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检索条件"主题词=Software algorithms"

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Securing IoT-enabled Metaverse with Authentication and Threat Classification Framework Using DL

Securing IoT-enabled Metaverse with Authentication and Threa...

引用

International Symposium on Advanced Computing and Communication (ISACC)

作者： Krisha Darji Chinmay Trivedi Rajesh Gupta Fenil Ramoliya Sudeep Tanwar Smita Agrawal Dept. of Comp. Science & Engg. Institute of Tech. Nirma University Ahmedabad India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

In the current technological landscape, the rise of Web 3.0 is notable. Alongside this, immersive technologies like Virtual Reality (VR) and Augmented Reality (AR) are gaining increased popularity. This is due to idea like the Metaverse, which is a spatial computing platform that offers virtual experiences replicating real environments. However, Metaverse also faces security vulnerabilities, such as the risk of identity theft, financial fraud, and the damage of virtual assets due to the public openness of the platform. To mitigate these paramount concerns, there has to be a robust user authentication and verification approach before granting access to these domains. We propose a Convolutional Neural Networks (CNN)-based approach, i.e., MetaSec, which focuses on analyzing incoming user traffic patterns to identify any anomaly in the Internet of Things (IoT)-enabled Metaverse environment. The performance evaluation of MetaSec has been done using various optimization algorithms such as Stochastic Gradient Descent (SGD), Adam, RMSprop, and Adagrad. The main goal of rigorous assessment is to identify the most effective optimization approach for enhancing virtual network security. Thus, MetaSec proactively uncovers potential susceptibility and minimises the risk of intrusion and harm to critical resources. These hardware and software plug-in ranges from computer systems, networks, databases, websites, and other network-based applications and services applied in the virtual domain.

关键词： Semantic Web Accuracy Metaverse software algorithms Authentication Traffic control software Spatial computing Convolutional neural networks Optimization

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Enhancing Fraud Detection via On-Chain Ethereum and Off-Chain X Data Fusion

Enhancing Fraud Detection via On-Chain Ethereum and Off-Chai...

引用

2025 2nd International Conference on algorithms, software Engineering and Network Security (ASENS)

作者： Yunpeng Niu Guang Li Yangtian Mi Jieying Zhou Weigang Wu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

Although Ethereum stands as the dominant blockchain for smart contracts and decentralized applications, faces persistent security challenges from fraudulent activities. Such activities often correlate with off-chain platform, such as blog platform and social media. Existing methods analyze fraud activities primarily rely on on-chain transaction data, neglecting interdependencies between on-chain and off-chain activities. In this paper, we observe that there are associations between airdrop campaigns in X platform, a famous social platform and Ethereum fraudulent activities. Further, we crawl Ethereum addresses and posts of these users in airdrop campaigns, and construct a cross-platform datasets from X to Ethereum, including matching pairs of Ethereum addresses to X users, Ethereum transactions and X post data. Due to inherent heterogeneity between Ethereum transactions (structured graphs) and X data (unstructured text/images), we design a multimodal fusion framework leveraging transformer architectures to fuse on-chain transaction features with off-chain content features (text and image representations). Finally, the fused features are leveraged to construct downstream fraud transactions classifiers. Experimental results demonstrate that classifiers using fused features outperform classifiers using transaction features, achieving a 12% improvement in Recall. Our findings highlight the critical role of off-chain data in enhancing fraud detection accuracy.

关键词： Accuracy Social networking (online) Soft sensors software algorithms Smart contracts Feature extraction Transformers Blockchains Fraud software engineering

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Development of an Algorithm for Intelligent Analysis of Data From a Microprocessor-Based Portable Spectrophotometer

Development of an Algorithm for Intelligent Analysis of Data...

引用

International Russian Smart Industry Conference (SmartIndustryCon)

作者： Bekruz Z. Parmanov Fayzullo M. Nazarov Maruf R. Tojiyev Department of Management Theory and Information Security Samarkand state university named fater Sharof Rashidov Samarkand Uzbekistan Department of Artificial intelligence and Information systems Samarkand state university named fater Sharof Rashidov Samarkand Uzbekistan

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

This article is devoted to the development of a microprocessor-based portable spectrophotometer for the analysis of substances in water, the development of an algorithm for analyzing data from the device, and the writing of software based on this algorithm. In addition, the article is devoted to assessing the applicability and relevance of this device for high-quality water monitoring. The article presents a structural diagram and an optical system diagram of a microprocessor-based portable spectrophotometer. In addition to the microcontroller, the portable device also uses at least two logical modules: a servo drive and a Bluetooth module. The principle of operation of the device's microcontroller is presented in the form of a structural diagram, the entire developed algorithm is divided into two main stages - a preparatory cycle and the main cycle in which samples are studied. A special algorithm has been developed that allows remote control of the spectrophotometer. In addition, the algorithm for intelligent data analysis has several operating modes and a calibration mode.

关键词： Data analysis Microcontrollers software algorithms Writing Search problems software Optical receivers Water monitoring Servomotors Remote control

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Privacy-Preserving Large Language Model in Terms of Secure Computing: A Survey

Privacy-Preserving Large Language Model in Terms of Secure C...

引用

2025 2nd International Conference on algorithms, software Engineering and Network Security (ASENS)

作者： Peiming Xu Huan Xu Maoqiang Chen Zhihong Liang Wenqian Xu Guangdong Provincial Key Laboratory of Power System Network Security China Southern Power Grid Electric Power Research Institute Guangzhou China China Southern Power Grid Company Limited Guangzhou China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

As large language models (LLMs) become increasingly embedded in diverse applications, from natural language processing to cybersecurity, the demand for robust privacy-preserving solutions has surged. This paper discusses privacy vulnerabilities in LLMs, identifying risks at system, application, and network levels. We categorize and evaluate current privacy-preserving methods, based on homomorphic encryption (HE), trusted execution environments (TEE), and secure multi-party computation (MPC), to assess their effectiveness in mitigating data exposure while supporting LLM performance. Building on these insights, we propose a novel privacy-preserving framework for fine-tuning and inference in LLMs. Our findings highlight existing gaps and propose future directions for developing secure, efficient, and scalable privacy-preserving LLM architectures.

关键词： Surveys Privacy Large language models Scalability software algorithms Pressing Multi-party computation Homomorphic encryption Standards software engineering

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Evaluating ReLink for Traceability Link Recovery in Practice

Evaluating ReLink for Traceability Link Recovery in Practice

引用

IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Ayberk Yaşa Cemhan Kaan Özaltan Görkem Ayten Fatih Kaplama Ömercan Devran Baykal Mehmet Uçar Eray Tüzün Stash Ankara Turkey RWTH Aachen University Aachen Germany Bilkent University Ankara Turkey Beko İstanbul Turkey

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

Traceability is important in the software development life cycle for managing the connections among various software artifacts, particularly pull requests (PRs) and issues. Developers often neglect to link these manually, reducing traceability. Existing algorithms to recover these links have limited application in closed-source projects. In this study, we share the experience of using ReLink, a predictive PR-issue linking tool with visualization capabilities, in a closed-source project environment. ReLink stands out due to its availability as a fully functional web application and its semi-automated nature, which enhances usability. ReLink determines missing links between PRs and issues based on a confidence score normalized between 0 and 100, calculated using text similarity and heuristic rules. The tool's effectiveness was evaluated in both an open-source project and an industry-based case study. In the open-source project, ReLink achieved a top-5 accuracy of 0.80 and a precision of 0.77. In the industrial case study, ReLink's effectiveness was validated by practitioners selecting the correct link from five issue suggestions, resulting in a top-5 accuracy of 0.86 and a mean reciprocal rank (MRR) of 0.84. Through this experience, we offer insights into both the benefits and challenges of implementing traceability link recovery in the industry and provide recommendations for practitioners seeking to bridge traceability gaps efficiently.

关键词： Industries Bridges Visualization Accuracy software algorithms Prediction algorithms software software development management

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Research Advances in Obstacle Avoidance Path Planning algorithms for Unmanned Aerial Vehicles

Research Advances in Obstacle Avoidance Path Planning Algori...

引用

2025 2nd International Conference on algorithms, software Engineering and Network Security (ASENS)

作者： Hui Li Xuliang Duan School of Electromechanical and Information Engineering Chengdu Agricultural College Chengdu China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

With the rapid advancement of drone technology, drones have demonstrated significant application potential in various fields such as agriculture, military, and logistics due to their efficiency and flexibility. However, the practical application of drones in obstacle avoidance tasks faces multiple challenges, including complex environmental perception, algorithmic performance bottlenecks, hardware compatibility issues, and system stability and reliability. These challenges have somewhat constrained the transformation of drones' potential into actual productivity. Therefore, research and development in obstacle avoidance technology for agricultural drones are crucial. This study analyzes the current difficulties and challenges faced by obstacle avoidance path planning algorithms, focusing on the classification, principles, advantages, and disadvantages of these algorithms, as well as their optimization and application in terms of real-time performance and efficiency. The study also looks forward to the future development trends of drone obstacle avoidance path planning algorithms, suggesting that future research will concentrate on the innovative integration of multiple algorithms, deeper fusion with AI technology, optimization of algorithms for specific environments, enhancement of real-time performance and robustness, coordinated hardware development, and improvement of multi-drone collaborative algorithms. These advancements aim to adapt to more complex flying and obstacle avoidance environments, thereby promoting the standardization and industrialization of drone technology.

关键词： software algorithms Standardization Autonomous aerial vehicles Path planning Real-time systems Hardware Classification algorithms Collision avoidance Optimization Drones

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Development and Design of Online Management Platform for Talent Cultivation Based on Artificial Intelligence

Development and Design of Online Management Platform for Tal...

引用

Digital Analysis and Processing, Intelligent Computation (DAPIC), International Conference on

作者： Chao Jiang Mao Li Chengdu Aircraft Industrial (Group) Coporation Ltd. Chengdu Sichuan China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

With the development of information technology, especially the rapid progress of artificial intelligence (AI) technology, the traditional education model is facing a transformation. This paper analyzes the challenges of the current education model, and combines the latest progress of AI technology to build a comprehensive platform that integrates the functions of course management, learning progress tracking, intelligent recommendation and data analysis. The platform adopts micro-service architecture to improve the maintainability, expansibility and deployment flexibility of the system. The functional modules include user management, course management, learning management, intelligent recommendation and data analysis, aiming at deeply analyzing students' behaviors through AI algorithm, providing teaching feedback for teachers and personalized learning suggestions for students, thus improving teaching and learning effects. In terms of key technologies and algorithms, the platform integrates natural language processing (NLP), machine learning, deep learning and knowledge mapping to improve the level of intelligence. Algorithm selection includes collaborative filtering algorithm for personalized recommendation, K-means clustering algorithm for learning behavior analysis, and decision tree algorithm for teaching effect evaluation. The development environment involves highperformance hardware and software configuration, including cloud server, Linux system, Python language, database technology, etc., as well as version control and deployment using tools such as Git and Docker. In the testing and debugging stage, the stability, performance and security of the platform are ensured through unit testing, integration testing, performance testing, user acceptance testing and security testing. The research in this paper provides an efficient and intelligent online management platform for the education field to meet the challenge of educational modernization.

关键词： Machine learning algorithms Data analysis Education software algorithms Clustering algorithms Learning (artificial intelligence) Natural language processing Stability analysis Security Testing

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Research on Key Technologies of Collaborative Spectrum Sensing Based on Differential Privacy

Research on Key Technologies of Collaborative Spectrum Sensi...

引用

Smart Grid and Green Energy (ICSGGE), International Conference on

作者： Rong Wang Dongtao Wang The College of Electrical Engineering and Automation Fuzhou University Fuzhou China Hunan Automotive Engineering Vocational University Zhuzhou China

ISBN: (数字)9798331542139

ISBN: (纸本)9798331542146

Aiming at the incentive mechanism issues in collaborative spectrum sensing and access for privacy protection in Internet of Things (IoT) crowd sensing, a multi-task winner selection mechanism based on differential privacy is proposed to address the privacy leakage in Mobile Crowd Sensing (MCS). This mechanism utilizes the exponential mechanism of differential privacy to select winners, protecting the cost privacy information of Crowd Sense Users (CSUs). Furthermore, to incentivize CSUs, a two-layer Stackelberg game algorithm is proposed. This algorithm can coordinate the compensation paid by Secondary Users (SUs) for spectrum access and the data quality reported by CSUs, thereby effectively enhancing the enthusiasm of CSUs to participate in sensing and the performance of collaborative spectrum detection. Simulation results using MATLAB software show that compared to traditional methods, the proposed algorithm increases the average user reward by $19.5 \%$.

关键词： Privacy Differential privacy software algorithms Collaboration Games Multitasking Sensors Internet of Things Game theory Signal to noise ratio

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Testing Environment for Validating Swarm algorithms in Randomized Settings

Testing Environment for Validating Swarm Algorithms in Rando...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),Conference on

作者： Luis Chavarria-Zamora Jason Leitón-Jiménez Luis Barboza-Artavia Pablo Soto-Quirós Escuela de Ingeniería en Computadores Instituto Tecnológico de Costa Rica Cartago Costa Rica Escuela de Matemática Instituto Tecnológico de Costa Rica Cartago Costa Rica

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Inspired by the collective behavior of organisms such as bee swarms and bird flocks, swarm algorithms are employed in artificial intelligence to tackle complex problems through the collaboration of multiple agents. This paper presents a digital validation environment designed to assess the performance of swarm algorithms. Two prominent algorithms were examined: Particle Swarm Optimization (PSO) and Directional Pheromone Walk (DPW). Given its effectiveness in territory exploration, DPW was selected for this study. Implementation was carried out using the open-source Crazyflie drone. The digital validation environment allows for the simulation and exploration of randomized terrains, facilitating the appropriate selection of swarm algorithms. The proposed integration with hardware enabled rapid and efficient system implementation, demonstrating a robust approach to algorithm validation in virtual environments. This setup allows users to optimize battery usage, costs, and the number of drones in advance for practical system deployment.

关键词： Heuristic algorithms Scalability software algorithms Virtual environments Reliability engineering software Particle swarm optimization System implementation Testing Drones

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Safety-Critical Scenario Generation for Self-Driving Systems Based on Domain Models

Safety-Critical Scenario Generation for Self-Driving Systems...

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2025 2nd International Conference on algorithms, software Engineering and Network Security (ASENS)

作者： Jinru Wang Xi Wang Computer Science Department Shanghai University Shanghai China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

The end-to-end automated driving system (ADS) has the capability to eliminate human errors present in driving tasks, thereby reducing the occurrence of traffic accidents and enhancing safety in the transportation sector. However, due to the complexity and opacity of its internal decision-making processes, which are often perceived as a black box, there is a potential for incorrect or unexpected extreme situations to arise, necessitating stringent robustness requirements. To comprehensively evaluate the performance of ADS, it is essential to generate a diverse array of safety-critical scenarios. This paper proposes a method aimed at generating test scenarios for ADS safety by combining domain models with hazardous factors that lead to violations or accidents. The method derives key objects and their environmental conditions that may cause accidents or violations from the hazardous factors obtained from corner cases. Based on this, we perform traversal operations on the domain model to create variations or reproductions of the identified objects and environmental conditions. Ultimately, through this series of operations, the generated safety-critical scenarios not only closely match real-world situations but also provide a reliable basis for further safety assessments. We simulated the generated scenarios on the industrial-grade ADS platform Apollo, successfully identifying safety violations.

关键词： Systematics software algorithms Transportation Ontologies Robustness Safety software reliability Scenario generation Accidents software engineering

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