Dr. Shen-Shyang Ho Profile

Dr. Shen-Shyang Ho

at Rowan Univ.

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Proceedings Article | 10 May 2019 Presentation + Paper

Automated information foraging for sensemaking

Phil DiBona, Shen-Shyang Ho

Proceedings Volume 11006, 110060D (2019) https://doi.org/10.1117/12.2518893

KEYWORDS: Data processing

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In preparations for Multi-Domain Operations and Battles, All-Source and OSINT intelligence analysts gather, assess, and extract relevant information from operational databases as well as publicly available information. This data, often unstructured text documents, is noisy with relevant snippets buried within the document corpus. The costs of exploratory search and exploitive document analysis required to find these hidden snippets of information often drive searches toward a small subset of documents. Additionally, modern search tools may reinforce the confirmation bias of analysts by providing only those documents that closely match their search query. Due to the potentially high tempo of multi-domain battle, the end result is a decision or hypothesis that is ill-considered and substantiated by potentially biased information. An automated information foraging framework can mitigate these challenges by automatically identifying a wide breadth of topics for the user, extracted directly from a document corpus. A semantic network formed from the constituent entities within a document corpus contains inherently valuable topological structures that can be used to generate topics and also guide the analyst?s information exploration. Leveraging a suite of information retrieval and graph analysis algorithms that analyze the semantic network, a framework is defined for assisting analysts in both exploring and exploiting relevant information from a corpus to support the sensemaking process.

Proceedings Article | 13 April 2009 Paper

Remote event detection and tracking using multiple heterogeneous satellite data fusion

Ashit Talukder, Shen-Shyang Ho

Proceedings Volume 7340, 73400D (2009) https://doi.org/10.1117/12.820488

KEYWORDS: Satellites, Sensors, Code division multiplexing, Motion models, Data fusion, Temporal resolution, Filtering (signal processing), Mining, Data mining, Data modeling

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We describe an automated remote cyclone detection and tracking approach using heterogeneous data from multiple satellites. Single Earth orbiting satellite has been used in the past to detect and track events such as cyclones but suffer from major drawbacks due to limited spatio-temporal coverage. Our novel approach addresses the challenges in using heterogeneous data from multiple data sources for knowledge discovery, tracking and mining of cyclones. Moreover, it offers better detection performance and spatio-temporal resolutions. Our solution is sufficiently powerful that it generalizes to multiple sensor measurement modalities. Our approach consists of: (i) feature extraction from each sensor measurement, (ii) an ensemble classifier for cyclone detection, and (iii) knowledge sharing between the different remote sensor measurements. Our extensive experimental results demonstrate (i) the superior performance of our cyclone detector compared to previous work on preprocessed historical data, (ii) stable performance of our cyclone detector when it is applied on different geographical regions (Western Pacific Ocean and the North Atlantic Ocean), (iii) meaningful knowledge derived from the cyclone detector output, and (iv) the performance quality of our automated cyclone detection and tracking solution closely match the cyclone best track information from the National Hurricane Center.

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