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作者： Gang Lin Yanchun Liang Xiaoyang Fu Guangshun Chen Shaocan Cai Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Department of Computer Science and TechnologySchool of Computer Science Department of Computer Science and Technology School of Computer ScienceZhuhai College of Jilin University

In order to generate a report for an enterprise where there is neither the API supporting from their existing website systems nor the granted database access rights approval,a daily business report generator system based on web scraping with k nearest neighbor(kNN)classification algorithm is proposed in this *** covers the web crawler technology that is to access existing website system and extract business *** k NN algorithm is applied to identify the verification code on the login page,and the brief daily report generating in a spreadsheet style *** with some OCR engine for image recognition,the system in Python can automatically generate the brief daily business reports by the kNN algorithm,which is better than some library with default training set on validating the verification code.

关键词： image Design of a Daily Brief Business Report Generator based on Web Scraping with KNN Algorithm Web KNN

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Extracting Attributes from Deep Web Interface Using Instances

Extracting Attributes from Deep Web Interface Using Instance...

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Computer Science and Information engineering, CSIE, WRI World Congress on FLAGGED

作者： Hao Liang Fei Ren Wanli Zuo Fengling He Junhua Wang College of Computer Science and Technology Jilin University Changchun China Department of Information Changchun Taxation College Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun China

There is myriad high quality information in the Deep Web and the feasible method to access the Deep Web is through the query interface of the Deep Web. Itpsilas necessary to extract abundant attributes and semantic relation description from the query interface. Automatic extracting attributes from the query interface and automatically translating a query is a solvable way for addressing the current limitations in accessing Deep Web data sources. We design a framework to automatically extract the attributes and instances from the query interface using the WordNet as a kind of ontology technique to enrich the semantic description of the attributes. Each attribute is extended into a candidate attribute set in the form of a hierarchy tree. At the same time, the hierarchy tree generated by ontology describes the semantic relation of the attributes in the same query interface. We carry out our experiments in the real-world domain. The results of the experiments showed the validation of query translation framework.

关键词： Data mining Ontologies Computer science Educational institutions Databases Programming profession HTML Laboratories Computer interfaces knowledge engineering

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Research and Design of Drawings Retrieval System Based on Spatial Relationships and Shape

Research and Design of Drawings Retrieval System Based on Sp...

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ISECS International Colloquium on Computing, Communication, Control, and Management, CCCM

作者： Haiyu Song Pengjie Wang Xiongfei Li Houjie Li Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin China The Research Institute of Computer Graphics and Image Processing Dalian Nationalities of University Dalian Liaoning China

CAD drawings reuse is of great importance in promoting design efficiency and productivity. The ability to retrieve CAD drawings is the core of drawings reuse task. Most of the existing drawings retrieval systems rely only on text descriptions. Due to complexity of visual feature, a little system is based on content. A simplified topological relationship is proposed to describe visual features for dimension reduction. An effective topological relationship vector is constructed by graph spectra. A novel retrieval system based on visual contents, including topological relationships, shape, and directional relationships, is proposed. The experiments show that the proposed approach and system are superior to the text-based and traditional content-based retrieval systems.

关键词： Shape Feature extraction Topology Indexing Design automation Data mining Visualization

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Joint-Motion Mutual Learning for Pose Estimation in Videos

arXiv

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arXiv 2024年

作者： Wu, Sifan Chen, Haipeng Yin, Yifang Hu, Sihao Feng, Runyang Jiao, Yingying Yang, Ziqi Liu, Zhenguang College of Computer Science and Technology Jilin University Changchun China A*STAR Singapore Georgia Institute of Technology Atlanta United States School of Artificial Intelligence Jilin University Changchun China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China Institute of Blockchain and Data Security Hangzhou China

Human pose estimation in videos has long been a compelling yet challenging task within the realm of computer vision. Nevertheless, this task remains difficult because of the complex video scenes, such as video defocus and self-occlusion. Recent methods strive to integrate multi-frame visual features generated by a backbone network for pose estimation. However, they often ignore the useful joint information encoded in the initial heatmap, which is a byproduct of the backbone generation. Comparatively, methods that attempt to refine the initial heatmap fail to consider any spatiotemporal motion features. As a result, the performance of existing methods for pose estimation falls short due to the lack of ability to leverage both local joint (heatmap) information and global motion (feature) dynamics. To address this problem, we propose a novel joint-motion mutual learning framework for pose estimation, which effectively concentrates on both local joint dependency and global pixel-level motion dynamics. Specifically, we introduce a context-aware joint learner that adaptively leverages initial heatmaps and motion flow to retrieve robust local joint feature. Given that local joint feature and global motion flow are complementary, we further propose a progressive joint-motion mutual learning that synergistically exchanges information and interactively learns between joint feature and motion flow to improve the capability of the model. More importantly, to capture more diverse joint and motion cues, we theoretically analyze and propose an information orthogonality objective to avoid learning redundant information from multi-cues. Empirical experiments show our method outperforms prior arts on three challenging benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion estimation

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A Novel Distributed Differential Privacy Preserving Based on Random Forest in Data Centers

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Procedia Computer Science 2022年 214卷 1531-1540页

作者： Xi Wang Weibei Fan Jing He Chi-Hung Chi Suzhou Institute of Industrial Technology Suzhou 215006 China Key laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 P.R. China College of Computer Nanjing University of Posts and Telecommunications Nanjing 210003 China Department of Neuroscience University of Oxford Oxford OX1 3QR England School of Computer Nanyang Technological University Nanyang Avenue 639798 Singapore

Traditional privacy-preserving technologies have been unable to provide adequate protection and are vulnerable to background knowledge. MapReduce is parallel distributed computing model which has the advantages of good scalability and high fault tolerance. In this paper, we design a differential privacy protection based on MapReduce for the security problems faced in the distributed environment. Firstly, we propose a random forest algorithm DPMRRF that satisfies differential privacy under the MapReduce framework. Secondly, we use the MapReduce framework in the Hadoop platform to make the classification results satisfy differential privacy by adding random noise values to the leaf nodes. Finally, through the design of the feature selection scheme combined with the index mechanism, three different privacy budget allocation methods are adopted, namely, equal distribution, equal distribution and equal distribution. Experiments show that the algorithm has better classification accuracy while ensuring data availability. The proposed algorithm effectively improves the classification accuracy and operating efficiency, and greatly reduces the amount of computation.

关键词： MapReduce Differential privacy protection Random forest Privacy budget

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The TPSO algorithm to solve geometric constraint problems

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Journal of computational Information Systems 2006年第4期2卷 1311-1316页

作者： Yuan, Hua Li, Wenhui Yi, Rongqin Zhao, Kong Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130012 China School of Computer Science and Engineering Changchun University of Technology Changchun 130012 China Suzhou Top Institute of Information Technology Suzhuo 215311 China

The constraint problem can be transformed to an optimization problem. Particle swarm optimization (PSO) is a new evolutionary computation technique. Even PSO has many attractive properties, but it lacks global search ability at the end of the run. This paper introduce a hybrid approach called the TPSO that simultaneously applies particle swarm optimization (PSO), and tabu search (TS) to create a generally well-performing search heuristics, and combat the problem of premature convergence. The new algorithm considers candidate solutions and their fitness as individuals, which are based on their recent search progress. The tabu search makes each particle to reset its record of its best position, to avoid making direction and velocity decisions on the basis of outdated information. The feasibility of the proposed method is demonstrated on Solving Geometric Constraint Problems.

关键词： Computer aided design

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Genetic algorithm with affinity propagation

Genetic algorithm with affinity propagation

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International Conference on Information and Automation (ICIA)

作者： Chunguo Wu Hao Gao Lianjiang Yu Yanchun Liang Rongwu Xiang College of Computer Science and Technology Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry Jilin University Changchun China School of Basic Courses Shenyang Pharmaceutical University Shenyang China

ISBN: (纸本)9781424457014

Classical genetic algorithm suffers heavy pressure of fitness evaluation for time-consuming optimization problems, e.g., aerodynamic design optimization, qualitative model learning in bioinformatics. To address this problem, we present a combination between genetic algorithms and clustering methods. Specifically, the clustering method used in this paper is affinity propagation. The numerical experiments demonstrate that the proposed method performs promisingly for well-known benchmark problems in the term of optimization accuracy.

关键词： Genetic algorithms Clustering methods Evolutionary computation Design optimization Optimization methods Design automation Computer science Educational technology Pharmaceutical technology knowledge engineering

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Game Theoretic Approaches in Vehicular Networks: A Survey

arXiv

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arXiv 2020年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Anil, Carie Cao, Dongpu College of Computer Science and Technology Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Engineering VIT-AP AmaravatiAndhra Pradesh522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

—In the era of the Internet of Things (IoT), vehicles and other intelligent components in Intelligent Transportation System (ITS) are connected, forming the Vehicular Networks (VNs) that provide efficient and secure traffic, ubiquitous access to information, and various applications. However, as the number of connected nodes keeps increasing, it is challenging to satisfy various and large amounts of service requests with different Quality of Service (QoS) and security requirements in the highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources so that either an individual or a group objectives can be achieved. Game theory, a theoretical framework designed for strategic interactions among rational decision-makers who faced with scarce resources, can be used to model and analyze individual or group behaviors of communication entities in VNs. This paper primarily surveys the recent advantages of GT used in solving various challenges in VNs. As VNs and GT have been extensively investigate34d, this survey starts with a brief introduction of the basic concept and classification of GT used in VNs. Then, a comprehensive review of applications of GT in VNs is presented, which primarily covers the aspects of QoS and security. Moreover, with the development of fifth-generation (5G) wireless communication, recent contributions of GT to diverse emerging technologies of 5G integrated into VNs are surveyed in this paper. Finally, several key research challenges and possible solutions for applying GT in VNs are outlined. Copyright © 2020, The Authors. All rights reserved.

关键词： Game theory

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An Enhanced Attentive Implicit Relation Embedding for Social Recommendation

SSRN

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SSRN 2022年

作者： Ma, Xintao Dong, Liyan Wang, Yuequn Li, Yongli Liu, Zhen Zhang, Hao College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China School of Information Science and Technology Northeast Normal University Changchun130117 China Graduate School of Engineering Nagasaki Institute of Applied Science 536 Aba-machi Nagasaki851-0193 Japan

Nowadays, recommendation system incorporating social networks has attracted a lot of research attention and is widely used to understand user preferences regarding social relations. Besides, graph neural networks(GNNs) have proven to learn graph-structured data effectively. Thus, integrating GNN into social recommendations has become one challenge. Many approaches encode both the user-item interaction and social relations to learn user preference. However, the implicit relations are also crucial to understanding the features of users and items. Thus, we propose the attentive implicit relation embedding for social recommendation(SR-AIR), which models the user-item interaction and social networks, utilizing a graph attention mechanism on implicit relations of users and items. We evaluate our framework on two real-world datasets and demonstrate that our framework performs the best compared with state-of-the-art baselines © 2022, The Authors. All rights reserved.

关键词： Recommender systems

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Automatic Controversy Detection Based on Heterogeneous Signed Attributed Network and Deep Dual-Layer Self-Supervised Community Analysis

SSRN

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SSRN 2023年

作者： Li, Ying Zhang, Xiao Liang, Yu Li, Qianqian Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Qianjin Street Jilin Changchun130012 China Institutes of Science and Development Chinese Academy of Sciences ZhongGuanCun BeiYiTiao Beijing100190 China School of Public Policy and Management University of Chinese Academy of Sciences ShiJingShan YuQuanLu Beijing100049 China

Controversial analysis entails identifying and scrutinizing contentious events or issues within a specific domain. Detecting controversy, particularly on social media, poses challenges due to the need to consider numerous contents and user interactions. Unlike previous work, rather than studying controversy on limited knowledge of the topic discussion, we introduce multidimensional and heterogeneous information to conduct a comprehensive evaluation. Our method to quantify controversy is based on heterogeneous signed attributed network. This framework constructs a comprehensive heterogeneous signed attributed network to integrate diverse semantic and structural information on social media, encompassing user, retweet, comment, summary, topic, sentiment information, and user attributes. We introduce a deep dual-layer self-supervised algorithm for community detection and controversial analysis within this network. We gauge controversy levels in hot events via community distinctions, betweenness centrality, and user representations. Extensive comparisons of state-of-the-art baseline approaches are performed. We find that existing methods shows instability on different datasets. Whereas our approach integrates the multidimensional features of topics and also takes into account various computational results, our method consistently yields stable results. A user-friendly web server has been developed for accessible analysis of other hot events (http://***/contro/). © 2023, The Authors. All rights reserved.

关键词： Social networking (online)

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