Reviewerly/exHarmony huggingface dataset:
On the Use of Deep Learning Models for Automatic Animal Classification of Native Species in the Amazon

Camera trap image analysis, although critical for habitat and species conservation, is often a manual, time-consuming, and expensive task. Thus, automating this process would allow large-scale research on biodiversity hotspots of large conspicuous mammals and bird species. This paper explores the use of deep learning species-level object detection and classification models for this task, using two state-of-the-art architectures, YOLOv5 and Faster R-CNN, for two species: white-lipped peccary and collared peccary. The dataset contains 7,733 images obtained after data augmentation from the Tiputini Biodiversity Station. The models were trained in 70% of the dataset, validated in 20%, and tested in 10% of the available data. The Faster R-CNN model achieved an average mAP (Mean Average Precision) of 0.26 at a 0.5 Intersection Over Union (IoU) threshold and 0.114 at a 0.5 to 0.95 IoU threshold, which is comparable with the original results of Faster R-CNN on the MS COCO dataset. Whereas, YOLOv5 achieved an average mAP of 0.5525 at a 0.5 IoU threshold, while its average mAP at a 0.5 to 0.95 IoU threshold is 0.37997. Therefore, the YOLOv5 model was shown to be more robust, having lower losses and a higher overall mAP value than Faster-RCNN and YOLOv5 trained on the MS COCO dataset. This is one of the first steps towards developing an automated camera trap analysis tool, allowing a large-scale analysis of population and habitat trends to benefit their conservation. The results suggest that hyperparameter fine-tuning would improve our models and allow us to extend this tool to other native species.
Methodological foundations for the formation of pre-professional IT competencies of high school students in children’s technoparks “Quantorium”

Modern state programs for the development of education have set an important task to prepare young people for successful professional self-realization, which implies the involvement of students in programs and activities of early career guidance, support of professional self-determination, and development of professional and socio-cultural competencies. The methods and principles of organizing educational activities of high school students on the basis of the children’s technopark “Quantorium” aim the development of pre-professional IT competencies. Classes in the technopark will make the preparation of students for modern and future IT professions more effective, and will also contribute to their readiness for self-realization and further professional development. Approaches to the development of pre-professional competencies of high school students are based on the project-oriented teaching methodology that promotes the development of teamwork, communication, and project management skills, technological competencies, and creative thinking. The analysis of the modular design principle of additional general educational programs of the technopark is carried out on the example of work in the direction “≪IT-quantum≫”, which is a part of the “Quantorium”. “ Quantorium” can become an effective tool to support educational activities aimed at the development of pre-professional competencies in high school students, contributing to the increase of interest in scientific, technical, and IT disciplines.
A Comparative Study of A*, RRT, and RRT* Algorithm for Path Planning in 2D Warehouse Configuration Space

Mobile robots are now being used in various interior and outdoor applications. The demands of Industry 4.0 are pushing robotic domains in the direction of decision-making. The autonomous robots should choose their direction based on their surroundings. Path planning for a mobile robot involves generation of a collision-free path from a current state to desired location while meeting particular optimization criteria through the surrounding, which includes obstacles. In this context, reliable and efficient path planning is critical. According to a thorough literature review, it was found that the A* and Rapidly Exploring Random Tree (RRTRapidly Exploring Random Tree (RRT)) algorithms, as well as their derivatives, are the most viable path planning algorithms for mobile robots. This paper presents a comparative study of A*, RRTRapidly Exploring Random Tree (RRT), and RRT*RRT* path planning algorithms in a 2D warehouseWarehouse environment. The efficiency of planners was correlated with the varied attributes of configuration spaces using several warehouseWarehouse configuration spaces. The path length, runtime, and number of nodes in the path were among the performance measures. Based on the results obtained, a best-suited algorithm was selected for warehouseWarehouse robot path planning.
Creating a Repeatable Nontechnical Skills Curriculum for the University of Southern Maine (USM) Cybersecurity Ambassador Program (CAP)

The workforce demand for skilled cybersecurity talent has exceeded its supply for years. Historically, the pedagogical approach was to identify and create curricula for the most in-demand technical knowledge, skills, and abilities (KSAs). Unfortunately, the field has tended to neglect nontechnical counterparts. However, recent literature suggests a core set of nontechnical KSAs that employers seek after. This study explored the codification of a nontechnical curriculum for a cybersecurity internship program at the University of Southern Maine (USM). The USM faculty created the Cybersecurity Ambassador Program that can serve students and the community. The service to students is to make them more attractive to employers. The benefit to the community is to provide cybersecurity awareness training to vulnerable populations. This discussion about the USM CAP serves as a case study for other programs considering this type of enrichment using an internship model. CAP started as an informal program, but this research used objective data to create repeatable blueprints. The researchers designed these lesson plans to help students progress from novices to competent in crucial nontechnical skills delineated in the National Initiative for Cybersecurity Education (NICE) Workforce framework. The team used a mixed methods approach to baseline Tier 1/novice students’ skill levels, place them in a cybersecurity enrichment program, track their progress, and determine program efficacy in helping them achieve beginner status. The information shared can serve as a point of departure for a case study that might guide other programs interested in doing similar work.
Recognizing and harnessing the transformational power of persistent identifiers (PIDs) for publicly-engaged scholars

While more recognizable publishing models continue to be favored across academic research and publishing systems, the diverse forms of knowledge emerging from publicly-engaged projects derived from working directly with and for communities requires novel and more dynamic publishing solutions. This article considers how the appropriate application of metadata and persistent identifiers to the processes and outputs of engaged scholarship are required to support the goals of the publicly-engaged humanities, and potentially lead to faster and more effective forms of impact for the people and places involved.
Project Werpa (Worksheets, E-Games, Remediation &amp; Peer Assistance): Numeracy Skill Enhancement for Students of San Pascual Senior High School 2

This study sought to ascertain the efficacy of Project WERPA in improving the student’s ability in select competencies in Mathematics. The least mastered competencies were determined by the test results made by the teacher-researcher. The descriptive research approach was employed in this study, with a questionnaire serving as the primary data collection instrument. According to the test results, most students struggle with the major topics in Mathematics such as rational, inverse, exponential, logarithmic functions, and logic. About the utilization of Project WERPA in the lessons, the respondents were very honest when they responded that the application of Project WERPA was very effective in stimulating their interests as well as very helpful in understanding the concepts in Mathematics. However, they were also very vocal when they responded that the school has no web-based learning environment and insufficient gadgets to cater to the needs of the students in the acquisition of Mathematics concepts using the unconventional method of the learning experience. The creation of a working website for Project WERPA will address the weak findings of the study. The included enrichment material and activity will consist of questions from the least mastered lessons to be answered by the students. Students will be more interested in participating in their Mathematics lesson if this digitally oriented material and activity are included because they will be employing technology- a type of innovation that today’s learners are good at controlling. Keywords: Project WERPA, Numeracy Skills, Least Mastered Skills and Competencies
R-universe now builds WASM binaries for all R packages

Abstract / TLDR R-universe now builds WASM binaries of all R packages for use in WebR applications (such as shinylive). For example to test the dev version of dplyr, you can open the WebR demo page and run:install.packages('dplyr', repos = c('https://tidyverse.r-universe.dev', 'https://repo.r-wasm.org')) As explained below, not all R packages are supported yet;
Improving activities in the processes of ensuring the quality of education in higher education schools and scientific institutes

In higher education institutions and scientific institutes, works are carried out to improve activities by implementing various ideas of the quality of education, treated as newer and more effective forms of ensuring a higher degree of mastery of knowledge and skills in performing the learned profession.
Professional examination programme in english on the speciality 014.021 secondary education (language and foreign literature (English language and literature))

Adhering to the structural and organisational continuity for the entrance examination on the basis of a bachelor’s degree, which provided for the verification of theoretical and practical preparation of applicants to study at Kyiv National Linguistic University at the second (Master’s) level of higher education, it has been found that the Professional Examination Programme on the speciality 014 Secondary Education, the major 014.021 English Language and Literature, the educational curriculum Teaching European Languages on the basis of combined technologies (English and another Western European language) (Melnyk, 2022) requires certain changes and new vision in order to optimally combine all aspects of assessing the overall level of English proficiency and, accordingly, with the objective results obtained, will ensure the successful process of obtaining a master’s degree. The paper outlines the Professional Examination Programme in English for applicants for the Master’s degree on the speciality 014.021 Secondary Education (Language and Foreign Literature (English Language and Literature). The eximination provides for the verification of theoretical and practical training of applicants, defines the required amount and content of knowledge in theoretical and practical English courses, and, accordingly, contains a list of literature on the practice of oral and written communication, lexicology, stylistics. The article covers the following components of the Professional Examination Programme: requirements for applicants; structure and content of the programme; criteria for assessing the results of applicants’ academic achievements; indicative topics; recommended literature.
Sharing, curation and metadata as essential components of the data management plan

This paper stresses the critical importance that data management plans actually be data management and sharing plans, that encourage data-sharing at the early stages of research; that indicate that data must be made as open as possible (and as closed as necessary); and that specify a minimum level of curation and collection of essential metadata. The author stresses that fact that it is no longer sufficient to post material openly and hope that others will be able to make use of it. The use of metadata and careful data curation of the data are essential to facilitate the discovery and successful re-use of data.
Intelligent Traffic System (ITS)

Abstract: One of the main issues of the modern day is effectively monitoring traffic. The influence on automobiles has increased in step with the population's exponential growth during the past century. The current transportation systems are under a great deal of strain as a result of this. Several traffic systems still operate today, ignoring the amount of traffic at that intersection, by assigning set timeslots to each traffic light. Millions of people lose valuable time in traffic as a result, while noise and air pollution also negatively impact the environment. In order to address these issues, we provide a system that controls traffic by using the main element of taking into account traffic density in real time. In order to accomplish its objective, this system makes use of cameras in addition to image processing.
AI-Assisted Chinese Language Teaching System in Distributed Sensor Networks

The Chinese learning system gained enormous importance to support the population of China with recent technologies. The AI and ML-assisted classes are useful for learners. However, it can cut down the employment of teachers. Various planning and act was passed to reform the Chinese education system with artificial intelligence and machine language. On the other hand, this education process is interesting, customized, and unsupervised based. It was observed that the human touch study process is more satisfying and clears doubts easily. The key object of data gathering sensor-based network is accumulating all the data from the nodes and sending those sensed data to the base station. These nodes are basically working while focusing on one thought: all the nodes present in the sensor-based system are well aware of all the other nodes currently working in the same network and transmitting data in the base station. Although AI comes with a few challenges like lack of resources, training, and various security issues, if these are overcome with proper strategies, AI can be the equipment to reign in the 21${}^{st}$ century. Additionally, LLMs and NLP modeling are described in the results that helped to understand the use of different models. Moreover, the Benefits of LLMs and NLP for Chinese language training are discussed in the study.
Indigenising Facebook language: Use of local languages in Facebook communication among a selected group of Kenyan internet users

This paper interrogates how Facebook use in Kenya is being localised to serve everyone: Local people and the elite. With approximately three billion monthly active users as of the second quarter of 2023, Facebook is the most used online social network globally. In the second quarter of 2017, the platform surpassed two billion active users, a feat accomplished in just over 13 years. Facebook (FB) has permeated the lives of millions of people and the way they relate to one another and share information. This article examines how selected Kenyans are indigenising Facebook by using other local languages. The article recognises the utility of FB as a novel tool to examine and interpret linguistic features for a selected group of Kenyan FB users. The article uses Herring’s Computer-Mediated Discourse Analysis (CMDA) theoretical framework. The research design used was both qualitative and quantitative. A purposive sampling procedure was used to arrive at eight FB friends in the 22-35 age bracket. This is the age that was found to use FB most in Kenya. The findings showed that Kenyans localised Facebook use in Kiswahili, vernacular, and Sheng.
Mixed Reality Technology: A Virtual Training Tool in Fluid Power Engineering

Abstract Fluid power systems can be expensive and difficult to access, making it challenging to provide hands-on training. This work discusses the incorporation of Mixed Reality (MR) technology in Fluid Power applications for providing a virtual training environment that simulates the behavior of fluid power systems, allowing users to receive immediate feedback on the system’s performance. Mixed reality is a digitized-based technology that integrates a virtual environment with our real world by utilizing real-world sensor data and computer models. This technology allows running simulations that examine the complexity of highly-coupled systems, producing new digital environments where physical and digital elements can interact in real-time. With all these features, MR technology can be a practical training tool for running virtual simulations that mimic real-life industry settings. It can extend the user with a virtual training environment, thus preparing the next generation of fluid power engineers and specialists. Throughout this work, we present the development and capabilities of a digitized virtual copy of a hydraulic excavator’s arm in an MR environment as a proof of concept. The MR arm module is developed and deployed using Microsoft’s Mixed Reality Tool Kit (MRTK) for Unity through HoloLens 2 MR headset. The MR development involves generating virtual copies of the mechanical and hydraulic subsystems, conducting the virtual assembly, and creating a user interface in the MR environment to visualize and interact with the model. The developed MR module enables visualizing the excavator’s internal structure, conducting the virtual assembly, and running virtual simulations, all of which assist in training future fluid power operators. It is an effective training tool that helps train junior engineers/technicians, cutting down on cost and time.
QUA-RC: the semi-synthetic dataset of multiple choice questions for assessing reading comprehension in Ukrainian

In this article we present the first dataset of multiple choice questions for assessing reading comprehension in Ukrainian. The dataset is based on the texts from the Ukrainian national tests for reading comprehension, and the MCQs themselves are created semi-automatically in three stages. The first stage was to use GPT-3 to generate the MCQs zero-shot, the second stage was to select MCQs of sufficient quality and revise the ones with minor errors, whereas the final stage was to expand the dataset with the MCQs written manually. The dataset is created by the Ukrainian language native speakers, one of whom is also a language teacher. The resulting corpus has slightly more than 900 MCQs, of which only 43 MCQs could be kept as they were generated by GPT-3.
Modeling the Happiness Index in Indonesia Using a Multiresponse Spline Nonparametric Regression Analysis Approach

Regression analysis is a statistical methodology that utilizes the relationship between two quantitative variables so that the response variable can be predicted. Multiresponse spline nonparametric regression is a regression model that has more than one correlated response variable where the regression function or curve is not assumed to form a certain pattern and is approximated by a spline function. Spline is a segmented polynomial model that able to handle increasing or decreasing data patterns with knot points indicating changes in data. The aim of this research is to obtain a multiresponse nonparametric spline regression model on the happiness index with the predictor variables used being literacy rate, open unemployment rate, population density, human development index, and unmet need for health services. The best model produced in this research is a linear spline model 1 knot points with a GCV values is 6,227930 and a coefficient of determination is 78,6021.
The effectiveness of moodle-based e-learning in enhancing digital literacy among village owned enterprises

Amid the global spread of the Covid-19 pandemic, including in Indonesia, several activities have been forcibly halted to prevent wider transmission and avoid more severe impacts. This decision has affected the dynamics of education and training, prompting an increase in the use of E-Learning applications. E-Learning has become a crucial method in creating a supportive learning environment and enhancing learners' performance. The development and expansion of e-learning are made easy with Learning Management System (LMS) software, such as Moodle. This study utilizes the ADDIE research model (Analyze, Design, Develop, Implement, and Evaluate). The data analysis techniques employed include feasibility analysis and learning outcome analysis. Learning outcomes are compared using the paired sample t-test, assisted by SPSS software. Media expert assessments yielded an average score of 4.54 for the presentation aspect, indicating an ideal score of 90.85%, while the programming aspect scored 84% in terms of idealness. Material expert assessments, on the other hand, received an average score of 4.62 for the learning aspect, with an ideal score of 92.3%, and 4.57 for the material aspect, indicating an ideal score of 91.4%. The paired sample t-test analysis produced a Sig. (2-tailed) value of 0.000 with a significance level of 5%. This result indicates that the Sig. (2-tailed) value is less than α = 0.05. Therefore, it can be concluded that the hypothesis is accepted. Consequently, it can be inferred that the use of Moodle-based E-Learning products is effective in enhancing digital literacy among the managers of Bina Sejahtera Village Owned Enterprises.
Comparison of Optimizers for Drone Signal Detection Using Convolutional Neural Networks (CNN)

Convolutional Neural Networks (CNNs) have emerged as a promising approach for drone signal detection from radio frequency (RF) data. The choice of optimizer significantly affects the performance of CNNs, as it influences the network's ability to learn complex patterns. In this paper, we conduct a comprehensive comparison of popular optimizers, including SGD, Adam, RMSprop, and Adagrad, and their variants, for drone signal detection using CNNs on an RF dataset. The results demonstrated that RMSprop outperformed other optimizers, particularly for larger datasets, maintaining consistently high accuracy with increased test data. Adam, Adamax, and AdamW also achieved impressive accuracy levels above 95%, with the exception of the Adadelta optimized model, which showed lower accuracy due to suboptimal performance during training. In conclusion, the RMSprop optimizer is the most suitable for drone signal detection using a CNN model, although other optimizers such as Adam, Adamax, and AdamW can also provide satisfactory results.
Experimental hierarchy of the nonclassicality of single-qubit states via potentials of entanglement, steering, and Bell nonlocality

Entanglement potentials are a promising way to quantify the nonclassicality of single-mode states.They are defined by the amount of entanglement (expressed by, e.g., the Wootters concurrence) obtained after mixing the examined single-mode state with a purely classical state; such as the vacuum or a coherent state.We generalize the idea of entanglement potentials to other quantum correlations: the EPR steering and Bell nonlocality, thus enabling us to study mutual hierarchies of these non-classicality potentials.Instead of the usual vacuum and one-photon superposition states, we experimentally test this concept using specially tailored polarization-encoded single-photon states.One polarization encodes a given non-classical single-mode state, while the other serves as the vacuum place-holder.This technique proves to be experimentally more convenient in comparison to the vacuum and a one-photon superposition as it does not require the vacuum detection.