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11th International Conference on Information Technology in Medicine and Education, ITME 2021

作者： Li, Hui-Ying Lei, Qing Zhang, Hong-Bo Du, Ji-Xiang Huaqiao University Department of Computer Science and Technology Xiamen China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen China

ISBN: (纸本)9781665406796

Action quality assessment(AQA) aims at achieving automatic evaluation the performance of human actions in video. Compared with action recognition problem, AQA focuses more on subtle differences both in spatial and temporal dimensions during the whole executing process of actions. However, most existing AQA methods tried to extract features directly from RGB videos through a 3D ConvNets, which makes the features mixed with useless scene information. To overcome this problem, We propose a deep pose feature learning AQA method that captured detailed and meaningful representations for skeleton information to discover the subtle motion difference of AQA problem. We first apply pose estimation method to obtain human skeleton data from RGB videos. Then a spatio-temporal graph convolutional network (ST-GCN) is employed to extract the dynamic changes of skeleton data and obtain the representative pose features. Finally, a regressor composed of three fully connected layers is developed to reduce the dimension of the obtained pose features and predict the final score. Experiments on MIT figure skating dataset have been extensively conducted, and the results demonstrate that the proposed method has achieved improvements that outperformed current state-of-the-art methods. © 2021 IEEE.

关键词： Musculoskeletal system

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Knowledge Distillation based Lightweight Adaptive Graph Convolutional Network for Skeleton-based action recognition 11

Knowledge Distillation based Lightweight Adaptive Graph Conv...

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11th International Conference on Information Technology in Medicine and Education, ITME 2021

作者： Qiu, Zhong-Xiang Zhang, Hong-Bo Lei, Qing Du, Ji-Xiang Huaqiao University Department of Computer Science and Technology Xiamen China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China

ISBN: (纸本)9781665406796

Skeleton-based human action recognition has received extensive attention due to its easy access to human skeleton data. However, the current mainstream skeleton-based action recognition methods have more or less the problem of overlarge parameters, which makes it difficult for these methods to meet the requirements of timeliness and accuracy. To solve this problem, we improve attention-enhanced adaptive graph convolutional neural network (AAGCN) to obtain a high-precision improved AAGCN (IAAGCN), and use it as teacher model to conduct knowledge distillation of our lightweight IAAGCN (LIAAGCN). The results of the tests on the NTU-RGBD dataset are validated by knowledge distillation to allow LIAAGCN to maintain good accuracy while keeping the parameters small. © 2021 IEEE.

关键词： Convolution

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High-Resolution Polymerization Network For Multi-Person Pose Estimation 11

High-Resolution Polymerization Network For Multi-Person Pose...

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11th International Conference on Information Technology in Medicine and Education, ITME 2021

作者： Bai, Yu-Fei Zhang, Hong-Bo Lei, Qing Du, Ji-Xiang Huaqiao University Department of Computer Science and Technology Xiamen China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen China

ISBN: (纸本)9781665406796

In this paper, we propose a High-Resolution Polymerization Network (HPN) for Multi-Person Pose Estimation. First, we use the separate channel convolution module High-Resolution Polymerization Block(HPB) in HPN to optimize the input features. The HPB module can segment local features on the channel multiple times and fuse different scales, learn more refined features to extract reliable keypoint coordinate representations. In addition, we observe that there is only a simple vertical identity mapping relationship among the features in the network structure. In order to further utilize the high-resolution features that contain rich spatial information, we extend the horizontal mapping of network characteristics, use parallel convolutional to link high and low resolution features across scales, enhance feature information by maintaining high-resolution representation, to achieve the purpose of precise keypoint positioning. Our method is evaluated on the COCO data set, and the current method has reached an advanced level. © 2021 IEEE.

关键词： Convolution

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A Multi-Stage Vision Transformer for Fine-grained Image Classification 11

A Multi-Stage Vision Transformer for Fine-grained Image Clas...

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11th International Conference on Information Technology in Medicine and Education, ITME 2021

作者： Huang, Zheng Du, Ji-Xiang Zhang, Hong-Bo Huaqiao University Department of Computer Science and Technology Xiamen China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China

ISBN: (纸本)9781665406796

Different from general image classification, Fine-grained visual classification(FGVC) aims at recognizing the subclass of an object. This task is challenging due to the differences between subcategories that might be very small. At present, Vision Transformer(ViT) achieved huge success in traditional classification tasks benefited from its innate attention mechanism. Recent works extend the vision trans-former into the FGVC task and achieve state-of-the-art performance. However, those transformer-based structures inevitably harm the discriminative regions while also requiring a high computational cost. To address these problems, we propose a pooling-based vision transformer model with overlapping patches(OP-ViT), which inserts the pooling layer into a transformer encoder to form a multi-stage hierarchical structure. In particular, the overlapping patches scheme can preserve the informational region effectively. We evaluate the model on two fine-grained mushroom datasets. The experimental results show that the proposed model achieves competitive performance with fewer parameters and computational overhead. © 2021 IEEE.

关键词： Image classification

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A secure access and associate deleting scheme for multi-replica in multi-cloud environment

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International Journal of High Performance Systems Architecture 2016年第3期6卷 143-152页

作者： Xiong, Jinbo Li, Suping Li, Xuan Zhang, Yuanyuan Jin, Biao Faculty of Software Fujian Normal University Fuzhou China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou China Fujian Engineering Research Center Public Service Big Data Mining and Application Fuzhou China

With the rapid development of cloud services in multi-cloud environment, it is convenient to generate multi-replica for user's data stored in multi-cloud servers. On the one hand, multi-replica is able to improve data availability and users' experience. But on the other hand, it becomes increasingly easy to disclose sensitive data and brings serious challenges to security and privacy for user's data and its multi-replica. In order to tackle the above issues, in this paper, we propose a secure access and associate deleting scheme for multi-replica (MADS) in multi-cloud environment, which is based on symmetric encryption algorithm, attribute-based encryption and replica location technology. We construct a novel multi-layered key structure, called multi-way search key tree (MSKT) that ensures no key material will be revealed, yet the data owner is able to control the master key and manage other keys by performing some tree operations. We also propose a data replica associated model, which is able to associate all the data replicas in multi-cloud environment. Comprehensive comparison and security analysis demonstrate that the proposed MADS scheme is effective and secure. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Sensitive data

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Experimental Analysis of Selective Imitation for Multifactorial Differential Evolution 14th

Experimental Analysis of Selective Imitation for Multifactor...

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Peng, Deming Cai, Yiqiao Fu, Shunkai Luo, Wei College of Computer Science and Technology Huaqiao University Quanzhou China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen361000 China

ISBN: (纸本)9789811534249

Recently, evolutionary multitasking optimization (EMTO) is proposed as a new emerging optimization paradigm to simultaneously solve multiple optimization tasks in a cooperative manner. In EMTO, the knowledge transfer between tasks is mainly carried out through the assortative mating and selective imitation operators. However, in the literature of EMTO, little study on the selective imitation operator has yet been done to provide a deeper insight in the knowledge transfer across different tasks. Based on this consideration, we firstly study the influence of the inheritance probability (IP) of the selective imitation on an EMTO algorithm, multifactorial differential evolution (MFDE), through the experimental analysis. Then, an adaptive inheritance mechanism (AIM) is introduced into the selective imitation operator of MFDE to automatically adjust the IP value for different tasks at different evolutionary stages. The experimental results on a suite of single-objective multitasking benchmark problems have demonstrated the effectiveness of AIM in enhancing the performance of MFDE. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Multitasking

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Legal Risks and Governance Paths for Generative AI - A Case Study of ChatGPT 13

Legal Risks and Governance Paths for Generative AI - A Case ...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Zhenfeng, Wang Quanwang, Lin Huihui, Gao Xiehua, Yu University of Fujian Province Key Laboratory of Big Data and Artificial Intelligence China MinNan Science and Technology University Fujian China School of Computer and Information MinNan Science and Technology University Quanzhou China University of Fujian Key Laboratory of Big Data and Artificial Intelligence Quanzhou China

ISBN: (纸本)9798350319156

The release of OpenAI's ChatGPT chatbot has sparked widespread concern around the world about the threats and legal issues of generative AI. Although this technological innovation has opened a new chapter in the field of artificial intelligence, it has also caused the public to worry about its possible negative effects. In order to solve the above problems and reasonably regulate the legal risks of generative artificial intelligence, this paper mainly adopts the literature, quantitative and qualitative analysis methods, etc. The first part mainly introduces the development process and current situation of generative artificial intelligence, the second part mainly discusses the impact of generative artificial intelligence on tort law, the third part mainly discusses the legal risks of generative artificial intelligence, and the fourth part mainly discusses the governance path of generative artificial intelligence legal risks. The last part is the conclusion of the research. Among them, the fourth part is the focus of this paper. By taking these issues into account, we can provide legal guarantees for the sustainable development of the generative AI industry and ensure that the widespread application of AI benefits human society[1]. © 2023 IEEE.

关键词： Artificial intelligence

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

arXiv

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arXiv 2025年

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fujian Key Laboratory of Network Fuzhou350002 China Computing and Intelligent Information Processing [Fuzhou University Fuzhou350116 China Department of Computer Science Faculty of Environment Science and Economy University of Exeter ExeterEX4 4QF United Kingdom School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Informatics University of Oslo Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Layer-Wise Learning Rate Optimization for Task-Dependent Fine-Tuning of Pre-Trained Models: An Evolutionary Approach

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-23页

作者： Bu, Chenyang Liu, Yuxin Huang, Manzong Shao, Jianxuan Ji, Shengwei Luo, Wenjian Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

The superior performance of large-scale pre-Trained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT), has received increasing attention in both academic and industrial research and has become one of the current research hotspots. A pre-Trained model refers to a model trained on large-scale unlabeled data, whose purpose is to learn general language representation or features for fine-Tuning or transfer learning in subsequent tasks. After pre-Training is complete, a small amount of labeled data can be used to fine-Tune the model for a specific task or domain. This two-stage method of "pre-Training+fine-Tuning"has achieved advanced results in natural language processing (NLP) tasks. Despite widespread adoption, existing fixed fine-Tuning schemes that adapt well to one NLP task may perform inconsistently on other NLP tasks given that different tasks have different latent semantic structures. In this article, we explore the effectiveness of automatic fine-Tuning pattern search for layer-wise learning rates from an evolutionary optimization perspective. Our goal is to use evolutionary algorithms to search for better task-dependent fine-Tuning patterns for specific NLP tasks than typical fixed fine-Tuning patterns. Experimental results on two real-world language benchmarks and three advanced pre-Training language models show the effectiveness and generality of the proposed framework. © 2024 held by the owner/author(s).

关键词： Semantics

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Probabilistic Reliability via Subsystem Structures of Arrangement Graph Networks

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IEEE Transactions on Reliability 2024年第1期73卷 279-289页

作者： Huang, Yanze Lin, Limei Xu, Li Hsieh, Sun-Yuan Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou350117 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid growth of the number of processors in a multiprocessor system, faulty processors occur in it with a probability that rises quickly. The probability of a subsystem with an appropriate size being fault-free in a definite time interval is a significant and practical measure of the reliability for a multiprocessor system, which characterizes the functionality of a multiprocessor system well. Motivated by the study of subgraph reliability, as well as the attractive structure and fault tolerance properties of (n, k)-arrangement graph A n, k, we focus on the subgraph reliability for A n, k under the probabilistic fault model in this article. First, we investigate intersections of no more than four subgraphs in A n, k, and classify all the intersecting modes. Second, we focus on the probability P(q, A n, k n-1, k-1) with which at least one (n-1, k-1)-subarrangement graph is fault-free in A n, k, when given a uniform probability q with which a single vertex is fault-free, and we establish the P(q, A n, kn-1, k-1) by adopting the principle of inclusion-exclusion under the probabilistic fault model. Finally, we study the probabilistic fault model involving a nonuniform probability with which a single vertex is fault-free, and we prove that the P(q, A n, kn-1, k-1) under both models is very close to the asymptotic value by both theoretical arguments and experimental results. © 1963-2012 IEEE.

关键词： Fault tolerance

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