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Design of a ML-based trust prediction model using intelligent TrustBoxes in challenged networks

International Journal of Systems, Control and Communications 2024年第3期15卷 209-234页

作者： Barai, Smritikona Kundu, Anindita Bhaumik, Parama Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu India Department of Information Technology Jadavpur University Kolkata India

Challenged networks (CNs) contain resource-constrained nodes deployed in regions where human intervention is difficult. Opportunistic networks (OppNets) are CNs with no predefined source-to-destination paths. Due to their inherent properties, CNs and OppNets are highly susceptible to black-hole (BH) attacks, resulting in degraded packet-delivery ratio. In this work, an ML-based trust prediction model (MLTPM) is proposed to identify potential BH nodes in OppNets. MLTPM uses a novel function to calculate the total-trust-value (TTV) of each node. Intelligent TrustBoxes are introduced in the network to identify possible BH nodes, using TTV, along with five more node-behaviour features. TrustBoxes reduce the computational overhead of the resource-constrained nodes. Three simulated scenarios are compared - no detection, non-ML-based detection, and MLTPM, each using epidemic, prophet, and spray-and-wait routing protocols. MLTPM performs best with spray-and-wait, exhibiting about 25.21% and 80% mean improvement in delivery-ratio and dropped-message numbers respectively, compared to non-ML-based detection. An overall 12.62% improvement in delivery-ratio and 26.7% improvement in dropped messages is observed using MLTPM, compared to the above-mentioned scenarios. © 2024 Inderscience Enterprises Ltd.

关键词： HTTP

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Influence of Ca Content on Microstructure and Mechanical Properties of Extruded Mg–Al–Ca–Mn Alloys

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Acta Metallurgica Sinica(English Letters) 2023年第3期36卷 426-438页

作者： Weiying Huang Jianhua Chen Zhen Jiang Xi Xiong Wei Qiu Jian Chen Xianwei Ren Liwei Lu Key Laboratory of Efficient&Clean Energy Utilization School of Energy and Power EngineeringChangsha University of Science&TechnologyChangsha 410114China School of Materials Science and Engineering North University of ChinaShuozhou 038507China Hunan Provincial Key Laboratory of High Efficiency and Precision Machining of Difficult‑to‑Cut Material Hunan University of Science and TechnologyXiangtan 411201China

Herein,the effects of Ca content on microstructure,texture and mechanical properties of extruded Mg–3Al–0.4Mn–xCa(x=0.4,0.8 and 1.2 wt%)rods are systematically *** results reveal that the alloy,with Ca content of 0.8 wt%,exhibits the highest strength and ductility,possessing an ultimate tensile strength of 267.57 MPa and elongation(EL)of 16%.This is mainly due to the gradual transformation of typical fiber texture into a texture with a[10-11]component parallel to the extrusion direction(ED),which increases the Schmid factor of pyramidal slip and enhances the activation rate of pyramidal(c+a)***,the as-formed spherical phases and segregation of Ca at grain boundaries render a significant influence on the strength and ductility of the alloy.

关键词： Mg-Al-Mn-Ca Microstructure Texture Mechanical properties

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Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

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computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

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A Cubic B-spline Model based on CGAN for Blade Airfoil Design 24

A Cubic B-spline Model based on CGAN for Blade Airfoil Desig...

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2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： He, Wenguang School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798400710681

Traditional aerodynamic performance studies often use parametric design methods, overlooking the smoothness coefficient’s impact on airfoil shape. Deep learning-based parametric design methods can optimize airfoil local shape, enhancing aerodynamic performance. However, it may cause model instability during the model training. Therefore, we proposed a cubic B-spline combined with a conditional generative adversarial network (CGAN) method, which can not only increase model stability but also optimize airfoil smoothness and aerodynamic performance. From the perspective of model stability, we found that airfoil samples generated by the CGAN network with a dynamic learning rate (DLR) are much more diverse than those with a fixed learning rate (FLR). We further investigated the influence of different smoothness coefficients on the aerodynamic performance of airfoil shape. The cubic B-spline with smoothness coefficients improves the Xfoil convergence for airfoil samples with Cl/Cd greater than 30. When the smoothness coefficient is 0.0025, Xfoil convergence with the cubic B-spline method reaches 72%, while that with the B-spline method is 70.75%. Moreover, the Max Cl/Cd value is enhanced with the cubic B-spline method. Particularly, compared to the highest Max Cl/Cd with the B-spline method, that with the cubic B-spline method achieves 88.06, increased by 10.72%. © 2024 Copyright held by the owner/author(s).

关键词： Aerodynamic stability

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A Robust Anomaly Detection Approach for IIoT Time Series 24

A Robust Anomaly Detection Approach for IIoT Time Series

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2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Lu, Zexin School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798400710681

Industrial Internet of Thing (IIoT) time series anomaly detection aims to learn distinguishable hidden features from data samples and identify anomalous patterns in the data. However, the complexity, large size, lack of labeling and mostly noise-adulterated IIoT time series data pose challenges to the anomaly detection task in practical applications. To this end, this paper proposes a robust IIoT time series anomaly detection method, called RobAD. Firstly, we slice the time series into patches with different lengths at multiple scales, and learn the local patterns of each patch at multiple scales, which helps to capture the structural details at different time scales. And then, this paper designs the ResMLP Encoder, which learns accurately correlations between multiple variables in each patch at multiple scales. This capability helps the model to resist noise interference, thereby enhancing its overall robustness. Next, after learning the cycle potential patterns and variable correlations, a Discrepancy Contrasting Block is introduced with the aim to increase the distance between positive and negative samples. This mechanism highlights the difference between normal and noise samples, thereby improving the accuracy of the anomaly detection task. Finally, this paper also optimizes the objective function to train a robust model. Numerous experiments show that the RobAD designed in this paper outperforms multiple SOTA models. © 2024 Copyright held by the owner/author(s).

关键词： Industrial internet of things (IIoT)

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Dynamic Low-Rank Adaptation Based Pruning Algorithm for Large Language Models 7

Dynamic Low-Rank Adaptation Based Pruning Algorithm for Larg...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Li, Linfeng Guo, Lei School of Computer Science and Technology Xinjiang University Urumqi China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350350890

Deep neural networks have been getting better and better performance while the parameter size has become larger and larger, and have now entered the era of large language models (LLM). However, large language models with huge number of parameters are difficult to be deployed on edge devices with limited computing power and memory resources. Network pruning techniques can reduce the storage and computation requirements of large language models. Although Dynamic Low-Rank Adaptation (DyLoRA) allows efficient fine-tuning of LLM, the deployment of LLM is still hindered by large model sizes and computational costs. In this paper, we propose a DyLoRA-based pruning algorithm that uses gradient updating of DyLoRA module to define the importance of parameters, and then prune the least important parameters based on the resulting importance scores. Through experiments on commonly used evaluation datasets, it is demonstrated that the proposed pruning algorithm is able to achieve 96.60% of the performance of the original model with 20% of the parameters pruned, which outperforms the previous pruning algorithms. © 2024 IEEE.

关键词： Deep neural networks

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Secure location-aware geocast routing in Internet of Vehicles

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Multimedia Tools and Applications 2025年第18期84卷 19669-19692页

作者： Kumar, Sunil Dohare, Upasana Kumar, Sushil Department of Computer Science and Information Technology Central University of Haryana Mahendragarh India School of Computing Science & Engineering Galgotias University Greater Noida India School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

Efficient message dissemination in Vehicular Ad Hoc Networks (VANETs) relies on robust connectivity between neighboring vehicular nodes, yet it is often compromised by malicious intruders. While recent literature proposes various approaches to enhance geocasting performance, security considerations for connectivity are often overlooked. To address this gap, we propose the Secure Location-Aware Cache Agent based Geocast Routing (SL-CAGR) protocol, emphasizing an Intrusion Prevention Technique (IPT) for message integrity preservation and a Connectivity Reliant Packet Forwarding (CRPF) algorithm for improved VANET connectivity. Our protocol introduces a network model for cache agent-based geocast routing on the Internet of Vehicle (IoV) environment, implements IPT using Diffie-Hellman digital signatures, and presents a secure geocast routing strategy focusing on re-caching and cache agent selection. Empirical simulations demonstrate SL-CAGR's superiority in packet delivery ratio, packet loss, duplication, hop count, end-to-end delay, and network load compared to these protocols. Simulation results demonstrate that SL-CAGR outperforms state-of-the-art techniques like CAG, CTRC, and D-Flooding. SL-CAGR stands out by merging intrusion prevention, secure geocast routing, and cache agent selection, ensuring robust security and efficiency in VANETs. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Vehicular ad hoc networks

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Reset-Free Reinforcement Learning via Multi-State Recovery and Failure Prevention for Autonomous Robots

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Tsinghua science and technology 2024年第5期29卷 1481-1494页

作者： Xu Zhou Benlian Xu Zhengqiang Jiang Jun Li Brett Nener School of Mechanical Engineering Changshu Institute of TechnologyChangshu 215500China School of Automation Nanjing University of Science and TechnologyNanjing 210094China School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou 215009China Faculty of Medicine and Health The University of SydneySydney 2006Australia Department of Electrical Electronic and Computer EngineeringThe University of Western AustraliaPerth 6009Australia

Reinforcement learning holds promise in enabling robotic tasks as it can learn optimal policies via trial and ***,the practical deployment of reinforcement learning usually requires human intervention to provide episodic resets when a failure *** manual resets are generally unavailable in autonomous robots,we propose a reset-free reinforcement learning algorithm based on multi-state recovery and failure prevention to avoid failure-induced *** multi-state recovery provides robots with the capability of recovering from failures by self-correcting its behavior in the problematic state and,more importantly,deciding which previous state is the best to return to for efficient *** failure prevention reduces potential failures by predicting and excluding possible unsafe actions in specific *** simulations and real-world experiments are used to validate our algorithm with the results showing a significant reduction in the number of resets and failures during the learning.

关键词： reinforcement learning manual reset multi-state recovery failure prediction

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E±cient Construction of Practical Python Call Graphs with Entity Knowledge Base

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International Journal of Software engineering and Knowledge engineering 2024年第7期34卷 999-1024页

作者： Cao, Yulu Chen, Lin Chen, Zhifei Zhong, Jiacheng Zhang, Xiaowei Wang, Linzhang State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

Call graphs facilitate various tasks in software engineering. However, for the dynamic language Python, the complex language features and external library dependencies pose enormous challenges for building the call graphs of real projects. Some program analysis techniques used for call graph construction in other languages are impractical for Python. In this paper, we present STAR, a practical technique for the construction of Python static call graphs. We reformulate call graph construction as an entity identification task. STAR leverages inter-module summary and cross-project dependencies to construct a fine-grained entity knowledge base to identify the possible nodes and edges of the call graph in the code, and then construct the call graph. Our evaluation of three benchmarks shows that (1) STAR improves recall in three benchmarks compared to three baseline tools. Especially, STAR improves the recall of reachable nodes and reachable edges compared with the state-of-the-art tool by 11.3% and 9.8%, respectively;(2) STAR achieves comparable performance as three baseline tools in execution time and memory usage and is more efficient in large projects;(3) STAR can be effectively used for the task of detecting vulnerability propagation with real-world cases. We expect our results will attract more exploration of practical methods and improve the application of Python call graphs. © World Scientific Publishing Company.

关键词： Python

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Sufficient learning: mining denser high-quality pixel-level labels for edge detection

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Neural Computing and Applications 2025年第14期37卷 8245-8260页

作者： Chen, Xiaodiao Yang, Wenya Wu, Wen Tao, Xiuting Mao, Xiaoyang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Media and Design Hangzhou Dianzi University Hangzhou310018 China Department of Computer Science and Engineering University of Yamanashi Kofu400-8511 Japan

The success of deep neural networks can largely be attributed to large-scale datasets with accurate annotations. In many practical applications, labels are annotated by multiple annotators, resulting in ambiguous labels. To mitigate the impact of such labels, existing deep edge detectors tend only to exploit sparsely reliable samples with high-confident labels by simple voting mechanism. However, lacking sufficient pixel-level learnable samples would severely degrade detection performance. To combat this issue, this work designs a sufficient learning framework for edge detection (SLED) to sufficiently explore reliable samples for model learning, which consists of three stages: sample selection, label refinement, and retraining. Firstly, we propose a sample selection scheme founded on history prediction, which aims to identify more reliable samples and locate unreliable regions containing potentially reliable samples. Secondly, we design an efficient label refinement strategy based on a graph convolutional network (GCN) to propagate reliable label information to unreliable nodes. This strategy can generate "new training targets" with denser, reliable labels for all images. Finally, we retrain the network with these new targets, obtaining a more robust model. Experimental results on three widely used benchmarks show that the proposed detector outperforms all compared SOTA methods. Our code will be released at https://***/wenya1994/SLED. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Pixels

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