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3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019

作者： Yan, Rong Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9783030260712

Traditional Pseudo Relevance Feedback (PRF) approaches fail to mode real-world intricate user activities. They naively assume that the first-pass top-ranked search results, i.e. the pseudo relevant set, have potentially relevant aspects for the user query. It is make the major challenge in PRF lies in how to get the reliability relevant feedback contents for the user real information need. Actually, there are two problems should not be ignored: (1) the assumed relevant documents are intertwined with the relevant and the non-relevant content, which influence the reliability of the expansion resource and can not concentrate in the real relevant portion;(2) even if the assumed relevant documents are real relevant to the user query, but they are always semantic redundance with various forms because the peculiarity of natural language expression. Furthermore, it will aggravate the "˜query drift' problem. To alleviate these problems, in this paper, we propose a novel PRF approach by diversifying feedback source, which main aim is to converge the relatively single semantic as well as diversity relevant information from the pseudo relevant set. The key idea behind our PRF approach is to construct an abstract pseudo content obtained from topical networks modeling over the set of top-ranked documents to represent the feedback documents, so as to cover as diverse aspects of the feedback set as possible in a small semantic granularity. Experimental results conducted in real datasets indicate that the proposed strategies show great promise for searching more reliable feedback source by helping to achieve query and search result diversity without giving up precision. © 2019, Springer Nature Switzerland AG.

关键词： Semantics

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Stylization-Based Architecture for Fast Deep Exemplar Colorization

Stylization-Based Architecture for Fast Deep Exemplar Colori...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhongyou Xu Tingting Wang Faming Fang Yun Sheng Guixu Zhang Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University Liverpool John Moores University

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Exemplar-based colorization aims to add colors to a grayscale image guided by a content related reference image. Existing methods are either sensitive to the selection of reference images (content, position) or extremely time and resource consuming, which limits their practical application. To tackle these problems, we propose a deep exemplar colorization architecture inspired by the characteristics of stylization in feature extracting and blending. Our coarse- to-fine architecture consists of two parts: a fast transfer sub-net and a robust colorization sub-net. The transfer sub- net obtains a coarse chrominance map via matching basic feature statistics of the input pairs in a progressive way. The colorization sub-net refines the map to generate the final results. The proposed end-to-end network can jointly learn faithful colorization with a related reference and plausible color prediction with unrelated reference. Extensive experimental validation demonstrates that our approach outperforms the state-of-the-art methods in less time whether in exemplar-based colorization or image stylization tasks.

关键词： Image color analysis computer architecture Feature extraction Task analysis Gray-scale Image reconstruction Robustness

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Look, listen, and attend: co-attention network for self-supervised audio-visual representation learning

arXiv

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arXiv 2020年

作者： Cheng, Ying Wang, Ruize Pan, Zhihao Feng, Rui Zhang, Yuejie Academy for Engineering and Technology Fudan University China School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

When watching videos, the occurrence of a visual event is often accompanied by an audio event, e.g., the voice of lip motion, the music of playing instruments. There is an underlying correlation between audio and visual events, which can be utilized as free supervised information to train a neural network by solving the pretext task of audio-visual synchronization. In this paper, we propose a novel self-supervised framework with co-attention mechanism to learn generic cross-modal representations from unlabelled videos in the wild, and further benefit downstream tasks. Specifically, we explore three different co-attention modules to focus on discriminative visual regions correlated to the sounds and introduce the interactions between them. Experiments show that our model achieves state-of-the-art performance on the pretext task while having fewer parameters compared with existing methods. To further evaluate the generalizability and transferability of our approach, we apply the pre-trained model on two downstream tasks, i.e., sound source localization and action recognition. Extensive experiments demonstrate that our model provides competitive results with other self-supervised methods, and also indicate that our approach can tackle the challenging scenes which contain multiple sound sources. Copyright © 2020, The Authors. All rights reserved.

关键词： Supervised learning

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Towards explainable multi-party learning: A contrastive knowledge sharing framework

arXiv

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arXiv 2021年

作者： Gao, Yuan Li, Jiawei Gong, Maoguo Xie, Yu Qin, A.K. The School of Electronic Engineering Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Shaanxi Province Xi'an710071 China The Key Laboratory of Computational Intelligence Chinese Information Processing Ministry of Education Shanxi University Taiyuan030006 China The department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia

Multi-party learning provides solutions for training joint models with decentralized data under legal and practical constraints. However, traditional multi-party learning approaches are confronted with obstacles such as system heterogeneity, statistical heterogeneity, and incentive design. How to deal with these challenges and further improve the efficiency and performance of multi-party learning has become an urgent problem to be solved. In this paper, we propose a novel contrastive multi-party learning framework for knowledge refinement and sharing with an accountable incentive mechanism. Since the existing parameter averaging method is contradictory to the learning paradigm of neural networks, we simulate the process of human cognition and communication, and analogize multiparty learning as a many-to-one knowledge sharing problem. The approach is capable of integrating the acquired explicit knowledge of each client in a transparent manner without privacy disclosure, and it reduces the dependence on data distribution and communication environments. The proposed scheme achieves significant improvement in model performance in a variety of scenarios, as we demonstrated through experiments on several real-world datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge management

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Optimizing Two-way Partial AUC with an End-to-end Framework

arXiv

引用

arXiv 2022年

作者： Yang, Zhiyong Xu, Qianqian Bao, Shilong He, Yuan Cao, Xiaochun Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Security Department of Alibaba Group Hangzhou311121 China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Shenzhen518107 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

The Area Under the ROC Curve (AUC) is a crucial metric for machine learning, which evaluates the average performance over all possible True Positive Rates (TPRs) and False Positive Rates (FPRs). Based on the knowledge that a skillful classifier should simultaneously embrace a high TPR and a low FPR, we turn to study a more general variant called Two-way Partial AUC (TPAUC), where only the region with TPR ≥ α, FPR ≤ β is included in the area. Moreover, a recent work shows that the TPAUC is essentially inconsistent with the existing Partial AUC metrics where only the FPR range is restricted, opening a new problem to seek solutions to leverage high TPAUC. Motivated by this, we present the first trial in this paper to optimize this new metric. The critical challenge along this course lies in the difficulty of performing gradient-based optimization with end-to-end stochastic training, even with a proper choice of surrogate loss. To address this issue, we propose a generic framework to construct surrogate optimization problems, which supports efficient end-to-end training with deep learning. Moreover, our theoretical analyses show that: 1) the objective function of the surrogate problems will achieve an upper bound of the original problem under mild conditions, and 2) optimizing the surrogate problems leads to good generalization performance in terms of TPAUC with a high probability. Finally, empirical studies over several benchmark datasets speak to the efficacy of our framework. © 2022, CC BY.

关键词： Stochastic systems

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LSTM-Based Distributed Conditional Generative Adversarial Network For Data-Driven 5G-Enabled Maritime UAV Communications

arXiv

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arXiv 2022年

作者： Rasheed, Iftikhar Asif, Muhammad Ihsan, Asim Khan, Wali Ullah Ahmed, Manzoor Rabie, Khaled The Department of Information and Communication Engineering The Islamia University of Bahawalpur Pakistan Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen Univerisyt Guangdong Shenzhen China The Department of Information and Communication Engineering Shanghai Jiao Tong University Shanghai China University of Luxembourg Luxembourg City1855 Luxembourg The College of Computer Science and Technology Qingdao University Qingdao266071 China The Department of Engineering Manchester Metropolitan University United Kingdom The Department of Electrical and Electronic Engineering University of Johannesburg P.O. Box 17011 Doornfontein Johannesburg2028 South Africa

5G enabled maritime unmanned aerial vehicle (UAV) communication is one of the important applications of 5G wireless network which requires minimum latency and higher reliability to support mission-critical applications. Therefore, lossless reliable communication with a high data rate is the key requirement in modern wireless communication systems. These all factors highly depend upon channel conditions. In this work, a channel model is proposed for air-to-surface link exploiting millimeter wave (mmWave) for 5G enabled maritime unmanned aerial vehicle (UAV) communication. Firstly, we will present the formulated channel estimation method which directly aims to adopt channel state information (CSI) of mmWave from the channel model inculcated by UAV operating within the Long Short Term Memory (LSTM)-Distributed Conditional generative adversarial network (DCGAN) i.e. (LSTM-DCGAN) for each beamforming direction. Secondly, to enhance the applications for the proposed trained channel model for the spatial domain, we have designed an LSTM-DCGAN based UAV network, where each one will learn mmWave CSI for all the distributions. Lastly, we have categorized the most favorable LSTM-DCGAN training method and emanated certain conditions for our UAV network to increase the channel model learning rate. Simulation results have shown that the proposed LSTM-DCGAN based network is vigorous to the error generated through local training. A detailed comparison has been done with the other available state-of-the-art CGAN network architectures i.e. stand-alone CGAN (without CSI sharing), Simple CGAN (with CSI sharing), multi-discriminator CGAN, federated learning CGAN and DCGAN. Simulation results have shown that the proposed LSTM-DCGAN structure demonstrates higher accuracy during the learning process and attained more data rate for downlink transmission as compared to the previous state of artworks. Copyright © 2022, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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A regional industry intelligence business platform based on adaptive clustering

引用

International Journal of Circuits, Systems and Signal processing 2020年 14卷 656-660页

作者： Liu, Junjie Cai, Danlin Zhu, Daxin Huang, Siyu Network Information Center Quanzhou Normal University Quanzhou China Fujian University Laboratory of Intelligent Computing and Information Processing Quanzhou China Fujian Provincial Key Laboratory of Data-Intensive Computing Quanzhou China College of Mathematics and Computer Science Quanzhou Normal University Quanzhou China

How to grasp comprehensive, timely, effective and accurate business competitive intelligence has become an urgent and critical issue for regional industrial clusters. Therefore, a Industrial Internet Platform for Regional Economic based on Adaptive Clustering called IIPRE is developed. The multi thread oriented extraction technology is used to collect the business intelligence data of specific industries. The data is clustered by rule-based machine learning, and the business information data model is used for analysis. Finally, the visualization report is generated by big data visualization software. The system uses Intelligent Retrieval technology to automatically complete the functions from acquisition to processing automatically, to generate data information to meet the application requirements, uses Automatic Classification technology to provide automatic classification function combining machine-based automatic learning and rule-based information, and uses Personalized Display technology to provide customizable personalized display pages for individuals, to organize and adjust the hot information, thematic information, clustering, related words, early warning, statistics and other information released by the system. The construction of the platform provides enterprises with comprehensive, timely, effective and accurate business competitive intelligence services, improves the strategic planning, competitive intelligence acquisition and industrial information sharing capabilities of regional industrial clusters, and will achieve tremendous economic and social benefits. © 2020, North Atlantic University Union NAUN. All rights reserved.

关键词： Data visualization

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Environmental Sound Recognition Based on Double-input Convolutional Neural Network Model

Environmental Sound Recognition Based on Double-input Convol...

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IEEE International Conference on Civil Aviation Safety and information technology (ICCASIT)

作者： Mei Wang Man Yao Liyan Luo Xin Liu Xiyu Song Wenshan Chu Shuting Guo Lu Bai Provincial Ministry of Education Key Laboratory of Cognitive Radio and Signal Processing Guilin University of Electronic Technology Guilin Guangxi College of Information Science and Engineering Guilin University of Technology Guilin Guangxi Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing Guilin University of Electronic Technology Guilin Guangxi

ISBN: (数字)9781728199481

ISBN: (纸本)9781728199498

Most environmental sound recognition models use acoustic features like log-mel spectrogram (Logmel) or mel frequency cepstral coefficient (MFCC) as an input training network model in recent years, but the result of recognition is unsatisfactory. These acoustic features were originally designed for speech recognition and music recognition, which may not represent environmental sound comprehensively. In this paper, we designed a double-input convolutional neural network model, adopting Logmel features and raw waveforms as inputs and extracting the respective features separately using a convolutional neural network, after that performing feature combination and recognition. The network model performs feature extraction directly on raw waveforms and can extract some specific information that may not be contained in these acoustic features, which is complementary to Logmel features and improves the feature's ability to represent environmental sound. The experiments over the GoogleAudioSet dataset showed that the proposed network model achieves a recognition result of 95.1%, which is better than the network model using a single feature and combining multiple acoustic features as input.

关键词： Training Speech recognition Feature extraction Safety Convolutional neural networks Mel frequency cepstral coefficient Spectrogram

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The First Competition on Resource-Limited Infrared Small Target Detection Challenge: Methods and Results

arXiv

引用

arXiv 2024年

作者： Li, Boyang Ying, Xinyi Li, Ruojing Liu, Yongxian Shi, Yangsi Li, Miao Zhang, Xin Hu, Mingyuan Wu, Chenyang Zhang, Yukai Wei, Hui Tang, Dongli Zhao, Jian Jin, Lei Xiao, Chao Ling, Qiang Lin, Zaiping Sheng, Weidong Peng, Chenxu Ke, Wei He, Yuhang Yang, Huoren Liu, Lingjie Liu, Ziqi Shi, Zelin Liu, Yunpeng Yu, Chuang Zhao, Jinmiao Xiang, Heng Yang, Shan Li, You Wang, Guoqing Li, Tianyu Zhou, Minghang Xie, Dongyu Lan, Chenxi Qiao, Chaofan Gao, Yupeng Liu, Yongxu Feng, Wei Ma, Zhihao Chen, Deping Song, Xiaopeng Yang, Jiuping Qiu, Zhaobing Ni, Rixiang Lin, Ye Luo, Changhai Zheng, Shuyuan Huang, Baojin Zhou, Xiaoqi Guo, Qingshan Zhang, Xuyang Li, Xiuhong Wu, Dangxuan Ma, Jian Zeng, Haodong Wang, Luyao Fu, Qiang Dai, Yimian Kou, Renke Song, Jian Feng, Changfeng Xiao, Mengxuan Xiong, Zihao Huang, Hong Liu, Yingxu Zhao, Quanyi National University of Defense Technology China China Telecom Institute of AI China Northwestern Polytechnical University China Beijing University of Posts and Telecommunications China Zhejiang Supcon Information Co. Ltd. China School of Physics Xi’an Jiaotong University China College of Artificial Intelligence Faculty of Electronic and Information Engineering Xi’an Jiaotong University China School of Software Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University China The Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China The Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Sichuan Zhongke Langxing Photoelectric Technology Co. Ltd. China Center for Future Media University of Electronic Science and Technology of China China Hangzhou Institute of Technology Xidian University China School of Electronic Engineering Xidian University China School of Mechanical Engineering and Automation Fuzhou University China Beijing Insitute of Technology Chongqing Innovation Center China Xinjiang University China Army Engineering University PLA Shijiazhuang Campus China Nanjing University of Science and Technology China Henan University of Technology China Chongqing University China

In this paper, we briefly summarize the first competition on resource-limited infrared small target detection (namely, LimitIRSTD). This competition has two tracks, including weakly-supervised infrared small target detection (Track 1) and lightweight infrared small target detection (Track 2). 46 and 60 teams successfully registered and took part in Tracks 1 and Track 2, respectively. The top-performing methods and their results in each track are described with details. This competition inspires the community to explore the tough problems in the application of infrared small target detection, and ultimately promote the deployment of this technology under limited resource. © 2024, CC BY-NC-SA.

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Urban Environmental Sound Monitoring Network Based on Combination of Mobile and Stationary Nodes

Urban Environmental Sound Monitoring Network Based on Combin...

引用

International Conference on information Science and Control Engineering (ICISCE)

作者： Jinquan Wei Liyan Luo Mei Wang Xiyu Song Liujun Zhang Man Yao Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing Guilin University of Electronic Technology Guilin Guangxi Provincial Ministry of Education Key Laboratory of Cognitive Radio and Signal Processing Guilin University of Electronic Technology Guilin Guangxi College of Information Science and Engineering Guilin University of Technology Guilin Guangxi

Urban noise has posed a threat to people's physical and mental health. Significant progress has been made in noise monitoring related projects at home and abroad, but these projects usually utilize stationary monitoring equipment to measure noise whose deployment is susceptible to the cost and complex urban environment and thus they cannot effectively cover the monitored area. In this paper, a kind of joint mobile and stationary nodes based urban environmental sound monitoring network is proposed to achieve low-cost, high-precision, and high-flexibility noise monitoring. The network adopts NETTY framework, which effectively alleviates the impact of high concurrency on server performance in the context of large-scale sensor nodes. In addition, this paper designs a visual monitoring platform for the display of noise distribution and abnormal sound events. In order to verify the performance of the system, 10 mobile nodes and 50 stationary nodes are deployed in the campus environment. The experimental results show that the proposed system can realize urban environmental noise monitoring with high performance in real scenes.

关键词： Visualization Costs Working environment noise Urban areas Mobile nodes Servers Noise measurement

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