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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Yuan, Jinliang Li, Qing Dang, Fan Mu, Xiaofang Qi, Hui Xu, Mengwei Wang, Shangguang Tsinghua University Department of Automation China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China China Taiyuan Normal University College of Computer Science and Technology China Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology China

ISBN: (纸本)9798331515966

Federated learning (FL) has been widely adopted as a privacy-preserving model training paradigm. However, traditional FL protocol heavily relies on data transmission between clients and servers across the wide-area network (WAN), which is tightly constrained and unreliable, therefore causing expensive communication and slow convergence. To this end, we propose a LAN-aware FL (LanFL) protocol, which can efficiently leverage the network capacity of the local-area network (LAN). By frequent model aggregation among the devices within the same LAN, we can significantly reduce the global aggregation across WAN, thus accelerating the training process. However, due to the unique challenges introduced by LAN, it's not easy to efficiently utilize LAN resources while preserving the original dignity of FL performance. Therefore, LanFL also incorporates several critical techniques: LAN-aware hierarchical aggregation, intraLAN device topology construction, and inter-LAN heterogeneous bandwidth coordination. Extensive real-world experiments are conducted and the experimental results show that LanFL can significantly accelerate FL training up to 6.0 ×, while preserving the model accuracy. © 2024 IEEE.

关键词： Network topology

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MGLGANï1/4ša generative adversarial model based on multi-layer graph convolution net and recurrent neural networks for link prediction

MGLGANï1/4ša generative adversarial model based on multi-l...

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2022 International Conference on Electronic Information Engineering, Big Data, and computer technology, EIBDCT 2022

作者： Song, Jiantong Xiao, Xiaoqiang Ning, Weixun Zhang, Xu State Key Laboratory of High Performance Computing HuNan ChangSha China Computer School National University of Defense Technology HuNan ChangSha China

ISBN: (纸本)9781510655133

Link prediction is a technology to find existing but unobserved links (static) or predict new links (dynamic) by studying the network topology. In real life, most networks change with time. Some recent works focus on the dynamic prediction of networks, but they do not solve the weight problem in link prediction well. At the same time, the methods based on matrix decomposition and node embedding used in most link prediction tasks have some problems, such as huge amount of calculation or can't better represent the time evolution. In this paper, we extend the method of predicting the weight between nodes in dynamic networks and propose a generative adversarial model based on multi-layer graph convolution and recurrent neural network. The model consists of a generator and discriminator trained during an adversarial process. Multi-layer graph convolution used to approximate the high order similarity of network graph, so as to compensate the time evolution characteristic that RNN network can only deal with low order similarity. Using the process of confrontation training, the model can learn the representation of robust time evolution and predict with high accuracy. Experiments on several real data sets show that our model has good adaptability better than several baseline models. © 2022 SPIE.

关键词： Generative adversarial networks

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TDLP: Time decay based link prediction method for dynamic networks

TDLP: Time decay based link prediction method for dynamic ne...

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2022 International Conference on Electronic Information Engineering, Big Data, and computer technology, EIBDCT 2022

作者： Zhang, Xu Xiao, Xiaoqiang Ning, Weixun Song, Jiantong State Key Laboratory of High Performance Computing HuNan ChangSha China Computer School National University of Defense Technology HuNan ChangSha China

ISBN: (纸本)9781510655133

Link prediction aims to find the missing link in current networks or estimate the likelihood the link will appear in the future. In many real-world scenarios, networks can be massive and drastically evolving. Many recent works concentrated on how to solve the procession of massive data, which cares less about the effects of the temporal information. In some dynamic networks, the edge that appears a long time ago will have fewer effects on the appearance of edges in the future. The accuracy of link prediction in dynamic networks can be further improved by adding temporal information in prediction measures. In this paper, we mainly explore the effects of the link temporal information in the graph stream link prediction. We design a graph stream-based framework to solve link prediction problems in dynamic networks which can provide a convenient platform to implement different link prediction methods for dynamic networks, and evaluate these methods on a fair basis. Then, we proposed the Time Decay-based Link Prediction (TDLP) method to improve the efficient of link prediction problem in the dynamic networks. TDLP extends the neighborhood-based measure with time decay functions in graph stream scenarios which can handle temporal information and massive data. Experiment results demonstrate that the accuracy of link prediction for dynamic networks can be efficiently improved in our TDLP method. © 2022 SPIE.

关键词： Metadata

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A Comprehensive Study of OOP-Related Bugs in C++ Compilers

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IEEE Transactions on Software Engineering 2025年 1-21页

作者： Wang, Bo Chen, Chong Chen, Junjie Xu, Bowen Ye, Chen Lin, Youfang Dong, Guoliang Sun, Jun Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence China Tianjin University College of Intelligence and Computing Tianjin300350 China North Carolina State University Department of Computer Science NC27695 United States Singapore Management University School of Computing and Information Systems Singapore178902 Singapore

Modern C++, a programming language characterized by its extensive use of object-oriented programming (OOP) features, is widely used for system programming. However, C++ compilers often struggle to correctly handle these sophisticated OOP features, resulting in numerous high-profile compiler bugs that can lead to crashes or miscompilation. Despite the significance of OOP-related bugs, existing studies largely overlook OOP features, hindering their ability to discover such bugs. To assist both compiler fuzzer designers and compiler developers, we conduct a comprehensive study of the compiler bugs caused by incorrectly handling C++ OOP-related features. First, we systematically extract 788 OOP-related C++ compiler bugs from GCC and LLVM. Second, derived from the core concepts of OOP and C++, we manually identified a two-level taxonomy of the OOP-related features leading to compiler bugs, which consists of 6 primary categories (e.g., Abstraction & Encapsulation, Inheritance, and Runtime Polymorphism), along with 17 secondary categories (e.g., Constructors & Destructors and Multiple Inheritance). Third, we systematically analyze the root causes, symptoms, fixes, options, and C++ standard versions of these bugs. Our analysis yields 13 key findings, highlighting that features related to the construction and destruction of objects lead to the highest number of bugs, crashes are the most frequent symptom, and while the average time from bug introduction to discovery is 1856 days, fixing the bug once discovered takes only 174 days on average. Additionally, more than half of the bugs can be triggered without any compiler options. These findings offer valuable insights not only for developing new compiler testing approaches but also for improving language design and compiler engineering. Inspired by these findings, we developed a proof-of-concept compiler fuzzer OOPFuzz, specifically targeting OOP-related bugs in C++ compilers. We applied it against the newest release versions

关键词： C++ (programming language)

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Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

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Journal of computer science & technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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Dynamic Task Scheduling for Coordinated Truck-Drone Parcel Delivery: A Hybrid Genetic Tabu Algorithm Approach 22

Dynamic Task Scheduling for Coordinated Truck-Drone Parcel D...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Li, Ruitai Yuan, Quan Liao, Tongxin Luo, Lijun Hu, Menglan Lai, Pan Zhang, Xiao South-Central Minzu University College of Computer Science China State Ethnic Affairs Commission Key Laboratory of Cyber-Physical Fusion Intelligent Computing China Huazhong University of Science and Technology School of Electronic Information and Communications China Hubei Key Laboratory of Smart Internet Technology China

ISBN: (纸本)9798331509712

With the rapid growth of the on-demand economy, logistics companies and merchants increasingly struggle to meet customer demands in dynamic and uncertain conditions. This paper studies the coordinated delivery of parcels by trucks and drones under such demands, proposing a Dynamic Task Scheduling Algorithm based on Hybrid Genetic Tabu algorithm (DTSAGT) for route optimization. Simulating dynamic customer demands with a Poisson distribution and statistical methods, the algorithm addresses timeliness issues due to variations in customer needs. It optimizes drone path planning and task allocation considering drone endurance and payload limits to minimize total delivery time. The algorithm includes three steps: initial solution construction, iterative optimization, and dynamic operations. Experimental results show that DTSAGT reduces the total service time by 15.05%, 34.13%, and 35.71% on average compared to the baseline algorithms. This paper's contribution is the combination of hybrid genetic and tabu search algorithms applied to dynamic task scheduling in truck-drone delivery, enhancing logistics efficiency. © 2024 IEEE.

关键词： Poisson distribution

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LprViT:An Efficient ViT for LiDAR-Based Place Recognition Using Range Image

LprViT:An Efficient ViT for LiDAR-Based Place Recognition Us...

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Communication, Image and Signal Processing (CCISP), International Conference on

作者： Rui He Yunzhe Xiao Shaowu Yang Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China

Place recognition is a crucial component in the realm of simultaneous localization and mapping (SLAM) and autonomous driving. It can also enhance the performance of loop closure detection and global localization. LiDAR-based place recognition usually relies on global feature descriptors to accurately express the similarity between LiDAR scans. However, it is time-consuming to extract and generate the global feature descriptors from the original LiDAR data. As a result, treating LiDAR-based point clouds place recognition as a 2D image problem using range projection is becoming an increasingly common and successful strategy. Vision Transformer (ViT) is a Transformer model based on self-attention mechanism for image classification. Through self-attention mechanism, ViT out-performs CNN at modeling the global information of images. We investigate that whether ViT can be transferred to LiDAR-based place recognition tasks. Firstly, we introduce a new convolutional stem to replace the original linear embedding layer in ViT. Secondly, we establish synergy by integrating low-level fine-grained features from new convolution layer with high-level coarse-grained features from ViT by skip connections. Thirdly, we aggregate these features to construct the global feature descriptor via the NetVLAD technique. The results of experiments on KITTI confirm that our method can operate online and achieve superior performance on par with existing approaches.

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Online Dynamic Scaling of Microservices with Fair Probabilistic Routing 22

Online Dynamic Scaling of Microservices with Fair Probabilis...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Chen, Yang Lin, Shisheng Hu, Yi Wang, Liangyuan Hu, Menglan Lai, Pan Xie, Yuanai College of Computer Science South-Central Minzu University China Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission China School of Electronic Information and Communications Huazhong University of Science and Technology China Hubei Key Laboratory of Smart Internet Technology China

ISBN: (纸本)9798331509712

Microservice architecture, as an emerging network architecture, has gained widespread adoption in latency-sensitive applications within the realm of mobile edge computing (MEC). In MEC networks, these latency-sensitive applications necessitate the concurrent processing of numerous service requests, which are composed of microservices. The complex dependencies between microservices and frequent data communication between servers contribute to the intricacy of deploying and routing microservice instances within the network. Moreover, the dynamic and unpredictable nature of service request traffic significantly complicates the timeliness and efficiency of service deployment and request routing strategies. However, existing research predominantly focuses on static network environments and neglects the time-varying characteristics of service request traffic in realistic scenarios. Consequently, we address the joint optimization problem of service deployment and request routing in the presence of dynamic service request traffic. To model the inherent data dependencies and analyze service request response latency, we employ the open Jackson queuing network. We propose a fine-grained microservice dynamic scaling (FMDS) algorithm to capture the dynamic fluctuations in service request traffic within the network. This algorithm scales microservice instances based on the principle of equal proportional change, obtaining a service deployment scheme that minimizes costs while satisfying latency constraints. Furthermore, we introduce a recursive path search algorithm that explores the service deployment scheme to determine the node forwarding probability for the entire network, adhering to the principles of fair routing. Simulation results show that the proposed method effectively improves network latency stability by 75% and enhances the timeliness of the service deployment strategy. © 2024 IEEE.

关键词： microservice request routing service deployment services computing

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A Thermochromic, Viscoelastic Nacre-like Nanocomposite for the Smart Thermal Management of Planar Electronics

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Nano-Micro Letters 2023年第10期15卷 182-196页

作者： Jiemin Wang Tairan Yang Zequn Wang Xuhui Sun Meng An Dan Liu Changsheng Zhao Gang Zhang Weiwei Lei College of Biomedical Engineering Sichuan UniversityChengdu 610064People’s Republic of China Institute for Frontier Materials Deakin UniversityWaurn Ponds CampusLocked Bag 20000GeelongVictoria 3220Australia College of Mechanical and Electrical Engineering Shaanxi University of Science and TechnologyXi’an 710021People’s Republic of China State Key Laboratory of Polymer Materials Engineering College of Polymer Science and EngineeringSichuan UniversityChengdu 610065People’s Republic of China Institute of High Performance Computing A*STAR Singapore 138632Singapore

Cutting-edge heat spreaders for soft and planar electronics require not only high thermal conductivity and a certain degree of flexibility but also remarkable self-adhesion without thermal interface materials, elasticity, arbitrary elongation along with soft devices, and smart properties involving thermal self-healing, thermochromism and so on. Nacre-like composites with excellent in-plane heat dissipation are ideal as heat spreaders for thin and planar electronics. However, the intrinsically poor viscoelasticity, i.e., adhesion and elasticity, prevents them from simultaneous self-adhesion and arbitrary elongation along with current flexible devices as well as incurring high interfacial thermal impedance. In this paper, we propose a soft thermochromic composite(STC) membrane with a layered structure, considerable stretchability, high in-plane thermal conductivity(~30 Wm^(-1) K^(-1)), low thermal contact resistance(~12 mm^2 KW^(-1), 4–5 times lower than that of silver paste), strong yet sustainable adhesion forces(~4607 Jm^(-2), 2220 Jm^(-2) greater than that of epoxy paste) and self-healing efficiency. As a self-adhesive heat spreader, it implements efficient cooling of various soft electronics with a temperature drop of 20℃ than the polyimide case. In addition to its self-healing function, the chameleon-like behavior of STC facilitates temperature monitoring by the naked eye, hence enabling smart thermal management.

关键词： Boron nitride nanosheets Nacre-inspired composites Viscoelastic Thermochromic Smart thermal management

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Sustainability Forecasting for Deep Learning Packages 31

Sustainability Forecasting for Deep Learning Packages

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Han, Junxiao Wang, Yunkun Liu, Zhongxin Bao, Lingfeng Liu, Jiakun Lo, David Deng, Shuiguang School of Computer & Computing Science Hangzhou City University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China School of Computing and Information Systems Singapore Management University Singapore Singapore

ISBN: (纸本)9798350330663

Deep Learning (DL) technologies have been widely adopted to tackle various tasks. In this process, through software dependencies, a multi-layer DL supply chain (SC) is formed, with DL frameworks acting as the root, DL packages acting as the bridge nodes, and downstream DL projects acting as the periphery. However, most Open Source Software (OSS) projects may fail. Considering the crucial position of DL packages in the DL SC, to foster the sustainable development of DL SCs and DL packages, we aim to forecast the long-term sustainability of DL packages. Here, sustained activity is adopted as the main proxy of sustainability, and the sustainability status is classified as 'sus-tainable' or 'dormant'. Relatedly, a DL package is considered as 'sustainable' if it has sustained activity in its last 12 months. Otherwise, it is deemed as 'dormant'. To this end, we propose an approach that begins with obtaining longitudinal features for each DL package in each month. Then, we develop a model to forecast the sustainability of DL packages by incorporating the longitudinal features, which can aptly predict sustainability with an accuracy of up to 0.81. Subsequently, an interpretable module is developed to interpret the determinants (i.e., important features) that impact the sustainability of DL packages. Finally, we generate sustainability trajectories for each DL package to better understand the monthly changes of their sustainability status. Our findings uncover that for most DL packages, fewer but more centralized developers and a balanced collaboration are more likely to help sustain the DL packages. Furthermore, although some DL packages are sustainable, their sustainability trajectories present statistically decreasing trends over time. Based on the findings, we shed light on the dynamic sustainability of DL packages, highlight future research directions, and provide practical suggestions to DL package maintainers, developers, users, and software engineering researchers. ©

关键词： Open source software

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