Reviewerly/exHarmony huggingface dataset:
Improving the Irradiance Data Measured by Silicon-Based Sensors

Silicon-based sensors are widely used for monitoring solar irradiance, in particular, in the field of Photovoltaic (PV) applications. We present a method to correct the global horizontal irradiance measured by silicon-based sensors that reduces the difference to the standard thermopile sensor measurements. A major motivation to use silicon-based sensors for the measurements of irradiance is their lower cost. In addition, their response time is much lower, and their spectral response is much closer to that of the PV systems. The analysis of the differences is based on evaluating four parameters that influence the sensor measurements, namely the temperature, cosine error, spectral mismatch, and calibration factor. Based on the analysis, a correction model is applied to the silicon sensors measurements. The model separates measurements under a clear sky and cloudy sky by combining the clearness index and the solar zenith angle. By applying the correction model on the measurements of the silicon-based sensor, the differences between sensor readings have been reduced significantly. The relative root mean squared difference (rRMSD) between the daily solar irradiation measured by both sensors decreased from 10.6% to 5.4% after applying the correction model, while relative mean absolute difference (rMAD) decreased from 7.4% to 2.5%. The difference in total annual irradiation decreased from 70 KWh/m2 (6.5%) to 15 kWh/m2 (1.5%) by the correction. The presented correction method shows promising results for a further improvement in the accuracy of silicon-based sensors.
THE QUESTION ABOUT INFORMATION TECHNOLOGIES IN THE ORGANIZATION OF THE INDEPENDENT WORK OF STUDENTS

In connection with the emergence of new technologies in the field of education, changes are taking place in the organization of the learning process. These changes also affect the organization of students' independent work. Independent work of students is becoming the most important component of the organization of the educational process that meets international educational standards, its effectiveness is increasing. The presented article analyzes the application of the electronic information and educational environment of the Samara State University of Communications, reflecting its success in terms of its application and use in organizing the independent work of students in the course of studying mathematics.
Measuring the Use of Social Media Networks (SMNs) in Knowledge Sharing, by Using Social Cognitive Theory (SCT) A Study Conducted in Some of Iraqi Universities

SMNs like Facebook, YouTube, Twitter, WhatsApp,..etc. are among the most popular sites on the Internet. These sites can provide a powerful means of sharing, organizing, finding information and knowledge. The popularity of these sites provides an opportunity to measure the use them in knowledge sharing, which needs a special scale, but unfortunately, there is no special scale for that. Thus, this study supposes to use SCT as a scale to measure the use of SMNs in electronic knowledge sharing due to it has been used to measure knowledge sharing with its traditional form. This study can help the decision-makers to use these SMNs to share the academics’ knowledge in educational institutes to the communities by adopting special plans and strategies to address the main factors in such cases that will help to increase the knowledge sharing between academics and communities. The aim of this study to know the amount of using SMNs by academics to share their own knowledge with the community, which will reflect on educating the community and disseminate the culture within the community. Additionally, to address what the main factor can affect them to share their knowledge with others. The study uses SCT which consists of three factors: (Personal, Environment, and Behavior) as an independent variable, while the dependent variable is: (Knowledge Sharing). Furthermore, the quantitative method is adopted in this study by using an electronic questionnaire through Google Documents with “Five Scale of Likert” to collect the data from participants (250), who are the staff of some Iraqi universities. SPSS has been used in analyzing the collected data. The findings of the study come up with the following: the environmental factor has the greatest influence, then the behavioural factor which is lesser and the personal factor has the lowest influence. Finally, the study found that there is a possibility and ability to measure knowledge sharing by using SCT electronically. &#x0D; &#x0D; Paper type: research paper.
UNIGE_SE @ PRELEARN: Utility for Automatic Prerequisite Learning from Italian Wikipedia

The present paper describes the approach proposed by the UNIGE_SE team to tackle the EVALITA 2020 shared task on Prerequisite Relation Learning (PRELEARN). We developed a neural network classifier that exploits features extracted both from raw text and the structure of the Wikipedia pages provided by task organisers as training sets. We participated in all four sub–tasks proposed by task organizers: the neural network was trained on different sets of features for each of the two training settings (i.e., raw and structured features) and evaluated in all proposed scenarios (i.e. in– and cross– domain). When evaluated on the official test sets, the system was able to get improvements compared to the provided baselines, even though it ranked third (out of three participants). This contribution also describes the interface we developed to compare multiple runs of our models.
Leveraging Fault Localisation to Enhance Defect Prediction

Software Quality Assurance (SQA) is a resource constrained activity. Research has explored various means of sup-porting that activity. For example, to aid in resource investment decisions, defect prediction identifies modules or changes that are likely to be defective in the future. To support repair activities, fault localisation identifies areas of code that are likely to require change to address known defects. Although the identification and localisation of defects are interdependent tasks, the synergy between defect prediction and fault localisation remains largely underexplored.We hypothesise that modifying code that was suspicious in the past is riskier than modifying code that was not. To validate our hypothesis, in this paper, we employ fault localisation, which localises the root cause of a program failure. We compute the past suspiciousness score of code changes to each fault, and use those scores to (1) define new features for training defect prediction models; and (2) guide the next actions of developers for a commit labelled as fix-inducing. An empirical study of three open-source projects confirms our hypothesis. The new suspiciousness features improve F1 score and balanced accuracy of Just-In-Time (JIT) defect prediction models by 4.2% to 92.2% and by 1.2% to 3.7%, respectively. When guiding developer actions, past code suspiciousness successfully guides developers to a defective file, inspecting two to nine fewer files on average, compared to the baselines based on previous findings on past faults. These results demonstrate the potential of synergies of fault localisation and defect prediction, and lay the groundwork for explorations of that combined space.
Parameter Settings Optimization in Map Reduce Big Data processing using the MOPSO Algorithm

Big data is a commodity that is highly valued in the entire globe.It is not just regarded as data but in the world of experts, we can derive intelligence from it.Because of its characteristics which are Variety, Value, Volume, Velocity, and the growing need of how it can be handled, Organizations are facing difficulties in ensuring optimal as well as affordable processing and storage of large datasets.One of the already existing models used for rapid processing together with storage in big data is known as Hadoop MapReduce.MapReduce is used for large-scale data processing in a parallel and distributed computing environment, while Hadoop is used for running applications and storing data in clusters of commodity hardware Furthermore, the Hadoop MapReduce framework needs to tune more than 190 configuration parameters which are mostly done manually.Due to complex interactions and large spaces between parameters, manual tuning is not effective.Even worse, these parameters must be tuned every time Hadoop MapReduce applications are run.The main goal of this research is to create an algorithm that will improve efficiency by automatically optimizing parameter settings when MapReduce jobs are running.The algorithm employs the Multi-Objective Particle Swarm Optimization (MOPSO) technique, which uses two objective functions to look for a Pareto optimal solution while optimizing the parameters.The results of the experiments have shown that the algorithm has remarkably improved MapReduce job performance in comparison to the use of default settings.
Integration of off-line &amp; on-line: the Concept and Action Choice of Online Teaching of Vocational Education in Post-epidemic Era

During the epidemic period, vocational colleges made a preliminary exploration of online teaching in response to the national call of "suspending classes without stopping learning",Due to lack of experience, many problems and deficiencies were exposed. This paper systematically analyzes and summarizes the crux of online teaching in vocational colleges during the epidemic period, and then combs out the reform ideas of online teaching,which provides certain guidance and action path for the normal operation of online teaching mechanism in the post-epidemic era—integration of off-line &amp; on-line, and concretely expounds the specific path of integration of off-line &amp; on-line towards normalization of from three levels: home-school integration, virtual-real integration and production-education integration.
The Effect of Bad Password Habits on Personal Data Breach

Users tend to utilize bad or weak passwords with memorable characteristics such as simple words from the dictionary and easy to remember sequence of numbers from birthdays.Poor or bad password habits lead to compromise of personal data privacy and allow hackers to gain unauthorised access to these passwords and use them for criminal and fraudulent cyber activities.The purpose of this research is to examine the impact of password habits among Malaysians on their personal data breaches.This study provides insights into the behaviour of users concerning their passwords use.This study used a positivism paradigm and applied a quantitative approach and used a convenience sampling technique to collect data from 297 respondents from Malaysian nationals.IBM SPSS AMOS 24 is used to conduct the analysis.The result from the study shows that "lacking the use of second-factor authentication' have a significant and a positive impact on the personal data breaches.Based on this finding, it can be concluded that the lack of second-factor authentication is an essential factor that significantly impacts personal data breach.This finding provides a different perspective from the usual connection of bad password habits of weak password length and combination, easy to guess the password and common password reuse to be the main contributing factor of the personal data breaches by the previous literature.The contribution of this research is the provision of empirical evidence that emphasise the need to continually beef up own security by correctly using second-factor authentication across individual accounts.Doing so is crucial to curb personal data breaches.
Workspace Design and Trajectory Planning of a Five Degree of Freedom Mobile Welding Manipulator for Spherical Objects

Mobile Manipulators (MM) has attracted a lot of researchers for incorporation in the robotics field owning to their multitude of applications in real world. Welding automation has its wide applications in industry like automobile manufacturing and power generation industry involving spherical tanks. The objective of this study is to design workspace and devise a methodology to plan position trajectory of welding tool that produces smooth welding while the mobile platform turns simultaneously. The robot proposed in this paper has the manipulator mounted on a platform moving as a turntable to increase the workspace and enhances the mobility of the manipulator. The earlier produces linear segment of weld while the later produces parabolic segment. The kinematic equations for mobile platform and the mounted manipulator are described in detail. The workspace of the robot is visualized based on computations of transformation matrices and jacobians structured based on kinematic equation. Trajectories for each joint, computed using inverse kinematic equations, are also presented. For spherical trajectories the solution of system equations is combined with constraint values for each manipulator joint, thus allowing computation of desired joint position at any time interval. The efficacy of the proposed methodology for the trajectory planning is tested through a case study. The simulation results of motion transformation, workspace and trajectory show that linear segments of the trajectory combine with parabolic trajectory segments smoothly with zero acceleration within designed reachable workspace. The experimental results verify the efficacy of application of presented kinematic and inverse kinematic models for welding of spherical objects.
Virtual Reality For Electrical Machine

Virtual Reality (VR) is an innovation that enables a client to collaborate with a computer-mimicked climate, regardless of whether that climate is a recreation of this present reality or a conjured universe. It is the way to find, feel and contact with the past, the present and what is to come. It is the vehicle to make our own reality, our own altered reality. It could go from making a computer game to taking a virtual tour of the universe, from wandering through our own fantasy house to finding a walk on a strange planet. With computer-generated reality, we can meet the most frightening and boring circumstances by playing safe and with a learning point of view.&#x0D; Mechanical maintenance preparation requires significant investment. Must be legitimate public information personnel before playing out any support on apparent devices and must have an appropriate maintenance and security setting applicable to the specific device support is performed on. Two organizations and scientific institutions face preparing limits when ordering maintenance for replacers or workers.&#x0D; Virtual Reality situations deal with the issue of access, cost, support, firm plan, and well-being. Virtual reality situations using Game-Engine phases (for this mode, Unity), Computer-Aided-Design display and C# programming to execute what is needed in a VR reality head where a student is perfectly suited to speak with environmental factors through kidnapping, contact, holding, dropping, tossing and more through existing virtual reality controllers.
Problems Faced By Students in Writing English Academic Summary

Purpose: This research investigates the problems faced by college students in writing English academic summary. Methodology/Approach/Design: The method used includes surface strategy taxonomy by Heidi Dulay and non-linguistic problem using personal interview. The data were collected from 35 original summary writing documents for the linguistic sources and Zoom meeting video recorder for the non-linguistic sources. The samples were 7 students in 5th semester of English Education Department, Islamic State Institute of Kediri. To gather data, the researchers employed a random sampling technique. The data analysis used descriptive qualitative methods. Results: The result of the data analysis shows that the students committed four types of surface strategy taxonomy: omission, addition, misformation, and miss ordering. Misformation is the type of common problem found in the students’ summary writing. In the personal interview, it was found that misunderstands about advanced-vocabulary is the common non-linguistic problem. These represent the problems faced by students. Practical Implications: This research study has implications for teachers and students. The results can be of use for improving students’ writing and designing alternative teaching skills for this important skill. Originality/Value: Summary writing process helps the students to expand their view about proper academic writing. This situation suggests that summary writing is a good strategy technique to be applied in college students. However, the ability in writing an academic summary using surface strategy taxonomy needs to be improved
Challenges of Integrating GeoGebra in the Teaching of Mathematics in South African High Schools

Technology is embraced as an integral part of modern society, and hence it is also important for education to integrate various aspects of technology into teaching and learning.The South African National Development Plan (NDP) also prescribes the inclusion of quality technology to enhance the classroom experiences of both teachers and learners.GeoGebra is one of the preferred mathematics software in South African high schools.This qualitative study explores the challenges of teachers in the integration of GeoGebra in the teaching of mathematics in South African high schools.Using interpretivism as a research paradigm, we listened to and recorded the views of teachers about challenges they experienced in the integration of GeoGebra.The data was elicited from four purposively selected teachers from the Bojanala District in the North West Province of South Africa by using unstructured interviews which were transcribed, coded and categorised into relevant themes.The selected teachers signed the consent forms as part of their willingness to participate voluntarily in the study.The study supports the view that teachers are pivotal factors in the integration of GeoGebra and hence the need to understand their challenges.The key finding revealed was that South African high schools were being robbed and vandalised of ICT equipment because of security issues.Further, each school owned only one data projector which stifles efforts to strengthen teachers" knowledge and skills in implementing the integration of GeoGebra into mathematics lessons.Consequently, this loss of equipment disadvantages teachers and learners concerning the integration of GeoGebra into mathematics lessons because learners did not have computers for exploring and experimenting, and teachers are becoming demotivated due to insufficient resources and a lack of modern skills to make mathematics lessons interesting and productive.
Modeling Age of Acquisition Norms Using Transformer Networks

The age at which children acquire words is an important psycholinguistic property for modeling the growth of children's semantic networks. Much work over the years has explored how to effectively exploit statistical models to predict the age at which a word will be acquired, ranging from simple linear regression to LSA and skip-gram. However, thus far no work has explored whether transformers are any better at modeling word acquisition, despite the superior performance they have achieved on a wide variety of natural language processing (NLP) benchmarks. In this paper, we explore using several transformer models to predict the age of acquisition norms for several datasets. We evaluate the quality of our models using various experiments based on prior work and compare the transformers against two baseline models. We obtain promising results overall, as the transformers can outperform the baselines in most cases.
Comparison between Meta-Heuristic Algorithms for Path Planning

Unmanned Aerial Vehicle (UAV) has been increasingly employed in several missions with a pre-defined path. Over the years, UAV has become necessary in complex environments, where it demands high computational cost and execution time for traditional algorithms. To solve this problem meta-heuristic algorithms are used. Meta-heuristics are generic algorithms to solve problems without having to describe each step until the result and search for the best possible answer in an acceptable computational time. The simulations are made in Python, with it, a statistical analyses was realized based on execution time and path length between algorithms Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Glowworm Swarm Optimization (GSO). Despite the GWO returns the paths in a shorter time, the PSO showed better performance with similar execution time and shorter path length. However, the reliability of the algorithms will depend on the size of the environment. PSO is less reliable in large environments, while the GWO maintains the same reliability.
Information and Communication Technologies (ICTs) Adoption by MSMEs and Local Poverty: An Empirical Evidence from Indonesia

This paper aims to examine a relationship between the adoption of Information and Communication Technologies (ICTs) by Micro, Small, and Medium Enterprises (MSMEs) and local poverty in Indonesia. Previous literature has shown that the adoption of ICTs is a significant catalyst to increasing a nation’s productivity from a macroeconomic perspective. This has imposed a new debate among researchers whether the adoption of ICTs can contribute to a more specific economic development goal: reducing poverty. Some researchers argue that the means of a broader economic development can be obtained through the informal sectors, MSMEs. Therefore, this paper argues that in order to find a more satisfactory result, the role of ICTs adoption in reducing poverty needs to be examined from within a more specific economic agent—the MSMEs. To test the hypothesis, we run OLS regression models with province and year fixed effects on our MSMEs survey data and local poverty measures. The results show a robust, negative relationship between the adoption of ICTs by MSMEs and the number of poor populations in the corresponding region, controlling other factors. DOI : https://doi.org/10.26905/jkdp.v25i2.5343
Exploring the Relationship between Writing Anxiety and the Pedagogical Use of Online Learning Medium among Malaysian University Students

According to United Nations in 2020, the pandemic of COVID-19 has caused the largest disruption of education systems in history.The sudden transition from traditional classroom to online classroom has caused many students to become anxious with their learning especially when they have to learn writing skills through several online mediums such as instant messenger (e. g.Whatsapp and Telegram), social media (e. g.Facebook, Instagram, Twitter and YouTube), live meeting session (e. g.Google Hangouts Meet, Zoom and Cisco Webex) and education tools (e. g.Google Classroom, Padlet and Kahoot).To address this issue, this qualitative study explored the reported experience of nine students from Diploma of Banking Studies who are taking the writing course through an online medium, namely Telegram.The students are prompted with a list of interview questions focusing on writing anxiety.Their responses were analysed, and two major themes were generated.The results revealed that the participants in this study described external and internal reasons as the causal factors to their writing anxiety.The internal reasons can be classified further into somatic anxiety, cognitive anxiety, and avoidance behaviour.The participants suggested a few major coping strategies to overcome their writing anxiety such as taking a deep breath, taking some time off from writing task, getting some entertainment and practising relaxation.