Trabalhando com literatura na disciplina de língua estrangeira

Anais do II Seminário Seminário Estadual PIBID do Paraná: tecendo saberes / organizado por Dulcyene Maria Ribeiro e Catarina Costa Fernandes — Foz do Iguaçu: Unioeste; Unila, 2014Este artigo apresenta o plano de ensino desenvolvido dentro do Programa Institucional de Bolsa de Iniciação à Docência (PIBID), Subprojeto Letras / Inglês da UNICENTRO (Universidade Estadual do Centro-Oeste), Campus de Irati. O trabalho aqui apresentado contempla as atividades desenvolvidas na área da Literatura Infanto-Juvenil com o objetivo de despertar o interesse dos alunos por meio das intervenções propostas. O plano de ensino representa a relação de sua temática com os principais assuntos abordados nos PCNs (Parâmetros Curriculares Nacionais) e nas DCEs (Diretrizes Curriculares da Educação Básica) em relação ao ensino de Língua Estrangeira Moderna (LEM). Espera-se, com o trabalho aqui relatado, propiciar condições para o reconhecimento, por parte dos alunos, da literatura em suas diversas manifestações midiática

Towards multimodal affective expression:merging facial expressions and body motion into emotion

Affect recognition plays an important role in human everyday life and it is a substantial way of communication through expressions. Humans can rely on different channels of information to understand the affective messages communicated with others. Similarly, it is expected that an automatic affect recognition system should be able to analyse different types of emotion expressions. In this respect, an important issue to be addressed is the fusion of different channels of expression, taking into account the relationship and correlation across different modalities. In this work, affective facial and bodily motion expressions are addressed as channels for the communication of affect, designed as an emotion recognition system. A probabilistic approach is used to combine features from two modalities by incorporating geometric facial expression features and body motion skeleton-based features. Preliminary results show that the presented approach has potential for automatic emotion recognition and it can be used for human robot interaction

On the effects of pseudorandom and quantum-random number generators in soft computing

In this work, we argue that the implications of pseudorandom and quantum-random number generators (PRNG and QRNG) inexplicably affect the performances and behaviours of various machine learning models that require a random input. These implications are yet to be explored in soft computing until this work. We use a CPU and a QPU to generate random numbers for multiple machine learning techniques. Random numbers are employed in the random initial weight distributions of dense and convolutional neural networks, in which results show a profound difference in learning patterns for the two. In 50 dense neural networks (25 PRNG/25 QRNG), QRNG increases over PRNG for accent classification at + 0.1%, and QRNG exceeded PRNG for mental state EEG classification by + 2.82%. In 50 convolutional neural networks (25 PRNG/25 QRNG), the MNIST and CIFAR-10 problems are benchmarked, and in MNIST the QRNG experiences a higher starting accuracy than the PRNG but ultimately only exceeds it by 0.02%. In CIFAR-10, the QRNG outperforms PRNG by + 0.92%. The n-random split of a Random Tree is enhanced towards and new Quantum Random Tree (QRT) model, which has differing classification abilities to its classical counterpart, 200 trees are trained and compared (100 PRNG/100 QRNG). Using the accent and EEG classification data sets, a QRT seemed inferior to a RT as it performed on average worse by − 0.12%. This pattern is also seen in the EEG classification problem, where a QRT performs worse than a RT by − 0.28%. Finally, the QRT is ensembled into a Quantum Random Forest (QRF), which also has a noticeable effect when compared to the standard Random Forest (RF). Ten to 100 ensembles of trees are benchmarked for the accent and EEG classification problems. In accent classification, the best RF (100 RT) outperforms the best QRF (100 QRF) by 0.14% accuracy. In EEG classification, the best RF (100 RT) outperforms the best QRF (100 QRT) by 0.08% but is extremely more complex, requiring twice the amount of trees in committee. All differences are observed to be situationally positive or negative and thus are likely data dependent in their observed functional behaviour

Affective facial expressions recognition for human-robot interaction

Affective facial expression is a key feature of nonverbal behaviour and is considered as a symptom of an internal emotional state. Emotion recognition plays an important role in social communication: human-to-human and also for humanto-robot. Taking this as inspiration, this work aims at the development of a framework able to recognise human emotions through facial expression for human-robot interaction. Features based on facial landmarks distances and angles are extracted to feed a dynamic probabilistic classification framework. The public online dataset Karolinska Directed Emotional Faces (KDEF) [1] is used to learn seven different emotions (e.g. angry, fearful, disgusted, happy, sad, surprised, and neutral) performed by seventy subjects. A new dataset was created in order to record stimulated affect while participants watched video sessions to awaken their emotions, different of the KDEF dataset where participants are actors (i.e. performing expressions when asked to). Offline and on-the-fly tests were carried out: leave-one-out cross validation tests on datasets and on-the-fly tests with human-robot interactions. Results show that the proposed framework can correctly recognise human facial expressions with potential to be used in human-robot interaction scenario

A Probabilistic Approach for Human Everyday Activities Recognition using Body Motion from RGB-D Images

In this work, we propose an approach that relies on cues from depth perception from RGB-D images, where features related to human body motion (3D skeleton features) are used on multiple learning classifiers in order to recognize human activities on a benchmark dataset. A Dynamic Bayesian Mixture Model (DBMM) is designed to combine multiple classifier likelihoods into a single form, assigning weights (by an uncertainty measure) to counterbalance the likelihoods as a posterior probability. Temporal information is incorporated in the DBMM by means of prior probabilities, taking into consideration previous probabilistic inference to reinforce current-frame classification. The publicly available Cornell Activity Dataset [1] with 12 different human activities was used to evaluate the proposed approach. Reported results on testing dataset show that our approach overcomes state of the art methods in terms of precision, recall and overall accuracy. The developed work allows the use of activities classification for applications where the human behaviour recognition is important, such as human-robot interaction, assisted living for elderly care, among others

Representation framework of perceived object softness characteristics for active robotic hand exploration

During the last years the principles and demands guiding the design and implementation of robotic platforms are changing. Nowadays, robotic platforms are tendency equipped with a conjugation of multi-modal artificial perception systems (stereo vision, tactile sensing) and complex actuation systems (multi-articulated robotic hands, arms and legs). This artificial perception systems are required by robotic systems to navigate and interact with the environment and persons. This work is focused in the artificial perception systems related with the robotic manipulation strategies used to dexterously interact with deformable objects in the environment.In this context, the robotic dexterous manipulation of objects require that the framework used to represent the characteristics of the deformable manipulated objects should be suitable to receive inputs from multiple exploratory elements(multi- fingered robotic hands) and to progressively update that representation status as long as the exploration progresses on time. The framework should be also designed to incorporate the uncertainty and errors associated to the sensing process in this type of dynamic environments, and to deal with novelty by characterizing objects of new softness characteristics to the system based on the previous knowledge and interactions with a restricted set of reference materials, that constitute the haptic memory of the system. In order to provide to the robotic hands the capability to differentiate deformable objects with distinct softness characteristics and to dexterously manipulate them, this work analyses the principles and strategies used by humans to successfully perform such type of tasks, using predominatelyhaptic information. During the object exploration, the per-ception and discrimination capability of softness characteristics depend on both cutaneous and kinesthetic information by executing press and release movements [2] - active haptic perception. This has been demonstrated byl experiments performed by Srinivasan and Lamotte [5]

Multimodal Bayesian Network for Artificial Perception

In order to make machines perceive their external environment coherently, multiple sources of sensory information derived from several different modalities can be used (e.g. cameras, LIDAR, stereo, RGB-D, and radars). All these different sources of information can be efficiently merged to form a robust perception of the environment. Some of the mechanisms that underlie this merging of the sensor information are highlighted in this chapter, showing that depending on the type of information, different combination and integration strategies can be used and that prior knowledge are often required for interpreting the sensory signals efficiently. The notion that perception involves Bayesian inference is an increasingly popular position taken by a considerable number of researchers. Bayesian models have provided insights into many perceptual phenomena, showing that they are a valid approach to deal with real-world uncertainties and for robust classification, including classification in time-dependent problems. This chapter addresses the use of Bayesian networks applied to sensory perception in the following areas: mobile robotics, autonomous driving systems, advanced driver assistance systems, sensor fusion for object detection, and EEG-based mental states classification. Document type: Part of book or chapter of boo

The southernmost range limit for the hidden angelshark Squatina occulta

Background: Angelsharks (Genus Squatina) are distributed in the southern Southwest Atlantic Ocean between southeastern Brazil and central Patagonia. The endangered hidden angelshark Squatina occulta is reported in the literature as ranging from Espírito Santo, Brazil to Southern Uruguay. Its presence in Argentine waters has been suspected but not verified so far. This study describes and analyzes a specimen of S. occulta found in Puerto Quequén 38° 40′S - 58° 50′W, Buenos Aires Province, Argentina. Results: An immature male of 578 mm total length and 1,450 g was collected from commercial landings of the bottom trawl fishery of Puerto Quequén. The specimen exhibited the coloration pattern, dermal denticle distribution, and tooth formula characteristic of S. occulta. Conclusions: Squatina guggenheim and S. argentina are already known to occur off Puerto Quequén. The present finding confirms the presence of a third species of angelshark in Argentina and constitutes the southernmost record of S. occulta.Fil: Estalles, María Lourdes. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencia Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales ; ArgentinaFil: Chiaramonte, Gustavo Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales ; ArgentinaFil: Faria, Vicente V.. Universidade Federal do Ceará; BrasilFil: Luzzatto, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales ; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencia Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Díaz de Astarloa, Juan Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencia Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentin