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Dual Dynamic Consistency Regularization for Semi-Supervised Domain Adaptation

IEEE Access 2024年 12卷 36267-36279页

作者： Ngo, Ba Hung Lam, Ba Thinh Nguyen, Thanh Huy Dinh, Quang Vinh Choi, Tae Jong Chonnam National University Graduate School of Data Science Gwangju61186 Korea Republic of University of Science Faculty of Information Technology Ho Chi Minh City700000 Viet Nam National Cheng Kung University Department of Biomedical Engineering Tainan70101 Taiwan Vietnamese-German University School of Electrical Engineering and Computer Science Ho Chi Minh City72000 Viet Nam

The Vision Transformer (ViT) model serves as a powerful model to capture and comprehend global information, particularly when trained on extensive datasets. Conversely, the Convolutional Neural Network (CNN) model is beneficial to training with small datasets for retaining essential local information. Inspired by these properties of ViT and CNN models, we introduce a hybrid framework that smoothly increases the cross-domain generalization in Semi-supervised Domain Adaptation (SSDA). To achieve this goal, we first train the ViT model on abundant labeled source data, while the CNN model is trained on a few labeled target samples. Then, these models dynamically exchange their knowledge for potential generalization to unlabeled target data via the proposed method, named Dual Dynamic Consistency Regularization (D2CR). Specifically, the ViT model provides its pseudo labels to update the global perspective for the CNN model. Similarly, the CNN model offers pseudo labels to complement the local perspective for the ViT model. The previous methods use a fixed threshold algorithm for the pseudo-labeling process. However, we utilize the dynamic threshold strategy to create pseudo labels for the bi-directional consistency regularization learning between the ViT and CNN models. We verify our approach across several SSDA benchmark datasets. The outstanding experimental results provide strong evidence of the effectiveness and superiority of our approach over previous state-of-the-art SSDA methods. © 2013 IEEE.

关键词： Neural network models

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Augmenting IoT Intrusion Detection System Performance Using Deep Neural Network

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computers, Materials & Continua 2023年第1期74卷 1351-1374页

作者： Nasir Sayed Muhammad Shoaib Waqas Ahmed Sultan Noman Qasem Abdullah M.Albarrak Faisal Saeed Department of Computer Science Islamia College PeshawarPeshawar25120Pakistan Department of Computer Science CECOS University of IT and Emerging SciencesPeshawar25000Pakistan Department of Electrical Engineering HITEC University TaxilaTaxila47080Pakistan Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK

Due to their low power consumption and limited computing power,Internet of Things(IoT)devices are difficult to ***,the rapid growth of IoT devices in homes increases the risk of *** detection systems(IDS)are commonly employed to prevent *** systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate *** have been made in the past to detect new attacks using machine learning and deep learning techniques,however,these efforts have been *** this paper,we propose two deep learning models to automatically detect various types of intrusion attacks in IoT ***,we experimentally evaluate the use of two Convolutional Neural Networks(CNN)to detect nine distinct types of attacks listed in the NF-UNSW-NB15-v2 *** accomplish this goal,the network stream data were initially converted to twodimensional images,which were then used to train the neural network *** also propose two baseline models to demonstrate the performance of the proposed ***,both models achieve high accuracy in detecting the majority of these nine attacks.

关键词： Internet of things intrusion detection system deep learning convolutional neural network supervised learning

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Insider Trading with a Random Deadline under Partial Observations:Maximal Principle Method

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Acta Mathematicae Applicatae Sinica 2022年第4期38卷 753-762页

作者： Kai XIAO Yong-hui ZHOU School of Mathematical Sciences Guizhou Normal UniversityGuiyang 550001China School of Big Data and Computer Science Guizhou Normal UniversityGuiyang 550001China

For a revised model of Caldentey and Stacchetti(Econometrica,2010)in continuous-time insider trading with a random deadline which allows market makers to observe some information on a risky asset,a closed form of its market equilibrium consisting of optimal insider trading intensity and market liquidity is obtained by maximum principle *** shows that in the equilibrium,(i)as time goes by,the optimal insider trading intensity is exponentially increasing even up to infinity while both the market liquidity and the residual information are exponentially decreasing even down to zero;(ii)the more accurate information observed by market makers,the stronger optimal insider trading intensity is such that the total expect profit of the insider is decreasing even go to zero while both the market liquidity and the residual information are decreasing;(iii)the longer the mean of random time,the weaker the optimal insider trading intensity is while the more both the residual information and the expected profit are,but there is a threshold of trading time,half of the mean of the random time,such that if and only if after it the market liquidity is increasing with the mean of random time increasing.

关键词： continuous-time insider trading random deadline partial observations filtering theory maximal principle

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Heart Disease Diagnosis Using the Brute Force Algorithm and Machine Learning Techniques

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computers, Materials & Continua 2022年第8期72卷 3195-3211页

作者： Junaid Rashid Samina Kanwal Jungeun Kim Muhammad Wasif Nisar Usman Naseem Amir Hussain Department of Computer Science and Engineering Kongju National UniversityCheonan31080Korea Department of Computer Science COMSATS University IslamabadWah Campus47040Pakistan School of Computer Science University of SydneySydneyNSW 2006Australia Centre of AI and Data Science Edinburgh Napier UniversityEdinburghEH114DYUK

Heart disease is one of the leading causes of death in the world *** of heart disease is a prominent topic in the clinical data *** increase patient survival rates,early diagnosis of heart disease is an important field of research in the medical *** are many studies on the prediction of heart disease,but limited work is done on the selection of *** selection of features is one of the best techniques for the diagnosis of heart *** this research paper,we find optimal features using the brute-force algorithm,and machine learning techniques are used to improve the accuracy of heart disease *** performance evaluation,accuracy,sensitivity,and specificity are used with split and cross-validation *** results of the proposed technique are evaluated in three different heart disease datasets with a different number of records,and the proposed technique is found to have superior *** selection of optimized features generated by the brute force algorithm is used as input to machine learning algorithms such as Support Vector Machine(SVM),Random Forest(RF),K Nearest Neighbor(KNN),and Naive Bayes(NB).The proposed technique achieved 97%accuracy with Naive Bayes through split validation and 95%accuracy with Random Forest through *** Bayes and Random Forest are found to outperform other classification approaches when accurately *** results of the proposed technique are compared with the results of the existing study,and the results of the proposed technique are found to be better than other ***,our proposed approach plays an important role in the selection of important features and the automatic detection of heart disease.

关键词： Heart disease brute force machine learning feature selection

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Expeditious detection and segmentation of bone mass variation in DEXA images using the hybrid GLCM-AlexNet approach

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Soft Computing 2024年第19期28卷 11633-11646页

作者： Amiya, Gautam Murugan, Pallikonda Rajasekaran Ramaraj, Kottaimalai Govindaraj, Vishnuvarthanan Vasudevan, Muneeswaran Thirumurugan, M. Zhang, Yu-Dong Abdullah, S. Sheik Thiyagarajan, Arunprasath Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Data Science Division School of Computer Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal India Consultant Orthopaedic Surgeon MGR Medical University Tamilnadu Chennai India School of Informatics University of Leicester LeicesterLE1 7RH United Kingdom

Osteoporosis (OP) is an osteometabolic disorder characterized by a lesser bone mineral density (BMD) and the disruption of bone tissue micro—architecture, resulting in a greater bone fragility and higher likelihoods of fractures. OP emerges once the bone mass declines faster than the body's capacity to replenish it, leading to a significant reduction in the strength of bone. OP impacts each and every bone throughout the body and provides no clinical signs until a fracture happens. Aging leads to the reduction in BMD, and the rate of percentage of fractures rises over time, usually causing mortality and morbidity. Numerous BMD evaluation methods are available, and they are used in a variety of settings by considering the location of the fracture. Dual-energy x-ray absorptiometry (DEXA/DXA) is recognized as the gold standard for predicting the fracture, since it is the most advanced, technologically affirmed, and also has excellent performance. According to the findings, most researchers do not endeavour the identification and the segmentation of low bone masses from DEXA images. Medical image segmentation supports in analyzing and visualizing the bone's low bone mass. The envisaged hybrid approach, that integrates GLCM for feature extraction and AlexNet for a low bone mass variation classification, provides segmented images that assist in categorizing bone health as normal, osteopenia, or osteoporosis. The developed algorithm's performance metrics, including Dice Co-efficient, Sensitivity, and Specificity, were 92.35, 90.26, and 92.42%, respectively. The Orthopedicians ascertained the efficacy of the outcomes rendered by the proposed algorithm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Fracture

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BERD+: A Generic Sequential Recommendation Framework by Eliminating Unreliable data with Item- and Attribute-level Signals

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ACM Transactions on Information Systems 2023年第2期42卷 1-33页

作者： Sun, Yatong Yang, Xiaochun Sun, Zhu Wang, Bin School of Computer Science and Engineering Northeastern University School of Computing Macquarie University China Australia Institute of High Performance Computing Centre for Frontier AI Research A*STAR Singapore School of Computer Science and Engineering Northeastern University China National Frontiers Science Center for Industrial Intelligence and Systems Optimization China Key Laboratory of Data Analytics and Optimization for Smart Industry [Northeastern University Ministry of Education China

A preliminary work of our study was published at IJCAI’21 [47], which is substantially extended in the following aspects: (1) In Section 1, we analyze the necessity of introducing item attributes for detecting unreliable instances, together with the problems and challenges that attributes may bring in. (2) In Section 2, we add discussions about the limitations of existing attribute-aware recommender systems (Section 2.2) and denoising methods (Section 2.3) in the context of detecting unreliable instances. (3) In Section 4.2, we further conduct an in-depth analysis at the attribute level to demonstrate the capability of attributes for rectifying instance loss and uncertainty, as well as the disturbance caused by attributes. (4) We generalize BERD to a generic framework BERD+ in Section 5.1, equipped with novel modules, i.e., HU-GCN (Section 5.2) and EPE (Section 5.4), which properly incorporate item attributes while reducing their disturbance for rectifying instance uncer tainty (Section 5.5) and loss (Section 5.6). The generic BERD+ can be flexibly plugged into existing SRSs for performance enhanced recommendation via eliminating unreliable data. (5) In Section 6.2, we apply our BERD+ framework to seven state-of-the-art SRSs on five real-world datasets to illustrate its superiority. (6) To avoid unfair comparison caused by item attributes, we build and compare with the baseline that combines the original BERD and an advanced attribute-aware recommender system, KSR [19]. (7) For more comprehensive comparison, in Section 6.2.2, we compare BRED+ with two state-of-the-art denoising approaches;in Section 6.2.3, to examine the efficacy of HU-GCN and EPE, we compare HU-GCN with various attribute embedding techniques, i.e., variants of graph neural networks, and compare EPE with different attribute fusing methods, i.e., adding, concatenation, and weighted sum. (8) In Section 6.2.4, a detailed ablation study is conducted to verify the effectiveness of each module of BERD+. (

关键词： Convolution

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VIPNet: Combining Viewpoint Information and Shape Priors for Instant Multi-view 3D Reconstruction 17th

VIPNet: Combining Viewpoint Information and Shape Priors fo...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Ye, Weining Li, Zhixuan Jiang, Tingting School of Computer Science Peking University Beijing100871 China College of Computing and Data Science Nanyang Technological University Singapore Singapore National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science National Biomedical Imaging Center Peking University Beijing100871 China

ISBN: (纸本)9789819609680

While the multi-view 3D reconstruction task has made significant progress, existing methods simply fuse multi-view image features without effectively leveraging available auxiliary information, especially the viewpoint information for guiding and associating features of different views. To this end, we propose to enhance multi-view 3D reconstruction with the power of viewpoint information. Specifically, a simple-yet-effective viewpoint estimator is designed to learn and provide comprehensive viewpoint knowledge for locating and associating learned features from different views. Moreover, to improve the 3D reconstruction quality when 2D images of only very few viewpoints are available, we propose to learn the shape prior knowledge to provide sufficient shape information for compensating the limited 2D observations. Overall, we present VIPNet, benefiting from Viewpoint Information and Shape Prior learning for high-quality multi-view 3D reconstruction. Extensive experiments validate the effectiveness of the proposed VIPNet, which achieves state-of-the-art performance on challenging datasets and shows well generalization ability in real-world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling

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Learning to Unlearn for Bayesian Personalized Ranking via Influence Function

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Chinese Journal of Electronics 2025年第3期34卷 990-1001页

作者： Chen Jundong Zhang Honglei Li Haoxuan Li Yidong Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University) Ministry of Education Beijing 100044 China School of Cyberspace Science and Technology Beijing Jiaotong University Beijing 100044 China School of Computer Science and Technology Beijing Jiaotong University Beijing 100044 China Center for Data Science Peking University Beijing 100871 China

Learning recommender models from vast amounts of behavioral data has become a mainstream paradigm in recent information systems. Conversely, with privacy awareness grown, there has been increasing attention to the removal of sensitive or outlier data from well-trained recommendation models (known as recommendation unlearning). However, current unlearning methods primarily focus on fully/partially retraining the entire model. Despite considerable performance, it inevitably introduces significant efficiency bottlenecks, which is impractical for latency-sensitive streaming services. While recent efforts exploit efficient unlearning in point-wise recommender tasks, these approaches overlook the partial order relationships between items, resulting in suboptimal performance in both recommendation and unlearning capabilities. In light of this, we explore learning to unlearn for Bayesian personalized ranking via influence function, which relies on a pair-wise ranking loss to model user preferences and item characteristics, making unlearning more challenging than in point-wise settings. Specifically, we propose an influence function-guided unlearning framework tailored for pair-wise ranking models to efficiently perform unlearning requests, which involves unlearning partial order relationships while handling negative samples appropriately during the unlearning process. Besides, we prove that our proposed method can theoretically match the performance of retraining counterparts. Finally, we conduct extensive experiments to validate the effectiveness and efficiency of our model.

关键词： Recommendation unlearning Influence function Bayesian personalized ranking The right to be forgotten

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Reinforcement Learning with UAV Assistance for Optimized Computation Offloading in Mobile Edge Computing 34

Reinforcement Learning with UAV Assistance for Optimized Com...

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34th Conference of Open Innovations Association FRUCT, FRUCT 2023

作者： Alabsi, Aisha Hawbani, Ammar Wang, Xingfu Alsamhi, Saeed Hamood Zhao, Liang Al-Dubai, Ahmed University of Science and Technology of China School of Computer Science and Technology China National University of Ireland Insight Centre for Data Analytics Galway Ireland Shenyang Aerospace University School of Computer Science China Computing School of Edinburgh Napier University United Kingdom

ISBN: (纸本)9789526524603

With the rise of computing-intensive applications like online gaming and telemedicine on user equipment (UE) and the evolution of 5G technology, there is a surge in demand for greater computing resources and power. Yet, UEs have limited resources and batteries. Mobile Cloud Computing (MCC) has emerged as a method to enhance UEs computing capabilities and conserve energy by transferring tasks to the cloud. Mobile Edge Computing (MEC) further aids by reducing delays, although it faces issues like limited resources and unpredictable network conditions. Unmanned Aerial Vehicles (UAVs) offer a remedy by serving as mobile stations for MEC, but optimal offloading decisions in UAV-assisted MEC remain intricate. Addressing this, I propose using Reinforcement Learning (RL), specifically Q-Learning, Deep Q Network (DQN), and Deep Deterministic Policy Gradient (DDPG), to enhance decision-making for offloading. Our focus is on energy efficiency and reduced service delay, and our simulations prove our method's efficacy in UAV-assisted MEC environments. © 2023 FRUCT Oy.

关键词： Mobile edge computing

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Scalable Transactional Stream Processing on Multicore Processors

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IEEE Transactions on Knowledge and data Engineering 2025年第7期37卷 4254-4269页

作者： Zhao, Jianjun Mao, Yancan Yang, Zhonghao Liu, Haikun Zhang, Shuhao Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China National University of Singapore 119077 Singapore Nanyang Technological University 639798 Singapore

Transactional stream processing engines (TSPEs) are central to modern stream applications handling shared mutable states. However, their full potential, particularly in adaptive scheduling, remains largely unexplored. We present MorphStream, a TSPE designed to optimize parallelism and performance for transactional stream processing on multicores. Through a unique three-stage execution paradigm (i.e., planning, scheduling, and execution), MorphStream enables adaptive scheduling under varying workload characteristics. Building on this foundation, MorphStream is further enhanced with support for non-deterministic state access, employing a stateful task precedence graph to handle undefined read/write sets at runtime while guaranteeing transaction semantics. Additionally, MorphStream incorporates a generalized framework for managing window-based operations, enabling efficient tracking and maintenance of overlapping windows using multi-versioned state management. These extensions enhance the system’s ability to process dynamic and irregular workloads. Experimental results demonstrate up to 3.4 times higher throughput and 69.1% lower latency compared to state-of-the-art TSPEs, validating its scalability and adaptability in real-world streaming scenarios. © 1989-2012 IEEE.

关键词： Multicore programming

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