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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Uludag, Mehmed K. Karakus, Murat Guler, Evrim Uludag, Suleyman University of Michigan Department of Computer Science and Engineering Ann Arbor United States Ankara University Department of Software Engineering Ankara06830 Turkey Bartin University Department of Computer Engineering Bartin74110 Turkey University of Michigan Department of Computer Science FlintMI48502 United States

ISBN: (纸本)9798350367942

software-Defined Networking (SDN) and Blockchain (BC) are transformative technologies reshaping network management and security, utilizing their synergies. SDN's centralized control enhances flexibility and efficiency but introduces vulnerabilities due to its single point of control. With its decentralized and immutable ledger, BC offers a solution by distributing control and providing a tamper-proof audit trail. This paper integrates these technologies to address blackhole attacks - a critical vulnerability where compromised SDN controllers disrupt network performance. We propose Blockchain-Enhanced SDN for Adaptive Path Finding (BeS4APF) algorithm against blackhole attacks in the multidomain SDNs. The algorithm maintains a high Packet Delivery Ratio (PDR) by dynamically adjusting network paths in response to node failures. The BeS4APF algorithm presented effectively maintains a high PDR by dynamically adjusting paths in response to node failures. The methodology involves monitoring the network, detecting compromised nodes, and recalculating optimal paths using Dijkstra's algorithm and node-disjoint path selection. Experiments with synthetic networks of varying sizes (from 60 to 120 domains) demonstrate that the algorithm successfully handles domain-compromising node attacks, maintaining high PDR even with increasing stochastic disruptions. Our preliminary results show that, while PDR slightly deviates and recovers in smaller networks, it stabilizes towards almost 100% in larger networks after initial adjustments. This work advances the integration of SDN and BC, offering a robust approach to securing modern networks against evolving stochastic attack scenarios and threats. © 2024 IEEE.

关键词： Stochastic systems

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Support vector machine with discriminative low-rank embedding

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1249-1262页

作者： Guangfei Liang Zhihui Lai Heng Kong Computer Vision Institute College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina Department of Breast and Thyroid Surgery BaoAn Central Hospital of ShenzhenShenzhenChina

Support vector machine(SVM)is a binary classifier widely used in machine ***,neglecting the latent data structure in previous SVM can limit the performance of SVM and its *** address this issue,the authors propose a novel SVM with discriminative low-rank embedding(LRSVM)that finds a discriminative latent low-rank subspace more suitable for SVM *** extension models of LRSVM are introduced by imposing different orthogonality constraints to prevent computational inaccuracies.A detailed derivation of the authors’iterative algorithms are given that is essentially for solving the SVM on the low-rank ***,some theorems and properties of the proposed models are presented by the *** is worth mentioning that the subproblems of the proposed algorithms are equivalent to the standard or the weighted linear discriminant analysis(LDA)*** indicates that the projection subspaces obtained by the authors’algorithms are more suitable for SVM classification compared to those from the LDA *** convergence analysis for the authors proposed algorithms are also ***,the authors conduct experiments on various machine learning data sets to evaluate the *** experiment results show that the authors’algorithms perform significantly better than other algorithms,which indicates their superior abilities on classification tasks.

关键词： iterative methods machine leaning support vector machunes

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An Approach to Optimize Symbolic Execution in Ethereum Smart Contracts 24

An Approach to Optimize Symbolic Execution in Ethereum Smart...

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24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Liu, Yi Cai, Lizhi Chai, Chenchen Ma, Yuan School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing & Evaluating Shanghai China

ISBN: (纸本)9798350365658

As the application of smart contracts in blockchain technology becomes increasingly widespread, their security issues have emerged as a focal point of both research and practice. Although symbolic execution technology has been extensively applied for the detection of security vulnerabilities in smart contracts, its limitations in terms of execution efficiency and code coverage remain significant. In response to these challenges, this study introduces an innovative method named HoneyToken-DetectorRev, which optimizes traditional symbolic execution through program slicing technology, aiming to enhance the efficiency and effectiveness of smart contract security detection. By selecting ten smart contracts from the Ethereum platform, which vary in business logic and complexity, we conducted an empirical analysis of HoneyToken-DetectorRev. The experimental results demonstrate that this method significantly improves the efficiency of smart contract detection without sacrificing precision. This research not only provides an effective technical approach for the security detection of smart contracts but also offers theoretical and practical guidance for further enhancing the security of blockchain applications. © 2024 IEEE.

关键词： Smart contract

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MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

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computers, Materials & Continua 2023年第6期75卷 5573-5595页

作者： Zhanyang Xu Jianchun Cheng Luofei Cheng Xiaolong Xu Muhammad Bilal School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory Novel Software Technology Nanjing UniversityNanjing210023China Department of Computer and Electronics Systems Engineering Hankuk University of Foreign StudiesYongin-siGyeonggi-do17035Korea

Federated learning has been used extensively in business inno-vation scenarios in various *** research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment ***,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise *** proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model ***,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and *** addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global *** results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify ***,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.

关键词： Federated learning feature selection credit risk assessment MSEs

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Understanding the Impact of Unexpected Cobot Movements on Human Stress Levels: A Time Series Classification Task

Understanding the Impact of Unexpected Cobot Movements on Hu...

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2024 Workshop on Embracing Human-Aware AI in Industry 5.0, HAII5.0 2024

作者： Shilon, Lior Shevtsov, Vladyslav Schillreff, Nadia Rother, Anne Mark, Shlomo Spiliopoulou, Myra Department of Software Engineering Sami Shamoon College of Engineering Ashdod Israel Faculty of Computer Science Otto-von-Guericke-University Magdeburg Germany

This study investigates the impact of unexpected stimuli on participants’ stress levels during human-robot interactions (HRI). We designed an experiment, where a cobot performs writing tasks, as well as some unexpected actions, intended to surprise the human observer. During this experiment, we are continuously monitoring physiological responses through Electrodermal Activity (EDA) and Electrocardiogram (ECG) sensors. Our goal was to capture the relationship between surprising actions by the cobot and indicators of change in the human stress levels. We employed time series classification techniques, namely ROCKET, Shapelet Transform and WEASEL, along with Support Vector Machine (SVM) analysis on temporal segments. This approach enabled us to develop a binary classification model on stress levels. Our research contributes to the design of AI systems that can detect changes in the stress levels of humans who interact with cobots that perform unexpected actions. Thus, we work towards the improvement of safety protocols in HRI. © 2024 Copyright for this paper by its authors.

关键词： Collaborative robots

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QUEST: Quadruple Multimodal Contrastive Learning with Constraints and Self-Penalization 38

QUEST: Quadruple Multimodal Contrastive Learning with Constr...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Song, Qi Gong, Tianxiang Gao, Shiqi Zhou, Haoyi Li, Jianxin School of Software Beihang University China School of Computer Science and Engineering Beihang University China Zhongguancun Laboratory Beijing China

Multimodal contrastive learning (MCL) has recently demonstrated significant success across various tasks. However, the existing MCL treats all negative samples equally and ignores the potential semantic association with positive samples, which limits the model's ability to achieve fine-grained alignment. In multi-view scenarios, MCL tends to prioritize shared information while neglecting modality-specific unique information across different views, leading to feature suppression and suboptimal performance in downstream tasks. To address these limitations, we propose a novel contrastive framework named QUEST: Quadruple Multimodal Contrastive Learning with Constraints and Self-Penalization. In the QUEST framework, we propose quaternion contrastive objectives and orthogonal constraints to extract sufficient unique information. Meanwhile, a shared information-guided penalization is introduced to ensure that shared information does not excessively influence the optimization of unique information. Our method leverages quaternion vector spaces to simultaneously optimize shared and unique information. Experiments on multiple datasets show that our method achieves superior performance in multimodal contrastive learning benchmarks. On public benchmark, our approach achieves state-of-the-art performance, and on synthetic shortcut datasets, we outperform existing baseline methods by an average of 97.95% on the CLIP model. © 2024 Neural information processing systems foundation. All rights reserved.

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New Algorithms for Distributed Fair k-Center Clustering: Almost Accurate as Sequential Algorithms 23

New Algorithms for Distributed Fair k-Center Clustering: Alm...

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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Wu, Xiaoliang Feng, Qilong Huang, Ziyun Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Xiangjiang Laboratory Changsha China Department of Computer Science and Software Engineering Penn State Erie The Behrend College Erie United States Department of Computer Science and Engineering State University of New York at Buffalo Buffalo United States Hunan Provincial Key Lab on Bioinformatics Central South University Xiangjiang Laboratory Changsha China

Fair clustering problems have been paid lots of attention recently. In this paper, we study the k-Center problem under the group fairness and data summarization fairness constraints, denoted as Group Fair k-Center (GFkC) and Data Summarization Fair k-Center (DSFkC), respectively, in the massively parallel computational (MPC) distributed model. The previous best results for the above two problems in the MPC model are a 9-approximation with violation 7 (WWW 2022) and a (17 + ∊)-approximation without fairness violation (ICML 2020), respectively. In this paper, we obtain a (3 + ∊)- approximation with violation 1 for the GFkC problem in the MPC model, which is almost as accurate as the best known approximation ratio 3 with violation 1 for the sequential algorithm of the GFkC problem. Moreover, for the DSFkC problem in the MPC model, we obtain a (4 + ∊)-approximation without fairness violation, which is very close to the best known approximation ratio 3 for the sequential algorithm of the DSFkC problem. Empirical experiments show that our distributed algorithms perform better than existing state-of-the-art distributed methods for the above two problems. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Machine learning

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Injections Attacks Efficient and Secure Techniques Based on Bidirectional Long Short Time Memory Model

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computers, Materials & Continua 2023年第9期76卷 3605-3622页

作者： Abdulgbar A.R.Farea Gehad Abdullah Amran Ebraheem Farea Amerah Alabrah Ahmed A.Abdulraheem Muhammad Mursil Mohammed A.A.Al-qaness School of Big Data&Software Engineering Chongqing UniversityChongqing401331China Department of Management Science Engineering Dalian University of TechnologyDalian116024China Software College Northeastern UniversityShenyang110169China Department of Information Systems College of Computer and Information ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Management Science and Engineering South China University of TechnologyGuangzhou510641China Department of Computer Engineering and Mathematics University of Rovira i VirgiliTarragonaSpain College of Physics and Electronic Information Engineering Zhejiang Normal UniversityJinhua321004China

E-commerce,online ticketing,online banking,and other web-based applications that handle sensitive data,such as passwords,payment information,and financial information,are widely *** web developers may have varying levels of understanding when it comes to securing an online *** Query language SQL injection and cross-site scripting are the two vulnerabilities defined by the OpenWeb Application Security Project(OWASP)for its 2017 Top Ten List Cross Site Scripting(XSS).An attacker can exploit these two flaws and launch malicious web-based actions as a result of these *** published articles focused on these attacks’binary *** article described a novel deep-learning approach for detecting SQL injection and XSS *** datasets for SQL injection and XSS payloads are combined into a single *** dataset is labeledmanually into three labels,each representing a kind of *** work implements some pre-processing algorithms,including Porter stemming,one-hot encoding,and the word-embedding method to convert a word’s text into a *** model used bidirectional long short-term memory(BiLSTM)to extract features automatically,train,and test the payload *** payloads were classified into three types by BiLSTM:XSS,SQL injection attacks,and *** outcomes demonstrated excellent performance in classifying payloads into XSS attacks,injection attacks,and non-malicious ***’s high performance was demonstrated by its accuracy of 99.26%.

关键词： Web security SQL injection XSS deep learning RNN LSTM BiLSTM

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Generative AI — The End of Systematic Reviews in PhD Projects?

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ACM Inroads 2024年第2期15卷 48-50页

作者： Zamani, Sanaz Sinha, Roopak Department of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand School of Information Technology Deakin University Melbourne Australia

In this opinion piece, we question the efficacy of students conducting systematic reviews (SRs) at the very start of their PhDs, especially now that we are riding, or drowning in, the Generative AI wave. How would the ubiquitous availability of Generative AI tools shape PhD research and supervision, and how can we capitalize on such disruptions? We report our reflections about early-stage PhD research from 10+ PhD supervisions completed by Sinha, and a recently started PhD project with Zamani as the student. We conducted a systematic literature review following traditional methods for the first four months and then revisited the task using ChatGPT and other related tools. We opine that while SRs still have value for new students, Generative AI must be incorporated, with care, to significantly enhance student learning, reinvest time savings into other, often-ignored skills, and improve the quality (breadth and depth) of SRs. © 2024 Association for Computing Machinery. All rights reserved.

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Autonomous Aspect-Image Instruction A2II: Q-Former Guided Multimodal Sentiment Classification 30

Autonomous Aspect-Image Instruction A2II: Q-Former Guided Mu...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Feng, Junjia Lin, Mingqian Shang, Lin Gao, Xiaoying State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Engineering and Computer Science Victoria University of Wellington New Zealand

ISBN: (纸本)9782493814104

Multimodal aspect-oriented sentiment classification (MABSC) task has garnered significant attention, which aims to identify the sentiment polarities of aspects by combining both language and vision information. However, the limited multimodal data in this task has become a big gap for the vision-language multimodal fusion. While large-scale vision-language pretrained models have been adapted to multiple tasks, their use for MABSC task is still in a nascent stage. In this work, we present an attempt to use the instruction tuning paradigm to MABSC task and leverage the ability of large vision-language models to alleviate the limitation in the fusion of textual and image modalities. To tackle the problem of potential irrelevance between aspects and images, we propose a plug-and-play selector to autonomously choose the most appropriate instruction from the instruction pool, thereby reducing the impact of irrelevant image noise on the final sentiment classification results. We conduct extensive experiments in various scenarios and our model achieves state-of-the-art performance on benchmark datasets, as well as in few-shot settings. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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