Miracle-dz/UniFAR-FTUs huggingface dataset:
Seminar Paper: Emotion Recognition from Physiological signals using Bio-sensors

This paper presents an introduction to various bio-signals used in Emotion recognition such as Electromyography (EMG) electrocardiogram (ECG), electrodermal response (EDR), Skin temperature, and Blood volume pulse (BVP) and respiration changes. This paper also discusses about various kinds of biosensors used to detect the Physiological signals. In the remaining of the paper we describe the Multimodal emotion elicitation and Acquisition of emotion-specific physiological signal database. It also deals with feature extraction technique based on the HilbertHuang Transform (HHT) method and classification techniques such as Support Vector Machine. the highest recognition accuracy is obtained by using the fission based HHT features method with 28 features, 76% of test cases are correctly classified.
Projects : the role of Communities of Practice and Mentoring in enhancing teachers ’ Continuing Professional Development

The assumption that learning is a distinct and primarily individual process, a process initiated, controlled and completed as the result of teaching interventions, is reflected upon the typical design, development and management of educational institutions around the globe. Similar assumptions appear to apply for teachers’ training and development. Over recent years, there has been an upsurge of interest concerning the planning and implementation of collaborative action research projects, denoting the value and significance of such initiatives in the field of teachers’ continuous professional development. Action research is a reflective process of progressive problem solving and it seems that through action-based inquiry teachers are enhanced to better understand and extend their professional activity as well as reflect on their teaching problems. The present paper aspires to reveal the way in which innovatory mentoring programmes combined with collaborative action research initiatives and communities of practice may help teachers to move from a position of dependency to one of greater independence and professional autonomy.
A Comparison of Document-at-a-Time and Score-at-a-Time Query Evaluation

We present an empirical comparison between document-at-a-time (DaaT) and score-at-a-time (SaaT) document ranking strategies within a common framework. Although both strategies have been extensively explored, the literature lacks a fair, direct comparison: such a study has been difficult due to vastly different query evaluation mechanics and index organizations. Our work controls for score quantization, document processing, compression, implementation language, implementation effort, and a number of details, arriving at an empirical evaluation that fairly characterizes the performance of three specific techniques: WAND (DaaT), BMW (DaaT), and JASS (SaaT). Experiments reveal a number of interesting findings. The performance gap between WAND and BMW is not as clear as the literature suggests, and both methods are susceptible to tail queries that may take orders of magnitude longer than the median query to execute. Surprisingly, approximate query evaluation in WAND and BMW does not significantly reduce the risk of these tail queries. Overall, JASS is slightly slower than either WAND or BMW, but exhibits much lower variance in query latencies and is much less susceptible to tail query effects. Furthermore, JASS query latency is not particularly sensitive to the retrieval depth, making it an appealing solution for performance-sensitive applications where bounds on query latencies are desirable.
Facial feature units localization using horizontal information of most significant bit planes

We present here an approach to find the exact position of some feature units related to human face images. We use the horizontal information in most significant bit planes of images to accomplish the task. Finding location of facial feature units is of importance as most human face recognition approaches take it as initial point. The prominent feature units in a face are eyes, nostrils and lips which are usually oriented in horizontal direction and visually significant in face image. The majority of the visually significant data in image can be extracted using higher order bits of that image. Our four step method consists of bit planes processing, separating horizontal information using wavelet transform (WT), binary thresholding and appropriate combination of Dilation and Erosion. The proposed method shows high accuracy in the presence of all real world situations like various gestures, illumination variations, closed eyes, and eyes with glasses. Key words: Bit planes, feature localization, facial features, face recognition and wavelet transform.
Auditory-visual interactions subserving goal-directed saccades in a complex scene.

This study addresses the integration of auditory and visual stimuli subserving the generation of saccades in a complex scene. Previous studies have shown that saccadic reaction times (SRTs) to combined auditory-visual stimuli are reduced when compared with SRTs to either stimulus alone. However, these results have been typically obtained with high-intensity stimuli distributed over a limited number of positions in the horizontal plane. It is less clear how auditory-visual interactions influence saccades under more complex but arguably more natural conditions, when low-intensity stimuli are embedded in complex backgrounds and distributed throughout two-dimensional (2-D) space. To study this problem, human subjects made saccades to visual-only (V-saccades), auditory-only (A-saccades), or spatially coincident auditory-visual (AV-saccades) targets. In each trial, the low-intensity target was embedded within a complex auditory-visual background, and subjects were allowed over 3 s to search for and foveate the target at 1 of 24 possible locations within the 2-D oculomotor range. We varied systematically the onset times of the targets and the intensity of the auditory target relative to background [i.e., the signal-to-noise (S/N) ratio] to examine their effects on both SRT and saccadic accuracy. Subjects were often able to localize the target within one or two saccades, but in about 15% of the trials they generated scanning patterns that consisted of many saccades. The present study reports only the SRT and accuracy of the first saccade in each trial. In all subjects, A-saccades had shorter SRTs than V-saccades, but were more inaccurate than V-saccades when generated to auditory targets presented at low S/N ratios. AV-saccades were at least as accurate as V-saccades but were generated at SRTs typical of A-saccades. The properties of AV-saccades depended systematically on both stimulus timing and S/N ratio of the auditory target. Compared with unimodal A- and V-saccades, the improvements in SRT and accuracy of AV-saccades were greatest when the visual target was synchronous with or leading the auditory target, and when the S/N ratio of the auditory target was lowest. Further, the improvements in saccade accuracy were greater in elevation than in azimuth. A control experiment demonstrated that a portion of the improvements in SRT could be attributable to a warning-cue mechanism, but that the improvements in saccade accuracy depended on the spatial register of the stimuli. These results agree well with earlier electrophysiological results obtained from the midbrain superior colliculus (SC) of anesthetized preparations, and we argue that they demonstrate multisensory integration of auditory and visual signals in a complex, quasi-natural environment. A conceptual model incorporating the SC is presented to explain the observed data.
Building a Flexible Object-Group-Oriented Framework to support Large Scale Cooperative and Adaptive Applications

In this paper we examine the requirements of large scale cooperative applications, namely those alternating multi-part synchronous work sessions with variable periods of asynchronous interactions and disconnected work. We discuss the topological organization of the large scale cooperative space and the materialization of an integrated generic platform to support those requirements. Our approach emphasizes the need of an adequate programming support. We will describe and characterize an object-grouporiented layered and wrapped infrastructure as a contribution to build generic programming frameworks offering high degree of orthogonality and, consequently, high flexibility.
A Two-step Method to Construct Credit Scoring Models with Data Mining Techniques

Credit scoring can be defined as a technique that helps credit providers decide whether to grant credit to consumers or customers. Its increasing importance can be seen from the growing popularity and application of credit scoring in consumer credit. There are advantages not only to construct effective credit scoring models to help improve the bottom-line of credit providers, but also to combine models to yield a better performing combined model. This paper has two objectives. First, it illustrates the use of data mining techniques to construct credit scoring models. Second, it illustrates the combination of credit scoring models to give a superior final model. The paper also highlights the prerequisites and limitations of the data mining approach.
All You Need Is Facebook Friends? Associations between Online and Face-to-Face Friendships and Health

Positive social relations are known to have a beneficial impact on health, however, little is known about the links of health with online relationships. In this study, we compare face-to-face and virtual friendships in their association with health. By building on previous results of studies conducted on the well-being of college students, we expect to find stronger associations of face-to-face friendships with health than of those established through Facebook. Furthermore, we expect to test the mediating role of social capital variables in this process. Two large-scale studies conducted in community samples (Study 1 = 350 urban residents; Study 2 = 803 urban and rural residents) showed that the number and quality of face-to-face friendships were directly associated with self-reported health status, however, the same did not occur with Facebook friendships. Moreover, the association of face-to-face friendships with health was totally mediated by bonding (mostly) but also bridging social capital. These results, replicated in both studies, were found controlling for confounding variables such as age, gender, education, living alone, self-esteem, and socioeconomic status. This pattern of results emphasizes the gains of face-to-face over online friendships for individuals’ health status in community samples.
A survey of CBT supervision in the UK: methods, satisfaction and training, as viewed by a selected sample of CBT supervision leaders

Abstract Internationally, clinical supervision has been increasingly recognized as a core competency and an essential requirement for clinical training. Over the past 10 years, frameworks for supervision competencies have been developed and promulgated in several countries, notably the UK, USA and Australia. But what is the current status of the actual practice of CBT supervision in the UK? We conducted an internet survey with a purposive sample of n=110 accredited British Association for Behavioural and Cognitive Psychotherapy (BABCP) supervisors and trainers (a 44% response rate), selected for their assumed expertise. The results were consistent with past surveys of Townend et al., indicating that the most frequently reported supervision methods tended to reflect many of the recommendations in widely disseminated supervision competency frameworks and recognized best practice statements. Overall, these CBT supervision leaders reported using an impressively wide range of methods, including much more frequent use of role-play, therapy recordings, and direct observation than reported in the Townend et al. surveys or in observational studies. Although satisfied in their supervisory role, respondents indicated the need for improved CBT supervisor training resources, with significant interest in developing competence instruments and group supervision methods. In conclusion, at least for this small sample of CBT supervision leaders in the UK, practice reflects international progress, but training resources are sought to maintain momentum.
African American Obese Women’s Perspectives Regarding Barriers to the Utilization of Bariatric Surgery: A Phenomenological Study

The purpose of the study was to explore how obese African American women conceptualize and construct meaning around obesity and bariatric surgery. The authors specifically focused on gaining insight regarding present barriers which may prevent African American women from considering weight loss surgery as a possible option. Utilizing phenomenology as methodological approach, 14 African American obese women, based on body mass index, participated in 30- to 60-minute semistructured interviews regarding their perceptions of bariatric surgery as a treatment intervention for weight reduction. In the study, three themes emerged, which included (a) the meaning of obesity and bariatric surgery, (b) issues in communication, and (c) barriers to bariatric surgery utilization. In the article, the authors discuss clinical implications for African American women and for the medical community, as well as offer recommendations for future research.
Trunk Fat Negatively Influences Skeletal and Testicular Functions in Obese Men: Clinical Implications for the Aging Male

Osteocalcin (OSCA) seems to act as a negative regulator of energy metabolism and insulin sensitivity. Evidence from male rodents suggests that OSCA may also regulate testosterone (T) synthesis. Using a cross-sectional design, we evaluated OSCA, 25(OH) vitamin D, T, 17β-estradiol (E2), homeostasis model assessment of insulin resistance (HOMA-IR), and body composition in 86 obese (mean BMI = 34) male subjects (18–69 yr old). Independently from BMI, an inverse relationship between trunk fat percentage and plasma T (r 2 = −0.26, P < 0.01) and between HOMA-IR and OSCA levels (r 2 = −0.22, P < 0.005) was found. OSCA levels, as well as vitamin D, decreased significantly for higher BMI with significant differences above 35 (P < 0.01). A direct correlation between T and bone mineral density at lumbar (BMDL) and neck (BMDH) (P < 0.001, r 2 = −0.20; P < 0.001, r 2 = −0.24) was found, independently from age. An inverse correlation between E2 levels, BMDL, and BMDH (P < 0.001, r 2 = −0.20; P < 0.001, r 2 = −0.19) was observed. These data provide new evidences that a relationship between trunk fat mass, insulin sensitivity, OSCA and T synthesis occurs. This new relationship with skeletal health has relevant implications for the aging male, suggesting OSCA as a novel marker of metabolic and gonadal health status.
Factores predictores de la satisfacción de estudiantes de cursos virtuales Prediction factors of student satisfaction in online courses

The easy access and daily use of recent information and communication technologies has led to an impressive development of university offers completely in online modality. These developments have also raised important questions about the determining factors that affect learning, performance and retention of the learners of these academic programs. One of these determinants is the degree to which virtual courses or programs satisfy the learners’ expectations. Specifically, this study investigated the predictor factors of learner satisfaction identified by Sun and colleagues (2008) among Hispanic learners. The questionnaire was translated into Spanish and filled out by 102 participants. The internal consistency analysis resulted in high reliability. Correlation analysis showed that all the factors studied, except computer anxiety, are significantly correlated with learner satisfaction. The stepwise regression analysis found that course flexibility, instructor attitude towards e-learning, student Internet
Fast Speaker Independent Large Vocabulary Continuous Speech Recognition

To build useful applications based on large vocabulary continuous speech recognition systems, such systems have to run in real time on common platforms. However, with most research focused on further reducing the recognition error rates, the topic of speed has been neglected in the development of speech recognition algorithms. I will present a speaker independent system that has been designed for fast speech recognition using vocabularies up to 65,000 words. Using the approaches presented in this thesis, this recognizer can now run in real time, 200 times faster than the original evaluation system. Important progress was made on the following topics: Tradeoos: a better understanding of the tradeoos between the computational eeort and the accuracy of the acoustic modeling provides a foundation to methodically develop faster algorithms. Algorithms: a number of new or improved algorithms were introduced and analyzed in this work, such as: Lookaheads: Lookaheads provide early estimates of acoustic and language model scores to the recognition process. These estimates can be used to restrict the search space at low risk. Generalized-BBI: the cross codebook bucket box intersection algorithm reduces the eeort for the observation probability computation, especially when used in speech recognition systems with very large continuous hidden Markov models. Frame Skipping: for many sections of the recorded speech, not all operations have to be performed for all frames provided by the preprocessing. The recognizer presented here is derived from existing evaluation systems. No specialized small recognizer had to be trained in order to get real time performance. Therefore, the new algorithms can also help to reduce the cycle time in developing new evaluation systems. All algorithms have been developed and tested on a state-of-the-art dictation system for the North-American-Business-News (NAB) Task. By testing on other databases, robustness with respect to new microphones and foreign accents has been veriied. It was also shown that the new algorithms can be ported to a variety of tasks such as a Japanese dictation system and the automatic transcription of spontaneously spoken German scheduling dialogues. uberpr uft.
A linearization free recursive prediction error method for combined state and parameter estimation for nonlinear systems

A novel method for combined state and parameter estimation is presented. The method is based on the recursive prediction error method for nonlinear system and is able to estimate states and parameters from nonlinear state-space models simultaneously. Moreover, because of the application of the deterministic sampling approach, no model linearization is required. A simple example (real-time parameter estimation for a three-tank system) is included to demonstrate the performance of the algorithm.
Measuring Semantic Distance using Distributional Profiles of Concepts

Semantic distance is a measure of how close or distant in meaning two units of language are. A large number of important natural language problems, including machine translation and word sense disambiguation, can be viewed as semantic distance problems. The two dominant approaches to estimating semantic distance are the WordNet-based semantic measures and the corpus-based distributional measures. In this thesis, I compare them, both qualitatively and quantitatively, and identify the limitations of each. This thesis argues that estimating semantic distance is essentially a property of concepts (rather than words) and that two concepts are semantically close if they occur in similar contexts. Instead of identifying the co-occurrence (distributional) profiles of words (distributional hypothesis), I argue that distributional profiles of concepts (DPCs) can be used to infer the semantic properties of concepts and indeed to estimate semantic distance more accurately. I propose a new hybrid approach to calculating semantic distance that combines corpus statistics and a published thesaurus (Macquarie Thesaurus). The algorithm determines estimates of the DPCs using the categories in the thesaurus as very coarse concepts and, notably, without requiring any sense-annotated data. Even though the use of only about 1000 concepts to represent the vocabulary of a language seems drastic, I show that the method achieves results better than the state-of-the-art in a number of natural language tasks. I show how cross-lingual DPCs can be created by combining text in one language with a thesaurus from another. Using these cross-lingual DPCs, we can solve problems in one, possibly resource-poor, language using a knowledge source from another, possibly resource-rich, language. I show that the approach is also useful in tasks that inherently involve two or more languages, such as machine translation and multilingual text summarization. The proposed approach is computationally inexpensive, it can estimate both semantic relatedness and semantic similarity, and it can be applied to all parts of speech. Extensive experiments on ranking word pairs as per semantic distance, real-word spelling correction, solving Reader’s Digest word choice problems, determining word sense dominance, word sense disambiguation, and word translation show that the new approach is markedly superior to previous ones.
Solving Einstein's Equations on Supercomputers

In 1916, Albert Einstein published his famous general theory of relativity, which contains the rules of gravity and provides the basis for modern theories of astrophysics and cosmology. For many years, physicists, astrophysicists and mathematicians have striven to develop techniques for unlocking the secrets contained in Einstein's theory of gravity; more recently, computational science research groups have added their expertise to the endeavor. Because the underlying scientific project provides such a demanding and rich system for computational science, techniques developed to solve Einstein's equations will apply immediately to a large family of scientific and engineering problems. The authors have developed a collaborative computational framework that allows remote monitoring and visualization of simulations, at the center of which lies a community code called Cactus. Many researchers in the general scientific computing community have already adopted Cactus, as have numerical relativists and astrophysicists. In June 1999, an international team of researchers at various sites ran some of the largest such simulations in numerical relativity yet undertaken, using a 256-processor SGI Origin 2000 supercomputer at the National Center for Supercomputing Applications (NCSA). Other globally distributed scientific teams are running visual simulations of Einstein's equations on the gravitational effects of colliding black holes.
The Research and Application of Visual Saliency and Adaptive Support Vector Machine in Target Tracking Field

The efficient target tracking algorithm researches have become current research focus of intelligent robots. The main problems of target tracking process in mobile robot face environmental uncertainty. They are very difficult to estimate the target states, illumination change, target shape changes, complex backgrounds, and other factors and all affect the occlusion in tracking robustness. To further improve the target tracking's accuracy and reliability, we present a novel target tracking algorithm to use visual saliency and adaptive support vector machine (ASVM). Furthermore, the paper's algorithm has been based on the mixture saliency of image features. These features include color, brightness, and sport feature. The execution process used visual saliency features and those common characteristics have been expressed as the target's saliency. Numerous experiments demonstrate the effectiveness and timeliness of the proposed target tracking algorithm in video sequences where the target objects undergo large changes in pose, scale, and illumination.