Data mining approaches to software fault diagnosis

Automatic identification of software faults has enormous practical significance. This requires characterizing program execution behavior and the use of appropriate data mining techniques on the chosen representation. In this paper, we use the sequence of system calls to characterize program execution. The data mining tasks addressed are learning to map system call streams to fault labels and automatic identification of fault causes. Spectrum kernels and SVM are used for the former while latent semantic analysis is used for the latter The techniques are demonstrated for the intrusion dataset containing system call traces. The results show that kernel techniques are as accurate as the best available results but are faster by orders of magnitude. We also show that latent semantic indexing is capable of revealing fault-specific features.

On the relation between K-means and PLSA

Non-negative matrix factorization [5](NMF) is a well known tool for unsupervised machine learning. It can be viewed as a generalization of the K-means clustering, Expectation Maximization based clustering and aspect modeling by Probabilistic Latent Semantic Analysis (PLSA). Specifically PLSA is related to NMF with KL-divergence objective function. Further it is shown that K-means clustering is a special case of NMF with matrix L2 norm based error function. In this paper our objective is to analyze the relation between K-means clustering and PLSA by examining the KL-divergence function and matrix L2 norm based error function.

Desenvolvimento e validação de modelo preditivo e avaliação de testes de diagnóstico por classe latente para o parasitismo por L. chagasi em cães atendidos no Hospital Veterinário Universitário da UFPI, Teresina

A leishmaniose visceral americana (LVA) é uma doença em expansão no Brasil, para a qual se dispõem de poucas, e aparentemente ineficientes, estratégias de controle. Um dos grandes problemas para a contenção da leishmaniose visceral americana é a falta de um método acurado de identificação dos cães infectados, considerados os principais reservatórios da doença no meio urbano. Neste sentido, a caracterização de marcadores clínico-laboratoriais da infecção neste reservatório e a avaliação mais adequada do desempenho de testes para diagnóstico da infecção podem contribuir para aumentar a efetividade das estratégias de controle da LVA. Com isso, o presente estudo tem dois objetivos principais: (1) desenvolver e validar um modelo de predição para o parasitismo por Leishmania chagasi em cães, baseado em resultados de testes sorológicos e sinais clínicos e (2) avaliar a sensibilidade e especificidade de critérios clínicos, sorológicos e parasitológicos para detecção de infecção canina por L. chagasi mediante análise de classe latente. O primeiro objetivo foi desenvolvido a partir de estudo em que foram obtidos dados de exames clínico, sorológico e parasitológico de todos os cães, suspeitos ou não de LVA, atendidos no Hospital Veterinário Universitário da Universidade Federal do Piauí (HVU-UFPI), em Teresina, nos anos de 2003 e 2004, totalizando 1412 animais. Modelos de regressão logística foram construídos com os animais atendidos em 2003 com a finalidade de desenvolver um modelo preditivo para o parasitismo com base nos sinais clínicos e resultados de sorologia por Imunofluorescência Indireta (IFI). Este modelo foi validado nos cães atendidos no hospital em 2004. Para a avaliação da área abaixo da curva ROC (auROC), sensibilidade, especificidade, valores preditivos positivo (VPP), valores preditivos negativo (VPN) e acurácia global, foram criados três modelos: um somente baseado nas variáveis clínicas, outro considerando somente o resultado sorológico e um último considerando conjuntamente a clínica e a sorologia. Dentre os três, o último modelo apresentou o melhor desempenho (auROC=90,1%, sensibilidade=82,4%, especificidade=81,6%, VPP=73,4%, VPN=88,2% e acurácia global=81,9%). Conclui-se que o uso de modelos preditivos baseados em critérios clínicos e sorológicos para o diagnóstico da leishmaniose visceral canina pode ser de utilidade no processo de avaliação da infecção canina, promovendo maior agilidade na contenção destes animais com a finalidade de reduzir os níveis de transmissão. O segundo objetivo foi desenvolvido por meio de um estudo transversal com 715 cães de idade entre 1 mês e 13 anos, com raça variada avaliados por clínicos veterinários no HVU-UFPI, no período de janeiro a dezembro de 2003. As sensibilidades e especificidades de critérios clínicos, sorológicos e parasitológicos para detecção de infecção canina por Leishmania chagasi foram estimadas por meio de análise de classe latente, considerando quatro modelos de testes e diferentes pontos de corte. As melhores sensibilidades estimadas para os critérios clínico, sorológico e parasitológico foram de 60%, 95% e 66%, respectivamente. Já as melhores especificidades estimadas para os critérios clínico, sorológico e parasitológico foram de 77%, 90% e 100%, respectivamente. Conclui-se que o uso do exame parasitológico como padrão-ouro para validação de testes diagnósticos não é apropriado e que os indicadores de acurácia dos testes avaliados são insuficientes e não justificam que eles sejam usados isoladamente para diagnóstico da infecção com a finalidade de controle da doença.

"Heroes" and "Villains" of World History across Cultures

Emergent properties of global political culture were examined using data from the World History Survey (WHS) involving 6,902 university students in 37 countries evaluating 40 figures from world history. Multidimensional scaling and factor analysis techniques found only limited forms of universality in evaluations across Western, Catholic/Orthodox, Muslim, and Asian country clusters. The highest consensus across cultures involved scientific innovators, with Einstein having the most positive evaluation overall. Peaceful humanitarians like Mother Theresa and Gandhi followed. There was much less cross-cultural consistency in the evaluation of negative figures, led by Hitler, Osama bin Laden, and Saddam Hussein. After more traditional empirical methods (e.g., factor analysis) failed to identify meaningful cross-cultural patterns, Latent Profile Analysis (LPA) was used to identify four global representational profiles: Secular and Religious Idealists were overwhelmingly prevalent in Christian countries, and Political Realists were common in Muslim and Asian countries. We discuss possible consequences and interpretations of these different representational profiles.

Hierarchical Video Summarization in Reference Subspace

In this paper, a hierarchical video structure summarization approach using Laplacian Eigenmap is proposed, where a small set of reference frames is selected from the video sequence to form a reference subspace to measure the dissimilarity between two arbitrary frames. In the proposed summarization scheme, the shot-level key frames are first detected from the continuity of inter-frame dissimilarity, and the sub-shot level and scene level representative frames are then summarized by using K-mean clustering. The experiment is carried on both test videos and movies, and the results show that in comparison with a similar approach using latent semantic analysis, the proposed approach using Laplacian Eigenmap can achieve a better recall rate in keyframe detection, and gives an efficient hierarchical summarization at sub shot, shot and scene levels subsequently.

Multimodal Biometric Human Recognition for Perceptual Human-Computer Interaction

In this paper, a novel video-based multimodal biometric verification scheme using the subspace-based low-level feature fusion of face and speech is developed for specific speaker recognition for perceptual human--computer interaction (HCI). In the proposed scheme, human face is tracked and face pose is estimated to weight the detected facelike regions in successive frames, where ill-posed faces and false-positive detections are assigned with lower credit to enhance the accuracy. In the audio modality, mel-frequency cepstral coefficients are extracted for voice-based biometric verification. In the fusion step, features from both modalities are projected into nonlinear Laplacian Eigenmap subspace for multimodal speaker recognition and combined at low level. The proposed approach is tested on the video database of ten human subjects, and the results show that the proposed scheme can attain better accuracy in comparison with the conventional multimodal fusion using latent semantic analysis as well as the single-modality verifications. The experiment on MATLAB shows the potential of the proposed scheme to attain the real-time performance for perceptual HCI applications.

Association Study of SNAP25 and Schizophrenia in Irish Family and Case-Control Samples

SNAP25 occurs on chromosome 20p12.2, which has been linked to schizophrenia in some samples, and recently linked to latent classes of psychotic illness in our sample. SNAP25 is crucial to synaptic functioning, may be involved in axonal growth and dendritic sprouting, and its expression may be decreased in schizophrenia. We genotyped 18 haplotype-tagging SNPs in SNAP25 in a sample of 270 Irish high-density families. Single marker and haplotype analyses were performed in FBAT and PDT. We adjusted for multiple testing by computing q values. Association was followed up in an independent sample of 657 cases and 411 controls. We tested for allelic effects on the clinical phenotype by using the method of sequential addition and 5 factor-derived scores of the OPCRIT. Nine of 18 SNPs had Pvalues

Visualising Textual Knowledge about Risks to Aid Risk Communication

This paper demonstrates a potential application for latent semantic analysis and similar techniques in visualising the differences between two levels of knowledge about a risk issue. The HIV/AIDS risk issue will be examined and the semantic clusters of key words in a technical corpora derived from specific literature about HIV/AIDS will be compared with the semantic clusters of those in more general corpora. It is hoped that these comparisons will create a fast and efficient complementary approach to the articulation of mental models of risk issues that could be used to target possible inconsistencies between expert and lay mental models.

Understanding the socio-economic distribution of multiple deprivation: An application of self-organising maps

In this paper we seek to contribute to recent efforts to develop and implement multi-dimensional approaches to social exclusion by applying self-organising maps (SOMs) to a set of material deprivation indicators from the Irish component of EU-SILC. The first stage of our analysis involves the identification of sixteen clusters that confirm the multi-dimensional nature of deprivation in contemporary Ireland and the limitations of focusing solely on income. In going beyond this mapping stage, we consider both patterns of socio-economic differentiation in relation to cluster membership and the extent to which such membership contributes to our understanding of economic stress. Our analysis makes clear the continuing importance of traditional forms of stratification relating to factors such as income, social class and housing tenure in accounting for patterns of multiple deprivation. However, it also confirms the role of acute life events and life cycle and location influences. Most importantly, it demonstrates that conclusions relating to the relative impact of different kinds of socio-economic influences are highly dependent on the form of deprivation being considered. Our analysis suggests that debates relating to the extent to which poverty and social exclusion have become individualized should take particular care to distinguish between different kinds of outcomes. Further analysis demonstrates that the SOM approach is considerably more successful than a comparable latent class analysis in identifying those exposed to subjective economic stress. (C) 2010 International Sociological Association Research Committee 28 on Social Stratification and Mobility. Published by Elsevier Ltd. All rights reserved.

Economic vulnerability, multidimensional deprivation and social cohesion in an enlarged European community

In this article we have sought to combine regional and social exclusion perspectives on economic exclusion in the enlarged European Community. Our analysis, based on the European Quality of Life Survey, confirms that while the economically vulnerable, identified through latent class analysis, constitute substantially larger groups in the poorer economic clusters, they are much more sharply differentiated from others in the richer clusters. While the economically vulnerable are also disadvantaged in relation to measures of multidimensional deprivation and social cohesion, between economic clusters differences on these dimensions cannot be accounted for by corresponding variations in levels and intensity of economic vulnerability. In fact, the impact of such vulnerability on social cohesion is greater in the more affluent clusters. Copyright © 2005 SAGE Publications.

Beyond the standard work model? Varieties of flexible working time in European economies

Working time has been among the first aspect of the employment relation to be the object of intense regulation at the national and supra-national level. This standard regulation of working time comprised a number of elements: full-time hours, rigid working schedules, strong employers’ control and clear boundaries around working time In spite of general claims about the erosion of this model, few studies have investigated this process in a comparative and empirical perspective. The aim of this paper is to investigate the diversity of working time arrangements in European economies by applying latent class analysis to data
from the European Working Conditions Survey (EWCS). This analysis shows the existence of six different types of working time organization highlighting five cross-national patterns: multiple flexibilities, extended flexibility, standard, rigid and fragmented time.

Overconfidence, sexual health awareness and sexual health risk among young female users of sexual health clinics

This study explored the patterning of young people’s sexual health competence, and how this relates to sexual health outcomes. A survey of 381 young people attending two sexual health clinics in Northern Ireland was carried out between 2009 and 2010. Latent profile analysis of self-rated decision making, self-rated sexual health knowledge, and knowledge of sexually transmitted disease questionnaire scores was used to determine typologies of sexual health competence. Analysis revealed three categories of sexual health competence and explored their association with other behaviours and social characteristics. Young people’s subjective opinion of their sexual health competency, when not matched with a corresponding knowledge of sexual health, could place people at an increased risk of poor sexual health outcomes. Greater levels of peer pressure to have sex and early sexual debut were associated with poorer sexual health knowledge. This finding warrants further investigation, as the importance of self-perceived competence for sexual health screening and education programmes are considerable.

Rapid detection of health-care-associated bloodstream infection in critical care using multipathogen real-time polymerase chain reaction technology: a diagnostic accuracy study and systematic review

Background: There is growing interest in the potential utility of real-time polymerase chain reaction (PCR) in diagnosing bloodstream infection by detecting pathogen deoxyribonucleic acid (DNA) in blood samples within a few hours. SeptiFast (Roche Diagnostics GmBH, Mannheim, Germany) is a multipathogen probe-based system targeting ribosomal DNA sequences of bacteria and fungi. It detects and identifies the commonest pathogens causing bloodstream infection. As background to this study, we report a systematic review of Phase III diagnostic accuracy studies of SeptiFast, which reveals uncertainty about its likely clinical utility based on widespread evidence of deficiencies in study design and reporting with a high risk of bias. 

Objective: Determine the accuracy of SeptiFast real-time PCR for the detection of health-care-associated bloodstream infection, against standard microbiological culture. 

Design: Prospective multicentre Phase III clinical diagnostic accuracy study using the standards for the reporting of diagnostic accuracy studies criteria. 

Setting: Critical care departments within NHS hospitals in the north-west of England. 

Participants: Adult patients requiring blood culture (BC) when developing new signs of systemic inflammation. 

Main outcome measures: SeptiFast real-time PCR results at species/genus level compared with microbiological culture in association with independent adjudication of infection. Metrics of diagnostic accuracy were derived including sensitivity, specificity, likelihood ratios and predictive values, with their 95% confidence intervals (CIs). Latent class analysis was used to explore the diagnostic performance of culture as a reference standard. 

Results: Of 1006 new patient episodes of systemic inflammation in 853 patients, 922 (92%) met the inclusion criteria and provided sufficient information for analysis. Index test assay failure occurred on 69 (7%) occasions. Adult patients had been exposed to a median of 8 days (interquartile range 4–16 days) of hospital care, had high levels of organ support activities and recent antibiotic exposure. SeptiFast real-time PCR, when compared with culture-proven bloodstream infection at species/genus level, had better specificity (85.8%, 95% CI 83.3% to 88.1%) than sensitivity (50%, 95% CI 39.1% to 60.8%). When compared with pooled diagnostic metrics derived from our systematic review, our clinical study revealed lower test accuracy of SeptiFast real-time PCR, mainly as a result of low diagnostic sensitivity. There was a low prevalence of BC-proven pathogens in these patients (9.2%, 95% CI 7.4% to 11.2%) such that the post-test probabilities of both a positive (26.3%, 95% CI 19.8% to 33.7%) and a negative SeptiFast test (5.6%, 95% CI 4.1% to 7.4%) indicate the potential limitations of this technology in the diagnosis of bloodstream infection. However, latent class analysis indicates that BC has a low sensitivity, questioning its relevance as a reference test in this setting. Using this analysis approach, the sensitivity of the SeptiFast test was low but also appeared significantly better than BC. Blood samples identified as positive by either culture or SeptiFast real-time PCR were associated with a high probability (> 95%) of infection, indicating higher diagnostic rule-in utility than was apparent using conventional analyses of diagnostic accuracy. 

Conclusion: SeptiFast real-time PCR on blood samples may have rapid rule-in utility for the diagnosis of health-care-associated bloodstream infection but the lack of sensitivity is a significant limiting factor. Innovations aimed at improved diagnostic sensitivity of real-time PCR in this setting are urgently required. Future work recommendations include technology developments to improve the efficiency of pathogen DNA extraction and the capacity to detect a much broader range of pathogens and drug resistance genes and the application of new statistical approaches able to more reliably assess test performance in situation where the reference standard (e.g. blood culture in the setting of high antimicrobial use) is prone to error.

A relação trabalho-família : o desafio para ontem

Tese de doutoramento, Psicologia (Psicologia dos Recursos Humanos, do Trabalho e das Organizações), Universidade de Lisboa, Faculdade de Psicologia, 2016

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal