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Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach

Generating High-quality Movie Tags from Social Reviews: A Le...

IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Zhenxiao Luo Guopin Tang Chen Wang Yipeng Zhou Xi Zheng Jessie Hui Wang Gang Liu Di Wu School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Faculty of Science and Engineering Macquarie University Sydney Australia Institute for Network Sciences and Cyberspace Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665417631

As a kind of keywords to describe video contents, tags are extremely beneficial for viewers to locate videos when they search the site. Additionally, tags also help video platform operators to better organize and recommend videos to platform users. Previous approaches are mainly based on manually tagging or tag propagation via video content analysis, which is time-consuming and resource-consuming. Especially, given the high volume of fresh movies generated per year, it is difficult to accurately tag all the movies manually. Moreover, video content analysis is also not easy considering typical features (e.g., long duration, complicated scenarios) of commercial movies. In this paper, we propose an automatic tagging algorithm called TagRec that exploits crowdsourced user reviews to generate accurate movie tags. We observe that user reviews contain rich information about movies (e.g., quality, actors) which can be learned to generate high-quality movie tags. Inspired by the above observation, we choose to transform the movie video tagging problem into a tag recommendation problem, in which tags are recommended to different movies by extracting knowledge from crowdsourced movie reviews. We take latent topics, tag co-occurrence probability and tag semantics into account, and formulate the problem as a recommendation optimization problem. We evaluate the performance of our pro-posed TagRec algorithm with a large-scale real-world dataset. Extensive experiments demonstrate that TagRec achieves 7.1 % and 9.6% improvement compared with other state-of-the-art methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain respectively.

关键词： Social computing Filtering Semantics Transforms Tagging Motion pictures Optimization

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Towards 6G wireless communication networks:vision, enabling technologies, and new paradigm shifts

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Science China(Information Sciences) 2021年第1期64卷 5-78页

作者： Xiaohu YOU Cheng-Xiang WANG Jie HUANG Xiqi GAO Zaichen ZHANG Mao WANG Yongming HUANG Chuan ZHANG Yanxiang JIANG Jiaheng WANG Min ZHU Bin SHENG Dongming WANG Zhiwen PAN Pengcheng ZHU Yang YANG Zening LIU Ping ZHANG Xiaofeng TAO Shaoqian LI Zhi CHEN Xinying MA Chih-Lin I Shuangfeng HAN Ke LI Chengkang PAN Zhimin ZHENG Lajos HANZO Xuemin (Sherman) SHEN Yingjie Jay GUO Zhiguo DING Harald HAAS Wen TONG Peiying ZHU Ganghua YANG Jun WANG Erik G.LARSSON Hien Quoc NGO Wei HONG Haiming WANG Debin HOU Jixin CHEN Zhe CHEN Zhangcheng HAO Geoffrey Ye LI Rahim TAFAZOLLI Yue GAO H.Vincent POOR Gerhard P.FETTWEIS Ying-Chang LIANG National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast University Purple Mountain Laboratories Shanghai Institute of Fog Computing Technology (SHIFT) ShanghaiTech University Research Center for Network Communication Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China (UESTC) China Mobile Research Institute School of Electronics and Computer Science University of Southampton Department of Electrical and Computer Engineering University of Waterloo Global Big Data Technologies Centre (GBDTC) University of Technology Sydney School of Electrical and Electronic Engineering The University of Manchester LiFi Research and Development Centre Institute for Digital CommunicationsSchool of EngineeringThe University of Edinburgh Huawei Technologies Canada Co. Ltd. Huawei Technologies Department of Electrical Engineering (ISY) Link?ping University Institute of Electronics Communications & Information Technology (ECIT)Queen's University Belfast State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast University School of Electrical and Computer Engineering Georgia Institute of Technology 5G Innovation Centre University of Surrey Princeton University Vodafone Chair Mobile Communications Systems Technische Universit?t Dresden Center for Intelligent Networking and Communications (CINC) University of Electronic Science and Technology of China (UESTC)

The fifth generation(5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability,and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation(6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle(UAV) communication networks, thus achieving a space-airground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6GHz, millimeter wave(mmWave), terahertz(THz),and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence(AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable f

关键词： 6G vision network architecture air interface and transmission technologies space-air-ground-sea integrated network all spectra artificial intelligence network security

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Research on data Fusion Method Based on Compressed Sensing

Research on Data Fusion Method Based on Compressed Sensing

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2019 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2019

作者： Zhang, Suzhi Chen, Xiaoni Yang, Rui Li, Penghui Cai, Qiang School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China Beijing Key Laboratory of Big Data Technology for Food Safety Beijng Technology and Business University Beijing China

ISBN: (纸本)9781728112220

In order to improve the efficiency and fusion effect of data fusion, according to the characteristics of compression sensing theory, this paper proposes a data fusion method based on independent data region division and compression sensing. In the method of this paper, the compressed sensing theory is used to sample the data to obtain the measured values, and the sample data is divided by independent region to form a joint region, then the weight coefficient of mutual information fusion is calculated, and the fused data are obtained according to the compressed sensing coefficient reconstruction method. Simulation results show that compared with the traditional fusion method, this method not only improves the data fusion effect, but also has good stability. © 2019 IEEE.

关键词： Compressed sensing

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data dimensionality reduction method combining intra-class and inter-class distance 2019

Data dimensionality reduction method combining intra-class a...

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2019 International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2019

作者： Zhang, Suzhi Chen, Xiaoni Li, Penghui Cai, Qiang Zhengzhou University of Light Industry School of Computer and Communication Engineering Zhengzhou China Beijng Technology and Business University Beijing Key Laboratory of Big Data Technology for Food Safety Beijing China

ISBN: (纸本)9781450372022

Traditional principal component analysis method has the problems of low computational efficiency and large memory consumption when faces high-dimension data. After the information entropy is introduced, although it reduces the memory and running time, but it cannot meet the requirements of the actual classification effect. In order to extract low-dimensional features with good discrimination ability, the idea of intra-class and inter-class distance is introduced, the principal component analysis data dimension reduction algorithm based on intra-class and inter-class distance is proposed. In this algorithm, firstly, the entropy of attribute information is calculated, and then the threshold value of information entropy is compared to it, which to realize the feature screening of data matrix. Then, the improved PCA algorithm based on the ideas of intra-class distance maximization and inter-class distance minimization is used to reduce the dimension of data. Finally, the data after dimension reduction is classified by KNN and SVM algorithms. Compared with the PCA, principal component analysis based on information entropy (E-PCA) and LDA algorithms, the experimental results show that the proposed algorithm can not only improve the result of dimension reduction, but also significantly improve the discrimination performance of low-dimensional data after dimension reduction. © 2019 Association for Computing Machinery.

关键词： Principal component analysis

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Correction to: A Tree-type Multiparty Quantum key Agreement Protocol Against Collusive Attacks

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International Journal of Theoretical Physics 2023年第5期62卷 1-1页

作者： Yang, Hao Lu, Songfeng Zhu, Jianxin Wu, Junjun Zhou, Qing Li, Tong Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center On Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China College of Management Shenzhen University Shenzhen China

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Joint multi-task cascade for instance segmentation

Joint multi-task cascade for instance segmentation

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作者： Wen, Yaole Hu, Fuyuan Ren, Jinchang Shang, Xinru Li, Linyan Xi, Xuefeng School of Electronic & Information Engineering Suzhou University of Science and Technology SuzhouJiangsu215009 China Centre for Signal and Image Processing University of Strathclyde Glasgow United Kingdom Suzhou Institute of Trade & Commerce SuzhouJiangsu215009 China Virtual Reality Key Laboratory of Intelligent Interaction and Application Technology of Suzhou Suzhou University of Science and Technology SuzhouJiangsu215009 China Suzhou Key Laboratory for Big Data and Information Service Suzhou University of Science and Technology SuzhouJiangsu215009 China

Instance segmentation requires both pixel-level classification accuracy and high-level semantic features at the target instance level, which is very challenging, and the cascade structure can effectively improve both of these problems. To make full use of the relationship between detection and segmentation, this paper proposes a joint multi-tasking cascade structure, which is not simply to cascade the two tasks of detection and segmentation, but to unitedly put them into multi-stage processing, and especially to integrate the information at different stages of the mask branch. The entire structure can effectively utilize the superior characteristics of each stage in the matter of detection and segmentation, thus improving the quality of mask prediction. The feature fusion process is introduced in the full convolution networks (FCN) branch, and the high-level and low-level features are effectively fused to enhance the contextual information of the picture semantic features. The experiments demonstrate the better results on the COCO dataset. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Semantics

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Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview

arXiv

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arXiv 2021年

作者： Fan, Zhaoxin Zhu, Yazhi He, Yulin Sun, Qi Liu, Hongyan He, Jun Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing China No. 59 Zhongguancun Street Haidian Dist Beijing100872 China Institute of Information Science Beijing Jiaotong University No.3 Shangyuancun Haidian Dist. Beijing China School of Economics and Management Tsinghua University Haidian Dist Beijing100084 China

Object pose detection and tracking has recently attracted increasing attention due to its wide applications in many areas, such as autonomous driving, robotics, and augmented reality. Among methods for object pose detection and tracking, deep learning is the most promising one that has shown better performance than others. However, survey study about the latest development of deep learning-based methods is lacking. Therefore, this study presents a comprehensive review of recent progress in object pose detection and tracking that belongs to the deep learning technical route. To achieve a more thorough introduction, the scope of this study is limited to methods taking monocular RGB/RGBD data as input and covering three kinds of major tasks: instance-level monocular object pose detection, category-level monocular object pose detection, and monocular object pose tracking. In our work, metrics, datasets, and methods of both detection and tracking are presented in detail. Comparative results of current state-of-the-art methods on several publicly available datasets are also presented, together with insightful observations and inspiring future research directions. Copyright © 2021, The Authors. All rights reserved.

关键词： Augmented reality

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Fusion of Super-Resolution and Semantic Segmentation Deep Models for Building Footprint Extraction From Aerial Images

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The Photogrammetric Record 2025年第190期40卷

作者： Ziyi Chen Yuhua Luo Jiaying Zhang Ronggang Guo Liai Deng Jinghua Liu Dilong Li Yongtao Yu Ammar Abulibdeh Cheng Wang Department of Computer Science and Technology Fujian Key Laboratory of big Data Intelligence and Security Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen FJ China Guojiao Spatial Information Technology (Beijing) co. Ltd Beijing China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian JS China Department of Humanities College of Arts and Sciences Qatar University Doha Qatar School of Informatics Xiamen University Xiamen FJ China

The spatial resolution of remotely sensed images has seen significant improvements; higher resolution facilitates the understanding of remote sensing images and improves the accuracy of building footprint extraction. Thus, this paper introduces a novel approach that combines super-resolution reconstruction with semantic segmentation deep models for building footprint extraction. To begin, a super-resolution reconstruction deep model is employed to enhance the resolution of the target images. Subsequently, training samples are generated using various data augmentation techniques based on the reconstructed images. Finally, a semantic segmentation deep model is trained to perform the final building footprint extraction. For validation, our proposed framework was tested on two publicly available aerial image datasets: the WHU building dataset and the Massachusetts building dataset. The results obtained from these datasets demonstrated the effectiveness of our method, achieving high Intersection over Union (IoU) scores of 90.57% and 75.02%, respectively. Furthermore, our approach outperformed seven other competing methods, showcasing its superior performance.

关键词： aerial image building footprint extraction deep learning semantic segmentation super-resolution

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From local to national metabolism:a review and a scale-up framework

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Ecosystem Health and Sustainability 2020年第1期6卷 334-352,333页

作者： Ningyu Yan Gengyuan Liu Maddalena Ripa Ning Wang Hongmei Zheng Francesco Gonella State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentBeijing Normal UniversityBeijingChina Beijing Engineering Research Center for Watershed Environmental Restoration&Integrated Ecological Regulation BeijingChina Institut De Ciencia I Tecnologia Ambientals Universitat Autonoma De BarcelonaBellaterraSpain Beijing Key Lab of Green Development Decision Based on Big Data Beijing Information Science and Technology UniversityChina Department of Resource Management Tangshan Normal UniversityTangshanChina Department of Molecular Sciences and Nanosystems UniversitàCa’Foscari VeneziaVeniceItaly

Research background:Countries are likely the most important subjects involved in the environmental control and response to global environmental issues,while the majority of the related metabolic analyses are focused on the metabolism at the city ***:Analyzing why and how to scale up the metabolic study from city to ***:Combining quantitative analysis with a literature review socio-metabolic research,since it is an effective method to study resource and environmental issues and has been applied at different ***:1)A single city or a smaller area is hardly self-sufficient,and its sustainability and resilience needs the support of the surrounding environment;2)At the country scale,systems exhibit a higher level of self-organization and a corresponding higher level of complexity,addressing the need for applying the metabolic theory at the national scale;3)The emergy analysis methods show its advantages in study metabolic processes for national metabolism;4)Input-output analysis plays an important role in and region ***:The interactions among and within the scales are nested,as well as the goals and ***,socio-metabolic research on scales differ in their priority goals,and the methods adopted must be targeted.

关键词： National metabolism socio-metabolic research sustainability scale-up

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Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search

arXiv

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arXiv 2020年

作者： Guo, Yong Chen, Yaofo Zheng, Yin Zhao, Peilin Chen, Jian Huang, Junzhou Tan, Mingkui School of Software Engineering South China University of Technology China Pazhou Laboratory China Weixin Group Tencent China Tencent AI Lab Tencent China Guangdong Key Laboratory of Big Data Analysis and Processing China

Neural architecture search (NAS) has become an important approach to automatically find effective architectures. To cover all possible good architectures, we need to search in an extremely large search space with billions of candidate architectures. More critically, given a large search space, we may face a very challenging issue of space explosion. However, due to the limitation of computational resources, we can only sample a very small proportion of the architectures, which provides insufficient information for the training. As a result, existing methods may often produce suboptimal architectures. To alleviate this issue, we propose a curriculum search method that starts from a small search space and gradually incorporates the learned knowledge to guide the search in a large space. With the proposed search strategy, our Curriculum Neural Architecture Search (CNAS) method significantly improves the search efficiency and finds better architectures than existing NAS methods. Extensive experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Curricula

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