Jue Wang – författare

This is the second edition of an introductory text that describes the principles of invariant measurement; how invariant measurement can be achieved using Rasch measurement theory; and how to use invariant measurement to solve a variety of measurement problems in the social, behavioral, and health sciences. Rasch models are used throughout the text, but brief comparisons of Rasch models to other item response theory (IRT) models are also provided.Written with students in mind, this new edition was class-tested to help maximize accessibility. Chapters open with an introduction and close with a discussion and summary. All chapters have been updated from the first edition, and a new chapter on explanatory Rasch models has been added. Features include numerous examples and exercises to demonstrate the main issues addressed in each chapter. Key terms are defined when first introduced and included in a helpful end-of-text glossary.This book also benefits from online materials which include the data sets used in the book, sample syntax files for running the Facets program, Excel files for creating item and person response functions, and links to related websites.This book will act as a supplementary text for graduate or advanced undergraduate courses on measurement or test theory, IRT, scaling theory, psychometrics, advanced measurement techniques, research methods, or evaluation research taught in education, psychology, and other social and health sciences. It will also appeal to practitioners and researchers in these fields who develop or use scales and instruments. Only a basic mathematical level is required, including a basic course in statistics, ensuring it is an accessible resource for students and researchers alike.

This is the second edition of an introductory text that describes the principles of invariant measurement; how invariant measurement can be achieved using Rasch measurement theory; and how to use invariant measurement to solve a variety of measurement problems in the social, behavioral, and health sciences. Rasch models are used throughout the text, but brief comparisons of Rasch models to other item response theory (IRT) models are also provided.Written with students in mind, this new edition was class-tested to help maximize accessibility. Chapters open with an introduction and close with a discussion and summary. All chapters have been updated from the first edition, and a new chapter on explanatory Rasch models has been added. Features include numerous examples and exercises to demonstrate the main issues addressed in each chapter. Key terms are defined when first introduced and included in a helpful end-of-text glossary.This book also benefits from online materials which include the data sets used in the book, sample syntax files for running the Facets program, Excel files for creating item and person response functions, and links to related websites.This book will act as a supplementary text for graduate or advanced undergraduate courses on measurement or test theory, IRT, scaling theory, psychometrics, advanced measurement techniques, research methods, or evaluation research taught in education, psychology, and other social and health sciences. It will also appeal to practitioners and researchers in these fields who develop or use scales and instruments. Only a basic mathematical level is required, including a basic course in statistics, ensuring it is an accessible resource for students and researchers alike.

Business Intelligence in Economic Forecasting: Technologies and Techniques discusses various Business Intelligence techniques including neural networks, support vector machine, genetic programming, clustering analysis, TEI@I, fuzzy systems, text mining, and many more. This publication serves as a valuable reference for professionals and researchers interested in BI technologies and their practical applications in economic forecasting, as well as policy makers in business organizations and governments.

To thrive, every living cell must continuously gauge and respond to changes in its environment. These changes are ultimately implemented by modulating gene expression, a process that relies on transcription by Nature’s most multivalent molecular machine, the RNA polymerase. This book covers progress made over the past decade understanding how this machine functions to compute the cellular state, from the atomistic structural level responsible for chemistry to the integrative level at which RNA polymerase interacts with the other key molecular machineries of the cell.

Intelligence and security informatics (ISI) is concerned with the study of the development and use of advanced information technologies and systems for national, international, and societal security-related applications. The annual IEEE International Conference series on ISI was started in 2003 and the first four meetings were held in the United States. In 2006, the Workshop on ISI (http://isi. se. cuhk. edu. hk/2006/) was held in Singapore in conjunction with the Pacific Asia Conference on Knowledge Discovery and Data Mining, with over 100 contributors and participants from all over the world. These past ISI conferences have brought together academic researchers, law enforcement and intelligence experts, information technology consultants, and practitioners to discuss their research and practice related to various ISI topics including ISI data management, data and text mining for ISI applications, terrorism informatics, deception and intent detection, terrorist and criminal social network analysis, public health and bio-security, crime analysis, cyber-infrastructure protection, transportation infrastructure security, policy studies and evaluation, and information assurance, among others. We continued this stream of ISI conferences by organizing the 2007 Pacific Asia Workshop on ISI (PAISI 2007) to especially provide a stimulating forum for ISI researchers in Pacific Asia and other regions of the world to exchange ideas and report research progress. PAISI 2007 was hosted by the Chinese Academy of Sciences, the University of Arizona, and the Chinese University of Hong Kong.

This book presents the most recent state-of-the-art algorithms on robust environmental perception and reliability control for intelligent vehicle systems. By integrating object detection, semantic segmentation, trajectory prediction, multi-object tracking, multi-sensor fusion, and reliability control in a systematic way, this book is aimed at guaranteeing that intelligent vehicles can run safely in complex road traffic scenes.Adopts the multi-sensor data fusion-based neural networks to environmental perception fault tolerance algorithms, solving the problem of perception reliability when some sensors fail by using data redundancy.Presents the camera-based monocular approach to implement the robust perception tasks, which introduces sequential feature association and depth hint augmentation, and introduces seven adaptive methods.Proposes efficient and robust semantic segmentation of traffic scenes through real-time deep dual-resolution networks and representation separation of vision transformers.Focuses on trajectory prediction and proposes phased and progressive trajectory prediction methods that is more consistent with human psychological characteristics, which is able to take both social interactions and personal intentions into account.Puts forward methods based on conditional random field and multi-task segmentation learning to solve the robust multi-object tracking problem for environment perception in autonomous vehicle scenarios.    Presents the novel reliability control strategies of intelligent vehicles to optimize the dynamic tracking performance and investigates the completely unknown autonomous vehicle tracking issues with actuator faults.