Moped task analysis: task descriptions. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Report of the ADAMHA Manpower Policy Analysis Task Force.

Mode of access: Internet.

DAEDALUS at SemEval-2014 Task 9: Comparing approaches for sentiment analysis in twitter

This paper describes our participation at SemEval- 2014 sentiment analysis task, in both contextual and message polarity classification. Our idea was to com- pare two different techniques for sentiment analysis. First, a machine learning classifier specifically built for the task using the provided training corpus. On the other hand, a lexicon-based approach using natural language processing techniques, developed for a ge- neric sentiment analysis task with no adaptation to the provided training corpus. Results, though far from the best runs, prove that the generic model is more robust as it achieves a more balanced evaluation for message polarity along the different test sets.

Decision analysis for abandoned mine reclamation site selection and planning : a report /

"August 1979."

ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization

Background: Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks. Results: We present ArrayMining.net, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining. Conclusion: ArrayMining.net is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases.

Brain decoding based on functional magnetic resonance imaging using machine learning : a comparative study

Brain decoding of functional Magnetic Resonance Imaging data is a pattern analysis task that links brain activity patterns to the experimental conditions. Classifiers predict the neural states from the spatial and temporal pattern of brain activity extracted from multiple voxels in the functional images in a certain period of time. The prediction results offer insight into the nature of neural representations and cognitive mechanisms and the classification accuracy determines our confidence in understanding the relationship between brain activity and stimuli. In this paper, we compared the efficacy of three machine learning algorithms: neural network, support vector machines, and conditional random field to decode the visual stimuli or neural cognitive states from functional Magnetic Resonance data. Leave-one-out cross validation was performed to quantify the generalization accuracy of each algorithm on unseen data. The results indicated support vector machine and conditional random field have comparable performance and the potential of the latter is worthy of further investigation.

Functional brain maps of Tower of London performance : a positron emission tomography and functional magnetic resonance imaging study

Regional cerebral blood flow (rCBF) and blood oxygenation level-dependent (BOLD) contrasts represent different physiological measures of brain activation. The present study aimed to compare two functional brain imaging techniques (functional magnetic resonance imaging versus [15O] positron emission tomography) when using Tower of London (TOL) problems as the activation task. A categorical analysis (task versus baseline) revealed a significant BOLD increase bilaterally for the dorsolateral prefrontal and inferior parietal cortex and for the cerebellum. A parametric haemodynamic response model (or regression analysis) confirmed a task-difficulty-dependent increase of BOLD and rCBF for the cerebellum and the left dorsolateral prefrontal cortex. In line with previous studies, a task-difficulty-dependent increase of left-hemispheric rCBF was also detected for the premotor cortex, cingulate, precuneus, and globus pallidus. These results imply consistency across the two neuroimaging modalities, particularly for the assessment of prefrontal brain function when using a parametric TOL adaptation.

Identifying Meaningful Places

Place identification refers to the process of analyzing sensor data in order to detect places, i.e., spatial areas that are linked with activities and associated with meanings. Place information can be used, e.g., to provide awareness cues in applications that support social interactions, to provide personalized and location-sensitive information to the user, and to support mobile user studies by providing cues about the situations the study participant has encountered. Regularities in human movement patterns make it possible to detect personally meaningful places by analyzing location traces of a user. This thesis focuses on providing system level support for place identification, as well as on algorithmic issues related to the place identification process. The move from location to place requires interactions between location sensing technologies (e.g., GPS or GSM positioning), algorithms that identify places from location data and applications and services that utilize place information. These interactions can be facilitated using a mobile platform, i.e., an application or framework that runs on a mobile phone. For the purposes of this thesis, mobile platforms automate data capture and processing and provide means for disseminating data to applications and other system components. The first contribution of the thesis is BeTelGeuse, a freely available, open source mobile platform that supports multiple runtime environments. The actual place identification process can be understood as a data analysis task where the goal is to analyze (location) measurements and to identify areas that are meaningful to the user. The second contribution of the thesis is the Dirichlet Process Clustering (DPCluster) algorithm, a novel place identification algorithm. The performance of the DPCluster algorithm is evaluated using twelve different datasets that have been collected by different users, at different locations and over different periods of time. As part of the evaluation we compare the DPCluster algorithm against other state-of-the-art place identification algorithms. The results indicate that the DPCluster algorithm provides improved generalization performance against spatial and temporal variations in location measurements.

Patterns in permuted binary matrices

Reorganizing a dataset so that its hidden structure can be observed is useful in any data analysis task. For example, detecting a regularity in a dataset helps us to interpret the data, compress the data, and explain the processes behind the data. We study datasets that come in the form of binary matrices (tables with 0s and 1s). Our goal is to develop automatic methods that bring out certain patterns by permuting the rows and columns. We concentrate on the following patterns in binary matrices: consecutive-ones (C1P), simultaneous consecutive-ones (SC1P), nestedness, k-nestedness, and bandedness. These patterns reflect specific types of interplay and variation between the rows and columns, such as continuity and hierarchies. Furthermore, their combinatorial properties are interlinked, which helps us to develop the theory of binary matrices and efficient algorithms. Indeed, we can detect all these patterns in a binary matrix efficiently, that is, in polynomial time in the size of the matrix. Since real-world datasets often contain noise and errors, we rarely witness perfect patterns. Therefore we also need to assess how far an input matrix is from a pattern: we count the number of flips (from 0s to 1s or vice versa) needed to bring out the perfect pattern in the matrix. Unfortunately, for most patterns it is an NP-complete problem to find the minimum distance to a matrix that has the perfect pattern, which means that the existence of a polynomial-time algorithm is unlikely. To find patterns in datasets with noise, we need methods that are noise-tolerant and work in practical time with large datasets. The theory of binary matrices gives rise to robust heuristics that have good performance with synthetic data and discover easily interpretable structures in real-world datasets: dialectical variation in the spoken Finnish language, division of European locations by the hierarchies found in mammal occurrences, and co-occuring groups in network data. In addition to determining the distance from a dataset to a pattern, we need to determine whether the pattern is significant or a mere occurrence of a random chance. To this end, we use significance testing: we deem a dataset significant if it appears exceptional when compared to datasets generated from a certain null hypothesis. After detecting a significant pattern in a dataset, it is up to domain experts to interpret the results in the terms of the application.

Metrologia de instrumentos endodônticos por microscopia eletrônica de varredura e análise digital de imagens: uma proposta metodológica

A padronização para a fabricação de instrumentos endodônticos em aço inoxidável contribuiu para o desenvolvimento de novos aspectos geométricos. Surgiram propostas de alterações no desenho da haste helicoidal, da seção reta transversal, da ponta, da conicidade e do diâmetro na extremidade (D0). Concomitantemente, o emprego de ligas em Níquel-Titânio possibilitou a produção de instrumentos acionados a motor, largamente empregados hoje. A cada ano a indústria lança instrumentos com diversas modificações, sem, contudo, disponibilizar informações suficientes quanto às implicações clínicas destas modificações. Existe um crescente interesse no estudo dos diferentes aspectos geométricos e sua precisa metrologia. Tradicionalmente, a aferição de aspectos geométricos de instrumentos endodônticos é realizada visualmente através de microscopia ótica. Entretanto, esse procedimento visual é lento e subjetivo. Este trabalho propõe um novo método para a metrologia de instrumentos endodônticos baseado no microscópio eletrônico de varredura e na análise digital das imagens. A profundidade de campo do MEV permite obter a imagem de todo o relevo do instrumento endodôntico a uma distância de trabalho constante. Além disso, as imagens obtidas pelo detector de elétrons retro-espalhados possuem menos artefatos e sombras, tornando a obtenção e análise das imagens mais fáceis. Adicionalmente a análise das imagens permite formas de mensuração mais eficientes, com maior velocidade e qualidade. Um porta-amostras específico foi adaptado para obtenção das imagens dos instrumentos endodônticos. Ele é composto de um conector elétrico múltiplo com terminais parafusados de 12 pólos com 4 mm de diâmetro, numa base de alumínio coberta por discos de ouro. Os nichos do conector (terminais fêmeas) têm diâmetro apropriado (2,5 mm) para o encaixe dos instrumentos endodônticos. Outrossim, o posicionamento ordenado dos referidos instrumentos no conector elétrico permite a aquisição automatizada das imagens no MEV. Os alvos de ouro produzem, nas imagens de elétrons retro-espalhados, melhor contraste de número atômico entre o fundo em ouro e os instrumentos. No porta-amostras desenvolvido, os discos que compõem o fundo em ouro são na verdade, alvos do aparelho metalizador, comumente encontrados em laboratórios de MEV. Para cada instrumento, imagens de quatro a seis campos adjacentes de 100X de aumento são automaticamente obtidas para cobrir todo o comprimento do instrumento com a magnificação e resolução requeridas (3,12 m/pixel). As imagens obtidas são processadas e analisadas pelos programas Axiovision e KS400. Primeiro elas são dispostas num campo único estendido de cada instrumento por um procedimento de alinhamento semi-automático baseado na inter-relação com o Axiovision. Então a imagem de cada instrumento passa por uma rotina automatizada de análise de imagens no KS400. A rotina segue uma sequência padrão: pré-processamento, segmentação, pós-processamento e mensuração dos aspectos geométricos.

An Empirical Study of the Use of Relevance Information in Inductive Logic Programming

Srinivasan, A., King, R. D. and Bain, M.E. (2003) An Empirical Study of the Use of Relevance Information in Inductive Logic Programming. Journal of Machine Learning Research. 4(Jul):369-383

Génération automatique de configurations et de scénarios d'utilisation d'outils de visualisation à partir de spécifications de tâches d'analyse de logiciels

Nous proposons une approche qui génère des scénarios de visualisation à partir des descriptions de tâches d'analyse de code. La dérivation de scénario est considérée comme un processus d'optimisation. Dans ce contexte, nous évaluons différentes possibilités d'utilisation d'un outil de visualisation donnée pour effectuer la tâche d'analyse, et sélectionnons le scénario qui nécessite le moins d'effort d'analyste. Notre approche a été appliquée avec succès à diverses tâches d'analyse telles que la détection des défauts de conception.

Análise de formas planas em imagens digitais: uma nova abordagem baseada na Transformada de Hough

Pós-graduação em Ciência da Computação - IBILCE