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Hetu: a highly efficient automatic parallel distributed deep learning system

science China(Information sciences) 2023年第1期66卷 314-315页

作者： Xupeng MIAO Xiaonan NIE Hailin ZHANG Tong ZHAO Bin CUI School of Computer Science Peking University Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications

On July 18, 2021, the PKU-DaiR Lab1)(data and Intelligence Research Lab at Peking University) openly released the source code of Hetu, a highly efficient and easy-to-use distributed deep learning(DL) framework. Hetu is the first distributed DL system developed by academic groups in Chinese universities, and takes into account both high availability in industry and innovation in academia. Through independent research and development, Hetu is completely decoupled from the existing DL systems and has unique characteristics. The public release of the Hetu system will help researchers and practitioners to carry out frontier MLSys(machine learning system) research and promote innovation and industrial upgrading.

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Event-triggered tracking control for a class of nonholonomic systems in chained form

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science China(Information sciences) 2023年第7期66卷 147-161页

作者： Liang XU Youfeng SU He Cai Center for Discrete Mathematics and Theoretical Computer Science Fuzhou University College of Computer and Data Science Fuzhou University School of Automation Science and Engineering South China University of Technology

In this study, the event-triggered asymptotic tracking control problem is considered for a class of nonholonomic systems in chained form for the time-varying reference input. First, to eliminate the ripple phenomenon caused by the imprecise compensation of the time-varying reference input, a novel time-varying event-triggered piecewise continuous control law and a triggering mechanism with a time-varying triggering function are developed. Second, an explicit integral input-to-state stable Lyapunov function is constructed for the time-varying closed-loop system regarding the sampling error as the external input. The origin of the closed-loop system is shown to be uniformly globally asymptotically stable for any global exponential decaying threshold signals, which in turn rules out the Zeno behavior. Moreover, infinitely fast sampling can be avoided by appropriately tuning the exponential convergence rate of the threshold signal. A numerical simulation example is provided to illustrate the proposed control approach.

关键词： event-triggered nonholonomic systems strict Lyapunov function tracking integral input-to-state stable

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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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science China(Information sciences) 2025年第2期68卷 103-119页

作者： Wenyu Cai Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

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Constrained Multiobjective Optimization via Relaxations on Both Constraints and Objectives

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6709-6722页

作者： Ming, Fei Xue, Bing Zhang, Mengjie Gong, Wenyin Zhen, Huixiang China University of Geosciences School of Computer Science Wuhan430074 China Victoria University of Wellington Center of Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand

Since most multiobjective optimization problems in real-world applications contain constraints, constraint-handling techniques (CHTs) are necessary for a multiobjective optimizer. However, existing CHTs give no relaxation to objectives, resulting in the elimination of infeasible dominated solutions that are promising (potentially useful but inferior) for detecting feasible regions and the constrained Pareto front (CPF). To overcome this drawback, in this work, we propose an objective relaxation technique that can preserve promising by relaxing objective function values, i.e., convergence, through an adaptively adjusted relaxation factor. Further, we develop a new constrained multiobjective optimization evolutionary algorithm (CMOEA) based on relaxations on both constraints and objectives. The proposed algorithm evolves one population by the constraint relaxation technique to preserve promising infeasible solutions and the other population by both objective and constraint relaxation techniques to preserve promising infeasible dominated solutions. In this way, our method can overcome the drawback of existing CHTs. Besides, an archive update strategy is designed to maintain encountered feasible solutions by the two populations to approximate the CPF. Experiments on challenging benchmark problems and real-world problems have demonstrated the superiority or at least competitiveness of our proposed CMOEA. Moreover, to verify the generality of the objective relaxation technique, we embed it into two existing CMOEA frameworks and the results show that it can significantly improve the performance in handling challenging problems. © 2020 IEEE.

关键词： Constrained optimization

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Resource investment for DDoS attack resistant SDN: a practical assessment

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science China(Information sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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Evolutionary Trainer-Based Deep Q-Network for Dynamic Flexible Job Shop Scheduling

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Liu, Yun Zhang, Fangfang Sun, Yanan Zhang, Mengjie College of Computer Science Sichuan University Chengdu PR China Center for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

Dynamic flexible job shop scheduling (DFJSS) aims to achieve the optimal efficiency for production planning in the face of dynamic events. In practice, deep Q-network (DQN) algorithms have been intensively studied for solving various DFJSS problems. However, these algorithms often cause moving targets for the given job-shop state. This will inevitably lead to unstable training and severe deterioration of the performance. In this paper, we propose a training algorithm based on genetic algorithm to efficiently and effectively address this critical issue. Specifically, a state feature extraction method is first developed, which can effectively represent different job shop scenarios. Furthermore, a genetic encoding strategy is designed, which can reduce the encoding length to enhance search ability. In addition, an evaluation strategy is proposed to calculate a fixed target for each job-shop state, which can avoid the parameter update of target networks. With the designs, the DQNs could be stably trained, thus their performance is greatly improved. Extensive experiments demonstrate that the proposed algorithm outperforms the state-of-the-art peer competitors in terms of both effectiveness and generalizability to multiple scheduling scenarios with different scales. In addition, the ablation study also reveals that the proposed algorithm can outperform the DQN algorithms with different updating frequencies of target networks. IEEE

关键词： Job shop scheduling

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A Facial Expression Recognition Method Integrating Uncertainty Estimation and Active Learning

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computers, Materials & Continua 2024年第10期81卷 533-548页

作者： Yujian Wang Jianxun Zhang Renhao Sun School of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China Data Space Research Institute of Hefei Comprehensive National Science Center Hefei230088China

The effectiveness of facial expression recognition(FER)algorithms hinges on the model’s quality and the availability of a substantial amount of labeled expression ***,labeling large datasets demands significant human,time,and financial *** active learning methods have mitigated the dependency on extensive labeled data,a cold-start problem persists in small to medium-sized expression recognition *** issue arises because the initial labeled data often fails to represent the full spectrum of facial expression *** paper introduces an active learning approach that integrates uncertainty estimation,aiming to improve the precision of facial expression recognition regardless of dataset scale *** method is divided into two primary ***,the model undergoes self-supervised pre-training using contrastive learning and uncertainty estimation to bolster its feature extraction ***,the model is fine-tuned using the prior knowledge obtained from the pre-training phase to significantly improve recognition *** the pretraining phase,the model employs contrastive learning to extract fundamental feature representations from the complete unlabeled *** features are then weighted through a self-attention mechanism with rank ***,data from the low-weighted set is relabeled to further refine the model’s feature extraction *** pre-trained model is then utilized in active learning to select and label information-rich samples more *** results demonstrate that the proposed method significantly outperforms existing approaches,achieving an improvement in recognition accuracy of 5.09%and 3.82%over the best existing active learning methods,Margin,and Least Confidence methods,respectively,and a 1.61%improvement compared to the conventional segmented active learning method.

关键词： Expression recognition active learning self-supervised learning uncertainty estimation

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Precise control of page cache for containers

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Frontiers of computer science 2024年第2期18卷 17-30页

作者： Kun WANG Song WU Shengbang LI Zhuo HUANG Hao FAN Chen YU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Container-based virtualization isbecoming increasingly popular in cloud computing due to its efficiency and *** isolation is a fundamental property of *** works have indicated weak resource isolation could cause significant performance degradation for containerized applications and enhanced resource ***,current studies have almost not discussed the isolation problems of page cache which is a key resource for *** leverage memory cgroup to control page cache ***,existing policy introduces two major problems in a container-based ***,containers can utilize more memory than limited by their cgroup,effectively breaking memory ***,the Os kernel has to evict page cache to make space for newly-arrived memory requests,slowing down containerized *** paper performs an empirical study of these problems and demonstrates the performance impacts on containerized *** we propose pCache(precise control of page cache)to address the problems by dividing page cache into private and shared and controlling both kinds of page cache separately and *** do so,pCache leverages two new technologies:fair account(f-account)and evict on demand(EoD).F-account splits the shared page cache charging based on per-container share to prevent containers from using memory for free,enhancing memory *** EoD reduces unnecessary page cache evictions to avoid the performance *** evaluation results demonstrate that our system can effectively enhance memory isolation for containers and achieve substantial performance improvement over the original page cache management policy.

关键词： page cache memory cgroup container isolation cloud computing

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Minimal Context-Switching data Race Detection with dataflow Tracking

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Journal of computer science & Technology 2024年第1期39卷 211-226页

作者：郑龙李洋辛杰刘海峰郑然廖小飞金海 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

data race is one of the most important concurrent anomalies in multi-threaded *** con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race ***,this constraint-based approach has serious limitations on helping programmers analyze and understand data ***,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the ***,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving *** ad hoc recommendation imposes too many context switches,which complicates the data race *** address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context ***,we reduce the false positives by ana-lyzing and tracking the dataflow in the *** this means,DFTracker thus reduces the unnecessary analysis of false race *** further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data *** experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race ***,DFTracker can be used as an effective tool to understand the data race for programmers.

关键词： data race satisfiability modulo theory multi-threaded program dynamic detection

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Internet of Brain, Thought, Thinking, and Creation

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Chinese Journal of Electronics 2022年第6期31卷 1025-1042页

作者： ZHANG Zhimin YIN Rui NING Huansheng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing Engineering Research Center for Cyberspace Data Analysis and Applications

Thinking space came into being with the emergence of human civilization. With the emergence and development of cyberspace, the interaction between those two spaces began to take place. In the collision of thinking and technology, new changes have taken place in both thinking space and cyberspace. To this end, this paper divides the current integration and development of thinking space and cyberspace into three stages, namely Internet of brain(IoB), Internet of thought(IoTh), and Internet of thinking(IoTk). At each stage, the contents and technologies to achieve convergence and connection of spaces are discussed. Besides, the Internet of creation(IoC) is proposed to represent the future development of thinking space and cyberspace. Finally, a series of open issues are raised, and they will become thorny factors in the development of the Io C stage.

关键词： thinking space IoTk Internet of thinking IoTh Internet of thought cyberspace development Internet of Things Internet of brain cyber-physical systems IoB human civilization emergence

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