Recerca sobre webs "antivacunes" a internet

L'objectiu d'aquest treball de recerca és identificar i caracteritzar continguts web "antivacunes" en llengua castellana o catalana.

Cultural differences on seeking information: an eye tracking study

The main goal of this research is to investigate how people with different cultural background differ in their interaction style and visual behavior on search engine results pages (SERP), more specifically between groups from the Middle Eastern region vs. Western Europe. We conducted a controlled eye-tracking experiment to explore and evaluate the visual behavior of Arabs and Spaniardusers when scanning through the first page of the search results in Google. Big differences can be observed in the 4 aspects studied: U.A.E. participants stayed on the SERPs for longer, they read more results and they read each snippet in a more complete way than Spaniards. In Spain, people tended to scan the SERP, reading less text on each snippet, and choose a result among the first top rankedones without hardly seeing those in bottom positions.

The Mixed Management of Patients' Medical Records: Responsibility Sharing Between the Patient and the Physician

Through this article, we propose a mixed management of patients' medical records, so as to share responsibilities between the patient and the Medical Practitioner by making Patients responsible for the validation of their administrative information, and MPs responsible for the validation of their Patients' medical information. Our proposal can be considered a solution to the main problem faced by patients, health practitioners and the authorities, namely the gathering and updating of administrative and medical data belonging to the patient in order to accurately reconstitute a patient's medical history. This method is based on two processes. The aim of the first process is to provide a patient's administrative data, in order to know where and when the patient received care (name of the health structure or health practitioner, type of care: out patient or inpatient). The aim of the second process is to provide a patient's medical information and to validate it under the accountability of the Medical Practitioner with the help of the patient if needed. During these two processes, the patient's privacy will be ensured through cryptographic hash functions like the Secure Hash Algorithm, which allows pseudonymisation of a patient's identity. The proposed Medical Record Search Engines will be able to retrieve and to provide upon a request formulated by the Medical ractitioner all the available information concerning a patient who has received care in different health structures without divulging the patient's identity. Our method can lead to improved efficiency of personal medical record management under the mixed responsibilities of the patient and the MP.

FOSS License Selection and Code Management

With nearly 2,000 free and open source software (FLOSS) licenses, software license proliferation¿ can be a major headache for software development organizations trying to speed development through software component reuse, as well as companies redistributing software packages as components of their products. Scope is one problem: from the Free Beer license to the GPL family of licenses to platform-specific licenses such as Apache and Eclipse, the number and variety of licenses make it difficult for companies to ¿do the right thing¿ with respect to the software components in their products and applications. In addition to the sheer number of licenses, each license carries within it the author¿s specific definition of how the software can be used and re-used. Permissive licenses like BSD and MIT make it easy; software can be redistributed and developers can modify code without the requirement of making changes publicly available. Reciprocal licenses, on the other hand, place varying restrictions on re-use and redistribution. Woe to the developer who snags a bit of code after a simple web search without understanding the ramifications of license restrictions.

Improving Information Problem Solving skills in Secondary Education through embedded instruction

This empirical study consists in an investigation of the effects, on the development of Information Problem Solving (IPS) skills, of a long-term embedded, structured and supported instruction in Secondary Education. Forty secondary students of 7th and 8th grades (13–15 years old) participated in the 2-year IPS instruction designed in this study. Twenty of them participated in the IPS instruction, and the remaining twenty were the control group. All the students were pre- and post-tested in their regular classrooms, and their IPS process and performance were logged by means of screen capture software, to warrant their ecological validity. The IPS constituent skills, the web search sub-skills and the answers given by each participant were analyzed. The main findings of our study suggested that experimental students showed a more expert pattern than the control students regarding the constituent skill ‘defining the problem’ and the following two web search sub-skills: ‘search terms’ typed in a search engine, and ‘selected results’ from a SERP. In addition, scores of task performance were statistically better in experimental students than in control group students. The paper contributes to the discussion of how well-designed and well-embedded scaffolds could be designed in instructional programs in order to guarantee the development and efficiency of the students’ IPS skills by using net information better and participating fully in the global knowledge society.

Text Classification and Indexing in IP Networks

Internet on elektronisen postin perusrakenne ja ollut tärkeä tiedonlähde akateemisille käyttäjille jo pitkään. Siitä on tullut merkittävä tietolähde kaupallisille yrityksille niiden pyrkiessä pitämään yhteyttä asiakkaisiinsa ja seuraamaan kilpailijoitansa. WWW:n kasvu sekä määrällisesti että sen moninaisuus on luonut kasvavan kysynnän kehittyneille tiedonhallintapalveluille. Tällaisia palveluja ovet ryhmittely ja luokittelu, tiedon löytäminen ja suodattaminen sekä lähteiden käytön personointi ja seuranta. Vaikka WWW:stä saatavan tieteellisen ja kaupallisesti arvokkaan tiedon määrä on huomattavasti kasvanut viime vuosina sen etsiminen ja löytyminen on edelleen tavanomaisen Internet hakukoneen varassa. Tietojen hakuun kohdistuvien kasvavien ja muuttuvien tarpeiden tyydyttämisestä on tullut monimutkainen tehtävä Internet hakukoneille. Luokittelu ja indeksointi ovat merkittävä osa luotettavan ja täsmällisen tiedon etsimisessä ja löytämisessä. Tämä diplomityö esittelee luokittelussa ja indeksoinnissa käytettävät yleisimmät menetelmät ja niitä käyttäviä sovelluksia ja projekteja, joissa tiedon hakuun liittyvät ongelmat on pyritty ratkaisemaan.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Como pesquisar o perfil patentário de um fármaco: o caso Efavirenz

The importance of the patent system for researchers, especially in chemistry and related areas, is undeniable. In this context, this work aims at guiding the search in major search engines of patents, in order to map the patents related to a specific chemical compound and identify the material that each patent document protects. In this case study, it was performed a search for the drug efavirenz to demonstrate how to conduct a literature search in patents databases and to map patent applications at national and international levels.

Kävijätietojen kerääminen ja hyödyntäminen osana verkkopalveluita

Kävijätietojen keräys ja tiedon hyödyntäminen ovat monelle yritykselle yksi lisäkeino saavuttaa kohdeasiakkaansa sekä tarjota heille uusia lisäpalveluita. Kävijätietojen seuraamiselle sekä näiden tietojen hyödyntämiselle löytyy lukuisia käyttökohteita aina hakukoneoptimoinnista uusien asiakkaiden etsimiseen. Moni yritys onkin löytänyt tästä itselleen uuden toimialan. Työn tavoitteena on toteuttaa IP-pohjainen kävijätietojen keräämiseen soveltuva ohjelma, jonka tietoja pystytään hyödyntämään yrityksessä, jonka tuotekonseptiin kuuluu tarjota loppukäyttäjälle maksuttomia hakemisto- ja yhteystietopalveluita. Työssä keskitytään erityisesti ylläpidettävän kävijätietorekisterin luomiseen, mutta esitellään myös, kuinka esimerkiksi yrityksen hakutuloksia voidaan kerätyillä kävijätiedoilla tehostaa sekä tarjota palveluja käyttäville asiakkaille uusia lisäpalveluita. Työssä pyritään hyödyntämään tarjolla olevia avoimiin lisensseihin pohjautuvia ratkaisuja. Tiedonkeruuta toteutettaessa on jo alussa otettava huomioon, että tiedosta ei saada täysin eksaktia, vaan suuntaa-antavaa. Tämä ei kuitenkaan poista sitä, ettei tietoa voisi hyväksikäyttää.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

2.6 MW:n tuulivoimakonvertterin nestejäähdytysjärjestelmän komponentit ja hinnat

Tässä tutkimuksessa selvitettiin nestejäähdytteisen 2.6 MW:n tuulivoimakonvertterin jäähdytysjärjestelmään kuuluvat komponentit ja niiden hinnat. Tällä pyritään helpottamaan uutta teknologiaa hyödyntävien jäähdytysjärjestelmien kehittäjiä kaupallistamaan tuotteensa selvittämällä heille nykyisten jäähdytysjärjestelmien hintaluokka. Työ tehtiin kirjallisuuskatsauksena ja lähteinä käytettiin pääasiassa ABB:lta saatavia julkisia materiaaleja. Komponenttien hinnat haettiin internetin hakukoneiden avulla, sekä lähettämällä tarjouspyyntöjä myyjille, jotka eivät hintojaan olleet suoraan ilmoittaneet. Hinnat komponenteista kerättiin 1, 10, 50 ja 100 kappaleen toimituserille, jolloin voitiin vertailla myös kuinka paljon edullisemmaksi sarjatuotanto tulisi, kuin yksittäisen tuotteen valmistaminen. Tuloksista huomattiin, että konvertterikaapiston sisäiset jäähdytysjärjestelmään kuuluvat komponentit muodostavat vain pienen osan siitä hinnasta, mikä aiheutuu ulkopuolisesta jäähdytysyksiköstä, johon kaapisto on kytkettävä. Käytettäessä yksittäistä jäähdytysyksikköä on koko jäähdytysjärjestelmän hinta 14-15 000 €, josta ulkoisen jäähdytysyksikön osuus on 12 000 €, eli yli 80 % kokonaishinnasta. Sarjatuotannossa hinta putoaa etenkin jäähdytysyksikön osalta huomattavasti. Uusia teknologioita kaupallistettaessa on niiden kokonaisuudessaan oltava tätä summaa halvempia, johon paras tapa päästä on keskittää huomionsa ulkoisen jäähdytysyksikön kustannusten laskemiseen. Toinen vaihtoehto kaupalliseen menestykseen, on tehdä samanhintainen jäähdytysjärjestelmä, mutta jäähdytysteholtaan huomattavasti tehokkaampi, sekä kooltaan pienempi.

Repository content - lost in Discovery?

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

pp-Blast: a "pseudo-parallel" Blast

We have developed a software called pp-Blast that uses the publicly available Blast package and PVM (parallel virtual machine) to partition a multi-sequence query across a set of nodes with replicated or shared databases. Benchmark tests show that pp-Blast running in a cluster of 14 PCs outperformed conventional Blast running in large servers. In addition, using pp-Blast and the cluster we were able to map all human cDNAs onto the draft of the human genome in less than 6 days. We propose here that the cost/benefit ratio of pp-Blast makes it appropriate for large-scale sequence analysis. The source code and configuration files for pp-Blast are available at http://www.ludwig.org.br/biocomp/tools/pp-blast.

The capillary supply of human skeletal muscle in health and disease

BACKGROUND: Capillaries function to provide a surface area for nutrient and waste exchange with cells. The capillary supply of skeletal muscle is highly organized, and therefore, represents an excellent choice to study factors regulating diffusion. Muscle is comprised of three specific fibre types, each with specific contractile and metabolic characteristics, which influence the capillary supply of a given muscle; in addition, both environmental and genetic factors influence the capillary supply, including aging, physical training, and various disease processes. OBJECTIVE: The present study was undertaken to develop and assess the functionality of a data base, from which virtual experiments can be conducted on the capillary supply of human muscle, and the adaptations of the capillary bed in muscle to various perturbations. METHODS: To create the database, an extensive search of the literature was conducted using various search engines, and the three key words - "capillary, muscle, and human". This search yielded 169 papers from which the data for the 46 variables on the capillary supply and fibre characteristics of muscle were extracted for inclusion in the database. A series of statistical analyses (ANOVA) were done on the capillary database to examine differences in skeletal muscle capillarization and fibre characteristics between young and old individuals, between healthy and diseased individuals, and between untrained, endurance trained, endurance welltrained, and resistance trained individuals, using SAS. RESULTS: There was a significantly higher capillarization in the young compared to the old individuals, in the healthy compared to the diseased individuals, and in the endurance-trained and endurance well-trained compared to the untrained individuals. CONCLUSIONS: The results of this study support the conclusion that the capillary supply of skeletal muscle is closely regulated by factors aimed at optimizing oxygen and nutrient supply and/or waste removal in response to changes in muscle mass and/or metabolic activity.

Dynamics in large scale networks

In this thesis we study the properties of two large dynamic networks, the competition network of advertisers on the Google and Bing search engines and the dynamic network of friend relationships among avatars in the massively multiplayer online game (MMOG) Planetside 2. We are particularly interested in removal patterns in these networks. Our main finding is that in both of these networks the nodes which are most commonly removed are minor near isolated nodes. We also investigate the process of merging of two large networks using data captured during the merger of servers of Planetside 2. We found that the original network structures do not really merge but rather they get gradually replaced by newcomers not associated with the original structures. In the final part of the thesis we investigate the concept of motifs in the Barabási-Albert random graph. We establish some bounds on the number of motifs in this graph.