T
Timothy Hanratty
Researcher at United States Army Research Laboratory
Publications - 74
Citations - 1135
Timothy Hanratty is an academic researcher from United States Army Research Laboratory. The author has contributed to research in topics: Value of information & Decision support system. The author has an hindex of 16, co-authored 74 publications receiving 1007 citations. Previous affiliations of Timothy Hanratty include United States Department of the Army.
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Proceedings ArticleDOI
GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media
TL;DR: This work proposes GMove, a group-level mobility modeling method using GeoSM data that alternates between user grouping and mobility modeling, and generates an ensemble of Hidden Markov Models (HMMs) to characterize group- level movement regularity.
Proceedings ArticleDOI
Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning
Chao Zhang,Keyang Zhang,Quan Yuan,Haoruo Peng,Yu Zheng,Timothy Hanratty,Shaowen Wang,Jiawei Han +7 more
TL;DR: CrossMap is presented, a novel cross-modal representation learning method that uncovers urban dynamics with massive GTSM data and significantly outperforms state-of-the-art methods for activity recovery and classification, but also achieves much better efficiency.
Journal ArticleDOI
Agents with shared mental models for enhancing team decision makings
TL;DR: The effectiveness of the decision-theoretic proactive communication strategy in improving team performance, and the effectiveness of information fusion as an approach to alleviating the information overload problem faced by distributed decision makers are evaluated.
Proceedings ArticleDOI
TrioVecEvent: Embedding-Based Online Local Event Detection in Geo-Tagged Tweet Streams
TL;DR: Crowdourcing is used to evaluate TrioVecEvent, a method that leverages multimodal embeddings to achieve accurate online local event detection and introduces discriminative features that can well characterize local events.
Proceedings ArticleDOI
MetaPAD: Meta Pattern Discovery from Massive Text Corpora
Meng Jiang,Jingbo Shang,Taylor Cassidy,Xiang Ren,Lance M. Kaplan,Timothy Hanratty,Jiawei Han +6 more
TL;DR: An efficient framework is proposed, called MetaPAD, which discovers meta patterns from massive corpora with three techniques: it develops a context-aware segmentation method to carefully determine the boundaries of patterns with a learnt pattern quality assessment function, which avoids costly dependency parsing and generates high-quality patterns.