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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Sim, Rachael Hwee Ling Zhang, Yehong Hoang, Trong Nghia Xu, Xinyi Low, Bryan Kian Hsiang Jaillet, Patrick Department of Computer Science National University of Singapore Singapore Peng Cheng Laboratory China School of Electrical Engineering and Computer Science Washington State University United States Dept. of Electrical Engineering and Computer Science MIT United States

ISBN: (纸本)9781713899921

Collaborative machine learning involves training models on data from multiple parties but must incentivize their participation. Existing data valuation methods fairly value and reward each party based on shared data or model parameters but neglect the privacy risks involved. To address this, we introduce differential privacy (DP) as an incentive. Each party can select its required DP guarantee and perturb its sufficient statistic (SS) accordingly. The mediator values the perturbed SS by the Bayesian surprise it elicits about the model parameters. As our valuation function enforces a privacy-valuation trade-off, parties are deterred from selecting excessive DP guarantees that reduce the utility of the grand coalition's model. Finally, the mediator rewards each party with different posterior samples of the model parameters. Such rewards still satisfy existing incentives like fairness but additionally preserve DP and a high similarity to the grand coalition's posterior. We empirically demonstrate the effectiveness and practicality of our approach on synthetic and real-world datasets. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Machine learning

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Adaptive Robust Control Integrated With Gaussian Processes for Quadrotors: Enhanced Accuracy, Fault Tolerance and Anti-Disturbance

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3235-3248页

作者： Liang, Weisheng Amer, Abdelhakim Mehndiratta, Mohit Chen, Zheng Yao, Bin Kayacan, Erdal Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310027 China Aarhus University Artificial Intelligence in Robotics Laboratory Department of Electrical and Computer Engineering Aarhus8000 Denmark GIM Robotics Espoo02650 Finland Zhejiang University Ocean College Ocean Research Center of Zhoushan Zhoushan316021 China Purdue University School of Mechanical Engineering West Lafayette47907 United States Department of Electrical Engineering and Information Technology Paderborn33098 Germany

With increasingly challenging applications for quadrotors, higher requirements are emerging for tracking accuracy and safety. While high accuracy is a prerequisite for complex tasks, safety is ensured through tolerance to actuator faults and resistance to external disturbances. In this article, adaptive robust control (ARC) integrated with Gaussian processes (GPs), i.e., ARC-GP, is proposed to achieve enhanced accuracy, fault tolerance, and anti-disturbance. These three requirements are interrelated and affected by uncertainties. The primary idea of this article is to categorize uncertainties into parametric and nonparametric types, which are then addressed through parameter adaptation and GP, respectively. First, a detailed dynamic model is established, including actuator models that reflect different types of faults corresponding to changes in different physical parameters. Then, parameter adaptation is designed, with direct and indirect methods adopted for different parameters. In particular, the actuator parameters are effectively estimated to achieve targeted fault compensation. Regarding GP for nonparametric uncertainties, its model parameters are also updated via parameter adaptation. The GP thereby also learns parameter estimation errors along with external disturbances. Accordingly, ARC controllers are designed, for which robust feedback terms are constructed to further mitigate uncertainties on the basis of the covariances predicted by GP. The experiments demonstrate that the proposed ARC-GP can actively tolerate various types of actuator faults and better resist wind disturbances. © 2013 IEEE.

关键词： Robust control

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A stacked ensemble approach to detect cyber attacks based on feature selection techniques

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 316-331页

作者： Urmi, Wahida Ferdose Uddin, Mohammed Nasir Uddin, Md Ashraf Talukder, Md. Alamin Hasan, Md. Rahat Paul, Souvik Chanda, Moumita Ayoade, John Khraisat, Ansam Hossen, Rakib Imran, Faisal Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia School of Information Technology Crown Institute of Higher Education 116 Pacific Highway North SydneyNSW2060 Australia Department of Cyber Security Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakoir Gazipur1750 Bangladesh Department of Electrical and Computer Engineering University of Ottawa Canada

The exponential growth of data and increased reliance on interconnected systems have heightened the need for robust network security. Cyber-Attack Detection Systems (CADS) are essential for identifying and mitigating threats through network traffic analysis. However, the effectiveness of CADS is highly dependent on selecting pertinent features. This research evaluates the impact of three feature selection techniques—Recursive Feature Elimination (RFE), Mutual Information (MI), and Lasso Feature Selection (LFS)—on CADS performance. We propose a novel stacked ensemble classification approach, combining Random Forest, XGBoost, and Extra-Trees classifiers with a Logistic Regression meta-model. Performance is assessed using CICIDS2017 and NSL-KDD datasets. Results show that RFE achieves 100% accuracy for Brute Force attacks, 99.99% for Infiltration and Web Attacks on CICIDS2017, and 99.95% accuracy for all attacks on NSL-KDD, marking a significant improvement over traditional methods. This study demonstrates that optimizing feature selection and leveraging diverse classifiers can substantially enhance the accuracy of CADS, providing stronger protection against evolving cyber threats. © 2024 The Authors

关键词： Feature Selection

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

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computer Systems science & engineering 2022年第10期43卷 275-289页

作者： V.Ulagamuthalvi G.Kulanthaivel A.Balasundaram Arun Kumar Sivaraman Department of Computer Science and Engineering Sathyabama UniversityChennai600119India Department of Electrical Electronics and Communication EngineeringNITTTRChennai600113India Centre for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India

One of the fast-growing disease affecting women’s health seriously is breast *** is highly essential to identify and detect breast cancer in the earlier *** paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer *** learning algorithms are fully automatic in learning,extracting,and classifying the features and are highly suitable for any image,from natural to medical *** methods focused on using various conventional and machine learning methods for processing natural and medical *** is inadequate for the image where the coarse structure matters *** of the input images are downscaled,where it is impossible to fetch all the hidden details to reach accuracy in *** deep learning algorithms are high efficiency,fully automatic,have more learning capability using more hidden layers,fetch as much as possible hidden information from the input images,and provide an accurate *** this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram *** performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.

关键词： Medical image processing deep learning convolution neural network breast cancer feature extraction classification

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Schottky barrier modulation of bottom contact SnO_(2) thin-film transistors via chloride-based combustion synthesis

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Journal of Materials science & Technology 2023年第17期148卷 199-208页

作者： Bongho Jang Junhee Lee Hongki Kang Jaewon Jang Hyuk-Jun Kwon Department of Electrical Engineering and Computer Science DGISTDaegu 42988South Korea School of Electronic and Electrical Engineering Kyungpook National UniversityDaegu 41566South Korea School of Electronics Engineering Kyungpook National UniversityDaegu 41566South Korea Convergence Research Advanced Centre for Olfaction DGISTDaegu 42988South Korea

The enhanced carrier flow at the interface between Au and SnO_(2)semiconductors,which initially form Schottky contacts,is realized using chloride-based combustion ***-based combustion sys-tems can achieve chlorine(Cl)doping effects as well as conversion to crystalline SnO_(2)films at clearly lower temperatures(∼250℃)than conventional *** to the Cl doping effect,the high carrier concentration can induce thin potential barriers at the metal/semiconductor(MS)junctions,resulting in carrier injection by *** a result,compared to conventional SnO_(2)thin-film transistors,the de-vices fabricated by combustion synthesis exhibit significantly improved electrical performance with field-effect mobility of 6.52 cm 2/Vs(∼13 times),subthreshold swing of 0.74 V/dec,and on/offratio of∼10^(7)below 300℃.Furthermore,because of the enhanced tunneling carriers induced by the narrowed barrier width,the Schottky barriers are significantly reduced from 0.83 to 0.29 eV(65%decrease)at 250℃and from 0.42 to 0.17 eV(60%decrease)at 400℃.Therefore,chloride-based combustion synthesis can con-tribute to developing SnO_(2)-based electronics and flexible devices by achieving both high-quality oxide films and improved current flow at the MS interface with low-temperature annealing.

关键词： SnO_(2) Combustion Sol-gel Schottky contact Thin-film transistors

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Expansive detector via hybrid temporal and transposed convolutional mechanism for weld proximity defects

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Soft Computing 2025年第6期29卷 3021-3034页

作者： Chen, Zihua Zhang, Runmei Chen, Zhong Yuan, Bin Zheng, Yu Li, Kuan-Ching School of Mechanical and Electrical Engineering Anhui Jianzhu University Hefei230000 China Department of Computer Science and Information Engr Providence University Taichung43301 Taiwan

Detection of weld proximity defects aims to recognize and classify different proximity defects in industrial welding. It presents some characteristics such as the small-target, localized diffusion, and dense distribution, which is difficult in steel-sheet welding engineering. Due to small-target size, localized diffusion, the similar and dense distribution, and imbalanced sample numbers, the existing detection models with the fixed receptive field are weak in detecting weld proximity defects. To solve the problem, we propose an expansive you-only-look-once detection model, called DRB-YOLOv5. It integrates a novel dilation causal convolution cross-stage partial network and the improved Focal Loss function. First, we provide a dilation causal transposed-convolution residual block cross-stage partial network to amplify and extract features of weld proximity defects effectively. Further, we reduce the block number of two middle Resblock Bodies in the Backbone of DRB-YOLOv5 to avoid network overfitting. Second, we design a Prediction Head via the Bias Focal Loss to balance positive and negative samples and improve the detection accuracy of various number-imbalanced weld defects. Finally, we compare the proposed model with some related existing models via the real weld proximity defects dataset. Experimental results show that the proposed DRB-YOLOv5 can present superior effects, mainly including mAP: 89.21%, F1: 83.50%, and recall: 80.73%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Bias focal loss Improved cross-stage partial network Object detection Weld proximity defects

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FPGA Hardware Trojan Detection: Golden-Free Machine Learning Approach

FPGA Hardware Trojan Detection: Golden-Free Machine Learning...

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2023 IEEE National Aerospace and Electronics Conference, NAECON 2023

作者： Ghimire, Ashutosh Amsaad, Fathi Hossain, Tanvir Hoque, Tamzidul Sherif, Ahmed Wright State University Department of Computer Science and Engineering United States University of Kansas Department of Electrical Engineering and Computer Science United States School of Computing Sciences and Computer Engineering The University of Southern Mississippi United States

ISBN: (纸本)9798350338782

Ensuring trust in the semiconductor IC supply chain necessitates the critical detection of Hardware Trojans, yet current methods relying on side-channel analysis often require the use of golden chips for verification. This research paper presents a novel approach to detect Hardware Trojans in the semiconductor IC supply chain, addressing the need for trust and eliminating the use of golden chips. By combining unsuper-vised machine learning and side-channel analysis, the proposed technique leverages unique features from on-chip ring-oscillator networks to identify anomalies through unsupervised clustering. Evaluation on FPGA chips with Trojan insertion demonstrated exceptional accuracy, surpassing alternative methods with a 99 % accuracy rate. The centroid-based clustering model exhibited superior performance with a slight edge in false positive rate and an fl score. This research contributes to enhancing trust in semiconductor IC supply chains by offering a fresh perspective on Hardware Trojan detection. © 2023 IEEE.

关键词： Hardware security

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Transformers for Vision: A Survey on Innovative Methods for computer Vision

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IEEE Access 2025年 13卷 95496-95523页

作者： Hassija, Vikas Palanisamy, Balamurugan Chatterjee, Arpita Mandal, Arpita Chakraborty, Debanshi Pandey, Amit Chalapathi, Gss Kumar, Dhruv Deemed to be University School of Computer Engineering Odisha Bhubaneswar751024 India Bennett University School of CSET Gautam Buddha Nagar Greater Noida201310 India Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani xPilani Campus Vidya Vihar Rajasthan Pilani333031 India Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Pilani Campus Vidya Vihar Rajasthan Pilani333031 India

Transformers have emerged as a groundbreaking architecture in the field of computer vision, offering a compelling alternative to traditional convolutional neural networks (CNNs) by enabling the modeling of long-range dependencies and global context through self-attention mechanisms. Originally developed for natural language processing, transformers have now been successfully adapted for a wide range of vision tasks, leading to significant improvements in performance and generalization. This survey provides a comprehensive overview of the fundamental principles of transformer architectures, highlighting the core mechanisms such as self-attention, multi-head attention, and positional encoding that distinguish them from CNNs. We delve into the theoretical adaptations required to apply transformers to visual data, including image tokenization and the integration of positional embeddings. A detailed analysis of key transformer-based vision architectures such as ViT, DeiT, Swin Transformer, PVT, Twins, and CrossViT are presented, alongside their practical applications in image classification, object detection, video understanding, medical imaging, and cross-modal tasks. The paper further compares the performance of vision transformers with CNNs, examining their respective strengths, limitations, and the emergence of hybrid models. Finally, current challenges in deploying ViTs, such as computational cost, data efficiency, and interpretability, and explore recent advancements and future research directions including efficient architectures, self-supervised learning, and multimodal integration are discussed. © 2013 IEEE.

关键词： Convolutional neural networks

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Developing a Smart Drip Infusion System to Elevate Precision in Healthcare 4

Developing a Smart Drip Infusion System to Elevate Precision...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Molefinyane, Sefemo Yahya, Abid Hamsadhwani, V. Rohini, R. Samikannu, Ravi Begam, K.Maruliya Botswana International University of Science and Technology Department of Electrical and Communications Systems Engineering Palapye Botswana Periyar Maniammai Institute of Science and Technology Department of Electrical and Electronics Engineering Thanjavur India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tiruchengode India Saveetha School of Engineering Saveetha Institute of Medical and Technology Science Department of Computer Science and Communications Engineering Tiruchengode India

ISBN: (纸本)9798331529635

Intravenous (IV) therapy is a critical component of modern healthcare, yet traditional IV systems are prone to human errors, manual adjustments, and limited monitoring capabilities, which can jeopardize patient safety and hinder healthcare efficiency. This paper presents the development of the Smartdrip IV Infusion System, a sophisticated and automated solution that integrates advanced sensors, control algorithms, and intuitive user interfaces to address these limitations. The Smartdrip system minimizes human intervention, provides real-time monitoring, and automates fluid administration with enhanced precision and safety features. The system demonstrates high reliability through simulation and prototype testing in detecting anomalies, issuing alerts, and ensuring accurate infusion rates, thereby improving patient outcomes and reducing healthcare provider workload. The results validate the Smartdrip system as a transformative approach to optimizing IV therapy in diverse clinical settings. © 2024 IEEE.

关键词： Automated Infusion System Drip Rate Monitoring Healthcare Automation Intravenous Therapy Patient

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