John J. Salerno

Social Computing, Behavioral-Cultural Modeling and Prediction

In the context of modernization and development, the complex adaptive systems framework can help address the coupling of macro social constraint and opportunity with individual agency. Combining system dynamics and agent based modeling, we formalize the Human Development (HD) perspective with a system of asymmetric, coupled nonlinear equations empirically validated from World Values Survey (WVS) data, capturing the core qualitative logic of HD theory. Using a simple evolutionary game approach, we fuse endogenously derived individual socio-economic attribute changes with Prisoner’s Dilemma spatial intra-societal economic transactions. We then explore a new human development dynamics (HDD) model behavior via quasi-global simulation methods to explore economic development, cultural plasticity, social and political change.

SAWA: an assistant for higher-level fusion and situation awareness

Situation awareness involves the identification and monitoring of relationships among level-one objects. This problem in general is intractable (i.e., there is a potentially infinite number of relations that could be tracked) and thus requires additional constraints and guidance defined by the user if there is to be any hope of creating practical situation awareness systems. This paper describes a Situation Awareness Assistant (SAWA) that facilitates the development of user-defined domain knowledge in the form of formal ontologies and rule sets and then permits the application of the domain knowledge to the monitoring of relevant relations as they occur in evolving situations. SAWA includes tools for developing ontologies in OWL and rules in SWRL and provides runtime components for collecting event data, storing and querying the data, monitoring relevant relations and viewing the results through a graphical user interface. An application of SAWA to a scenario from the domain of supply logistics is also presented.

Topic taxonomy adaptation for group profiling

A topic taxonomy is an effective representation that describes salient features of virtual groups or online communities. A topic taxonomy consists of topic nodes. Each internal node is defined by its vertical path (i.e., ancestor and child nodes) and its horizonal list of attributes (or terms). In a text-dominant environment, a topic taxonomy can be used to flexibly describe a groups interests with varying granularity. However, the stagnant nature of a taxonomy may fail to timely capture the dynamic change of a groups interest. This article addresses the problem of how to adapt a topic taxonomy to the accumulated data that reflects the change of a groups interest to achieve dynamic group profiling. We first discuss the issues related to topic taxonomy. We next formulate taxonomy adaptation as an optimization problem to find the taxonomy that best fits the data. We then present a viable algorithm that can efficiently accomplish taxonomy adaptation. We conduct extensive experiments to evaluate our approachs efficacy for group profiling, compare the approach with some alternatives, and study its performance for dynamic group profiling. While pointing out various applications of taxonomy adaption, we suggest some future work that can take advantage of burgeoning Web 2.0 services for online targeted marketing, counterterrorism in connecting dots, and community tracking.

Social Computing, Behavioral Modeling, and Prediction

Social computing concerns the study of social behavior and context based on computational systems. Behavioral modeling reproduces the social behavior, and allows for experimenting with and deep understanding of behavior, patterns, and potential outcomes. The pervasive use of computer and Internet technologies provides an unprecedented environment where people can share opinions and experiences, offer suggestions and advice, debate, and even conduct experiments. Social computing facilitates behavioral modeling in model building, analysis, pattern mining, anticipation, and prediction. The proceedings from this interdisciplinary workshop provide a platform for researchers, practitioners, and graduate students from sociology, behavioral and computer science, psychology, cultural study, information systems, and operations research to share results and develop new concepts and methodologies aimed at advancing and deepening our understanding of social and behavioral computing to aid critical decision making.

Applying data mining in investigating money laundering crimes

In this paper, we study the problem of applying data mining to facilitate the investigation of money laundering crimes (MLCs). We have identified a new paradigm of problems --- that of automatic community generation based on uni-party data, the data in which there is no direct or explicit link information available. Consequently, we have proposed a new methodology for Link Discovery based on Correlation Analysis (LDCA). We have used MLC group model generation as an exemplary application of this problem paradigm, and have focused on this application to develop a specific method of automatic MLC group model generation based on timeline analysis using the LDCA methodology, called CORAL. A prototype of CORAL method has been implemented, and preliminary testing and evaluations based on a real MLC case data are reported. The contributions of this work are: (1) identification of the uni-party data community generation problem paradigm, (2) proposal of a new methodology LDCA to solve for problems in this paradigm, (3) formulation of the MLC group model generation problem as an example of this paradigm, (4) application of the LDCA methodology in developing a specific solution (CORAL) to the MLC group model generation problem, and (5) development, evaluation, and testing of the CORAL prototype in a real MLC case data.

Issues and challenges of knowledge representation and reasoning methods in situation assessment (Level 2 Fusion)

Situation assessment (SA) involves deriving relations among entities, e.g., the aggregation of object states (i.e. classification and location). While SA has been recognized in the information fusion and human factors literature, there still exist open questions regarding knowledge representation and reasoning methods to afford SA. For instance, while lots of data is collected over a region of interest, how does this information get presented to an attention constrained user? The information overload can deteriorate cognitive reasoning so a pragmatic solution to knowledge representation is needed for effective and efficient situation understanding. In this paper, we present issues associated with Level 2 (Situation Assessment) including: (1) user perception and perceptual reasoning representation, (2) knowledge discovery process models, (3) procedural versus logical reasoning about relationships, (4) user-fusion interaction through performance metrics, and (5) syntactic and semantic representations. While a definitive conclusion is not the aim of the paper, many critical issues are proposed in order to characterize future successful strategies to knowledge representation and reasoning strategies for situation assessment.

Realizing situation awareness within a cyber environment

Situation Awareness (SA) problems all require an understanding of current activities, an ability to anticipate what may happen next, and techniques to analyze the threat or impact of current activities and predictions. These processes of SA are common regardless of the domain and can be applied to the detection of cyber attacks. This paper will describe the application of a SA framework to implementing Cyber SA, describe some metrics for measuring and evaluating systems implementing Cyber SA, and discuss ongoing work in this area. We conclude with some ideas for future activities.

Evaluating algorithmic techniques in supporting situation awareness

How well does an algorithm support its purpose and user base? Has automation provided the user with the ability to augment their production, quality or responsiveness? In a number of systems today these questions can be answered by either Measures of Performance (MOP) or Measures of Effectiveness (MOE). However, the fusion community has not yet developed sufficient measures and has only recently devoted a concerted effort to address this deficiency. In this paper, we will summarize work in metrics for the lower levels of fusion (object ID, tracking, etc) and discuss whether these same metrics still apply to the higher levels (Situation Awareness), or if other approaches are necessary. We conclude this paper with a set of future activities and direction.

A situation awareness model applied to multiple domains

Full Spectrum Dominance, or as defined by Joint Vision 2020, the ability to be persuasive in peace, decisive in war and preeminent in any form of conflict, cannot be accomplished without the ability to know what the adversary is currently doing as well as the capacity to correctly anticipate the adversarys future actions. A key component in the ability to predict the adversarys intention is Situation Awareness (SA). In this paper we provide a discussion of an SA model, examine a specific instantiation of the model and demonstrate how it has been applied to two specific domains: Global Monitoring and Cyber Awareness. We conclude this paper with a discussion on future work.

Information fusion for situational awareness

Information Fusion is beginning to defined in our process. We conclude with a receive increased attention not only within the discussion on metrics. military, but also within the civilian sector. The notion of maintaining constant awareness of ones 1.1 Background surroundings is not a new military goal, but to provide this capability through computers has been a long standing and challenging problem. In this Over the years, more than thirty fusion longpstandin and will aloenging paous tc Iq this wmodels