49,477 research outputs found

    A novel Big Data analytics and intelligent technique to predict driver's intent

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    Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

    Animal welfare science: recent publication trends and future research priorities

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    Animal welfare science is a young and thriving field. Over the last two decades, the output of scientific publications on welfare has increased by c. 10-15% annually (tripling as a proportion of all science papers logged by ISI’s Web of Science), with just under half the c. 8500 total being published in the last 4 years. These papers span an incredible 500+ journals, but around three quarters have been in 80 animal science, veterinary, ethology, conservation and specialized welfare publications, and nearly 25% are published in just two: Animal Welfare and Applied Animal Behaviour Science. Farmed animals – especially mammals – have attracted by far the most research. This broadly reflects the vastness of their populations and the degree of public concern they elicit; poultry, however, are under-studied, and farmed fish ever more so: fish have only recently attracted welfare research, and are by far the least studied of all agricultural species, perhaps because of ongoing doubts about their sentience. We predict this farm animal focus will continue in the future, but embracing more farmed fish, reptiles and invertebrates, and placing its findings within broader international contexts such as environmental and food security concerns. Laboratory animals have been consistently well studied, with a shift in recent years away from primates and towards rodents. Pets, the second largest animal sector after farmed animals, have in contrast been little studied considering their huge populations (cats being especially overlooked): we anticipate research on them increasing in the future. Captive wild animals, especially mammals, have attracted a consistent level of welfare research over the last two decades. Given the many thousands of diverse species kept by zoos, this must, and we predict will, increase. Future challenges and opportunities including refining the use of preference tests, stereotypic behaviour, corticosteroid outputs and putative indicators of positive affect, to enable more valid conclusions about welfare; investigating the evolution and functions of affective states; and last but not least, identifying which taxonomic groups and stages of development are actually sentient and so worthy of welfare concern

    Network destabilization and transition in depression : new methods for studying the dynamics of therapeutic change

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    The science of dynamic systems is the study of pattern formation and system change. Dynamic systems theory can provide a useful framework for understanding the chronicity of depression and its treatment. We propose a working model of therapeutic change with potential to organize findings from psychopathology and treatment research, suggest new ways to study change, facilitate comparisons across studies, and stimulate treatment innovation. We describe a treatment for depression that we developed to apply principles from dynamic systems theory and then present a program of research to examine the utility of this application. Recent methodological and technological developments are also discussed to further advance the search for mechanisms of therapeutic change

    Semantic discovery and reuse of business process patterns

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    Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

    Emotions in context: examining pervasive affective sensing systems, applications, and analyses

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    Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

    Machine Understanding of Human Behavior

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    A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior
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