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Personalized Recommendation Based on Entity Attributes and Graph Features 12

Personalized Recommendation Based on Entity Attributes and G...

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12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Zhu, Yi Dong, Bingbing Sha, Zhiqing Yangzhou University School of Information Engineering Yangzhou China Key Laboratory of Knowledge Engineering With Big Data Hefei University of Technology Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Institute of Big Knowledge Science Hefei University of Technology Hefei China

ISBN: (纸本)9781665438582

With the rapid increase in the amount of website data, it has been a more difficult task for users to get the infor-mation they are interested in. Personalized recommendation is an important bridge to find the information which users really need on the website. Many recent studies have introduced additional attribute information about users and/or items to the rating matrix for alleviating the problem of data sparsity. In order to make full use of the attribute information and scoring matrix, deep learning based recommendation methods are proposed, especially the autoencoder model has attracted much attention because of its strong ability to learn hidden features. However, most of the existing autoencoder- based models require that the dimension of the input layer is equal to the dimension of the output layer, which may increase model complexity and certain information loss when using attribute information. In addition, as users' awareness of privacy protection increases, user attribute information is difficult to obtain. To address the above problems, in this paper, we propose a hybrid personalized recommendation model, which uses a semi-autoencoder to jointly embed the item's score vector and internal graph features (short for Co-Agpre). Specifically, we regard the user-item historical interaction matrix as a bipartite graph, and the Laplacian of the user-item co-occurrence graph is utilized to obtain the graph features of the item for solving the problem of sparse attributes. Then a semi-autoencoder is introduced to learn the hidden features of the item and perform rating prediction. The proposed model can flexibly use information from different sources to reduce the complexity of the model. Experiments on two real-world datasets demonstrate the effectiveness of the proposed Co-Agpre compared with state-of-the-art methods. © 2021 IEEE.

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Collaborative innovation community capacity building for electronic records security management 17

Collaborative innovation community capacity building for ele...

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17th International Conference on Intellectual Capital, knowledge Management and Organisational Learning, ICICKM 2020

作者： Wang, Lili An, Xiaomi Xu, Jicang Huang, Jie Guo, Mingjun Hu, Jufang School of Information Resource Management Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing China Renmin University of China Libraries Beijing China

ISBN: (纸本)9781912764723

With more and more governmental business going digital and collaborative to the best of whole government value and public value, electronic records security management (ERSM) faces great challenges. There is a growing understanding that standardization is the key to the successful ERSM, when governmental business requires cross-boundary collaboration. However, previous studies of collaborative innovation community capacity building (CICCB) on ERSM are mainly from the perspective of archival study to the best of long term preservation of electronic archives,with little concerns about needs of different interested parties. Few studies have been taken from interdisciplinary perspective and integrated use of international standards of business continuity management, records management, knowledge management and information security management to the best value of knowledge sharing and continuity of business, records, knowledge and security service. Based on prior findings and the integrated use of ISO management systems standards (ISO/IEC 27001, 2013;ISO 15489-1, 2016;ISO 30401, 2018;ISO 22301, 2019;ISO 30301, 2019), this study analyses the motivation mechanisms of CICCB for ERSM, then develops an extended conceptual framework for CICCB, including cognitive collaboration which means shared knowledge and values on ERSM for all types of interested parties that enable continual improvement;interested parties collaboration which means human interactions on shared rules for community practice on ERSM that enable improved transparency and accountability;process collaboration which means shared standards, guidelines and models for whole process control that enable electronic records controllable with characteristics of authenticity, reliability, integrity, usability, confidentiality and availability;technical collaboration which means shared solutions that enable electronic records systems reliable, secure, compliant, comprehensive, systematic. The above collaborations need com

关键词： knowledge management

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RGB-D Visual Odometry Based on Semantic Feature Points in Dynamic Environments 1st

RGB-D Visual Odometry Based on Semantic Feature Points in ...

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Wang, Hao Wang, Yincan Fang, Baofu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Anhui230009 China School of Computer Science and Information Engineering HeFei University of Technology Anhui Hefei230009 China

ISBN: (纸本)9783030930486

Various algorithms of traditional visual Simultaneous Localization and Mapping (SLAM) can well match with static scenes, but mismatches will occur in dynamic scenes, which makes the positioning and mapping of the SLAM system produce large errors. Therefore, this paper proposed a visual odometry algorithm based on semantic feature points, which can improve the positioning accuracy in dynamic scenes. The algorithm combined semantic information to detect dynamic objects, and then detects and eliminates dynamic feature points. This paper conducted an extensive evaluation of the system and compared it with ORB-SLAM3 and other dynamic scene SLAM systems. The experimental results show that this method greatly improves the positioning accuracy of the camera and the robustness of the system in a highly complex dynamic environment, which verifies the advancement and effectiveness of the algorithm in this paper. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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What and how affect influence the consequence of product trial 20

What and how affect influence the consequence of product tri...

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20th Pacific Asia Conference on Information Systems, PACIS 2016

作者： Sun, Kai Zuo, Meiyun Kong, Dong Key Laboratory of Data Engineering and Knowledge Engineering School of Business Renmin University of China Beijing China Research Institute of Smart Aged Care School of Information National Academy of Development and Strategy Renmin University of China Beijing China

ISBN: (纸本)9789860491029

IT product trial is an important method to promote consumers' attitude toward IT product. Affect is one of the most essential factors and components in determining consumers' attitude. However, previous studies pay more attention to the affective response to trial, which measure the holistic feeling about product trial experience. Affect in product trial is complex and multipartite that different parts may have different influence on consumers' attitude. Therefore, we build the Affective Response Framework to divide consumers' affect into affective response to product, affective response to behaviour, and affective response to environment. And then we analyse the relationship between three components of affective response and consumers' attitude. In order to test the hypothesis, we launch a field study of new IT product trial (an electroencephalogram product) among 205 college students. Results reveal that affective response to product and behaviour have positive influence on attitude toward product;affective response to behaviour and environment have positive influence on attitude toward trial.

关键词： Students

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RPR-Net: A Point Cloud-Based Rotation-Aware Large Scale Place Recognition Network 17th

RPR-Net: A Point Cloud-Based Rotation-Aware Large Scale Plac...

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17th European Conference on Computer Vision, ECCV 2022

作者： Fan, Zhaoxin Song, Zhenbo Zhang, Wenping Liu, Hongyan He, Jun Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing100872 China Department of Management Science and Engineering Tsinghua University Beijing100084 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9783031250552

Point cloud-based large scale place recognition is an important but challenging task for many applications such as Simultaneous Localization and Mapping (SLAM). Taking the task as a point cloud retrieval problem, previous methods have made delightful achievements. However, how to deal with catastrophic collapse caused by rotation problems is still under-explored. In this paper, to tackle the issue, we propose a novel Point Cloud-based Rotation-aware Large Scale Place Recognition Network (RPR-Net). In particular, to solve the problem, we propose to learn rotation-invariant features in three steps. First, we design three kinds of novel Rotation-Invariant Features (RIFs), which are low-level features that can hold the rotation-invariant property. Second, using these RIFs, we design an attentive module to learn rotation-invariant kernels. Third, we apply these kernels to previous point cloud features to generate new features, which is the well-known SO(3) mapping process. By doing so, high-level scene-specific rotation-invariant features can be learned. We call the above process an Attentive Rotation-Invariant Convolution (ARIConv). To achieve the place recognition goal, we build RPR-Net, which takes ARIConv as a basic unit to construct a dense network architecture. Then, powerful global descriptors used for retrieval-based place recognition can be sufficiently extracted from RPR-Net. Experimental results on prevalent datasets show that our method achieves comparable results to existing state-of-the-art place recognition models and significantly outperforms other rotation-invariant baseline models when solving rotation problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Network architecture

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Uncertainty-guided Mutual Consistency Training for Semi-supervised Biomedical Relation Extraction

Uncertainty-guided Mutual Consistency Training for Semi-supe...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Mao, Bing Jia, Chang Huang, Yucheng He, Kai Wu, Jialun Gong, Tieliang Li, Chen Xi'an Jiaotong University School of Computer Science and Technology Xi'an China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an China

ISBN: (纸本)9781665468190

Biomedical relation extraction seeks to automatically extract biomedical relations from biomedical text, which plays an important role in biomedical studies. However, constructing high-quality biomedical annotation data is not only time-consuming but also requires a high level of knowledge in the biomedical field. To alleviate this problem, Semi-supervised Biomedical Relation Extraction aims to extract relation facts from the limited labeled data and the more readily available unlabeled samples. Existing works can be roughly categorized as self-training methods and self-ensembling methods. The former aims to generate pseudo labels, which may lead to the gradual drift problem. The latter aims to encourage the output of one model to be consistent with the other model, where the acquisition of the model is tedious. To alleviate these issues, we propose a novel Uncertainty-Guided Mutual Consistency Training framework(UG-MCT) for semi-supervised Biomedical relation extraction. Specifically, our framework consists of two models with the same structure, which differ only when updating their weights, and then an intersecting pseudo-label mechanism is designed to convert the prediction discrepancies of the two models into mutual consistency training loss, thus promoting the consistency of model predictions. In addition, we utilize uncertainty as guided information to assist the model in focusing on the confident pseudo labels and mitigate the noise of inaccurate pseudo labeling during training. Thus, our model is very simple and efficient while mitigating the noise introduced by pseudo-labels. UG-MCT is evaluated on multiple datasets in different settings and the experimental results demonstrate that our method is highly effective in semi-supervised biomedical relation extraction compared to the state-of-the-art. © 2022 IEEE.

关键词： Extraction

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Automated vision inspection of rail surface cracks: A double-layer data driven framework 48

Automated vision inspection of rail surface cracks: A double...

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48th International Conference on Computers and Industrial engineering, CIE 2018

作者： Zhuang, Li Wang, Long Zhang, Zijun School of Data Science City University of Hong Kong Hong Kong Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China

A double-layer data-driven framework for the automated vision inspection of the rail surface cracks is proposed in this paper. Based on images of rails, the proposed framework is capable to detect the location of cracks firstly and next automatically obtain the boundary of cracks via a feature-based linear iterative crack aggregation (FLICA). Extended Haar-like features are applied to develop significant features for identifying cracks in images. Built on extended Haar-like features, a cascading classifier ensemble integrating three single cascading classifiers with a major voting scheme is proposed to decide the presence of cracks in the image. Each single cascading classifier is composed of a sequence of stage classifiers trained by the LogitBoost algorithm. A scalable sliding window carrying the cascading classifier ensemble is applied to scan images of rail tracks, which is identified by the Otsu’s method, and detect cracks. After completing the crack registration in the first layer, the FLICA is developed to discover boundaries of cracks. The effectiveness of the proposed data-driven framework for identifying rail surface cracks is validated with the rail images provided by the China Railway Corporation and Hong Kong Mass Transit Railway (MRT). Six benchmarking methods, the Otsu’s method, mean shift, the visual detection system, the geometrical approach, fully convolutional networks and the U-net, are utilized to prove advantages of the proposed framework. Results of the validation and comparative analyses demonstrate that the proposed framework is most effective in the rail surface crack detection. © 2018, Curran Associates Inc. All rights reserved.

关键词： Crack detection

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Information propagation in online social networks based on user behavior

Information propagation in online social networks based on u...

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3rd International Conference on Information Computing and Applications, ICICA 2012

作者： Li, Niu Han, Xiaoting Key Laboratory of Ministry of Education for Data Engineering and Knowledge Engineering Renmin University of China Beijing China School of Information Resource Management Renmin University of China Beijing China School of Economics and Management Beihang University Beijing China

ISBN: (纸本)9783642340611

Along with the development of Internet and Web2.0, online social networks (OSNs) are becoming an important information propagation platform. Therefore, it is of great significance to study the information propagation rules in OSNs. An information propagation model named IP-OSN is proposed in this paper, and some simulation experiments are carried out to investigate the mechanism of information propagation. From the experimental results, we can see that along with the information propagation, the number of known nodes increases and reaches its maximum, then keep an unchanging status. Moreover, from the user behavior aspect, we find that different user behavior in OSNs causes different information propagation results, the more users who are willing to diffuse information, the more scope the information can propagate and the faster the information diffuses. Findings in this paper are meaningful for theory of information propagation and complex networks. © 2012 Springer-Verlag.

关键词： Social networking (online)

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knowledge Enhanced Coreference Resolution via Gated Attention

Knowledge Enhanced Coreference Resolution via Gated Attentio...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： He, Kai Mao, Bing Zhou, Xiangyu Li, Yufei Gong, Tieliang Li, Chen Wu, Jialun Xi'an Jiaotong University School of Computer Science and Technology Xi'an China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an China

ISBN: (纸本)9781665468190

Coreference resolution aims at linking all mentions that refer to the same entity, which are widely adopted in many biomedical and bioinformatics tasks, such as biomedical knowledge graph construction and metabolic pathway integration. Many recent studies focus on improving neural model structures. However, we argue that a practical method that integrates commonsense knowledge can further improve coreference resolution performance, because commonsense delivers extra prior knowledge for reasoning and can enhance related representations, rather than naive mention-context occurrence modeling. In this work, we propose an effective method to integrate external commonsense knowledge into a neural coreference resolution model. Specially, a gated attention mechanism is employed in our method to leverage commonsense according to different contexts. By using ConceptNet as the knowledge base in three span-ranking backbone models, the models can yield significant performance gains on used datasets. We also achieve improvements in tasks of long-term mention detection and cross-sentence coreferences after incorporating knowledge. © 2022 IEEE.

关键词： Model structures

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Interference and power constrained broadcast and multicast routing in wireless ad hoc networks using directional antennas

Interference and power constrained broadcast and multicast r...

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作者： Li, Zheng Li, Deying Liu, Ming School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 610054 China

In this paper, we address the interference and power constrained broadcast/multicast routing problem (D-IPCB/M) in wireless ad hoc networks using directional antenna as a starting point, which jointly considers low-interference and energy-efficiency issues. Then we study the delay-bounded interference and power constrained broadcast/multicast routing problem (DB-D-IPCB/M). An approximation algorithm and a heuristic algorithm with low time complexity are proposed for the D-IPCB/M and DB-D-IPCB/M problem, respectively. Finally, we explore and investigate the multi-constrained subgraph optimization (MCSO) problem. We prove its NP-hard and propose an approximation scheme with theoretical performance guarantee for this class of optimization problems. This is a general result originated from the study of D-IPCB/M and DB-D-IPCB/M problems, which can be applicable to solve different optimization problems such as the multi-constrained broadcast/multicast routing problems. Broadcast/multicast message by using the routing trees found by our algorithms tends to not only have less channel collisions but also save energy to extend network lifetime. The theoretical results are evaluated by simulation studies. © 2010 Elsevier B.V. All rights reserved.

关键词： Approximation algorithms

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