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The name "Arduino" made its international debut in 2005, marking the age of Arduino as one of the most user-friendly and cost-effective microcontroller boards (MCBs) for novices. The science implementation of Arduino boards in automation, networking and data acquisition has been increasing steadily. This study provides a thorough Bibliometric analysis from 1122 papers focused on the Scopus database of published microcontroller research, from the first year the Arduino keyword appeared in 2008 until 2022. Various science articles indexed by Scopus and referring to the use of Arduino MCBs are selected. The Bibliometric analysis explores comprehensive and general key attributes that form a trend from the Scopus articles based on authors, titles, publication years, keywords, citations, affiliations, abstracts, funding information, and languages. The generated data is visualized and analyzed to find patterns that appear within the time span. This study found a significant increase in the number of articles on Arduino boards in Biology, Physics, Chemistry, Science, and STEM category of the paper. Despite using only the Scopus database, this study opens up to view the direction of the growing application of Arduino boards in Science. The use of Bibliometric analysis maps the scientific implementation of Arduino boards as an extensive guide for future collaborations in education and industry., Comment: 18 pages, 6 figures, and 9 tables

In thermochemistry experiments, a large volume and relatively high concentration of chemicals are often used to get a significant temperature change using an analogue thermometer. A greener experimental protocol is developed as an alternative using modern microcontroller boards, ChemDuino. The aim of this research is to develop a low cost and pocket sized prototype for measuring temperature of solutions using Arduino Uno and sensor based on green chemistry and Do It Yourself methods. The construction of ChemDuino for temperature sensing is made in the simplest manner for individuals with little knowledge of electronics. The details of all components are stipulated in this paper. This research was conducted in three phases, which includes preliminary, prototype development, and assessment phase. The device is used to determine the enthalpy change of neutralization of NaOH and HCl solution. The enthalpy change of neutralization, kJ/mol at 25.0 {\deg}C from literature is -57.13 kJ/mol, whereas the enthalpy change of neutralization, kJ/mol at 25.0 {\deg}C from the experiment using ChemDuino Calorimetry is -56.87 kJ/mol with the estimated standard deviation of 1.9. The device has successfully measured the temperature change of the reaction at a relatively lower concentration. ChemDuino Calorimetry has a great potential, because of its reliability and accuracy in measurements, inexpensive setup, and interconnectivity., Comment: 7 pages, 4 figures, 2 tables

We introduce a new family of non-linear neural network activation functions that mimic the properties induced by the widely-used Mat\'ern family of kernels in Gaussian process (GP) models. This class spans a range of locally stationary models of various degrees of mean-square differentiability. We show an explicit link to the corresponding GP models in the case that the network consists of one infinitely wide hidden layer. In the limit of infinite smoothness the Mat\'ern family results in the RBF kernel, and in this case we recover RBF activations. Mat\'ern activation functions result in similar appealing properties to their counterparts in GP models, and we demonstrate that the local stationarity property together with limited mean-square differentiability shows both good performance and uncertainty calibration in Bayesian deep learning tasks. In particular, local stationarity helps calibrate out-of-distribution (OOD) uncertainty. We demonstrate these properties on classification and regression benchmarks and a radar emitter classification task., Comment: To appear in Advances in Neural Information Processing Systems (NeurIPS 2020)

We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-Text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and dialogue-act-based meaning representation tasks by utilizing techniques such as: tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization. Our data and code can be found at https://github.com/Yale-LILY/dart., Comment: NAACL 2021