@article{DBLP:journals/corr/abs-1908-02624,
  author       = {Yolanda Gil and
                  Bart Selman},
  title        = {A 20-Year Community Roadmap for Artificial Intelligence Research in
                  the {US}},
  journal      = {CoRR},
  volume       = {abs/1908.02624},
  year         = {2019},
  url          = {http://arxiv.org/abs/1908.02624},
  eprinttype    = {arXiv},
  eprint       = {1908.02624},
  timestamp    = {Sat, 23 Jan 2021 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1908-02624.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{pujara2019common,
  title={A Common Framework for Developing Table Understanding Models.},
  author={Pujara, Jay and Rajendran, Arunkumar and Ghasemi-Gol, Majid and Szekely, Pedro A},
  booktitle={ISWC Satellites},
  pages={133--136},
  year={2019}
}

@inproceedings{Tiwari2019AComparativeStudyof,
 author = {Tiwari, Abhishek  and Cassani, Raymundo and   Narayanan, Shrikanth and  Falk, Tiago},
 booktitle = {In proceedings of 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC19)},
 location = {Berlin, Germany},
 title = {A Comparative Study of Stress and Anxiety Prediction in Ecological Settings Using a Smart-shirt and a Smart-bracelet},
 year = {2019},
 month = {Jul}
}

@misc{huang_2019, 
title={A Gentle Introduction to Graph Neural Network (Basics, DeepWalk, and GraphSage)}, url={https://towardsdatascience.com/a-gentle-introduction-to-graph-neural-network-basics-deepwalk-and-graphsage-db5d540d50b3}, 
publisher={Towards Data Science}, 
author={Huang, Kung-Hsiang}, 
year={2019}, 
month={Feb}}

@InProceedings{cardenas-EtAl:2019:N19-1,
  author    = {Cardenas, Ronald  and  Lin, Ying  and  Ji, Heng  and  May, Jonathan},
  title     = {A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages},
  booktitle = {Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota},
  publisher = {Association for Computational Linguistics},
  pages     = {2428--2439},
  abstract  = {Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but practical taggers need to ground their clusters as well. Grounding generally requires reference labeled data, a luxury a low-resource language might not have. In this work, we describe an approach for low-resource unsupervised POS tagging that yields fully grounded output and requires no labeled training data. We find the classic method of Brown et al. (1992) clusters well in our use case and employ a decipherment-based approach to grounding. This approach presumes a sequence of cluster IDs is a `ciphertext' and seeks a POS tag-to-cluster ID mapping that will reveal the POS sequence. We show intrinsically that, despite the difficulty of the task, we obtain reasonable performance across a variety of languages. We also show extrinsically that incorporating our POS tagger into a name tagger leads to state-of-the-art tagging performance in Sinhalese and Kinyarwanda, two languages with nearly no labeled POS data available. We further demonstrate our tagger's utility by incorporating it into a true `zero-resource' variant of the MALOPA (Ammar et al., 2016) dependency parser model that removes the current reliance on multilingual resources and gold POS tags for new languages. Experiments show that including our tagger makes up much of the accuracy lost when gold POS tags are unavailable.},
  url       = {http://www.aclweb.org/anthology/N19-1252}
}

Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but practical taggers need to ground their clusters as well. Grounding generally requires reference labeled data, a luxury a low-resource language might not have. In this work, we describe an approach for low-resource unsupervised POS tagging that yields fully grounded output and requires no labeled training data. We find the classic method of Brown et al. (1992) clusters well in our use case and employ a decipherment-based approach to grounding. This approach presumes a sequence of cluster IDs is a `ciphertext' and seeks a POS tag-to-cluster ID mapping that will reveal the POS sequence. We show intrinsically that, despite the difficulty of the task, we obtain reasonable performance across a variety of languages. We also show extrinsically that incorporating our POS tagger into a name tagger leads to state-of-the-art tagging performance in Sinhalese and Kinyarwanda, two languages with nearly no labeled POS data available. We further demonstrate our tagger's utility by incorporating it into a true `zero-resource' variant of the MALOPA (Ammar et al., 2016) dependency parser model that removes the current reliance on multilingual resources and gold POS tags for new languages. Experiments show that including our tagger makes up much of the accuracy lost when gold POS tags are unavailable.

@inproceedings{Schuler2019,
	abstract = {Databases are well suited to the task of describing and organizing research datasets, however, the difficulties of using database management systems effectively have resulted in their limited usage among domain scientists. Scientists operate in an environment that is changing steadily with new experimental protocols, instruments, and discoveries that impact what datasets they generate and how they describe and organize them. In order to manage datasets for a scientific application, scientists need to routinely revise their database schemas to reflect these changes. Unfortunately, evolving a database is one of the well-known and most difficult aspects of database usage. The conventional data definition and manipulation languages offer relatively low-level programming abstractions to perform complex database evolution tasks, and therefore require specialized technical skills not possessed by most domain scientists. A simplified means of expressing database evolution operations can reduce the effort of keeping the scientific database in sync with changing requirements. This paper presents a high-level, user-oriented, schema evolution framework with an algebra of specialized schema modification operators. The approach allows introduction of novel operators as motivated by new requirements and is amenable to well established optimization techniques for efficient planning and execution. We present the framework and its implementation, and we demonstrate its utility in an exemplar use case and performance evaluation.},
	address = {{New York, NY, USA}},
	author = {Schuler, Robert E. and Kessleman, Carl},
	booktitle = {Proceedings of the 31st {{International Conference}} on {{Scientific}} and {{Statistical Database Management}}},
	doi = {10.1145/3335783.3335787},
	file = {/Users/schuler/Zotero/storage/T8CSAR3I/Schuler and Kesselman - 2019 - A High-level User-oriented Framework for Database .pdf},
	isbn = {978-1-4503-6216-0},
	keywords = {Database evolution,Schema evolution,Scientific databases},
	pages = {157--168},
	publisher = {{Association for Computing Machinery}},
	series = {{{SSDBM}} '19},
	title = {A {{High-Level User-Oriented Framework}} for {{Database Evolution}}},
	year = {2019},
	bdsk-url-1 = {https://doi.org/10.1145/3335783.3335787}}

Databases are well suited to the task of describing and organizing research datasets, however, the difficulties of using database management systems effectively have resulted in their limited usage among domain scientists. Scientists operate in an environment that is changing steadily with new experimental protocols, instruments, and discoveries that impact what datasets they generate and how they describe and organize them. In order to manage datasets for a scientific application, scientists need to routinely revise their database schemas to reflect these changes. Unfortunately, evolving a database is one of the well-known and most difficult aspects of database usage. The conventional data definition and manipulation languages offer relatively low-level programming abstractions to perform complex database evolution tasks, and therefore require specialized technical skills not possessed by most domain scientists. A simplified means of expressing database evolution operations can reduce the effort of keeping the scientific database in sync with changing requirements. This paper presents a high-level, user-oriented, schema evolution framework with an algebra of specialized schema modification operators. The approach allows introduction of novel operators as motivated by new requirements and is amenable to well established optimization techniques for efficient planning and execution. We present the framework and its implementation, and we demonstrate its utility in an exemplar use case and performance evaluation.

@inproceedings{Cen2019APersonalVisualComfort,
 author = {Cen, Lingkai and Choi, Joon-Ho and Yao, Xiaomeng and Gil, Yolanda and Narayanan, Shrikanth and Pentz,  Maryann},
 booktitle = {In proceedings of 10th International Conference on Indoor Air Quality, Ventilation and Energy Conservation in Buildings (IAQVEC2019)},
 location = {Bari, Italy},
 month = {Sep},
 title = {A Personal Visual Comfort Model: Predict Individuals Visual Comfort Using Occupant Eye Pupil Size and Machine Learning},
 year = {2019}
}

@inproceedings{cen-etal-iaqvec19,
author = {Lingkai Cen and Joon-Ho Choi and Xiaomeng Yao and Yolanda Gil and Shrikanth Narayanan and Maryann Pentz},
title = {A Personal Visual Comfort Model: Predict an Individual\textquotesingle s Visual Comfort Using Occupant Eye Pupil Sizes and Machine Learning},
booktitle = {Tenth International Conference on Indoor Air Quality, Ventilation and Energy Conservation in Buildings, IOP Conference Series in Materials Science and Engineering (609)4},
address = {Bari, Italy},
year = {2019}
}

@InProceedings{	  bogol-iwsg-2019,
  Title		= {A Secure Gateway for Enabling ASIC Design Collaborations},
  Author	= {Bogol, Steve and Brenner, Paul and Brinckman, Adam and
		  Deelman, Ewa and Ferreira da Silva, Rafael and Gupta,
		  Sandeep and Nabrzyski, Jarek and Park, Soowang and Perez,
		  Damian and Rynge, Mats and Taylor, Ian and Vahi, Karan and
		  Werf, Matt Vander and Rucker Sarah and Wyngaard,
		  Sebastian},
  BookTitle	= {11th International Workshop on Science Gateways (IWSG
		  2019)},
  Year		= {2019},
  Pages		= {},
  DOI		= {},
  Note		= {Funding Acknowledgments: DARPA HR0011-16-C-0043}
}

@article{DBLP:journals/corr/abs-1909-06516,
  author    = {Mozhdeh Gheini and
               Jonathan May},
  title     = {A Universal Parent Model for Low-Resource Neural Machine Translation
               Transfer},
  journal   = {CoRR},
  volume    = {abs/1909.06516},
  year      = {2019},
  url       = {http://arxiv.org/abs/1909.06516},
  archivePrefix = {arXiv},
  eprint    = {1909.06516},
  timestamp = {Mon, 23 Sep 2019 18:07:15 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1909-06516.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{jalal2019cooperative,
  title={A cooperative machine learning approach for pedestrian navigation in indoor IoT},
  author={Jalal Abadi, Marzieh and Luceri, Luca and Hassan, Mahbub and Chou, Chun Tung and Nicoli, Monica},
  journal={Sensors},
  volume={19},
  number={21},
  pages={4609},
  year={2019},
  publisher={MDPI}
}

@article{Alex2019Amodulararchitecturefor,
 author = {Alexander, Rachel  and  Sorensen, Tanner and  Toutios, Asterios and  Narayanan, Shrikanth},
 bib2html_rescat = {span},
 doi = {https://doi.org/10.1121/1.5139413},
 journal = {J. Acoust. Soc. Am.},
 link = {http://sail.usc.edu/publications/files/Alexander-JASA2020.pdf},
 month = {dec},
 number = {6},
 pages = {4458-4471},
 title = {A modular architecture for articulatory synthesis from gestural specification},
 volume = {146},
 year = {2019}
}

@article{PendergrassBuyskeJeffEtAl2019,
	abstract = {We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one {SNP} is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the {Population Architecture using Genomics and Epidemiology} (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype. To do this, we performed a PheWAS, testing each {SNP} on the Metabochip for an association with up to 273 phenotypes in the participating PAGE I study sites. We identified 133 putative pleiotropic variants, defined as SNPs associated at an empirically derived p-value threshold of p<0.01 in two or more PAGE study sites for two or more phenotype classes. We further annotated these PheWAS-identified variants using publicly available functional data and local genetic ancestry. Amongst our novel findings is SPARC rs4958487, associated with increased glucose levels and hypertension. SPARC has been implicated in the pathogenesis of diabetes and is also known to have a potential role in fibrosis, a common consequence of multiple conditions including hypertension. The SPARC example and others highlight the potential that PheWAS approaches have in improving our understanding of complex disease architecture by identifying novel relationships between genetic variants and an array of common human phenotypes.},
	author = {Pendergrass, Sarah A. and Buyske, Steven and Jeff, Janina M. and Frase, Alex and Dudek, Scott and Bradford, Yuki and Ambite, Jose-Luis and Avery, Christy L. and Buzkova, Petra and Deelman, Ewa and Fesinmeyer, Megan D. and Haiman, Christopher and Heiss, Gerardo and Hindorff, Lucia A. and Hsu, Chun-Nan and Jackson, Rebecca D. and Lin, Yi and Le Marchand, Loic and Matise, Tara C. and Monroe, Kristine R. and Moreland, Larry and North, Kari E. and Park, Sungshim L. and Reiner, Alex and Wallace, Robert and Wilkens, Lynne R. and Kooperberg, Charles and Ritchie, Marylyn D. and Crawford, Dana C.},
	citation-subset = {IM},
	completed = {2020-04-06},
	country = {United States},
	doi = {10.1371/journal.pone.0226771},
	issn = {1932-6203},
	issn-linking = {1932-6203},
	issue = {12},
	journal = {PloS one},
	keywords = {African Americans, genetics; Aged; Atherosclerosis, genetics; Epidemiologic Studies; Female; Genetic Pleiotropy; Genome-Wide Association Study; Humans; Male; Metagenomics; Middle Aged; Phenomics; Polymorphism, Single Nucleotide},
	nlm-id = {101285081},
	owner = {NLM},
	pages = {e0226771},
	pii = {PONE-D-19-33442},
	pmc = {PMC6938343},
	pmid = {31891604},
	url = {https://pubmed.ncbi.nlm.nih.gov/31891604/},

	pubmodel = {Electronic-eCollection},
	pubstate = {epublish},
	revised = {2020-04-30},
	title = {A phenome-wide association study ({PheWAS}) in the {Population Architecture using Genomics and Epidemiology} ({PAGE}) study reveals potential pleiotropy in {African Americans}.},
	volume = {14},
	year = {2019},
	bdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/31891604/},
	bdsk-url-2 = {https://doi.org/10.1371/journal.pone.0226771}}

We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one SNP is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the Population Architecture using Genomics and Epidemiology (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype. To do this, we performed a PheWAS, testing each SNP on the Metabochip for an association with up to 273 phenotypes in the participating PAGE I study sites. We identified 133 putative pleiotropic variants, defined as SNPs associated at an empirically derived p-value threshold of p<0.01 in two or more PAGE study sites for two or more phenotype classes. We further annotated these PheWAS-identified variants using publicly available functional data and local genetic ancestry. Amongst our novel findings is SPARC rs4958487, associated with increased glucose levels and hypertension. SPARC has been implicated in the pathogenesis of diabetes and is also known to have a potential role in fibrosis, a common consequence of multiple conditions including hypertension. The SPARC example and others highlight the potential that PheWAS approaches have in improving our understanding of complex disease architecture by identifying novel relationships between genetic variants and an array of common human phenotypes.

@InProceedings{sticca2019:ASHG,
  author = 	 {E.L. Sticca and G.M. Belbin and J. Soto and R. Shemirani and D. Nelson and  S. Gravel and J.L. Ambite and G.L. Wojcik and E.E. Kenny and C.R. Gignoux},
  title = 	 {A pipeline for large-scale simulation of population-based linkage.},
  booktitle = {Annual Meeting of the American Society of Human Genetics},
  year = 	 {2019},
  address = 	 {Houston, TX},
  note = 	 {Abstract + Poster},
}

@inproceedings{Martinez2019Asystemforthe,
 author = {Martinez, Victor and Ramakrishna, Anil and Chiu, Ming-Chang and Singla, Karan and Narayanan, Shrikanth},
 booktitle = {In proceedings of Proceedings of the 8th International Conference on Affective  Computing Intelligent Interaction},
 location = {Cambridge, UK},
 month = {September},
 title = {A system for the 2019 Sentiment, Emotion and  Cognitive State Task of DARPAs LORELEI project},
 year = {2019}
}

@article{lu2019universal,
  title={A universal measure for network traceability},
  author={Lu, Xin and Horn, Abigail L and Su, Jiahao and Jiang, Jiang},
  journal={Omega},
  volume={87},
  pages={191--204},
  year={2019},
  publisher={Elsevier}
}

@article{DBLP:journals/corr/abs-1903-00788,
  author       = {Ayush Jaiswal and
                  Yue Wu and
                  Wael AbdAlmageed and
                  Iacopo Masi and
                  Premkumar Natarajan},
  title        = {{AIRD:} Adversarial Learning Framework for Image Repurposing Detection},
  journal      = {CoRR},
  volume       = {abs/1903.00788},
  year         = {2019},
  url          = {http://arxiv.org/abs/1903.00788},
  eprinttype    = {arXiv},
  eprint       = {1903.00788},
  timestamp    = {Sat, 30 Mar 2019 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1903-00788.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@InProceedings{	  ferreiradasilva-iccs-2019,
  Author	= {Ferreira da Silva, Rafael and Orgerie, Anne-C\'{e}cile and
		  Casanova, Henri and Tanaka, Ryan and Deelman, Ewa and
		  Suter, Fr\'{e}d\'{e}ric},
  Title		= {Accurately Simulating Energy Consumption of I/O-intensive
		  Scientific Workflows},
  BookTitle	= {Computational Science -- ICCS 2019},
  Year		= {2019},
  Pages		= {138--152},
  Publisher	= {Springer International Publishing},
  DOI		= {10.1007/978-3-030-22734-0_11},
  Note		= {Funding Acknowledgments: NSF 1642335, NSF 1664162, DOE
		  DE-SC0012636}
}

@inbook{Toutios2018AdvancesInVocalTractImaging,
 address = {New York, NY},
 author = {Toutios, Asterios and Byrd, Dani and Goldstein, Louis and Narayanan, Shrikanth S.},
 bib2html_rescat = {span},
 link = {},
 publisher = {Routledge London and New York},
 title = {Advances in vocal tract imaging and analysis},
 year = {2019},
 month = {Apr}
}

@article{DBLP:journals/f1000research/HornAAABBBBBFGGKLLMMOST19,
  author       = {John D. Van Horn and
                  Sumiko Abe and
                  Jos{\'{e}} Luis Ambite and
                  Teresa K. Attwood and
                  Niall Beard and
                  Louisa Bellis and
                  Avnish Bhattrai and
                  Alex A. T. Bui and
                  Gully Burns and
                  Lily Fierro and
                  Jonathan Gordon and
                  Jeffrey S. Grethe and
                  Jeana Kamdar and
                  Xiaoyu Lei and
                  Kristina Lerman and
                  Annette McGrath and
                  Nicola J. Mulder and
                  Caroline O'Driscoll and
                  Crystal Stewart and
                  Sonika Tyagi},
  title        = {Advancing the international data science workforce through shared
                  training and education},
  journal      = {F1000Research},
  volume       = {8},
  pages        = {251},
  year         = {2019},
  url          = {https://doi.org/10.12688/f1000research.18357.1},
  doi          = {10.12688/F1000RESEARCH.18357.1},
  timestamp    = {Mon, 28 Aug 2023 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/f1000research/HornAAABBBBBFGGKLLMMOST19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/mta/RiazAPCMN19,
  author       = {Sidra Riaz and
                  Zahid Ali and
                  Unsang Park and
                  Jongmoo Choi and
                  Iacopo Masi and
                  Prem Natarajan},
  title        = {Age-invariant face recognition using gender specific 3D aging modeling},
  journal      = {Multim. Tools Appl.},
  volume       = {78},
  number       = {17},
  pages        = {25163--25183},
  year         = {2019},
  url          = {https://doi.org/10.1007/s11042-019-7694-1},
  doi          = {10.1007/S11042-019-7694-1},
  timestamp    = {Mon, 11 May 2020 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/mta/RiazAPCMN19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{Sharon2019AnEmpiricalStudyof,
 author = {Sharon, Rini and Narayanan, Shrikanth and Sur, Mriganka and Murthy, Hema},
 booktitle = {Proceedings of ICASSP},
 location = {Brighton, UK},
 month = {May},
 title = {An Empirical Study of Speech Processing in the Brain by Analyzing the Temporal Syllable Structure in Speech-input Induced EEG},
 year = {2019}
}

@inproceedings{Garijo:2019:III:3308557.3308711,
 author = {Garijo, Daniel and Khider, Deborah and Ratnakar, Varun and Gil, Yolanda and Deelman, Ewa and da Silva, Rafael Ferreira and Knoblock, Craig and Chiang, Yao-Yi and Pham, Minh and Pujara, Jay and Vu, Binh and Feldman, Dan and Mayani, Rajiv and Cobourn, Kelly and Duffy, Chris and Kemanian, Armen and Shu, Lele and Kumar, Vipin and Khandelwal, Ankush and Tayal, Kshitij and Peckham, Scott and Stoica, Maria and Dabrowski, Anna and Hardesty-Lewis, Daniel and Pierce, Suzanne},
 title = {An Intelligent Interface for Integrating Climate, Hydrology, Agriculture, and Socioeconomic Models},
 booktitle = {Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion},
 series = {IUI '19},
 year = {2019},
 isbn = {978-1-4503-6673-1},
 location = {Marina del Ray, California},
 pages = {111--112},
 numpages = {2},
 url = {http://doi.acm.org/10.1145/3308557.3308711},
 urlPaper = {http://usc-isi-i2.github.io/papers/daniel19-iui.pdf},
 doi = {10.1145/3308557.3308711},
 acmid = {3308711},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {environmental modeling, intelligent workflow systems, model integration, scientific discovery},
} 

@InProceedings{	  garijo-iui-2019,
  Author	= {Garijo, Daniel and Khider, Deborah and Ratnakar, Varun and
		  Gil, Yolanda and Deelman, Ewa and Ferreira da Silva, Rafael
		  and Knoblock, Craig and Chiang, Yao-Yi and Pham, Minh and
		  Pujara, Jay and Vu, Binh and Feldman, Dan and Mayani, Rajiv
		  and Cobourn, Kelly and Duffy, Chris and Kemanian, Armen and
		  Shu, Lele and Kumar, Vipin and Khandelwal, Ankush and
		  Tayal, Kshitij and Peckham, Scott and Stoica, Maria and
		  Dabrowski, Anna and Hardesty-Lewis, Daniel and Pierce,
		  Suzanne},
  Title		= {An Intelligent Interface for Integrating Climate,
		  Hydrology, Agriculture, and Socioeconomic Models},
  BookTitle	= {ACM 24th International Conference on Intelligent User
		  Interfaces (IUI'19)},
  Year		= {2019},
  Pages		= {111--112},
  DOI		= {10.1145/3308557.3308711},
  Note		= {Funding Acknowledgments: DARPA W911NF-18-1-0027}
}

@article{Albash_2019,
	doi = {10.1088/2058-9565/ab13ea},
	url = {https://doi.org/10.1088%2F2058-9565%2Fab13ea},
	year = 2019,
	month = {apr},
	publisher = {{IOP} Publishing},
	volume = {4},
	number = {2},
	pages = {02LT03},
	author = {Tameem Albash and Victor Martin-Mayor and Itay Hen},
	title = {Analog errors in Ising machines},
	journal = {Quantum Science and Technology},
	abstract = {Recent technological breakthroughs have precipitated the availability of specialized devices that promise to solve NP-Hard problems faster than standard computers. These ‘Ising Machines’ are however analog in nature and as such inevitably have implementation errors. We find that their success probability decays exponentially with problem size for a fixed error level, and we derive a sufficient scaling law for the error in order to maintain a fixed success probability. We corroborate our results with experiment and numerical simulations and discuss the practical implications of our findings.}
}

Recent technological breakthroughs have precipitated the availability of specialized devices that promise to solve NP-Hard problems faster than standard computers. These ‘Ising Machines’ are however analog in nature and as such inevitably have implementation errors. We find that their success probability decays exponentially with problem size for a fixed error level, and we derive a sufficient scaling law for the error in order to maintain a fixed success probability. We corroborate our results with experiment and numerical simulations and discuss the practical implications of our findings.

@article{npjQ,
	Abstract = {Quantum annealing has the potential to provide a speedup over classical algorithms in solving optimization problems. Just as for any other quantum device, suppressing Hamiltonian control errors will be necessary before quantum annealers can achieve speedups. Such analog control errors are known to lead to {\$}{\$}J{\$}{\$}J-chaos, wherein the probability of obtaining the optimal solution, encoded as the ground state of the intended Hamiltonian, varies widely depending on the control error. Here, we show that {\$}{\$}J{\$}{\$}J-chaos causes a catastrophic failure of quantum annealing, in that the scaling of the time-to-solution metric becomes worse than that of a deterministic (exhaustive) classical solver. We demonstrate this empirically using random Ising spin glass problems run on the two latest generations of the D-Wave quantum annealers. We then proceed to show that this doomsday scenario can be mitigated using a simple error suppression and correction scheme known as quantum annealing correction (QAC). By using QAC, the time-to-solution scaling of the same D-Wave devices is improved to below that of the classical upper bound, thus restoring hope in the speedup prospects of quantum annealing.},
	Author = {Pearson, Adam and Mishra, Anurag and Hen, Itay and Lidar, Daniel A.},
	Da = {2019/11/28},
	Date-Added = {2020-05-11 16:55:01 -0700},
	Date-Modified = {2020-05-11 16:55:01 -0700},
	Doi = {10.1038/s41534-019-0210-7},
	Id = {Pearson2019},
	Isbn = {2056-6387},
	Journal = {npj Quantum Information},
	Number = {1},
	Pages = {107},
	Title = {Analog errors in quantum annealing: doom and hope},
	Ty = {JOUR},
	Url = {https://doi.org/10.1038/s41534-019-0210-7},
	Volume = {5},
	Year = {2019},
	Bdsk-Url-1 = {https://doi.org/10.1038/s41534-019-0210-7}}

Quantum annealing has the potential to provide a speedup over classical algorithms in solving optimization problems. Just as for any other quantum device, suppressing Hamiltonian control errors will be necessary before quantum annealers can achieve speedups. Such analog control errors are known to lead to \$}{\$J\$}{\$J-chaos, wherein the probability of obtaining the optimal solution, encoded as the ground state of the intended Hamiltonian, varies widely depending on the control error. Here, we show that \$}{\$J\$}{\$J-chaos causes a catastrophic failure of quantum annealing, in that the scaling of the time-to-solution metric becomes worse than that of a deterministic (exhaustive) classical solver. We demonstrate this empirically using random Ising spin glass problems run on the two latest generations of the D-Wave quantum annealers. We then proceed to show that this doomsday scenario can be mitigated using a simple error suppression and correction scheme known as quantum annealing correction (QAC). By using QAC, the time-to-solution scaling of the same D-Wave devices is improved to below that of the classical upper bound, thus restoring hope in the speedup prospects of quantum annealing.

@article{Slutskii_2019,
	doi = {10.1088/1367-2630/ab51f9},
	url = {https://doi.org/10.1088%2F1367-2630%2Fab51f9},
	year = 2019,
	month = {nov},
	publisher = {{IOP} Publishing},
	volume = {21},
	number = {11},
	pages = {113025},
	author = {Mikhail Slutskii and Tameem Albash and Lev Barash and Itay Hen},
	title = {Analog nature of quantum adiabatic unstructured search},
	journal = {New Journal of Physics},
	abstract = {The quantum adiabatic unstructured search algorithm is one of only a handful of quantum adiabatic optimization algorithms to exhibit provable speedups over their classical counterparts. With no fault tolerance theorems to guarantee the resilience of such algorithms against errors, understanding the impact of imperfections on their performance is of both scientific and practical significance. We study the robustness of the algorithm against various types of imperfections: limited control over the interpolating schedule, Hamiltonian misspecification, and interactions with a thermal environment. We find that the unstructured search algorithm’s quadratic speedup is generally not robust to the presence of any one of the above non-idealities, and in some cases we find that it imposes unrealistic conditions on how the strength of these noise sources must scale to maintain the quadratic speedup.}
}

The quantum adiabatic unstructured search algorithm is one of only a handful of quantum adiabatic optimization algorithms to exhibit provable speedups over their classical counterparts. With no fault tolerance theorems to guarantee the resilience of such algorithms against errors, understanding the impact of imperfections on their performance is of both scientific and practical significance. We study the robustness of the algorithm against various types of imperfections: limited control over the interpolating schedule, Hamiltonian misspecification, and interactions with a thermal environment. We find that the unstructured search algorithm’s quadratic speedup is generally not robust to the presence of any one of the above non-idealities, and in some cases we find that it imposes unrealistic conditions on how the strength of these noise sources must scale to maintain the quadratic speedup.

@article{pepke2019,
	Author = {Pepke, Shirley and Nelson, William M and {Ver Steeg}, Greg},
	Date-Added = {2020-01-20 20:40:51 +0000},
	Date-Modified = {2020-01-20 20:40:51 +0000},
	Journal = {JoVE (Journal of Visualized Experiments)},
	Number = {152},
	Pages = {e60431},
	Title = {Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal},
	Year = {2019}}

@article{luceri2019analyzing,
  title={Analyzing and inferring human real-life behavior through online social networks with social influence deep learning},
  author={Luceri, Luca and Braun, Torsten and Giordano, Silvia},
  journal={Applied network science},
  volume={4},
  number={1},
  pages={1--25},
  year={2019},
  publisher={Springer}
}

@InProceedings{	  chap2019precs,
  Author	= {Chapp, Dylan and Rorabaugh, Danny and Brown, Duncan A. and
		  Deelman, Ewa and Vahi, Karan and Welch, Von and Taufer,
		  Michela},
  Title		= {Applicability Study of the PRIMAD Model to LIGO
		  Gravitational Wave Search Workflows},
  Year		= {2019},
  DOI		= {10.1145/3322790.3330591},
  BookTitle	= {Proceedings of the 2nd International Workshop on Practical
		  Reproducible Evaluation of Computer Systems (P-RECS'19)},
  Pages		= {1–6}
}

@article{li_applying_2019,
	title = {Applying {Multivariate} {Segmentation} {Methods} to {Human} {Activity} {Recognition} {From} {Wearable} {Sensors}' {Data}.},
	volume = {7},
	copyright = {(c)Kenan Li, Rima Habre, Huiyu Deng, Robert Urman, John Morrison, Frank D Gilliland, Jose Luis Ambite, Dimitris Stripelis, Yao-Yi Chiang, Yijun Lin, Alex AT Bui, Christine King, Anahita Hosseini, Eleanne Van Vliet, Majid Sarrafzadeh, Sandrah P Eckel. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 07.02.2019.},
	issn = {2291-5222 2291-5222},
	url = {https://mhealth.jmir.org/2019/2/e11201/},
	doi = {10.2196/11201},
	abstract = {BACKGROUND: Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity. OBJECTIVE: We aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points. METHODS: We applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict  physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones (HARuS) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma (BREATHE) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and  randomly concatenating the 60 activity bouts. Each dataset was divided into {\textasciitilde}90\%  training and {\textasciitilde}10\% holdout testing. RESULTS: In the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06\% (the highest accuracy rate was 91.79\% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4\% of predictions for 6 physical activities. CONCLUSIONS: In a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as expected,  it was slightly lower in the more real-world study using wrist-worn smartwatches  in children (BREATHE) than in the more tightly controlled study using waist-worn  smartphones in adults (HARuS). We implemented GGS in an offline setting, but it could be adapted for real-time prediction with streaming data.},
	language = {eng},
	number = {2},
	journal = {JMIR mHealth and uHealth},
	author = {Li, Kenan and Habre, Rima and Deng, Huiyu and Urman, Robert and Morrison, John and Gilliland, Frank D. and Ambite, Jose Luis and Stripelis, Dimitris and Chiang, Yao-Yi and Lin, Yijun and Bui, Alex At and King, Christine and Hosseini, Anahita and Vliet, Eleanne Van and Sarrafzadeh, Majid and Eckel, Sandrah P.},
	month = feb,
	year = {2019},
	pmid = {30730297},
	pmcid = {PMC6386646},
	keywords = {machine learning, physical activity, smartphone, statistical data analysis wearable devices},
	pages = {e11201},
}

BACKGROUND: Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity. OBJECTIVE: We aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points. METHODS: We applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones (HARuS) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma (BREATHE) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and randomly concatenating the 60 activity bouts. Each dataset was divided into ~90% training and ~10% holdout testing. RESULTS: In the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06% (the highest accuracy rate was 91.79% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4% of predictions for 6 physical activities. CONCLUSIONS: In a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as expected, it was slightly lower in the more real-world study using wrist-worn smartwatches in children (BREATHE) than in the more tightly controlled study using waist-worn smartphones in adults (HARuS). We implemented GGS in an offline setting, but it could be adapted for real-time prediction with streaming data.

@article{Proctor2019ArticulatoryCharacterizationofEnglish,
 abstract = {Articulation of liquid consonants in onsets and codas by four speakers of General American English was examined using real-time MRI. Midsagittal tongue posture was compared for laterals and rhotics produced in each syllable margin, adjacent to 13 different vowels and diphthongs. Vowel articulation was examined in words without liquids, before each liquid, and after each liquid, to assess the coarticulatory influence of each segment on the others. Overall, nuclear vocalic postures were more influenced by coda rhotics than onset rhotics or laterals in either syllable margin. Laterals exhibited greater temporal and spatial independence between coronal and dorsal gestures. Rhotics were produced with a variety of speaker-specific postures, but were united by a greater degree of coarticulatory resistance to vowel context, patterns consistent with greater coarticulatory influence on adjacent vowels, and less allophonic variation across syllable positions than laterals.},
 author = {Proctor, Michael and Walker, Rachel and Smith, Caitlin and Szalay, Tunde and Goldstein, Louis and Narayanan, Shrikanth},
 doi = {https://doi.org/10.1016/j.wocn.2019.100921},
 issn = {0095-4470},
 journal = {J. Phonetics},
 keywords = {Liquid consonant, Rhotic, Lateral, Coarticulation, Syllable structure},
 link = {http://sail.usc.edu/publications/files/proctor_jphon-2019.pdf},
 pages = {100921},
 title = {Articulatory Characterization of English Liquid-Final Rimes},
 url = {http://www.sciencedirect.com/science/article/pii/S0095447018300457},
 volume = {77},
 year = {2019},
 month = {Aug}
}

Articulation of liquid consonants in onsets and codas by four speakers of General American English was examined using real-time MRI. Midsagittal tongue posture was compared for laterals and rhotics produced in each syllable margin, adjacent to 13 different vowels and diphthongs. Vowel articulation was examined in words without liquids, before each liquid, and after each liquid, to assess the coarticulatory influence of each segment on the others. Overall, nuclear vocalic postures were more influenced by coda rhotics than onset rhotics or laterals in either syllable margin. Laterals exhibited greater temporal and spatial independence between coronal and dorsal gestures. Rhotics were produced with a variety of speaker-specific postures, but were united by a greater degree of coarticulatory resistance to vowel context, patterns consistent with greater coarticulatory influence on adjacent vowels, and less allophonic variation across syllable positions than laterals.

@article{Park2019Auto-TuningSpectralClusteringfor,
 author = {Park, Tae Jin and Han, Kyu and Kumar, Manoj and Narayanan, Shrikanth},
 doi = {10.1109/LSP.2019.2961071},
 journal = {IEEE Signal Processing Letters},
 title = {Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap},
 year = {2019},
 month = {Mar}
}

@inproceedings{aistats2019,
	Author = {Shuyang Gao and Robert Brekelmans and Greg {Ver Steeg} and Aram Galstyan},
	Booktitle = {Proceedings of the 22nd International Conference on AI and Statistics (AISTATS)},
	Date-Added = {2019-01-24 17:43:17 +0000},
	Date-Modified = {2019-01-24 17:44:47 +0000},
	Title = {Auto-encoding Correlation Explanation},
	Year = {2019}}

@inproceedings{gao2019auto,
  title={Auto-encoding total correlation explanation},
  author={Gao, Shuyang and Brekelmans, Rob and Ver Steeg, Greg and Galstyan, Aram},
  booktitle={The 22nd International Conference on Artificial Intelligence and Statistics},
  pages={1157--1166},
  year={2019},
  organization={PMLR}
}

@article{uhl2019automated,
  title={Automated extraction of human settlement patterns from historical topographic map series using weakly supervised convolutional neural networks},
  author={Uhl, Johannes H and Leyk, Stefan and Chiang, Yao-Yi and Duan, Weiwei and Knoblock, Craig A},
  journal={IEEE Access},
  year={2019},
  publisher={IEEE},
  urlLink={https://ieeexplore.ieee.org/document/8946322},
  urlPaper={http://usc-isi-i2.github.io/papers/uhl19-ieee.pdf}
}

@InProceedings{mayani2019a:ASHG,
  author = 	 {R. Mayani and K. Vahi and J.L. Ambite and S. Sharma and M. Azaro and S. Wilson and B. Ruocco and G. Davis and M. Romanella and L. Brzustowicz and E. Deelman and Y. Arens},
  title = 	 {Automated processing of phenotypic data submissions},
  booktitle = {Annual Meeting of the American Society of Human Genetics},
  year = 	 {2019},
  address = 	 {Houston, TX},
  note = 	 {Abstract + Poster},
}

@inproceedings{shi2019automatic,
  title={Automatic Adaptation to Sensor Replacements},
  author={Shi, Yuan and Kumar, TK Satish and Knoblock, Craig A},
  booktitle={The Thirty-Second International Flairs Conference},
  year={2019}
}

@article{duan2019automatic,
  title={Automatic alignment of contemporary vector data and georeferenced historical maps using reinforcement learning},
  author={Duan, Weiwei and Chiang, Yao-Yi and Leyk, Stefan and Uhl, Johannes H and Knoblock, Craig A},
  journal={International Journal of Geographical Information Science},
  pages={1--26},
  year={2019},
  publisher={Taylor \& Francis}
}

@article{ALOBAID2019100472,
	title        = {Automating ontology engineering support activities with OnToology},
	author       = {Ahmad Alobaid and Daniel Garijo and María Poveda-Villalón and Idafen Santana-Perez and Alba Fernández-Izquierdo and Oscar Corcho},
	year         = 2019,
	journal      = {Journal of Web Semantics},
	volume       = 57,
	pages        = 100472,
	doi          = {10.1016/j.websem.2018.09.003},
	issn         = {1570-8268},
	url          = {https://dgarijo.com/papers/JWSsupporting.pdf},
	keywords     = {Ontology engineering, Ontology evaluation, Ontology documentation, Ontology publication},
	abstract     = {Due to the increasing uptake of semantic technologies, ontologies are now part of a good number of information systems. As a result, software development teams that have to combine ontology engineering activities with software development practices are facing several challenges, since these two areas have evolved, in general, separately. In this paper we present OnToology, an approach to manage ontology engineering support activities (i.e., documentation, evaluation, releasing and versioning). OnToology is a web-based application that builds on top of Git-based environments and integrates existing semantic web technologies. We have validated OnToology against a set of representative requirements for ontology development support activities in distributed environments, and report on a survey of the system to assess its usefulness and usability.}
}

Due to the increasing uptake of semantic technologies, ontologies are now part of a good number of information systems. As a result, software development teams that have to combine ontology engineering activities with software development practices are facing several challenges, since these two areas have evolved, in general, separately. In this paper we present OnToology, an approach to manage ontology engineering support activities (i.e., documentation, evaluation, releasing and versioning). OnToology is a web-based application that builds on top of Git-based environments and integrates existing semantic web technologies. We have validated OnToology against a set of representative requirements for ontology development support activities in distributed environments, and report on a survey of the system to assess its usefulness and usability.

@article{ambite2019bd2k,
  title={BD2K Training Coordinating Center's ERuDIte: the Educational Resource Discovery Index for Data Science},
  author={Ambite, Jose Luis and Fierro, Lily and Gordon, Jonathan and Burns, Gully and Geigl, Florian and Lerman, Kristina and Van Horn, John D},
  journal={IEEE Transactions on Emerging Topics in Computing},
  year={2019},
  publisher={IEEE}
}

@Article{ambite2019:tetc-DONTCITEUPDATE2021,
  author = 	 {Jos\'{e} Luis Ambite and Lily Fierro and Jonathan Gordon and Gully A. Burns and Florian Geigl and Kristina Lerman and John D. Van Horn},
  title = 	 {{BD2K Training Coordinating Center’s ERuDIte: the Educational Resource Discovery Index for Data Science}},
  journal = 	 {IEEE Transactions on Emerging Topics in Computing},
  year = 	 {2019},
  DOI = {10.1109/TETC.2019.2903466},
  note = {10.1109/TETC.2019.2903466},
  publisher = {IEEE Computer Society},
  address = {Los Alamitos, CA, USA},
  month = {Jan}
}

@InBook{mythodologies-2019,
  author =    {Joseph A. Dane and Jonathan May},
  title =        {Begging The Question: Critical Reasoning in Chaucer Studies, Book History, and Humanistic Inquiry},
  chapter =      {III.3: Evidence and Artificial Intelligence},
  publisher =    {Marymount Institute Press},
  year =         2019,
  number =    {II},
  series =    {Mythodologies},
  pages =     {295--208}}

@article{ghazarian2019better,
  title={Better automatic evaluation of open-domain dialogue systems with contextualized embeddings},
  author={Ghazarian, Sarik and Wei, Johnny Tian-Zheng and Galstyan, Aram and Peng, Nanyun},
  journal={arXiv preprint arXiv:1904.10635},
  year={2019}
}

@misc{bd2k-erudite,
  author = {{BD2K-TCC}},
  title = {{Big Data to Knowledge (BD2K) Training Coordinating Center (TCC) Educational Resource Discovery Index (ERuDIte) as Linked Data}},
  howpublished = {https://doi.org/10.5281/zenodo.2553415},
  year = {2019}, 
  note = {[Online; accessed 16-August-2021]}
}

@article{Mathew2019BiomedicalNE,
  title={Biomedical Named Entity Recognition via Reference-Set Augmented Bootstrapping},
  author={Joel Mathew and Shobeir Fakhraei and J. Ambite},
  journal={ArXiv},
  year={2019},
  volume={abs/1906.00282}
}

@inproceedings{khachatrian2019biorelex,
  title={Biorelex 1.0: Biological relation extraction benchmark},
  author={Khachatrian, Hrant and Nersisyan, Lilit and Hambardzumyan, Karen and Galstyan, Tigran and Hakobyan, Anna and Arakelyan, Arsen and Rzhetsky, Andrey and Galstyan, Aram},
  booktitle={Proceedings of the 18th BioNLP Workshop and Shared Task},
  pages={176--190},
  year={2019}
}

@inproceedings{Mundnich2019BluetoothbasedIndoorLocalization,
 author = {Mundnich, Karel and Girault, Benjamin and Narayanan, Shrikanth},
 booktitle = {Proceedings of ICASSP},
 link = {http://sail.usc.edu/publications/files/Mundnich-ICASSP2019.pdf},
 location = {Brighton, UK},
 month = {May},
 title = {Bluetooth based Indoor Localization using Triplet Embeddings},
 year = {2019}
}

@inproceedings{chevalier2019bootkeeper,
  title={BootKeeper: Validating Software Integrity Properties on Boot Firmware Images},
  author={Chevalier, Ronny and Cristalli, Stefano and Hauser, Christophe and Shoshitaishvili, Yan and Wang, Ruoyu and Kruegel, Christopher and Vigna, Giovanni and Bruschi, Danilo and Lanzi, Andrea},
  booktitle={Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy},
  pages={315--325},
  year={2019}
}

@InProceedings{8902700,
  author = {A. Tiwari and S. Narayanan and T. H. Falk},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Breathing Rate Complexity Features for “In-the-Wild” Stress and Anxiety Measurement},
  year = {2019},
  pages = {1-5},
  abstract = {Features extracted from respiratory activity signals have been shown to carry information about mental states such as anxiety and mental stress. Such findings, however, are based on studies conducted mostly in controlled laboratory environments with artificially-induced psychological responses. While this assures that high quality data are collected, the amount of data is limited and the transferability of the findings to more ecologically-appropriate natural settings (i.e., “in-the-wild”) remains unknown. In this paper, we propose new non-linear complexity measures computed from four different respiration activity time series (i.e., inter-breath interval, inhale-to-exhale ratio, inhale/exhale amplitude envelope, and interbreath difference) and show their discriminatory power for anxiety and stress monitoring in the workplace. The new features are tested on a dataset collected from 200 hospital workers (nurses and staff) during their normal work shifts. The proposed features are shown to be complementary to conventional measures of breathing rate and depth.},
  keywords = {diseases;feature extraction;medical signal processing;patient care;pneumodynamics;psychology;time series;artificially-induced psychological responses;ecologically-appropriate natural settings;nonlinear complexity measures;inter-breath interval;stress monitoring;respiratory activity signals;mental states;breathing rate complexity features;anxiety measurement;respiration activity time series;in-the-wild stress;feature extraction;Feature extraction;Stress;Benchmark testing;Biomedical measurement;Stress measurement;Time series analysis;Entropy},
  doi = {10.23919/EUSIPCO.2019.8902700},
  issn = {2076-1465},
  month = {Sep.},
}

Features extracted from respiratory activity signals have been shown to carry information about mental states such as anxiety and mental stress. Such findings, however, are based on studies conducted mostly in controlled laboratory environments with artificially-induced psychological responses. While this assures that high quality data are collected, the amount of data is limited and the transferability of the findings to more ecologically-appropriate natural settings (i.e., “in-the-wild”) remains unknown. In this paper, we propose new non-linear complexity measures computed from four different respiration activity time series (i.e., inter-breath interval, inhale-to-exhale ratio, inhale/exhale amplitude envelope, and interbreath difference) and show their discriminatory power for anxiety and stress monitoring in the workplace. The new features are tested on a dataset collected from 200 hospital workers (nurses and staff) during their normal work shifts. The proposed features are shown to be complementary to conventional measures of breathing rate and depth.

@inproceedings{ferreiradasilva2019escience,
    title = {Bridging Concepts and Practice in eScience via Simulation-driven Engineering},
    author = {Ferreira da Silva, Rafael and Casanova, Henri and Tanaka, Ryan and Suter, Frederic},
    booktitle = {Workshop on Bridging from Concepts to Data and Computation for eScience (BC2DC'19), 15th International Conference on eScience (eScience)},
    year = {2019},
    pages = {609--614},
    doi = {10.1109/eScience.2019.00084},
    note = {Funding Acknowledgements: NSF 1642335, NSF 1923539}
}

@INPROCEEDINGS{Chiang2019-yx,
  title     = "{Building Explainable Predictive Analytics for Location-Dependent
               Time-Series Data}",
  booktitle = "{Proceedings of the 2019 IEEE First International Conference on
               Cognitive Machine Intelligence (CogMI)}",
  author    = "Chiang, Y Y and Lin, Y and Franklin, M and Eckel, S P and
               Ambite, J L and Ku, W",
  pages     = "202--209",
  month     =  dec,
  year      =  2019,
  url       = "http://dx.doi.org/10.1109/CogMI48466.2019.00037",
  url-file      = "papers/Chiang-et-al.-2019-Building-Explainable-Predictive-Analytics-for-Location-Dependent-Time-Series-Data.pdf",
  doi       = "10.1109/CogMI48466.2019.00037"
}

@article{burns2019building,
  title={Building deep learning models for evidence classification from the open access biomedical literature},
  author={Burns, Gully A and Li, Xiangci and Peng, Nanyun},
  journal={Database},
  volume={2019},
  year={2019},
  publisher={Narnia}
}

@InProceedings{Moura19b,
	author = 	"Giovane C. M. Moura and John Heidemann and
 Ricardo de O. Schmidt and Wes Hardaker",
	title = 	"Cache Me If You Can: Effects of {DNS} {Time-to-Live}",
        booktitle =     "Proceedings of the " # "ACM Internet Measurement Conference",
        year =          2019,
	sortdate = "2019-10-31",
	project = "ant, lacanic, divoice, paaddos, nipet, ddidd",
	jsubject = "network_security",
        pages =      "to appear",
        month =      oct,
        address =    "Amsterdam, the Netherlands",
        publisher =  "ACM",
        jlocation =   "johnh: pafile",
	keywords = 	"anycast, dns, ttl, ddos, root ddos",
	doi = "https://doi.org/10.1145/3355369.3355568",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Moura19b.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Moura19b.pdf",
	blogurl = "https://ant.isi.edu/blog/?p=1355",
	dataurl =	"https://ant.isi.edu/datasets/dns/#Moura19b_data",
	myorganization =	"USC/Information Sciences Institute",
	copyrightholder = "authors",
	abstract = "DNS depends on extensive caching for good performance, and every DNS
zone owner must set \emph{Time-to-Live} (TTL) values to control their DNS
caching.  Today there is relatively little guidance backed by research
about how to set TTLs, and operators must balance conflicting demands
of caching against agility of configuration.  
Exactly how TTL value choices affect operational
networks is quite challenging to understand due to interactions across the
distributed DNS service, where resolvers receive TTLs in different ways
(answers and hints), TTLs are specified in multiple places (zones and
their parent's glue), and while DNS resolution must be security-aware.
This paper provides the first careful evaluation of how these multiple,
interacting factors affect the effective cache lifetimes of DNS records,
and provides recommendations for how to configure DNS TTLs based on
our findings.  We provide recommendations in TTL choice for different
situations, and for where they must be configured.  We show that longer
TTLs have significant promise in reducing latency, reducing it from
183ms to 28.7ms for one country-code TLD.",
}

DNS depends on extensive caching for good performance, and every DNS zone owner must set \emphTime-to-Live (TTL) values to control their DNS caching. Today there is relatively little guidance backed by research about how to set TTLs, and operators must balance conflicting demands of caching against agility of configuration. Exactly how TTL value choices affect operational networks is quite challenging to understand due to interactions across the distributed DNS service, where resolvers receive TTLs in different ways (answers and hints), TTLs are specified in multiple places (zones and their parent's glue), and while DNS resolution must be security-aware. This paper provides the first careful evaluation of how these multiple, interacting factors affect the effective cache lifetimes of DNS records, and provides recommendations for how to configure DNS TTLs based on our findings. We provide recommendations in TTL choice for different situations, and for where they must be configured. We show that longer TTLs have significant promise in reducing latency, reducing it from 183ms to 28.7ms for one country-code TLD.

@inproceedings{Moura:2019:CMY:3355369.3355568,
 author = {Moura, Giovane C. M. and Heidemann, John and Schmidt, Ricardo de O. and Hardaker, Wes},
 title = {Cache Me If You Can: Effects of DNS Time-to-Live},
 booktitle = {Proceedings of the Internet Measurement Conference},
 series = {IMC '19},
 year = {2019},
 isbn = {978-1-4503-6948-0},
 location = {Amsterdam, Netherlands},
 pages = {101--115},
 numpages = {15},
 url = {http://doi.acm.org/10.1145/3355369.3355568},
 pdfurl = {https://www.isi.edu/~hardaker/papers/2019-10-cache-me-ttls.pdf},
 doi = {10.1145/3355369.3355568},
 acmid = {3355568},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {DNS, caching, recursive DNS servers},
 ISIArea="NET",
}

@TechReport{Moura19a,
	author = 	"Giovane C. M. Moura and John Heidemann and
        		Ricardo de O. Schmidt and Wes Hardaker",
	title = 	"Cache Me If You Can: Effects of {DNS} {Time-to-Live} (extended)",
	institution = 	"USC/Information Sciences Institute",
	year = 		2019,
	sortdate = "2018-07-16",
	number = 	"ISI-TR-734b",
	note = "Released May 2018, updated Sept.~2019",
	project = "ant, lacanic, divoice, paaddos, nipet, ddidd",
	jsubject = "network_security",
	month = 	jul,
	jlocation = 	"johnh: pafile",
	keywords = 	"anycast, dns, ttl, ddos, root ddos",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Moura19a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Moura19a.pdf",
	xxxblogurl = "https://ant.isi.edu/blog/?p=1192",
	otherurl = "ftp://ftp.isi.edu/isi-pubs/tr-734.pdf",
	dataurl =	"https://ant.isi.edu/datasets/dns/#Moura19a_data",
	myorganization =	"USC/Information Sciences Institute",
	copyrightholder = "authors",
	abstract = "
DNS depends on extensive caching for good performance, and every DNS
zone owner must set Time-to-Live (TTL) values to control their
DNS caching.  Today there is relatively little guidance backed by
research about how to set TTLs, and operators must balance conflicting
demands of caching against agility of configuration.  Exactly how TTL
value choices affect operational networks is quite challenging to
understand for several reasons:  DNS is a distributed service, DNS
resolution is security-sensitive, and resolvers require multiple types
of information as they traverse the DNS hierarchy.  These complications
mean there are multiple frequently interacting, places TTLs can be
specified.  This paper provides the first careful evaluation of how
these factors affect the effective cache lifetimes of DNS records, and
provides recommendations for how to configure DNS TTLs based on our
findings.  We provide recommendations in TTL choice for different
situations, and for where they must be configured.  We show that longer
TTLs have significant promise, reducing median latency from 183ms to
28.7ms for one country-code TLD.
",
}

DNS depends on extensive caching for good performance, and every DNS zone owner must set Time-to-Live (TTL) values to control their DNS caching. Today there is relatively little guidance backed by research about how to set TTLs, and operators must balance conflicting demands of caching against agility of configuration. Exactly how TTL value choices affect operational networks is quite challenging to understand for several reasons: DNS is a distributed service, DNS resolution is security-sensitive, and resolvers require multiple types of information as they traverse the DNS hierarchy. These complications mean there are multiple frequently interacting, places TTLs can be specified. This paper provides the first careful evaluation of how these factors affect the effective cache lifetimes of DNS records, and provides recommendations for how to configure DNS TTLs based on our findings. We provide recommendations in TTL choice for different situations, and for where they must be configured. We show that longer TTLs have significant promise, reducing median latency from 183ms to 28.7ms for one country-code TLD.

@article{Turalska2019,
  title = {Cascading failures in scale-free interdependent networks},
  author = {Turalska, Malgorzata and Burghardt, Keith and Rohden, Martin and Swami, Ananthram and D'Souza, Raissa M.},
  journal = {Phys. Rev. E},
  volume = {99},
  issue = {3},
  pages = {032308},
  numpages = {9},
  year = {2019},
  month = {Mar},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.99.032308},
  url = {https://link.aps.org/doi/10.1103/PhysRevE.99.032308}
}

@InProceedings{	  thomas-escience-2019,
  Title		= {Characterization of In Situ and In Transit Analytics of
		  Molecular Dynamics Simulations for Next-generation
		  Supercomputers},
  Author	= {Thomas, Stephen and Wyatt, Michael and Do, Tu Mai Anh and
		  Pottier, Lo\"ic and Ferreira da Silva, Rafael and
		  Weinstein, Harel and Cuendet, Michel A. and Estrada, Trilce
		  and Deelman, Ewa and Taufer, Michela},
  BookTitle	= {15th International Conference on eScience (eScience)},
  Year		= {2019},
  Pages		= {188--198},
  DOI		= {10.1109/eScience.2019.00027},
  Note		= {Funding Acknowledgments: NSF 1741040}
}

@INPROCEEDINGS{Tavabi2019characterizing,
  author =       {Nazgol Tavabi and Nathan Bartley and Andres Abeliuk and Sandeep Soni and Emilio Ferrara and Kristina Lerman},
  title =        {Characterizing Activity on the Deep and DarkWeb},
   booktitle =    {Proceedings of the Companion to The Web Conference: CyberSafety Workshop},
  year =         {2019},
 }

@article{Badawy2019characterizing,
  title={Characterizing the 2016 Russian IRA influence campaign},
  author={Badawy, Adam and Addawood, Aseel and Lerman, Kristina and Ferrara, Emilio},
  journal={Social Network Analysis and Mining},
  volume={9},
  number={1},
  pages={31},
  year={2019},
  publisher={Springer}
}

@article{zhu_climate_2019,
	title = {Climate models can correctly simulate the continuum of global-average temperature variability},
	volume = {116},
	issn = {0027-8424},
	url = {https://github.com/khider/khider.github.io/blob/master/papers/zhu_pnas.pdf},
	doi = {10.1073/pnas.1809959116},
	abstract = {Climate models are foundational to formulations of climate policy and must successfully reproduce key features of the climate system. The temporal spectrum of observed global surface temperature is one such critical benchmark. This spectrum is known to obey scaling laws connecting astronomical forcings, from orbital to annual scales. We provide evidence that the current hierarchy of climate models is capable of reproducing the increase in variance in global-mean temperature at low frequencies. We suggest that successful climate predictions at decadal-to-centennial horizons hinge critically on the accuracy of initial and boundary conditions, particularly for the deep ocean state.Climate records exhibit scaling behavior with large exponents, resulting in larger fluctuations at longer timescales. It is unclear whether climate models are capable of simulating these fluctuations, which draws into question their ability to simulate such variability in the coming decades and centuries. Using the latest simulations and data syntheses, we find agreement for spectra derived from observations and models on timescales ranging from interannual to multimillennial. Our results confirm the existence of a scaling break between orbital and annual peaks, occurring around millennial periodicities. That both simple and comprehensive ocean–atmosphere models can reproduce these features suggests that long-range persistence is a consequence of the oceanic integration of both gradual and abrupt climate forcings. This result implies that Holocene low-frequency variability is partly a consequence of the climate system’s integrated memory of orbital forcing. We conclude that climate models appear to contain the essential physics to correctly simulate the spectral continuum of global-mean temperature; however, regional discrepancies remain unresolved. A critical element of successfully simulating suborbital climate variability involves, we hypothesize, initial conditions of the deep ocean state that are consistent with observations of the recent past.},
	number = {18},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Zhu, Feng and Emile-Geay, Julien and McKay, Nicholas P. and Hakim, Gregory J. and Khider, Deborah and Ault, Toby R. and Steig, Eric J. and Dee, Sylvia and Kirchner, James W.},
	year = {2019},
	pages = {8728--8733},
}

Climate models are foundational to formulations of climate policy and must successfully reproduce key features of the climate system. The temporal spectrum of observed global surface temperature is one such critical benchmark. This spectrum is known to obey scaling laws connecting astronomical forcings, from orbital to annual scales. We provide evidence that the current hierarchy of climate models is capable of reproducing the increase in variance in global-mean temperature at low frequencies. We suggest that successful climate predictions at decadal-to-centennial horizons hinge critically on the accuracy of initial and boundary conditions, particularly for the deep ocean state.Climate records exhibit scaling behavior with large exponents, resulting in larger fluctuations at longer timescales. It is unclear whether climate models are capable of simulating these fluctuations, which draws into question their ability to simulate such variability in the coming decades and centuries. Using the latest simulations and data syntheses, we find agreement for spectra derived from observations and models on timescales ranging from interannual to multimillennial. Our results confirm the existence of a scaling break between orbital and annual peaks, occurring around millennial periodicities. That both simple and comprehensive ocean–atmosphere models can reproduce these features suggests that long-range persistence is a consequence of the oceanic integration of both gradual and abrupt climate forcings. This result implies that Holocene low-frequency variability is partly a consequence of the climate system’s integrated memory of orbital forcing. We conclude that climate models appear to contain the essential physics to correctly simulate the spectral continuum of global-mean temperature; however, regional discrepancies remain unresolved. A critical element of successfully simulating suborbital climate variability involves, we hypothesize, initial conditions of the deep ocean state that are consistent with observations of the recent past.

@inproceedings{DBLP:conf/semweb/KejriwalS19,
  author    = {Mayank Kejriwal and
               Pedro A. Szekely},
  editor    = {Mari Carmen Su{\'{a}}rez{-}Figueroa and
               Gong Cheng and
               Anna Lisa Gentile and
               Christophe Gu{\'{e}}ret and
               C. Maria Keet and
               Abraham Bernstein},
  title     = {Co-LOD: Continuous Space Linked Open Data},
  booktitle = {Proceedings of the {ISWC} 2019 Satellite Tracks (Posters {\&} Demonstrations,
               Industry, and Outrageous Ideas) co-located with 18th International
               Semantic Web Conference {(ISWC} 2019), Auckland, New Zealand, October
               26-30, 2019},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2456},
  pages     = {333--337},
  publisher = {CEUR-WS.org},
  year      = {2019},
  url       = {http://ceur-ws.org/Vol-2456/paper94.pdf},
  timestamp = {Tue, 07 Sep 2021 14:10:34 +0200},
  biburl    = {https://dblp.org/rec/conf/semweb/KejriwalS19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@INPROCEEDINGS{8831193,
  author={Imes, Connor and Zhang, Huazhe and Zhao, Kevin and Hoffmann, Henry},
  booktitle={2019 IEEE International Conference on Autonomic Computing (ICAC)},
  title={CoPPer: Soft Real-Time Application Performance Using Hardware Power Capping},
  year={2019},
  volume={},
  number={},
  pages={31-41},
  abstract={Dynamic voltage and frequency scaling (DVFS) has been the cornerstone of innumerable software approaches to meeting application timing requirements with minimal energy. However, recent trends in technology-e.g., moving voltage converters on chip-favor hardware control of DVFS, as hardware can both react faster to external events and perform fine-grained power management across a device. We respond to these trends with CoPPer, which instead uses hardware power capping to meet application performance requirements with high energy efficiency. We find that meeting performance requirements with power capping is more challenging than using DVFS because the relationship between power and performance is non-linear and has diminishing returns at high power values. CoPPer overcomes these difficulties by using adaptive control to approximate non-linearities and a novel gain limit to avoid over-allocating power when it is no longer beneficial. We evaluate CoPPer with 20 parallel applications and compare it to both a classic linear DVFS controller and to a sophisticated control-theoretic, model-driven software DVFS manager. CoPPer provides all the functionality of the sophisticated DVFS-based approach, without requiring a user-specified model or time-consuming, exhaustive application/system pre-characterization. Compared to DVFS, CoPPer's gain limit reduces energy by 6% on average and by 12% for memory-bound applications. For high performance requirements, the energy savings are even greater: 8% on average and 18% for memory-bound applications.},
  keywords={energy conservation;multiprocessing systems;power aware computing;soft real-time application performance;hardware power capping;application timing requirements;minimal energy;voltage converters;chip-favor hardware control;fine-grained power management;adaptive control;model-driven software DVFS manager;sophisticated DVFS-based approach;memory-bound applications;dynamic voltage and frequency scaling;energy efficiency;parallel applications;linear DVFS controller;CoPPer;Copper;Software;Hardware;Timing;Power demand;Computational modeling;Market research;performance;power cap;rapl;control theory;dvfs;self aware systems;adaptive control},
  doi={10.1109/ICAC.2019.00015},
  ISSN={2474-0756},
  month={June},
  ISIArea = {CAS}
}

Dynamic voltage and frequency scaling (DVFS) has been the cornerstone of innumerable software approaches to meeting application timing requirements with minimal energy. However, recent trends in technology-e.g., moving voltage converters on chip-favor hardware control of DVFS, as hardware can both react faster to external events and perform fine-grained power management across a device. We respond to these trends with CoPPer, which instead uses hardware power capping to meet application performance requirements with high energy efficiency. We find that meeting performance requirements with power capping is more challenging than using DVFS because the relationship between power and performance is non-linear and has diminishing returns at high power values. CoPPer overcomes these difficulties by using adaptive control to approximate non-linearities and a novel gain limit to avoid over-allocating power when it is no longer beneficial. We evaluate CoPPer with 20 parallel applications and compare it to both a classic linear DVFS controller and to a sophisticated control-theoretic, model-driven software DVFS manager. CoPPer provides all the functionality of the sophisticated DVFS-based approach, without requiring a user-specified model or time-consuming, exhaustive application/system pre-characterization. Compared to DVFS, CoPPer's gain limit reduces energy by 6% on average and by 12% for memory-bound applications. For high performance requirements, the energy savings are even greater: 8% on average and 18% for memory-bound applications.

@conference {RN849,
	title = {Collaborating to leverage R\&E network infrastructures between Africa, Brazil, and the U.S.},
	booktitle = {UbuntuNet-Connect 2019},
	year = {2019},
	month = {2019},
	type = {Conference Paper},
	address = {Antananarivo, Madagascar},
	url = {https://urldefense.com/v3/__https://repository.ubuntunet.net/handle/10.20374/271__;!!FjuHKAHQs5udqho!OrfqNEjI8IYnpYEY3Wc7S4oSY6Kf82MHM8jGAJar7qa8MoXzNwtTTC2HiJsVSREP5YicoFyGz7HN7A$ },
	author = {Heidi Morgan and Julio Ibarra and Jeronimo Bezerra and Lopez, Luis Fernandez and Vasilka Chergarova and Donald A. Cox and Michael Stanton and Hazin, Aluizio and Lotz, Len and Mammen, Siju}
}

@article{muric2019collaboration,
  title={Collaboration Drives Individual Productivity},
  author={Muri{\'c}, Goran and Abeliuk, Andres and Lerman, Kristina and Ferrara, Emilio},
  journal={Proceedings of the ACM on Human-Computer Interaction},
  volume={3},
  number={CSCW},
  pages={1--24},
  year={2019},
  publisher={ACM New York, NY, USA}
}

@article{DBLP:journals/corr/abs-1911-11787,
  author       = {Goran Muric and
                  Andr{\'{e}}s Abeliuk and
                  Kristina Lerman and
                  Emilio Ferrara},
  title        = {Collaboration Drives Individual Productivity},
  journal      = {CoRR},
  volume       = {abs/1911.11787},
  year         = {2019},
  url          = {http://arxiv.org/abs/1911.11787},
  eprinttype    = {arXiv},
  eprint       = {1911.11787},
  timestamp    = {Sun, 02 Oct 2022 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1911-11787.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@Article{	  brinckman-fgcs-2018,
  Title		= {Collaborative Circuit Designs using the CRAFT Repository},
  Author	= {Brinckman, Adam and Deelman, Ewa and Gupta, Sandeep and
		  Nabrzyski, Jarek and Park, Soowang and Ferreira da Silva,
		  Rafael and Taylor, Ian J. and Vahi, Karan},
  Journal	= {Future Generation Computer Systems},
  Volume	= {94},
  Number	= {},
  Pages		= {841--853},
  Year		= {2019},
  DOI		= {10.1016/j.future.2018.01.018},
  Note		= {Funding Acknowledgments: DARPA HR0011-16-C-0043}
}

@inproceedings{embar:akbc19ws,
    author = "Embar, Varun and Pujara, Jay and Getoor, Lise",
    bib_url = "/pubs/bib/embar-akbc19ws.bib",
    booktitle = "AKBC Workshop on Federated KBs and the Open Knowledge Network",
    pdf_url = "/pubs/2019/embar-akbc19ws/embar-akbc19ws.pdf",
    sec = "ws",
    title = "Collective Alignment of Large-scale Ontologies",
    year = "2019"
}

@inprcoeedings{Yin2019ComprehensibleCT,
  title={Comprehensible Context-driven Text Game Playing},
  author={Xusen Yin and Jonathan May},
  booktitle={Proc. 2019 IEEE Conference on Games (CoG)},
  year={2019},
  month=August,
  pages={1-8}
}

@article{datta2019computational,
  title={Computational requirements for real-time ptychographic image reconstruction},
  author={Datta, Kaushik and Rittenbach, Andrew and Kang, Dong-In and Walters, John Paul and Crago, Stephen P and Damoulakis, John},
  journal={Applied Optics},
  volume={58},
  number={7},
  pages={B19--B27},
  year={2019},
  publisher={Optical Society of America}
}

@inproceedings{DBLP:conf/asunam/ZhangK19,
  author    = {Shuo Zhang and
               Mayank Kejriwal},
  editor    = {Francesca Spezzano and
               Wei Chen and
               Xiaokui Xiao},
  title     = {Concept drift in bias and sensationalism detection: an experimental
               study},
  booktitle = {{ASONAM} '19: International Conference on Advances in Social Networks
               Analysis and Mining, Vancouver, British Columbia, Canada, 27-30 August,
               2019},
  pages     = {601--604},
  publisher = {{ACM}},
  year      = {2019},
  url       = {https://doi.org/10.1145/3341161.3343690},
  doi       = {10.1145/3341161.3343690},
  timestamp = {Sun, 19 Jan 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/asunam/ZhangK19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{richey_considerations_2019,
	title = {Considerations for {Globigerinoides} ruber ({White} and {Pink}) {Paleoceanography}: {Comprehensive} {Insights} {From} a {Long}-{Running} {Sediment} {Trap}},
	volume = {34},
	url = {https://github.com/khider/khider.github.io/blob/master/papers/Richey_et_al-2019-Paleoceanography_and_Paleoclimatology.pdf},
	doi = {10.1029/2018PA003417},
	abstract = {Abstract We present a detailed analysis of the seasonal distribution, size, morphological variability, and geochemistry of co-occurring pink and white chromotypes of Globigerinoides ruber from a high-resolution (1–2 weeks) and long-running sediment trap time series in the northern Gulf of Mexico. We find no difference in the seasonal flux of the two chromotypes. Although flux of G. ruber is consistently lowest in winter, the flux-weighted signal exported to marine sediments represents mean annual conditions in the surface mixed layer. We observe the same morphological diversity among pink specimens of G. ruber as white. Comparison of the oxygen and carbon isotopic composition (δ18O and δ13C) of two morphotypes (sensu stricto and sensu lato) of pink G. ruber reveals the isotopes to be indistinguishable. The test size distribution within the population varies seasonally, with the abundance of large individuals increasing (decreasing) with increasing (decreasing) sea surface temperature. We find no systematic offsets in the Mg/Ca and δ18O of co-occurring pink and white G. ruber. The sediment trap data set shows that the Mg/Ca-temperature sensitivity for both chromotypes is much lower than the canonical 9\%/°C, which can likely be attributed to the secondary influence of both salinity and pH on foraminiferal Mg/Ca. Using paired Mg/Ca and δ18O, we evaluate the performance of a suite of published equations for calculating sea surface temperature, sea surface salinity, and isotopic composition of seawater (δ18Osw), including a new salinity-δ18Osw relationship for the northern Gulf of Mexico from water column observations.},
	number = {3},
	journal = {Paleoceanography and Paleoclimatology},
	author = {Richey, Julie N. and Thirumalai, Kaustubh and Khider, Deborah and Reynolds, Caitlin E. and Partin, Judson W. and Quinn, Terrence M.},
	year = {2019},
	keywords = {Globigerinoides ruber, Gulf of Mexico, Mg/Ca, Sediment Trap, morphotypes, planktic foraminifera},
	pages = {353--373},
}

Abstract We present a detailed analysis of the seasonal distribution, size, morphological variability, and geochemistry of co-occurring pink and white chromotypes of Globigerinoides ruber from a high-resolution (1–2 weeks) and long-running sediment trap time series in the northern Gulf of Mexico. We find no difference in the seasonal flux of the two chromotypes. Although flux of G. ruber is consistently lowest in winter, the flux-weighted signal exported to marine sediments represents mean annual conditions in the surface mixed layer. We observe the same morphological diversity among pink specimens of G. ruber as white. Comparison of the oxygen and carbon isotopic composition (δ18O and δ13C) of two morphotypes (sensu stricto and sensu lato) of pink G. ruber reveals the isotopes to be indistinguishable. The test size distribution within the population varies seasonally, with the abundance of large individuals increasing (decreasing) with increasing (decreasing) sea surface temperature. We find no systematic offsets in the Mg/Ca and δ18O of co-occurring pink and white G. ruber. The sediment trap data set shows that the Mg/Ca-temperature sensitivity for both chromotypes is much lower than the canonical 9%/°C, which can likely be attributed to the secondary influence of both salinity and pH on foraminiferal Mg/Ca. Using paired Mg/Ca and δ18O, we evaluate the performance of a suite of published equations for calculating sea surface temperature, sea surface salinity, and isotopic composition of seawater (δ18Osw), including a new salinity-δ18Osw relationship for the northern Gulf of Mexico from water column observations.

@conference {Schmidt2019,
    title = {Constellations in the Cloud: Virtualizing Remote Sensing Systems},
    booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
    year = {2019},
    author = {A. G. Schmidt, V. Venugopalan, M. Paolieri, and M. French}
}
  
    

@inproceedings{schmidt2019b,
	author = {Andrew G. Schmidt and Venugopalan, Vivek and Marco Paolieri and French, M.},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
	title = {Constellations in the cloud: Virtualizing remote sensing systems},
	year = {2019}}

@inproceedings{mhamdi-etal-2019-contextualized,
    title = "Contextualized Cross-Lingual Event Trigger Extraction with Minimal Resources",
    author = "M{'}hamdi, Meryem  and
      Freedman, Marjorie  and
      May, Jonathan",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1061",
    doi = "10.18653/v1/K19-1061",
    pages = "656--665",
    abstract = "Event trigger extraction is an information extraction task of practical utility, yet it is challenging due to the difficulty of disambiguating word sense meaning. Previous approaches rely extensively on hand-crafted language-specific features and are applied mainly to English for which annotated datasets and Natural Language Processing (NLP) tools are available. However, the availability of such resources varies from one language to another. Recently, contextualized Bidirectional Encoder Representations from Transformers (BERT) models have established state-of-the-art performance for a variety of NLP tasks. However, there has not been much effort in exploring language transfer using BERT for event extraction. In this work, we treat event trigger extraction as a sequence tagging problem and propose a cross-lingual framework for training it without any hand-crafted features. We experiment with different flavors of transfer learning from high-resourced to low-resourced languages and compare the performance of different multilingual embeddings for event trigger extraction. Our results show that training in a multilingual setting outperforms language-specific models for both English and Chinese. Our work is the first to experiment with two event architecture variants in a cross-lingual setting, to show the effectiveness of contextualized embeddings obtained using BERT, and to explore and analyze its performance on Arabic.",
}

Event trigger extraction is an information extraction task of practical utility, yet it is challenging due to the difficulty of disambiguating word sense meaning. Previous approaches rely extensively on hand-crafted language-specific features and are applied mainly to English for which annotated datasets and Natural Language Processing (NLP) tools are available. However, the availability of such resources varies from one language to another. Recently, contextualized Bidirectional Encoder Representations from Transformers (BERT) models have established state-of-the-art performance for a variety of NLP tasks. However, there has not been much effort in exploring language transfer using BERT for event extraction. In this work, we treat event trigger extraction as a sequence tagging problem and propose a cross-lingual framework for training it without any hand-crafted features. We experiment with different flavors of transfer learning from high-resourced to low-resourced languages and compare the performance of different multilingual embeddings for event trigger extraction. Our results show that training in a multilingual setting outperforms language-specific models for both English and Chinese. Our work is the first to experiment with two event architecture variants in a cross-lingual setting, to show the effectiveness of contextualized embeddings obtained using BERT, and to explore and analyze its performance on Arabic.

@inproceedings{han2019contextualized,
  title={Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding},
  author={Han, Rujun and Liang, Mengyue and Alhafni, Bashar and Peng, Nanyun},
  booktitle={2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2019), Workshop on Narrative Understanding},
  year={2019}
}

@article{Soma2019Coregulationoftherapistand,
 author = {Soma, Christina S. and Baucom, Brian R. W. and Xiao, Bo and Butner, Jonathan E. and Hilpert, Peter and Narayanan,  Shrikanth and Atkins, David C. and Imel, Zac E.},
 journal = {Psychotherapy Research},
 title = {Coregulation of therapist and client emotion during psychotherapy},
 year = {2019},
 month = {Nov}
}

@inproceedings{liu2019coupled,
  title={Coupled clustering of time-series and networks},
  author={Liu, Yike and Zhu, Linhong and Szekely, Pedro and Galstyan, Aram and Koutra, Danai},
  booktitle={Proceedings of the 2019 SIAM International Conference on Data Mining},
  pages={531--539},
  year={2019},
  organization={Society for Industrial and Applied Mathematics}
}

@inproceedings{shbita2019creating,
  title={Creating a FAIR Data Catalog to Support Scientific Modeling},
  author={Shbita, Basel and Vu, Binh and Feldman, Dan and Pham, Minh and Rajendran, Arunkumar and Knoblock, Craig A and Pujara, Jay and Chiang, Yao-Yi},
  booktitle={Proceedings of the Workshop on Advanced Knowledge Technologies for Science in a FAIR World},
  location={San Diego, CA},
  year={2019},
  urlPaper={http://usc-isi-i2.github.io/papers/shbita19-escience.pdf},
  urlSlides={http://usc-isi-i2.github.io/slides/shbita19-escience-slides.pdf}
}

@article{Sorensen2019Cross-ModalCoordinationofFace-Directed,
 author = {Sorensen, Tanner and Zane, Emily and Feng, Tiantian and Narayanan, Shrikanth and Grossman, Ruth},
 doi = {10.1038/s41598-019-54587-z},
 journal = {Scientific Reports},
 link = {http://sail.usc.edu/publications/files/sorensen2019ASD.pdf},
 month = {Dec},
 number = {18301},
 title = {Cross-Modal Coordination of Face-Directed Gaze and Emotional Speech Production in School-Aged Children and Adolescents with ASD},
 volume = {9},
 year = {2019}
}

@article{uddin2019cross,
  title={Cross-lingual Dependency Parsing with Unlabeled Auxiliary Languages},
  author={Uddin Ahmad, Wasi and Zhang, Zhisong and Ma, Xuezhe and Chang, Kai-Wei and Peng, Nanyun},
  journal={arXiv preprint arXiv:1909.09265},
  year={2019}
}

@inproceedings{ahmad2019cross,
  title={Cross-lingual Dependency Parsing with Unlabeled Auxiliary Languages},
  author={Ahmad, Wasi Uddin and Zhang, Zhisong and Ma, Xuezhe and Chang, Kai-Wei and Peng, Nanyun},
  booktitle={The 2019 SIGNLL Conference on Computational Natural Language Learning (CoNLL)},
  year={2019}
}

@inproceedings{pan-etal-2019-cross,
    title = "Cross-lingual Joint Entity and Word Embedding to Improve Entity Linking and Parallel Sentence Mining",
    author = "Pan, Xiaoman  and
      Gowda, Thamme  and
      Ji, Heng  and
      May, Jonathan  and
      Miller, Scott",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6107",
    doi = "10.18653/v1/D19-6107",
    pages = "56--66",
    abstract = "Entities, which refer to distinct objects in the real world, can be viewed as language universals and used as effective signals to generate less ambiguous semantic representations and align multiple languages. We propose a novel method, CLEW, to generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia. We replace each anchor link in the source language with its corresponding entity title in the target language if it exists, or in the source language otherwise. A cross-lingual joint entity and word embedding learned from this kind of data not only can disambiguate linkable entities but can also effectively represent unlinkable entities. Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking. Experimental results show that CLEW significantly advances the state-of-the-art: up to 3.1{\%} absolute F-score gain for unsupervised cross-lingual entity linking. Moreover, it provides reliable alignment on both the word/entity level and the sentence level, and thus we use it to mine parallel sentences for all (302, 2) language pairs in Wikipedia.",
}

Entities, which refer to distinct objects in the real world, can be viewed as language universals and used as effective signals to generate less ambiguous semantic representations and align multiple languages. We propose a novel method, CLEW, to generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia. We replace each anchor link in the source language with its corresponding entity title in the target language if it exists, or in the source language otherwise. A cross-lingual joint entity and word embedding learned from this kind of data not only can disambiguate linkable entities but can also effectively represent unlinkable entities. Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking. Experimental results show that CLEW significantly advances the state-of-the-art: up to 3.1% absolute F-score gain for unsupervised cross-lingual entity linking. Moreover, it provides reliable alignment on both the word/entity level and the sentence level, and thus we use it to mine parallel sentences for all (302, 2) language pairs in Wikipedia.

@inproceedings{pan-etal-2019-cross,
    title = "Cross-lingual Joint Entity and Word Embedding to Improve Entity Linking and Parallel Sentence Mining",
    author = "Pan, Xiaoman  and
      \textbf{Gowda, Thamme}  and
      Ji, Heng  and
      May, Jonathan  and
      Miller, Scott",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6107",
    doi = "10.18653/v1/D19-6107",
    pages = "56--66",
    abstract = "Entities, which refer to distinct objects in the real world, can be viewed as language universals and used as effective signals to generate less ambiguous semantic representations and align multiple languages. We propose a novel method, CLEW, to generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia. We replace each anchor link in the source language with its corresponding entity title in the target language if it exists, or in the source language otherwise. A cross-lingual joint entity and word embedding learned from this kind of data not only can disambiguate linkable entities but can also effectively represent unlinkable entities. Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking. Experimental results show that CLEW significantly advances the state-of-the-art: up to 3.1{\%} absolute F-score gain for unsupervised cross-lingual entity linking. Moreover, it provides reliable alignment on both the word/entity level and the sentence level, and thus we use it to mine parallel sentences for all (302, 2) language pairs in Wikipedia.",
}

Entities, which refer to distinct objects in the real world, can be viewed as language universals and used as effective signals to generate less ambiguous semantic representations and align multiple languages. We propose a novel method, CLEW, to generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia. We replace each anchor link in the source language with its corresponding entity title in the target language if it exists, or in the source language otherwise. A cross-lingual joint entity and word embedding learned from this kind of data not only can disambiguate linkable entities but can also effectively represent unlinkable entities. Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking. Experimental results show that CLEW significantly advances the state-of-the-art: up to 3.1% absolute F-score gain for unsupervised cross-lingual entity linking. Moreover, it provides reliable alignment on both the word/entity level and the sentence level, and thus we use it to mine parallel sentences for all (302, 2) language pairs in Wikipedia.

@InProceedings{huang-ji-may:2019:N19-1,
  author    = {Huang, Lifu  and  Ji, Heng  and  May, Jonathan},
  title     = {Cross-lingual Multi-Level Adversarial Transfer to Enhance Low-Resource Name Tagging},
  booktitle = {Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota},
  publisher = {Association for Computational Linguistics},
  pages     = {3823--3833},
  abstract  = {We focus on improving name tagging for low-resource languages using annotations from related languages. Previous studies either directly project annotations from a source language to a target language using cross-lingual representations or use a shared encoder in a multitask network to transfer knowledge. These approaches inevitably introduce noise to the target language annotation due to mismatched source-target sentence structures. To effectively transfer the resources, we develop a new neural architecture that leverages multi-level adversarial transfer: (1) word-level adversarial training, which projects source language words into the same semantic space as those of the target language without using any parallel corpora or bilingual gazetteers, and (2) sentence-level adversarial training, which yields language-agnostic sequential features. Our neural architecture outperforms previous approaches on CoNLL data sets. Moreover, on 10 low-resource languages, our approach achieves up to 16\% absolute F-score gain over all high-performing baselines on cross-lingual transfer without using any target-language resources.},
  url       = {http://www.aclweb.org/anthology/N19-1383}
}

We focus on improving name tagging for low-resource languages using annotations from related languages. Previous studies either directly project annotations from a source language to a target language using cross-lingual representations or use a shared encoder in a multitask network to transfer knowledge. These approaches inevitably introduce noise to the target language annotation due to mismatched source-target sentence structures. To effectively transfer the resources, we develop a new neural architecture that leverages multi-level adversarial transfer: (1) word-level adversarial training, which projects source language words into the same semantic space as those of the target language without using any parallel corpora or bilingual gazetteers, and (2) sentence-level adversarial training, which yields language-agnostic sequential features. Our neural architecture outperforms previous approaches on CoNLL data sets. Moreover, on 10 low-resource languages, our approach achieves up to 16% absolute F-score gain over all high-performing baselines on cross-lingual transfer without using any target-language resources.

@inproceedings{subburathinam-etal-2019-cross,
    title = "Cross-lingual Structure Transfer for Relation and Event Extraction",
    author = "Subburathinam, Ananya  and
      Lu, Di  and
      Ji, Heng  and
      May, Jonathan  and
      Chang, Shih-Fu  and
      Sil, Avirup  and
      Voss, Clare",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1030",
    doi = "10.18653/v1/D19-1030",
    pages = "313--325",
    abstract = "The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages. We investigate the suitability of cross-lingual structure transfer techniques for these tasks. We exploit relation- and event-relevant language-universal features, leveraging both symbolic (including part-of-speech and dependency path) and distributional (including type representation and contextualized representation) information. By representing all entity mentions, event triggers, and contexts into this complex and structured multilingual common space, using graph convolutional networks, we can train a relation or event extractor from source language annotations and apply it to the target language. Extensive experiments on cross-lingual relation and event transfer among English, Chinese, and Arabic demonstrate that our approach achieves performance comparable to state-of-the-art supervised models trained on up to 3,000 manually annotated mentions: up to 62.6{\%} F-score for Relation Extraction, and 63.1{\%} F-score for Event Argument Role Labeling. The event argument role labeling model transferred from English to Chinese achieves similar performance as the model trained from Chinese. We thus find that language-universal symbolic and distributional representations are complementary for cross-lingual structure transfer.",
}

The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages. We investigate the suitability of cross-lingual structure transfer techniques for these tasks. We exploit relation- and event-relevant language-universal features, leveraging both symbolic (including part-of-speech and dependency path) and distributional (including type representation and contextualized representation) information. By representing all entity mentions, event triggers, and contexts into this complex and structured multilingual common space, using graph convolutional networks, we can train a relation or event extractor from source language annotations and apply it to the target language. Extensive experiments on cross-lingual relation and event transfer among English, Chinese, and Arabic demonstrate that our approach achieves performance comparable to state-of-the-art supervised models trained on up to 3,000 manually annotated mentions: up to 62.6% F-score for Relation Extraction, and 63.1% F-score for Event Argument Role Labeling. The event argument role labeling model transferred from English to Chinese achieves similar performance as the model trained from Chinese. We thus find that language-universal symbolic and distributional representations are complementary for cross-lingual structure transfer.

@InProceedings{	  vahi-escience-2019,
  Title		= {Custom Execution Environments with Containers in
		  Pegasus-enabled Scientific Workflows},
  Author	= {Vahi, Karan and Rynge, Mats and Papadimitriou, George and
		  Brown, Duncan and Mayani, Rajiv and Ferreira da Silva,
		  Rafael and Deelman, Ewa and Mandal, Anirban and Lyons, Eric
		  and Zink, Michael},
  BookTitle	= {15th International Conference on eScience (eScience)},
  Year		= {2019},
  Location	= {San Diego, CA, USA},
  Pages		= {281--290},
  DOI		= {10.1109/eScience.2019.00039},
  Note		= {Funding Acknowledgments: NSF 1664162, NSF 1826997, NSF
		  1443047}
}

@InProceedings{	  deelman-escience-2019,
  Title		= {Cyberinfrastructure Center of Excellence Pilot: Connecting
		  Large Facilities Cyberinfrastructure},
  Author	= {Deelman, Ewa and Mandal, Anirban and Pascucci, Valerio and
		  Sons, Susan and Wyngaard, Jane and Vardeman II, Charles F
		  and Petruzza, Steve and Baldin, Ilya and Christopherson,
		  Laura and Mitchell, Ryan and Pottier, Lo\"ic and Rynge,
		  Mats and Scott, Erik and Vahi, Karan and Kogank, Marina and
		  Mann, Jasmine A and Gulbransen, Tom and Allen, Daniel and
		  Barlow, David and Bonarrigo, Santiago and Clark, Chris and
		  Goldman, Leslie and Goulden, Tristan and Harvey, Phil and
		  Hulsander, David and Jacob, Steve and Laney, Christine and
		  Lobo-Padilla, Ivan and Sampson, Jeremey and Staarmann, John
		  and Stone, Steve},
  BookTitle	= {15th International Conference on eScience (eScience)},
  Year		= {2019},
  Location	= {San Diego, CA, USA},
  Pages		= {},
  DOI		= {},
  Note		= {Funding Acknowledgments: NSF 1842042}
}

@article{
 title = {Cyberinfrastructure Requirements to Enhance Multi-messenger Astrophysics},
 type = {article},
 year = {2019},
 websites = {http://arxiv.org/abs/1903.04590},
 month = {3},
 day = {11},
 id = {2bcb05ba-9331-34ad-b009-f2f4cba04f58},
 created = {2020-04-22T21:44:57.173Z},
 accessed = {2020-04-22},
 file_attached = {true},
 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 last_modified = {2020-05-11T14:43:33.833Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 citation_key = {Chang2019},
 private_publication = {false},
 abstract = {The identification of the electromagnetic counterpart of the gravitational wave event, GW170817, and discovery of neutrinos and gamma-rays from TXS 0506+056 heralded the new era of multi-messenger astrophysics. As the number of multi-messenger events rapidly grow over the next decade, the cyberinfrastructure requirements to handle the increase in data rates, data volume, need for event follow up, and analysis across the different messengers will also explosively grow. The cyberinfrastructure requirements to enhance multi-messenger astrophysics will both be a major challenge and opportunity for astronomers, physicists, computer scientists and cyberinfrastructure specialists. Here we outline some of these requirements and argue for a distributed cyberinfrastructure institute for multi-messenger astrophysics to meet these challenges.},
 bibtype = {article},
 author = {Chang, Philip and Allen, Gabrielle and Anderson, Warren and Bianco, Federica B. and Bloom, Joshua S. and Brady, Patrick R. and Brazier, Adam and Cenko, S. Bradley and Couch, Sean M. and DeYoung, Tyce and Deelman, Ewa and Etienne, Zachariah B and Foley, Ryan J. and Fox, Derek B and Golkhou, V. Zach and Grant, Darren R and Hanna, Chad and Holley-Bockelmann, Kelly and Howell, D. Andrew and Huerta, E. A. and Johnson, Margaret W. G. and Juric, Mario and Kaplan, David L. and Katz, Daniel S. and Keivani, Azadeh and Kerzendorf, Wolfgang and Kopper, Claudio and Lam, Michael T. and Lehner, Luis and Marka, Zsuzsa and Marka, Szabolcs and Nabrzyski, Jarek and Narayan, Gautham and O'Shea, Brian W. and Petravick, Donald and Quick, Rob and Street, Rachel A. and Taboada, Ignacio and Timmes, Frank and Turk, Matthew J. and Weltman, Amanda and Zhang, Zhao}
}

The identification of the electromagnetic counterpart of the gravitational wave event, GW170817, and discovery of neutrinos and gamma-rays from TXS 0506+056 heralded the new era of multi-messenger astrophysics. As the number of multi-messenger events rapidly grow over the next decade, the cyberinfrastructure requirements to handle the increase in data rates, data volume, need for event follow up, and analysis across the different messengers will also explosively grow. The cyberinfrastructure requirements to enhance multi-messenger astrophysics will both be a major challenge and opportunity for astronomers, physicists, computer scientists and cyberinfrastructure specialists. Here we outline some of these requirements and argue for a distributed cyberinfrastructure institute for multi-messenger astrophysics to meet these challenges.

@inproceedings{kline19cacoe,
 title={{Cybersecurity Experimentation at Program Scale: Guidelines and Principles for Future Testbeds}},
 author={Kline, Erik and Schwab, Stephen},
 booktitle={Cyber Range Applications and Technologies (CACOE)},
 year={2019}
 }

@inproceedings{vu:kcap19,
    author = "Vu, Binh and Knoblock, Craig and Pujara, Jay",
    acceptrate = "18\%",
    bib_url = "/pubs/bib/vu-kcap19.bib",
    booktitle = "ACM International Conference on Knowledge Capture (K-CAP)",
    doi_url = "https://doi.org/10.1145/3308558.3313711",
    pdf_url = "/pubs/2019/vu-kcap19/vu-kcap19.pdf",
    sec = "conf",
    title = "D-REPR: A Language for Describing and Mapping Diversely-Structured Data Sources to RDF",
    year = "2019"
}

@inproceedings{vu2019d,
  title={D-REPR: A Language for Describing and Mapping Diversely-Structured Data Sources to RDF},
  author={Vu, Binh and Pujara, Jay and Knoblock, Craig A},
  booktitle={Proceedings of the 10th International Conference on Knowledge Capture},
  pages={189--196},
  year={2019},
  Urlpaper = {http://usc-isi-i2.github.io/papers/vu19-kcap.pdf},
  UrlSlides = {http://usc-isi-i2.github.io/slides/vu2019-slides.pdf}
}

@Misc{Hardaker19b,
title="DANE update metrics",
author="Viktor Dukhovni and  Wes Hardaker",
month=06,
year=2019,
sortdate = "2019-06-22",
URL="https://65.schedule.icann.org/meetings/1058208",
project = "ant, broot",
keywords="dns, dnssec, dane, security",
howpublished="Talk at ICANN DNSSEC Workshop",
pdfurl="https://ant.isi.edu/~hardaker/presentations/2019-06-DANE-hardaker-dukhovni.pdf"
}

% wjh:isi:gawseedresearch,

@Misc{Heidemann19a,
	author = 	"John Heidemann and Wes Hardaker and Jelena
                  Mirkovic and ASM Rizvi and Robert Story",
	title = 	"{DDoS} Defense in Depth for {DNS} (DDIDD)",
	howpublished = "Invited talk at the Trusted CI Webinar",
	month = 	dec,
	year = 	2019,
	sortdate = 	"2019-12-09", 
	project = "ant, ddidd, paaddos, diiner",
	jsubject = "topology_modeling",
	jlocation = 	"johnh: pafile",
	keywords = 	"anti-DDoS, network security, B-Root, invited talks",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Heidemann19a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Heidemann19a.pdf",
	videourl = "https://youtu.be/g_IivqPLdQM",
	myorganization =	"USC/Information Sciences Institute",
	copyrightholder = "authors",
	abstract = "
he DDIDD Project (DDoS Defense in Depth for DNS) is
applying existing and developing new defenses against
Distributed-Denial-of-Service attacks for operational DNS
infrastructure.  We are building a defense-in-depth approach to
mitigate Distributed Denial-of-Service attacks for DNS servers, with
approaches to filter spoofed traffic, identify known-good traffic when
possible, and employ cloud-based scaling to handle the largest
attacks.  We are working with USC's B-Root team to test our approaches
as a case study, and are making approaches open source as they become
available.  This talk will summarize the project and our overall
approach, provide details about some of our early filters and filter
selection, and describe where we plan to go in the remaining year.
",
}

he DDIDD Project (DDoS Defense in Depth for DNS) is applying existing and developing new defenses against Distributed-Denial-of-Service attacks for operational DNS infrastructure. We are building a defense-in-depth approach to mitigate Distributed Denial-of-Service attacks for DNS servers, with approaches to filter spoofed traffic, identify known-good traffic when possible, and employ cloud-based scaling to handle the largest attacks. We are working with USC's B-Root team to test our approaches as a case study, and are making approaches open source as they become available. This talk will summarize the project and our overall approach, provide details about some of our early filters and filter selection, and describe where we plan to go in the remaining year.

@inproceedings{chatziagapi2019data,
	title = {Data augmentation using {GANs} for speech emotion recognition.},
	booktitle = {Interspeech},
	author = {Chatziagapi, Aggelina and Paraskevopoulos, Georgios and Sgouropoulos, Dimitris and Pantazopoulos, Georgios and Nikandrou, Malvina and Giannakopoulos, Theodoros and Katsamanis, Athanasios and Potamianos, Alexandros and Narayanan, Shrikanth},
	year = {2019},
	keywords = {⛔ No DOI found},
	pages = {171--175},
}

@inproceedings{mehrabi2019debiasing,
  title={Debiasing community detection: The importance of lowly connected nodes},
  author={Mehrabi, Ninareh and Morstatter, Fred and Peng, Nanyun and Galstyan, Aram},
  booktitle={2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},
  pages={509--512},
  year={2019},
  organization={IEEE}
}

@inproceedings{inproceedings,
author = {Han, Rujun and Hsu, I-Hung and Yang, Mu and Galstyan, Aram and Weischedel, Ralph and Peng, Nanyun},
year = {2019},
month = {01},
pages = {666-106},
title = {Deep Structured Neural Network for Event Temporal Relation Extraction},
doi = {10.18653/v1/K19-1062}
}

@inproceedings{han2019deep,
  title={Deep structured neural network for event temporal relation extraction},
  author={Han, Rujun and Hsu, I and Yang, Mu and Galstyan, Aram and Weischedel, Ralph and Peng, Nanyun and others},
booktitle={Proceedings  of the 2019 SIGNLL Conference on Computational Natural Language Learning (CoNLL)},
 year={2019}
}

@article{DBLP:journals/ijcv/ChangTHMNM19,
  author       = {Feng{-}Ju Chang and
                  Anh Tuan Tran and
                  Tal Hassner and
                  Iacopo Masi and
                  Ram Nevatia and
                  G{\'{e}}rard G. Medioni},
  title        = {Deep, Landmark-Free {FAME:} Face Alignment, Modeling, and Expression
                  Estimation},
  journal      = {Int. J. Comput. Vis.},
  volume       = {127},
  number       = {6-7},
  pages        = {930--956},
  year         = {2019},
  url          = {https://doi.org/10.1007/s11263-019-01151-x},
  doi          = {10.1007/S11263-019-01151-X},
  timestamp    = {Fri, 13 Mar 2020 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/ijcv/ChangTHMNM19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{tandon2019defending,
  title={Defending Web Servers Against Flash Crowd Attacks},
  author={Tandon, Rajat and Palia, Abhinav and Ramani, Jaydeep and Paulsen, Brandon and Bartlett, Genevieve and Mirkovic, Jelena},
  booktitle={Midscale Education and Research Infrastructure and Tools (MERIT) Workshop},
  pages={1--2},
  year={2019},
  organization={IEEE}
}

@inproceedings{habif2019demonstration,
  title={Demonstration of the Generalized Kennedy Receiver as a Near Quantum-Optimal Measurement for the Discrimination of Weak Classical Optical States},
  author={Habif, Jonathan and Jagannathan, Arunkumar},
  booktitle={APS Meeting Abstracts},
  year={2019}
}

@inproceedings{gartenstein2019demonstration,
  title={Demonstration of the Generalized-Kennedy Receiver for Quantum-Limited Discrimination of Photon-Starved Classical Light},
  author={Gartenstein, Samuel and Habif, Jonathan L and Jagannathan, Arun},
  booktitle={Laser Science},
  pages={JTu3A--112},
  year={2019},
  organization={Optical Society of America}
}

@inproceedings{rajagopala2019b,
	author = {Abhi D. Rajagopala and Ron Sass and Andrew G. Schmidt},
	booktitle = {International Symposium on Memory Systems (MEMSYS)},
	title = {Design Considerations for Mapping FPGA High-Level Synthesis Algorithms to Next-Generation Memory Devices},
	year = {2019}}

@article{Imel2018Designandpilotfeasibility,
 author = {Imel, Zac and Pace, Brian and Soma, Christina and Tanana, Michael and Hirsch, Tad and  Gibson, James and Georgiou, Panayiotis and Narayanan, Shrikanth and Atkins, David},
 journal = {Pyschotherapy},
 title = {Design and pilot feasibility of an automated, machine learning feedback system for Motivational Interviewing Psychotherapy},
 year = {2019},
 month = {Apr}
}

@inproceedings{mirkovicdesigning,
  title={Designing for Fallible Humans},
  author={Mirkovic, Jelena and Woo, Simon S},
  booktitle={Humans and CyberSecurity Workshop (HACS)},
  year={2019}
}

@article{nettasinghe2019diffusion,
  title={Diffusion in social networks: Effects of monophilic contagion, friendship paradox and reactive networks},
  author={Nettasinghe, Buddhika and Krishnamurthy, Vikram and Lerman, Kristina},
  journal={IEEE Transactions on Network Science and Engineering},
  year={2019},
  publisher={IEEE}
}

@article{li2019discourse,
  title={Discourse Tagging for Scientific Evidence Extraction},
  author={Li, Xiangci and Burns, Gully and Peng, Nanyun},
  journal={arXiv preprint arXiv:1909.04758},
  year={2019}
}

@inproceedings{Feng2019DiscoveringOptimalVariable-lengthTime,
 author = {Feng, Tiantian and Narayanan, Shrikanth},
 booktitle = {Proceedings of ICASSP},
 location = {Brighton, UK},
 month = {May},
 title = {Discovering Optimal Variable-length Time Series Motifs in Large-Scale Wearable Recordings of Human Bio-behavioral Signals},
 year = {2019}
}

@inproceedings{mullenbach-etal-2019-nuclear,
    title = "Do Nuclear Submarines Have Nuclear Captains? A Challenge Dataset for Commonsense Reasoning over Adjectives and Objects",
    author = "Mullenbach, James  and
      Gordon, Jonathan  and
      Peng, Nanyun  and
      May, Jonathan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1625",
    doi = "10.18653/v1/D19-1625",
    pages = "6051--6057",
    abstract = "How do adjectives project from a noun to its parts? If a motorcycle is red, are its wheels red? Is a nuclear submarine{'}s captain nuclear? These questions are easy for humans to judge using our commonsense understanding of the world, but are difficult for computers. To attack this challenge, we crowdsource a set of human judgments that answer the English-language question {``}Given a whole described by an adjective, does the adjective also describe a given part?{''} We build strong baselines for this task with a classification approach. Our findings indicate that, despite the recent successes of large language models on tasks aimed to assess commonsense knowledge, these models do not greatly outperform simple word-level models based on pre-trained word embeddings. This provides evidence that the amount of commonsense knowledge encoded in these language models does not extend far beyond that already baked into the word embeddings. Our dataset will serve as a useful testbed for future research in commonsense reasoning, especially as it relates to adjectives and objects",
}

How do adjectives project from a noun to its parts? If a motorcycle is red, are its wheels red? Is a nuclear submarine's captain nuclear? These questions are easy for humans to judge using our commonsense understanding of the world, but are difficult for computers. To attack this challenge, we crowdsource a set of human judgments that answer the English-language question ``Given a whole described by an adjective, does the adjective also describe a given part?'' We build strong baselines for this task with a classification approach. Our findings indicate that, despite the recent successes of large language models on tasks aimed to assess commonsense knowledge, these models do not greatly outperform simple word-level models based on pre-trained word embeddings. This provides evidence that the amount of commonsense knowledge encoded in these language models does not extend far beyond that already baked into the word embeddings. Our dataset will serve as a useful testbed for future research in commonsense reasoning, especially as it relates to adjectives and objects

@inproceedings{DBLP:conf/icdar/PengWC19,
  author    = {Xujun Peng and
               Chao Wang and
               Huaigu Cao},
  title     = {Document Binarization via Multi-resolutional Attention Model with
               DRD Loss},
  booktitle = {2019 International Conference on Document Analysis and Recognition,
               ICDAR 2019, Sydney, Australia, September 20-25, 2019},
  pages     = {45--50},
  publisher = {IEEE},
  year      = {2019},
  url       = {https://doi.org/10.1109/ICDAR.2019.00017},
  doi       = {10.1109/ICDAR.2019.00017},
  timestamp = {Tue, 04 Feb 2020 13:28:39 +0100},
  biburl    = {https://dblp.org/rec/conf/icdar/PengWC19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1906-02858,
  author       = {Joe Mathai and
                  Iacopo Masi and
                  Wael AbdAlmageed},
  title        = {Does Generative Face Completion Help Face Recognition?},
  journal      = {CoRR},
  volume       = {abs/1906.02858},
  year         = {2019},
  url          = {http://arxiv.org/abs/1906.02858},
  eprinttype    = {arXiv},
  eprint       = {1906.02858},
  timestamp    = {Fri, 14 Jun 2019 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1906-02858.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@techreport{szekely2019domain,
  title={Domain-Specific Insight Graphs},
  author={Szekely, Pedro and Kejriwal, Mayank},
  year={2019},
  institution={University of Southern California Marina del Rey United States}
}

@book{DBLP:series/sbcs/Kejriwal19,
  author    = {Mayank Kejriwal},
  title     = {Domain-Specific Knowledge Graph Construction},
  series    = {Springer Briefs in Computer Science},
  publisher = {Springer},
  year      = {2019},
  url       = {https://doi.org/10.1007/978-3-030-12375-8},
  doi       = {10.1007/978-3-030-12375-8},
  isbn      = {978-3-030-12374-1},
  timestamp = {Fri, 15 Mar 2019 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/series/sbcs/Kejriwal19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@TechReport{Rizvi19a,
	author = 	"{ASM} Rizvi and John Heidemann and Jelena Mirkovic",
	title = 	"Dynamically Selecting Defenses to {DDoS} for {DNS} (extended)",
	institution = 	"USC/Information Sciences Institute",
	year = 		2019,
	sortdate = 		"2019-12-03", 
	project = "ant, ddidd, paaddos",
	jsubject = "routing",
	number =	"ISI-TR-736",
	month =		may,
	jlocation =	"johnh: pafile",
	keywords =	"ddos, filtering, hop-count, rcode, dns",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Rizvi19a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Rizvi19a.pdf",
	myorganization =	"USC/Information Sciences Institute",
	copyrightholder = "authors",
	abstract = "
Distributed Denial-of-Service (DDoS) attacks exhaust resources,
leaving a server unavailable to legitimate clients.  The Domain Name
System (DNS) is frequently the target of DDoS attacks, and its
connectionless communication makes it an easy target for spoofing
attacks.  A large body of prior work has focused on specific filters
or anti-spoofing techniques, but DDoS threats continue to grow,
augmented by the addition of millions of Internet-of-Things (IoT)
devices.  We propose two approaches to DDoS-defense:  first, we
propose having a \emph{library} of defensive filters ready, each
applicable to different attack types and with different levels of
selectivity.  Second, we suggest \emph{automatically selecting} the
best defense mechanism at attack start, and re-evaluating that choice
during the attack to account for polymorphic attacks.  While
commercial services deploy automatic defenses today, there are no
detailed public descriptions of how they work---our contribution is to
document one automated approach, and to show the importance of
multiple types of defenses.  We evaluate our approach against captured
DDoS attacks against a root DNS server, using analysis and testbed
experimentation with real DNS servers.  Our automated system can
detect attack events within 15\,s, and choose the best defense within
40\,s.  We show that we can reduce 23\% CPU usage and 63\% egress
network bandwidth with the same memory consumption and with little
collateral damage.
",
}

Distributed Denial-of-Service (DDoS) attacks exhaust resources, leaving a server unavailable to legitimate clients. The Domain Name System (DNS) is frequently the target of DDoS attacks, and its connectionless communication makes it an easy target for spoofing attacks. A large body of prior work has focused on specific filters or anti-spoofing techniques, but DDoS threats continue to grow, augmented by the addition of millions of Internet-of-Things (IoT) devices. We propose two approaches to DDoS-defense: first, we propose having a \emphlibrary of defensive filters ready, each applicable to different attack types and with different levels of selectivity. Second, we suggest \emphautomatically selecting the best defense mechanism at attack start, and re-evaluating that choice during the attack to account for polymorphic attacks. While commercial services deploy automatic defenses today, there are no detailed public descriptions of how they work—our contribution is to document one automated approach, and to show the importance of multiple types of defenses. We evaluate our approach against captured DDoS attacks against a root DNS server, using analysis and testbed experimentation with real DNS servers. Our automated system can detect attack events within 15\,s, and choose the best defense within 40\,s. We show that we can reduce 23% CPU usage and 63% egress network bandwidth with the same memory consumption and with little collateral damage.

@article{journals/Lin,
  author = {Lin, Anne and Abeliuk, Andr{\'{e}}s and Emilio, Ferrara},
  Journal = {IEEE International Conference on Big Data (Big Data)},
  title = {Effects of network structure on subjective preference diversity.},
  year = 2019,
  organization={IEEE},
  pages={3026-3031},
  doi={10.1109/BigData47090.2019.9005454}
}

@article{Travadi2018EfficientEstimation,
 author = {Travadi, Ruchir and Narayanan, Shrikanth S.},
 bib2html_rescat = {},
 doi = {https://doi.org/10.1016/j.csl.2018.07.003},
 journal = {Computer Speech and Language},
 link = {https://www.sciencedirect.com/science/article/pii/S0885230818300147?via%3Dihub},
 month = {Jan},
 pages = {43-64},
 title = {Efficient Estimation and Model Generalization for the Total Variability Model},
 volume = {53},
 year = {2019}
}

@article{elkholy_efficient_2019,
	title = {Efficient and Robust Skeleton-Based Quality Assessment and Abnormality Detection in Human Action Performance},
	rights = {All rights reserved},
	journaltitle = {{IEEE} journal of biomedical and health informatics},
	author = {Elkholy, Amr and Hussein, Mohamed and Gomaa, Walid and Damen, Dima and Saba, Emmanuel},
	year = {2019},
	file = {Snapshot:C\:\\Users\\Mohamed Hussein\\Zotero\\storage\\HLN5C3W5\\8664567.html:text/html}
}

@InProceedings{shemirani2019:ASHG,
  author = 	 {R. Shemirani and G.M. Belbin and E.E. Kenny and C.R. Gignoux and J.L. Ambite},
  title = 	 {Efficient and rapid clustering of identity-by-descent tracts in biobank-scale datasets},
  booktitle = {Annual Meeting of the American Society of Human Genetics},
  year = 	 {2019},
  address = 	 {Houston, TX},
  note = 	 {Abstract + Poster},
}

@article{harutyunyan2019efficient,
  title={Efficient covariance estimation from temporal data},
  author={Harutyunyan, Hrayr and Moyer, Daniel and Khachatrian, Hrant and Steeg, Greg Ver and Galstyan, Aram},
  journal={arXiv preprint arXiv:1905.13276},
  year={2019}
}

@Article{ref8,
author={Hayes, Bryan D.
and Bartlett, Genevieve},
title={Emotional States in Clickbait Headlines},
year={2019},
}

@InProceedings{	  ferreiradasilva2019btsd,
  Title		= {Empowering Agroecosystem Modeling with HTC Scientific
		  Workflows: The Cycles Model Use Case},
  Author	= {Ferreira da Silva, Rafael and Mayani, Rajiv and Shi,
		  Yuning and Kemanian, Armen R. and Rynge, Mats and Deelman,
		  Ewa},
  BookTitle	= {First International Workshop on Big Data Tools, Methods,
		  and Use Cases for Innovative Scientific Discovery (BTSD)},
  Year		= {2019},
  Pages		= {4545--4552},
  DOI		= {10.1109/BigData47090.2019.9006107},
  Note		= {Funding Acknowledgments: DARPA W911NF-18-1-0027, NSF
		  1664162}
}

@InProceedings{	  rodero-gateways-2019,
  Title		= {Enabling Data Streaming-based Science Gateway through
		  Federated Cyberinfrastructure},
  Author	= {Rodero, Ivan and Qin, Yubo and Valls, Jesus and Simonet,
		  Anthony and Villalobos, J.J. and Parashar, Manish and Youn,
		  Chooban and Wang, Cong and Thareja, Komal and Ruth, Paul
		  and Papadimitriou, George and Lyons, Eric and Zink,
		  Michael},
  BookTitle	= {Gateways 2019},
  Year		= {2019},
  Location	= {San Diego, CA, USA},
  Pages		= {},
  DOI		= {},
  Note		= {Funding Acknowledgments: NSF 1835692, NSF 1745246, NSF
		  1826997}
}

@article{Hagedorn2018EngineeringInnovationinSpeech,
 author = {Hagedorn, Christina and  Sorensen, Tanner and Lammert, Adam  and Toutios, Asterios and Goldstein, Louis and Byrd, Dani and Narayanan, Shrikanth},
 doi = {10.1044/2018_PERS-SIG19-2018-0003},
 journal = {SIG 19 Speech Science Perspectives of ASHA},
 link = {http://sail.usc.edu/publications/files/Hagedorn-SIGPerspective2019.pdf},
 month = {Apr},
 number = {2},
 pages = {411-420},
 title = {Engineering Innovation in Speech Science: Data and Technologies},
 volume = {4},
 year = {2019}
}

@inproceedings{schmidt2019a,
	author = {Travis Haroldsen and Matthew French and Andrew G. Schmidt and D. Khamar},
	booktitle = {Government Microcircuit Applications \& Critical Technology Conference (GOMACTech)},
	title = {Enhanced Independent Functional Testing of Xilinx FPGAs},
	year = {2019}}

@conference {Haroldsen2019,
    title = {Enhanced Independent Functional Testing of Xilinx FPGAs},
    booktitle = {Government Microcircuit Applications and Critical Technology Conference (GOMACTech)},
    year = {2019},
    month = {April},
    address = {Albuquerque, NM},
    author = {T. Haroldsen, M. French, A. Schmidt, and D. Khamar}
} 
   
   

@InProceedings{mayani2019b:ASHG,
  author = 	 {B.G. Ruocco and R. Mayani and S. Sharma and S. Wilson and K. Vahi and S. Voinea and G. Davis and J. Valentine-Cooper and W. Valentine-Cooper and J. Mathew and M. Romanella and Y. Arens and E. Deelman and M.A. Azaro  and V.J. Vieland and J.L. Ambite and L.M. Brzustowicz},
  title = 	 {Enhancing access to data at the {NIMH Repository and Genomics Resource}},
  booktitle = {Annual Meeting of the American Society of Human Genetics},
  year = 	 {2019},
  address = 	 {Houston, TX},
  note = 	 {Abstract + Poster},
}

@inproceedings{pujara:akbc19ws,
    author = "Pujara, Jay and Raschid, Louiqa and Hoberg, Gerard and Phillips, Gordon and Knoblock, Craig",
    bib_url = "/pubs/bib/pujara-akbc19ws.bib",
    booktitle = "AKBC Workshop on Federated KBs and the Open Knowledge Network",
    pdf_url = "/pubs/2019/pujara-akbc19ws/pujara-akbc19ws.pdf",
    sec = "ws",
    title = "Enterprise OKN: A Federated Knowledge Graph for Financial Data",
    year = "2019"
}

@article{thawani2019entity,
  title={Entity Linking to Knowledge Graphs to Infer Column Types and Properties.},
  author={Thawani, Avijit and Hu, Minda and Hu, Erdong and Zafar, Husain and Divvala, Naren Teja and Singh, Amandeep and Qasemi, Ehsan and Szekely, Pedro A and Pujara, Jay},
  journal={SemTab ISWC},
  volume={2019},
  pages={25--32},
  year={2019}
}

@inproceedings{thawani:semtab19,
    author = "Thawani, Avijit and Hu, Minda and Hu, Erdong and Zafar, Husain and Divvala, Naren Teja and Singh, Amandeep and Qasemi, Ehsan and Pujara, Jay",
    booktitle = "The Semantic Web Challenge on Tabular Data to Knowledge Graph Matching at ISWC",
    sec = "ws",
    title = "Entity linking to knowledge graphs to infer column types and properties.",
    year = "2019"
}

@article{PhysRevApplied.12.011003,
  title = {Equation Planting: A Tool for Benchmarking Ising Machines},
  author = {Hen, Itay},
  journal = {Phys. Rev. Applied},
  volume = {12},
  issue = {1},
  pages = {011003},
  numpages = {7},
  year = {2019},
  month = {Jul},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevApplied.12.011003},
  url = {https://link.aps.org/doi/10.1103/PhysRevApplied.12.011003}
}

@inproceedings{wang2019espresso,
  title={Espresso: A Fast End-to-end Neural Speech Recognition Toolkit},
  author={Wang, Yiming and Chen, Tongfei and Xu, Hainan and Ding, Shuoyang and Lv, Hang and Shao, Yiwen and Peng, Nanyun and Xie, Lei and Watanabe, Shinji and Khudanpur, Sanjeev},
  booktitle={The 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
  year={2019}
}

@inproceedings{yan2019estimating,
  title={Estimating individualized daily self-reported affect with wearable sensors},
  author={Yan, Shen and Hosseinmardi, Homa and Kao, Hsien-Te and Narayanan, Shrikanth and Lerman, Kristina and Ferrara, Emilio},
  booktitle={2019 IEEE International Conference on Healthcare Informatics (ICHI)},
  pages={1--9},
  year={2019},
  organization={IEEE}
}

@article{Barash_2019,
	doi = {10.1088/1367-2630/ab2e39},
	url = {https://doi.org/10.1088%2F1367-2630%2Fab2e39},
	year = 2019,
	month = {jul},
	publisher = {{IOP} Publishing},
	volume = {21},
	number = {7},
	pages = {073065},
	author = {Lev Barash and Jeffrey Marshall and Martin Weigel and Itay Hen},
	title = {Estimating the density of states of frustrated spin systems},
	journal = {New Journal of Physics},
	abstract = {Estimating the density of states (DOS) of systems with rugged free energy landscapes is a notoriously difficult task of the utmost importance in many areas of physics ranging from spin glasses to biopolymers. DOS estimation has also recently become an indispensable tool for the benchmarking of quantum annealers when these function as samplers. Some of the standard approaches suffer from a spurious convergence of the estimates to metastable minima, and these cases are particularly hard to detect. Here, we introduce a sampling technique based on population annealing enhanced with a multi-histogram analysis and report on its performance for spin glasses. We demonstrate its ability to overcome the pitfalls of other entropic samplers, resulting in some cases in large scaling advantages that can lead to the uncovering of new physics. The new technique avoids some inherent difficulties in established approaches and can be applied to a wide range of systems without relevant tailoring requirements. Benchmarking of the studied techniques is facilitated by the introduction of several schemes that allow us to achieve exact counts of the degeneracies of the tested instances.}
}

Estimating the density of states (DOS) of systems with rugged free energy landscapes is a notoriously difficult task of the utmost importance in many areas of physics ranging from spin glasses to biopolymers. DOS estimation has also recently become an indispensable tool for the benchmarking of quantum annealers when these function as samplers. Some of the standard approaches suffer from a spurious convergence of the estimates to metastable minima, and these cases are particularly hard to detect. Here, we introduce a sampling technique based on population annealing enhanced with a multi-histogram analysis and report on its performance for spin glasses. We demonstrate its ability to overcome the pitfalls of other entropic samplers, resulting in some cases in large scaling advantages that can lead to the uncovering of new physics. The new technique avoids some inherent difficulties in established approaches and can be applied to a wide range of systems without relevant tailoring requirements. Benchmarking of the studied techniques is facilitated by the introduction of several schemes that allow us to achieve exact counts of the degeneracies of the tested instances.

@article{luceri2019evolution,
  title={Evolution of bot and human behavior during elections},
  author={Luceri, Luca and Deb, Ashok and Giordano, Silvia and Ferrara, Emilio},
  journal={First Monday},
  year={2019}
}

@inproceedings{brekelmans2019exact,
  title={Exact Rate-Distortion in Autoencoders via Echo Noise},
  author={Brekelmans, Rob and Moyer, Daniel and Galstyan, Aram and Steeg, Greg Ver},
  booktitle={Advances in Neural Information Processing Systems},
  url={https://arxiv.org/abs/1904.07199},
  year={2019}
}

@inproceedings{brekelmans2019exact,
  title={Exact rate-distortion in autoencoders via echo noise},
  author={Brekelmans, Rob and Moyer, Daniel and Galstyan, Aram and Ver Steeg, Greg},
  booktitle={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@inproceedings{DBLP:conf/sigir/KejriwalSS19,
  author    = {Mayank Kejriwal and
               Runqi Shao and
               Pedro A. Szekely},
  editor    = {Benjamin Piwowarski and
               Max Chevalier and
               {\'{E}}ric Gaussier and
               Yoelle Maarek and
               Jian{-}Yun Nie and
               Falk Scholer},
  title     = {Expert-Guided Entity Extraction using Expressive Rules},
  booktitle = {Proceedings of the 42nd International {ACM} {SIGIR} Conference on
               Research and Development in Information Retrieval, {SIGIR} 2019, Paris,
               France, July 21-25, 2019},
  pages     = {1353--1356},
  publisher = {{ACM}},
  year      = {2019},
  url       = {https://doi.org/10.1145/3331184.3331392},
  doi       = {10.1145/3331184.3331392},
  timestamp = {Thu, 14 Oct 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/sigir/KejriwalSS19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@InProceedings{	  mitchell2019btsd,
  Title		= {Exploration of Workflow Management Systems Emerging
		  Features from Users Perspectives},
  Author	= {Mitchell, Ryan and Pottier, Lo\"ic and Jacobs, Steve and
		  Ferreira da Silva, Rafael and Rynge, Mats and Vahi, Karan
		  and Deelman, Ewa},
  BookTitle	= {First International Workshop on Big Data Tools, Methods,
		  and Use Cases for Innovative Scientific Discovery (BTSD)},
  Year		= {2019},
  Pages		= {4537--4544},
  DOI		= {10.1109/BigData47090.2019.9005494},
  Note		= {Funding Acknowledgments: NSF 1842042}
}

@inproceedings{yao2019extensible,
  title={Extensible and Scalable Entity Resolution for Financial Datasets Using RLTK},
  author={Yao, Yixiang and Szekely, Pedro and Pujara, Jay},
  booktitle={Proceedings of the 5th Workshop on Data Science for Macro-modeling with Financial and Economic Datasets},
  pages={1--1},
  year={2019}
}

@article{zhang2019extracting,
  title={Extracting geographic features from the Internet: A geographic information mining framework},
  author={Zhang, Ying and Ma, Qunfei and Chiang, Yao-Yi and Knoblock, Craig and Zhang, Xin and Yang, Puhai and Gao, Minghe and Hu, Xiang},
  journal={Knowledge-Based Systems},
  volume={174},
  pages={57--72},
  year={2019},
  publisher={Elsevier},
  urlLink={https://doi.org/10.1016/j.knosys.2019.02.031},
  urlPaper={http://usc-isi-i2.github.io/papers/zhang19-elseviar.pdf}
}

@article{DBLP:journals/ijcv/MasiTHSM19,
  author       = {Iacopo Masi and
                  Anh Tuan Tran and
                  Tal Hassner and
                  Gozde Sahin and
                  G{\'{e}}rard G. Medioni},
  title        = {Face-Specific Data Augmentation for Unconstrained Face Recognition},
  journal      = {Int. J. Comput. Vis.},
  volume       = {127},
  number       = {6-7},
  pages        = {642--667},
  year         = {2019},
  url          = {https://doi.org/10.1007/s11263-019-01178-0},
  doi          = {10.1007/S11263-019-01178-0},
  timestamp    = {Fri, 13 Mar 2020 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/ijcv/MasiTHSM19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@conference {RN678,
	title = {Factors influencing the adoption of Software Defined Networking by Research and Educational Networks},
	booktitle = {The annual Americas{\textquoteright} Conference on Information Systems (AMCIS) 2019},
	year = {2019},
	type = {Conference Proceedings},
	url = {https://urldefense.com/v3/__https://aisel.aisnet.org/amcis2019/adoption_diffusion_IT/adoption_diffusion_IT/10/__;!!FjuHKAHQs5udqho!OrfqNEjI8IYnpYEY3Wc7S4oSY6Kf82MHM8jGAJar7qa8MoXzNwtTTC2HiJsVSREP5YicoFy-7bQtMA$ },
	author = {Vasilka Chergarova and Jeronimo Bezerra and Julio Ibarra and Heidi Morgan}
}

@article{Timmons2019Family-of-OriginAggression,
 author = {Timmons, Adela and Han, Sohyun and Chaspari, Theodora and Kim, Yehsong and  Pettit, Corey and  Narayanan, Shrikanth and Margolin, Gayla},
 journal = {Physiology and Behavior},
 title = {Family-of-Origin Aggression, Dating Aggression, and Physiological Stress Reactivity in Daily Life},
 year = {2019},
 month  = {Jul}
}

@article{ver2019fast,
  title={Fast structure learning with modular regularization},
  author={Ver Steeg, Greg and Harutyunyan, Hrayr and Moyer, Daniel and Galstyan, Aram},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  year={2019}
}

@INPROCEEDINGS{Sayyadiharikandeh2019finding,
  author =       {Mohsen Sayyadiharikandeh and Jonathan Gordon and Jose Luis Ambite and Kristina Lerman},
  title =        {Finding Prerequisite Relations using the Wikipedia Clickstream},
  booktitle =    {Proceedings of the Companion to The Web Conference: CyberSafety Workshop},
  year =         {2019},
  keywords =     {},
}

@inproceedings{DBLP:conf/scalespace/DeutschMS19,
  author       = {Shay Deutsch and
                  Iacopo Masi and
                  Stefano Soatto},
  editor       = {Jan Lellmann and
                  Martin Burger and
                  Jan Modersitzki},
  title        = {Finding Structure in Point Cloud Data with the Robust Isoperimetric
                  Loss},
  booktitle    = {Scale Space and Variational Methods in Computer Vision - 7th International
                  Conference, {SSVM} 2019, Hofgeismar, Germany, June 30 - July 4, 2019,
                  Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {11603},
  pages        = {25--37},
  publisher    = {Springer},
  year         = {2019},
  url          = {https://doi.org/10.1007/978-3-030-22368-7\_3},
  doi          = {10.1007/978-3-030-22368-7\_3},
  timestamp    = {Tue, 25 Jun 2019 13:16:41 +0200},
  biburl       = {https://dblp.org/rec/conf/scalespace/DeutschMS19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{sac,
  title = {Future of physical quantum annealers: impediments and hopes},
  author = {Albash, T and Hen, Itay},
  journal = {Science and Culture},
  volume = {85},
  pages = {163-170},
  year = {2019},
}

@article{li2019gaia,
  title={Gaia at sm-kbp 2019-a multi-media multi-lingual knowledge extraction and hypothesis generation system},
  author={Li, Manling and Lin, Ying and Subburathinam, Ananya and Whitehead, Spencer and Pan, Xiaoman and Lu, Di and Wang, Qingyun and Zhang, Tongtao and Huang, Lifu and Ji, Heng and others},
  journal={Proceedings of TAC KBP},
  year={2019}
}

@article{Mundnich2019Generatinglabelsforregression,
 author = {Mundnich, Karel and Booth, Brandon and  Girault, Benjamin and Narayanan, Shrikanth},
 journal = {Pattern Recognition Letters},
 link = {http://sail.usc.edu/publications/files/1-s2.0-S0167865519302752-main (1).pdf},
 month = {December},
 pages = {385-392},
 title = {Generating labels for regression of subjective constructs using triplet embeddings},
 volume = {128},
 year = {2019}
}

@Article{	  wojcik2019genetic,
  Title		= {Genetic analyses of diverse populations improves discovery
		  for complex traits},
  Author	= {Wojcik, Genevieve L and Graff, Mariaelisa and Nishimura,
		  Katherine K and Tao, Ran and Haessler, Jeffrey and Gignoux,
		  Christopher R and Highland, Heather M and Patel, Yesha M
		  and Sorokin, Elena P and Avery, Christy L and Gillian M.
		  Belbin and Stephanie A. Bien and Iona Cheng and Sinead
		  Cullina and Chani J. Hodonsky and Yao Hu and Laura M.
		  Huckins and Janina Jeff and Anne E. Justice and Jonathan M.
		  Kocarnik and Unhee Lim and Bridget M. Lin and Yingchang Lu
		  and Sarah C. Nelson and Sung-Shim L. Park and Hannah
		  Poisner and Michael H. Preuss and Melissa A. Richard and
		  Claudia Schurmann and Veronica W. Setiawan and Alexandra
		  Sockell and Karan Vahi and Marie Verbanck and Abhishek
		  Vishnu and Ryan W. Walker and Kristin L. Young and Niha
		  Zubair and Victor Acuña-Alonso and Jose Luis Ambite and
		  Kathleen C. Barnes and Eric Boerwinkle and Erwin P.
		  Bottinger and Carlos D. Bustamante and Christian Caberto
		  and Samuel Canizales-Quinteros and Matthew P. Conomos and
		  Ewa Deelman and Ron Do and Kimberly Doheny and Lindsay
		  Fernández-Rhodes and Myriam Fornage and Benyam Hailu and
		  Gerardo Heiss and Brenna M. Henn and Lucia A. Hindorff and
		  Rebecca D. Jackson and Cecelia A. Laurie and Cathy C.
		  Laurie and Yuqing Li and Dan-Yu Lin and Andres
		  Moreno-Estrada and Girish Nadkarni and Paul J. Norman and
		  Loreall C. Pooler and Alexander P. Reiner and Jane Romm and
		  Chiara Sabatti and Karla Sandoval and Xin Sheng and Eli A.
		  Stahl and Daniel O. Stram and Timothy A. Thornton and
		  Christina L. Wassel and Lynne R. Wilkens and Cheryl A.
		  Winkler and Sachi Yoneyama and Steven Buyske and
		  Christopher A. Haiman and Charles Kooperberg and Le
		  Marchand and Ruth J. F. Loos and Tara C. Matise and Kari E.
		  North and Ulrike Peters and Eimear E. Kenny and Christopher
		  S. Carlson},
  Journal	= {Nature},
  Volume	= {570},
  Pages		= {514--518},
  Year		= {2019},
  Publisher	= {Nature Publishing Group},
  DOI		= {10.1038/s41586-019-1310-4}
}

@article{LinNadkarniTaoEtAl2019,
	abstract = {Chronic kidney disease (CKD) is common and disproportionally burdens United States ethnic minorities. Its genetic determinants may differ by disease severity and clinical stages. To uncover genetic factors associated CKD severity among high-risk ethnic groups, we performed genome-wide association studies ({GWAS}) in diverse populations within the {Population Architecture using Genomics and Epidemiology} (PAGE) study. We assembled multi-ethnic genome-wide imputed data on CKD non-overlapping cases [4,150 mild to moderate CKD, 1,105 end-stage kidney disease (ESKD)] and non-CKD controls for up to 41,041 PAGE participants (African Americans, Hispanics/Latinos, East Asian, Native Hawaiian, and American Indians). We implemented a generalized estimating equation approach for {GWAS} using ancestry combined data while adjusting for age, sex, principal components, study, and ethnicity. The {GWAS} identified a novel genome-wide associated locus for mild to moderate CKD nearby   (rs10906850,   = 3.7 × 10 ) that replicated in the United Kingdom Biobank white British (  = 0.008). Several variants at the   locus were associated with ESKD including the   G1 rs73885319 (  = 1.2 × 10 ). There was no overlap among associated loci for CKD and ESKD traits, even at the previously reported   locus (  = 0.76 for CKD). Several additional loci were associated with CKD or ESKD at  -values below the genome-wide threshold. These loci were often driven by variants more common in non-European ancestry. Our genetic study identified a novel association at   for CKD and showed for the first time strong associations of the   variants with ESKD across multi-ethnic populations. Our findings suggest differences in genetic effects across CKD severity and provide information for study design of genetic studies of CKD in diverse populations.},
	author = {Lin, Bridget M. and Nadkarni, Girish N. and Tao, Ran and Graff, Mariaelisa and Fornage, Myriam and Buyske, Steven and Matise, Tara C. and Highland, Heather M. and Wilkens, Lynne R. and Carlson, Christopher S. and Park, S. Lani and Setiawan, V. Wendy and Ambite, Jose Luis and Heiss, Gerardo and Boerwinkle, Eric and Lin, Dan-Yu and Morris, Andrew P. and Loos, Ruth J. F. and Kooperberg, Charles and North, Kari E. and Wassel, Christina L. and Franceschini, Nora},
	country = {Switzerland},
	doi = {10.3389/fgene.2019.00494},
	issn = {1664-8021},
	issn-linking = {1664-8021},
	journal = {Frontiers in genetics},
	keywords = {APOL1; chronic kidney disease stages; diverse populations; end stage kidney disease; genetics; genome-wide association studies; single nucleotide polymorphisms},
	nlm-id = {101560621},
	owner = {NLM},
	pages = {494},
	pmc = {PMC6544117},
	pmid = {31178898},
	url = {https://pubmed.ncbi.nlm.nih.gov/31178898/},

	pubmodel = {Electronic-eCollection},
	pubstate = {epublish},
	revised = {2020-09-28},
	title = {Genetics of Chronic Kidney Disease Stages Across Ancestries: The {PAGE} Study.},
	volume = {10},
	year = {2019},
	bdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/31178898/},
	bdsk-url-2 = {https://doi.org/10.3389/fgene.2019.00494}}

Chronic kidney disease (CKD) is common and disproportionally burdens United States ethnic minorities. Its genetic determinants may differ by disease severity and clinical stages. To uncover genetic factors associated CKD severity among high-risk ethnic groups, we performed genome-wide association studies (GWAS) in diverse populations within the Population Architecture using Genomics and Epidemiology (PAGE) study. We assembled multi-ethnic genome-wide imputed data on CKD non-overlapping cases [4,150 mild to moderate CKD, 1,105 end-stage kidney disease (ESKD)] and non-CKD controls for up to 41,041 PAGE participants (African Americans, Hispanics/Latinos, East Asian, Native Hawaiian, and American Indians). We implemented a generalized estimating equation approach for GWAS using ancestry combined data while adjusting for age, sex, principal components, study, and ethnicity. The GWAS identified a novel genome-wide associated locus for mild to moderate CKD nearby (rs10906850, = 3.7 × 10 ) that replicated in the United Kingdom Biobank white British ( = 0.008). Several variants at the locus were associated with ESKD including the G1 rs73885319 ( = 1.2 × 10 ). There was no overlap among associated loci for CKD and ESKD traits, even at the previously reported locus ( = 0.76 for CKD). Several additional loci were associated with CKD or ESKD at -values below the genome-wide threshold. These loci were often driven by variants more common in non-European ancestry. Our genetic study identified a novel association at for CKD and showed for the first time strong associations of the variants with ESKD across multi-ethnic populations. Our findings suggest differences in genetic effects across CKD severity and provide information for study design of genetic studies of CKD in diverse populations.

@misc{huang__2019, 
title={Hands-on Graph Neural Networks with PyTorch Geometric}, 
url={https://towardsdatascience.com/hands-on-graph-neural-networks-with-pytorch-pytorch-geometric-359487e221a8}, publisher={Towards Data Science}, 
author={Huang, Kung-Hsiang}, 
year={2019}, 
month={Mar}}

@inproceedings{habif2019harvesting,
  title={Harvesting Planck Radiation for Free-Space Optical Communications in the Long-Wave Infrared Band},
  author={Habif, Jonathan L and Jagannathan, Arunkumar},
  booktitle={Laser Science},
  pages={JTu4A--59},
  year={2019},
  organization={Optical Society of America}
}

@incollection{kocoloski2019hobbes,
  title={Hobbes: A multi-kernel infrastructure for application composition},
  author={Kocoloski, Brian and Lange, John and Pedretti, Kevin and Brightwell, Ron},
  booktitle={Operating Systems for Supercomputers and High Performance Computing},
  pages={241--267},
  year={2019},
  publisher={Springer, Singapore}
}

@article{Pqsp,
  title = {How quantum is the speedup in adiabatic unstructured search?},
  author = {Hen, Itay},
  journal = {Quant. Inf. Proc.},
  volume = {18},
  pages = {162},
  year = {2019},
 }
 
 

@inproceedings{kothari2019boundaries,
  title="Human-Computability Boundaries",
  author={Kothari, Vijay and Anantharaman, Prashant and Jenkins, Ira and Millian, Michael and Bratus, Sergey and Blythe, Jim and Koppel, Ross and Smith, Sean},
  booktitle={International Workshop on Security Protocols},
  year={2019}
}

@InProceedings{Bogutz19a,
        author =        "Ryan Bogutz and Yuri Pradkin and John Heidemann",
        title =         "Identifying Important Internet Outages",
        booktitle =     "Sixth National Symposium for NSF REU Research in Data Science, Systems, and Security",
        year =          "2019",
	sortdate = 		"2019-12-12", 
	project = "ant, lacanic, divoice, iiovadr, isireu, reu",
	jsubject = "routing",
	month =		dec,
	jlocation =	"johnh: pafile",
	keywords =	"network outage detection, reporting",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Bogutz19a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Bogutz19a.pdf",
	blogurl = "https://ant.isi.edu/blog/?p=1376",
	myorganization =	"USC/Information Sciences Institute",
	copyrightholder = "authors",
	abstract = "Today, outage detection systems can track outages across the whole
IPv4 Internet---millions of networks.  However, it becomes difficult
to find meaningful, interesting events in this huge dataset, since
three months of data can easily include 660M observations and
thousands of outage events.  We propose
an \emph{outage reporting system} that sifts through this data
to find the most \emph{interesting} events.
We explore multiple metrics to evaluate
``interesting'', reflecting the size and severity of outages.  We show
that defining interest as the product of size by severity works well,
avoiding degenerate cases like complete outages affecting a few
people, and apparently large outages that affect only a small fraction
of people in an area.  We have integrated outage reporting into our
existing public website (\url{https://outage.ant.isi.edu}) with the
goal of making near-real-time outage information accessible to the
general public.  Such data can help answer questions like ``what are
the most significant outages today?'', ``did Flordia have major
problems in an ongoing hurricane?'', and ``are there power outages in
Venezuela?''.",
}

Today, outage detection systems can track outages across the whole IPv4 Internet—millions of networks. However, it becomes difficult to find meaningful, interesting events in this huge dataset, since three months of data can easily include 660M observations and thousands of outage events. We propose an \emphoutage reporting system that sifts through this data to find the most \emphinteresting events. We explore multiple metrics to evaluate ``interesting'', reflecting the size and severity of outages. We show that defining interest as the product of size by severity works well, avoiding degenerate cases like complete outages affecting a few people, and apparently large outages that affect only a small fraction of people in an area. We have integrated outage reporting into our existing public website (˘rlhttps://outage.ant.isi.edu) with the goal of making near-real-time outage information accessible to the general public. Such data can help answer questions like ``what are the most significant outages today?'', ``did Flordia have major problems in an ongoing hurricane?'', and ``are there power outages in Venezuela?''.

@TechReport{Bogutz19b,
        author =        "Ryan Bogutz and Yuri Pradkin and John Heidemann",
        title =         "Identifying Important Internet Outages (extended)",
	institution = 	"USC/Information Sciences Institute",
	year = 		2019,
	sortdate = 		"2019-11-12", 
	project = "ant, lacanic, divoice, iiovadr, isireu",
	jsubject = "routing",
	number =	"ISI-TR-735",
	month =		oct,
	jlocation =	"johnh: pafile",
	keywords =	"network outage detection, reporting",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Bogutz19b.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Bogtutz19b.pdf",
	blogurl = "https://ant.isi.edu/blog/?p=1376",
	myorganization =	"USC/Information Sciences Institute",
	copyrightholder = "authors",
	abstract = "Today, outage detection systems can track outages across the whole
IPv4 Internet---millions of networks.  However, it becomes difficult
to find meaningful, interesting events in this huge dataset, since
three months of data can easily include 660M observations and
thousands of outage events.  We propose
an \emph{outage reporting system} that sifts through this data
to find the most \emph{interesting} events.
We explore multiple metrics to evaluate
``interesting'', reflecting the size and severity of outages.  We show
that defining interest as the product of size by severity works well,
avoiding degenerate cases like complete outages affecting a few
people, and apparently large outages that affect only a small fraction
of people in an area.  We have integrated outage reporting into our
existing public website (\url{https://outage.ant.isi.edu}) with the
goal of making near-real-time outage information accessible to the
general public.  Such data can help answer questions like ``what are
the most significant outages today?'', ``did Flordia have major
problems in an ongoing hurricane?'', and ``are there power outages in
Venezuela?''.",
}

Today, outage detection systems can track outages across the whole IPv4 Internet—millions of networks. However, it becomes difficult to find meaningful, interesting events in this huge dataset, since three months of data can easily include 660M observations and thousands of outage events. We propose an \emphoutage reporting system that sifts through this data to find the most \emphinteresting events. We explore multiple metrics to evaluate ``interesting'', reflecting the size and severity of outages. We show that defining interest as the product of size by severity works well, avoiding degenerate cases like complete outages affecting a few people, and apparently large outages that affect only a small fraction of people in an area. We have integrated outage reporting into our existing public website (˘rlhttps://outage.ant.isi.edu) with the goal of making near-real-time outage information accessible to the general public. Such data can help answer questions like ``what are the most significant outages today?'', ``did Flordia have major problems in an ongoing hurricane?'', and ``are there power outages in Venezuela?''.

@inproceedings{Martinez2019IdentifyingTherapistandClient,
 author = {Martinez, Victor and Flemotomos, Nikolaos and Ardulov, Victor and Somandepalli, Krishna and Goldberg, Simon and Imel, Zac and Atkins, David and Narayanan, Shrikanth},
 booktitle = {In proceedings of Proceedings of Interspeech},
 location = {Graz, Austria},
 month = {September},
 title = {Identifying Therapist and Client Personae for Therapeutic Alliance Estimation},
 year = {2019}
}

@phdthesis{ilievski2019identity,
  title={Identity of Long-Tail Entities in Text},
  author={Ilievski, Filip},
  year={2019},
  school={Vrije Universiteit Amsterdam}
}

@inproceedings{rajagopala2009a,
	author = {Abhi D. Rajagopala and Ron Sass and Andrew G. Schmidt},
	booktitle = {International Workshop on FPGAs for Software Programmers (FSP)},
	title = {Impact of Off-Chip Memories on HLS-Generated Circuits},
	year = {2019}}

@InProceedings{cullina2019:ASHG,
  author = 	 {S. Cullina and G.M. Belbin and R. Shemirani and J.L. Ambite and C.R. Gignoux and E.E. Kenny},
  title = 	 {Impact of fine-scale population structure in the UK Biobank on Mendelian disease variants},
  booktitle =    {Annual Meeting of the American Society of Human Genetics},
  year = 	 {2019},
  address = 	 {Houston, TX},
  note = 	 {Abstract + Poster},
}

@article{rughani2019improved,
  title={Improved CubeSat Mission Reliability Using a Rigorous Top-Down Systems-Level Approach},
  author={Rughani, Rahul and Rogers, Rebecca A and Allam, Jeremy J and Narayanan, Sriram and Patil, Piyush and Clarke, Kyle and Lariviere, Marcel and Du Plessis, Justin and Villafana, Lizvette and Healy, Denis and others},
  booktitle={70th International Astronautical Congress (IAC), Washington DC, United States},
  year={2019}
}

@article{Chen2019REALTAG-Real-TimeMRI,
 author = {Chen, Weiyi  and Byrd, Dani and Narayanan, Shrikanth  and Nayak, Krishna},
 journal = {Magenetic Resonance in Medicine},
 title = {Improved real-time tagged MRI using REALTAG},
 year = {2019},
 month = {Dec}
}

@techreport{bartlettimproving,
  title={Improving Testbed Experiment Design Through Shifting User Interface Emphasis},
  author={Bartlett, Genevieve and Mirkovic, Jelena and Blythe, Jim},
  number={ISI-TR-737},
  year={2019}
}

@TechReport{Baltra19a,
	author = 	"Guillermo Baltra and John Heidemann",
	title = 	"Improving the Optics of Active Outage Detection (extended)",
	institution = 	"USC/Information Sciences Institute",
	year = 		2019,
	sortdate = 		"2018-05-16", 
	project = "ant, lacanic, divoice, iiovadr",
	jsubject = "routing",
	number =	"ISI-TR-733",
	month =		may,
	jlocation =	"johnh: pafile",
	keywords =	"network outage detection",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Baltra19a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Baltra19a.pdf",
	myorganization =	"USC/Information Sciences Institute",
	copyrightholder = "authors",
	abstract = "
There is a growing interest in carefully observing the reliability of
the Internet's edge.  Outage information can inform our understanding
of Internet reliability and planning, and it can help guide
operations.  Outage detection algorithms using active probing from
third parties have been shown to be accurate for most of the Internet,
but inaccurate for blocks that are sparsely occupied.  Our
contributions include a definition of outages, which we use to
determine how many independent observers are required to determine
global outages.  We propose a new \emph{Full Block Scanning} (FBS)
algorithm that gathers more information for sparse blocks to reduce
false outage reports.  We also propose \emph{ISP Availability Sensing}
(IAS) to detect maintenance activity using only external information.
We study a year of outage data and show that FBS has a True Positive
Rate of 86\%, and show that IAS detects maintenance events in a large
U.S.~ISP.
",
}

There is a growing interest in carefully observing the reliability of the Internet's edge. Outage information can inform our understanding of Internet reliability and planning, and it can help guide operations. Outage detection algorithms using active probing from third parties have been shown to be accurate for most of the Internet, but inaccurate for blocks that are sparsely occupied. Our contributions include a definition of outages, which we use to determine how many independent observers are required to determine global outages. We propose a new \emphFull Block Scanning (FBS) algorithm that gathers more information for sparse blocks to reduce false outage reports. We also propose \emphISP Availability Sensing (IAS) to detect maintenance activity using only external information. We study a year of outage data and show that FBS has a True Positive Rate of 86%, and show that IAS detects maintenance events in a large U.S. ISP.

@inproceedings{Feng2019ImputingMissingDataIn,
 author = {Feng, Tiantian and  Narayanan, Shrikanth},
 booktitle = {In proceedings of 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC19)},
 location = {Berlin, Germany},
 month = {July},
 title = {Imputing  Missing  Data  In  Large-Scale  Multivariate  Biomedical Wearable  Recordings  Using  Bidirectional  Recurrent  Neural  Networks With  Temporal  Activation},
 year = {2019}
}

@inproceedings{tran2019increased,
  title={Increased Fault-Tolerance and Real-Time Performance Resiliency for Stream Processing Workloads through Redundancy},
  author={Tran, Geoffrey Phi and Walters, John Paul and Crago, Stephen},
  booktitle={2019 IEEE International Conference on Services Computing (SCC)},
  pages={51--55},
  year={2019},
  organization={IEEE}
}

@article{Burghardt2018Jury,
    author = {Burghardt, Keith AND Rand, William AND Girvan, Michelle},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Inferring models of opinion dynamics from aggregated jury data},
    year = {2019},
    month = {07},
    volume = {14},
    url = {https://doi.org/10.1371/journal.pone.0218312},
    pages = {1-15},
    abstract = {Jury deliberations provide a quintessential example of collective decision-making, but few studies have probed the available data to explore how juries reach verdicts. We examine how features of jury dynamics can be better understood from the joint distribution of final votes and deliberation time. To do this, we fit several different decision-making models to jury datasets from different places and times. In our best-fit model, jurors influence each other and have an increasing tendency to stick to their opinion of the defendant’s guilt or innocence. We also show that this model can explain spikes in mean deliberation times when juries are hung, sub-linear scaling between mean deliberation times and trial duration, and unexpected final vote and deliberation time distributions. Our findings suggest that both stubbornness and herding play an important role in collective decision-making, providing a nuanced insight into how juries reach verdicts, and more generally, how group decisions emerge.},
    number = {7},
    doi = {10.1371/journal.pone.0218312}
}

Jury deliberations provide a quintessential example of collective decision-making, but few studies have probed the available data to explore how juries reach verdicts. We examine how features of jury dynamics can be better understood from the joint distribution of final votes and deliberation time. To do this, we fit several different decision-making models to jury datasets from different places and times. In our best-fit model, jurors influence each other and have an increasing tendency to stick to their opinion of the defendant’s guilt or innocence. We also show that this model can explain spikes in mean deliberation times when juries are hung, sub-linear scaling between mean deliberation times and trial duration, and unexpected final vote and deliberation time distributions. Our findings suggest that both stubbornness and herding play an important role in collective decision-making, providing a nuanced insight into how juries reach verdicts, and more generally, how group decisions emerge.

@inproceedings{barnhart2019initial,
  title={Initial Safety Posture Investigations for Earth Regime Rendezvous and Proximity Operations},
  author={Barnhart, David A and Rughani, Rahul and Allam, Jeremy J and Clarke, Kyle W},
  booktitle={10th International Association for the Advancement of Space Safety (IAASS) Conference, El Segundo, California, USA},
  pages={15--17},
  year={2019}
}

@InProceedings{	  rynge-pearc-2019,
  Author	= {Rynge, Mats and Vahi, Karan and Deelman, Ewa and Mandal,
		  Anirban and Baldin, Ilya and Bhide, Omkar and Heiland,
		  Randy and Welch, Von and Hill, Raquel and Poehlman, William
		  L. and Feltus, F. Alex},
  Title		= {Integrity Protection for Scientific Workflow Data:
		  Motivation and Initial Experiences},
  BookTitle	= {Proceedings of the Practice and Experience in Advanced
		  Research Computing on Rise of the Machines (Learning)},
  Series	= {PEARC '19},
  Year		= {2019},
  ISBN		= {978-1-4503-7227-5},
  Location	= {Chicago, IL, USA},
  Pages		= {17:1--17:8},
  articleno	= {17},
  numpages	= {8},
  URL		= {http://doi.acm.org/10.1145/3332186.3332222},
  DOI		= {10.1145/3332186.3332222},
  acmid		= {3332222},
  Publisher	= {ACM},
  Address	= {New York, NY, USA},
  Note		= {Funding Acknowledgments: NSF 1642070, NSF 1642053, NSF
		  1642090. Best Paper in Advanced Research Computing Software
		  and Applications Track. The Phil Andrews Most
		  Transformative Contribution Award.}
}

@article{DBLP:journals/cacm/GilPBBBBCEGHHHK19,
  author       = {Yolanda Gil and
                  Suzanne A. Pierce and
                  Hassan A. Babaie and
                  Arindam Banerjee and
                  Kirk D. Borne and
                  Gary S. Bust and
                  Michelle Cheatham and
                  Imme Ebert{-}Uphoff and
                  Carla P. Gomes and
                  Mary C. Hill and
                  John D. Horel and
                  Leslie Hsu and
                  Jim Kinter and
                  Craig A. Knoblock and
                  David M. Krum and
                  Vipin Kumar and
                  Pierre F. J. Lermusiaux and
                  Yan Liu and
                  Chris North and
                  Victor Pankratius and
                  Shanan Peters and
                  Beth Plale and
                  Allen Pope and
                  Sai Ravela and
                  Juan Restrepo and
                  Aaron J. Ridley and
                  Hanan Samet and
                  Shashi Shekhar},
  title        = {Intelligent systems for geosciences: an essential research agenda},
  journal      = {Commun. {ACM}},
  volume       = {62},
  number       = {1},
  pages        = {76--84},
  year         = {2019},
  url          = {https://doi.org/10.1145/3192335},
  doi          = {10.1145/3192335},
  timestamp    = {Tue, 07 May 2024 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/cacm/GilPBBBBCEGHHHK19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{Chen2019IntermittentlyTaggedReal-TimeMRI,
 author = {Chen, Weiyi  and Byrd, Dani and Narayanan, Shrikanth  and Nayak, Krishna},
 journal = {Magnetic Resonance in Medicine},
 month = {Aug},
 number = {2},
 pages = {600-613},
 title = {Intermittently Tagged Real-Time MRI Reveals Internal Tongue Motion during Speech Production},
 volume = {82},
 year = {2019}
}

@inproceedings{han2019joint,
  title={Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction},
  author={Han, Rujun and Ning, Qiang and Peng, Nanyun},
  booktitle={2019 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year={2019}
}

@inproceedings{sahil_aaai2019,
	Author = {Sahil Garg and Aram Galstyan and Greg {Ver Steeg} and Irina Rish and Guillermo Cecchi and Shuyang Gao},
	Booktitle = {Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence},
	Date-Added = {2019-01-24 17:39:42 +0000},
	Date-Modified = {2019-01-24 17:46:34 +0000},
	Title = {Kernelized Hashcode Representations for Biomedical Relation Extraction},
	Year = {2019}}

@inproceedings{garg2019kernelized,
  title={Kernelized hashcode representations for relation extraction},
  author={Garg, Sahil and Galstyan, Aram and Ver Steeg, Greg and Rish, Irina and Cecchi, Guillermo and Gao, Shuyang},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  number={01},
  pages={6431--6440},
  year={2019}
}

@article{DBLP:journals/semweb/KejriwalSL19,
  author    = {Mayank Kejriwal and
               Juan F. Sequeda and
               Vanessa Lopez},
  title     = {Knowledge graphs: Construction, management and querying},
  journal   = {Semantic Web},
  volume    = {10},
  number    = {6},
  pages     = {961--962},
  year      = {2019},
  url       = {https://doi.org/10.3233/SW-190370},
  doi       = {10.3233/SW-190370},
  timestamp = {Tue, 28 Jan 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/semweb/KejriwalSL19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/icassp/Shi0CBN19,
  author    = {Xiangyang Shi and
               Yue Wu and
               Huaigu Cao and
               Gully Burns and
               Prem Natarajan},
  title     = {Layout-aware Subfigure Decomposition for Complex Figures in the Biomedical
               Literature},
  booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing,
               ICASSP 2019, Brighton, United Kingdom, May 12-17, 2019},
  pages     = {1343--1347},
  publisher = {IEEE},
  year      = {2019},
  url       = {https://doi.org/10.1109/ICASSP.2019.8683824},
  doi       = {10.1109/ICASSP.2019.8683824},
  timestamp = {Wed, 16 Oct 2019 14:14:52 +0200},
  biburl    = {https://dblp.org/rec/conf/icassp/Shi0CBN19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{Fang2019GAN,
  title="Learning Adversarial Interactions in Stackelberg Security Games with Limited Data",
  author={Fang, Boli and Jiang, Miao and Tregubov, Alexey and Blythe, Jim and Ferrara, Emilio},
  booktitle={NeurIPS workshop on Bridging Game Theory and Deep Learning},
  year={2019},
  ISIArea = {CSEC, ML}
}

@article{DBLP:journals/corr/abs-1911-06959,
  author       = {Nazgol Tavabi and
                  Homa Hosseinmardi and
                  Jennifer L. Villatte and
                  Andr{\'{e}}s Abeliuk and
                  Shrikanth S. Narayanan and
                  Emilio Ferrara and
                  Kristina Lerman},
  title        = {Learning Behavioral Representations from Wearable Sensors},
  journal      = {CoRR},
  volume       = {abs/1911.06959},
  year         = {2019},
  url          = {http://arxiv.org/abs/1911.06959},
  eprinttype    = {arXiv},
  eprint       = {1911.06959},
  timestamp    = {Tue, 03 Dec 2019 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1911-06959.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{pham2019learning,
  title={Learning Data Transformations with Minimal User Effort},
  author={Pham, Minh and Knoblock, Craig A and Pujara, Jay},
  booktitle={2019 IEEE International Conference on Big Data (Big Data)},
  pages={657--664},
  year={2019},
  organization={IEEE},
  urlPaper = {http://usc-isi-i2.github.io/papers/minh19-ieee.pdf},
  urlSlides = "http://usc-isi-i2.github.io/slides/pham-ieee19-slides.pptx"
}

@inproceedings{Vu:2019:LSM:3308558.3313711,
 author = {Vu, Binh and Knoblock, Craig and Pujara, Jay},
 title = {Learning Semantic Models of Data Sources Using Probabilistic Graphical Models},
 booktitle = {The World Wide Web Conference},
 series = {WWW '19},
 year = {2019},
 isbn = {978-1-4503-6674-8},
 location = {San Francisco, CA, USA},
 pages = {1944--1953},
 numpages = {10},
 url = {http://doi.acm.org/10.1145/3308558.3313711},
 doi = {10.1145/3308558.3313711},
 acmid = {3313711},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Semantic models, knowledge graph, linked data, ontology, probabilistic graphical models, semantic web},
} 

@inproceedings{Greer2019LearningSharedVectorRepresentations,
 author = {Greer, Timothy and Singla, Karan and Ma, Benjamin and Narayanan, Shrikanth},
 booktitle = {Proceedings of ICASSP},
 location = {Brighton, UK},
 month = {May},
 title = {Learning Shared Vector Representations of Lyrics and Chords in Music},
 year = {2019}
}

@inproceedings{huang-etal-2019-learning,
    title = "Learning a Unified Named Entity Tagger from Multiple Partially Annotated Corpora for Efficient Adaptation",
    author = "Huang, Xiao  and
      Dong, Li  and
      Boschee, Elizabeth  and
      Peng, Nanyun",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1048",
    doi = "10.18653/v1/K19-1048",
pages = "515--527",
}

@article{l2019lessons,
  title={Lessons Learned: Recommendations For Implementing a Longitudinal Study Using Wearable and Environmental Sensors in a Health Care Organization},
  author={L'Hommedieu, Michelle and L'Hommedieu, Justin and Begay, Cynthia and Schenone, Alison and Dimitropoulou, Lida and Margolin, Gayla and Falk, Tiago and Ferrara, Emilio and Lerman, Kristina and Narayanan, Shrikanth},
  journal={JMIR mHealth and uHealth},
  volume={7},
  number={12},
  pages={e13305},
  year={2019},
  publisher={JMIR Publications Inc., Toronto, Canada}
}

@inproceedings{Addawood2019linguistic,
  title={Linguistic Cues to Deception: Identifying Political Trolls on Social Media},
  author={Addawood, Aseel and Badawy, Adam and Lerman, Kristina and Ferrara, Emilio},
  booktitle={Proceedings of the International AAAI Conference on Web and Social Media},
  volume={13},
  number={01},
  pages={15--25},
  year={2019}
}

@inproceedings{markovic-et-al-sciknow2019,
	title        = {Linking Abstract Plans of Scientific Experiments to their Corresponding Execution Traces},
	author       = {Milan Markovic and Daniel Garijo and Peter Edwards},
	year         = 2019,
	booktitle    = {Proceedings of the Third Workshop on Capturing Scientific Knowledge (SciKnow 2019), held in conjunction with the 2019 ACM International Conference on Knowledge Capture (K-CAP)},
	address      = {Los Angeles, California},
	volume       = 2526,
	issn         = {1613-0073},
	url          = {http://ceur-ws.org/Vol-2526/short1.pdf}
}

@article{Ambite_Gordon_Fierro_Burns_Mathew_2019, title={Linking Educational Resources on Data Science}, volume={33}, url={https://ojs.aaai.org/index.php/AAAI/article/view/4989}, DOI={10.1609/aaai.v33i01.33019404}, abstractNote={<p>The availability of massive datasets in genetics, neuroimaging, mobile health, and other subfields of biology and medicine promises new insights but also poses significant challenges. To realize the potential of big data in biomedicine, the National Institutes of Health launched the Big Data to Knowledge (BD2K) initiative, funding several centers of excellence in biomedical data analysis and a Training Coordinating Center (TCC) tasked with facilitating online and inperson training of biomedical researchers in data science. A major initiative of the BD2K TCC is to automatically identify, describe, and organize data science training resources available on the Web and provide personalized training paths for users. In this paper, we describe the construction of ERuDIte, the Educational Resource Discovery Index for Data Science, and its release as linked data. ERuDIte contains over 11,000 training resources including courses, video tutorials, conference talks, and other materials. The metadata for these resources is described uniformly using Schema.org. We use machine learning techniques to tag each resource with concepts from the Data Science Education Ontology, which we developed to further describe resource content. Finally, we map references to people and organizations in learning resources to entities in DBpedia, DBLP, and ORCID, embedding our collection in the web of linked data. We hope that ERuDIte will provide a framework to foster open linked educational resources on the Web.</p>}, number={01}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Ambite, José Luis and Gordon, Jonathan and Fierro, Lily and Burns, Gully and Mathew, Joel}, year={2019}, month={Jul.}, pages={9404-9409} }

@InProceedings{ambite:iaai2019,
  author = 	 {Jos\'{e} Luis Ambite and Jonathan Gordon and Lily Fierro and Gully Burns and Joel Matthew},
  title = 	 {Linking Educational Resources on Data Science},
  booktitle = {Proceedings of the 31st Innovative Applications of Artificial Intelligence Conference (IAAI)},
  year = 	 {2019},
  address = 	 {Honolulu, Hawaii},
}

@article{horn2019locating,
  title={Locating the source of large-scale outbreaks of foodborne disease},
  author={Horn, Abigail L and Friedrich, Hanno},
  journal={Journal of the Royal Society Interface},
  volume={16},
  number={151},
  pages={20180624},
  year={2019},
  publisher={The Royal Society}
}

@article{DBLP:journals/corr/abs-1907-06745,
  author    = {Mayank Kejriwal and
               Peilin Zhou},
  title     = {Low-supervision urgency detection and transfer in short crisis messages},
  journal   = {CoRR},
  volume    = {abs/1907.06745},
  year      = {2019},
  url       = {http://arxiv.org/abs/1907.06745},
  eprinttype = {arXiv},
  eprint    = {1907.06745},
  timestamp = {Tue, 23 Jul 2019 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1907-06745.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{khider2019mint,
  title={MINT: An intelligent interface for understanding the impacts of climate change on hydrological, agricultural and economic systems},
  author={Khider, Deborah and Gil, Yolanda and Cobourn, Kelly M and Deelman, Ewa and Duffy, Christopher and da Silva, Rafael Ferreira and Kemanian, Armen and Knoblock, Craig and Kumar, Vipin and Peckham, Scott Dale and others},
  booktitle={AGU Fall Meeting 2019},
  year={2019},
  organization={AGU}
}

@article{Goldberg2019Machinelearningandnatural,
 author = {Goldberg, Simon B. and Flemotomos, Nikolaos and Martinez, Victor R. and Tanana, Michael and Kuo, Patty and Pace,  Brian T. and Villatte,  Jennifer L. and Georgiou, Panayiotis and Epps, Jake Van and Imel, Zac E. and Narayanan, Shrikanth and Atkins, David C.},
 journal = {Journal of Counseling Psychology},
 title = {Machine learning and natural language processing in psychotherapy research: Alliance as example use case},
 year = {2019}
}

@article{mehrabi2019man,
  title={Man is to Person as Woman is to Location: Measuring Gender Bias in Named Entity Recognition},
  author={Mehrabi, Ninareh and Gowda, Thamme and Morstatter, Fred and Peng, Nanyun and Galstyan, Aram},
  journal={arXiv preprint arXiv:1910.10872},
  year={2019}
}

@inproceedings{blythe2019massive,
  title={Massive Multi-Agent Data-Driven Simulations of the GitHub Ecosystem},
  author={Blythe, Jim and Bollenbacher, John and Huang, Di and Hui, Pik-Mai and Krohn, Rachel and Pacheco, Diogo and Muric, Goran and Sapienza, Anna and Tregubov, Alexey and Ahn, Yong-Yeol and others},
  booktitle={International Conference on Practical Applications of Agents and Multi-Agent Systems},
  pages={3--15},
  year={2019},
  organization={Springer, Cham}
}

@inproceedings{blythe-emilio_PAAMS19, 
	title={Massive Multi-Agent Data-Driven Simulationsof the GitHub Ecosystem}, 
	author={Blythe, James and Ferrara, Emilio and Huang, Di and Lerman, Kristina and Muric, Goran and Sapienza,Anna and Tregubov, Alexey and Pacheco, Diogo and Bollenbacher, John and Flammini, Alessandro and Hui, Pik-Mai and Menczer, Filippo}, 
	booktitle={International Conference on Autonomous Agents and Multiagent Systems PAAMS}, 
	year={2019}
}

@inproceedings{krohn2019massive,
  title={Massive Multi-agent Data-Driven Simulations of the GitHub Ecosystem},
  author={Krohn, Rachel and Pacheco, Diogo and Muric, Goran and Sapienza, Anna and Tregubov, Alexey and Ahn, Yong-Yeol and Flammini, Alessandro and Lerman, Kristina and Menczer, Filippo and Weninger, Tim and others},
  booktitle={Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection: 17th International Conference, PAAMS 2019, {\'A}vila, Spain, June 26-28, 2019, Proceedings},
  volume={11523},
  pages={3},
  year={2019},
  organization={Springer}
}

@article{reing2019,
	Author = {Reing, Kyle and {Ver Steeg}, Greg and Galstyan, Aram},
	Date-Added = {2020-01-20 21:45:30 +0000},
	Date-Modified = {2020-01-20 21:50:01 +0000},
	Journal = {IEEE Transactions on Information Theory},
	Publisher = {IEEE},
	Title = {Maximizing Multivariate Information with Error-Correcting Codes},
	Year = {2019}}

@inproceedings{lepe2019measuring,
  title={Measuring Student Learning On Network Testbeds},
  author={Lepe, Paul and Aggarwal, Aashray and Mirkovic, Jelena and Mache, Jens and Weiss, Richard and Weinmann, David},
  booktitle={Midscale Education and Research Infrastructure and Tools (MERIT) Workshop},
  pages={1--2},
  year={2019},
  organization={IEEE}
}

@Article{	  ferreiradasilva-fgcs-bb-2019,
  Title		= {Measuring the Impact of Burst Buffers on Data-Intensive
		  Scientific Workflows},
  Author	= {Ferreira da Silva, Rafael and Callaghan, Scott and Do, Tu
		  Mai Anh and Papadimitriou, George and Deelman, Ewa},
  Journal	= {Future Generation Computer Systems},
  Volume	= {101},
  Number	= {},
  Pages		= {208--220},
  Year		= {2019},
  DOI		= {10.1016/j.future.2019.06.016},
  Note		= {Funding Acknowledgments: DOE DESC0012636, NSF 1664162, NSF
		  1741040}
}

@inproceedings{ananth2019mismorph,
  title="Mismorphism: the Heart of the Weird Machine",
  author={Anantharaman, Prashant and Kothari, Vijay and Jenkins, Ira and Millian, Michael and Bratus, Sergey and Blythe, Jim and Koppel, Ross and Smith, Sean},
  booktitle={International Workshop on Security Protocols},
  year={2019}
}

@inproceedings{narayanan2019mission,
  title={Mission Dodona: Electronic Power System Design, Analysis and Integration},
  author={Narayanan, Sriram and Rughani, Rahul and Rogers, Rebecca and Clarke, Kyle and Allam, Jeremy},
  booktitle={33rd Annual Conference on Small Satellites, Logan, Utah, USA},
  year={2019}
}

@conference {RN644,
	title = {Mitigating soft failures using network analytics and SDN to support distributed bandwidth-intensive scientific instruments over international networks},
	booktitle = {SubOptic Conference 2019},
	year = {2019},
	month = {2019},
	type = {Conference Paper},
	address = {New Orleans, LA},
	author = {Jeronimo Bezerra and Julio Ibarra and David Boertjes and Franco Santillo and Williford, Lance and Heidi Morgan and Chip Cox and Luiz Lopez}
}

@inproceedings{narayanan2019mitigation,
  title={Mitigation of security attacks in the SDN data plane using P4-enabled switches},
  author={Narayanan, Niranjhana and Sankaran, Ganesh C and Sivalingam, Krishna M},
  booktitle={2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)},
  pages={1--6},
  year={2019},
  organization={IEEE}
}

@inproceedings{mixhop,
 author={Sami Abu-El-Haija AND Bryan Perozzi AND Amol Kapoor AND Hrayr Harutyunyan
         AND Nazanin Alipourfard AND Kristina Lerman AND Greg Ver Steeg AND Aram Galstyan},
 title={MixHop: Higher-Order Graph Convolution Architectures via Sparsified Neighborhood Mixing},
 booktitle = {International Conference on Machine Learning (ICML)},
 year = {2019},
}

@article{DBLP:journals/corr/abs-1905-00067,
  author       = {Sami Abu{-}El{-}Haija and
                  Bryan Perozzi and
                  Amol Kapoor and
                  Hrayr Harutyunyan and
                  Nazanin Alipourfard and
                  Kristina Lerman and
                  Greg Ver Steeg and
                  Aram Galstyan},
  title        = {MixHop: Higher-Order Graph Convolutional Architectures via Sparsified
                  Neighborhood Mixing},
  journal      = {CoRR},
  volume       = {abs/1905.00067},
  year         = {2019},
  url          = {http://arxiv.org/abs/1905.00067},
  eprinttype    = {arXiv},
  eprint       = {1905.00067},
  timestamp    = {Mon, 03 Jun 2019 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1905-00067.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{abu2019mixhop,
  title={Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing},
  author={Abu-El-Haija, Sami and Perozzi, Bryan and Kapoor, Amol and Alipourfard, Nazanin and Lerman, Kristina and Harutyunyan, Hrayr and Steeg, Greg Ver and Galstyan, Aram},
  journal={arXiv preprint arXiv:1905.00067},
  year={2019}
}

@inproceedings{abu2019mixhop,
  title={Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing},
  author={Abu-El-Haija, Sami and Perozzi, Bryan and Kapoor, Amol and Alipourfard, Nazanin and Lerman, Kristina and Harutyunyan, Hrayr and Ver Steeg, Greg and Galstyan, Aram},
  booktitle={International Conference on Machine Learning},
  pages={21--29},
  year={2019},
  organization={PMLR}
}

@inproceedings{Nasir2019ModelingInterpersonalLinguisticCoordination,
 author = {Nasir, Md and Chakravarthula, Sandeep Nallan and Baucom, Brian and Atkins, David and Georgiou, Panayiotis and Narayanan, Shrikanth},
 booktitle = {In proceedings of Proceedings of Interspeech},
 location = {Graz, Austria},
 month = {September},
 title = {Modeling Interpersonal  Linguistic Coordination in Conversations using Word Movers Distance},
 year = {2019}
}

@article{Zane2018Motion-CapturePatternsofVoluntarily,
 author = {Zane, Emily and Yang, Zhaojun and Pozzan, Lucia and Guha, Tanaya and Narayanan, Shrikanth and Grossman, Ruth},
 doi = {10.1007/s10803-018-3811-7},
 journal = {Journal of autism and developmental disorders},
 link = {http://sail.usc.edu/publications/files/Zane2018_Article_Motion-CapturePatternsOfVolunt.pdf},
 month = {Mar},
 number = {3},
 pages = {1062-1079},
 title = {Motion-Capture Patterns of Voluntarily Mimicked Dynamic Facial Expressions in Children and Adolescents With and Without ASD},
 volume = {49},
 year = {2019}
}

@inproceedings{DBLP:conf/interspeech/JatiPPP0TGN19,
  author       = {Arindam Jati and
                  Raghuveer Peri and
                  Monisankha Pal and
                  Tae Jin Park and
                  Naveen Kumar and
                  Ruchir Travadi and
                  Panayiotis G. Georgiou and
                  Shrikanth Narayanan},
  title        = {Multi-Task Discriminative Training of Hybrid {DNN-TVM} Model for Speaker
                  Verification with Noisy and Far-Field Speech},
  booktitle    = {Interspeech 2019, 20th Annual Conference of the International Speech
                  Communication Association, Graz, Austria, 15-19 September 2019},
  pages        = {2463--2467},
  year         = {2019},
  crossref     = {DBLP:conf/interspeech/2019},
  url          = {https://doi.org/10.21437/Interspeech.2019-3010},
  doi          = {10.21437/INTERSPEECH.2019-3010},
  timestamp    = {Fri, 29 Jan 2021 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/conf/interspeech/JatiPPP0TGN19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{Gibson2019Multi-labelMulti-taskDeepLearning,
 author = {Gibson, James and Atkins, David and Creed, Torrey and Imel, Zac and Georgiou, Panayiotis and Narayanan, Shrikanth},
 doi = {10.1109/TAFFC.2019.2952113},
 journal = {IEEE Transactions on Affective Computing},
 link = {http://sail.usc.edu/publications/files/Gibson-TAFFC-2019.pdf},
 title = {Multi-label Multi-task Deep Learning for Behavioral Coding},
 year = {2019},
 month = {Nov}
}

@article{Booth2019MultimodalHumanandEnvironmental,
 author = {Booth, Brandon M and Mundnich, Karel and Feng, Tiantian and Nadarajan, Amrutha and Falk, Tiago H and Villatte, Jennifer L and Ferrara, Emilio and Narayanan, Shrikanth},
 journal = {J Med Internet Res (JMIR)},
 link = {http://sail.usc.edu/publications/files/document(3).pdf},
 number = {8},
 pages = {e12832},
 title = {Multimodal Human and Environmental Sensing for Longitudinal Behavioral Studies in Naturalistic Settings: Framework for Sensor Selection, Deployment, and Management},
 volume = {21},
 year = {2019}
}

@inproceedings{Greer2019MultimodalViewintoMusic,
 author = {Greer, Timothy and Ma, Benjamin Sachs, Matthew  and Habibi, Assal and Narayanan, Shrikanth},
 booktitle = {In proceedings of 27th ACM International Conference on Multimedia (ACM MM 2019)},
 location = {Nice, France},
 month = {Oct},
 title = {Multimodal View into Musics Effect on Human Neural, Physiological, and Emotional Experience},
 year = {2019}
}

@article{harutyunyan2019multitask,
  title={Multitask learning and benchmarking with clinical time series data},
  author={Harutyunyan, Hrayr and Khachatrian, Hrant and Kale, David C and Ver Steeg, Greg and Galstyan, Aram},
  journal={Scientific data},
  volume={6},
  number={1},
  pages={1--18},
  year={2019},
  publisher={Nature Publishing Group}
}

@inproceedings{DBLP:conf/interspeech/Somandepalli0JG19,
  author       = {Krishna Somandepalli and
                  Naveen Kumar and
                  Arindam Jati and
                  Panayiotis G. Georgiou and
                  Shrikanth Narayanan},
  title        = {Multiview Shared Subspace Learning Across Speakers and Speech Commands},
  booktitle    = {Interspeech 2019, 20th Annual Conference of the International Speech
                  Communication Association, Graz, Austria, 15-19 September 2019},
  pages        = {2320--2324},
  year         = {2019},
  crossref     = {DBLP:conf/interspeech/2019},
  url          = {https://doi.org/10.21437/Interspeech.2019-3130},
  doi          = {10.21437/INTERSPEECH.2019-3130},
  timestamp    = {Fri, 29 Jan 2021 17:41:10 +0100},
  biburl       = {https://dblp.org/rec/conf/interspeech/Somandepalli0JG19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{48352,
    title	= {N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification},
    author	= {Sami Abu-El-Haija and Amol Kapoor and Bryan Perozzi and Joonseok Lee},
    year	= {2019},
    URL	= {http://auai.org/uai2019/proceedings/papers/310.pdf},
    booktitle	= {Uncertainty in Artificial Intelligence}
}

@proceedings {RN851,
	title = {NRE-011: Big Data Express: A Scalable and High-performance Data Transfer Platform},
	journal = {Supercomputing Conference (SC19)},
	year = {2019},
	type = {Conference Proceedings},
	address = {Denver, CO},
	url = {https://urldefense.com/v3/__https://sc19.supercomputing.org/app/uploads/2019/11/SC19-NRE-011.pdf__;!!FjuHKAHQs5udqho!OrfqNEjI8IYnpYEY3Wc7S4oSY6Kf82MHM8jGAJar7qa8MoXzNwtTTC2HiJsVSREP5YicoFyfN6n2Uw$ },
	author = {Lu, Qiming and Zhang, Liang and Wu, Wenji and Demar, Phil and Chen, Jim and Joe Mambretti and Guok, Chin and Monga, Inder and Lehman, Tom and Yang, Xi and Kim, Jin and Cho, Buseung and Julio Ibarra and Heidi Morgan and Jeronimo Bezerra and Vasilka Chergarova and Zahir, Adil and Malenstein, Gerben van}
}

@proceedings {RN852,
	title = {NRE-019: Global Petascale to Exascale Workflows for Data Intensive Science Accelerated by Next Generation Programmable SDN Architectures and Machine Learning Applications},
	journal = {Supercomputing Conference (SC19)},
	year = {2019},
	type = {Conference Proceedings},
	address = {Denver, CO},
	url = {https://urldefense.com/v3/__https://sc19.supercomputing.org/app/uploads/2019/11/SC19-NRE-019.pdf__;!!FjuHKAHQs5udqho!OrfqNEjI8IYnpYEY3Wc7S4oSY6Kf82MHM8jGAJar7qa8MoXzNwtTTC2HiJsVSREP5YicoFxRthXLow$ },
	author = {Newman, Harvey and Balcas, Justas and Sirvinskas, Raimondas and Iordache, Catalin and Chiu, Joseph and Anderson, Stuart and Barayoga, Juan and Chang, Jin and Boyd, Dawn and Watanabe, Larry and Williams, Don S. and Mughal, Azher and Jeff Kantor and Matt Kollross and Julio Ibarra and Jeronimo Bezerra and Zahir, Adil and Heidi Morgan and Yang, Richard and Xiang, Qiao and Zhang, Jensen and Wang, X. Tony and Guo, Dong and Yu, Dennis and Wang, May and Leet, Christoher and Chen, Shenshen and Le, Franck and Lim, Yeon-sup and Pourbaix, Yuki de and Mishra, Vinod and Yang, Xi and Lehman, Tom and Duarte, Javier and DeFanti, Tom and Smarr, Larry and Graham, John and Hutton, Tom and Wuerthwein, Frank and Papadopoulos, Phil and Harris, Phil and Monga, Inder and Guok, Chin and MacAuley, John and Sim, Alex and Hazen, Damian and Sim, Alex and Novaes, Sergio and Iope, Rogerio and Leal, Beraldo and Gomes, Marco and Beruchi, Artur and Joe Mambretti and Chen, Jim and Szalay, Alex and Malenstein, Gerben van and Demar, Phil and Winkler, Linda and Shawn McKee and Yeh, Edmund and RanLi and Wu, Yuanhao and Christos Papadopoulos and Fan, Chengyu and Shannigrahi, Susmit and Lixia Zhang and Jashal, Brij and Mazumdar, Kajari and Hess, John and Fox, Louis and Lopez, Luis and Michael Stanton and Moura, Alex and Sandra Jaque and Albert Astudillo and Lyonnais, Marc and Wilson, Rod and Wilby, Nick and Williford, Lance}
}

@proceedings {RN850,
	title = {NRE-023: International Data Transfer over AmLight Express and Protect (ExP)},
	journal = {Supercomputing Conference (SC19)},
	year = {2019},
	type = {Conference Proceedings},
	address = {Denver, CO},
	url = {https://urldefense.com/v3/__https://sc19.supercomputing.org/app/uploads/2019/11/SC19-NRE-023.pdf__;!!FjuHKAHQs5udqho!OrfqNEjI8IYnpYEY3Wc7S4oSY6Kf82MHM8jGAJar7qa8MoXzNwtTTC2HiJsVSREP5YicoFzE-lttHA$ },
	author = {Newman, Harvey and Lehman, Tom and Julio Ibarra and Jeronimo Bezerra and Zahir, Adil and Vasilka Chergarova and Heidi Morgan and Clark, Russel and Sean Donovan and Novaes, Sergio and Iope, Rogerio and Leal, Beraldo and Lopez, Luis and Michael Stanton and Lima, Renata Frez de and Albert Astudillo}
}

@inproceedings{garg2019nearly,
  title={Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction},
  author={Garg, Sahil and Galstyan, Aram and Ver Steeg, Greg and Cecchi, Guillermo A},
  booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
  pages={4026--4036},
  year={2019}
}
%%2018

@article{DBLP:journals/ans/KejriwalK19,
  author    = {Mayank Kejriwal and
               Rahul Kapoor},
  title     = {Network-theoretic information extraction quality assessment in the
               human trafficking domain},
  journal   = {Appl. Netw. Sci.},
  volume    = {4},
  number    = {1},
  pages     = {44:1--44:26},
  year      = {2019},
  url       = {https://doi.org/10.1007/s41109-019-0154-z},
  doi       = {10.1007/s41109-019-0154-z},
  timestamp = {Fri, 18 Sep 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/ans/KejriwalK19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article {CHUNG2019512,
	title = {Novel network services for supporting big data science research},
	journal = {Future Generation Computer Systems},
	volume = {98},
	year = {2019},
	pages = {512 - 521},
	abstract = {To interconnect research facilities across wide geographic areas, network operators deploy science networks, also referred to as Research and Education (R\&E) networks. These networks allow experimenters to establish dedicated network connections between research facilities for transferring large amounts of data. Recently, R\&E networks have started using Software-Defined Networking (SDN) and Software-Defined Exchanges (SDX) for deploying these connections. AtlanticWave/SDX is a response to the growing demand to support end-to-end network services spanning multiple SDN domains. However, requesting these services is a challenging task for domain-expert scientists, because the interfaces of the R\&E networks have been developed by network operators for network operators. In this paper, we propose interfaces that allow domain-expert scientists to reserve resources of the scientific network using abstractions that focus on their data transfer needs for scientific workflow management. Recent trends in the networking field pursue better interfaces for requesting network services (e.g., intent-based networking). Although intents are sufficient for the needs of network operations, they are not abstract enough in most cases to be used by domain-expert scientists. This is an issue we are addressing in the AtlanticWave/SDX design: network operators and domain-expert scientists will have their own interfaces focusing on their specific needs.},
	keywords = {AtlanticWave/SDX, Interface, Science workflows, Software-defined exchange},
	issn = {0167-739X},
	doi = {https://urldefense.com/v3/__https://doi.org/10.1016/j.future.2019.03.047__;!!FjuHKAHQs5udqho!OrfqNEjI8IYnpYEY3Wc7S4oSY6Kf82MHM8jGAJar7qa8MoXzNwtTTC2HiJsVSREP5YicoFxcV6n5Kg$ },
	url = {https://urldefense.com/v3/__http://www.sciencedirect.com/science/article/pii/S0167739X18314651__;!!FjuHKAHQs5udqho!OrfqNEjI8IYnpYEY3Wc7S4oSY6Kf82MHM8jGAJar7qa8MoXzNwtTTC2HiJsVSREP5YicoFxxbv95yg$ },
	author = {Joaquin Chung and Sean Donovan and Jeronimo Bezerra and Heidi Morgan and Julio Ibarra and Russ Clark and Henry Owen}
}

To interconnect research facilities across wide geographic areas, network operators deploy science networks, also referred to as Research and Education (R&E) networks. These networks allow experimenters to establish dedicated network connections between research facilities for transferring large amounts of data. Recently, R&E networks have started using Software-Defined Networking (SDN) and Software-Defined Exchanges (SDX) for deploying these connections. AtlanticWave/SDX is a response to the growing demand to support end-to-end network services spanning multiple SDN domains. However, requesting these services is a challenging task for domain-expert scientists, because the interfaces of the R&E networks have been developed by network operators for network operators. In this paper, we propose interfaces that allow domain-expert scientists to reserve resources of the scientific network using abstractions that focus on their data transfer needs for scientific workflow management. Recent trends in the networking field pursue better interfaces for requesting network services (e.g., intent-based networking). Although intents are sufficient for the needs of network operations, they are not abstract enough in most cases to be used by domain-expert scientists. This is an issue we are addressing in the AtlanticWave/SDX design: network operators and domain-expert scientists will have their own interfaces focusing on their specific needs.

@inproceedings{fakhraei2019nseen,
  title={Nseen: Neural semantic embedding for entity normalization},
  author={Fakhraei, Shobeir and Mathew, Joel and Ambite, Jos{\'e} Luis},
  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages={665--680},
  year={2019},
  organization={Springer}
}

@inproceedings{DBLP:conf/eScience/GarijoOKRG19,
  author       = {Daniel Garijo and
                  Maximiliano Osorio and
                  Deborah Khider and
                  Varun Ratnakar and
                  Yolanda Gil},
  title        = {OKG-Soft: An Open Knowledge Graph with Machine Readable Scientific
                  Software Metadata},
  booktitle    = {15th International Conference on eScience, eScience 2019, San Diego,
                  CA, USA, September 24-27, 2019},
  pages        = {349--358},
  publisher    = {{IEEE}},
  year         = {2019},
  url          = {https://doi.org/10.1109/eScience.2019.00046},
  doi          = {10.1109/ESCIENCE.2019.00046},
  timestamp    = {Sat, 21 Mar 2020 17:14:48 +0100},
  biburl       = {https://dblp.org/rec/conf/eScience/GarijoOKRG19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{Agrawal2019OnEvaluatingCNNrepresentations,
 author = {Agrawal, Taruna and Gupta, Rahul and  Narayanan, Shrikanth},
 booktitle = {Proceedings of ICASSP},
 month = {May},
 title = {On Evaluating CNN representations for Low resource medical image classification},
 year = {2019}
}

@inproceedings{Huang2019OnRoleandLocation,
 author = {Huang, Che-Wei and Narayanan, Shrikanth},
 booktitle = {Proceedings of ICASSP},
 location = {Brighton, UK},
 month = {May},
 title = {On Role and Location of Normalization Before Model-based Data Augmentation in Residual Blocks for Classification Tasks},
 year = {2019}
}

@inproceedings{mirzaalian_lsci_fpad_2019,
	title = {On the Effectiveness of Laser Speckle Contrast Imaging and Deep Neural Networks for Detecting Known and Unknown Fingerprint Presentation Attacks},
	booktitle = {International Conference on Biometrics 2019 (ICB)},
	year = {2019},
	author = {Hengameh Mirzaalian and Hussein, Mohamed and Wael AbdAlmageed}
}

@article{marvianLidarHen,
	Abstract = {Quantum many-body systems whose Hamiltonians are non-stoquastic, i.e., have positive off-diagonal matrix elements in a given basis, are known to pose severe limitations on the efficiency of Quantum Monte Carlo algorithms designed to simulate them, due to the infamous sign problem. We study the computational complexity associated with `curing'non-stoquastic Hamiltonians, i.e., transforming them into sign-problem-free ones. We prove that if such transformations are limited to single-qubit Clifford group elements or general single-qubit orthogonal matrices, finding the curing transformation is NP-complete. We discuss the implications of this result.},
	Author = {Marvian, Milad and Lidar, Daniel A. and Hen, Itay},
	Da = {2019/04/05},
	Date-Added = {2020-05-11 17:23:21 -0700},
	Date-Modified = {2020-05-11 17:23:21 -0700},
	Doi = {10.1038/s41467-019-09501-6},
	Id = {Marvian2019},
	Isbn = {2041-1723},
	Journal = {Nature Communications},
	Number = {1},
	Pages = {1571},
	Title = {On the computational complexity of curing non-stoquastic Hamiltonians},
	Ty = {JOUR},
	Url = {https://doi.org/10.1038/s41467-019-09501-6},
	Volume = {10},
	Year = {2019},
	Bdsk-Url-1 = {https://doi.org/10.1038/s41467-019-09501-6}}
	

Quantum many-body systems whose Hamiltonians are non-stoquastic, i.e., have positive off-diagonal matrix elements in a given basis, are known to pose severe limitations on the efficiency of Quantum Monte Carlo algorithms designed to simulate them, due to the infamous sign problem. We study the computational complexity associated with `curing'non-stoquastic Hamiltonians, i.e., transforming them into sign-problem-free ones. We prove that if such transformations are limited to single-qubit Clifford group elements or general single-qubit orthogonal matrices, finding the curing transformation is NP-complete. We discuss the implications of this result.

@inproceedings{tregubov_blythe_abstractions, 
	title={Optimization of Large-scale Agent-based Simulations through Automated Abstraction and Simplification}, 
	author={Tregubov, Alexey and Blythe, James }, 
	booktitle={AAMAS International Workshop on Multi-Agent-Based Simulation}, 
	year={2019}
}

@inproceedings{blythe-tregubov_abstractions, 
	title={Optimization of Large-scale Agent-based Simulations through AutomatedAbstraction and Simplification}, 
	author={Blythe, James and Tregubov, Alexey}, 
	booktitle={International Joint Conference on Artificial Intelligence}, 
	year={2019}
}

@article{doi:10.1029/2019PA003632,
	title        = {PaCTS 1.0: A Crowdsourced Reporting Standard for Paleoclimate Data},
	author       = {Khider, D. and Emile-Geay, J. and McKay, N. P. and Gil, Y. and Garijo, D. and Ratnakar, V. and Alonso-Garcia, M. and Bertrand, S. and Bothe, O. and Brewer, P. and Bunn, A. and Chevalier, M. and Comas-Bru, L. and Csank, A. and Dassié, E. and DeLong, K. and Felis, T. and Francus, P. and Frappier, A. and Gray, W. and Goring, S. and Jonkers, L. and Kahle, M. and Kaufman, D. and Kehrwald, N. M. and Martrat, B. and McGregor, H. and Richey, J. and Schmittner, A. and Scroxton, N. and Sutherland, E. and Thirumalai, K. and Allen, K. and Arnaud, F. and Axford, Y. and Barrows, T. and Bazin, L. and Pilaar Birch, S. E. and Bradley, E. and Bregy, J. and Capron, E. and Cartapanis, O. and Chiang, H.-W. and Cobb, K. M. and Debret, M. and Dommain, R. and Du, J. and Dyez, K. and Emerick, S. and Erb, M. P. and Falster, G. and Finsinger, W. and Fortier, D. and Gauthier, Nicolas and George, S. and Grimm, E. and Hertzberg, J. and Hibbert, F. and Hillman, A. and Hobbs, W. and Huber, M. and Hughes, A. L. C. and Jaccard, S. and Ruan, J. and Kienast, M. and Konecky, B. and Le Roux, G. and Lyubchich, V. and Novello, V. F. and Olaka, L. and Partin, J. W. and Pearce, C. and Phipps, S. J. and Pignol, C. and Piotrowska, N. and Poli, M.-S. and Prokopenko, A. and Schwanck, F. and Stepanek, C. and Swann, G. E. A. and Telford, R. and Thomas, E. and Thomas, Z. and Truebe, S. and von Gunten, L. and Waite, A. and Weitzel, N. and Wilhelm, B. and Williams, J. and Williams, J. J. and Winstrup, M. and Zhao, N. and Zhou, Y.},
	year         = 2019,
	journal      = {Paleoceanography and Paleoclimatology},
	volume       = 34,
	number       = 10,
	pages        = {1570--1596},
	doi          = {10.1029/2019PA003632},
	url          = {https://dgarijo.com/papers/agu_2019.pdf},
	keywords     = {standards, FAIR, paleoclimate, paleoceanography, data, best practices},
	eprint       = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2019PA003632}
}

@inproceedings{Shbita2019,
	title = {Parsing, Representing and Transforming Units of Measure},
	author = {Basel Shbita and Arun Rajendran and Jay Pujara and Craig A. Knoblock},
	booktitle = {Proceedings of the Conference on Modeling the World's Systems},
	location = {Pittsburgh, PA},
	year = {2019},
  Urlpaper = {http://usc-isi-i2.github.io/papers/shbita19-mws.pdf}
  }
	

@TechReport{Guo19a,
        author =        "Hang Guo and Xun Fan and Anh Cao and Geoff
                  Outhred and John Heidemann",
        title =         "Peek Inside the Closed World: Evaluating Autoencoder-Based Detection of {DDoS} to Cloud",
	institution = 	"arXiv",
        year =          2019,
	sortdate = 		"2019-12-16", 
	project = "ant, lacanic",
	jsubject = "topology_modeling",
        number =     "arXiv:1912.05590v2 [cs.NI]",
        month =      dec,
	jlocation = 	"johnh: pafile",
	keywords = 	"ddos, cloud, machine learning, autoencoder",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Guo19a.html",
	otherurl =		"https://ant.isi.edu/%7ehangguo/papers/Guo19a.pdf",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Guo19a.pdf",
	blogurl = "https://ant.isi.edu/blog/?p=1401",
        abstract = "Machine-learning-based anomaly detection (ML-based AD) has been
successful at detecting DDoS events in the lab. However published
evaluations of ML-based AD have only had limited data and have not
provided insight into why it works. To address limited evaluation
against real-world data, we apply autoencoder, an existing ML-AD
model, to 57 DDoS attack events captured at 5 cloud IPs from a major
cloud provider. To improve our understanding for why ML-based AD works
or not works, we interpret this data with feature attribution and
counterfactual explanation. We show that our version of autoencoders
work well overall: our models capture nearly all malicious flows to 2
of the 4 cloud IPs under attacks (at least 99.99\%) but generate a few
false negatives (5\% and 9\%) for the remaining 2 IPs. We show that our
models maintain near-zero false positives on benign flows to all 5
IPs. Our interpretation of results shows that our models identify
almost all malicious flows with non-whitelisted (non-WL) destination
ports (99.92\%) by learning the full list of benign destination ports
from training data (the normality). Interpretation shows that although
our models learn incomplete normality for protocols and source ports,
they still identify most malicious flows with non-WL protocols and
blacklisted (BL) source ports (100.0\% and 97.5\%) but risk false
positives. Interpretation also shows that our models only detect a few
malicious flows with BL packet sizes (8.5\%) by incorrectly inferring
these BL sizes as normal based on incomplete normality learned. We
find our models still detect a quarter of flows (24.7\%) with abnormal
payload contents even when they do not see payload by combining
anomalies from multiple flow features. Lastly, we summarize the
implications of what we learn on applying autoencoder-based AD in
production.problme?Machine-learning-based anomaly detection (ML-based
AD) has been successful at detecting DDoS events in the lab. However
published evaluations of ML-based AD have only had limited data and
have not provided insight into why it works. To address limited
evaluation against real-world data, we apply autoencoder, an existing
ML-AD model, to 57 DDoS attack events captured at 5 cloud IPs from a
major cloud provider. To improve our understanding for why ML-based AD
works or not works, we interpret this data with feature attribution
and counterfactual explanation. We show that our version of
autoencoders work well overall: our models capture nearly all
malicious flows to 2 of the 4 cloud IPs under attacks (at least
99.99\%) but generate a few false negatives (5\% and 9\%) for the
remaining 2 IPs. We show that our models maintain near-zero false
positives on benign flows to all 5 IPs. Our interpretation of results
shows that our models identify almost all malicious flows with
non-whitelisted (non-WL) destination ports (99.92\%) by learning the
full list of benign destination ports from training data (the
normality). Interpretation shows that although our models learn
incomplete normality for protocols and source ports, they still
identify most malicious flows with non-WL protocols and blacklisted
(BL) source ports (100.0\% and 97.5\%) but risk false
positives. Interpretation also shows that our models only detect a few
malicious flows with BL packet sizes (8.5\%) by incorrectly inferring
these BL sizes as normal based on incomplete normality learned. We
find our models still detect a quarter of flows (24.7\%) with abnormal
payload contents even when they do not see payload by combining
anomalies from multiple flow features. Lastly, we summarize the
implications of what we learn on applying autoencoder-based AD in
production.",
}

Machine-learning-based anomaly detection (ML-based AD) has been successful at detecting DDoS events in the lab. However published evaluations of ML-based AD have only had limited data and have not provided insight into why it works. To address limited evaluation against real-world data, we apply autoencoder, an existing ML-AD model, to 57 DDoS attack events captured at 5 cloud IPs from a major cloud provider. To improve our understanding for why ML-based AD works or not works, we interpret this data with feature attribution and counterfactual explanation. We show that our version of autoencoders work well overall: our models capture nearly all malicious flows to 2 of the 4 cloud IPs under attacks (at least 99.99%) but generate a few false negatives (5% and 9%) for the remaining 2 IPs. We show that our models maintain near-zero false positives on benign flows to all 5 IPs. Our interpretation of results shows that our models identify almost all malicious flows with non-whitelisted (non-WL) destination ports (99.92%) by learning the full list of benign destination ports from training data (the normality). Interpretation shows that although our models learn incomplete normality for protocols and source ports, they still identify most malicious flows with non-WL protocols and blacklisted (BL) source ports (100.0% and 97.5%) but risk false positives. Interpretation also shows that our models only detect a few malicious flows with BL packet sizes (8.5%) by incorrectly inferring these BL sizes as normal based on incomplete normality learned. We find our models still detect a quarter of flows (24.7%) with abnormal payload contents even when they do not see payload by combining anomalies from multiple flow features. Lastly, we summarize the implications of what we learn on applying autoencoder-based AD in production.problme?Machine-learning-based anomaly detection (ML-based AD) has been successful at detecting DDoS events in the lab. However published evaluations of ML-based AD have only had limited data and have not provided insight into why it works. To address limited evaluation against real-world data, we apply autoencoder, an existing ML-AD model, to 57 DDoS attack events captured at 5 cloud IPs from a major cloud provider. To improve our understanding for why ML-based AD works or not works, we interpret this data with feature attribution and counterfactual explanation. We show that our version of autoencoders work well overall: our models capture nearly all malicious flows to 2 of the 4 cloud IPs under attacks (at least 99.99%) but generate a few false negatives (5% and 9%) for the remaining 2 IPs. We show that our models maintain near-zero false positives on benign flows to all 5 IPs. Our interpretation of results shows that our models identify almost all malicious flows with non-whitelisted (non-WL) destination ports (99.92%) by learning the full list of benign destination ports from training data (the normality). Interpretation shows that although our models learn incomplete normality for protocols and source ports, they still identify most malicious flows with non-WL protocols and blacklisted (BL) source ports (100.0% and 97.5%) but risk false positives. Interpretation also shows that our models only detect a few malicious flows with BL packet sizes (8.5%) by incorrectly inferring these BL sizes as normal based on incomplete normality learned. We find our models still detect a quarter of flows (24.7%) with abnormal payload contents even when they do not see payload by combining anomalies from multiple flow features. Lastly, we summarize the implications of what we learn on applying autoencoder-based AD in production.

@inproceedings{deb2019perils,
  title={Perils and challenges of social media and election manipulation analysis: The 2018 us midterms},
  author={Deb, Ashok and Luceri, Luca and Badaway, Adam and Ferrara, Emilio},
  booktitle={Companion proceedings of the 2019 world wide web conference},
  pages={237--247},
  year={2019}
}

@inproceedings{kouki:iui19,
    author = "Kouki, Pigi and Schaffer, James and Pujara, Jay and O'Donovan, John and Getoor, Lise",
    acceptrate = "25\%",
    bib_url = "/pubs/bib/kouki-iui19.bib",
    booktitle = "ACM International Conference on Intelligent User Interfaces",
    doi_url = "https://doi.org/10.1145/3301275.3302306",
    note = "\textbf{Winner of Outstanding Paper award}",
    pdf_url = "/pubs/2019/kouki-iui19/kouki-iui19.pdf",
    sec = "conf",
    title = "Personalized Explanations for Hybrid Recommender Systems",
    year = "2019"
}

@inproceedings{goldfarb2019plan,
  title={Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation},
  author={Goldfarb-Tarrant, Seraphina and Feng, Haining and Peng, Nanyun},
  booktitle={2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2019), Demonstrations Track},
  volume={4},
  pages={89--97},
  year={2019}
}

@inproceedings{yao2019plan,
  title={Plan-And-Write: Towards Better Automatic Storytelling},
  author={Yao, Lili and Peng, Nanyun and Ralph, Weischedel and Knight, Kevin and Zhao, Dongyan and Yan, Rui},
  booktitle={The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)},
  year={2019}
}

@article{article,
author = {Yao, Lili and Peng, Nanyun and Weischedel, Ralph and Knight, Kevin and Zhao, Dongyan and Yan, Rui},
year = {2019},
month = {07},
pages = {7378-7385},
title = {Plan-and-Write: Towards Better Automatic Storytelling},
volume = {33},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
doi = {10.1609/aaai.v33i01.33017378}
}

@TechReport{Qadeer19b,
        author =        "Abdul Qadeer and John Heidemann",
        title =         "Plumb: Efficient Processing of Multi-User Pipelines (Poster)",
        institution =   "USC/Information Sciences Institute",
        year =          2019,
	sortdate = 		"2019-01-17", 
	project = "ant, lacanic, retrofuturebridge",
	jsubject = "network_big_data",
        month =      jan,
        jlocation =   "johnh: pafile",
        keywords =   "big data, hadoop, plumb, DNS, streaming data,
                  data processing, workflow",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Qadeer19b.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Qadeer19b.pdf",
	xblogurl = "https://ant.isi.edu/blog/?p=xxx",
	myorganization =	"USC/Information Sciences Institute",
	copyrightholder = "authors",
        abstract = "This document is a one-page abstract and a 
		copy of the poster
                  presented at ACM SOCC in October 2018
		 about Plumb, a workflow system
                  for multi-stage workflows where parts of computation
                  and output are shared across different groups.
		  The abstract without poster is available
		from the ACM as \url{https://doi.org/10.1145/3267809.3275461}.",
}

This document is a one-page abstract and a copy of the poster presented at ACM SOCC in October 2018 about Plumb, a workflow system for multi-stage workflows where parts of computation and output are shared across different groups. The abstract without poster is available from the ACM as ˘rlhttps://doi.org/10.1145/3267809.3275461.

@article{PhysRevApplied.11.044083,
  title = {Power of Pausing: Advancing Understanding of Thermalization in Experimental Quantum Annealers},
  author = {Marshall, Jeffrey and Venturelli, Davide and Hen, Itay and Rieffel, Eleanor G.},
  journal = {Phys. Rev. Applied},
  volume = {11},
  issue = {4},
  pages = {044083},
  numpages = {23},
  year = {2019},
  month = {Apr},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevApplied.11.044083},
  url = {https://link.aps.org/doi/10.1103/PhysRevApplied.11.044083}
}

@article{Ardi19a,
	author          = {Ardi, Calvin and Heidemann, John},
	title           = {Precise Detection of Content Reuse in the Web},
	journal         = "ACM Computer Communication Review",
	project         = "ant, mega",
	sortdate        = "2019-05-22",
	issue_date      = {April 2019},
	volume          = {49},
	number          = {2},
	month           = apr,
	year            = {2019},
	issn            = {0146-4833},
	pages           = {9--24},
	numpages        = {16},
	url             = "http://doi.acm.org/10.1145/3336937.3336940",
	pdfurl          = "https://ccronline.sigcomm.org/wp-content/uploads/2019/05/acmdl19-299.pdf",
	blogurl         = "https://ant.isi.edu/blog/?p=1311",
	doi             = "https://doi.org/10.1145/3336937.3336940",
	acmid           = {3336940},
	publisher       = "ACM",
	address         = {New York, NY, USA},
	keywords        = {content duplication, content reuse, duplicate detection, phishing},
	institution     = "USC/Information Sciences Institute",
	myorganization  = "USC/Information Sciences Institute",
	copyrightholder = "authors",
	abstract        = {
With vast amount of content online, it is not surprising that unscrupulous
entities "borrow" from the web to provide content for advertisements, link
farms, and spam. Our insight is that cryptographic hashing and fingerprinting
can efficiently identify content reuse for web-size corpora. We develop two
related algorithms, one to automatically *discover* previously unknown
duplicate content in the web, and the second to *precisely detect* copies of
discovered or manually identified content. We show that *bad neighborhoods*,
clusters of pages where copied content is frequent, help identify copying in
the web. We verify our algorithm and its choices with controlled experiments
over three web datasets: Common Crawl (2009/10), GeoCities (1990s–2000s), and a
phishing corpus (2014). We show that our use of cryptographic hashing is much
more precise than alternatives such as locality-sensitive hashing, avoiding the
thousands of false-positives that would otherwise occur. We apply our approach
in three systems: discovering and detecting duplicated content in the web,
searching explicitly for copies of Wikipedia in the web, and detecting phishing
sites in a web browser. We show that general copying in the web is often benign
(for example, templates), but 6–11% are commercial or possibly commercial. Most
copies of Wikipedia (86%) are commercialized (link farming or advertisements).
For phishing, we focus on PayPal, detecting 59% of PayPal-phish even without
taking on intentional cloaking.
},
}

With vast amount of content online, it is not surprising that unscrupulous entities "borrow" from the web to provide content for advertisements, link farms, and spam. Our insight is that cryptographic hashing and fingerprinting can efficiently identify content reuse for web-size corpora. We develop two related algorithms, one to automatically *discover* previously unknown duplicate content in the web, and the second to *precisely detect* copies of discovered or manually identified content. We show that *bad neighborhoods*, clusters of pages where copied content is frequent, help identify copying in the web. We verify our algorithm and its choices with controlled experiments over three web datasets: Common Crawl (2009/10), GeoCities (1990s–2000s), and a phishing corpus (2014). We show that our use of cryptographic hashing is much more precise than alternatives such as locality-sensitive hashing, avoiding the thousands of false-positives that would otherwise occur. We apply our approach in three systems: discovering and detecting duplicated content in the web, searching explicitly for copies of Wikipedia in the web, and detecting phishing sites in a web browser. We show that general copying in the web is often benign (for example, templates), but 6–11% are commercial or possibly commercial. Most copies of Wikipedia (86%) are commercialized (link farming or advertisements). For phishing, we focus on PayPal, detecting 59% of PayPal-phish even without taking on intentional cloaking.

@inproceedings{Ma2019PredictingHuman-ReportedEnjoymentResponses,
 author = {Ma, Benjamin and Greer, Timothy and Sachs, Matthew and Habibi, Assal and Kaplan, Jonas and Narayanan, Shrikanth},
 booktitle = {In proceedings of Proceedings of the 8th International Conference on Affective  Computing Intelligent Interaction},
 location = {Cambridge, UK},
 month = {September},
 title = {Predicting Human-Reported Enjoyment Responses  in Happy and Sad Music},
 year = {2019}
}

@article{fennell2019predicting,
  title={Predicting and explaining behavioral data with structured feature space decomposition},
  author={Fennell, Peter G and Zuo, Zhiya and Lerman, Kristina},
  journal={EPJ Data Science},
  volume={8},
  number={1},
  pages={23},
  year={2019},
  doi={https://doi.org/10.1140/epjds/s13688-019-0201-0},
  publisher={Springer Berlin Heidelberg}
}

@inproceedings{Zhuohao2019PredictionofTherapistBehaviors,
 author = {Chen, Zhuohao and Singla, Karan and Gibson, James and Can, Dogan and Imel, Zac and Atkins, David and Georgiou, Panayiotis and Narayanan, Shrikanth},
 booktitle = {Proceedings of ICASSP},
 location = {Brighton, UK},
 month = {May},
 title = {Prediction of Therapist Behaviors in Addiction Counseling by Exploiting Class Confusions},
 year = {2019}
}

@misc{1901.03192,
  added-at = {2019-02-01T00:00:00.000+0100},
  author = {Abeliuk, Andr{\'{e}}s and Elbassioni, Khaled M. and Rahwan, Talal and Cebri\'an, Manuel and Rahwan, Iyad},
  biburl = {https://www.bibsonomy.org/bibtex/2c3bee26678b85a0981fdc6d88a459709/dblp},
  ee = {http://arxiv.org/abs/1901.03192},
  interhash = {c72f5169661ea56ad08cb2f2bf3a6e50},
  intrahash = {c3bee26678b85a0981fdc6d88a459709},
  keywords = {dblp},
  timestamp = {2019-02-02T11:37:04.000+0100},
  title = {Price of Anarchy in Algorithmic Matching of Romantic Partners.},
  url = {http://dblp.uni-trier.de/db/journals/corr/corr1901.html#abs-1901-03192},
  month = jan,
  year = 2019,
  howpublished = {http://arxiv.org/abs/1901.03192}
}

@proceedings{DBLP:conf/kcap/2019,
  editor    = {Mayank Kejriwal and
               Pedro A. Szekely and
               Rapha{\"{e}}l Troncy},
  title     = {Proceedings of the 10th International Conference on Knowledge Capture,
               {K-CAP} 2019, Marina Del Rey, CA, USA, November 19-21, 2019},
  publisher = {{ACM}},
  year      = {2019},
  url       = {https://doi.org/10.1145/3360901},
  doi       = {10.1145/3360901},
  isbn      = {978-1-4503-7008-0},
  timestamp = {Sat, 30 May 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/kcap/2019.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{semeval-2019-international,
    title = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2000",
}

@inproceedings{he2019pun,
  title={Pun Generation with Surprise},
  author={He, He and Peng, Nanyun and Liang, Percy},
  booktitle={2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2019)},
  volume={1},
  year={2019}
}

@inproceedings{venugopalan2019a,
	author = {Vivek Venugopalan and G. Kolhe and Andrew G. Schmidt and Joshua Monson and Matthew French and Hu, Y. and P. A. Beerel and P. Nuzzo},
	booktitle = {In Government Microcircuit Applications \& Critical Technology Conference (GOMACTech)},
	title = {Quantifying Security and Overheads for Obfuscation of Integrated Circuits},
	year = {2019}}

@conference{Venugopalan2019Quantifying-Sec,
	author = {Venugopalan, Vivek and Kolhe, Gaurav and Schmidt, Andrew and Hu, Yinghua and Beerel, Peter A and Nuzzo, Pierluigi and Monson, Joshua and French, Matthew},
	booktitle = {Government Microcircuit Applications \& Critical Techology Conference (GOMACTech)},
	date-added = {2020-01-20 18:26:09 -0500},
	date-modified = {2020-01-20 18:26:09 -0500},
	doi = {https://apps.dtic.mil/docs/citations/AD1075410},
	keywords = {intellectual property , cryptography , reverse engineering , semiconductors , signal processing , algorithms , dynamic range , standards , manufacturing , integrated circuits , fabrication , databases , linear programming , integer programming , security},
	month = mar,
	title = {{Quantifying Security and Overheads for Obfuscation of Integrated Circuits}},
	year = {2019},
	Bdsk-Url-1 = {https://apps.dtic.mil/docs/citations/AD1075410}}

@conference {Venugopalan2019,
    title = {Quantifying Security and Overheads for Obfuscation of Integrated Circuits},
    booktitle = {Government Microcircuit Applications and Critical Technology Conference (GOMACTech)},
    year = {2019},
    month = {April},
    address = {Albuquerque, NM},
    author = {V. Venugopalan, G. Kolhe, A. Schmidt, J. Monson, M. French, Y. Hu, P. A. Beerel, P. Nuzzo}
} 
   
   
   

@article{journals/Chong,
  author = {Chong, sunshine and Abeliuk, Andr{\'{e}}s},
  Journal = {IEEE International Conference on Big Data (Big Data)},
  title = {Quantifying the Effects of Recommendation Systems.},
  year = 2019,
  organization={IEEE},
  pages={3008-3015},
  doi={10.1109/BigData47090.2019.9005951}
}

@inproceedings{sun2019quantitative,
  title={Quantitative laser speckle contrast imaging for presentation attack detection in biometric authentication systems},
  author={Sun, Claire and Jagannathan, Arun and Habif, Jonathan L and Hussein, Mohamed and Spinoulas, Leonidas and Abd-Almageed, Wael},
  booktitle={Smart Biomedical and Physiological Sensor Technology XV},
  volume={11020},
  pages={1102008},
  year={2019},
  organization={International Society for Optics and Photonics}
}

@conference{habif_lsci_fpad_spie_2019,
	title = {Quantitative laser speckle contrast imaging for presentation attack detection in biometric authentication systems},
	booktitle = {SPIE Defense and Commercial Systems},
	year = {2019},
	author = {Arun Jagannathan and Claire Sun and Leonidas Spinoulas and Hussein, Mohamed and Jonathan Habif and Abd-Almageed, Wael}
}

@article{habif2019quantum,
  title={Quantum-limited discrimination of laser light and thermal light},
  author={Habif, Jonathan L and Jagannathan, Arunkumar and Gartenstein, Samuel and Amory, Phoebe and Guha, Saikat},
  journal={arXiv preprint arXiv:1912.06718},
  year={2019}
}

@inproceedings{lignos2019real,
  title={Real-World Causal Relationship Discovery from Text.},
  author={Lignos, Constantine and Palen-Michel, Chester and Singer, Oskar and Szekely, Pedro A and Boschee, Elizabeth},
  booktitle={ISWC Satellites},
  pages={101--104},
  year={2019}
}

@inproceedings{DBLP:conf/cvpr/SabirCJAMN19,
  author       = {Ekraam Sabir and
                  Jiaxin Cheng and
                  Ayush Jaiswal and
                  Wael AbdAlmageed and
                  Iacopo Masi and
                  Prem Natarajan},
  title        = {Recurrent Convolutional Strategies for Face Manipulation Detection
                  in Videos},
  booktitle    = {{IEEE} Conference on Computer Vision and Pattern Recognition Workshops,
                  {CVPR} Workshops 2019, Long Beach, CA, USA, June 16-20, 2019},
  pages        = {80--87},
  publisher    = {Computer Vision Foundation / {IEEE}},
  year         = {2019},
  url          = {http://openaccess.thecvf.com/content\_CVPRW\_2019/html/Media\_Forensics/Sabir\_Recurrent\_Convolutional\_Strategies\_for\_Face\_Manipulation\_Detection\_in\_Videos\_CVPRW\_2019\_paper.html},
  timestamp    = {Mon, 30 Aug 2021 17:04:30 +0200},
  biburl       = {https://dblp.org/rec/conf/cvpr/SabirCJAMN19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{jin2019recurrent,
  title={Recurrent event network for reasoning over temporal knowledge graphs},
  author={Jin, Woojeong and Zhang, Changlin and Szekely, Pedro and Ren, Xiang},
  journal={arXiv preprint arXiv:1904.05530},
  year={2019}
}

@article{jin2019recurrent,
  title={Recurrent event network: Global structure inference over temporal knowledge graph},
  author={Jin, Woojeong and Jiang, He and Qu, Meng and Chen, Tong and Zhang, Changlin and Szekely, Pedro and Ren, Xiang},
  journal={arXiv preprint arXiv:1904.05530},
  year={2019}
}

@inproceedings{luceri2019red,
  title={Red bots do it better: Comparative analysis of social bot partisan behavior},
  author={Luceri, Luca and Deb, Ashok and Badawy, Adam and Ferrara, Emilio},
  booktitle={Companion proceedings of the 2019 world wide web conference},
  pages={1007--1012},
  year={2019}
}

@inproceedings{zahka2019reducing,
  title={Reducing Kernel Surface Areas for Isolation and Scalability},
  author={Zahka, Daniel and Kocoloski, Brian and Keahey, Kate},
  booktitle={International Conference on Parallel Processing},
  year={2019}
}

@inproceedings{Som2019ReinforcingSelf-expressiveRepresentationWith,
 author = {Somandepalli, Krishna and Narayanan, Shrikanth},
 booktitle = {Proceedings of ICASSP},
 location = {Brighton, UK},
 month = {May},
 title = {Reinforcing Self-expressive Representation With Constraint Propagation for Face Clustering in Movies},
 year = {2019}
}

@article{PhysRevE.99.033306,
  title = {Resolution of the sign problem for a frustrated triplet of spins},
  author = {Hen, Itay},
  journal = {Phys. Rev. E},
  volume = {99},
  issue = {3},
  pages = {033306},
  numpages = {10},
  year = {2019},
  month = {Mar},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.99.033306},
  url = {https://link.aps.org/doi/10.1103/PhysRevE.99.033306}
}

@article{DBLP:journals/corr/abs-1903-03691,
  author       = {Ayush Jaiswal and
                  Shuai Xia and
                  Iacopo Masi and
                  Wael AbdAlmageed},
  title        = {RoPAD: Robust Presentation Attack Detection through Unsupervised Adversarial
                  Invariance},
  journal      = {CoRR},
  volume       = {abs/1903.03691},
  year         = {2019},
  url          = {http://arxiv.org/abs/1903.03691},
  eprinttype    = {arXiv},
  eprint       = {1903.03691},
  timestamp    = {Sun, 31 Mar 2019 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1903-03691.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{Hebbar2019RobustSpeechActivityDetection,
 author = {Hebbar, Rajat and Somandepalli, Krishna and Narayanan, Shrikanth},
 booktitle = {Proceedings of ICASSP},
 location = {Brighton, UK},
 month = {May},
 title = {Robust Speech Activity Detection in Movie Audio: Data Resources and Experimental Evaluation},
 year = {2019}
}

@inproceedings{Flemotomos2019RoleSpecificLatticeRescoring,
 author = {Flemotomos, Nikolaos and Georgiou, Panayiotis and Atkins, David and Narayanan, Shrikanth},
 booktitle = {Proceedings of ICASSP},
 location = {Brighton, UK},
 month = {May},
 title = {Role Specific Lattice Rescoring for Speaker Role Recognition From Speech Recognition Outputs},
 year = {2019}
}

@article{PhysRevA.99.042334,
  title = {Role of nonstoquastic catalysts in quantum adiabatic optimization},
  author = {Albash, Tameem},
  journal = {Phys. Rev. A},
  volume = {99},
  issue = {4},
  pages = {042334},
  numpages = {11},
  year = {2019},
  month = {Apr},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevA.99.042334},
  url = {https://link.aps.org/doi/10.1103/PhysRevA.99.042334}
}

@inproceedings{Muller:2019:RRR:3355369.3355570,
     author = {M\"{u}ller, Moritz and Thomas, Matthew and Wessels, Duane and Hardaker, Wes and Chung, Taejoong and Toorop, Willem and Rijswijk-Deij, Roland van},
     title = {Roll, Roll, Roll Your Root: A Comprehensive Analysis of the First Ever DNSSEC Root KSK Rollover},
     booktitle = {Proceedings of the Internet Measurement Conference},
     series = {IMC '19},
     year = {2019},
     isbn = {978-1-4503-6948-0},
     jlocation = {Amsterdam, Netherlands},
     pages = {1--14},
     numpages = {14},
     url = {http://doi.acm.org/10.1145/3355369.3355570},
     pdfurl = {https://ant.isi.edu/~hardaker/papers/2019-10-ksk-roll.pdf},
     doi = {10.1145/3355369.3355570},
     acmid = {3355570},
     publisher = {ACM},
     address = {New York, NY, USA},
     note="Distinguished Paper Award",
     xISIArea="Networking",
     project="ant",
} 

@article{morstatter_sage_2019,
	title = {{SAGE}: A Hybrid Geopolitical Event Forecasting System},
	url = {https://www.ijcai.org/proceedings/2019/955},
	doi = {10.24963/ijcai.2019/955},
	shorttitle = {{SAGE}},
	abstract = {Electronic proceedings of {IJCAI} 2019},
	pages = {6557--6559},
	author = {Morstatter, Fred and Galstyan, Aram and Satyukov, Gleb and Benjamin, Daniel and Abeliuk, Andres and Mirtaheri, Mehrnoosh and Atanasov, Pavel and Joseph, Regina and Leskovec, Jure and Catasta, Michele and Beger, Andreas and Sethi, Rajiv and Sosic, Rok and Abbas, Ali and Ward, Michael and Himmelstein, Mark and Steyvers, Mark and Bennet, Stephen and Matsui, Akira and Ferrara, Emilio and Hossain, {KSM} Tozammel and Szekely, Pedro and Budescu, David},
	urldate = {2020-01-08},
	date = {2019},
	year = {2019},
	file = {Snapshot:C\:\\Users\\benjamin\\Zotero\\storage\\HSEI4727\\955.html:text/html}
}

Electronic proceedings of IJCAI 2019

@inproceedings{morstatter2019sage,
  title={SAGE: A Hybrid Geopolitical Event Forecasting System.},
  author={Morstatter, Fred and Galstyan, Aram and Satyukov, Gleb and Benjamin, Daniel and Abeliuk, Andres and Mirtaheri, Mehrnoosh and Hossain, KSM Tozammel and Szekely, Pedro A and Ferrara, Emilio and Matsui, Akira and others},
  booktitle={IJCAI},
  volume={1},
  pages={6557--6559},
  year={2019}
}

@inproceedings{boschee-etal-2019-saral,
    title = "{SARAL}: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage",
    author = "Boschee, Elizabeth  and
      Barry, Joel  and
      Billa, Jayadev  and
      Freedman, Marjorie  and
      Gowda, Thamme  and
      Lignos, Constantine  and
      Palen-Michel, Chester  and
      Pust, Michael  and
      Khonglah, Banriskhem Kayang  and
      Madikeri, Srikanth  and
      May, Jonathan  and
      Miller, Scott",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3004",
    doi = "10.18653/v1/P19-3004",
    pages = "19--24",
    abstract = "With the increasing democratization of electronic media, vast information resources are available in less-frequently-taught languages such as Swahili or Somali. That information, which may be crucially important and not available elsewhere, can be difficult for monolingual English speakers to effectively access. In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed. The SARAL system achieved the top end-to-end performance in the most recent IARPA MATERIAL CLIR+summarization evaluations. Our demonstration system provides end-to-end open query retrieval and summarization capability, and presents the original source text or audio, speech transcription, and machine translation, for two low resource languages.",
}

With the increasing democratization of electronic media, vast information resources are available in less-frequently-taught languages such as Swahili or Somali. That information, which may be crucially important and not available elsewhere, can be difficult for monolingual English speakers to effectively access. In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed. The SARAL system achieved the top end-to-end performance in the most recent IARPA MATERIAL CLIR+summarization evaluations. Our demonstration system provides end-to-end open query retrieval and summarization capability, and presents the original source text or audio, speech transcription, and machine translation, for two low resource languages.

@inproceedings{boschee-etal-2019-saral,
    title = "{SARAL}: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage",
    author = "Boschee, Elizabeth  and
      Barry, Joel  and
      Billa, Jayadev  and
      Freedman, Marjorie  and
      \textbf{Gowda, Thamme}  and
      Lignos, Constantine  and
      Palen-Michel, Chester  and
      Pust, Michael  and
      Khonglah, Banriskhem Kayang  and
      Madikeri, Srikanth  and
      May, Jonathan  and
      Miller, Scott",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3004",
    doi = "10.18653/v1/P19-3004",
    pages = "19--24",
    abstract = "With the increasing democratization of electronic media, vast information resources are available in less-frequently-taught languages such as Swahili or Somali. That information, which may be crucially important and not available elsewhere, can be difficult for monolingual English speakers to effectively access. In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed. The SARAL system achieved the top end-to-end performance in the most recent IARPA MATERIAL CLIR+summarization evaluations. Our demonstration system provides end-to-end open query retrieval and summarization capability, and presents the original source text or audio, speech transcription, and machine translation, for two low resource languages.",
}

With the increasing democratization of electronic media, vast information resources are available in less-frequently-taught languages such as Swahili or Somali. That information, which may be crucially important and not available elsewhere, can be difficult for monolingual English speakers to effectively access. In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed. The SARAL system achieved the top end-to-end performance in the most recent IARPA MATERIAL CLIR+summarization evaluations. Our demonstration system provides end-to-end open query retrieval and summarization capability, and presents the original source text or audio, speech transcription, and machine translation, for two low resource languages.

@inproceedings{DBLP:conf/asunam/KejriwalZ19a,
  author    = {Mayank Kejriwal and
               Peilin Zhou},
  editor    = {Francesca Spezzano and
               Wei Chen and
               Xiaokui Xiao},
  title     = {{SAVIZ:} interactive exploration and visualization of situation labeling
               classifiers over crisis social media data},
  booktitle = {{ASONAM} '19: International Conference on Advances in Social Networks
               Analysis and Mining, Vancouver, British Columbia, Canada, 27-30 August,
               2019},
  pages     = {705--708},
  publisher = {{ACM}},
  year      = {2019},
  url       = {https://doi.org/10.1145/3341161.3343703},
  doi       = {10.1145/3341161.3343703},
  timestamp = {Sun, 19 Jan 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/asunam/KejriwalZ19a.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@Misc{Hardaker19a,
title="SMTP Security Options",
author="Wes Hardaker and  Viktor Dukhovni",
month=06,
year=2019,
sortdate = "2019-06-22",
URL="https://65.schedule.icann.org/meetings/1058207",
pdfurl="https://ant.isi.edu/~hardaker/presentations/2019-06-SMTP-security-options.pdf",
keywords="dns, dnssec, dane, security",
project = "ant, broot",
howpublished="Talk at ICANN DNSSEC Workshop",
}

% wjh:icann:dnssecworkshop:2019danemetrics,

@InProceedings{	  edwards-gateways-2019,
  Title		= {Search SRA Gateway for Metagenomics Data},
  Author	= {Edwards, Robert and Abeysinghe, Eroma and Pierce, Marlon
		  and Rynge, Mats},
  BookTitle	= {Gateways 2019},
  Year		= {2019},
  Pages		= {},
  DOI		= {}
}

@inproceedings{Hu2019a,
	author = {Yinghua Hu and Vivek V. Menon and Andrew Schmidt and Joshua Monson and Matthew French and Pierluigi Nuzzo},
	booktitle = {ACM-IEEE International Conference on Formal Methods and Models for System Design (MEMOCODE)},
	title = {Security-driven Metrics and Models for Efficient Evaluation of Logic Encryption Schemes},
	year = {2019}}

@conference {Hu2019,
    title = {Security-driven metrics and models for efficient evaluation of logic encryption schemes},
    booktitle = {ACM-IEEE International Conference on Formal Methods and Models for System Design (MEMOCODE)},
    year = {2019},
    author = {Yinghua Hu, Vivek V. Menon, Andrew G. Schmidt, Joshua S. Monson, Matthew French, Pierluigi Nuzzo}
  }
    

@inproceedings{markovic-et-al2019,
	title        = {Semantic Modelling of Plans and Execution Traces for Enhancing Transparency of IoT Systems},
	author       = {M. {Markovic} and D. {Garijo} and P. {Edwards} and W. {Vasconcelos}},
	year         = 2019,
	month        = oct,
	booktitle    = {2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS)},
	volume       = {},
	number       = {},
	pages        = {110--115},
	doi          = {10.1109/IOTSMS48152.2019.8939260},
	url          = {https://dgarijo.com/papers/IoT_Systems_AAM2019.pdf},
	keywords     = {}
}

@inproceedings{carrillo-et-al-sciknow2019,
	title        = {Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees},
	author       = {Manuel Carrillo Garcia, Juan and Garijo, Daniel and Crowley, Mark and Carrillo, Rober and Gil, Yolanda and Borda, Katherine},
	year         = 2019,
	booktitle    = {Proceedings of the Third Workshop on Capturing Scientific Knowledge (SciKnow 2019), held in conjunction with the 2019 ACM International Conference on Knowledge Capture (K-CAP)},
	address      = {Los Angeles, California},
	volume       = 2526,
	issn         = {1613-0073},
	url          = {http://ceur-ws.org/Vol-2526/paper1.pdf}
}

@inproceedings{carrillo-et-al-sciknow19,
  title = {Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees},
  author = {Carrillo Garcia, Juan Manuel and Daniel Garijo and Mark Crowley and Rober Carrillo and Yolanda Gil and Katherine Borda},
  booktitle = {Proceedings of the Third Workshop on Capturing Scientific Knowledge (SciKnow), held in conjunction with the 2019 ACM International Conference on Knowledge Capture (K-CAP'19)},
  address = {Los Angeles, CA},
  url = {https://knowledgecaptureanddiscovery.github.io/yolanda_gil_website/papers/carrillo-et-al-sciknow19.pdf},
  year = 2019
}

@inproceedings{srivastava-etal-psb19,
	title        = {Semantic Workflows for Benchmark Challenges: Enhancing Comparability, Reusability and Reproducibility},
	author       = {Srivastava, Arunima and Adusumilli, Ravali and Boyce, Hunter and Garijo, Daniel and Ratnakar, Varun and Ratnakar, Rajiv and Yu, Thomas and Machiraju, Raghu and Gil, Yolanda and Mallick, Parag},
	year         = 2019,
	booktitle    = {Proceedings of the Pacific Symposium on Biocomputing (PSB)},
	url          = {http://www.isi.edu/~gil/papers/srivastava-etal-psb19.pdf}
}

@inproceedings{DBLP:conf/psb/SrivastavaABGRM19,
  author       = {Arunima Srivastava and
                  Ravali Adusumilli and
                  Hunter Boyce and
                  Daniel Garijo and
                  Varun Ratnakar and
                  Rajiv Mayani and
                  Thomas Yu and
                  Raghu Machiraju and
                  Yolanda Gil and
                  Parag Mallick},
  editor       = {Russ B. Altman and
                  A. Keith Dunker and
                  Lawrence Hunter and
                  Marylyn D. Ritchie and
                  Teri E. Klein},
  title        = {Semantic workflows for benchmark challenges: Enhancing comparability,
                  reusability and reproducibility},
  booktitle    = {Biocomputing 2019: Proceedings of the Pacific Symposium, The Big Island
                  of Hawaii, Hawaii, USA, January 3-7, 2019},
  pages        = {208--219},
  year         = {2019},
  url          = {http://psb.stanford.edu/psb-online/proceedings/psb19/srivastava.pdf},
  timestamp    = {Thu, 12 Mar 2020 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/conf/psb/SrivastavaABGRM19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{PhysRevA.99.032324,
  title = {Sensitivity of quantum speedup by quantum annealing to a noisy oracle},
  author = {Muthukrishnan, Siddharth and Albash, Tameem and Lidar, Daniel A.},
  journal = {Phys. Rev. A},
  volume = {99},
  issue = {3},
  pages = {032324},
  numpages = {14},
  year = {2019},
  month = {Mar},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevA.99.032324},
  url = {https://link.aps.org/doi/10.1103/PhysRevA.99.032324}

  
}

@inproceedings{hauser2019sleak,
  title={Sleak: automating address space layout derandomization},
  author={Hauser, Christophe and Menon, Jayakrishna and Shoshitaishvili, Yan and Wang, Ruoyu and Vigna, Giovanni and Kruegel, Christopher},
  booktitle={Proceedings of the 35th Annual Computer Security Applications Conference},
  pages={190--202},
  year={2019}
}

@inproceedings{thurlow2019:sled,
  title={{Sled: System-Loader for Ephemeral Devices}},
  author={Thurlow, Lincoln and Goodfellow, Ryan},
  booktitle={The 5th IEEE INFOCOM Workshop on Computer and Networking Experimental Research using Testbeds 2019 (IEEE CNERT 2019)},
  year={2019},
  publisher={{IEEE}},
  month={April},
}

@inproceedings{9006447,
	title        = {SoMEF: A Framework for Capturing Scientific Software Metadata from its Documentation},
	author       = {A. {Mao} and D. {Garijo} and S. {Fakhraei}},
	year         = 2019,
	booktitle    = {2019 IEEE International Conference on Big Data (Big Data)},
	pages        = {3032--3037},
  doi          = {10.1109/BigData47090.2019.9006447},
	url          = {https://dgarijo.com/papers/SoMEF.pdf}
}

@article{pfeffer2019software,
  title={Software Adaptation for an Unmanned Undersea Vehicle},
  author={Pfeffer, Avi and Wu, Curt and Fry, Gerald and Lu, Kenny and Marotta, Steve and Reposa, Mike and Shi, Yuan and Kumar, TK Satish and Knoblock, Craig A and Parker, David and Irfan Muhammad and Chris Novakovic},
  journal={IEEE Software},
  volume={36},
  number={2},
  pages={91--96},
  year={2019},
  publisher={IEEE},
  urlLink={https://ieeexplore.ieee.org/document/8648259},
  urlPaper={http://usc-isi-i2.github.io/papers/pfeffer19-ieee.pdf}
}

@inproceedings{bezouska2019spacecraft,
  title={Spacecraft Pose Estimation and Swarm Localization performance under Varying Illumination and Viewing Conditions},
  author={Bezouska, William and Barnhart, David},
  booktitle={AAS/AIAA Space Flight Mechanics Meeting},
  number={AAS 19-405},
  year={2019}
}

@inproceedings{Nadarajan2019SpeakerAgnosticForegroundSpeech,
 author = {Nadarajan, Amrutha and Somandepalli, Krishna and Narayanan, Shrikanth},
 booktitle = {Proceedings of ICASSP},
 location = {Brighton, UK},
 month = {May},
 title = {Speaker Agnostic Foreground Speech Detection From Audio Recordings in Workplace Settings From Wearable Recorders},
 year = {2019}
}

@inproceedings{Park2019SpeakerDiarizationwithLexical,
 author = {Park, Taejin and Han, Kyu and Huang, Jing and He, Xiaodong and Zhou, Bowen and Georgiou, Panayiotis and Narayanan, Shrikanth},
 booktitle = {In proceedings of Proceedings of Interspeech},
 location = {Graz, Austria},
 month = {September},
 title = {Speaker Diarization with Lexical Information},
 year = {2019}
}

@Article{	  dblp:journals/corr/abs-1905-06911,
  Author	= {Derek Weitzel and Mari{\'{a}}n Zvada and Ilija Vukotic and
		  Robert W. Gardner and Brian Bockelman and Mats Rynge and
		  Edgar Fajardo Hernandez and Brian Lin and Matyas Selmeci},
  Title		= {StashCache: {A} Distributed Caching Federation for the
		  Open Science Grid},
  Journal	= {CoRR},
  Volume	= {abs/1905.06911},
  Year		= {2019},
  URL		= {http://arxiv.org/abs/1905.06911},
  ArchivePrefix	= {arXiv},
  EPrint	= {1905.06911},
  timestamp	= {Tue, 28 May 2019 12:48:08 +0200},
  biburl	= {https://dblp.org/rec/bib/journals/corr/abs-1905-06911},
  bibsource	= {dblp computer science bibliography, https://dblp.org}
}

@InProceedings{eckel2019,
  author = 	 {Sandrah P. Eckel and  Kenan Li and  Huiyu Deng and  Shujing Xu and  Rima Habre and  Mariam Girguis and  Alex Bui and  Kathy Sward and  Ramkiran Gouripeddi and  Scott Collingwood and  Robert Urman and  John Morrison and  Meredith Franklin and  Jose Luis Ambite and  Yao-Yi Chiang and  Dimitrios Stripelis and  Yijun Lin and Frank D. Gilliland},
  title = 	 {Statistical and machine learning methods for using sensor monitoring systems to predict asthma exacerbations},
  booktitle = {Military Health System Research Symposium},
  year = 	 {2019},
  address = 	 {Kissimmee, FL},
  note = 	 {Invited Abstract},
}

@inproceedings{Tiwari2019StressandAnxietyMeasurement,
 author = {Tiwari, Abhishek   and  Narayanan, Shrikanth and  Falk, Tiago},
 booktitle = {In proceedings of 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC19)},
 location = {Berlin, Germany},
 month = {Jul},
 title = {Stress and Anxiety Measurement "In-the-Wild" Using Quality-aware Multi-scale HRV Features},
 year = {2019}
}

@inproceedings{Kumar2019Subspacetechniquesfortask,
 author = {Kumar, Mari Ganesh and Saranya, M. S.  and  Narayanan, Shrikanth and Sur, Mriganka  and  Murthy, Hema},
 booktitle = {In proceedings of 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC19)},
 location = {Berlin, Germany},
 month = {July},
 title = {Subspace techniques for task independent EEG person identification},
 year = {2019}
}

@inproceedings{rughani2019swarm,
  title={Swarm RPO and Docking Simulation on a 3DOF Air Bearing Platform},
  author={Rughani, Rahul and Villafana, Lizvette and Barnhart, David A},
  booktitle={70th International Astronautical Congress (IAC), Washington DC, United States},
  pages={21--25},
  year={2019}
}

@inproceedings{menon2019a,
	author = {Vivek V. Menon and G. Kolhe and Andrew G. Schmidt and J. {Monson} and French, M. and Hu, Y. and P. A. Beerel and P. Nuzzo},
	booktitle = {In IEEE Cybersecurity Development (SecDev)},
	title = {System-Level Framework for Logic Obfuscation with Quantified Metrics for Evaluation},
	year = {2019}}

@conference {Menon2019,
    title = {System-Level Framework for Logic Obfuscation with Quantified Metrics for Evaluation},
    booktitle = {IEEE Secure Development Conference (SecDev)},
    year = {2019},
    author = {V. Menon, G. Kohle, A. G. Schmidt, J. Monson, M. French, Y. Hu, and P. Nuzzo}
}

   

@inproceedings{Szekely2019T2WMLAC,
	title        = {T2WML: A Cell-Based Language to Map Tables into Wikidata Records},
	author       = {Pedro A. Szekely and Daniel Garijo and Jay Pujara and Divij Bhatia and Jiasheng Wu},
	year         = 2019,
	booktitle    = {ISWC Satellites},
	url          = {http://ceur-ws.org/Vol-2456/paper12.pdf}
}

@inproceedings{szekely:kcap19,
    author = "Szekely, Pedro and Garijo, Daniel and Bhatia, Divij and Wu, Jiasheng and Yao, Yixiang and Pujara, Jay",
    acceptrate = "18\%",
    bib_url = "/pubs/bib/szekely-kcap19.bib",
    booktitle = "ACM International Conference on Knowledge Capture (K-CAP)",
    doi_url = "https://doi.org/10.1145/3308558.3313711",
    pdf_url = "/pubs/2019/szekely-kcap19/szekely-kcap19.pdf",
    sec = "conf",
    title = "T2WML: Table To Wikidata Mapping Langauge",
    year = "2019"
}

@inproceedings{szekely-garijo-et-al2019,
	title        = {T2WML: Table To Wikidata Mapping Language},
	author       = {Szekely, Pedro and Garijo, Daniel and Bhatia, Divij and Wu, Jiasheng and Yao, Yixiang and Pujara, Jay},
	year         = 2019,
	booktitle    = {Proceedings of the 10th International Conference on Knowledge Capture},
	location     = {Marina Del Rey, CA, USA},
	publisher    = {Association for Computing Machinery},
	address      = {New York, NY, USA},
	series       = {K-CAP ’19},
	pages        = {267–270},
	doi          = {10.1145/3360901.3364448},
	isbn         = 9781450370080,
	url          = {https://dgarijo.com/papers/t2wmlKCAP2019.pdf},
	numpages     = 4,
	keywords     = {entity linking, wikidata, rdf, knowledge graphs}
}

@inproceedings{gol:icdm19,
    Author = "Ghasemi-Gol, Majid and Pujara, Jay and Szekely, Pedro",
    acceptrate = "9\%",
    bib_url = "/pubs/bib/gol-icdm19.bib",
    booktitle = "International Conference on Data Mining",
    doi_url = "",
    pdf_url = "/pubs/2019/gol-icdm19/gol-icdm19.pdf",
    sec = "conf",
    title = "Tabular Cell Classification Using Pre-Trained Cell Embeddings",
    year = "2019"
}

@inproceedings{gol2019tabular,
  title={Tabular cell classification using pre-trained cell embeddings},
  author={Gol, Majid Ghasemi and Pujara, Jay and Szekely, Pedro},
  booktitle={2019 IEEE International Conference on Data Mining (ICDM)},
  pages={230--239},
  year={2019},
  organization={IEEE}
}

@inproceedings{meng2019target,
  title={Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing},
  author={Meng, Tao and Peng, Nanyun and Chang, Kai-Wei},
  booktitle={2019 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year={2019}
}

@article{Sorensen2019Task-dependenceofArticulatorSynergies,
 author = {Sorensen, Tanner and Toutios, Asterios and Goldstein, Louis and Narayanan, Shrikanth},
 doi = {10.1121/1.5093538},
 journal = {The Journal of the Acoustical Society of America},
 link = {http://sail.usc.edu/publications/files/1.5093538.pdf},
 month = {Mar},
 number = {3},
 title = {Task-dependence of Articulator Synergies},
 volume = {145},
 year = {2019}
}

@inproceedings{DBLP:conf/aaaifs/JuvinaLWGNM19,
  author    = {Ion Juvina and
               Othalia Larue and
               Colin Widmer and
               Subhashini Ganapathy and
               Srikanth Nadella, 
               Brandon S. Minnery,
               Ramshaw, L., 
               Servan-Schreiber, E., 
               Balick, M., and
               Weischedel, R. },
  editor    = {Adam Amos{-}Binks and
               Dustin Dannenhauer and
               Rogelio E. Cardona{-}Rivera and
               Gene A. Brewer},
  title     = {Task-offload Tools Improve Productivity and Performance in Geopolitical
               Forecasting},
  booktitle = {Short Paper Proceedings of the Workshop on Cognitive Systems for Anticipatory
               Thinking {(COGSAT} 2019) co-located with the {AAAI} Fall Symposium
               2019, Arlington, USA, November, 2019},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2558},
  publisher = {CEUR-WS.org},
  year      = {2019},
  url       = {http://ceur-ws.org/Vol-2558/short6.pdf},
  timestamp = {Wed, 11 Mar 2020 17:00:44 +0100},
  biburl    = {https://dblp.org/rec/conf/aaaifs/JuvinaLWGNM19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{tanaka2019eduhpc,
    title = {Teaching Parallel and Distributed Computing Concepts in Simulation with WRENCH},
    author = {Tanaka, Ryan and Casanova, Henri and Ferreira da Silva, Rafael},
    booktitle = {Workshop on Education for High-Performance Computing (EduHPC)},
    year={2019},
    volume={},
    number={},
    pages={1-9},
    doi={10.1109/EduHPC49559.2019.00006},
    note = {Funding Acknowledgements: NSF 1642335, NSF 1923539}
}

@article{mirkovictechreport,
  title={Techreport ISI-TR-699 LegoTG: Composable Traffic Generation with a Custom Blueprint},
  author={Mirkovic, Jelena and Bartlett, Genevieve},
  year={2019}
  
}

@inproceedings{blythe2019darpa,
  title={The DARPA SocialSim Challenge: Massive Multi-Agent Simulations of the Github Ecosystem},
  author={Blythe, James and Ferrara, Emilio and Huang, Di and Lerman, Kristina and Muric, Goran and Sapienza, Anna and Tregubov, Alexey and Pacheco, Diogo and Bollenbacher, John and Flammini, Alessandro and others},
  booktitle={Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems},
  pages={1835--1837},
  year={2019},
  organization={International Foundation for Autonomous Agents and Multiagent Systems}
}

@inproceedings {dcomp-cset19,
author = {Ryan Goodfellow and Stephen Schwab and Erik Kline and Lincoln Thurlow and Geoff Lawler},
title = {{The DComp Testbed}},
booktitle = {12th {USENIX} Workshop on Cyber Security Experimentation and Test ({CSET} 19)},
year = {2019},
address = {Santa Clara, CA},
url = {https://www.usenix.org/conference/cset19/presentation/goodfellow},
publisher = {{USENIX} Association},
month = aug,
}

@Article{	  deelman-cise-2019,
  Title		= {The Evolution of the Pegasus Workflow Management
		  Software},
  Author	= {Deelman, Ewa and Vahi, Karan and Rynge, Mats and Mayani,
		  Rajiv and Ferreira da Silva, Rafael and Papadimitriou,
		  George and Livny, Miron},
  Journal	= {Computing in Science Engineering},
  Volume	= {21},
  Number	= {4},
  Pages		= {22--36},
  Year		= {2019},
  DOI		= {10.1109/MCSE.2019.2919690},
  Note		= {Funding Acknowledgments: NSF 1664162, NSF 1148515, DOE
		  DESC0012636, NSF 1642053}
}

@inproceedings{Park2019TheSecondDIHARDchallenge:,
 author = {Park, Taejin and Kumar, Manoj and Flemotomos, Nikolaos and Pal, Monisankha and Peri, Raghuveer and Lahiri, Rimita and Georgiou, Panayiotis and Narayanan, Shrikanth},
 booktitle = {In proceedings of Proceedings of Interspeech},
 location = {Graz, Austria},
 month = {September},
 title = {The  Second DIHARD challenge: System Description for USC-SAIL Team},
 year = {2019}
}

@inproceedings{sheng2019woman,
  title={The Woman Worked as a Babysitter: On Biases in Language Generation},
  author={Sheng, Emily and Chang, Kai-Wei and Natarajan, Premkumar and Peng, Nanyun},
  booktitle={2019 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year={2019}
}

@inproceedings{horn2019network,
  title={The network source location problem in the context of foodborne disease outbreaks},
  author={Horn, Abigail L and Friedrich, Hanno},
  booktitle={Dynamics On and Of Complex Networks III: Machine Learning and Statistical Physics Approaches 10},
  pages={151--165},
  year={2019},
  organization={Springer}
}

@Article{	  deelman-ijhpca-2019,
  Title		= {The role of machine learning in scientific workflows},
  Author	= {Deelman, Ewa and Mandal, Anirban and Jiang, Ming and
		  Sakellariou, Rizos},
  Journal	= {The International Journal of High Performance Computing
		  Applications},
  Volume	= {},
  Number	= {},
  Pages		= {},
  Year		= {2019},
  DOI		= {10.1177/1094342019852127},
  Note		= {Funding Acknowledgments: LLNL-ABS-755833
		  DE-AC52-07NA27344, DOE DESC0012636, NSF 1839900,
		  LLNL-JRNL-765200}
}

@conference{Kshirsagar2019Thin-Wing-EMA-S,
	author = {Kshirsagar, Parag and Venugopalan, Vivek},
	booktitle = {Government Microcircuit Applications \& Critical Techology Conference (GOMACTech)},
	date-added = {2020-01-20 18:26:09 -0500},
	date-modified = {2020-01-20 18:26:09 -0500},
	doi = {https://apps.dtic.mil/docs/citations/AD1075251},
	keywords = {flight control systems , thin wings , control systems , change detection , anomaly detection , embedded systems , control surfaces , air force , platforms , probability distribution functions , cyberattacks , denial of service attack , pattern recognition , machine learning , military research},
	month = mar,
	title = {{Thin Wing EMA-System on Chip with Synchronized TRust and Assurance (SaSTRA)}},
	year = {2019},
	Bdsk-Url-1 = {https://apps.dtic.mil/docs/citations/AD1075251}}

@article{Travadi2019TotalVariabilityLayerin,
 author = {Travadi, Ruchir and Narayanan, Shrikanth},
 doi = {https://doi.org/10.1109/LSP.2019.2910400},
 journal = {IEEE Signal Processing Letters},
 link = {https://ieeexplore.ieee.org/document/8686172},
 month = {Jun},
 pages = {893-897},
 title = {Total Variability Layer in Deep Neural Network Embeddings for Speaker Verification},
 volume = {26},
 year = {2019}
}

@inproceedings{schuler_toward_2019,
	address = {San Diego, CA, USA},
	title = {Toward {FAIR} {Knowledge} {Turns} in {Bioinformatics}},
	isbn = {978-1-72811-867-3},
	url = {https://ieeexplore.ieee.org/document/8982988/},
	doi = {10.1109/BIBM47256.2019.8982988},
	urldate = {2022-01-14},
	booktitle = {2019 {IEEE} {International} {Conference} on {Bioinformatics} and {Biomedicine} ({BIBM})},
	publisher = {IEEE},
	author = {Schuler, Robert and Bugacov, Alejandro and Blow, Matthew and Kesselman, Carl},
	month = nov,
	year = {2019},
	pages = {1240--1242},
}

@inproceedings{Booth2019TowardRobustInterpretableHuman,
 author = {Booth, Brandon and Feng, Tiantian and Jangalwa, Abhishek and Narayanan, Shrikanth},
 booktitle = {Proceedings of ICASSP},
 location = {Brighton, UK},
 month = {May},
 title = {Toward Robust Interpretable Human Movement Pattern Analysis in a Workplace Setting},
 year = {2019}
}

@InProceedings{	  lyons-escience-2019,
  Title		= {Toward a Dynamic Network-centric Distributed Cloud
		  Platform for Scientific Workflows: A Case Study for
		  Adaptive Weather Sensing},
  Author	= {Lyons, Eric and Papadimitriou, George and Wang, Cong and
		  Thareja, Komal and Ruth, Paul and Villalobos, J.J. and
		  Rodero, Ivan and Deelman, Ewa and Zink, Michael and Mandal,
		  Anirban},
  BookTitle	= {15th International Conference on eScience (eScience)},
  Year		= {2019},
  Location	= {San Diego, CA, USA},
  Pages		= {67--76},
  DOI		= {10.1109/eScience.2019.00015},
  Note		= {Funding Acknowledgments: NSF 1826997}
}

@inproceedings{DBLP:conf/vldb/GarijoFRYEMWBBG19,
  author       = {Daniel Garijo and
                  Shobeir Fakhraei and
                  Varun Ratnakar and
                  Qifan Yang and
                  Hanna Endrias and
                  Yibo Ma and
                  Regina Wang and
                  Michael Bornstein and
                  Joanna Bright and
                  Yolanda Gil and
                  Neda Jahanshad},
  editor       = {Vijay Gadepally and
                  Timothy G. Mattson and
                  Michael Stonebraker and
                  Fusheng Wang and
                  Gang Luo and
                  Yanhui Laing and
                  Alevtina Dubovitskaya},
  title        = {Towards Automated Hypothesis Testing in Neuroscience},
  booktitle    = {Heterogeneous Data Management, Polystores, and Analytics for Healthcare
                  - {VLDB} 2019 Workshops, Poly and DMAH, Los Angeles, CA, USA, August
                  30, 2019, Revised Selected Papers},
  series       = {Lecture Notes in Computer Science},
  volume       = {11721},
  pages        = {249--257},
  publisher    = {Springer},
  year         = {2019},
  url          = {https://doi.org/10.1007/978-3-030-33752-0\_18},
  doi          = {10.1007/978-3-030-33752-0\_18},
  timestamp    = {Tue, 16 Aug 2022 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/vldb/GarijoFRYEMWBBG19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{gil-etal-iui19,
  author = {Yolanda Gil and James Honaker and Shikhar Gupta and Yibo Ma and Vito D\textquotesingle Orazio and Daniel Garijo and Shruti Gadewar and Qifan Yang and Neda Jahanshad},
  title = {Towards Human-Guided Machine Learning},
  booktitle = {Proceedings of the 24th ACM International Conference on Intelligent User Interfaces (IUI)},
  address = {Marina del Rey, CA},
  url = {https://knowledgecaptureanddiscovery.github.io/yolanda_gil_website/papers/gil-etal-iui19.pdf},
  ee = {http://doi.org/10.1145/3301275.3302324},
  doi = {110.1145/3301275.3302324},
  year = {2019}
}

@inproceedings{DBLP:conf/iui/GilHGMDGGYJ19,
  author       = {Yolanda Gil and
                  James Honaker and
                  Shikhar Gupta and
                  Yibo Ma and
                  Vito D'Orazio and
                  Daniel Garijo and
                  Shruti Gadewar and
                  Qifan Yang and
                  Neda Jahanshad},
  editor       = {Wai{-}Tat Fu and
                  Shimei Pan and
                  Oliver Brdiczka and
                  Polo Chau and
                  Gaelle Calvary},
  title        = {Towards human-guided machine learning},
  booktitle    = {Proceedings of the 24th International Conference on Intelligent User
                  Interfaces, {IUI} 2019, Marina del Ray, CA, USA, March 17-20, 2019},
  pages        = {614--624},
  publisher    = {{ACM}},
  year         = {2019},
  url          = {https://doi.org/10.1145/3301275.3302324},
  doi          = {10.1145/3301275.3302324},
  timestamp    = {Tue, 16 Aug 2022 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/iui/GilHGMDGGYJ19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@InProceedings{	  papadimitriou-indis-2019,
  Title		= {Training Classifiers to Identify TCP Signatures in
		  Scientific Workflows},
  Author	= {Papadimitriou, George and Kiran, Mariam and Wang, Cong and
		  Mandal, Anirban and Deelman, Ewa},
  BookTitle	= {2019 IEEE/ACM Innovating the Network for Data-Intensive
		  Science (INDIS)},
  Year		= {2019},
  Location	= {Denver, CO, USA},
  Pages		= {61-68},
  DOI		= {10.1109/INDIS49552.2019.00012},
  Month		= {Nov},
  Note		= {Funding Acknowledgments: DOE DESC0012636}
}

@inproceedings{pourdamghani-etal-2019-translating,
    title = "Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation",
    author = "Pourdamghani, Nima  and
      Aldarrab, Nada  and
      Ghazvininejad, Marjan  and
      Knight, Kevin  and
      May, Jonathan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1293",
    doi = "10.18653/v1/P19-1293",
    pages = "3057--3062",
    abstract = "Given a rough, word-by-word gloss of a source language sentence, target language natives can uncover the latent, fully-fluent rendering of the translation. In this work we explore this intuition by breaking translation into a two step process: generating a rough gloss by means of a dictionary and then {`}translating{'} the resulting pseudo-translation, or {`}Translationese{'} into a fully fluent translation. We build our Translationese decoder once from a mish-mash of parallel data that has the target language in common and then can build dictionaries on demand using unsupervised techniques, resulting in rapidly generated unsupervised neural MT systems for many source languages. We apply this process to 14 test languages, obtaining better or comparable translation results on high-resource languages than previously published unsupervised MT studies, and obtaining good quality results for low-resource languages that have never been used in an unsupervised MT scenario.",
}

Given a rough, word-by-word gloss of a source language sentence, target language natives can uncover the latent, fully-fluent rendering of the translation. In this work we explore this intuition by breaking translation into a two step process: generating a rough gloss by means of a dictionary and then `translating' the resulting pseudo-translation, or `Translationese' into a fully fluent translation. We build our Translationese decoder once from a mish-mash of parallel data that has the target language in common and then can build dictionaries on demand using unsupervised techniques, resulting in rapidly generated unsupervised neural MT systems for many source languages. We apply this process to 14 test languages, obtaining better or comparable translation results on high-resource languages than previously published unsupervised MT studies, and obtaining good quality results for low-resource languages that have never been used in an unsupervised MT scenario.

@inproceedings{Booth2019TrapezoidalSegmentedRegression:A,
 author = {Booth, Brandon and Narayanan, Shrikanth},
 booktitle = {In proceedings of Proceedings of the 8th International Conference on Affective  Computing Intelligent Interaction},
 location = {Cambridge, UK},
 month = {Sep},
 title = {Trapezoidal Segmented  Regression: A Novel Continuous-scale Real-time Annotation Approximation Algorithm},
 year = {2019}
}

@inproceedings{DBLP:conf/sigir/GheiniK19,
  author    = {Mozhdeh Gheini and
               Mayank Kejriwal},
  editor    = {Jon Degenhardt and
               Surya Kallumadi and
               Utkarsh Porwal and
               Andrew Trotman},
  title     = {Unsupervised Product Entity Resolution using Graph Representation
               Learning},
  booktitle = {Proceedings of the {SIGIR} 2019 Workshop on eCommerce, co-located
               with the 42st International {ACM} {SIGIR} Conference on Research and
               Development in Information Retrieval, eCom@SIGIR 2019, Paris, France,
               July 25, 2019},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2410},
  publisher = {CEUR-WS.org},
  year      = {2019},
  url       = {http://ceur-ws.org/Vol-2410/paper26.pdf},
  timestamp = {Wed, 12 Feb 2020 16:44:59 +0100},
  biburl    = {https://dblp.org/rec/conf/sigir/GheiniK19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{woo2019using,
  title={Using Episodic Memory for User Authentication},
  author={Woo, Simon S and Artstein, Ron and Kaiser, Elsi and Le, Xiao and Mirkovic, Jelena},
  journal={ACM Transactions on Privacy and Security (TOPS)},
  volume={22},
  number={2},
  pages={11},
  year={2019},
  publisher={ACM}
}

@inproceedings{GiannakopoulosCBMI19,
 author = {T. {Giannakopoulos} and S. {Dimopoulos} and G. {Pantazopoulos} and A. {Chatziagapi} and D. {Sgouropoulos} and A. {Katsamanis} and A. {Potamianos} and S. {Narayanan}},
 booktitle = {Proceedings 2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
 month = {Se},
 title = {Using Oliver API for emotion-aware movie content characterization},
 year = {2019}
}

@Article{	  ferreiradasilva-fgcs-2019,
  Title		= {Using Simple PID-inspired Controllers for Online Resilient
		  Resource Management of Distributed Scientific Workflows},
  Author	= {Ferreira da Silva, Rafael and Filgueira, Rosa and Deelman,
		  Ewa and Pairo-Castineira, Erola and Overton, Ian Michael
		  and Atkinson, Malcolm},
  Journal	= {Future Generation Computer Systems},
  Volume	= {95},
  Number	= {},
  Pages		= {615-628},
  Year		= {2019},
  DOI		= {10.1016/j.future.2019.01.015},
  Note		= {Funding Acknowledgments: DOE DE-SC0012636}
}

@inproceedings{Martinez2019ViolenceRatingPredictionfrom,
 author = {Martinez, Victor and Somandepalli, Krishna and Singla, Karan and Ramakrishna, Anil and Uhls, Yalda and Narayanan, Shrikanth},
 booktitle = {In proceedings of Proceedings of Thirty-Third AAAI Conference on Artificial Intelligence},
 link = {http://sail.usc.edu/publications/files/martinez2019violence.pdf},
 location = {Honolulu},
 month = {January},
 title = {Violence Rating Prediction from Movie Scripts},
 year = {2019}
}

@inproceedings{bezouska2019visual,
  title={Visual sensor selection for satellite swarm cooperative localization},
  author={Bezouska, William and Barnhart, David},
  booktitle={SPIE Defense and Security Conference},
  year={2019},
  url = {https://doi.org/10.1117/12.2518809}
}

@inproceedings{rajagopala2019a,
	author = {Abhi D. Rajagopala and Ron Sass and Andrew G. Schmidt},
	booktitle = {International Conference on ReConFigurable Computing and FPGAs},
	title = {Volcan: System Integration of HLS and HMC with FPGAs},
	year = {2019}}

@inproceedings{garijo-and-szekely-sciknow2019,
	title        = {WDPlus: Leveraging Wikidata to Link and Extend Tabular Data},
	author       = {Daniel Garijo and Pedro Szekely},
	year         = 2019,
	booktitle    = {Proceedings of the Third Workshop on Capturing Scientific Knowledge (SciKnow 2019), held in conjunction with the 2019 ACM International Conference on Knowledge Capture (K-CAP)},
	address      = {Los Angeles, California},
	volume       = 2526,
	issn         = {1613-0073},
	url          = {http://ceur-ws.org/Vol-2526/short4.pdf}
}

@inproceedings{huang-etal-2019-matters,
    title = "What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis",
    author = "Huang, Xiaolei  and
      May, Jonathan  and
      Peng, Nanyun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1672",
    doi = "10.18653/v1/D19-1672",
    pages = "6394--6400",
    abstract = "Building named entity recognition (NER) models for languages that do not have much training data is a challenging task. While recent work has shown promising results on cross-lingual transfer from high-resource languages, it is unclear what knowledge is transferred. In this paper, we first propose a simple and efficient neural architecture for cross-lingual NER. Experiments show that our model achieves competitive performance with the state-of-the-art. We further explore how transfer learning works for cross-lingual NER on two transferable factors: sequential order and multilingual embedding. Our results shed light on future research for improving cross-lingual NER.",
}

Building named entity recognition (NER) models for languages that do not have much training data is a challenging task. While recent work has shown promising results on cross-lingual transfer from high-resource languages, it is unclear what knowledge is transferred. In this paper, we first propose a simple and efficient neural architecture for cross-lingual NER. Experiments show that our model achieves competitive performance with the state-of-the-art. We further explore how transfer learning works for cross-lingual NER on two transferable factors: sequential order and multilingual embedding. Our results shed light on future research for improving cross-lingual NER.

@inproceedings{Badawy2019falls,
  title={Who Falls for Online Political Manipulation?},
  author={Badawy, Adam and Lerman, Kristina and Ferrara, Emilio},
  booktitle={Companion Proceedings of The 2019 World Wide Web Conference},
  pages={162--168},
  year={2019},
  organization={ACM}
}

@Article{	  2018-vahi-adass-weak-leansing,
  Title		= {Workflows using Pegasus: Enabling Dark Energy Survey
		  Pipelines},
  Author	= {Vahi, Karan and Wang, Michael H. and Chang, Chihway and
		  Dodelson, Scott and Rynge, Mats and Deelman, Ewa},
  Journal	= {Astronomical Data Analysis Software and Systems XXVIII},
  Volume	= {523},
  Year		= {2019},
  Pages		= {689--692},
  Note		= {Funding Acknowledgments: NSF 1664162}
}

@article{DBLP:journals/fi/KejriwalS19,
  author    = {Mayank Kejriwal and
               Pedro A. Szekely},
  title     = {myDIG: Personalized Illicit Domain-Specific Knowledge Discovery with
               No Programming},
  journal   = {Future Internet},
  volume    = {11},
  number    = {3},
  pages     = {59},
  year      = {2019},
  url       = {https://doi.org/10.3390/fi11030059},
  doi       = {10.3390/fi11030059},
  timestamp = {Thu, 14 Oct 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/fi/KejriwalS19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{mirkovic2018dew,
  title={$\{$DEW$\}$: Distributed Experiment Workflows},
  author={Mirkovic, Jelena and Bartlett, Genevieve and Blythe, Jim},
  booktitle={11th $\{$USENIX$\}$ Workshop on Cyber Security Experimentation and Test ($\{$CSET$\}$ 18)},
  year={2018}
}

@proceedings{DBLP:conf/icassp/2018,
  title     = {2018 {IEEE} International Conference on Acoustics, Speech and Signal
               Processing, {ICASSP} 2018, Calgary, AB, Canada, April 15-20, 2018},
  publisher = {{IEEE}},
  year      = {2018},
  url       = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8450881},
  isbn      = {978-1-5386-4658-8},
  timestamp = {Tue, 18 Sep 2018 09:50:15 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/icassp/2018},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{Lim20183DDynamicMRIof,
 author = {Lim, Yongwan and Zhu, Yinghua and Lingala, Sajan Goud and Byrd, Dani and Narayanan, Shrikanth},
 doi = {10.1002/mrm.27570},
 journal = {Magnetic Resonance in Medicine},
 link = {http://sail.usc.edu/publications/files/mrm.27570.pdf},
 month = {Nov},
 pages = {10},
 title = {3D Dynamic MRI of the Vocal Tract During Natural Speech},
 year = {2018}
}

@inproceedings{conf/aies/KimKAADTR18,
  added-at = {2019-01-14T00:00:00.000+0100},
  author = {Kim, Richard and Kleiman-Weiner, Max and Abeliuk, Andr{\'{e}}s and Awad, Edmond and Dsouza, Sohan and Tenenbaum, Joshua B. and Rahwan, Iyad},
  biburl = {https://www.bibsonomy.org/bibtex/2cf8c9c97e22d43ee7ccae651a9b74b97/dblp},
  booktitle = {AIES},
  crossref = {conf/aies/2018},
  editor = {Furman, Jason and Marchant, Gary E. and Price, Huw and Rossi, Francesca},
  ee = {https://doi.org/10.1145/3278721.3278770},
  interhash = {5ccbd8b607de2994d8e437208c9abc65},
  intrahash = {cf8c9c97e22d43ee7ccae651a9b74b97},
  isbn = {978-1-4503-6012-8},
  keywords = {dblp},
  pages = {197-203},
  publisher = {ACM},
  timestamp = {2019-01-15T11:37:54.000+0100},
  title = {A Computational Model of Commonsense Moral Decision Making.},
  url = {http://dblp.uni-trier.de/db/conf/aies/aies2018.html#KimKAADTR18},
  doi = {10.1145/3278721.3278770},
  month = feb,
  year = 2018
}

@inproceedings{lawton2018forest,
  title={A Forest Mixture Bound for Block-Free Parallel Inference},
  author={Lawton, Neal and Galstyan, Aram and Steeg, Greg Ver},
  booktitle={Uncertainty in Artificial Intelligence (UAI)},
  year={2018}
}

@inproceedings{neal_fm,
	Author = {Neal Lawton and Greg {Ver Steeg} and Aram Galstyan},
	Booktitle = {Proc. of the Conference on Uncertainty in Artificial Intelligence (UAI)},
	Date-Added = {2018-05-23 21:14:14 +0000},
	Date-Modified = {2018-05-23 21:16:01 +0000},
	Title = {A Forest Mixture Bound for Block-Free Parallel Inference},
	Year = {2018}}

@inproceedings{haija2018highorder,
  author={Sami Abu-El-Haija AND Nazanin Alipourfard AND Hrayr Harutyunyan AND Amol Kapoor AND Bryan Perozzi},
  title={A Higher-Order Graph Convolutional Layer},
  booktitle={NeurIPS 2018 Workshop},
  year={2018},
}

@Article{	  tovar-tpds-2017,
  Title		= {A Job Sizing Strategy for High-Throughput Scientific
		  Workflows},
  Author	= {Tovar, Benjamin and Ferreira da Silva, Rafael and Juve,
		  Gideon and Deelman, Ewa and Allcock, William and Thain,
		  Douglas and Livny, Miron},
  Journal	= {IEEE Transactions on Parallel and Distributed Systems},
  Volume	= {29},
  Number	= {2},
  Pages		= {240--253},
  Year		= {2018},
  DOI		= {10.1109/TPDS.2017.2762310},
  Note		= {Funding Acknowledgments: DOE ER26110}
}

@inproceedings{Kumar2018KnowledgeDriven,
 address = {Hyderabad, India},
 author = {Kumar, Manoj and Chebolu, Pooja and Kim, So Hyun and Martinez, Kassandra and Lord, Catherine and Narayanan, Shrikanth S.},
 bib2html_rescat = {care},
 booktitle = {Proceedings of InterSpeech},
 link = {},
 month = {Sep},
 title = {A Knowledge Driven Structural Segmentation Approach for Play-Talk Classification during Autism Assessment},
 year = {2018}
}

@inproceedings{Singla2018MultitaskApproach,
 abstract = {We present a novel multi-task modeling approach to learning multilingual distributed representations of text. Our system learns word and sentence embeddings jointly by training a multilingual skip-gram model together with a cross-lingual sentence similarity model. Our architecture can transparently use both monolingual and sentence aligned bilingual corpora to learn multilingual embeddings, thus covering a vocabulary significantly larger than the vocabulary of the bilingual corpora alone. Our model shows competitive performance in a standard cross-lingual document classification task. We also show the effectiveness of our method in a limited resource scenario.},
 address = {Melbourne, Australia},
 author = {Singla, Karan and Can, Dogan and Narayanan, Shrikanth S.},
 booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
 doi = {10.18653/v1/P18-2035},
 link = {https://www.aclweb.org/anthology/P18-2035.pdf},
 month = {Jul},
 pages = {214-220},
 publisher = {Association for Computational Linguistics},
 title = {A Multi-task Approach to Learning Multilingual Representations},
 year = {2018}
}

We present a novel multi-task modeling approach to learning multilingual distributed representations of text. Our system learns word and sentence embeddings jointly by training a multilingual skip-gram model together with a cross-lingual sentence similarity model. Our architecture can transparently use both monolingual and sentence aligned bilingual corpora to learn multilingual embeddings, thus covering a vocabulary significantly larger than the vocabulary of the bilingual corpora alone. Our model shows competitive performance in a standard cross-lingual document classification task. We also show the effectiveness of our method in a limited resource scenario.

@inproceedings{Booth2018ANovelMethodfor,
 author = {Booth, Brandon and Mundnich, Karel and Narayanan, Shrikanth},
 booktitle = {In proceedings of ICASSP},
 doi = {10.1109/ICASSP.2018.8461645},
 link = {http://sail.usc.edu/publications/files/08461645.pdf},
 location = {Calgary},
 month = {Apr},
 title = {A Novel Method for Human Bias Correction of Continuous- Time Annotations},
 year = {2018}
}

@article{DBLP:journals/corr/abs-1801-05881,
  author    = {Mayank Kejriwal and
               Yao Gu},
  title     = {A Pipeline for Post-Crisis Twitter Data Acquisition},
  journal   = {CoRR},
  volume    = {abs/1801.05881},
  year      = {2018},
  url       = {http://arxiv.org/abs/1801.05881},
  eprinttype = {arXiv},
  eprint    = {1801.05881},
  timestamp = {Mon, 13 Aug 2018 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1801-05881.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{garijo2018ontosoft,
	title        = {A Semantic Model Catalog to Support Comparison and Reuse},
	author       = {Garijo, Daniel and Khider, Deborah and Gil, Yolanda and Carvalho, Lucas and Essawy, Bakinam and Pierce, Suzanne and Hardesty, Daniel Lewis and Ratnakar, Varun and Peckham, Scott and Duffy, Chris and Goodal, Jonathan},
	year         = 2018,
	booktitle    = {Proceedings of the 9th International Congress on Environmental Modelling and Software},
	url          = {http://dgarijo.com/papers/IEMSs2018-OntoSoft.pdf}
}

@inproceedings{garijo2018ontosoft,
	title        = {A Semantic Model Catalog to Support Comparison and Reuse},
	author       = {Daniel Garijo and Deborah Khider and Yolanda Gil and Lucas Carvalho and Bakinam Essawy and Suzanne Pierce and Daniel Hardesty Lewis and Varun Ratnakar and Scott Peckham and Chris Duffy and Jonathan Goodal},
	year         = 2018,
	booktitle    = {Proceedings of the 9th International Congress on Environmental Modelling and Software},
	url          = {https://dgarijo.com/papers/IEMSs2018-OntoSoft.pdf}
}

@inproceedings{garijo-etal-iemss18,
  author = {Daniel Garijo and Deborah Khider and Yolanda Gil and Lucas Carvalho and Bakinam Essawy and Suzanne Pierce 
  	and Daniel H. Lewis and Varun Ratnakar and Scott D. Peckham and Chris Duffy and Jonathan Goodall},
  title = {A Semantic Model Registry to Support Comparison and Reuse},
  booktitle = {Proceedings of the Ninth International Congress on Environmental Modeling and Software},
  address = {Ft Collins, CO},
  year = {2018}
}

@article{potok_study_2018,
	title = {A {Study} of {Complex} {Deep} {Learning} {Networks} on {High}-{Performance}, {Neuromorphic}, and {Quantum} {Computers}},
	volume = {14},
	url = {https://doi.org/10.1145/3178454},
	doi = {10.1145/3178454},
	number = {2},
	journal = {JETC},
	author = {Potok, Thomas E. and Schuman, Catherine D. and Young, Steven R. and Patton, Robert M. and Spedalieri, Federico M. and Liu, Jeremy and Yao, Ke-Thia and Rose, Garrett S. and Chakma, Gangotree},
	year = {2018},
	pages = {19:1--19:21},
}

@inproceedings{menon2018binary,
  title={A binary analysis approach to retrofit security in input parsing routines},
  author={Menon, Jayakrishna and Hauser, Christophe and Shoshitaishvili, Yan and Schwab, Stephen},
  booktitle={2018 IEEE Security and Privacy Workshops (SPW)},
  pages={306--322},
  year={2018},
  organization={IEEE}
}

@article{faust2018hyaluronic,
  title={A hyaluronic acid binding peptide-polymer system for treating osteoarthritis},
  author={Faust, Heather J and Sommerfeld, Sven D and Rathod, Sona and Rittenbach, Andrew and Banerjee, Sangeeta Ray and Tsui, Benjamin MW and Pomper, Martin and Amzel, Mario L and Singh, Anirudha and Elisseeff, Jennifer H},
  journal={Biomaterials},
  volume={183},
  pages={93--101},
  year={2018},
  publisher={Elsevier}
}

@proceedings {12524,
	title = {AARCLight{\textendash}New opportunities for South Atlantic R\&E Network collaboration between Africa, Brazil, and the US},
	journal = {UbuntuNet Connect Conference 2018},
	year = {2018},
	month = {11/2018},
	address = {Zanzibar, Tanzania},
	url = {https://urldefense.com/v3/__http://aarclight.net/wp-content/uploads/2018/11/AARCLightWhitePaperUC2018.pdf__;!!FjuHKAHQs5udqho!OrfqNEjI8IYnpYEY3Wc7S4oSY6Kf82MHM8jGAJar7qa8MoXzNwtTTC2HiJsVSREP5YicoFykCYhwOw$ },
	author = {Heidi Morgan and Julio Ibarra and Jeronimo Bezerra and Lopez, Luis Fernandez and Vasilka Chergarova and Cox III, Donald A and Alvarez, Gabriella E and Michael Stanton and Hazin, Aluizio and Lotz, Len and Mammen, Siju}
}

@article{Vaz2018AcousticDenoisingusingDictionary,
 author = {Vaz, Colin  and  Ramanarayanan, Vikram and Narayanan, Shrikanth},
 journal = {IEEE/ACM Transactions on Audio, Speech and Language Processing},
 title = {Acoustic Denoising using Dictionary Learning with Spectral and Temporal Regularization},
 year = {2018},
 month = {Jan}
}

@article{liu_adiabatic_2018,
	title = {Adiabatic {Quantum} {Computation} {Applied} to {Deep} {Learning} {Networks}},
	volume = {20},
	url = {https://doi.org/10.3390/e20050380},
	doi = {10.3390/e20050380},
	number = {5},
	journal = {Entropy},
	author = {Liu, Jeremy and Spedalieri, Federico M. and Yao, Ke-Thia and Potok, Thomas E. and Schuman, Catherine D. and Young, Steven R. and Patton, Robert M. and Rose, Garrett S. and Chakma, Gangotree},
	year = {2018},
	pages = {380},
	file = {Liu et al. - 2018 - Adiabatic Quantum Computation Applied to Deep Lear.pdf:C\:\\Users\\ktyao\\Zotero\\storage\\V9TW3J8J\\Liu et al. - 2018 - Adiabatic Quantum Computation Applied to Deep Lear.pdf:application/pdf},
}

@article{RevModPhys.90.015002,
  title = {Adiabatic quantum computation},
  author = {Albash, Tameem and Lidar, Daniel A.},
  journal = {Rev. Mod. Phys.},
  volume = {90},
  issue = {1},
  pages = {015002},
  numpages = {64},
  year = {2018},
  month = {Jan},
  publisher = {American Physical Society},
  doi = {10.1103/RevModPhys.90.015002},
  url = {https://link.aps.org/doi/10.1103/RevModPhys.90.015002}
}

@inproceedings{kumar2018,
    author = "Kumar, T. K. Satish and Xu, Hong and Tang, Zheng and Kumar, Anoop and Rogers, Craig Milo and Knoblock, Craig A.",
    year = "2018",
    pages = "38--43",
    booktitle = "Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference (FLAIRS)",
    title = "Alert Generation in Execution Monitoring Using Resource Envelopes",
    Urlpaper = {http://usc-isi-i2.github.io/papers/tksk18-flairs.pdf},
    URLslides = "http://files.hong.me/papers/kumar2018-slides.pdf"
}

@inproceedings{embar:kbcom18,
    author = "Embar, Varun and Farnadi, Golnoosh and Pujara, Jay and Getoor, Lise",
    bib_url = "/pubs/bib/embar-kbcom18.bib",
    booktitle = "{WSDM Workshop on Knowledge Base Construction, Reasoning and Mining}",
    pdf_url = "/pubs/2018/embar-kbcom18/embar-kbcom18.pdf",
    sec = "ws",
    title = "Aligning Product Categories using Anchor Products",
    year = "2018"
}

@inproceedings{DBLP:conf/aies/HundmanGKB18,
  author    = {Kyle Hundman and
               Thamme Gowda and
               Mayank Kejriwal and
               Benedikt Boecking},
  editor    = {Jason Furman and
               Gary E. Marchant and
               Huw Price and
               Francesca Rossi},
  title     = {Always Lurking: Understanding and Mitigating Bias in Online Human
               Trafficking Detection},
  booktitle = {Proceedings of the 2018 {AAAI/ACM} Conference on AI, Ethics, and Society,
               {AIES} 2018, New Orleans, LA, USA, February 02-03, 2018},
  pages     = {137--143},
  publisher = {{ACM}},
  year      = {2018},
  url       = {https://doi.org/10.1145/3278721.3278782},
  doi       = {10.1145/3278721.3278782},
  timestamp = {Fri, 25 Dec 2020 01:15:08 +0100},
  biburl    = {https://dblp.org/rec/conf/aies/HundmanGKB18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{hundman2018lurking,
author = {Hundman, Kyle and \textbf{Gowda, Thamme} and Kejriwal, Mayank and Boecking, Benedikt},
title = {Always Lurking: Understanding and Mitigating Bias in Online Human Trafficking Detection},
year = {2018},
isbn = {9781450360128},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3278721.3278782},
doi = {10.1145/3278721.3278782},
booktitle = {Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society},
pages = {137–143},
numpages = {7},
keywords = {bias mitigation, clustering, web crawling, human trafficking, text classification},
location = {New Orleans, LA, USA},
series = {AIES '18},
}

  

@article{Smith2018Anindividuallytailoredfamily-centered,
 author = {Smith, Justin D and Berkel, Cady and Jordan, Neil  and Atkins, David C  and Narayanan, Shrikanth S  and Gallo, Carlos  and Grimm, Kevin J and  Dishion, Thomas J and Mauricio, Anne M  and Rudo-Stern, Jenna  and  Meachum, Mariah K and  Winslow, Emily and Bruening, Meg M},
 doi = {10.1186/s13012-017-0697-2},
 journal = {Implementation Science},
 link = {http://sail.usc.edu/publications/files/Smith_et_al-2018-Implementation_Science.pdf},
 month = {Jan},
 number = {11},
 pages = {1-15},
 title = {An individually tailored family-centered intervention for pediatric obesity in primary care: Study protocol of a randomized type II hybrid implementation-effectiveness trial (Raising Healthy Children study)},
 volume = {13},
 year = {2018}
}

@article{Ramanarayanan2018AnalysisofSpeechProduction,
 author = {Ramanarayanan, Vikram and Tilsen, Sam and Proctor, Michael and Toger, Johannes and Goldstein, Louis and Nayak, Krishna and Narayanan, Shrikanth S.},
 bib2html_rescat = {span},
 doi = {https://doi.org/10.1016/j.csl.2018.04.002},
 journal = {Computer Speech and Language},
 link = {http://sail.usc.edu/publications/files/Ramanarayanan-CSL2018.pdf},
 month = {Nov},
 pages = {1-22},
 title = {Analysis of Speech Production Real-Time MRI},
 volume = {52},
 year = {2018}
}

@InProceedings{Hardaker18a,
        author =        "Wes Hardaker",
	title = "Analyzing and Mitigating Privacy with the {DNS} Root Service",
        booktitle =     "Proceedings of the " # " {ISOC} {NDSS} Workshop on {DNS} Privacy ",
        year =          2018,
	myorganization =	"USC/Information Sciences Institute",
	sortdate = 	"2018-02-18", 
	project = "ant, earr, researchroot, nipet",
        month =      feb,
        address =    "San Diego, California, USA",
        publisher =  "The Internet Society",
	jlocation = 	"johnh: pafile",
	keywords = 	"dns, privacy, data leak, PII",
	pdfurl = "https://ant.isi.edu/%7ehardaker/papers/2018-02-ndss-analyzing-root-privacy.pdf",
	abstract = "Processing of all DNS requests start at the root of the DNS tree and
make use of either cached data from previous requests, or by
traversing the DNS tree for the missing information. When QNAME
minimization is not in use, queries forwarded to the parental nodes in
the DNS tree may leak private DNS query data. In this paper we examine
31 days during the month of January 2017 of queries sent from two
recursive resolvers placed in two residential networks to the DNS root
server operated by USC/ISI's, analyzing the leaked QNAMEs for an
impact on the network’s privacy.  We then compare a few DNS privacy
preserving techniques against the privacy analysis against these
networks. Finally, we introduce a new solution called ``LocalRoot''that
enables users to entirely mitigate privacy concerns when interacting
with the DNS root server system, while other solutions fail to
completely protect users from all privacy analysis methods.",
}



% wjh:icann:dnssecworkshop:2019danemtasts,                  

Processing of all DNS requests start at the root of the DNS tree and make use of either cached data from previous requests, or by traversing the DNS tree for the missing information. When QNAME minimization is not in use, queries forwarded to the parental nodes in the DNS tree may leak private DNS query data. In this paper we examine 31 days during the month of January 2017 of queries sent from two recursive resolvers placed in two residential networks to the DNS root server operated by USC/ISI's, analyzing the leaked QNAMEs for an impact on the network’s privacy. We then compare a few DNS privacy preserving techniques against the privacy analysis against these networks. Finally, we introduce a new solution called ``LocalRoot''that enables users to entirely mitigate privacy concerns when interacting with the DNS root server system, while other solutions fail to completely protect users from all privacy analysis methods.

@inproceedings{Badawy2018asonam,
    abstract = {Until recently, social media was seen to promote democratic discourse on
social and political issues. However, this powerful communication platform has
come under scrutiny for allowing hostile actors to exploit online discussions
in an attempt to manipulate public opinion. A case in point is the ongoing {U.S}.
Congress' investigation of Russian interference in the 2016 {U.S}. election
campaign, with Russia accused of using trolls (malicious accounts created to
manipulate) and bots to spread misinformation and politically biased
information. In this study, we explore the effects of this manipulation
campaign, taking a closer look at users who re-shared the posts produced on
Twitter by the Russian troll accounts publicly disclosed by {U.S}. Congress
investigation. We collected a dataset with over 43 million election-related
posts shared on Twitter between September 16 and October 21, 2016, by about 5.7
million distinct users. This dataset included accounts associated with the
identified Russian trolls. We use label propagation to infer the ideology of
all users based on the news sources they shared. This method enables us to
classify a large number of users as liberal or conservative with precision and
recall above 90\%. Conservatives retweeted Russian trolls about 31 times more
often than liberals and produced 36x more tweets. Additionally, most retweets
of troll content originated from two Southern states: Tennessee and Texas.
Using state-of-the-art bot detection techniques, we estimated that about 4.9\%
and 6.2\% of liberal and conservative users respectively were bots. Text
analysis on the content shared by trolls reveals that they had a mostly
conservative, {pro-Trump} agenda. Although an ideologically broad swath of
Twitter users was exposed to Russian Trolls in the period leading up to the
2016 {U.S}. Presidential election, it was mainly conservatives who helped amplify
their message.},
    booktitle = {Proceedings of The international conference series on Advances in Social Network Analysis and Mining  (ASONAM-2018)},
    title = {Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign},
    url = {http://arxiv.org/abs/1802.04291},
    year = {2018},
    keywords = {fake-news}
}

Until recently, social media was seen to promote democratic discourse on social and political issues. However, this powerful communication platform has come under scrutiny for allowing hostile actors to exploit online discussions in an attempt to manipulate public opinion. A case in point is the ongoing U.S. Congress' investigation of Russian interference in the 2016 U.S. election campaign, with Russia accused of using trolls (malicious accounts created to manipulate) and bots to spread misinformation and politically biased information. In this study, we explore the effects of this manipulation campaign, taking a closer look at users who re-shared the posts produced on Twitter by the Russian troll accounts publicly disclosed by U.S. Congress investigation. We collected a dataset with over 43 million election-related posts shared on Twitter between September 16 and October 21, 2016, by about 5.7 million distinct users. This dataset included accounts associated with the identified Russian trolls. We use label propagation to infer the ideology of all users based on the news sources they shared. This method enables us to classify a large number of users as liberal or conservative with precision and recall above 90%. Conservatives retweeted Russian trolls about 31 times more often than liberals and produced 36x more tweets. Additionally, most retweets of troll content originated from two Southern states: Tennessee and Texas. Using state-of-the-art bot detection techniques, we estimated that about 4.9% and 6.2% of liberal and conservative users respectively were bots. Text analysis on the content shared by trolls reveals that they had a mostly conservative, pro-Trump agenda. Although an ideologically broad swath of Twitter users was exposed to Russian Trolls in the period leading up to the 2016 U.S. Presidential election, it was mainly conservatives who helped amplify their message.

@inproceedings{Badawy2018analyzing,
  title={Analyzing the digital traces of political manipulation: the 2016 Russian interference Twitter campaign},
  author={Badawy, Adam and Ferrara, Emilio and Lerman, Kristina},
  booktitle={2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},
  pages={258--265},
  year={2018},
  organization={IEEE}
}

@inproceedings{duan2018:autocarto,
	author = "Weiwei Duan and Yao-Yi Chiang and Craig A. Knoblock and Stefan Leyk and Johannes H. Uhl",
	title = "Automatic Generation of Precisely Delineated Geographic Features from Georeferenced Historical Maps Using Deep Learning",
	booktitle = {The 22nd International Research Symposium on Computer-based Cartography and GIScience (Autocarto/UCGIS)},
	Year = {2018},
  Url = {https://www.ucgis.org/assets/docs/AutoCarto-UCGIS%202018%20Proceedings.pdf}
	}

@TechReport{Heidemann18b,
	author = 	"John Heidemann and Yuri Pradkin and Aqib Nisar",
	title = 	"Back Out: End-to-end Inference of Common
                  Points-of-Failure in the Internet (extended)",
	institution = 	"USC/Information Sciences Institute",
	year = 		2018,
	sortdate = 		"2018-02-02", 
	project = "ant, lacanic, retrofuturebridge, duoi",
	jsubject = "routing",
	number =	"ISI-TR-724",
	month =		feb,
	jlocation =	"johnh: pafile",
	keywords =	"network outage detection, clustering, visualization",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Heidemann18b.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Heidemann18b.pdf",
	myorganization =	"USC/Information Sciences Institute",
	copyrightholder = "authors",
	abstract = "
Internet reliability has many potential weaknesses:  fiber
rights-of-way at the physical layer, exchange-point congestion from
DDOS at the network layer, settlement disputes between organizations
at the financial layer, and government intervention the political
layer.  This paper shows that we
can \emph{discover common points-of-failure}
at \emph{any} of these layers by observing
correlated failures.  We use \emph{end-to-end} observations from
data-plane-level connectivity of edge hosts in the Internet.  We
identify \emph{correlations in connectivity:}  networks that usually
fail and recover at the same time suggest common point-of-failure.  We
define two new algorithms to meet these goals.  First, we define a
computationally-efficient algorithm to create a \emph{linear ordering}
of blocks to make correlated failures apparent to a human analyst.
Second, we develop an \emph{event-based clustering} algorithm that
directly networks with correlated failures, suggesting common
points-of-failure.  Our algorithms scale to real-world datasets of
millions of networks and observations:  linear ordering
is O(n log n) time and event-based clustering parallelizes with Map/Reduce.  We
demonstrate them on three months of outages for 4 million
/24 network prefixes, showing high recall (0.83 to 0.98) and precision
(0.72 to 1.0) for blocks that respond.  We also show that our
algorithms generalize to identify correlations in anycast catchments
and routing.
",
}

Internet reliability has many potential weaknesses: fiber rights-of-way at the physical layer, exchange-point congestion from DDOS at the network layer, settlement disputes between organizations at the financial layer, and government intervention the political layer. This paper shows that we can \emphdiscover common points-of-failure at \emphany of these layers by observing correlated failures. We use \emphend-to-end observations from data-plane-level connectivity of edge hosts in the Internet. We identify \emphcorrelations in connectivity: networks that usually fail and recover at the same time suggest common point-of-failure. We define two new algorithms to meet these goals. First, we define a computationally-efficient algorithm to create a \emphlinear ordering of blocks to make correlated failures apparent to a human analyst. Second, we develop an \emphevent-based clustering algorithm that directly networks with correlated failures, suggesting common points-of-failure. Our algorithms scale to real-world datasets of millions of networks and observations: linear ordering is O(n log n) time and event-based clustering parallelizes with Map/Reduce. We demonstrate them on three months of outages for 4 million /24 network prefixes, showing high recall (0.83 to 0.98) and precision (0.72 to 1.0) for blocks that respond. We also show that our algorithms generalize to identify correlations in anycast catchments and routing.

@inproceedings{jaiswal_bidirectional_2018,
	title = {Bidirectional {Conditional} {Generative} {Adversarial} {Networks}},
	booktitle = {Asian {Conference} on {Computer} {Vision}},
	author = {Jaiswal, A. and Wu, Y. and AbdAlmageed, W. and Natarajan, P.},
	month = dec,
	year = {2018}
}

@inproceedings{conf/www/0001VDMCKR18,
  added-at = {2018-11-21T00:00:00.000+0100},
  author = {Tang, Jie and Vazirgiannis, Michalis and Dong, Yuxiao and Malliaros, Fragkiskos D. and Cochez, Michael and Kejriwal, Mayank and Rettinger, Achim},
  biburl = {https://www.bibsonomy.org/bibtex/20a5171dd3ffd42886d47db078fdefec9/dblp},
  booktitle = {WWW (Companion Volume)},
  crossref = {conf/www/2018c},
  editor = {Champin, Pierre-Antoine and Gandon, Fabien and Lalmas, Mounia and Ipeirotis, Panagiotis G.},
  ee = {https://doi.org/10.1145/3184558.3192293},
  interhash = {a36dd765842f8e361bc97d9b635669e8},
  intrahash = {0a5171dd3ffd42886d47db078fdefec9},
  keywords = {dblp},
  pages = {943-944},
  publisher = {ACM},
  timestamp = {2024-04-09T12:55:59.000+0200},
  title = {BigNet 2018 Chairs' Welcome & Organization.},
  url = {http://dblp.uni-trier.de/db/conf/www/www2018c.html#0001VDMCKR18},
  year = 2018
}

@inproceedings{rajagopala2018a,
	author = {Abhilash Devalapura Rajagopala and Ron Sass and Andrew G. Schmidt and Matthew French},
	booktitle = {IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM)},
	title = {Bridging the Gap between Advanced Memory and Heterogeneous Architectures},
	year = {2018}}

@inproceedings{lin2018:semsci,
    author    = "Chun Lin and Hang Su and Craig A. Knoblock and Yao-Yi Chiang and Weiwei Duan and Stefan Leyk and Johannes H. Uhl",
    title     = "Building Linked Data from Historical Maps",
    booktitle = "Proceedings of the ISWC 2018 Workshop on Enabling Open Semantic Science (SemSci 2018)",
    year      = "2018",
    Urlpaper = {http://usc-isi-i2.github.io/papers/lin18.pdf},
    urlslides = {http://usc-isi-i2.github.io/slides/lin08-semsci.pdf}
	}

	

@inproceedings{wu_busternet:_2018,
	title = {{BusterNet}: {Detecting} {Copy}-{Move} {Image} {Forgery} with {Source}/{Target} {Localization}},
	url = {http://openaccess.thecvf.com/content_ECCV_2018/papers/Rex_Yue_Wu_BusterNet_Detecting_Copy-Move_ECCV_2018_paper.pdf},
	booktitle = {European {Conference} on {Computer} {Vision} ({ECCV})},
	author = {Wu, Y. and AbdAlmageed, W. and Natarajan, P.},
	month = sep,
	year = {2018},
	pages = {168--184}
}

@INPROCEEDINGS{Alipourfard2018wsdm,
  author =       {Nazanin Alipourfard and Peter Fennell and Kristina Lerman},
  title =        {Can you Trust the Trend? Discovering Simpson�s Paradoxes in Social Data},
  booktitle =    {Proceedings of the 11th International ACM Conference on Web Search and Data Mining (WSDM)},
  year =         {2018},
  pages =        {},
  doi={10.1145/3159652.3159684},
  publisher =    {ACM},
  abstract =     {We investigate how Simpson�s paradox affects analysis of trends
in social data. According to the paradox, the trends observed in
data that has been aggregated over an entire population may be
quite different from, and even opposite to, those of the underlying
subgroups. Failure to take this effect into account can lead
analysis to wrong conclusions. We present a statistical method to
automatically identify Simpson�s paradox in data by comparing
statistical trends in the aggregate data to those in the disaggregated
subgroups. We apply the approach to data from Stack Exchange, a
popular question-answering platform, to analyze factors affecting
answerer performance, specifically, the likelihood that an answer
provided by a user will be accepted by the asker as the best answer
to his or her question. Our analysis confirms a known Simpson�s
paradox and identifies several new instances. These paradoxes provide
novel insights into user behavior on Stack Exchange.},
  keywords =     {social-dynamics},
}

We investigate how Simpson�s paradox affects analysis of trends in social data. According to the paradox, the trends observed in data that has been aggregated over an entire population may be quite different from, and even opposite to, those of the underlying subgroups. Failure to take this effect into account can lead analysis to wrong conclusions. We present a statistical method to automatically identify Simpson�s paradox in data by comparing statistical trends in the aggregate data to those in the disaggregated subgroups. We apply the approach to data from Stack Exchange, a popular question-answering platform, to analyze factors affecting answerer performance, specifically, the likelihood that an answer provided by a user will be accepted by the asker as the best answer to his or her question. Our analysis confirms a known Simpson�s paradox and identifies several new instances. These paradoxes provide novel insights into user behavior on Stack Exchange.

@article{jaiswal_capsulegan:_2018,
	title = {{CapsuleGAN}: {Generative} {Adversarial} {Capsule} {Network}},
	shorttitle = {{CapsuleGAN}},
	url = {https://arxiv.org/abs/1802.06167},
	journal = {European Conference on Computer Vision Workshop on Brain-Driven Computer Vision},
	author = {Jaiswal, A. and AbdAlmageed, W. and Natarajan, P.},
	month = sep,
	year = {2018}
}

@inproceedings{kline-components-trident18,
  author    = {E. Kline and
               G. Bartlett and
               G. Lawler and
               R. Story and
               M. Elkins},
  title     = {{Capturing Domain Knowledge Through Extensible Components}},
  booktitle = {Testbeds and Research Infrastructures for the Development of Networks and Communications - 13th {EAI} International Conference, TridentCom 2018, Shanghai, China},
  pages     = {141--156},
  month     = {December},
  year      = {2018},
  url       = {https://doi.org/10.1007/978-3-030-12971-2\_9},
  doi       = {10.1007/978-3-030-12971-2\_9},
  timestamp = {Wed, 06 Feb 2019 15:10:39 +0100}
}

@article{Huang2018CharacterizingTypesofConvolution,
 author = {Huang, Che-Wei and Narayanan, Shrikanth},
 journal = {IEEE Transactions on Affective Computing},
 title = {Characterizing Types of Convolution in Deep Convolutional Recurrent Neural Networks for Robust Speech Emotion Recognition},
 year = {2018},
 month ={Jan}
}

@inproceedings{chang:sysml18,
    author = "Chang, Johnnie and Chen, Robert and Pujara, Jay and Getoor, Lise",
    acceptrate = "57.1\%",
    bib_url = "/pubs/bib/chang-sysml18.bib",
    booktitle = "SysML Conference",
    pdf_url = "/pubs/2018/chang-sysml18/chang-sysml18.pdf",
    sec = "conf-inf",
    title = "Clustering System Data using Aggregate Measures",
    year = "2018"
}

@inproceedings{lee2018collaborative,
  author={Joonseok Lee AND Sami Abu-El-Haija AND Balakrishnan Varadarajan AND Paul Natsev},
  title={Collaborative deep metric learning for video understanding},
  booktitle={SIGKDD Knowledge Discovery and Data Mining},
  year={2018},
}

@article{kouki:kais19,
    Author = "Kouki, Pigi and Pujara, Jay and Marcum, Christopher and Koehly, Laura and Getoor, Lise",
    Journal = "Knowledge and Information Systems",
    Title = "Collective Entity Resolution in Multi-relational Familial Networks",
    Year = "2018",
    bib_url = "/pubs/bib/kouki-kais19.bib",
    doi_url = "https://doi.org/10.1007/s10115-018-1246-2",
    impactfactor = "2.94",
    number = "3",
    pages = "1547-1581",
    pdf_url = "/pubs/2019/kouki-kais19/kouki-kais19.pdf",
    sec = "journal",
    volume = "61"
}

@InProceedings{	  2108-bhide-workflows-blockchain,
  Title		= {Combating Workflow Failures with Integrity-based
		  Checkpoints and Blockchain},
  Author	= {Bhide, Omkar and Hill, Raquel and Vahi, Karan and Rynge,
		  Mats and Velch, Von},
  BookTitle	= {6th International Workshop on Distributed Storage and
		  Blockchain Technologies for Big Data},
  Year		= {2018},
  Note		= {Funding Acknowledgments: NSF 1642070, 1642053, and
		  1642090}
}

@inproceedings{Flemotomos2018CombinedSpeaker,
 address = {Hyderabad, India},
 author = {Flemotomos, Nikolaos and Papadopoulos, Pavlos and Gibson, James and Narayanan, Shrikanth S.},
 bib2html_rescat = {},
 booktitle = {Proceedings of InterSpeech},
 link = {},
 month = {Sep},
 title = {Combined Speaker Clustering and Role Recognition in Conversational Speech},
 year = {2018}
}

@article{mt2019,
  author  = {Ryan Gabbard and Jay DeYoung and Constantine Lignos and Marjorie Freedman and Ralph Weischedel}, 
  title   = {Combining rule-based and statistical mechanisms for low-resource named entity recognition},
  journal = {Machine Translation},
  year    = 2018,
  number  = 32,
  pages   = {31-43},
  month   = 6,
  volume  = 31
}

@TechReport{Dainotti18a,
        author =        "Alberto Dainotti and John Heidemann and Alistair King and Ramakrishna
Padmanabhan and Yuri Pradkin",
        title =         "Common Outage Data Format, version 1.0",
        institution =   "USC/Information Sciences Institute",
        year =          2018,
	sortdate = 		"2018-10-29", 
	project = "ant, divoice",
	jsubject = "routing",
        number =     "ISI-TR-729",
        month =      oct,
        jlocation =   "johnh: pafile",
        keywords =   "internet outage, NIDE, file format",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Dainotti18a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Dainotti18a.pdf",
	myorganization =	"USC/Information Sciences Institute",
	copyrightholder = "authors",
        abstract = "This document defines a data format for exchanging information about Internet outages.  It specifies the semantics data about network outages, and two syntaxes that can be used to represent this information.  This format is designed to support reports from Internet outage detection systems such as Trinocular, Thunderping, and IODA.",
}

This document defines a data format for exchanging information about Internet outages. It specifies the semantics data about network outages, and two syntaxes that can be used to represent this information. This format is designed to support reports from Internet outage detection systems such as Trinocular, Thunderping, and IODA.

@inproceedings{Ramakrishna2018ComputationalHumor,
 address = {Hyderabad, India},
 author = {Ramakrishna, Anil and Greer, Timothy and Atkins, David and Narayanan, Shrikanth S.},
 bib2html_rescat = {aaa},
 booktitle = {Proceedings of InterSpeech},
 link = {},
 month = {Sep},
 title = {Computational modeling of conversational humor in psychotherapy},
 year = {2018}
}

@Article{Lerman2018jcss,
author="Lerman, Kristina",
title="Computational social scientist beware: Simpson's paradox in behavioral data",
journal="Journal of Computational Social Science",
year="2018",
month="Jan",
day="01",
volume="1",
number="1",
pages="49--58",
abstract="Observational data about human behavior are often heterogeneous, i.e., generated by subgroups within the population under study that vary in size and behavior. Heterogeneity predisposes analysis to Simpson's paradox, whereby the trends observed in data that have been aggregated over the entire population may be substantially different from those of the underlying subgroups. I illustrate Simpson's paradox with several examples coming from studies of online behavior and show that aggregate response leads to wrong conclusions about the underlying individual behavior. I then present a simple method to test whether Simpson's paradox is affecting results of analysis. The presence of Simpson's paradox in social data suggests that important behavioral differences exist within the population, and failure to take these differences into account can distort the studies' findings.",
issn="2432-2725",
doi="10.1007/s42001-017-0007-4",
url="https://doi.org/10.1007/s42001-017-0007-4"
}

Observational data about human behavior are often heterogeneous, i.e., generated by subgroups within the population under study that vary in size and behavior. Heterogeneity predisposes analysis to Simpson's paradox, whereby the trends observed in data that have been aggregated over the entire population may be substantially different from those of the underlying subgroups. I illustrate Simpson's paradox with several examples coming from studies of online behavior and show that aggregate response leads to wrong conclusions about the underlying individual behavior. I then present a simple method to test whether Simpson's paradox is affecting results of analysis. The presence of Simpson's paradox in social data suggests that important behavioral differences exist within the population, and failure to take these differences into account can distort the studies' findings.

@inproceedings{Kusumastuti-informs18,
	title={Concentrated vs. Distributed Defense as a Deterrent to Cyber Attackers: A Behavioral Analog Simulation Game},
	author={Kusumastuti, Sarah and Rosoff, Heather and Blythe, Jim and John, Richard},
	booktitle={INFORMS Annual Meeting},
	year={2018}}

@article{lindstrom_conserved_2018,
	title = {Conserved and {Divergent} {Features} of {Human} and {Mouse} {Kidney} {Organogenesis}.},
	doi = {doi: 10.1681/ASN.2017080887},
	abstract = {Human kidney function is underpinned by approximately 1,000,000 nephrons, although the number varies substantially, and low nephron number is linked to disease. Human kidney development initiates around 4 weeks of gestation and ends around 34-37 weeks of gestation. Over this period, a reiterative inductive process establishes the nephron complement. Studies have provided insightful anatomic descriptions of human kidney development, but the limited histologic views are not readily accessible to a broad audience. In this first paper in a series providing comprehensive insight into human kidney formation, we examined human kidney development in 135 anonymously donated human kidney specimens. We documented kidney development at a macroscopic and cellular level through histologic analysis, RNA in situ hybridization, immunofluorescence studies, and transcriptional profiling, contrasting human development (4-23 weeks) with mouse development at selected stages (embryonic day 15.5 and postnatal day 2). The high-resolution histologic interactive atlas of human kidney organogenesis generated can be viewed at the GUDMAP database (www.gudmap.org) together with three-dimensional reconstructions of key components of the data herein. At the anatomic level, human and mouse kidney development differ in timing, scale, and global features such as lobe formation and progenitor niche organization. The data also highlight differences in molecular and cellular features, including the expression and cellular distribution of anchor gene markers used to identify key cell types in mouse kidney studies. These data will facilitate and inform in vitro efforts to generate human kidney structures and comparative functional analyses across mammalian species.},
	journal = {Journal of the American Society of Nephrology},
	author = {Lindström, NO and McMahon, JA and Guo, J and Tran, T and Guo, Q and Rutledge, E and Parvez, RK and Saribekyan, G and Schuler, RE and Liao, C and Kim, AD and Abdelhalim, A and Ruffins, SW and Thornton, ME and Basking, L and Grubbs, B and Kesselman, C and McMahon, AP},
	month = feb,
	year = {2018},
}

Human kidney function is underpinned by approximately 1,000,000 nephrons, although the number varies substantially, and low nephron number is linked to disease. Human kidney development initiates around 4 weeks of gestation and ends around 34-37 weeks of gestation. Over this period, a reiterative inductive process establishes the nephron complement. Studies have provided insightful anatomic descriptions of human kidney development, but the limited histologic views are not readily accessible to a broad audience. In this first paper in a series providing comprehensive insight into human kidney formation, we examined human kidney development in 135 anonymously donated human kidney specimens. We documented kidney development at a macroscopic and cellular level through histologic analysis, RNA in situ hybridization, immunofluorescence studies, and transcriptional profiling, contrasting human development (4-23 weeks) with mouse development at selected stages (embryonic day 15.5 and postnatal day 2). The high-resolution histologic interactive atlas of human kidney organogenesis generated can be viewed at the GUDMAP database (www.gudmap.org) together with three-dimensional reconstructions of key components of the data herein. At the anatomic level, human and mouse kidney development differ in timing, scale, and global features such as lobe formation and progenitor niche organization. The data also highlight differences in molecular and cellular features, including the expression and cellular distribution of anchor gene markers used to identify key cell types in mouse kidney studies. These data will facilitate and inform in vitro efforts to generate human kidney structures and comparative functional analyses across mammalian species.

@inproceedings{shi2018constraint,
  title={Constraint-Based Learning for Sensor Failure Detection and Adaptation},
  author={Shi, Yuan and Kumar, TK Satish and Knoblock, Craig A},
  booktitle={2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)},
  pages={328--335},
  year={2018},
  organization={IEEE},
  Urlpaper = "{http://usc-isi-i2.github.io/papers/shi18-ieee.pdf}"
}  

@inproceedings{DBLP:conf/aaai/KejriwalS18,
  author    = {Mayank Kejriwal and
               Pedro A. Szekely},
  editor    = {Sheila A. McIlraith and
               Kilian Q. Weinberger},
  title     = {Constructing Domain-Specific Search Engines With No Programming},
  booktitle = {Proceedings of the Thirty-Second {AAAI} Conference on Artificial Intelligence,
               (AAAI-18), the 30th innovative Applications of Artificial Intelligence
               (IAAI-18), and the 8th {AAAI} Symposium on Educational Advances in
               Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February
               2-7, 2018},
  pages     = {8204--8205},
  publisher = {{AAAI} Press},
  year      = {2018},
  url       = {https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16990},
  timestamp = {Thu, 18 Feb 2021 14:22:23 +0100},
  biburl    = {https://dblp.org/rec/conf/aaai/KejriwalS18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{el-ghaish_covp3dj_2018,
	title = {{CovP}3DJ: Skeleton-parts-based-covariance Descriptor for Human Action Recognition.},
	rights = {All rights reserved},
	shorttitle = {{CovP}3DJ},
	pages = {343--350},
	booktitle = {{VISIGRAPP} (5: {VISAPP})},
	author = {El-Ghaish, Hany A. and Shoukry, Amin and Hussein, Mohamed E.},
	year = {2018},
	file = {Full Text:C\:\\Users\\Mohamed Hussein\\Zotero\\storage\\IMVBTDKU\\El-Ghaish et al. - 2018 - CovP3DJ Skeleton-parts-based-covariance Descripto.pdf:application/pdf}
}

@InProceedings{	  jordan-11ncee-2018,
  Title		= {CyberShake Models of Seismic Hazards in Southern and
		  Central California},
  Author	= {Jordan, T. H. and Callaghan, S. and Graves, R. W. and
		  Wang, F. and Milner, K. R. and Goulet, C. A. and Maechling,
		  P. J. and Olsen, K. B. and Cui, Y. and Juve, G. and Vahi,
		  K. and Yu, J. and Deelman, E. and Gill, D.},
  BookTitle	= {11th National Conference in Earthquake Engineering},
  Year		= {2018},
  Pages		= {},
  DOI		= {}
}

@INPROCEEDINGS{Sapienza2018www,
  author =       {Anna Sapienza and Sindhu Kiranmai Ernala and
               Alessandro Bessi and
               Kristina Lerman and
               Emilio Ferrara},
  title =        {DISCOVER: Mining Online Chatter for Emerging Cyber Threats},
  booktitle =    {Proceedings of WWW Companion for the Third International Workshop on Computational Methods for CyberSafety},
  year =         {2018},
  }

@INPROCEEDINGS{Tavabi2018iaai,
  author =       {Nazgol Tavabi and Palash Goyal and Mohammed Almukaynizi and Paulo Shakarian and Kristina Lerman},
  title =        {DarkEmbed: Exploit Prediction with Neural Language Models},
  booktitle =    {Proceedings of AAAI Conference on Innovative Applications of AI (IAAI2018)},
  year =         {2018},
  url = {https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17304}
  }

@inproceedings{kothari-chi18,
	title={Data Privacy and the Elusive Goal of Empowering the User},
	author={Kothari, Vijay and Smith, Sean and Blythe, Jim and Koppel, Ross},
	booktitle={CHI workshop on Exploring Individual Differences in Privacy},
	year={2018}}

@article{Wagstaff2018Deepmars,
title={{Deep Mars}: {CNN} Classification of {M}ars Imagery for the {PDS} Imaging Atlas}, volume={32}, 
author={Wagstaff, Kiri and Lu, You and Stanboli, Alice and Grimes, Kevin and \textbf{Gowda, Thamme} and Padams, Jordan}, 
url={https://ojs.aaai.org/index.php/AAAI/article/view/11404},
 number={1},
 journal={Proceedings of the AAAI Conference on Artificial Intelligence},
 year={2018}, month={Apr.},
 }

@inproceedings{sabir_deep_2018,
	title = {Deep {Multimodal} {Image}-{Repurposing} {Detection}},
	url = {https://arxiv.org/abs/1808.06686},
	booktitle = {{ACM} {Multimedia} 2018},
	author = {Sabir, E. and AbdAlmageed, W. and Wu, Y. and Natarajan, P.},
	month = oct,
	year = {2018}
}

@article{Wu2018pre,
  title = {Degree correlations amplify the growth of cascades in networks},
  author = {Wu, Xin-Zeng and Fennell, Peter G. and Percus, Allon G. and Lerman, Kristina},
  journal = {Phys. Rev. E},
  volume = {98},
  issue = {2},
  pages = {022321},
  numpages = {8},
  year = {2018},
  month = {Aug},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.98.022321},
  url = {https://link.aps.org/doi/10.1103/PhysRevE.98.022321},
  keywords =     {social-networks},
}

@article{dibaji2018delay,
  title={Delay-aware wide-area control of power systems over sparse communications with analytical guarantees},
  author={Dibaji, Seyed Mehran and Annaswamy, Anuradha and Chakrabortty, Aranya and Hussain, Alefiya},
  year={2018},
  publisher={Institute of Electrical and Electronics Engineers (IEEE)}
}

@inproceedings{DBLP:conf/psb/HornFK0SBALOSJB18,
  author       = {John Darrell Van Horn and
                  Lily Fierro and
                  Jeana Kamdar and
                  Jonathan Gordon and
                  Crystal Stewart and
                  Avnish Bhattrai and
                  Sumiko Abe and
                  Xiaoxiao Lei and
                  Caroline O'Driscoll and
                  Aakanchha Sinha and
                  Priyambada Jain and
                  Gully Burns and
                  Kristina Lerman and
                  Jos{\'{e}} Luis Ambite},
  editor       = {Russ B. Altman and
                  A. Keith Dunker and
                  Lawrence Hunter and
                  Marylyn D. Ritchie and
                  Teri E. Klein},
  title        = {Democratizing data science through data science training},
  booktitle    = {Biocomputing 2018: Proceedings of the Pacific Symposium, The Big Island
                  of Hawaii, Hawaii, USA, January 3-7, 2018},
  pages        = {292--303},
  year         = {2018},
  url          = {http://psb.stanford.edu/psb-online/proceedings/psb18/vanhorn.pdf},
  timestamp    = {Thu, 12 Mar 2020 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/conf/psb/HornFK0SBALOSJB18.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{PhysRevX.8.031016,
  title = {Demonstration of a Scaling Advantage for a Quantum Annealer over Simulated Annealing},
  author = {Albash, Tameem and Lidar, Daniel A.},
  journal = {Phys. Rev. X},
  volume = {8},
  issue = {3},
  pages = {031016},
  numpages = {26},
  year = {2018},
  month = {Jul},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevX.8.031016},
  url = {https://link.aps.org/doi/10.1103/PhysRevX.8.031016}
}

@inproceedings{Sebastian2018DenoisingAndRawWaveform,
 address = {Hyderabad, India},
 author = {Sebastian, Jilt and Kumar, Manoj and Kumar D S., Pavan and Doss, Mathew Magimai and Murthy, Hema and Narayanan, Shrikanth S.},
 bib2html_rescat = {},
 booktitle = {Proceedings of InterSpeech},
 link = {},
 month = {Sep},
 title = {Denoising and Raw-waveform Networks for Weakly-Supervised Gender Identification on Noisy Speech},
 year = {2018}
}

@InProceedings{Guo18a,
	author = 	"Hang Guo and John Heidemann",
	title = 	"Detecting {ICMP} Rate Limiting in the {Internet}",
        booktitle =     "Proceedings of the " # " Passive and Active Measurement Workshop",
        year =          2018,
	sortdate = 		"2018-03-26", 
	project = "ant, retrofuturebridge, lacrend",
	jsubject = "topology_modeling",
        pages =      "to appear",
        month =      mar,
        address =    "Berlin, Germany",
        publisher =  "Springer",
        jlocation =   "johnh: pafile",
	keywords = 	"icmp, rate limiting",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Guo18a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Guo18a.pdf",
	blogurl = "https://ant.isi.edu/blog/?p=1176",
	abstract = "ICMP active probing is the center of many network measurements.  Rate
limiting to ICMP traffic, if undetected, could distort measurements
and create false conclusions.  To settle this concern, we look
systematically for ICMP rate limiting in the Internet.  We 
create \emph{FADER}, a new algorithm that can identify rate limiting from
user-side traces with minimal new measurement traffic.  We validate
the accuracy of FADER with many different network configurations in
testbed experiments and show that it almost always detects rate
limiting.  With this confidence, we apply our algorithm to a random
sample of the whole Internet, showing that \emph{rate limiting exists}
but that \emph{for slow probing rates, rate-limiting is very rare}.
For our random sample of 40,493 /24 blocks (about 2\% of the
responsive space), we confirm 6 blocks (0.02\%!) see rate limiting at
0.39 packets/s per block.  We look at higher rates in public datasets
and suggest that fall-off in responses as rates approach 1 packet/s
per /24 block is consistent with rate limiting.  We also show that
even very slow probing (0.0001 packet/s) can encounter rate limiting
of NACKs that are concentrated at a single router near the prober.",
}

ICMP active probing is the center of many network measurements. Rate limiting to ICMP traffic, if undetected, could distort measurements and create false conclusions. To settle this concern, we look systematically for ICMP rate limiting in the Internet. We create \emphFADER, a new algorithm that can identify rate limiting from user-side traces with minimal new measurement traffic. We validate the accuracy of FADER with many different network configurations in testbed experiments and show that it almost always detects rate limiting. With this confidence, we apply our algorithm to a random sample of the whole Internet, showing that \emphrate limiting exists but that \emphfor slow probing rates, rate-limiting is very rare. For our random sample of 40,493 /24 blocks (about 2% of the responsive space), we confirm 6 blocks (0.02%!) see rate limiting at 0.39 packets/s per block. We look at higher rates in public datasets and suggest that fall-off in responses as rates approach 1 packet/s per /24 block is consistent with rate limiting. We also show that even very slow probing (0.0001 packet/s) can encounter rate limiting of NACKs that are concentrated at a single router near the prober.

@TechReport{Guo18c,
        author =        "Hang Guo and John Heidemann",
        title =         "Detecting IoT Devices in the Internet (Extended)",
	institution = 	"USC/Information Sciences Institute",
        year =          2018,
	sortdate = 		"2018-07-16", 
	project = "ant, lacanic",
	jsubject = "topology_modeling",
        number =     "ISI-TR-726B",
        note =     "(updated March 2017 to 726B)",
        month =      jul,
	jlocation = 	"johnh: pafile",
	keywords = 	"iot, detection, traffic analysis",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Guo18c.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Guo18c.pdf",
	blogurl = "https://ant.isi.edu/blog/?p=1216",
	abstract = "Distributed Denial-of-Service (DDoS) attacks launched from compromised
Internet-of-Things (IoT) devices have shown how vulnerable the
Internet is to large-scale DDoS attacks.  To understand the risks of
these attacks requires learning about these IoT devices:  where are
they? how many are there?  how are they changing?  This paper
describes three new methods to find IoT devices on the Internet:
server IP addresses in traffic, server names in DNS queries, and
manufacturer information in TLS certificates.  Our primary methods (IP
addresses and DNS names) use knowledge of servers run by the
manufacturers of these devices.  We have developed these approaches
with 10 device models from 7 vendors.  Our third method uses TLS
certificates obtained by active scanning.  We have applied our
algorithms to a number of observations.  With our IP-based algorithm,
we report detections from a university campus over 4 months and from
traffic transiting an IXP over 10 days.  We apply our DNS-based
algorithm to traffic from 8 root DNS servers from 2013 to 2018 to
study AS-level IoT deployment.  We find substantial growth (about
$3.5\times$) in AS penetration for 23 types of IoT devices and modest
increase in device type density for ASes detected with these device
types (at most 2 device types in 80\% of these ASes in 2018).  DNS
also shows substantial growth in IoT deployment in residential
households from 2013 to 2017.  Our certificate-based algorithm finds
254k IP cameras and network video recorders from 199 countries around
the world.
",
}

Distributed Denial-of-Service (DDoS) attacks launched from compromised Internet-of-Things (IoT) devices have shown how vulnerable the Internet is to large-scale DDoS attacks. To understand the risks of these attacks requires learning about these IoT devices: where are they? how many are there? how are they changing? This paper describes three new methods to find IoT devices on the Internet: server IP addresses in traffic, server names in DNS queries, and manufacturer information in TLS certificates. Our primary methods (IP addresses and DNS names) use knowledge of servers run by the manufacturers of these devices. We have developed these approaches with 10 device models from 7 vendors. Our third method uses TLS certificates obtained by active scanning. We have applied our algorithms to a number of observations. With our IP-based algorithm, we report detections from a university campus over 4 months and from traffic transiting an IXP over 10 days. We apply our DNS-based algorithm to traffic from 8 root DNS servers from 2013 to 2018 to study AS-level IoT deployment. We find substantial growth (about $3.5×$) in AS penetration for 23 types of IoT devices and modest increase in device type density for ASes detected with these device types (at most 2 device types in 80% of these ASes in 2018). DNS also shows substantial growth in IoT deployment in residential households from 2013 to 2017. Our certificate-based algorithm finds 254k IP cameras and network video recorders from 199 countries around the world.

@inproceedings{garg2018dialogue,
  title={Dialogue Modeling Via Hash Functions},
  author={Garg, Sahil and Cecchi, Guillermo A and Rish, Irina and Gao, Shuyang and Ver Steeg, Greg and Ghazarian, Sarik and Goyal, Palash and Galstyan, Aram},
  booktitle={LaCATODA@ IJCAI},
  year={2018}
}

@INPROCEEDINGS{Kao2018besc,
  author =       {Hsien-Te Kao and Homa Hosseinmardi and Shen Yan and Michelle Hasan and Shrikanth Narayanan and  Kristina Lerman and Emilio Ferrara},
  title =        {Discovering Latent Psychological Structures from Self-report Assessments of Hospital Workers},
  booktitle =    {The 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing},
  year =         {2018},
  keywords =     {behavioral-modeling},
}

@article{DBLP:journals/corr/abs-1806-03342,
  author       = {Palash Goyal and
                  K. S. M. Tozammel Hossain and
                  Ashok Deb and
                  Nazgol Tavabi and
                  Nathan Bartley and
                  Andr{\'{e}}s Abeliuk and
                  Emilio Ferrara and
                  Kristina Lerman},
  title        = {Discovering Signals from Web Sources to Predict Cyber Attacks},
  journal      = {CoRR},
  volume       = {abs/1806.03342},
  year         = {2018},
  url          = {http://arxiv.org/abs/1806.03342},
  eprinttype    = {arXiv},
  eprint       = {1806.03342},
  timestamp    = {Mon, 13 Aug 2018 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1806-03342.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@Article{kocarnik2018,
  author = 	 {Kocarnik JM and Richard M and Graff M and Haessler J and Bien S and Carlson C and Carty CL and Reiner AP and Avery CL and Ballantyne CM and LaCroix AZ and Assimes TL and Barbalic M and Pankratz N and Tang W and Tao R and Chen D and Talavera GA and Daviglus ML and Chirinos-Medina DA and Pereira R and Nishimura K and Bužková P and Best LG and Ambite JL and Cheng I and Crawford DC and Hindorff LA and Fornage M and Heiss G and North KE and Haiman CA and Peters U and Le Marchand L and Kooperberg C.},
  title = 	 {Discovery, fine-mapping, and conditional analyses of genetic variants associated with C-reactive protein in multiethnic populations using the Metabochip in the Population Architecture using Genomics and Epidemiology ({PAGE}) study.},
  journal = 	 {Human Molecular Genetics},
  year = 	 {2018},
  volume = 	 {27},
  number = 	 {16},
  pages = 	 {2940–2953},
  month = 	 {Aug},
  note = 	 {doi:10.1093/hmg/ddy211; PMID: 29878111},
}

@Article{Wei18a,
	author = 	"Lan Wei and John Heidemann",
	title = 	"Does Anycast Hang up on You ({UDP} and {TCP})?",
        journal =       " {IEEE} Transactions on Network and Service Management",
        year =          2018,
	jlocation = 	"johnh: pafile",
	keywords = 	"passive observation, internet census",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Wei18a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Wei18a.pdf",
	xxxblogurl = "https://ant.isi.edu/blog/?p=1007-xxx",
	myorganization =	"USC/Information Sciences Institute",
	sortdate = 	"2018-02-11", 
	Project = "ant, retrofuturebridge, lacrend, researchroot, nipet",
	jsubject = "network_security",
        volume =     "15",
        number =     "2",
        pages =      "707--717",
        month =      feb,
        oldnote =       "Accepted for publication Feb. 2018",
	abstract = "Anycast-based services today are widely used commercially, with
several major providers serving thousands of important
websites. However, to our knowledge, there has been only limited study
of how often anycast fails because routing changes interrupt
connections between users and their current anycast site. While the
commercial success of anycast CDNs means anycast usually works well,
do some users end up shut out of anycast? In this paper we examine
data from more than 9000 geographically distributed vantage points
(VPs) to 11 anycast services to evaluate this question. Our
contribution is the analysis of this data to provide the first
quantification of this problem, and to explore where and why it
occurs. We see that about 1\% of VPs are \emph{anycast unstable},
reaching a different anycast site frequently (sometimes every
query). Flips back and forth between two sites in 10 seconds are
observed in selected experiments for given service and VPs. Moreover,
we show that anycast instability is \emph{persistent} for some VPs---a
few VPs never see a stable connections to certain anycast services
during a week or even longer. The vast majority of VPs only saw
unstable routing towards one or two services instead of instability
with all services, suggesting the cause of the instability lies
somewhere in the path to the anycast sites. We point out that for
highly-unstable VPs, their probability to hit a given site is
constant, suggesting load balancing might be the cause to anycast
routing flipping. Finally, we directly examine TCP flipping and show
that it is much rarer than UDP flipping, but does occur in about
0.15\% (VP, letter) combinations. Moreover, we show concrete cases in
which TCP connection timeout in anycast connection due to per-packet
flipping. Our findings confirm the common wisdom that anycast almost
always works well, but provide evidence that a small number of
locations in the Internet where specific anycast services are never
stable."
,}

Anycast-based services today are widely used commercially, with several major providers serving thousands of important websites. However, to our knowledge, there has been only limited study of how often anycast fails because routing changes interrupt connections between users and their current anycast site. While the commercial success of anycast CDNs means anycast usually works well, do some users end up shut out of anycast? In this paper we examine data from more than 9000 geographically distributed vantage points (VPs) to 11 anycast services to evaluate this question. Our contribution is the analysis of this data to provide the first quantification of this problem, and to explore where and why it occurs. We see that about 1% of VPs are \emphanycast unstable, reaching a different anycast site frequently (sometimes every query). Flips back and forth between two sites in 10 seconds are observed in selected experiments for given service and VPs. Moreover, we show that anycast instability is \emphpersistent for some VPs—a few VPs never see a stable connections to certain anycast services during a week or even longer. The vast majority of VPs only saw unstable routing towards one or two services instead of instability with all services, suggesting the cause of the instability lies somewhere in the path to the anycast sites. We point out that for highly-unstable VPs, their probability to hit a given site is constant, suggesting load balancing might be the cause to anycast routing flipping. Finally, we directly examine TCP flipping and show that it is much rarer than UDP flipping, but does occur in about 0.15% (VP, letter) combinations. Moreover, we show concrete cases in which TCP connection timeout in anycast connection due to per-packet flipping. Our findings confirm the common wisdom that anycast almost always works well, but provide evidence that a small number of locations in the Internet where specific anycast services are never stable.

@inproceedings{DBLP:conf/www/SzekelyK18,
  author    = {Pedro A. Szekely and
               Mayank Kejriwal},
  editor    = {Pierre{-}Antoine Champin and
               Fabien Gandon and
               Mounia Lalmas and
               Panagiotis G. Ipeirotis},
  title     = {Domain-specific Insight Graphs {(DIG)}},
  booktitle = {Companion of the The Web Conference 2018 on The Web Conference 2018,
               {WWW} 2018, Lyon , France, April 23-27, 2018},
  pages     = {433--434},
  publisher = {{ACM}},
  year      = {2018},
  url       = {https://doi.org/10.1145/3184558.3186203},
  doi       = {10.1145/3184558.3186203},
  timestamp = {Thu, 14 Oct 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/www/SzekelyK18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{vossen2018don,
  title={Don’t annotate, but validate: A data-to-text method for capturing event data},
  author={Vossen, Piek and Ilievski, Filip and Postma, Marten and Segers, Roxane},
  booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year={2018}
}

@inproceedings{DBLP:conf/icassp/Billa18,
  author    = {Jayadev Billa},
  title     = {Dropout Approaches for {LSTM} Based Speech Recognition Systems},
  booktitle = {2018 {IEEE} International Conference on Acoustics, Speech and Signal
               Processing, {ICASSP} 2018, Calgary, AB, Canada, April 15-20, 2018},
  pages     = {5879--5883},
  year      = {2018},
  crossref  = {DBLP:conf/icassp/2018},
  url       = {https://doi.org/10.1109/ICASSP.2018.8462544},
  doi       = {10.1109/ICASSP.2018.8462544},
  timestamp = {Tue, 18 Sep 2018 14:34:15 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/icassp/Billa18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{Lim2018DynamicOff-resonanceCorrectionfor,
 author = {Lim, Yongwan and Lingala, Sajan Goud and Narayanan, Shrikanth S and Nayak, Krishna S},
 doi = {10.1002/mrm.27373},
 journal = {Magnetic Resonance in Medicine},
 link = {http://sail.usc.edu/publications/files/lim2018dynamic.pdf},
 month = {May},
 pages = {1-13},
 title = {Dynamic Off-resonance Correction for Spiral Real-Time MRI of Speech},
 year = {2018}
}

@InProceedings{zhang-EtAl:2018:N18-5,
  author    = {Zhang, Boliang  and  Lin, Ying  and  Pan, Xiaoman  and  Lu, Di  and  May, Jonathan  and  Knight, Kevin  and  Ji, Heng},
  title     = {ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {41--45},
  abstract  = {We demonstrate ELISA-EDL, a state-of-the-art re-trainable system to extract entity mentions from low-resource languages, link them to external English knowledge bases, and visualize locations related to disaster topics on a world heatmap. We make all of our data sets, resources and system training and testing APIs publicly available for research purpose.},
  url       = {http://www.aclweb.org/anthology/N18-5009}
}

We demonstrate ELISA-EDL, a state-of-the-art re-trainable system to extract entity mentions from low-resource languages, link them to external English knowledge bases, and visualize locations related to disaster topics on a world heatmap. We make all of our data sets, resources and system training and testing APIs publicly available for research purpose.

@inproceedings{mcmahon-etal-incf18,
  author = {Agnes McMahon and Daniel Garijo and Ryan Espiritu and Faisal Rashid and MiHyun Jang and Tejal Patted and Varun Ratnakar and Yolanda Gil and Paul Thompson and Neda Jahanshad},
  title = {ENIGMA-ODS: A Platform for Global Neuroscience Collaborations in the ENIGMA Consortium},
  booktitle = {INCF Neuroinformatics Congress},
  address = {Montreal, Canada},
  year = {2018}
}

@inproceedings{czajkowski_ermrest_2018,
	address = {Bozen-Bolzano Italy},
	title = {{ERMrest}: a web service for collaborative data management},
	isbn = {978-1-4503-6505-5},
	shorttitle = {{ERMrest}},
	doi = {10.1145/3221269.3222333},
	abstract = {The foundation of data oriented scientific collaboration is the abil- ity for participants to find, access and reuse data created during the course of an investigation, what has been referred to as the FAIR principles. In this paper, we describe ERMrest, a collaborative data management service that promotes data oriented collabora- tion by enabling FAIR data management throughout the data life cycle. ERMrest is a RESTful web service that promotes discovery and reuse by organizing diverse data assets into a dynamic entity relationship model. We present details on the design and implemen- tation of ERMrest, data on its performance and its use by a range of collaborations to accelerate and enhance their scientific output.},
	language = {en},
	urldate = {2022-01-29},
	booktitle = {Proceedings of the 30th {International} {Conference} on {Scientific} and {Statistical} {Database} {Management}},
	publisher = {ACM},
	author = {Czajkowski, Karl and Kesselman, Carl and Schuler, Robert E. and Tangmunarunkit, Hongsuda},
	month = jul,
	year = {2018},
	pages = {1--12},
}

The foundation of data oriented scientific collaboration is the abil- ity for participants to find, access and reuse data created during the course of an investigation, what has been referred to as the FAIR principles. In this paper, we describe ERMrest, a collaborative data management service that promotes data oriented collabora- tion by enabling FAIR data management throughout the data life cycle. ERMrest is a RESTful web service that promotes discovery and reuse by organizing diverse data assets into a dynamic entity relationship model. We present details on the design and implemen- tation of ERMrest, data on its performance and its use by a range of collaborations to accelerate and enhance their scientific output.

@INPROCEEDINGS{tmpca,
  author={Su, Yuanhang and Huang, Yuzhong and Kuo, C.-C. Jay},
  booktitle={2018 24th International Conference on Pattern Recognition (ICPR)}, 
  title={Efficient Text Classification Using Tree-structured Multi-linear Principal Component Analysis}, 
  year={2018},
  volume={},
  number={},
  pages={585-590},
  doi={10.1109/ICPR.2018.8545832}
}

@incollection{goyal2018embedding,
  title={Embedding networks with edge attributes},
  author={Goyal, Palash and Hosseinmardi, Homa and Ferrara, Emilio and Galstyan, Aram},
  booktitle={Proceedings of the 29th on Hypertext and Social Media},
  pages={38--42},
  year={2018}
}

@InProceedings{	  do-sc-18,
  Title		= {Enabling Data Analytics Workflows using Node-Local
		  Storage},
  Author	= {Do, Tu Mai Anh and Jiang, Ming and Gallagher, Brian and
		  Chu, Albert and Harrison, Cyrus and Vahi, Karan and
		  Deelman, Ewa},
  BookTitle	= {The International Conference for High Performance
		  Computing, Networking, Storage, and Analysis (SC18),
		  Poster},
  Year		= {2018},
  Note		= {Funding Acknowledgments: LLNL-ABS-755833
		  DE-AC52-07NA27344, NSF 1741040}
}

@Article{MONSON2018148,
  author   = {Joshua S. Monson and Brad L. Hutchings},
  title    = {Enhancing debug observability for HLS-based FPGA circuits through source-to-source compilation},
  journal  = {Journal of Parallel and Distributed Computing},
  year     = {2018},
  volume   = {117},
  pages    = {148 - 160},
  issn     = {0743-7315},
  doi      = {https://doi.org/10.1016/j.jpdc.2018.02.012},
  keywords = {FPGA, Debug, High level synthesis, Observability, Instrumentation, Productivity},
  url      = {http://www.sciencedirect.com/science/article/pii/S0743731518300893},
}

@InProceedings{Imana18a,
        author =        "Basileal Imana and Aleksandra Korolova and John Heidemann",
        title =         "Enumerating Privacy Leaks in {DNS} Data
                  Collected Above the Recursive",
        booktitle =     "Proceedings of the " # " {ISOC} {NDSS} Workshop on {DNS} Privacy ",
        year =          2018,
	myorganization =	"USC/Information Sciences Institute",
	sortdate = 	"2018-02-18", 
	project = "ant, earr, retrofuturebridge, lacrend, researchroot, nipet",
        month =      feb,
        address =    "San Diego, California, USA",
        publisher =  "The Internet Society",
	jlocation = 	"johnh: pafile",
	keywords = 	"dns, privacy, data leak, PII",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Imana18a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Imana18a.pdf",
	talkurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Imana18a_talk.pdf",
	dataseturl =	"https://ant.isi.edu/datasets/readmes/DITL_B_Root-20170411.README.txt",
	blogurl = "https://ant.isi.edu/blog/?p=1158",
	icon = "Imana18a_icon.png",
	abstract = "As with any information system consisting of data derived from
people’s actions, DNS data is vulnerable to privacy risks. In DNS,
users make queries through recursive resolvers to authoritative
servers. Data collected below (or in) the recursive resolver directly
exposes users, so most prior DNS data sharing focuses on queries above
the recursive resolver. Data collected above a recursive resolver has
largely been seen as posing a minimal privacy risk since recursive
resolvers typically aggregate traffic for many users, thereby hiding
their identity and mixing their traffic. Although this assumption is
widely made, to our knowledge it has not been verified. In this paper
we re-examine this assumption for DNS traffic above the recursive
resolver. First, we show that two kinds of information appear in query
names above the recursive resolver: IP addresses and sensitive domain
names, such as those pertaining to health, politics, or personal or
lifestyle information. Second, we examine how often these classes of
potentially sensitive names appear in Root DNS traffic, using 48 hours
of B-Root data from April 2017.",
}

As with any information system consisting of data derived from people’s actions, DNS data is vulnerable to privacy risks. In DNS, users make queries through recursive resolvers to authoritative servers. Data collected below (or in) the recursive resolver directly exposes users, so most prior DNS data sharing focuses on queries above the recursive resolver. Data collected above a recursive resolver has largely been seen as posing a minimal privacy risk since recursive resolvers typically aggregate traffic for many users, thereby hiding their identity and mixing their traffic. Although this assumption is widely made, to our knowledge it has not been verified. In this paper we re-examine this assumption for DNS traffic above the recursive resolver. First, we show that two kinds of information appear in query names above the recursive resolver: IP addresses and sensitive domain names, such as those pertaining to health, politics, or personal or lifestyle information. Second, we examine how often these classes of potentially sensitive names appear in Root DNS traffic, using 48 hours of B-Root data from April 2017.

@inproceedings{tregubuov:10.1007/978-3-319-62217-0_39,
	author={Tregubov, Alexey and Lane, Jo Ann and Boehm, Barry},
	editor={Madni, Azad M. and Boehm, Barry and Ghanem, Roger G. and Erwin, Daniel and Wheaton, Marilee J.},
	title={Evaluation of Cross-Project Multitasking in Software Projects},
	booktitle={Disciplinary Convergence in Systems Engineering Research},
	year={2018},
	publisher={Springer International Publishing},
	address={Cham},
	pages={559--572},
	isbn={978-3-319-62217-0}
}

@Misc{		  suraj-hpc-asia18-event-triggers,
  Author	= {Pandey, Suraj and Vahi, Karan and Ferreira da Silva,
		  Rafael and Deelman, Ewa and Jian, Ming and Harrison, Cyrus
		  and Chu, Al and Casanova, Henri},
  Title		= {Event-Based Triggering and Management of Scientific
		  Workflow Ensembles},
  Year		= {2018},
  URL		= {http://sighpc.ipsj.or.jp/HPCAsia2018/poster/post102s2-file1.pdf},
  Note		= {Poster presented at the HPC Asia 2018: Tokyo, Japan}
}

@inproceedings{lin_exploiting_2018,
	address = {Seattle, WA},
	title = {Exploiting spatiotemporal patterns for accurate air quality forecasting using deep learning},
	booktitle = {Proceedings of the 26th {ACM} {SIGSPATIAL} {International} {Conference} on {Advances} in {Geographic} {Information} {Systems}},
	author = {Lin, Yijun and Mago, Nikhit and Gao, Yu and Li, Yaguang and Chiang, Yao-Yi and Shahabi, Cyrus and Ambite, Jose-Luis},
	year = {2018},
}

@inproceedings{uhl2018,
	author = {Johannes Uhl and Stefan Leyk and  Yao-Yi Chiang and Weiwei Duan and Craig A. Knoblock},
	title = {Exploring the Potential of Deep Learning for Settlement Symbol Extraction from Historical Map Documents},
	editor = {Freundschuh S.M. and Sinton D.},
	booktitle = {The 22nd International Research Symposium on Computer-based Cartography and GIScience},
	address = {Madison, WI},
	year = 2018,
	pages = {22--24},
	url = {https://www.ucgis.org/assets/docs/AutoCarto-UCGIS%202018%20Proceedings.pdf}
}

@inproceedings{Papadopoulos2018ExploringRelationship,
 address = {Hyderabad, India},
 author = {Papadopoulos, Pavlos and Vaz, Colin and Narayanan, Shrikanth S.},
 bib2html_rescat = {},
 booktitle = {Proceedings of InterSpeech},
 link = {},
 month = {Sep},
 title = {Exploring the Relationship between Conic Affinity of NMF Dictionaries and Speech Enhancement Metrics},
 year = {2018}
}

@conference {Monson2018,
    title = {Extending the USRP/RFNOC Framework for Implementing Latency-Sensitive Radio Systems},
    booktitle ={ Wireless Innovation Forum, WinnComm },
    year = {2018},
    author = {M. Monson, Z. Cao, P. Liu, M. French, and T. Haroldsen}
} 
   
   
    

@inproceedings{blythe-tregubov18, 
	title={FARM: Architecture for Distributed Agent-based Social Simulations}, 
	author={Blythe, Jim and Tregubov, Alexey}, 
	booktitle={IJCAI/AAMAS Workshop on Massively Multi-Agent Systems}, 
	year={2018},
	ISIArea = {CSEC, ML}
}

@inproceedings{gupta2018feature,
  title={Feature Selection Methods For Understanding Business Competitor Relationships},
  author={Gupta, Rahul and Pujara, Jay and Knoblock, Craig A and Sharanappa, Shushyam M and Pulavarti, Bharat and Hoberg, Gerard and Phillips, Gordon},
  booktitle={Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets},
  year={2018},
  organization={ACM},
  Urlpaper = {http://usc-isi-i2.github.io/papers/rahul18-dsmm.pdf},
  urlSlides = "http://usc-isi-i2.github.io/slides/pujara-sigmod_dsmm18-slides.pptx"
}

@inproceedings{hussein_fingerprint_2018,
	title = {Fingerprint {Presentation} {Attack} {Detection} {Using} {A} {Novel} {Multi}-{Spectral} {Capture} {Device} and {Patch}-{Based} {Convolutional} {Neural} {Networks}},
	booktitle = {{IEEE} {Workshop} on {Information} {Forensics} and {Security}},
	author = {Hussein, M. and Spinoulas, L. and Xiong, F. and AbdAlmageed, W.},
	month = dec,
	year = {2018}
}

@article{Mishra2018,
	Abstract = {Closed-system quantum annealing is expected to sometimes fail spectacularly in solving simple problems for which the gap becomes exponentially small in the problem size. Much less is known about whether this gap scaling also impedes open-system quantum annealing. Here, we study the performance of a quantum annealing processor in solving such a problem: a ferromagnetic chain with sectors of alternating coupling strength that is classically trivial but exhibits an exponentially decreasing gap in the sector size. The gap is several orders of magnitude smaller than the device temperature. Contrary to the closed-system expectation, the success probability rises for sufficiently large sector sizes. The success probability is strongly correlated with the number of thermally accessible excited states at the critical point. We demonstrate that this behavior is consistent with a quantum open-system description that is unrelated to thermal relaxation, and is instead dominated by the system's properties at the critical point.},
	Author = {Mishra, Anurag and Albash, Tameem and Lidar, Daniel A.},
	Da = {2018/07/25},
	Date-Added = {2019-04-29 17:39:08 -0700},
	Date-Modified = {2019-04-29 17:39:08 -0700},
	Doi = {10.1038/s41467-018-05239-9},
	Id = {Mishra2018},
	Isbn = {2041-1723},
	Journal = {Nature Communications},
	Number = {1},
	Pages = {2917},
	Title = {Finite temperature quantum annealing solving exponentially small gap problem with non-monotonic success probability},
	Ty = {JOUR},
	Url = {https://doi.org/10.1038/s41467-018-05239-9},
	Volume = {9},
	Year = {2018}
	}

Closed-system quantum annealing is expected to sometimes fail spectacularly in solving simple problems for which the gap becomes exponentially small in the problem size. Much less is known about whether this gap scaling also impedes open-system quantum annealing. Here, we study the performance of a quantum annealing processor in solving such a problem: a ferromagnetic chain with sectors of alternating coupling strength that is classically trivial but exhibits an exponentially decreasing gap in the sector size. The gap is several orders of magnitude smaller than the device temperature. Contrary to the closed-system expectation, the success probability rises for sufficiently large sector sizes. The success probability is strongly correlated with the number of thermally accessible excited states at the critical point. We demonstrate that this behavior is consistent with a quantum open-system description that is unrelated to thermal relaxation, and is instead dominated by the system's properties at the critical point.

@inproceedings{morstatter2018alt,
  title={From alt-right to alt-rechts: Twitter analysis of the 2017 german federal election},
  author={Morstatter, Fred and Shao, Yunqiu and Galstyan, Aram and Karunasekera, Shanika},
  booktitle={Companion Proceedings of the The Web Conference 2018},
  pages={621--628},
  year={2018}
}

@inproceedings{Booth2018FusingAnnotationswithMajority,
 author = {Booth, Brandon M and Mundnich, Karel and Narayanan, Shrikanth},
 booktitle = {In proceedings of Audio/Visual Emotion Challenge and Workshop A},
 doi = {10.1145/3266302.3266312},
 link = {http://sail.usc.edu/publications/files/p83-booth.pdf},
 location = {Seoul, South Korea},
 month = {Oct},
 title = {Fusing Annotations with Majority Vote Triplet Embeddings},
 year = {2018}
}

@inproceedings{DBLP:conf/tac/ZhangSSHLPLZWWJ18,
  author    = {Tongtao Zhang and
               Ananya Subburathinam and
               Ge Shi and
               Lifu Huang and
               Di Lu and
               Xiaoman Pan and
               Manling Li and
               Boliang Zhang and
               Qingyun Wang and
               Spencer Whitehead and
               Heng Ji and
               Alireza Zareian and
               Hassan Akbari and
               Brian Chen and
               Ruiqi Zhong and
               Steven Shao and
               Emily Allaway and
               Shih{-}Fu Chang and
               Kathleen R. McKeown and
               Dongyu Li and
               Xin Huang and
               Kexuan Sun and
               Xujun Peng and
               Ryan Gabbard and
               Marjorie Freedman and
               Mayank Kejriwal and
               Ram Nevatia and
               Pedro A. Szekely and
               T. K. Satish Kumar and
               Ali Sadeghian and
               Giacomo Bergami and
               Sourav Dutta and
               Miguel E. Rodr{\'{\i}}guez and
               Daisy Zhe Wang},
  title     = {{GAIA} - {A} Multi-media Multi-lingual Knowledge Extraction and Hypothesis
               Generation System},
  booktitle = {Proceedings of the 2018 Text Analysis Conference, {TAC} 2018, Gaithersburg,
               Maryland, USA, November 13-14, 2018},
  publisher = {{NIST}},
  year      = {2018},
  url       = {https://tac.nist.gov/publications/2018/participant.papers/TAC2018.GAIA.proceedings.pdf},
  timestamp = {Thu, 21 Jan 2021 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/tac/ZhangSSHLPLZWWJ18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{mdpi18,
	author    = {Miguel Pires and
	Srivatsan Ravi and
	Rodrigo Rodrigues},
	title     = {Generalized Paxos Made Byzantine (and Less Complex)},
	journal   = {Algorithms},
	volume    = {11},
	number    = {9},
	pages     = {141},
	year      = {2018}
	}

@InProceedings{	  estrada-graphic-2018,
  Author	= {Estrada, Trilce and Benson, Jeremy and Carrillo-Cabada,
		  Hector and Razavi, Asghar and Cuendet, Michel and
		  Weinstein, Harel and Deelman, Ewa and Taufer, Michela},
  Year		= {2018},
  Month		= {08},
  Pages		= {315-324},
  Title		= {Graphic Encoding of Macromolecules for Efficient
		  High-Throughput Analysis},
  BookTitle	= {2018 ACM International Conference on Bioinformatics,
		  Computational Biology, and Health Informatics},
  DOI		= {10.1145/3233547.3233607},
  Note		= {Funding Acknowledgments: NSF 1741040}
}

@inproceedings{woo2018guidedpass,
  title={GuidedPass: Helping Users to Create Strong and Memorable Passwords},
  author={Woo, Simon S and Mirkovic, Jelena},
  booktitle={International Symposium on Research in Attacks, Intrusions, and Defenses (RAID)},
  pages={250--270},
  year={2018},
  organization={Springer}
}

@article{shi2018handling,
  title={Handling Anti-Virtual Machine Techniques in Malicious Software},
  author={Shi, Hao and Mirkovic, Jelena and Alwabel, Abdulla},
  journal={ACM Transactions on Privacy and Security (TOPS)},
  volume={21},
  number={1},
  pages={2},
  year={2018},
  publisher={ACM}
}

@inproceedings{weisbach2018hardware,
  title={Hardware performance variation: a comparative study using lightweight kernels},
  author={Weisbach, Hannes and Gerofi, Balazs and Kocoloski, Brian and H{\"a}rtig, Hermann and Ishikawa, Yutaka},
  booktitle={International Conference on High Performance Computing},
  pages={246--265},
  year={2018},
  organization={Springer, Cham}
}

@InProceedings{	  thapa-pearc18-2018,
  Author	= {Thapa, Suchandra and Gardner, Robert W. and Herner,
		  Kenneth and Hufnagel, Dirk and Lesny, David and Rynge,
		  Mats},
  Title		= {Homogenizing OSG and XSEDE: Providing Access to XSEDE
		  Allocations Through OSG Infrastructure},
  BookTitle	= {Proceedings of the Practice and Experience on Advanced
		  Research Computing},
  Series	= {PEARC '18},
  Year		= {2018},
  ISBN		= {978-1-4503-6446-1},
  Location	= {Pittsburgh, PA, USA},
  Pages		= {15:1--15:7},
  articleno	= {15},
  numpages	= {7},
  URL		= {http://doi.acm.org/10.1145/3219104.3219157},
  DOI		= {10.1145/3219104.3219157},
  acmid		= {3219157},
  Publisher	= {ACM},
  Keywords	= {ATLAS, Bridges, CMS, CVMFS, OSG, PSC, Stampede2, TACC,
		  XSEDE, distributed data access}
}

@INPROCEEDINGS{8457637, 
author={Skalicky,Sam and Monson, Joshua and Schmidt, Andrew and M. French, Matthew}, 
booktitle={2018 IEEE 26th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)}, 
title={Hot \& Spicy: Improving Productivity with Python and HLS for FPGAs}, 
year={2018}, 
volume={}, 
number={}, 
pages={85-92}, 
keywords={field programmable gate arrays;image processing;Python;open-source infrastructure;tool suite;Python functions;HLS-suitable C functions;FPGA EDA tool flow;FPGA experts;high-level Python abstractions;FPGA development flow;Python image processing application;productivity;FPGA accelerators;Python source code;Hot Spicy tools;Python implementation;C++ based drivers;Python C wrapper bindings;compilers;optimizations;hand-optimized OpenCV library implementation;Tools;Python;Field programmable gate arrays;Acceleration;Hardware design languages;Syntactics;Hardware;Python;HLS;SDSoC;Pynq}, 
doi={10.1109/FCCM.2018.00022}, 
ISSN={2576-2621}, 
month={April},}

@inproceedings{skalicky2018a,
	author = {Sam Skalicky and Monson, Joshua and Andrew G. Schmidt and French, M.},
	booktitle = {IEEE International Symposium on Field Customizeable Computing Machines},
	title = {Hot \& Spicy: Improving Productivity with Python and HLS for FPGAs},
	year = {2018}}

@INPROCEEDINGS{8457637,
author={S. {Skalicky} and J. {Monson} and A. {Schmidt} and M. {French}},
booktitle={2018 IEEE 26th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)},
title={Hot and Spicy: Improving Productivity with Python and HLS for FPGAs},
year={2018},
volume={},
number={},
pages={85-92},
keywords={field programmable gate arrays;image processing;Python;open-source infrastructure;tool suite;Python functions;HLS-suitable C functions;FPGA EDA tool flow;FPGA experts;high-level Python abstractions;FPGA development flow;Python image processing application;productivity;FPGA accelerators;Python source code;Hot Spicy tools;Python implementation;C++ based drivers;Python C wrapper bindings;compilers;optimizations;hand-optimized OpenCV library implementation;Tools;Python;Field programmable gate arrays;Acceleration;Hardware design languages;Syntactics;Hardware;Python;HLS;SDSoC;Pynq},
doi={10.1109/FCCM.2018.00022},
ISSN={2576-2621},
month={April}
}
   
   
   

@article{DBLP:journals/computer/NamK18,
  author    = {Daye Nam and
               Mayank Kejriwal},
  title     = {How Do Organizations Publish Semantic Markup? Three Case Studies Using
               Public Schema.org Crawls},
  journal   = {Computer},
  volume    = {51},
  number    = {6},
  pages     = {42--51},
  year      = {2018},
  url       = {https://doi.org/10.1109/MC.2018.2701635},
  doi       = {10.1109/MC.2018.2701635},
  timestamp = {Wed, 12 Aug 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/computer/NamK18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{rawls_how_2018,
	title = {How {To} {Efficiently} {Increase} {Resolution} in {Neural} {OCR} {Models}},
	journal = {2018 2nd International Workshop on Arabic Script Analysis and Recognition (ASAR)},
	author = {Rawls, Stephen and Cao, Huaigu and Mathai, Joe and Natarajan, Prem},
	year = {2018},
}

@article{el-ghaish_human_2018,
	title = {Human Action Recognition Based on Integrating Body Pose, Part Shape, and Motion},
	volume = {6},
	rights = {All rights reserved},
	pages = {49040--49055},
	journaltitle = {{IEEE} Access},
	author = {El-Ghaish, Hany and Hussein, Mohamed E. and Shoukry, Amin and Onai, Rikio},
	year = {2018},
	file = {Snapshot:C\:\\Users\\Mohamed Hussein\\Zotero\\storage\\PUGJ699M\\8453782.html:text/html}
}

@inproceedings{pujara:dsmm18,
    author = "Pujara, Jay",
    bib_url = "/pubs/bib/pujara-dsmm18.bib",
    booktitle = "Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets",
    doi_url = "https://dl.acm.org/doi/10.1145/3220547.3220559",
    organization = "ACM SIGMOD",
    pdf_url = "/pubs/2018/pujara-sigmod18ws/pujara-dsmm18.pdf",
    sec = "ws",
    title = "Hybrid Link Prediction for Competitor Relationships",
    year = "2018"
}

@inproceedings{Kumar2018IMPROVINGSEMI-SUPERVISEDCLASSIFICATIONFOR,
 author = {Kumar, Manoj and Pavlos Papadopoulos, Pavlos and Travadi, Ruchir  and Bone, Daniel and Narayanan, Shrikanth},
 booktitle = {Proceedings of ICASSP},
 location = {Calgary, Canada},
 month = {Apr},
 title = {IMPROVING SEMI-SUPERVISED CLASSIFICATION FOR LOW-RESOURCE SPEECH INTERACTION APPLICATIONS},
 year = {2018}
}

@InProceedings{Guo18b,
        author =        "Hang Guo and John Heidemann",
        title =         "IP-Based {IoT} Device Detection",
        booktitle =     "Proceedings of the " # " {ACM} SIGCOMM Workshop on {IoT} Security and Privacy ",
        year =          2018,
	sortdate = 		"2018-08-20", 
	project = "ant, retrofuturebridge, lacanic",
	jsubject = "topology_modeling",
        month =      aug,
	pages = "36--42",
        address =    "Budapest, Hungary",
        publisher =  "ACM",
        jlocation =   "johnh: pafile",
        keywords =   "iot, detection, scanning",
        doi =        "https://doi.org/10.1145/3229565.3229572",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Guo18b.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Guo18b.pdf",
	blogurl = "https://ant.isi.edu/blog/?p=1197",
	dataseturl = "https://ant.isi.edu/datasets/iot/",
	abstract =
"Recent IoT-based DDoS attacks have exposed how vulnerable the
Internet can be to millions of insufficiently
secured IoT devices. To understand the risks of
these attacks requires learning about these IoT
devices---where are they, how many are there, how
are they changing? In this paper, we propose a new
method to find IoT devices in Internet to begin to
assess this threat. Our approach requires
observations of flow-level network traffic and
knowledge of servers run by the manufacturers of the
IoT devices. We have developed our approach with 10
device models by 7 vendors and controlled
experiments. We apply our algorithm to observations
from 6 days of Internet traffic at a college campus
and partial traffic from an IXP to detect IoT
devices.",
}

Recent IoT-based DDoS attacks have exposed how vulnerable the Internet can be to millions of insufficiently secured IoT devices. To understand the risks of these attacks requires learning about these IoT devices—where are they, how many are there, how are they changing? In this paper, we propose a new method to find IoT devices in Internet to begin to assess this threat. Our approach requires observations of flow-level network traffic and knowledge of servers run by the manufacturers of the IoT devices. We have developed our approach with 10 device models by 7 vendors and controlled experiments. We apply our algorithm to observations from 6 days of Internet traffic at a college campus and partial traffic from an IXP to detect IoT devices.

@inproceedings{DBLP:conf/interspeech/Billa18,
  author    = {Jayadev Billa},
  title     = {{ISI} {ASR} System for the Low Resource Speech Recognition Challenge
               for Indian Languages},
  booktitle = {Interspeech 2018, 19th Annual Conference of the International Speech
               Communication Association, Hyderabad, India, 2-6 September 2018.},
  pages     = {3207--3211},
  year      = {2018},
  crossref  = {DBLP:conf/interspeech/2018},
  url       = {https://doi.org/10.21437/Interspeech.2018-2473},
  doi       = {10.21437/Interspeech.2018-2473},
  timestamp = {Tue, 11 Sep 2018 10:03:33 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/interspeech/Billa18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{hassanein_identifying_2018,
	title = {Identifying motion pathways in highly crowded scenes: A non-parametric tracklet clustering approach},
	rights = {All rights reserved},
	shorttitle = {Identifying motion pathways in highly crowded scenes},
	journaltitle = {Computer Vision and Image Understanding},
	author = {Hassanein, Allam S. and Hussein, Mohamed E. and Gomaa, Walid and Makihara, Yasushi and Yagi, Yasushi},
	year = {2018},
	file = {Snapshot:C\:\\Users\\Mohamed Hussein\\Zotero\\storage\\8AAURRR9\\S1077314218301887.html:text/html}
}

@inproceedings{wu_image_2018,
	title = {Image {Copy}-{Move} {Forgery} {Detection} via an {End}-to-{End} {Deep} {Neural} {Network}},
	isbn = {978-1-5386-4886-5},
	url = {doi.ieeecomputersociety.org/10.1109/WACV.2018.00211},
	doi = {10.1109/WACV.2018.00211},
	booktitle = {2018 {IEEE} {Winter} {Conference} on {Applications} of {Computer} {Vision} ({WACV})},
	author = {Wu, Y. and AbdAlmageed, W. and Natarajan, P.},
	month = mar,
	year = {2018},
	keywords = {affine transforms, convolution, Feature extraction, feedforward neural nets, image forensics},
	pages = {1907--1915}
}

@inproceedings{cheng_image--gps_2018,
	title = {Image-to-{GPS} {Verification} {Through} {A} {Bottom}-{Up} {Pattern} {Matching} {Network}},
	booktitle = {Asian {Conference} on {Computer} {Vision}},
	author = {Cheng, J. and Wu, Y. and AbdAlmageed, W. and Natarajan, P.},
	month = dec,
	year = {2018}
}

@conference {Zhu2018,
    title = {Improved Metrics for Obfuscated ICs,” Government Microcircuit Applications and Critical Technology Conference (GOMACTech)},
    booktitle = {Government Microcircuit Applications and Critical Technology Conference (GOMACTech)},
    year = {2018},
    month = {April},
    address = {Miami, FL},
    author = {M. Zhu, M. French, and P. Beerel}
} 
   
   

@inproceedings{Hebbar2018ImprovingGenderIdentification,
 address = {Hyderabad, India},
 author = {Hebbar, Rajat and Somandepalli, Krishna and Narayanan, Shrikanth S.},
 bib2html_rescat = {mica},
 booktitle = {Proceedings of InterSpeech},
 link = {},
 month = {Sep},
 title = {Improving Gender Identification in Movie Audio using Cross-Domain Data},
 year = {2018}
}

@Article{Hermjakob2018,
author="Hermjakob, Ulf
and Li, Qiang
and Marcu, Daniel
and May, Jonathan
and Mielke, Sebastian J.
and Pourdamghani, Nima
and Pust, Michael
and Shi, Xing
and Knight, Kevin
and Levinboim, Tomer
and Murray, Kenton
and Chiang, David
and Zhang, Boliang
and Pan, Xiaoman
and Lu, Di
and Lin, Ying
and Ji, Heng",
title="Incident-Driven Machine Translation and Name Tagging for Low-resource Languages",
journal="Machine Translation",
year="2018",
month="Jun",
day="01",
volume="32",
number="1",
pages="59--89",
abstract="We describe novel approaches to tackling the problem of natural language processing for low-resource languages. The approaches are embodied in systems for name tagging and machine translation (MT) that we constructed to participate in the NIST LoReHLT evaluation in 2016. Our methods include universal tools, rapid resource and knowledge acquisition, rapid language projection, and joint methods for MT and name tagging.",
issn="1573-0573",
doi="10.1007/s10590-017-9207-1",
url="https://doi.org/10.1007/s10590-017-9207-1"
}

We describe novel approaches to tackling the problem of natural language processing for low-resource languages. The approaches are embodied in systems for name tagging and machine translation (MT) that we constructed to participate in the NIST LoReHLT evaluation in 2016. Our methods include universal tools, rapid resource and knowledge acquisition, rapid language projection, and joint methods for MT and name tagging.