LIAAD - Indexed Articles in Conferences

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Browsing LIAAD - Indexed Articles in Conferences by Title

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    2D-Interval Predictions for Time Series
    ( 2011) Luís Torgo ; Orlando Shigueo Junior
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    Accelerating Recommender Systems using GPUs
    ( 2015) André Valente Rodrigues ; Alípio Jorge ; Inês Dutra
    We describe GPU implementations of the matrix recommender algorithms CCD++ and ALS. We compare the processing time and predictive ability of the GPU implementations with existing multi- core versions of the same algorithms. Results on the GPU are better than the results of the multi- core versions (maximum speedup of 14.8).
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    Active Learning from Video Streams in a Multi-Camera Scenario
    ( 2014) Samaneh Khoshrou ; Jaime Cardoso ; Luís Filipe Teixeira
    While video surveillance systems are spreading everywhere, extracting meaningful information from what they are recording is still prohibitively expensive. There is a major effort under way in order to make this process economical by including an intelligent software that eases the burden of the system. In this paper, we introduce an incremental learning framework to classify parallel data streams generated in a multi-camera surveillance scenario. The framework exploits active learning strategies in order to interact wisely with operators to address various problems that exist in such non-stationary environments, such as concept drift and concept evolution. If we look at the problem as mining parallel streams, the framework can address learning from uneven parallel streams applying a class-based ensemble, a problem that has not been addressed before. Favourable results indicate the success of the framework.
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    Acute Kidney Injury Detection: An Alarm System to Improve Early Treatment
    ( 2017) Ana Rita Nogueira ; Carlos Ferreira ; João Gama
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    An Agent-based Model of the Earth System & Climate Change
    ( 2016) Yassine Baghoussi ; Pedro Campos ; Rossetti,RJF
    Simulation is a computer-based experimentation tool suitable to determine the efficacy of a previously untried decision. In this paper, we present a model of climate change. The goal behind this project is to provide a test-bed to evaluate theories related to the Earth system so as to test and evaluate metrics such as greenhouse gases and climate change in general. The proposed approach is based on a multi-agent model which has as input a representation of nature and as output the changes that will occur on Earth within a given instant of time. Most views about climate change do not take into account the real severity of the subject matter; however, the present perspective is given in a way so as to make non-experts aware of the risks that are threatening life on Earth. Just recently, the general population has developed considerable sensitivity to these issues. One important contribution of this work is to use agent-based modeling and simulation as an instructional tool that will allow people to easily understand all aspects involved in the preservation of the environment in a more aware and responsible way.
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    Algorithm selection via meta-learning and sample-based active testing
    ( 2015) Salisu Mamman Abdulrhaman ; Pavel Brazdil ; Van Rijn,JN ; Vanschoren,J
    Identifying the best machine learning algorithm for a given problem continues to be an active area of research. In this paper we present a new method which exploits both meta-level information acquired in past experiments and active testing, an algorithm selection strategy. Active testing attempts to iteratively identify an algorithm whose performance will most likely exceed the performance of previously tried algorithms. The novel method described in this paper uses tests on smaller data sample to rank the most promising candidates, thus optimizing the schedule of experiments to be carried out. The experimental results show that this approach leads to considerably faster algorithm selection.
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    Anáiise de Variância Dual
    ( 2011) Adelaide Figueiredo
    Consideremos a abordagem dual da abordagem clássica de estatística multivariada em que os indivíduos estão fixos e as variáveis são escolhidas aleatoriamente de uma população de variáveis. Supomos k grupos de variáveis centradas e reduzidas e associamos a cada grupo uma distribuição de Watson. Para vermos se estes grupos são distintos testamos se as direções privilegiadas das distribuições de Watson diferem significativamente usando a análise de variância dual. Analisamos a potência deste teste para dois grupos e diferentes dimensões de esfera. Pretendemos aplicar esta abordagem a dados reais.
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    AND Parallelism for ILP: The APIS System
    ( 2014) Rui Camacho ; Ramos,R ; Nuno Fonseca
    Inductive Logic Programming (ILP) is a well known approach to Multi-Relational Data Mining. ILP systems may take a long time for analyzing the data mainly because the search (hypotheses) spaces are often very large and the evaluation of each hypothesis, which involves theorem proving, may be quite time consuming in some domains. To address these efficiency issues of ILP systems we propose the APIS (And ParallelISm for ILP) system that uses results from Logic Programming AND-parallelism. The approach enables the partition of the search space into sub-spaces of two kinds: sub-spaces where clause evaluation requires theorem proving; and sub-spaces where clause evaluation is performed quite efficiently without resorting to a theorem prover. We have also defined a new type of redundancy (Coverage-equivalent redundancy) that enables the prune of significant parts of the search space. The new type of pruning together with the partition of the hypothesis space considerably improved the performance of the APIS system. An empirical evaluation of the APIS system in standard ILP data sets shows considerable speedups without a lost of accuracy of the models constructed.
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    Anomaly detection from telecommunication data using three-way analysis
    ( 2012) Márcia Barbosa Oliveira ; João Gama ; Hadi Fanaee Tork
    So far, several supervised and unsupervised solutions have been provided for detecting failures in telecommunication networks. Among them, unsupervised approaches attracted more attention since no labeled data is required. Principal component analysis (PCA) is a wellknown unsupervised technique to solve this type of problem when data is organized in matrix form. However, PCA may fail to capture all the significant interactions established among di erent dimensions when applied to higher-order data. When dealing with three, instead of two dimensions, three-way factorization methods are more suitable since they are able to explicitly take into account the interactions among the three dimensions, without collapsing the raw data. Since an important property of telecommunication data is its temporal-sequential nature, the temporal dimension should be considered along with the other two dimensions in order to gain insights regarding its evolution. The aim of this paper is to demonstrate t
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    Application-specific traffic anomaly detection using universal background model
    ( 2015) Alizadeh,H ; Samaneh Khoshrou ; Zuquete,A
    This paper presents an application-specific intrusion detection framework in order to address the problem of detecting intrusions in individual applications when their traffic exhibits anomalies. The system is based on the assumption that authorized traffic analyzers have access to a trustworthy binding between network traffic and the source application responsible for it. Given traffic flows generated by individual genuine application, we exploit the GMM-UBM (Gaussian Mixture Model-Universal Background Model) method to build models for genuine applications, and thereby form our detection system. The system was evaluated on a public dataset collected from a real network. Favorable results indicate the success of the framework. Copyright © 2015 ACM.
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    An Approach to Extract Proper Implications Set from High-dimension Formal Contexts using Binary Decision Diagram
    ( 2018) Santos,P ; Neves,J ; Paula Raissa Silva ; Dias,SM ; Zárate,L ; Song,M ; 7134
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    Arbitrated Ensemble for Solar Radiation Forecasting
    ( 2017) Vítor Manuel Cerqueira ; Luís Torgo ; Carlos Manuel Soares
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    Arbitrated Ensemble for Time Series Forecasting
    ( 2017) Vítor Manuel Cerqueira ; Luís Torgo ; Pinto,F ; Carlos Manuel Soares
    This paper proposes an ensemble method for time series forecasting tasks. Combining different forecasting models is a common approach to tackle these problems. State-of-the-art methods track the loss of the available models and adapt their weights accordingly. Metalearning strategies such as stacking are also used in these tasks. We propose a metalearning approach for adaptively combining forecasting models that specializes them across the time series. Our assumption is that different forecasting models have different areas of expertise and a varying relative performance. Moreover, many time series show recurring structures due to factors such as seasonality. Therefore, the ability of a method to deal with changes in relative performance of models as well as recurrent changes in the data distribution can be very useful in dynamic environments. Our approach is based on an ensemble of heterogeneous forecasters, arbitrated by a metalearning model. This strategy is designed to cope with the different dynamics of time series and quickly adapt the ensemble to regime changes. We validate our proposal using time series from several real world domains. Empirical results show the competitiveness of the method in comparison to state-of-the-art approaches for combining forecasters. © 2017, Springer International Publishing AG.
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    Assessing topic discovery evaluation measures on Facebook publications of political activists in Brazil
    ( 2016) Arian Rodrigo Pasquali ; Canavarro,M ; Ricardo Campos ; Alípio Jorge
    Automatic topic detection in document collections is an important tool for various tasks. In particular, it is valuable for studying and understanding socio-political phenomena. A currently relevant example is the automatic analysis of streams of posts issued by different activist groups in the current Brazilian turmoil, through the analysis of the generated streams of texts published on the web. It is useful to determine the relative importance of the different topics identified. We can find in the literature proposals for measuring topic relevance. In this paper, we adopt two of such measures and apply them to data sets extracted from Facebook pages related to Brazilian political activism. On top of the analysis, we then carry an experimental evaluation of the human interpretability for these two measures by comparing their outcomes with the opinion of three Brazilian professionals from the field of Communication Science and media-activists. Copyright 2016 ACM.
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    Automatic Classification of Anuran Sounds Using Convolutional Neural Networks
    ( 2016) Juan Gariel Colonna ; Peet,T ; Carlos Ferreira ; Alípio Jorge ; Elsa Ferreira Gomes ; João Gama
    Anurans (frogs or toads) are closely related to the ecosystem and they are commonly used by biologists as early indicators of ecological stress. Automatic classification of anurans, by processing their calls, helps biologists analyze the activity of anurans on larger scale. Wireless Sensor Networks (WSNs) can be used for gathering data automatically over a large area. WSNs usually set restrictions on computing and transmission power for extending the network's lifetime. Deep Learning algorithms have gathered a lot of popularity in recent years, especially in the field of image recognition. Being an eager learner, a trained Deep Learning model does not need a lot of computing power and could be used in hardware with limited resources. This paper investigates the possibility of using Convolutional Neural Networks with Mel-Frequency Cepstral Coefficients (MFCCs) as input for the task of classifying anuran sounds. © 2016 ACM.
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    BHP universality in energy sources
    ( 2014) Helena Ferreira ; Rui Gonçalves ; Alberto Pinto
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    A Biased Random-key Genetic Algorithm for Placement of Virtual Machines across Geo-Separated Data Centers
    ( 2015) Stefanello,F ; Aggarwal,V ; Buriol,LS ; José Fernando Gonçalves ; Resende,MGC
    Cloud computing has recently emerged as a new technology for hosting and supplying services over the Internet. This technology has brought many benefits, such as eliminating the need for maintaining expensive computing hardware and allowing business owners to start from small and increase resources only when there is a rise in service demand. With an increasing demand for cloud computing, providing performance guarantees for applications that run over cloud become important. Applications can be abstracted into a set of virtual machines with certain guarantees depicting the quality of service of the application. In this paper, we consider the placement of these virtual machines across multiple data centers, meeting the quality of service requirements while minimizing the bandwidth cost of the data centers. This problem is a generalization of the NP-hard Generalized Quadratic Assignment Problem (GQAP). We formalize the problem and propose a novel algorithm based on a biased random-key genetic algorithm (BRKGA) to find nearoptimal solutions for the problem. The experimental results show that the proposed algorithm is effective in quickly finding feasible solutions and it produces better results than a baseline aproach provided by a commercial solver and a multi-start algorithm.
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    Biased Resampling Strategies for Imbalanced Spatio-Temporal Forecasting
    ( 2019) Moniz,N ; Mariana Rafaela Oliveira ; Torgo,L ; Santos Costa,V ; 6110
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    Bipartite Graphs for Monitoring Clusters Transitions
    ( 2010) João Gama ; Márcia Barbosa Oliveira
    The study of evolution has become an important research issue, especially in the last decade, due to a greater awareness of our world's volatility. As a consequence, a new paradigm has emerged to respond more effectively to a class of new problems in Data Mining. In this paper we address the problem of monitoring the evolution of clusters and propose the MClusT framework, which was developed along the lines of this new Change Mining paradigm. MClusT includes a taxonomy of transitions, a tracking method based in Graph Theory, and a transition detection algorithm. To demonstrate its feasibility and applicability we present real world case studies, using datasets extracted from Banco de Portugal and the Portuguese Institute of Statistics. We also test our approach in a benchmark dataset from TSDL. The results are encouraging and demonstrate the ability of MClusT framework to provide an efficient diagnosis of clusters transitions
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    A Bounded Neural Network for Open Set Recognition
    ( 2015) Douglas Oliveira Cardoso ; Franca,F ; João Gama
    Open set recognition is, more than an interesting research subject, a component of various machine learning applications which is sometimes neglected: it is not unusual the existence of learning systems developed on the top of closed-set assumptions, ignoring the error risk involved in a prediction. This risk is strictly related to the location in feature space where the prediction has to be made, compared to the location of the training data: the more distant the training observations are, less is known, higher is the risk. Proper handling of this risk can be necessary in various situation where classification and its variants are employed. This paper presents an approach to open set recognition based on an elaborate distance-like computation provided by a weightless neural network model. The results obtained in the proposed test scenarios are quite interesting, placing the proposed method among the current best ones.