The Experts below are selected from a list of 1992 Experts worldwide ranked by ideXlab platform
Mohammad Yamin - One of the best experts on this subject based on the ideXlab platform.
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A distributed smart fusion framework based on hard and soft sensors
International Journal of Information Technology, 2017Co-Authors: Girija Chetty, Mohammad YaminAbstract:In this paper we propose a novel intelligent processing approach based on hard and soft sensor fusion for obtaining better Actionable Intelligence from automatic computer based decision support systems. The proposed smart fusion framework with particular focus on combining heterogeneous, multimedia, multimodal real-time big data streams—from hard and soft smart phone sensors, allows synergistic fusion to be achieved, leading to better operational Intelligence from the computer based decision support systems. The details of this framework implementation with a component based software platform—the msifStudio, and its evaluation for some of the use case application scenarios is presented here.
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A smart fusion framework for multimodal object, activity and event detection
2016Co-Authors: Girija Chetty, Mohammad YaminAbstract:With an increasing diffusion of wearable technologies and mobile sensor systems, along with entrenchment of social media networks and crowdsourced information systems in every aspect of modern society, an unavoidable reality is that of continuous, pervasive and ubiquitous sensing, monitoring, surveillance and detection of every type of object, activity, event and incident at a global scale. This rapid proliferation has provided immense opportunities to make use of comprehensive information from a diverse array of multimodal, multi-view, and multisensory data streams for developing efficient and robust, automated computer based decision support systems. Further, with the availability of the complementary and the supplementary information in terms of auxiliary meta-data from the social networks, human experts and the crowdsourced communities, it is possible to obtain better Actionable Intelligence from these systems. In this paper, we propose a novel computational framework for addressing this gap. The proposed smart fusion framework with particular focus on combining heterogeneous, multimodal real-time big data streams — with information from different types of sensor and auxiliary information drawn from human experts and opinion scores in the loop, allows synergistic fusion to be achieved, leading to better Actionable Intelligence from the computer based decision support systems. The details of this framework implementation with a component based software platform — the msifStudio, and its evaluation for some of the use case application scenarios is presented here.
Girija Chetty - One of the best experts on this subject based on the ideXlab platform.
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A distributed smart fusion framework based on hard and soft sensors
International Journal of Information Technology, 2017Co-Authors: Girija Chetty, Mohammad YaminAbstract:In this paper we propose a novel intelligent processing approach based on hard and soft sensor fusion for obtaining better Actionable Intelligence from automatic computer based decision support systems. The proposed smart fusion framework with particular focus on combining heterogeneous, multimedia, multimodal real-time big data streams—from hard and soft smart phone sensors, allows synergistic fusion to be achieved, leading to better operational Intelligence from the computer based decision support systems. The details of this framework implementation with a component based software platform—the msifStudio, and its evaluation for some of the use case application scenarios is presented here.
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A smart fusion framework for multimodal object, activity and event detection
2016Co-Authors: Girija Chetty, Mohammad YaminAbstract:With an increasing diffusion of wearable technologies and mobile sensor systems, along with entrenchment of social media networks and crowdsourced information systems in every aspect of modern society, an unavoidable reality is that of continuous, pervasive and ubiquitous sensing, monitoring, surveillance and detection of every type of object, activity, event and incident at a global scale. This rapid proliferation has provided immense opportunities to make use of comprehensive information from a diverse array of multimodal, multi-view, and multisensory data streams for developing efficient and robust, automated computer based decision support systems. Further, with the availability of the complementary and the supplementary information in terms of auxiliary meta-data from the social networks, human experts and the crowdsourced communities, it is possible to obtain better Actionable Intelligence from these systems. In this paper, we propose a novel computational framework for addressing this gap. The proposed smart fusion framework with particular focus on combining heterogeneous, multimodal real-time big data streams — with information from different types of sensor and auxiliary information drawn from human experts and opinion scores in the loop, allows synergistic fusion to be achieved, leading to better Actionable Intelligence from the computer based decision support systems. The details of this framework implementation with a component based software platform — the msifStudio, and its evaluation for some of the use case application scenarios is presented here.
P Virga - One of the best experts on this subject based on the ideXlab platform.
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maai media analytics for Actionable Intelligence
International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Upendra V Chaudhari, Sarah Conrod, Alexander Faisman, Giridharan Iyengar, Dimitri Kanevsky, M Kogan, Ganesh N Ramaswamy, P VirgaAbstract:Government agencies, corporations, and police departments are plagued by information overload. The inability of fully analyze fragments of data scattered across the organizations reduces productivity, and more and more of these fragments are being gathered every day thanks to tools like the Internet and digital audio/video recorders. However, since much of this information is stored in computer systems and networks, it is possible to develop tools that will automatically analyze, assemble and associate the fragments so that precious human resources can focus on the analysis and interpretation of just those fragments that may contain valuable insight. Media analytics for Actionable Intelligence (MAAI) is a combination of automatic, semi-automatic and manual tools which provide three basic levels of analysis: automatic services, manual feedback, and higher level mining (e.g. timeline, social network, hypothesis generation) which allow investigators/analysts to act more efficiently and accurately.
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ICASSP - MAAI: Media analytics for Actionable Intelligence
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Upendra V Chaudhari, Sarah Conrod, Alexander Faisman, Giridharan Iyengar, Dimitri Kanevsky, M Kogan, Ganesh N Ramaswamy, P VirgaAbstract:Government agencies, corporations, and police departments are plagued by information overload. The inability of fully analyze fragments of data scattered across the organizations reduces productivity, and more and more of these fragments are being gathered every day thanks to tools like the Internet and digital audio/video recorders. However, since much of this information is stored in computer systems and networks, it is possible to develop tools that will automatically analyze, assemble and associate the fragments so that precious human resources can focus on the analysis and interpretation of just those fragments that may contain valuable insight. Media analytics for Actionable Intelligence (MAAI) is a combination of automatic, semi-automatic and manual tools which provide three basic levels of analysis: automatic services, manual feedback, and higher level mining (e.g. timeline, social network, hypothesis generation) which allow investigators/analysts to act more efficiently and accurately.
George Siemens - One of the best experts on this subject based on the ideXlab platform.
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supporting Actionable Intelligence reframing the analysis of observed study strategies
Learning Analytics and Knowledge, 2020Co-Authors: Jelena Jovanovic, Shane Dawson, Srecko Joksimovic, George SiemensAbstract:Models and processes developed in learning analytics research are increasing in sophistication and predictive power. However, the ability to translate analytic findings to practice remains problematic. This study aims to address this issue by establishing a model of learner behaviour that is both predictive of student course performance, and easily interpreted by instructors. To achieve this aim, we analysed fine grained trace data (from 3 offerings of an undergraduate online course, N=1068) to establish a comprehensive set of behaviour indicators aligned with the course design. The identified behaviour patterns, which we refer to as observed study strategies, proved to be associated with the student course performance. By examining the observed strategies of high and low performers throughout the course, we identified prototypical pathways associated with course success and failure. The proposed model and approach offers valuable insights for the provision of process-oriented feedback early in the course, and thus can aid learners in developing their capacity to succeed online.
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LAK - Supporting Actionable Intelligence: reframing the analysis of observed study strategies
Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, 2020Co-Authors: Jelena Jovanovic, Shane Dawson, Srećko Joksimović, George SiemensAbstract:Models and processes developed in learning analytics research are increasing in sophistication and predictive power. However, the ability to translate analytic findings to practice remains problematic. This study aims to address this issue by establishing a model of learner behaviour that is both predictive of student course performance, and easily interpreted by instructors. To achieve this aim, we analysed fine grained trace data (from 3 offerings of an undergraduate online course, N=1068) to establish a comprehensive set of behaviour indicators aligned with the course design. The identified behaviour patterns, which we refer to as observed study strategies, proved to be associated with the student course performance. By examining the observed strategies of high and low performers throughout the course, we identified prototypical pathways associated with course success and failure. The proposed model and approach offers valuable insights for the provision of process-oriented feedback early in the course, and thus can aid learners in developing their capacity to succeed online.
Upendra V Chaudhari - One of the best experts on this subject based on the ideXlab platform.
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maai media analytics for Actionable Intelligence
International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Upendra V Chaudhari, Sarah Conrod, Alexander Faisman, Giridharan Iyengar, Dimitri Kanevsky, M Kogan, Ganesh N Ramaswamy, P VirgaAbstract:Government agencies, corporations, and police departments are plagued by information overload. The inability of fully analyze fragments of data scattered across the organizations reduces productivity, and more and more of these fragments are being gathered every day thanks to tools like the Internet and digital audio/video recorders. However, since much of this information is stored in computer systems and networks, it is possible to develop tools that will automatically analyze, assemble and associate the fragments so that precious human resources can focus on the analysis and interpretation of just those fragments that may contain valuable insight. Media analytics for Actionable Intelligence (MAAI) is a combination of automatic, semi-automatic and manual tools which provide three basic levels of analysis: automatic services, manual feedback, and higher level mining (e.g. timeline, social network, hypothesis generation) which allow investigators/analysts to act more efficiently and accurately.
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ICASSP - MAAI: Media analytics for Actionable Intelligence
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Upendra V Chaudhari, Sarah Conrod, Alexander Faisman, Giridharan Iyengar, Dimitri Kanevsky, M Kogan, Ganesh N Ramaswamy, P VirgaAbstract:Government agencies, corporations, and police departments are plagued by information overload. The inability of fully analyze fragments of data scattered across the organizations reduces productivity, and more and more of these fragments are being gathered every day thanks to tools like the Internet and digital audio/video recorders. However, since much of this information is stored in computer systems and networks, it is possible to develop tools that will automatically analyze, assemble and associate the fragments so that precious human resources can focus on the analysis and interpretation of just those fragments that may contain valuable insight. Media analytics for Actionable Intelligence (MAAI) is a combination of automatic, semi-automatic and manual tools which provide three basic levels of analysis: automatic services, manual feedback, and higher level mining (e.g. timeline, social network, hypothesis generation) which allow investigators/analysts to act more efficiently and accurately.
