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2023 World Wide Web Conference, WWW 2023

作者： Gu, Yin Zhang, Kai Liu, Qi Lin, Xin Huang, Zhenya Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9781450394161

In online games, predicting massive battle outcomes is a fundamental task of many applications, such as team optimization and tactical formulation. Existing works do not pay adequate attention to the massive battle. They either seek to evaluate individuals in isolation or mine simple pair-wise interactions between individuals, neither of which effectively captures the intricate interactions between massive units (e.g., individuals). Furthermore, as the team size increases, the phenomenon of diminishing marginal utility of units emerges. Such a diminishing pattern is rarely noticed in previous work, and how to capture it from data remains a challenge. To this end, we propose a novel Massive battle outcome predictor with margiNal Effect modules, namely MassNE, which comprehensively incorporates individual effects, cooperation effects (i.e., intra-team interactions) and suppression effects (i.e., inter-team interactions) for predicting battle outcomes. Specifically, we design marginal effect modules to learn how units' marginal utility changing respect to their number, where the monotonicity assumption is applied to ensure rationality. In addition, we evaluate the current classical models and provide mathematical proofs that MassNE is able to generalize several earlier works in massive settings. Massive battle datasets generated by StarCraft II APIs are adopted to evaluate the performances of MassNE. Extensive experiments empirically demonstrate the effectiveness of MassNE, and MassNE can reveal reasonable cooperation effects, suppression effects, and marginal utilities of combat units from the data. © 2023 ACM.

关键词： Forecasting

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NewsThumbnail: Automatic Generation of News Video Thumbnail

NewsThumbnail: Automatic Generation of News Video Thumbnail

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Li, Jinyu Lin, Shujin Zhou, Fan Wang, Ruomei School of Computer Science and Engineering National Engineering Research Center of Digital Life China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion China Sun Yat-sen University School of Communication and Design Guangzhou China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Reading news is an important way for people to obtain information. People can quickly sort out the context of events through a short news video. However, there are numerous news generated around the world every day. It's challenging to locate the interesting video. Thumbnails are often used as video covers and play an important role in displaying video content and driving views. Video owners can choose from individual images or elaborate thumbnails to upload to the site. But manually selecting from a large number of frames is time-consuming, and customizing thumbnails requires a high degree of expertise. Therefore, this paper proposes an automatic generation method of news video thumbnail, which can screen out semantically similar contents according to user query and combine them into a thumbnail. In order to facilitate the screening of graphic materials, we also propose a video content structuring method based on multiple cues, which can accurately segment the video into theme units. At the same time, we designed a visual system to display thumbnails and designed a user survey to investigate the performance of this method in news retrieval and understanding. Compared with peer methods, the thumbnails generated by our method can help users better understand the video content and locate the videos they are interested in. © 2022 IEEE.

关键词： Video recording

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Towards High-Performance Graph Processing: From a Hardware/Software Co-Design Perspective

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Journal of Computer science & Technology 2024年第2期39卷 245-266页

作者：廖小飞赵文举金海姚鹏程黄禹王庆刚赵进郑龙张宇邵志远 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph *** domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software *** article presents a graph processing ecosystem from hardware to *** start by introducing a series of hardware accelerators as the foundation of this ***,the codesigned parallel graph systems and their distributed techniques are presented to support graph ***,we introduce our efforts on novel graph applications and hardware *** results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.

关键词： graph processing hardware accelerator software system high performance ecosystem

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3D garment segmentation based on semi-supervised learning method

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Journal of Fiber Bioengineering and Informatics 2015年第4期8卷 657-665页

作者： Huang, Mian Liu, Li Liu, Ruomei Fu, Xiaodong Liu, Lijun Yunnan Provincial Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China National Engineering Research Center of Digital Life State-Province Joint Laboratory of Digital Home Interactive Applications School of Data and Computer Science Sun Yat-sen University Guangzhou China

In this paper, we propose a semi-supervised learning method to simultaneous segmentation and labeling of parts in 3D garments. The key idea in this work is to analyze 3D garments using semi-supervised learning method which can label parts in various 3D garments. We first develop an objective function based on Conditional Random Field (CRF) model to learn the prior knowledge of garment components from a set of training examples. Then, we exploit an effective training method that utilizes JointBoost classifiers based on the co-analysis for garments. And we modify the JointBoost to automatically cluster the segmented components without requiring manual parameter tuning. The purpose of our method is to relieve the manual segmentation and labeling of components in 3D garment collections. Finally, the experimental results show the performance of our proposed method is effective. © 2015 Binary Information Press & Textile Bioengineering and Informatics Society December 2015.

关键词： Random processes

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Blockchain-Based Multi-Cloud data Storage System Disaster Recovery

Blockchain-Based Multi-Cloud Data Storage System Disaster Re...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Feiyu Zhou, Jian-Tao College of Computer Science Inner Mongolia University Inner Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350337020

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads. © 2023 IEEE.

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BMDP:Blockchain-Based Multi-Cloud Storage data Provenance 26

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Feiyu Zhou, Jian-Tao Guo, Xu Inner Mongolia University College of Computer Science Inner Mongolia Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software China

ISBN: (纸本)9798350331684

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme's safety and do simulation experiments to show that the scheme has practicality. © 2023 IEEE.

关键词： Smart contract

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A survey of major techniques of clothing in vision

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Journal of Fiber Bioengineering and Informatics 2015年第4期8卷 725-732页

作者： Huang, Dongyan Liu, Li Fu, Xiaodong Liu, Lijun Huang, Qingsong Yunnan Provincial Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China National Engineering Research Center of Digital Life State-Province Joint Laboratory of Digital Home Interactive Applications School of Data and Computer Science Sun Yat-sen University Guangzhou China

Clothing in vision plays an important role in many applications of digital clothing, virtual fitting, fashion design and manufacturing. The research of clothing in vision is full of technical challenges, such as background detection and features extraction. In this paper, we introduce the latest remarkable works of clothing in vision area, including clothing segmentation, clothing recognition and clothing retrieval. There are many remarkable advances on the basis of the predecessors' achievements in the three fields. Firstly, we provide an overview of the techniques for clothing segmentation and clothing recognition. Then, we introduce and analyze the representative works on clothing retrieval. Finally, we forecast broader application prospect and future research work of clothing in vision. © 2015 Binary Information Press & Textile Bioengineering and Informatics Society December 2015.

关键词： Textiles

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Research on Digital Identity Technology and Application Based on Identification Code and Trusted Account Blockchain fusion

Research on Digital Identity Technology and Application Base...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Wei Zhao Ning Yang Guangli Li Keli Zhang China Information Security Research Institute Co. Ltd Beijing China National Engineering Laboratory for Big Data Analysis and Applications Center for Data Science Beijing China

ISBN: (纸本)9781665412971

Digital identity is one of the key elements to build a trust system in the digital economy era. Digital identity has empowered social governance and public services in the new era in many fields, and promoted the rapid development of digital economy. In this paper, a trusted digital identity based on identification code and trusted account blockchain fusion is proposed. Through the unified standard, unified coding identifier, combined with identity-based cryptographic algorithm and blockchain, identification code and trusted account blockchain fusion is realized, and a real, effective, safe and credible digital identity technology system is constructed. Trusted digital identity is a bridge connecting the virtual world and the physical world. It plays a strong supporting role in realizing the identity fusion and ‘digital twin’ of the physical world offline and the virtual world. It is the core element of the construction of new smart cities and the development of digital economy.

关键词： Seminars Bridges Codes Smart cities big data Encoding Blockchains

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Semantic Template-based Convolutional Neural Network for Text Classification

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ACM Transactions on Asian and Low-Resource Language Information Processing 2023年第11期22卷 1-21页

作者： Chang, Yung-Chun Ng, Siu Hin Chen, Jung-Peng Liang, Yu-Chi Hsu, Wen-Lian Graduate Institute of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Snhcc Tigp Academia Sinica National Cheng Chi University Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering College of Information and Electrical Engineering Asia University Taichung Taiwan Pervasive Ai Research Labs Ministry of Science and Technology Hsinchu City Taiwan

We propose a semantic template-based distributed representation for the convolutional neural network called Semantic Template-based Convolutional Neural Network (STCNN) for text categorization that imitates the perceptual behavior of human comprehension. STCNN is a highly automatic approach that learns semantic templates that characterize a domain from raw text and recognizes categories of documents using a semantic-infused convolutional neural network that allows a template to be partially matched through a statistical scoring system. Our experiment results show that STCNN effectively classifies documents in about 140,000 Chinese news articles into predefined categories by capturing the most prominent and expressive patterns and achieves the best performance among all compared methods for Chinese topic classification. Finally, the same knowledge can be directly used to perform a semantic analysis task. © 2023 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantics

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Research on traffic vehicle target detection method based on improved YOLOv7 24

Research on traffic vehicle target detection method based on...

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7th International Conference on Image and Graphics Processing, ICIGP 2024

作者： Zhang, Feng Sun, Quan Yuan, Shuai School of Electrical and Control Engineering Shenyang Jianzhu University China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center China

ISBN: (纸本)9798400716720

With the increase in the number of vehicles in our country, traffic accidents have become frequent. The real-time detection of dense traffic vehicles is particularly important, which can promote the development of autonomous driving and lay a good foundation for intelligent transportation. Aiming at the problems such as high vehicle overlap rate, high small target missing rate and poor real-time detection in traffic vehicle data set, a traffic vehicle target detection method based on improved YOLOv7 was proposed. Firstly, the neck network structure of the original YOLOv7 is lightweight and introduced into the GhostSlimPAN paradigm structure to reduce the number of model parameters and operation cost, and improve the accuracy of detection of small target vehicles. Then, the original loss function CIoU is improved to replusion loss function to reduce the missed and false detection rate caused by mutual occlusion between vehicles. In order to verify the effect of the improved model, the vehicle dataset was selected for testing and verification. The experimental results show that compared with the original YOLOv7 algorithm, the mAP@0.5 of the improved YOLOv7 algorithm on this dataset reaches 88.2%, which is 1.8% higher than that of the benchmark network. The improved algorithm can be applied to vehicle target detection in daily environment, providing key technical support for intelligent transportation and autonomous driving. © 2024 ACM.

关键词： Autonomous vehicles

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