检索结果-内蒙古大学图书馆

Cooperative Sensing and Heterogeneous Information Fusion in VCPS: A Multi-agent Deep Reinforcement Learning Approach

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Xu, Xincao Liu, Kai Dai, Penglin Xie, Ruitao Cao, Jingjing Luo, Jiangtao The College of Computer Science Chongqing University Chongqing400040 China The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China The National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The School of Transportation and Logistics Engineering Wuhan University of Technology Hubei430063 China The Electronic Information and Networking Research Institute Chongqing University of Posts and Telecommunications Chongqing400065 China

Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing a new metric called Age of View (AoV). Specifically, we first present the system architecture where heterogeneous information can be cooperatively sensed and uploaded via vehicle-to-infrastructure (V2I) communications in vehicular edge computing (VEC). Logical views are constructed by fusing the heterogeneous information at edge nodes. Further, we formulate the problem by deriving a cooperative sensing model based on the multi-class M/G/1 priority queue, and defining the AoV by modeling the timeliness, completeness and consistency of the logical views. On this basis, a multi-agent deep reinforcement learning solution is proposed. In particular, the system state includes vehicle sensed information, edge cached information and view requirements. The vehicle action space consists of the sensing frequencies and uploading priorities of information. A difference-reward-based credit assignment is designed to divide the system reward, which is defined as the VCPS quality, into the difference reward for vehicles. Edge node allocates V2I bandwidth to vehicles based on predicted vehicle trajectories and view requirements. Finally, we build the simulation model and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solution. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

Virtual Reality, Augmented Reality and Mixed Reality on the Marketing of Film and Television Creation Industry 3rd

学校读者我要写书评

暂无评论

Virtual Reality, Augmented Reality and Mixed Reality on the ...

3rd EAI International Conference on 6G for Future Wireless Networks, 6GN 2020

作者： Ge, Rui Chen, Hsuan-Yu Hsiao, Tsung-Chih Chang, Yu-Tzu Wu, Zhen-Yu College of Computer Science and Technology Huaqiao University Quanzhou Fujian China Department of Radiology Tri-Service General Hospital National Defense Medical Center Taipei Taiwan Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Quanzhou China Department of Marketing and Logistics Management Chaoyang University of Technology Taichung Taiwan Department of Information Management National Penghu University of Science and Technology Penghu Taiwan

ISBN: (纸本)9783030639402

Virtual reality and augmented reality technology has been on the road to scientific research for many years since the end of the twentieth century, and although the scope of mixed reality application is relatively narrow compared to the first two applications, we have seen the results. Realistic contents also have exploring progress in the field of film and television creation in recent years, such as virtual reality technology can be utilized to make film clips. However, in fact, these technologies can be applied in film and television creation far more than that. I then illustrate some other aspects which these realistic contents can also be applied to in film and television creation in the future and the influence on the industry. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Augmented reality

data-Driven Single Image Deraining: A Comprehensive Review and New Perspectives

学校读者我要写书评

暂无评论

TechRxiv

TechRxiv 2021年

作者： Zhang, Zhao Wei, Yanyan Zhang, Haijun Yang, Yi Yan, Shuicheng Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei230009 China Shenzhen China Centre for Artificial Intelligence University of Technology Sydney SydneyNSW Australia Department of Electrical and Computer Engineering National University of Singapore Singapore Hefei University of Technology China

Single Image Deraining task aims at recovering the rain-free background from an image degraded by rain streaks and rain accumulation. For the powerful fitting ability of deep neural networks and massive training data, data-driven deep SID methods obtained significant improvement over traditional ones. Current SID methods usually focus on improving the deraining performance by proposing different kinds of deraining networks, while neglecting the interpretation of the solving process. As a result, the generalization ability may still be limited in real-world scenarios, and the deraining results also cannot effectively improve the performance of subsequent high-level tasks (e.g., object detection). To explore these issues, we in this paper re-examine the three important factors (i.e., data, rain model and network architecture) for the SID problem, and specifically analyze them by proposing new and more reasonable criteria (i.e., general vs. specific, synthetical vs. mathematical, black-box vs. white-box). We also study the relationship of the three factors from a new perspective of data, and reveal two different solving paradigms (explicit vs. implicit) for the SID task. We further discuss the current mainstream data-driven SID methods from five aspects, i.e., training strategy, network pipeline, domain knowledge, data preprocessing, and objective function, and some useful conclusions are summarized by statistics. Besides, we profoundly studied one of the three factors, i.e., data, and measured the performance of current methods on different datasets through extensive experiments to reveal the effectiveness of SID data. Finally, with the comprehensive review and in-depth analysis, we draw some valuable conclusions and suggestions for future research. © 2021, CC BY.

关键词： Object detection

Dictionary Pair-based data-Free Fast Deep Neural Network Compression

学校读者我要写书评

暂无评论

Dictionary Pair-based Data-Free Fast Deep Neural Network Com...

IEEE International Conference on data Mining (ICDM)

作者： Yangcheng Gao Zhao Zhang Haijun Zhang Mingbo Zhao Yi Yang Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Harbin Institute of Technology (Shenzhen) Xili University Town Shenzhen China City University of Hong Kong Hong Kong SAR Centre for Artificial Intelligence University of Technology Sydney Sydney NSW Australia

ISBN: (纸本)9781665423991

Deep neural network (DNN) compression can reduce the memory footprint of deep networks effectively, so that the deep model can be deployed on the portable devices. However, most of the existing model compression methods cost lots of time, e.g., vector quantization or pruning, which makes them inept to the real-world applications that need fast online computation. In this paper, we therefore explore how to accelerate the model compression process by reducing the computation cost. Then, we propose a new deep model compression method, termed Dictionary Pair-based data-Free Fast DNN Compression, which aims at reducing the memory consumption of DNNs without extra training and can greatly improve the compression efficiency. Specifically, our proposed method performs tensor decomposition on the DNN model with a fast dictionary pair learning-based reconstruction approach, which can be deployed on different layers (e.g., convolution and fully-connection layers). Given a pre-trained DNN model, we first divide the parameters (i.e., weights) of each layer into a series of partitions for dictionary pair-based fast reconstruction, which can potentially discover more fine-grained information and provide the possibility for parallel model compression. Then, dictionaries of less memory occupation are learned to reconstruct the weights. Extensive experiments on popular DNNs (i.e., VGG-16, ResNet-18 and ResNet-50) showed that our proposed weight compression method can significantly reduce the memory footprint and speed up the compression process, with less performance loss.

关键词： Deep learning Training Performance evaluation Dictionaries Tensors Costs Computational modeling

DE-RSTC: A rational secure two-party computation protocol based on direction entropy

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Chen, Yuling Ma, Juan Wang, Xianmin Zhang, Xinyu Zhou, Huiyu State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University Of Electronic Technology Guilin541000 China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510000 China University of Leicester LeicesterLe1 7RH United Kingdom

Rational secure multi-party computation (RSMC) means two or more rational parties to complete a function on private inputs. In the process, the rational parties choose strategies to maximize utility, which will cause players to maliciously execute the protocol and undermine the fairness and correctness of the protocol. To solve this problem, we leverage game theory to propose the direction entropy-based solution. First, we utilize the direction vector of the direction entropy to examine the player's strategy uncertainty and quantify its strategy from different dimensions. Specifically, when parties choose a cooperation strategy, the direction vector is positive, and the information transmitted is positive, conversely, it is negative information. Then, we provide mutual information to construct new utility functions for the players. What's more, we measure the mutual information of players to appraise their strategies. Finally, we prove in detail the protocol we gave, and the result show that the fairness problem in rational secure two-party computation. We also prove that the proposed protocol reaches the Nash equilibrium. Furthermore, we conduct experiments using mutual information to construct utility, and the results show that the utility obtained when the player is honest will be higher. Copyright © 2021, The Authors. All rights reserved.

关键词： Entropy

Adaptive feature selection with weighted fuzzy rough sets for noisy data

学校读者我要写书评

暂无评论

Fuzzy Sets and Systems 2025年 518卷

作者： Xiaoling Yang Hongmei Chen Tianrui Li Shan Feng Jihong Wan Yiyu Yao School of Mathematical Sciences Sichuan Normal University China School of Computing and Artificial Intelligence Southwest Jiaotong University China Department of Computer Science University of Regina Regina Canada National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University China

The existence of noise in data may decrease the performance of the classification model. An intuitive assumption suggests that samples with a higher local density have a greater probability of being correctly classified. Moreover, there are differences in the importance of features. It should further increase the influence of important features on the classification task. Motivated by these issues, we propose a sample-driven adaptive feature selection method. It aims to the impact of noise and obtain the adaptive importance of features. In this paper, the Weighted Fuzzy Rough Sets model (WFRS) is proposed by utilizing more reliable samples to address noisy data. Firstly, the approximation margin is derived by applying the WFRS model within the original feature space from the sample's perspective. It serves as a basis for deriving the feature weights. On this basis, the weighted feature space is obtained. This space compresses the features with the lower weight and stretches the features with higher weight. Then, we obtain a specific fuzzy relation to maximize the within-class samples and minimize the between-class samples in the weighted feature space. Further, an evaluation function of features is constructed to model the uncertainty of fuzzy positive and non-negative regions. A Sample-driven adaptive Feature selection algorithm based on the Weighted fuzzy rough sets model (SFW) is designed to capture the varying importance of features by considering the local density of samples. The weighted features are subsequently selected to facilitate the downstream classification tasks. Experimental results show the WFRS model's robustness, the effectiveness of SFW, and its superiority compared to alternative methods.

关键词：

MedBench: A Comprehensive, Standardized, and Reliable Benchmarking System for Evaluating Chinese Medical Large Language Models

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Liu, Mianxin Ding, Jinru Xu, Jie Hu, Weiguo Li, Xiaoyang Zhu, Lifeng Bai, Zhian Shi, Xiaoming Wang, Benyou Song, Haitao Liu, Pengfei Zhang, Xiaofan Wang, Shanshan Li, Kang Wang, Haofen Ruan, Tong Huang, Xuanjing Sun, Xin Zhang, Shaoting Shanghai Artificial Intelligence Laboratory Shanghai200232 China Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine Shanghai200025 China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China The Shanghai Artificial Intelligence Research Institute Shanghai Jiao Tong University Shanghai200240 China The School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Qing Yuan Research Institute Shanghai Jiao Tong University Shanghai200240 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Sichuan University West China Hospital Chengdu610041 China The College of Design and Innovation Tongji University Shanghai200092 China The Department of Computer Science and Technology East China University of Science and Technology Shanghai200237 China The School of Computer Science Fudan University Shanghai200433 China Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine Shanghai200092 China

Ensuring the general efficacy and goodness for human beings from medical large language models (LLM) before real-world deployment is crucial. However, a widely accepted and accessible evaluation process for medical LLM, especially in the Chinese context, remains to be established. In this work, we introduce "MedBench", a comprehensive, standardized, and reliable benchmarking system for Chinese medical LLM. First, MedBench assembles the currently largest evaluation dataset (300,901 questions) to cover 43 clinical specialties and performs multi-facet evaluation on medical LLM. Second, MedBench provides a standardized and fully automatic cloud-based evaluation infrastructure, with physical separations for question and ground truth. Third, MedBench implements dynamic evaluation mechanisms to prevent shortcut learning and answer remembering. Applying MedBench to popular general and medical LLMs, we observe unbiased, reproducible evaluation results largely aligning with medical professionals' perspectives. This study establishes a significant foundation for preparing the practical applications of Chinese medical LLMs. MedBench is publicly accessible at https://***. © 2024, CC BY.

关键词： Benchmarking

FDDH: Fast discriminative discrete hashing for large-scale cross-modal retrieval

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Liu, Xin Wang, Xingzhi Cheung, Yiu-Ming Department of Computer Science Huaqiao University Xiamen Key Laboratory of Computer Vision and Pattern Recognition Fujian Key Laboratory of Big Data Intelligence and Security Xiamen361021 China School of Electronics and Information Technology Sun Yat-sen University Guangzhou510006 China Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong

Cross-modal hashing, favored for its effectiveness and efficiency, has received wide attention to facilitating efficient retrieval across different modalities. Nevertheless, most existing methods do not sufficiently exploit the discriminative power of semantic information when learning the hash codes, while often involving time-consuming training procedure for handling the large-scale dataset. To tackle these issues, we formulate the learning of similarity-preserving hash codes in terms of orthogonally rotating the semantic data so as to minimize the quantization loss of mapping such data to hamming space, and propose an efficient Fast Discriminative Discrete Hashing (FDDH) approach for large-scale cross-modal retrieval. More specifically, FDDH introduces an orthogonal basis to regress the targeted hash codes of training examples to their corresponding semantic labels, and utilizes ϵ-dragging technique to provide provable large semantic margins. Accordingly, the discriminative power of semantic information can be explicitly captured and maximized. Moreover, an orthogonal transformation scheme is further proposed to map the nonlinear embedding data into the semantic subspace, which can well guarantee the semantic consistency between the data feature and its semantic representation. Consequently, an efficient closedform solution is derived for discriminative hash code learning, which is very computationally efficient. In addition, an effective and stable online learning strategy is presented for optimizing modality-specific projection functions, featuring adaptivity to different training sizes and streaming data. The proposed FDDH approach theoretically approximates the bi-Lipschitz continuity, runs sufficiently fast, and also significantly improves the retrieval performance over the state-of-the-art methods. The source code is released at: https://***/starxliu/FDDH. © 2021, CC0.

关键词： Hash functions

Comparative analysis of time irreversibility and amplitude irreversibility based on joint permutation

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Yao, Wenpo Yao, Wenli Xu, Rongshuang Wang, Jun School of Geographic and Biologic Information Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210023 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Ministry of Education China Department of Hydraulic Engineering School of Civil Engineering Tsinghua University Beijing100084 China School of Applied Meteorology Nanjing University of Information Science & Technology Nanjing210044 China

Although time irreversibility (TIR) and amplitude irreversibility (AIR) are relevant concepts for nonequilibrium analysis, their association has received little attention. This paper conducts a systematic comparative analysis of the relationship between TIR and AIR based on statistical descriptions and numerical simulations. To simplify the quantification of TIR and AIR, the amplitude permutation and global information of the associated vector are combined to produce a joint probability estimation. Chaotic logistic, Henon, and Lorenz series are generated to evaluate TIR and AIR according to surrogate theory, and the distributions of joint permutations for these model series are measured to compare the degree of TIR and AIR. The joint permutation TIR and AIR are then used to investigate nonequilibrium pathological features in epileptic electroencephalography data. Test results suggest that epileptic brain electrical activities, particular those during the onset of seizure, manifest higher nonequilibrium characteristics. According to the statistical definitions and targeted pairs of joint permutations in the chaotic model data, TIR and AIR are fundamentally different nonequilibrium descriptors from time- and amplitude-reversibility, respectively, and thus require different forms of numerical analysis. At the same time, TIR and AIR both provide measures for fluctuation theorems associated with nonequilibrium processes, and have similar probabilistic differences in the pairs of joint permutations and consistent outcomes when used to analyze both the model series and real-world signals. Overall, comparative analysis of TIR and AIR contributes to our understanding of nonequilibrium features and broadens the scope of quantitative nonequilibrium measures. Additionally, the construction of joint permutations contributes to the development of symbolic time series analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Electroencephalography