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2011 International Conference on Future Communication, Computing, Control and Management, ICF4C 2011

作者： Ding-Wei, Li Bing, Li Peng, He Wei-Feng, Pan State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 China Complex Networks Research Center Wuhan University Wuhan 430072 China School of Computer Science and Information Engineering Zhejiang Gongshan University Hangzhou 310018 China

ISBN: (纸本)9783642273100

How to identify important classes in an object-oriented system is a significant task for resources allocation and decision making. However, the work on ranking the importance of classes is scarce. This paper introduces a weighted software network model to represent the structure of object-oriented software, and based on which an indicator, IC, is defined to measure the importance of classes. By ranking the ICs of all classes we can obtain the important classes we should focus on. Manually inspection of the source code verifies the meaningfulness of the classes obtained and also validates the feasibility of our approach. © 2012 Springer-Verlag GmbH.

关键词： complex networks

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FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring

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Journal of computer Science & Technology 2023年第5期38卷 970-984页

作者：胡春雨忽丽莎袁林陆佃杰吕蕾陈益强 Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 25000China Institute of Information Technology Hebei University of Economics and BusinessShijiazhuang 050061China School of Information Science and Engineering Shandong Normal UniversityJinan 250358China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power ***,most wearable health data is distributed across dfferent organizations,such as hospitals,research institutes,and companies,and can only be accessed by the owners of the data in compliance with data privacy *** first challenge addressed in this paper is communicating in a privacy-preserving manner among different *** second technical challenge is handling the dynamic expansion of the federation without model *** address the first challenge,we propose a horizontal federated learning method called Federated Extremely Random Forest(FedERF).Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model *** on FedERF,we present a federated incremental learning method called Federated Incremental Extremely Random Forest(FedIERF)to address the second technical *** introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model *** experiments show that FedERF achieves comparable performance with non-federated methods,and FedIERF effectively addresses the dynamic expansion of the *** opens up opportunities for cooperation between different organizations in wearable health monitoring.

关键词： federated learning incremental learning random forest wearable health monitoring

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Biological Classification System Knowledge Graph and Semi-automatic Construction of Its Invertebrate Fossil Branches

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Journal of Earth Science 2024年第6期35卷 2119-2128页

作者： Shaochun Dong Yukun Shi Yizao Ran Haijun Wu Yiying Deng Junxuan Fan Xinyu Dai School of Earth Sciences and Engineering Frontiers Science Center for Critical Earth Material CyclingNanjing UniversityNanjing210046China Jiangsu Deep-Time Digital Earth Research Center for Excellence Suzhou215004China School of Computer Science Nanjing UniversityNanjing210046China School of Resources and Environmental Engineering Hefei University of TechnologyHefei230009China State Key Laboratory for Mineral Deposits Research Nanjing210046China National Key Laboratory for Novel Software Technology Nanjing210046China

Biological classification is the foundation of biology and paleontology,as it arranges all the organisms in a hierarchy that humans can easily follow and *** is further used to reconstruct the evolution of life.A biological classification system(BCS)that includes all the established fossil taxa would be both useful and challenging for uncovering the life *** fossil taxa were originally recorded in various published books and articles written by natural languages,the primary step is to organize all those taxa information in a manner that can be deciphered by a computer system.A Knowledge Graph(KG)is a formalized description framework of semantic knowledge,which represents and retrieves knowledge in a machine-understandable way,and therefore provides an eligible method to represent the *** this paper,a model of the BCS KG including the ontology and fact layers is *** put it into practice,the ontology layer of the invertebrate fossil branches was manually developed,while the fact layer was automatically constructed by extracting information from 46 volumes of the Treatise of Invertebrate Paleontology series with the help of natural language processing *** a result,27348 taxa nodes spanning fourteen taxonomic ranks were extracted with high accuracy and high efficiency,and the invertebrate fossil branches of the BCS KG was thus *** study demonstrates that a properly designed KG model and its automatic construction with the help of natural language processing are reliable and efficient.

关键词： biological classification system knowledge graph ontology invertebrate fossil big data

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Scale-adaptive low-resolution person re-identification via learning a discriminating surface 25

Scale-adaptive low-resolution person re-identification via l...

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Zheng, Wang Hu, Ruimin Yu, Yi Jiang, Junjun Chao, Liang Jinqiao, Wang State Key Laboratory of Software Engineering School of Computer Wuhan University China Digital Content and Media Sciences Research Division National Institute of Informatics Japan School of Computer Science China University of Geosciences China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China National Engineering Research Center for Multimedia Software Collaborative Innovation Center of Geospatial Technology Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China

Person re-identification, as an important task in video surveillance and forensics applications, has been widely studied. But most of previous approaches are based on the key assumption that images for comparison have the same resolution and a uniform scale. Some recent works investigate how to match low resolution query images against high resolution gallery images, but still assume that the low-resolution query images have the same scale. In real scenarios, person images may not only be with low-resolution but also have different scales. Through investigating the distance variation behavior by changing image scales, we observe that scale-distance functions, generated by image pairs under different scales from the same person or different persons, are distinguishable and can be classified as feasible (for a pair of images from the same person) or infeasible (for a pair of images from different persons). The scale-distance functions are further represented by parameter vectors in the scale-distance function space. On this basis, we propose to learn a discriminating surface separating these feasible and infeasible functions in the scale-distance function space, and use it for reidentifying persons. Experimental results on two simulated datasets and one public dataset demonstrate the effectiveness of the proposed framework.

关键词： Vector spaces

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An Improvement of Coherent Semantic Attention for Image Inpainting 1

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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Wang, Weiqing Gu, Enming Fang, Wei School of Computer & Software Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Jiangsu Key Laboratory of Computer Information Process Technology SooChow University Suzhou China

ISBN: (数字)9789811580833

ISBN: (纸本)9789811580826

In some image restoration algorithms of the past, they often do not consider the continuity between pixels, and the internal features of the hole region. The mapping to the image semantically does not take into account the continuity of the feature, resulting in the color of the fault. Or the deformation of the edge contour of the image. In some of the current popular algorithms and models, we can clearly see the color faults and artificial repair traces from their repair results. These discontinuities are mainly because these methods ignore the semantic relevance and feature continuity of the hole region. Therefore, if we want to get a better image repair effect. We have to improve on semantic relevance and feature continuity. We validated the effectiveness of our proposed method in image restoration tasks on the CelebA and Places2 datasets, and our results yielded a better visual experience in some images than existing methods. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Semantics

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EFFICIENT MULTI-STEP AUDIO OBJECT CODING WITH LIMITED RESIDUAL INFORMATION

EFFICIENT MULTI-STEP AUDIO OBJECT CODING WITH LIMITED RESIDU...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Hu, Chenhao Hu, Ruimin Wang, Xiaochen Wu, Yulin Liu, Wenke National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Research Institute of Wuhan University Shenzhen China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China

ISBN: (纸本)9781665438643

Spatial audio object coding (SAOC) is an effective method to transmit multiple audio objects. Audio systems can provide personalized services under this framework. However, this method causes frequency aliasing distortion, which severely impacts the listening experience. The multi-step SAOC (MS-SAOC) scheme was proposed to enhance the sound quality of each audio object by using residual information. Compared with SAOC, the bit-rate increases three times due to the residual data of multiple objects. In this paper, an efficient multi-step residual coding method is proposed to reduce the residual bit-rate of MS-SAOC. A two-level filter is designed to remove redundant residual information, and the limited residual information can efficiently compensate for frequency aliasing distortion. From experiment results, the residual bit-rate is half of MS-SAOC, and the sound quality is maintained at the Good-Excellent level. © 2021 IEEE

关键词： Audio systems

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LOW BITRATES AUDIO OBJECT CODING USING CONVOLUTIONAL AUTO-ENCODER AND DENSENET MIXTURE MODEL

LOW BITRATES AUDIO OBJECT CODING USING CONVOLUTIONAL AUTO-EN...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Wu, Yulin Hu, Ruimin Hu, Chenhao Ke, Shanfa Li, Gang Wang, Xiaochen National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Research Institute of Wuhan University Shenzhen China

ISBN: (纸本)9781665438643

The efficient transmission of the audio objects can be achieved by spatial audio object coding (SAOC) method that conveys a mono downmix signal together with side information parameters that enable object reconstruction in the decoder. To allow the transmission of audio objects at low bitrates, we present a new audio coding method with convolutional auto-encoder (CAE) and dense convolutional network (DenseNet) mixture model, optimizing the compression of side information parameters of audio objects. It has two main advantages: 1) Different from the linear transform methods, CAE can dig the nonlinear relationship of side information parameters and can effectively reduce the dimension of side information parameters;2) DenseNet is adding in the decoder to make full use of low dimensional features of side information parameters, which improves the audio quality at low bitrate. Experiments show that our method outperforms baseline methods permitting bitrates as low as 1 kbps per object. © 2021 IEEE

关键词： Mixtures

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SPATIAL AUDIO OBJECT CODING BASED ON TIME-FREQUENCY SHIFTING AND SCHEDULING

SPATIAL AUDIO OBJECT CODING BASED ON TIME-FREQUENCY SHIFTING...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Hu, Chenhao Hu, Ruimin Wang, Xiaochen Wu, Yulin National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Research Institute of Wuhan University Shenzhen China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China

ISBN: (纸本)9781665438643

Spatial audio object coding (SAOC) is an effective method to transmit multiple audio objects. It divides the full frequency band into 28 subbands and extracts spatial parameters for decoding. In this way, objects can be encoded into a downmix signal with a few parameters. However, using the same parameters in one subband will cause frequency aliasing distortion, which severely impacts the listening experience. Existing studies to enhance SAOC cannot eliminate the aliasing distortion of all objects effectively. This paper describes a new structure to balance the bit-rate and decode quality based on time-frequency (TF) shifting and scheduling. In this structure, a TF shifting strategy (contains global shifting and local shifting) is proposed to reduce frequency aliasing distortion. Furthermore, a scheduling strategy is used to decide which part should be shifted according to the degree of aliasing. From the experiment results, the performance of the proposed method is better than SAOC and other enhanced methods. © 2021 IEEE computer Society. All rights reserved.

关键词： Decoding

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Scoring Tourist Attractions Based on Sentiment Lexicon

Scoring Tourist Attractions Based on Sentiment Lexicon

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2017 IEEE 第2届先进信息技术,电子与自动化控制国际会议(IAEAC 2017)

作者： Yi Ding Bing Li Yuqi Zhao Can Cheng State Key Lab of Software Engineering & School of Computer Wuhan University School of Computer Wuhan Vocational College of Software and Engineering International School of Software & State Key Laboratory of Software Engineering Wuhan University Research Center of Complex Network Wuhan University

Sentiment analysis uses NLP(Natural Language Processing) technology to analyze people’s sentiment or *** can treat the subjectivity,sentiment and opinion of text by *** recent years,

ISBN: (纸本)9781467389808

Sentiment analysis uses NLP(Natural Language Processing) technology to analyze people’s sentiment or *** can treat the subjectivity,sentiment and opinion of text by *** recent years,

关键词： Tourist attraction Sentiment analysis Review Score Lexicon Dictionaries as Topic Sentiment analysis opinion Natural Language Processing

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Illuminate Low-Light Image via Coarse-to-fine Multi-level network 27th

Illuminate Low-Light Image via Coarse-to-fine Multi-level Ne...

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27th International Conference on MultiMedia Modeling, MMM 2021

作者： Qiu, Yansheng Chen, Jun Wang, Xiao Jang, Kui National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China

ISBN: (纸本)9783030678319

Images under low light or against light are of low readability and visibility which in turn cause performance degradation of many computer vision tasks. As the crucial research objects for low-brightness image enhancement, reflectance as an invariant has color information, and illuminance as a variable has brightness information. Existing technologies prefer to represent the reflectance and illumination in the same network depth directly, which ignores the internal discrepancy and correlation of these two components, further causing the details vanished and artifacts noises. Consequently, we propose coarse-to-fine multi-level decouple and fusion network (CMDFN) to decompose the restoration tasks into two sub-tasks, and use decouple network (CMDN) to learn the joint features of reflectance and illumination at different network depths. To refine the joint representation and revise color information, fusion network (CMFN) is applied to promote the illuminations map by extracting the complementary and redundant information from the reflectance map. Finally, we integrate the refined illumination and reflectance to reproduce the predicted image. Especially, in CMFN, we introduce a process based on discriminate attention mechanism to balance the noise. Experimental results have demonstrated that our proposed network is superior other state of the art methods, both visually and quantitatively. © 2021, Springer Nature Switzerland AG.

关键词： Reflection

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