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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Wang, Congcong Wang, Chen Shen, Jian Vijayakumar, Pandi Bhuiyan, Zakirul Alam School of Software Nanjing University of Information Science & Technology Nanjing210044 China School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China University College of Engineering Tindivanam Department of Computer Science and Engineering Tamilnadu 604001 India Fordham University Department of Computer and Information Sciences NY10458 United States

ISBN: (纸本)9798350304602

With the rapid development of smart grid technology, a new challenge has been brought about by the massive data generated from distributed devices. Data aggregation technology plays a crucial role in smart grid schemes, in terms of energy data statistics and resource allocation. However, achieving fast error-checking and improving device fault-tolerance are still open issues in the smart grid data aggregation. To address these issues, this paper proposes an error-tolerant data aggregation scheme integrated with error-checking mechanisms. The scheme proposes a tree-based aggregation structure, facilitating swift error-checking of data. We combine masking values with user anonymity in the ElGamal cryptographic scheme to ensure the security of sensitive data and resist collusion attacks. Both theoretical and experimental analyses demonstrate that this scheme enables a lightweight, secure data aggregation, ensuring enhanced reliability and error-checking. © 2023 IEEE.

关键词： Fault tolerance

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KAN-HyperpointNet for Point Cloud Sequence-Based 3D Human Action Recognition

KAN-HyperpointNet for Point Cloud Sequence-Based 3D Human Ac...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhaoyu Chen Xing Li Qian Huang Qiang Geng Tianjin Yang Shihao Han College of Information Science and Technology & Artificial Intelligence Nanjing Forestry University College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Point cloud sequence-based 3D action recognition has achieved impressive performance and efficiency. However, existing point cloud sequence modeling methods cannot adequately balance the precision of limb micro-movements with the integrity of posture macro-structure, leading to the loss of crucial information cues in action inference. To overcome this limitation, we introduce D-Hyperpoint, a novel data type generated through a D-Hyperpoint Embedding module. D-Hyperpoint encapsulates both regional-momentary motion and global-static posture, effectively summarizing the unit human action at each moment. In addition, we present a D-Hyperpoint KANsMixer module, which is recursively applied to nested groupings of D-Hyperpoints to learn the action discrimination information and creatively integrates Kolmogorov-Arnold Networks (KAN) to enhance spatio-temporal interaction within D-Hyperpoints. Finally, we propose KAN-HyperpointNet, a spatio-temporal decoupled network architecture for 3D action recognition. Extensive experiments on two public datasets: MSR Action3D and NTU-RGB+D 60, demonstrate the state-of-the-art performance of our method.

关键词： Point cloud compression Limbs Three-dimensional displays Speech recognition Signal processing Network architecture Acoustics Human activity recognition Speech processing

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Data Augmentation for Sequential Recommendation: A Survey

arXiv

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arXiv 2024年

作者： Dang, Yizhou Yang, Enneng Liu, Yuting Guo, Guibing Jiang, Linying Zhao, Jianzhe Wang, Xingwei Software College Northeastern University China School of Computer Science and Engineering Northeastern University China

As an essential branch of recommender systems, sequential recommendation (SR) has received much attention due to its well-consistency with real-world situations. However, the widespread data sparsity issue limits the SR model’s performance. Therefore, researchers have proposed many data augmentation (DA) methods to mitigate this phenomenon and have achieved impressive progress. In this survey, we provide a comprehensive review of DA methods for SR. We start by introducing the research background and motivation. Then, we categorize existing methodologies regarding their augmentation principles, objects, and purposes. Next, we present a comparative discussion of their advantages and disadvantages, followed by the exhibition and analysis of representative experimental results. Finally, we outline directions for future research and summarize this survey. We also maintain a repository with a paper list at https://***/KingGugu/DA-CL-4Rec. © 2024, CC BY.

关键词： Recommender systems

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Lightweight underwater garbage target detection algorithm based on improved YOLOV7-TINY 4

Lightweight underwater garbage target detection algorithm ba...

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4th International Conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Liu, Xinpei Xiong, Hailing Gao, Mingjie Liu, Wei College of Computer and Information Science Squthwest School of Software Southwest University Chongqing400715 China College of Electronic and Information Engineering Southwest University Chongqing China Business School The Taihu Lake University Chongqing China Silkworm Academy Palace Southwest University Chongqing China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

In the field of object detection, deep learning has been used extensively, especially in algorithms like Yolov7, which have achieved significant accuracy improvements. However, traditional convolutional neural networks are computationally intensive and require powerful GPU support, making its deployment on embedded devices difficult. This presents a problem for researchers as the high device requirements hinder their related research work. Consequently, more people opt to use lightweight networks, such as Yolov7-tiny. However, when using Yolov7-tiny for underwater garbage detection, it has been observed that while achieving good accuracy in mAP (mean Average Precision) at IOU (Intersection over Union) of 0.5, the performance is not satisfactory in the mAP range of 0.5 to 0.95. This limitation may be attributed to the trade-off in network performance during the process of model lightweighting. To address these issues, An enhanced Yolov7-tiny method for the detection of underwater trash objects is proposed in this paper. First, the algorithm employs an enhanced Ghost convolutional feature extraction module, which starts with conventional convolutions using a smaller number of channels, then performs grouped convolutions to obtain partial output maps with features. Finally, the maps with features obtained from the first convolutional step are added to the channels obtained from the second grouped convolution step. This design effectively reduces model complexity while extracting richer feature information. Secondly, the algorithm utilizes the CA (Channel Attention) mechanism to weight channels based on their positional information, thereby efficiently extracting features. The network can concentrate more on important feature regions by learning position weights in the feature maps. Lastly, the algorithm combines the Repeated Weighted Bi-directional Feature Pyramid Network (BIFPN) for feature fusion. BIFPN employs multiple down-sampling steps for short skip connections,

关键词： Convolution

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CD-FL:Cataract Images Based Disease Detection Using Federated Learning

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computer Systems science & engineering 2023年第11期47卷 1733-1750页

作者： Arfat Ahmad Khan Shtwai Alsubai Chitapong Wechtaisong Ahmad Almadhor Natalia Kryvinska Abdullah Al Hejaili Uzma Ghulam Mohammad Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Information Systems Department Faculty of ManagementComenius University in BratislavaOdbojárovBratislava440Slovakia Faculty of Computers&Information Technology Computer Science DepartmentUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan

A cataract is one of the most significant eye problems worldwide that does not immediately impair vision and progressively worsens over *** cataract prediction based on various imaging technologies has been addressed recently,such as smartphone apps used for remote health monitoring and eye *** recent years,advances in diagnosis,prediction,and clinical decision support using Artificial Intelligence(AI)in medicine and ophthalmology have been *** to privacy concerns,a lack of data makes applying artificial intelligence models in the medical field *** address this issue,a federated learning framework named CDFL based on a VGG16 deep neural network model is proposed in this *** study collects data from the Ocular Disease Intelligent Recognition(ODIR)database containing 5,000 patient *** significant features are extracted and normalized using the min-max normalization *** the federated learning-based technique,the VGG16 model is trained on the dataset individually after receiving model updates from two *** transferring the attributes to the global model,the suggested method trains the local *** global model subsequently improves the technique after integrating the new *** client analyses the results in three rounds to decrease the over-fitting *** experimental result shows the effectiveness of the federated learning-based technique on a Deep Neural Network(DNN),reaching a 95.28%accuracy while also providing privacy to the patient’s *** experiment demonstrated that the suggested federated learning model outperforms other traditional methods,achieving client 1 accuracy of 95.0%and client 2 accuracy of 96.0%.

关键词： Privacy-preserving cataract disease federated learning fundus images healthcare smartphone applications machine learning

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Using Multinomial Naive Bayes Machine Learning Method to Classify, Detect, and Recognize Programming Language Source Code 23

Using Multinomial Naive Bayes Machine Learning Method to Cla...

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23rd International Arab Conference on Information Technology, ACIT 2022

作者： Odeh, Ayman Hussien Odeh, Munther Odeh, Nada Science- College of Engineering Al Ain University Software Engineering and Computer Al Ain United Arab Emirates Oldenburg University Department of Mathematics and Science Oldenburg Germany

ISBN: (纸本)9798350320244

Processing programming languages are very similar to processing natural languages, especially high-level languages such as Python, Java, C#, C, C++, and others. Therefore, the natural language processing concepts can be applied as one of the most important branches of artificial intelligence in detecting, recognizing, and classification scripts written in different programming languages. The programming language script classification can be counted as a classical machine learning problem. This research aims to present a model using Multinomial Naïve Bayes (MNB) artificial intelligence algorithm to identify and classify the programming language used in writing the source code file provided as an input for the proposed model. A set of categorized files containing source codes will be used in training the proposed model, and then the model will be able to automatically detect and classify a new script into one of the already trained categories. The machine learning method called NB Multinomial will be used to implement this matter. This work is very important for Mufti-programming language editors such as Visual Studio Code, Notepad+, and others, where the user can paste the source code, and the editor will recognize the programming language automatically. © 2022 IEEE.

关键词： Machine learning

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EMG gesture signal analysis towards diagnosis of upper limb using dual-pathway convolutional neural network

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Mathematical Biosciences and engineering 2024年第4期21卷 5712-5734页

作者： Qamar, Hafiz Ghulam Murtza Qureshi, Muhammad Farrukh Mushtaq, Zohaib Zubariah, Zubariah Rehman, Muhammad Zia ur Samee, Nagwan Abdel Mahmoud, Noha F. Gu, Yeong Hyeon Al-Masni, Mohammed A. School of Electrical Engineering Yanshan University Hebei Qinhuangdao066104 China Department of Electrical Engineering Riphah International University Islamabad44000 Pakistan Department of Electrical Electronics and Computer Systems College of Engineering and Technology University of Sargodha Sargodha40100 Pakistan Department of Physiotherapy Isfandyar Bukhari Civil Hospital District Headquarter Hospital Attock43600 Pakistan Department of Biomedical Engineering Riphah International University Islamabad44000 Pakistan Department of Health Science and Technology Aalborg University Aalborg9220 Denmark Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Department of Artificial Intelligence Data Science College of Software & Convergence Technology Sejong University Seoul05006 Korea Republic of

This research introduces a novel dual-pathway convolutional neural network (DP-CNN) architecture tailored for robust performance in Log-Mel spectrogram image analysis derived from raw multichannel electromyography signals. The primary objective is to assess the effectiveness of the proposed DP-CNN architecture across three datasets (NinaPro DB1, DB2, and DB3), encompassing both able-bodied and amputee subjects. Performance metrics, including accuracy, precision, recall, and F1-score, are employed for comprehensive evaluation. The DP-CNN demonstrates notable mean accuracies of 94.93 ± 1.71% and 94.00 ± 3.65% on NinaPro DB1 and DB2 for healthy subjects, respectively. Additionally, it achieves a robust mean classification accuracy of 85.36 ± 0.82% on amputee subjects in DB3, affirming its efficacy. Comparative analysis with previous methodologies on the same datasets reveals substantial improvements of 28.33%, 26.92%, and 39.09% over the baseline for DB1, DB2, and DB3, respectively. The DP-CNN’s superior performance extends to comparisons with transfer learning models for image classification, reaffirming its efficacy. Across diverse datasets involving both able-bodied and amputee subjects, the DP-CNN exhibits enhanced capabilities, holding promise for advancing myoelectric control. © 2024 the Author(s), licensee AIMS Press.

关键词： Convolutional neural networks

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Reparameterizable Dual-Resolution Network for Real-time Semantic Segmentation

arXiv

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arXiv 2024年

作者： Yang, Guoyu Wang, Yuan Shi, Daming College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China

Semantic segmentation plays a key role in applications such as autonomous driving and medical image. Although existing real-time semantic segmentation models achieve a commendable balance between accuracy and speed, their multi-path blocks still affect overall speed. To address this issue, this study proposes a Reparameterizable Dual-Resolution Network (RDRNet) dedicated to real-time semantic segmentation. Specifically, RDRNet employs a two-branch architecture, utilizing multi-path blocks during training and reparameterizing them into singlepath blocks during inference, thereby enhancing both accuracy and inference speed simultaneously. Furthermore, we propose the Reparameterizable Pyramid Pooling Module (RPPM) to enhance the feature representation of the pyramid pooling module without increasing its inference time. Experimental results on the Cityscapes, CamVid, and Pascal VOC 2012 datasets demonstrate that RDRNet outperforms existing state-of-the-art models in terms of both performance and speed. The code is available at https://***/gyyang23/RDRNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Timely Status Update in TDMA: Round-Based or User-Based?

Timely Status Update in TDMA: Round-Based or User-Based?

引用

Information science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Xinhui Han Jian Feng College of Computer Science and Software Engineering Shenzhen University Shenzhen China

This paper investigates the information freshness of two time division multiple access (TDMA) systems: round-based TDMA and user-based TDMA. The age of information (AoI) metric is used to represent the freshness of information, measured by the time since the generation of the most recently received packet. In a conventional round-based TDMA system, multiple users send their packets in rounds, regardless of whether their packets are successfully received by the receiver, possibly leading to a high average AoI of the system. In contrast, we propose a user-based TDMA system that guarantees successful packet reception for each user through feedback before proceeding to the next user. We evaluate the average AoI and average Peak AoI (PAoI) of both TDMA systems. Our theoretical and simulation results indicate that while both systems exhibit the same average PAoI, the user-based TDMA system has a lower average AoI compared with its round-based counterpart. Our findings provide new insights into the design of TDMA systems with different information freshness requirements.

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Accelerate Bit Manipulation in XiangShan Processer Using RISC-V B Extension and Instruction Fusion

Accelerate Bit Manipulation in XiangShan Processer Using RIS...

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2022 International Conference on Cloud Computing, Internet of Things, and computer Applications, CICA 2022

作者： Zhang, Fawang Tang, Dan Cai, Ye College of Computer Science and Software Engineering Shenzhen University Shenzhen China State Key Laboratory of Computer Architecture Institute of Computing Technology Beijing China

ISBN: (纸本)9781510656673

In the field of high-performance computing, some application scenarios make extensive use of bit manipulation. RISC-V foundation issues B extension to reduce the number of instructions during the static compilation. But this extension does not cover the more frequent operations fully. Instruction fusion technology can fuse multiple instructions into one instruction at the decode stage to dynamically reduce the number of instructions. Therefore, this technology can supplement RISC-V B extension uncovered instruction, enhancing the processor’s capabilities for bit manipulation. This paper implements the RISC-V B extension 1.0 version on the Xiangshan processor. For accelerating more bit manipulation, we profiled general benchmarks by the simulator, proposed ten new instructions fusion targets, and implemented a fusion decode unit. Finally, we use Coremark and SPEC CPU 2006 integer to evaluate the performance. The Coremark results show that dynamic instructions reduce by 18.14%, and the performance improves by 12.09%. © 2022 SPIE.

关键词： Decoding

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