This degree project is dedicated to Waymark in Stockholm. Waymark is a consulting company where they, among other projects, develops a web application for one of the major banks in Sweden. Today the application is fully functional and deployed at the bank’s application server. Much of the work today is about maintenance but a lot of time goes to solve the bugs that the bank reports back to Waymark. One feature that was considered missing was to read the logs from the production environment in a smoothly manner. This functionality seemed appropriate for a degree project of ten weeks. The work is about solving this task and developing a prototype to be delivered to Waymark during the project. To solve this problem different log-analysis frameworks are tested to see how well they would fit. The web application is written in Java 7 and running on a WebSphere at the bank. The web layer is written in Wicket, jQuery and CSS3. Service layer running JPA2 and Google Guice used for dependency injection. For logging Log4J is used. The result turned out to be a built-in functionality in the application that manages the monitoring of the logs. Through the admin GUI, a specific log page can be opened where the user can read and filter the results to find the desired log messages. Configuration of Log4jJ has been done. Where on disk and how logs are stored has been discussed. Testing of the new functionality has been a high priority throughout the project. / Detta examensarbete är utfört åt Waymark i Stockholm. Waymark är ett IT-konsultbolag där bland annat en intern webbapplikation åt en storbank utvecklas. Applikationen är fullt fungerande och körs på bankens interna server. Idag förvaltar Waymark applikationen men ny funktionalitet införs konstant samtidigt som mycket arbete handlar om att lösa systemets buggar som banken rapporterar. En funktion som saknades var att kunna läsa loggarna från produktion på ett smidigt sätt. Denna funktionalitet ansågs som en passande uppgift för ett examensarbete på 10 veckor. Arbetet kretsar kring att utifrån den givna webbapplikationen undersöka hur denna funktionalitet skulle kunna se ut samt att utveckla fram en prototyp som levereras till Waymark innan projektets slut. I arbetet granskas logganalyserings ramverk för att se hur väl de skulle passa in i detta projekt. Webbapplikationen är skriven i Java 7 och körs på en WebSphere hos banken. Webblagret är skrivet i Wicket, jQuery och CSS3. Servicelagret kör JPA2 och Google Guice används för dependency injection. För loggning används Log4J. Resultatet blev en inbyggd funktionalitet i applikationen som sköter monitorering av loggarna. Via ett admin GUI kan en specifik loggsida öppnas där användaren kan söka och filtrera resultaten för att hitta önskade loggutskrifter. Konfiguration i Log4J har också genomförts. Var på disk och hur loggarna ska sparas har diskuterats. Testning av den nya funktionaliteten har haft hög prioritet genom hela projektet.

Trabalho realizado na Adclick, orientado pelo Engº Pedro Roque / Tese de mestrado integrado. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 2012

Föreningen Sportfiskarna samlar in statistik över fisket i Sverige för användning inom forskning och förvaltning. I och med lanseringen av applikationer för smarta telefoner har antalet inrapporterade fångster ökat och ett behov av att hantera data både till och från dessa applikationer har uppstått. Denna uppsats beskriver skapandet av Sportfiskarnas webbplats FångstDataBanken. Webbplatsens huvudsakliga syfte är att administrera informationen som finns tillgänglig i de mobila applikationerna men också att kunna hantera de inrapporterade fångsterna för statistiska ändamål. Projektets resultat är en webbplats skriven i ASP.NET MVC som tillfredsställer Sportfiskarnas behov. Webbplatsen utgör kärnan i Sportfiskarnas system för att hantera fångstrapportering och kommer att tas i bruk under sommaren 2015. / The Swedish Anglers Association (Sportfiskarna) collects statistics on fishing in Sweden for use in research and management. With the launch of applications for smart phones, the number of reported catches has increased and the need to manage data both to and from these applications has arisen. This dissertation describes the creation of Sportfiskarna’s website FångsDataBanken. The main purpose of the site is to administer the information provided in the mobile applications but also to deal with the reported catches for statistical purposes. The result of the project is a website written in ASP.NET MVC that satisfies the customer’s requirements. The site is the core of Sportfiskarna's system to manage the catch reporting and will become operational in the summer of 2015.

<p>Cognitive load theory (CLT) lays out a tripartite scheme concerned with how learners cognitively interact with instructional materials during learning and problem solving. Cognitive load refers to the utilization of working memory resources, and CLT designates three types of cognitive load as intrinsic cognitive load, extraneous cognitive load, and germane cognitive load. Intrinsic cognitive load is related to the intrinsic complexity of the material. Extraneous cognitive load is concerned with unnecessary utilization of cognitive resources due to suboptimal instructional design. Germane cognitive load results from processing the intrinsic load and schema acquisition. The expertise reversal effect follows as a consequence of CLT.  </p> <p>The expertise reversal effect (ERE) states that instructional materials that are beneficial to low prior knowledge (LPK) learners may be detrimental to high prior knowledge (HPK) learners. Less guided materials have been shown to reduce extraneous cognitive load for these learners and therefore produce a greater benefit.  </p> <p>In this work we present the development of online instructional modules that deliver content in two distinct styles, differentiated by their use of guiding features. the high level guidance version (HLG) uses guiding features, such as animations and voice narration, which have been shown to benefit LPK learners. Alternatively, guiding features have been shown to be destructive to the learning of HPK students. The low level guidance (LLG) version uses text in place of voice narration and pop-up content in place of continuous animations. Both versions led to a statistically significant improvement from pre-test to post-test. However, both HPK and LPK students showed a preference for the HLG version of the module, contrary to the ERE. Future work will focus on improving the ability to indentify HPK and LPK students, and refining methods for providing optimal instructional materials for these cohorts.  </p> <p>Meanwhile, the use of machine learning is an emerging trend in education. Machine learning has been used in roles such as automatic scoring of essays in scientific argumentation tasks and providing feedback to students in real time. In this work we report our results on two projects using machine learning in education. In one project we used machine learning to predict students’ correctness on a physics problem given an essay outlining their approach to solving the problem. Our overall accuracy in predicting problem correctness given a student’s strategy essay was 80%. We were able to detect students whose approach would lead to an incorrect solution at a rate of 87%. However, deploying this model to provide real-time feedback would necessitate performance improvement. Planned future work on this problem includes hand grading essays to produce a label that reflects the scientific merit of each essay, using more sophisticated models (like Google’s B.E.R.T.), and generalizing to a larger set of problems. </p> <p>In another study, we used data about students’ prior academic behavior to predict academic risk in a first-year algebra based physics course. Their final course grade was used to define their risk category as; B- and above is designated low risk, and C+ and below is designated as high-risk. Using a mix of numerical and category features such as high school gpa, ACT/SAT scores, gender, and ethnicity we were able to predict student academic risk with 75% overall accuracy. Students with a very high grade (A) or students with a very low grade (D,F,W) were identified at a rate 92% and 88% (respectively).</p> <p>Prior work has shown that performance can be greatly increased by including in-class features into the model. Future work will focus on obtaining raw data, rather than using curved scores reported to the university registrar. Also, obtaining more batches of data to improve predictive power with existing models developed in this study.<br> </p>

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Submitted by Jaqueline Silva (jtas29@gmail.com) on 2014-09-15T17:01:52Z No. of bitstreams: 2 Filipe Marinho de Brito.pdf: 17131597 bytes, checksum: 477190303473bc899a0003fa4fa5d986 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Jaqueline Silva (jtas29@gmail.com) on 2014-09-15T17:49:43Z (GMT) No. of bitstreams: 2 Filipe Marinho de Brito.pdf: 17131597 bytes, checksum: 477190303473bc899a0003fa4fa5d986 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-09-15T17:49:43Z (GMT). No. of bitstreams: 2 Filipe Marinho de Brito.pdf: 17131597 bytes, checksum: 477190303473bc899a0003fa4fa5d986 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2013-09-03 / The operation planning process for hydrothermal generation systems is complex and depends on a large demand of organized data to find rational strategies of operation. These data although vast, often are shown in closed formats, tabular or graphical to satisfy a specific interest. What comes as closest as raw data, that allows the application of mathematical models to find rational strategies of operation are the decks. Although the wide range of papers developed to solve this planning, these are usually applied in a restricted sample of data because there is a great difficulty in reading the decks, for example the lack of standarts in the identification of a plant and the presence of binary files. In order to solve the problem in obtaining and handling data of plants, the Faraday System was developed, which promotes reading, organization, standardization, reporting and editing data of plants from decks and other information bases, as data flows reading from governmental institutions sites. The Faraday system is a web application platform, focusing on mobile computing, and can be accessed by tablets, smartphones or any devices that have access to browser and internet, introducing the science of energy research in mobile computing. / O processo de planejamento de operações de sistemas de geração hidrotérmicos é complexo e depende de uma grande demanda de dados organizados para encontrar estratégias racionais de operação. Estes dados apesar de vastos, costumam ser apresentados em formatos fechados, gráficos ou tabulados de modo a atender a um interesse específico. O que mais se aproxima de dados mais embrutecidos, que possibilitem a aplicação de modelos matemáticos para encontrar estratégias racionais de operação, são os decks. Apesar da ampla gama de trabalhos científicos desenvolvidos no sentido de solucionar o planejamento, estes costumam ser aplicados em uma amostra restrita de dados pois existe uma dificuldade grande na leitura dos decks, dentre eles pode-se citar a falta de padronização na identificação de uma usina e a presença de arquivos binários. Visando solucionar o problema na obtenção e manipulação de dados de usinas, foi desenvolvido o Sistema Faraday, que promove a leitura, organização, padronização, emissão de relatórios e edição de dados de usinas provinientes de decks e outras bases de informação, como dados de leitura de vazão provinientes de sites de instituições governamentais. O Sistema Faraday é uma aplicação web, multiplataforma, com foco na computação móvel, podendo ser acessada por tablets, smartphones ou quaisquer dispositivos que possuam navegador e acesso à internet; introduzindo a ciência da pesquisa energética na computação móvel.