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4th International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, GRMSE 2016

作者： Ding, Yi Li, Bing Zhao, Yuqi Liao, Fengling State Key Lab of Software Engineering and School of Computer Wuhan University Wuhan430072 China International School of Software and State Key Laboratory of Software Engineering Wuhan University Wuhan430072 China Research Center of Complex Network Wuhan University Wuhan430072 China School of Computer Wuhan Vocational College of Software and Engineering Wuhan430205 China

ISBN: (纸本)9789811039652

With the development of vocational education in China, millions of students graduate every year. Many graduates can’t find the compatible job, even out of work. At the same time, many enterprises complain that the ability of graduates can’t meet their requirements. They found that it is difficult to find suitable talents. In recent years, publishing recruitment information through the Internet has become a common action of many enterprises, especially in the recruitment website. Because enterprises have clearly put forward the qualification and responsibility of posts that the candidates should have in the recruitment information, we designed and implemented a solution to help vocational colleges improving the employment rate and the quality of teaching. It crawl recruitment information from recruitment website and extract qualification and responsibility of posts. On the one hand, the recruitment information can help students obtain employment;on the other hand, the qualification and responsibility of posts can help vocational colleges to closely track the enterprises’ demand for talents’ vocational skill, formulate and optimize the talents training scheme timely so as to ensure the quality of talents. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Students

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Building connectome analysis tools with representation learning on neuronal skeleton and circuit topology

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Neural networks 2025年

作者： Minghui Liao Guojia Wan Wenbin Hu Bo Du School of Computer Science Wuhan University Wuhan China National Engineering Research Center for Multimedia Software Wuhan University Wuhan China Institute of Artificial Intelligence Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

Analyzing connectome plays a significant role in the investigation of neurological diseases and brain research. However, the efficiency of utilizing anatomical, physiological, or molecular characteristics of neurons is relatively low and costly. With the advancements in volume electron microscopy(VEM) and analysis techniques for brain tissue, we are able to obtain whole-brain connectome consisting neuronal high-resolution morphology and connectivity information. Nevertheless, few tools are built based on such data for automated connectome analysis. In this paper, we introduce a connectome analysis tool based on a representation learning model termed NeuNet. NeuNet consists of three key components: Connectome Encoder, Skeleton Encoder, and Readout Layer, which together integrate information pertaining to neuronal connectivity and morphology. Furthermore, we reprocess and release a brain neuron reconstruction dataset from a Drosophila Nerve Cord VEM data. We apply the proposed tool to tasks related to connectome analysis, including neuron classification, brain circuit layout, neuron retrieval and neuron morphology description, and the experiments demonstrate the effectiveness of our tool. We will soon release our code and data on https://***/WHUminghui/ConnectomeAnalysisTool .

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Interpolation Method of Head-Related Transfer Functions Based on Common-Pole/Zero Modeling

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China Communications 2020年第10期17卷 170-182页

作者： Wei Chen Xiaochen Wang Ruimin Hu Gang Li Weiping Tu National Engineering Research Center for Multimedia Software School of Computer ScienceWuhan UniversityWuhan 430072China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan UniversityWuhan 430072China Research Institute of Wuhan University in Shenzhen China

The head-related transfer function(HRTF)involves the cues for human auditory localization,which turns it into an essential item of virtual auditory display *** practice,the interpolation of HRTF is necessary for the virtual auditory display systems to achieve high spatial *** geometric-based interpolation methods are generally restrained by the spatial distribution of reference on *** the spatial distribution is sparse,the accuracy of interpolation decreases ***,an interpolation method using the common-pole/zero model and the fitting neural network is ***,we propose a common-pole/zero model to represent HRTFs across multiple subjects,in which the low-dimensional features of the measured HRTFs are ***,for a new spatial direction,we predict the corresponding low-dimensional HRTF with a fitting neural ***,we reconstruct the high-dimensional HRTF from the predicted low-dimensional *** simulation results suggest that the proposed method outperforms other interpolation methods such as Linear_AMBC,Bilinear_AMBC,and the Combination method.

关键词： HRTF interpolation fitting neural network common-pole/zero model

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Pedestrian Attributes Recognition in Surveillance Scenarios with Hierarchical Multi-Task CNN Models

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China Communications 2018年第12期15卷 208-219页

作者： Wenhua Fang Jun Chen Ruimin Hu National Engineering Research Center for Multimedia Software Computer School of Wuhan UniversityWuhan 430072China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan UniversityWuhan 430072China Collaborative Innovation Center of Geospatial Technology Wuhan 430079China

Pedestrian attributes recognition is a very important problem in video surveillance and video forensics. Traditional methods assume the pedestrian attributes are independent and design handcraft features for each one. In this paper, we propose a joint hierarchical multi-task learning algorithm to learn the relationships among attributes for better recognizing the pedestrian attributes in still images using convolutional neural networks(CNN). We divide the attributes into local and global ones according to spatial and semantic relations, and then consider learning semantic attributes through a hierarchical multi-task CNN model where each CNN in the first layer will predict each group of such local attributes and CNN in the second layer will predict the global attributes. Our multi-task learning framework allows each CNN model to simultaneously share visual knowledge among different groups of attribute categories. Extensive experiments are conducted on two popular and challenging benchmarks in surveillance scenarios, namely, the PETA and RAP pedestrian attributes datasets. On both benchmarks, our framework achieves superior results over the state-of-theart methods by 88.2% on PETA and 83.25% on RAP, respectively.

关键词： attributes recognition CNN multi-task learning

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A hierarchical similarity based job recommendation service framework for university students

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Frontiers of computer Science 2017年第5期11卷 912-922页

作者： Rui LIU Wenge RONG Yuanxin OUYANG Zhang XIONG State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China Engineering Research Center of Advanced Computer Application Technology Ministry of Education Beihang University Beijing 100191 China School of Computer Science and Engineering Beihang University Beijing 100191 China

When people want to move to a new job, it is often difficult since there is too much job information available. To select an appropriate job and then submit a resume is tedious. It is particularly difficult for university students since they normally do not have any work experience and also are unfamiliar with the job market. To deal with the informa- tion overload for students during their transition into work, a job recommendation system can be very valuable. In this research, after fully investigating the pros and cons of current job recommendation systems for university students, we propose a student profiling based re-ranking framework. In this system, the students are recommended a list of potential jobs based on those who have graduated and obtained job offers over the past few years. Furthermore, recommended employers are also used as input for job recommendation result re-ranking. Our experimental study on real recruitment data over the past four years has shown this method＇s potential.

关键词： job recommendation students similarity time,re-ranking

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Urban Scene Semantic Segmentation with Insufficient Labeled Data

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China Communications 2019年第11期16卷 212-221页

作者： Qi Zheng Jun Chen Peng Huang Ruimin Hu National Engineering Research Center for Multimedia Software Computer School of Wuhan UniversityWuhan 430072China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan UniversityWuhan 430072China Collaborative Innovation Center of Geospatial Technology Wuhan 430079China

Semantic segmentation of urban scenes is an enabling factor for a wide range of *** the development of deep learning in recent years,semantic segmentation tasks using high-capacity models have achieved considerable successes on large ***,the pixel-level annotation process,especially for urban scene images with various objects,is tedious and labor ***,the scale of the unlabeled data,which is currently easy to collect,is often much larger than labeled ***,using the abundant unlabeled data to make up the loss of the segmentation model from insufficient labeled data is of great *** this paper,we propose a semi-supervised method based on reinforcement learning to capture the contextual information from the unlabeled data to improve the model trained on the small scale labeled *** quantitative and qualitative experiments have shown the effectiveness of the proposed method.

关键词： semantic segmentation semi-supervised learning reinforcement learning

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Attribute-Based Signature with Policy-and-Endorsement Mechanism

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Journal of computer Science & Technology 2010年第6期25卷 1293-1304页

作者：王怀习朱岩冯荣权 Stephen S.Yau LMAM School of Mathematical SciencesPeking University Electronic Engineering Institute Institute of Computer Science and Technology Peking University Key Laboratory of Network and Software Security Assurance (Peking University) Ministry of Education Information Assurance Center and School of Computing Informatics and Decision Systems Engineering Arizona State University

In this paper a new signature scheme,called Policy-Endorsing Attribute-Based Signature,is developed to correspond with the existing Ciphertext-Policy Attribute-Based *** signature provides a policy-and-endorsement *** this mechanism a single user,whose attributes satisfy the predicate,endorses the *** signature allows the signer to announce his endorsement using an access policy without having to reveal the identity of the *** security of this signature,selfless anonymity and existential unforgeability,is based on the Strong Diffie-Hellman assumption and the Decision Linear assumption in bilinear map groups.

关键词： cryptography fine-grained access control attribute-based signature policy-and-endorsement selfless anonymity existential unforgeability

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Robust Background Subtraction Method via Low-Rank and Structured Sparse Decomposition

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China Communications 2018年第7期15卷 156-167页

作者： Minsheng Ma Ruimin Hu Shihong Chen Jing Xiao Zhongyuan Wang National Engineering Research Center for Multimedia Software School of Computer ScienceWuhan UniversityWuhan 430072China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan UniversityWuhan 430072China Collaborative Innovation Center of Geospatial Technology Wuhan 430079China

Background subtraction is a challenging problem in surveillance scenes. Although the low-rank and sparse decomposition(LRSD) methods offer an appropriate framework for background modeling, they fail to account for image's local structure, which is favorable for this problem. Based on this, we propose a background subtraction method via low-rank and SILTP-based structured sparse decomposition, named LRSSD. In this method, a novel SILTP-inducing sparsity norm is introduced to enhance the structured presentation of the foreground region. As an assistance, saliency detection is employed to render a rough shape and location of foreground. The final refined foreground is decided jointly by sparse component and attention map. Experimental results on different datasets show its superiority over the competing methods, especially under noise and changing illumination scenarios.

关键词： background subtraction LRSD structured sparse SILTP

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Learning a generalizable re-identification model from unlabelled data with domain-agnostic expert

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Visual Intelligence 2024年第1期2卷 1-13页

作者： Liu, Fangyi Ye, Mang Du, Bo National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Hubei Luojia Laboratory Wuhan University Wuhan 430072 China

In response to real-world scenarios, the domain generalization (DG) problem has spurred considerable research in person re-identification (ReID). This challenge arises when the target domain, which is significantly different from the source domains, remains unknown. However, the performance of current DG ReID relies heavily on labor-intensive source domain annotations. Considering the potential of unlabeled data, we investigate unsupervised domain generalization (UDG) in ReID. Our goal is to create a model that can generalize from unlabeled source domains to semantically retrieve images in an unseen target domain. To address this, we propose a new approach that trains a domain-agnostic expert (DaE) for unsupervised domain-generalizable person ReID. This involves independently training multiple experts to account for label space inconsistencies between source domains. At the same time, the DaE captures domain-generalizable information for testing. Our experiments demonstrate the effectiveness of this method for learning generalizable features under the UDG setting. The results demonstrate the superiority of our method over state-of-the-art techniques. We will make our code and models available for public use. © The Author(s) 2024.

关键词： Domain generalization (DG) Domain-agnostic expert (DaE) Label space inconsistencies Unlabeled source domains

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A sensitive object-oriented approach to big surveillance data compression for social security applications in smart cities

A sensitive object-oriented approach to big surveillance dat...

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作者： Xiao, Jing Wang, Zhongyuan Chen, Yu Liao, Liang Xiao, Jun Zhan, Gen Hu, Ruimin State Key Laboratory of Software Engineering Computer School of Wuhan University China National Engineering Research Center for Multimedia Software Computer School of Wuhan University China Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Collaborative Innovation Center of Geospatial Technology China

Surveillance has become a fairly common practice with the global boom in "smart cities". How to efficiently store and manage the vast quantities of surveillance data is a persistent challenge in terms of analyzing social security problems. Developing data compression technology under the analytic requirements of surveillance data is the key to solving the storage problem. Criminal investigation demands the quality preservation of sensitive objects, typically pedestrians, human faces, vehicles, and license plates;however, the analytical value of surveillance data is rapidly lost as the compression ratio increases. In this paper, we propose a sensitive object-oriented regions of interest-based coding strategy for preserving the analytical value of surveillance data. In the proposed method, instead of generating a saliency map based on human visual perception, we consider saliency as a set of characteristics important for object detection and recognition. By making this modification, almost all sensitive objects necessary in a criminal investigation are assigned high saliency value rather than only one or two salient regions. Motions in the temporal domain are integrated to place emphasis on moving objects, namely moving sensitive objects, which then gain the highest saliency. Finally, a saliency-based rate control algorithm embedded in High Efficiency Video Coding is used to maintain the quality of sensitive objects in the encoded video under a fixed bitrate. Experiments were conducted on two analytical indexes: Feature similarity and object detection accuracy. The results showed that by achieving the same feature similarity and object detection accuracy, our method can save 20% and 40% bitrate over High Efficiency Video Coding, respectively, for the storage of big surveillance data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Object detection

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