This volume focuses on the advances in the Science, Technology, Higher Education, Society in the Conceptual Age, which are a critical aspect in the design of any technological system. The ideas and practical solutions described in the book are the outcome of dedicated research by academics and practitioners aiming to advance theory and practice in this dynamic and all-encompassing discipline. This book highlight new research in different fields for which the upcoming Conceptual Age is a common point. Leading researchers will continue to provide new ideas and guidance for those involved in creating contemporary and future conditions in the field of higher education, social sciences and new technologies. Research papers formed in various areas including psychology, management, life sciences, ergonomics and higher education issues.

This book reports on practical approaches for facilitating the process of achieving excellence in the management and leadership of organizational resources. It shows how the principles of creating shared value can be applied to ensure faster learning, training, business development, and social renewal. In particular, the book presents novel methods and tools for tackling the complexity of management and learning in both business organizations and society. It covers ontologies, intelligent management systems, methods for creating knowledge and value added. It gives novel insights into time management and operations optimization, as well as advanced methods for evaluating customers’ satisfaction and conscious experience. Based on the AHFE 2016 International Conference on Human Factors, Business Management and Society, held on July 27-31, 2016, Walt Disney World®, Florida, USA, the book provides both researchers and professionals with new tools and inspiring ideas for achieving excellence in various business activities.

As a bold provocation to reimagine what the philosophy of education might mean in the 21st century, this book responds to the exhaustion of present theoretical models and indeed the degradation of fabulative thought in its current prospectus. The contributors, from Asia, the Americas, and Europe, proffer a frank response to the everyday reality of the classroom where teachers compete with electronic devices for the attention of students whose minds are literally elsewhere, cocooned in the noospheric ether. Outside of lecture halls the world is suffering the rise of fascism, panic, and anger driven by precarious employment, and a looming fatalism and resignation in the face of ecological calamity. These developments have led to an avalanche of psychical woes afflicting young people ranging from trauma, the loss of hope and, in extremis, violence and suicide. The concerned and committed writers of this volume therefore raise the timely question of the return of utopia as a fitting, desperate, and indeed necessary response to the ecological, existential, and pedagogical crises spreading across the planet. At this most crucial juncture in human history, the excellent contributions to this book offer singularly unique perspectives regarding the possibility/impossibility of utopia. This book was originally published as a special issue of the journal, Educational Philosophy and Theory.

Neurodevelopmental disorders encompass a group of conditions that can typically manifest during early childhood. These include intellectual disabilities, communication disorders, autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), specific learning disorder (SLD), and motor disorders. Early identification and assessment can be a valuable tool in determining diagnosis, prognosis, functional abilities, and formulating clinical intervention for both children and adults with neurodevelopmental disorders. Emerging Trends in the Diagnosis and Intervention of Neurodevelopmental Disorders is an essential reference source that discusses and disseminates contributions covering recent trends in diagnosis, assessment, and intervention techniques. Featuring research on topics such as clinical linguistics, neural connectivity, and animal-assisted therapy, this book is ideally designed for speech pathologists, social workers, occupational therapists, psychologists, psychiatrists, neurologists, pediatricians, researchers, clinicians, and academicians seeking coverage on neural and developmental disorder identification and strategies for clinician support and therapies.

Data sources such as simulations, sensor networks across many application domains generate large volumes of time-series data which exhibit characteristics that evolve over time. Visual data analysis methods can help us in exploring and understanding the underlying patterns present in time-series data but, due to their ever-increasing size, the visual data analysis process can become complex. Large data sets can be handled using data abstraction techniques by transforming the raw data into a simpler format while, at the same time, preserving significant features that are important for the user. When dealing with time-series data, abstraction techniques should also take into account the underlying temporal characteristics. This thesis focuses on different data abstraction and pattern identification methods particularly in the cases of large 1D time-series and 2D spatio-temporal time-series data which exhibit spatiotemporal discontinuity. Based on the dimensionality and characteristics of the data, this thesis proposes a variety of efficient data-adaptive and user-controlled data abstraction methods that transform the raw data into a symbol sequence. The transformation of raw time-series into a symbol sequence can act as input to different sequence analysis methods from data mining and machine learning communities to identify interesting patterns of user behavior. In the case of very long duration 1D time-series, locally adaptive and user-controlled data approximation methods were presented to simplify the data, while at the same time retaining the perceptually important features. The simplified data were converted into a symbol sequence and a sketch-based pattern identification was then used to identify patterns in the symbolic data using regular expression based pattern matching. The method was applied to financial time-series and patterns such as head-and-shoulders, double and triple-top patterns were identified using hand drawn sketches in an interactive manner. Through data smoothing, the data approximation step also enables visualization of inherent patterns in the time-series representation while at the same time retaining perceptually important points. Very long duration 2D spatio-temporal eye tracking data sets that exhibit spatio-temporal discontinuity was transformed into symbolic data using scalable clustering and hierarchical cluster merging processes, each of which can be parallelized. The raw data is transformed into a symbol sequence with each symbol representing a region of interest in the eye gaze data. The identified regions of interest can also be displayed in a Space-Time Cube (STC) that captures both the temporal and contextual information. Through interactive filtering, zooming and geometric transformation, the STC representation along with linked views enables interactive data exploration. Using different sequence analysis methods, the symbol sequences are analyzed further to identify temporal patterns in the data set. Data collected from air traffic control officers from the domain of Air traffic control were used as application examples to demonstrate the results.