Análise biogeográfica da dieta de um predador de topo ao longo da região Paleártica

Existem vários estudos sobre a dieta de predadores de topo a uma escala local, mas o estudo da dieta de uma espécie ao longo de diferentes regiões geográficas poderá permitir a deteção de certos padrões e variações ecológicas. Para estudar a variação biogeográfica da dieta do bufo-real (Bubo bubo) ao longo do Paleártico criou-se uma base de dados baseada em 192 estudos. Pretendia-se analisar padrões em macro-escala de descritores da dieta e avaliar o efeito de diferentes condições ambientais, de descritores da paisagem e da disponibilidade de presas. Registaram-se 346813 presas de 698 espécies distintas. As principais presas são os lagomorfos e os roedores, enquanto que as aves constituem importantes presas alternativas. Temperaturas mais elevadas favorecem a diversidade trófica, mas esta diminui com a latitude e a longitude. Os resultados revelam a natureza oportunista do bufo-real e a sua elevada adaptabilidade trófica a diferentes habitats e condições ambientais; Biogeographic analysis of a top predator's diet across the Paleartic Region Summary: There are several studies about top predators' diet at a local scale, but studying a species diet across different geographic regions may allow the detection of certain patterns and variations which might influence its ecological features. We created a database of 192 papers to study the diet of the Eurasian Eagle Owl (Bubo bubo) across the Palearctic region. We analysed large scale biogeographical patterns of diet descriptors in relation to different environmental conditions, including climate, landscape and prey availability. We recorded 346816 preys from 698 different species. The main prey groups are lagomorphs and rodents, whereas birds represent important alternative prey. Higher temperatures favour a higher trophic diversity, which in turn decreases at lower latitudes and longitudes. The results reveal the opportunistic nature of the Eurasian Eagle Owl and its high trophic adaptability to different habitats and environments.

Integration of process planning and scheduling using mobile-agent based approach in a networked manufacturing environment

Effective and efficient implementation of intelligent and/or recently emerged networked manufacturing systems require an enterprise level integration. The networked manufacturing offers several advantages in the current competitive atmosphere by way to reduce, by shortening manufacturing cycle time and maintaining the production flexibility thereby achieving several feasible process plans. The first step in this direction is to integrate manufacturing functions such as process planning and scheduling for multi-jobs in a network based manufacturing system. It is difficult to determine a proper plan that meets conflicting objectives simultaneously. This paper describes a mobile-agent based negotiation approach to integrate manufacturing functions in a distributed manner; and its fundamental framework and functions are presented. Moreover, ontology has been constructed by using the Protégé software which possesses the flexibility to convert knowledge into Extensible Markup Language (XML) schema of Web Ontology Language (OWL) documents. The generated XML schemas have been used to transfer information throughout the manufacturing network for the intelligent interoperable integration of product data models and manufacturing resources. To validate the feasibility of the proposed approach, an illustrative example along with varied production environments that includes production demand fluctuations is presented and compared the proposed approach performance and its effectiveness with evolutionary algorithm based Hybrid Dynamic-DNA (HD-DNA) algorithm. The results show that the proposed scheme is very effective and reasonably acceptable for integration of manufacturing functions.

Versioning Concept Schemes for Persistent Retrieval

Things change. Words change, meaning changes and use changes both words and meaning. In information access systems this means concept schemes such as thesauri or clas- sification schemes change. They always have. Concept schemes that have survived have evolved over time, moving from one version, often called an edition, to the next. If we want to manage how words and meanings - and as a conse- quence use - change in an effective manner, and if we want to be able to search across versions of concept schemes, we have to track these changes. This paper explores how we might expand SKOS, a World Wide Web Consortium (W3C) draft recommendation in order to do that kind of tracking.The Simple Knowledge Organization System (SKOS) Core Guide is sponsored by the Semantic Web Best Practices and Deployment Working Group. The second draft, edited by Alistair Miles and Dan Brickley, was issued in November 2005. SKOS is a “model for expressing the basic structure and content of concept schemes such as thesauri, classification schemes, subject heading lists, taxonomies, folksonomies, other types of controlled vocabulary and also concept schemes embedded in glossaries and terminologies” in RDF. How SKOS handles version in concept schemes is an open issue. The current draft guide suggests using OWL and DCTERMS as mechanisms for concept scheme revision.As it stands an editor of a concept scheme can make notes or declare in OWL that more than one version exists. This paper adds to the SKOS Core by introducing a tracking sys- tem for changes in concept schemes. We call this tracking system vocabulary ontogeny. Ontogeny is a biological term for the development of an organism during its lifetime. Here we use the ontogeny metaphor to describe how vocabularies change over their lifetime. Our purpose here is to create a conceptual mechanism that will track these changes and in so doing enhance information retrieval and prevent document loss through versioning, thereby enabling persistent retrieval.

Gestion de contexte dans un habitat intelligent à base d’ontologie : modélisation, implantation et validation

Le laboratoire DOMUS développe des applications sensibles au contexte dans une perspective d’intelligence ambiante. L’architecture utilisée présentement pour gérer le contexte a atteint ses limites en termes de capacité d’évoluer, d’intégration de nouvelles sources de données et de nouveaux capteurs et actionneurs, de capacité de partage entre les applications et de capacité de raisonnement. Ce projet de recherche a pour objectif de développer un nouveau modèle, un gestionnaire de contexte et de proposer une architecture pour les applications d’assistance installées dans un habitat intelligent. Le modèle doit répondre aux exigences suivantes : commun, abstrait, évolutif, décentralisé, performant et une accessibilité uniforme. Le gestionnaire du contexte doit permettre de gérer les événements et offrir des capacités de raisonnement sur les données et le contexte. La nouvelle architecture doit simplifier le développement d’applications d’assistance et la gestion du contexte. Les applications doivent pouvoir se mettre à jour si le modèle de données évolue dans le temps sans nécessiter de modification dans le code source. Le nouveau modèle de données repose sur une ontologie définie avec le langage OWL 2 DL. L’architecture pour les applications d’assistance utilise le cadre d’applications Apache Jena pour la gestion des requêtes SPARQL et un dépôt RDF pour le stockage des données. Une bibliothèque Java a été développée pour gérer la correspondance entre le modèle de données et le modèle Java. Le serveur d’événements est basé sur le projet OpenIoT et utilise un dépôt RDF. Il fournit une API pour la gestion des capteurs / événements et des actionneurs / actions. Les choix d’implémentation et l’utilisation d’une ontologie comme modèle de données et des technologies du Web sémantique (OWL, SPARQL et dépôt RDF) pour les applications d’assistance dans un habitat intelligent ont été validés par des tests intensifs et l’adaptation d’applications déjà existantes au laboratoire. L’utilisation d’une ontologie a pour avantage une intégration des déductions et du raisonnement directement dans le modèle de données et non au niveau du code des applications.

Análisis de las barreras para la unificación de una Historia Clínica Electrónica -HCE- en Colombia

Objetivo: Identificar las barreras para la unificación de una Historia Clínica Electrónica –HCE- en Colombia. Materiales y Métodos: Se realizó un estudio cualitativo. Se realizaron entrevistas semiestructuradas a profesionales y expertos de 22 instituciones del sector salud, de Bogotá y de los departamentos de Cundinamarca, Santander, Antioquia, Caldas, Huila, Valle del Cauca. Resultados: Colombia se encuentra en una estructuración para la implementación de la Historia Clínica Electrónica Unificada -HCEU-. Actualmente, se encuentra en unificación en 42 IPSs públicas en el departamento de Cundinamarca, el desarrollo de la HCEU en el país es privado y de desarrollo propio debido a las necesidades particulares de cada IPS. Conclusiones: Se identificaron barreras humanas, financieras, legales, organizacionales, técnicas y profesionales en los departamentos entrevistados. Se identificó que la unificación de la HCE depende del acuerdo de voluntades entre las IPSs del sector público, privado, EPSs, y el Gobierno Nacional.

Criação de uma ontologia e respectiva povoação a partir do processamento de relatórios médicos

A evolução tecnológica tem provocado uma evolução na medicina, através de sistemas computacionais voltados para o armazenamento, captura e disponibilização de informações médicas. Os relatórios médicos são, na maior parte das vezes, guardados num texto livre não estruturado e escritos com vocabulário proprietário, podendo ocasionar falhas de interpretação. Através das linguagens da Web Semântica, é possível utilizar antologias como modo de estruturar e padronizar a informação dos relatórios médicos, adicionando¬ lhe anotações semânticas. A informação contida nos relatórios pode desta forma ser publicada na Web, permitindo às máquinas o processamento automático da informação. No entanto, o processo de criação de antologias é bastante complexo, pois existe o problema de criar uma ontologia que não cubra todo o domínio pretendido. Este trabalho incide na criação de uma ontologia e respectiva povoação, através de técnicas de PLN e Aprendizagem Automática que permitem extrair a informação dos relatórios médicos. Foi desenvolvida uma aplicação, que permite ao utilizador converter relatórios do formato digital para o formato OWL. ABSTRACT: Technological evolution has caused a medicine evolution through computer systems which allow storage, gathering and availability of medical information. Medical reports are, most of the times, stored in a non-structured free text and written in a personal way so that misunderstandings may occur. Through Semantic Web languages, it’s possible to use ontology as a way to structure and standardize medical reports information by adding semantic notes. The information in those reports can, by these means, be displayed on the web, allowing machines automatic information processing. However, the process of creating ontology is very complex, as there is a risk creating of an ontology that not covering the whole desired domain. This work is about creation of an ontology and its population through NLP and Machine Learning techniques to extract information from medical reports. An application was developed which allows the user to convert reports from digital for¬ mat to OWL format.

Integration of heterogeneous data sources and automated reasoning in healthcare and domotic IoT systems

In recent years, IoT technology has radically transformed many crucial industrial and service sectors such as healthcare. The multi-facets heterogeneity of the devices and the collected information provides important opportunities to develop innovative systems and services. However, the ubiquitous presence of data silos and the poor semantic interoperability in the IoT landscape constitute a significant obstacle in the pursuit of this goal. Moreover, achieving actionable knowledge from the collected data requires IoT information sources to be analysed using appropriate artificial intelligence techniques such as automated reasoning. In this thesis work, Semantic Web technologies have been investigated as an approach to address both the data integration and reasoning aspect in modern IoT systems. In particular, the contributions presented in this thesis are the following: (1) the IoT Fitness Ontology, an OWL ontology that has been developed in order to overcome the issue of data silos and enable semantic interoperability in the IoT fitness domain; (2) a Linked Open Data web portal for collecting and sharing IoT health datasets with the research community; (3) a novel methodology for embedding knowledge in rule-defined IoT smart home scenarios; and (4) a knowledge-based IoT home automation system that supports a seamless integration of heterogeneous devices and data sources.

Knowledge-based Chord Embeddings

This thesis develops AI methods as a contribution to computational musicology, an interdisciplinary field that studies music with computers. In systematic musicology a composition is defined as the combination of harmony, melody and rhythm. According to de La Borde, harmony alone "merits the name of composition". This thesis focuses on analysing the harmony from a computational perspective. We concentrate on symbolic music representation and address the problem of formally representing chord progressions in western music compositions. Informally, chords are sets of pitches played simultaneously, and chord progressions constitute the harmony of a composition. Our approach combines ML techniques with knowledge-based techniques. We design and implement the Modal Harmony ontology (MHO), using OWL. It formalises one of the most important theories in western music: the Modal Harmony Theory. We propose and experiment with different types of embedding methods to encode chords, inspired by NLP and adapted to the music domain, using both statistical (extensional) knowledge by relying on a huge dataset of chord annotations (ChoCo), intensional knowledge by relying on MHO and a combination of the two. The methods are evaluated on two musicologically relevant tasks: chord classification and music structure segmentation. The former is verified by comparing the results of the Odd One Out algorithm to the classification obtained with MHO. Good performances (accuracy: 0.86) are achieved. We feed a RNN for the latter, using our embeddings. Results show that the best performance (F1: 0.6) is achieved with embeddings that combine both approaches. Our method outpeforms the state of the art (F1 = 0.42) for symbolic music structure segmentation. It is worth noticing that embeddings based only on MHO almost equal the best performance (F1 = 0.58). We remark that those embeddings only require the ontology as an input as opposed to other approaches that rely on large datasets.