Reviewerly/exHarmony huggingface dataset:
Effect of Equipment on the Accuracy of Accelerometer-Based Human Activity Recognition in Extreme Environments

A little explored area of human activity recognition (HAR) is in people operating in relation to extreme environments, e.g., mountaineers. In these contexts, the ability to accurately identify activities, alongside other data streams, has the potential to prevent death and serious negative health events to the operators. This study aimed to address this user group and investigate factors associated with the placement, number, and combination of accelerometer sensors. Eight participants (age = 25.0 ± 7 years) wore 17 accelerometers simultaneously during lab-based simulated mountaineering activities, under a range of equipment and loading conditions. Initially, a selection of machine learning techniques was tested. Secondly, a comprehensive analysis of all possible combinations of the 17 accelerometers was performed to identify the optimum number of sensors, and their respective body locations. Finally, the impact of activity-specific equipment on the classifier accuracy was explored. The results demonstrated that the support vector machine (SVM) provided the most accurate classifications of the five machine learning algorithms tested. It was found that two sensors provided the optimum balance between complexity, performance, and user compliance. Sensors located on the hip and right tibia produced the most accurate classification of the simulated activities (96.29%). A significant effect associated with the use of mountaineering boots and a 12 kg rucksack was established.
DEVELOPMENT OF DIGITAL COMPETENCE OF FUTURE TEACHERS OF GERMAN AS A FOREIGN LANGUAGE AT THE UNIVERSITY OF LJUBLJANA

In modern society, digital technology is becoming increasingly important and, at the same time, is experiencing a growing interest in the research landscape. Without digital technology, active participation in professional or private life is no longer imaginable. The European Union also takes this fact into account by establishing digital competence as one of the key competencies for lifelong learning (European Commission 2006, 2018). The development of digital competence and the use of digital technologies in the teaching-learning process also poses major challenges for teacher educators and future teachers, as teachers are expected to contribute to better teaching quality through the use of ICT while serving as role models for the next generations. On the one hand, teachers must have their own digital competence, and on the other hand, they should design lessons in such a way that learners also develop the necessary digital competence. Based on the relevant literature, it can be seen that the concept of digital competence has changed with the development of digital technologies. According to the survey, the course proved to be a successful dovetailing of theory and practise, enabling future teachers to acquire sufficient digital competence for beginners in both areas (learning and teaching). The selfassessment shows that they are able to plan and design lessons and integrate digital media into various learning processes. They can create collaborative learning opportunities and are able to promote self-directed learning. Future teachers will be able to communicate successfully using digital media. They are less digitally literate in the area of assessing learning outcomes. Although today's students belong to the generation of digital natives, they do not automatically have highly developed digital competence that they can easily apply in their professional lives, nor should it go unmentioned that digital competence varies widely among future teachers. For this reason, designing the course in the context of teacher training is more than necessary.
Robust Data Sampling in Machine Learning: A Game-Theoretic Framework for Training and Validation Data Selection

How to sample training/validation data is an important question for machine learning models, especially when the dataset is heterogeneous and skewed. In this paper, we propose a data sampling method that robustly selects training/validation data. We formulate the training/validation data sampling process as a two-player game: a trainer aims to sample training data so as to minimize the test error, while a validator adversarially samples validation data that can increase the test error. Robust sampling is achieved at the game equilibrium. To accelerate the searching process, we adopt reinforcement learning aided Monte Carlo trees search (MCTS). We apply our method to a car-following modeling problem, a complicated scenario with heterogeneous and random human driving behavior. Real-world data, the Next Generation SIMulation (NGSIM), is used to validate this method, and experiment results demonstrate the sampling robustness and thereby the model out-of-sample performance.
A methodology for Answer-Based Subjective Online Examination System

Both computers and the internet have ingratiated themselves deeply into daily lives, freeing one from a lot of paperwork. The assessment of a student is also an essential component of the work because it shows how well-versed and knowledgeable a student is in a certain area. Students who employ traditional methods of assessment may do it in an unfair manner. The assessment of students' response sheets would be handled by various universities and institutes with the use of automatic agents that evaluate the answers. The study outlines a method for assessing students' descriptive responses. The suggested approach will automatically compare students' responses to expected responses. Therefore, the inference process necessary for the construction of such a system is the subject of this paper. For one word and one-sentence-based answers, this system is workable. For implementation, WordNet and WordNet handling APIs like RiTa are employed along with Java.
BellDiagonalQudits: A package for entanglement analyses of mixed maximally entangled qudits

In the field of quantum information and technology, entanglement in quantum systems called qudits is regarded as resource for quantum-based or quantum-assisted information processing tasks.It allows new ways of information processing like quantum computation or quantum teleportation and provides possibilities for speedups in a variety of algorithms with applications like search or optimization.Despite its theoretical and practical relevance, there is no general method to determine whether a given quantum state is entangled or not due to a special and hard to detect form of entanglement called bound entanglement.Bipartite Bell states are maximally entangled states of two qudits and are of high relevance for application in quantum technologies.Mixtures of those states can be entangled, including its bound form, or separable.Leveraging their special properties, Bell states can be numerically generated and analyzed for their entanglement properties by various methods implemented in this Julia package.
A GUI Based Application for PDF Processing Tools Using Python &amp; CustomTkinter

Abstract: This paper presents a Graphical User Interface (GUI) application for PDF processing tools and file conversion tools. The application provides a user-friendly interface for users to perform various operations on PDF documents, such as splitting, merging, extracting, rotating, and deleting pages. Depending on the user's needs, the user can perform these operations on every page, even pages, odd pages, random specific pages, all pages after some nth page, and between some specific ranges. The application is designed to be user-friendly and simple to use, allowing users to complete tasks quickly and easily. Nowadays, most universities and schools provide their students with all of their course materials online, therefore many students access these resources via PDF files. Since this application operates without an internet connection and is therefore advantageous to all users, Using the PyPDF2 library for PDFs, the Python graphics package Tkinter is used to develop the graphical user interface and UI library CustomTkinter which provides fresh, modern, and completely customizable widgets. Many students upload their files to the internet in order to use these tools, but our offline GUI application provides all of these tools for free. It also includes PDF encryption, decryption, and file conversion tools. These tools are simple to use and can help users save time and effort.
Patent Trend Analysis: Extended Reality (XR) and Future Virtual Adventure

Abstract Patent trend analysis plays an effective and important role in innovation tendencies and investigating high-tech progressions among technology field. The interdisciplinary applications of XR technology have led to scattered scholarly works and fragmentary insights to be translated into practice. XR is an umbrella term that encompasses both virtual reality (VR), augmented reality (AR) and mixed reality (MR), among others. Despite the promising outlook, this nascent technology has been shrouded by uncertain possibilities, making the adoption of XR technology much slower than expected. Thus, there is a demanding need for a critical review and synthesis XR patent analysis in order to support this emerging field in information system (IS). To accomplish this goal, the study analysis the current patent trend from 2016 to 2021 total of 412 patents through the software of INNOVUE. In summary, this study identifies the major trends in development and forecasts the future of XR adoption. Keywords: Extended reality (XR), augmented reality (AR), virtual reality (VR) and mixed reality (MR), Patent trend.
K-MEANS AND ELBOW METHOD FOR CLUSTER ANALYSIS OF ELEMENTARY SCHOOL DATA

This research was conducted to find the groups of elementary schools in the Special Capital Region of Jakarta, also known as DKI Jakarta. Elementary school data were selected because it is the first stage of formal education in Indonesia. This research used K-means clustering with the elbow method to determine optimal cluster numbers. The optimal cluster number is three with Cluster 2 having the most members, followed by Cluster 1 and Cluster 0. The data distribution of Cluster 2 shows that the second-most student body and public schools located in East and West Jakarta have an adequate student-to-teacher ratio based on Article 17 of Government Regulation 74, 2008.
KAJIAN DAN OPINI MAHASISWA TERHADAP PENGGUNAAN APLIKASI TEACHMINT PADA MATAKULIAH LOGIKA MATEMATIKA

The Teachmint application is an all in one learning application based in India and has started to develop everywhere, one of them is in Indonesia, as well as the languages used are also available in various languages including Indonesian. This application is quite complete in its use, namely there is a live video session facility that allows direct interaction between lecturers and students, there is a blackboard that is not limited to the number of pages as if students are studying offline and is immediately saved automatically both in pdf and video form recording. The drawbacks of this application can almost be said to be non-existent, apart from the need for a stable internet network and quota to be fulfilled like other video call applications so if all of these are not fulfilled then of course this application will not be optimal. This study discusses the views or opinions of students as users during mathematical logic lectures. Based on the results of questionnaires and interviews conducted, it was found that this application is good for online learning because it can store all lecture materials together so that they are not scattered and all are fully documented, both in the pdf version and the video version as a result of the automatic recording during the live session.
Communicating knowledge and embodied experiences of personal menstrual cup use through YouTube

Background: Despite being a normal part of life for many, menstruation is often stigmatized resulting in reduced knowledge of reusable products such as menstrual cups. YouTube videos can raise awareness of menstrual cups and challenge stigma, but little is known about the content creators or what content is communicated in the videos. Aim: Firstly, to examine vlogger characteristics and the content of videos sharing personal experiences of cup use. Secondly, to evaluate the potential of these videos as a way of communicating about menstrual health. Methods: A content analysis of the 100 most popular menstrual cup videos on YouTube followed by a qualitative thematic analysis of 50 videos containing personal experiences. Results: The content of the videos was captured in four themes: embodied knowledge; technology enabled intimacies; persuasive narratives and a collective sense of community. Discussion: Vloggers communicated knowledge and embodied experiences of personal cup use. This represents a step towards destigmatizing menstruation and menstruants’ bodies, but issues around the complexities of vloggers’ motivations and the situatedness of vloggers remain. Conclusions: Given the paucity of information on menstruation and reusable menstrual products, YouTube videos provide a potentially valuable resource for health communication and education especially for younger menstruants.
Meningkatan Kemampuan Membaca dengan Menggunakan Metode Multisensori VAKT Pada Siswa Sekolah Dasar

Reading ability is a child's ability to recognize letters and words, then connect them with sounds, and understand the meaning of the writing that is read starting with the ability to listen to letters correctly and precisely. This pkm focuses on the fourth grade students of SDN 75 Bengkulu Tengah as the main object. Instruments used in the form of observation, and documentation. The results of the analysis can be obtained that the reading ability of fourth grade students at SD Negeri 75 Central Bengkulu is still very low. The influencing factors are internal and external factors. Our efforts to improve reading skills at SD Negeri 75 Bengkulu Tengah are providing motivation to learn, practicing reading, repeating lessons that have been given and holding reading learning activities outside of school hours.
Smart Village: Mobile-Based Community Reporting Application

Smart Village innovatively uses information technology to improve quality of life and efficiency. This study seeks to cover research gaps, contribute to the research field, and serve as a platform for primary probabilistic research by overcoming a proposed concept about The Community Reporting application as a solution for mobile phones. This application development uses the DART language with the Flutter Framework. The research stages are divided into four parts: problem analysis, literature study, system development, and report writing. The UAT test on the community reporting application got a score of 81.43% from community respondents. Furthermore, the community reporting administration system scored 88.3% of the village apparatus respondents. Therefore, the developed application could be well received. The government and community should contribute to implementing government and developing villages based on information technology.
Mathematic Modeling and Control of Underwater Cable-Driven Parallel Robot

Cable-driven parallel robots are mechanisms that control the position and attitude of an object using multiple cables. Typical cable-driven parallel robots have been studied for use on land, but not for use in water. This is because the position and attitude control of object and motion analysis become difficult underwater due to the large deformation of cables caused by fluid forces. In this paper, we propose an underwater cable-driven parallel robot consisting of multiple on-water robots and derive a dynamics model that considers the cable dynamics underwater. Then, we proposed a position and attitude control system for the suspended object based on the inverse kinematics problem for the suspended object and studied its effectiveness by simulation by using the planar two degrees-of-freedom underwater cable-driven parallel robot.
Machine Learning Models for Network Traffic Classification in Programmable Logic

Network traffic classification via machine learning on network packet payloads has emerged as an active area of research for network security due to the high accuracy machine learning models have achieved in classifying payloads. For effective deployment as part of network security, these machine learning models must not only classify malicious packet payloads accurately, they must also identify anomalous payloads and perform inference at speeds generally faster than 10,000 packets per second to be effective. This work explores the inference speeds and accuracy of several neural network models implemented in programmable logic on various field programmable gate arrays (FPGA), including the Xilinx VC1902 and Xilinx Zynq Ultrascale+. This work also presents the design and performance of both an autoencoder and variational autoencoder programmed on the FPGA for identifying anomalous packet payloads. The performance benefits of the FPGA implementation for this type of packet payload inspection driven by machine learning are compared against graphics processing unit (GPU) inference implementations run on two state-of-the-art datacenter GPU devices, the NVIDIA V100 and A100. The model accuracy difference between the FPGA and GPU implementations was 4% or less while the Xilinx VC1902 outperformed both the NVIDIA V100 and A100 for inference speeds on all the models explored except the variational autoencoder.
Textual Augmentation Techniques Applied to Low Resource Machine Translation: Case of Swahili

In this work we investigate the impact of applying textual data augmentation tasks to low resource machine translation. There has been recent interest in investigating approaches for training systems for languages with limited resources and one popular approach is the use of data augmentation techniques. Data augmentation aims to increase the quantity of data that is available to train the system. In machine translation, majority of the language pairs around the world are considered low resource because they have little parallel data available and the quality of neural machine translation (NMT) systems depend a lot on the availability of sizable parallel corpora. We study and apply three simple data augmentation techniques popularly used in text classification tasks; synonym replacement, random insertion and contextual data augmentation and compare their performance with baseline neural machine translation for English-Swahili (En-Sw) datasets. We also present results in BLEU, ChrF and Meteor scores. Overall, the contextual data augmentation technique shows some improvements both in the EN -&gt; SW and SW -&gt; EN directions. We see that there is potential to use these methods in neural machine translation when more extensive experiments are done with diverse datasets.
Exploring machine learning techniques for fake profile detection in online social networks

&lt;span lang="EN-US"&gt;The online social network is the largest network, more than 4 billion users use social media and with its rapid growth, the risk of maintaining the integrity of data has tremendously increased. There are several kinds of security challenges in online social networks (OSNs). Many abominable behaviors try to hack social sites and misuse the data available on these sites. Therefore, protection against such behaviors has become an essential requirement. Though there are many types of security threats in online social networks but, one of the significant threats is the fake profile. Fake profiles are created intentionally with certain motives, and such profiles may be targeted to steal or acquire sensitive information and/or spread rumors on online social networks with specific motives. Fake profiles are primarily used to steal or extract information by means of friendly interaction online and/or misusing online data available on social sites. Thus, fake profile detection in social media networks is attracting the attention of researchers. This paper aims to discuss various machine learning (ML) methods used by researchers for fake profile detection to explore the further possibility of improvising the machine learning models for speedy results.&lt;/span&gt;
Sensitivity of solar panel energy conversion at sunrise and sunset on three weather fluctuations in equatorial climate

&lt;span lang="EN-US"&gt;The high sunlight intensity in tropical and equatorial regions makes the potential for installing photovoltaic (PV) panels. However, the initial design of PV installations must be analyzed. Their implementation is carried out in buildings with load power for household electricity scale. For this reason, the panel reliability system could be efficient by designing the initial PV requirements using systematic measurements. Collecting data on fluctuating sunlight intensity (unpredictable weather) conditions needs the use of manual measuring tools, namely digital light meters and PV data with sensor integration. The research sample consists of three fluctuating hot weather conditions, namely hot-sunny, hot-cloudy and hot-rainy conditions. These weather conditions were taken because the climate of West Sumatra tends to shift clouds which sometimes cover the sun's rays. The peak PV output for direct current (DC) power generated during hot- sunny conditions reaches 1827.17 W, in sunny-cloudy weather it reaches around 1626.85 W and during sunny-rainy weather conditions the resulting output is 1161.81 W. From daily measurements, the results show that the efficiency of the PV system is strongly influenced by the prevailing weather climate.&lt;/span&gt;
Cost-sensitive PLM-based Approach for Arabic and English Cyberbullying Classification

Abstract Cyberbullying is a hurtful phenomenon that spreads widely on social networks and negatively affects the lives of individuals. Detecting this phenomenon is of utmost necessity to make the digital environment safer for youth. This study uses a bilingual classification of cyberbullying on Ara-bic and English datasets. A four-module approach is proposed. It consists of preprocessing the textual data, generating sentence embeddings, performing the classification, and evaluating the results of the models. The approach relies on two strategies based on transfer learning of pre-trained NLP models. The first uses PLMs (ELMo, Universal Sentence Encoder, BERT, distilBERT, and RoBERTa) to generate sentence embeddings, while the second adopts a fine-tuning procedure of BERT-based PLMs for cyberbullying classification. Due to the frequent class imbalance problem in the research literature, this study used cost-sensitive learning algorithms trained to maximize the Recall/F1 score. The aim is to search for the best classification model that most accurately separates the cyber-bullying and non-cyberbullying classes. The models achieve 75-84%.