Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

VizRank: Data Visualization Guided by Machine Learning

Data visualization plays a crucial role in identifying interesting patterns in exploratory data analysis. Its use is, however, made difficult by the large number of possible data projections showing different attribute subsets that must be evaluated by the data analyst. In this paper, we introduce a method called VizRank, which is applied on classified data to automatically select the most useful data projections. VizRank can be used with any visualization method that maps attribute values to points in a two-dimensional visualization space. It assesses possible data projections and ranks them by their ability to visually discriminate between classes. The quality of class separation is estimated by computing the predictive accuracy of k-nearest neighbor classifier on the data set consisting of x and y positions of the projected data points and their class information. The paper introduces the method and presents experimental results which show that VizRank's ranking of projections highly agrees with subjective rankings by data analysts. The practical use of VizRank is also demonstrated by an application in the field of functional genomics

Evolutionary games on graphs

Game theory is one of the key paradigms behind many scientific disciplines from biology to behavioral sciences to economics. In its evolutionary form and especially when the interacting agents are linked in a specific social network the underlying solution concepts and methods are very similar to those applied in non-equilibrium statistical physics. This review gives a tutorial-type overview of the field for physicists. The first three sections introduce the necessary background in classical and evolutionary game theory from the basic definitions to the most important results. The fourth section surveys the topological complications implied by non-mean-field-type social network structures in general. The last three sections discuss in detail the dynamic behavior of three prominent classes of models: the Prisoner's Dilemma, the Rock-Scissors-Paper game, and Competing Associations. The major theme of the review is in what sense and how the graph structure of interactions can modify and enrich the picture of long term behavioral patterns emerging in evolutionary games.Comment: Review, final version, 133 pages, 65 figure

Automated segmentation, tracking and evaluation of bacteria in microscopy images

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaMost of the investigation in microbiology relies on microscope imaging and needs to be complemented with reliable methods of computer assisted image processing, in order to avoid manual analysis. In this work, a method to assist the study of the in vivo kinetics of protein expression from Escherichia coli cells was developed. Confocal fluorescence microscopy (CFM) and Differential Interference Contrast (DIC) microscopy images were acquired and processed using the developed method. This method comprises two steps: the first one is focused on the cells detection using DIC images. The latter aligns both DIC and CFM images and computes the fluorescence level emitted by each cell. For the first step, the Gradient Path Labelling (GPL) algorithm was used which produces a moderate over-segmented DIC image. The proposed algorithm, based on decision trees generated by the Classification and Regression Trees (CART) algorithm, discards the backgrounds regions and merges the regions belonging to the same cell. To align DIC/fluorescence images an exhaustive search of the relative position and scale parameters that maximizes the fluorescence inside the cells is made. After the cells have been located on the CFM images, the fluorescence emitted by each cell is evaluated. The discard classifier performed with an error rate of 1:81% 0:98% and the merge classifier with 3:25% 1:37%. The segmentation algorithm detected 93:71% 2:06% of the cells in the tested images. The tracking algorithm correctly followed 64:52% 16:02% of cells and the alignment method successfully aligned all the tested images

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

A Trading Agent Framework Using Plain Strategies & Machine Learning

O mundo das mercados de apostas desportivas (trading) está em constante crescimento e com isso as pessoas estão a tentar melhorar os resultados do seu trading usando agentes automáticos de trading. Em analogia com os mercados financeiros, as operações de compra e venda são substituídas por apostas a favor e contra (Back and Lay respetivamente).Esta tese descreve uma framework para ser usada no desenvolvimento de agentes automáticos de trading nos mercados da Betfair, utilizando uma interface de programação escrita em Java. A Betfair processa mais de cinco milhões de transações diárias (como fazer uma aposta) que representa mais do que todas as trocas feitas nas bolsas de ações Europeias combinadas. A Betfair está disponível 24 horas por dia, 7 dias por semana. Neste trabalho foram desenvolvidos dois agentes de trading, DealerAgent e HorseLayAgent, de acordo com a framework supra mencionada.Os agentes mencionados atuam nos mercados "Para Ganhar" em corridas de cavalos do Reino Unido. Usam estratégias planas em conjunto com métodos de machine learning para melhorar os seus resultados de lucro/perda. Os agentes desenvolvidos foram submetidos a testes de viabilidade usando dados dos mercados "Para Ganhar" de corridas de cavalos do mercado de apostas Betfair, de Janeiro, Fevereiro e Maço de 2014.The world of online sports betting exchange (trading) is growing every day and with that people are trying to improve their trading by using automated trading. In analogy to the financial markets the buy and sell operations are replaced by betting for and against (Back and Lay).This thesis describes a framework to be used to develop automated trading agents at Betfair sports markets using a Java programming interface. Betfair processes more than five million transactions (such as placing a bet) every day which is more than all European stock exchanges combined. Betfair is available 24 hours a day 7 days a week. For this thesis were developed two trading agents, DealerAgent and HorseLayAgent, accordingly with the presented framework. The agents mentioned above act on To Win horse racing markets in United Kingdom. They use plain strategies together with machine learning methods to improve the profit/loss results. The developed agents were submitted to viability tests using data from Betfair To Win horse racing markets from January, February and March of 2014