175,487 research outputs found

    A gentle transition from Java programming to Web Services using XML-RPC

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    Exposing students to leading edge vocational areas of relevance such as Web Services can be difficult. We show a lightweight approach by embedding a key component of Web Services within a Level 3 BSc module in Distributed Computing. We present a ready to use collection of lecture slides and student activities based on XML-RPC. In addition we show that this material addresses the central topics in the context of web services as identified by Draganova (2003)

    The Limited Effect of Graphic Elements in Video and Augmented Reality on Children’s Listening Comprehension

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    There is currently significant interest in the use of instructional strategies in learning environments thanks to the emergence of new multimedia systems that combine text, audio, graphics and video, such as augmented reality (AR). In this light, this study compares the effectiveness of AR and video for listening comprehension tasks. The sample consisted of thirty-two elementary school students with different reading comprehension. Firstly, the experience, instructions and objectives were introduced to all the students. Next, they were divided into two groups to perform activities—one group performed an activity involving watching an Educational Video Story of the Laika dog and her Space Journey available by mobile devices app Blue Planet Tales, while the other performed an activity involving the use of AR, whose contents of the same history were visualized by means of the app Augment Sales. Once the activities were completed participants answered a comprehension test. Results (p = 0.180) indicate there are no meaningful differences between the lesson format and test performance. But there are differences between the participants of the AR group according to their reading comprehension level. With respect to the time taken to perform the comprehension test, there is no significant difference between the two groups but there is a difference between participants with a high and low level of comprehension. To conclude SUS (System Usability Scale) questionnaire was used to establish the measure usability for the AR app on a smartphone. An average score of 77.5 out of 100 was obtained in this questionnaire, which indicates that the app has fairly good user-centered design

    An original framework for understanding human actions and body language by using deep neural networks

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    The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

    Attention and Anticipation in Fast Visual-Inertial Navigation

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    We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to estimate its state using an on-board camera and an inertial sensor, without any prior knowledge of the external environment. We consider the case in which the robot can allocate limited resources to VIN, due to tight computational constraints. Therefore, we answer the following question: under limited resources, what are the most relevant visual cues to maximize the performance of visual-inertial navigation? Our approach has four key ingredients. First, it is task-driven, in that the selection of the visual cues is guided by a metric quantifying the VIN performance. Second, it exploits the notion of anticipation, since it uses a simplified model for forward-simulation of robot dynamics, predicting the utility of a set of visual cues over a future time horizon. Third, it is efficient and easy to implement, since it leads to a greedy algorithm for the selection of the most relevant visual cues. Fourth, it provides formal performance guarantees: we leverage submodularity to prove that the greedy selection cannot be far from the optimal (combinatorial) selection. Simulations and real experiments on agile drones show that our approach ensures state-of-the-art VIN performance while maintaining a lean processing time. In the easy scenarios, our approach outperforms appearance-based feature selection in terms of localization errors. In the most challenging scenarios, it enables accurate visual-inertial navigation while appearance-based feature selection fails to track robot's motion during aggressive maneuvers.Comment: 20 pages, 7 figures, 2 table

    Design, Implementation, and Evaulation of GIS-Based Learning Materials in an Introductory Geoscience Course

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    Little is known about how well GIS-based learning lives up to its potential for improving students' skills in problem solving, analysis, and spatial visualization. This article describes a study in which researchers determined ways to quantify student learning that occurred with a GIS-based module on plate tectonics and geologic hazards, and to improve the materials design with the use of classroom observations and field testing. The study found that student difficulties in working with GIS-based activities can be overcome by making some features of the GIS transparent to the user, that a lack of basic geography skills can interfere in the progression of a GIS-based activity, and that some conceptual difficulties can be overcome by providing guiding questions that help students interrogate visual data. In addition, it was noted that some misconceptions in interpretation of two-dimensional maps and three-dimensional block diagrams can persist even after direct instruction. In general, a positive correlation was noted between spatial thinking and GIS-based learning. Educational levels: Graduate or professional
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