The Requirements of Using Provenance in e-Science Experiments

In e-Science experiments, it is vital to record the experimental process for later use such as in interpreting results, verifying that the correct process took place or tracing where data came from. The process that led to some data is called the provenance of that data, and a provenance architecture is the software architecture for a system that will provide the necessary functionality to record, store and use process documentation. However, there has been little principled analysis of what is actually required of a provenance architecture, so it is impossible to determine the functionality they would ideally support. In this paper, we present use cases for a provenance architecture from current experiments in biology, chemistry, physics and computer science, and analyse the use cases to determine the technical requirements of a generic, technology and application-independent architecture. We propose an architecture that meets these requirements and evaluate a preliminary implementation by attempting to realise two of the use cases.

Specification and Runtime Verification of Java Card Programs

COSTA, Umberto Souza da; MOREIRA, Anamaria Martins; MUSICANTE, Martin A. Specification and Runtime Verification of Java Card Programs. Electronic Notes in Theoretical Computer Science. [S.l:s.n], 2009.

Conditional reasoning: A key to assessing computer-based knowledge-building communication processes

This article describes a methodological approach to conditional reasoning in online asynchronous learning environments such as Virtual-U VGroups, developed by SFU, BC, Canada, consistent with the notion of meaning implication: If part of a meaning C is embedded in B and a part of a meaning B is embedded in A, then A implies C in terms of meaning [Piaget 91]. A new transcript analysis technique was developed to assess the flows of conditional meaning implications and to identify the occurrence of hypotheses and connections among them in two human science graduate mixed-mode online courses offered in the summer/spring session of 1997 by SFU. Flows of conditional meaning implications were confronted with Virtual-U VGroups threads and results of the two courses were compared. Findings suggest that Virtual-U VGroups is a knowledge-building environment although the tree-like Virtual-U VGroups threads should be transformed into neuronal-like threads. Findings also suggest that formulating hypotheses together triggers a collaboratively problem-solving process that scaffolds knowledge-building in asynchronous learning environments: A pedagogical technique and an built-in tool for formulating hypotheses together are proposed. © Springer Pub. Co.

Computer-assisted analysis of p53 and PCNA expression in oral lesions infected with human papillomavirus

OBJECTIVE: To carry out a retrospective study to determine whether human papillomavirus (HPV) infection and immunohistochemical expression of p53 and proliferating cell nuclear antigen (PCNA) are related to the risk of oral cancer. STUDY DESIGN: Fifty-seven oral biopsies, consisting of 30 oral squamous papillomas (OSPs) and 27 oral squamous cell carcinomas (OSCCs) were tested for the presence of HPV 6/11 and 16/18 by in situ hybridization using catalyzed signal amplification and in situ hybridization. p53 And PCNA expression was analyzed by immunohistochemistry and evaluated quantitatively by image analysis. RESULTS: Nineteen of the 57 oral lesions (33.3%) were positive for HPV. HPV 6/11 was found in 6 of 30 (20%) OSPs and 1 of 27 (3.7%) OSCCs. HPV 16/18 was found in 10 of 27 (37%) OSCCs and 2 of 30 (6.7%) OSPs. Sixteen of the 19 HPV-positive cases (84.2%) were p53 negative; 5 (9%) were HPV 6/11 and 11 (19%) HPV 16/18, with an inverse correlation between the presence of HPV DNA and p53 expression (P=.017, P < .05). PCNA expression appeared in 18 (94.7%) of HPV positive cases, showing that HPV 16/18 was associated with intensity of PCNA expression and with OSCCs (P=.037, P < .05). CONCLUSION: Quantitative evaluation of p53 by image analysis showed an inverse correlation between p53 expression and HPV presence, suggesting protein degradation. Image analysis also demonstrated that PCNA expression was more intense in HPV DNA 16/18 OSCCs. These findings suggest involvement of high-risk HPV types in oral carcinogenesis.

Real-time crowd density estimation using images

This paper presents a technique for real-time crowd density estimation based on textures of crowd images. In this technique, the current image from a sequence of input images is classified into a crowd density class. Then, the classification is corrected by a low-pass filter based on the crowd density classification of the last n images of the input sequence. The technique obtained 73.89% of correct classification in a real-time application on a sequence of 9892 crowd images. Distributed processing was used in order to obtain real-time performance. © Springer-Verlag Berlin Heidelberg 2005.

Methods and tools for encoding the WordNet.Br sentences, concept glosses, and conceptual-semantic relations

This paper presents the overall methodology that has been used to encode both the Brazilian Portuguese WordNet (WordNet.Br) standard language-independent conceptual-semantic relations (hyponymy, co-hyponymy, meronymy, cause, and entailment) and the so-called cross-lingual conceptual-semantic relations between different wordnets. Accordingly, after contextualizing the project and outlining the current lexical database structure and statistics, it describes the WordNet.Br editing GUI that was designed to aid the linguist in carrying out the tasks of building synsets, selecting sample sentences from corpora, writing synset concept glosses, and encoding both language-independent conceptual-semantic relations and cross-lingual conceptual-semantic relations between WordNet.Br and Princeton WordNet © Springer-Verlag Berlin Heidelberg 2006.

Precipitates segmentation from scanning electron microscope images through machine learning techniques

The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis. © 2011 Springer-Verlag Berlin Heidelberg.

Improving accuracy and speed of optimum-path forest classifier using combination of disjoint training subsets

The Optimum-Path Forest (OPF) classifier is a recent and promising method for pattern recognition, with a fast training algorithm and good accuracy results. Therefore, the investigation of a combining method for this kind of classifier can be important for many applications. In this paper we report a fast method to combine OPF-based classifiers trained with disjoint training subsets. Given a fixed number of subsets, the algorithm chooses random samples, without replacement, from the original training set. Each subset accuracy is improved by a learning procedure. The final decision is given by majority vote. Experiments with simulated and real data sets showed that the proposed combining method is more efficient and effective than naive approach provided some conditions. It was also showed that OPF training step runs faster for a series of small subsets than for the whole training set. The combining scheme was also designed to support parallel or distributed processing, speeding up the procedure even more. © 2011 Springer-Verlag.

LUTS: A lightweight user-level transaction scheduler

Software Transactional Memory (STM) systems have poor performance under high contention scenarios. Since many transactions compete for the same data, most of them are aborted, wasting processor runtime. Contention management policies are typically used to avoid that, but they are passive approaches as they wait for an abort to happen so they can take action. More proactive approaches have emerged, trying to predict when a transaction is likely to abort so its execution can be delayed. Such techniques are limited, as they do not replace the doomed transaction by another or, when they do, they rely on the operating system for that, having little or no control on which transaction should run. In this paper we propose LUTS, a Lightweight User-Level Transaction Scheduler, which is based on an execution context record mechanism. Unlike other techniques, LUTS provides the means for selecting another transaction to run in parallel, thus improving system throughput. Moreover, it avoids most of the issues caused by pseudo parallelism, as it only launches as many system-level threads as the number of available processor cores. We discuss LUTS design and present three conflict-avoidance heuristics built around LUTS scheduling capabilities. Experimental results, conducted with STMBench7 and STAMP benchmark suites, show LUTS efficiency when running high contention applications and how conflict-avoidance heuristics can improve STM performance even more. In fact, our transaction scheduling techniques are capable of improving program performance even in overloaded scenarios. © 2011 Springer-Verlag.

A study on automatic methods based on mathematical morphology for Martian dust devil tracks detection

This paper presents three methods for automatic detection of dust devils tracks in images of Mars. The methods are mainly based on Mathematical Morphology and results of their performance are analyzed and compared. A dataset of 21 images from the surface of Mars representative of the diversity of those track features were considered for developing, testing and evaluating our methods, confronting their outputs with ground truth images made manually. Methods 1 and 3, based on closing top-hat and path closing top-hat, respectively, showed similar mean accuracies around 90% but the time of processing was much greater for method 1 than for method 3. Method 2, based on radial closing, was the fastest but showed worse mean accuracy. Thus, this was the tiebreak factor. © 2011 Springer-Verlag.

Metrics to support the evaluation of association rule clustering

Many topics related to association mining have received attention in the research community, especially the ones focused on the discovery of interesting knowledge. A promising approach, related to this topic, is the application of clustering in the pre-processing step to aid the user to find the relevant associative patterns of the domain. In this paper, we propose nine metrics to support the evaluation of this kind of approach. The metrics are important since they provide criteria to: (a) analyze the methodologies, (b) identify their positive and negative aspects, (c) carry out comparisons among them and, therefore, (d) help the users to select the most suitable solution for their problems. Some experiments were done in order to present how the metrics can be used and their usefulness. © 2013 Springer-Verlag GmbH.

Teoria da ressonância adaptativa através da linguagem Java para detecção e classificação de e-mails indesejados

Pós-graduação em Engenharia Elétrica - FEIS

Previsão de séries temporais financeiras por meio de redes neurais dinâmicas e processos de transformação de dados: uma abordagem empírico-comparativa

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A transformada wavelet na fusão de imagens de sensoriamento remoto

Pós-graduação em Matematica Aplicada e Computacional - FCT

Aplicação de redes neurais artificiais no monitoramento da operação de dressagem

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)