Indexation et interrogation de pages web décomposées en blocs visuels

Cette thèse porte sur l'indexation et l'interrogation de pages Web. Dans ce cadre, nous proposons un nouveau modèle : BlockWeb, qui s'appuie sur une décomposition de pages Web en une hiérarchie de blocs visuels. Ce modèle prend en compte, l'importance visuelle de chaque bloc et la perméabilité des blocs au contenu de leurs blocs voisins dans la page. Les avantages de cette décomposition sont multiples en terme d'indexation et d'interrogation. Elle permet notamment d'effectuer une interrogation à une granularité plus fine que la page : les blocs les plus similaires à une requête peuvent être renvoyés à la place de la page complète. Une page est représentée sous forme d'un graphe acyclique orienté dont chaque nœud est associé à un bloc et étiqueté par l'importance de ce bloc et chaque arc est étiqueté la perméabilité du bloc cible au bloc source. Afin de construire ce graphe à partir de la représentation en arbre de blocs d'une page, nous proposons un nouveau langage : XIML (acronyme de XML Indexing Management Language), qui est un langage de règles à la façon de XSLT. Nous avons expérimenté notre modèle sur deux applications distinctes : la recherche du meilleur point d'entrée sur un corpus d'articles de journaux électroniques et l'indexation et la recherche d'images sur un corpus de la campagne d'ImagEval 2006. Nous en présentons les résultats.This thesis is about indexing and querying Web pages. We propose a new model called BlockWeb, based on the decomposition of Web pages into a hierarchy of visual blocks. This model takes in account the visual importance of each block as well as the permeability of block's content to their neighbor blocks on the page. Splitting up a page into blocks has several advantages in terms of indexing and querying. It allows to query the system with a finer granularity than the whole page: the most similar blocks to the query can be returned instead of the whole page. A page is modeled as a directed acyclic graph, the IP graph, where each node is associated with a block and is labeled by the coefficient of importance of this block and each arc is labeled by the coefficient of permeability of the target node content to the source node content. In order to build this graph from the bloc tree representation of a page, we propose a new language : XIML (acronym for XML Indexing Management Language), a rule based language like XSLT. The model has been assessed on two distinct dataset: finding the best entry point in a dataset of electronic newspaper articles, and images indexing and querying in a dataset drawn from web pages of the ImagEval 2006 campaign. We present the results of these experiments.AIX-MARSEILLE3-Bib. élec. (130559903) / SudocSudocFranceF

A Probabilistic Framework for Information Modelling and Retrieval Based on User Annotations on Digital Objects

Annotations are a means to make critical remarks, to explain and comment things, to add notes and give opinions, and to relate objects. Nowadays, they can be found in digital libraries and collaboratories, for example as a building block for scientific discussion on the one hand or as private notes on the other. We further find them in product reviews, scientific databases and many "Web 2.0" applications; even well-established concepts like emails can be regarded as annotations in a certain sense. Digital annotations can be (textual) comments, markings (i.e. highlighted parts) and references to other documents or document parts. Since annotations convey information which is potentially important to satisfy a user's information need, this thesis tries to answer the question of how to exploit annotations for information retrieval. It gives a first answer to the question if retrieval effectiveness can be improved with annotations. A survey of the "annotation universe" reveals some facets of annotations; for example, they can be content level annotations (extending the content of the annotation object) or meta level ones (saying something about the annotated object). Besides the annotations themselves, other objects created during the process of annotation can be interesting for retrieval, these being the annotated fragments. These objects are integrated into an object-oriented model comprising digital objects such as structured documents and annotations as well as fragments. In this model, the different relationships among the various objects are reflected. From this model, the basic data structure for annotation-based retrieval, the structured annotation hypertext, is derived. In order to thoroughly exploit the information contained in structured annotation hypertexts, a probabilistic, object-oriented logical framework called POLAR is introduced. In POLAR, structured annotation hypertexts can be modelled by means of probabilistic propositions and four-valued logics. POLAR allows for specifying several relationships among annotations and annotated (sub)parts or fragments. Queries can be posed to extract the knowledge contained in structured annotation hypertexts. POLAR supports annotation-based retrieval, i.e. document and discussion search, by applying an augmentation strategy (knowledge augmentation, propagating propositions from subcontexts like annotations, or relevance augmentation, where retrieval status values are propagated) in conjunction with probabilistic inference, where P(d -> q), the probability that a document d implies a query q, is estimated. POLAR's semantics is based on possible worlds and accessibility relations. It is implemented on top of four-valued probabilistic Datalog. POLAR's core retrieval functionality, knowledge augmentation with probabilistic inference, is evaluated for discussion and document search. The experiments show that all relevant POLAR objects, merged annotation targets, fragments and content annotations, are able to increase retrieval effectiveness when used as a context for discussion or document search. Additional experiments reveal that we can determine the polarity of annotations with an accuracy of around 80%

Multidimensional computation and visualisation for marine controlled source electromagnetic methods

The controlled source electromagnetic method is improving the search for oil and gas in marine settings and is becoming an integral component of many exploration toolkits. While the level of detail and benefit obtained from recorded electromagnetic data sets is limited to the tools available, interpretation is fundamentally restricted by non-unique and equivalent solutions. I create the tools necessary to rapidly compute and visualise multi-dimensional electromagnetic fields generated for a variety of controlled source electromagnetic surveys. This thesis is divided into two parts: the creation of an electromagnetic software framework and the electromagnetic research applications.The creation of a new electromagnetic software framework is covered in Part I. Steps to create and test a modern electromagnetic data structure, three-dimensional visualisation and interactive graphical user interface from the ground up are presented. Bringing together several computer science disciplines ranging from parallel computing, networking and computer human interaction to three-dimensional visualisation, a package specifically tailored to marine controlled source electromagnetic compuation is formed. The electromagnetic framework is comprised of approximately 100,000 lines of new Java code and several third party libraries, which provides low-level graphical, network and execution cross-platform functionality. The software provides a generic framework to integrate most computational engines and algorithms into the coherent global electromagnetic package enabling the interactive forward modelling, inversion and visualisation of electromagnetic data.Part II is comprised of several research applications utilising the developed electromagnetic software framework. Cloud computing and streamline visualisation are covered. These topics are covered to solve several problems in modern controlled source electromagnetic methods. Large 3D electromagnetic modelling and inversion may require days or even weeks to be performed on a single-threaded personal computers. A massively parallelised electromagnetic forward modelling and inversion methods can dramatically was created to improve computational time. The developed ’macro’ parallelisation method facilitated the reduction in computational time by several orders of magnitude with relatively little additional effort and without modification of the internal electromagnetic algorithm. The air wave is a significant component of marine controlled source electromagnetic surveys however there is controversy and confusion over its defintion. The airwave has been described as a reflected, refracted, direct or diffusing wave, which has lead to confusion over its physical reality

Mapping Nanomedicine Terminology in the Regulatory Landscape

A common terminology is essential in any field of science and technology for a mutual understanding among different communities of experts and regulators, harmonisation of policy actions, standardisation of quality procedures and experimental testing, and the communication to the general public. It also allows effective revision of information for policy making and optimises research fund allocation. In particular, in emerging scientific fields with a high innovation potential, new terms, descriptions and definitions are quickly generated, which are then ambiguously used by stakeholders having diverse interests, coming from different scientific disciplines and/or from various regions. The application of nanotechnology in health -often called nanomedicine- is considered as such emerging and multidisciplinary field with a growing interest of various communities. In order to support a better understanding of terms used in the regulatory domain, the Nanomedicines Working Group of the International Pharmaceutical Regulators Forum (IPRF) has prioritised the need to map, compile and discuss the currently used terminology of regulatory scientists coming from different geographic areas. The JRC has taken the lead to identify and compile frequently used terms in the field by using web crawling and text mining tools as well as the manual extraction of terms. Websites of 13 regulatory authorities and clinical trial registries globally involved in regulating nanomedicines have been crawled. The compilation and analysis of extracted terms demonstrated sectorial and geographical differences in the frequency and type of nanomedicine related terms used in a regulatory context. Finally 31 relevant and most frequently used terms deriving from various agencies have been compiled, discussed and analysed for their similarities and differences. These descriptions will support the development of harmonised use of terminology in the future. The report provides necessary background information to advance the discussion among stakeholders. It will strengthen activities aiming to develop harmonised standards in the field of nanomedicine, which is an essential factor to stimulate innovation and industrial competitiveness.JRC.F.2-Consumer Products Safet

Field Drilling Data Cleaning and Preparation for Data Analytics Applications

Throughout the history of oil well drilling, service providers have been continuously striving to improve performance and reduce total drilling costs to operating companies. Despite constant improvement in tools, products, and processes, data science has not played a large part in oil well drilling. With the implementation of data science in the energy sector, companies have come to see significant value in efficiently processing the massive amounts of data produced by the multitude of internet of thing (IOT) sensors at the rig. The scope of this project is to combine academia and industry experience to analyze data from 13 different wells drilled in an area of 2 x 4 miles. The data was collected in the same rig and contains over 12 million electronic drilling recorder data points, driller’s activity logs and well profiles. The main focus is to propose a detailed workflow to clean and process real drilling data. Once cleaned, the data can be fed into data analytics platforms and machine learning models to efficiently analyze trends and plan future well more efficiently. This roadmap will serve as a basis for drilling optimization. The objective of this work is to detail the various steps needed to prepare field drilling data for business analysis, as well discuss about data analytics and machine learning application in drilling operations. The results to be presented are the detailed workflow and description of the data preparation steps, an example analysis of the drilling data and an example application of a machine learning model in drilling

Products and Services

Today’s global economy offers more opportunities, but is also more complex and competitive than ever before. This fact leads to a wide range of research activity in different fields of interest, especially in the so-called high-tech sectors. This book is a result of widespread research and development activity from many researchers worldwide, covering the aspects of development activities in general, as well as various aspects of the practical application of knowledge

Scalable Graph Algorithms in a High-Level Language Using Primitives Inspired by Linear Algebra

This dissertation advances the state of the art for scalable high-performance graph analytics and data mining using the language of linear algebra. Many graph computations suffer poor scalability due to their irregular nature and low operational intensity. A small but powerful set of linear algebra primitives that specifically target graph and data mining applications can expose sufficient coarse-grained parallelism to scale to thousands of processors.In this dissertation we advance existing distributed memory approaches in two important ways. First, we observe that data scientists and domain experts know their analysis and mining problems well, but suffer from little HPC experience. We describe a system that presents the user with a clean API in a high-level language that scales from a laptop to a supercomputer with thousands of cores. We utilize a Domain-Specific Embedded Language with Selective Just-In-Time Specialization to ensure a negligible performance impact over the original distributed memory low-level code. The high-level language enables ease of use, rapid prototyping, and additional features such as on-the-fly filtering, runtime-defined objects, and exposure to a large set of third-party visualization packages.The second important advance is a new sparse matrix data structure and set of algorithms. We note that shared memory machines are dominant both in stand-alone form and as nodes in distributed memory clusters. This thesis offers the design of a new sparse-matrix data structure and set of parallel algorithms, a reusable implementation in shared memory, and a performance evaluation that shows significant speed and memory usage improvements over competing packages. Our method also offers features such as in-memory compression, a low-cost transpose, and chained primitives that do not materialize the entire intermediate result at any one time. We focus on a scalable, generalized, sparse matrix-matrix multiplication algorithm. This primitive is used extensively in many graph algorithms such as betweenness centrality, graph clustering, graph contraction, and subgraph extraction

Development and testing of porous ionizer materials, part I Summary report, Feb. 1965 - May 1966

Development and testing of porous tungsten ionizer materials for cesium contact engine

Development of a framework for the classification of antibiotics adjuvants

Dissertação de mestrado em BioInformaticsThroughout the last decades, bacteria have become increasingly resistant to available antibiotics, leading to a growing need for new antibiotics and new drug development methodologies. In the last 40 years, there are no records of the development of new antibiotics, which has begun to shorten possible alternatives. Therefore, finding new antibiotics and bringing them to market is increasingly challenging. One approach is finding compounds that restore or leverage the activity of existing antibiotics against biofilm bacteria. As the information in this field is very limited and there is no database regarding this theme, machine learning models were used to predict the relevance of the documents regarding adjuvants. In this project, the BIOFILMad - Catalog of antimicrobial adjuvants to tackle biofilms application was developed to help researchers save time in their daily research. This application was constructed using Django and Django REST Framework for the backend and React for the frontend. As for the backend, a database needed to be constructed since no database entirely focuses on this topic. For that, a machine learning model was trained to help us classify articles. Three different algorithms were used, Support-Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR), combined with a different number of features used, more precisely, 945 and 1890. When analyzing all metrics, model LR-1 performed the best for classifying relevant documents with an accuracy score of 0.8461, a recall score of 0.6170, an f1-score of 0.6904, and a precision score of 0.7837. This model is the best at correctly predicting the relevant documents, as proven by the higher recall score compared to the other models. With this model, our database was populated with relevant information. Our backend has a unique feature, the aggregation feature constructed with Named Entity Recognition (NER). The goal is to identify specific entity types, in our case, it identifies CHEMICAL and DISEASE. An association between these entities was made, thus delivering the user the respective associations, saving researchers time. For example, a researcher can see with which compounds "pseudomonas aeruginosa" has already been tested thanks to this aggregation feature. The frontend was implemented so the user could access this aggregation feature, see the articles present in the database, use the machine learning models to classify new documents, and insert them in the database if they are relevant.Ao longo das últimas décadas, as bactérias tornaram-se cada vez mais resistentes aos antibióticos disponíveis, levando a uma crescente necessidade de novos antibióticos e novas metodologias de desenvolvimento de medicamentos. Nos últimos 40 anos, não há registos do desenvolvimento de novos antibióticos, o que começa a reduzir as alternativas possíveis. Portanto, criar novos antibióticos e torna-los disponíveis no mercado é cada vez mais desafiante. Uma abordagem é a descoberta de compostos que restaurem ou potencializem a atividade dos antibióticos existentes contra bactérias multirresistentes. Como as informações neste campo são muito limitadas e não há uma base de dados sobre este tema, modelos de Machine Learning foram utilizados para prever a relevância dos documentos acerca dos adjuvantes. Neste projeto, foi desenvolvida a aplicação BIOFILMad - Catalog of antimicrobial adjuvants to tackle biofilms para ajudar os investigadores a economizar tempo nas suas pesquisas. Esta aplicação foi construída usando o Django e Django REST Framework para o backend e React para o frontend. Quanto ao backend, foi necessário construir uma base de dados, pois não existe nenhuma que se concentre inteiramente neste tópico. Para isso, foi treinado um modelo machine learning para nos ajudar a classificar os artigos. Três algoritmos diferentes foram usados: Support-Vector Machine (SVM), Random Forest (RF) e Logistic Regression (LR), combinados com um número diferente de features, mais precisamente, 945 e 1890. Ao analisar todas as métricas, o modelo LR-1 teve o melhor desempenho para classificar artigos relevantes com uma accuracy de 0,8461, um recall de 0,6170, um f1-score de 0,6904 e uma precision de 0,7837. Este modelo foi o melhor a prever corretamente os artigos relevantes, comprovado pelo alto recall em comparação com os outros modelos. Com este modelo, a base de dados foi populda com informação relevante. O backend apresenta uma caracteristica particular, a agregação construída com Named-Entity-Recognition (NER). O objetivo é identificar tipos específicos de entidades, no nosso caso, identifica QUÍMICOS e DOENÇAS. Esta classificação serviu para formar associações entre entidades, demonstrando ao utilizador as respetivas associações feitas, permitindo economizar o tempo dos investigadores. Por exemplo, um investigador pode ver com quais compostos a "pseudomonas aeruginosa" já foi testada graças à funcionalidade de agregação. O frontend foi implementado para que o utilizador possa ter acesso a esta funcionalidade de agregação, ver os artigos presentes na base de dados, utilizar o modelo de machine learning para classificar novos artigos e inseri-los na base de dados caso sejam relevantes