CoCo, a web interface for corpora compilation

CoCo is a collaborative web interface for the compilation of linguistic resources. In this demo we are presenting one of its possible applications: paraphrase acquisition.

O6-Methylguanine-DNA methyltransferase protein expression by immunohistochemistry in brain and non-brain systemic tumours: systematic review and meta-analysis of correlation with methylation-specific polymerase chain reaction

Background: The DNA repair protein O6-Methylguanine-DNA methyltransferase (MGMT) confers resistance to alkylating agents. Several methods have been applied to its analysis, with methylation-specific polymerase chain reaction (MSP) the most commonly used for promoter methylation study, while immunohistochemistry (IHC) has become the most frequently used for the detection of MGMT protein expression. Agreement on the best and most reliable technique for evaluating MGMT status remains unsettled. The aim of this study was to perform a systematic review and meta-analysis of the correlation between IHC and MSP. Methods A computer-aided search of MEDLINE (1950-October 2009), EBSCO (1966-October 2009) and EMBASE (1974-October 2009) was performed for relevant publications. Studies meeting inclusion criteria were those comparing MGMT protein expression by IHC with MGMT promoter methylation by MSP in the same cohort of patients. Methodological quality was assessed by using the QUADAS and STARD instruments. Previously published guidelines were followed for meta-analysis performance. Results Of 254 studies identified as eligible for full-text review, 52 (20.5%) met the inclusion criteria. The review showed that results of MGMT protein expression by IHC are not in close agreement with those obtained with MSP. Moreover, type of tumour (primary brain tumour vs others) was an independent covariate of accuracy estimates in the meta-regression analysis beyond the cut-off value. Conclusions Protein expression assessed by IHC alone fails to reflect the promoter methylation status of MGMT. Thus, in attempts at clinical diagnosis the two methods seem to select different groups of patients and should not be used interchangeably.

Classifier combination for in vivo magnetic resonance spectra of brain tumours

In this paper we present a multi-stage classifier for magnetic resonance spectra of human brain tumours which is being developed as part of a decision support system for radiologists. The basic idea is to decompose a complex classification scheme into a sequence of classifiers, each specialising in different classes of tumours and trying to reproducepart of the WHO classification hierarchy. Each stage uses a particular set of classification features, which are selected using a combination of classical statistical analysis, splitting performance and previous knowledge.Classifiers with different behaviour are combined using a simple voting scheme in order to extract different error patterns: LDA, decision trees and the k-NN classifier. A special label named "unknown¿ is used when the outcomes of the different classifiers disagree. Cascading is alsoused to incorporate class distances computed using LDA into decision trees. Both cascading and voting are effective tools to improve classification accuracy. Experiments also show that it is possible to extract useful information from the classification process itself in order to helpusers (clinicians and radiologists) to make more accurate predictions and reduce the number of possible classification mistakes.

Fluid dynamic modelling and simulation of circulating fluidized bed reactors: importance of the interface turbulence transfer

The main objective of this work is to analyze the importance of the gas-solid interface transfer of the kinetic energy of the turbulent motion on the accuracy of prediction of the fluid dynamic of Circulating Fluidized Bed (CFB) reactors. CFB reactors are used in a variety of industrial applications related to combustion, incineration and catalytic cracking. In this work a two-dimensional fluid dynamic model for gas-particle flow has been used to compute the porosity, the pressure, and the velocity fields of both phases in 2-D axisymmetrical cylindrical co-ordinates. The fluid dynamic model is based on the two fluid model approach in which both phases are considered to be continuous and fully interpenetrating. CFB processes are essentially turbulent. The model of effective stress on each phase is that of a Newtonian fluid, where the effective gas viscosity was calculated from the standard k-epsilon turbulence model and the transport coefficients of the particulate phase were calculated from the kinetic theory of granular flow (KTGF). This work shows that the turbulence transfer between the phases is very important for a better representation of the fluid dynamics of CFB reactors, especially for systems with internal recirculation and high gradients of particle concentration. Two systems with different characteristics were analyzed. The results were compared with experimental data available in the literature. The results were obtained by using a computer code developed by the authors. The finite volume method with collocated grid, the hybrid interpolation scheme, the false time step strategy and SIMPLEC (Semi-Implicit Method for Pressure Linked Equations - Consistent) algorithm were used to obtain the numerical solution.

Design and evaluation of web interface signs to improve web usability : a semiotic framework

Technological innovations, the development of the internet, and globalization have increased the number and complexity of web applications. As a result, keeping web user interfaces understandable and usable (in terms of ease-of-use, effectiveness, and satisfaction) is a challenge. As part of this, designing userintuitive interface signs (i.e., the small elements of web user interface, e.g., navigational link, command buttons, icons, small images, thumbnails, etc.) is an issue for designers. Interface signs are key elements of web user interfaces because ‘interface signs’ act as a communication artefact to convey web content and system functionality, and because users interact with systems by means of interface signs. In the light of the above, applying semiotic (i.e., the study of signs) concepts on web interface signs will contribute to discover new and important perspectives on web user interface design and evaluation. The thesis mainly focuses on web interface signs and uses the theory of semiotic as a background theory. The underlying aim of this thesis is to provide valuable insights to design and evaluate web user interfaces from a semiotic perspective in order to improve overall web usability. The fundamental research question is formulated as What do practitioners and researchers need to be aware of from a semiotic perspective when designing or evaluating web user interfaces to improve web usability? From a methodological perspective, the thesis follows a design science research (DSR) approach. A systematic literature review and six empirical studies are carried out in this thesis. The empirical studies are carried out with a total of 74 participants in Finland. The steps of a design science research process are followed while the studies were designed and conducted; that includes (a) problem identification and motivation, (b) definition of objectives of a solution, (c) design and development, (d) demonstration, (e) evaluation, and (f) communication. The data is collected using observations in a usability testing lab, by analytical (expert) inspection, with questionnaires, and in structured and semi-structured interviews. User behaviour analysis, qualitative analysis and statistics are used to analyze the study data. The results are summarized as follows and have lead to the following contributions. Firstly, the results present the current status of semiotic research in UI design and evaluation and highlight the importance of considering semiotic concepts in UI design and evaluation. Secondly, the thesis explores interface sign ontologies (i.e., sets of concepts and skills that a user should know to interpret the meaning of interface signs) by providing a set of ontologies used to interpret the meaning of interface signs, and by providing a set of features related to ontology mapping in interpreting the meaning of interface signs. Thirdly, the thesis explores the value of integrating semiotic concepts in usability testing. Fourthly, the thesis proposes a semiotic framework (Semiotic Interface sign Design and Evaluation – SIDE) for interface sign design and evaluation in order to make them intuitive for end users and to improve web usability. The SIDE framework includes a set of determinants and attributes of user-intuitive interface signs, and a set of semiotic heuristics to design and evaluate interface signs. Finally, the thesis assesses (a) the quality of the SIDE framework in terms of performance metrics (e.g., thoroughness, validity, effectiveness, reliability, etc.) and (b) the contributions of the SIDE framework from the evaluators’ perspective.

Validation of a simple and inexpensive method for the quantitation of infarct in the rat brain

A gravimetric method was evaluated as a simple, sensitive, reproducible, low-cost alternative to quantify the extent of brain infarct after occlusion of the medial cerebral artery in rats. In ether-anesthetized rats, the left medial cerebral artery was occluded for 1, 1.5 or 2 h by inserting a 4-0 nylon monofilament suture into the internal carotid artery. Twenty-four hours later, the brains were processed for histochemical triphenyltetrazolium chloride (TTC) staining and quantitation of the schemic infarct. In each TTC-stained brain section, the ischemic tissue was dissected with a scalpel and fixed in 10% formalin at 0ºC until its total mass could be estimated. The mass (mg) of the ischemic tissue was weighed on an analytical balance and compared to its volume (mm³), estimated either by plethysmometry using platinum electrodes or by computer-assisted image analysis. Infarct size as measured by the weighing method (mg), and reported as a percent (%) of the affected (left) hemisphere, correlated closely with volume (mm³, also reported as %) estimated by computerized image analysis (r = 0.88; P < 0.001; N = 10) or by plethysmography (r = 0.97-0.98; P < 0.0001; N = 41). This degree of correlation was maintained between different experimenters. The method was also sensitive for detecting the effect of different ischemia durations on infarct size (P < 0.005; N = 23), and the effect of drug treatments in reducing the extent of brain damage (P < 0.005; N = 24). The data suggest that, in addition to being simple and low cost, the weighing method is a reliable alternative for quantifying brain infarct in animal models of stroke.

Automated UI testing. WBCT, a computer-automated user test environment tool.

Today, the user experience and usability in software application are becoming a major design issue due to the adaptation of many processes using new technologies. Therefore, the study of the user experience and usability might be included in every software development project and, thus, they should be tested to get traceable results. As a result of different testing methods to evaluate the concepts, a non-expert on the topic might have doubts on which option he/she should opt for and how to interpret the outcomes of the process. This work aims to create a process to ease the whole testing methodology based on the process created by Seffah et al. and a supporting software tool to follow the procedure of these testing methods for the user experience and usability.

Effectiveness of computer training on attentional skills

This paper presents two studies, both examining the efficacy of a computer programme (Captain's Log) in training attentional skills. The population of interest is the traumatically brain injured. Study #1 is a single-case design that offers recommendations for the second, .larger (N=5) inquiry. Study #2 is an eight-week hierarchical treatment programme with a multi-based testing component. Attention, memory, listening comprehension, locus-of-control, self-esteem, visuo-spatial, and general outcome measures are employed within the testing schedule. Results suggest that any improvement was a result of practice effects. With a few single-case exceptions, the participants showed little improvement in the dependent measures.

Is more information always good? : investigating the impact of website interface features on e-retailer's sales performance

A number of frameworks have been suggested for online retailing, but still there exists little consensus among researchers and practitioners regarding the appropriate amount of information critical and essential to the improvement of customers' satisfaction and their purchase intention. Against this backdrop, this study contributes to the current practical and theoretical discussions and conversations about how information search and perceived risk theories can be applied to the management of online retailer website features. This paper examines the moderating role of website personalization in studying the relationship between information content provided on the top US retailers' websites, and customer satisfaction and purchase intention. The study also explores the role played by customer satisfaction and purchase intention in studying the relationship between information that is personalized to the needs of individual customers and online retailers' sales performance. Results indicate that the extent of information content features presented to online customers alone is not enough for companies looking to satisfy and motivate customers to purchase. However, information that is targeted to an individual customer influences customer satisfaction and purchase intention, and customer satisfaction in tum serves as a driver to the retailer's online sales performance.

Sleep and information processing in individuals who have sustained a traumatic brain injury

Individuals who have sustained a traumatic brain injury (TBI) often complain of t roubl e sleeping and daytime fatigue but little is known about the neurophysiological underpinnings of the s e sleep difficulties. The fragile sleep of thos e with a TBI was predicted to be characterized by impairments in gating, hyperarousal and a breakdown in sleep homeostatic mechanisms. To test these hypotheses, 20 individuals with a TBI (18- 64 years old, 10 men) and 20 age-matched controls (18-61 years old, 9 men) took part in a comprehensive investigation of their sleep. While TBI participants were not recruited based on sleep complaint, the fmal sample was comprised of individuals with a variety of sleep complaints, across a range of injury severities. Rigorous screening procedures were used to reduce potential confounds (e.g., medication). Sleep and waking data were recorded with a 20-channel montage on three consecutive nights. Results showed dysregulation in sleep/wake mechanisms. The sleep of individuals with a TBI was less efficient than that of controls, as measured by sleep architecture variables. There was a clear breakdown in both spontaneous and evoked K-complexes in those with a TBI. Greater injury severities were associated with reductions in spindle density, though sleep spindles in slow wave sleep were longer for individuals with TBI than controls. Quantitative EEG revealed an impairment in sleep homeostatic mechanisms during sleep in the TBI group. As well, results showed the presence of hyper arousal based on quantitative EEG during sleep. In wakefulness, quantitative EEG showed a clear dissociation in arousal level between TBls with complaints of insomnia and TBls with daytime fatigue. In addition, ERPs indicated that the experience of hyper arousal in persons with a TBI was supported by neural evidence, particularly in wakefulness and Stage 2 sleep, and especially for those with insomnia symptoms. ERPs during sleep suggested that individuals with a TBI experienced impairments in information processing and sensory gating. Whereas neuropsychological testing and subjective data confirmed predicted deficits in the waking function of those with a TBI, particularly for those with more severe injuries, there were few group differences on laboratory computer-based tasks. Finally, the use of correlation analyses confirmed distinct sleep-wake relationships for each group. In sum, the mechanisms contributing to sleep disruption in TBI are particular to this condition, and unique neurobiological mechanisms predict the experience of insomnia versus daytime fatigue following a TBI. An understanding of how sleep becomes disrupted after a TBI is important to directing future research and neurorehabilitation.

Integrated Management of Interface Power (IMIP) Framework

La présence importante de plusieurs réseaux sans-fils de différentes portées a encouragée le développement d’une nouvelle génération d’équipements portables sans-fils avec plusieurs interfaces radio. Ainsi, les utilisateurs peuvent bénéficier d’une large possibilité de connectivité aux réseaux sans-fils (e.g. Wi-Fi [1], WiMAX [2], 3G [3]) disponibles autour. Cependant, la batterie d’un nœud mobile à plusieurs interfaces sera rapidement épuisée et le temps d’utilisation de l’équipement sera réduit aussi. Pour prolonger l’utilisation du mobile les standards, des réseaux sans-fils, on définie (individuellement) plusieurs états (émission, réception, sleep, idle, etc.); quand une interface radio n’est pas en mode émission/réception il est en mode sleep/idle où la consommation est très faible, comparée aux modes émission/réception. Pourtant, en cas d’équipement portable à multi-interfaces radio, l’énergie totale consommée par les interfaces en mode idle est très importante. Autrement, un équipement portable équipé de plusieurs interfaces radio augmente sa capacité de connectivité mais réduit sa longévité d’utilisation. Pour surpasser cet inconvénient on propose une plate-forme, qu'on appelle IMIP (Integrated Management of Interface Power), basée sur l’extension du standard MIH (Media Independent Handover) IEEE 802.21 [4]. IMIP permet une meilleure gestion d’énergie des interfaces radio, d’un équipement mobile à multi-radio, lorsque celles-ci entrent en mode idle. Les expérimentations que nous avons exécutées montrent que l’utilisation de IMIP permet d'économiser jusqu'a 80% de l'énergie consommée en comparaison avec les standards existants. En effet, IMIP permet de prolonger la durée d'utilisation d'équipements à plusieurs interfaces grâce à sa gestion efficace de l'énergie.

Évaluation des jeux Kinect à l’aide du suivi physiologique, du suivi oculaire et des réactions faciales du joueur.

Les données sont analysées par le logiciel conçu par François Courtemanche et Féthi Guerdelli. L'expérimentation des jeux a eu lieu au Laboratoire de recherche en communication multimédia de l'Université de Montréal.

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.

“Improved Feature Extraction and Classification Techniques for Multispectral Brain Magnetic Resonance Images”

Magnetic Resonance Imaging (MRI) is a multi sequence medical imaging technique in which stacks of images are acquired with different tissue contrasts. Simultaneous observation and quantitative analysis of normal brain tissues and small abnormalities from these large numbers of different sequences is a great challenge in clinical applications. Multispectral MRI analysis can simplify the job considerably by combining unlimited number of available co-registered sequences in a single suite. However, poor performance of the multispectral system with conventional image classification and segmentation methods makes it inappropriate for clinical analysis. Recent works in multispectral brain MRI analysis attempted to resolve this issue by improved feature extraction approaches, such as transform based methods, fuzzy approaches, algebraic techniques and so forth. Transform based feature extraction methods like Independent Component Analysis (ICA) and its extensions have been effectively used in recent studies to improve the performance of multispectral brain MRI analysis. However, these global transforms were found to be inefficient and inconsistent in identifying less frequently occurred features like small lesions, from large amount of MR data. The present thesis focuses on the improvement in ICA based feature extraction techniques to enhance the performance of multispectral brain MRI analysis. Methods using spectral clustering and wavelet transforms are proposed to resolve the inefficiency of ICA in identifying small abnormalities, and problems due to ICA over-completeness. Effectiveness of the new methods in brain tissue classification and segmentation is confirmed by a detailed quantitative and qualitative analysis with synthetic and clinical, normal and abnormal, data. In comparison to conventional classification techniques, proposed algorithms provide better performance in classification of normal brain tissues and significant small abnormalities.

Content-Based Image Retrieval of Axial Brain Slices Using a Novel LBP with a Ternary Encoding

Retrieval of similar anatomical structures of brain MR images across patients would help the expert in diagnosis of diseases. In this paper, modified local binary pattern with ternary encoding called modified local ternary pattern (MOD-LTP) is introduced, which is more discriminant and less sensitive to noise in near-uniform regions, to locate slices belonging to the same level from the brain MR image database. The ternary encoding depends on a threshold, which is a user-specified one or calculated locally, based on the variance of the pixel intensities in each window. The variancebased local threshold makes the MOD-LTP more robust to noise and global illumination changes. The retrieval performance is shown to improve by taking region-based moment features of MODLTP and iteratively reweighting the moment features of MOD-LTP based on the user’s feedback. The average rank obtained using iterated and weighted moment features of MOD-LTP with a local variance-based threshold, is one to two times better than rotational invariant LBP (Unay, D., Ekin, A. and Jasinschi, R.S. (2010) Local structure-based region-of-interest retrieval in brain MR images. IEEE Trans. Inf. Technol. Biomed., 14, 897–903.) in retrieving the first 10 relevant images