Selecting Highly Efficient Sets of Subdomains for Mutation Adequacy

Test selection techniques are used to reduce the human effort involved in software testing. Most research focusses on selecting efficient sets of test cases according to various coverage criteria for directed testing. We introduce a new technique to select efficient sets of sub domains from which new test cases can be sampled at random to achieve a high mutation score. We first present a technique for evolving multiple sub domains, each of which target a different group of mutants. The evolved sub domains are shown to achieve an average 160% improvement in mutation score compared to random testing with six real world Java programs. We then present a technique for selecting sets of the evolved sub domains to reduce the human effort involved in evaluating sampled test cases without reducing their fault finding effectiveness. This technique significantly reduces the number of sub domains for four of the six programs with a negligible difference in mutation score

Ensemble classification and signal image processing for genus Gyrodactylus (Monogenea)

This thesis presents an investigation into Gyrodactylus species recognition, making use of machine learning classification and feature selection techniques, and explores image feature extraction to demonstrate proof of concept for an envisaged rapid, consistent and secure initial identification of pathogens by field workers and non-expert users. The design of the proposed cognitively inspired framework is able to provide confident discrimination recognition from its non-pathogenic congeners, which is sought in order to assist diagnostics during periods of a suspected outbreak. Accurate identification of pathogens is a key to their control in an aquaculture context and the monogenean worm genus Gyrodactylus provides an ideal test-bed for the selected techniques. In the proposed algorithm, the concept of classification using a single model is extended to include more than one model. In classifying multiple species of Gyrodactylus, experiments using 557 specimens of nine different species, two classifiers and three feature sets were performed. To combine these models, an ensemble based majority voting approach has been adopted. Experimental results with a database of Gyrodactylus species show the superior performance of the ensemble system. Comparison with single classification approaches indicates that the proposed framework produces a marked improvement in classification performance. The second contribution of this thesis is the exploration of image processing techniques. Active Shape Model (ASM) and Complex Network methods are applied to images of the attachment hooks of several species of Gyrodactylus to classify each species according to their true species type. ASM is used to provide landmark points to segment the contour of the image, while the Complex Network model is used to extract the information from the contour of an image. The current system aims to confidently classify species, which is notifiable pathogen of Atlantic salmon, to their true class with high degree of accuracy. Finally, some concluding remarks are made along with proposal for future work

Toward an affect-sensitive multimodal human-computer interaction

The ability to recognize affective states of a person... This paper argues that next-generation human-computer interaction (HCI) designs need to include the essence of emotional intelligence -- the ability to recognize a user's affective states -- in order to become more human-like, more effective, and more efficient. Affective arousal modulates all nonverbal communicative cues (facial expressions, body movements, and vocal and physiological reactions). In a face-to-face interaction, humans detect and interpret those interactive signals of their communicator with little or no effort. Yet design and development of an automated system that accomplishes these tasks is rather difficult. This paper surveys the past work in solving these problems by a computer and provides a set of recommendations for developing the first part of an intelligent multimodal HCI -- an automatic personalized analyzer of a user's nonverbal affective feedback

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Enhancing video game performance through an individualized biocybernetic system

Biocybernetic systems are physiological software systems that explicitly utilize physiological signals to control or adapt software functionality (Pope et al., 1995.) These systems have tremendous potential for innovation in human computer interaction by using physiological signals to infer a user\u27s emotional and mental states (Allanson & Fairclough, 2004; Fairclough, 2008). Nevertheless, development of these systems has been ultimately hindered by two fundamental challenges. First, these systems make generalizations about physiological indicators of cognitive states across populations when, in fact, relationships between physiological responses and cognitive states are specific to each individual (Andreassi, 2006). Second, they often employ largely inconsistent retrospective techniques to subjectively infer user\u27s mental state (Fairclough, 2008). An individualized biocybernetic system was developed to address the fundamental challenges of biocybernetic research. This system was used to adapt video game difficulty through real-time classifications of physiological responses to subjective appraisals. A study was conducted to determine the system\u27s ability to improve player\u27s performance. The results provide evidence of significant task performance increase and higher attained task difficulty when players interacted with the game using the system than without. This work offers researchers with an alternative method for software adaptation by conforming to the individual characteristics of each user