The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Christina Gengqing Chi - One of the best experts on this subject based on the ideXlab platform.
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changing perceptions and Reasoning Process comparison of residents pre and post event attitudes
Annals of Tourism Research, 2018Co-Authors: Christina Gengqing Chi, Zhe OuyangAbstract:Abstract Upon a systematic assessment of how residents’ trust in government(s) and attachment to a marquee event influence their evaluations of the event’s impacts and subsequent attitudes towards the hosting of the event, this study further explores the dynamic nature of residents’ subjective evaluations and corresponding attitudes to the event. In line with the confirmation bias theory, findings clearly demonstrate that residents’ trust in government(s), attachment to the event, perceptions of the event’s impacts and ultimate support to the event have changed in a predictable manner over time. Moreover, findings indicate that individuals’ direct experience with the event alters the associations between their cognitive/affective evaluations and attitudes towards the event, with a shifted focus to the cognitive evaluations after the event.
Kun Bai - One of the best experts on this subject based on the ideXlab platform.
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read attend and exclude multi choice reading comprehension by mimicking human Reasoning Process
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020Co-Authors: Chenbin Zhang, Congjian Luo, Ao Liu, Bing Bai, Kun BaiAbstract:Multi-Choice Reading Comprehension~(MCRC) is an essential task where a machine selects the correct answer from multiple choices given a context document and a corresponding question. Existing methods usually make predictions based on a single-round Reasoning Process with the attention mechanism, however, this may be insufficient for tasks that require a more complex Reasoning Process. To effectively comprehend the context and select the correct answer from different perspectives, we propose the Read-Attend-Exclude (RAE) model which is motivated by what human readers do for MCRC in multi-rounds Reasoning Process. Specifically, the RAE model includes four components: the Scan Reading Module, the Attended Intensive Reading Module, the Answer Exclusion Module, and the Gated Fusion Module that makes the final decisions collectively based on the aforementioned three modules. Extensive experiments demonstrate the strong results of the proposed model on the DREAM dataset and the effectiveness of all proposed modules.
Zhe Ouyang - One of the best experts on this subject based on the ideXlab platform.
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changing perceptions and Reasoning Process comparison of residents pre and post event attitudes
Annals of Tourism Research, 2018Co-Authors: Christina Gengqing Chi, Zhe OuyangAbstract:Abstract Upon a systematic assessment of how residents’ trust in government(s) and attachment to a marquee event influence their evaluations of the event’s impacts and subsequent attitudes towards the hosting of the event, this study further explores the dynamic nature of residents’ subjective evaluations and corresponding attitudes to the event. In line with the confirmation bias theory, findings clearly demonstrate that residents’ trust in government(s), attachment to the event, perceptions of the event’s impacts and ultimate support to the event have changed in a predictable manner over time. Moreover, findings indicate that individuals’ direct experience with the event alters the associations between their cognitive/affective evaluations and attitudes towards the event, with a shifted focus to the cognitive evaluations after the event.
Chenbin Zhang - One of the best experts on this subject based on the ideXlab platform.
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read attend and exclude multi choice reading comprehension by mimicking human Reasoning Process
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020Co-Authors: Chenbin Zhang, Congjian Luo, Ao Liu, Bing Bai, Kun BaiAbstract:Multi-Choice Reading Comprehension~(MCRC) is an essential task where a machine selects the correct answer from multiple choices given a context document and a corresponding question. Existing methods usually make predictions based on a single-round Reasoning Process with the attention mechanism, however, this may be insufficient for tasks that require a more complex Reasoning Process. To effectively comprehend the context and select the correct answer from different perspectives, we propose the Read-Attend-Exclude (RAE) model which is motivated by what human readers do for MCRC in multi-rounds Reasoning Process. Specifically, the RAE model includes four components: the Scan Reading Module, the Attended Intensive Reading Module, the Answer Exclusion Module, and the Gated Fusion Module that makes the final decisions collectively based on the aforementioned three modules. Extensive experiments demonstrate the strong results of the proposed model on the DREAM dataset and the effectiveness of all proposed modules.
Deb Ghosh - One of the best experts on this subject based on the ideXlab platform.
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a probabilistic Reasoning model formulation and control strategy
Decision Support Systems, 1996Co-Authors: Sumit Sarkar, Deb GhoshAbstract:Abstract It has been recognized that past experiences of a decision maker often plays a pivotal role in solving new problem instances. Therefore, the ability to model human Reasoning Processes has become an important subject of research in recent years. In many applications, the Reasoning Process must deal with uncertainty inherent in the problem domain. This research addresses the issue of supporting the model formulation and data acquisition Processes for situations that (i) operate under uncertain conditions, and (ii) utilize evidential information that is gathered in stages. A theoretical framework is presented for the probabilistic formulation of the Reasoning Process that incorporates past experiences. The model is validated by testing its performance on simulated data, and is shown to work well when a sufficiently large number of cases are available for estimating probabilities. The probabilistic Reasoning system can revise beliefs in an intuitively appealing and theoretically sound manner when information is acquired in an incremental fashion. Two dynamic information gathering strategies are discussed for such a Reasoning system, one using information theoretic techniques, and the other using decision theoretic techniques.
