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6th ACM International Conference on Multimedia in Asia, MMAsia 2024

作者： Yang, Hairui Wang, Ning Wang, Zhihui Wang, Lei University of Electronic Science and Technology of China Zhejiang Huzhou China International School of Information Science and Engineering Dalian University of Technology Liaoning Dalian China Software School Dalian University of Technology Liaoning Dalian China

ISBN: (纸本)9798400712739

Sketch-based 3D model retrieval is a challenging task in computer vision, requiring simultaneous solutions to discriminative feature learning and cross-modal semantic *** methods typically project sketches and 3D models into a shared feature space for both feature learning and semantic alignment, often resulting in the sub-optimal match of query sketch and retrieved 3D *** paper proposes a novel sketch-based 3D model retrieval method based on cross-modal ***, discriminative features of 3D models are learned without interference from ***, sketch feature learning and semantic alignment are guided by learned prototypes of 3D model ***, cross-modal representations generated by vision-language pre-trained models are introduced as category similarity priors to enhance the discriminative nature of sketch *** results demonstrate that the proposed method outperforms existing methods in retrieval performance, achieving a 2.5% improvement in mean average precision (mAP) on SHREC 2016. © 2024 Copyright held by the owner/author(s).

关键词： 3D modeling

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Zero Trust Networks: Evolution and Application from Concept to Practice

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Computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

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A Combination Model of Periodic and Non-periodic Events for Temporal Knowledge Graph Event Prediction

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Chinese Journal of Electronics 2025年第3期34卷 952-961页

作者： Chen Yue Huang Yongzhong School of Computer Science and Information Security Guilin University of Electronic Technology Guilin 541004 China School of Data Science and Software Engineering Wuzhou University Wuzhou 543002 China

Temporal knowledge graph (TKG) reasoning aims to predict missing facts or future events at given timestamps and has attracted more and more attention in recent years. Existing TKG reasoning methods mainly focus on the interactions between entities and ignore the associations between events where the entities involve. In addition, the characteristics of different types of events have not been studied and exploited, which reduces the performance of event prediction. To address these problems, this paper proposes a combination model of periodic and non-periodic events (CM-PNP). Specifically, there are two basic components designed to process different types of events. The periodic component of CM-PNP learns the recurrent pattern of periodic events and encodes the temporal information in the manner of timespan to prevent the unseen timestamp issue. The non-periodic component of CM-PNP introduces extra information (e.g., entity attributes) to represent non-periodic events, and predicts this type of events according to the related historical events. A combination model of multiple sub-models that focus on encoding different parts of the event is used to improve the performance of single model. The periodic and non-periodic components are combined by a gate block. The experimental results on three real-world datasets demonstrate that CM-PNP outperforms the existing baselines.

关键词： Temporal knowledge graph Temporal knowledge graph reasoning Event prediction Embedding learning Data mining

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Locating What You Need: Towards Adapting Diffusion Models to OOD Concepts In-the-Wild 38

Locating What You Need: Towards Adapting Diffusion Models to...

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Yang, Jianan Gao, Chenchao Xiao, Zhiqing Zhao, Junbo Wu, Sai Chen, Gang Wang, Haobo College of Computer Science and Technology Zhejiang University China Institute of Blockchain and Data Security China School of Software Technology Zhejiang University China International School of Information Science and Engineering Dalian University of Technology China

The recent large-scale text-to-image generative models have attained unprecedented performance, while people established adaptor modules like LoRA and DreamBooth to extend this performance to even more unseen concept tokens. However, we empirically find that this workflow often fails to accurately depict the out-of-distribution concepts. This failure is highly related to the low quality of training data. To resolve this, we present a framework called Controllable Adaptor Towards Out-of-Distribution Concepts (CATOD). Our framework follows the active learning paradigm which includes high-quality data accumulation and adaptor training, enabling a finer-grained enhancement of generative results. The aesthetics score and concept-matching score are two major factors that impact the quality of synthetic results. One key component of CATOD is the weighted scoring system that automatically balances between these two scores and we also offer comprehensive theoretical analysis for this point. Then, it determines how to select data and schedule the adaptor training based on this scoring system. The extensive results show that CATOD significantly outperforms the prior approaches with an 11.10 boost on the CLIP score and a 33.08% decrease on the CMMD metric. © 2024 Neural information processing systems foundation. All rights reserved.

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An Efficient Convolutional Multi-Scale Vision Transformer for Image Classification 2

An Efficient Convolutional Multi-Scale Vision Transformer fo...

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2nd International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Zhang, Ji Chen, Zhihao Ge, Yiyuan Yu, Mingxin Beijing Information Science and Technology University School of Instrument Science and Opto-Electronics Engineering Beijing China Beijing Information Science and Technology University Computer School Beijing China Jilin University Department of Software Engineering Changchun China

ISBN: (纸本)9798350331417

This paper introduces an innovative and efficient multi-scale Vision Transformer (ViT) for the task of image classification. The proposed model leverages the inherent power of transformer architecture and combines it with the concept of multi-scale processing generally used in convolutional neural networks (CNNs). The work aims to address the limitations of conventional ViTs which typically operate at a single scale, hence overlooking the hierarchical structure in visual data. The multi-scale ViT enhances classification performance by processing image features at different scales, effectively capturing both low-level and high-level semantic information. Extensive experimental results demonstrate the superior performance of the proposed model over standard ViTs and other state-of-the-art image classification methods, signifying the effectiveness of the multi-scale approach. This research opens new avenues for incorporating scale-variance in transformer-based models for improved performance in vision tasks. © 2023 IEEE.

关键词： Classification Convolutional neural networks Multi-scale Vision Transformer

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Design of Multimedia Database Online Integration System Based on Artificial Intelligence 2

Design of Multimedia Database Online Integration System Base...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Li, Jiaqi Xiong, Gaojie Wu, Xing Harbin Institute of Technology Computer Sciense and Technology Guangdong Shenzhen China University of Electronic Science and Technology School of Information and Software Engineering Sichuan Chengdu China Zhejiang Vocational Academy of Art Zhejiang Hangzhou China

ISBN: (纸本)9798350317558

Multimedia database online integration system plays an important role in today's information age. However, the performance of the integrated system is limited by the problems of low accuracy, insufficient recall rate and long running time of traditional methods in processing multimedia data. This paper designs an online integration system based on artificial intelligence (AI) and verifies its effectiveness and feasibility through experiments so as to overcome these problems. In the study, multimedia datasets suitable for the experimental needs are selected and the AI-based online integration system is compared with traditional methods. Through the analysis of accuracy, recall, F1 value and mean square error, it is found that the online integration system based on AI has obvious advantages in multimedia data integration. The results of this paper demonstrate the feasibility and effectiveness of an online integration system based on AI. It is able to enhance the accuracy and efficiency of multimedia data integration while reducing runtime and resource consumption. © 2024 IEEE.

关键词： artificial intelligence integration efficiency multimedia database online integration

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Graph-based anomaly detection using regression on HTTP 2

Graph-based anomaly detection using regression on HTTP

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2nd International Symposium on Computer technology and information Science, ISCTIS 2022

作者： Wu, Han Jiang, Zhupeng He, Fengyu Computer Science and Engineering Beijing Information Science Technology University Beijing China Software Engineering Beijing Information Science Technology University Beijing China

ISBN: (纸本)9781510660533

Detecting anomalies in HTTP request data is a vital security task. With big data becoming ubiquitous, techniques for structured graph data have been focused on recent years. As nodes in graphs have long-distance correlations, detecting anomaly in plain structured graph data is practical. This paper proposes a node-level feature-based regression detection method. Given a graph generated from a snapshot of HTTP request data collected by API gateway and considering clustering coefficient and empirical inspired rules, construct a regression model to dig out substantially deviate nodes. Extensive experimental studies on a real-world request dataset demonstrate that it performs relatively prominent and favorably to HBOS (a concurrent density-based method) and iForest (a linear time complexity model-based method with a low memory requirement) in terms of ROC-AUC and processing time. © 2022 SPIE.

关键词： Big data

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A Method for Maximizing Social Network Influence Considering Node Reading Effect and Delay 6

A Method for Maximizing Social Network Influence Considering...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Su, Sheng Li, Jingshu Liu, Wenxian Liu, Qiuxia Zhao, Fei Tian, Yuan Tian, Ye University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Luzhou Laojiao Group Co. Ltd Department of Information Luzhou China

ISBN: (纸本)9798350325485

The dissemination of articles in social networks is not only related to the centrality of nodes, but also to the reading effect and delay of nodes. In order to more accurately measure the influence of nodes in the process of article dissemination, we propose a social network impact maximization method that considers the reading effect and delay of nodes. The reading effect of a node refers to the probability of forwarding and reading articles after receiving them, while the delay of a node refers to the time delay of forwarding or reading articles after receiving information. This method calculates the influence of each node during the article dissemination process by establishing an influence model that includes reading effect and delay, and selects some nodes as seed nodes. This method can improve the dissemination effect of articles and help design more effective social network marketing strategies and advertising placement plans. © 2023 IEEE.

关键词： Marketing

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Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management 24

Application of Machine Learning Optimization in Cloud Comput...

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5th International Conference on Computer information and Big Data Applications, CIBDA 2024

作者： Zhang, Yifan Huang, Jiaxin Liu, Bo Xu, Jingyu Gong, Yulu Wan, Weixiang Shaanxi Xi’an China Information Studies Trine University Phoenix United States Software Engineering Zhejiang University HangZhou China Computer Information Technology Northern Arizona University FlagstaffAZ United States Electronics & Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798400718106

In recent years, cloud computing has been widely used. Cloud computing refers to the centralized computing resources, users through the access to the centralized resources to complete the calculation, the cloud computing center will return the results of the program processing to the user. Cloud computing is not only for individual users, but also for enterprise users. By purchasing a cloud server, users do not have to buy a large number of computers, saving computing costs. According to a report by China Economic News Network, the scale of cloud computing in China has reached 209.1 billion yuan. Rational allocation of resources plays a crucial role in cloud computing. In the resource allocation of cloud computing, the cloud computing center has limited cloud resources, and users arrive in sequence. Each user requests the cloud computing center to use a certain number of cloud resources at a specific time. © 2024 ACM.

关键词： Cloud computing

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Improving students' cognitive abilities in online environment based on neurofeedback 14

Improving students' cognitive abilities in online environmen...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Jamil, Nuraini Lakas, Abderrahmane Belkacem, Abdelkader Nasreddine College of Information Technology United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates College of Information Technology United Arab Emirates University Department of Computer and Network Engineering United Arab Emirates

ISBN: (纸本)9798350399431

The brain-computer interface (BCI) and eye-tracking technologies can potentially improve the learning environment in education. Cognitive BCIs can give a deep knowledge of brain functioning, enabling the creation of more effective learning approaches and improving brain-based abilities. This study proposes a neurofeedback strategy based on BCI and eye-tracking to collect factual data (monitoring students' brainwaves and eye movement) and analyze their cognitive capacities during online learning. This study aims to create patterns regarding students' learning behavior based on brain and eye movement responses to learning activities as part of the learning environment. As a result, teachers may adapt to new pedagogical ideas and a more flexible delivery style. © 2023 IEEE.

关键词： Brain

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