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multiple measures OF MONITORING MENTAL WORKLOAD

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2000年第3期 100-104页

作者：张智君

This study explored the possibility of developing a technique combining multiple measures of mental workload. Dual task technique was applied. Twenty subjects were asked to perform computer simulated monitoring tasks with three different levels of difficulties. A reaction time performance with digital stimuli was added as subsidiary task. The four indexes, accurate reaction time of primary task, variation rate of accurate reaction time of subsidiary task, weighted subjective workload rating and variation rate of heart rate variance, were used and tested. The correlation matrix of the four indexes was examined with principal component analysis technique, and two mathematical models of comprehensive mental workload indexes (CMWIA, CMWIB) were then advanced. It is verified that these two comprehensive indexes were far more sensitive for monitoring mental workload as compared with any one of the preceding four indexes. It was concluded that it was useful to construct some comprehensive mental workload indexes for practical industrial purposes.

关键词： monitoring mental workload multiple measures mental overload

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Learning multiple metrics for ranking

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Frontiers of Materials Science 2011年第3期5卷 259-267页

作者： Xiubo GENG Xue-Qi CHENG Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

Directly optimizing an information retrieval （IR） metric has become a hot topic in the field of learning to rank. Conventional wisdom believes that it is better to train for the loss function on which will be used for evaluation. But we often observe different results in reality. For example, directly optimizing averaged preci- sion achieves higher performance than directly optimizing precision@3 when the ranking results are evaluated in terms of precision@3. This motivates us to combine multiple metrics in the process of optimizing IR metrics. For simplicity we study learning with two metrics. Since we usually conduct the learning process in a restricted hypothesis space, e.g., linear hypothesis space, it is usually difficult to maximize both metrics at the same time. To tackle this problem, we propose a relaxed approach in this paper. Specifically, we incorporate one metric within the constraint while maximizing the other one. By restricting the feasible hypothesis space, we can get a more robust ranking model. Empirical results on the benchmark data set LETOR show that the relaxed approach is superior to the direct linear combination approach, and also outperforms other baselines.

关键词： leaming to rank multiple measures directoptimization

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习近平新时代中华民族共同体理论的多维阐释

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民族问题研究 2020年第10期

作者：代宏丽敖日格乐

党的十八大以来，以习近平同志为核心的党中央提出了一系列中华民族共同体理论：一是从中华民族大家庭、中华民族优秀传统文化、中华民族共同体意识、中华民族伟大民族精神、中华民族伟大复兴梦等多维度认知中华民族；二是从民族复合体... 详细信息

党的十八大以来，以习近平同志为核心的党中央提出了一系列中华民族共同体理论：一是从中华民族大家庭、中华民族优秀传统文化、中华民族共同体意识、中华民族伟大民族精神、中华民族伟大复兴梦等多维度认知中华民族；二是从民族复合体、政治共同体、经济共同体、文化共同体、命运共同体等多重视角阐释中华民族共同体；三是从制度基础、物质基础、思想基础、社会基础、法治基础等多方面铸牢中华民族共同体意识。

关键词：中华民族共同体共同体理论多维认知多重视角多方铸牢 the community of the Chinese nation theory on the community with a shared future multidimensional perspective multiple dimensions multiple measures

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