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Personalized Service System Based on Hybrid Filtering for Digital Library

Tsinghua Science and technology 2007年第1期12卷 1-8页

作者：高凤荣邢春晓杜小勇王珊 Web and Software Technology R&D Center Research Institute of Information Technology Tsinghua University School of Information Renmin University of China

Personalized service systems are an effective way to help users obtain recommendations for unseen items, within the enormous volume of information available based on their preferences. The most commonly used personalized service system methods are collaborative filtering, content-based filtering, and hybrid filtering. Unfortunately, each method has its drawbacks. This paper proposes a new method which unified partition-based collaborative filtering and meta-information filtering. In partition-based collaborative filtering the user-item rating matrix can be partitioned into low-dimensional dense matrices using a matrix clustering algorithm. Recommendations are generated based on these low-dimensional matrices. Additionally, the very low ratings problem can be solved using meta-information filtering. The unified method is applied to a digital resource management system. The experimental results show the high efficiency and good performance of the new approach.

关键词： personalized service system content-based filtering collaborative filtering user preferences model category-based collaborative filtering meta-information filtering

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Data source recommendation for building mashup applications

Data source recommendation for building mashup applications

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7th web Information Systems and Applications Conference, WISA 2010, 5th Workshop on Semantic web and Ontology, SWON 2010, 4th Workshop on Electronic Government technology and Application, EGTA 2010

作者： Cao, Jiawei Xing, Chunxiao Web and Software Technology Research Center Tsinghua University Bejing China

ISBN: (纸本)9780769541938

The emergence of mashup is gaining tremendous popularity and its application can be seen in a large number of domains. Along with the development of mashup technology, several mashup editors have been produced by the industry which can assist users to build mashups. However, with the increasing service and information sources distributed across the entire web space, even an easy to use mashup editor for nonprogrammers is not sufficient. In this paper, we apply the item based top-N recommendation algorithm which is widely used in e-Commerce area to recommend data source to the users while they are building mashups based on collected data of existing mashups. We also conduct experiment to evaluate the parameters of the recommendation algorithm and finally achieve very satisfactory results. © 2010 IEEE.

关键词： Data Source Internet Mashup Recommendation web 2.0 web 2.0 data source e-commerce electronic commerce information resources information sources mashup editor mashup technology recommender systems top-N recommendation algorithm

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Data Source Recommendation for Building Mashup Applications

Data Source Recommendation for Building Mashup Applications

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web Information Systems and Applications Conference (WISA)

作者： Jiawei Cao Chunxiao Xing Web and Software Technology Research Center Tsinghua University Beijing China

关键词： Mashups Buildings Computational modeling Data models Algorithm design and analysis Training Google

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WFMS: web File Management as a Service

WFMS: Web File Management as a Service

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International Symposium on Information Science and Engineering, ISISE

作者： Quansong Deng Yong Zhang Chunxiao Xing Web and Software Technology (WeST) Research and Development center Tsinghua University Beijing China

Generally, every website provides its own resource upload and download modules for users to share resources. However, the resources uploaded to websites' servers are often lack of unified and professional management. Whatpsilas more, when the amount of user using the upload and download modules increases, it will put a big pressure on the websitepsilas server in term of bandwidth. To solve these problems, this paper designed and implemented a web resource supporting platform-WFMS (web File Management System) which can provide a unified interface for multiple Wbsites to upload, download and manage resources professionally, and decrease the developing costs and bandwidth pressure as well.

关键词： Resource management Bandwidth Costs IP networks web page design Computer networks Distributed computing Information science research and development management technology management

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Optimizing the NASA technical report server

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INTERNET research-ELECTRONIC NETWORKING APPLICATIONS AND POLICY 1996年第1期6卷 64-&页

作者： Nelson, ML Maa, MH Michael L. Nelson is the “webmaster” for NASA Langley Research Center Computer Systems Branch Mail Stop 157A 15 Langley Boulevard Hampton VA 23681‐0001 USA. Tel: 804 8648511 Fax: 804 8648342 E‐mail: <m.l.nelson@larc.nasa.gov> . He has also worked with others to implement report servers software servers technology transfer databases and other World Wide Web services and is also the technical lead for NASA Langley Research Center’s workstation clustering activities. Ming‐Hokng Maa is a NASA Langley Research Center Cooperative Education Student e‐mail: <m.h.maa@larc.nasa.gov> pursuing his BS in computer engineering/computer science at the Massachusetts Institute of Technology. He has previously held research positions at nView Corp. and the Inter‐National Research Institute Inc Ming‐Hokng Maa is a NASA Langley Research Center Cooperative Education Student e‐mail: <m.h.maa@larc.nasa.gov> pursuing his BS in computer engineering/computer science at the Massachusetts Institute of Technology. He has previously held research positions at nView Corp. and the Inter‐National Research Institute Inc

Explains how the NASA Technical Report Server, a World Wide web report distribution NASA technical publications service, has been modified for performance enhancement, greater protocol support and human interface optimization. Results include: parallel database queries, significantly decreasing user access times by an average factor of 2.3;access from clients behind firewalls and/or proxies which truncate excessively long Uniform Resource Locators;access to non-Wide Area Information Server (WAIS) databases and compatibility with the Z39-50.3 protocol;and a streamlined user interface.

关键词： distribution information retrieval information services Internet

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Collaborative filtering recommendation algorithm incorporated with user interest change

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Jisuanji Yanjiu yu Fazhan/Computer research and Development 2007年第2期44卷 296-301页

作者： Xing, Chunxiao Gao, Fengrong Zhan, Sinan Zhou, Lizhu Web and Software Technology Research and Development Center Research Institute of Information Technology Tsinghua University Beijing 100084 China Institute of Software Department of Computer Science and Technology Tsinghua University Beijing 100084 China

Collaborative filtering is one of the most successful technologies for building recommender systems, and is extensively used in many personalized systems. However, existing collaborative filtering algorithms do not consider the change of user interests. For this reason, the systems may recommend unsatisfactory items when user's interest has changed. To solve this problem, two new data weighting methods: time-based data weight and item similarity-based data weight are proposed, to adaptively track the change of user interests. Based on the analysis, the advantages of both weighting methods are combined efficiently and applied to the recommendation generation process. Experimental results show that the proposed algorithm outperforms the traditional item-based collaborative filtering algorithm.

关键词： Database systems

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Spectral-spatial anomaly detection of hyperspectral data based on improved isolation forest

TechRxiv

引用

TechRxiv 2021年

作者： Song, Xiangyu Aryal, Sunil Ting, Kai Ming Liu, Zhen He, Bin Changchun130033 China The University of Chinese Academy of Sciences Beijing100049 China The School of Information Technology Deakin University 75 Pigdons Rd Waurn Ponds VIC3216 Australia National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Big Data Research Center and Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

Anomaly detection in hyperspectral image is affected by redundant bands and the limited utilization capacity of spectral-spatial information. In this article, we propose a novel Improved Isolation Forest (IIF) algorithm based on the assumption that anomaly pixels are more susceptible to isolation than the background pixels. The proposed IIF is a modified version of the Isolation Forest (iForest) algorithm, which addresses the poor performance of iForest in detecting local anomalies and anomaly detection in high-dimensional data. Further, we propose a spectral-spatial anomaly detector based on IIF (SSIIFD) to make full use of global and local information, as well as spectral and spatial information. To be specific, first, we apply the Gabor filter to extract spatial features, which are then employed as input to the Relative Mass Isolation Forest (ReMass-iForest) detector to obtain the spatial anomaly score. Next, original images are divided into several homogeneous regions via the Entropy Rate Segmentation (ERS) algorithm, and the preprocessed images are then employed as input to the proposed IIF detector to obtain the spectral anomaly score. Finally, we fuse the spatial and spectral anomaly scores by combining them linearly to predict anomaly pixels. The experimental results on four real hyperspectral data sets demonstrate that the proposed detector outperforms other state-of-the-art methods. © 2021, CC BY.

关键词： Anomaly detection

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Semantically enabling pharmacogenomic data for the realization of personalized medicine (vol 13, pg 201, 2012)

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PHARMACOGENOMICS 2012年第5期13卷 628-628页

作者： Samwald, M. Coulet, A. Huerga, I Section for Medical Expert & Knowledge-Based Systems Center for Medical Statistics Informatics & Intelligent Systems Medical University of Vienna Spitalgasse 23 A-1090 Vienna Austria Institute of Software Technology & Interactive Systems University of Technology Vienna Favoritenstrasse 9–11/188 A-1040 Vienna Austria LORIA – INRIA Nancy–Grand-Est Campus Scientifique-BP 239 54506 Vandoeuvre-lès-Nancy Cedex France Elsevier 1600 John F Kennedy Blvd. Suite 1800 Philadelphia PA 19103-2899 USA Predictive Medicine Inc. 37 Mansion Drive Topsfield MA 01983 USA Tetherless World Constellation Rensselaer Polytechnic Institute 110 8th Street Winslow 2143 Troy NY 12180 USA Division of Biomedical Statistics & Informatics Department of Health Sciences Research Mayo Clinic 200 First Street SW Rochester MN 55901 USA Genomics Education Initiative 609 Sycamore Avenue Fullerton CA 92831 USA Department of Chemistry & Chemical Biology Northeastern University 360 Huntington Avenue Boston MA 02115 USA World Wide Web Consortium/MIT 32 Vassar Street Cambridge MA 02140 USA Department of Biology School of Computer Science Institute of Biochemistry Carleton University 1125 Colonel By Drive Ottawa ON K1T 2T4 Canada Department of Medical Statistics & Bioinformatics Leiden University Medical Center/Informatics Institute University of Amsterdam Einthovenweg 20 2333 ZC Leiden The Netherlands

Understanding how each individual’s genetics and physiology influences pharmaceutical response is crucial to the realization of personalized medicine and the discovery and validation of pharmacogenomic biomarkers is key to its success. However, integration of genotype and phenotype knowledge in medical information systems remains a critical challenge. The inability to easily and accurately integrate the results of biomolecular studies with patients’ medical records and clinical reports prevents us from realizing the full potential of pharmacogenomic knowledge for both drug development and clinical practice. Herein, we describe approaches using Semantic web technologies, in which pharmacogenomic knowledge relevant to drug development and medical decision support is represented in such a way that it can be efficiently accessed both by software and human experts. We suggest that this approach increases the utility of data, and that such computational technologies will become an essential part of personalized medicine, alongside diagnostics and pharmaceutical products.

关键词： clinical decision support systems knowledge representation ontologies personalized medicine pharmacogenomics translational medicine

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NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

arXiv

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arXiv 2022年

作者： Li, Yawei Zhang, Kai Timofte, Radu Van Gool, Luc Kong, Fangyuan Li, Mingxi Liu, Songwei Du, Zongcai Liu, Ding Zhou, Chenhui Chen, Jingyi Han, Qingrui Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Qiao, Yu Dong, Chao Sun, Long Pan, Jinshan Zhu, Yi Zong, Zhikai Liu, Xiaoxiao Hui, Zheng Yang, Tao Ren, Peiran Xie, Xuansong Hua, Xian-Sheng Wang, Yanbo Ji, Xiaozhong Lin, Chuming Luo, Donghao Tai, Ying Wang, Chengjie Zhang, Zhizhong Xie, Yuan Cheng, Shen Luo, Ziwei Yu, Lei Wen, Zhihong Wu, Qi Li, Youwei Fan, Haoqiang Sun, Jian Liu, Shuaicheng Huang, Yuanfei Jin, Meiguang Huang, Hua Liu, Jing Zhang, Xinjian Wang, Yan Long, Lingshun Li, Gen Zhang, Yuanfan Cao, Zuowei Sun, Lei Alexander, Panaetov Wang, Yucong Cai, Minjie Wang, Li Tian, Lu Wang, Zheyuan Ma, Hongbing Liu, Jie Chen, Chao Cai, Yidong Tang, Jie Wu, Gangshan Wang, Weiran Huang, Shirui Lu, Honglei Liu, Huan Wang, Keyan Chen, Jun Chen, Shi Miao, Yuchun Huang, Zimo Zhang, Lefei Ayazoglu, Mustafa Xiong, Wei Xiong, Chengyi Wang, Fei Li, Hao Wen, Ruimian Yang, Zhijing Zou, Wenbin Zheng, Weixin Ye, Tian Zhang, Yuncheng Kong, Xiangzhen Arora, Aditya Zamir, Syed Waqas Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Gao, Dandan Zhou, Dengwen Ning, Qian Tang, Jingzhu Huang, Han Wang, Yufei Peng, Zhangheng Li, Haobo Guan, Wenxue Gong, Shenghua Li, Xin Liu, Jun Wang, Wanjun Zeng, Kun Lin, Hanjiang Chen, Xinyu Fang, Jinsheng Zhang, Shuhao Zhang, Yuhao Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Tan, Wei Chen, Hao Xu, Qian Narang, Pratik Singh, Usneek Sameen, Syed Khaitan, Harsh Yinghua, Liu Tianlin, Zhang Xiaoming, Zhang Meng, Dingxuan Tian, Chunwei Morshed, Mashrur M. Ahsan, Ahmad Omar Computer Vision Lab ETH Zurich Switzerland University of Würzburg Germany ByteDance Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University China ByteDance Inc China NetEase Inc. China Shenzhen Institutes of Advanced Technology CAS China University of Macau China Shanghai AI Lab Shanghai China Nanjing University of Science and Technology China Amazon Web Services United States China Alibaba DAMO Academy EFC Yuhang District Zhejiang Hangzhou China East China Normal University China Youtu Lab Tencent China Megvii Technology China University of Electronic Science and Technology of China China School of Artificial Intelligence Beijing Normal University China Alibaba Group China Bilibili AI China Nankai-Baidu Joint Lab Nankai University Tianjin China Platform Technologies Tencent Online Video China Higher School of Economics Russia Huawei Moscow Research Center Russia Hunan University China Xidian University China Xilinx Technology Beijing Limited China College of Information Science and Engineering Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Nanjing University China McMaster University Canada School of Telecommunication Engineering Xidian University Xi’an China School of Computer Science Wuhan University Wuhan China School of Mathematical Science University of Electronic Science and Technology of China Chengdu China School of Computer Science The University of Sydney Sydney Australia Aselsan Research Ankara Turkey School of Electronic and Information Engineering South-Central University for Nationalities Wuhan China Guangdong University of Technology China Fujian Normal University Fuzhou University Jimei University China Design Group China Abu Dhabi United Arab Emirates Monash University Melbourne Australia Mohamed bin Zayed University of AI United Arab Emirates North China Electric Power University Changping District Beijing China The School of Art

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution. © 2022, CC BY.

关键词： Chemical activation

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