1,299 research outputs found

    Digital Technologies for Teaching English as a Foreign/Second Language: a collective monograph

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    Колективна монографія розкриває різні аспекти використання цифрових технологій у навчанні англійської мови як іноземної/другої мови (цифровий сторітелінг, мобільні застосунки, інтерактивне навчання і онлайн-ігри, тощо) та надає освітянам і дослідникам ресурс для збагачення їхньої професійної діяльності. Окрема увага приділена цифровим інструментам для впровадження соціально-емоційного навчання та інклюзивної освіти на уроках англійської мови. Для вчителів англійської мови, методистів, викладачів вищих закладів освіти, науковців, здобувачів вищої освіти

    Systems of State-Owned Enterprises: from Public Entrepreneurship to State Shareholding

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    This thesis outlines a new analytical perspective on state ownership through the original concept of systems of state-owned enterprises (SOSOEs). It is argued that the SOSOEs concept adequately captures the evolution of state-owned enterprises (SOEs) in modern capitalist economies, challenging and enriching existing economic theories as well as contributing to reinstate the policy instrumentality of state ownership. The concept is defined from a comparative case study analysis of two distinct SOSOEs, operating within the same national context in different time periods. The first case concerns the Istituto per la Ricostruzione Industriale (IRI), Italy’s former and most relevant state holding company, that played a central role in the Country’s post-WWII economic development. This thesis advances an interpretation of IRI’s economic function based on an original empirical investigation of its archival and documentary sources, focusing on its main public policy missions and on its display of industrial entrepreneurship features. The second case examines the current Italian system of SOEs, assessing the still relevant presence of SOEs in the Italian national context and evaluating the overall governance of the system through a set of interviews with leading executives. Despite the similarity in size and sectoral diversification, the two SOSOEs differ significantly in terms of their operating configurations. In fact, they could be assimilated to two dichotomous ideal types: the IRI SOSOEs represents a template for the policy-oriented and dynamic ‘public entrepreneurship’ model, while the current Italian SOSOEs resembles the policy-neutral and passive ‘state shareholding’ variant. Implicit in these results is the opportunity for current SOSOEs to embrace a public entrepreneurship configuration, in order to exploit the full policy potential of state ownership in driving economic change. The thesis concludes with a proposal for reforming Italy’s current SOSOEs via the creation of a state holding company

    Deep Reinforcement Learning for Robotic Tasks: Manipulation and Sensor Odometry

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    Research in robotics has frequently focused on artificial intelligence (AI). To increase the effectiveness of the learning process for the robot, numerous studies have been carried out. To be more effective, robots must be able to learn effectively in a shorter amount of time and with fewer resources. It has been established that reinforcement learning (RL) is efficient for aiding a robot's learning. In this dissertation, we proposed and optimized RL algorithms to ensure that our robots learn well. Research into driverless or self-driving automobiles has exploded in the last few years. A precise estimation of the vehicle's motion is crucial for higher levels of autonomous driving functionality. Recent research has been done on the development of sensors to improve the localization accuracy of these vehicles. Recent sensor odometry research suggests that Lidar Monocular Visual Odometry (LIMO) can be beneficial for determining odometry. However, the LIMO algorithm has a considerable number of errors when compared to ground truth, which motivates us to investigate ways to make it far more accurate. We intend to use a Genetic Algorithm (GA) in our dissertation to improve LIMO's performance. Robotic manipulator research has also been popular and has room for development, which piqued our interest. As a result, we researched robotic manipulators and applied GA to Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) (GA+DDPG+HER). Finally, we kept researching DDPG and created an algorithm named AACHER. AACHER uses HER and many independent instances of actors and critics from the DDPG to increase a robot's learning effectiveness. AACHER is used to evaluate the results in both custom and existing robot environments.In the first part of our research, we discuss the LIMO algorithm, an odometry estimation technique that employs a camera and a Lidar for visual localization by tracking features from their measurements. LIMO can estimate sensor motion via Bundle Adjustment based on reliable keyframes. LIMO employs weights of the vegetative landmarks and semantic labeling to reject outliers. LIMO, like many other odometry estimating methods, has the issue of having a lot of hyperparameters that need to be manually modified in response to dynamic changes in the environment to reduce translational errors. The GA has been proven to be useful in determining near-optimal values of learning hyperparameters. In our study, we present and propose the application of the GA to maximize the performance of LIMO's localization and motion estimates by optimizing its hyperparameters. We test our approach using the well-known KITTI dataset and demonstrate how the GA supports LIMO to lower translation errors in various datasets. Our second contribution includes the use of RL. Robots using RL can select actions based on a reward function. On the other hand, the choice of values for the learning algorithm's hyperparameters could have a big impact on the entire learning process. We used GA to find the hyperparameters for the Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER). We proposed the algorithm GA+DDPG+HER to optimize learning hyperparameters and applied it to the robotic manipulation tasks of FetchReach, FetchSlide, FetchPush, FetchPick\&Place, and DoorOpening. With only a few modifications, our proposed GA+DDPG+HER was also used in the AuboReach environment. Compared to the original algorithm (DDPG+HER), our experiments show that our approach (GA+DDPG+HER) yields noticeably better results and is substantially faster. In the final part of our dissertation, we were motivated to use and improve DDPG. Many simulated continuous control problems have shown promising results for the DDPG, a unique Deep Reinforcement Learning (DRL) technique. DDPG has two parts: Actor learning and Critic learning. The performance of the DDPG technique is therefore relatively sensitive and unstable because actor and critic learning is a considerable contributor to the robot’s total learning. Our dissertation suggests a multi-actor-critic DDPG for reliable actor-critic learning as an improved DDPG to further enhance the performance and stability of DDPG. This multi-actor-critic DDPG is further combined with HER, called AACHER. The average value of numerous actors/critics is used to replace the single actor/critic in the traditional DDPG approach for improved resistance when one actor/critic performs poorly. Numerous independent actors and critics can also learn from the environment in general. In all the actor/critic number combinations that are evaluated, AACHER performs better than DDPG+HER

    Potential connection between positive frustration in family leisure time and the promotion of adolescent autonomy

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    Family relationships during leisure time in adolescence have the potential to promote positive development, particularly in terms of autonomy. However, the scientific literature that links specifically positive family leisure to the development of adolescent autonomy is scarce, and lower when analyzing the role of frustration in leisure time. Grounded in Self-Determination Theory (SDT) this article examines the potential relationship between positive frustration in family leisure time and the promotion of adolescent autonomy. For that purpose, the manuscript addresses four objectives to be discussed consecutively: (1) to delimit the concept of adolescent autonomy and point out the difficulty of parental support; (2) to explore positive frustration, a concept aligned with Csikszentmihalyi’s theory of flow, as a construct that can promote socio-emotional development in adolescence; (3) to describe the components of family leisure; and (4) to understand how the experience of optimal frustration may be linked to the development of adolescent autonomy during family leisure time. From this central question, several additional inquiries emerge: the interplay of frustration and failure in adolescence, the importance of parents and adolescents spending quality time together, the enjoyment in structured family leisure time, the autonomy-supportive parenting in leisure time activities in relation to daily activities, the need to strengthen adolescent bonds developed in infancy, and the complexity of paternal and maternal autonomy granting

    Visual Guidance for Unmanned Aerial Vehicles with Deep Learning

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    Unmanned Aerial Vehicles (UAVs) have been widely applied in the military and civilian domains. In recent years, the operation mode of UAVs is evolving from teleoperation to autonomous flight. In order to fulfill the goal of autonomous flight, a reliable guidance system is essential. Since the combination of Global Positioning System (GPS) and Inertial Navigation System (INS) systems cannot sustain autonomous flight in some situations where GPS can be degraded or unavailable, using computer vision as a primary method for UAV guidance has been widely explored. Moreover, GPS does not provide any information to the robot on the presence of obstacles. Stereo cameras have complex architecture and need a minimum baseline to generate disparity map. By contrast, monocular cameras are simple and require less hardware resources. Benefiting from state-of-the-art Deep Learning (DL) techniques, especially Convolutional Neural Networks (CNNs), a monocular camera is sufficient to extrapolate mid-level visual representations such as depth maps and optical flow (OF) maps from the environment. Therefore, the objective of this thesis is to develop a real-time visual guidance method for UAVs in cluttered environments using a monocular camera and DL. The three major tasks performed in this thesis are investigating the development of DL techniques and monocular depth estimation (MDE), developing real-time CNNs for MDE, and developing visual guidance methods on the basis of the developed MDE system. A comprehensive survey is conducted, which covers Structure from Motion (SfM)-based methods, traditional handcrafted feature-based methods, and state-of-the-art DL-based methods. More importantly, it also investigates the application of MDE in robotics. Based on the survey, two CNNs for MDE are developed. In addition to promising accuracy performance, these two CNNs run at high frame rates (126 fps and 90 fps respectively), on a single modest power Graphical Processing Unit (GPU). As regards the third task, the visual guidance for UAVs is first developed on top of the designed MDE networks. To improve the robustness of UAV guidance, OF maps are integrated into the developed visual guidance method. A cross-attention module is applied to fuse the features learned from the depth maps and OF maps. The fused features are then passed through a deep reinforcement learning (DRL) network to generate the policy for guiding the flight of UAV. Additionally, a simulation framework is developed which integrates AirSim, Unreal Engine and PyTorch. The effectiveness of the developed visual guidance method is validated through extensive experiments in the simulation framework
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