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Ontology-supported exploratory search for physical training exercises

Ontology-supported exploratory search for physical training ...

ISWC 2015 Posters and Demonstrations Track, ISWC-P and D 2015 - co-located with the 14th International Semantic Web Conference, ISWC 2015

作者： Kotzyba, Michael Ponomaryov, Denis Low, Thomas Thiel, Marcus Glimm, Birte Nürnberger, Andreas Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Institute of Artificial Intelligence University of Ulm Germany

In order to provide a qualitative support to the users during a web search, it is important to have access to a multitude of information sources simultaneously. Depending on the available domain expertise, the user can get an overview and insight to different information perspectives and compare the quality of resources. In this work, we present an exploratory search engine that simultaneously accesses internet resources and a local knowledge base given in the form of ontologies. The ontologies are used to define the search context and can be extended during exploration with new information, thus making the search process adaptive and iterative. We demonstrate our system on an example from the fitness domain, where the user searches for physical training exercises on the Web to complete a personal training plan.

关键词： Ontology

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A framework to provide customized reuse of open corpus content for adaptive systems 15

A framework to provide customized reuse of open corpus conte...

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26th ACM Conference on Hypertext and Social Media, HT 2015

作者： Bayomi, Mostafa CNGL Centre for Global Intelligent Content Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland

ISBN: (纸本)9781450333955

One of the main services that Adaptive Systems offer to their users is the provision of content that is tailored to individual user's needs. Some Adaptive Systems use a closed corpus content that has been prepared for them a priori, hence, they accept only a narrow field of content. Furthermore, the content is tightly coupled with other parts of the system, which also hinders its reusability. To address these limitations, recent systems started to make use of open Web content to provide a wider variety of content. Previous approaches have attempted to harness the information available on the web by providing adaptive systems with customizable information objects. Since adaptive systems are evolving towards the Semantic Web and the use of ontologies, existing systems are limited by their ability to service these documents solely through keyword-based queries. In this research we propose a novel framework that extends existing content provision system, Slicepedia. Our framework uses the conceptual representation of content to segment it in a semantic manner. The framework removes unnecessary content from web pages, such as navigation bars, and then semantically reveals the structural representation of text to build a tree-like hierarchy. This tree can be traversed to obtain different levels of content granularity that facilitate content discoverability and adaptivity. © 2015 ACM.

关键词： Adaptive systems

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Introduction

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) 2016年 9792 LNCS卷 1-8页

作者： González-Beltrán, Alejandra Osborne, Francesco Peroni, Silvio Oxford e-Research Centre University of Oxford Oxford United Kingdom Data Science Group Knowledge Media Institute The Open University Milton Keynes United Kingdom Digital and Semantic Publishing Laboratory Department of Computer Science and Engineering University of Bologna Bologna Italy

We provide the motivation and overview of the SAVE-SD workshop series and introduce the manuscripts that were accepted as part of these proceedings of the SAVE-SD 2016 edition. © Springer International Publishing...

ISBN: (纸本)9783319536361

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Event-based clustering for reducing labeling costs of incident-related microposts 2

Event-based clustering for reducing labeling costs of incide...

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2nd International Workshop on Mining Urban data, MUD 2015 - co-located with 32nd International Conference on Machine Learning, ICML 2015

作者： Schulz, Axel Ristoski, Petar Fürnkranz, Johannes Janssen, Frederik DB Mobility Logistics AG Telecooperation Lab Technische Universität Darmstadt Germany Data and Web Science Group University of Mannheim Germany Knowledge Engineering Group Technische Universität Darmstadt Germany

Automatically identifying the event type of event-related information in the sheer amount of social media data makes machine learning inevitable. However, this is highly dependent on (1) the number of correctly labeled instances and (2) labeling costs. Active learning has been proposed to reduce the number of instances to label. Though, current approaches focus on the thematic dimension, i.e., the event type, for selecting instances to label;other metadata such as spatial and temporal information that is helpful for achieving a more fine-grained clustering is currently not taken into account. Also, labeling quality is always assumed to be perfect as currently no qualitative information is present for manual event type labeling. In this paper, we present a novel event-based clustering strategy that makes use of temporal, spatial, and thematic metadata to determine instances to label. Furthermore, we also inspect the quality of the manual labeling in a crowdsourcing study by comparing experts and non-experts. An evaluation on incident-related tweets shows that (i) labels provided by crowdsourcing are of acceptable quality and (ii) our selection strategy for active learning outperforms current state-of-theart approaches even with few labeled instances. Copyright © 2015 for this paper by its authors.

关键词： Metadata

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Event-based clustering for reducing labeling costs of event-related microposts 9

Event-based clustering for reducing labeling costs of event-...

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9th International Conference on Web and Social Media, ICWSM 2015

作者： Schulz, Axel Janssen, Frederik Ristoski, Petar Fürnkranz, Johannes DB Mobility Logistics AG Germany Telecooperation Lab Technische Universität Darmstadt Germany Data and Web Science Group University of Mannheim Germany Knowledge Engineering Group Technische Universität Darmstadt Germany

ISBN: (纸本)9781577357339

Automatically identifying the event type of event-related information in the sheer amount of social media data makes machine learning inevitable. However, this is highly dependent on (1) the number of correctly labeled instances and (2) labeling costs. Active learning has been proposed to reduce the number of instances to label. Albeit the thematic dimension is already used, other metadata such as spatial and temporal information that is helpful for achieving a more fine-grained clustering is currently not taken into account. In this paper, we present a novel event-based clustering strategy that makes use of temporal, spatial, and thematic metadata to determine instances to label. An evaluation on incident-related tweets shows that our selection strategy for active learning outperforms current state-of-the-art approaches even with few labeled instances. © Copyright 2015, Association for the Advancement of Artificial Intelligence. All rights reserved.

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Subgroup discovery as a method for generating test ontologies

Subgroup discovery as a method for generating test ontologie...

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Learning, knowledge, Adaptation Workshops, LWA 2015: knowledge Discovery, data Mining and Machine Learning, KDML 2015, knowledge Management, FGWM 2015, Information Retrieval, IR 2015 and database Systems, FGDB 2015

作者： Knoell, Daniel Rieder, Constantin Atzmueller, Martin Scherer, Klaus Peter Karlsruhe Institute of Technology Eggenstein-LeopoldshafenD-76344 Germany University of Kassel Research Center for Information System Design Knowledge and Data Engineering Group Wilhelmshöher Allee 73 Kassel34121 Germany

For the validation of ontologies, test cases can be applied. In order to ease the acquisition of such test cases, automatic methods can be used. This paper introduces such an approach to unit testing of ontologies using subgroup discovery methods. The tests are given by small ontologies, which will be compared to an existing ontology. The generation of the test ontologies uses subgroup discovery methods to derive concepts and relations from real data which refers to the ontology. For each concept of the ontology, we apply subgroup discovery in order to identify subgroup patterns consisting of concepts that are (un-) correlated with the target concept. Then, subgroups with very high (or low) confidence will be selected for the testing ontology. We exemplify the approach using real-world data from the ophthalmology domain, and present first results and experiences of a case study demonstrating the test case generation process. Copyright © 2015 by the paper's authors.

关键词： Ontology

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Adaptive class association rule mining for human activity recognition 6

Adaptive class association rule mining for human activity re...

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6th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2015

作者： Atzmueller, Martin Kibanov, Mark Hayat, Naveed Trojahn, Matthias Kroll, Dennis University of Kassel Research Center for Information System Design Knowledge and Data Engineering Group Kassel Germany Volkswagen AG Wolfsburg Germany University of Kassel Research Center for Information System Design Department for Communication Technology Kassel Germany

The analysis of human activity data is an important research area in the context of ubiquitous and social environments. Using sensor data obtained by mobile devices, e. g., utilizing accelerometer sensors contained in mobile phones, behavioral patterns and models can then be obtained. However, the utilized models are often not simple to interpret by humans in order to facilitate assessment, evaluation and validation, e. g., in computational social science or in medical contexts. In this paper, we propose a novel approach for generating interpretable rule sets for classification: We present an adaptive framework for mining class association rules using subgroup discovery, and analyze different techniques for obtaining the final classifier. The approach is investigated in the context of human activity recognition. For our evaluation, we apply real-world activity data collected using mobile phone sensors. Copyright © 2015 by the paper's authors.

关键词： Cellular telephones

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Temporal evolution of contacts and communities in networks of face-to-face human interactions

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Science China(Information Sciences) 2014年第3期57卷 29-45页

作者： Mark KIBANOV Martin ATZMUELLER Christoph SCHOLZ Gerd STUMME Knowledge and Data Engineering Group University of KasselWilhelmshher Allee 73Kassel D-34121Germany

Temporal dynamics of social interaction networks as well as the analysis of communities are key aspects to gain a better understanding of the involved processes,important influence factors,their efects,and their structural *** this article,we analyze temporal dynamics of contacts and the evolution of communities in networks of face-to-face *** our application context,we consider four scientific *** a structural level,we focus on static and dynamic properties of the contact ***,we analyze the resulting community structure using state-of-the-art automatic community detection ***,we analyze the evolution of contacts and communities over time to consider the stability of the respective ***,we assess diferent factors which have an influence on the quality of community ***,we provide first important insights into the evolution of contacts and communities in face-to-face contact networks.

关键词： social network analysis community detection face-to-face contact networks temporal networks community stability

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Global, regional, and national burden of neck pain, 1990–2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021

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The Lancet Rheumatology 2024年第3期6卷 e142-e155页

作者： Wu, Ai-Min Cross, Marita Elliott, James M. Culbreth, Garland T. Cousin, Ewerton Haile, Lydia M. Steinmetz, Jaimie D. Hagins, Hailey Kopec, Jacek A. Brooks, Peter M. Woolf, Anthony D. Kopansky-Giles, Deborah R. Walton, David M. Treleaven, Julia M. Dreinhoefer, Karsten E. Betteridge, Neil Abbasifard, Mitra Abbasi-Kangevari, Zeinab Addo, Isaac Yeboah Adesina, Miracle Ayomikun Adnani, Qorinah Estiningtyas Sakilah Aithala, Janardhana P. Alhalaiqa, Fadwa Alhalaiqa Naji Alimohamadi, Yousef Amiri, Sohrab Amu, Hubert Antony, Benny Arabloo, Jalal Aravkin, Aleksandr Y. Asghari-Jafarabadi, Mohammad Atomsa, Gamechu Hunde Azadnajafabad, Sina Azzam, Ahmed Y. Baghdadi, Soroush Balogun, Saliu A. Balta, Asaminew Birhanu Banach, Maciej Banakar, Morteza Barrow, Amadou Bashiri, Azadeh Bekele, Alehegn Bensenor, Isabela M. Bhardwaj, Pankaj Bhat, Ajay Nagesh Bilchut, Awraris Hailu Briggs, Andrew M. Buchbinder, Rachelle Cao, Chao Chaurasia, Akhilanand Chirinos-Caceres, Jesus Lorenzo Christensen, Steffan Wittrup McPhee Coberly, Kaleb Dadras, Omid Dai, Xiaochen de Luca, Katie Dehghan, Azizallah Dong, Huan-Ji Ekholuenetale, Michael Elhadi, Muhammed Eshetu, Habitu Birhan Eskandarieh, Sharareh Etaee, Farshid Fagbamigbe, Adeniyi Francis Fares, Jawad Fatehizadeh, Ali Feizkhah, Alireza Ferreira, Manuela L. Ferreira, Nuno Fischer, Florian Franklin, Richard Charles Ganesan, Balasankar Gebremichael, Mathewos Alemu Gerema, Urge Gholami, Ali Ghozy, Sherief Gill, Tiffany K. Golechha, Mahaveer Goleij, Pouya Golinelli, Davide Graham, Simon Matthew Haj-Mirzaian, Arvin Harlianto, Netanja I. Hartvigsen, Jan Hasanian, Mohammad Hassen, Mohammed Bheser Hay, Simon I. Hebert, Jeffrey J. Heidari, Golnaz Hoveidaei, Amir Human Hsiao, Alexander Kevin Ibitoye, Segun Emmanuel Iwu, Chidozie C.D. Jacob, Louis Janodia, Manthan Dilipkumar Jin, Yingzhao Jonas, Jost B. Joshua, Charity Ehimwenma Kandel, Himal Khader, Yousef Saleh Khajuria, Himanshu Khan, Ejaz Ahmad Khan, Moien A.B. Khatatbeh, Moawiah Mohammad Khateri, Sorour Khayat Kashani, Hamid Reza Khonji, Mohammad Saeid Department of Orthopaedics The Second Affiliated Hospital of Wenzhou Medical University Wenzhou China Faculty of Medicine and Health University of Sydney Sydney NSW Australia Global Alliance for Musculoskeletal Health Sydney NSW Australia Kolling Institute of Medical Research University of Sydney Northern Sydney Local Health District St Leonards NSW Australia Department of Physical Therapy and Human Movement Sciences Northwestern University Chicago IL United States Institute for Health Metrics and Evaluation University of Washington Seattle WA United States School of Population and Public Health University of British Columbia Vancouver BC Canada Arthritis Research Canada Richmond BC Canada US Department of Veterans Affairs (VA) Birmingham AL United States Centre for Health Policy University of Melbourne Melbourne VIC Australia Bone and Joint Research Group Royal Cornwall Hospital Truro United Kingdom Department of Family and Community Medicine University of Toronto Toronto ON Canada Department of Research and Innovation Canadian Memorial Chiropractic College Toronto ON Canada Department of Physical Therapy University of Sydney Sydney NSW Australia School of Physical Therapy Western University London ON Canada School of Health and Rehabilitation Sciences The University of Queensland Brisbane Australia Center of Musculoskeletal Surgery Charité Universitätsmedizin Berlin (Charité Medical University Berlin) Berlin Germany London United Kingdom Department of Internal Medicine Rafsanjan University of Medical Sciences Rafsanjan Iran Clinical Research Development Unit Rafsanjan University of Medical Sciences Rafsanjan Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Tehran University of Medical Sciences Tehran Iran Centre for Social Research in Health University of New South Wales Sydney NSW

Background: Neck pain is a highly prevalent condition that leads to considerable pain, disability, and economic cost. We present the most current estimates of neck pain prevalence and years lived with disability (YLDs) from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) by age, sex, and location, with forecasted prevalence to 2050. Methods: Systematic reviews identified population-representative surveys used to estimate the prevalence of and YLDs from neck pain in 204 countries and territories, spanning from 1990 to 2020, with additional data from opportunistic review. Medical claims data from Taiwan (province of China) were also included. Input data were pooled using DisMod-MR 2.1, a Bayesian meta-regression tool. Prevalence was forecast to 2050 using a mixed-effects model using Socio-demographic Index as a predictor and multiplying by projected population estimates. We present 95% UIs for every metric based on the 2·5th and 97·5th percentiles of 100 draws of the posterior distribution. Findings: Globally, in 2020, neck pain affected 203 million (95% uncertainty interval [UI] 163–253) people. The global age-standardised prevalence rate of neck pain was estimated to be 2450 (1960–3040) per 100 000 population and global age-standardised YLD rate was estimated to be 244 (165–346) per 100 000. The age-standardised prevalence rate remained stable between 1990 and 2020 (percentage change 0·2% [–1·3 to 1·7]). Globally, females had a higher age-standardised prevalence rate (2890 [2330–3620] per 100 000) than males (2000 [1600–2480] per 100 000), with the prevalence peaking between 45 years and 74 years in male and female sexes. By 2050, the estimated global number of neck pain cases is projected to be 269 million (219–322), with an increase of 32·5% (23·9–42·3) from 2020 to 2050. Decomposition analysis of the projections showed population growth was the primary contributing factor, followed by population ageing. Interpretation: Although age-standardi

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Enhancing Student Engagement through Personalized Motivations

Enhancing Student Engagement through Personalized Motivation...

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International Conference on Advanced Learning Technologies (ICALT)

作者： Athanasios Staikopoulos Ian OKeeffe Bilal Yousuf Owen Conlan Eddie Walsh Vincent Wade Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin University of Dublin Trinity College Dublin IE

A lack of appropriate engagement in online learning courses is a significant factor leading to poor performance and high dropout rates. This paper presents a framework that provides personalized motivations in the form of feedback, advices and reminders in order to enhance the engagement of students with online learning. It also describes the design, implementation and evaluation of the framework which has been used by over one hundred students. The paper focuses on how the framework by modelling and monitoring the level of engagement of students, it provides tailored motivations to stimulate and sustain their engagement. Finally, the paper compares the results from a previous student cohort (where no motivations were provided) to highlight how the framework has significantly increased the level of engagement of students.

关键词： Monitoring Engines Education Context Electronic mail Least squares approximations Portals

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