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VulnSlicer: A Java Vulnerability Analysis Tool Based on Vulnerability Slice 10

VulnSlicer: A Java Vulnerability Analysis Tool Based on Vuln...

10th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, DependSys 2024

作者： Liao, Chaohao Zhang, Shihe Liu, Renhua University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9798331540500

Due to its exceptional performance and memory security, Java is extensively employed in enterprise applications, thereby rendering any vulnerability in Java applications capable of causing significant repercussions. In recent years, due to the frequent occurrence of network attacks, new attack methods continue to emerge while software systems become increasingly intricate and unstable under the weight of historical burdens. Consequently, static analysis techniques are imperative for identifying security risks in code during the coding phase. The prevailing approach in static analysis involves transforming code into a standardized intermediate representation and conducting vulnerability detection, without code execution. However, this methodology often yields detection results accompanied by a considerable false positive rate. We propose VulnSlicer, a tool based on graph similarity matching and vulnerability subgraph slice technology. This tool not only enables efficient vulnerability retrieval but also effectively reduces the false positive rate by leveraging the slice mechanism to save historical vulnerabilities. Additionally, it facilitates rapid matching of accumulated vulnerabilities in the database and further mitigates false positives through the introduction of a keyframe mechanism. In the experiment, we conducted a comparative analysis of several existing static detection tools to assess their effectiveness in detecting vulnerabilities within the same codebase. Experiments show that VulnSlicer has decent accuracy and lower false positive rate. © 2024 IEEE.

关键词： Java programming language

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Transaction transmission model for blockchain channels based on non-cooperative games

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science China(Information sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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Research on Spiking Neural Network with Additional Time Dimension for Sequencial Modeling 21

Research on Spiking Neural Network with Additional Time Dime...

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Zhang, Enqi School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331519254

Spiking neural networks have gained significant traction in image and sequence tasks due to low power consumption. However, a comprehensive theoretical understanding of how information is stored within spike trains remains underexplored. This study analyzes the fundamental mechanisms of information storage and transmission in spike trains from a theoretical standpoint and introduces a new framework: the spiking neural network model with multiple time dimensions. Given the equivalence of this model's nature to artificial neural networks with quantized activation values, we evaluated the performance of these equivalent ANNs in sequential tasks. The experimental results establish a theoretical foundation that could guide the future development of more efficient and interpretable spiking neural networks for sequential tasks. © 2024 IEEE.

关键词： Neural network models

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Heterogeneous distributed data mining method based on decision tree classification algorithm 5

Heterogeneous distributed data mining method based on decisi...

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5th International Conference on computers, Information Processing and Advanced Education, CIPAE 2024

作者： Zheng, Yingzi School of Computer Science and Engineering Guangzhou Institute of Science and Technology Guangzhou510540 China

ISBN: (纸本)9798331527662

The mining of heterogeneous distributed data is a key technology for insight data traceability and classification feature analysis. A heterogeneous distributed data mining method based on decision tree classification algorithm is proposed, and a structured heterogeneous distributed big data label classification model is constructed. The decision tree shift of heterogeneous distributed data is achieved by combining the label classification statistical method. The decision tree classification algorithm is used to achieve label classification statistics and state transfer of distributed big data. Based on the purity deviation difference and direction deviation, the segmentation values in data label classification are reconstructed and the decision tree structure layer is reorganized. The arithmetic difference of each segmentation block of distributed heterogeneous big data is solved, and more embedding capacity is obtained through histogram shift algorithm. The statistical related parameter feature quantity of data is extracted, and screening is carried out according to feature attributes, using mutual information to implement decision tree classification and big data mining for the feature attributes to be embedded. And simulation tests were conducted on the data mining model under different attack models. The simulation results showed that using this method for heterogeneous distributed data mining can design corresponding shift directions and distances for each heterogeneous data distribution area. There will be no errors in the mining process, which improves the traceability and anti attack ability of the data. Moreover, the distortion of the data is small, and the traceability and classification ability of the data are strong. © 2024 IEEE.

关键词： Data consistency

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BadTTS: Identifying Vulnerabilities in Neural Text-to-Speech Models

BadTTS: Identifying Vulnerabilities in Neural Text-to-Speech...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Rui Li, Hongwei Jiang, Wenbo Zhang, Ran He, Jiaming University of Electronic Science and Technology of China School of Computer Science and Engineering China China

ISBN: (纸本)9798350351255

With the widespread use of deep learning systems in many applications, adversaries have strong incentives to perform attacks against these systems for their adversarial purposes. Reports have indicated that backdoor attacks on deep neural networks represent a novel form of threat. In this attack, the adversary will inject backdoors into the benign model and then mislead the model to classify the input containing backdoor triggers as a target label specified by the adversary. Existing research mainly focuses on backdoor attacks in image and text models, little attention has been paid to the backdoor attacks on text-to-speech (TTS) models. We conduct a systematic investigation of backdoor attacks on text-to-speech models and propose BadTTS, the first backdoor attack against TTS models, which is a general backdoor attack framework that tampers with input texts in three semantic levels to generate malicious output speech, including Char-Backdoor, Word-Backdoor, and Sentence-Backdoor. Our method not only efficiently injects backdoors into a TTS model but is also stealthy and has little impact on the synthesized speech quality. We implement the backdoor attack in a black-box fine-tuning setting, where the adversary has no knowledge of model architectures except for a small amount of training data. We perform empirical experiments on three representative and widely studied TTS models, indicating that backdoors can be injected into TTS models within a few fine-tuning steps. Additionally, we conduct experiments to explore the impact of different types of triggers, as well as the intermediate outputs of models, which provide insights for potential defenses against backdoor attacks. © 2024 IEEE.

关键词： Adversarial machine learning

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Predefined-time distributed multiobjective optimization for network resource allocation

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science China(Information sciences) 2023年第7期66卷 58-72页

作者： Kunpeng ZHANG Lei XU Xinlei YI Zhengtao DING Karl H.JOHANSSON Tianyou CHAI Tao YANG State Key Laboratory of Synthetical Automation for Process Industries Northeastern University School of Electrical Engineering and Computer Science KTH Royal Institute of Technology School of Electrical and Electronic Engineering The University of Manchester

We consider the multiobjective optimization problem for the resource allocation of the multiagent network, where each agent contains multiple conflicting local objective functions. The goal is to find compromise solutions minimizing all local objective functions subject to resource constraints as much as possible, i.e., the Pareto optimums. To this end, we first reformulate the multiobjective optimization problem into one single-objective distributed optimization problem by using the weighted Lppreference index,where the weighting factors of all local objective functions are obtained from the optimization procedure so that the optimizer of the latter is the desired Pareto optimum of the former. Next, we propose novel predefined-time algorithms to solve the reformulated problem by time-based generators. We show that the reformulated problem is solved within a predefined time if the local objective functions are strongly convex and smooth. Moreover, the settling time can be arbitrarily preset since it does not depend on the initial values and designed parameters. Finally, numerical simulations are presented to illustrate the effectiveness of the proposed algorithms.

关键词： distributed optimization multiobjective optimization predefined-time algorithms time-based generators weighted ${L_p}$ preference index

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An Uncertainty Decision Model for Partial Observable Agents by Dynamically Sharing Group Utilities 9

An Uncertainty Decision Model for Partial Observable Agents ...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Xia, Shengqi Zhang, Shangyan Xu, Yang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350376548

A decision-making model for intelligent agents is proposed in this study, emphasizing the maximization of group utility to address the challenges of multi-agent decision-making in dynamic and partially observable environments. The model functions on the principle of agents collectively forming utility, thereby mitigating limitations arising from incomplete observations through shared utilities. By prioritizing the enhancement of group utility, the model facilitates the establishment of joint behavior and action plans for the agents. A representative experiment is conducted within the context of Pac-Man gameplay, where the ghosts aim to encircle the Pac-Man. Through controlled experiments, the cooperative decision-making capability of the model is assessed, revealing a significant improvement in the performance of the multi-agent system. The findings demonstrate that adopting a decision-making approach centered on group utility diminishes the constraints arising from individual agent observations, particularly within extensive search spaces. Consequently, the model proves beneficial in aiding multi-agent systems in efficiently accomplishing their designated tasks. © 2024 IEEE.

关键词： Intelligent agents

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High Order Time Shift Keying Modulation for Ambient Backscatter Communications

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IEEE Transactions on Communications 2024年第6期73卷 3792-3803页

作者： Chen, Jixiang Guan, Quansheng Rong, Yue Li, Dong Chen, Weiqi Yu, Hua South China University Of Technology School Of Electronic And Information Engineering Guangzhou510640 China Curtin University School Of Electrical Engineering Computing And Mathematical Sciences BentleyWA6102 Australia Macau University Of Science And Technology School Of Computer Science And Engineering 999078 China Guangdong University Of Finance School Of Internet Finance And Information Engineering Guangzhou China

Ambient backscatter communication (AmBC) is a newly cutting-edge technology for the Internet of Things, which utilizes the ambient radio frequency signal as the carrier to transmit information. Existing works focus on the simple on-off keying modulation which has low channel utilization. However, it is not desirable to develop the high-order modulation in the power domain due to the weak strength of the backscattered signal. In this paper, we extend the high-order modulation in the time domain instead, i.e., high-order time shift keying (TSK). Since the channel coherent time is unknown at the receiver and the tag, the detection methods with training symbols will have a huge performance degradation if the channels are changed and training symbols become outdated. To overcome this challenge, we further propose the transition-aided TSK (TA-TSK) modulation and the frequency-shifting TSK (FS-TSK) modulation, which do not need to send training symbols at the tag. These two methods can work well even if the channel coherent time is as short as one time slot. Meanwhile, the detection methods for TSK are developed and the corresponding closed-form bit-error-rate (BER) expressions are obtained. Simulation results show that a high modulation order is more suitable for the M-ary phase shift keying source than the complex Gaussian source. The high order TSK can provide at least 2 dB signal-to-noise ratio (SNR) gain at the same BER compared to the on-off keying. © 1972-2012 IEEE.

关键词： Signal to noise ratio

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HiLo Optical Sectioning Algorithm Based on the Rolling Ball Method 5

HiLo Optical Sectioning Algorithm Based on the Rolling Ball ...

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5th International Conference on Intelligent Computing and Human-computer Interaction, ICHCI 2024

作者： Ma, Rui Jiang, Jingrui Tao, Yuchuan Liu, Jiang School of Information Engineering Southwest University of Science and Technology Mianyang China School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350368284

Traditional structured illumination microscopy techniques still face issues such as complex hardware structures, high optical path costs, low efficiency of optical sectioning reconstruction algorithms, and long image acquisition times. To address these problems, this text designs and builds a high-speed portable three-dimensional optical sectioning microscopy system based on DMD and LED, which greatly simplifies the optical path structure and reduces hardware costs. Additionally, through in-depth analysis of traditional optical sectioning reconstruction algorithms, a HiLo optical sectioning reconstruction algorithm based on the rolling ball method (RB-HiLo) is proposed. Experimental results show that compared to the traditional RMS algorithm, this new algorithm improves image acquisition efficiency by 22.8%, enhances optical sectioning reconstruction efficiency by 20.7%, and demonstrates lower background intensity, more effective deblurring, and higher signal-To-noise ratio in the reconstructed optical sectioning images as indicated by the normalized intensity distribution curves. © 2024 IEEE.

关键词： Image reconstruction

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Solving Directed Multiway Cut Faster Than 2n 32

Solving Directed Multiway Cut Faster Than 2n

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32nd Annual European Symposium on Algorithms, ESA 2024

作者： Xiao, Mingyu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9783959773386

In the Directed Multiway Cut problem, we are given a directed graph G = (V, E) and a subset T ⊆ V, called the terminal set. The aim is to find a minimum sized set S ⊆ V \ T, such that after deleting S, no directed path exists from one terminal to another terminal in the remaining graph. It has been an open question whether Directed Multiway Cut can be solved faster than the trivial running-time bound O∗(2|V|). In this paper, we provide a positive answer to this question, presenting an algorithm with a running-time bound O(1.9967|V|) © Mingyu Xiao;licensed under Creative Commons License CC-BY 4.0.

关键词： Directed graphs

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