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2023 World Wide Web Conference, WWW 2023

作者： Dong, Zeqian He, Qiang Chen, Feifei Jin, Hai Gu, Tao Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia School of Computing Macquarie University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Training machine learning (ML) models on mobile and Web-of-Things (WoT) has been widely acknowledged and employed as a promising solution to privacy-preserving ML. However, these end-devices often suffer from constrained resources and fail to accommodate increasingly large ML models that crave great computation power. Offloading ML models partially to the cloud for training strikes a trade-off between privacy preservation and resource requirements. However, device-cloud training creates communication overheads that delay model training tremendously. This paper presents EdgeMove, the first device-edge training scheme that enables fast pipelined model training across edge devices and edge servers. It employs probing-based mechanisms to tackle the new challenges raised by device-edge training. Before training begins, it probes nearby edge servers' training performance and bootstraps model training by constructing a training pipeline with an approximate model partitioning. During the training process, EdgeMove accommodates user mobility and system dynamics by probing nearby edge servers' training performance adaptively and adapting the training pipeline proactively. Extensive experiments are conducted with two popular DNN models trained on four datasets for three ML tasks. The results demonstrate that EdgeMove achieves a 1.3 × -2.1 × speedup over the state-of-the-art scheme. © 2023 ACM.

关键词： Internet of things

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Incremental SDN Deployment to Achieve Load Balance in ISP Networks 16th

Incremental SDN Deployment to Achieve Load Balance in ISP ...

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16th International Conference on Algorithmic Aspects in Information and Management, AAIM 2022

作者： Cheng, Yunlong Zhou, Hao Gao, Xiaofeng Zheng, Jiaqi Chen, Guihai MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9783031160806

Software defined network (SDN) decouples control planes from data planes and integrates them into a logically centralized controller. With capture of the global view, the controller can dynamically and timely reply to the changes of network states. However, replacing the entire traditional networks, e.g., Internet Service Provider (ISP) networks, with SDNs is difficult and computationally expensive. Hence, incremental deployment of partial SDN devices has received much attention. In this paper, we consider the k-LB problem, i.e., upgrading at most k legacy switches to SDN switches to achieve load balance. We claim that k-LB problem is NP-hard and there is no polynomial time (N+ M) 1-ϵ -approximation algorithm for any constant ϵ> 0 unless P= NP, where N (M) is the number of switches (links) in the network. Given these negative results, we propose an effective greedy algorithm and claim that it reaches an approximation guarantee of cavgcminM, where cavg (cmin ) is the average (minimum) link capacity. Large-scale simulations on real ISP networks show that our greedy algorithm achieves near optimal performance and decreases the maximum link utilization by 30% on average compared with the state of the art. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Approximation algorithms

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SOFTENED SYMBOL GROUNDING FOR NEURO-SYMBOLIC SYSTEMS

arXiv

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arXiv 2024年

作者： Li, Zenan Yao, Yuan Chen, Taolue Xu, Jingwei Cao, Chun Ma, Xiaoxing Lü, Jian State Key Lab of Novel Software Technology Department of Computer Science and Technology Nanjing University China Department of Computer Science Birkbeck University of London United Kingdom

Neuro-symbolic learning generally consists of two separated worlds, i.e., neural network training and symbolic constraint solving, whose success hinges on symbol grounding, a fundamental problem in AI. This paper presents a novel, softened symbol grounding process, bridging the gap between the two worlds, and resulting in an effective and efficient neuro-symbolic learning framework. Technically, the framework features (1) modeling of symbol solution states as a Boltzmann distribution, which avoids expensive state searching and facilitates mutually beneficial interactions between network training and symbolic reasoning;(2) a new MCMC technique leveraging projection and SMT solvers, which efficiently samples from disconnected symbol solution spaces;(3) an annealing mechanism that can escape from sub-optimal symbol groundings. Experiments with three representative neuro-symbolic learning tasks demonstrate that, owining to its superior symbol grounding capability, our framework successfully solves problems well beyond the frontier of the existing proposals. © 2024, CC BY.

关键词： Boltzmann equation

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PipeEdge: A Trusted Pipelining Collaborative Edge Training based on Blockchain 23

PipeEdge: A Trusted Pipelining Collaborative Edge Training b...

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2023 World Wide Web Conference, WWW 2023

作者： Yuan, Liang He, Qiang Chen, Feifei Dou, Ruihan Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Mathematics University of Waterloo Canada National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collaborate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collaborate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads. © 2023 ACM.

关键词： Deep neural networks

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Medical Image Fusion Solid Works Supporting Multiple Techniques with Feature-Level Transforms 2

Medical Image Fusion Solid Works Supporting Multiple Techniq...

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Mutha, Rakhi Pathak, Devendra Nath Ahuja, Vandana Bhandari, Prabhakar Ramesh Rahi, Pankaj Mamodiya, Udit Lovely Professional University Department of Computer Application Punjab India Poornima College of Engineering Department of Computer Science and Engineering Rajasthan Jaipur India Swami Vivekanand Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India Sevadal Mahila Mahavidyalaya Department of Microbiology Umred Road Maharashtra Nagpur India Poornima Institute of Engineering and Technology Department of Artificial Intelligence & Data Science Rajasthan Jaipur India Piet Aicte Idea Lab Poornima Institute of Engineering and Technology Department of Electrical Engineering Rajasthan Jaipur India

ISBN: (纸本)9781665491990

The use of group classifiers on similar features has resulted in computationally expensive and tedious, making them unsuitable for online applications. The clustering techniques are evaluated for both inter-subject and intra-subject variations, and it is discovered that intra-subject order execution is superior. Another approach to dealing with object recognition from medical images is considered. This is known as similarity matching, and it is a computationally simple process as compared to costly and complex characterization computations. The major challenges that occur in the image processing are high noise levels, limited image resolution, and geometric deformations throughout the image. © 2023 IEEE.

关键词： Image resolution

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities 24

On the Effectiveness of Function-Level Vulnerability Detecto...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hong Kong Jin YinHu Laboratory Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Colorado Colorado Springs Department of Computer Science Colorado Springs Colorado United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400702174

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. © 2024 ACM.

关键词： Open source software

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BinaryAI: Binary Software Composition Analysis via Intelligent Binary Source Code Matching 24

BinaryAI: Binary Software Composition Analysis via Intellige...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Jiang, Ling An, Junwen Huang, Huihui Tang, Qiyi Nie, Sen Wu, Shi Zhang, Yuqun Southern University of Science and Technology Shenzhen China Tencent Security Keen Lab Shanghai China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Southern University of Science and Technol-ogy Department of Computer Science and Engineering Shenzhen China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation China

ISBN: (纸本)9798400702174

While third-party libraries (TPLs) are extensively reused to enhance productivity during software development, they can also introduce potential security risks such as vulnerability propagation. Software composition analysis (SCA), proposed to identify reused TPLs for reducing such risks, has become an essential procedure within modern DevSecOps. As one of the mainstream SCA techniques, binary-to-source SCA identifies the third-party source projects contained in binary files via binary source code matching, which is a major challenge in reverse engineering since binary and source code exhibit substantial disparities after compilation. The existing binary-to-source SCA techniques leverage basic syntactic features that suffer from redundancy and lack robustness in the large-scale TPL dataset, leading to inevitable false positives and compromised recall. To mitigate these limitations, we introduce BinaryAI, a novel binary-to-source SCA technique with two-phase binary source code matching to capture both syntactic and semantic code features. First, BinaryAI trains a transformer-based model to produce function-level embeddings and obtain similar source functions for each binary function accordingly. Then by applying the link-time locality to facilitate function matching, BinaryAI detects the reused TPLs based on the ratio of matched source functions. Our experimental results demonstrate the superior performance of BinaryAI in terms of binary source code matching and the downstream SCA task. Specifically, our embedding model outperforms the state-of-the-art model CodeCMR, i.e., achieving 22.54% recall@l and 0.34 MRR compared with 10.75% and 0.17 respectively. Additionally, BinaryAI outperforms all existing binary-to-source SCA tools in TPL detection, increasing the precision from 73.36% to 85.84% and recall from 59.81% to 64.98% compared with the well-recognized commercial SCA product Black Duck. E-https://*** © 2024 ACM.

关键词： Syntactics

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Roland: Robust In-band Parallel Communication for Magnetic MIMO Wireless Power Transfer System 42

Roland: Robust In-band Parallel Communication for Magnetic M...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Zhou, Wangqiu Zhou, Hao Cui, Xiang Wang, Xinyu Wang, Xiaoyan Liu, Zhi Linke Lab School of Computer Science and Technology University of Science and Technology of China China University of Science and Technology of China Cas Key Laboratory of Wireless-Optical Communications China Deqing Alpha Innovation Institute Huzhou China Ibaraki University Graduate School of Science and Engineering Ibaraki Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo182-8585 Japan

ISBN: (纸本)9798350334142

In recent years, receiver (RX) feedback communication has attracted increasing attention to enhance the charging performance for magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems. People prefer to adopt the in-band implementation with minimal overhead costs. However, the influence of RX-RX coupling couldn't be directly ignored like that in the RFID field, i.e., strong couplings and relay phenomenon. In order to solve these two critical issues, we propose a Robust layer-level in-band parallel communication protocol for MIMO MRC-WPT systems (called Roland). Technically, we first utilize the observed channel decomposability to construct group-level channel relationship graph for eliminating the interference caused by strong RX-RX couplings. Then, we generalize such method to deal with the RX dependency due to relay phenomenon. Finally, we conduct extensive experiments on a prototype testbed to evaluate the effectiveness of the proposed scheme. The results demonstrate that our Roland could provide ≥95% average decoding accuracy for concurrent feedback communication of 14 devices. Compared with the state-of-the-art solution, the proposed protocol Roland can achieve an average decoding accuracy improvement of 20.41%. © 2023 IEEE.

关键词： MIMO systems

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Generalized Multi-protocol label Switching based Virtualization for Cloud Computing 1

Generalized Multi-protocol Label Switching based Virtualizat...

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1st International Conference on Connected Systems and Intelligence, CSI 2022

作者： Saxena, Rajat Patel, Ajay Singh Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Cloud computing Lab Department of Computer Science and Engineering Indore India Cloud computing Lab Department of Computer Science and Engineering Indore India

ISBN: (纸本)9781665458153

Generalized Multi Protocol label Switching (GMPLS) or Multi-Protocol Lambda Switching is a new technology which produce amplification in Multi Protocol label Switching (MPLS) to provide support network switching, space switching, and packet switching for time and wavelength. Thus, we can say that GMPLS is extension of MPLS which provides resilience and restoration by automatic switching. In this paper, we provide a Docker based simu-1ation environment which emulates a complex network and create insight functioning of GMPLS. The Docker based simulation environment is light-weight and scalable. It emulates large and complex network with minimal specifications. We have tested this Docker based GMPLS simulation testbed that depends on scaled network resources. Our method has shown tremendous improvement over the other virtualization methods. In this paper, we do a comparative study based on UML, Virtual Box, and Docker and found that Docker consumes very less resources when compared to UML and Virtual Box. In a system with 1 TB Hard disk space and 16 GB RAM, we can design and study a topology with 30-40 nodes using Virtual Box and a topology with 10-15 nodes using UML. Whereas, with Docker a topology with 300-nodes can be designed and tested. Thus scalability is limited by system specifications. © 2022 IEEE.

关键词： multi-protocol label switching

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An Ensemble Model for Gait Classification in Children and Adolescent with Cerebral Palsy: A Low-Cost Approach 1

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11th International Conference on Soft Computing for Problem Solving, SocProS 2022

作者： Chakraborty, Saikat Sambhavi, Sruti Panda, Prashansa Nandy, Anup Odisha Bhubaneswar India Machine Intelligence and Bio-motion Research Lab Department of Computer Science and Engineering National Institute of Technology Rourkela Odisha Rourkela India

ISBN: (数字)9789811965258

ISBN: (纸本)9789811965241

A fast and precise automatic gait diagnostic system is an urgent need for real-time clinical gait assessment. Existing machine intelligence-based systems to detect cerebral palsy gait have often ignored the crucial issue of performance and computation speed trade-off. This study, in a low-cost experimental setup, proposes an ensemble model by combining fast and deep neural networks. The proposed system demonstrates a competing result with an overall $${\approx }82\%$$ of detection accuracy (sensitivity: $${\approx }78\%$$, specificity: $${\approx }84\%$$, and F1-score: $${\approx }83\%$$ ). Although the improvement in detection performance is marginal, the computation speed increased remarkably from state of the art. From the perspective of computation time and performance trade-off, the proposed model demonstrated to be competing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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