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A comprheensive review of visual programming tools for Arduino 17

A comprheensive review of Visual Programming Tools for Ardui...

18th Latin American Robotics Symposium (LARS) / 12th Brazilian Symposium on Robotics (SBR) / 11th Workshop on Robotics in Education (WRE)

作者： Melo, Julio Fidelis, Melquiades Alves, Sidney Freitas, Ulisses Dantas, Rummenigge Univ Fed Rio Grande do Norte Natal RN Brazil

ISBN: (纸本)9780738111537

This document presents a systematic review of visual programming tools for the Arduino Board. The Arduino board is an embedded platform that focus on enabling an easy way to teach embedded systems, but, due to its success, it ended up being used in different teaching levels, ranging from primary to university. The board was also applied in the teaching of robotics and other contexts since it is relatively cheap, easy to program and its community has produced a considerable amount of free educational material. Even though Arduino is a success it is still maturing in terms of educational purposes, aside the electronic knowledge that needs to be explored, the board is programmed, by default, with c++, which is considered not friendly to Computer Thinking beginners. Trying to solve this problem, many solutions apply visual programming, which is proven to make it easy for unexperienced and even children to learn the basis of Computational Thinking, in the educational context with Arduino. Even though there are many solutions applying visual programming for Arduino, there are no systematic review or survey on this subject, meaning that it is hard to identify where are the gaps and which are the problems that are already solved. Therefore, on this paper, it was performed a systematic review on the subject, the method found 372 papers, which 31 were selected for full review. The reviewed solutions where discussed and categorized, we expect that the presented results could be used as guideline for new research in the area and by educators and companies that are looking for visual programming tools for the Arduino board. © 2020 IEEE.

关键词： Arduino visual programming tools Systematic Review Survey

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DeepFlow: A Flow-Based visual programming Tool for Deep Learning Development 25

DeepFlow: A Flow-Based Visual Programming Tool for Deep Lear...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Calò, Tommaso De Russis, Luigi Dipartimento di Automatica e Informatica Politecnico di Torino Torino Torino Italy

ISBN: (纸本)9798400713064

visual programming tools have recently been introduced to enable Deep Learning (DL) development without the need for expertise in traditional programming languages and frameworks. However, these tools often exhibit limitations in scalability for complex architectures and lack real-time debugging capabilities. This paper introduces DeepFlow, a flow-based visual programming tool (VPT) designed to address these challenges by leveraging the inherently visual nature of DL models as sequences of learnable functions. DeepFlow incorporates hierarchical abstraction mechanisms through "supernodes"to support model scalability, which is crucial for modern, complex architectures. Additionally, it introduces interactive debugging in the model design phase, allowing developers to validate network architectures before execution. We conducted a user study with 16 DL developers, involving typical DL model design tasks. We assessed DeepFlow using quantitative usability metrics, and post-task interviews to evaluate user perceptions and workflow integration across different expertise levels. Results demonstrated high usability and user satisfaction, and highlighted DeepFlow's effectiveness for rapid model iteration and as a learning aid for complex DL architectures, while also identifying areas for improvement. © 2025 Copyright held by the owner/author(s).

关键词： visual programming tools

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Flexible Techniques for Fast Developing and Remotely Controlling DIY Robots, with AI Flavor

Flexible Techniques for Fast Developing and Remotely Control...

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International Conference on Educational Robotics (EDUROBOTICS)

作者： Loukatos, Dimitrios Kahn, Ken Alimisis, Dimitris Agr Univ Athens Dept Nat Resources Management & Agr Engn Athens Greece EDUMOTIVA European Lab Educ Technol Athens Greece Univ Oxford Dept Educ Oxford England

ISBN: (纸本)9783030181413;9783030181406

During the last years we are witnessing a very successful osmosis between innovative and cost-effective credit card - sized computers and education. These computers, equipped with low cost sensors or actuators, can be the "heart" of various DIY robotic artefacts. This environment allows for a mixture of thinking and making activities that can be very meaningful in terms of pedagogy and science. Indeed, similar practices, usually referred as STEM or STEAM activities, are applied in many educational institutions, from primary schools up to universities, with most of the effort to focus on secondary school students. The overall process, although promising at the beginning, is not always straightforward to keep up with. More specifically, as students get more experience, they develop a hunger for more complicated scenarios that usually demand features like remote interaction with simple Artificial Intelligence - A.I. capabilities or sophisticated control of their robotic artefacts. At this moment, trainers should be able to propose simple and stable techniques to their students for implementing such features in their constructions. This paper proposes flexible methods for this to be done by exploiting the very popular MIT App Inventor and Snap! visual programming environments, in conjunction with a modified tiny web server module, written in Python, that runs on a Raspberry Pi credit card - sized computer. Furthermore, this paper reports on simple techniques being used to make robust enough robots by low cost everyday/ recyclable materials like cardboard, wood, plastic bottles or broken toys.

关键词： DIY robots Remote control visual programming tools AI App Inventor Snap! Raspberry Pi

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Discovering feature relevancy and dependency by kernel-guided probabilistic model- building evolution

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BIODATA MINING 2017年第1期10卷 12-12页

作者： Rodriguez, Nestor Rojas-Galeano, Sergio Univ Distrital FJC Sch Engn Bogota Colombia

Background: Discovering relevant features (biomarkers) that discriminate etiologies of a disease is useful to provide biomedical researchers with candidate targets for further laboratory experimentation while saving costs;dependencies among biomarkers may suggest additional valuable information, for example, to characterize complex epistatic relationships from genetic data. The use of classifiers to guide the search for biomarkers (the so-called wrapper approach) has been widely studied. However, simultaneously searching for relevancy and dependencies among markers is a less explored ground. Results: We propose a new wrapper method that builds upon the discrimination power of a weighted kernel classifier to guide the search for a probabilistic model of simultaneous marginal and interacting effects. The feasibility of the method was evaluated in three empirical studies. The first one assessed its ability to discover complex epistatic effects on a large-scale testbed of generated human genetic problems;the method succeeded in 4 out of 5 of these problems while providing more accurate and expressive results than a baseline technique that also considers dependencies. The second study evaluated the performance of the method in benchmark classification tasks;in average the prediction accuracy was comparable to two other baseline techniques whilst finding smaller subsets of relevant features. The last study was aimed at discovering relevancy/dependency in a hepatitis dataset;in this regard, evidence recently reported in medical literature corroborated our findings. As a byproduct, the method was implemented and made freely available as a toolbox of software components deployed within an existing visual data-mining workbench. Conclusions: The mining advantages exhibited by the method come at the expense of a higher computational complexity, posing interesting algorithmic challenges regarding its applicability to large-scale datasets. Extending the probabilistic assumptions

关键词： Relevancy discovery Dependency estimation Feature selection Epistasis Hepatitis dataset visual programming tools

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