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  1. Free, publicly-accessible full text available August 1, 2024
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  3. Wireless cameras can be used to gather situation awareness information (e.g., humans in distress) in disaster recovery scenarios. However, blindly sending raw video streams from such cameras, to an operations center or controller can be prohibitive in terms of bandwidth. Further, these raw streams could contain either redundant or irrelevant information. Thus, we ask “how do we extract accurate situation awareness information from such camera nodes and send it in a timely manner, back to the operations center?” Towards this, we design ACTION, a framework that (a) detects objects of interest (e.g., humans) from the video streams, (b) combines these streams intelligently to eliminate redundancies and (c) transmits only parts of the feeds that are sufficient in achieving a desired detection accuracy to the controller. ACTION uses small amounts of metadata to determine if the objects from different camera feeds are the same. A resource-aware greedy algorithm is used to select a subset of video feeds that are associated with the same object, so as to provide a desired accuracy, for being sent to the operations center. Our evaluations show that ACTION helps reduce the network usage up to threefold, and yet achieves a high detection accuracy of ≈ 90%. 
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  4. Mining textual patterns in news, tweets, papers, and many other kinds of text corpora has been an active theme in text mining and NLP research. Previous studies adopt a dependency parsing-based pattern discovery approach. However, the parsing results lose rich context around entities in the patterns, and the process is costly for a corpus of large scale. In this study, we propose a novel typed textual pattern structure, called meta pattern, which is extended to a frequent, informative, and precise subsequence pattern in certain context. We propose an efficient framework, called MetaPAD, which discovers meta patterns from massive corpora with three techniques: (1) 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; (2) it identifies and groups synonymous meta patterns from multiple facets—their types, contexts, and extractions; and (3) it examines type distributions of entities in the instances extracted by each group of patterns, and looks for appropriate type levels to make discovered patterns precise. Experiments demonstrate that our proposed framework discovers high-quality typed textual patterns efficiently from different genres of massive corpora and facilitates information extraction. 
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