Reviewerly/exHarmony huggingface dataset:
Effective and Low-Cost Arduino based Dual-Axis Solar Tracker

Abstract This paper presents research on dual-axis solar tracker utilizing solar irradiance most efficiently. The high cost is the main challenge in effectively harnessing solar technology due to the higher cost of materials and equipment for the system since the solar tracking device need more equipment to manage the control system. The objective of this research is to design a dual-axis solar tracker using Arduino as the main component in the control system. In order to maximize the effectiveness of this work, the structure of the tracker is designed to accommodate the solar panels with minimal power consumption and cost. This research is focused on improving the full collection of output power from the solar panel using a tracking system. The experiment shows the performance comparison between the dual-axis solar tracker and a fixed tilted solar panel.
Continuous Testing Improvement Model

Continuous Delivery is a practice where high-quality software is built in a way that it can be released into production at any time. However, systematic literature reviews and surveys performed as part of this Doctoral Research report that both the literature and the industry are still facing problems related to testing using practices like Continuous Delivery or Continuous Deployment. Thus, we propose Continuous Testing Improvement Model (CTIM) as a solution to the testing problems in continuous software development environments. It brings together proposals and approaches from different authors which are presented as good practices grouped by type of tests and divided into four levels. These levels indicate an improvement hierarchy and an evolutionary path in the implementation of Continuous Testing. Also, an application called EvalCTIM was developed to support the appraisal of a testing process using the proposed model. Finally, to validate the model, an action-research methodology was employed through an interpretive theoretical evaluation followed by case studies conducted in real software development projects. After several improvements made as part of the validation outcomes, the results demonstrate that the model can be used as a solution for implementing Continuous Testing gradually at companies using Continuous Deployment or Continuous Delivery and measuring its progress.
A Monte-Carlo Analysis of Monetary Impact of Mega Data Breaches

The monetary impact of mega data breaches has been a significant concern for enterprises. The study of data breach risk assessment is a necessity for organizations to have effective cybersecurity risk management. Due to the lack of available data, it is not easy to obtain a comprehensive understanding of the interactions among factors that affect the cost of mega data breaches. The Monte Carlo analysis results were used to explicate the interactions among independent variables and emerging patterns in the variation of the total data breach cost. The findings of this study are as follows: The total data breach cost varies significantly with personally identifiable information (PII) and sensitive personally identifiable information (SPII) with unique patterns. Second, SPII must be a separate independent variable. Third, the multilevel factorial interactions between SPII and the other independent variables elucidate subtle patterns in the total data breach cost variation. Fourth, class action lawsuit (CAL) categorical variables regulate the variation in the total data breach cost.
Effects of initial states on the quantum correlations in the generalized Grover search algorithm*

We investigate the correlations between two qubits in the Grover search algorithm with arbitrary initial states by numerical simulation. Using a set of suitable bases, we construct the reduced density matrix and give the numerical expression of correlations relating to the iterations. For different initial states, we obtain the concurrence and quantum discord compared with the success probability in the algorithm. The results show that the initial states affect the correlations and the limit point of the correlations in the searching process. However, the initial states do not influence the whole cyclical trend.
Pengimplementasian Sistem Rekomendasi Musik Dengan Metode Collaborative Filtering

Nowdays, Recommendation Systems are widely used on various platforms such as e-commerce, online cinemas, youtube, and online music streaming platforms. The Recommendation System is a machine learning algorithm that has goal to aim of providing predictions in the form of values or an action on an item that is given or given to a number of users, so that users can find new items that match what user likes and preferences of each user.The Recommendation System has a variety of methods that can be implemented, one of which is the collaborative screening method. Collaborative filtering method works by providing predictions on a number of items to be given to the user based on the interaction value the user gives with these items. However, the collaborative filtering method itself has various types of algorithms which can be broadly divided into 2, namely the KNN algorithm and Matrix Factorization.In this study, a collaborative filtering based music Recommendation System was implemented. Then conducted a study on the evaluation of the music Recommendation System using the KNN algorithm with a music Recommendation System using a Matrix Factorization algorithm.
Emergent practices and material conditions in learning and teaching with technologies

How technology is reshaping and transforming pedagogical practices around the world is one of the major ongoing enquiries in the field with a mounting body of research over the past few decades. Th...
Object Segmentation Without Labels with Large-Scale Generative Models

The recent rise of unsupervised and self-supervised learning has dramatically reduced the dependency on labeled data, providing effective image representations for transfer to downstream vision tasks. Furthermore, recent works employed these representations in a fully unsupervised setup for image classification, reducing the need for human labels on the fine-tuning stage as well. This work demonstrates that large-scale unsupervised models can also perform a more challenging object segmentation task, requiring neither pixel-level nor image-level labeling. Namely, we show that recent unsupervised GANs allow to differentiate between foreground/background pixels, providing high-quality saliency masks. By extensive comparison on standard benchmarks, we outperform existing unsupervised alternatives for object segmentation, achieving new state-of-the-art.
The Influence of BYOD Concept on Development of Learning Process in Universities

Education is one of the largest markets promoting implementation of Bring Your Own Device BYOD. The BYOD model was originated in colleges and universities, being stimulated by technologically advanced students, who demanded it, and administrators of educational entities, who agreed that allowance to get access to the network using personal devices was a competitive advantage. Nowadays this concept attracts great attention. People depend on their personal devices and want to have the opportunity to use them anywhere in order to make their life simpler and more efficient. While BYOD implementation increases, teachers determine new methods of integration of mobile devices into learning. The use of personal mobile devices of students for learning seems to be attractive for universities, since these devices would help to reduce expenses and to support teaching and learning. The research objective: to detect the level of influence of BYOD concept on learning process. In the conclsuoins authors confirm that BYOD is the dominant model in universities.
Prosocial or Selfish? Agents with Different Behaviors for Contract Negotiation Using Reinforcement Learning

We present an effective technique for training deep learning agents capable of negotiating on a set of clauses in a contract agreement using a simple communication protocol. We use Multi-Agent Reinforcement Learning to train both agents simultaneously as they negotiate with each other in the training environment. We also model selfish and prosocial behavior to varying degrees in these agents. Empirical evidence is provided showing consistency in agent behaviors. We further train a meta-agent with a mixture of behaviors by learning an ensemble of different models using reinforcement learning. Finally, to ascertain the deployability of the negotiating agents, we conducted experiments pitting the trained agents against human players. Results demonstrate that the agents are able to hold their own against human players, often emerging as winners in the negotiation. Our experiments demonstrate that the meta-agent is able to reasonably emulate human behavior.
Secure Data Sharing Based on Linear Congruetial Method in Cloud Computing

Each day the need for cloud computing increases to provide flexible storing and cloud services and according to the users’ wishes. If a certain owner of data wishes to share this data with another user, the security of data will form a complicated problem. In the current research, a secure data sharing and storing system is suggested on the cloud, with offering data secrecy and access control authenticity for data. The suggested model reduces the load on the central management and offers encryption and decryption for the files shared by the users. The suggested system consists of three fundamental entities: cloud service (CS), certificate authority (CA), and users, where CA is the trusted entity to generate keys and distribute them on other entities and to encrypt and decrypt files.
Implementasi Crime Prevention Through Environmental Design (CPTED) Pada Fasilitas Parkir Sepeda Motor (Studi Kasus: Kampus Fakultas Ilmu Budaya USU)

Most often crime that occurs on the USU campus is motorcycle theft. In fact, several motorcycle theft on the USU campus is covered by electronic media. So there is a need to make a motorcycle parking model to reducing and preventing the opportunity for crime to occur. This research aims to make a model of Faculty of Cultural Studies USU campus motorcycle parking with the CPTED concept. This research using the descriptive qualitative method. This research started with a physical analysis of existing conditions in the motorcycle parking area, then the author developing a motorcycle parking model with the CPTED concept. The result of this research is motorcycle parking model in Faculty of Cultural Studies USU campus is focusing on six ways, i.e: (1) using hedge as fence; (2) using automated system parking portal and pedestrian way from/to parking area; (3) construct parking lot roofing; (4) construct students gathering space; (5) using speed bump; (6) using signage. By applying this six ways, the Faculty of Cultural Studies USU campus motorcycle parking is expected to free from any crime.
An innovative network based on double receptive field and Recursive Bi-directional Long Short-Term Memory

Abstract Sequence recognition of natural scene images has always been an important research topic in the field of computer vision. CRNN has been proven to be a popular end-to-end character sequence recognition network. However, the problem of wide characters is not considered under the setting of CRNN. The CRNN is less effective in recognizing long dense small characters. Aiming at the shortcomings of CRNN, we proposed an improved CRNN network, named CRNN-RES, based on BiLSTM and multiple receptive fields. Specifically, on the one hand, the CRNN-RES uses a dual pooling core to enhance the CNN network’s ability to extract features. On the other hand, by improving the last RNN layer, the BiLSTM is changed to a shared parameter BiLSTM network using recursive residuals, which reduces the number of network parameters and improves the accuracy. In addition, we designed a structure that can flexibly configure the length of the input data sequence in the RNN layer, called the CRFC layer. Comparing the CRNN-RES network proposed in this paper with the original CRNN network, the extensive experiments show that when recognizing English characters and numbers, the parameters of CRNN-RES is 8197549, which decreased 133,752 parameters compare with CRNN. In the public dataset ICDAR 2003 (IC03), ICDAR 2013 (IC13), IIIT 5k-word (IIIT5k), and Street View Text (SVT), the CRNN-RES obtain the accuracy of 96.90%, 89.85%, 83.63%, and 82.96%, which higher than CRNN by 1.40%, 3.15%, 5.43%, and 2.16% respectively.
Impact of Human Resources Development on Economic Growth: An Appraisal

The study focused on the inter relationship that exist among educational (formal and non-formal) training, human resources development and economic growth. Opinions of 60 participants were randomly sampled through a research instrument titled “impact of human resources management on economic growth indices” (HRMEGI). The study aimed at assessing role of formal and non-formal education on quality of human resources and evaluating contributions of human resources to economic growth. Findings revealed that there is a reverse causal relationship between quality of human resources and economic growth, and enrollments into the three (3) tiers of education have varying degree of contribution to economic growth. Hypotheses tested revealed a positive and significant relationship between educational training and quality of human resources, as well as human resources management, development strategy and economic growth. Findings suggests that increased funding of education at all levels, Research Institutes, genuine commitment to staff training and development in public and private sectors, putting in place policies aimed at reducing poverty, unemployment and inequality wealth distribution in the economy would impact human resources development which would further translates positively on economic growth.
Pronunciation Learning Strategy Use of EFL Learners: Focused on 5th and 6th Graders in Primary School

This study aims to investigate pronunciation learning strategies (PLS) use of 5th and 6th graders learning EFL in Korean elementary schools, including strategies they employ most and least frequently, and to discuss the relationship between PLS use and their grade, gender, and the level of interest in English. The questionnaire data about PLS use from 207 students were analyzed by descriptive statistics, t-tests, ANOVAs, and post-hoc tests. The responses to open-ended questions were qualitatively analyzed. Participants showed medium strategy use in six PLS categories. They also reported using memory and cognitive strategies most frequently, and metacognitive strategies least frequently. The most frequently used strategies involved repeating aloud and trying to remember the teachers’ pronunciation, whereas planning pronunciation learning and avoiding saying difficult words were used least frequently. Fifth graders and females used more strategies with higher mean scores than sixth graders and males. However, two strategies such as checking the sounds of words on a computer and learning pronunciation with peers varied significantly by grade, and a significant gender difference occurred in one strategy like practicing sounds in words and then in sentences. Noticeably, EFL learners’ levels of interest in English had the most significant effect on overall PLS use.
The Expansion of E-learning in the UAE

The COVID-19 pandemic has forced higher education institutions (HEI) in the United Arab Emirates (UAE) and globally to become more innovative in their online teaching and learning approach. This chapter presents the conceptual basis for understanding the feasibility of implementing e-learning in the UAE. Rogers’s (2003) diffusion innovation theory is used to assess which factors affect the UAE’s public HEIs’ intention and ability to adopt e-learning during the COVID-19 era. It was found that to lower the complexities the pandemic presents and promote the UAE’s post-crisis growth, public HEIs should rework their traditional higher education system into a blended learning model while safeguarding the UAE’s social and cultural standards for e-learning. Overall, with the observed benefits of increased access and developing a new generation of technologically skilled creative thinkers, the UAE’s advanced infrastructure is equipped and ready to become a global hub for e-learning.
American and Indian Sign Language Translation using Computer Vision

Even with all the recent technological advancements, the hearing and speech impaired community still struggles to communicate as seamlessly as their more privileged peers do. This is both a social and economic concern as it comes in the way of total inclusivity of this community in social circles, as well as puts companies under pressure as the barricade to free flowing communication between people who are and are not hearing/speech impaired leads to loss of productivity and thus resources, as it is not viable for companies to hire many translators. A lack of medium for spontaneous communication must not come in the way of socializing, employment, or productivity. In keeping with the demography, we developed an android based application which will translate Indian Sign Language (ISL) being gestured by a user to English text in real time. A comparative analysis of the proposed system to translate American Sign Language (ASL) and ISL is also presented. The work aims to provide a platform to the hearing and speech impaired to express their views and opinions thus enabling them to eliminate their dependencies.
Face Recognition Based Smart and Robust Attendance Monitoring using Deep CNN

It is difficult for teachers to deal with student attendance during classes, whether online or offline since they do it by hand as they use their teaching time. To solve this problem, the smart and insightful attendance management system can be used. Authentication leads to the biggest impediment. The current structure uses biometric authentication, such as voice analysis and signature verification. The study suggested a system of attendance tracking built on facial recognition that can strengthen traditional biometric authentication. The architecture is a relationship between computers and humans and addresses a robust method of authentication. To identify a face, the system uses HOG and SVM and uses an existing database for labeling attendance. The experimental results show the device can automatically identify the faces recorded by the camera accurately and we can detect the face more precisely and efficiently with the use of the SVM classifier.
Models for supporting the operating scenarios during a life cycle in automated systems

The paper presents models for describing the operating scenarios of automated systems. The authors introduce and analyze the category “the life cycle of automated system scenarios”. The life cycle consists of a sequence of stages. The leading success factor of any scenario is the support of the scenario during all stages of its life cycle. The most important stages of the scenario life cycle are the following: the stage of forming the event set for generating the scenario, the stage of performing the sequence of scenario events and the stage of situational analysis of the external and internal environment of the event. In the article it is proposed to use a theoretical set model in order to select an element from a set of alternatives. The elements are events for designing a scenario. The model uses fuzzy logic and is based on the process of controlling an array of parameters if variants are available. The model is used to support the stage of forming a set of events for generating the scenario. The mathematical model of automated system operation which is used to support the stage of performing the sequence of scenario events is suggested in the article. The model is based on the principle of information logistics: each parameter (a set of data) is in the right place in the automated system, ‘just-in-time’ and in the required format. A modified frame model is proposed to support situational analysis. Situational analysis is used to operate the event algorithms and to determine the next event in the scenario. The modified frame model is based on the use of fuzzy logic procedures in a frame network.
Evaluation of Heavy Metals Pollution Status of the Groundwater around Riruwai Mining Area, Kano State, Nigeria

The aim of this study was to investigate the heavy metals pollution status of the groundwater of Riruwai mining area, Kano State, Nigeria. A total of 31 groundwater samples were collected from five sampling locations which include: underground mining site (RGWI), tap water, (RGW2), surface mining ponds (RGW3), borehole (RGW4) and the well water samples (RGW5) during the dry and rainy seasons. The concentrations of seven heavy metals (As, Cd, Cr, Hg, Mn, Pb and Zn) and some physical parameters (pH, EC, and TDS) were determined using Atomic Absorption Spectrometer and Deluxe Water and Soil Analysis Kit respectively. The results of the analysis revealed that pH, EC and TDS were within the desirable limits recommended by WHO and NSDWQ in all sampling locations except in RGW1 and RGW3 for both seasons. The concentrations of heavy metals ranged as follow: As (ND to 0.15), Cd (ND to 0.1), Cr (ND to 0.25), Hg (ND to 0.09), Mn (0.12 to 0.66), Pb (0.003 to 0.06) and Zn (2.29 to 11.75), and As (0.005 to 020), Cd (0.001 to 0.15), Cr (0.001 to 0.17), Hg (ND to 0.14), Mn (0.16 to 0.92), Pb (0.007 to 0.09) and Zn (2.85 to 14.05) during the dry and rainy seasons respectively. The heavy metals concentrations changed along with the sampling sites in decreasing order of RGW1 &gt; RGW2 &gt; RGW3 &gt; RGW4 &gt; RGW5 in both seasons. The concentrations of all heavy metals were above the desirable limits recommended by WHO and NSDWQ during both seasons except well water samples (RGW5) and some few locations where the heavy metals were not detected. Therefore, it is recommended that water resources of the Riruwai mining area should be monitored closely, and more efforts should be made to reduce heavy metals concentrations level, particularly As, Cd, Cr and Zn.
Symmetry-via-Duality: Invariant Neural Network Densities from Parameter-Space Correlators

Parameter-space and function-space provide two different duality frames in which to study neural networks. We demonstrate that symmetries of network densities may be determined via dual computations of network correlation functions, even when the density is unknown and the network is not equivariant. Symmetry-via-duality relies on invariance properties of the correlation functions, which stem from the choice of network parameter distributions. Input and output symmetries of neural network densities are determined, which recover known Gaussian process results in the infinite width limit. The mechanism may also be utilized to determine symmetries during training, when parameters are correlated, as well as symmetries of the Neural Tangent Kernel. We demonstrate that the amount of symmetry in the initialization density affects the accuracy of networks trained on Fashion-MNIST, and that symmetry breaking helps only when it is in the direction of ground truth.