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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Sepehr Jalayer Yiming Xiao Marta Kersten-Oertel Gina Cody School of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

With more and more availability of both functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI), there is increasing interest and evidence that brain network analysis can enable new possibilities to understand brain function and disease. We have developed an open-source virtual reality (VR) platform, VRNConnect, for exploring brain connectivity data to enable researchers, students, and clinicians to map the different regions of the brain and study their functions in a more interactive, intuitive and engaging way. VRNConnect allows users to interact with the data to view various graph network measurements such as node strength, degree, and clustering coefficient by using the Brain Connectivity Analysis tools during runtime. We performed a user study to de-termine the intuitiveness and usability of the platform as well as looked at differences in controller versus gesture interaction methods. Results showed that VRNConnect is easy to use, easy to learn and intuitive. In terms of the interaction methods, almost all participants performed significantly better, doing tasks with the controller as opposed to hand gestures in both task completion time and task errors. VRNConnect showed promising results in both usability scale (SUS) and cognitive load (NASA TLX), showing the potential to be used as an academic and analytical tool, with a customizable design for importing connectivity data from different modalities and flexible and intuitive interaction methods.

关键词： Three-dimensional displays Runtime Virtual environments Tutorials Network analyzers Functional magnetic resonance imaging Usability

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Learning Order Forest for Qualitative-Attribute Data Clustering 27

Learning Order Forest for Qualitative-Attribute Data Cluster...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhao, Mingjie Feng, Sen Zhang, Yiqun Li, Mengke Lu, Yang Cheung, Yiu-Ming School of Computer Science and Technology Guangdong University of Technology Guangzhou China Shenzhen China School of Computer Science and Software Engineering Shenzhen University Shenzhen China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China Department of Computer Science Hong Kong Baptist University Hong Kong

ISBN: (纸本)9781643685489

Clustering is a fundamental approach to understanding data patterns, wherein the intuitive Euclidean distance space is commonly adopted. However, this is not the case for implicit cluster distributions reflected by qualitative attribute values, e.g., the nominal values of attributes like symptoms, marital status, etc. This paper, therefore, discovered a tree-like distance structure to flexibly represent the local order relationship among intra-attribute qualitative values. That is, treating a value as the vertex of the tree allows to capture rich order relationships among the vertex value and the others. To obtain the trees in a clustering-friendly form, a joint learning mechanism is proposed to iteratively obtain more appropriate tree structures and clusters. It turns out that the latent distance space of the whole dataset can be well-represented by a forest consisting of the learned trees. Extensive experiments demonstrate that the joint learning adapts the forest to the clustering task to yield accurate results. Comparisons of 10 counterparts on 12 real benchmark datasets with significance tests verify the superiority of the proposed method. Source code of the proposed method is available at [39]. © 2024 The Authors.

关键词： Adversarial machine learning

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Applied Technology Research on the Construction of Bidirectional Knowledge Graph Curriculum System Based on SmartKG

Applied Technology Research on the Construction of Bidirecti...

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Artificial Intelligence and Education (WAIE), International Workshop on

作者： Su Jiang Wei Luo Hengji Cui School of Computer Science and Software Engineering Dalian Neusoft University of Information Dalian China

ISBN: (数字)9798350378894

ISBN: (纸本)9798350378900

Amidst the rapid advancements in artificial intelligence technology, it is imperative to apply these technological developments to the realm of education to enhance information-based teaching methodologies. This article introduces a knowledge management framework that aims to visualize curriculum knowledge systems using knowledge graphs in the educational sector. Taking courses like Fundamentals of computer Systems as exemplars, the article demonstrates the techniques for constructing knowledge graphs. Additionally, SmartKG's ease of deployment and application allows students to construct their own visual knowledge graphs, fostering a deeper comprehension of knowledge and fostering the profound integration of AI and teaching. Through the analysis of the curriculum system and students' self-constructed knowledge graphs, the efficacy of utilizing knowledge graphs in learning has been validated, propelling the application and practice of intelligent classroom teaching.

关键词： Visualization Conferences Knowledge based systems Knowledge graphs Educational technology Propulsion Big Data Market research Knowledge management Information technology

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FPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework

FPPL: An Efficient and Non-IID Robust Federated Continual Le...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： He, Yuchen Shen, Chuyun Wang, Xiangfeng Jin, Bo Shanghai Formal-Tech Information Technology Co. Lt Shanghai200062 China East China Normal University School of Computer Science and Technology Shanghai200062 China TongJi University School of Software Engineering Shanghai200092 China

ISBN: (纸本)9798350362480

Federated continual learning (FCL) aims to learn from sequential data stream in the decentralized federated learning setting, while simultaneously mitigating the catastrophic forgetting issue in classical continual learning. Existing FCL methods usually employ typical rehearsal mechanisms, which could result in privacy violations or additional onerous storage and computational burdens. In this work, an efficient and non-IID robust federated continual learning framework, called Federated Prototype-Augmented Prompt Learning (FPPL), is proposed. The FPPL can collaboratively learn lightweight prompts augmented by prototypes without rehearsal. On the client side, a fusion function is employed to fully leverage the knowledge contained in task-specific prompts for alleviating catastrophic forgetting. Additionally, global prototypes aggregated from the server are used to obtain unified representation through contrastive learning, mitigating the impact of non-IID-derived data heterogeneity. On the server side, locally uploaded prototypes are utilized to perform debiasing on the classifier, further alleviating the performance degradation caused by both non-IID and catastrophic forgetting. Empirical evaluations demonstrate the effectiveness of FPPL, achieving notable performance with an efficient design while remaining robust to diverse non-IID degrees. Code is available at: https://***/ycheoo/FPPL. © 2024 IEEE.

关键词： Federated learning

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Identification and Visualization of Spatial and Temporal Trends in Textile Industry

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computers, Materials & Continua 2023年第2期74卷 4165-4181页

作者： Umair Yousaf Muhammad Asif Shahbaz Ahmed Noman Tahir Azeem Irshad Akber Abid Gardezi Muhammad Shafiq Jin-Ghoo Choi Habib Hamam Department of Computer Science National Textile UniversityFaisalabad38000Pakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Faculty of Engineering Uni de MonctonMonctonNB E1A3E9Canada International Institute of Technology and Management Commune d’AkandaBPLibreville1989Gabon School of Electrical Engineering Department of Electrical and Electronic Eng.ScienceUniversity of JohannesburgJohannesburg2006South Africa Spectrum of Knowledge Production&Skills Development Sfax3027Tunisia

The research volume increases at the study rate,causing massive text *** to these enormous text corpora,we are drowning in data and starving for ***,recent research employed different text mining approaches to extract information from this text *** proposed approaches extract meaningful and precise phrases that effectively describe the text’s *** extracted phrases are commonly termed ***,these key phrases are employed to determine the different fields of study ***,these key phrases can also be used to determine the spatiotemporal trends in the various research *** this research,the progress of a research field can be better revealed through spatiotemporal bibliographic trend ***,an effective spatiotemporal trend extraction mechanism is required to disclose textile research trends of particular regions during a specific *** study collected a diversified dataset of textile research from 2011–2019 and different countries to determine the research *** data was collected from various open access ***,this research determined the spatiotemporal trends using quality *** research also focused on finding the research collaboration of different countries in a particular research *** research collaborations of other countries’researchers show the impact on import and export of those *** visualization approach is also incorporated to understand the results better.

关键词： Text mining spatiotemporal trend research collaboration

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A ResNet-Based DVFS Regulator for Heterogeneous Multi-Core Mobile Processors

A ResNet-Based DVFS Regulator for Heterogeneous Multi-Core M...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Hu, Shibo Xu, Xinzi Chen, Yuze Qin, Muyun Lian, Yong Zhao, Yang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Xiaomi Mobile Software Co. Ltd. Beijing China York University Department of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350350821

Dynamic voltage and frequency scaling (DVFS) is commonly used for the balance of performance and power of mobile processors. Conventional DVFS regulators take the processor cores with the same power and performance weight showing their inability in the regulation of heterogeneous processors. This paper proposes a core-aware frequency-power evaluation scheme that utilizes a multilayer perceptron (MLP) to assess the efficacy of DVFS regulation actions. Additionally, a ResNet-based DVFS regulator, trained with the assistance of the MLP, is developed for heterogeneous multi-core processors. Evaluated on Xiaomi 13 Pro powered by a Qualcomm Snapdragon 8 Gen 2, the proposed DVFS approach achieves an up to ×1.47 improvement in FPS Performance Per Watt (FPPW) compared with the Qualcomm's default DVFS governor in video recording scenario. © 2024 IEEE.

关键词： Dynamic frequency scaling

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Relationship between factors influencing the software development process and software defects 13

Relationship between factors influencing the software develo...

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13th International Conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Gordieiev, Oleksandr Gordieieva, Daria Rainer, Austen Pishchukhina, Olga Lutsk National Technical University Department of Software engineering Lutsk Ukraine Queen's University Belfast School of Electronics Electrical Engineering and Computer Science Belfast United Kingdom Lutsk National Technical University Department of Computer Engineering and Cybersecurity Lutsk Ukraine

ISBN: (纸本)9798350396119

Poor software quality can lead to crashes, failures or downtime, the consequences of which may be lost profits, financial costs, leakage or loss of data, accidents, human casualties, material losses or environmental disasters. The total cost of poor software quality for IT companies in the USA is estimated at $2.41 trillion per year and tends to increase. Among the identified costs, the cost of unsuccessful projects is estimated at $260 billion, and the total cost of operational failures caused by poor quality software is estimated at $1.81 trillion. Late finding and fixing of software defects directly affects the success of the project as a whole, since it significantly increases the cost and development time, and affects its quality. This paper discussed the relationship between factors affecting the software development process and various groups of defects. It has been found that the leading groups of defects affecting the software quality are the defects in software requirements and defects in the design of software interfaces for human-computer interaction with the total percentage of influence of 33.3%. © 2023 IEEE.

关键词： Defects

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Using analytic hierarchy process to evaluate deep learning for infrared target recognition

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Multimedia Tools and Applications 2024年第38期83卷 86229-86245页

作者： Xu, Laixiang Cao, Bingxu Wei, Xinhong Li, Bei Su, Jingfeng Ning, Shiyuan Zhao, Junmin School of Computer and Data Science Henan University of Urban Construction Pingdingshan467036 China School of Information Engineering Luohe Vocational Technology College Henan University of Technology Luohe Institute of Technology Luohe462000 China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Pingdingshan467036 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan467036 China Department of Software Information China Electronics Technology Group Corporation 36-th Research Institute Jiaxing314033 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan467036 China

In order to promote the evaluation performance of deep learning infrared automatic target recognition (ATR) algorithms in the complex environment of air-to-air missile research, we proposed an analytic hierarchy process (AHP) evaluation system consisting of the target recognition ability, the generalization recognition ability, the anti-interference ability, the background suppression ability, and the cost metrics ability. According to detailed quantitative analysis and experimental results, we establish probability evaluation models and get their probability values. Compared with traditional evaluation systems, our proposed method implementation is showing high reliability. Similar superior performances are also achieved, such as quick, comprehensive, and scientific. Through the above research, the key technology of deep learning performance evaluation in automatic target recognition has been broken through, which provides some basis and guidance for the analysis of infrared imaging-guided weapon systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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Multi-View User Preference Modeling for Personalized Text-to-Image Generation

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IEEE Transactions on Multimedia 2025年 27卷 3082-3091页

作者： Zhang, Huaiwen Wu, Tianci Wei, Yinwei Inner Mongolia University College of Computer Science Hohhot010021 China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot010021 China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot010021 China Shandong University School of Software Jinan250100 China

Personalized text-to-image generation aims to synthesize images tailored to individual user preferences. Existing methods primarily generate customized content using a few reference images, which often struggle to mine user preferences from historical records, and thus fail to synthesize truly personalized content. In addition, it is difficult to directly incorporate the extracted feature of user preferences into the feature space of the generation model, since there exists a considerable gap between them. In this paper, we propose a novel multi-view personalized text-to-image generation method based on the diffusion model, named MVP-Diffusion, which learns instance- and user-level preferences from historical records and integrates them into the generation model. For instance-level user preference modeling, we employ a chain-of-thought prompting strategy to deduce preference keywords and integrate them into input prompts with the aid of a large language model. For user-level preference modeling, we construct a learnable embedding for each user to capture more comprehensive preferences by analyzing their historical records. An adaptive user preference fusion module is proposed to inject user preferences into the generation model via a set of learnable parameters. Experimental results demonstrate that the proposed method significantly enhances the personalization of the generated images compared to the other personalized text-to-image generation methods. © 1999-2012 IEEE.

关键词： Adaptation models User preference Text to image Large language models Training Analytical models Image synthesis Feature extraction Diffusion models Symbols

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Power in computer Security and Privacy: A Critical Lens

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IEEE Security and Privacy 2023年第2期21卷 48-52页

作者： Redmiles, Elissa M. Bennett, Mia M. Kohno, Tadayoshi Max Planck Institute for Software Systems Saarland66123 Germany University of Washington SeattleWA98195 United States Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA98195 United States

Critical theory is an approach to research focused on acknowledging and dismantling power structures. In this piece, we illustrate the ways in which security and privacy research already takes a critical approach and ... 详细信息

关键词： computer networks

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