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Bevers: A General, Simple, And Performant Framework For Automatic Fact Verification, Mitchell Dehaven
Bevers: A General, Simple, And Performant Framework For Automatic Fact Verification, Mitchell Dehaven
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
Fact verification has become an important process, primarily done manually by humans, to verify the authenticity of claims and statements made online. Increasingly, social media companies have utilized human effort to debunk false claims on their platforms, opting to either tag the content as misleading or false, or removing it entirely to combat misinformation on their sites. In tandem, the field of automatic fact verification has become a subject of focus among the natural language processing (NLP) community, spawning new datasets and research. The most popular dataset is the Fact Extraction and VERification (FEVER) dataset. In this thesis an end-to-end …
Using Contextual Bandits To Improve Traffic Performance In Edge Network, Aziza Al Zadjali
Using Contextual Bandits To Improve Traffic Performance In Edge Network, Aziza Al Zadjali
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
Edge computing network is a great candidate to reduce latency and enhance performance of the Internet. The flexibility afforded by Edge computing to handle data creates exciting range of possibilities. However, Edge servers have some limitations since Edge computing process and analyze partial sets of information. It is challenging to allocate computing and network resources rationally to satisfy the requirement of mobile devices under uncertain wireless network, and meet the constraints of datacenter servers too. To combat these issues, this dissertation proposes smart multi armed bandit algorithms that decide the appropriate connection setup for multiple network access technologies on the …