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arXiv 2025年

作者： Cheng, Zhipeng Xia, Xiaoyu Wang, Hong Liwang, Minghui Chen, Ning Fan, Xuwei Wang, Xianbin School of Future Science and Engineering Soochow University Suzhou215006 China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai201804 China Qingdao China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Department of Electrical and Computer Engineering Western University ON Canada

Edge inference (EI) is a key solution to address the growing challenges of delayed response times, limited scalability, and privacy concerns in cloud-based Deep Neural Network (DNN) inference. However, deploying DNN models on resource-constrained edge devices faces more severe challenges, such as model storage limitations, dynamic service requests, and privacy risks. This paper proposes a novel framework for privacy-aware joint DNN model deployment and partition optimization to minimize long-term average inference delay under resource and privacy constraints. Specifically, the problem is formulated as a complex optimization problem considering model deployment, user-server association, and model partition strategies. To handle the NP-hardness and future uncertainties, a Lyapunov-based approach is introduced to transform the long-term optimization into a single-time-slot problem, ensuring system performance. Additionally, a coalition formation game model is proposed for edge server association, and a greedy-based algorithm is developed for model deployment within each coalition to efficiently solve the problem. Extensive simulations show that the proposed algorithms effectively reduce inference delay while satisfying privacy constraints, outperforming baseline approaches in various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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Ciphertext-only fault analysis of GIFT lightweight cryptosystem

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Science China(information Sciences) 2022年第3期65卷 263-265页

作者： Wei LI Shan CAO Dawu GU Jiayao LI Tianpei CAI Menglin WANG Li SUN Zhiqiang LIU Ya LIU School of Computer Science and Technology Donghua University Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai Key Laboratory of Computing and Systems Shanghai Jiao Tong University Shanghai Key Laboratory of Integrate Administration Technologies for Information Security Shanghai Jiao Tong University Department of Computer Science and Engineering University of Shanghai for Science and Technology

Dear editor,The GIFT cryptosystem was proposed by Banik et al. [1]in CHES 2017. It can be widely applied to protect RFID tags and other low-resource devices. It has an SPN structure with a fixed 128-bit key size and two flexible variants of 64-bit and 128-bit block sizes. In simulations, GIFT achieves good performance and surpasses both SIMON and SKINNY [1]. In 2013, Fuhr et al. [2] proposed a ciphertextonly fault analysis(CFA) of AES for three types of faults:zero-byte fault, zero-nibble fault,

关键词： lightweight cryptosystem GIFT ciphertext-only fault analysis cryptanalysis fault analysis

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A data-driven frequency scaling approach for deadline-aware energy efficient scheduling on Graphics Processing Units (GPUs)

arXiv

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arXiv 2020年

作者： Ilager, Shashikant Muralidhar, Rajeev Rammohanrao, Kotagiri Buyya, Rajkumar Laboratory School of Computing and Information Systems University of Melbourne

Modern computing paradigms, such as cloud computing, are increasingly adopting GPUs to boost their computing capabilities primarily due to the heterogeneous nature of AI/ML/deep learning workloads. However, the energy consumption of GPUs is a critical problem. Dynamic Voltage Frequency Scaling (DVFS) is a widely used technique to reduce the dynamic power of GPUs. Yet, configuring the optimal clock frequency for essential performance requirements is a non-trivial task due to the complex nonlinear relationship between the application's runtime performance characteristics, energy, and execution time. It becomes more challenging when different applications behave distinctively with similar clock settings. Simple analytical solutions and standard GPU frequency scaling heuristics fail to capture these intricacies and scale the frequencies appropriately. In this regard, we propose a data-driven frequency scaling technique by predicting the power and execution time of a given application over different clock settings. We collect the data from application profiling and train the models to predict the outcome accurately. The proposed solution is generic and can be easily extended to different kinds of workloads and GPU architectures. Furthermore, using this frequency scaling by prediction models, we present a deadline-aware application scheduling algorithm to reduce energy consumption while simultaneously meeting their deadlines. We conduct real extensive experiments on NVIDIA GPUs using several benchmark applications. The experiment results have shown that our prediction models have high accuracy with the average RMSE values of 0.38 and 0.05 for energy and time prediction, respectively. Also, the scheduling algorithm consumes 15.07% less energy as compared to the baseline policies. Copyright © 2020, The Authors. All rights reserved.

关键词： Clocks

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Heuristic Algorithms for Placing Geomagnetically Induced Current Blocking Devices

arXiv

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arXiv 2023年

作者： Ryu, Minseok Attia, Ahmed Barnes, Arthur Bent, Russell Leyffer, Sven Mate, Adam School of Computing and Augmented Intelligence Arizona State University TempeAZ United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Information Systems and Modeling Group United States Applied Mathematics and Plasma Physics Group Los Alamos National Laboratory Los AlamosNM United States

We propose a new heuristic approach for solving the challenge of determining optimal placements for geomagnetically induced current blocking devices on electrical grids. Traditionally, these determinations are approached by formulating the problem as mixed-integer nonlinear programming models and solving them using optimization solvers based on the spatial branch-and-bound algorithm. However, computing an optimal solution using the solvers often demands substantial computational time due to their inability to leverage the inherent problem structure. Therefore, in this work we propose a new heuristic approach based on a three-block alternating direction method of multipliers algorithm, and we compare it with an existing stochastic learning algorithm. Both heuristics exploit the structure of the problem of interest. We test these heuristic approaches through extensive numerical experiments conducted on the EPRI-21 and UIUC-150 test systems. The outcomes showcase the superior performance of our methodologies in terms of both solution quality and computational speed when compared with conventional solvers. Copyright © 2023, The Authors. All rights reserved.

关键词： Heuristic algorithms

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Accurate Neuroanatomy Segmentation Using 3D Spatial and Anatomical Attention Neural Networks 14

Accurate Neuroanatomy Segmentation Using 3D Spatial and Anat...

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Cheng, Hewei Ren, Zhengyu Li, Peiyang Tian, Yin Wang, Wei Li, Zhangyong Fan, Yong Research Center of Biomedical Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing Engineering Laboratory of Digital Medical Equipment and Systems Chongqing University of Posts and Telecommunications Chongqing China Chongqing Engineering Research Center of Medical Electronics & Information Technology Chongqing University of Posts and Telecommunications Chongqing China Center for Biomedical Image Computing and Analytics Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States

ISBN: (纸本)9781510657564

Brain structure segmentation from 3D magnetic resonance (MR) images is a prerequisite for quantifying brain morphology. Since typical 3D whole brain deep learning models demand large GPU memory, 3D image patch-based deep learning methods are favored for their GPU memory efficiency. However, existing 3D image patch-based methods are not well equipped to capture spatial and anatomical contextual information that is necessary for accurate brain structure segmentation. To overcome this limitation, we develop a spatial and anatomical context-aware network to integrate spatial and anatomical contextual information for accurate brain structure segmentation from MR images. Particularly, a spatial attention block is adopted to encode spatial context information of the 3D patches, an anatomical attention block is adopted to aggregate image information across channels of the 3D patches, and finally the spatial and anatomical attention blocks are adaptively fused by an element-wise convolution operation. Moreover, an online patch sampling strategy is utilized to train a deep neural network with all available patches of the training MR images, facilitating accurate segmentation of brain structures. Ablation and comparison results have demonstrated that our method is capable of achieving promising segmentation performance, better than state-of-the-art alternative methods by 3.30% in terms of Dice scores. © 2022 SPIE.

关键词： Magnetic resonance imaging

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Saliency-based Explainable Artificial Intelligence (XAI) for Interpreting U-Net Semantic Segmentation of Synthetic Liquid Spray Images

Saliency-based Explainable Artificial Intelligence (XAI) for...

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computing (ICOCO), IEEE International Conference on

作者： Teck Wai Chin Sian Lun Lau Richard T. K. Wong Luis Bravo Rahul Babu Koneru Prashant Khare Dept. of Computing and Information Systems School of Engineering and Technology Sunway University Bandar Sunway Malaysia Dept. of Engineering School of Engineering and Technology Sunway University Bandar Sunway Malaysia DEVCOM Army Research Laboratory Army Research Directorate Aberdeen Proving Ground MD Department of Aerospace Engineering University of Maryland College Park Maryland USA Department of Aerospace Engineering University of Cincinnati Cincinnati OH

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

Deep learning models, particularly U-Net, have been widely used for image segmentation tasks in various domains such as medical, biological, autonomous driving, and industrial applications. However, the black-box nature of these models raises public concerns about the lack of transparency and interpretability of their decision-making processes. Unlike typical static objects, the segmentation of dynamic objects from synthetic fluid datasets are more complicated as the properties like position and shape change over time. This paper investigates these issues by exploring the uses of saliency-based Explainable Artificial Intelligence (XAI) techniques such as Class Activation Mapping (CAM), Gradient-weighted Class Activation Mapping (Grad-CAM), Grad-CAM++ and Segmentation Grad-CAM (Seg-Grad-CAM) to generate heatmap visualizations for providing insights into the prediction of U-net semantic segmentation. Using a dataset of synthetic liquid spray images, we systematically apply these techniques to visualize the model’s attention and highlights the properties and regions of the image that are influential in the model’s predictions for different classes such as lobe, ligaments and droplets. Our results demonstrate that while all techniques provide interpretable visualizations, Seg-Grad-CAM and Grad-CAM++ offer better localization of salient features, providing more precise explanations for U-Net’s segmentation decisions. The comparison of the results using quantitative analysis such as Pointing Game Accuracy and Mean Localization Error also indicated that Seg-Grad-CAM and Grad-CAM++ are more effective for interpreting complex segmentation models in liquid spray analysis.

关键词： Location awareness Visualization Analytical models Liquids Accuracy Explainable AI Semantic segmentation Predictive models Brain modeling Ligaments

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A survey on hand gesture recognition for mobile devices

A survey on hand gesture recognition for mobile devices

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作者： Lahiani, Houssem Neji, Mahmoud National School of Electronics and Telecommunications University of Sfax Tunisia Tunisia Multimedia Information Systems and Advanced Computing Laboratory Faculty of Economics and Management University of Sfax Sfax Tunisia

As smartphones and mobile devices become ubiquitous in the daily life of human beings, facilitating interaction with them will have a positive impact on their use. This paper presents an emerging and important area for research on human-machine interaction, involving interaction with mobile devices. Hand gesture recognition is already a quite mature field and its integration on advanced technologies like smartphones makes a survey an interesting and needed contribution. The design of interactive mobile applications should differ from traditional desktop applications. For this purpose, the paper discusses some concepts that help to understand new challenges and introduces techniques that may be useful to properly exploit these devices. This paper gives also an overview of recent works done on vision-based and contact-based hand gesture recognition systems for mobile devices. Used techniques and approaches are also discussed. At the end, we conclude with some reflections. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Palmprint recognition

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Small file read performance optimization based on redis cache in distributed storage system 13th

Small file read performance optimization based on redis cach...

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13th International Conference on Genetic and Evolutionary computing, ICGEC 2019

作者： Wei, Bizhong Deng, Liqiang Fan, Ya Ye, Miao School of Computer Science and Information Security Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology GuilinGuangxi541004 China

ISBN: (纸本)9789811533075

At present, small files stored in most of the distributed storage systems account for a large proportion, how to improve the storage efficiency of small file has long been a hot issue in the academic community. In the existing methods, researchers usually integrate small files into content-related large files for association storage. The shortcoming is that if we read some small files in a large file, the entire large file must be read into the cache space, which greatly reduces the utilization of the cache space. To solve this defect, this paper designs a scheme based on the combination of cache replacement optimization algorithm and multi-level cache dynamic elimination mechanism. We compute the content heat value of the cache object by the cache replacement optimization algorithm, and then through the multi-level cache dynamic elimination mechanism we expel the object with low content heat value in the cache space to reserve enough space for the new arrived object. This proposed method not only improves the cache space utilization, but also increases the cache data search efficiency and hit rate. Series of experiments and their result show that the cache hit rate obtained by our proposed method is 14% higher than the LRU cache replacement algorithm and 52% higher than the LFU cache replacement algorithm, which can optimize the reading performance of small files based on Redis cache. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Dynamics

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Data-Driven Analysis of Skin Cancer Classification with Convolutional Neural Networks for E-Health Applications

Data-Driven Analysis of Skin Cancer Classification with Conv...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Imran Ahmed Misbah Ahmad Abdellah Chehri Gwanggil Jeon School of Computing and Information Science Anglia Ruskin University Cambridge UK Animal and Agriculture Department Hartpury University Gloucester UK Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol UK Department of Mathematics and Computer Science The Royal Military College of Canada (RMC) Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

This study explores the effectiveness of Convolutional Neural Networks (CNNs) in automatically classifying skin cancer for e-health applications. The trained model showcases impressive performance by leveraging the HAM10000 dataset, which includes a wide range of skin lesion images from seven different classes. The parameters and architecture of the CNN model are presented in a systematic manner, providing valuable insights into the reasoning behind its design. The model is optimized using the Adam optimizer and annealing techniques to ensure efficient convergence. The model’s performance is assessed on validation and test datasets, showcasing an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. This study highlights the significant potential of CNN as a powerful tool for automating the diagnosis of skin cancer, which is in line with the growing trend of using deep learning for medical image analysis.

关键词： Deep learning Systematics Image analysis Market research Skin Convolutional neural networks Lesions Global communication Medical diagnostic imaging Skin cancer

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Security Enhancing for IRS-Aided NOMA Networks via Constructive Interference Precoding

Security Enhancing for IRS-Aided NOMA Networks via Construct...

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International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (MAPE)

作者： Jingying Bao Yang Cao Xiaoqi Qin Lexi Xu Nan Zhao Dusit Niyato School of Information and Communication Engineering Dalian University of Technology Dalian China Engineering Systems and Design Pillar Singapore University of Technology and Design Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Research Institute China United Network Communications Corporation Beijing China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331533922

ISBN: (纸本)9798331533939

In this paper, we propose a constructive interference precoding (CIP) enabled secure intelligent reflecting surface (IRS)-non-orthogonal multiple access (NOMA) scheme, lever-aging both the inter-user interference and artificial noise to boost the legitimate transmission of far user, while inducing the eavesdropper to decode the deceptive information. Based on this CIP-NOMA scheme, we minimize the transmit power under the perfect channel state information (CSI), subject to the CIP constraint for the far user and eavesdropper, while guaranteeing the quality of service and successive interference cancellation for the near user and the IRS unit modulus constraint. To handle this non-convex problem, an alternating optimization algorithm is proposed. Specifically, by alternately optimizing the precoding vectors at the base station and the IRS reflecting matrix via the successive convex approximation and the penalty-based algorithm, respectively, a reliable solution can be obtained. Simulation results demonstrate that the proposed scheme can achieve better security performance with less energy consumption compared to the conventional NOMA.

关键词： Microwave antennas Wireless communication NOMA Precoding Simulation Noise Approximation algorithms Vectors Security Reliability

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