899 resultados para information bottleneck method


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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

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In the recent past, hardly anyone could predict this course of GIS development. GIS is moving from desktop to cloud. Web 2.0 enabled people to input data into web. These data are becoming increasingly geolocated. Big amounts of data formed something that is called "Big Data". Scientists still don't know how to deal with it completely. Different Data Mining tools are used for trying to extract some useful information from this Big Data. In our study, we also deal with one part of these data - User Generated Geographic Content (UGGC). The Panoramio initiative allows people to upload photos and describe them with tags. These photos are geolocated, which means that they have exact location on the Earth's surface according to a certain spatial reference system. By using Data Mining tools, we are trying to answer if it is possible to extract land use information from Panoramio photo tags. Also, we tried to answer to what extent this information could be accurate. At the end, we compared different Data Mining methods in order to distinguish which one has the most suited performances for this kind of data, which is text. Our answers are quite encouraging. With more than 70% of accuracy, we proved that extracting land use information is possible to some extent. Also, we found Memory Based Reasoning (MBR) method the most suitable method for this kind of data in all cases.

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Based in internet growth, through semantic web, together with communication speed improvement and fast development of storage device sizes, data and information volume rises considerably every day. Because of this, in the last few years there has been a growing interest in structures for formal representation with suitable characteristics, such as the possibility to organize data and information, as well as the reuse of its contents aimed for the generation of new knowledge. Controlled Vocabulary, specifically Ontologies, present themselves in the lead as one of such structures of representation with high potential. Not only allow for data representation, as well as the reuse of such data for knowledge extraction, coupled with its subsequent storage through not so complex formalisms. However, for the purpose of assuring that ontology knowledge is always up to date, they need maintenance. Ontology Learning is an area which studies the details of update and maintenance of ontologies. It is worth noting that relevant literature already presents first results on automatic maintenance of ontologies, but still in a very early stage. Human-based processes are still the current way to update and maintain an ontology, which turns this into a cumbersome task. The generation of new knowledge aimed for ontology growth can be done based in Data Mining techniques, which is an area that studies techniques for data processing, pattern discovery and knowledge extraction in IT systems. This work aims at proposing a novel semi-automatic method for knowledge extraction from unstructured data sources, using Data Mining techniques, namely through pattern discovery, focused in improving the precision of concept and its semantic relations present in an ontology. In order to verify the applicability of the proposed method, a proof of concept was developed, presenting its results, which were applied in building and construction sector.

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PURPOSE: Patients preparing to undergo surgery should not suffer needless anxiety. This study aimed to evaluate anxiety levels on the day before surgery as related to the information known by the patient regarding the diagnosis, surgical procedure, or anesthesia. METHOD: Patients reported their knowledge of diagnosis, surgery, and anesthesia. The Spielberger State-Trait Anxiety Inventory (STAI) was used to measure patient anxiety levels. RESULTS: One hundred and forty-nine patients were selected, and 82 females and 38 males were interviewed. Twenty-nine patients were excluded due to illiteracy. The state-anxiety levels were alike for males and females (36.10 ± 11.94 vs. 37.61 ± 8.76) (mean ± SD). Trait-anxiety levels were higher for women (42.55 ± 10.39 vs. 38.08 ± 12.25, P = 0.041). Patient education level did not influence the state-anxiety level but was inversely related to the trait-anxiety level. Knowledge of the diagnosis was clear for 91.7% of patients, of the surgery for 75.0%, and of anesthesia for 37.5%. Unfamiliarity with the surgical procedure raised state-anxiety levels (P = 0.021). A lower state-anxiety level was found among patients who did not know the diagnosis but knew about the surgery (P = 0.038). CONCLUSIONS: Increased knowledge of patients regarding the surgery they are about to undergo may reduce their state-anxiety levels.

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Sustainability is frequently defined by its three pillars: economically viable, socially equitable, and environmentally bearable. Consequently the evaluation of the sustainability of any decision, public or private, requires information on these three dimensions. This paper focuses on social sustainability. In the context of renewable energy sources, the examination of social sustainability requires the analysis of not only the efficiency but also the equity of its welfare impacts. The present paper proposes and applies a methodology to generate the information necessary to do a proper welfare analysis of the social sustainability of renewable energy production facilities. This information is key both for an equity and an efficiency analysis. The analysis focuses on the case of investments in renewable energy electricity production facilities, where the impacts on local residents’ welfare are often significantly different than the welfare effects on the general population. We apply the contingent valuation method to selected facilities across the different renewable energy power plants located in Portugal and conclude that local residents acknowledge differently the damage sustained by the type, location and operation of the plants. The results from these case studies attest to the need of acknowledging and quantifying the negative impacts on local communities when assessing the economic viability, social equity and environmental impact of renewable energy projects.

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The diagnosis of historic masonry walls is an intricate and complex field and has been an object of research for many years. This paper aims to propose practical methodologies for the diagnosis of historic masonry walls, specifically based on their typological characteristics. In order to develop such procedures, information relating to historic masonry typologies in Portugal, classified as rural, urban and military was gathered and techniques for the assessment of historic masonry were studied. All information was integrated to develop a pattern typology oriented methodology. Developed methodology was tested and validated in a small diagnosis campaign carried out in the Guimarães Castle. Methodology was proven to be advantageous and although the study is limited and focused on the Portuguese architectural specificities, it still holds global classifications, and therefore can be useful for any diagnosis procedure of a historic masonry wall.

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A conventional method for seismic strengthening of masonry walls is externally application of reinforced concrete layer (shotcrete). However, due to the lack of analytical and experimental information on the behavior of strengthened walls, the design procedures are usually followed based on the empirical relations. Using these design procedures have resulted in massive strengthening details in retrofitting projects. This paper presents a computational framework for nonlinear analysis of strengthened masonry walls and its versatility has been verified by comparing the numerical and experimental results. Based on the developed numerical model and available experimental information, design relations and failure modes are proposed for strengthened walls in accordance with the ASCE 41 standard. Finally, a sample masonry structure has been strengthened using the proposed and available conventional methods. It has been shown that using the proposed method results in lower strengthening details and appropriate (ductile) failure modes

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Information security is concerned with the protection of information, which can be stored, processed or transmitted within critical information systems of the organizations, against loss of confidentiality, integrity or availability. Protection measures to prevent these problems result through the implementation of controls at several dimensions: technical, administrative or physical. A vital objective for military organizations is to ensure superiority in contexts of information warfare and competitive intelligence. Therefore, the problem of information security in military organizations has been a topic of intensive work at both national and transnational levels, and extensive conceptual and standardization work is being produced. A current effort is therefore to develop automated decision support systems to assist military decision makers, at different levels in the command chain, to provide suitable control measures that can effectively deal with potential attacks and, at the same time, prevent, detect and contain vulnerabilities targeted at their information systems. The concept and processes of the Case-Based Reasoning (CBR) methodology outstandingly resembles classical military processes and doctrine, in particular the analysis of “lessons learned” and definition of “modes of action”. Therefore, the present paper addresses the modeling and design of a CBR system with two key objectives: to support an effective response in context of information security for military organizations; to allow for scenario planning and analysis for training and auditing processes.

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In recent decades, an increased interest has been evidenced in the research on multi-scale hierarchical modelling in the field of mechanics, and also in the field of wood products and timber engineering. One of the main motivations for hierar-chical modelling is to understand how properties, composition and structure at lower scale levels may influence and be used to predict the material properties on a macroscopic and structural engineering scale. This chapter presents the applicability of statistic and probabilistic methods, such as the Maximum Likelihood method and Bayesian methods, in the representation of timber’s mechanical properties and its inference accounting to prior information obtained in different importance scales. These methods allow to analyse distinct timber’s reference properties, such as density, bending stiffness and strength, and hierarchically consider information obtained through different non, semi or destructive tests. The basis and fundaments of the methods are described and also recommendations and limitations are discussed. The methods may be used in several contexts, however require an expert’s knowledge to assess the correct statistic fitting and define the correlation arrangement between properties.

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Finfish pots have emerged as a “responsible” gear, when used in combination with conservational and technical measures to sustain fisheries. Previous trials in Irish waters have offered no published reported data and so three designs tested in the current study provide new information on this gear. The most successful traps in terms of fish catch were rigid steel framed rectangular pots used to target Conger eel. Although commercial yield was low (0.2 per trap haul), potential existed for a viable pot fishery. Deployment and storage of Norwegian floating pots was conducted with relative ease but performance in the water was poor resulting in loss of gear. Catch returns were notable even though effort was restricted as mega-faunal by-catch was a problem, which lead to ending this trial. From these initial trials it was evident that catch rates were low compared to established Norwegian fisheries (3.6 cod per pot), which resulted in the utilisation of pots, already established in the crustacean fishery, to find species readily accessible to pot capture. Although fished and designed differently, these gears provided an opportunity to establish the benefits of pot fishing to fish quality and to determine the effects on by-catch. The fishing effects of three catching methods (pots, angling and trawl) and the effects of air exposure on the physiological status of a common by-catch, the lesser spotted dogfish Scyliorhinus canícula (L.) were examined using a range of physiological biomarkers (plasma catecholamine, glucose, lactate, muscle pH and muscle lactate). Physiological responses of fish to an emersion stress regime resulted in a significant metabolic disturbance in groups, but may not have weakened the overall health of these fish, as signified in the revival of some metabolites. Plasma glucose and lactate concentrations did not however recovery to baseline levels indicating that to achieve an accurate profile, responses should be determined by a suite of biomarkers. Responses did not demonstrate that samples from the pots were significantly less stressed than for the other two methods; angling and trawling, which are in contrast to many other studies. Employment of finfish potting therefore in Irish waters needs further consideration before further promotion as a more responsible method to supplement or replace established techniques.

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The use of Geographic Information Systems has revolutionalized the handling and the visualization of geo-referenced data and has underlined the critic role of spatial analysis. The usual tools for such a purpose are geostatistics which are widely used in Earth science. Geostatistics are based upon several hypothesis which are not always verified in practice. On the other hand, Artificial Neural Network (ANN) a priori can be used without special assumptions and are known to be flexible. This paper proposes to discuss the application of ANN in the case of the interpolation of a geo-referenced variable.

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MOTIVATION: Microarray results accumulated in public repositories are widely reused in meta-analytical studies and secondary databases. The quality of the data obtained with this technology varies from experiment to experiment, and an efficient method for quality assessment is necessary to ensure their reliability. RESULTS: The lack of a good benchmark has hampered evaluation of existing methods for quality control. In this study, we propose a new independent quality metric that is based on evolutionary conservation of expression profiles. We show, using 11 large organ-specific datasets, that IQRray, a new quality metrics developed by us, exhibits the highest correlation with this reference metric, among 14 metrics tested. IQRray outperforms other methods in identification of poor quality arrays in datasets composed of arrays from many independent experiments. In contrast, the performance of methods designed for detecting outliers in a single experiment like Normalized Unscaled Standard Error and Relative Log Expression was low because of the inability of these methods to detect datasets containing only low-quality arrays and because the scores cannot be directly compared between experiments. AVAILABILITY AND IMPLEMENTATION: The R implementation of IQRray is available at: ftp://lausanne.isb-sib.ch/pub/databases/Bgee/general/IQRray.R. CONTACT: Marta.Rosikiewicz@unil.ch SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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BACKGROUND AND STUDY AIMS: Various screening methods for colorectal cancer (CRC) are promoted by professional societies; however, few data are available about the factors that determine patient participation in screening, which is crucial to the success of population-based programs. This study aimed (i) to identify factors that determine acceptance of screening and preference of screening method, and (ii) to evaluate procedure success, detection of colorectal neoplasia, and patient satisfaction with screening colonoscopy. PATIENTS AND METHODS: Following a public awareness campaign, the population aged 50 - 80 years was offered CRC screening in the form of annual fecal occult blood tests, flexible sigmoidoscopy, a combination of both, or colonoscopy. RESULTS: 2731 asymptomatic persons (12.0 % of the target population) registered with and were eligible to take part in the screening program. Access to information and a positive attitude to screening were major determinants of participation. Colonoscopy was the method preferred by 74.8 % of participants. Advanced colorectal neoplasia was present in 8.5 %; its prevalence was higher in males and increased with age. Significant complications occurred in 0.5 % of those undergoing colonoscopy and were associated with polypectomy or sedation. Most patients were satisfied with colonoscopy and over 90 % would choose it again for CRC screening. CONCLUSIONS: In this population-based study, only a small proportion of the target population underwent CRC screening despite an extensive information campaign. Colonoscopy was the preferred method and was safe. The determinants of participation in screening and preference of screening method, together with the distribution of colorectal neoplasia in different demographic categories, provide a rationale for improving screening procedures.