Deception in context: coding nonverbal cues, situational variables and risk of detection

There are many situations in which deception may arise and understanding the behaviors associated with it are compounded by various contexts in which it may occur. This paper sets out a coding protocol for identifying cues to deception and reports on three studies, in which deception was studied in different contexts. The contexts involved manipulating risks (i.e., probability) of being detected and reconnaissance, both of which are related to terrorist activities. Two of the studies examined the impact of changing the risks of deception detection, whilst the third investigated increased cognitive demand of duplex deception tasks including reconnaissance and deception. In all three studies, cues to deception were analyzed in relation to observable body movements and subjective impressions given by participants. In general, the results indicate a pattern of hand movement reduction by deceivers, and suggest the notion that raising the risk of detection influences deceivers? behaviors. Participants in the higher risk condition displayed increased negative affect (found in deceivers) and tension (found in both deceivers and truth-tellers) than those in lower risk conditions

Associating Facial Expressions and Upper-Body Gestures with Learning Tasks for Enhancing Intelligent Tutoring Systems

Learning involves a substantial amount of cognitive, social and emotional states. Therefore, recognizing and understanding these states in the context of learning is key in designing informed interventions and addressing the needs of the individual student to provide personalized education. In this paper, we explore the automatic detection of learner’s nonverbal behaviors involving hand-over-face gestures, head and eye movements and emotions via facial expressions during learning. The proposed computer vision-based behavior monitoring method uses a low-cost webcam and can easily be integrated with modern tutoring technologies. We investigate these behaviors in-depth over time in a classroom session of 40 minutes involving reading and problem-solving exercises. The exercises in the sessions are divided into three categories: an easy, medium and difficult topic within the context of undergraduate computer science. We found that there is a significant increase in head and eye movements as time progresses, as well as with the increase of difficulty level. We demonstrated that there is a considerable occurrence of hand-over-face gestures (on average 21.35%) during the 40 minutes session and is unexplored in the education domain. We propose a novel deep learning approach for automatic detection of hand-over-face gestures in images with a classification accuracy of 86.87%. There is a prominent increase in hand-over-face gestures when the difficulty level of the given exercise increases. The hand-over-face gestures occur more frequently during problem-solving (easy 23.79%, medium 19.84% and difficult 30.46%) exercises in comparison to reading (easy 16.20%, medium 20.06% and difficult 20.18%)

From COBOL to Business Rules—Extracting Business Rules from Legacy Code

The reverse engineering project described in this paper is aimed at documenting a 6.4 million lines of code COBOL/IMS/DB2 system for world-wide car leasing. The ultimate goal is to re-implement that system. The system was originally developed in the 1980s with less than 3 million code lines and has since evolved to its current size. It survived the year 2000 date change and the Euro conversion as well as several major company reorganizations to preserve the continuity of the leasing service. Finally, after 30 years of service it is planned to retire the system. However, the first two attempts to replace it, one by automatically converting it and the other by replacing it with a standard package ended in failure. It is now planned to rewrite the system based on a specification derived from the current code base. That specification includes among other documents a documentation of the processing rules. The extracted rules are intended to act as guide to those writing the new code

Deception Detection in Online Automated Job Interviews

This research-in-progress paper presents a conceptual system for automated deception detection in online interviewing. The design proposes video recordings of responses to predefined, structured interview question sets variously selected based on the desired behavioral metric of interest, such as competence, social skills, or in this case, veracity. Raw behavioral data extracted from video responses is refined to produce indicators of behavioral metrics. A prototype implementation of the design was built and tested experimentally using a job interview scenario. Results of the experimental analysis provide evidence of the potential of the concept