Les théories implicites des agresseurs sexuelles d'enfants

Objectif: Ce mémoire avait pour objectif d’examiner le contenu cognitif du discours d’agresseurs sexuels d’enfants dans un échantillon d’hommes francophones afin de déterminer s’il est possible de reproduire les catégories de théories implicites telles que proposées par Ward et Keenan (1999). Le but était également d’investiguer la possibilité de retrouver de nouvelles théories implicites. Méthode: 20 entrevues semi-structurées ont été réalisées auprès d’un échantillon d’agresseurs sexuels d’enfants judiciarisés (Centre hospitalier Robert-Giffard, Établissement Montée St-François). À partir de ces entrevues, 2 juges indépendants ont sélectionné le contenu cognitif du discours des agresseurs sexuels. Ce contenu a ensuite été classé thématiquement. Chacun des thèmes émergents a fait l’objet d’une analyse indépendante afin de déterminer si les catégories permettaient une classification optimale des distorsions cognitives. Les juges ont donc repris 3 entrevues et ont recodifié les données afin de comparer la codification. Les données ont été analysées à l’aide de NVivo, un logiciel d’analyse de données qualitatives. Les résultats ont été discutés et comparés aux résultats de Ward et Keenan (1999). Résultats: Les analyses ont permis de trouver 6 théories implicites. Les théories Le droit d’agir à sa guise, Le monde est incontrôlable et L’agression ne cause pas de tort aux enfants étaient identiques à leur version originale. La théorie Le monde est dangereux variait de sa version originale car aucun lien causal n’a été trouvé entre la perception des adultes et des enfants. Deux visions uniques et indépendantes l’une de l’autre ont plutôt été observées. Aussi, les résultats ont montré que les agresseurs partageaient une image dichotomique de la femme. D’ailleurs, ce résultat est consistant avec la théorie implicite Les femmes sont dangereuses de Polaschek et Ward (2004). La théorie Les enfants sont des êtres sexuels variait de sa version originale quant à sa conceptualisation. Les enfants sont des partenaires de vie est une nouvelle théorie implicite n’ayant pas été discutée par Ward et Keenan. Ce résultat est consistant avec les recherches de Wilson (1999) sur la congruence émotionnelle envers les enfants des agresseurs sexuels.

Pervasive technologies for workplace management

Purpose – Facilities managers have less visibility of how buildings are being used due to flexible working and unpredictable workers. The purpose of this paper is to examine the current issues in workspace management and an automatic solution through radio frequency identification (RFID) that could provide real time information on the volume and capacity of buildings. Design/methodology/approach – The study described in this paper is based on a case study at a facilities management (FM) department. The department is examining a ubiquitous technology in the form of innovative RFID for security and workspace management. Interviews and observations are conducted within the facilities department for the initial phase of the implementation of RFID technology. Findings – Research suggests that work methods are evolving and becoming more flexible. With this in mind, facilities managers face new challenges to create a suitable environment for an unpredictable workforce. RFID is one solution that could provide facilities managers with an automatic way of examining space in real time and over a wider area than currently possible. RFID alone for space management is financially expensive but by making the application multiple for other areas makes more business sense. Practical implications – This paper will provide practicing FM and academics with the knowledge gained from the application of RFID in this organisation. While the concept of flexible working seems attractive, there is an emerging need to provide various forms of spaces that enable employees' satisfaction and enhance the productivity of the organisation. Originality/value – The paper introduces new thinking on the subject of “workspace management”. It highlights the current difficulties in workspace management and how an RFID solution will benefit workspace methods.

Pervasive technologies for workspace management

Purpose – Facilities managers have less visibility of how buildings are being used due to flexible working and unpredictable workers. The purpose of this paper is to examine the current issues in workspace management and an automatic solution through radio frequency identification (RFID) that could provide real time information on the volume and capacity of buildings. Design/methodology/approach – The study described in this paper is based on a case study at a facilities management (FM) department. The department is examining a ubiquitous technology in the form of innovative RFID for security and workspace management. Interviews and observations are conducted within the facilities department for the initial phase of the implementation of RFID technology. Findings – Research suggests that work methods are evolving and becoming more flexible. With this in mind, facilities managers face new challenges to create a suitable environment for an unpredictable workforce. RFID is one solution that could provide facilities managers with an automatic way of examining space in real time and over a wider area than currently possible. RFID alone for space management is financially expensive but by making the application multiple for other areas makes more business sense. Practical implications – This paper will provide practicing FM and academics with the knowledge gained from the application of RFID in this organisation. While the concept of flexible working seems attractive, there is an emerging need to provide various forms of spaces that enable employees’ satisfaction and enhance the productivity of the organisation. Originality/value – The paper introduces new thinking on the subject of “workspace management”. It highlights the current difficulties in workspace management and how an RFID solution will benefit workspace methods.

Supressão de interferência em sistemas de banda ultra larga e classificação automática de modulação

Esta tese apresenta duas contribuições distintas na área de sistemas de comunicações sem fi o. Primeiro, é apresentada uma formulação analítica para a análise de desempenho de sistemas utilizando multiplexação multibanda por divisão ortogonal na frequência (MB-OFDM, do inglês Multi-Band Orthogonal Frequency-Division Multiplexing ) com um ltro notch para mitigar a interferência em banda estreita causada por outros sistemas que operam dentro da faixa de frequências alocada para sistemas UWB. Em seguida, um novo front end para classificação automática de modulações com o uso de aprendizado discriminativo é proposto. Esse front end pode ser utilizado por qualquer classi cador discriminativo e consiste em ordenar magnitude e fase do símbolos recebidos. Os resultados obtidos pelo classi cador proposto mostraram-se competitivos com outros algoritmos já existentes na literatura.

[Strategies for reducing the CT radiation dose]

The rapid technical advances in computed tomography have led to an increased number of clinical indications. Unfortunately, at the same time the radiation exposure to the population has also increased due to the increased total number of CT examinations. In the last few years various publications have demonstrated the feasibility of radiation dose reduction for CT examinations with no compromise in image quality and loss in interpretation accuracy. The majority of the proposed methods for dose optimization are easy to apply and are independent of the detector array configuration. This article reviews indication-dependent principles (e.g. application of reduced tube voltage for CT angiography, selection of the collimation and the pitch, reducing the total number of imaging series, lowering the tube voltage and tube current for non-contrast CT scans), manufacturer-dependent principles (e.g. accurate application of automatic modulation of tube current, use of adaptive image noise filter and use of iterative image reconstruction) and general principles (e.g. appropriate patient-centering in the gantry, avoiding over-ranging of the CT scan, lowering the tube voltage and tube current for survey CT scans) which lead to radiation dose reduction.

Visual-auditory differences in duration discrimination of intervals in the subsecond and second range

A common finding in time psychophysics is that temporal acuity is much better for auditory than for visual stimuli. The present study aimed to examine modality-specific differences in duration discrimination within the conceptual framework of the Distinct Timing Hypothesis. This theoretical account proposes that durations in the lower milliseconds range are processed automatically while longer durations are processed by a cognitive mechanism. A sample of 46 participants performed two auditory and visual duration discrimination tasks with extremely brief (50-ms standard duration) and longer (1000-ms standard duration) intervals. Better discrimination performance for auditory compared to visual intervals could be established for extremely brief and longer intervals. However, when performance on duration discrimination of longer intervals in the 1-s range was controlled for modality-specific input from the sensory-automatic timing mechanism, the visual-auditory difference disappeared completely as indicated by virtually identical Weber fractions for both sensory modalities. These findings support the idea of a sensory-automatic mechanism underlying the observed visual-auditory differences in duration discrimination of extremely brief intervals in the millisecond range and longer intervals in the 1-s range. Our data are consistent with the notion of a gradual transition from a purely modality-specific, sensory-automatic to a more cognitive, amodal timing mechanism. Within this transition zone, both mechanisms appear to operate simultaneously but the influence of the sensory-automatic timing mechanism is expected to continuously decrease with increasing interval duration.

Area-Efficient Linear Regression Architecture for Real-Time Signal Processing on FPGAs

Linear regression is a technique widely used in digital signal processing. It consists on finding the linear function that better fits a given set of samples. This paper proposes different hardware architectures for the implementation of the linear regression method on FPGAs, specially targeting area restrictive systems. It saves area at the cost of constraining the lengths of the input signal to some fixed values. We have implemented the proposed scheme in an Automatic Modulation Classifier, meeting the hard real-time constraints this kind of systems have.

Photonic processing for wideband cancellation and spectral discrimination of RF signals

Photonic signal processing is used to implement common mode signal cancellation across a very wide bandwidth utilising phase modulation of radio frequency (RF) signals onto a narrow linewidth laser carrier. RF spectra were observed using narrow-band, tunable optical filtering using a scanning Fabry Perot etalon. Thus functions conventionally performed using digital signal processing techniques in the electronic domain have been replaced by analog techniques in the photonic domain. This technique was able to observe simultaneous cancellation of signals across a bandwidth of 1400 MHz, limited only by the free spectral range of the etalon. © 2013 David M. Benton.

Second-order cyclostationarity-based detection and classification of LTE SC-FDMA signals for cognitive radio

Cognitive radio (CR) was developed for utilizing the spectrum bands efficiently. Spectrum sensing and awareness represent main tasks of a CR, providing the possibility of exploiting the unused bands. In this thesis, we investigate the detection and classification of Long Term Evolution (LTE) single carrier-frequency division multiple access (SC-FDMA) signals, which are used in uplink LTE, with applications to cognitive radio. We explore the second-order cyclostationarity of the LTE SC-FDMA signals, and apply results obtained for the cyclic autocorrelation function to signal detection and classification (in other words, to spectrum sensing and awareness). The proposed detection and classification algorithms provide a very good performance under various channel conditions, with a short observation time and at low signal-to-noise ratios, with reduced complexity. The validity of the proposed algorithms is verified using signals generated and acquired by laboratory instrumentation, and the experimental results show a good match with computer simulation results.

Reciprocal Modulation of Cognitive and Emotional Aspects in Pianistic Performances

Background: High level piano performance requires complex integration of perceptual, motor, cognitive and emotive skills. Observations in psychology and neuroscience studies have suggested reciprocal inhibitory modulation of the cognition by emotion and emotion by cognition. However, it is still unclear how cognitive states may influence the pianistic performance. The aim of the present study is to verify the influence of cognitive and affective attention in the piano performances. Methods and Findings: Nine pianists were instructed to play the same piece of music, firstly focusing only on cognitive aspects of musical structure (cognitive performances), and secondly, paying attention solely on affective aspects (affective performances). Audio files from pianistic performances were examined using a computational model that retrieves nine specific musical features (descriptors) - loudness, articulation, brightness, harmonic complexity, event detection, key clarity, mode detection, pulse clarity and repetition. In addition, the number of volunteers' errors in the recording sessions was counted. Comments from pianists about their thoughts during performances were also evaluated. The analyses of audio files throughout musical descriptors indicated that the affective performances have more: agogics, legatos, pianos phrasing, and less perception of event density when compared to the cognitive ones. Error analysis demonstrated that volunteers misplayed more left hand notes in the cognitive performances than in the affective ones. Volunteers also played more wrong notes in affective than in cognitive performances. These results correspond to the volunteers' comments that in the affective performances, the cognitive aspects of piano execution are inhibited, whereas in the cognitive performances, the expressiveness is inhibited. Conclusions: Therefore, the present results indicate that attention to the emotional aspects of performance enhances expressiveness, but constrains cognitive and motor skills in the piano execution. In contrast, attention to the cognitive aspects may constrain the expressivity and automatism of piano performances.

Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization

Liver steatosis is mainly a textural abnormality of the hepatic parenchyma due to fat accumulation on the hepatic vesicles. Today, the assessment is subjectively performed by visual inspection. Here a classifier based on features extracted from ultrasound (US) images is described for the automatic diagnostic of this phatology. The proposed algorithm estimates the original ultrasound radio-frequency (RF) envelope signal from which the noiseless anatomic information and the textural information encoded in the speckle noise is extracted. The features characterizing the textural information are the coefficients of the first order autoregressive model that describes the speckle field. A binary Bayesian classifier was implemented and the Bayes factor was calculated. The classification has revealed an overall accuracy of 100%. The Bayes factor could be helpful in the graphical display of the quantitative results for diagnosis purposes.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Automatic sleep apnea detection and sleep classification using the ECG and the SpO2 signals

Dissertation for a Masters Degree in Computer and Electronic Engineering

The contribution of multitemporal information from multispectral satellite images for automatic land cover classification at the national scale

Thesis submitted to the Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Information Management – Geographic Information Systems

Unsupervised automatic music genre classification

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática