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International Conference on Computer and Communications (ICCC)

作者： Dawen Zheng Lusheng Wang Caihong Kai Min Peng Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Service robots play an increasingly important role in people's daily life. The density of pedestrians is large and the movement is irregular in pedestrian-robot mixed traffic flows. Robots are prone to collision with pedestrians, and the tasks to be offloaded are closely related to pedestrians. How to analyze the tasks of robots and select the appropriate roadside unit is an important issue. In this paper, the social force model is used to predict the positions of pedestrians and robots, taking into account the influence of various forces to avoid collisions. A task offloading resource optimization algorithm with position prediction is proposed. According to the predicted information, the size and position distribution of all tasks in the scenario are obtained, and then the neural network trained beforehand based on deep Q-Iearning is used to generate a task offloading strategy. The simulation results show that the running time of the proposed algorithm is very short, and the resource allocation required for task offloading is completed in advance based on the predicted information before robots arriving the corresponding positions. Besides, the algorithm significantly reduces the task offloading delay.

关键词： Service robots Simulation Force Predictive models Prediction algorithms Delays Task analysis

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Shingles-based structural clustering of web documents

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Journal of Computational Information Systems 2008年第6期4卷 2777-2785页

作者： Xia, Tian Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China School of Information Resource Management Renmin University of China Beijing 100872 China

Web document structural clustering is a useful task for many web intelligent applications, however, processing based on the structure of web documents have not yet received strong attention. In this paper, we propose a shingles-based approach to clustering web documents by structure. Firstly, semi-structured web documents are converted into structured tree which composed of a set of limited nodes, and structural features are extracted by shingles. Secondly, we define document distance and structural distance matrix, and then structural similarity is calculated according to this matrix. Finally, we cluster the document structure based on modified k-means algorithm. Different from existing methods, we construct shingles not only including real vertical paths, but also virtual horizontal paths. Weight factors are also considered to optimize the algorithm. Experimental results show the effectiveness of the new shingles-based similarity measurement and the structural clustering. The proposed document similarity, as well as the structural shingles analysis, could be applied to other web-based research issues. © 2008 Binary Information Press.

关键词： Information systems

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Constructing Corpus for Query-oriented XML Text Summarization

Constructing Corpus for Query-oriented XML Text Summarizatio...

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2010 International Conference on Management of e-Commerce and e-Government(第四届电子商务与电子政务管理国际会议 ICMeCG 2010)

作者： Shihan WU Dexi LIU Xianpei JIAO Jiangxi Key Laboratory of Data and Knowledge Engineering School of Information Technology Jiangxi University of Finance & Economics Nanchang Jiangxi China

XML Retrieval is becoming the focus study of the field of Information Retrieval and database. Summarization of the results which come from the XML search engines will alleviate the read burden of user's. However, as the basis of this study, the construction of the query-oriented XML text summarization corpus has not yet received enough attention. In this paper, we introduce our works on constructing this kind of corpus, including the selection of topics and XML elements/documents, construction process and the feature of the constructed corpus. Up to now, the corpus has 25 English query topics, including 422 elements for summarization, and 32 Chinese topics which including 402 elements. For each topic, 4 pieces of extracted summaries and 4 pieces of generated summaries are made manually by 4 experts.

关键词： Query-oriented XML Automatic summarization Corpus

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Convolution Neural Network for Renal Function Assessment Based on Glomerular Filtration Rate

Convolution Neural Network for Renal Function Assessment Bas...

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2021 International Conference on Advanced Technologies and Applications of Modern Industry, ATAMI 2021

作者： Wu, Congzhong Wang, Lulu Yang, Zhen Wang, Jing Han, Li Li, Fei Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei Anhui230601 China School of Computer and Information Hefei University of Technology Hefei Anhui230601 China The Second Hospital of Anhui Medical University Hefei Anhui230601 China

Objective: To develop a Convolution Neural Network (CNN) and discuss its performance in the estimation of Glomerular Fifiltration Rate (GFR) for patients with chronic kidney disease (CKD). Methods: A total of 112 patients with chronic kidney disease were enrolled in this study. The GFR is measured by 99mTc-DTPA renal dynamic and used as standard GFR after normalization by body surface area imaging. We established a CNN model and verified the performance of the model by comparing the GFR predicted by the model with the standard GFR. It turned out that the CNN could better evaluate the GFR of patients which is superior to the CG formula, MDRD formula, CKD-EPI formula and GRNN model. Conclusions: The CNN significantly evaluated GFR for patients with CKD, and it showed better performance than traditional methods and GRNN model and closer to the results of 99mTc-DTPA. The experimental results demonstrated that CNN could be used to estimate GFR. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Neural network models

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Incremental computation for MEDIAN cubes in what-if analysis

Incremental computation for MEDIAN cubes in what-if analysis

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Joint International Conference on Advances in data and Web Management, APWeb/WAIM 2009

作者： Xiao, Yanqin Zhang, Yansong Wang, Shan Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China Computer Center Hebei University Baoding Hebei 071002 China Department of Computer Science Harbin Finance College Harbin 150030 China

ISBN: (纸本)9783642006715

What-if analysis is an important type of DSS analysis processing procedure. It analyzes hypothetical scenarios based on historical data. The data cube view must be updated when the what-if condition is changed. Since source data must be kept in order to compute the new aggregate value when new tuples are inserted or deleted, in what-if analysis, incrementally computing a data cube for holistic aggregation functions is a difficult problem. In this paper, we adopt delta cube strategy and work area technique to incrementally compute data cube for MEDIAN function. The size of work area has important influence on the efficiency of the incremental computing. This paper optimizes the size of work area based on the number and the cardinality of dimension attributes of the cuboid. Performance study shows that our algorithms are effective over large databases. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Geometry

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Investigating different syntactic context types and context representations for learning word embeddings

Investigating different syntactic context types and context ...

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2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017

作者： Li, Bofang Liu, Tao Zhao, Zhe Tang, Buzhou Drozd, Aleksandr Rogers, Anna Du, Xiaoyong School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Shenzhen Graduate School Harbin Institute of Technology China Global Scientific Information and Computing Center Tokyo Institute of Technology China Department of Computer Science University of Massachusetts Lowell United States

ISBN: (纸本)9781945626838

The number of word embedding models is growing every year. Most of them are based on the co-occurrence information of words and their contexts. However, it is still an open question what is the best definition of context. We provide a systematical investigation of 4 different syntactic context types and context representations for learning word embeddings. Comprehensive experiments are conducted to evaluate their effectiveness on 6 extrinsic and intrinsic tasks. We hope that this paper, along with the published code, would be helpful for choosing the best context type and representation for a given task. © 2017 Association for Computational Linguistics.

关键词： Embeddings

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CLDG: Contrastive Learning on Dynamic Graphs 39

CLDG: Contrastive Learning on Dynamic Graphs

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Xi'an Jiaotong University Department of Computer Science and Technology China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Xi'an Jiaotong University Department of Distance Education China Beihang University School of Software China Beihang University Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9798350322279

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph's augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2023 IEEE.

关键词： Graph neural networks

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Sentiment Analysis of Semester Learning Essays in Design Education

Sentiment Analysis of Semester Learning Essays in Design Edu...

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2023 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2023

作者： Wang, Z.H. Ming, Z.J. Wang, G.X. Mistree, F. Allen, J.K. School of Mechanical Engineering Beijing Institute of Technology Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing China Ministry of Industry and Information Technology Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Beijing China The University of Oklahoma The Systems Realization Laboratory Norman United States

ISBN: (纸本)9798350323153

AME4163: Principles of engineering Design is a design, build and test course offered at the University of Oklahoma, Norman, USA. Throughout the semester students are expected to reflect on authentic and immersive experiences and write Take-aways and Learning Statements. At the end of the semester each student submits a summative Semester Learning Essay. Between 2019-2021 we collected about 10,000 Take-aways and Learning Statements from the Semester Learning Essays of nearly 400 students. In this paper, we attempt to help instructors gain insight of what students have learned through these materials and thence improve the delivery of the course in the future. We analyze their Take-aways from summative Semester Learning Essays by using dictionary-based sentiment analysis to assess students' subjective feelings toward what they have learned. The results prove that we gain insight into their learning thereby providing instructors with evidence-based guidance on modifying the course from different perspectives. The method proposed is generalizable to courses that involve authentic immersive experiences. © 2023 IEEE.

关键词： Assessment of learning learning by reflecting on doing sentiment analysis summative assessment

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Efficient incremental computation of CUBE in multiple versions what-if analysis

Efficient incremental computation of CUBE in multiple versio...

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Joint International Conference on Advances in data and Web Management, APWeb/WAIM 2009

ISBN: (纸本)9783642006715

What-if analysis is an important method to analyze the hypothetical scenarios based on the historical data. It provides useful information for the decision- maker. Multiple versions are critical to what-if analysis. In this paper, we analyze the problem of multiple versions data processing and the incremental computation of cubes in what-if analysis, proposing a strategy to process multiple versions of what-if data. Our solution can adopt different data processing methods according to the type of aggregate functions, the efficiency of multiple versions data processing is improved. Furthermore, we proposes an algorithm of incremental computation of CUBE for max(min) function to reduce the access time of fact table, and the experimental results showed that the performance is improved about 10%. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Decision making

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Link Prediction for Opportunistic Networks Based on Hybrid Similarity Metrics and E-LSTM-D Models 24th

Link Prediction for Opportunistic Networks Based on Hybrid...

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24th International Conference on Web Information Systems engineering, WISE 2023

作者： Yang, Xiaoying Li, Lijie Xu, Gang Song, Ming Zhang, Feng College of Computer Science Inner Mongolia University Hohhot010021 China Inner Mongolia A.R. Key Laboratory of Data Mining and Knowledge Engineering Inner Mongolia University Hohhot010021 China School of Computer and Information Technology Shanxi University Taiyuan030000 China

ISBN: (纸本)9789819972531

Link prediction is a crucial issue in opportunistic networks routing research. Static link prediction methods ignore the historical information of network evolution, which affects the prediction accuracy. In this paper, we propose a link prediction method (HS-LSTM) that combines hybrid similarity metrics and the E-LSTM-D model. The approach extracts structural features using hybrid similarity metrics, constructs a structural feature matrix, and applies the E-LSTM-D model to extract temporal features and predict the links that have not yet appeared in the network. Our experiments on three real datasets of opportunistic networks (Infocom5, MIT, and Haggle) shows that our proposed method outperforms the baseline method in terms of prediction accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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