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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Chunhua Jin Lulu Li Jiahao Wang Youwang Sun Ying Jin Guanhua Chen Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Federated learning is a distributed machine learning paradigm. It entails training a global model across diverse organizations while ensuring privacy constraints are upheld. Despite its promising potential, challenges arise when adversaries attempt to infer private information from exchanged parameters or compromise the global model. Although the development of multiple protocols to mitigate security threats, safeguarding the privacy of individual participants while countering Byzantine adversaries remains a challenge. Furthermore, the frequent transmission of gradients between the master server and participants results in significant communication overhead, thereby limiting the training efficiency of federated learning. In this study, We propose an efficient privacy-preserving federated learning scheme with Byzantine robustness (EFL-PB). For privacy protection and Byzantine robustness, we propose a novel aggregation strategy based on the multi-message shuffle protocol in differential privacy. For enhancing communication efficiency, we develop an adaptive gradient compression scheme. Theoretical analysis demonstrates that our multi-message shuffle protocol can satisfy differential privacy. Experimental results on the MNIST, FashionMNIST, and CIFAR-10 datasets validate the robustness and efficiency of the EFL-PB scheme. As well, the EFL-PB scheme exhibits strong performance on non-iid datasets.

关键词： Training Privacy Differential privacy Protocols Federated learning Resists Robustness Security Servers Protection

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Garbage Image Classification Based on Improved ShuffleNet Fusion Algorithm

Garbage Image Classification Based on Improved ShuffleNet Fu...

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Kaikai Shang Shaozhang Xiao Ken Lin Kangliang Xiao Yin Xia Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Aims to address the problems of low accuracy of existing garbage classification and low classification ac curacy of models, large model size and difficulty of deployment on portable devices. A lightweight garbage image classification model is proposed by merging the improved lightweight model ShuffleNet V2 and MobileViT. Firstly, the structure of the ShuffleNet V2 model is adjusted and improved to enhance the cross-channel information interaction ability of the model, which improves the feature information interaction ability between the channels of the ShuffleNet V 2 model; secondly, the improved ShuffleNet V2 model is fused with the MobileViT model, the advantage of the MobileViT module in global feature information extraction is used to overcome the shortcomings of the ShuffleNet V2 network, which is difficult to extract global feature information. Comparative experiments are conducted on public datasets to evaluate the performance of the model. The experimental results show that the accuracy of the proposed SNViT model is significantly better than the D enseNet-121 model, and also has a better classification performance compared to EfficientNet V2, MobileNet V2 and other models, while the precision rate, recall rate and F1 value are also improved. The improved SNViT model improves the accuracy of the image classification task and achieves high classification performance, while the number of parameters is small and easy to train and deploy.

关键词： Accuracy Convolution Computational modeling Merging Feature extraction Information retrieval Data models Data mining Image classification Periodic structures

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An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm

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computers, Materials & Continua 2024年第6期79卷 5201-5223页

作者： Chen Zhang Liming Liu Yufei Yang Yu Sun Jiaxu Ning Yu Zhang Changsheng Zhang Ying Guo Software College Northeastern UniversityShenyang110169China School of Information Science and Engineering Shenyang Ligong UniversityShenyang110159China China Telecom Digital Intelligence Technology Co. Ltd.Beijing100035China College of Computer Science and Engineering Ningxia Institute of Science and TechnologyShizuishan753000China

The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave *** preferentially selects the best-performing *** tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search *** address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search ***,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its ***,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test *** results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.

关键词： Flying foxes optimization(FFO)algorithm opposition-based learning niching techniques swarm intelligence metaheuristics evolutionary algorithms

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A Method Based on Genetic Programming to Automatically Construct Factors for Annual Report Scenarios 20

A Method Based on Genetic Programming to Automatically Const...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Ma, Yan Zhang, Changsheng Gao, Yan Guo, Ying School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China College of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan China

ISBN: (纸本)9798350356328

Factors have always played an important role in stock analysis, but they are only effective for specific problems in specific scenarios. Therefore, constructing factors timely and quickly for different scenarios is an urgent problem to be solved. Although some experts have constructed factors, they need to manually construct factors for each scenario, and the construction process consumes time and effort. The annual report is an important and common scenario. It is an important form of information disclosure and financial reporting. Therefore, this paper proposes a method for automatically constructing factors based on genetic programming that combines expert experience for this scenario. It incorporates the knowledge and insights of experts in the field of stock analysis into the process of automati-cally constructing factors, and continuously adjusts and improves the combination of factors through genetic programming to adapt to the needs of the scenario. The effectiveness of this method is verified through empirical analysis and its advantages in specific scenarios are demonstrated. © 2024 IEEE.

关键词： Genetic programming

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ScDCMAC:adaptive feedback clustering of scRNA-seq data with deep gene correlation mining

ScDCMAC:adaptive feedback clustering of scRNA-seq data with ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pang, Shanchen Wang, Ruiqian Wu, Wenhao Li, Hengxiao Wu, Jiarui Zhang, Yuanyuan Wang, Shudong College of Computer Science and Technology Qingdao Institute of Software Qingdao China Qingdao University of Technology School of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350386226

Cluster assignment is crucial for analyzing single-cell RNA sequencing (scRNA-seq) data, essential for studying cell diversity and biological functions at the single-cell level. Deep learning-based clustering methods have been extensively applied in scRNA-seq data analysis. However, current deep models frequently overlook the deep exploration of potential gene correlation, resulting in networks lacking guidance from gene correlation information when learning hidden layer features. This paper proposes an adaptive feedback fuzzy clustering method based on deep gene correlation mining(scDCMAC). The deep gene correlation mining network employs contrast learning to compute the contrast loss on two cell similarity matrices, constructed from denoised autoencoder hidden layer features and original input features, to uncover potential gene correlation. The adaptive feedback clustering network further denoises the hidden layer features using a self-attention mechanism, computes the affiliation matrix, and supervises the denoising autoencoder's dimensionality reduction using the affiliation matrix. Experiments on 13 real scRNA-seq datasets demonstrate that scDCMAC excels in clustering accuracy and stability, showing great potential in scRNA-seq sample analysis. © 2024 IEEE.

关键词： Dimensionality reduction

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IEARACDA: Predicting circRNA-disease associations based on information enhancement and arctangent rank approximation

IEARACDA: Predicting circRNA-disease associations based on i...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pang, Shanchen Song, Zheqi Liu, Tiyao Wu, Wenhao Zhang, Yuanyuan Wang, Shudong College of Computer Science and Technology Qingdao Institute of Software Qingdao China Qingdao University of Technology School of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350386226

Circular RNAs (circRNAs) are non-coding RNA molecules that play a significant role in cell regulation and disease occurrence. In recent years, the use of computational methods to predict circRNAs associated with diseases has become a research focus. However, these methods are often constrained by the noise interference and an insufficient exploration of the underlying information in known associations. Therefore, this paper proposes an information enhancement and arctangent rank approximation based method to predict potential circRNA-disease associations (IEARACDA). Firstly, information enhancement methods are used to make circRNA and disease similarity information more complete and to reduce the sparsity of known association. Subsequently, the arctangent rank approximation method is employed to mitigate the potential bias issues that may arise from the nuclear norm, and optimization is carried out using the inexact augmented Lagrange multiplier (IALM) method. Finally, potential associations are predicted through heterogeneous graph inference method. The experimental results demonstrate that IEARACDA exhibits superior performance compared to existing methods. Furthermore, case studies for hepatocellular carcinoma and lung cancer provide additional confirmation of the accuracy and practical significance of the model predictions. © 2024 IEEE.

关键词： Lung cancer

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MemSpate: Memory Usage Protocol Guided Fuzzing 1

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25th International Conference on Formal engineering Methods, ICFEM 2024

作者： Fu, Zhiyuan Jiang, Jiacheng Wen, Cheng Xu, Zhiwu Qin, Shengchao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangzhou Institute of Technology Xidian University Xi’An China

ISBN: (数字)9789819606177

ISBN: (纸本)9789819606160

Memory safety vulnerabilities are high-risk and common vulnerabilities in software testing, often leading to a series of system errors. Fuzz testing is widely recognized as one of the most effective methods for detecting vulnerabilities, including memory safety ones. However, current fuzzing solutions typically only partially address memory usage, limiting their ability to detect memory safety vulnerabilities. In this paper, we introduce MemSpate, a dedicated fuzzer designed to detect memory safety vulnerabilities. Utilizing a more comprehensive memory usage protocol, MemSpate identifies the memory operation sequences that may violate the protocol and estimates the overall memory consumption to exceed an acceptable limit. It then monitors the coverage of these operation sequences and tracks the maximum memory consumption, both of which are used as a new feedback mechanism to guide the fuzzing process. We evaluated MemSpate on 12 real-world open-source programs and compared its performance with 5 state-of-the-art fuzzers. The results demonstrate that MemSpate surpasses all other fuzzers in terms of discovering memory safety vulnerabilities. Furthermore, our experiments have led to the discovery of 4 previously unknown vulnerabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Safety testing

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UEBCS: software Development technology Based on Component Selection 1

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Zhao, Yingnan Qi, Xuezhao Li, Jian Lu, Dan College of Computer Science and Technology Harbin Engineering University Harbin150009 China Harbin Institute of Technology Software Engineering Co. Ltd. Harbin China

ISBN: (数字)9789819998937

ISBN: (纸本)9789819998920

Current software development has moved away from the traditional manual workshop model and emphasizes improving software product quality. To address the issue of repetitive work, software reuse techniques can be adopted to continually enhance the quality and efficiency of software development. software reuse primarily involves reutilizing existing software knowledge during the software development process, effectively reducing maintenance costs incurred during development and controlling the overall software development expenses. software components are an effective form of supporting software product reuse and serve as the core technology for enabling software reuse. Component-based software engineering techniques emphasize the use of reusable software "components" to design and construct programs, aiming to assemble these components within a software architecture to achieve software reuse and improve the quality and productivity of software products. However, selecting the most suitable components from the results of component retrieval requires understanding the different usages of each component in the retrieval results. The existing methods suffer from excessive reliance on manual approaches and errors caused by inter-component relationships. Therefore, this paper proposes a component selection technique called UEBCS (Usage Example-Based Component Selection). This technique leverages steps such as clustering analysis and hierarchical classification to achieve optimal component selection. UEBCS has shown excellent results in terms of both efficiency and accuracy in selecting components. This method provides technical support for software developers in the software development process and holds significant practical significance for enhancing software development quality and efficiency, as well as promoting the deepening development of the software industry. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer software reusability

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Deep Reinforcement Learning for Solving the Vehicle Routing Problem in Practical Logistics 13

Deep Reinforcement Learning for Solving the Vehicle Routing ...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Cai, Junchuang Zhang, Xinzhi Lin, Qiuzhen Dong, Lisha Chen, Weineng Ming, Zhong Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen Institute of Information Technology College of Sino-German Robotics Shenzhen China SSouth China University of Technology College of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350308365

The vehicle routing problem with simultaneous pickup-delivery and time windows (VRPPDTW) is applicable to a wide range of practical scenarios within the domains of transportation and logistics. When addressing this complex problem, conventional heuristic algorithms have exhibited limitations in effectiveness, primarily due to the need for expert knowledge in heuristic design, which often prioritizes immediate improvements and leads to convergence towards local optima. To overcome this challenge, we propose a deep Q-network-based iterated local search algorithm (DQN-ILS). This algorithm employs a deep Q-learning network to automatically learn a policy for selecting heuristics to improve the current solution. Simultaneously, an adaptive perturbation strategy is designed to avoid becoming trapped in local optima. In this way, our algorithm can better balance the trade-off between exploration and exploitation when solving VRPPDTW. Experimental studies conducted on a real test suite of large-scale VRPPDTW instances validate the superiority of the proposed DQN-ILS over several recently proposed methods. © 2024 IEEE.

关键词： Deep reinforcement learning

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Research on Script-Based software Component Development 1

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Zhao, Yingnan Sun, Yang Fan, Bin Lu, Dan College of Computer Science and Technology Harbin Engineering University Harbin150009 China Harbin Institute of Technology Software Engineering Co. Ltd. Harbin China

ISBN: (数字)9789819998937

ISBN: (纸本)9789819998920

As software demand proliferates and software size and complexity increase, traditional software development models face enormous challenges. As a result, new software development techniques are being explored to meet the requirements of software development. The development of new software development techniques has begun to meet the requirements of software development. Based on many years of experience in object-oriented and component-oriented software design methods, we propose an innovative four-factor scripted analysis and design method for state transfer using the knowledge of computer compilation principles on finite state machine (FSM) circuit module automation. It provides a methodological and empirical reference for programmers in the design and development of application systems using dynamic languages. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer software reusability

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