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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Huang, Haocheng Li, Yangfan Zhou, Xinlian Hunan University of Science and Technology The School of Computer Science and Engineering Xiangtan China Central South University The School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350330717

Point clouds play a crucial role in various fields such as robotics, 3D modeling, and autonomous driving. DGCNN, as a representative work in this domain, has exhibited superior performance compared to classic models like PointNet++. Such models often display significant computational demands and encounter notable performance bottlenecks, so how to accelerate their calculations has become a noteworthy issue. Nevertheless, due to the inherent disparities between point cloud tasks and conventional convolution tasks, existing algorithms and architectural paradigms designed for convolution frequently prove to be ill-suited for point clouds, such as Tensor Core (TC). There have been studies focused on reducing DGCNN model complexity to improve computational efficiency, but research specifically tailored to TC acceleration remains relatively limited. Therefore, we present an encapsulation module that bridges the computation of model in Torch level with the components of the CUDA backend, introducing TC into DGCNN. It dynamically adjusts parameters and computation flow based on input data and transforms batch computations into streaming computations. Additionally, we propose a new computational organization approach, which involves the restructuring of two critical computation steps within DGCNN: the Get Graph Feature operation and the Conv operation. It significantly enhances the computational speed of these two operations while reducing memory redundancy of tensor data involved in the intermediate processes. In the experiment, our modifications achieve an average acceleration of 1.80X for the Get Graph Feature operation and 1.46X for the Conv operation, all while not compromising result accuracy. © 2023 IEEE.

关键词： Classification Convolution Dynamic graph update GPU Neural Networks Point Cloud Tensor Core

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A deep graph convolution network with attention for clustering scRNA-seq data

A deep graph convolution network with attention for clusteri...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Xiang Yu, Junnan Peng, Li Li, Min Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350337488

Identifying cell types is a primary objective in single-cell RNA sequencing (scRNA-seq) analysis, and clustering is a widely used method for achieving this goal. However, the abundance of data and the presence of noise pose significant challenges for single-cell clustering. We propose a novel method called scGCNClustering, which leverages a deep graph convolution network (GCN) with attention. scGCNClustering comprises two core models. The first model is a deep GCN encoder that effectively preserves both the topological structure and feature information in scRNA-seq data, enabling accurate cell segregation. Additionally, we incorporate an attention module into the GCN encoder to capture precise global similarities between cells. The second model is a deep zero-inflated negative binomial (ZINB) decoder, which approximates the true distribution of scRNA-seq data. Extensive analysis conducted on six real scRNA-seq datasets demonstrates that scGCNClustering achieves promising performance in scRNA-seq clustering. © 2023 IEEE.

关键词： Clustering Graph convolution network Single cell RNA-seq

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Observing suppressed polarization in flexible ferroelectric negative capacitance field effect transistors

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Journal of Materiomics 2024年第4期10卷 762-769页

作者： Chuanlai Ren Liyufen Dai Congbing Tan Guangtong Yuan Ke Qu Jinbin Wang Xiangli Zhong Mingqiang Huang Jiyan Dai Gaokuo Zhong Jiangyu Li Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518055GuangdongChina Guangdong Provincial Key Laboratory of Functional Oxide Materials and Devices Southern University of Science and TechnologyShenzhen518055GuangdongChina School of Materials Science and Engineering Xiangtan UniversityXiangtan411105HunanChina School of Physics and Electronics Hunan University of Science and TechnologyXiangtan411201HunanChina Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen518055GuangdongChina Department of Applied Physics The Hong Kong Polytechnic UniversityHong Kong999077China

Negative capacitance(NC)has the potential to enable low power microelectronics beyond the fundamental thermionic limit,and it has been theorized that the thermodynamically unstable NC of ferroelectrics can be stabilized by linear dielectric,making negative capacitance ferroelectric field effect transistors(NC-FeFET)***,the validity of NC as a physical concept for ferroelectrics remain contentious despite numerous theoretical and experimental investigations,and the intrinsic ferroelectric NC with suppressed polarization has not been demonstrated except locally at vortex *** NC-FeFET with subthreshold swing(SS)lower than 60 mV/dec limit has been reported,such device characteristics has not been directly connected to suppressed polarization at materials’level,and alternative mechanisms other than NC have also been *** we demonstrate stable sub-60 mV/dec SS with hysteresis free Isingle bondV in NC-FeFET based on SrTiO_(3)/Pb(Zr_(0.1)Ti_(0.9))O_(3)/SrTiO_(3) heterostructure,and observe its suppressed polarization at both macroscopic and microscopic *** intrinsic ferroelectric NC thus is experimentally confirmed and directly connected to NC-FeFET performance,and the mica-based device is also highly flexible and robust under cyclic bending as well as extended heating.

关键词： Negative capacitance field-effect transistors Ferroelectric Pulse laser deposition Mica

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Multi-domain feature-enhanced attribute updater for generalized zero-shot learning

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Neural Computing and Applications 2025年第14期37卷 8397-8414页

作者： Shi, Yuyan Jiang, Chenyi Song, Feifan Ye, Qiaolin Long, Yang Zhang, Haofeng School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China School of Information Science and Technology Nanjing Forestry University Jiangsu Nanjing210037 China Department of Computer Science Durham University DurhamDH1 3LE United Kingdom

The goal of generalized zero-shot learning (GZSL) is to transfer knowledge from seen classes to unseen classes. However, a significant challenge is the single-category attributes are often inadequate to capture the intra-class variability of diverse image samples. During training, the model tends to cluster samples of seen categories based solely on semantic descriptions, overlooking subtle yet important visual differences. To address these challenges, this paper introduces an instance-level attribute updating method, termed as multi-domain feature-enhanced attribute updater (MDAU), which establishes a one-to-one alignment between visual and semantic by obtaining multiple instance-level attributes that closely correspond to the natural intra-class distribution of the image, in contrast to the many-to-one alignment typically used in traditional GZSL approaches. Specifically, we initially compute feature weights for each individual image, and then, the visual information is incorporated as conditions to facilitate the personalized updating of class attributes. We evaluate our model on three benchmark datasets, the results show that our model can achieve the state-of-the-art performance and detailed analysis also illustrates the effectiveness of each proposed module. Code is available at https://***/S-Silvia/MDAU. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Zero-shot learning

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Deploying Hybrid Ensemble Machine Learning Techniques for Effective Cross-Site Scripting(XSS)Attack Detection

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computers, Materials & Continua 2024年第10期81卷 707-748页

作者： Noor Ullah Bacha Songfeng Lu Attiq Ur Rehman Muhammad Idrees Yazeed Yasin Ghadi Tahani Jaser Alahmadi School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430073China Department of Computer Science and Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain12555Abu Dhabi Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive *** detection methods often fail to keep pace with the evolving sophistication of cyber *** paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression(LR),Support Vector Machines(SVM),eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Deep Neural Networks(DNN).Utilizing the XSS-Attacks-2021 dataset,which comprises 460 instances across various real-world trafficrelated scenarios,this framework significantly enhances XSS attack *** approach,which includes rigorous feature engineering and model tuning,not only optimizes accuracy but also effectively minimizes false positives(FP)(0.13%)and false negatives(FN)(0.19%).This comprehensive methodology has been rigorously validated,achieving an unprecedented accuracy of 99.87%.The proposed system is scalable and efficient,capable of adapting to the increasing number of web applications and user demands without a decline in *** demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant ***,despite the computational complexity introduced by the hybrid ensemble approach,strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time *** for easy integration with existing web security systems,our framework supports adaptable Application Programming Interfaces(APIs)and a modular design,facilitating seamless augmentation of current *** innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.

关键词： Cross-site scripting machine learning XSS detection stacking ensemble learning hybrid learning

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Precise control of page cache for containers

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Frontiers of computer science 2024年第2期18卷 17-30页

作者： Kun WANG Song WU Shengbang LI Zhuo HUANG Hao FAN Chen YU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Container-based virtualization isbecoming increasingly popular in cloud computing due to its efficiency and *** isolation is a fundamental property of *** works have indicated weak resource isolation could cause significant performance degradation for containerized applications and enhanced resource ***,current studies have almost not discussed the isolation problems of page cache which is a key resource for *** leverage memory cgroup to control page cache ***,existing policy introduces two major problems in a container-based ***,containers can utilize more memory than limited by their cgroup,effectively breaking memory ***,the Os kernel has to evict page cache to make space for newly-arrived memory requests,slowing down containerized *** paper performs an empirical study of these problems and demonstrates the performance impacts on containerized *** we propose pCache(precise control of page cache)to address the problems by dividing page cache into private and shared and controlling both kinds of page cache separately and *** do so,pCache leverages two new technologies:fair account(f-account)and evict on demand(EoD).F-account splits the shared page cache charging based on per-container share to prevent containers from using memory for free,enhancing memory *** EoD reduces unnecessary page cache evictions to avoid the performance *** evaluation results demonstrate that our system can effectively enhance memory isolation for containers and achieve substantial performance improvement over the original page cache management policy.

关键词： page cache memory cgroup container isolation cloud computing

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Research on Enhanced Multi-head Self-Attention Social Recommendation Algorithm Based on Graph Neural Network

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第7期51卷 754-764页

作者： Teng, Yue Yang, Kai Yue Teng is a postgraduate student at the School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In today’s society, communication among people has become more frequent and extensive due to the rapid development of science, technology, and the Internet. This vast communication occurs in real life and virtual online worlds. However, with the overwhelming amount of information available, people often struggle with finding and accessing the information they need, leading to frustration. To address this challenge, we conducted a study to extract valuable information and insights from users’ social relationships. While existing models can help us understand and analyze users’ social relationships to some extent, there are still significant challenges in extracting valuable insights. Therefore, we conducted in-depth research to improve the GraphRec model and created a new social recommendation model called the CWYGNN model. The CWYGNN model is unique because it can identify deeper connections in users’ social interactions through in-depth learning of the social sequences between users. This is important for understanding users’ behavioral patterns, predicting their needs, and providing personalized services. To validate the effectiveness of the CWYGNN model, we conducted experiments and comparisons on several public datasets. The experimental results show that the CWYGNN model outperforms similar approaches in processing user social relationships and extracting valuable insights. This result provides a new way of thinking and methodology to help us better understand and serve users and ultimately enhance their overall experience. © (2024) International Association of Engineers. All rights reserved.

关键词： Recommender systems

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Graph CA: Learning From Graph Counterfactual Augmentation for Knowledge Tracing

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2108-2123页

作者： Xinhua Wang Shasha Zhao Lei Guo Lei Zhu Chaoran Cui Liancheng Xu School of Information Science and Engineering Shandong Normal UniversityJinan 250358China School of Computer Science and Technology Shandong University of Finance and EconomicsJinan 250014China

With the popularity of online learning in educational settings, knowledge tracing(KT) plays an increasingly significant role. The task of KT is to help students learn more effectively by predicting their next mastery of knowledge based on their historical exercise sequences. Nowadays, many related works have emerged in this field, such as Bayesian knowledge tracing and deep knowledge tracing methods. Despite the progress that has been made in KT, existing techniques still have the following limitations: 1) Previous studies address KT by only exploring the observational sparsity data distribution, and the counterfactual data distribution has been largely ignored. 2) Current works designed for KT only consider either the entity relationships between questions and concepts, or the relations between two concepts, and none of them investigates the relations among students, questions, and concepts, simultaneously, leading to inaccurate student modeling. To address the above limitations,we propose a graph counterfactual augmentation method for knowledge tracing. Concretely, to consider the multiple relationships among different entities, we first uniform students, questions, and concepts in graphs, and then leverage a heterogeneous graph convolutional network to conduct representation *** model the counterfactual world, we conduct counterfactual transformations on students’ learning graphs by changing the corresponding treatments and then exploit the counterfactual outcomes in a contrastive learning framework. We conduct extensive experiments on three real-world datasets, and the experimental results demonstrate the superiority of our proposed Graph CA method compared with several state-of-the-art baselines.

关键词： Contrastive learning counterfactual representation graph neural network knowledge tracing

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Collaborative Edge Server Placement for Maximizing QoS with Distributed Data Cleaning

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IEEE Transactions on Services Computing 2025年第03期18卷 1321-1335页

作者： Liang, Yuzhu Yin, Mujun Wang, Wenhua Liu, Qin Wang, Liang Zheng, Xi Wang, Tian Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai519000 China Huaqiao University College of Computer Science and Technology Xiamen361021 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Northwestern Polytechnical University School of Computer Science Xi'an710072 China Macquarie University School of Computing NSW2109 Australia

The proliferation of contaminated data on Internet of Things (IoT) devices has the potential to undermine the accuracy of data-driven decision-making by altering the distribution of original data. Existing data cleaning methods primarily depend on cloud center or cloud-edge cooperation, leading to prolonged data transmission delays and reduced cleaning accuracy. In this study, we identify edge server placement as a crucial step aligned with data cleaning and view the collaborative edge server placement with distributed data cleaning (SPDC) as a holistic problem. We comprehensively quantify the complexity of our issue through the analysis of numerous scenarios. To address this problem, we introduce a novel distributed collaborative edge framework comprising two key stages: server placement and data cleaning. We propose an optimized clustering algorithm for the former, considering the data distribution on the IoT layer and the constraints of the edge layer. For the latter, we introduce a gossip-based data cleaning algorithm that fully utilizes edge collaboration to enhance data cleaning accuracy. The algorithm exhibits an approximate performance complexity of O(ln m), where m represents the number of users' tasks. Both theoretical analysis and experimental results reveal that our algorithm an average improvement in data cleaning accuracy of 9.02% and a reduction in delay of 36.61%, surpassing the performance of state-of-the-art works in various scenarios. © 2008-2012 IEEE.

关键词： Data accuracy

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Method and Practice of Trusted Embedded Computing and Data Transmission Protection Architecture Based on Android

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Chinese Journal of Electronics 2024年第3期33卷 623-634页

作者： Yichuan WANG Wen GAO Xinhong HEI Yanning DU School of Computer Science and Engineering Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology

In recent years,the rapid development of Internet technology has constantly enriched people's daily life and gradually changed from the traditional computer terminal to the mobile *** with it comes the security problems brought by the mobile *** for Android system,due to its open source nature,malicious applications continue to emerge,which greatly threatens the data security of ***,this paper proposes a method of trusted embedded static measurement and data transmission protection architecture based on Android to reduce the risk of data leakage in the process of terminal storage and *** conducted detailed data and feasibility analysis of the proposed method from the aspects of time consumption,storage overhead and *** experimental results show that this method can detect Android system layer attacks such as self-booting of the malicious module and improve the security of data encryption and transmission process *** with the native system,the additional performance overhead is small.

关键词： TrustZone Static measurement Data leakage Android

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