Thomas O. Meservy

Transforming software development: an MDA road map

The Model Driven Architecture initiative shifts the focus of software development from writing code to building models. At appropriate abstraction levels, such models can help customers and development teams translate their understanding of the problem domain into more reliable working code more quickly.

An Approach for Intent Identification by Building on Deception Detection

Past research in deception detection at the University of Arizona has guided the investigation of intent detection. A theoretical foundation and model for the analysis of intent detection is proposed. Available test beds for intent analysis are discussed and two proof-of-concept studies exploring nonverbal communication within the context of deception detection and intent analysis are shared.

Detecting Concealment of Intent in Transportation Screening: A Proof of Concept

Transportation and border security systems have a common goal: to allow law-abiding people to pass through security and detain those people who intend to harm. Understanding how intention is concealed and how it might be detected should help in attaining this goal. In this paper, we introduce a multidisciplinary theoretical model of intent concealment along with three verbal and nonverbal automated methods for detecting intent: message feature mining, speech act profiling, and kinesic analysis. This paper also reviews a program of empirical research supporting this model, including several previously published studies and the results of a proof-of-concept study. These studies support the model by showing that aspects of intent can be detected at a rate that is higher than chance. Finally, this paper discusses the implications of these findings in an airport-screening scenario.

Evaluation of Competing Candidate Solutions in Electronic Networks of Practice

Electronic networks of practice have become a prevalent means for acquiring new knowledge. Knowledge seekers commonly turn to online repositories constructed by these networks to find solutions to domain-specific problems and questions. Yet little is understood about the process by which such knowledge is evaluated and adopted by knowledge seekers. This study examines how individuals filter knowledge encountered in online forums, a common platform for knowledge exchange in an electronic network of practice. Drawing on dual process theory, we develop research hypotheses regarding both central and peripheral evaluation of knowledge. These hypotheses are examined in a field experiment in which participants evaluate online solutions for computer programming problems. Results show that peripheral cues source expertise and validation have a greater influence on knowledge filtering decisions than does the content quality of the solution. Moreover, elaboration increases the effect of content quality but does not seem to attenuate the effect of peripheral cues. Implications for research and practice are discussed.

Detecting Deceptive Chat-Based Communication Using Typing Behavior and Message Cues

Computer-mediated deception is prevalent and may have serious consequences for individuals, organizations, and society. This article investigates several metrics as predictors of deception in synchronous chat-based environments, where participants must often spontaneously formulate deceptive responses. Based on cognitive load theory, we hypothesize that deception influences response time, word count, lexical diversity, and the number of times a chat message is edited. Using a custom chatbot to conduct interviews in an experiment, we collected 1,572 deceitful and 1,590 truthful chat-based responses. The results of the experiment confirm that deception is positively correlated with response time and the number of edits and negatively correlated to word count. Contrary to our prediction, we found that deception is not significantly correlated with lexical diversity. Furthermore, the age of the participant moderates the influence of deception on response time. Our results have implications for understanding deceit in chat-based communication and building deception-detection decision aids in chat-based systems.

HMM-Based Deception Recognition from Visual Cues

Behavioral indicators of deception and behavioral state are extremely difficult for humans to analyze. This research effort attempts to leverage automated systems to augment humans in detecting deception by analyzing nonverbal behavior on video. By tracking faces and hands of an individual, it is anticipated that objective behavioral indicators of deception can be isolated, extracted and synthesized to create a more accurate means for detecting human deception. Blob analysis, a method for analyzing the movement of the head and hands based on the identification of skin color is presented. A proof-of-concept study is presented that uses Blob analysis to extract visual cues and events, throughout the examined videos. The integration of these cues is done using a hierarchical hidden Markov model to explore behavioral state identification in the detection of deception, mainly involving the detection of agitated and over-controlled behaviors

Dynamic collaboration: participant-driven agile processes for complex tasks

Groups and decision makers are increasingly running headlong into complexity as interconnections and interdependencies between individuals and organizations continue to grow, decision time horizons shrink and more work is being performed by distributed teams. These factors are driving up overall problem space complexity and limiting the effectiveness of time-tested decision and collaboration processes. Increases in problem space complexity lead to higher equivocality in collaborative processes and associated products. Electronic collaboration support tools and associated process management schemes have proven successful in many challenging contexts. However, current collaboration process management schemes and tools may not be able to effectively handle more complex tasks. We posit that heightened problem space complexity must be addressed with commensurate process and technological support for collaborative efforts. To achieve truly agile collaborative solutions, we propose Dynamic Collaboration -- a process management scheme that utilizes group consensus, and process evolution via iterative process alignment and product refinement phases to meet the challenges posed by complexity and equivocality.

Identification of deceptive behavioral cues extracted from video

This research project investigates a novel approach for deriving behavioral deception cues from videotaped interactions. Researchers utilized inputs extracted from video to construct a set of two-dimensional spatial features. The features for thirty-eight video interactions were then analyzed using discriminant analysis and logistic regression. Through this exploratory study, the team has identified a number of promising features that help discriminate deception from truth. The techniques explored hold promise for the creation of near real time systems for transportation security professionals.

The Business Rules Approach and Its Effect on Software Testing

Codification and testing of business rules in application programs has historically been a challenge in software engineering. Many organizations have adopted the business rules approach to formalize and compartmentalize business rules as a separate component from application code. This article investigates and presents the effects of the business rules approach on testing activities in the software development life cycle at a Fortune 500 corporation. The findings suggest that the business rules approach has the potential to engage testing personnel early in the development process and to improve the efficiency and effectiveness of testing activities.

Exploring Knowledge Filtering Processes in Electronic Networks of Practice

Abstract Electronic networks of practice (ENPs) have become an important mechanism for knowledge exchange among loosely connected individuals who share common knowledge interests. While prior research has explored factors that influence knowledge contribution in such networks, less is understood about the process by which individuals evaluate and ultimately adopt knowledge from ENPs. This study examines the process of knowledge filtering in online ENP forums. Drawing from dual process and information-evaluation theories, we hypothesize that performance on a knowledge-filtering task will be influenced by the constancy and directionality of search patterns employed by knowledge seekers. Hypotheses are tested in an experiment that utilized an eye tracker to record gaze data from professional software developers using an experimental ENP forum. By combining information-evaluation and dual process theory perspectives, our results deepen the insights offered in extant information-processing literature by showing that higher filtering accuracy is associated with (a) constant evaluation of some types of information attributes (solution content) but not others (peripheral cues), and (b) increasing attribute-based processing over time.