In many insider crimes, managers and other coworkers observed that the offenders had exhibited signs of stress, disgruntlement, or other issues, but no alarms were raised. Barriers to using such psychosocial indicators include the inability to recognize the signs and the failure to record the behaviors so that they can be assessed. A psychosocial model was developed to assess an employee's behavior associated with an increased risk of insider abuse. The model is based on case studies and research literature on factors/correlates associated with precursor behavioral manifestations of individuals committing insider crimes. To test the model's agreement with human resources and management professionals, we conducted an experiment with positive results. If implemented in an operational setting, the model would be part of a set of management tools for employee assessment to identify employees who pose a greater insider threat.
The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). The model parameters (e.g., etiology, climatic, spatial, cultural) and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological) were recorded and reviewed. A component of this review is the identification of verification and validation (V&V) methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology Readiness Level definitions.
SummaryA model was developed to assess employees' behavioral manifestations of a number of psychological and personality predispositions that are hypothesized to indicate an increased risk of insider abuse. This psychosocial model is based on case studies and research literature on factors and correlates associated with behavioral precursors of individuals committing insider crimes. In many of these crimes, managers and other coworkers observed that the offenders had exhibited signs of stress, disgruntlement, or other issues, but no alarms were raised. Barriers to using such psychosocial indicators include the inability to recognize the signs and the failure to record the behaviors so that they can be assessed.The model has been implemented as a Bayesian belief network, designed with the help of human resources staff experienced in evaluating workplace behaviors. We conducted an experiment to assess the agreement of the model's risk assessment output with judgments of human resources and management professionals on the relative insider threat risks of a collection of sample scenarios. The model exhibited strong agreement with judgments of the human experts, suggesting that it has potential as a tool to raise an alarm about employees who pose higher insider threat risks. While additional testing is needed, we suggest that combining this type of analysis with more traditional cyber/workstation monitoring tools can ease the processing burden and improve performance of computer-assisted insider threat monitoring and detection.v
SummaryDespite the growth of the visual analytics (VA) field, there has been limited systematic testing and evaluation to determine the effectiveness of VA solutions for improving knowledge discovery and decision making. The VA community acknowledges the need for a more scientific foundation to guide research on and evaluation of VA tools. A practical methodology and framework will not only inform the design of VA systems but also facilitate establishment of metrics to evaluate their effectiveness. This report describes the findings of a research project with the following scientific and operational objectives in support of the VA community: (a) Enhance understanding of the role of VA in knowledge discovery and insight; (b) Identify more rigorous scientific methods to evaluate effectiveness of VA tools; and (c) Inform design of deployable VA solutions based on this theoretical foundation.U.S. Department of Homeland Security end users do not merely want more displays and tools; they need operational/deployable solutions that enhance information processing and decision making. There is also a need for user testing methodologies and metrics to assess performance effectiveness of VA tools in operationally relevant contexts. To this end, the present research examined scientific literature in cognitive science, human factors, and related fields to identify concepts and research results that inform the application of VA technologies to meet operational challenges. By updating previous taxonomies for VA approaches and applications, we hope to provide a more comprehensive framework and benchmarks for this expanding field.In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions.The following conclusions and recommendations are provided for advancing the VA field and for continuing and expanding this research program in cognitive foundations for VA.Recommendations for future research:• More research is needed on sensemaking/problem solving and the analytic process to help align visualization technologies and representation techniques to user's mental models and thought processes.• Research is required to advance the science and engineering practices of VA tool evaluation.• Research is needed to develop more effective means of communicating the results of analyses to stakeholders (intuitive and natural ways of conveying findings as well as providing rationale and background information supporting the decisions and recommendations).• A more directed application of cognitive theories and results of empirical research on critical decisio...
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