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IEEE International Conference on Automated Software Engineering (ASE)

作者： Yiru Chen Chenxi Zhang Zhen Dong Dingyu Yang Xin Peng Jiayu Ou Hong Yang Zheshun Wu Xiaojun Qu Wei Li School of Computer Science Fudan University China Shanghai Key Laboratory of Data Science Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Alibaba Group China

A fault in large online service systems often triggers numerous alerts due to the complex business and component dependencies among services, which is known as “alert storm”. In a short time, an online service system may generate a huge amount of alert data. This poses a challenge for on-call engineers to identify alerts that are associated with a system failure for root cause analysis. In this paper, we propose DyAlert, a dynamic graph neural networks-based approach for linking alerts that might be triggered by a same fault to reduce the burden of on-call engineers in the fault analysis. Our insight is that alerts are often triggered by alert propagation when a system failure occurs, e.g., alert $a$ would lead to the occurrence of alert $b$ . Whether two alerts should be linked depends on if one alert is triggered by the propagation of the other. Leveraging this insight, we design a dynamic graph (namely Alert-Metric Dynamic Graph) that describes the propagation process of alerts. Based on the dynamic graph, we train a neural networks-based model to predict alert links. We evaluate DyAlert with real-world data collected from an online service system running 85 business units and about 30,000 different services in a large enterprise. The results show that DyAlert is effective in predicting alert links and it outperforms the state-of-the-art approaches with an average increase of 0.259 in F1-score.

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Query-Centered Temporal Community Search via Time-Constrained Personalized PageRank

arXiv

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arXiv 2023年

作者： Lin, Longlong Yuan, Pingpeng Li, Rong-Hua Zhu, Chunxue Qin, Hongchao Jin, Hai Jia, Tao College of Computer and Information Science Southwest University Chongqing400715 China National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technololgy Beijing Institute of Technology Beijing100081 China

Existing temporal community search suffers from two defects: (i) they ignore the temporal proximity between the query vertex q and other vertices but simply require the result to include q. Thus, they find many temporal irrelevant vertices (these vertices are called query-drifted vertices) to q for satisfying their cohesiveness, resulting in q being marginalized;(ii) their methods are NP-hard, incurring high costs for exact solutions or compromised qualities for approximate/heuristic algorithms. Inspired by these, we propose a novel problem named query-centered temporal community search to circumvent query-drifted vertices. Specifically, we first present a novel concept of Time-Constrained Personalized PageRank to characterize the temporal proximity between q and other vertices. Then, we introduce a model called β-temporal proximity core, which can combine temporal proximity and structural cohesiveness. Subsequently, our problem is formulated as an optimization task that finds a β-temporal proximity core with the largest β. To solve our problem, we first devise an exact and near-linear time greedy removing algorithm that iteratively removes unpromising vertices. To improve efficiency, we then design an approximate two-stage local search algorithm with bound-based pruning techniques. Finally, extensive experiments on eight real-life datasets and nine competitors show the superiority of the proposed solutions. © 2023, CC BY.

关键词： Iterative methods

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Attempt to synthesize the proton-unbound Bi182,183 isotopes

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Physical Review C 2024年第1期110卷 014326-014326页

作者： H. Huang H. Y. Wu C. Y. Guo W. Q. Zhang Q. B. Zeng D. X. Wang Z. Liu Z. H. Li A. N. Andreyev S. Antalic B. Andel A. Sitarčík H. Hua X. H. Yu Z. X. Zhou X. Wang H. Y. Lu Z. G. Gan Z. Y. Zhang L. Ma M. H. Huang H. B. Yang M. M. Zhang X. J. Wen G. S. Li F. F. Zeng X. X. Xu J. J. Liu H. Jian H. F. Zhu Q. R. Gao Z. Qin J. R. Wang Z. M. Jia S. W. Cao P. F. Zhai Y. J. Li X. H. Zhou F. S. Zhang Beijing 100049 China D-64291 Darmstadt Germany Key Laboratory of Nuclear Data School of Physics and School of Physics Engineering and Technology Advanced Science Research Center (ASRC) Department of Nuclear Physics and Biophysics School of Space Science and Physics The Key Laboratory of Beam Technology of Ministry of Education College of Nuclear Science and Technology Institute of Radiation Technology

The near-symmetric complete fusion reaction Kr78+Ag107→Bi*185 was studied at the gas-filled recoil separator SHANS with an attempt to synthesize the extremely neutron-deficient proton-unbound Bi182,183 isotopes. No decay events which could be attributed to them were observed. The two- and three-particle evaporation residues Hg180,179 (αp, αpn) and Pb183,182 (pn, p2n) were identified. Their production cross sections have been measured at two bombarding energies. Based on the yields of Pb182,183 in the present work and the systematics of the ratios between the cross sections of p(x−1)n and xn evaporation channels for the most neutron-deficient odd-Z nuclei above lead, the upper limits for the half-lives of Bi182,183 were estimated to be less than 0.3µs.

关键词： Lifetimes & widths Low & intermediate energy heavy-ion reactions Nuclear structure & decays 150 ≤ A ≤ 189

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Multikernel:Operating system solution to generalized functional safety

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Security and Safety 2023年第2期2卷 30-43页

作者： Yijing Song Huasheng Dai Jinhu Jiang Weihua Zhang School of Computer Science Fudan UniversityShanghai 200433China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Institute of Big Data Fudan UniversityShanghai 200433China Parallel Processing Institute Fudan UniversityShanghai 200433China

With the trend of digitalization,intelligence,and networking sweeping the world,functional safety and cyber security are increasingly intertwined and overlapped,evolving into the issue of generalized functional *** system reliability technology and network defense technology cannot provide quantifiable design implementation theories and *** the cornerstone of software systems,operating systems in particular are in need of efficient safety *** DHR architecture is a mature and comprehensive solution,and it is necessary to implement an OS-level DHR architecture,for which the multi-kernel operating system is a good *** multi-kernel operating system takes the kernel as the processing scenario element and constructs redundancy,heterogeneity,and dynamism on the kernel,so it has the generalized robustness of the DHR *** article analyzes the significance and requirements of OS-level DHR architecture,and systematically explains how the multi-kernel operating system responds to the requirements of OS-level DHR architecture by analyzing the technical routes of multi-kernel operating systems and develops an operating system solution idea for the generalized functionally safety.

关键词： Metaverse New infrastructure Security base Highly trusted network Trusted identiry Trusted circulation of data

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Balanced training for sparse GANs 23

Balanced training for sparse GANs

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yite Wang Jing Wu Naira Hovakimyan Ruoyu Sun University of Illinois Urbana-Champaign School of Data Science The Chinese University of Hong Kong Shenzhen China and Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data

Over the past few years, there has been growing interest in developing larger and deeper neural networks, including deep generative models like generative adversarial networks (GANs). However, GANs typically come with high computational complexity, leading researchers to explore methods for reducing the training and inference costs. One such approach gaining popularity in supervised learning is dynamic sparse training (DST), which maintains good performance while enjoying excellent training efficiency. Despite its potential benefits, applying DST to GANs presents challenges due to the adversarial nature of the training process. In this paper, we propose a novel metric called the balance ratio (BR) to study the balance between the sparse generator and discriminator. We also introduce a new method called balanced dynamic sparse training (ADAPT), which seeks to control the BR during GAN training to achieve a good trade-off between performance and computational cost. Our proposed method shows promising results on multiple datasets, demonstrating its effectiveness. Our code is available at https://***/YiteWang/ADAPT.

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3L-AODV: Three Layer Security Protocol for Grayhole Attack Mitigation in MANET 8th

3L-AODV: Three Layer Security Protocol for Grayhole Attack ...

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8th International Congress on Information and Communication Technology, ICICT 2023

作者： Kamel, Mohammed B. M. Abdullah, Wisam Dawood Hamoud, Alaa Khalaf Valadares, Dalton C. G. Shareiyat, Ammar Ligeti, Peter Department of Computer Algebra Eotvos Lorand University Budapest Hungary Institute for Data Science Cloud Computing and IT Security Furtwangen University of Applied Sciences Furtwangen im Schwarzwald Germany Department of Computer Science University of Kufa Najaf Iraq Department of Computer Science University of Tikrit Tikrit Iraq Department of Computer Information Systems University of Basrah Basrah Iraq Virtus RDI Center Federal University of Campina Grande Campina Grande Brazil Federal Institute of Pernambuco Pernambuco Caruaru Brazil Department of CSS University of Stockholm Stockholm Sweden Department of Vulnerability Operations Center Orange Cyberdefense Stockholm Sweden

ISBN: (纸本)9789819930425

As the mobile ad hoc networks infrastructure has no centralized points of connections, the traffic from a source to a destination passes through number of intermediate nodes. When more than one neighbor node is available, the traffic passes through one of them based on some criteria. If a node behaves maliciously in all of the connections and drops the traffic, it creates a security blackhole, while a malicious node that behaves normally is some of the connections creates a security grayhole. Since the malicious node behaves normally in some connections, mitigating this attack is challenging. In this paper, we proposed 3L-AODV, a security protocol that uses three layers of protection and detection to mitigate the grayhole attack. As it does not require any modification on the AODV protocol, it can be implemented on any network that its routing is based on AODV. The analysis of our results shows that 3L-AODV could mitigate the grayhole attack without affecting the performance of the network. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile ad hoc networks

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RIS-Assisted Passive Localization (RAPL): An Efficient Zero-Overhead Framework Using Conditional Sample Mean

arXiv

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arXiv 2025年

作者： Yao, Jiawei Mao, Yijie Chen, Mingzhe Hu, Ye School of Information Science and Technology ShanghaiTech University Shanghai201210 China Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States

Reconfigurable Intelligent Surface (RIS) has been recognized as a promising solution for enhancing localization accuracy. Traditional RIS-based localization methods typically rely on prior channel knowledge, beam scanning, and pilot-based assistance. These approaches often result in substantial energy and computational overhead, and require real-time coordination between the base station (BS) and the RIS. To address these challenges, in this work, we move beyond conventional methods and introduce a novel data-driven, multiple RISs-assisted passive localization approach (RAPL). The proposed method includes two stages, the angle-of-directions (AoDs) between the RISs and the user is estimated by using the conditional sample mean in the first stage, and then the user’s position is determined based on the estimated multiple AoD pairs in the second stage. This approach only utilizes the existing communication signals between the user and the BS, relying solely on the measurement of received signal power at each BS antenna for a set of randomly generated phase shifts across all RISs. Moreover, by obviating the need for real-time RIS phase shift optimization or user-to-BS pilot transmissions, the method introduces no additional communication overhead, making it highly suitable for deployment in real-world networks. The proposed scheme is then extended to multi-RIS scenarios considering both parallel and cascaded RIS topologies. Numerical results show that the proposed RAPL improves localization accuracy while significantly reducing energy and signaling overhead compared to conventional methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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RoomGen: 3D Interior Furniture Layout Generation

RoomGen: 3D Interior Furniture Layout Generation

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Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Wenzhe Zeng Xingyuan Liu School of Software South China Normal University Guangzhou China Aberdeen Institute of Data Science and Artificial Intelligence South China Normal University Guangzhou China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

Lowering the costs of generating realistic indoor scenes upon customer’s requests, automatic synthesis of diverse indoor furniture layouts opens the door to various innovative business models for the real estate and home improvement industry. With the continuous development of deep generative networks, the quality of their generation results is constantly improving and occupies an important position in industrial design. Therefore, how to utilize deep generative networks to design indoor 3D scene layout with high quality and high level is of great significance to improve the design efficiency. This paper introduce RoomGen, a 3D Generative Adversarial Network (GAN) tailored for this purpose. RoomGen integrates an enhanced Variational Autoencoder (VAE) and a UNet++ discriminator for discriminate feature learning. Specifically, VAE introduces residual module in the encoder and deep supervision in the decoder, which improves the fidelity of the generated layouts. In addition, RoomGen incorporates a novel attention mechanism in the UNet++ recognizer, which enables it to capture fine-grained features and better distinguish between real and generated layouts. Experimental results demonstrate RoomGen can proficiently generate high-quality indoor furniture layouts with impressive reconstruction accuracy. Additionally, RoomGen’s capability to produce diverse and realistic scenes offers a more cost-effective solution for the real estate and home improvement sectors. Moreover, it provides a fully automated solution for designing virtual reality scenes and 3D game environments.

关键词： Representation learning Industries Solid modeling Three-dimensional displays Layout Virtual reality Games Generative adversarial networks Decoding Image reconstruction

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Autonomous Robot Navigation in Unknown Terrains using Generative Adversarial Networks

Autonomous Robot Navigation in Unknown Terrains using Genera...

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Soft computing for Security Applications (ICSCSA), International Conference on

作者： P Chitra K Sridar Sudha Govindan Britto Raj S Akshya. J Mani Deepak Choudhry M. Sundarrajan Department of Artificial Intelligence and Data Science St Joseph's Institute of Technology Chennai Department of Computer Science and Engineering Veerammal Engineering College Dindigul Department of Computer Science and Engineering Sona College of Technology Salem Department of Computer Science and Engineering RRASE College Of Engineering Chennai Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai Department of Networking and Communications SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Autonomous Robot Navigation in unknown and dynamic environments is considered one of the grand challenges in robotics. These motions that guide it span from rescue operations to planetary exploration. Classical methods for path planning, including A*, Dijkstra, and RRT, were unable to balance the efficiency and adaptability of motion planning over complex terrain. This may have a powerful solution using Generative Adversarial Networks (GANs) with advanced machine learning techniques to improve the ability of robotic navigation. This study proposes a GAN-based approach for designing an effective self-adaptive path planning and obstacle avoidance system for autonomous robots. Despite performing well in structured environments, traditional techniques normally fail in analysing the unstructured terrains with real-time adjustments and dynamic changes due to the high computational costs and lack of flexibility involved. In this regard, the proposed GAN-based system resolves these issues by rating navigation paths concerning safety and efficiency. Quantitative analysis demonstrates that within a forest, desert, urban, or off-road environment, the computational times by using the developed strategy turn out to be 0.12 seconds, 0.10 seconds, 0.14 seconds, and 0.13 seconds, respectively, behaving much lower than what can be achieved with other algorithms. This approach proves much superior in terms of computational efficiency path smoothness and adaptability under different environmental conditions in applications related to autonomous robot navigation systems.

关键词： Navigation Statistical analysis Machine learning Generative adversarial networks Path planning Real-time systems Generators Safety Computational efficiency Autonomous robots

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GPU-based Parallel Technique for Solving the N-Similarity Problem in Textual data Mining 3

GPU-based Parallel Technique for Solving the N-Similarity Pr...

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3rd International Conference on Distributed computing and High Performance computing, DCHPC 2024

作者： Fazlali, Mahmood Mirhosseini, Mina Shahsavari, Mahyar Shafarenko, Alex Mashinchi, Mashallah University of Hertfordshire Cyber Security and Computing Research Group Department of Computer Science Hertfordshire United Kingdom Shahid Beheshti University Faculty of Mathematical Sciences Department of Computer and Data Sciences Tehran Iran Radboud University Donders Institute for Brain Cognition and Behaviour Ai Department Nijmegen Netherlands Shahid Bahonar University of Kerman Faculty of Mathematics and Computer Department of Statistics Kerman Iran

ISBN: (纸本)9798350381580

An important issue in data mining and information retrieval is the problem of multiple similarity or n-similarity. This problem entails finding a group of n data points with the highest similarity within a large dataset. Exact methods to solve this problem exist but come with high time and space complexities. Additionally, various metaheuristic algorithms have been proposed, including genetic algorithms, gravitational search algorithms, particle swarm optimization, imperialist competitive algorithms, and fuzzy imperialist competitive algorithms. These metaheuristics are capable of finding near-optimal solutions within a reasonable timeframe, although there is no guarantee of achieving exact results. In this paper, we employ a parallelization technique using CUDA to expedite the exact method. We conduct experiments on textual datasets to identify a group of n textual documents with the highest similarity to each other. The experimental results demonstrate that the proposed parallel exact method significantly reduces execution time compared to the best sequential approach and CPU multi-core implementation. Furthermore, it is evident that the proposed method requires less memory space than the exact method. © 2024 IEEE.

关键词： Large datasets

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