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International Conference on Services Systems and Services Management, ICSSSM

作者： Changyu Wang Meiyun Zuo Yifan Bo Key Laboratory of Data Engineering and Knowledge Engineering and School of Business Renmin University of China Beijing China

knowledge transfer is an important type of knowledge service. Based on existing studies on knowledge transfer between employees, this paper builds a research model of factors affecting the pre-retiree's intention to transfer knowledge to the younger employee. data collected from 4 leaders and 7 pre-retirees in different industries through face-to-face interview is used to test and improve the model, then we gain a theoretical model which posits that the pre-retiree's knowledge transfer intention is influenced by the pre-retiree's perceptions of younger employees and emotions to organization, the younger employee's attitudes towards pre-retirees, the younger employee's learning intention and ability and work attitudes, communication difference between the pre-retiree and the younger employee. Our results contribute to the improvement of research on pre-retirees' knowledge transfer and have implications for management practices.

关键词： knowledge transfer Interviews Organizations data models Educational institutions Analytical models Cognition

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A study of SQL-on-Hadoop systems

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8807卷 154-166页

作者： Chen, Yueguo Qin, Xiongpai Bian, Haoqiong Chen, Jun Dong, Zhaoan Du, Xiaoyong Gao, Yanjie Liu, Dehai Lu, Jiaheng Zhang, Huijie Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China School of Information Renmin University of China Beijing100872 China

ISBN: (纸本)9783319130200

Hadoop is now the de facto standard for storing and processing big data, not only for unstructured data but also for some structured data. As a result, providing SQL analysis functionality to the big data resided in HDFS becomes more and more important. Hive is a pioneer system that support SQL-like analysis to the data in HDFS. However, the performance of Hive is not satisfactory for many applications. This leads to the quick emergence of dozens of SQL-on-Hadoop systems that try to support interactive SQL query processing to the data stored in HDFS. This paper firstly gives a brief technical review on recent efforts of SQLon- Hadoop systems. Then we test and compare the performance of five representative SQL-on-Hadoop systems, based on some queries selected or derived from the TPC-DS benchmark. According to the results, we show that such systems can benefit more from the applications of many parallel query processing techniques that have been widely studied in the traditional MPP analytical databases. © Springer International Publishing Switzerland 2014.

关键词： Big data

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data Cleaning of Binary Sensor Events in Activity Recognition by Cluster-Based Methods 7th

Data Cleaning of Binary Sensor Events in Activity Recognitio...

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7th International Conference on Human Aspects of IT for the Aged Population, ITAP 2021, held as part of the 23rd International Conference, HCI International 2021

作者： Zhao, Chunyang Que, Xia Yin, Yue Xing, Xiaoman Yang, Jiaoyun An, Ning Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Medical Imaging Department Chinese Academy of Sciences Suzhou China

ISBN: (纸本)9783030781101

The Ambient Assisted Living (AAL) systems use sensors to detect the daily behavior of older adults and provide necessary assistance based on changes in their cognitive status and physical functions, thus enabling older adults to maintain their independence at home. However, the effectiveness of the AAL systems depends on the accuracy of the data provided by sensors. Namely, when a human error or a hardware failure occurs, the activity recognition model can become inaccurate. This inaccuracy hinders the identification of critical and potentially life-threatening activities. Although there are many methods for cleaning sensor data, there is no method for binary sensors deployed in smart homes. By considering noisy sensor events and unintentional forgetting of turning off the device, this paper proposes two clustering-based methods for denoising and splitting binary sensor events to address possible inaccuracy due to the two mentioned problems. The effectiveness of the proposed methods is verified by the experiments using four machine learning models and three real-world smart home datasets and adopting different sensor configurations. The experimental results demonstrate that compared to the original unprocessed datasets, by combining the two proposed methods, the average accuracy and F-measure are improved by 15.00% and 17.25%, respectively. © 2021, Springer Nature Switzerland AG.

关键词： Automation

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Assisted Diagnostic Learning Based on Concept Lattice

Assisted Diagnostic Learning Based on Concept Lattice

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2021 International Conference on Signal Processing and Communication Technology, SPCT 2021

作者： Shen, Xiajiong Li, Xingchen Han, Daojun Zhang, Yanfeng Henan Key Laboratory of Big Data Analysis and Processing Henan Univ. HA Kaifeng475004 China School of Computer and Information Engineering Henan Univ. HA Kaifeng475004 China Institute of Data and Knowledge Engineering Henan Univ. HA Kaifeng475004 China

ISBN: (数字)9781510653344

ISBN: (纸本)9781510653337

With the continuous development of education, online education is becoming more and more popular with learners because of its convenience. However, online education has the problem of insufficient interaction with students. Students still need to rely on the students themselves to judge the lack of knowledge in practice tasks, which will greatly affect the learning efficiency. In this paper, we propose a method based on formal concept analysis to analyze the knowledge that may not be mastered, and a method of intensive practice for students. First of all, establishing the formal context of student error test questions, construct the concept lattice to calculate the degree of importance of the knowledge concept that may not be mastered, judge the student's mastery of the knowledge concept, and give feedback. Secondly, construct the concept lattice of the unpractised test set, and calculate the similarity of the concept extension set that meets the threshold, find similar topics and push them, and realize targeted training for students who have not mastered the knowledge concepts. © 2022 SPIE.

关键词： Students

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Stable Learning via Triplex Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5267-5276页

作者： Yang, Shuai Jiang, Tingting Dang, Qianlong Gu, Lichuan Wu, Xindong Anhui Agricultural University School of Information and Artificial Intelligence Hefei230036 China Anhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing Hefei230036 China Northwest A & F University College of Science Yangling712100 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

Stable learning aims to learn a model that generalizes well to arbitrary unseen target domain by leveraging a single source domain. Recent advances in stable learning have focused on balancing the distribution of confounders for each feature to eliminate spurious correlations. However, previous studies treat all features equally without considering the difficulties of confounder balancing associated with different features, and regard irrelevant features as confounders, deteriorating generalization performance. To tackle these issues, this article proposes a novel triplex learning (TriL) based stable learning algorithm, which performs sample reweighting, causal feature selection, and representation learning to remove spurious correlations. Specifically, first, TriL adaptively assigns weights to the confounder balancing term of each feature in accordance with the difficulties of confounder balancing, and aligns the confounder distribution of each feature by learning a group of sample weights. Second, TriL integrates the sample weights into a weighted cross-entropy model to compute causal effects of features for excluding irrelevant features from the confounder set. Finally, TriL relearns a set of sample weights and uses them to guide a new supervised dual-autoencoder containing two classifiers to learn feature representations. TriL forces the results of two classifiers to remain consistent for removing spurious correlations by using a cross-classifier consistency regularization. Extensive experiments on synthetic and two real-world datasets show the superiority of TriL compared with seven methods. © 2024 IEEE.

关键词： Feature Selection

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Measuring China's Real Estate Financial Innovation from the Perspective of Government,Enterprises and the Public:Index Compilation and Its Spatial-Temporal Characteristics Analysis

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Journal of Systems Science and Information 2023年第1期11卷 1-34页

作者： Jichang DONG Lijun YIN Xiaoting LIU Xiuting LI School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China China Construction Second Engineering Bureau Co.Ltd Beijing 100070China

In recent years,China has witnessed the rapid development in housing finance,and there have emerged constantly real estate finance innovations;however,there exists no relevant index for measuring the innovations of China's real estate *** on the perspectives of the governments,enterprises and the public,this paper constructs the"innovation index of real estate finance"on a quarterly basis from 2009 to 2019,with the method of empowerment which combines the subjective method(analytic hierarchy process)and the objective one(range coefficient method).It clearly and concretely depicts the innovations in housing finance and the related temporal-spatial characteristics in China since the outbreak of the financial crisis in *** index covers 30 provinces,autonomous regions and municipalities directly under the central government,and analyzes its temporal and spatial *** findings show that there exist a strong spatial autocorrelation and a big regional difference in innovations.

关键词： governments enterprises public real estate finance innovation

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Fast incremental structure from motion based on parallel bundle adjustment

Fast incremental structure from motion based on parallel bun...

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作者： Cao, Mingwei Zheng, Liping Jia, Wei Liu, Xiaoping Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei230009 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology Hefei230009 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

Structure from motion has attracted a lot of research in recent years, with new state-of-the-art approaches coming almost every year. One of its advantages over 3D reconstruction is that it can be used for any cameras (UAVs, depth sensor, light field) and produces relatively accurate point clouds and camera parameters. One of its disadvantages compared to other approaches is that it is computationally expensive. In this paper, we design a novel structure-from-motion framework to reduce the computational cost and implement a parallel bundle adjustment on GPU device for large-scale optimization. In our framework, the local bundle adjustment is added into the architecture of the incremental structure from motion;namely, the point clouds and camera’s parameters are optimized when an additional number of images was added. Then, the purpose is not only to improve the quality of the produced point clouds but also to reduce computation time via parallel bundle adjustment. We conduct extensively experiments on several challenging datasets and make comparison with the state-of-the-art methods. Experimental results show that the proposed method has the best performance in terms of accuracy and efficiency. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cameras

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An Economic Semantic Web Platform

An Economic Semantic Web Platform

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International Conference on Grid and Cooperative Computing Workshops (GCCW)

作者： Xiaoyong Du He Hu Man Li Wenjuan Wu Guo Qin Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China Beijing China

The paper describes an ongoing project which implements a subject-oriented semantic Web platform at Renmin Univ. of China. The economic semantic Web platform (ESWP) contains three components: collaborative ontology developing environment and repository system (CODERS); economic ontology annotation Web services (ConAnnotator); economic ontology and annotated resources. We describe each of these components in detail and illustrate some use cases of the ESWP

关键词： Semantic Web Ontologies Environmental economics Web services Collaboration Humans Software agents Application software Helium Laboratories

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Computing Degree of Association Based on Different Semantic Relationships

Computing Degree of Association Based on Different Semantic ...

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International Workshop on database and Expert Systems Applications

作者： Xuan Tian Xiaoyong Du Haihua Li School of Information Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China

In domain ontologies, there is usually no weight assigned to the link between two concepts. This has been considered as one of main obstacles in using ontologies. Semantic Association (SA) is to depict the correlation of two concepts, and can be measured as the weight of the link. In this paper, we defined Degree of Association (DOA) to measure SA from a concept to its direct-related concept in domain ontology, and proposed a Language-Model-Based Method (LMBM) to compute DOA. Our idea comes from the intuition that the semantic relationship between two concepts implies certain semantic association of them. We took probabilistic model for computing DOA, and used Maximum Likelihood Estimation to estimate parameters. We tested the proposed method on two different domain ontologies, and applied it in experiments of semantic query expansion. Experimental results show the benefit of our approach and demonstrate the promising effectiveness over semantic query expansion.

关键词： Ontologies Information retrieval Direction of arrival estimation Parameter estimation data mining databases Expert systems Computer applications Laboratories data engineering

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In-Network Execution of External Join for Sensor Networks

In-Network Execution of External Join for Sensor Networks

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International Conference on Web-Age Information Management Workshops (WAIM)

作者： Yongxuan Lai Yilong Chen Hong Chen School of Information Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China

ISBN: (纸本)9781424432004;9780769531854

Recently there have been growing interests in the applications of wireless sensor networks such as traffic tracking, environmental surveillance, and network monitoring. In these applications, the exploration of the relationship and linkage of sensing data with other data sources can be naturally expressed by the external join, where the sensory tuples join with an external table at the base station. However, executing such kind of join queries in a highly distributed and resource-constraint sensor network is a challenging task. In this paper, we propose a partition-based algorithm called NEJA (in-network external join algorithm) for the external join processing in sensor networks. NEJA organizes the sensory data of the network through an optimized "value-to-storage" mapping, according to which each storage point stores the tuples that belong to the same subrange on the joint attribute. Then the subrange of each storage point is further partitioned into unit ranges, and tuples in the same unit range wisely choose their joining point that incurs the least communication cost based on a cost metric according to the latest historical statistics. Also, NEJA adopts some optimization techniques to handle the changes of sensory data and uses approximate approaches to cut down the maintenance cost of the mechanism. The experimental results indicate that our scheme is effective in reducing the amount of transmissions for the real time external join processing, especially when the external table has a relatively large size.

关键词： Base stations Joints Wireless sensor networks Sensors Partitioning algorithms Distributed databases Energy efficiency

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