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Multi-item surveys are frequently used to study scores on latent factors, like human values, attitudes and behavior. Such studies often include a comparison, between specific groups of individuals, either at one or multiple points in time. If such latent factor means are to be meaningfully compared, the measurement structures including the latent factor and their survey items should be stable across groups and/or over time, that is ‘invariant’. Recent developments in statistics have provided new analytical tools for assessing measurement invariance (MI). The aim of this special issue is to provide a forum for a discussion of MI, covering some crucial ‘themes’: (1) ways to assess and deal with measurement non-invariance; (2) Bayesian and IRT methods employing the concept of approximate measurement invariance; and (3) new or adjusted approaches for testing MI to fit increasingly complex statistical models and specific characteristics of survey data. The special issue started with a kick-off meeting where all potential contributors shared ideas on potential papers. This expert workshop was organized at Utrecht University in The Netherlands and was funded by the Netherlands Organization for Scientific Research (NWO-VENI-451-11-008). After the kick-off meeting the authors submitted their papers, all of which were reviewed by experts in the field. The papers in the eBook are listed in alphabetical order, but in the editorial the papers are introduced thematically. Although it is impossible to cover all areas of relevant research in the field of MI, papers in this eBook provide insight on important aspects of measurement invariance. We hope that the discussions included in this special issue will stimulate further research on MI and facilitate further discussions to support the understanding of the role of MI in multi-item surveys.

Non-invariance --- Partial Invariance --- Structural Equation Modeling --- bayesian statistics --- cross national surveys --- Measurement invariance --- Approximate invariance --- multiple group analysis

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Computer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation of algorithms that can automatically process visual data to recognize objects, track and recover their shape and spatial layout.  This unique text/reference presents a broad selection of cutting-edge research, covering both theoretical and practical aspects of the three main areas in computer vision: reconstruction, registration, and recognition. The book provides an in-depth overview of challenging areas, in addition to descriptions of novel algorithms that exploit machine learning and pattern recognition techniques to infer the semantic content of images and videos.  Topics and features: Investigates visual features, trajectory features, and stereo matching Reviews the main challenges of semi-supervised object recognition, and a novel method for human action categorization Presents a framework for the visual localization of MAVs, and for the use of moment constraints in convex shape optimization Examines solutions to the co-recognition problem, and distance-based classifiers for large-scale image classification Describes how the four-color theorem can be used in early computer vision for solving MRF problems where an energy is to be minimized Introduces a Bayesian generative model for understanding indoor environments, and a boosting approach for generalizing the k-NN rule Discusses the issue of scene-specific object detection, and an approach for making temporal super resolution video from a single input image sequence  This must-read collection will be of great value to advanced undergraduate and graduate students of computer vision, pattern recognition and machine learning. Researchers and practitioners will also find the book useful for understanding and reviewing current approaches in computer vision.

Computer science --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- computervisie --- patroonherkenning --- machine learning --- Bayesian statistics --- object recognition --- computers --- grafische vormgeving --- KI (kunstmatige intelligentie) --- computerkunde --- AI (artificiële intelligentie) --- Computer vision.

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This symposium was born as a research forum to present and discuss original, rigorous and significant contributions on Artificial Intelligence-based (AI) solutions—with a strong, practical logic and, preferably, with empirical applications—developed to aid the management of organizations in multiple areas, activities, processes and problem-solving; what we call Management Intelligent Systems (MiS).   This volume presents the proceedings of these activities in a collection of contributions with many original approaches. They address diverse Management and Business areas of application such as decision support, segmentation of markets, CRM, product design, service personalization, organizational design, e-commerce, credit scoring, workplace integration, innovation management, business database analysis, workflow management, location of stores, etc. A wide variety of AI techniques have been applied to these areas such as multi-objective optimization and evolutionary algorithms, classification algorithms, ant algorithms, fuzzy rule-based systems, intelligent agents, Web mining, neural networks, Bayesian models, data warehousing, rough sets, etc. This volume also includes a track focused on the latest research on Intelligent Systems and Technology Enhanced Learning (iTEL), as well as its impacts for learners and institutions. It aims at bringing together researchers and developers from both the professional and the academic realms to present, discuss and debate the latest advances on intelligent systems and technology-enhanced learning   The symposium was organized by the Soft Computing and Intelligent Information Systems Research Group (http://sci2s.ugr.es) of the University of Granada (Spain) and the Bioinformatics, Intelligent System and Educational Technology Research Group (http:// bisite.usal.es/) of the University of Salamanca (Spain). The present edition was held in Salamanca (Spain) on May 22–24, 2013.  .

Applied physical engineering --- Artificial intelligence. Robotics. Simulation. Graphics --- neuronale netwerken --- fuzzy logic --- cybernetica --- bio-informatica --- Bayesian statistics --- webdesign --- KI (kunstmatige intelligentie) --- ingenieurswetenschappen --- robots --- AI (artificiële intelligentie) --- Management information systems. --- Information resources management.

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Speech is the most natural mode of communication and yet attempts to build systems which support robust habitable conversations between a human and a machine have so far had only limited success. A key reason is that current systems treat speech input as equivalent to a keyboard or mouse, and behaviour is controlled by predefined scripts that try to anticipate what the user will say and act accordingly. But speech recognisers make many errors and humans are not predictable; the result is systems which are difficult to design and fragile in use. Statistical methods for spoken dialogue management takes a radically different view. It treats dialogue as the problem of inferring a user's intentions based on what is said. The dialogue is modelled as a probabilistic network and the input speech acts are observations that provide evidence for performing Bayesian inference. The result is a system which is much more robust to speech recognition errors and for which a dialogue strategy can be learned automatically using reinforcement learning. The thesis describes both the architecture, the algorithms needed for fast real-time inference over very large networks, model parameter estimation and policy optimisation. This ground-breaking work will be of interest both to practitioners in spoken dialogue systems and to cognitive scientists interested in models of human behaviour.

Affective and dynamic functions --- Cognitive psychology --- Psychology --- Statistical science --- Operational research. Game theory --- Psychiatry --- Neuropathology --- Applied physical engineering --- Computer science --- Computer. Automation --- beeldverwerking --- medische psychologie --- toegepaste psychologie --- spraaktechnologie --- stochastische analyse --- machine learning --- Bayesian statistics --- computers --- cognitieve psychologie --- bewustzijn --- neuropsychologie --- informatietechnologie --- ingenieurswetenschappen --- computerkunde --- signaalverwerking --- statistisch onderzoek --- Speech processing systems. --- Discourse analysis.

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“Particle Filters for Random Set Models” presents coverage of state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based  on the Monte Carlo statistical method. The resulting  algorithms, known as particle filters, in the last decade have become one of the essential tools for stochastic filtering, with applications ranging from  navigation and autonomous vehicles to bio-informatics and finance. While particle filters have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. These recent developments have dramatically widened the scope of applications, from single to multiple appearing/disappearing objects, from precise to imprecise measurements and measurement models. This book is ideal for graduate students, researchers, scientists and engineers interested in Bayesian estimation.

Mathematics --- Operational research. Game theory --- Applied physical engineering --- Computer science --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- neuronale netwerken --- beeldverwerking --- AGV (autonoom geleide voertuigen) --- fuzzy logic --- ICT (informatie- en communicatietechnieken) --- cybernetica --- stochastische analyse --- bio-informatica --- Bayesian statistics --- computers --- informatiesystemen --- informatietechnologie --- wiskunde --- KI (kunstmatige intelligentie) --- ingenieurswetenschappen --- computerkunde --- robots --- signaalverwerking --- AI (artificiële intelligentie) --- Random sets. --- Stochastic processes. --- Estimation theory.