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共 90579 条记录，以下是431-440 订阅

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A Lightweight Approach for Detection of software Design Smells

A Lightweight Approach for Detection of Software Design Smel...

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International conference on Advancements in Computational Sciences (ICACS)

作者： Sabiyyah Sabir Ghulam Rasool Department of Computer Science COMSATS University Islamabad Lahore Campus Lahore Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

In software engineering, design smells can make software systems harder to maintain and lower their overall quality. Traditional methods, primarily focusing on code-level analysis, often miss design inconsistencies that manifest during the design phase. This paper introduces a light weight approach that leverages the Web Ontology Language (OWL) to detect design smells at the software design level. By incorporating ontological reasoning and automated checks against established design principles, the approach offers a proactive strategy for identifying and addressing design smells before they escalate into more significant issues in the implementation phase. We demonstrate the approach’s effectiveness through its application to several open-source Java projects, achieving notable precision and recall rates.

关键词： Java software maintenance software design OWL software quality software systems Cognition software reliability software engineering software development management

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SNNGX: Securing Spiking Neural Networks with Genetic XOR Encryption on RRAM-based Neuromorphic Accelerator 24

SNNGX: Securing Spiking Neural Networks with Genetic XOR Enc...

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43rd International conference on computer-Aided Design, ICCAD 2024

作者： Wong, Kwunhang Wang, Songqi Huang, Wei Zhang, Xinyuan He, Yangu Lai, Karl M.H. Jiao, Yuzhong Lin, Ning Qi, Xiaojuan Chen, Xiaoming Wang, Zhongrui ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400710773

Biologically plausible Spiking Neural Networks (SNNs), characterized by spike sparsity, are growing tremendous attention over intellectual edge devices and critical bio-medical applications as compared to artificial neural networks (ANNs). However, there is a considerable risk from malicious attempts to extract white-box information (i.e., weights) from SNNs, as attackers could exploit well-trained SNNs for profit and white-box adversarial concerns. There is a dire need for intellectual property (IP) protective measures. In this paper, we present a novel secure software-hardware co-designed RRAM-based neuromorphic accelerator for protecting the IP of SNNs. software-wise, we design a tailored genetic algorithm with classic XOR encryption to target the least number of weights that need encryption. From a hardware perspective, we develop a low-energy decryption module, meticulously designed to provide zero decryption latency. Extensive results from various datasets, including NMNIST, DVSGesture, EEGMMIDB, Braille Letter, and SHD, demonstrate that our proposed method effectively secures SNNs by encrypting a minimal fraction of stealthy weights, only 0.00005% to 0.016% weight bits. Additionally, it achieves a substantial reduction in energy consumption, ranging from ×59 to ×6780, and significantly lowers decryption latency, ranging from ×175 to ×4250. Moreover, our method requires as little as one sample per class in dataset for encryption and addresses hessian/gradient-based search insensitive problems. This strategy offers a highly efficient and flexible solution for securing SNNs in diverse applications1 © 2024 Copyright is held by the owner/author(s).

关键词： Medical computing

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Advancing Evasion: Distributed Backdoor Attacks in Federated Learning 25th

Advancing Evasion: Distributed Backdoor Attacks in Federate...

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25th International conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Wang, Jian Shen, Hong Ke, Wei Faculty of Applied Sciences Macao Polytechnic University China School of Engineering and Technology Central Queensland University Rockhampton Australia School of Software Technology Guangzhou Institute of Software Guangzhou China

ISBN: (纸本)9789819642069

Federated Learning (FL) is vulnerable to backdoor attacks through data poisoning if the data is not scrutinized, as malicious participants can inject backdoor triggers in normal samples, leading to poisoned updates. Distributed backdoor attacks pose a greater threat than centralized ones, as they often use fixed pixel blocks as triggers, increasing the risk of detection. This paper presents a novel distributed backdoor attack strategy that leverages edge structure poisoning to circumvent existing defense mechanisms, employing a distributed poisoning strategy to evade current defense mechanisms, thereby enhancing the stealth of the attack. Experimental results on multiple benchmark datasets demonstrate that this method is more effective and stealthy compared to other backdoor attack methods. Furthermore, this paper also proposes targeted defense strategies based on the experimental results, offering a new perspective on the security of FL systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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The Implementation of Biomimetic Structural Optimization software in a Supercomputing Environment 5th

The Implementation of Biomimetic Structural Optimization So...

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5th Polish Congress of Mechanics and 25th International conference on computer Methods in Mechanics, PCM-CMM 2023

作者： Nowak, Michal Polak, Jan Napierala, Oskar Department of Virtual Engineering Poznan University of Technology Poland ul. Jana Pawla II 24 Poznan60-965 Poland

ISBN: (纸本)9783031731600

The Poznan University of Technology team has developed a biomimetic topology optimization method, which is a structural optimization system based on trabecular bone surface remodeling. The basis of the algorithm formulation was the phenomenon of bone adaptation to mechanical stimulation. The effective use of the developed algorithms will require careful study of how they are implemented. The biomimetic topology optimization has been also extended and applied to shape optimization in 3D elasticity and compliance optimization under multiple load cases. In the paper the supercomputing environment enabling parallel calculations for both preparation of computational grids and structural calculations is described. The speed-up and efficiency tests were performed on Poznan Supercomputing and Networking Center environment. Practical conclusions from the implementation of the Biomimetic Structural Optimization System in a supercomputing environment will be presented. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Shape optimization

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Emotion-Aware Synthetic Speech Detection with Multi-level Fusion Features 2nd

Emotion-Aware Synthetic Speech Detection with Multi-level Fu...

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2nd International conference on the Frontiers of Robotics and software engineering, FRSE 2024

作者： Xiang, Lingyun Zhou, Zhiguo Liu, Yangfan School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China

ISBN: (纸本)9789819631605

Current synthetic speech detection methods often overlook the emotional distinctions between synthetic and real speech. To comprehensively leverage emotional information and deep features to further enhance the accuracy of distinguishing synthetic speeches, this paper proposes an emotion-aware synthetic speech detection method by fusing multi-level different features. Initially, a multi-scale emotional feature extraction model is devised to extract emotional features from 3-D log-Mels. Considering that the emotional features learned by a single model may not be comprehensive enough, the emotion2vec model is utilized to automatically learn additional emotional features from the temporal domain of the original speech. Subsequently, by employing a co-attention network, the extracted two kinds of emotional features are both incorporated into the deep features learned by the end-to-end model Rawnet2, thereby obtaining highly sensitive and weighted deep features. Finally, the extracted emotional features and the weighted deep features are fused to classify synthetic speech from real speech. Experimental results on several datasets demonstrate that the proposed method outperforms baseline methods, with a detection accuracy exceeding 0.97. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Speech recognition

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Design of a Scaled-Vehicle Research Platform for software-Defined Vehicles

Design of a Scaled-Vehicle Research Platform for Software-De...

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Mechatronics and Robotics engineering (ICMRE), International conference on

作者： Sung Bhin Oh Young Soo Do Se Jeong Lim Hyeok Jun Choi Sung Keun Cha Hyeong Keun Hong Seong Hyeon Lim Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

Modern vehicles are integrated systems of complex software that support advanced features such as autonomous driving and over-the-air updates. software-defined vehicles (SDVs) are emerging as a future automotive paradigm for managing increasing software complexity by centralizing computing resources and enabling flexible software updates and feature deployment. However, most SDV research is conducted in simulation environments because using real vehicles presents many challenges, including high cost, safety concerns, and implementation difficulties. In this paper, we present the design of an SDV research platform using a 1/5-scale vehicle model to overcome these limitations of SDV research. The platform implements a zone architecture and integrates various embedded systems to replicate the distributed computing environment of a modern vehicle. The platform allows researchers to experiment with different SDV concepts such as distributed computing, OTA updates, middleware, and vehicle software management without the constraints of full-scale vehicle testing. Our design demonstrates the feasibility of using extended vehicles for SDV research and provides insights into platform design for SDV research.

关键词： Mechatronics Embedded systems Wireless networks computer architecture Safety Distributed computing Middleware Robots Automotive engineering Testing

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Enhancing Sovereign Allocation of Resources in Cloud Milieus Using a Causal Dilated Geometric Algebra Approach for Dynamic Scalability 2

Enhancing Sovereign Allocation of Resources in Cloud Milieus...

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Katru, Chandrasekhar Rao Srinivasan, Sathish Kumar, Mohan Yadwad, Sunita Satish, G. Maranan, Ramya Lead Software Engineer Indian LandSC29707 United States Machine Learning & AI Development Oracle Cloud Infrastructure Division San FranciscoCA United States Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru India Parul institute of Engineering and Technology Parul University Computer science and Engineering Faculty of Engineering and Technology Gujarat India Sree Dattha Institute of Engineering and Science Department of Electrical and Electronics Engineering Telangana Sheriguda India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

Cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle these resources dynamically in spite of fluctuating workloads and service delivery requirements. Static models of resource management suffer from scalability and inability to react on the fly and accurately predict the demand thus causing Service Level Agreement (SLA) breaches and affecting the Quality of Service (QoS) as well as energy consumption profile. Advanced and elaborative cloud workloads require an intelligent system that can analyse the temporal and spatial patterns without impacting the overall system's performance significantly. Inspired by these challenges, this research proposes a Causal Dilated Geometric Algebra Convolutional Transformer Network (CATNet) along with the Prairie-Dog Optimizer (P-DogO), referred to as CAT-DogNet to learn and predict the dynamic scalability. CATNet integrates Causal Dilated Convolutional Networks in time domain for temporal structure capturing and Geometric Algebra Transformer for spatial resource coding. P-DogO adjusts weight parameters and losses for resource allocation, enhancing them depending on a work environment. A set of comprehensive assessments demonstrate a 0.85% SLA violation rate, average response time of less than 10ms, and overall 12.3% less energy consumption than baseline methods. The system had favourable predictability rates over 99.4% for the resource demand estimate. CAT-DogN et presents a sound and agnostic architecture for autonomous resource allocation in cloud environment, which indeed extend the state of art by considering flexibility, power consumption and QoS compliance. © 2025 IEEE.

关键词： Infrastructure as a service (IaaS)

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Fair and Communication-Efficient Personalized Federated Learning 18th

Fair and Communication-Efficient Personalized Federated Lea...

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18th International conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Zheng, Yifu Zheng, Xu Wang, Tingqi Mu, Chong Zhakiyev, Nurkhat School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Science and Innovation Astana IT University Astana Kazakhstan

ISBN: (纸本)9783031714665

Personalized Federated Learning (pFL) is among the most popular tasks in distributed deep learning, which compensates for mutual knowledge and enables device-specific model personalization. However, the effectiveness of pFL is severely impeded by challenges on fairness and significant communication overhead, where devices holding essential samples have to devote extensive resources for model training. To address these issues, we introduce the Fair and Communication-Efficient Personalized Federated Learning (FCE-PFL) framework, which harmonizes performance and device fairness while maintaining communication costs. Based on the Dempster-Shafer Theory, FCE-PFL employs assistance and contribution metrics to quantify the auxiliary information a client receives and provides. Then FCE-PFL balances and adjusts the involvement of devices in each training iteration, so as to facilitate fair training by constraining the maximum resource consumption per device and reduce the overall communication overhead. Our framework has been proven to be superior to existing methods in accuracy through rigorous experiments on the CIFAR10 and CIFAR100 datasets, demonstrating its potential as a fair and efficient pFL solution. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Automatic Prompt engineering: The Case of Requirements Classification 31st

Automatic Prompt Engineering: The Case of Requirements Cla...

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31st International Working conference on Requirements engineering: Foundation for software Quality, REFSQ 2025

作者： Zadenoori, Mohammad Amin Zhao, Liping Alhoshan, Waad Ferrari, Alessio Pisa Italy University of Manchester Manchester United Kingdom Imam Mohammad ibn Saud Islamic University Riyadh Saudi Arabia Dublin Ireland

ISBN: (纸本)9783031885303

Context and motivation: Large language models (LLMs) are increasingly used to address requirements engineering (RE) tasks, including trace-link recovery, legal compliance, model generation, and others. Question/problem: Most of the existing studies rely on static, non-adaptive prompting strategies that do not fully harness the models’ capabilities. Specifically, these studies overlook the potential of automatic prompting engineering (APE), a technique that allows LLMs to self-generate and fine-tune prompts to improve task performance. Principal ideas/results: This research preview aims to study the effectiveness of APE techniques in LLM-powered RE tasks. As a preliminary step, we perform a benchmarking study in which we compare APE techniques with more traditional prompting solutions for the task of requirements classification. Our results show that, on average and with some exceptions, APE outperforms the baselines. We outline research avenues, including the evaluation and tailoring of APE for other RE tasks, and considering the human-in-the-loop. Contribution: To the best of our knowledge, this is the first study to introduce APE in RE, paving the way for a deeper exploration of LLMs’ potential in this field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Requirements engineering

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Examples of the Use of Knowledge engineering for a Better Exploitation of Massive Company Data 26th

Examples of the Use of Knowledge Engineering for a Better Ex...

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26th International conference on Artificial Intelligence and Applications, ICAI 2024, held as part of the World Congress in computer Science, computer engineering and Applied Computing, CSCE 2024

作者： Vegega, Cinthia Pollo-Cattaneo, María F. Olivas, José A. Research Group on Methodologies for Software Engineering (GEMIS) National Technological University Buenos Aires Regional Faculty Buenos Aires Argentina Soft Management of Internet and Learning (SMILe) Castilla-La Mancha University Ciudad Real Spain

ISBN: (纸本)9783031866227

Hybrid Intelligence facilitates the attainment of complex objectives through the integration of Human Intelligence and Artificial Intelligence. The amalgamation of Knowledge engineering and Machine Learning introduces novel approaches to structuring knowledge, culminating in the development of Hybrid Intelligent systems. In this context, a process model is proposed for the intelligent exploitation of massive data under the guidance of Knowledge engineering. This model places emphasis on the systematization of data exploitation, considering existing data and domain knowledge. Several examples use cases are presented, cultivated over numerous years, demonstrating the generalizability of the proposed method. These use cases systematically address common tasks, enhancing outcomes in data utilization across different domains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Artificial Intelligence Hybrid Intelligence Knowledge engineering Massive Data Process Model

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