Reviewerly/exHarmony huggingface dataset:
AUGMENTED REALITY INTERFACE FOR PC

Augmented Reality (AR) and Virtual Reality (VR) headsets are becoming more accessible and cheaper, especially headsets that allow mobile phones to be used as a display for VR/AR applications. VR is being utilized heavily on platforms like Google Cardboard. Whereas AR applications which use the camera of the mobile device for situational awareness with virtual elements overlaid on top of the camera view are useful for productivity and commercial applications. We propose an Android mobile application made using Unity3D that provides additional virtual displays for PC that can be used as large virtual workspace instead of large monitors. It is implemented with the AR display which is on top of the viewport. The user can record the screen and work anywhere while connected to the same local area network. Keywords: Virtual Reality, Augmented Reality, Socket Programming, Unity3D
A Short Overview of the Main Concepts of Artificial Neural Networks

A significant increase in computer performance, the accumulation of a large amount of data necessary for training deep neural networks, the development of training methods for neural networks that allow you to quickly and efficiently train networks consisting of a hundred or more layers, has led to significant progress in training deep neural networks. This allowed deep neural networks to take a leading position among machine learning methods. In this work, neural network paradigms (and their methods of training and functioning) considers, such as Rosenblatt perceptron, multilayer perceptrons, radial basis function network, Kohonen network, Hopfield network, Boltzmann machine, and deep neural networks. As a result of comparative consideration of these paradigms, it can be concluded that they all successfully solve the tasks set before them, but now, deep neural networks are the most effective mechanism for solving intellectual practical tasks.
Efektivitas Penggunaan Quizizz sebagai Media Penilaian Siswa pada Mata Pelajaran TIK

This study uses a quantitative descriptive analysis method. The research was conducted by teaching ICT material in two meetings, then at the end of the second meeting a learning evaluation was carried out. Evaluation of learning is done by giving questions about the material that has been taught. The subjects of this study were 23 students of class VIII SMPN 19 Pontianak. This research was conducted by giving questionnaires to students of SMP Negeri 19 Pontianak to collect data. Based on the questionnaire distributed to class X students of SMP Negeri 19 Pontianak, the following data were obtained based on 23 respondents who filled out the questionnaire/questionnaire survey and then processed it using a Likert scale calculation. Based on the statistics mentioned above, the majority of students answered positively or agreed with the evaluation of student motivation in Information and Communication Technology (ICT) subjects. Based on the summary of the 12 indicators questionnaire, it can be concluded that the use of Quizizz in class VIII SMP Negeri 19 Pontianak was successful with an average percentage of 94.17%.
Post-covid Tourist Satisfaction in the Hospitality industry: Reflections from Jordan

This study sets out to investigate the effects of COVID-19 on the tourism industry in Jordan by studying the satisfaction of tourists after the lockdown. This study focuses on three main areas that may influence tourists' satisfaction. The tourist satisfaction study was conducted using a questionnaire designed and distributed among tourists who used the services. Other than that, all the tourist guides from the six travel agencies involved in this study were interviewed for cross-checking purposes. There was a shift in the satisfaction level after the pandemic's peak. The three main areas that determine satisfaction are services provided by travel agencies, facilities for tourists at their destinations, and accommodations. The findings suggested the tourists demanded proper COVID-19 standard operating procedures in addition to the basic requirements of the services provided. Data will inform stakeholders on the preparation of services provided for the post-COVID-19 tourism industry.
Design of Infak Payment Information System Using Borland Delphi 7 (Case Study: Aceh Energy &amp; Mineral Resources Department)

The purpose of the study is to create an Infaq Payment Information System using Delphi 7 to make Infak Data Payments contained in the Aceh Energy and Mineral Resources Office where there are advantages when compared to Microsoft Excel in terms of Infaq Payments including in terms of Output, where the system that will be the author developed can load more than one Output. In the process of the author's research regarding infaq payments at the aceh energy and mineral resources service, in the current system there are often obstacles or problems that arise. This happens because there are several things that quite interfere with the Financial Monitoring process, including; The length of the infaq payment process is one of the main obstacles, the use of microsoft excel is considered too complicated in terms of input, processing, information, the process of accessing / uploading data will take longer and waste more energy, and the possibility of errors or mistakes is more risky due to everything something that can manually interfere with the accuracy of the user. In application development, the researcher uses the prototype method. From the results of the research, the calculation application can be used as payment at the Aceh Energy &amp; Mineral Resources Department and is designed using Delphi, then the results of the evaluation of the current desktop application display with prototype design are known that each category has an increase in usability value. Content, Organization and Readability previously had a value of 0.61 and increased to 0.66. The Navigation category was previously 0.62 to 0.67. The previous User Interface Design category was 0.63 to 0.70 and the previous Performance and Effectiveness category was 0.64 to 0.72.
Sedimentological and Geochemical Study of Foundation Zone Sediment at Baiji City, North Iraq

The study includes grain size analysis, clay minerals, selected heavy metals, total dissolved solids and chemical properties. The grain size analyses of (78) samples indicate that the upper layers of the floodplain are fine where the lower layers are coarser. The identification of abundance clay minerals are; Kaolinite, Montmorilonite, Illite, Chlorite and Palygorskite. The (pH) of the sediments ranged (7.07–7.33) with average (7.22), the values of (EC) ranged (1.71-6.42) with average (3.45) and the variation may be due to the variation of climate conditions, while the sulfates SO3 between (0.256% –8.985%) with average (1.70), and that may due to presence of primary or secondary gypsum. The (T.S.S) ranged between (0.490%–12.550%) with average (3.42) and organic content range between (0.342%–3.272%) with average (1.55).&#x0D; The concentrations of Co, Zn, Sr, Ni, Pb, and Cd in (ppm), indicate that Co, Zn, and Ni elements are higher than the standards in the earth crust while the others within the limits of the earth crust or less than that.
Effect of Fragility on Growth and Poverty in Nigeria: A Disaggregate State-Level Analysis

Why some nations are wealthier than others are one of the most contentious and enigmatic questions in international development economics. This has necessitated plausible explanations for the reasons behind Africa's poor development record over the past 50 years. Among other factors, fragility arising from different conflicts in African countries has been ranked as a key factor that undermines the development of the continent. Nigeria found itself in this set due to growing conflicts in different parts of the country. Consequently, fragility worsened the country’s development due to the huge associated economic and social costs. More so, conflict-affected countries are characterized by the worst socio-economic outcomes. Hence, existing studies have been preoccupied with the understanding of the relationship between fragility and economic growth as well as fragility and poverty. To provide evidence in the context of Nigeria, this paper, therefore, empirically investigated the fragility-growth nexus, as well as the fragility-poverty nexus, in a sample of 36 states and the Federal Capital Territory (FCT) in Nigeria. We further considered the macroeconomic and socio-political relationships in fragile and non-fragile states of Nigeria. Using data covering the period between 2011 and 2015, both the static approach (Ordinary Least Squares, Fixed Effect, and Random Effect) and the dynamic approach (Difference and System Generalized Method of Moments) were explored to provide answers to some key questions in the study. The results showed that the neoclassical and socio-political approaches complement each other. Specifically, fragility significantly weakened economic growth and further worsened poverty levels among the states. This suggests that conflict-related fragility creates an unstable environment that discourages economic activities and aggravates hunger among the population. More so, the results indicated that only debt enhances economic growth while income reduces poverty in both fragile and non-fragile states. Hence, conflict resolution is crucial to addressing conflicts in different parts of the country. Also, the country needs to explore various strategies (security infrastructure, and human capital) to overcome fragility, enhance economic growth, and combat poverty.
Acceptance of Bitcoin by Investors in the Southern Region of Thailand

Bitcoin has become an asset that is gaining popularity among investors due to the trend of transitioning into the digital society, inflation, and the use of Bitcoin as a store of value. This research aims to study the acceptance of Bitcoin among investors in southern Thailand, classified by personal factors, through a survey of 400 samples. The data was analyzed using statistical methods such as independent sample t-test and one-way ANOVA to test hypotheses. The study found that most of the sample group were males, over 40 years old but under 50 years old, with a bachelor's degree or equivalent, an average monthly income of 30,001-45,000 baht, and had experience in investing in the SET market. Gender and experience in investing in the SET market did not affect the acceptance of Bitcoin, but Differences in age, education level, and income have an impact on the acceptance of Bitcoin. At least one pair of groups showed significantly different acceptance levels: investors aged over 40 but under 50 were more likely to accept Bitcoin than investors aged over 30 but under 40 and investors aged under 30. Investors with educational levels lower than a bachelor's degree tend to have lower acceptance of Bitcoin compared to those with a master's degree or higher, as well as investors with a bachelor's degree or equivalent. And an average monthly income of 30,001-45,000 baht were more likely to accept Bitcoin than those with an average monthly income over 45,000 baht and those with an average monthly income of 15,000-30,000 baht. The significance level was 0.05. This research is beneficial to relevant organizations in planning for providing information and implementing measures to regulate Bitcoin based on the personal characteristics of investors or the general population. Further studies could examine attitudes and investment behaviors among different generations within the sample group.
Novel Framework forEnhancing Data Quality using Data Correlation Factor in Wireless Sensor Network

Sensors are randomly deployed in Wireless Sensor Network (WSN) and it is observed that the sensor density is increasing rapidly.Owing to which there is presence of similar forms of singular event data captured by different sensors.This problem of data correlation is closely associated with cluster formation process, in which energy efficiency is emphasized compared to data quality.Existing reviews show that clustering approaches require major revisions in order to ensure better data quality.Therefore, the proposed system introduces a clustering mechanism that uses data correlation as the essential parameter for the selection of clusterhead, in order to control the transmission of error-prone data packets during the process of data aggregation.Using analytical research methodology, the proposed system introduces three sequential clustering algorithms for ensuring better selection of clusterhead on the basis of best data correlation value.The simulated outcome of the proposed study shows that, it offers better data quality in contrast tothe existing system.
A Novel Approach towards Movie Recommender System using Deep Learning Techniques

Recommender systems have become a key technology to help the users in interacting with the increasingly larger data and information available online.The rapid advancements in Deep Learning techniques have been very useful in recommendation systems as it enhances the overall performance and accuracy of the recommendation systems.This paper attempts to work on a hybrid recommendation model by considering a weighted average of top N recommendations from both content based and collaborative based filtering methods and hence eliminating their individual shortcomings.A LightFM module has been also used to evaluate the loss functions on this hybrid model and to capture the latent features about attributes of users and items.Thereafter, a class of two-layer undirected graphical models, called Restricted Boltzmann Machine (RBM) and Auto-encoder is successfully applied to the Movielens data set to provide the accurate recommendations.This study shows that the proposed approach outperform the traditional recommender systems in terms of accuracy.
Blockchain in Educational Development: Potentialities and Issues—Towards sophisticated Digital Education Systems

Blockchainhas become an important digital technology component in the Information Technology field and applicable in diverse areas of society and community.Blockchain and its gradual development lead it to become an emerging technology and called as 'Blockchain Technology.Blockchain simply offers a system of recording information and which are not possible to change due to robust and secure systems.Blockchain is a kind of digital ledger transaction that spreads over the entire communications or networks.Due to the adopted mechanism in Blockchain it is difficult to change or hack or check within the systems and therefore this technology is rising gradually as it is using encrypted and distributed databases connected with transactions.Blockchain is a digital ledger system and that further depends on different independent record, and keep recoded data in a particular place.Blockchain is dedicated in tangible and intangible asset management and further concerned pieces of data stored in the same place.Normally, it is being started its uses in the financial segment regarding proper digital currency transactions but gradually it has become an important tool in diverse areas such as in tangible and intangible asset management.As far as the Education sector is concerned it is useful in both onsite and online education systems.However online and e-learning are more suited with Blockchain.Today in educational institutions many third parties are being used in proper educational delivery and Blockchainis being used in proper safeguarding in newer educational and training models.This Paper is about Blockchain with its concepts, features and applications in educational systems, management and the teaching-learning process.Work also suggested the issues and concerns of Blockchain in educational development and management.
Website performance analysis in relation to the Visibility indicator of the Webometrics ranking: case study TOP 5 universities from the Republic of Moldova &amp; Romania

The Webometrics Ranking of World Universities periodically brings to the attention of mass media, but also to the society of the Republic of Moldova, information relevant for future students regarding the positions of higher education institutions in the country. A website is simply a prerequisite of online existence for a university. Most of the time, the university’s webpage is the place where one can present detailed information about institution’s activity, educational services provided, research activity, etc., this being the official source of information for future students, future graduates, but also university partners. The purpose of this paper is to analyze the webpages of the TOP 5 universities ranked by Webometrics from the Republic of Moldova and Romania, in terms of the visibility indicator of this university ranking. The paper has the task to make a comparative evaluation of the universities’ webpages so that the conclusions formulated later could contribute to the improvement of the Moldovan universities’ positions in the Impact Rank section of the Webometrics Ranking of World’s Universities.
Crime Anomaly Detection using CNN and Ensemble Model

Every single day, thousands of crimes are perpetrated, and hundreds may be probably taking place right now throughout the world. Without a doubt, crime is viewed as a social blight. Nothing can truly stop it, no matter what is done. Surveillance cameras, on the other hand, can dramatically minimize it. Using public surveillance camera systems to prevent, document, and minimize crime can be a cost-effective solution. Installing enough cameras to detect crimes in progress and integrating technology to automate the monitoring of the live stream from these cameras will result in the most effective systems. Because of its self-learning characteristics, the advanced Artificial Intelligence surveillance system is constantly learning and improving. The Deep Learning Algorithms applied in this work processes videos using electronic devices like cameras in real-time termed as image processing, saving both human resources and a great deal of time. The highest accuracy of 86.6% was attained by Ensemble Model, followed by Inception Model with SGD Optimizer, Leaky Relu Activation Function giving an accuracy of 83.43%. Hence, anomalies were detected efficiently using decision making in real-time surveillance scenarios.
INVESTIGATING THE E-SERVICE ADOPTION OF SMEs IN OMAN USING THE TECHNOLOGY READINESS MODEL 2.0 WITH A FOCUS ON BUSINESS SIZE

The study examines the technology readiness of SMEs for the adoption of e-services based on company size. This reveals opportunities to better exploit the potential of e-services adoption for SMEs in Oman. The problem this study focused is that despite the increasing availability and benefits of e-services, many small and medium enterprises (SMEs) in Oman have been slow to adopt them. This lack of adoption can limit their ability to compete and grow in an increasingly digital market. Studies show that business size plays an important role in the adoption of e-services, and this is one of the main reasons for conducting this study. A quantitative survey method was used to select the sample among SMEs in Oman. SmartPLS 4.0 was used to conduct a multigroup analysis (MGA) to determine the moderating effect of firm size of SMEs in Oman.The main findings show that motivators (optimism and innovativeness) are important predictors of SMEs' willingness to adopt e-services. The study also found that among demographic factors, firm size significantly explains differences between SMEs' readiness to adopt e-services, while it is predicted by motivators and inhibitors.This study has added to the literature by investigating the moderating effect of firm size on SMEs' readiness to adopt e-services in Oman. It could also provide additional benefits to many SMEs that could lead to the adoption of e-services.&#x0D; Keywords: Technology Readiness, Technology Adoption, E-services, SME, Business Size.
Prediction of stock purchase decisions using artificial neural network method

The difficulty of determining a stock purchase decision is a problem to benefit from stock transactions. This study aims to give a person's decision to buy one of the issuer's company shares to get a profit on the same day. The dataset used in this study came from the investing.com website in the form of daily data shares of PT Indofood CBP Sukses Makmur Tbk with ICBP stock code for the period January - September 2019. The attributes used in this study were the opening price, highest price, lowest price, closing price, transaction volume, day representation, and decisions. The dataset that has been collected was normalized using the Min-Max method to facilitate data processing. This research used the backpropagation neural network method and used the 10-Fold Cross Validation and Confusion Matrix for validation. The results of this study indicate that the backpropagation neural network method uses the bipolar activation function with training cycles of 2000 and learning rate of 0.03 has the best performance namely 69.35% of accuracy, 67.65% of precision, 74.19% of recall and 30.65% of error rate for prediction of stock purchase decisions per day in the form of buy or not.
Comparison Between the Kernel Functions Used in Estimating the Fuzzy Regression Discontinuous Model

Some experiments need to know the extent of their usefulness to continue providing them or not. This is done through the fuzzy regression discontinuous model, where the Epanechnikov Kernel and Triangular Kernel were used to estimate the model by generating data from the Monte Carlo experiment and comparing the results obtained. It was found that the. Epanechnikov Kernel has a least mean squared error.
A MOBILE ROBOT PATH PLANNING USING GENETIC ALGORITHM

Mobile robot path planning is an important task that involves finding an optimal path from an initial location to a goal. This paper presents a novel approach to this problem utilizing a genetic algorithm. The proposed approach formulates the problem as an optimization problem, where the objective is to minimize the total cost of a given path. The genetic algorithm is then used to search for an optimal solution, which is found by evolving a population of paths over multiple generations. The proposed approach is tested and evaluated using a simulated robotic environment, and the results demonstrate its efficacy in finding optimal paths. KEYWORD: Mobile Robots, Path Planning, Genetic Algorithm, Optimization, Simulation.
DETECTION OF COMPLEX SEMANTICS FROM UNSTRUCTURED DATA USING NLP &amp; CONVOLUTION NEURAL NETWORK TECHNIQUE

The majority of data on computers nowadays still takes the form of unstructured text. The inherent ambiguity of natural language makes it incredibly difficult but also highly profitable to find hidden information or comprehend complex semantics in unstructured text. In this paper, we present combination of Natural Language Processing (NLP) and Convolution Neural Network (CNN) hybrid architecture for detection of complex semantics from unstructured data that enables different users to make understand formal semantic knowledge to be extracted from an unstructured text corpus. Keywords— Convolution Neural Network, Natural Language Processing, Controlled Natural Language, Text Mining, Information Extraction.