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arXiv 2024年

作者： Tao, Wei Zhou, Yucheng Wang, Yanlin Zhang, Hongyu Wang, Haofen Zhang, Wenqiang Shanghai Engineering Research Center of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China School of Software Engineering Sun Yat-sen University Guangdong Zhuhai519082 China School of Big Data and Software Engineering Chongqing University Chongqing China College of Design and Innovation Tongji University Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Academy for Engineering and Technology Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University 220 Handan Road Shanghai200433 China

Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practice commits), while the rest do not. On the basis of our empirical study, we discover that training on good-practice commits significantly contributes to the commit message generation. Motivated by this finding, we propose a novel knowledge-aware denoising learning method called KADEL. Considering that good-practice commits constitute only a small proportion of the dataset, we align the remaining training samples with these good-practice commits. To achieve this, we propose a model that learns the commit knowledge by training on good-practice commits. This knowledge model enables supplementing more information for training samples that do not conform to good practice. However, since the supplementary information may contain noise or prediction errors, we propose a dynamic denoising training method. This method composes a distribution-aware confidence function and a dynamic distribution list, which enhances the effectiveness of the training process. Experimental results on the whole MCMD dataset demonstrate that our method overall achieves state-of-the-art performance compared with previous methods. © 2024, CC BY.

关键词： Sampling

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Erratum to "Multi-View Face Synthesis via Progressive Face Flow"

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2021年 30卷 6700页

作者： Yangyang Xu Xuemiao Xu Jianbo Jiao Keke Li Cheng Xu Shengfeng He School of Computer Science and Engineering South China University of Technology Guangzhou China State Key Laboratory of Subtropical Building Science the Key Laboratory of Big Data and Intelligent Robot Ministry of Education and the Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information South China University of Technology Guangzhou China University of Oxford Oxford U.K.

In the above article [1], unfortunately, Fig. 5 was not displayed correctly with many empty images. The correct version is supplemented here.

关键词： Faces

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Erratum to: Incremental algorithms for the maximum internal spanning tree problem

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Science China Information Sciences 2022年第10期65卷 1-1页

作者： Zhu, Xianbin Li, Wenjun Yang, Yongjie Wang, Jianxin School of Computer Science and Engineering Central South University Changsha China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China Chair of Economic Theory Saarland University Saarbrücken Germany

In the original paper [1], there are two errors on page 5. The rules in the algorithm are not sufficient since we omitted one rule during publication. The following rule should be added to the paper. Rule 3: If P1 = (u, v) and P2 = (x, y) are two maximal leaf-paths both with two vertices in T, where u and x are leaves in T and (v, y) ∈ E(G), we remove (y′, y) and then add (v, y) to T where y′ is the non-leaf neighbor of y in T. The expression of Algorithm 2 is not accurate. It should be modified as follows. Line 3: Exhaustively apply Rules 1–3, and for i ∈ {2, 3} only apply Rule i when none of Rules j where 1 ⩽ j < i is applicable. Line 8: Determine the longest path P among all paths whose endpoints have nonempty neighbors in T \ V(T′).

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A Survey of Recent Advances in Optimization Methods for Wireless Communications

arXiv

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arXiv 2024年

作者： Liu, Ya-Feng Chang, Tsung-Hui Hong, Mingyi Wu, Zheyu So, Anthony Man-Cho Jorswieck, Eduard A. Yu, Wei The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China Shenzhen Research Institute of Big Data China The Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States The Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong The Institute for Communication Technology Technische Universitat Braunschweig Germany The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada

Mathematical optimization is now widely regarded as an indispensable modeling and solution tool for the design of wireless communications systems. While optimization has played a significant role in the revolutionary progress in wireless communication and networking technologies from 1G to 5G and onto the future 6G, the innovations in wireless technologies have also substantially transformed the nature of the underlying mathematical optimization problems upon which the system designs are based and have sparked significant innovations in the development of methodologies to understand, to analyze, and to solve those problems. In this paper, we provide a comprehensive survey of recent advances in mathematical optimization theory and algorithms for wireless communication system design. We begin by illustrating common features of mathematical optimization problems arising in wireless communication system design. We discuss various scenarios and use cases and their associated mathematical structures from an optimization perspective. We then provide an overview of recently developed optimization techniques in areas ranging from nonconvex optimization, global optimization, and integer programming, to distributed optimization and learning-based optimization. The key to successful solution of mathematical optimization problems is in carefully choosing or developing suitable algorithms (or neural network architectures) that can exploit the underlying problem structure. We conclude the paper by identifying several open research challenges and outlining future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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A reinforcement-learning-based energy-efficient framework for multi-task video analytics pipeline

arXiv

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arXiv 2021年

作者： Zhao, Yingying Dong, Mingzhi Wang, Yujiang Feng, Da Lv, Qin Dick, Robert P. Li, Dongsheng Lu, Tun Gu, Ning Shang, Li School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Department of Computing Imperial College London London United Kingdom Software Service Company Limited Beijing China University of Colorado Boulder BoulderCO United States Department of Electrical Engineering and Computer Science College of Engineering University of Michigan Ann ArborMI United States Microsoft Research Asia Shanghai China School of Computer Science Fudan University Shanghai China

Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and reliance on complex inference algorithms, which limits its adoption in energy-constrained applications. Motivated by the observation of high and variable spatial redundancy and temporal dynamics in video data streams, we design and evaluate an adaptive-resolution optimization framework to minimize the energy use of multitask video analytics pipelines. Instead of heuristically tuning the input data resolution of individual tasks, our framework utilizes deep reinforcement learning to dynamically govern the input resolution and computation of the entire video analytics pipeline. By monitoring the impact of varying resolution on the quality of high-dimensional video analytics features, hence the accuracy of video analytics results, the proposed end-to-end optimization framework learns the best non-myopic policy for dynamically controlling the resolution of input video streams to globally optimize energy efficiency. Governed by reinforcement learning, optical flow is incorporated into the framework to minimize unnecessary spatio-temporal redundancy that leads to re-computation, while preserving accuracy. The proposed framework is applied to video instance segmentation which is one of the most challenging computer vision tasks, and achieves better energy efficiency than all baseline methods of similar accuracy on the YouTube-VIS dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

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An Explainable and Lightweight Deep Learning Model with Attention Mechanism for Efficient Lung Disease Detection

An Explainable and Lightweight Deep Learning Model with Atte...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qian Tingting Sohaib Asif Yuke Lin Jincao Yao Enyu Wang Vicky Yang Wang Dong Xu Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital) Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence Taizhou China Center of Intelligent Diagnosis and Therapy (Taizhou) Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Taizhou China Wenling Institute of Big Data and Artificial Intelligence in Medicine Taizhou China Department of Diagnostic Ultrasound Imaging & Interventional Therapy Zhejiang Cancer Hospital Chinese Academy of Sciences China Zhejiang Chinese Medical University Hangzhou China Department of Radiology Imaging Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital) Taizhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

COVID-19 has rapidly spread across the world as an extremely contagious disease. Therefore, early detection of the virus is essential to effectively control its transmission and prevent further spread. It is widely reported in the literature that convolutional neural networks (CNNs) are commonly utilized for COVID-19 detection. However, the majority of these models require substantial computational resources and possess a large number of parameters, rendering them less feasible for deployment on real-time devices. This study proposes a solution to address COVID-19 detection challenges by introducing LWIA-Net, a rapid and highly efficient lightweight CNN architecture. The LWIA-Net is equipped with channel attention squeeze and excitation (SE) blocks, as well as a naïve inception module, to enhance the network’s learning ability, reduce computational complexity, and maintain a high level of detection performance. By incorporating the naïve inception module, the LWIA-Net enables multi-level feature extraction, while the inclusion of the SE block helps focuses on informative channels. This integration contributes to the production of high-quality image features and effectively reduces redundancy. We performed thorough experiments using both our locally developed dataset and a publicly available dataset. The statistical analysis demonstrate that LWIA-Net achieves superior performance compared to pre-trained CNN architectures, despite its extremely lightweight architecture which demands low computational cost, memory space, and consists of only 1.11 million parameters. We conducted a comprehensive ablation study to gain a deeper understanding of the significance of each component and to verify their individual contributions to the LWIA-Net. Additionally, we utilized Grad-CAM analysis to confirm that the proposed model effectively identifies and emphasizes the most critical regions of interest. Experimental results have shown that LWIA-Net achieves remarkable accuracy rate

关键词： COVID-19 Statistical analysis Computational modeling Pulmonary diseases Redundancy Computer architecture Production Rendering (computer graphics) Real-time systems Convolutional neural networks

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Inequality-Type Zhang Equivalency Originating from Neural Dynamics for Time-Varying Problems Solving

Inequality-Type Zhang Equivalency Originating from Neural Dy...

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IEEE International Conference on Mechatronics and Automation

作者： Yunong Zhang Liangjie Ming Min Yang Binbin Qiu Zhonghua Li School of Data and Computer Science Sun Yat-sen University Guangzhou P. R. China Research Institute of Sun Yat-sen University in Shenzhen Shenzhen P. R. China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou P. R. China School of Electronics and Information Technology Sun Yat-sen University Guangzhou P. R. China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou P. R. China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

In this paper, a concept of Zhang equivalency (ZE) is presented, and its type of inequality to inequality (simply termed, inequality type) is further discussed. By applying Zhang neural dynamics method, we thus propose the order-1, order2, and order-3 ZE general formulas of such an inequality type, respectively. Then, the inequalities in four different situations are investigated, i.e., the robot situation, matrix situation (including matrix-vector situation), state-control situation, and state situation. We propose the corresponding order-1, order-2, and order-3 ZE formulas to prepare for solving corresponding time-varying inequality-related problems.

关键词： Mechatronics Automation Conferences Linear matrix inequalities Problem-solving Robots

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Event-triggering-based H_∞ load frequency control for multi-area cyber–physical power system under DoS attacks

Franklin Open

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Franklin Open 2023年 3卷

作者： Liu, Xingyue Shi, Kaibo Zhou, Kun Wen, Shiping Tang, Yiqian Tang, Lin School of Electronic Information and Electrical Engineering Chengdu University Chengdu 610106 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou 310018 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology Sydney Sydney 2007 NSW Australia Engineering Research Center of Power Quality of Ministry of Education Anhui University Hefei 230601 China Institute of Electronic and Information Engineering of University of Electronic Science and Technology of China in Guangdong 523808 China

In this work, the event-triggering-based resilient H∞ load frequency control (LFC) scheme under denial-of-service (DoS) attacks is developed for multi-area cyber–physical power system (MA-CPPS) with electric vehicle... 详细信息

In this work, the event-triggering-based resilient H_∞ load frequency control (LFC) scheme under denial-of-service (DoS) attacks is developed for multi-area cyber–physical power system (MA-CPPS) with electric vehicles (EVs). In MA-CPPS, the sampled measurements are transmitted through the communication network, which may be subjected to the energy-limited DoS attack with a resilient index of maximum allowable continuous data losses. Meanwhile, a novel adaptive event-triggering mechanism (AETM) characterized by parameters disturbances and dynamic adjustment mechanism of triggering threshold can effectively reduce the occupation of network bandwidth and computational burden in practical operation of sampled-data LFC in MA-CPPS. Hence a sampled-data LFC model of considering energy-limited DoS attack and AETM is established firstly. Then based on a novel AETM transmission interval-dependent loop functional approach, a resilient H_∞ state-feedback controller is designed with guaranteed H_∞ performance and resilient index of energy-limited DoS attack. Finally, the effectiveness and superiority of our proposed scheme are verified by one-area and three-areas LFC CPPS. © 2023 The Authors

关键词： Adaptive event-triggering mechanism Cyber–physical power system Denial-of-service attacks Resilient H∞ load frequency control scheme Transmission interval-dependent loop functional approach

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Evaluation and application of shallow buried tunnel construction safety based on combined empowerment-multidimensional cloud model

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IOP Conference Series: Earth and Environmental Science 2021年第1期791卷

作者： Shang Liu Jie Song Run Xu Changyong Zhang Guoqiang Zhong Chao Wang Shandong Provincial Communications Planning and Design Institute Group Co. LTD Shandong Jinan China Shandong Province highway traffic safety big data engineering laboratory Shandong Jinan China Key Laboratory of tunnel information construction and intelligent control transportation industry Shandong Jinan China

In order to study the influence of various factors on the construction safety of shallow buried tunnels, a multifactor evaluation model consisting of 9 indicators, such as tunnel size, the buried depth, surrounding rock condition and construction management, is established from the perspective of '4M1E' by combining the characteristics of shallow buried tunnel construction by borehole-blasting method. It combines the advantages of subjective weighting method AHP and objective weighting method CRITIC, and determines the comprehensive weight values through the combined weighting method, which is used to improve the weight assignment of multi-dimensional normal cloud model, and evaluates the stability of the surrounding rock at the entrance and exit of the shallow buried tunnel based on the concepts of expectation, entropy and super entropy of the cloud model. And then it accurately obtains the construction safety of the entrance and exit of the shallow buried tunnel, and the evaluation results have scientific guidance for the control of construction risks.

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An Improved Adam Optimization Algorithm Combining Adaptive Coefficients and Composite Gradients Based on Randomized Block Coordinate Descent

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Computational intelligence and neuroscience 2023年第1期2023卷 4765891页

作者： Miaomiao Liu Dan Yao Zhigang Liu Jingfeng Guo Jing Chen School of Computer and Information Technology Northeast Petroleum University Daqing 163318 China. Heilongjiang Key Laboratory of Petroleum Big Data and Intelligent Analysis Daqing 163318 China. College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China.

An improved Adam optimization algorithm combining adaptive coefficients and composite gradients based on randomized block coordinate descent is proposed to address issues of the Adam algorithm such as slow convergence, the tendency to miss the global optimal solution, and the ineffectiveness of processing high-dimensional vectors. The adaptive coefficient is used to adjust the gradient deviation value and correct the search direction firstly. Then, the predicted gradient is introduced, and the current gradient and the first-order momentum are combined to form a composite gradient to improve the global optimization ability. Finally, the random block coordinate method is used to determine the gradient update mode, which reduces the computational overhead. Simulation experiments on two standard datasets for classification show that the convergence speed and accuracy of the proposed algorithm are higher than those of the six gradient descent methods, and the CPU and memory utilization are significantly reduced. In addition, based on logging data, the BP neural networks optimized by six algorithms, respectively, are used to predict reservoir porosity. Results show that the proposed method has lower system overhead, higher accuracy, and stronger stability, and the absolute error of more than 86% data is within 0.1%, which further verifies its effectiveness.

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