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Intelligent Systems, IEEE

Issue 5 • Date Sept.-Oct. 2011

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Displaying Results 1 - 18 of 18
  • [Front cover]

    Page(s): c1
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  • [Front cover]

    Page(s): c2
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  • Contents

    Page(s): 1
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  • A Report on the San Francisco Board Meeting

    Page(s): 2 - 4
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  • In the News

    Page(s): 5 - 9
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  • Hierarchical and Modular Surveillance Systems in ITS

    Page(s): 10 - 15
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    Over the last 30 years, video surveillance systems have been a key part of intelligent transportation systems (ITSs), which use various image sensors to capture visual information about vehicles and pedestrians to obtain real-time knowledge of traffic conditions. Specifically, they capture vehicles' visual appearances and support mining more information about them through ve hicle detection, localization, and classification; license plate recognition; vehicle-behavior analysis; and so forth. They also help generate overall vehicle statistics such as estimations of flow rate, average speed, and density. In addition, video surveillance systems can capture pedestrian visual information to support their detection and behavior analysis, especially their interactions with vehicles, which can help identify impending traffic accidents. View full abstract»

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  • Brain Informatics

    Page(s): 16 - 21
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  • Using Brain Imaging to Interpret Student Problem Solving

    Page(s): 22 - 29
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    We have been exploring whether multi voxel pattern analysis (MVPA) of functional magnet resonance imaging (fMRI) data can be used to infer the mental states of students learning mathematics. This approach has shown considerable success in tracking static mental states such as whether a person is thinking about a location or an animal. Applying this to our case involves significant challenges not faced in many MVPA applications because it is necessary to track changing student states over time. The paths of states that students take in solving problems can be quite variable. Nevertheless, we have achieved relatively high accuracy in determining what step a student is on when solving a sequence of problems and whether that step is being performed correctly. Hidden Markov models can then be used to combine behavioral and brain-imaging data from an intelligent tutoring system to track mental states during student's problem-solving episodes. View full abstract»

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  • Cyber-Individual Meets Brain Informatics

    Page(s): 30 - 37
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    To help people live better in today's digitally explosive environment, the authors envision a Cyber-Individual (Cyber-I) that is the counterpart of a real individual in the physical world. View full abstract»

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  • Imaging the Social Brain by Simultaneous Hyperscanning during Subject Interaction

    Page(s): 38 - 45
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    Advances in neuroelectric recordings and computational tools allow investigation of interactive brain activity and connectivity in a group of subjects engaged in social interactions. View full abstract»

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  • EEG-Based Cognitive Interfaces for Ubiquitous Applications: Developments and Challenges

    Page(s): 46 - 53
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    Technical advances in the neuroelectric recordings and in the computational tools for the analysis of the brain activity and connectivity make it now possible to follow and to quantify, in real time, the interactive brain activity in a group of subjects engaged in social interactions. The degree of interaction between persons can then be assessed by "reading" their neuroelectric activities. Imaging the social brain can thus open a new area of study in neuroscience. View full abstract»

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  • Pattern Recognition Approaches for Identifying Subcortical Targets during Deep Brain Stimulation Surgery

    Page(s): 54 - 63
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    This paper presents the approach for identifying subcortical targets during deep brain stimulation surgery.Pattern recognition approaches can help localize neural targets for therapeutic neurostimulation, such as deep brain stimulation of the subthalamic nucleus in Parkinson's disease. A variety of neurophysiological signals are routinely collected from patients with brain diseases in the hope that such signals contain meaningful patterns that reflect underlying pathological brain functions. However, little is understood about the underlying mechanisms of brain diseases because those data are often massive and noisy. Data processing tools might help extract meaningful, yet hidden, information from massive neuro physiological data for both clinical and scientific uses. View full abstract»

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  • Analyzing Neural Interaction Characteristics in a Monkey's Motor Cortex during Reach-to-Grasp Tasks

    Page(s): 64 - 71
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    Applying a dynamic Bayesian network model can help detect neural interactions and analyze the characteristics of a monkey's motor cortex during reach-to-grasp tasks. View full abstract»

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  • Franchise Experts

    Page(s): 72 - 77
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    This article harks back to the origins of this periodical as IEEE Expert Systems. Even while expert systems as a field or paradigm was morphing into intelligent systems, it was recognized that cognitive task analysis was critical in the design of new technologies. Furthermore, as a part of cognitive task analysis, it is crucial to conduct some form of proficiency scale to help identify the experts whose knowledge and skill might be revealed and specified in the creation of reasoning and knowledge-based systems. In this article, we will advance the claim that identifying and studying franchise experts can contribute to the design of intelligent systems. Of further interest is the possibility that the knowledge elicited from such experts might be invaluable for the practice of accelerated learning. We refer to "franchise" experts because they are not only expert in their chosen technical domain but also expert with regard to the organizations to which they belong. As a concept map organizer within a "knowledge model," some of the nodes are appended with icons that link to other concept maps that drill down into details, technical documentation, schematics, URLs, and so forth. This concept map shows that the franchise expert's knowledge refers to organizational structures and topics. View full abstract»

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  • Smart health and wellbeing [Trends & Controversies]

    Page(s): 78 - 90
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    In light of such overwhelming interest from governments and academia in adopting and advancing IT for effective healthcare, there are great opportunities for researchers and practitioners alike to invest efforts in conducting innovative and high-impact healthcare IT research. This IEEE Intelligent Systems Trends and Controversies (T&C) Department hopes to raise awareness and highlight selected recent research that helps move us toward such goals. This department includes three articles on Smart Health and Wellbeing from distinguished experts in computer science, information systems, and medicine. Each article presents unique perspectives, advanced computational methods, and selected results and examples. View full abstract»

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  • Identifying Social Influence in Networks Using Randomized Experiments

    Page(s): 91 - 96
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    The recent availability of massive amounts of networked data generated by email, instant messaging, mobile phone communications, micro blogs, and online social networks is enabling studies of population-level human interaction on scales orders of magnitude greater than what was previ ously possible.1'2 One important goal of applying statistical inference techniques to large networked datasets is to understand how behavioral conta gions spread in human social networks. More pre cisely, understanding how people influence or are influenced by their peers can help us understand the ebb and flow of market trends, product adoption and diffusion, the spread of health behaviors such as smoking and exercise, the productivity of information workers, and whether particular indi viduals in a social network have a disproportion ate amount of influence on the system. View full abstract»

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  • [Advertisement - Back cover]

    Page(s): c3
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  • [Advertisement - Back cover]

    Page(s): c4
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Aims & Scope

IEEE Intelligent Systems serves users, managers, developers, researchers, and purchasers who are interested in intelligent systems and artificial intelligence, with particular emphasis on applications.

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Meet Our Editors

Editor-in-Chief
Daniel Zeng
University of Arizona