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7th International Conference on Social Media and Society, SMSociety 2016

作者： Sanders, W. Scott Nutakki, Gopi Chand Nasraoui, Olfa Department of Communication University of Louisville United States Knowledge Discovery and Web Mining Lab Dept. of Computer Engineering and Computer Science Speed School of Engineering University of Louisville United States

ISBN: (纸本)9781450339384

Online third party marketplaces link buyers and sellers by providing a neutral platform for exchange. However, this requires buyers to assess the quality of goods without being able to handle or sample them. Recent research has proposed extending the warranting principle, an emerging theory of online interpersonal impression formation, to the judgements consumer make about products online. The warranting principle holds that information that is more difficult to manufacture or manipulate, such as an individualized photo of an auction item, should be more informative to a consumers judgement of the product than information which is easy to falsify or manipulate. While this theoretical extension assumes that all online goods are capable of being assessed in the same way, some goods (i.e. experience goods) are more difficult to assess in online marketplaces because they must be experienced before their true quality and characteristics can be known. The current study examines 2,401 completed auctions from eBay revealing that the relationship between warranting cues and price discounts is moderated by the type of good being sold. The theoretical contributions and limitations of the study are discussed. © Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Electronic commerce

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作者： Nguyen, Linh Anh Nguyen, Thi Hong Khanh Nguyen, Ngoc-Thanh Ha, Quang-Thuy Division of Knowledge and System Engineering for ICT Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Institute of Informatics University of Warsaw Warsaw Poland Electric Power University Hoang Quoc Viet Tu Liem Hanoi Viet Nam Department of Information Systems Faculty of Computer Science and Management Wroclaw University of Science and Technology Str. Wyb. Wyspianskiego 27 Wroclaw50-370 Poland Faculty of Information Technology VNU University of Engineering and Technology Xuan Thuy Hanoi Viet Nam

We introduce comparisons w.r.t. information between interpretations in paraconsistent description logics and use them to define bisimilarity for such logics. This notion is useful for concept learning in description logics when inconsistencies occur. We give preservation results and the Hennessy-Milner property for comparisons w.r.t. information in paraconsistent description logics. As consequences, we obtain also invariance results and the Hennessy-Milner property for bisimilarity in paraconsistent description logics. © 2017 - IOS Press and the authors. All rights reserved.

关键词： Data description

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A Web-Based Platform for People with Memory Problems and Their Caregivers (CAREGIVERSPRO-MMD):Mixed-Methods Evaluation of Usability

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JMIR Formative Research 2018年第1期2卷 e4页

作者： Zafeiridi, Paraskevi Paulson, Kevin Dunn, Rosie Wolverson, Emma White, Caroline Thorpe, Jonathan Adrian Antomarini, Marco Cesaroni, Francesca Scocchera, Francesca Landrin-Dutot, Isabelle Malherbe, Laetitia Lingiah, Hendi Berard, Marie Girones, Xavier Quintana, Maria Cortes, Ulises Barrue, Cristian Cortes, Atia Paliokas, Ioannis Votis, Konstantinos Tzovaras, Dimitrios School of Engineering and Computer Science University of Hull School of Health and Social Work Aire Building Cottingham Road Hull HU67RX United Kingdom Cooperativa Sociale Onlus Marche Onlus Ancona Italy Department of Internal Medicine Geriatrics and Therapeutics Rouen University Hospital Department of Geriatrics Rouen Cedex France Faculty of Health Sciences University of Vic Central University of Catalonia Manresa Spain Knowledge Engineering & Machine Learning Group Universitat Polit cnica de Catalunya Barcelona Spain Information Technologies Institute Centre for Research and Technology Hellas Thessaloniki Greece

Background: The increasing number of people with dementia (PwD) drives research exploring Web-based support interventions to provide effective care for larger populations. In this concept, a Web-based platform (CAREGIVERSPRO-MMD, 620911) was designed to (1) improve the quality of life for PwD, (2) reduce caregiver burden, (3) reduce the financial costs for care, and (4) reduce administration time for health and social care professionals. Objective: The objective of this study was to evaluate the usability and usefulness of CAREGIVERSPRO-MMD platform for PwD or mild cognitive impairment (MCI), informal caregivers, and health and social care professionals with respect to a wider strategy followed by the project to enhance the user-centered approach. A secondary aim of the study was to collect recommendations to improve the platform before the future pilot study. Methods: A mixed methods design was employed for recruiting PwD or MCI (N=24), informal caregivers (N=24), and professionals (N=10). Participants were asked to rate their satisfaction, the perceived usefulness, and ease of use of each function of the platform. Qualitative questions about the improvement of the platform were asked when participants provided low scores for a function. Testing occurred at baseline and 1 week after participants used the platform. The dropout rate from baseline to the follow-up was approximately 10% (6/58). Results: After 1 week of platform use, the system was useful for 90% (20.75/23) of the caregivers and for 89% (5.36/6) of the professionals. When users responded tomore than 1 question per platform function, the mean of satisfied users per function was calculated. These user groups also provided positive evaluations for the ease of use (caregivers: 82%, 18.75/23;professionals: 97%, 5.82/6) and their satisfaction with the platform (caregivers: 79%, 18.08/23;professionals: 73%, 4.36/6). Ratings from PwD were lower than the other groups for usefulness (57%, 13/23), ease of use (41%

关键词： Caregivers Dementia Social support Technology

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Sequential clustering and contextual importance measures for incremental update summarization 26

Sequential clustering and contextual importance measures for...

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26th International Conference on Computational Linguistics, COLING 2016

作者： Zopf, Markus Mencía, Eneldo Loza Fürnkranz, Johannes Research Training Group AIPHES Knowledge Engineering Group Department of Computer Science Technische Universität Darmstadt Hochschulstraße 10 Darmstadt64289 Germany

ISBN: (纸本)9784879747020

Unexpected events such as accidents, natural disasters and terrorist attacks represent an information situation where it is crucial to give users access to important and non-redundant information as early as possible. Previous work uses either a fast but inaccurate pipeline approach or a precise but slow clustering approach. Instead, we propose to use sequential clustering for grouping information so that we are able to publish sentences at each time step. By doing so, we combine the best of both clustering and pipeline approaches and create a fast and precise real-time system. Experiments on the TREC Temporal Summarization 2015 shared task dataset show that our system achieves better results compared to the state-of-the-art. © 1963-2018 ACL.

关键词： Real time systems

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Beyond centrality and structural features: Learning information importance for text summarization 20

Beyond centrality and structural features: Learning informat...

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20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016

ISBN: (纸本)9781945626197

Most automatic text summarization systems proposed to date rely on centrality and structural features as indicators for information importance. In this paper, we argue that these features cannot reliably detect important information in heterogeneous document collections. Instead, we propose CPSum, a summarizer that learns the importance of information objects from a background source. Our hypothesis is tested on a multi-document corpus where we remove centrality and structural features. CPSum proves to be able to perform well in this challenging scenario whereas reference systems fail. © 2016 Association for Computational Linguistics.

关键词： Text processing

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CNME: A system for Chinese news meta-data extraction 1

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5th Joint International Conference on Semantic Technology, JIST 2015

作者： Xia, Junbo Xie, Fei Zhang, Mengdi Su, Yu Luan, Huanbo Knowledge Engineering Group Department of Computer Science and Technology Tsinghua University Beijing100084 China Communication Technology Bureau Xinhua News Agency Beijing100803 China

ISBN: (数字)9783319316765

ISBN: (纸本)9783319316758

News mining has gained increasing attention because of the overwhelming news produced everyday. Lots of news portals such as Sina (http://***) and Chinanews (http://***. com) develop tools to manage the billions of news and provide services to meet all kinds of needs. News analysis applications conduct news mining work and reveal valuable information. What they all need is news metadata, the fundamental element to support news analysis work. To extract and maintain meta-data of news becomes an important and challenging task. In this paper, we present a system specialized for Chinese news metadata extraction. It can identify 28 kinds of meta-data and provides not only a pipeline to extract them but also a systematic way for management. It facilitates the organizing and conducting of news mining processes and improves efficiency by avoiding duplication of work. More specifically, it introduces an innovative way to categorize news based on words’ ability to represent category. It also adapts existing methods to extract keywords, entities and event elements. Integration of our system on news mining applications has proved its valuable contribution for news analysis work. © Springer International Publishing Switzerland 2016.

关键词： Metadata

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Mapping the Human Body at Cellular Resolution -- The NIH Common Fund Human BioMolecular Atlas Program

arXiv

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arXiv 2019年

作者： Snyder, Michael P. Lin, Shin Posgai, Amanda Atkinson, Mark Regev, Aviv Rood, Jennifer Rozenblatt-Rosen, Orit Gaffney, Leslie Hupalowska, Anna Satija, Rahul Gehlenborg, Nils Shendure, Jay Laskin, Julia Harbury, Pehr Nystrom, Nicholas A. Bar-Joseph, Ziv Zhang, Kun Börner, Katy Lin, Yiing Conroy, Richard Procaccini, Dena Roy, Ananda L. Pillai, Ajay Brown, Marishka Galis, Zorina S. Cai, Long Shendure, Jay Trapnell, Cole Lin, Shin Jackson, Dana Snyder, Michael P. Nolan, Garry Greenleaf, William James Lin, Yiing Plevritis, Sylvia Ahadi, Sara Lee, Hayan Nevins, Stephanie Esplin, Ed Horning, Aaron Bahmani, Amir Zhang, Kun Sun, Xin Jain, Sanjay Hagood, James Kharchenko, Peter Atkinson, Mark Bodenmiller, Bernd Brusko, Todd Clare-Salzler, Michael Nick, Harry Otto, Kevin Posgai, Amanda Wasserfall, Clive Maffioletti, Sergio Caprioli, Richard M. Spraggins, Jeffrey M. Gutierrez, Danielle Patterson, Nathan Heath Neumann, Elizabeth K. Harris, Raymond deCaestecker, Mark de Plas, Raf Van Lau, Ken Cai, Long Yuan, Guo-Cheng Zhu, Qian Dries, Ruben Yin, Peng Saka, Sinem K. Kishi, Jocelyn Y. Wang, Yu Goldaracena, Isabel Laskin, Julia Ye, DongHye Burnum-Johnson, Kristin E. Piehowski, Paul D. Ansong, Charles Zhu, Ying Harbury, Pehr Desai, Tushar Mulye, Jay Chou, Peter Nagendran, Monica Bar-Joseph, Ziv Teichmann, Sarah A. Paten, Benedict Murphy, Robert F. Ma, Jian Kiselev, Vladimir Yu Kingsford, Carl Ricarte, Allyson Ruffalo, Matthew Gehlenborg, Nils Kharchenko, Peter Vella, Margaret McCallum, Chuck Börner, Katy Cross, Leonard E. Friedman, Samuel H. Heiland, Randy Herr, Bruce Macklin, Paul Quardokus, Ellen M. Record, Lisel Sluka, James P. Weber, Griffin M. Nystrom, Nicholas A. Silverstein, Jonathan C. Blood, Philip D. Ropelewski, Alexander J. Shirey, William E. Mabee, Paula Lenhardt, W. Christopher Robasky, Kimberly Michailidis, Stavros Satija, Rahul Marioni, John Regev, Aviv Butler, Andrew Stuart, Tim Fisher, Eyal Ghazanfar, Shila Rood, Jennifer Gaffney, Leslie Ersalan, Gokcen Biancalani, Tommaso Conroy, Richard Procaccini, Dena Roy, Anan School of Medicine Stanford University 300 Pasteur Drive United States Department of Genetics Stanford School of Medicine StanfordCA94305 United States Department of Medicine University of Washington SeattleWA98195 United States Department of Pathology University of Florida Diabetes Institute GainesvilleFL32610 United States Klarman Cell Observatory Broad Institute of MIT and Harvard CambridgeMA02142 United States Howard Hughes Medical Institute Koch Institute of Integrative Cancer Research Department of Biology Massachusetts Institute of Technology CambridgeMA02140 United States New York Genome Center New York CityNY10013 United States New York University New YorkNY10012 United States Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Department of Genome Sciences University of Washington SeattleWA98105 United States Department of Chemistry Purdue University West LafayetteIN47907 United States Department of Biochemistry Stanford University School of Medicine StanfordCA94305 United States Pittsburgh Supercomputing Center Carnegie Mellon University PittsburghPA15213 United States Department of Computational Biology School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Department of Bioengineering University of California San Diego La JollaCA92093 United States Department of Intelligent Systems Engineering School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Surgery Washington University School of Medicine St LouisMO63110 United States Division of Program Coordination Planning and Strategic Initiatives National Institutes of Health BethesdaMD20892 United States National Human Genome Research Institute National Institutes of Health BethesdaMD20892 United States National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Caltech-UW TMC Departmen

Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

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QCBA: Improving Rule Classifiers Learned from Quantitative Data by Recovering Information Lost by Discretisation

arXiv

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arXiv 2017年

作者： Kliegr, Tomáš Izquierdo, Ebroul Department of Information and Knowledge Engineering Faculty of Informatics and Statistics University of Economics Prague W Churchill sq. 4 Prague Czech Republic School of Electronic Engineering and Computer Science Queen Mary United Kingdom University of London Mile End Rd. London United Kingdom

A prediscretisation of numerical attributes which is required by some rule learning algorithms is a source of inefficiencies. This paper describes new rule tuning steps that aim to recover lost information in the discretisation and new pruning techniques that may further reduce the size of rule models and improve their accuracy. The proposed QCBA method was initially developed to postprocess quantitative attributes in models generated by the Classification based on associations (CBA) algorithm, but it can also be applied to the results of other rule learning approaches. We demonstrate the effectiveness on the postprocessing of models generated by five association rule classification algorithms (CBA, CMAR, CPAR, IDS, SBRL) and two first-order logic rule learners (FOIL2 and PRM). Benchmarks on 22 datasets from the UCI repository show smaller size and the overall best predictive performance for FOIL2+QCBA compared to all seven baselines. Postoptimised CBA models have a better predictive performance compared to the state-of-the-art rule learner CORELS in this benchmark. The article contains an ablation study for the individual postprocessing steps and a scalability analysis on the KDD’99 Anomaly detection dataset. © 2017, CC BY.

关键词： Classification (of information)

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Metacognitive learning approach for online tool condition monitoring

arXiv

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arXiv 2017年

作者： Pratama, Mahardhika Dimla, Eric Lai, Chow Yin Lughofer, Edwin School of Computer Science and Engineering Nanyang Technological University Singapore Mechanical Engineering Programme Area Faculty of Engineering Universiti Teknologi Brunei Jalan Tungku Link Gadong Bandar Seri BegawanBE1410 Brunei School of Engineering RMIT University CarltonVIC3053 Australia Department of Knowledge-Based Mathematical Systems Johannes Kepler University Linz Austria

As manufacturing processes become increasingly automated, so should tool condition monitoring (TCM) as it is impractical to have human workers monitor the state of the tools continuously. Tool condition is crucial to ensure the good quality of products - Worn tools affect not only the surface quality but also the dimensional accuracy, which means higher reject rate of the products. Therefore, there is an urgent need to identify tool failures before it occurs on the fly. While various versions of intelligent tool condition monitoring have been proposed, most of them suffer from a cognitive nature of traditional machine learning algorithms. They focus on the how-to-learn process without paying attention to other two crucial issues - what-to-learn, and when-to-learn. The what-to-learn and the when-to-learn provide self-regulating mechanisms to select the training samples and to determine time instants to train a model. A novel tool condition monitoring approach based on a psychologically plausible concept, namely the metacognitive scaffolding theory, is proposed and built upon a recently published algorithm - recurrent classifier (rClass). The learning process consists of three phases: what-to-learn, how-to-learn, when-to-learn and makes use of a generalized recurrent network structure as a cognitive component. Experimental studies with real-world manufacturing data streams were conducted where rClass demonstrated the highest accuracy while retaining the lowest complexity over its counterparts. Copyright © 2017, The Authors. All rights reserved.

关键词： Learning algorithms

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Multidimensional knowledge representation of text analytics results in knowledge bases

Multidimensional knowledge representation of text analytics ...

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IEEE International Conference on Electro-Information Technology

作者： Johannes Zenkert Madjid Fathi Department of Electrical Engineering and Computer Science Institute of Knowledge Based Systems and Knowledge Management Germany

ISBN: (纸本)9781467399869

In the age of digitization, intelligent systems have to cope with an ever-growing amount of data. Therefore, knowledge representation plays a key-role for applications to handle continuously created data and to enable an access on flexible and extensively well-described data structures. This paper introduces a knowledge base design which has the capability of dimensional structuring of semantically-related data and explains how text analytic results can be integrated into a knowledge base. The paper discusses the main advantages of this design and shows how the data can be arranged in the knowledge base. The multidimensional structure of the knowledge base helps to resolve one of the main challenges of knowledge discovery which is the extraction of meaningful information from data in a context.

关键词： knowledge based systems Metadata Tagging Text analysis Hidden Markov models Organizations

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