Primusinter (ím)pares - análise da presença dos académicos nos plateaux informativos da TV portuguesa

Os académicos estão a tornar-se uma presença cada vez mais visível nos plateaux informativos da TV portuguesa. Não são um grupo muito diversificado. Pelo contrário. Apresentam-se como uma confraria que é oriunda das universidades de Lisboa e pertence a um reduzido número de campos de saberes. Aos plateaux televisivos portugueses dificilmente chegará o fazer-ciência concreto. O que chega são alguns dos seus actores, o que não significa que o consigam por um efeito de reconhecimento inter pares, mas, antes, por um efeito de verdadeiras imparidades: porque já adquiriram suficiente capital simbólico em zonas exteriores ao campo científico. Zonas mais limítrofes, como o campo institucional-académico, ou zonas mais afastadas, como o campo comunicacional.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

Vision-based portuguese sign language recognition system

Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign lan- guages are not standard and universal and the grammars differ from country to coun- try. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of fea- tures and an accuracy of 99.6% with a second dataset of features. Although the im- plemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.

An integrated system for human action recognition from video using hidden Markov model

In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.

Enrollment time as a requirement for biometric hand recognition systems

Biometric systems are increasingly being used as a means for authentication to provide system security in modern technologies. The performance of a biometric system depends on the accuracy, the processing speed, the template size, and the time necessary for enrollment. While much research has focused on the first three factors, enrollment time has not received as much attention. In this work, we present the findings of our research focused upon studying user’s behavior when enrolling in a biometric system. Specifically, we collected information about the user’s availability for enrollment in respect to the hand recognition systems (e.g., hand geometry, palm geometry or any other requiring positioning the hand on an optical scanner). A sample of 19 participants, chosen randomly apart their age, gender, profession and nationality, were used as test subjects in an experiment to study the patience of users enrolling in a biometric hand recognition system.

Assessing student individual performance within PBL teams: findings from the implementation of a new mechanism

This paper reports on the experience of the implementation of a new mechanism to assess individual student contribution within project work, where students work in teams to solve a large-scale open-ended interdisciplinary project. The study takes place at the University of Minho, with first year engineering students, enrolled in the Industrial Management and Engineering (Integrated Masters) degree. The aim of this paper is to describe the main principles and procedures underlying the assessment mechanism created and also provide some feedback from its first implementation, based on the students, lecturers and tutors perceptions. For data collection, a survey was sent to all course lecturers and tutors involved in the assessment process. Students also contributed with suggestions, both on a workshop held at the end of the project and also by answering a survey on the overall satisfaction with PBL experience. Findings show a positive level of acceptance of the new mechanism by the students and also by the lecturers and tutors. The study identified the need to clarify the criteria used by the lecturers and the exact role of the tutor, as well as the need for further improvement of its features and procedures. Some recommendations are also issued regarding technical aspects related to some of the steps of the procedures, as well as the need for greater support on the adjustment and final setting of the individual grades.

Contribution of the evaluation strategy and the use of hearing protection for the uncertainty associated with occupational exposure to noise

Doctoral Thesis for PhD degree in Industrial and Systems Engineering

Exposição à escrita e rendimento académico na língua portuguesa: um estudo com alunos do 5.º e 7.º ano

Dissertação de mestrado integrado em Psicologia

Empatia no desenvolvimento típico: relação entre autorrelatos e comportamentos empáticos

Dissertação de mestrado integrado em Psicologia

Intra and inter-individual differences in social intelligence of Portuguese students

Social intelligence is a favorable condition for career decision-making and development. The social intelligence indices of Portuguese students in school years prior to a career transition are characterized and intra and interindividual differences are analyzed. Participants were 1095 students (552, 50.4% women) with a mean age of 14.78 years (SD = 1.86), in the 8th (542, 49.5%), 10th (295, 26.9%) and 11th (258, 23.6%) grades. The Cognitive Test of Social Intelligence (PCIS) was administered at two moments, six months apart. Results indicate that the 8th grade obtained higher average scores in Problem Solving, Motivation and Self-confidence (time 1), while the 10th grade obtained better results in Problem Solving, Motivation and Familiarity (time 2). Between the assessment moments, all school years register an increase in Problem Solving and Self-confidence in social situations. These results constitute favorable psychological conditions for the promotion of ethical questioning in career guidance interventions.

The cystic fibrosis microbiome in an ecological perspective and its impact in antibiotic therapy

The recent focus on the cystic fibrosis (CF) complex microbiome has led to the recognition that the microbes can interact between them and with the host immune system, affecting the disease progression and treatment routes. Although the main focus remains on the interactions between traditional pathogens, growing evidence supports the contribution and the role of emergent species. Understanding the mechanisms and the biological effects involved in polymicrobial interactions may be the key to improve effective therapies and also to define new strategies for disease control. This review focuses on the interactions between microbe-microbe and host-microbe, from an ecological point of view, discussing their impact on CF disease progression. There are increasing indications that these interactions impact the success of antimicrobial therapy. Consequently, a new approach where therapy is personalized to patients by taking into account their individual CF microbiome is suggested.

The contribution of phenolic acids to the anti-inflammatory activity of mushrooms: Screening in phenolic extracts, individual parent molecules and synthesized glucuronated and methylated derivatives

In the present study, the ethanolic extracts of fourteen edible mushrooms were investigated for their anti-inflammatory potential in LPS (lipopolysaccharide) activated RAW 264.7 macrophages. Furthermore the extracts were chemically characterized in terms of phenolic acids and related compounds. The identified molecules (p-hydroxybenzoic, p-coumaric and cinnamic acids) and their glucuronated and methylated derivatives obtained by chemical synthesis were also evaluated for the same bioactivity, in order to establish structure-activity relationships and to comprehend the effects of in vivo metabolism reactions in the activity of the compounds. The extracts of Pleurotus ostreatus, Macrolepiota procera, Boletus impolitus and Agaricus bisporus revealed the strongest anti-inflammatory potential (EC50 values 96 ± 1 to 190 ± 6 µg/mL, and also the highest concentration of cinnamic acid (656 to 156 µg/g), which was also the individual compound with the highest anti-inflammatory activity. The derivatives of p-coumaric acid revealed the strongest properties, specially the derivative methylated in the carboxylic group (CoA-M1) that exhibited similar activity to the one showed by dexamethaxone used as anti-inflammatory standard; by contrast, the derivatives of p-hydroxybenzoic revealed the lowest inhibition of NO production. All in all, whereas the conjugation reactions change the chemical structure of phenolic acids and may increase or decrease their activity, the glucuronated and methylated derivatives of the studied compounds are still displaying anti-inflammatory activity.