256 research outputs found

    Topic Assisted Fusion to Re-Rank Texts for Multi-Faceted Information Retrieval

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    We propose to develop a framework for an intelligent business information system with multi-faceted data analysis capabilities that supports complex decision making processes. Reasoning and Learning of contextual factors from texts of financial services data are core aspects of the proposed framework. As part of the proposed framework, we present an approach for the ordering of contextual information from textual data with the help of latent topics identified from the web corpus. The web corpus is prepared by specifically using a number of financial services sources on the web that describe various aspects of mobile payments and services. The proposed approach first performs weighting of query terms and retrieves the initial set of texts from the web corpus. We use Latent Dirichlet Allocation (LDA) on this web corpus to identify the topics that relate to the contextual features of various financial services/products. The retrieved texts are scored based on the identified topics that could cover a variety of contextual factors. We performed subjective evaluation to identify the relevance of the contextual information retrieved, and found that the proposed approach captures a variety of key contexts pertaining to user information needs in a better way with the support of topic assisted contextual factors

    INCREMENTAL QUERY PROCESSING IN INFORMATION FUSION SYSTEMS

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    This dissertation studies the methodology and techniques of information retrieval in fusion systems where information referring to same objects is assessed on the basis of data from multiple heterogeneous data sources. A wide range of important applications can be categorized as information fusion systems e.g. multisensor surveillance system, local search system, multisource medical diagnose system, and so on. Up to the time of this dissertation, most information retrieval methods in fusion systems are highly domain specific, and most query systems do not address fusion problem with enough efforts. In this dissertation, I describe a broadly applicable query based information retrieval approach in general fusion systems: user information needs are interpreted as fusion queries, and the query processing techniques e.g. source dependence graph (SDG), query refinement and optimization are described. Aiming to remove the query building bottleneck, a novel incremental query method is proposed, which can eliminate the accumulated complexity in query building as well as in query execution. Query pattern is defined to capture and reuse repeated structures in the incremental queries. Several new techniques for query pattern matching and learning are described in detail. Some important experiments in a real-world multisensor fusion system, i.e. the intelligent vehicle tracking (IVET) system, have been presented to validate the proposed methodology and techniques

    Fuzzy-description logic for supporting the rehabilitation of the elderly

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    [EN] According to the latest statistics, the proportion of the elderly (+65) is increasing and is expected to double within the European Union in a period of 50 years. This ageing is due to the improvement of quality of life and advances in medicine in the last decades. Gerontechnology is receiving a great deal of attention as a way of providing the elderly with sustainable products, environments, and services combining gerontology and technology. One of the most important aspects to consider by gerontechnology is the mobility/rehabilitation technologies, because there is an important relationship between mobility and the elderly's quality of life. Telerehabilitation systems have emerged to allow the elderly to perform their rehabilitation exercises remotely. However, in many cases, the proposed systems assist neither the patients nor the experts about the progress of the rehabilitation. To address this problem, we propose in this paper, a fuzzy-semantic system for evaluating patient's physical state during the rehabilitation process based on well-known standard for patients' evaluation. Moreover, a tool called FINE has been developed that facilitates the evaluation be accomplished in a semi-automatic way first asking patients to carry out a set of standard tests and then inferencing their state by means of a fuzzy-semantic approach using the data captured during the rehabilitation tasks.This research was funded by the Spanish Ministry of Economy and Competitiveness and by EU FEDER funds under project grants TIN2016-79100-R and TIN2015-72931-EXP. It has also been funded by the Junta de Comunidades de Castilla¿La Mancha scholarship 2018-UCLM1-9131Moya, A.; Navarro, E.; Jaén Martínez, FJ.; González, P. (2020). Fuzzy-description logic for supporting the rehabilitation of the elderly. Expert Systems. 37(2):1-16. https://doi.org/10.1111/exsy.12464116372Alamri, A., Cha, J., & El Saddik, A. (2010). AR-REHAB: An Augmented Reality Framework for Poststroke-Patient Rehabilitation. IEEE Transactions on Instrumentation and Measurement, 59(10), 2554-2563. doi:10.1109/tim.2010.2057750Antoniou, G., & van Harmelen, F. (2004). Web Ontology Language: OWL. Handbook on Ontologies, 67-92. doi:10.1007/978-3-540-24750-0_4Bobillo F.(2008).Managing vagueness in ontologies. Universidad de Granada.Bobillo F. (2015).The fuzzyDL system. Retrieved July 10 2018 fromhttp://www.umbertostraccia.it/cs/software/fuzzyDL/fuzzyDL.htmlBobillo, F., Delgado, M., & Gómez-Romero, J. (2012). DeLorean: A reasoner for fuzzy OWL 2. Expert Systems with Applications, 39(1), 258-272. doi:10.1016/j.eswa.2011.07.016Bobillo, F., & Straccia, U. (2016). The fuzzy ontology reasoner fuzzyDL. 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International Journal of Man-Machine Studies, 8(3), 313-327. doi:10.1016/s0020-7373(76)80003-xHsieh, Y.-W., Hsueh, I.-P., Chou, Y.-T., Sheu, C.-F., Hsieh, C.-L., & Kwakkel, G. (2007). Development and Validation of a Short Form of the Fugl-Meyer Motor Scale in Patients With Stroke. Stroke, 38(11), 3052-3054. doi:10.1161/strokeaha.107.490730Karime, A., Eid, M., Alja’am, J. M., Saddik, A. E., & Gueaieb, W. (2014). A Fuzzy-Based Adaptive Rehabilitation Framework for Home-Based Wrist Training. IEEE Transactions on Instrumentation and Measurement, 63(1), 135-144. doi:10.1109/tim.2013.2277536Krynicki, K., Jaen, J., & Navarro, E. (2016). An ACO-based personalized learning technique in support of people with acquired brain injury. Applied Soft Computing, 47, 316-331. doi:10.1016/j.asoc.2016.04.039Leap Motion INC. (2018).Leap Motion. Retrieved July 10 2018 fromhttps://www.leapmotion.com/Lukasiewicz, T., & Straccia, U. (2008). Managing uncertainty and vagueness in description logics for the Semantic Web. Journal of Web Semantics, 6(4), 291-308. doi:10.1016/j.websem.2008.04.001Metz, D. . (2000). Mobility of older people and their quality of life. Transport Policy, 7(2), 149-152. doi:10.1016/s0967-070x(00)00004-4Nassabi M. H. Den Akker H. &Vollenbroek‐Hutten M. (2014).An ontology‐based recommender system to promote physical activity for pre‐frail elderly 181–184.Navarro, E., González, P., López-Jaquero, V., Montero, F., Molina, J. P., & Romero-Ayuso, D. (2018). Adaptive, Multisensorial, Physiological and Social: The Next Generation of Telerehabilitation Systems. Frontiers in Neuroinformatics, 12. doi:10.3389/fninf.2018.00043OpenNI Pioneering Members. (2018).OpenNI. Retrieved July 10 2018 fromhttp://openni.ru/about/index.htmlOrbbec 3D. (2018).Orbbec Astra Pro. Retrieved July 10 2018 fromhttps://orbbec3d.com/product‐astra‐pro/Rodríguez, A. C., Roda, C., Montero, F., González, P., & Navarro, E. (2015). An Interactive Fuzzy Inference System for Teletherapy of Older People. Cognitive Computation, 8(2), 318-335. doi:10.1007/s12559-015-9356-6Shaughnessy, M., Resnick, B. M., & Macko, R. F. (2006). Testing a Model of Post-Stroke Exercise Behavior. Rehabilitation Nursing, 31(1), 15-21. doi:10.1002/j.2048-7940.2006.tb00005.xSu, C.-J., Chiang, C.-Y., & Huang, J.-Y. (2014). Kinect-enabled home-based rehabilitation system using Dynamic Time Warping and fuzzy logic. Applied Soft Computing, 22, 652-666. doi:10.1016/j.asoc.2014.04.020Velozo, C. A., & Woodbury, M. L. (2011). Translating measurement findings into rehabilitation practice: An example using Fugl-Meyer Assessment-Upper Extremity with patients following stroke. The Journal of Rehabilitation Research and Development, 48(10), 1211. doi:10.1682/jrrd.2010.10.0203W3C. (2012).OWL 2 web ontology language. Retrieved July 10 2018 from https://www.w3.org/TR/owl2‐overview/Zadeh, L. A. (1965). Fuzzy sets. 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    A systematic review of data quality issues in knowledge discovery tasks

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    Hay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.Large volume of data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust

    Vehicle Integrated Prognostic Reasoner (VIPR) 2010 Annual Final Report

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    Honeywell's Central Maintenance Computer Function (CMCF) and Aircraft Condition Monitoring Function (ACMF) represent the state-of-the art in integrated vehicle health management (IVHM). Underlying these technologies is a fault propagation modeling system that provides nose-to-tail coverage and root cause diagnostics. The Vehicle Integrated Prognostic Reasoner (VIPR) extends this technology to interpret evidence generated by advanced diagnostic and prognostic monitors provided by component suppliers to detect, isolate, and predict adverse events that affect flight safety. This report describes year one work that included defining the architecture and communication protocols and establishing the user requirements for such a system. Based on these and a set of ConOps scenarios, we designed and implemented a demonstration of communication pathways and associated three-tiered health management architecture. A series of scripted scenarios showed how VIPR would detect adverse events before they escalate as safety incidents through a combination of advanced reasoning and additional aircraft data collected from an aircraft condition monitoring system. Demonstrating VIPR capability for cases recorded in the ASIAS database and cross linking them with historical aircraft data is planned for year two

    Developing a computational framework for explanation generation in knowledge-based systems and its application in automated feature recognition

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    A Knowledge-Based System (KBS) is essentially an intelligent computer system which explicitly or tacitly possesses a knowledge repository that helps the system solve problems. Researches focusing on building KBSs for industrial applications to improve design quality and shorten research cycle are increasingly attracting interests. For the early models, explanability is considered as one of the major benefits of using KBSs since that most of them are generally rule-based systems and the explanation can be generated based on the rule traces of the reasoning behaviors. With the development of KBS, the definition of knowledge base is becoming much more general than just using rules, and the techniques used to solve problems in KBS are far more than just rule-based reasoning. Many Artificial Intelligence (AI) techniques are introduced, such as neural network, genetic algorithm, etc. The effectiveness and efficiency of KBS are thus improved. However, as a trade-off, the explanability of KBS is weakened. More and more KBSs are conceived as black-box systems that do not run transparently to users, resulting in loss of trusts for the KBSs. Developing an explanation model for modern KBSs has a positive impact on user acceptance of the KBSs and the advices they provided. This thesis proposes a novel computational framework for explanation generation in KBS. Different with existing models which are usually built inside a KBS and generate explanations based on the actual decision making process, the explanation model in our framework stands outside the KBS and attempts to generate explanations through the production of an alternative justification that is unrelated to the actual decision making process used by the system. In this case, the knowledge and reasoning approaches in the explanation model can be optimized specially for explanation generation. The quality of explanation is thus improved. Another contribution in this study is that the system aims to cover three types of explanations (where most of the existing models only focus on the first two): 1) decision explanation, which helps users understand how a KBS reached its conclusion; 2) domain explanation, which provides detailed descriptions of the concepts and relationships within the domain; 3) software diagnostic, which diagnoses user observations of unexpected behaviors of the system or some relevant domain phenomena. The framework is demonstrated with a case of Automated Feature Recognition (AFR). The resulting explanatory system uses Semantic Web languages to implement an individual knowledge base only for explanatory purpose, and integrates a novel reasoning approach for generating explanations. The system is tested with an industrial STEP file, and delivers good quality explanations for user queries about how a certain feature is recognized

    Automatic Generation of Personalized Recommendations in eCoaching

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    Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

    Stratégies pour le raisonnement sur le contexte dans les environnements d assistance pour les personnes âgées

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    Tirant parti de notre expérience avec une approche traditionnelle des environnements d'assistance ambiante (AAL) qui repose sur l'utilisation de nombreuses technologies hétérogènes dans les déploiements, cette thèse étudie la possibilité d'une approche simplifiée et complémentaire, ou seul un sous-ensemble hardware réduit est déployé, initiant un transfert de complexité vers le côté logiciel. Axé sur les aspects de raisonnement dans les systèmes AAL, ce travail a permis à la proposition d'un moteur d'inférence sémantique adapté à l'utilisation particulière à ces systèmes, répondant ainsi à un besoin de la communauté scientifique. Prenant en compte la grossière granularité des données situationnelles disponible avec une telle approche, un ensemble de règles dédiées avec des stratégies d'inférence adaptées est proposé, implémenté et validé en utilisant ce moteur. Un mécanisme de raisonnement sémantique novateur est proposé sur la base d'une architecture de raisonnement inspiré du système cognitif. Enfin, le système de raisonnement est intégré dans un framework de provision de services sensible au contexte, se chargeant de l'intelligence vis-à-vis des données contextuelles en effectuant un traitement des événements en direct par des manipulations ontologiques complexes. L ensemble du système est validé par des déploiements in-situ dans une maison de retraite ainsi que dans des maisons privées, ce qui en soi est remarquable dans un domaine de recherche principalement cantonné aux laboratoiresLeveraging our experience with the traditional approach to ambient assisted living (AAL) which relies on a large spread of heterogeneous technologies in deployments, this thesis studies the possibility of a more stripped down and complementary approach, where only a reduced hardware subset is deployed, probing a transfer of complexity towards the software side, and enhancing the large scale deployability of the solution. Focused on the reasoning aspects in AAL systems, this work has allowed the finding of a suitable semantic inference engine for the peculiar use in these systems, responding to a need in this scientific community. Considering the coarse granularity of situational data available, dedicated rule-sets with adapted inference strategies are proposed, implemented, and validated using this engine. A novel semantic reasoning mechanism is proposed based on a cognitively inspired reasoning architecture. Finally, the whole reasoning system is integrated in a fully featured context-aware service framework, powering its context awareness by performing live event processing through complex ontological manipulation. the overall system is validated through in-situ deployments in a nursing home as well as private homes over a few months period, which itself is noticeable in a mainly laboratory-bound research domainEVRY-INT (912282302) / SudocSudocFranceF

    Goal Reasoning: Papers from the ACS Workshop

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    This technical report contains the 14 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2015 Conference on Advances in Cognitive Systems (ACS-15) in Atlanta, Georgia on 28 May 2015. This is the fourth in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy; the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012; and the third was the Goal Reasoning Workshop at ACS-13 in Baltimore, Maryland in December 2013
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