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v Conference Committee
SESSION 1 INFORMATION FUSION AND ANALYSIS
9122 02 Automatic theory generation from analyst text files using coherence networks [9122-1]
S. C. Shaffer, The Pennsylvania State Univ. (United States)
9122 03 Using Complex Event Processing (CEP) and vocal synthesis techniques to improve comprehension of sonified human-centric data [9122-2]
J. Rimland, M. Ballora, The Pennsylvania State Univ. (United States)
9122 04 A data fusion approach to indications and warnings of terrorist attacks [9122-3]
D. McDaniel, G. Schaefer, Silver Bullet Solutions, Inc. (United States)
9122 05 Warfighter information services: lessons learned in the intelligence domain [9122-4]
S. E. Bray, Defence Science and Technology Lab. (United Kingdom)
9122 06 A survey of automated methods for sensemaking support [9122-5]
J. Llinas, Univ. at Buffalo (United States)
SESSION 2 INFORMATION VISUALIZATION
9122 07 Neural network based visualization of collaborations in a citizen science project [9122-6]
A. M. M. Morais, R. D. C. Santos, Instituto Nacional de Pesquisas Espaciais (Brazil); M. J. Raddick, Johns Hopkins Univ. (United States)
9122 08 Visualizing common operating picture of critical infrastructure [9122-7]
L. Rummukainen, L. Oksama, J. Timonen, J. Vankka, National Defence Univ. (Finland)
9122 09 Visualization of multi-INT fusion data using Java Viewer (JVIEW) [9122-8]
E. Blasch, A. Aved, J. Nagy, S. Scott, Air Force Research Lab. (United States)
9122 0A A visual analytic framework for data fusion in investigative intelligence [9122-9]
G. Cai, The Pennsylvania State Univ. (United States); G. Gross, J. Llinas, Univ. at Buffalo (United States); D. Hall, The Pennsylvania State Univ. (United States)
9122 0B Human terrain exploitation suite: applying visual analytics to open source information [9122-10]
T. Hanratty, J. Richardson, M. Mittrick, J. Dumer, E. Heilman, H. Roy, S. Kase, U.S. Army Research Lab. (United States)
SESSION 3 BIG DATA AND INFORMATION MANAGEMENT
9122 0C Profile-based autonomous data feeding: an approach to the information retrieval problem in a high communications latency environment [9122-11]
J. Straub, The Univ. of North Dakota (United States)
9122 0D Exploiting social media for Army operations: Syrian crisis use case [9122-12]
S. E. Kase, E. K. Bowman, U.S. Army Research Lab. (United States); M. T. Al Amin, T. Abdelzaher, Univ. of Illinois at Urbana-Champaign (United States)
9122 0E A qualitative readiness-requirements assessment model for enterprise big-data infrastructure investment [9122-13]
M. M. Olama, A. W. McNair, S. R. Sukumar, J. J. Nutaro, Oak Ridge National Lab. (United States)
9122 0G Utilizing semantic wiki technology for intelligence analysis at the tactical edge [9122-16]
E. Little, Modus Operandi, Inc. (United States)
SESSION 4 PARTICIPATORY SENSING & COGNITION
9122 0H User-centric incentive design for participatory mobile phone sensing [9122-17]
W. Gao, H. Lu, The Univ. of Tennessee Knoxville (United States)
9122 0I Conversational sensing [9122-18]
A. Preece, C. Gwilliams, C. Parizas, D. Pizzocaro, Cardiff Univ. (United Kingdom); J. Z. Bakdash, U.S. Army Research Lab. (United States); D. Braines, IBM United Kingdom Ltd. (United Kingdom)
9122 0J Using cognitive architectures to study issues in team cognition in a complex task environment [9122-19]
P. R. Smart, Univ. of Southampton (United Kingdom); K. Sycara, Y. Tang, Carnegie Mellon Univ. (United States)
9122 0K Language and dialect identification in social media analysis [9122-21]
S. Tratz, D. Briesch, U.S. Army Research Lab. (United States); J. Laoudi, ARTI (United States); C. Voss, V. M. Holland, U.S. Army Research Lab. (United States)
9122 0L Application of the JDL data fusion process model to hard/soft information fusion in the condition monitoring of aircraft [9122-22]
J. T. Bernardo, The Pennsylvania State Univ. (United States)
9122 0M Predicting student success using analytics in course learning management systems [9122-23]
M. M. Olama, G. Thakur, A. W. McNair, S. R. Sukumar, Oak Ridge National Lab. (United States)
Wolfgang Schade, Technische Universität Clausthal (Germany) and Fraunhofer Heinrich-Hertz-Institut (Germany)
Barbara D. Broome, U.S. Army Research Laboratory (United States)
David L. Hall, The Pennsylvania State University (United States)
James Llinas, University at Buffalo (United States)
Conference Program Committee
Nina M. Berry, Sandia National Laboratories, California (United States)
John S. Eicke, U.S. Army Research Laboratory (United States)
James Fink, U. S. Army Intelligence Center of Excellence (United States)
Timothy P. Hanratty, U.S. Army Research Laboratory (United States)
James Hendler, Rensselaer Polytechnic Institute (United States)
John E. Lavery, U.S. Army Research Laboratory (United States)
Bob Madahar, Defence Science and Technology Laboratory (United Kingdom)
Paul Sajda, Columbia University (United States)
Alan Steinberg, Georgia Tech Research Institute (United States)
Edward L. Waltz, BAE Systems (United States)
During the past five years, extensive research has been conducted to develop methods for correlating and fusing data from physical sensors (“hard” data) and from human observers (“soft” data). Hard sensor data generally involves signals, images or scalar information related to the location, identification and characterization of entities, e.g., humans and vehicles, while soft data typically involves textual information, e.g., observations, inferences, and comments, from human observers. Modern Information Fusion systems are also exploiting contextual information, e.g., socio-cultural data, much of which is also of a soft type. With the rapid proliferation of mobile communications devices and increased global connectivity, the need to fuse hard and soft data becomes an increasingly ubiquitous problem. Applications involve areas such as environmental monitoring, citizen science, military situation awareness and assessment, and emergency response. There are numerous challenges involved in hard and soft data fusion based on issues such as: the inherent differences in level of abstraction of hard versus soft data (viz., hard data about observed entities represented by signals, images, vectors and scalars versus semantic meta-data based on human observations and inferences); challenges in characterizing the performance of physical sensors versus human observers; differences in data rates; and issues in correlation and association.
The 2014 SPIE Sensing Technology and Applications Conference included the second annual session on the “Next-Generation Analyst”. This conference followed on the model and success of a similarly-themed conference held at SPIE DSS in 2013. The focus this year was on research and advances in hard and soft data fusion. The papers provided an overview and discussion of the state of the art in four sessions: (1) information fusion and analysis, (2) information visualization, (3) big data and information management, and (4) participatory sensing and cognition. Across the four thematic sessions listed, over 20 papers authored by a set of international authors from the USA, the UK, Brazil, and Finland covered top-level research issues toward realizing new capabilities for analysis in very complex environments.
The papers covered a broad range of topics. However, one core theme that emerged was that of the semantic complexities in dealing with language-based data, to include the extensive domain of social media and even data collected by through crowd-sourcing. It was shown for example that for some languages of interest today, even building a capability to recognize a language or dialect of interest can be challenging. Architectural concepts for modern analysis suites included virtual knowledge bases, agent-based approaches, Wiki-based concepts, and user-centric approaches. Visualization ideas and the associated area of human-computer interfacing were also addressed in papers exploring sonification concepts for analyst alerting, multi-functional utilities and visualizations, and techniques for maintaining user interest and attention. Among other topics, the concern for input quality, especially in open-source and social-media environments was reflected in many papers, and new frameworks for analysis, such as argumentation-based methods, were also discussed.
Barbara D. Broome
David L. Hall