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作者： Hu, Yonghua Zhang, Xin Wang, Shuying Liang, Wei Li, Kuan-Ching School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China

Array-unit dual-usage register is a kind of register resource that can be read or written as a whole or individually. It is mainly configured in processors with SIMD processing units and provides register-level speed data transfer between the scalar and vector processing units. To improve the efficiency of algorithms by using an array-unit dual-usage register, we investigate in this article the problem of adapting register allocation to code containing array-unit dual-usage register names. We propose a corresponding global register allocation method by combining the allocation of regular registers with array-unit dual-usage register, ensuring that the names of array-unit dual-usage register can be used in the input code of register allocation. Moreover, we present the processing framework of this method and the specific algorithms of some related vital aspects and demonstrate the working principles of the algorithms by an example. Experimental studies were conducted on a platform based on the FT-M7002 DSP core, and showing that our register allocation method can effectively handle codes containing array-unit dual-usage register names and support relevant application algorithms to improve their data transfer scheme. For some typical algorithms with input matrix, substantial performance improvements of twofold or higher are achieved. © 2022 John Wiley & Sons, Ltd.

关键词： Data transfer

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Blockchain-Based Cognitive Computing Model for Data Security on a Cloud Platform

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computers, Materials & Continua 2023年第12期77卷 3305-3323页

作者： Xiangmin Guo Guangjun Liang Jiayin Liu Xianyi Chen Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Jiangsu Police InstituteNanjing210031China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China

Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and ***,information shoplifting poses significant threats,potentially leading to poor performance and privacy ***-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business *** ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing(HD2C)***,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the ***2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of ***“Data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to *** with Blockchain allows quick consensus through smart member selection and *** HD2C paradigm significantly improves the computational processing efficiency of intelligent *** analysis results derived from IIoT datasets confirm HD2C *** compared to other consensus algorithms,the Blockchain PoA’s foundational cost is *** accuracy and memory utilization evaluation results predict the total benefits of the *** comparison to the values 0.004 and 0.04,the value of 0.4 achieves good *** to the experiment results,the number of transactions per second has minimal impact on memory *** findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.

关键词： Blockchain Internet of Things(IoT) blockchain based cognitive computing Hybridized Data Driven Cognitive Computing(HD2C) Federated Learning(FL) Proof of Authority(PoA)

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Heart failure prediction: a comparative analysis of machine learning algorithms

Heart failure prediction: a comparative analysis of machine ...

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2024 International Conference on Optics, Electronics, and Communication engineering, OECE 2024

作者： Dahri, Fida Husain Laghari, Asif Ali Sajnani, Dileep Kumar Shazia, Asima Kumar, Teerath School of Computer Science and Engineering Southeast University Jiangsu Nanjing211100 China Software College Shenyang Normal University Liaoning Shenyang110034 China National University of Computer and Emerging Sciences Islamabad Pakistan

ISBN: (纸本)9781510685437

In recent, machine learning techniques have been employed to predict various diseases such as lung, blood, liver, heart, etc. As the heart is considered one of the human body’s significant organs, this research proposes a comparative analysis of heart disease failure using various machine learning algorithms. Since the ECG and EEG are primary sources for analyzing heart performance, the lipid profile also provides information to determine good and bad cholesterol levels. Although this research proposes a comparative analysis of Heart Failure (HF) and other Cardiovascular diseases (CVDs), it assists in finding a better way to predict the root cause of HF and CVDs. The machine learning models are employed and trained, such as the Support Vector Classifier (SVC), Random Forest Classifier (RF), Logistic Regression (LR), Decision Tree Classifier (DT), and K-Nearest Neighbors Classifier (KNN) on the real-time dataset from Kaggle to predict and classify heart disease patients. The experimental set-up outcomes show that the Logistic Regression (LR) classifier has proven the best accuracy (88.00%) among all other machine learning classifiers. Our research results significantly contribute to predicting (HF) and nurturing advancements in AI-powered tools for improved heart failure (HF) prediction and patient care. © 2024 SPIE.

关键词： Logistic regression

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Emotionally Challenging Games Can Satisfy Older Adults' Psychological Needs: From Empirical Study to Design Guidelines 25

Emotionally Challenging Games Can Satisfy Older Adults' Psyc...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Zhou, Min Peng, Xiaolan Liu, Binjie Denisova, Alena Barathi, Soumya C. Li, Zhuying Xie, Xurong Huang, Jin Tian, Feng Institute of Software Chinese Academy of Sciences Beijing China Department of Computer Science University of York York United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798400713941

Older adults often struggle to meet their psychological needs due to retirement and living alone. Recent studies suggest that games featuring emotional challenge (EC) can help fulfill basic psychological needs such as autonomy, competence, and relatedness by facilitating emotional exploration. However, it remains unclear whether older adults can benefit from EC games, whether they find this genre enjoyable, and how these games should be designed to better meet their needs. This work explores older adults' experiences and perceptions of playing EC games through two studies. The first study involved playing Detroit: Become Human, revealing that older adults derived multifaceted psychological experiences from playing the game. The second study involved a custom-designed game scenario tailored to older adults, demonstrating that meaningful choices significantly influenced autonomy need satisfaction. Based on these findings, we offer five design guidelines for developing EC games that satisfy psychological needs of older adults. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Design Guidelines Emotional Challenge Older Adults Psychological Need Satisfaction Video Games

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Enhanced Diagnosis of Diabetic Retinopathy Utilizing Convolutional Neural Networks: A Deep Learning Approach

Enhanced Diagnosis of Diabetic Retinopathy Utilizing Convolu...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Singh, Neelam Srivastava, Rohit Mahdi, Hussain Falih Sati, Subhangi Joshi, Purvika Sar, Ayan Singh, Devansh Choudhury, Tanupriya Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India School of Computer Science University of Petroleum and Energy Studies Bidholi Uttarakhand Dehradun248007 India University of Diyala Department of Computer and Software Engineering Baquba Iraq Thapar University Punjab Patiala147001 India

ISBN: (纸本)9798350379716

Diabetic Retinopathy (DR) is one of the most severe eye complications associated with diabetes, hence the need to diagnose and treat at the initial stages. The current approaches of DR detection that use ophthalmoscopic inspection of the optic fundus images are subjective and take an ample amount of time. This paper focuses on the application of Convolutional Neural Networks (CNNs) in automating DR detection as well as its classification process using large archive annotated datasets for training and testing. The structure of our CNN excels in capturing stipples of the retinal image and reaches a level higher than 90% accuracy while remaining sensitive and specific. By presenting feature maps and class activation maps, there is an improved credibility as the process the model goes through is explained. The analysis shows that using the CNN-based system can bring notable benefits in the form of early detection, a decrease in the amount of work performed by ophthalmologists, and enhancement of patient outcomes. Future work seeks to implement this model in clinical practice and work to overcome problems such as data privacy and real-time processing, thus demonstrating the revolutionary impact that deep learning holds for ophthalmology. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on rectal tumor identification method by convolutional neural network based on multi-feature fusion

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International Journal for engineering Modelling 2021年第2期34卷 31-41页

作者： Liang, Zhuang Wu, Jiansheng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114044 China

Aiming at the obscure features of tumors in rectal CT images and their complexity, this paper proposes a multi-feature fusion-based convolutional neural network rectal tumor recognition method and uses it to model rectal tumors for classification experiments. This method extracts the convolutional features from rectal CT images using Alexnet, VGG16, ResNet, and DenseNet, respectively. At the same time, local features such as histogram of oriented gradient, local binary pattern, and HU moment invariants are extracted from this image. The above local features are fused linearly with the convolutional features. Then we put the new fused features into the fully connected layer for image classification. The experimental results finally reached the accuracy rates of 92.6 %, 93.1 %, 91.7 %, and 91.1 %, respectively. Comparative experiments show that this method improves the accuracy of rectal tumor recognition. © 2021, University of Split. All rights reserved.

关键词： Tumors

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Three-dimensional reconstruction of industrial parts from a single image

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 340-351页

作者： Zhenxing Xu Aizeng Wang Fei Hou Gang Zhao School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China Key Laboratory of Aeronautics Smart Manufacturing Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

This study proposes an image-based three-dimensional(3D)vector reconstruction of industrial parts that can gener-ate non-uniform rational B-splines(NURBS)surfaces with high fidelity and *** contributions of this study include three parts:first,a dataset of two-dimensional images is constructed for typical industrial parts,including hex-agonal head bolts,cylindrical gears,shoulder rings,hexagonal nuts,and cylindrical roller bearings;second,a deep learning algorithm is developed for parameter extraction of 3D industrial parts,which can determine the final 3D parameters and pose information of the reconstructed model using two new nets,CAD-ClassNet and CAD-ReconNet;and finally,a 3D vector shape reconstruction of mechanical parts is presented to generate NURBS from the obtained shape *** final reconstructed models show that the proposed approach is highly accurate,efficient,and practical.

关键词： Three-dimensional reconstruction Non-uniform rational B-splines Industrial parts Deep learning

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A Digital Twin Based Framework for Real-Time Machine Condition Monitoring 19

A Digital Twin Based Framework for Real-Time Machine Conditi...

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19th IEEE International Conference on Automation science and engineering, CASE 2023

作者： Chen, Zixiao Choudhury, Madhurjya Dev Blincoe, Kelly Dhupia, Jaspreet Singh University of Auckland Department of Mechanical and Mechatronics Engineering New Zealand School of Engineering and Computer Science Victoria University of Wellington New Zealand University of Auckland Department of Electrical Computer and Software Engineering New Zealand

ISBN: (纸本)9798350320695

Condition Monitoring (CM) is an important approach to extending the life of complex equipment by forecasting the outcome of an event before catastrophic failure occurs. Recent advancements in digital twins (DT) offer additional benefits to machine condition monitoring. In this study, a framework based on DT for real-time condition monitoring of industrial machines is proposed. The multi-layer DT framework consists of a physical entity (PE), virtual equipment (VE), edge device, fidelity service and digital twin services. The virtual equipment is a replica of the physical entity or the monitored machine. It also contains a cloud platform to store data online and an application to interface with the cloud enabling users to check the data remotely. The fidelity service ensures conformity between the PE and the VE. The digital service provides optimal operation and maintenance schedules based on the data from both physical and virtual spaces. The integration of the edge layer enables real-time handling of high-frequency machine data for effective health monitoring. The validity of the proposed framework is demonstrated with a case study based on monitoring a critical component of an industrial drivetrain test rig. The features of the framework allow end-users to visualize the component's real-time health status remotely. © 2023 IEEE.

关键词： Condition monitoring

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Enhancing Federated Learning by One-Shot Transferring of Intermediate Features from Clients 10

Enhancing Federated Learning by One-Shot Transferring of Int...

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10th IEEE International Conference on Data science and Advanced Analytics, DSAA 2023

作者： Deng, Youxingzhu Zhou, Yipeng Liu, Gang Wang, Jessie Hui Shui, Yu Shenzhen University College of Computer Science and Software Engineering Shenzhen China Macquarie University School of Computing Faculty of Science and Engineering Sydney Australia Institute for Network Sciences and Cyberspace Tsinghua University Peking China University of Technology Sydney School of Computer Science Sydney Australia

ISBN: (纸本)9798350345032

Federated learning (FL) is an emerging paradigm using a parameter server (PS) to coordinate multiple decentralized clients for training a common model without exposing their raw data. Despite its amazing capability in preserving data privacy, FL confronts two significant challenges that have not been sufficiently addressed by existing works, which are: 1) heterogeneous data distributed on clients given that the PS cannot alter data locations. 2) limited computation resources of FL clients who may conduct model training with mobile devices. To tackle these challenges, we propose a novel federated one-shot transferring of intermediate features (FedOTF) algorithm. More specifically, FedOTF consists of two stages: feature extraction and model reconstruction. To overcome challenge 1, clients in stage 1 only collaboratively train a small model in a federated manner, with the objective to extract features. In stage 2, intermediate features (generated by the small model trained by stage 1) are transferred from clients to the PS so that a large model can be trained to overcome challenge 2. In addition, the design of FedOTF is robust, which can flexibly diminish the number of communication rounds of stage 1 when network capacity is limited, and reduce the amount of exposed features when privacy is concerned. To verify the superiority of FedOTF, we conduct comprehensive experiments with real datasets. The experiment results demonstrate that FedOTF can significantly improve the model utility of FL because a better model can be finally obtained by the PS without incurring heavy computational load on clients. Besides, we conduct robustness evaluation of FedOTF which can achieve stable performance when varying network capacity and privacy requirement. © 2023 IEEE.

关键词： Privacy-preserving techniques

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Intelligent Tutoring for Large-Scale Personalized Programming Learning Based on Knowledge Graph 53

Intelligent Tutoring for Large-Scale Personalized Programmin...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Li, Ying Qiu, Jincheng Yang, Runze Zhu, Tongyu Sheng, Hao Gui, ShiJie Liang, Yu School of Computer Science and Engineering Beihang University Beijing China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing China

ISBN: (纸本)9798350336429

The development of learning analytics technology has created the necessary conditions for the implementation of large-scale personalized education. By taking advantage of the current advancements available, we can adopt a deep integration of 'artificial intelligence + education', take educational big data as the research object, aim at achieving precise teaching, and use cognitive diagnosis as a means to construct an intelligent guidance model. The model will conform to a learner's cognitive patterns and curriculum characteristics to meet his personalized learning needs under different knowledge states. Firstly, use knowledge map and abstract syntax trees to extract the characteristics of teaching objectives from questions and standard programing codes. Secondly, Use 'target characteristics - learning characteristics - learning sequence - learning effect' cognitive diagnosis model to assess student achievement in learning. Finally, according to the correlation between knowledge points, the initial learning path consisting of all relevant knowledge points is identified from a knowledge map. And the most optimal learning path is selected based on the importance of knowledge points and student's learning situation. There are three innovation points in this paper. First, the traditional method which only focuses on discovering the one-dimensional knowledge features implicated in the questions, is extended to construct a knowledge-ability multidimensional feature matrix. Secondly, it optimized static cognitive diagnosis into dynamic cognitive arbitrariness, which has a modeling capability of dynamic temporal sequence. Thirdly, it regarded the correlation between knowledge points, and the importance of knowledge points as the heuristic conditions for constructing a learning path. © 2023 IEEE.

关键词： Cognitive Diagnosis Intelligent Guidance Model Knowledge Graph Personalized Learning Path

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