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An ontology-driven personalization framework for designing theory-driven self-management interventions

An ontology-driven personalization framework for designing t...

AIME 2013 Joint Workshop on Knowledge Representation for Healthcare and Process-Oriented Information Systems in Healthcare, KR4HC 2013/ProHealth 2013

作者： Abidi, Syed Sibte Raza Abidi, Samina NICHE Research Lab Faculty of Computer Science Dalhousie University Canada Medical Informatics Faculty of Medicine Dalhousie University Canada

ISBN: (纸本)9783319039152

We present a patient-centered self-management framework that aims to assist individuals to achieve self-efficacy in terms of self-management of their chronic condition. We have incorporated an evidence-driven behavior model-i.e. the Social Cognition Theory (SCT)-to personalize the selfmanagement educational content based on the individual's health and psychosocial profile. We have taken a knowledge management approach to the development of the self-management framework where we have modeled the SCT, educational content and strategies, assessment tools and the personalization logic using an OWL-DL based ontology. The execution of the knowledge encapsulated within the SCT ontology allows for the dynamic generation of a patient's profile and the selection of the relevant self-management strategies, educational and motivational messages. We applied our self-management framework to develop a self-management program for cardiac conditions. © Springer International Publishing Switzerland 2013.

关键词： Ontology

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ASIS&T annual meeting pre-conference activities: SIG/USE research symposium context in information behavior research

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Bulletin of the Association for Information science and Technology 2015年第3期41卷

作者： Lu Xiao Assistant Professor Kyung-Sun Kim Professor Rong Tang Associate Professor Lisa Given Professor Denise E. Agosto Associate Professor Gary Burnett Professor Amanda Waugh Ph.D. Student Faculty of Information & Media Studies Department of Computer Science at the University of Western Ontario School of Library and Information Studies University of Wisconsin Madison Simmons and director Simmons GSLIS Usability Lab. School of Information Studies Charles Sturt University College of Computing and Informatics at Drexel University College of Communication and Information at Florida State University College of Information Studies University of Maryland College Park

EDITOR'S SUMMARY ASIS&T's Special Interest Group/Information Needs, Seeking and Use (SIG/USE) met during the 2014 Annual Meeting for the group's 14th Annual Research Symposium, focusing on Context in Information Behavior Research . Keynote speaker J. David Johnson set the tone as he encouraged research into the context of information activities outside the usual settings and using varied theoretical perspectives and tools. Attendees also heard 14 lightning talks exploring conceptual and methodological issues. Speakers considered time and emotion as dominant contextual influences in information behavior, the role of information overload and ways diverse contexts affect seeking and providing information. Discussion of research methods encouraged a mixed-method approach and analytic bracketing and illuminated how study participants create their own information context. During the world café session, tablemates discussed how information context is constructed and evolves, research gaps and available methodologies. The symposium ended with presentation of awards for outstanding research, best paper and poster and for travel to pursue studies. Gary Marchionini was recognized for his outstanding contributions to information behavior research throughout his career.

关键词： information needs information seeking information use user behavior contextual information honors

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AgreementMakerLight results for OAEI 2013 8

AgreementMakerLight results for OAEI 2013

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8th International Workshop on Ontology Matching, OM 2013, Co-located with the 12th International Semantic Web Conference, ISWC 2013

作者： Faria, Daniel Pesquita, Catia Santos, Emanuel Cruz, Isabel F. Couto, Francisco M. LASIGE Dept. Informatics Faculty of Sciences Portugal ADVIS Lab. Dept. Computer Science University of Illinois Chicago United States

AgreementMakerLight (AML) is an automated ontology matching framework based on element-level matching and the use of external resources as background knowledge. This paper describes the configuration of AML for the OAEI 2013 competition and discusses its results. Being a newly developed and still incomplete system, our focus in this year's OAEI were the anatomy and large biomedical ontologies tracks, wherein background knowledge plays a critical role. Nevertheless, AML was fairly successful in other tracks as well, showing that in many ontology matching tasks, a lightweight approach based solely on element-level matching can compete with more complex approaches.

关键词： Ontology

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CoDS: A representative sampling method for relational databases

CoDS: A representative sampling method for relational databa...

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24th International Conference on Database and Expert Systems Applications, DEXA 2013

作者： Buda, Teodora Sandra Cerqueus, Thomas Murphy, John Kristiansen, Morten Performance Engineering Lab. School of Computer Science and Informatics University College Dublin Ireland IBM Collaboration Solutions IBM Software Group Dublin Ireland

ISBN: (纸本)9783642402845

Database sampling has become a popular approach to handle large amounts of data in a wide range of application areas such as data mining or approximate query evaluation. Using database samples is a potential solution when using the entire database is not cost-effective, and a balance between the accuracy of the results and the computational cost of the process applied on the large data set is preferred. Existing sampling approaches are either limited to specific application areas, to single table databases, or to random sampling. In this paper, we propose CoDS: a novel sampling approach targeting relational databases that ensures that the sample database follows the same distribution for specific fields as the original database. In particular it aims to maintain the distribution between tables. We evaluate the performance of our algorithm by measuring the representativeness of the sample with respect to the original database. We compare our approach with two existing solutions, and we show that our method performs faster and produces better results in terms of representativeness. © 2013 Springer-Verlag.

关键词： Cost effectiveness

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Feature Based Encryption Technique for Securing Forensic Biometric Image Data Using AES and Visual Cryptography

Feature Based Encryption Technique for Securing Forensic Bio...

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International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)

作者： Quist-Aphetsi Kester Laurent Nana Anca Christine Pascu Sophie Gire J.M. Eghan Nii Narku Quaynor Faculty of Informatics Ghana Technology University College Accra Ghana Lab-STICC (UMR CNRS 6285) University of Brest Brest cedex France Department of Computer Science and Information Technology University of Cape Coast Cape Coast Ghana HCTI EA 4249 and Lab-STICC (UMR CNRS 6285) European University of Brittany UBO Brest France University of Cape Coast Cape Coast Central GH

With the current emergence of biometric image data applications on devices such as smart phones, security cameras, personal computers etc, there is a need for securing the image templates obtained from crime scenes as well as such devices before storing them in locations such as the cloud etc. In this paper, we proposed an encryption technique of securing the biometric image data collected from devices with an approach of feature based encryption technique. The biometric image data was encrypted using advanced encryption process and visual cryptography method. The method engaged the encrypted feature extracted from the plain image in the encryption process and used in to encrypt the image based on a visual cryptographic technique. Analysis of the plain and ciphered image was done and there was no pixel expansion in the process. The implementation was done using MATlab.

关键词： Encryption Visualization Biomedical imaging Ciphers Feature extraction Entropy

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Stream-based event prediction using Bayesian and bloom filters 13

Stream-based event prediction using Bayesian and bloom filte...

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2013 4th ACM/SPEC International Conference on Performance Engineering, ICPE 2013

作者： Wang, Miao Holub, Viliam Murphy, John O'Sullivan, Patrick Performance Engineering Lab. School of Computer Science and Informatics University College Dublin Dublin Ireland Collaboration Solution System IBM Software Group Dublin Ireland

ISBN: (纸本)9781450316361

Nowadays, enterprise software systems store a large amount of operational information in logs. Manually analysing these data can be time-consuming and error-prone. Although a static knowledge database eases the task to capture recurring problems, maintaining such a knowledge repository requires periodic knowledge updates by domain experts. Moreover, as the repository grows, the problem of memory efficiency will also arise. Our goal is to enable administrators to efficiently capture interesting data in a high volume stream of events in real-time. We are proposing a statistical approach for software applications to be automatically trained with a smaller dataset to efficiently predict interesting data under such conditions. The proposed solution maintains a stable memory usage by migrating keywords from a dynamic data structure to fixed sized data structures (Bloom Filter). In particular, the solution has achieved better prediction results by enhancing the Bayesian theory with belief modifiers. © 2013 ACM.

关键词： Forecasting

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Exploring unkown networks using a cooperative mas-based approach

Exploring unkown networks using a cooperative mas-based appr...

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25th European Modeling and Simulation Symposium, EMSS 2013

作者： Carvalho, Pedro Simeão Rossetti, Rosaldo J. F. Rocha, Ana Paula Oliveira, Eugénio C. Informatics Engineering Department Artificial Intelligence and Computer Science Lab University of Porto Rua Dr. Roberto Frias S/N 4200-465 Porto Portugal

ISBN: (纸本)9788897999225

This paper reports on a novel method to explore and map an entirely unknown network using a cooperative Multi-Agent System (MAS) to extract knowledge or information from nodes and connections. We consider the likely presence of obstacles, eventually making the network disconnected. The MAS architecture is applicable to a vast range of scenarios. Our main goal is to discover the entire network as quickly as possible, characterizing its nodes' meta-structures. In this paper, we propose a novel method that relies on agents that can communicate to each other through simple messages, ensuring that there is no resource sharing. The proposed method is compared to other two non-cooperative methods through simulation, in order to establish a basis for comparison. Preliminary results show that our cooperative approach produces better results than the other two implemented and guarantees that the entire network is explored at the end. © 2013 DIME UNIVERSITÀ DI GENOVA.

关键词： Multi agent systems

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Parallel field ranking

Parallel field ranking

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作者： Ji, Ming Lin, Binbin He, Xiaofei Cai, Deng Han, Jiawei Department of Computer Science University of Illinois Urbana-Champaign IL United States Center for Evolutionary Medicine and Informatics Biodesign Institute Arizona State University United States State Key Lab of CAD and CG College of Computer Science Zhejiang University China

Recently, ranking data with respect to the intrinsic geometric structure (manifold ranking) has received considerable attentions, with encouraging performance in many applications in pattern recognition, information retrieval and recommendation systems. Most of the existing manifold ranking methods focus on learning a ranking function that varies smoothly along the data manifold. However, beyond smoothness, a desirable ranking function should vary monotonically along the geodesics of the data manifold, such that the ranking order along the geodesics is preserved. In this article, we aim to learn a ranking function that varies linearly and therefore monotonically along the geodesics of the data manifold. Recent theoretical work shows that the gradient field of a linear function on the manifold has to be a parallel vector field. Therefore, we propose a novel ranking algorithm on the data manifolds, called Parallel Field Ranking. Specifically, we try to learn a ranking function and a vector field simultaneously. We require the vector field to be close to the gradient field of the ranking function, and the vector field to be as parallel as possible. Moreover, we require the value of the ranking function at the query point to be the highest, and then decrease linearly along the manifold. Experimental results on both synthetic data and real data demonstrate the effectiveness of our proposed algorithm. © 2013 ACM.

关键词： Recommender systems

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Multi-person identification and localization for ambient assistive living

Multi-person identification and localization for ambient ass...

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1st International Conference on Distributed, Ambient, and Pervasive Interactions, DAPI 2013, Held as Part of 15th International Conference on Human-computer Interaction, HCI International 2013

作者： Galatas, Georgios Ferdous, Shahina Makedon, Fillia Heracleia Human Centered Computing Lab Computer Science and Engineering Dept. University of Texas at Arlington United States Institute of Informatics and Telecommunications NCSR Demokritos Athens Greece

ISBN: (纸本)9783642393501

In this paper, we present a novel, non-intrusive system that uses RFID technology and the Kinect sensor in order to identify and track multiple people in an assistive apartment. RFID is used for both identification and location estimation while information from the Kinect sensor is used for accurate localization. Data from the various modalities is fused using two techniques. During the experiments conducted, our system exhibited high accuracy, thus proving the effectiveness of the proposed design. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Radio frequency identification (RFID)

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A novel cardiovascular decision support framework for effective clinical risk assessment

A novel cardiovascular decision support framework for effect...

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IEEE Symposium on Computational Intelligence in Healthcare and e-health

作者： Kamran Farooq Jan Karasek Hicham Atassi Amir Hussain Peipei Yang Calum MacRae Mufti Mahmud Bin Luo Warner Slack Computing Science and Mathematics Division University of Stirling Stirling UK Department of Telecommunications Brno University of Technology Brno Czech Republic National Laboratory of Pattern Recognition Chinese Academy of Science Beijing China Harvard Medical School Brigham and Women's Hospital Boston US COSIPRA Lab University of Stirling Stirling United Kingdom Institute of Information Technology Jahangirnagar University Savar Dhaka Bangladesh Theoretical Neurobiology and Neuroengineering Lab University of Antwerp Belgium School of Computer Science and Technology Anhui University Anhui China Beth Israel Clinical Informatics Division Harvard Medical School Boston US

The aim of this study is to help improve the diagnostic and performance capabilities of Rapid Access Chest Pain Clinics (RACPC), by reducing delay and inaccuracies in the cardiovascular risk assessment of patients with chest pain by helping clinicians effectively distinguish acute angina patients from those with other causes of chest pain. Key to our new approach is (1) an intelligent prospective clinical decision support framework for primary and secondary care clinicians, (2) learning from missing/impartial clinical data using Bernoulli mixture models and Expectation Maximisation (EM) techniques, (3) utilisation of state-of-the-art feature section, pattern recognition and data mining techniques for the development of intelligent risk prediction models for cardiovascular patients. The study cohort comprises of 632 patients suspected of cardiac chest pain. A retrospective data analysis of the clinical studies evaluating clinical risk factors for chest pain patients was performed for the development of RACPC specific risk assessment models to distinguish between cardiac and non cardiac chest pain. A comparative analysis case study of machine learning methods was carried out for predicting RACPC clinical outcomes using real patient data acquired from Raigmore Hospital in Inverness, UK. The proposed framework was also validated using the University of Cleveland's Heart Disease dataset which contains 76 attributes, but all published experiments refer to using a subset of 14 of them. Experiments with the Cleveland database (based on 18 clinical features of 270 patients) were concentrated on attempting to distinguish the presence of heart disease (values 1, 2, 3, 4) from absence (value 0). The new clinical models, having been evaluated in clinical practice, resulted in very good predictive power, demonstrating general performance improvement over benchmark multivariate statistical classifiers. As part of these case studies, various online RACPC risk assessment prototypes

关键词： Pain Diseases Educational institutions Heart Data models Electronic mail Guidelines

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