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Differentially Private Ensemble Classifiers for Data Streams [article]

Lovedeep Gondara, Ke Wang, Ricardo Silva Carvalho
2021 arXiv   pre-print
Our method outperforms competitors on real-world and simulated datasets for varying settings of privacy, concept drift, and data distribution.  ...  Adapting to evolving data characteristics (concept drift) while protecting data owners' private information is an open challenge.  ...  International Conference on Web Search and Data Mining (WSDM ’22), February 21–25, 2022, Tempe, AZ, USA.  ... 
arXiv:2112.04640v1 fatcat:3gffqrkxffcfznisfbceeczj6y

Wikipedia Research and Tools: Review and Comments

Finn Årup Nielsen
2012 Social Science Research Network  
I here give an overview of Wikipedia and wiki research and tools. Well over 1,000 reports have been published in the field and there exist dedicated scientific meetings for Wikipedia research.  ...  Acknowledgment Thanks to Daniel Kinzler, Torsten Zesch, Felipe Ortega, Piotr Konieczny, Claudia Koltzenburg and James Heilman for pointing to references and tools Thanks also to Chitu Okoli, Mohamad Mehdi  ...  , Mostafa Mesgari and Arto Lanamäki with whom I am writing systematic reviews about Wikipedia research. 14  ... 
doi:10.2139/ssrn.2129874 fatcat:h4znerp2efhn5j5bdi5rl7addi

Algorithmic Fairness Datasets: the Story so Far [article]

Alessandro Fabris, Stefano Messina, Gianmaria Silvello, Gian Antonio Susto
2022 arXiv   pre-print
Progress in fair Machine Learning hinges on data, which can be appropriately used only if adequately documented.  ...  Unfortunately, the algorithmic fairness community suffers from a collective data documentation debt caused by a lack of information on specific resources (opacity) and scatteredness of available information  ...  Acknowledgements The authors would like to thank the following researchers and dataset creators for the useful feedback on the data briefs: Alain Barrat, Luc Behaghel, Asia Biega, Marko Bohanec, Chris  ... 
arXiv:2202.01711v3 fatcat:kd546yklwjhvtkrbhtzgbzb2xm

HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning

Mingyu Derek Ma, Muhao Chen, Te-Lin Wu, Nanyun Peng
2021 Findings of the Association for Computational Linguistics: EMNLP 2021   unpublished
Mining, WSDM 2014, New York, NY, USA, February Xing Wang, Zhaopeng Tu, Longyue Wang, and Shum- 24-28, 2014, pages 243–252. ing Shi. 2019.  ...  In Seventh ACM In Proceedings of the Web Conference 2021, pages International Conference on Web Search and Data 3291–3304.  ... 
doi:10.18653/v1/2021.findings-emnlp.353 fatcat:77n6w46xgbflfmsgclc7w5se3a

Algorithmic Fairness Datasets: the Story so Far [article]

Alessandro Fabris, Stefano Messina, Gianmaria Silvello, Gian Antonio Susto
2022
Data-driven algorithms are being studied and deployed in diverse domains to support critical decisions, directly impacting on people's well-being.  ...  Algorithmic fairness progress hinges on data, which can be used appropriately only if adequately documented.  ...  Acknowledgements The authors would like to thank the following researchers and dataset creators for the useful feedback on the data briefs: Alain Barrat, Luc  ... 
doi:10.48550/arxiv.2202.01711 fatcat:mav36x3w5namjhurzpevtsmsju

Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings [article]

Kiran Ramnath, Mark Hasegawa-Johnson
2020
We also employ a new image representation technique we call 'Image-as-Knowledge' to enable this capability, alongside a simple one-step CoAttention mechanism to attend to text and image during QA.  ...  We develop a novel QA architecture that allows us to reason over incomplete KGs, something current FVQA state-of-the-art (SOTA) approaches lack due to their critical reliance on fact retrieval.  ...  In Seventh ACM International Conference on Web Search and Data Mining, WSDM 2014, New York, NY, USA, February 24-28, 2014, pages 523- 532.  ... 
doi:10.48550/arxiv.2012.15484 fatcat:lpsipbjzvndf3kg6w2ag3dkasu

Mapping (Dis-)Information Flow about the MH17 Plane Crash

Mareike Hartmann, Yevgeniy Golovchenko, Isabelle Augenstein
2019 Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda   unpublished
We accepted a total of 24 papers: 10 for the regular track and 14 for the shared task.  ...  We further featured a shared task on the identification of propaganda in news articles. The task included two subtasks with different levels of complexity.  ...  bias by making users aware of what they are reading, thus promoting media literacy and critical thinking, which is arguably the best way to address disinformation and "fake news."  ... 
doi:10.18653/v1/d19-5006 fatcat:77l3dndrkvfmlhjt6qvnrassgi