The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographica...The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human vision.We have created an Automated Human Intelligence System(AHIS)to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face shape.Finally,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.展开更多
In this contribution, we present iHEARu-PLAY, an online, multi-player platform for crowdsourced database collection and labelling, including the voice analysis application (VoiLA), a free web-based speech classificati...In this contribution, we present iHEARu-PLAY, an online, multi-player platform for crowdsourced database collection and labelling, including the voice analysis application (VoiLA), a free web-based speech classification tool designed to educate iHEARu-PLAY users about state-of-the-art speech analysis paradigms. Via this associated speech analysis web interface, in addition, VoiLA encourages users to take an active role in improving the service by providing labelled speech data. The platform allows users to record and upload voice samples directly from their browser, which are then analysed in a state-of-the-art classification pipeline. A set of pre-trained models targeting a range of speaker states and traits such as gender, valence, arousal, dominance, and 24 different discrete emotions is employed. The analysis results are visualised in a way that they are easily interpretable by laymen, giving users unique insights into how their voice sounds. We assess the effectiveness of iHEARu-PLAY and its integrated VoiLA feature via a series of user evaluations which indicate that it is fun and easy to use, and that it provides accurate and informative results.展开更多
Human reliability analysis(HRA) is an expansion of man-machine engineering. It is also a new multidisciplinary based on behavioral science, cognitive science, information processing, system analysis and probability st...Human reliability analysis(HRA) is an expansion of man-machine engineering. It is also a new multidisciplinary based on behavioral science, cognitive science, information processing, system analysis and probability statistics in order to analyze, predict, reduce and prevent human errors. Firstly, the quantitative analysis model of HRA is proposed based on Markov process theory by using human error probability(HEP) and error correction cycle(ECC) as parameters. And human reliability evaluation criterion is built. Then, the HRA process considering error correction is proposed based on cognitive reliability and error analysis method(CREAM). Finally, according to the characteristics of armored vehicle system, common performance condition(CPC) in CREAM is improved.A reliability impact index is characterized by the overall contexts of tasks. Human reliability evaluation criterion of armored vehicle system is formulated. And the result of HRA is obtained based on the method presented in this paper. In addition, the relative weights are estimated by combining scale of 10/10—18/2 and analytical hierarchy process(AHP), and the triangular fuzzy number considering confidence factor and optimism index is adopted in order to reduce the subjectivity. The analysis results show that the method presented in this paper is reasonable and feasible. Meantime, the method can provide guidance for human reliability analysis of other weapon systems.展开更多
文摘The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human vision.We have created an Automated Human Intelligence System(AHIS)to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face shape.Finally,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.
基金supported by the European Community’s Seventh Framework Programme(No.338164)(ERC Starting Grant iHEARu)
文摘In this contribution, we present iHEARu-PLAY, an online, multi-player platform for crowdsourced database collection and labelling, including the voice analysis application (VoiLA), a free web-based speech classification tool designed to educate iHEARu-PLAY users about state-of-the-art speech analysis paradigms. Via this associated speech analysis web interface, in addition, VoiLA encourages users to take an active role in improving the service by providing labelled speech data. The platform allows users to record and upload voice samples directly from their browser, which are then analysed in a state-of-the-art classification pipeline. A set of pre-trained models targeting a range of speaker states and traits such as gender, valence, arousal, dominance, and 24 different discrete emotions is employed. The analysis results are visualised in a way that they are easily interpretable by laymen, giving users unique insights into how their voice sounds. We assess the effectiveness of iHEARu-PLAY and its integrated VoiLA feature via a series of user evaluations which indicate that it is fun and easy to use, and that it provides accurate and informative results.
基金the Technical Basis Projects of China’s Ministry of Industry and Information Technology(No.ZQ092012B003)
文摘Human reliability analysis(HRA) is an expansion of man-machine engineering. It is also a new multidisciplinary based on behavioral science, cognitive science, information processing, system analysis and probability statistics in order to analyze, predict, reduce and prevent human errors. Firstly, the quantitative analysis model of HRA is proposed based on Markov process theory by using human error probability(HEP) and error correction cycle(ECC) as parameters. And human reliability evaluation criterion is built. Then, the HRA process considering error correction is proposed based on cognitive reliability and error analysis method(CREAM). Finally, according to the characteristics of armored vehicle system, common performance condition(CPC) in CREAM is improved.A reliability impact index is characterized by the overall contexts of tasks. Human reliability evaluation criterion of armored vehicle system is formulated. And the result of HRA is obtained based on the method presented in this paper. In addition, the relative weights are estimated by combining scale of 10/10—18/2 and analytical hierarchy process(AHP), and the triangular fuzzy number considering confidence factor and optimism index is adopted in order to reduce the subjectivity. The analysis results show that the method presented in this paper is reasonable and feasible. Meantime, the method can provide guidance for human reliability analysis of other weapon systems.
