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A multi-objective cuckoo search algorithm using generalized Lèvy flight and dissimilar egg identification for multispectral image thresholding

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Applied Soft Computing 2025年 175卷

作者： Pal, Ramen Roy, Pritam Mallick, Srijon Mukhopadhyay, Somnath Sarkar, Sunita Hinchey, Mike Department of Computer Science and Engineering Assam University Silchar Assam India Cognizant Technology Solutions India Pvt. Ltd. India Samsung Semiconductor India Research India Lero the SFI Research Centre for Software Department of Computer Science & Information Systems University of Limerick Limerick Ireland

Cuckoo Search (CS) stands as a highly efficient meta-heuristic optimization algorithm. Existing literature showcases the ability of CS in multi-objective scenarios, delineated by the three fundamental rules. However, the first rule of the algorithm incurs the cost of a generation to update a single nest, while the third rule necessitates hit-and-trial methods for parameter adjustment. To address these concerns, a multi-objective cuckoo search algorithm is proposed in this paper. The algorithm builds upon a generalized concept of Lèvy Flight for generating new solutions. Problem-specific, constraint-based strategies for identifying the best nest and dissimilar eggs are also introduced. The algorithm is further applied to solve multispectral remote sensing image thresholding problem. Prior studies have underscored the efficiency of entropy and clustering-based thresholding methods over other techniques. Nevertheless, most entropy-based approaches entail converting color images to grayscale before segmentation, potentially sacrificing crucial spectral information and consequently degrading segmentation algorithm's performance. To avoid these limitations, this research introduces an entropy-based thresholding method to segment a color image without converting it to grayscale. The experiments are carried out using very high resolution (VHR) and coarse resolution (CR) multispectral (MS) images from the satellite sensors Pl'eidas-1B and Sentinel-2b, respectively. The proposed methods undergo validation against four state-of-the-art techniques on benchmark functions and six clustering indexes, respectively. © 2025 Elsevier B.V.

关键词： Heuristic algorithms

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A Conceptual Deep Learning Model for Real-Time Routin 16

A Conceptual Deep Learning Model for Real-Time Routin

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16th IEEE International Conference on Signal-Image Technology and Internet-Based systems, SITIS 2022

作者： Ikidid, Abdelouafi Fazziki, Abdelaziz El Sadgal, Mohammed Ghazouani, Mohamed El Ichahane, My Youssef Cadi Ayyad University Computer Systems Engineering Laboratory Computer Science Dept Marrakesh Morocco Chouaib Doukkali University Polydisciplinary Faculty of Sidi Bennour El Jadida Morocco Chouaib Doukkali University Information Technologiy Laboratory El Jadida Morocco

ISBN: (纸本)9781665464956

Urban congestion has been a known problem since the first urban revolution throughout the world. Today's major metropolises are synonymous with traffic congestion and complicated urban circulation. This paper introduces a traffic management system based on agent technology and deep learning model. It acts on the phase layouts constituted of sequences and length to reduce average delay time, maximize intersection throughput, and suggest a routing planning scheme to avoid congested intersection. In the optimization model, lights of each intersection are controlled by a group of agents. The system uptake the ability of the agent to communicate, coordinate and collaborate, to decompose the routing process into decentralized architecture. The proposed model was instantiated in the ANYLOGIC simulator. © 2022 IEEE.

关键词： Traffic congestion

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ELU-GCN: EFFECTIVELY LABEL-UTILIZING GRAPH CONVOLUTIONAL NETWORK

arXiv

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arXiv 2024年

作者： Huang, Jincheng Mo, Yujie Shi, Xiaoshuang Feng, Lei Zhu, Xiaofeng School of Computer Science and Engineering University of Electronic Science and Technology of China China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

The message-passing mechanism of graph convolutional networks (i.e., GCNs) enables label information to be propagated to a broader range of neighbors, thereby increasing the utilization of labels. However, the label information is not always effectively utilized in the traditional GCN framework. To address this issue, we propose a new two-step framework called ELU-GCN. In the first stage, ELU-GCN conducts graph learning to learn a new graph structure (i.e., ELU-graph), which enables GCNs to effectively utilize label information. In the second stage, we design a new graph contrastive learning on the GCN framework for representation learning by exploring the consistency and mutually exclusive information between the learned ELU graph and the original graph. Moreover, we theoretically demonstrate that the proposed method can ensure the generalization ability of GCNs. Extensive experiments validate the superiority of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Robust Sliding Manifold Design for Uncertain Linear systems

Robust Sliding Manifold Design for Uncertain Linear Systems

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European Control Conference (ECC)

作者： Edoardo Vacchini Nikolas Sacchi Michele Cucuzzella Antonella Ferrara Department of Electrical Computer Science and Biomedical Engineering University of Pavia Pavia Italy Center for Systems and Control ENTEG Faculty of Science and Engineering (FSE) University of Groningen (UG) Groningen Netherlands

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper proposes a novel approach for the design of stabilizing sliding manifolds for linear systems affected by model uncertainties and external disturbances. In classical sliding mode control approaches, rejecting model uncertainties and external disturbances often relies on designing a dis-continuous control law with a suitable gain. Specifically, the greater the uncertainty, the larger the control gain. However, this approach might be detrimental to the plant. Instead, the proposed technique deals with this problem by focusing on the design of a suitable sliding manifold, where stability is guaranteed despite model uncertainties. This approach exhibits several benefits such as not needing any further identification process and designing a smaller control gain.

关键词： Manifolds Linear systems Uncertainty RLC circuits Stability analysis Mathematical models Robustness

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On the Need to Use Smart Contracts in Enterprise Application Integration 25

On the Need to Use Smart Contracts in Enterprise Application...

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25th Ibero-American Conference on Software engineering, CIbSE 2022

作者： Parahyba, Fernando Dornelles, Eldair F. Roos-Frantz, Fabricia Frantz, Rafael Z. Molina-Jiménez, Carlos Reina Quintero, Antonia M. Bocanegra, José Sawicki, Sandro Unijuí University Brazil Department of Computer Science and Technology University of Cambridge United Kingdom Department of Language and Systems University of Seville Spain Department of Systems and Computing Engineering Universidad de los Andes Colombia

ISBN: (纸本)9781713855583

Integration processes involve Business Constraints and Service Level Agreements that, with current technology, are not monitored or enforced automatically at run–time. This approach leaves the participants with no means of supervising the development of their interactions or of collecting indisputable evidence to ease the resolution of disputes that can potentially emerge. In this paper, to address the issue, we suggest the inclusion of smart contracts in integration processes to supervise and mediate, at run–time, the agreements to which the participants commit. We discuss the requirements that smart contracts for integration processes need to meet and the challenges involved in writing, executing, deploying, and verifying them. © 2022 CIbSE 2022 - XXV Ibero-American Conference on Software engineering. All rights reserved.

关键词： Smart contract

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Learning Recovery Strategies for Dynamic Self-healing in Reactive systems

arXiv

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arXiv 2024年

作者： Sanabria, Mateo Dusparic, Ivana Cardozo, Nicolás Systems and Computing Engineering Department Universidad de los Andes Colombia School of Computer Science and Statistics Trinity College Dublin Ireland

Self-healing systems depend on following a set of predefined instructions to recover from a known failure state. Failure states are generally detected based on domain specific specialized metrics. Failure fixes are applied at predefined application hooks that are not sufficiently expressive to manage different failure types. Self-healing is usually applied in the context of distributed systems, where the detection of failures is constrained to communication problems, and resolution strategies often consist of replacing complete components. However, current complex systems may reach failure states at a fine granularity not anticipated by developers (for example, value range changes for data streaming in IoT systems), making them unsuitable for existing self-healing techniques. To counter these problems, in this paper we propose a new self-healing framework that learns recovery strategies for healing fine-grained system behavior at run time. Our proposal targets complex reactive systems, defining monitors as predicates specifying satisfiability conditions of system properties. Such monitors are functionally expressive and can be defined at run time to detect failure states at any execution point. Once failure states are detected, we use a Reinforcement Learning-based technique to learn a recovery strategy based on users’ corrective sequences. Finally, to execute the learned strategies, we extract them as Context-oriented Programming variations that activate dynamically whenever the failure state is detected, overwriting the base system behavior with the recovery strategy for that state. We validate the feasibility and effectiveness of our framework through a prototypical reactive application for tracking mouse movements, and the DeltaIoT exemplar for self-healing systems. Our results demonstrate that with just the definition of monitors, the system is effective in detecting and recovering from failures between 55% - 92% of the cases in the first application, and at pa

关键词： Functional programming

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A Trend Analysis of Test Smells in Python Test Code Over Commit History

A Trend Analysis of Test Smells in Python Test Code Over Com...

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Euromicro Conference on Software engineering and Advanced Applications (SEAA)

作者： Yuki Fushihara Hirohisa Aman Sousuke Amasaki Tomoyuki Yokogawa Minoru Kawahara Department of Computer Science Faculty of Engineering Ehime University Ehime Japan Center for Information Technology Ehime University Ehime Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama Japan

Software testing is an essential activity for developing and maintaining high-quality software. Unit testing with test code (test cases) is a fundamental testing activity, and developers can test their production code whenever they create or modify the code. However, such quality assurance relies on the correctness of the test code. If a test code had a flaw, it would mislead the developers about the hidden faults and prevent early detection of the faults. This paper focuses on "test smells," which may cause test code flaws in Python programs, and analyzes their changing trends over commit history (code changes) toward better Python test code management. Through an empirical data analysis of 100 open-source projects, the paper reports the following findings: (1) a few kinds of test smells constitute the majority of smells detected in the studied projects, and (2) most kinds of smells tend to increase over commits, i.e., many test smells are likely to have remained in test code as technical debt.

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Secure-JobChain: Revolutionizing Job Recruitment with Blockchain Technology

Secure-JobChain: Revolutionizing Job Recruitment with Blockc...

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Intelligent systems and Advanced Applications (ICISAA), International Conference on

作者： S. Pavithra S. Geethanjali B. Vidya S. Suria B.S. Rahul R Kishore Computer Science and Engineering Rajiv Gandhi College of Engineering and Technology Pondicherry India Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur India Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

The traditional job recruitment process is time consuming and also undergoes certain difficulties such as data privacy, lack of transparency, and inefficient credential verification. These difficulties lead to mistrust and also delay the process of hiring. There is a need for improvement in this process that could benefit both the companies and the job applicants. In this paper, a decentralized recruitment platform based on blockchain technology is proposed. This proposed technique will overcome the above-mentioned difficulties by ensuring secure, transparent, and efficient connections between the recruitment companies and the prospective candidates. This solution uses the merits of blockchain i.e., immutability and automation to record job postings, applications, and hiring decisions on an immutable ledger, protecting personal data with cryptographic techniques and automating qualification verification with smart contracts. By implementing the decentralization concepts in Job recruitment, it is proven that the transparency in the interview process is improved by $\mathbf{9 0 \%}$ and fraud risks are reduced considerably. Moreover, the efficiency of the entire job recruitment process is improved largely. Thus, the decentralized solution proposed in this paper demonstrates its potential in revolutionizing the job recruitment process by providing a secure, transparent and efficient hiring ecosystem

关键词： Data privacy Smart contracts Ecosystems Companies User experience Blockchains Recording Reliability Recruitment Qualifications

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Edge-aware Feature Aggregation Network for Polyp Segmentation

arXiv

引用

arXiv 2023年

作者： Zhou, Tao Zhang, Yizhe Chen, Geng Zhou, Yi Wu, Ye Fan, Deng-Ping PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering NPU Xi’an China School of Computer Science and Engineering Southeast University Nanjing China Computer Vision Lab ETH Zürich Zürich Switzerland

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer (CRC) in clinical practice. However, due to scale variation and blurry polyp boundaries, it is still a challenging task to achieve satisfactory segmentation performance with different scales and shapes. In this study, we present a novel Edge-aware Feature Aggregation Network (EFA-Net) for polyp segmentation, which can fully make use of cross-level and multi-scale features to enhance the performance of polyp segmentation. Specifically, we first present an Edge-aware Guidance Module (EGM) to combine the low-level features with the high-level features to learn an edge-enhanced feature, which is incorporated into each decoder unit using a layer-by-layer strategy. Besides, a Scale-aware Convolution Module (SCM) is proposed to learn scale-aware features by using dilated convolutions with different ratios, in order to effectively deal with scale variation. Further, a Cross-level Fusion Module (CFM) is proposed to effectively integrate the cross-level features, which can exploit the local and global contextual information. Finally, the outputs of CFMs are adaptively weighted by using the learned edge-aware feature, which are then used to produce multiple side-out segmentation maps. Experimental results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and effectiveness. Our implementation code and segmentation maps will be publicly at https://***/taozh2017/EFANet. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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A Survey of Edge Computing Privacy and Security Threats and Their Countermeasures

A Survey of Edge Computing Privacy and Security Threats and ...

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IEEE computer Society Annual Symposium on VLSI

作者： Ahmed Shafee Tasneem A. Awaad Ahmed Moro Department of Engineering and Computer Science Adams State University Alamosa CO USA Department of Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo Egypt Siemens Digital Industries Software Lifecycle Collaboration Software Segment Cairo Egypt

ISBN: (数字)9798350354119

ISBN: (纸本)9798350354126

Recent advancements in Artificial Intelligence (AI) and the proliferation of the Internet of Things (loT) and mobile devices have generated enormous data volumes, necessitating advanced data processing closer to data sources through edge computing to enhance efficiency and reduce latency. Nonetheless, privacy concerns in cloud storage and data transmission, alongside the inadequacy of traditional privacy protections, have highlighted the vulnerability to sophisticated data breaches. This paper offers a survey on edge computing, emphasizing privacy and security challenges, evaluating threats, and exploring solutions to promote the development of secure, robust and privacy-preserving edge computing frameworks.

关键词： Surveys Data privacy Cloud computing Privacy Very large scale integration Data processing Security

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