Specification and detection of feature interactions using MSCs

New network architectures, such as the Intelligent Network (IN), have evolved in response to the changing needs and demands for advanced and sophisticated telecommunications services. However, as more services are introduced into the network, a new problem of interactions between various services/features, becomes more prominent. This problem arises when multiple services or features interfere with each other and produce unexpected results, which disturb the users. This thesis presents my work in modeling features and detecting feature interactions using Message Sequence Charts (MSCs). The modeling technique is based on the Advanced Intelligent Network (AIN) architecture and call models. To effectively detect feature interactions, we propose an MSC feature specification style which embodies several important aspects of features directly related to feature interactions. Based on the modeling of features using the specification style, we propose a new approach for detecting feature interactions. This approach includes definitions, classification of feature interactions, and specific detection algorithms for various types of interactions. We developed a prototyped feature interaction detection tool to implement our approach. With this tool, we are able to detect many interactions described in the Bellcore feature interaction benchmark. Our detection technique has maintained its consistency and accuracy in detecting these interactions. However, some limitations of our approach prevent us from detecting certain types of interactions. Combining our feature specification style, detection approach and tool, we propose a general framework for feature specification and interaction detection for IN services

Multimodal Polynomial Fusion for Detecting Driver Distraction

Distracted driving is deadly, claiming 3,477 lives in the U.S. in 2015 alone. Although there has been a considerable amount of research on modeling the distracted behavior of drivers under various conditions, accurate automatic detection using multiple modalities and especially the contribution of using the speech modality to improve accuracy has received little attention. This paper introduces a new multimodal dataset for distracted driving behavior and discusses automatic distraction detection using features from three modalities: facial expression, speech and car signals. Detailed multimodal feature analysis shows that adding more modalities monotonically increases the predictive accuracy of the model. Finally, a simple and effective multimodal fusion technique using a polynomial fusion layer shows superior distraction detection results compared to the baseline SVM and neural network models.Comment: INTERSPEECH 201

Detecting Low Rapport During Natural Interactions in Small Groups from Non-Verbal Behaviour

Rapport, the close and harmonious relationship in which interaction partners are "in sync" with each other, was shown to result in smoother social interactions, improved collaboration, and improved interpersonal outcomes. In this work, we are first to investigate automatic prediction of low rapport during natural interactions within small groups. This task is challenging given that rapport only manifests in subtle non-verbal signals that are, in addition, subject to influences of group dynamics as well as inter-personal idiosyncrasies. We record videos of unscripted discussions of three to four people using a multi-view camera system and microphones. We analyse a rich set of non-verbal signals for rapport detection, namely facial expressions, hand motion, gaze, speaker turns, and speech prosody. Using facial features, we can detect low rapport with an average precision of 0.7 (chance level at 0.25), while incorporating prior knowledge of participants' personalities can even achieve early prediction without a drop in performance. We further provide a detailed analysis of different feature sets and the amount of information contained in different temporal segments of the interactions.Comment: 12 pages, 6 figure

Review of computer vision in intelligent environment design

This paper discusses and compares the use of vision based and non-vision based technologies in developing intelligent environments. By reviewing the related projects that use vision based techniques in intelligent environment design, the achieved functions, technical issues and drawbacks of those projects are discussed and summarized, and the potential solutions for future improvement are proposed, which leads to the prospective direction of my PhD research