Lexical Influences on Spoken Spondaic Word Recognition in Hearing-Impaired Patients.

Top-down contextual influences play a major part in speech understanding, especially in hearing-impaired patients with deteriorated auditory input. Those influences are most obvious in difficult listening situations, such as listening to sentences in noise but can also be observed at the word level under more favorable conditions, as in one of the most commonly used tasks in audiology, i.e., repeating isolated words in silence. This study aimed to explore the role of top-down contextual influences and their dependence on lexical factors and patient-specific factors using standard clinical linguistic material. Spondaic word perception was tested in 160 hearing-impaired patients aged 23-88 years with a four-frequency average pure-tone threshold ranging from 21 to 88 dB HL. Sixty spondaic words were randomly presented at a level adjusted to correspond to a speech perception score ranging between 40 and 70% of the performance intensity function obtained using monosyllabic words. Phoneme and whole-word recognition scores were used to calculate two context-influence indices (the j factor and the ratio of word scores to phonemic scores) and were correlated with linguistic factors, such as the phonological neighborhood density and several indices of word occurrence frequencies. Contextual influence was greater for spondaic words than in similar studies using monosyllabic words, with an overall j factor of 2.07 (SD = 0.5). For both indices, context use decreased with increasing hearing loss once the average hearing loss exceeded 55 dB HL. In right-handed patients, significantly greater context influence was observed for words presented in the right ears than for words presented in the left, especially in patients with many years of education. The correlations between raw word scores (and context influence indices) and word occurrence frequencies showed a significant age-dependent effect, with a stronger correlation between perception scores and word occurrence frequencies when the occurrence frequencies were based on the years corresponding to the patients' youth, showing a "historic" word frequency effect. This effect was still observed for patients with few years of formal education, but recent occurrence frequencies based on current word exposure had a stronger influence for those patients, especially for younger ones.

Recognition of prior learning in online postgraduate education

The recognition of prior experiential learning (RPEL) involves the assessment ofskills and knowledge acquired by an individual through previous experience, which isnot necessarily related to an academic context. RPEL practices are far from generalisedin higher education, and there is a lack of specific guidelines on how to implement RPLprograms in particular settings, such as management education or online programs. TheRPEL pilot program developed in a Spanish virtual university is used throughout thearticle as the basis for further reflection on the design and implementation of RPEL inonline postgraduate education in the business field. The role of competences as a centraltheoretical foundation for RPEL is explained, and the context and characteristics of theRPEL program described. Special attention is paid to the key elements of the program¿sdesign and to the practical aspects of its implementation. The results of the program areassessed and general conclusions and suggestions for further research are discussed.

Shared feature extraction for nearest neighbor face recognition

In this paper, we propose a new supervised linearfeature extraction technique for multiclass classification problemsthat is specially suited to the nearest neighbor classifier (NN).The problem of finding the optimal linear projection matrix isdefined as a classification problem and the Adaboost algorithmis used to compute it in an iterative way. This strategy allowsthe introduction of a multitask learning (MTL) criterion in themethod and results in a solution that makes no assumptions aboutthe data distribution and that is specially appropriated to solvethe small sample size problem. The performance of the methodis illustrated by an application to the face recognition problem.The experiments show that the representation obtained followingthe multitask approach improves the classic feature extractionalgorithms when using the NN classifier, especially when we havea few examples from each class

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

Optical Character Recognition for Symbian OS

This Master's thesis addresses the design and implementation of the optical character recognition (OCR) system for a mobile device working on the Symbian operating system. The developed OCR system, named OCRCapriccio, emphasizes the modularity, effective extensibility and reuse. The system consists of two parts which are the graphical user interface and the OCR engine that was implemented as a plug-in. In fact, the plug-in includes two implementations of the OCR engine for enabling two types of recognition: the bitmap comparison based recognition and statistical recognition. The implementation results have shown that the approach based on bitmap comparison is more suitable for the Symbian environment because of its nature. Although the current implementation of bitmap comparison is lacking in accuracy, further development should be done in its direction. The biggest challenges of this work were related to developing an OCR scheme that would be suitable for Symbian OS Smartphones that have limited computational power and restricted resources.

Towards a lightweight adaptative multicomponential affective recognition system

As part of the Affective Computing research field, the development of automatic affective recognition systems can enhance human-computer interactions by allowing the creation of interfaces that react to the user's emotional state. To that end, this Master Thesis brings affect recognition to nowadays most used human computer interface, mobile devices, by developing a facial expression recognition system able to perform detection under the difficult conditions of viewing angle and illumination that entails the interaction with a mobile device. Moreover, this Master Thesis proposes to combine emotional features detected from expression with contextual information of the current situation, to infer a complex and extensive emotional state of the user. Thus, a cognitive computational model of emotion is defined that provides a multicomponential affective state of the user through the integration of the detected emotional features into appraisal processes. In order to account for individual differences in the emotional experience, these processes can be adapted to the culture and personality of the user.

Physical Realization of an Optical Automatic Target Recognition System

In the modern warfare there is an active development of a new trend connected with a robotic warfare. One of the critical elements of robotics warfare systems is an automatic target recognition system, allowing to recognize objects, based on the data received from sensors. This work considers aspects of optical realization of such a system by means of NIR target scanning at fixed wavelengths. An algorithm was designed, an experimental setup was built and samples of various modern gear and apparel materials were tested. For pattern testing the samples of actively arm engaged armies camouflages were chosen. Tests were performed both in clear atmosphere and in the artificial extremely humid and hot atmosphere to simulate field conditions.

Is professional recognition in plastic surgery related to activity in research

OBJECTIVE: To evaluate the relation of medical research, with the participation of prominent plastic surgeon in Congress.METHODS: We reviewed the scientific programs of the last 3 Brazilian Congress of Surgery, were selected 21 Brazilian plástic surgeons invited to serve as panelists or speakers in roundtable sessions in the last 3 congresses (Group 1). We randomly selected and paired by other members (associates) of the Brazilian Society of Plastic Surgery, with no participation in congress as speaker (Group 2). We conducted a search for articles published in journals indexed in Medline, Lilacs and SciELO for all doctors selected during the entire academic career and the last 5 years from March 2007 until March 2012. We assessed the research activity through the simple counting of the number of publications in indexed journals for each professional. The number of publications groups was compared.RESULTS: articles produced throughout career: Group 1- 639 articles (average of 30.42 items each). Group 2- 79 articles (mean 3.95 articles each). Difference between medias: p <0.001.CONCLUSION: The results demonstrate that the Brazilian Society of Plastic Surgery seeking professionals with a greater number of publications and journals of higher impact. This approach encourages new members to pursue a higher qualification, and give security to congressmen, they can rely on the existence of a technical criterion in the choice of speakers.

Evaluation of Bubble Formation and Break Up in Suppression Pools by Using Pattern Recognition Methods

During a possible loss of coolant accident in BWRs, a large amount of steam will be released from the reactor pressure vessel to the suppression pool. Steam will be condensed into the suppression pool causing dynamic and structural loads to the pool. The formation and break up of bubbles can be measured by visual observation using a suitable pattern recognition algorithm. The aim of this study was to improve the preliminary pattern recognition algorithm, developed by Vesa Tanskanen in his doctoral dissertation, by using MATLAB. Video material from the PPOOLEX test facility, recorded during thermal stratification and mixing experiments, was used as a reference in the development of the algorithm. The developed algorithm consists of two parts: the pattern recognition of the bubbles and the analysis of recognized bubble images. The bubble recognition works well, but some errors will appear due to the complex structure of the pool. The results of the image analysis were reasonable. The volume and the surface area of the bubbles were not evaluated. Chugging frequencies calculated by using FFT fitted well into the results of oscillation frequencies measured in the experiments. The pattern recognition algorithm works in the conditions it is designed for. If the measurement configuration will be changed, some modifications have to be done. Numerous improvements are proposed for the future 3D equipment.

MHC-restricted antigen presentation and recognition: constraints on gene, recombinant and peptide vaccines in humans

The target of any immunization is to activate and expand lymphocyte clones with the desired recognition specificity and the necessary effector functions. In gene, recombinant and peptide vaccines, the immunogen is a single protein or a small assembly of epitopes from antigenic proteins. Since most immune responses against protein and peptide antigens are T-cell dependent, the molecular target of such vaccines is to generate at least 50-100 complexes between MHC molecule and the antigenic peptide per antigen-presenting cell, sensitizing a T cell population of appropriate clonal size and effector characteristics. Thus, the immunobiology of antigen recognition by T cells must be taken into account when designing new generation peptide- or gene-based vaccines. Since T cell recognition is MHC-restricted, and given the wide polymorphism of the different MHC molecules, distinct epitopes may be recognized by different individuals in the population. Therefore, the issue of whether immunization will be effective in inducing a protective immune response, covering the entire target population, becomes an important question. Many pathogens have evolved molecular mechanisms to escape recognition by the immune system by variation of antigenic protein sequences. In this short review, we will discuss the several concepts related to selection of amino acid sequences to be included in DNA and peptide vaccines.

Evidence for the expression of native Mycobacterium tuberculosis phospholipase C: recognition by immune sera and detection of promoter activity

The genome of Mycobacterium tuberculosis H37Rv contains three contiguous genes (plc-a, plc-b and plc-c) which are similar to the Pseudomonas aeruginosa phospholipase C (PLC) genes. Expression of mycobacterial PLC-a and PLC-b in E. coli and M. smegmatis has been reported, whereas expression of the native proteins in M. tuberculosis H37Rv has not been demonstrated. The objective of the present study was to demonstrate that native PLC-a is expressed in M. tuberculosis H37Rv. Sera from mice immunized with recombinant PLC-a expressed in E. coli were used in immunoblots to evaluate PLC-a expression. The immune serum recognized a 49-kDa protein in immunoblots against M. tuberculosis extracts. No bands were visible in M. tuberculosis culture supernatants or extracts from M. avium, M. bovis and M. smegmatis. A 550-bp DNA fragment upstream of plc-a was cloned in the pJEM12 vector and the existence of a functional promoter was evaluated by detection of ß-galactosidase activity. ß-Galactosidase activity was detected in M. smegmatis transformed with recombinant pJEM12 grown in vitro and inside macrophages. The putative promoter was active both in vitro and in vivo, suggesting that expression is constitutive. In conclusion, expression of non-secreted native PLC-a was demonstrated in M. tuberculosis.

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.