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SSRN 2024年

作者： Feng, Zhimin Luo, Xiaobing Wei, Wei School of Mathematics and Statistics Xinyang Normal University Xinyang China School of Computer Technology and Software Engineering Wuhan Polytechnic Wuhan China Center of Intelligent Computing and Applied Statistics School of Mathematics Physics and Statistics Shanghai University of Engineering Science Shanghai201620 China

A graph is called $H$-free if the graph has no subgraph being isomorphic to $H$. The classical Tur\'an problem aims to determine the maximum size of an $H$-free graph with given order. In 2010, Nikiforov introduced the spectral Tur\'an-type problem: Determine the maximum spectral radius of an $H$-free graph with given order. He also presented the following conjecture: For sufficiently large $\nu$ and for $k\geq 2$, the graph $K_k\vee (\overline{K_{\nu-k-2}}\cup K_2)$ is the unique $\nu$-vertex $\{C_{2k+1}, C_{2k+2}\}$-free graph having the largest spectral radius and $K_k\vee \overline{K_{\nu-k}}$ is the unique $\nu$-vertex $C_{2k+2}$-free graph having the largest spectral radius. Recently, this conjecture was confirmed by Cioab\u{a}, Desai and Tait. In this contribution, we resolve the Nikiforov's even cycle conjecture for bipartite graphs. Let $\nu$ and $k$ be two integers satisfying $\nu\geq 1$ if $k=1,$ and $\nu \geq \left(\frac{7^22^{24}k\left(2k-1\right)^2(k+1)^{20}} {(2k-3)^2}\right)^{\frac{k+1}{k-1}}$ if $k\geq 2$. Then we show that the complete bipartite graph $K_{k,\nu-k}$ uniquely maximizes the spectral radius over the set of $C_{2k+2}$-free bipartite graphs of order $\nu$. © 2024, The Authors. All rights reserved.

关键词： Graph theory

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AgileShard: Turning the Sharded Blockchain into a Real-Time Transaction Processing System

AgileShard: Turning the Sharded Blockchain into a Real-Time ...

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Real-Time Systems Symposium (RTSS)

作者： Jianfeng Shi Heng Wu Wang Liu Heran Gao Wenbo Zhang University of Chinese Academy of Sciences Beijing China Software Engineering Technology Research and Development Center Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Nanjing China Nanjing institute of software technology Nanjing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

Blockchain, as an emerging transaction processing system, suffers from low throughput and high latency. Sharded blockchains can significantly increase throughput by dividing nodes into groups (i.e., shards) to process disjoint transactions in parallel. However, the diverse latency requirements of transactions in current sharded blockchains are not well met. Three challenges prevent the sharded blockchain from becoming a real-time transaction processing system, namely, the static block size, the first-come-first-served transaction packing strategy, and the load imbalance. Therefore, this paper proposes 3 methods to help turn the sharded blockchain into a real-time transaction processing system. First, we propose an inter-shard dynamic block size negotiation method that enables shards to adaptively determine the globally optimal block size based on the deadlines of pending transactions. Then, we propose a DAG-based transaction packing method for reducing the number of deadline violations and improving parallelism. Finally, we propose a minimum-cost-flow-based shard reconfiguration method to address load imbalance. Under real datasets on Ethereum, experimental results show that AgileShard using the above three methods can effectively improve the transaction deadline satisfaction rate.

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Imperceptible and Targeted Physical Attacks on Deep Learning-Based Speech Semantic Communications

Imperceptible and Targeted Physical Attacks on Deep Learning...

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IEEE Conference on Wireless Communications and Networking

作者： Yuhao Hua Yang Xu Chen Lyu Jia Liu Yulong Shen Weidong Yang Norio Shiratori Hangzhou Institute of Technology Xidian University Hangzhou China School of Computer Science and Technology Xidian University Xi'an China MoE Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics Shanghai China Center for Strategic Cyber Resilience Research and Development National Institute of Informatics Tokyo Japan Research and Development Initiative Chuo University Tokyo Japan

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The deep learning-based semantic communication system (DeepSC) is designed to improve the efficiency and accuracy of information transmission, by leveraging joint source-channel coding techniques to extract relevant semantic features. However, existing research on attack methods targeting DeepSC has primarily focused on text and image domains, leaving the speech domain largely unexplored. To this end, this paper proposes Iterative Semantic Gradient Update (ISGU), a novel approach for crafting physical layer adversarial attacks on DeepSC for speech transmission (DeepSC-ST). Specifically, we introduce a joint loss that combines the semantic similarity loss with Connectionist Temporal Classification loss to expedite the generation process of targeted attacks against the DeepSC-ST. In addition, we design an algorithm to generate adversarial examples, enhancing their imperceptibility by meticulously controlling the perturbation power added to the input speech. Extensive experiments indicate that ISGU is capable of rapidly generating highly covert adversarial examples, with a notably high attack success rate.

关键词： Training Perturbation methods Oral communication Information processing Speech enhancement Semantic communication Physical layer Feature extraction Security Iterative methods

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BTM: Black-Box Testing for DNN Based on Meta-Learning

BTM: Black-Box Testing for DNN Based on Meta-Learning

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Zhuangyu Zhang Zhiyi Zhang Ziyuan Wang Fang Chen Zhiqiu Huang Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China

Deep learning is widely used in security fields like autonomous driving, but testing deep learning models poses challenges due to low generation efficiency and limited error detection. Current white-box test case generation methods rely on neuron coverage, but black-box testing is crucial when model details cannot be accessed. Prior methods also needed more consideration for error diversity under lower time resources. In this paper, we propose a novel approach based on the intuition that two models trained on the same classification task learn similar rules at a coarse-grained level. We employ meta-learning to train universal meta perturbation on a surrogate model, which increases neuron coverage on the surrogate model while inducing misclassifications in the original model (thus enhancing the adequacy of the initial tests). Subsequently, we apply data initialization to discover a wider range of errors faster. To accelerate test case generation and reduce resource consumption, we introduce a set of acceleration techniques based on image prediction probabilities, minimizing the time spent exploring irrelevant regions in images. Finally, we evaluate our approach on a well-known dataset and several well-known models, demonstrating its effectiveness.

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Efficient adaptive test case selection for DNNs robustness enhancement

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Journal of Systems and software 2025年 229卷

作者： Zhang, Zhiyi Meng, Huanze Ding, Yuchen Chen, Shuxian Yao, Yongming Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Jiangsu Nanjing China School of Mathematical Science Yangzhou University Jiangsu Yangzhou China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China Army Engineering of PLA Jiangsu Nanjing China

Deep neural networks (DNNs) have been widely used in various fields, and testing for DNN-based software has become increasingly important. To discover potential faults in DNNs, a large number of test cases and their corresponding labels are required. However, labeling so many test cases consumes enormous costs. Although there have been many test case selection techniques for DNN models, these techniques still have problems such as high overhead, low efficiency, and poor diversity. To address this problem, this paper proposes an efficient adaptive test case selection method based on the principle of uniform distribution of test cases called EATS. Based on the idea of adaptive testing, EATS combines the uncertainty of the model and the diversity of faults to calculate the distance of test cases and sort them, then gives priority to test cases with a higher probability of causing faults. We conduct experiments on four popular datasets and four representative DNN models. Experiment results show that, compared with the existing eight methods, EATS performs better in uniformity of test case distribution, diversity of errors found, model optimization, and optimization efficiency. © 2025 Elsevier Inc.

关键词： software testing

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Lcvd: Loop-Oriented Code Vulnerability Detection Via Graph Neural Network

SSRN

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SSRN 2023年

作者： Wang, Mingke Tao, Chuanqi Guo, Hongjing College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Due to the unique mechanism and complex structure, loops in programs can easily lead to various vulnerabilities such as dead loops, memory leaks, resource depletion, etc. Traditional approaches to loop-oriented program analysis (e.g. loop summarization) are costly with a high rate of false positives in complex software systems. To address the issues above, recent works have applied deep learning (DL) techniques to vulnerability detection. However, existing DL-based approaches mainly focused on the general characteristics of most vulnerabilities without considering the semantic information of specific vulnerabilities. As a typical structure in programs, loops are highly iterative with multi-paths. Currently, there is a lack of available approaches to represent loops, as well as useful methods to extract the implicit vulnerability patterns. Therefore, this paper introduces LCVD, an automated loop-oriented code vulnerability detection approach. LCVD represents the source code as the Loop-flow Abstract Syntax Tree (LFAST), which focuses on interleaving multi-paths around loop structures. Then a novel Loop-flow Graph Neural Network (LFGNN) is proposed to learn both the local and overall structure of loop-oriented vulnerabilities. The experimental results demonstrate that LCVD outperforms the three static analysis-based and four state-of-the-art DL-based vulnerability detection approaches across evaluation settings. © 2023, The Authors. All rights reserved.

关键词： Flow graphs

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Source-likelihood Mapping Using POMDP in Autonomous Hydrothermal Vent Prospecting 32nd

Source-likelihood Mapping Using POMDP in Autonomous Hydrothe...

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32nd International Ocean and Polar Engineering Conference, ISOPE 2022

作者： Li, Jinlong Pang, Shuo Jiu, Haifeng Jiang, Yin Marine Design & Research Institute of China Shanghai China Department of Electrical Engineering and Computer science Embry-Riddle Aeronautical University FL United States Department of Mechanics Harbin University of Commerce Harbin China China Eastern Technology Application Research and Development Center Co. LTD Shanghai China

ISBN: (纸本)9781880653814

Chemical signals contained in hydrothermal fluid provide the clues to infer the location of hydrothermal vent by autonomous underwater vehicle (AUV). A partially observable Markov decision process (POMDP) is introduced to model the searching area and observation behavior in non-buoyant plume height. A POMDP-based belief state estimation method is introduced to maintain source-likelihood mapping. A real-time simulation platform is built to verify the source-likelihood mapping approach which provides a novel solution to effectively infer the vent location. The influences of memory length of flow sensor information on mapping is studied, and the length is not recommended to be very long. © 2022 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： Mapping

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A CPK-Based Identity Authentication Scheme for IoT

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computer Systems Science & Engineering 2022年第3期40卷 1217-1231页

作者： Mingming Zhang Jiaming Mao Yuanyuan Ma Liangjie Xu Chuanjun Wang Ran Zhao Zhihao Li Lu Chen Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information&Telecommunication BranchNanjing210008China State Grid Key Laboratory of Information&Network Security Institute of Information and CommunicationGlobal Energy Interconnection Research InstituteNanjing210003China Anhui Jiyuan Software Co.Ltd SGITGHefei230088China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Engineering Research Center of Broadband Wireless Communication Technology of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

As the power Internet of Things(IoT)enters the security construction stage,the massive use of perception layer devices urgently requires an identity authentication scheme that considers both security and *** existing public key infrastructure(PKI)-based security authentication scheme is currently difficult to apply in many terminals in *** key distribution and management costs are high,which hinders the development of power IoT security *** Public Key(CPK)technology uses a small number of seeds to generate unlimited public *** is very suitable for identity authentication in the power Internet of *** this paper,we propose a novel identity authentication scheme for power *** scheme combines the physical unclonable function(PUF)with improved CPK technology to achieve mutual identity authentication between power IoT terminals and *** proposed scheme does not require third-party authentication and improves the security of identity authentication for power ***,the scheme reduces the resource consumption of power IoT *** improved CPK algorithm solves the key collision problem,and the third party only needs to save the private key and the public key *** results show that the amount of storage resources occupied in our scheme is *** proposed scheme is more suitable for the power IoT.

关键词： Power Internet of Things terminal equipment identity authentication CPK PUF

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Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution

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IEEE Transactions on Multimedia 2025年

作者： Fu, Congrui Yuan, Hui Jiang, Shiqi Zhang, Guanghui Shen, Liquan Hamzaoui, Raouf Center of Experimental Teaching China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China Shandong University School of software Shandong Jinan China Shandong University School of Computer Science and Technology China Shanghai University School of Communication and Information Engineering Shanghai China De Montfort University School of Engineering and Sustainable Development LeicesterLE1 9BH United Kingdom

By converting low-frame-rate, low-resolution videos into high-frame-rate, high-resolution ones, space-time video super-resolution techniques can enhance visual experiences and facilitate more efficient information dissemination. We propose a convolutional neural network (CNN) for space-time video super-resolution, namely GIRNet. Our method combines long-term global information and short-term local information from the video to better extract complete and accurate spatial-temporal information. To generate highly accurate features and thus improve performance, the proposed network integrates a feature-level temporal interpolation module with deformable convolutions and a global spatial-temporal information-based residual convolutional long short-term memory (convLSTM) module. In the feature-level temporal interpolation module, we leverage deformable convolution, which adapts to deformations and scale variations of objects across different scene locations. This provides a more efficient solution than conventional convolution for extracting features from moving objects. Our network effectively uses forward and backward feature information to determine inter-frame offsets, leading to the direct generation of interpolated frame features. In the global spatial-temporal information-based residual convLSTM module, the first convLSTM is used to derive global spatial-temporal information from the input features, and the second convLSTM uses the previously computed global spatial-temporal information feature as its initial cell state. This second convLSTM adopts residual connections to preserve spatial information, thereby enhancing the output features. Experiments on the Vimeo90K dataset show that the proposed method outperforms open source state-of-the-art techniques in peak signal-to-noise-ratio (by 1.45 dB, 1.14 dB, and 0.2 dB over STARnet, TMNet, and 3DAttGAN, respectively), structural similarity index(by 0.027, 0.023, and 0.006 over STARnet, TMNet, and 3DAttGAN, respectiv

关键词： Long short-term memory

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Model Checking Quantum Continuous-Time Markov Chains 32

Model Checking Quantum Continuous-Time Markov Chains

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32nd International Conference on Concurrency Theory, CONCUR 2021

作者： Xu, Ming Mei, Jingyi Guan, Ji Yu, Nengkun MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai China Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai China State Key Lab of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9783959772037

Verifying quantum systems has attracted a lot of interests in the last decades. In this paper, we initialise the model checking of quantum continuous-time Markov chain (QCTMC). As a real-time system, we specify the temporal properties on QCTMC by signal temporal logic (STL). To effectively check the atomic propositions in STL, we develop a state-of-the-art real root isolation algorithm under Schanuel's conjecture;further, we check the general STL formula by interval operations with a bottom-up fashion, whose query complexity turns out to be linear in the size of the input formula by calling the real root isolation algorithm. A running example of an open quantum walk is provided to demonstrate our method. © Ming Xu, Jingyi Mei, Ji Guan, and Nengkun Yu;.

关键词： Model checking

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