Influência dos resultados da pesquisa de clima nas decisões dos gestores do Universidade do Vale do Taquari UNIVATES

As decisões de uma organização podem levá-la ao sucesso assim como ao fracasso. Para que elas sejam o mais acertivas possíveis, é preciso informações seguras, que podem ser geradas a partir da pesquisa de clima, pois quando adotada como um processo contínuo de gestão, subsidia planos estratégicos, melhorias no ambiente de trabalho e acompanhamento de processos e pessoas, indicando caminhos para decisões gerenciais. Dentro deste contexto, surge o interesse de investigação desta pesquisa, que é identificar se os resultados da pesquisa de clima influenciam as decisões dos gestores do Universidade do Vale do Taquari UNIVATES. Para isso, desenvolveu-se uma pesquisa aplicada quanto à natureza; abordagem quantitativa quanto ao problema; exploratória com uma etapa descritiva quanto aos objetivos; e bibliográfica, documental e de campo quanto aos procedimentos técnicos. A coleta de dados deu-se por meio da aplicação de questionários a quarenta e um (41) gestores. O estudo mostra que, para a maioria dos gestores, os resultados da pesquisa de clima influenciam a tomada de suas decisões

Reservoir Computing Architectures for Modeling Robot Navigation Systems

This thesis proposes a new efficient and biologically inspired way of modeling navigation tasks for autonomous mobile robots having restrictions on cost, energy consumption, and computational complexity (such as household and assistant robots). It is based on the recently proposed Reservoir Computing approach for training Recurrent Neural Networks. Robot Navigation Systems Autonomous mobile robots must be able to safely and purposefully navigate in complex dynamic environments, preferentially considering a restricted amount of computational power as well as limited energy consumption. In order to turn these robots into commercially viable domestic products with intelligent, abstract computational capabilities, it is also necessary to use inexpensive sensory apparatus such as a few infra-red distance sensors of limited accuracy. Current state-of-the-art methods for robot localization and navigation require fully equipped robotic platforms usually possessing expensive laser scanners for environment mapping, a considerable amount of computational power, and extensive explicit modeling of the environment and of the task. This thesis The research presented in this thesis is a step towards creating intelligent autonomous mobile robots with abstract reasoning capabilities using a limited number of very simple raw noisy sensory signals, such as distance sensors. The basic assumption is that the low-dimensional sensory signal can be projected into a high-dimensional dynamic space where learning and computation is performed by linear methods (such as linear regression), overcoming sensor aliasing problems commonly found in robot navigation tasks. This form of computation is known in the literature as Reservoir Computing (RC), and the Echo State Network is a particular RC model used in this work and characterized by having the high-dimensional space implemented by a discrete analog recurrent neural network with fading memory properties. This thesis proposes a number of Reservoir Computing architectures which can be used in a variety of autonomous navigation tasks, by modeling implicit abstract representations of an environment as well as navigation behaviors which can be sequentially executed in the physical environment or simulated as a plan in deliberative goal-directed tasks. Navigation attractors A navigation attractor is a reactive robot behavior defined by a temporal pattern of sensory-motor coupling through the environment space. Under this scheme, a robot tends to follow a trajectory with attractor-like characteristics in space. These navigation attractors are characterized by being robust to noise and unpredictable events and by having inherent collision avoidance skills. In this work, it is shown that an RC network can model not only one behavior, but multiple navigation behaviors by shifting the operating point of the dynamical reservoir system into different \emph{sub-space attractors} using additional external inputs representing the selected behavior. The sub-space attractors emerge from the coupling existing between the RC network, which controls the autonomous robot, and the environment. All this is achieved under an imitation learning framework which trains the RC network using examples of navigation behaviors generated by a supervisor controller or a human. Implicit spatial representations From the stream of sensory input given by distance sensors, it is possible to construct implicit spatial representations of an environment by using Reservoir Computing networks. These networks are trained in a supervised way to predict locations at different levels of abstraction, from continuous-valued robot's pose in the global coordinate's frame, to more abstract locations such as small delimited areas and rooms of a robot environment. The high-dimensional reservoir projects the sensory input into a dynamic system space, whose characteristic fading memory disambiguates the sensory space, solving the sensor aliasing problems where multiple different locations generate similar sensory readings from the robot's perspective. Hierarchical networks for goal-directed navigation It is possible to model navigation attractors and implicit spatial representations with the same type of RC network. By constructing an hierarchical RC architecture which combines the aforementioned modeling skills in two different reservoir modules operating at different timescales, it is possible to achieve complex context-dependent sensory-motor coupling in unknown environments. The general idea is that the network trained to predict the location and orientation of the robot in this architecture can be used to select appropriate navigation attractors according to the current context, by shifting the operating point of the navigation reservoir to a sub-space attractor. As the robot navigates from one room to the next, a corresponding context switch selects a new reactive navigation behavior. This continuous sequence of context switches and reactive behaviors, when combined with an external input indicating the destination room, leads ultimately to a goal-directed navigation system, purely trained in a supervised way with examples of sensory-motor coupling. Generative modeling of environment-robot dynamics RC networks trained to predict the position of the robot from the sensory signals learns forward models of the robot. By using a generative RC network which predicts not only locations but also sensory nodes, it is possible to use the network in the opposite direction for predicting local environmental sensory perceptions from the robot position as input, thus learning an inverse model. The implicit map learned by forward models can be made explicit, by running the RC network in reverse: predict the local sensory signals given the location of the robot as input (inverse model). which are fed back to the reservoir, it is possible to internally predict future scenarios and behaviors without actually experiencing them in the current environment (a process analogous to dreaming), constituting a planning-like capability which opens new possibilities for deliberative navigation systems. Unsupervised learning of spatial representations In order to achieve a higher degree of autonomy in the learning process of RC-based navigation systems which use implicit learned models of the environment for goal-directed navigation, a new architecture is proposed. Instead of using linear regression, an unsupervised learning method which extracts slowly-varying output signals from the reservoir states, called Slow Feature Analysis, is used to generate self-organized spatial representations at the output layer, without the requirement of labeling training data with the desired locations. It is shown experimentally that the proposed RC-SFA architecture is empowered with an unique combination of short-term memory and non-linear transformations which overcomes the hidden state problem present in robot navigation tasks. In addition, experiments with simulated and real robots indicate that spatial activations generated by the trained network show similarities to the activations of CA1 hippocampal cells of rats (a specific group of neurons in the hippocampus)

Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban Environments

Deriving robust control policies for realistic urban navigation scenarios is not a trivial task. In an end-to-end approach, these policies must map high-dimensional images from the vehicle's cameras to low-level actions such as steering and throttle. While pure Reinforcement Learning (RL) approaches are based exclusively on rewards,Generative Adversarial Imitation Learning (GAIL) agents learn from expert demonstrations while interacting with the environment, which favors GAIL on tasks for which a reward signal is difficult to derive. In this work, the hGAIL architecture was proposed to solve the autonomous navigation of a vehicle in an end-to-end approach, mapping sensory perceptions directly to low-level actions, while simultaneously learning mid-level input representations of the agent's environment. The proposed hGAIL consists of an hierarchical Adversarial Imitation Learning architecture composed of two main modules: the GAN (Generative Adversarial Nets) which generates the Bird's-Eye View (BEV) representation mainly from the images of three frontal cameras of the vehicle, and the GAIL which learns to control the vehicle based mainly on the BEV predictions from the GAN as input.Our experiments have shown that GAIL exclusively from cameras (without BEV) fails to even learn the task, while hGAIL, after training, was able to autonomously navigate successfully in all intersections of the city

Desenvolvimento de um sistema de telemetria para aquisição de sinais fisiológicos com aplicação em programas de reabilitação cardíaca

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica

Evolutionary fuzzy system for architecture control in a constructive neural network

This work describes an evolutionary system to control the growth of a constructive neural network for autonomous navigation. A classifier system generates Takagi-Sugeno fuzzy rules and controls the architecture of a constructive neural network. The performance of the mobile robot guides the evolutionary learning mechanism. Experiments show the efficiency of the classifier fuzzy system for analyzing if it is worth inserting a new neuron into the architecture

Supervised Learning of Internal Models for Autonomous Goal-oriented Robot Navigation using Reservoir Computing

In this work we propose a hierarchical architec- ture which constructs internal models of a robot environment for goal-oriented navigation by an imitation learning process. The proposed architecture is based on the Reservoir Computing paradigm for training Recurrent Neural Networks (RNN). It is composed of two randomly generated RNNs (called reservoirs), one for modeling the localization capability and one for learning the navigation skill. The localization module is trained to detect the current and previously visited robot rooms based only on 8 noisy infra-red distance sensors. These predictions together with distance sensors and the desired goal location are used by the navigation network to actually steer the robot through the environment in a goal-oriented manner. The training of this architecture is performed in a supervised way (with examples of trajectories created by a supervisor) using linear regression on the reservoir states. So, the reservoir acts as a temporal kernel projecting the inputs to a rich feature space, whose states are linearly combined to generate the desired outputs. Experimental results on a simulated robot show that the trained system can localize itself within both simple and large unknown environments and navigate successfully to desired goals

A importância das mídias sociais em empresa varejista do segmento de moda: O Caso Alpha / The importance of social media in a fashion retail company: The Alpha Case

O objetivo geral da presente pesquisa é demonstrar a importância das mídias sociais em empresa varejista do segmento de moda. Utilizou-se o método de estudo de caso (entrevistas emi-estruturadas e análise documental) para se analisar a empresa ALPHA, estabelecida na cidade de São Paulo. Como principais resultados, pôde-se constatar que: (a) 30% do investimento utilizado pela empresa destina-se à divulgação de produtos em mídias sociais, sendo o Instagram um dos mais utilizados por seus usuários; (b) as vendas de vestuário, via web site (e-commerce) e aplicativo, representam em média 0,15% do seu faturamento; (c) a era digital e a visibilidade proporcionada pela empresa nesses canais eletrônicos, colocaram-na em evidência, despertando o desejo de um novo público de se tornar seu consumidor; assim, as redes sociais utilizadas pela ALPHA são ferramentas essenciais e imprescindíveis ao desenvolvimento de sua estratégia

Reconfiguração do mercado de trabalho: políticas públicas de inclusão social e pacto social

Na medida em que se estuda as mutações do mundo do trabalho, verificamos que a sociedade está se mobilizando no sentido de encontrar fórmulas-alternativas, como resposta deste ramo jurídico (direito do trabalho) a atual fase do capitalismo, da globalização, da crise econômica e da revolução tecnológica do último século, que provocaram mudanças nas relações de trabalho, ensejando novas formas de organização de produção e a discussão de conceitos até a então firmemente arraigados na doutrina e jurisprudência. O recente debate cientifico em tema de trabalho se caracteriza por um consenso sempre mais difuso segundo o qual o direito do trabalho deveria ser reformulado como um direito ao mercado de trabalho que reformula constantemente seus paradigmas em razão do mercado econômico e político advindos de diversas matizes, especialmente da globalização internacional, que seria a nova revolução, sutil e contundente, modificadora e complexa, mudando os tratamentos sociais e espaços, interesses e equações econômicas, os processos e resultados produtivos, valores e conceitos culturais, vontades e valores políticos, pessoas e instituições. O presente artigo é especialmente direcionado para a importância do dialogo social, através dos pactos sociais, a partir “Libro Verde”, adotados na União Européia, que objetivam uma discussão entre vários atores sociais sobre a temática trabalho, inclusão social, mercado e flexibilização

Towards a neural hierarchy of time scales for motor control

Animals show remarkable rich motion skills which are still far from realizable with robots. Inspired by the neural circuits which generate rhythmic motion patterns in the spinal cord of all vertebrates, one main research direction points towards the use of central pattern generators in robots. On of the key advantages of this, is that the dimensionality of the control problem is reduced. In this work we investigate this further by introducing a multi-timescale control hierarchy with at its core a hierarchy of recurrent neural networks. By means of some robot experiments, we demonstrate that this hierarchy can embed any rhythmic motor signal by imitation learning. Furthermore, the proposed hierarchy allows the tracking of several high level motion properties (e.g.: amplitude and offset), which are usually observed at a slower rate than the generated motion. Although these experiments are preliminary, the results are promising and have the potential to open the door for rich motor skills and advanced control