Usability testing is an important part of product design that offers developers insight into a product’s ability to help users achieve their goals. Despite the usefulness of usability testing, human usability evaluations are costly and time-intensive processes. Developing methods to reduce the time and costs of usability evaluations is important for organizations to improve the usability of their products without expensive investments. One prospective solution to this is the application of facial emotion recognition to automate the collection of qualitative metrics normally identified by human usability evaluators. In this paper, facial emotion recognition (FER) was applied to mock usability recordings to evaluate how well FER could parse moments of emotional significance. To determine the accuracy of FER in this context, a FER Python library created by Justin Shenk was compared with data tags produced by human reporters. This study found that the facial emotion recognizer could only match its emotion recognition output with less than 40% of the human-reported emotion timestamps and less than 78% of the emotion data tags were recognized at all. The current lack of consistency with the human reported emotions found in this thesis makes it difficult to recommend using FER for parsing moments of semantic significance over conventional human usability evaluators.

Inspection of the published research shows that sex differences in cognition in the general population have been widely cited with the direction of the advantage depending on the domain being examined. The most prevalent claims are that men are better than women at visuospatial and mathematical tasks whereas women have superior verbal skills and perform better than men on tasks assessing episodic memory. There is also some evidence that women are more accurate than men at identifying facial expressions of emotion. A more in-depth examination of the literature, however, reveals that evidence of such differences is not as conclusive as would at first appear. Not only is the direction and magnitude of sex differences dependent on the cognitive domain but also on the individual tasks. Some visuospatial tasks show no difference (e.g. figure copying) whist men have been shown to be better than women at confrontation naming (a verbal task). Alzheimer’s disease is a heterogeneous illness that affects the elderly. It manifests with deficits in cognitive abilities and behavioural difficulties. It has been suggested that some of the behavioural issues may arise from difficulties with recognising facial emotion expressions. There have been claims that AD affects men and women differently: women have been reported as being more likely to develop AD and showing a greater dementia severity than men with equivalent neuropathology. Despite this, research into sex differences in cognition in AD is scarce, and conflicting. This research was concerned with the effect of sex on the cognitive abilities of AD patients. The relative performance of men and women with AD was compared to that of elderly controls. The study focused on the verbal, visuospatial and facial emotion recognition domains. Data was collected and analysed from 70 AD patients (33 male, 37 female), 62 elderly controls (31 male, 31 female) and 80 young adults (40 male, 40 female). Results showed those with AD demonstrate cognitive deficits compared to elderly controls in verbal and visuospatial tasks but not in the recognition of facial emotions. There were no significant sex differences in either the young adults or the healthy elderly controls but sex differences favouring men emerged in the AD group for figure copying and recall and for confrontation naming. Given that elderly men and women perform equivalently for these tasks, this represents a deterioration in women’s cognitive abilities, relative to men’s. Further evidence of such an adverse effect of AD was apparent in other tasks, too: for most verbal and visuospatial tasks, either an effect favouring women in the elderly is reversed or a male advantage increases in magnitude. There is no evidence of sex differences in facial emotion recognition for any group. This suggests that the lack of published findings reporting on sex differences in this domain is due to the difficulty in getting null findings accepted for publication. The scarcity of research examining sex differences in other domains is also likely to be due to this bias.

Emoções exercem influência direta sobre a vida humana, mediando a maneira como os indivíduos interagem e se relacionam, seja em âmbito pessoal ou social. Por essas razões, o desenvolvimento de interfaces homem-máquina capazes de manter interações mais naturais e amigáveis com os seres humanos se torna importante. No desenvolvimento de robôs sociais, assunto tratado neste trabalho, a adequada interpretação do estado emocional dos indivíduos que interagem com os robôs é indispensável. Assim, este trabalho trata do desenvolvimento de um modelo matemático para o reconhecimento do estado emocional humano por meio de expressões faciais. Primeiramente, a face humana é detectada e rastreada por meio de um algoritmo; então, características descritivas são extraídas da mesma e são alimentadas no modelo de reconhecimento de estados emocionais desenvolvidos, que consiste de um classificador de emoções instantâneas, um filtro de Kalman e um classificador dinâmico de emoções, responsável por fornecer a saída final do modelo. O modelo é otimizado através de um algoritmo de têmpera simulada e é testado sobre diferentes bancos de dados relevantes, tendo seu desempenho medido para cada estado emocional considerado. / Emotions have direct influence on the human life and are of great importance in relationships and in the way interactions between individuals develop. Because of this, they are also important for the development of human-machine interfaces that aim to maintain natural and friendly interactions with its users. In the development of social robots, which this work aims for, a suitable interpretation of the emotional state of the person interacting with the social robot is indispensable. The focus of this work is the development of a mathematical model for recognizing emotional facial expressions in a sequence of frames. Firstly, a face tracker algorithm is used to find and keep track of a human face in images; then relevant information is extracted from this face and fed into the emotional state recognition model developed in this work, which consists of an instantaneous emotional expression classifier, a Kalman filter and a dynamic classifier, which gives the final output of the model. The model is optimized via a simulated annealing algorithm and is experimented on relevant datasets, having its performance measured for each of the considered emotional states.

Emoções exercem influência direta sobre a vida humana, mediando a maneira como os indivíduos interagem e se relacionam, seja em âmbito pessoal ou social. Por essas razões, o desenvolvimento de interfaces homem-máquina capazes de manter interações mais naturais e amigáveis com os seres humanos se torna importante. No desenvolvimento de robôs sociais, assunto tratado neste trabalho, a adequada interpretação do estado emocional dos indivíduos que interagem com os robôs é indispensável. Assim, este trabalho trata do desenvolvimento de um modelo matemático para o reconhecimento do estado emocional humano por meio de expressões faciais. Primeiramente, a face humana é detectada e rastreada por meio de um algoritmo; então, características descritivas são extraídas da mesma e são alimentadas no modelo de reconhecimento de estados emocionais desenvolvidos, que consiste de um classificador de emoções instantâneas, um filtro de Kalman e um classificador dinâmico de emoções, responsável por fornecer a saída final do modelo. O modelo é otimizado através de um algoritmo de têmpera simulada e é testado sobre diferentes bancos de dados relevantes, tendo seu desempenho medido para cada estado emocional considerado. / Emotions have direct influence on the human life and are of great importance in relationships and in the way interactions between individuals develop. Because of this, they are also important for the development of human-machine interfaces that aim to maintain natural and friendly interactions with its users. In the development of social robots, which this work aims for, a suitable interpretation of the emotional state of the person interacting with the social robot is indispensable. The focus of this work is the development of a mathematical model for recognizing emotional facial expressions in a sequence of frames. Firstly, a face tracker algorithm is used to find and keep track of a human face in images; then relevant information is extracted from this face and fed into the emotional state recognition model developed in this work, which consists of an instantaneous emotional expression classifier, a Kalman filter and a dynamic classifier, which gives the final output of the model. The model is optimized via a simulated annealing algorithm and is experimented on relevant datasets, having its performance measured for each of the considered emotional states.

The thesis aims to develop a deep learning model for facial emotion recognition using Convolutional Neural Network algorithm and Multiclass Classification along with Hyper-parameter tuning using Bayesian Optimization to improve the performance of the model. The developed model recognizes seven basic emotions in images of human beings such as fear, happy, surprise, sad, neutral, disgust and angry using FER-2013 dataset.

A Ansiedade de Performance Musical (APM) é considerada uma condição caracterizada por apreensão persistente e intensa diante da apresentação musical pública, desproporcional ao nível de aptidão, treino e preparo do músico. Os sintomas ocorrem em uma escala de gravidade contínua que em seu extremo afeta a aptidão musical devido a sintomas ao nível físico, comportamental e cognitivo, além de déficits no processamento cognitivo e cognição social, em especial na capacidade de reconhecimento de expressões faciais de emoção (REFE). Assim, intervenções que possam corrigir esses vieses com eficácia são necessárias. Nesse sentido, os objetivos dos estudos que compõem esta tese são: a) avaliar o REFE em músicos com diferentes níveis de APM; b) realizar uma revisão sistemática da literatura de forma a trazer evidências sobre os efeitos das substâncias ansiolíticas no REFE em indivíduos saudáveis; e c) conduzir um ensaio clínico, cross over, randomizado, duplo cego e controlado por placebo para testar o efeito da OCT em músicos com alto/baixo nível de APM no REFE, nos indicadores de humor/ansiedade e na cognição negativa. Método: Para se atender ao objetivo realizou-se um estudoobservacional, transversal, com 150 músicos de ambos os sexos, de diferentes estilos musicais, os quais realizaram uma tarefa de REFE, após serem classificados quanto aos níveis de APM.Para atender-se o segundo objetivo conduziu-se uma revisão sistemática da literatura seguindo-se as diretrizes do PRISMA e do Cochrane Handbook for SystematicReviewsofInterventions. Por fim, para alcançar o terceiro objetivo, 43 músicos do sexo masculino, de diferentes estilos musicais participaram de um ensaio clínico, randomizado, cross over, controlado por placebo, no qual testou-se a eficácia de 24UI de OCT intranasal. Resultados:Os resultados evidenciaram que os músicos com altos níveis de APM apresentam um prejuízo global no REFE, expresso, sobretudo pela dificuldade no reconhecimento adequado da emoção alegria, a qual está associada aos sinais de aprovação social. A revisão da literatura evidenciou que poucas substâncias foram testadas até momento, e que as alterações no REFE foram específicas e dependentes do mecanismo de ação da substância no sistema nervoso central, dose e forma de administração. O ensaio clínico apontou uma melhora no reconhecimento da emoção alegria,somente em músicos com altos níveis de APM, após o uso agudo da OCT. Conclusão:O REFE mostrou-se alterado de forma específica em músicos com altos níveis de APM, os quais podem ser corrigidos através do uso da OCT intranasal, a qual desponta como uma substância promissora para o uso clínico / Musical Performance Anxiety (MPA) is considered a condition characterized by persistent and intense apprehension in circumstances involving public musical presentation, disproportionate to the musician\'s aptitude level, training and preparation. The symptoms occur on a continuous severity scale that affects, at its extreme, the musical aptitude due to symptoms at the physical, behavioral and cognitive levels, as well as interfering with cognitive processing and social cognition, especially in the facial emotion recognition (FER) ability. Thus, interventions that can effectively correct these deviances are necessary. Therefore, the aims of the studies that compose this thesis are: a) to analyze the (FER) in musicians with different levels of MPA; b) to carry out a systematic review of the literature in order to present evidence about the effects of anxiolytic substances on FER in healthy individuals; c) to conduct a randomized, double-blind, placebo-controlled, cross-over clinical trial to test the OT effect on musicians with high/low MPA level in FER, mood/anxiety indicators and negative cognition. Methods: To achieve the first aim of this study, a cross-sectional, observational study was conducted with 150 musicians of both sexes, of different musical styles, who performed a FER task, after being classified according to the MPA levels. As for the second aim, a systematic literature review was carried out in accordance with the PRISMA guidelines and the Cochrane Handbook for Systematic Reviews of Interventions. Finally, for the third aim, 43 male musicians of different musical styles have participated in a randomized, placebo-controlled, cross-over clinical trial in which the 24UI of intranasal OT efficiency was tested. Results: The results showed that musicians with high levels of MPA present a global impairment in FER, expressed mainly by the difficulty in the appropriate recognition of the emotion of joy, which is associated with signs of social approval. The review of the literature showed that few substances have been tested so far, and that the changes in FER were specific and dependent on the substance mechanism of action in the central nervous system, dose and form of administration. The clinical trial presented an improvement in the recognition of the emotion of joy, only in musicians with high levels of MPA, after the OT acute use. Conclusion: The FER was specifically altered in musicians with high levels of MPA, which can be corrected with the use of intranasal OT, which appears as a promising substance for clinical use

The aim of the current study was to investigate the roles of meta-mood experience on the mood congruency effect in recognizing emotions from neutral facial expressions. For this aim, three scales were translated and adapted to Turkish, namely Brief Mood Introspection Scale (BMIS), State Meta-Mood Scale (SMMS), and Trait Meta-Mood Scale (TMMS). The reliability and validity analyses came out to be satisfactory. For the main analyses, an experimental study was conducted. The experimental design consisted of the administration of the Brief Symptom Inventory, Pre- induction Brief Mood Introspection Scale, Trait Meta-MoodScale, and Basic Personality Traits Inventory in the first step, followed by a sad mood induction procedure and the administration of Post- Brief Symptom Inventory, and State Meta-Mood Scale in the second step. The last step consisted of the administration of the NimStim Set of Facial Expressions. For the main analyses regarding mood congruency only the v mislabelings of neutral faces as sad or happy were considered. The results revealed that among personality traits Agreeableness was negatively associated with perceiving fast displayed neutral faces as sad. After controlling for personality traits / however, unpleasant mood measured before the mood induction procedure was positively associated with perceiving neutral faces as sad. When perceiving slow displayed neutral faces as happy were examined, it was found that anxiety was positively associated with such a bias. After controlling for symptomatology, among personality traits, extraversion and conscientiousness were found to be negatively associated with mislabelling slow displayed neutral faces as happy. Among the evaluative domain of the SMMS, typicality was found to be negatively associated with such a bias / and lastly, among the regulatory domain of the SMMS, emotional repair was found to be negatively associated with mislabelling slow displayed neutral faces as happy.

No description available.

Объектом исследования являются методы машинного обучения, позволяющие фильтровать данные, и методы разработки информационных платформ. Фильтрация данных подобного типа применяется в такой области, как поисковые системы, чтобы на основе запроса выдать пользователю релевантные результаты. Предмет исследования – разработка модели машинного обучения, фильтрующей текстовые данные, и информационной платформы для отображения отфильтрованных данных. Особенностями исследования являются открытая реализация полного проекта, то есть она доступна каждому, и возможность его модификации. Для обучения модели был использован самостоятельно составленный набор научных работ, информационная платформа была разработана с нуля. Итоговая модель LSTM, выбранная методом сравнения метрик, показала результат предсказания соответствия целевой тематике в 90%, что позволяет говорить о ее возможном внедрении в соответствующие Интернет-ресурсы, так как они гарантированно уменьшат объем научных работ, проверяемых вручную. / The object of the research is machine learning methods that allow filtering text data obtained from the information platform. Filtering of this type of data is used in such an area as search engines to give relevant results to the user based on a query. Within the framework of this dissertation, it was proposed to apply machine learning methods to filter a set of scientific papers based on their title and target label in the form of the subject of the work. The features of the study are the open implementation of the full project, that is, it is available to everyone, and the possibility of its modification. A self-compiled set of scientific papers was used to train the model, the information platform was developed from scratch. The final LSTM model, chosen by the method of comparing metrics, showed the result of predicting compliance with the target topic in 95%, which allows us to talk about its possible implementation in the relevant Internet resources, since they are guaranteed to reduce the volume of scientific papers checked manually.