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pro vyhledávání: '"Radford, Benjamin J."'
Many dynamical systems in the real world are naturally described by latent states with intrinsic orderings, such as "ally", "neutral", and "enemy" relationships in international relations. These latent states manifest through countries' cooperative v
Externí odkaz:
http://arxiv.org/abs/2212.04130
Autor:
Radford, Benjamin J.
Text data are an important source of detailed information about social and political events. Automated systems parse large volumes of text data to infer or extract structured information that describes actors, actions, dates, times, and locations. On
Externí odkaz:
http://arxiv.org/abs/2107.00080
We propose a new task and dataset for a common problem in social science research: "upsampling" coarse document labels to fine-grained labels or spans. We pose the problem in a question answering format, with the answers providing the fine-grained la
Externí odkaz:
http://arxiv.org/abs/2105.11260
Autor:
Radford, Benjamin J.
Publikováno v:
Workshop Proceedings of AESPEN at LREC 2020
Previous efforts to automate the detection of social and political events in text have primarily focused on identifying events described within single sentences or documents. Within a corpus of documents, these automated systems are unable to link ev
Externí odkaz:
http://arxiv.org/abs/2005.02966
Autor:
Radford, Benjamin J.
Publikováno v:
Working Notes of CLEF 2019 (2019)
The CLEF 2019 ProtestNews Lab tasks participants to identify text relating to political protests within larger corpora of news data. Three tasks include article classification, sentence detection, and event extraction. I apply multitask neural networ
Externí odkaz:
http://arxiv.org/abs/2005.02954
As the amount of cyber data continues to grow, cyber network defenders are faced with increasing amounts of data they must analyze to ensure the security of their networks. In addition, new types of attacks are constantly being created and executed g
Externí odkaz:
http://arxiv.org/abs/1808.10742
We evaluate methods for applying unsupervised anomaly detection to cybersecurity applications on computer network traffic data, or flow. We borrow from the natural language processing literature and conceptualize flow as a sort of "language" spoken b
Externí odkaz:
http://arxiv.org/abs/1805.03735
We show that a recurrent neural network is able to learn a model to represent sequences of communications between computers on a network and can be used to identify outlier network traffic. Defending computer networks is a challenging problem and is
Externí odkaz:
http://arxiv.org/abs/1803.10769
Akademický článek
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Autor:
Radford, Benjamin J.1 (AUTHOR)
Publikováno v:
International Interactions. Jul/Aug2022, Vol. 48 Issue 4, p739-758. 20p.
