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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jiali Zhou Xin Li Qinhui Wang Xiaolin Qin WeiWei Miao Jianwei Tian College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Military Training and Management Army Command College Nanjing China State Grid Jiangsu Electric Power Co. Ltd. Information & Telecommunication Branch China State Grid Hunan Electric Power Co. Ltd. Information & Telecommunication Branch China

Service Mesh has become one of the most popular microservices governance frameworks. In the service governance function of traffic management, how to design an efficient strategy for request distribution to minimize response time has attracted wide research interest. Generally, a traffic policy can be pre-set manually. However, such a static policy always performs terribly when the load changes dynamically. That is, Service Mesh cannot adaptively select the optimal policy when setting service version diversion and selecting instances on the service subset. To address this issue, in this paper, we designed an adaptive strategy based on split-flow to achieve global load balancing. Furthermore, we implement such a strategy by developing a plug-in named DTMA for Istio, a typical representative of Service Mesh. By obtaining the Service Mesh node’s topology and global instance load distribution in real-time, Istio updates the optimal split-flow weight and selects the suitable load balancing strategy. Extensive experimental results show that our algorithm can reduce the response time by nearly half and keep a load of nodes and instances stable compared with Istio’s native configuration. The optimization effect of minimizing the response time is achieved through real-time load balancing.

关键词： Automation Source coding Scalability Microservice architectures Elasticity Load management Real-time systems

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Biased feature learning for occlusion invariant face recognition 29

Biased feature learning for occlusion invariant face recogni...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Shao, Changbin Huo, Jing Qi, Lei Feng, Zhen-Hua Li, Wenbin Dong, Chuanqi Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Jiangsu University of Science and Technology Zhenjiang China Department of Computer Science Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

ISBN: (纸本)9780999241165

To address the challenges posed by unknown occlusions, we propose a Biased Feature Learning (BFL) framework for occlusion-invariant face recognition. We first construct an extended dataset using a multi-scale data augmentation method. For model training, we modify the label loss to adjust the impact of normal and occluded samples. Further, we propose a biased guidance strategy to manipulate the optimization of a network so that the feature embedding space is dominated by non-occluded faces. BFL not only enhances the robustness of a network to unknown occlusions but also maintains or even improves its performance for normal faces. Experimental results demonstrate its superiority as well as the generalization capability with different network architectures and loss functions. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Face recognition

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Sequential Mechanisms for Multi-type Resource Allocation 21

Sequential Mechanisms for Multi-type Resource Allocation

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International Conference on Autonomous Agents and Multiagent Systems

作者： Sujoy Sikdar Xiaoxi Guo Haibin Wang Lirong Xia Yongzhi Cao Department of Computer Science Binghamton University Key Laboratory of High Confidence Software Technologies (MOE) Department of Computer Science and Technology Peking University Department of Computer Science Rensselaer Polytechnic Institute

ISBN: (纸本)9781713832621

Several resource allocation problems involve multiple types of resources, with a different agency being responsible for "locally" allocating the resources of each type, while a central planner wishes to provide a guarantee on the properties of the final allocation given agents' preferences. We study the relationship between properties of the local mechanisms, each responsible for assigning all of the resources of a designated type, and the properties of a sequential mechanism which is composed of these local mechanisms, one for each type, applied sequentially, under lexicographic preferences, a well studied model of preferences over multiple types of resources in artificial intelligence and economics. We show that when preferences are O-legal, meaning that agents share a common importance order on the types, sequential mechanisms satisfy the desirable properties of anonymity, neutrality, non-bossiness, or Pareto-optimality if and only if every local mechanism also satisfies the same property, and they are applied sequentially according to the order O. Our main results are that under O-legal lexicographic preferences, every mechanism satisfying strategyproofness and a combination of these properties must be a sequential composition of local mechanisms that are also strategyproof, and satisfy the same combinations of properties.

关键词： Multi-type resource allocation Sequential mechanisms Computational social choice Mechanism design mechanism design Preferences Resource utilization Sequential management of resources

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey

arXiv

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arXiv 2025年

作者： Tian, Shuang Zhang, Tao Liu, Jiqiang Wang, Jiacheng Wu, Xuangou Zhu, Xiaoqiang Zhang, Ruichen Zhang, Weiting Yuan, Zhenhui Mao, Shiwen Kim, Dong In School of Software Engineering Beijing Jiaotong University Beijing100044 China School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Computer Science and Technology An hui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui University of Technology Anhui243002 China College of Computing and Data Science Nanyang Technological University Singapore School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Engineering University of Warwick CoventryCV4 7AL United Kingdom Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Department of Electrical and Computer Engineering College of Information and Communication Engineering Sungkyunkwan University Suwon16419 Korea Republic of

—With the rapid development of technology and the acceleration of digitalisation, the frequency and complexity of cyber security threats are increasing. Traditional cybersecurity approaches, often based on static rules and predefined scenarios, are struggling to adapt to the rapidly evolving nature of modern cyberattacks. There is an urgent need for more adaptive and intelligent defence strategies. The emergence of Large Language Model (LLM) provides an innovative solution to cope with the increasingly severe cyber threats, and its potential in analysing complex attack patterns, predicting threats and assisting real-time response has attracted a lot of attention in the field of cybersecurity, and exploring how to effectively use LLM to defend against cyberattacks has become a hot topic in the current research field. This survey examines the applications of LLM from the perspective of the cyber attack lifecycle, focusing on the three phases of defense reconnaissance, foothold establishment, and lateral movement, and it analyzes the potential of LLMs in Cyber Threat Intelligence (CTI) tasks. Meanwhile, we investigate how LLM-based security solutions are deployed and applied in different network scenarios. It also summarizes the internal and external risk issues faced by LLM during its application. Finally, this survey also points out the facing risk issues and possible future research directions in this domain. Copyright © 2025, The Authors. All rights reserved.

关键词： Cyber attacks

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PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction and Reconstruction of Multivariate Time Series

PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Zhiying Xiong Qilin Fan Kai Wang Xiuhua Li Xu Zhang Qingyu Xiong School of Big Data and Software Engineering Chongqing University Chongqing China Chongqing Key Laboratory of Digital Cinema Art Theory and Technology Chongqing University Chongqing China School of Computer Science and Technology Harbin Institute of Technology Weihai China Research Institute of Cyberspace Security Harbin Institute of Technology Weihai China Haihe Laboratory of Information Technology Application Innovation Tianjin China Department of Computer Science University of Exeter Exeter UK

Detecting anomalies for key performance indicator (KPI) data is of paramount importance to ensure the quality and reliability of network services. However, building the anomaly detection system for KPI is challenging due to the scarcity of abnormal labels and highly dynamic or even unseen patterns. In this paper, we propose an unsupervised KPI anomaly detection framework named PRAD by jointly optimizing prediction-based and reconstruction-based modules. Specifically, PRAD employs two parallel graph attention networks (GATs) to learn metric-oriented and time-oriented relationships. A temporal convolutional attention network (TCAN) is used to capture complex long-term dependencies. Furthermore, to tackle the overfitting issue in the variational auto-encoder (VAE), PRAD designs a reconstruction-based module that combines VAE with a generative adversarial network (GAN). The experimental results on the two public datasets demonstrate that the proposed PRAD outperforms existing anomaly detection methods.

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Automatic synthesis of generalized winning strategies of impartial combinatorial games using SMT solvers 29

Automatic synthesis of generalized winning strategies of imp...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Wu, Kaisheng Fang, Liangda Xiong, Liping Lai, Zhao-Rong Qiao, Yong Chen, Kaidong Rong, Fei Department of Computer Science Jinan University China Department of Computer Science South China Normal University China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology China

ISBN: (纸本)9780999241165

Strategy representation and reasoning has recently received much attention in artificial intelligence. Impartial combinatorial games (ICGs) are a type of elementary and fundamental games in game theory. One of the challenging problems of ICGs is to construct winning strategies, particularly, generalized winning strategies for possibly infinitely many instances of ICGs. In this paper, we investigate synthesizing generalized winning strategies for ICGs. To this end, we first propose a logical framework to formalize ICGs based on the linear integer arithmetic fragment of numeric part of PDDL. We then propose an approach to generating the winning formula that exactly captures the states in which the player can force to win. Furthermore, we compute winning strategies for ICGs based on the winning formula. Experimental results on several games demonstrate the effectiveness of our approach. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Game theory

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Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning

arXiv

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arXiv 2024年

作者： Wang, Tianfu Shen, Li Fan, Qilin Xu, Tong Liu, Tongliang Xiong, Hui Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science University of Science and Technology of China Hefei230027 China The School of Big Data and Software Engineering Chongqing University ChinaScience Chongqing401331 China JD Explore Academy The UBTECH Sydney Artificial Intelligence Centre The School of Information Technologies Faculty of Engineering and Information Technologies The University of Sydney DarlingtonNSW2008 Australia Thrust of Artificial Intelligence Nansha Guangdong Guangzhou511400 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

As an essential resource management problem in network virtualization, virtual network embedding (VNE) aims to allocate the finite resources of physical network to sequentially arriving virtual network requests (VNRs) with different resource demands. Since this is an NP-hard combinatorial optimization problem, many efforts have been made to provide viable solutions. However, most existing approaches have either ignored the admission control of VNRs, which has a potential impact on long-term performances, or not fully exploited the temporal and topological features of the physical network and VNRs. In this paper, we propose a deep Hierarchical Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for VNE, named HRL-ACRA. Specifically, the whole VNE process is decomposed into an upper-level policy for deciding whether to admit the arriving VNR or not and a lower-level policy for allocating resources of the physical network to meet the requirement of VNR through the HRL approach. Considering the proximal policy optimization as the basic training algorithm, we also adopt the average reward method to address the infinite horizon problem of the upper-level agent and design a customized multi-objective intrinsic reward to alleviate the sparse reward issue of the lower-level agent. Moreover, we develop a deep feature-aware graph neural network to capture the features of VNR and physical network and exploit a sequence-to-sequence model to generate embedding actions iteratively. Finally, extensive experiments are conducted in various settings, and show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue. Our code is available at https://***/GeminiLight/hrl-acra. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Static code analyzer recommendation via preference mining

引用

Expert Systems with Applications 2025年 285卷

作者： Ge, Xiuting Fang, Chunrong Li, Xuanye Shang, Ye Zhang, Mengyao Pan, Ya China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education China The State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Southwest University of Science and Technology China

Static Code Analyzers (SCAs) have played a critical role in software quality assurance. However, SCAs with various static analysis techniques suffer from different levels of false positives and false negatives, thereby yielding varying effectiveness in SCAs. To detect more defects in a given project, it is a possible way to use more available SCAs for scanning this project. Due to producing unacceptable costs and overpowering warnings, invoking all available SCAs for the defect detection of a given project is impractical in real scenarios. To address the above problem, we are the first to propose a practical SCA recommendation approach via preference mining, which aims to select the most effective SCA for a given project. Specifically, our approach performs the SCA effectiveness evaluation to obtain the correspondingly optimal SCAs on projects under test. Subsequently, our approach performs the SCA preference mining via the project characteristics, thereby analyzing the intrinsic relation between projects under test and the correspondingly optimal SCAs. Finally, our approach constructs the SCA recommendation model based on projects under test and the associated characteristics. We conduct the experimental evaluation on three popular SCAs as well as 213 open-source and large-scale projects. The results present that our constructed SCA recommendation model outperforms four typical baselines by 2 ∼ 11 times. © 2025

关键词： Redundancy

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When to update your model: constrained model-based reinforcement learning 22

When to update your model: constrained model-based reinforce...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Tianying Ji Yu Luo Fuchun Sun Mingxuan Jing Fengxiang He Wenbing Huang Department of Computer Science and Technology Tsinghua University Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences JD Explore Academy *** Inc Gaoling School of Artificial Intelligence Renmin University of China and Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

ISBN: (纸本)9781713871088

Designing and analyzing model-based RL (MBRL) algorithms with guaranteed monotonic improvement has been challenging, mainly due to the interdependence between policy optimization and model learning. Existing discrepancy bounds generally ignore the impacts of model shifts, and their corresponding algorithms are prone to degrade performance by drastic model updating. In this work, we first propose a novel and general theoretical scheme for a non-decreasing performance guarantee of MBRL. Our follow-up derived bounds reveal the relationship between model shifts and performance improvement. These discoveries encourage us to formulate a constrained lower-bound optimization problem to permit the monotonicity of MBRL. A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns. Motivated by these analyses, we design a simple but effective algorithm CMLO (Constrained Model-shift Lower-bound Optimization), by introducing an event-triggered mechanism that flexibly determines when to update the model. Experiments show that CMLO surpasses other state-of-the-art methods and produces a boost when various policy optimization methods are employed.

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