Software defect prediction using maximal information coefficient and fast correlation-based filter feature selection

Software quality ensures that applications that are developed are failure free. Some modern systems are intricate, due to the complexity of their information processes. Software fault prediction is an important quality assurance activity, since it is a mechanism that correctly predicts the defect proneness of modules and classifies modules that saves resources, time and developers’ efforts. In this study, a model that selects relevant features that can be used in defect prediction was proposed. The literature was reviewed and it revealed that process metrics are better predictors of defects in version systems and are based on historic source code over time. These metrics are extracted from the source-code module and include, for example, the number of additions and deletions from the source code, the number of distinct committers and the number of modified lines. In this research, defect prediction was conducted using open source software (OSS) of software product line(s) (SPL), hence process metrics were chosen. Data sets that are used in defect prediction may contain non-significant and redundant attributes that may affect the accuracy of machine-learning algorithms. In order to improve the prediction accuracy of classification models, features that are significant in the defect prediction process are utilised. In machine learning, feature selection techniques are applied in the identification of the relevant data. Feature selection is a pre-processing step that helps to reduce the dimensionality of data in machine learning. Feature selection techniques include information theoretic methods that are based on the entropy concept. This study experimented the efficiency of the feature selection techniques. It was realised that software defect prediction using significant attributes improves the prediction accuracy. A novel MICFastCR model, which is based on the Maximal Information Coefficient (MIC) was developed to select significant attributes and Fast Correlation Based Filter (FCBF) to eliminate redundant attributes. Machine learning algorithms were then run to predict software defects. The MICFastCR achieved the highest prediction accuracy as reported by various performance measures.School of ComputingPh. D. (Computer Science

Rejection-oriented learning without complete class information

Machine Learning is commonly used to support decision-making in numerous, diverse contexts. Its usefulness in this regard is unquestionable: there are complex systems built on the top of machine learning techniques whose descriptive and predictive capabilities go far beyond those of human beings. However, these systems still have limitations, whose analysis enable to estimate their applicability and confidence in various cases. This is interesting considering that abstention from the provision of a response is preferable to make a mistake in doing so. In the context of classification-like tasks, the indication of such inconclusive output is called rejection. The research which culminated in this thesis led to the conception, implementation and evaluation of rejection-oriented learning systems for two distinct tasks: open set recognition and data stream clustering. These system were derived from WiSARD artificial neural network, which had rejection modelling incorporated into its functioning. This text details and discuss such realizations. It also presents experimental results which allow assess the scientific and practical importance of the proposed state-of-the-art methodology.Aprendizado de Máquina é comumente usado para apoiar a tomada de decisão em numerosos e diversos contextos. Sua utilidade neste sentido é inquestionável: existem sistemas complexos baseados em técnicas de aprendizado de máquina cujas capacidades descritivas e preditivas vão muito além das dos seres humanos. Contudo, esses sistemas ainda possuem limitações, cuja análise permite estimar sua aplicabilidade e confiança em vários casos. Isto é interessante considerando que a abstenção da provisão de uma resposta é preferível a cometer um equívoco ao realizar tal ação. No contexto de classificação e tarefas similares, a indicação desse resultado inconclusivo é chamada de rejeição. A pesquisa que culminou nesta tese proporcionou a concepção, implementação e avaliação de sistemas de aprendizado orientados `a rejeição para duas tarefas distintas: reconhecimento em cenário abertos e agrupamento de dados em fluxo contínuo. Estes sistemas foram derivados da rede neural artificial WiSARD, que teve a modelagem de rejeição incorporada a seu funcionamento. Este texto detalha e discute tais realizações. Ele também apresenta resultados experimentais que permitem avaliar a importância científica e prática da metodologia de ponta proposta

Learning in behavioural robotics

The research described in this thesis examines how machine learning mechanisms can be used in an assembly robot system to improve the reliability of the system and reduce the development workload, without reducing the flexibility of the system. The justification foi' this is that for a robot to be performing effectively it is frequently necessary to have gained experience of its performance under a particular configuration before that configuration can be altered to produce a performance improvement. Machine learning mechanisms can automate this activity of testing, evaluating and then changing.From studying how other researchers have developed working robot systems the activities which require most effort and experimentation are:-• The selection of the optimal parameter settings. • The establishment of the action-sensor couplings which are necessary for the effective handling of uncertainty. • Choosing which way to achieve a goal.One way to implement the first two kinds of learning is to specify a model of the coupling or the interaction of parameters and results, and from that model derive an appropriate learning mechanism that will find a parametrisation for that model that will enable good performance to be obtained. From this starting point it has been possible to show how equal, or better performance can be obtained by using iearning mechanisms which are neither derived from nor require a model of the task being learned. Instead, by combining iteration and a task specific profit function it is possible to use a generic behavioural module based on a learning mechanism to achieve the task.Iteration and a task specific profit function can also be used to learn which behavioural module from a pool of equally competent modules is the best at any one time to use to achieve a particular goal. Like the other two kinds of learning, this successfully automates an otherwise difficult test and evaluation process that would have to be performed by a developer. In doing so effectively, it, like the other learning that has been used here, shows that instead of being a peripheral issue to be introduced to a working system, learning, carried out in the right way, can be instrumental in the production of that working system

Video content analysis for automated detection and tracking of humans in CCTV surveillance applications

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The problems of achieving high detection rate with low false alarm rate for human detection and tracking in video sequence, performance scalability, and improving response time are addressed in this thesis. The underlying causes are the effect of scene complexity, human-to-human interactions, scale changes, and scene background-human interactions. A two-stage processing solution, namely, human detection, and human tracking with two novel pattern classifiers is presented. Scale independent human detection is achieved by processing in the wavelet domain using square wavelet features. These features used to characterise human silhouettes at different scales are similar to rectangular features used in [Viola 2001]. At the detection stage two detectors are combined to improve detection rate. The first detector is based on shape-outline of humans extracted from the scene using a reduced complexity outline extraction algorithm. A Shape mismatch measure is used to differentiate between the human and the background class. The second detector uses rectangular features as primitives for silhouette description in the wavelet domain. The marginal distribution of features collocated at a particular position on a candidate human (a patch of the image) is used to describe statistically the silhouette. Two similarity measures are computed between a candidate human and the model histograms of human and non human classes. The similarity measure is used to discriminate between the human and the non human class. At the tracking stage, a tracker based on joint probabilistic data association filter (JPDAF) for data association, and motion correspondence is presented. Track clustering is used to reduce hypothesis enumeration complexity. Towards improving response time with increase in frame dimension, scene complexity, and number of channels; a scalable algorithmic architecture and operating accuracy prediction technique is presented. A scheduling strategy for improving the response time and throughput by parallel processing is also presented

Maintenance Management of Wind Turbines

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements

Resolution enhancement of thermal infrared images via high resolution class-map and statistical methods

Remote sensing from long stand-off distances offers numerous advantages. As our ability to extract information from data has increased, so has the need for high spatial resolution. Such results are often not available due to technological or financial limitations on the detectors which scan the scene and produce the imagery. Therefore, for many years to come, spatial resolution enhancement using additional data from a variety of sources shall remain popular and cost-effective. Work has been on-going at the Rochester Institute of Technology\u27s Center for Imaging Science in the spatial resolution enhancement of thermal infrared imagery. Background thermal imaging theory is presented and the most recent work by the Digital Imaging and Remote Sensing group is reviewed. A literature search of materials published on the topic since 1985 is included: numerous methods and techniques are presented. Based upon these concepts several areas of study were carried out. All investigations undertaken were confined to cases that ensure radiometric fidelity across image processing operations, since derivation of accurate temperature or emissivity maps necessitate this requirement. Given a low spatial resolution thermal band, these methods produced a high resolution estimate thereof based on enhancement using: (1) a single panchromatic band, (2) a high resolution class-map derived from multi-spectral bands and (3) a statistically based combination of multi-spectral bands

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Biologically inspired computational structures and processes for autonomous agents and robots

Recent years have seen a proliferation of intelligent agent applications: from robots for space exploration to software agents for information filtering and electronic commerce on the Internet. Although the scope of these agent applications have blossomed tremendously since the advent of compact, affordable computing (and the recent emergence of the World Wide Web), the design of such agents for specific applications remains a daunting engineering problem;Rather than approach the design of artificial agents from a purely engineering standpoint, this dissertation views animals as biological agents, and considers artificial analogs of biological structures and processes in the design of effective agent behaviors. In particular, it explores behaviors generated by artificial neural structures appropriately shaped by the processes of evolution and spatial learning;The first part of this dissertation deals with the evolution of artificial neural controllers for a box-pushing robot task. We show that evolution discovers high fitness structures using little domain-specific knowledge, even in feedback-impoverished environments. Through a careful analysis of the evolved designs we also show how evolution exploits the environmental constraints and properties to produce designs of superior adaptive value. By modifying the task constraints in controlled ways, we also show the ability of evolution to quickly adapt to these changes and exploit them to obtain significant performance gains. We also use evolution to design the sensory systems of the box-pushing robots, particularly the number, placement, and ranges of their sensors. We find that evolution automatically discards unnecessary sensors retaining only the ones that appear to significantly affect the performance of the robot. This optimization of design across multiple dimensions (performance, number of sensors, size of neural controller, etc.) is implicitly achieved by the evolutionary algorithm without any external pressure (e.g., penalty on the use of more sensors or neurocontroller units). When used in the design of robots with limited battery capacities , evolution produces energy-efficient robot designs that use minimal numbers of components and yet perform reasonably well. The performance as well as the complexity of robot designs increase when the robots have access to a spatial learning mechanism that allows them to learn, remember, and navigate to power sources in the environment;The second part of this dissertation develops a computational characterization of the hippocampal formation which is known to play a significant role in animal spatial learning. The model is based on neuroscientific and behavioral data, and learns place maps based on interactions of sensory and dead-reckoning information streams. Using an estimation mechanism known as Kalman filtering, the model explicitly deals with uncertainties in the two information streams, allowing the robot to effectively learn and localize even in the presence sensing and motion errors. Additionally, the model has mechanisms to handle perceptual aliasing problems (where multiple places in the environment appear sensorily identical), incrementally learn and integrate local place maps, and learn and remember multiple goal locations in the environment. We show a number of properties of this spatial learning model including computational replication of several behavioral experiments performed with rodents. Not only does this model make significant contributions to robot localization, but also offers a number of predictions and suggestions that can be validated (or refuted) through systematic neurobiological and behavioral experiments with animals