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检索条件"任意字段=International Conference on Systems, Computing Science and Software Engineering"

共 152602 条记录，以下是71-80 订阅

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An efficient formal verification method for concurrent programs 4

An efficient formal verification method for concurrent progr...

引用

4th international conference on Network Communication and Information Security, ICNCIS 2024

作者： Zhang, Jianyu Zhang, Long Zhang, Jingjing Yang, Feng University of Electronic Science and Technology of China 2006 Xiyuan Avenue Western District High-Tech Zone Chengdu611731 China National Key Laboratory of Science and Technology on Information System Security No. 13 Dachen Road Fengtai District Beijing100141 China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

Formal methods (FM) are a pivotal technology for ensuring the safety and reliability of software systems. These methods are centered around the application of precise mathematical principles to verify the security and dependability of software. Formal methods have been widely employed in the verification of critical systems, with the increasing significance of concurrent programs in the context of the growing prevalence of parallel computing architectures like multi-core processors. However, the efficiency of formal methods in verifying concurrent programs is often low, which hinders their broader adoption and application. Addressing this issue, this study proposes an efficient formal verification method tailored for concurrent programs. This method bifurcates the verification process of concurrent programs into two stages: proving sequential correctness and verifying other attributes. In the first stage, leveraging theorem-proving strategies and large language model (LLM) technology, the method assists in proving the sequential correctness of concurrent programs. The second stage, based on model checking technology, involves transforming the program to narrow down the verification scope before using model checking tools to verify other security attributes of the program. Experimental results have shown that the proposed method significantly reduces the state space that needs to be searched during verification, thereby enhancing the efficiency of concurrent program verification. © 2025 SPIE.

关键词： Model checking

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Dilated convolution model for lightweight neural network 2nd

Dilated convolution model for lightweight neural network

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2nd international conference on Next-Generation computing and Information systems, ICNGCIS 2023

作者： Gupta, Sachin Goyal, Priya Vyas, Bhuman Shabaz, Mohammad Bala, Suchitra Sameen, Aws Zuhair Department of CSE Maharaja Agrasen Institute of Technology India Department Computer Science and Engineering Manipal University Jaipur India Credit Acceptance Canton United States Model Institute of Engineering and Technology J&K Jammu India ICT & Cognitive Systems Sri Krishna Arts And Science College India College of Medical Techniques Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9781032738659

Lightweight convolutional neural networks are being studied further in order to better deploy deep convolutional neural networks to edge devices, minimize the number of model parameters in deep neural networks, and reduce network complexity. On accomplish the lightweight goal, the full convolution is performed to the remaining network structure. To increase the light effect, it is merged with point-by-point convolution to build an improved dilated convolution lightweight approach. The enhanced dilated convolution is coupled with conventional convolution to lessen the attenuation of accuracy, and a lightweight fusion dilated convolution is presented. The improved dilated convolution light approach offers the most substantial lightweight benefit, according to the data. Furthermore, the lightweight fusion dilated convolution approach decreases the amount of model parameters and offers the best speed-accuracy trade-off. © 2025 The Author(s).

关键词： Convolution

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Sentiment Analysis Scheme for Image-Text Comments based on Multimodal Deep Learning and Spatiotemporal Attention 6

Sentiment Analysis Scheme for Image-Text Comments based on M...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Fanchang TangYaLing Yadav, Amit Sah, Abhishek Pratap Khan, Asif Department of Software Engineering Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University NT Australia School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Application Integral University Lucknow India

ISBN: (纸本)9798331522667

A sentiment analysis scheme for image and text comments based on multimodal deep learning and spatiotemporal attention is proposed to address the issues of incomplete spatiotemporal considerations, incomplete implementation details, and cutting-edge theoretical algorithms in graphic and textual sentiment analysis schemes. The proposed model has clear layering including data preprocessing layer, modal encoding layer, modal fusion layer, sentiment classification layer, loss function and optimizer, evaluation and feedback. The implementation details of each layer are introduced. The entire scheme model incorporates Multimodal Fusion Neural Network (MFNN) deep learning and spatiotemporal attention mechanism, which makes the scheme perform well in terms of security, robustness and performance, making up for the shortcomings of existing research schemes. © 2025 IEEE.

关键词： Contrastive Learning

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DASHCHEF: A Metric Recommendation Service for Online systems Using Graph Learning 28th

DASHCHEF: A Metric Recommendation Service for Online Systems...

引用

28th international conference on engineering of Complex Computer systems (ICECCS)

作者： He, Zilong Huang, Tao Chen, Pengfei Li, Ruipeng Wang, Rui Zheng, Zibin Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Tencent Shenzhen Peoples R China Sun Yat Sen Univ Sch Software Engn Guangzhou Peoples R China

ISBN: (纸本)9783031664557;9783031664564

To ensure the high availability of modern online systems, effective maintenance is of critical importance. Today's software maintenance techniques for online systems heavily rely on metrics, which are time series data that can describe the real-time state of a system from various perspectives. Typically, software engineers generate dashboards with metrics to aid software maintenance. Though several attempts have been devoted to metric analysis for automatic software maintenance, the primary step, i.e., dashboard generation, remains manual to a large extent. In this paper, we develop a metric recommendation service, which can automate the dashboard generation practice and greatly ease the burden in maintaining an online system. Specifically, we analyze the needs of two essential steps of online system maintenance, i.e., anomaly detection and fault diagnosis, and design metric recommendation mechanisms for them respectively. Graph learning techniques are employed in the automation of metric recommendation. Our experiments demonstrate that the proposed approach can achieve an F1-score of 0.912 in selecting metrics for anomaly detection, and an accuracy of 0.859 in retrieving metrics for faults diagnosis, which significantly outperforms the compared baselines.

关键词： metric recommendation graph learning software maintenance online systems

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Enhancing software Defect Prediction in Ansible Scripts Using Code-Smell-Guided Prompting with Large Language Models in Edge-Cloud Infrastructures 4th

Enhancing Software Defect Prediction in Ansible Scripts Usin...

引用

24th international conference Web engineering (ICWE)

作者： Hong, Hyunsun Lee, Sungu Ryu, Duksan Baik, Jongmoon Korea Adv Inst Sci & Technol Daejeon South Korea Jeonbuk Natl Univ Jeonju South Korea

ISBN: (纸本)9783031751097;9783031751103

In edge-cloud systems, the quality of infrastructure deployment is crucial for delivering high-quality services, especially when using popular Infrastructure as Code (IaC) tools like Ansible. Ensuring the reliability of such large-scale code systems poses a significant challenge due to the limited testing resources. software defect prediction (SDP) addresses this limitation by identifying defectprone software modules, allowing developers to prioritize testing resources effectively. This paper introduces a Large Language Model (LLM)-based approach for SDP in Ansible scripts with Code-Smell-guided Prompting (CSP). CSP leverages code smell indicators extracted from Ansible scripts to refine prompts given to LLMs, enhancing their understanding of code structure concerning defects. Our experimental results demonstrate that CSP variants, particularly the Chain of Thought CSP (CoT-CSP), outperform traditional prompting strategies, as evidenced by improved F1-scores and Recall. To the best of our knowledge, this is the first attempt to employ LLMs for SDP in Ansible scripts. By employing a code smell-guided prompting strategy tailored for Ansible, we anticipate that the proposed method will enhance software quality assurance and reliability, thereby increasing the overall reliability of edge-cloud systems.

关键词： Edge-cloud Ansible Large Language Models software Defect Prediction

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Maintaining Privacy in Contact Tracing systems for Pandemics Through Edge computing 1

Maintaining Privacy in Contact Tracing Systems for Pandemics...

引用

1st international conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Tejaswini, Virupakshi Sirisala, NageswaraRao K.S.R.M. College of Engineering Department of Computer Science and Engineering A.P Kadapa India

ISBN: (纸本)9798350355611

Data processing and publication are crucial today with the advancements in technologies such as edge computing and IoT(Internet of Things). A vast volume of data is being generated by internet-connected devices, often on a continuous basis. The COVID-19 pandemic data exemplifies this trend of constant data flow, as the global medical emergency has necessitated ongoing data collection. This data must be processed and analyzed, and subsequently published, while ensuring personal privacy is upheld. Edge computing devices can handle various tasks like data preprocessing and analysis in any setting. It is crucial to process data before uploading it to the cloud and sharing it with advisories or media outlets, as improper handling could lead to privacy breaches. To protect personal privacy, measures such as differential privacy techniques are implemented to protect data. Edge computing technologies offer several advantages, including efficient data processing close to the origin to minimize internet data transmission capacity use and enhanced security for confidential information on public cloud infrastructures. © 2025 IEEE.

关键词： Data Sharing Differential Privacy (DP) Edge computing Individual Privacy

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Integrating Clinical Expertise into software Development: Evaluating the Use of openEHR Archetypes for Requirements Elicitation in Healthcare Applications

Integrating Clinical Expertise into Software Development: Ev...

引用

18th international Joint conference on Biomedical engineering systems and Technologies, BIOSTEC 2025

作者： Silva, José Araújo, André Computing Institute Federal University of Alagoas Av. Lourival Melo Mota S/N Cidade Universitária Maceió Brazil

OpenEHR archetypes are standardized frameworks designed to model clinical information in healthcare systems, enabling a consistent and integrated representation of complex data. These models encompass common clinical elements such as symptoms, diagnoses, and treatments, ensuring that information is understood and applied uniformly across different contexts. This paper investigates the use of openEHR archetypes in the requirements elicitation and specification process for clinical systems, highlighting their potential to integrate healthcare professionals’ knowledge into software development. The literature review reveals a significant gap in the participation of these professionals during the requirements elicitation phase, especially in studies that apply archetypes. Quantitative and qualitative results positively perceive the methodology used, highlighting clarity, collaboration, and alignment with end-user needs. Statistical analysis using the Wilcoxon test presented significant p-values, indicating that professionals considered the method straightforward, intuitive, and conducive to engagement, with real opportunities for contribution to the validation of requirements. The qualitative data reinforce the importance of a collaborative environment and suggest the need for deeper involvement of healthcare professionals at all process stages. In conclusion, this research indicates that applying openEHR archetypes, combined with more significant interaction with healthcare professionals, is promising for integrating clinical expertise effectively and directly into developing clinical systems. © 2025 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Application programs

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A Hybrid Correlation Feature Selection Based Support Vector Machine (CFS-SVM) for Effective software Defect Prediction 4th

A Hybrid Correlation Feature Selection Based Support Vector ...

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4th international conference on Smart IoT systems: Innovations and computing, SSIC 2023

作者： Medhunhashini, D.R. Jeen Marseline, K.S. Sri Krishna Arts and Science College Coimbatore India

ISBN: (纸本)9789819751990

software engineering is seamlessly developed with advancement in the development tools and framework as so the development of the defect also. software defect prediction focus on ensuring and delivering quality software product. Machine learning algorithms plays a vital role in effectively predicting the software bugs early in the software life cycle. The main objective of this study is to identify a hybrid machine learning model incorporating Correlation feature selection. The radial basis function kernel of SVM maps the features in the input space. The CFS based SVM model encompasses a higher defect prediction when employed. The CM1 dataset from NASA spacecraft instrument were subjective for the evaluation of CFS based SVM model. Confusion matrix, Precision, Recall, and F1 Score are the metrics for CFS based SVM. The results show a higher rate of prediction with 64.32% for precision, 65.13% for Recall, and 64.72% for F Score, respectively. This study has been able to show the defective classes have a big impact on software defect prediction and opting the correct parameters with correlation feature selection effectively performs predictions among the other classifiers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： NASA

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DyPSS: Dynamic Priority Signalling System for Emergency Vehicles using Fog and YOLOv8 17

DyPSS: Dynamic Priority Signalling System for Emergency Vehi...

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17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Kakde, Anuj Nath, Shubha Brata Ghosh, Soumya K. Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331531195

Efficient navigation of emergency response vehicles (ERVs) through urban congestion is crucial to life-saving efforts, yet traditional traffic systems often slow down their swift passage. In this work, we introduce Dynamic Priority Signalling System (DyPSS), an intelligent, multi-layered traffic management solution designed to minimize ERV delays by creating "green corridors"at traffic signals. Using technologies like fog computing, cloud computing, global positioning system and deep learning, DyPSS adjusts traffic lights instantly to clear congestion as an ERV approaches. Simulation testing was conducted using SUMO and it demonstrated DyPSS's potential to significantly reduce ERV travel time in dense urban settings with minimal disruption to general traffic flow. This system could drastically improve emergency response by reducing delays and helping responders reach their destinations faster, potentially saving more lives. © 2025 IEEE.

关键词： Emergency vehicles

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Mitigating Security Challenges in Edge computing: Attacks, Defence Strategies, and Algorithms 3

Mitigating Security Challenges in Edge Computing: Attacks, D...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Matta, Venkata Durga Rao Maheswara Rao, V.V.R. Battula, Venkata Sai Jayasurya Addagatla, Mahesh Babu Computer Science and Engineering Department Vishnu Institute of Technology Andhra Pradesh Bhimavaram India Computer Science and Engineering Department Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram India

ISBN: (纸本)9798331527549

Edge computing has emerged as a crucial paradigm to fulfill the increasing demand for rapid data processing, low latency, and efficient resource utilization, particularly in applications such as the Internet of Things (IoT), smart cities, autonomous vehicles, and automated industrial systems. By processing data closer to its source, edge computing minimizes reliance on centralized cloud infrastructures, thereby improving response times and optimizing bandwidth. However, this transition also presents significant security challenges, including heightened risks of Distributed Denial of Service (DDoS) attacks, data breaches, and unauthorized physical access to edge nodes. This paper conducts a comprehensive analysis of these security vulnerabilities, presenting a framework that integrates advanced defence mechanisms and algorithms tailored to enhance data protection and resource management in edge environments. Our findings indicate that the proposed strategies effectively mitigate identified threats while maintaining system performance. Furthermore, we outline ongoing challenges related to scalability, interoperability, and data governance, providing insights into future research avenues aimed at fortifying edge computing systems against evolving security threats. This study contributes to a deeper understanding of the critical role edge computing plays in the domain of next-generation computing technologies. © 2025 IEEE.

关键词： Cloud security

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