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8th International Conference on Digital Home, ICDH 2020

作者： Chen, Xiangping Ma, Yingxun Zhou, Fan Lin, Ge Zhang, Zhian Sun Yat-sen University Guangdong Key Lab. for Big Data Anal. and Simulat. of Pub. Opinion the Sch. of Commun. and Design Guangzhou China Sun Yat-sen University School of Data Science and Computer National Engineering Research Center of Digital Life Guangzhou China

ISBN: (纸本)9781728192345

Recently, rumor detection has attracted lots of research interest because of the quick development of social networks and the widely spread of rumor. The exiting studies is mainly about the open social network such as Weibo and Facebook. However, there is little research about closed social networks, especially Wechat. As a famous app with more than one billion daily active user, the tweets in Wechat can attract lots of attention, which gives the spread of rumor tweets more chance. In this paper, we empirically study the tweets spread in Wechat. We first analyze the text information and users' behavior of Wechat tweets, then we come up with several important features needed for rumor detection. Furthermore, we propose a new model which can capture the impact of both users' behavior and text information. We conduct experiments using a large tweets dataset collected in Wechat, the result shows that our proposed method can effectively detect the rumor tweets. © 2020 IEEE.

关键词： Large dataset

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Research on personalized learning path management based on blockchain technology 16

Research on personalized learning path management based on b...

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16th IEEE International Conference on Computer Science and Education, ICCSE 2021

作者： Changhua, Zhao Chunqiao, Mi Huaihua University School of Computer Science and Engineering Hunan Huaihua418000 China Key Lab. of Wuling-Mt. Hlth. Big Data Intelligent Proc. and Applic. in Hunan Province Universities Hunan Huaihua418000 China Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua18000 China

ISBN: (纸本)9781665414685

Aiming at the problem that learners' personal information, courses and relevant certificates can not provide detailed information about learning depth and learning or teaching methods, this paper takes learners' learning information, learning record smart contract and Ethereum Blockchain related technology as the research object, aiming to use Ethereum Blockchain and smart contract to connect learners' learning records. On the one hand, it can use the block data stored in the Blockchain to easily verify the authenticity of the data and ensure the privacy and security of the data. On the other hand, it can provide personalized learning path for learners and be used to evaluate a person's educational achievement, employment suitability and intelligence evaluation. © 2021 IEEE.

关键词： Smart contract

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Size-Reduced Patch Antenna Loaded with a Metamaterial Substrate Made of Periodic Metal Strips Operating at 900MHz 13

Size-Reduced Patch Antenna Loaded with a Metamaterial Substr...

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13th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2021

作者： Lu, Hao Xu, Xiaofei Shanghai University School Of Communication And Engineering Shanghai200444 China Key Lab. Of Specialty Fiber Opt. And Optical Access Networks And Shanghai Inst. For Adv. Commun. And Data Sci. Shanghai University Shanghai200444 China

ISBN: (纸本)9781665434379

A microwave patch antenna is studied with reduced patch size operating at 900MHz. The new antenna is featured with a metamaterial substrate that is made from periodic metal strips. One antenna is numerically demonstrated with a patch size of 54mm×60mm. It resonates at 898MHz with a relative bandwidth of 3.8%. The electrical patch size is only 0.16×0.18, much beyond the 0.5 limitation for a conventional patch antenna. The metamaterial antenna is shown with a very high efficiency 95.8%, even for such a small prototype. © 2021 IEEE.

关键词： Microstrip antennas

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Robot Manipulator Control via Solving Four-Layered Time-Variant Equations Including Linear, Nonlinear Equalities and Inequalities 11

Robot Manipulator Control via Solving Four-Layered Time-Vari...

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11th International Conference on Intelligent Control and Information Processing, ICICIP 2021

作者： Zhu, Xinhui Zhang, Li Shi, Yang Wang, Jing Li, Jian Xinyang Normal University School of Computer and Information Technology Xinyang464000 China Henan Key Lab. of Analysis and Application of Education Big Data Xinyang Normal University Xinyang464000 China Yangzhou University School of Information Engineering Yangzhou225127 China

ISBN: (纸本)9781665425155

Robot manipulator control is a complicated multi-tasking problem in reality. It includes not only basic tracking task, but also additional tasks, such as conquering joint angle limits, posture control. However, most existing works only consider the goal of tracking and formulate it as single-layered time-variant problems, which leads to impracticality. In this work, robot manipulator control problem is formulated as four-layered time-variant equations including linear, nonlinear equalities and inequalities. Each layer formulates one subtask: The first layer of nonlinear equality describes basic tracking task based on forward kinematics;The second layer and third layer are inequalities, which describe joint angle upper and lower limits;The last layer is a linear equality with respect to joint angle velocity, which could be designed by user to describe other task, such as posture control. To solve this complicated four-layered time-variant problem, it is converted as single-layered equation based on the zeroing neural dynamics method. Then, continuous-time solution is proposed. Furthermore, discrete-time algorithm is proposed based on a third-order time-discretization formula and continuous-time solution. Numerical experiments illustrate the effectiveness and superiority compared to existing work. © 2021 IEEE.

关键词： Industrial robots

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A Multi-Dimensional Assessment Model and Its Application in E-learning Courses of Computer Science 20

A Multi-Dimensional Assessment Model and Its Application in ...

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21st Annual Conference of the Special Interest Group in Information Technology Education, SIGITE 2020

作者： Luo, Jiwen Lu, Feng Wang, Tao Key Lab. of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781450370455

Computer science is a practical discipline. It is always a great challenge to evaluate students' computer practice using computer-aided means for large scale students. We always need to address problems such as suspected plagiarism and deviation of the overall difficulty factor. In this paper, a multi-dimensional assessment model is designed for CS courses based on the detailed practice processing data in an E-learning system. The model comprehensively evaluates the students' learning process and results in three aspects of correctness, originality, and quality detection. Besides, the teacher can easily participate in the assessment according to their needs. The correctness is an essential requirement, and the originality is based on the clustering results of students' behaviors after clone detection to curb homework plagiarism. SonarQube is used to detect code quality and put forward higher requirements for codes. Manual participation intelligence has improved the flexibility and applicability of the model to a certain extent. We applied this model on the EduCoder online education platform and carried out a comprehensive analysis of 485 students in the Parallel Programming Principles and Practice Class of Huazhong University of Science and Technology. Experiment results confirm the distinction, rationality, and fairness of the model in assessing student performance. It not only gives students a credible, comprehensive score in large-scale online practical programming courses but also gives teachers and students corresponding suggestions based on the evaluation results. Furthermore, the model can be extended to other online education platforms. © 2020 ACM.

关键词： Students

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Semi-supervised Multi-lab.l Learning with Missing lab.ls via Correlation Information

Semi-supervised Multi-Label Learning with Missing Labels via...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zexian Xie Peipei Li Jinling Jiang Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Knowledge Engineering Research Center Zhejiang Lab Hangzhou Zhejiang China

In multi-lab.l learning, each instance is associated with a set of lab.ls simultaneously. Most existing studies assume that the set of lab.ls for each instance is complete. However, it is generally difficult to obtain all the relevant lab.ls of each instance, and only a partial or even empty set of relevant lab.ls is availab.e, which is called semi-supervised multi-lab.l learning with missing lab.ls. To tackle this problem, we propose a novel framework that considers lab.l correlations and instance correlations to recover the missing lab.ls and utilizes a large amount of unlab.led data simultaneously to improve the classification performance. Specifically, a new supplementary lab.l matrix is firstly obtained by learning the lab.l correlation. Secondly, considering each class lab.l may be decided by some specific characteristics of its own, a lab.l-specific data representation is hence learned for each class lab.l. Thirdly, instance correlations are utilized not only to recover the missing lab.ls, but also to propagate the supervision information from lab.led instances to unlab.led ones. In addition, a united objective function is designed to facilitate the above processing and an accelerated proximal gradient method is adopted to solve the optimization problem. Finally, extensive experimental results conducted on several benchmark datasets demonstrate the effectiveness of the proposed method compared to competing ones.

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Small scale dog face detection using improved Faster RCNN 3

Small scale dog face detection using improved Faster RCNN

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3rd International Conference on Electronic Communication and Artificial Intelligence, IWECAI 2022

作者： Mu, Lina Shen, Zhijun Liu, Jiangping Gao, Jing College of Computer And Information Engineering Inner Mongolia Agricultural University Huhhot010018 China School of Computer And Information Engineering FuYang Normal University Fuyang236037 China Inner Mongolia Autonomous Reg. Key Lab. of Big Data Res. And Applic. of Agric. And Animal Husbandry Huhhot010018 China

ISBN: (纸本)9781665479974

In order to achieve fast localization and detection of dog face in intelligent dog management system, a dog face detection algorithm based on improved Faster RCNN was proposed. To obtain the feature extraction backbone network suitable for small size targets, the algorithm extracts the features of the detection target by constructing the residual convolution network, and fuses the features of different layers of the convolution network with the feature pyramid structure. Then, the region proposal network is used to generate and filter the possible regions of the target, and the bilinear interpolation method is used to calculate the bounding box coordinate values to achieve accurate location. Using Tsinghua dog image dataset to train the model, an improved Faster RCNN model adapted to small scale targets is obtained. The experimental results show that the improved method proposed in this paper maintains high precision in the detection of small size dog face, and can realize the detection of different scale objects, which has certain engineering practical value. © 2022 IEEE.

关键词： Convolution

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arXiv

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arXiv 2017年

作者： Dong, Jianfeng Li, Xirong Xu, Duanqing College of Computer Science and Technology Zhejiang University Hangzhou310027 China Key Lab of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing100872 China

—In order to retrieve unlab.led images by textual queries, cross-media similarity computation is a key ingredient. Although novel methods are continuously introduced, little has been done to evaluate these methods together with large-scale query log analysis. Consequently, how far have these methods brought us in answering real-user queries is unclear. Given baseline methods that use relatively simple text/image matching, how much progress have advanced models made is also unclear. This paper takes a pragmatic approach to answering the two questions. Queries are automatically categorized according to the proposed query visualness measure, and later connected to the evaluation of multiple cross-media similarity models on three test sets. Such a connection reveals that the success of the state-of-the-art is mainly attributed to their good performance on visual-oriented queries, which account for only a small part of real-user queries. To quantify the current progress, we propose a simple text2image method, representing a novel query by a set of images selected from large-scale query log. Consequently, computing cross-media similarity between the query and a given image boils down to comparing the visual similarity between the given image and the selected images. Image retrieval experiments on the challenging Clickture dataset show that the proposed text2image is a strong baseline, comparing favorably to recent deep learning alternatives. Copyright © 2017, The Authors. All rights reserved.

关键词： Image retrieval

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A Method of Realizing External Network Access to Intranet for Embedded Web Server 2

A Method of Realizing External Network Access to Intranet fo...

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2nd International Conference on Electronic Information Technology and Smart Agriculture, ICEITSA 2022

作者： Zhang, Xian Qu, Taiguo Tang, Pengju Liu, Yiwen Key Lab. of Intelligent Contr. Technol. for Wuling Mountain Ecological Agriculture in Hunan Province Huaihua China Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Wuling Huaihua China College of Computer Science and Electronic Engineering Hunan University ChangSha China School of Computer Science and Engineering Huaihua University Huaihua China

ISBN: (纸本)9781665464017

Based on the application requirements of the embedded Web server for external network access to the internal network, this paper designs and builds an embedded Web server scheme for external network access to the internal network based on the embedded Boa server and phddns program. This solution mainly transplants the Boa server and phddns program in embedded devices. Boa provides a Web server application function, and phddns software realizes the mapping and binding mapping between the IP address and the domain name of embedded devices in the intranet. An experiment is designed to test this scheme's connection success rate, response time, stable running time, domain name mapping success rate, CPU utilization and other performance indicators. The test results show that this scheme can better meet the system requirements of external network access to the internal network. Therefore, this scheme can support the information and intelligent monitoring and control of embedded devices and has good practical value and application prospects. © 2022 IEEE.

关键词： Intranets

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Personalized Clustering via Targeted Representation Learning

arXiv

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arXiv 2024年

作者： Geng, Xiwen Zhao, Suyun Yu, Yixin Peng, Borui Du, Pan Chen, Hong Li, Cuiping Wang, Mengdie Key Lab of Data Engineering and Knowledge Engineering MOE Renmin University of China China School of Information Renmin University of China China School of Statistics Remin University of China China

Clustering traditionally aims to reveal a natural grouping structure within unlab.led data. However, this structure may not always align with users’ preferences. In this paper, we propose a personalized clustering method that explicitly performs targeted representation learning by interacting with users via modicum task information (e.g., must-link or cannot-link pairs) to guide the clustering direction. We query users with the most informative pairs, i.e., those pairs most hard to cluster and those most easy to miscluster, to facilitate the representation learning in terms of the clustering preference. Moreover, by exploiting attention mechanism, the targeted representation is learned and augmented. By leveraging the targeted representation and constrained contrastive loss as well, personalized clustering is obtained. Theoretically, we verify that the risk of personalized clustering is tightly bounded, guaranteeing that active queries to users do mitigate the clustering risk. Experimentally, extensive results show that our method performs well across different clustering tasks and datasets, even when only a limited number of queries are availab.e. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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