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International Conference on Automatic Face and Gesture recognition

作者： Xing Zhao Shuang Yang Shiguang Shan Xilin Chen Zhejiang University of Technology Hangzhou China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728130798

ISBN: (纸本)9781728130804

Lip reading has received an increasing research interest in recent years due to the rapid development of deep learning and its widespread potential applications. One key point to obtain good performance for the lip reading task depends heavily on how effective the representation can be used to capture the lip movement information and meanwhile to resist the noises resulted by the change of pose, lighting conditions, speaker's appearance, speaking speed and so on. Towards this target, we propose to introduce the mutual information constraints on both the local feature's level and the global sequence's level to enhance the relations of them with the speech content. On the one hand, we require the features generated at each time step to carry a strong relation with the speech content by imposing the local mutual information maximization constraint (LMIM), so as to improve the model's ability to discover fine-grained lip movements and the finegrained differences between words with similar pronunciation, such as “spend” and “spending”. On the other hand, we introduce the mutual information maximization constraint on the global sequence's level (GMIM), to make the model be able to pay more attention to discriminate key frames related with the speech content, and less to various noises appeared in the speaking process. By combining these two advantages together, the proposed method is expected to be both discriminative and robust for effective lip reading. To verify this method, we evaluate on two large-scale benchmarks whose videos are collected from several TV shows with a wide coverage of the speaking conditions. We perform a detailed analysis and comparison on several aspects, including the comparison with the baseline of the LMIM and GMIM, and the visualization of the learned representation. The results not only prove the effectiveness of the proposed method but also report new state-of-the-art performance on both the two benchmarks.

关键词： Lips Hidden Markov models Mutual information Task analysis Visualization Feature extraction Deep learning

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A Novel Data Association Algorithm Based on Fuzzy Logic for Visual Object Tracking

A Novel Data Association Algorithm Based on Fuzzy Logic for ...

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics

作者： Liangqun Li Xiyang Zhan Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

In this paper, we propose a novel fuzzy logic data association algorithm to resolve the problem of visual multi-object tracking. First of all, a fuzzy logic data association approach is proposed to calculate the association probabilities between the objects and observations (or detection responses) by use of a set of fuzzy if-then rules. Based on the similarities of object appearance, shape and motion models, several fuzzy inference systems are designed to confirm the fuzzy membership degrees between the objects and observations, the association probability is generated by weighted affinities, where the weight of the affinity is the output of fuzzy logic. Finally, the experiment results on several public sequences demonstrate that the proposed algorithm has superiorities over other comparatively leading tracking algorithms in terms of accuracy and robustness.

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An Optimal Tracking Control Method with Q-learning for Discrete-time Linear Switched System

An Optimal Tracking Control Method with Q-learning for Discr...

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第三十九届中国控制会议

作者： Shangwei Zhao Jingcheng Wang Hongyuan Wang Haotian Xu Automation Department Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University

In this paper, a novel approach combining Q-learning method and two-stage optimization is proposed to solve linear quadratic tracking(LQT) problem for unknown discrete-time switched system. An augmented system consisting of switched subsystem and reference trajectory is built and Q-learning method is introduced to identify each subsystem without requiring any knowledge of augmented system state dynamics. Then, based on the optimal control laws for each subsystem obtained by Q-learning in advance, the optimal hybrid control law including switching mode and control input is obtained by the two-stage optimization framework. Furthermore, the optimality of the algorithm is proved. Finally, a simulation case is used to testify the effectiveness of the proposed algorithm.

关键词： Tracking control Linear switched system Q-learning

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Improve the Performance of LSM-Tree based on key-Value via Multithreading

Improve the Performance of LSM-Tree based on Key-Value via M...

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International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Yuan Gao Ping Xie Wendi Hua Meng Lv Jiating Lu College of Computer of Qinghai Normal University Xining P. R. China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining P. R. China Key Laboratory of Internet of Things of Qinghai Province Xining P. R. China Academy of Plateau Science and Sustainability Xining P. R. China School of Physical and Electrical Engineering Qinghai Normal University Xining P. R. China

ISBN: (纸本)9781665428903

In the era of big data, key-value storage systems based on Log-Structure Merge tree (LSM-tree) are widely used in numerous industries. LSM-Tree is divided into two parts, one part is in the memory and the other part is on the hard disk. When writing data, first write to the memory, when the Immutable Memtable in the memory is full of data, then write to the disk to output a Sorted String Table (SSTable) file. However, the use of writing data from memory to disk is single-threaded. When the Immutable Memtable writes data to the disk, it will block other threads. This article uses multiple Immutable Memtables and multi-threaded writing to solve the single-thread blocking problem. By running the benchmarks of LevelDB to analyze the experiment, the experimental results indicate a higher improvement.

关键词： Industries Multithreading Instruction sets Writing Big Data Benchmark testing Hard disks

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iFeedback: exploiting user feedback for real-time issue detection in large-scale online service systems 19

iFeedback: exploiting user feedback for real-time issue dete...

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Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering

作者： Wujie Zheng Haochuan Lu Yangfan Zhou Jianming Liang Haibing Zheng Yuetang Deng Fudan University Shanghai China and Tencent Inc. Shenzhen China Fudan University Shanghai China and Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Tencent Inc. Shenzhen China

ISBN: (纸本)9781728125084

Large-scale online systems are complex, fast-evolving, and hardly bug-free despite the testing efforts. Backend system monitoring cannot detect many types of issues, such as UI related bugs, bugs with small impact on backend system indicators, or errors from third-party co-operating systems, etc. However, users are good informers of such issues: They will provide their feedback for any types of issues. This experience paper discusses our design of iFeedback, a tool to perform real-time issue detection based on user feedback texts. Unlike traditional approaches that analyze user feedback with computation-intensive natural language processing algorithms, iFeedback is focusing on fast issue detection, which can serve as a system life-condition monitor. In particular, iFeedback extracts word combination-based indicators from feedback texts. This allows iFeedback to perform fast system anomaly detection with sophisticated machine learning algorithms. iFeedback then further summarizes the texts with an aim to effectively present the anomaly to the developers for root cause analysis. We present our representative experiences in successfully applying iFeedback in tens of large-scale production online service systems in ten months.

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Temperature prediction of generator carbon brush based on LSTM neural network

Temperature prediction of generator carbon brush based on LS...

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Chinese Automation Congress (CAC)

作者： Qing Zhang Tingyang Jiao Yinxing Ma Youmin Zhang Xiaohui Wu Pengyue Wu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China The Faculty of Computing Science The University of Alberta Edmonton Alberta Canada Concordia University Montreal Canada Huaneng Weihai Power Generation Co. Ltd Wei’hai China Xi’an Thermal Power Research Institute Co. Ltd Xi’an China

ISBN: (纸本)9781665426480

In order to improve the prediction accuracy of the carbon brush temperature trend of the generator, analyzing the operating state of the generator more accurately, and reduce other accidents like the unplanned outage caused by carbon brush failure of the generator in the power plant. A multi-step temperature prediction model based on the LSTM (Long Short-Term Memory) neural network is proposed. The data comes from real operating carbon brush temperature of Weihai Power Plant in ten days. Then the temperature prediction model is established to achieve an accurate prediction of the carbon brush temperature in the future day. The prediction error is stable within 0.4 °C. By comparing the predicted results of the BP model and Elman model, the error between the predicted results of each model and the actual temperature data is analyzed. By comparing the indicators and analyzing the actual curves, the results show that LSTM neural network has higher accuracy in predicting the temperature of generator carbon brush. This method is of reference significance for the accurate analysis of the operation state of the generator and the load analysis of the excitation system.

关键词： Analytical models Temperature distribution Brushes Neural networks Predictive models Market research Generators

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Maximal information Complemented Refinement Network for Gland Instance Segmentation

Maximal Information Complemented Refinement Network for Glan...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Bowen Xu Yankai Wang Dawei Yang Wei Zhang Ye Zhang Yajie Kong Wenqiang Zhang Academy for Engineering and Technology Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Fine Mechanics and Physics Chinese Academy of Sciences Changchun Institute of Optics

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

The analysis of glandular morphology is a crucial step to determine the presence and grade of cancer. The rise of computational pathology has led to the development of automated segmentation to overcome the time-consuming manual segmentation. Although the existing encoder-decoder networks haved made significant progress, the downsample operation causes fine-grain information loss. It deteriorates boundaries' localization especially in malignant cases. In this paper, we propose a maximal information complemented refinement network based on UNet. We extend the skip connection with two information complement, aggregate spatial detail information by reuse low-level features, and introduce semantic information by high-level feature guidance. Besides, a weighted cross-entropy loss and generalized dice loss is used to tackle the fuzzy boundary and class imbalance. We evaluated our model against a dozen recent deep learning models on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset. Extensive experiments show that our proposal achieves the best overall performance, immensely improves the performance of malignant cases.

关键词： Semantics Glands Cancer Feature extraction Fuses Decoding Morphology

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A proximal algorithm for solving split monotone variational inclusions

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2020年第3期82卷 43-52页

作者： Zhao, Xiaopeng Yao, Jen-Chih Yao, Yonghong School of Mathematical Sciences Tianjin Polytechnic University Tianjin300387 China Center for General Education China Medical University Taichung Taiwan The Key Laboratory of Intelligent Information and Big Data Processing of NingXia Province North Minzu University Yinchuan750021 China

In this paper, we study the split monotone variational inclusion problem in Hilbert spaces. We suggest a proximal algorithm for finding a solution of the split monotone variational inclusion problem. Strong convergenc... 详细信息

关键词： Variational techniques

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Forecasting the cold-chain logistics demand of guangxi self-produced agricultural products 8

Forecasting the cold-chain logistics demand of guangxi self-...

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8th International Conference on Logistics and Systems Engineering 2018

作者： Yue, Guoyou Panichakarn, Boonsub School of Logistics and Supply Chain Naresuan University Phitsanulok Thailand Guangxi Key Laboratory Cultivation Base of Cross-border E-commerce Intelligent Information Processing Guangxi University of Finance and Economics Nanning China

ISBN: (纸本)9781921712685

Guangxi is a large producer of agricultural products in China. With the improvement of people's living standards and the improvement of the demand for the quality and safety of agricultural products, the guarantee, quantity, consumption, and extension of the marketing time of agricultural products have become the focus of attention of the agricultural products industry. The demand prediction of cold-chain logistics for agricultural products and the construction and improvement of cold-chain logistics system need to be solved urgently. In this paper, the output of four kinds of agricultural products produced by Guangxi from 2006 to 2016, including aquatic products, livestock, vegetables and fruits, was found in the Guangxi Statistical Yearbook by classification. The weighted moving average forecasting method is used to predict the production of the four kinds of agricultural products produced by Guangxi in the next few years, and the demand for different cold-chain logistics for each kind of agricultural products is analyzed by Delphi method. Finally, the demand of low-temperature and high-temperature cold-chain logistics for Guangxi self-produced agricultural products from 2017 to 2022 is calculated, which provides a reference for Guangxi to construct and perfect the cold-chain logistics system of agricultural products. © 2019 Proceedings of the 8th International Conference on Logistics and Systems Engineering 2018. All rights reserved.

关键词： Agricultural products

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Optimizing Two-way Partial AUC with an End-to-end Framework

arXiv

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arXiv 2022年

作者： Yang, Zhiyong Xu, Qianqian Bao, Shilong He, Yuan Cao, Xiaochun Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Security Department of Alibaba Group Hangzhou311121 China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Shenzhen518107 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

The Area Under the ROC Curve (AUC) is a crucial metric for machine learning, which evaluates the average performance over all possible True Positive Rates (TPRs) and False Positive Rates (FPRs). Based on the knowledge that a skillful classifier should simultaneously embrace a high TPR and a low FPR, we turn to study a more general variant called Two-way Partial AUC (TPAUC), where only the region with TPR ≥ α, FPR ≤ β is included in the area. Moreover, a recent work shows that the TPAUC is essentially inconsistent with the existing Partial AUC metrics where only the FPR range is restricted, opening a new problem to seek solutions to leverage high TPAUC. Motivated by this, we present the first trial in this paper to optimize this new metric. The critical challenge along this course lies in the difficulty of performing gradient-based optimization with end-to-end stochastic training, even with a proper choice of surrogate loss. To address this issue, we propose a generic framework to construct surrogate optimization problems, which supports efficient end-to-end training with deep learning. Moreover, our theoretical analyses show that: 1) the objective function of the surrogate problems will achieve an upper bound of the original problem under mild conditions, and 2) optimizing the surrogate problems leads to good generalization performance in terms of TPAUC with a high probability. Finally, empirical studies over several benchmark datasets speak to the efficacy of our framework. © 2022, CC BY.

关键词： Stochastic systems

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