CEST: a Cognitive Event based Semi-automatic Technique for behavior segmentation

This work introduces CEST, a Cognitive Event based Semiautomatic Technique for behavior segmentation. The technique was inspired by an everyday cognitive process. Humans, in fact, make sense of what happens to them by breaking the continuous stream of activity into smaller units, through a process known as segmentation. A cognitive theory, the Event Segmentation Theory, provides a computational and neurophysiological account of this process, describing how the detection of changes in the current situation drive boundary perception. CEST was designed with the aim of providing affective researchers with a tool to semi-automatically segment behavior. Researchers investigating behavior, as a matter of fact, often need to parse their research data into simpler units, either manually or automatically. To perform segmentation, the technique combines manual annotations and the output of change-point detection algorithms, techniques from time-series research that afford the detection of abrupt changes in time-series. CEST is inherently multidisciplinary: it is, to the best of our knowledge, the first attempt to adopt a cognitive science perspective on the issue of (semi) automatic behavior segmentation. CEST is a general-purpose technique, as it aims at providing a tool for segmenting behavior across research areas. In this manuscript, we detail the theories behind the design of CEST and the results of two experimental studies aimed at assessing the feasibility of the approach on both single and group scenarios. Most importantly, we present the results of the evaluation of CEST on a data-set of dance performances. We explore seven different techniques for change-point detection that could be leveraged to achieve semi-automatic segmentation through CEST and illustrate how two different bayesian algorithms led to the highest scores. Upon selecting the best algorithms, we measured the effect of the temporal grain of the analysis on the performance. Overall, our results support the idea of a semiautomatic segmentation technique for behavior segmentation. The output of the analysis mirrors cognitive science research on segmentation and on event structure perception. The work also tackles new challenges that may arise from our approach

FALSE MISBEHAVIOUR ELIMINATION IN WATCHDOG MONITORING SYSTEM USING CHANGE POINT IN A WIRELESS SENSOR NETWORK

Wireless Sensor Networks are to be widely deployed in the near future for data monitoring in commercial, industrial and military applications. Though much research has focused on making these networks feasible and useful security has received very little attention. Sensor networks are exposed to variety of attacks like eavesdropping, message tampering, selective forward, gray hole attack, and Wormhole and Sybil attacks. Watchdog is a kind of behaviour monitoring mechanism which is the base of many trust systems in Ad hoc and Wireless Sensor Network. Current watchdog mechanism only evaluates its next-hop’s behaviour and propagates the evaluation result to other nodes by broadcasting, which is neither energy efficient nor attack resilient. The fundamental problem of secure neighbour discovery is studied which is importunate in protecting the network from different forms of attacks. In this paper an improved watchdog monitoring mechanism is proposed by using the process of change point detection. By implementing this change point detection algorithm in watchdog mechanism, the limitations of the existing watchdog mechanism are overcome. From this the exact malicious node can be found out and the data will be routed through a secure path bypassing the malicious node. Finally to analyze the efficiency of this algorithm, the results obtained from the proposed algorithm and the existing algorithms are compared

Speaker segmentation and clustering

This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker clustering, deterministic and probabilistic algorithms are examined. A comparative assessment of the reviewed algorithms is undertaken, the algorithm advantages and disadvantages are indicated, insight to the algorithms is offered, and deductions as well as recommendations are given. Rich transcription and movie analysis are candidate applications that benefit from combined speaker segmentation and clustering. © 2007 Elsevier B.V. All rights reserved