Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Inferring implicit relevance from physiological signals

Ongoing growth in data availability and consumption has meant users are increasingly faced with the challenge of distilling relevant information from an abundance of noise. Overcoming this information overload can be particularly difficult in situations such as intelligence analysis, which involves subjectivity, ambiguity, or risky social implications. Highly automated solutions are often inadequate, therefore new methods are needed for augmenting existing analysis techniques to support user decision making. This project investigated the potential for deep learning to infer the occurrence of implicit relevance assessments from users' biometrics. Internal cognitive processes manifest involuntarily within physiological signals, and are often accompanied by 'gut feelings' of intuition. Quantifying unconscious mental processes during relevance appraisal may be a useful tool during decision making by offering an element of objectivity to an inherently subjective situation. Advances in wearable or non-contact sensors have made recording these signals more accessible, whilst advances in artificial intelligence and deep learning have enhanced the discovery of latent patterns within complex data. Together, these techniques might make it possible to transform tacit knowledge into codified knowledge which can be shared. A series of user studies recorded eye gaze movements, pupillary responses, electrodermal activity, heart rate variability, and skin temperature data from participants as they completed a binary relevance assessment task. Participants were asked to explicitly identify which of 40 short-text documents were relevant to an assigned topic. Investigations found this physiological data to contain detectable cues corresponding with relevance judgements. Random forests and artificial neural networks trained on features derived from the signals were able to produce inferences with moderate correlations with the participants' explicit relevance decisions. Several deep learning algorithms trained on the entire physiological time series data were generally unable to surpass the performance of feature-based methods, and instead produced inferences with low correlations with participants' explicit personal truths. Overall, pupillary responses, eye gaze movements, and electrodermal activity offered the most discriminative power, with additional physiological data providing diminishing or adverse returns. Finally, a conceptual design for a decision support system is used to discuss social implications and practicalities of quantifying implicit relevance using deep learning techniques. Potential benefits included assisting with introspection and collaborative assessment, however quantifying intrinsically unknowable concepts using personal data and abstruse artificial intelligence techniques were argued to pose incommensurate risks and challenges. Deep learning techniques therefore have the potential for inferring implicit relevance in information-rich environments, but are not yet fit for purpose. Several avenues worthy of further research are outlined

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

Reports to the President

A compilation of annual reports for the 1989-1990 academic year, including a report from the President of the Massachusetts Institute of Technology, as well as reports from the academic and administrative units of the Institute. The reports outline the year's goals, accomplishments, honors and awards, and future plans