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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Xiao, Shuai Zhang, Yangyang Jiang, Liming Wang, Zhengxia School of Computer Science and Technology Hainan University China School of Information and Communication Engineering Hainan University China Software Co. Ltd. China

ISBN: (纸本)9798350352214

Medical Visual Language Pretraining (MVLP) utilizes textual reports for weak supervision to improve the learning of medical visual representations, showing promise in various medical image analysis tasks. While previous studies have focused on image and report correspondences within the same instances, they have overlooked high-level semantic associations, such as disease-level semantic correspondences, across different instances. To address this issue, we propose an Advanced Semantic Information Guided Multi-Scale Alignment Framework (ASIMSA) with two main modules: 1) Image-Report Alignment (IRA) module for learning shared representations between image and report of the same instance, and 2) Disease-Entity Alignment (DEA) module for learning disease entity correspondences across different instances. Extensive experiments on zero-shot classification, fine-tune classification, and semantic segmentation tasks validate the effectiveness of our proposed method, demonstrating its stable and superior performance. © 2024 IEEE.

关键词： Semantic Segmentation

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Parallel Assembly Sequence Planning Based on Sparrow Search Algorithms 22

Parallel Assembly Sequence Planning Based on Sparrow Search ...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： He, Haichen Liu, Weibin Zhang, Yinan Song, Shasha Cheng, Yue Liu, Ziheng Xing, Weiwei Beijing Jiaotong University School of Computer Science and Technology China Crrc Information Technology Co. Ltd. China Beijing Jiaotong University School of Software Engineering China

ISBN: (纸本)9798331509712

Assembly sequence planning (ASP) is the most important process in the product lifecycle. This paper aims to propose a parallel assembly sequence planning method based on sparrow search algorithm (SSA) to improve the assembly quality and efficiency. The proposed method divides the problem into assembly unit division (AUD) part and ASP part. In AUD part, we use the markov clustering algorithm (MCA) to solve the AUD problem. In ASP part, the ASP evaluation system is designed for the assembly part sequence planning (APSP) problem and assembly unit sequence planning (AUSP) problem, and the discrete artificial sparrow search algorithm (DASSA) is proposed for solving the discrete APSP problem. The results of comparison experiment verify the effectiveness of the proposed method. Compared with genetic algorithm (GA) and particle swarm optimization (PSO), the proposed DASSA gets the optimal results and fastest convergence. The method proposed solves ASP problem to efficiently perform parallel assembly sequence planning and reduce production time, making it cost-effective in intelligent manufacturing. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Color Image Encryption Based on 4D Hyperchaotic System and RSA Algorithm Combined Scrambling and Diffusion 5

Color Image Encryption Based on 4D Hyperchaotic System and R...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Dong, Zhiqing Zhang, Zhao Zhou, Hongyan Chen, Xue-Bo University of Science and Technology Liaoning School of Computer Science and Software Engineering Anshan China University of Science and Technology Liaoning School of Electronics and Information Engineering Anshan China

ISBN: (纸本)9798350325294

In this paper, a color image encryption scheme based on 4D hyperchaos system and RSA algorithm is proposed. Firstly, RSA algorithm is used to generate the initial value of 4D hyperchaotic system, and the key stream is iteratively generated. Then, the color image is divided into RGB three channels. In order to change the position of image pixels, Arnold scrambling is performed on the image. Finally, simultaneous scrambling and diffusion algorithms are used to encrypt the preprocessed images for a second time. Experimental results show that the encryption scheme proposed in this paper is effective. Compared with the existing algorithms, this encryption scheme improves the encryption efficiency, has strong anti-aggression and robustness. © 2023 IEEE.

关键词： Cryptography

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Distributed Truss Computation in Dynamic Graphs

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Tsinghua Science and technology 2023年第5期28卷 873-887页

作者： Ziwei Mo Qi Luo Dongxiao Yu Hao Sheng Jiguo Yu Xiuzhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266200China State Key Laboratory of Software Development Environment School of Computer Science and Engineering and the Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Big Data Institute Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph *** k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a *** works mostly focus on truss computation in static graphs by sequential ***,the graphs are constantly changing dynamically in the real *** study distributed truss computation in dynamic graphs in this *** particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed *** decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition ***,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the *** experiments have been conducted to show the scalability and efficiency of the proposed algorithm.

关键词： distributed algorithm dynamic graph graph mining cohesive subgraph k-truss

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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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Pre-trained SAM as data augmentation for image segmentation

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CAAI Transactions on Intelligence technology 2025年第1期10卷 268-282页

作者： Junjun Wu Yunbo Rao Shaoning Zeng Bob Zhang Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhouChina School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengduChina Pattern Analysis and Machine Intelligence Research Group Department of Computer and Information ScienceUniversity of MacaoMacaoChina

Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the ***,data augmentation mainly involved some simple transformations of ***,in order to increase the diversity and complexity of data,more advanced methods appeared and evolved to sophisticated generative ***,these methods required a mass of computation of training or *** this paper,a novel training-free method that utilises the Pre-Trained Segment Anything Model(SAM)model as a data augmentation tool(PTSAM-DA)is proposed to generate the augmented annotations for *** the need for training,it obtains prompt boxes from the original annotations and then feeds the boxes to the pre-trained SAM to generate diverse and improved *** this way,annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation *** comparative experiments on three datasets are conducted,including an in-house dataset,ADE20K and *** this in-house dataset,namely Agricultural Plot Segmentation Dataset,maximum improvements of 3.77%and 8.92%are gained in two mainstream metrics,mIoU and mAcc,***,large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.

关键词： data augmentation image segmentation large model segment anything model

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Conceptualizing Processes in Robotic Process Automation: A Basic Meta-Model 10

Conceptualizing Processes in Robotic Process Automation: A B...

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10th International Conference on Systems and Informatics, ICSAI 2024

作者： Zhang, Lingtong Yang, Jing Ouyang, Chun Yu, Yang Pan, Maolin School of Computer Science and Engineering Sun Yat-sen University GuangZhou China Queensland University of Technology Australia School of Software Engineering Sun Yat-sen University ZhuHai China

ISBN: (纸本)9798331510138

Robotic Process Automation (RPA) uses software to mimic human actions in performing repetitive, time-consuming tasks, enabling organizations to strategically reallocate their workforce towards more value-driven activities. RPA can be used to build new intelligent systems that combine business with Artificial Intelligence (AI). However, despite its widespread adoption, RPA lacks standardization and a theoretical foundation, giving rise to misconception. For example, it has caused confusion between RPA and the established field of Business Process Automation (BPA), which has prevented organizations from properly using RPA in new intelligent systems. In this paper, we address the gap by proposing a basic meta-model for conceptualizing processes in RPA. Our research involves identifying key characteristics of processes suitable for RPA, deriving fundamental concepts of processes in RPA, and articulating the interrelations between them. Built upon such a conceptual foundation, we conduct a comparative analysis between RPA and BPA, clarifying the differences between the two. We further demonstrate the practical applicability of our proposed basic meta-model by identifying an instantiation of the meta-model manifested in three mainstream RPA software products, respectively. Our research contributes to laying the conceptual foundations of RPA and facilitating its standardization. © 2024 IEEE.

关键词： Intelligent robots

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Zero Trust Networks: Evolution and Application from Concept to Practice

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computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

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QCMAM: Quantitative Clustering Method for Machine Learning integrating Attention Mechanism 9

QCMAM: Quantitative Clustering Method for Machine Learning i...

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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Chai, YuChang Xiao, Shuai Jiang, Liming Xu, ZhiWang School of Computer Science and Technology Hainan University China Software Co. Ltd. China School of Information and Communication Engineering Hainan University China

ISBN: (纸本)9798350352214

Multiple Myeloma (MM) is a common malignant tumor in hematological diseases, and its incidence increases year by year. The membrane protein CD138 interacts with the proliferation and evolution of cancer cells during the development of MM. It is an important basis for the diagnosis of MM to obtain the data of malignant plasma cell membrane protein CD138 by single molecule localization microscopy and quantify its abnormal expression. The traditional quantitative analysis method has many drawbacks, such as a large amount of data, low speed of calculation and low accuracy. Therefore, we propose a new quantitative clustering analysis method based on machine learning to judge the expression of membrane protein CD138. By fusing the external attention mechanism, the model pays more attention to the statistical characteristics of complete membrane protein data samples, and accurately quantifies the expression of CD138 in the cluster. In addition, the quantitative analysis of the clustering results in this paper provides convenience for pathologists to study the interactions between proteins. Finally, this article uses simulation data and experimental data of membrane protein CD138 to verify the accuracy and feasibility of the algorithm. © 2024 IEEE.

关键词： Cluster analysis

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Metamorphic Relation Patterns for Metamorphic Testing, Exploration and Robustness

Metamorphic Relation Patterns for Metamorphic Testing, Explo...

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作者： Ying, Zhihao Towey, Dave Bellotti, Anthony Chua, Caslon Quan Zhou, Zhi School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo China Department of Computer Science and Software Engineering Swinburne University of Technology HawthornVIC Australia School of Computing and Information Technology University of Wollongong WollongongNSW Australia

Metamorphic testing (MT) is an effective software quality assurance method;it uses metamorphic relations (MRs) to examine the inputs and outputs of multiple test cases. Metamorphic exploration (ME) and metamorphic robustness testing (MRT) are advanced additions to the MT literature: ME can enable better understanding, and thus testing, of systems;MRT can test software robustness without an oracle. Metamorphic relation patterns (MRPs) are abstractions or templates for multiple concrete MRs. The performance of MT/ME/MRT relies on the quality of MRs, but the identification of MRs is often a manual task requiring some knowledge of MRPs, creative thinking and a good understanding of the system. In other words, the effective identification of MRs remains a challenge and requires further study. In this paper, we propose formal definitions to describe the relationship between multiple MRPs in order to classify them. We also identify a series of new MRPs and propose a new MT framework that can guide the identification and application of MRPs. Based on our definitions, we present the notion of MRP family trees and further propose two trees by classifying previously published MRPs and our proposed MRPs, in order to make it easier for users to find their target MRPs for reuse, reference or inference. Finally, we report on case studies using the MRPs and our framework to guide the identification of MRs for MT/ME/MRT and successfully detect multiple violations. © 2025 John Wiley & Sons Ltd.

关键词： software testing

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