This paper presents a new teaching tool with the goal of facilitating the learning of basic programming concepts among high school students and university freshmen. The tool incorporates a user-friendly visual interfa...
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ISBN:
(数字)9798350391084
ISBN:
(纸本)9798350391091
This paper presents a new teaching tool with the goal of facilitating the learning of basic programming concepts among high school students and university freshmen. The tool incorporates a user-friendly visual interface and robotics, expanding on the existing Fluxprog project. New features were implemented, such as variables, logical and mathematical operations, conditional structures, and loops, to motivate students to engage in challenges that promote logical thinking. This paper presents the design, implementation and evaluation of a programming teaching tool using a visual language based on flowcharts, with subsequent data collection through a questionnaire and analysis of preliminary results to evaluate the user's experience while interacting with the tool.
This article describes the estimation of a 3D point using a Kinect sensor and the Robot Operating System (ROS) along with You Only Look Once (YOLO) for object detection. The Kinect sensor provides RGB-D images, which ...
This article describes the estimation of a 3D point using a Kinect sensor and the Robot Operating System (ROS) along with You Only Look Once (YOLO) for object detection. The Kinect sensor provides RGB-D images, which are used to create a Point Cloud representing the geometry of the environment. ROS is used as a robotics development framework, while YOLO is employed to identify objects in the scene. The article presents the packages used, the datasets used for measurement, and the configuration of ROS and YOLO. Additionally, the functionalities of RViz, a 3D visualization tool used in the tests, are explored. Furthermore, it covers the methods employed, the acquired data, and an analysis of the error margin in relation to the measurement of the distance between the Kinect and the object. The findings and techniques presented in this study contribute to addressing the challenges faced in the RoboCup@Home competition, specifically in the context of object manipulation tasks.
Robocup@Home proposes a challenge related to Person Recognition: after presented, a new ‘operator’ should become ‘immediately’ recognizable by the robot. The presentation procedure may require the operator to corr...
Robocup@Home proposes a challenge related to Person Recognition: after presented, a new ‘operator’ should become ‘immediately’ recognizable by the robot. The presentation procedure may require the operator to correctly interact with the robot, following a certain procedure, as instructed by the robot itself (for example, staying in front of the robot, so that the robot can take pictures of this person). In this paper, we propose the use of the KNN (K-Nearest Neighbor) supervised machine learning algorithm to include a new ‘operator’ in a database of persons recognizable by the robot. This algorithm uses information taken from an image segmentation of the face of the operator. The experiment evaluates how long it takes to include a new operator if the robot has from 1 to 12 current operators, evaluating also how long it takes to include this operator based on 1, 2 or more images of the new operator, taken from slightly different points of view. The results confirm that KNN can be used to ‘present to the robot’ up to 13 new operators, with up to 15 images for each operator, in less than 60 seconds.
In this paper we address the task of hierarchical bird species identification from audio recordings. We evaluate three types of approaches to deal with hierarchical classification problems: the flat classification app...
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ISBN:
(纸本)9781479906505
In this paper we address the task of hierarchical bird species identification from audio recordings. We evaluate three types of approaches to deal with hierarchical classification problems: the flat classification approach, the local-model per parent node classifier approach and the global-model hierarchical-classification approach. For the flat and local-model classification approach we employ the classic Naive Bayes algorithm. For the global-model approach we use the Global Model Naive Bayes (GMNB) algorithm. As in the classical Naive Bayes, the algorithm computes prior probabilities and likelihoods, but these computations take into account the hierarchical classification scenario: it assumes that any example which belongs to a given class will also belong to all its ancestor classes. In the current application, the employed class hierarchy is the standard scientific taxonomy of birds used in Biology. In order to deal with the bird songs we obtain features by computing several acoustic quantities from intervals of the audio signal. We conduct three experiments in order to compare the three different approaches to the hierarchical bird species identification problem. Our experimental results show that the use of the GMNB hierarchical classification algorithm outperforms both the flat and local-model approaches (Using the Hierarchical F-measure metric);hence the use of a global-model approach (such as the GMNB) can be a feasible way to improve the classification performance for problems with a large number of classes.
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