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  • [Front cover]
    IEEE Intell. Syst. (IF 4.464) Pub Date : 2019-11-22

    Presents the front cover for this issue of the publication.

    更新日期:2020-01-04
  • Masthead
    IEEE Intell. Syst. (IF 4.464) Pub Date : 2019-11-22

    Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

    更新日期:2020-01-04
  • Table of contents
    IEEE Intell. Syst. (IF 4.464) Pub Date : 2019-11-22

    Presents the table of contents for this issue of the publication.

    更新日期:2020-01-04
  • Detecting Thermal Discomfort of Drivers Using Physiological Sensors and Thermal Imaging
    IEEE Intell. Syst. (IF 4.464) Pub Date : 2019-08-30
    Mohamed Abouelenien; Mihai Burzo

    Recent technological developments have been used extensively in manufacturing vehicles in order to improve the driving experience and add multiple safety features. This article introduces a novel machine learning approach using physiological sensors and thermal imaging of the subjects to detect human thermal discomfort in order to develop a fully automated climate control system in the vehicles that does not need any explicit input from individuals. To achieve this goal, a dataset of thermal videos and physiological signals from 50 subjects is collected, an extensive analysis of different feature sets is conducted, a multimodal approach is experimented, and a cascaded classification system is proposed. Our results evidently show the capability of specific feature sets of detecting human thermal discomfort as well as the superior performance of integrating multimodal features.

    更新日期:2020-01-04
  • IEEE TRANSACTIONS ON BIG DATA
    IEEE Intell. Syst. (IF 4.464) Pub Date : 2019-11-22

    Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

    更新日期:2020-01-04
  • Keyword Generation for Sponsored Search Advertising: Balancing Coverage and Relevance
    IEEE Intell. Syst. (IF 4.464) Pub Date : 2019-09-03
    Han Nie; Yanwu Yang; Daniel Zeng

    How to automatically generate a pool of keywords used by potential consumers is a challenging issue for advertisers in sponsored search advertising (SSA). Such a keyword pool serves as the base for market research and determines the feasible space of consequent keyword related decisions. This article presents a novel method for keyword generation with Wikipedia as a corpus of the source text (WIKG). Starting with a few seed keywords, the WIKG supports flexible keywords generation by taking advantage of Wikipedia's rich link structure to construct a graph of entry articles in an iterative way. The termination condition is determined by a threshold reflecting the tradeoff between coverage of the generated keyword set and its relevance to seed keywords. Experimental results show that the WIKG outperforms three baselines derived from the extant literature, in terms of both coverage and relevance.

    更新日期:2020-01-04
  • A Formal Graphical Language of Interdependence in Teamwork
    IEEE Intell. Syst. (IF 4.464) Pub Date : 2019-09-19
    Changyun Wei; Koen V. Hindriks; M. Birna van Riemsdijk; Catholijn M. Jonker

    Agents in teamwork may be highly interdependent on each other, and the awareness of interdependences is an important requirement for designing and consequently implementing a multiagent system. In this article, we propose a formal graphical and domain-independent language that can facilitate the identification of comprehensive interdependences among the agents in teamwork. Moreover, a formal semantics is also introduced to precisely express and explain the properties of a graphical structure. The novel feature of the graphical language is that it complements the Interdependence Analysis Color Scheme in a way that explicitly models negative influences and, in addition, provides a visual-communication aid for developers. To demonstrate the applicability and sufficiency of the graphical language in a variety of domains, our case studies include a multirobot scenario and a human-robot scenario.

    更新日期:2020-01-04
  • Security & Privacy
    IEEE Intell. Syst. (IF 4.464) Pub Date : 2019-11-22

    Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

    更新日期:2020-01-04
  • IEEE Computer Society Has You Covered!
    IEEE Intell. Syst. (IF 4.464) Pub Date : 2019-11-22

    Advertisement, IEEE..

    更新日期:2020-01-04
  • IEEE Computer Society
    IEEE Intell. Syst. (IF 4.464) Pub Date : 2019-11-22

    Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

    更新日期:2020-01-04
  • Keep Your Career Options Open
    IEEE Intell. Syst. (IF 4.464) Pub Date : 2019-11-22

    Advertisement, IEEE.

    更新日期:2020-01-04
  • Learning Setting-Generalized Activity Models for Smart Spaces.
    IEEE Intell. Syst. (IF 4.464) Pub Date : 2011-04-05
    Diane J Cook

    The data mining and pervasive computing technologies found in smart homes offer unprecedented opportunities for providing context-aware services, including health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to provide these services, smart environment algorithms need to recognize and track activities that people normally perform as part of their daily routines. However, activity recognition has typically involved gathering and labeling large amounts of data in each setting to learn a model for activities in that setting. We hypothesize that generalized models can be learned for common activities that span multiple environment settings and resident types. We describe our approach to learning these models and demonstrate the approach using eleven CASAS datasets collected in seven environments.

    更新日期:2019-11-01
  • The CKC Challenge: Exploring Tools for Collaborative Knowledge Construction.
    IEEE Intell. Syst. (IF 4.464) Pub Date : 2008-01-01
    Natalya F Noy,Abhita Chugh,Harith Alani

    The great success of Web 2.0 is mainly fuelled by an infrastructure that allows web users to create, share, tag, and connect content and knowledge easily. The tools for developing structured knowledge in this manner have started to appear as well. However, there are few, if any, user studies that are aimed at understanding what users expect from such tools, what works and what doesn't. We organized the Collaborative Knowledge Construction (CKC) Challenge to assess the state of the art for the tools that support collaborative processes for creation of various forms of structured knowledge. The goal of the Challenge was to get users to try out different tools and to learn what users expect from such tools-features that users need, features that they like or dislike. The Challenge task was to construct structured knowledge for a portal that would provide information about research. The Challenge design contained several incentives for users to participate. Forty-nine users registered for the Challenge; thirty-three of them participated actively by using the tools. We collected extensive feedback from the users where they discussed their thoughts on all the tools that they tried. In this paper, we present the results of the Challenge, discuss the features that users expect from tools for collaborative knowledge constructions, the features on which Challenge participants disagreed, and the lessons that we learned.

    更新日期:2019-11-01
  • Imaging the Social Brain by Simultaneous Hyperscanning During Subject Interaction.
    IEEE Intell. Syst. (IF 4.464) Pub Date : 2012-01-31
    Laura Astolfi,Jlenia Toppi,Fabrizio De Vico Fallani,Giovanni Vecchiato,Febo Cincotti,Christopher T Wilke,Han Yuan,Donatella Mattia,Serenella Salinari,Bin He,Fabio Babiloni

    更新日期:2019-11-01
  • How Will the Internet of Things Enable Augmented Personalized Health?
    IEEE Intell. Syst. (IF 4.464) Pub Date : 2018-06-12
    Amit Sheth,Utkarshani Jaimini,Hong Yung Yip

    The Internet of Things refers to network-enabled technologies, including mobile and wearable devices, which are capable of sensing and actuation as well as interaction and communication with other similar devices over the Internet. The IoT is profoundly redefining the way we create, consume, and share information. Ordinary citizens increasingly use these technologies to track their sleep, food intake, activity, vital signs, and other physiological statuses. This activity is complemented by IoT systems that continuously collect and process environment-related data that has a bearing on human health. This synergy has created an opportunity for a new generation of healthcare solutions.

    更新日期:2019-11-01
  • Opportunities for Operations Research in Medical Decision Making.
    IEEE Intell. Syst. (IF 4.464) Pub Date : 2015-01-20
    Sait Tunc,Oguzhan Alagoz,Elizabeth Burnside

    更新日期:2019-11-01
  • Recent Advances in Computational Epidemiology.
    IEEE Intell. Syst. (IF 4.464) Pub Date : 2013-07-01
    Madhav Marathe,Naren Ramakrishnan

    更新日期:2019-11-01
  • Topic-Aware Physical Activity Propagation in a Health Social Network.
    IEEE Intell. Syst. (IF 4.464) Pub Date : 2016-04-19
    Nhathai Phan,Javid Ebrahimi,Dave Kil,Brigitte Piniewski,Dejing Dou

    Modeling physical activity propagation, such as physical exercise level and intensity, is the key to preventing the conduct that can lead to obesity; it can also help spread wellness behavior in a social network.

    更新日期:2019-11-01
Contents have been reproduced by permission of the publishers.
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