Automatic classification of adventitious respiratory sounds: a (un)solved problem?

(1) Background: Patients with respiratory conditions typically exhibit adventitious respiratory sounds (ARS), such as wheezes and crackles. ARS events have variable duration. In this work we studied the influence of event duration on automatic ARS classification, namely, how the creation of the Other class (negative class) affected the classifiers’ performance. (2) Methods: We conducted a set of experiments where we varied the durations of the other events on three tasks: crackle vs. wheeze vs. other (3 Class); crackle vs. other (2 Class Crackles); and wheeze vs. other (2 Class Wheezes). Four classifiers (linear discriminant analysis, support vector machines, boosted trees, and convolutional neural networks) were evaluated on those tasks using an open access respiratory sound database. (3) Results: While on the 3 Class task with fixed durations, the best classifier achieved an accuracy of 96.9%, the same classifier reached an accuracy of 81.8% on the more realistic 3 Class task with variable durations. (4) Conclusion: These results demonstrate the importance of experimental design on the assessment of the performance of automatic ARS classification algorithms. Furthermore, they also indicate, unlike what is stated in the literature, that the automatic classification of ARS is not a solved problem, as the algorithms’ performance decreases substantially under complex evaluation scenarios.publishe

Building Persuasive Robots with Social Power Strategies

Can social power endow social robots with the capacity to persuade? This paper represents our recent endeavor to design persuasive social robots. We have designed and run three different user studies to investigate the effectiveness of different bases of social power (inspired by French and Raven's theory) on peoples' compliance to the requests of social robots. The results show that robotic persuaders that exert social power (specifically from expert, reward, and coercion bases) demonstrate increased ability to influence humans. The first study provides a positive answer and shows that under the same circumstances, people with different personalities prefer robots using a specific social power base. In addition, social rewards can be useful in persuading individuals. The second study suggests that by employing social power, social robots are capable of persuading people objectively to select a less desirable choice among others. Finally, the third study shows that the effect of power on persuasion does not decay over time and might strengthen under specific circumstances. Moreover, exerting stronger social power does not necessarily lead to higher persuasion. Overall, we argue that the results of these studies are relevant for designing human--robot-interaction scenarios especially the ones aiming at behavioral change

Emotionally-relevant features for classification and regression of music lyrics

This research addresses the role of lyrics in the music emotion recognition process. Our approach is based on several state of the art features complemented by novel stylistic, structural and semantic features. To evaluate our approach, we created a ground truth dataset containing 180 song lyrics, according to Russell’s emotion model. We conduct four types of experiments: regression and classification by quadrant, arousal and valence categories. Comparing to the state of the art features (ngrams - baseline), adding other features, including novel features, improved the F-measure from 69.9%, 82.7% and 85.6% to 80.1%, 88.3% and 90%, respectively for the three classification experiments. To study the relation between features and emotions (quadrants) we performed experiments to identify the best features that allow to describe and discriminate each quadrant. To further validate these experiments, we built a validation set comprising 771 lyrics extracted from the AllMusic platform, having achieved 73.6% F-measure in the classification by quadrants. We also conducted experiments to identify interpretable rules that show the relation between features and emotions and the relation among features. Regarding regression, results show that, comparing to similar studies for audio, we achieve a similar performance for arousal and a much better performance for valence

Rapid tooling for plastic injection moulding using indirect rapid tooling processes

Rapid Prototyping (RP) and Rapid Tooling (RT) are well known processesfor rapidly develop new products and consequently reduce the time to market. InPortugal, many companies are still not exploring the full advantages of usingthese technologies for new products development and produce reduced runs.This work presents some results obtained with composite tools manufactured forthermoplastics injection. These tools are obtained by casting a aluminium filledresin or by arc spray metal tooling over Rapid Prototyping models.Different properties, such as: moulds roughness, hardness and the wear weredetermined and compared, and the suitability of these processes are evaluated torapidly produce prototype moulds to inject thermoplastic models and pre series

Cutaneous mucormycosis in a young immunocompetent trauma patient: a case report

Introduction: Mucormycosis, a rare and life-threatening infection, is caused by microorganisms of the Mucorales order. It affects almost exclusively immunocompromised and diabetic patients, requiring extensive surgical debridement and prolonged antifungal therapy. Discussion/Results: We report the case of a 26-year-old immunocompetent woman, presenting with cutaneous mucormycosis after suffering blunt force trauma. This rare occurrence of mucormycosis in an immunocompetent patient reinforces the importance of elevated clinical suspicion and early initiation of adequate surgical and antifungal treatment. Conclusion: Mucormycosis is a challenging condition with potentially devastating consequences. Timely diagnosis and appropriate management are vital to mitigate the morbidity and mortality associated with this condition

Sistemas de classificação musical com redes neuronais

Como resultado da evolução e inovação tecnológicas, a indústria da distribuição electrónica de música tem tido um enorme crescimento. Desta forma, tarefas como a classificação automática de géneros musicais tornam-se um forte motivo para o incremento da investigação na área. O reconhecimento automático de géneros musicais envolve tarefas como a extracção de características das músicas e o desenvolvimento de classificadores que utilizem essas características. Neste estudo pretendeu-se, através de 3 problemas de classificação independentes, classificar peças de música clássica. Foi construído um protótipo para um sistema real de classificação, onde de um conjunto de músicas não catalogadas, foram automaticamente extraídos dez segmentos de seis segundos cada. Cada segmento musical foi classificado individualmente utilizando redes neuronais, tendo sido, para tal, extraídas 40 características por segmento. Cada música foi classificada no género mais representado pelos seus segmentos.As a result of recent technological innovations, there has been a tremendous growth in the Electronic Music Distribution industry. In this way, tasks such us automatic music genre classification address new and exciting research challenges. Automatic music genre recognition involves issues like feature extraction and development of classifiers using the obtained features. In this study we aim to classify classical music in subgenres, through three independent classification problems. Therefore, we extract 40 features for each one of the musical segments and we use neural nets as classifiers. Afterwards, due to the quality of the obtained results, a prototype system for automatic music classification of entire songs (not only segments) was built. We use 10 extracts for each song, uniformly distributed throughout the song. Each song is classified according to the most representative genre in all extracts