Reviewerly/exHarmony huggingface dataset:
Question Classification for Helpdesk Support Forum Using Support Vector Machine and Naïve Bayes Algorithm

The helpdesk support system is now essential in ensuring the journey of support services runs more systematically. One of the elements that contribute to the non-uniformity of the question data in the Helpdesk Support System is the diversity of services and users. Most questions asked in the system are in various forms and sentence styles but usually offer the same meaning making its hard for automation of the question classification process. This has led to problems such as the tickets being forwarded to the wrong resolver group, causing the ticket transfer process to take longer response. The key findings in the exploration results revealed that tickets with a high number of transfer transactions take longer to complete than tickets compared to no transfer transaction. Thus, this research aims to develop an automated question classification model for the Helpdesk Support System by applying supervised machine learning methods: Naïve Bayes (NB) and Support Vector Machine (SVM). The domain will use a readily available dataset from the IT Unit. The results using these techniques are then evaluated using confusion matrix and classification report evaluation, including precision, recall, and F1-Measure measurement. The outcomes showed that the SVM algorithm and TF-IDF feature extraction outperformed in terms of accuracy score compared to the NB algorithm. It is expected that this study will have a significant impact on the productivity of team technical and system owners in dealing with the increasing number of comments, feedback, and complaints presented by end-users.
Enhancing the Developer Experience (DX) in Docker Supported Projects

Docker is undeniably powerful and revolutionary in how containerized system development is developed today, but it is apparent that the learning curve for it should be addressed, as it typically is complex at times, especially for beginners. One of the fundamental tasks in a Docker workflow is Dockerfile configurations, which at times require ample time to study and observe for attaining the best practices, even the appropriate result. This issue undeniably affects the developer experience. Developer Experience (DX), being a derived field from User Experience (UX) that has been getting traction for the past few years concerns developers’ innate ability to perceive tasks as enjoyable, painful, or perhaps some other sets of emotions. The goal of DX is to evaluate all those factors in order to improve the software development experience, which consequently affects how the project is delivered. In resonance with that, this work aims to enhance the DX by way of proposing and incorporating supporting interaction tools, both based on CLI and GUI as the interface type, with two different permutations: CLI and GUI. The DX of both has to be evaluated by the experts, who are of experienced developers, regardless of whether they have knowledge of Docker or not. The method to test and evaluate two different solutions is conducted qualitatively, with each respondent having a different order of evaluating the two solutions. The qualitative data is thematically analyzed, resulting in GUI being the best option among the two. The contribution of this research is the design guidelines for GUI and CLI-based tools development that enhance the Developer Experience (DX) in the scaffolding of Dockerfile and docker-compose.yml for projects that use Docker.
A systematic performance comparison of two Smooth Pursuit detection algorithms in Virtual Reality depending on target number, distance, and movement patterns

We compared the performance of two smooth-pursuit-based object selection algorithms in Virtual Reality (VR). To assess the best algorithm for a range of configurations, we systematically varied the number of targets to choose from, their distance, and their movement pattern (linear and circular). Performance was operationalized as the ratio of hits, misses and non-detections. Averaged over all distances, the correlation-based algorithm performed better for circular movement patterns compared to linear ones (F(1,11) = 24.27, p &lt; .001, η² = .29). This was not found for the difference-based algorithm (F(1,11) = 0.98, p = .344, η² = .01). Both algorithms performed better in close distances compared to larger ones (F(1,11) = 190.77, p &lt; .001, η² = .75 correlation-based, and F(1,11) = 148.20, p &lt; .001, η² = .42, difference-based). An interaction effect for distance x movement emerged. After systematically varying the number of targets, these results could be replicated, with a slightly smaller effect. Based on performance levels, we introduce the concept of an optimal threshold algorithm, suggesting the best detection algorithm for the individual target configuration. Learnings of adding the third dimension to the detection algorithms and the role of distractors are discussed and suggestions for future research added.
The Importance of Stabilizing the Syrian Pound Exchange Rate for Achieving Economic Stability

The aim of this article is to measure the impact of the fluctuations in the real exchange rate of the Syrian pound on the key macroeconomic indicators of the Syrian economy during the period 1961- 2020 to analyze the role of the real exchange rate in achieving economic stability. Since 2011, the Syrian economy suffered from a devastating war that effected all economic and social aspects of business activities leading to high fluctuations and instability of the macroeconomic ecosystem. During the first 40 years of study, the political stability in Syria has not been translated into economic stability on at the macroeconomic level. Moreover, during the last 11 years, the political instability has worsened the economic situation. To achieve the objective of this study, we applied autoregressive model and simple regression model on a various macroeconomic variables including the real exchange rate. The results show that the real exchange rate has a significant positive impact on the balance of payment, inflation, GDP gap, and negative impact on Real GDP, unemployment rate, monetary supply (M2).
THE EVOLUTION OF SANCTIONS EVASION: HOW CRYPTOCURRENCY IS THE NEW GAME IN EVADING SANCTION AND HOW TO STOP IT

When one country illegally invades another sovereign country, repeatedly, utilizing the mechanism of sanctions to try and curb the misconduct, has become a favored approach among democratic countries.Russia once again invaded Ukraine in the early part of 2022, defying all international pressure, to refrain from the illegal act.The rapid response from the international community was a litany of sanctions intended to cripple and deter Russia's actions.Sanctions evasions are not a new challenge for sanctioning countries and agencies.A United Nations (UN) report notes that low levels of governmental oversight in the cryptocurrency sector have enabled North Korea to generate income at an alarming rate.The efficacy of financial sanctions in this way is consistently undermined through illicit cryptocurrency transactions.As the cryptocurrency sphere exceeds forty-two million users worldwide, the question on those issuing sanctions remains: If cryptocurrency is left unregulated, will financial sanctions lose their power?This article will outline the use of sanctions as a preferred foreign policy tool and how they work.I look at the various sanctions the United States, European Union, United Nations have levied against the Russian Federation in response to repeated invasions of Ukraine's sovereign territory.I will also analyze cryptocurrency, defining what it is, how it works to lay the groundwork for the analysis of the current cryptocurrency regulations and how this relates to concerns of illicit activity within the cryptocurrency sphere, as a means for sanctions evasion.Several countries including The Russian Federation (Russia), The Bolivarian Republic of Venezuela (Venezuela), The Islamic Republic of Iran (Iran) and The Democratic People's Republic of North Korea (North Korea) are using innovative cybercrimes and other crypto-based efforts to evade economic and financial sanctions.This article will consider the pushback on regulation from the crypto industry as well as illuminating the loopholes that are causing increased concern and current incidences of illicit activity internationally.Finally, I propose a few areas of consideration for creating an international regulatory framework to help combat the evasion of financial sanctions, using cryptocurrencies.
Product Fake Reviews Detection with Sentiment Analysis Using Machine Learning

Abstract: Recently, Sentiment Analysis (SA) has become one of the most interesting topics in text analysis, due to its promising commercial benefits. One of the main issues facing SA is how to extract emotions inside the opinion, and how to detect fake positive reviews and fake negative reviews from opinion reviews. Moreover, the opinion reviews obtained from users can be classified into positive or negative reviews, which can be used by a consumer to select a product. This paper aims to classify product reviews into groups of positive or negative polarity by using machine learning algorithms. In this study, we analyse online product reviews using SA methods in order to detect fake reviews. SA and text classification methods are applied to a dataset of product reviews. More specifically, we compare five supervised machine learning algorithms: Support Vector Machine (SVM), for sentiment classification of reviews using two different datasets, including product review dataset V2.0 and product reviews dataset V1.0. The measured results of our experiments show that the SVM algorithm outperforms other algorithms, and that it reaches thehighest accuracy not only in text classification, but also in detecting fake reviews.
DreamWalk: Dynamic Remapping and Multiperspectivity for Large-Scale Redirected Walking

Redirected walking (RDW) provides an immersive user experience in virtual reality applications. In RDW, the size of the physical play area is limited, which makes it challenging to design the virtual path in a larger virtual space. Mainstream RDW approaches rigidly manipulate gains to guide the user to follow predetermined rules. However, these methods may cause simulator sickness, boundary collision, and reset. Static mapping approaches warp the virtual path through expensive vertex replacement in the stage of model pre-processing. They are restricted to narrow spaces with non-looping pathways, partition walls, and planar surfaces. These methods fail to provide a smooth walking experience for large-scale open scenes. To tackle these problems, we propose a novel approach that dynamically redirects the user to walk in a non-linear virtual space. More specifically, we propose a Bezier-curve-based mapping algorithm to warp the virtual space dynamically and apply multiperspective fusion for visualization augmentation. We conduct comparable experiments to show its superiority over state-of-the-art large-scale redirected walking approaches on our self-collected photogrammetry dataset.
Don’t Play with Gas: Relating Metrics with Smart Contract Deployment Costs

Smart Contracts (SC) are computer programs that run on blockchains and can be executed automatically in a deterministic way, when pre-determined conditions are met. Currently, Ethereum is the biggest blockchain network with more than 200,000 SCs deployed every month[1](#fn-0002). The main mechanism for financially managing and securing such networks is “ Gas Consumption ”. In particular, a gas cost is assigned to each operation that alters the blockchain state, based on the SC size and complexity. Thus, the cost that a SC incurs to its owner and users is related to the internal structure of the SC. By considering that the average cost for deploying a Smart Contract can reach up to thousands of euros, it becomes obvious that internal quality of SCs is of great importance. To this end, in this article we present a comprehensive analysis of the correlation of a set of code metrics (e.g., size, complexity) with the actual gas required to deploy Smart Contracts. The empirical evidence that we provide rely on the analysis of over 90,000 SCs. In addition to the produced empirical knowledge, in most of the cases validating the theoretical expectation, we have implemented a web-based application (Smart Contracts Quality Analysis Platform—SCQAP) that visualizes the findings, enabling the on-demand creation of correlation diagrams, and offers access to a public repository of our data (metrics and deployment gas consumption) via a REST API. To the best of our knowledge this is the biggest empirical study on SCs, which: (a) sets up the scene for further large-scale studies on Smart Contracts (through tooling and public dataset); and (b) provides guidance to software practitioners on parameters that can inflate deployment costs.
THE EFFECT OF JEOPARDY GAME ON STUDENTS’ READING COMPREHENSION

The problem of this research is does jeopardy game give an effecton reading students’comprehension. This research is conduct for eight-grade SMP Advent 2 Medan in academic year 2021/2022. This quantitative research is experimental research uses pretest, treatment, and post-test design. The population of this study is SMP Advent 2 Medan and samples are 46 students. Data are collected through multiple-choice tests consisting of 20 questions with four options. Jeopardy game contains independent variable of the study, while understanding the students reading is a dependent variable. According to compilation, test result shows the average score of exsperimental group is 100 which is considered very good, and the control group is 95, which is considered very good. Conclusion of this research is using jeopardy game as an effective strategy to enhance the reading of understanding students at SMP Advent 2 Medan.
Implementation of a Logistic Map to Calculate the Bits Required for Digital Image Steganography Using the Least Significant Bit (LSB) Method

The LSB method in steganography usually only uses the last bit or the last few bits that are the same for all pixels.This is very easy to solve by using a bitwise shift left operation so that the last bit becomes the leading bit (MSB).Some techniques combine steganography and cryptography through two different processes.In this study, a new technique is proposed to perform steganography and cryptography together.The random sequence obtained from the logistic map is used to determine the number of bits in the LSB method.Furthermore, testing was carried out on several grayscale images.The result obtained is that the hidden images cannot be opened easily.The level of sensitivity is very small, reaching 10 -15 .
Aplikasi Layanan Service Kendaraan Bermotor (Carimontir) Using Design Thinking Method

Many ownership of vehicles is an opportunity for repair service owners in Indonesia, to plan operations on Carimontir's business plan, operational plans are carried out based on a strategic plan by setting operational objectives in line with the company's strategic plan in accordance with setting objectives. After determining operational objectives, Carimontir determines targets that in accordance with QSPM, namely the number of business partners and being anywhere &amp; anytime. Then according to the business level strategy, namely the differentiation strategy by having artificial intelligence technology in the form of Chat Bots. The idea for the display of the application must be easy for everyone to understand by applying a minimalist design concept accompanied by symbols that imply the function of the user of the feature. In designing the Carimontir system process technology using the latest version of the system so that application work will run smoothly without lag, the latest security system is an important part of system security and company data.
Faktor-Faktor yang Mempengaruhi Pengelolaan Dana Bantuan Oprasional Sekolah (BOS) pada Satuan Pendidikan di Kabupaten Bengkalis

This study aims to examine and analyze the influence of the competence of human resources contained in the treasurer and school leadership as well as community participation on the management of BOS funds in the Education unit in Bengkalis Regency. The population in this study were all elementary / junior high schools in the scope of the Bengkalis Regency Education Office, while the sample amounted to 176 schools consisting of 126 elementary schools and 50 junior high schools. Respondents were taken by means of random sampling, in order to obtain a total of 528 respondents consisting of the principal, teacher/treasurer and the local community/school committee. Data analysis uses quantitative analysis methods that use numbers and statistical calculations. The data analysis tool in this study uses structural equation modeling-partial least square (SEM-PLS) using WarpPLS version 5.0 software. Based on the results of the analysis, it can be concluded that the competence of treasurer human resources, managerial competence of school principals and community participation affect the management of boss funds in the education unit in Bengkalis Regency.
Human Age Predication from Face Images Based on Combining Deep Wavelet Network and Machine Learning Algorithms

Due to the numerous variances in face appearance, age estimation using facial images is a difficult subject.Many factors can affect the estimation of human age such as race, face post, gender, lifestyle, etc.By considering more factors, the optimum performance may be obtained.In this study, we proposed a method to predict the age of facial images.The proposed method consists of four main stages: (1) Preprocessing.(2) Face alignment and cropping.(3) Feature extraction by using Deep Wavelet Network (DWN).( 4) Age prediction.Five of the machine learning classifiers (K-nearest neighbor, support vector machine, Naïve Bayes, decision tree, and random forest) were suggested in this proposal to combine with DWN and then select the best performance one.Two DWNs trained for male and female faces separately, so we have to classify faces before inputting to one of the two networks (classifying faces' gender is out of this study's scope).The performance of predicting the age was measured first when the age was divided into eleven age groups, where the accuracy was 97% for the females and 98% for the males.Also, we secondly measured the performance when the age was divided into seventeen age groups (five years for each group) with an accuracy of 91% for female and 92% for male faces.
Extremes in the Concentrations of CCN at Mt. Musala and Synoptic Classifications

According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change the aerosols are having generally a negative but decreasing in magnitude contribution to the effective radiative forcing (ERF) and, therefore, they act against the global warming. Leading role in this ERF balance play the aerosols involved in aerosol-cloud interaction, named the cloud condensation nuclei (CCN). In the present article we study the distribution of CCN as monitored by the Basic Environmental Observatory Musala in 2016 and their performance in different atmospheric circulations. As first step, we determine the days with extrema in the concentration and sort them separately for minima and maxima using the circulation scheme of Jenkinson–Collison Types that comprises 26 types of circulations. Thus we determine the most frequent types of circulation occurring in the studied period. Consequently, for each of these days we obtain the backward trajectories using the Hybrid Single-particle Lagrangian Integrated Trajectory model. This permits to match the prevailing types of circulation with the origin of air masses, and, on the other hand, their origin with respect to prevailing height of the trajectory.
Comparison of Missing Data Imputation Methods on Mixture Cure Survival Analysis Results in a Cardiovascular Disease Cohort

Abstract The goal of this study was to investigate the effect of missing data in the covariate variable on the amount of lost information in the cure mixture survival analysis model. For this purpose, data from patients of a cardiovascular cohort in Sulaimani Hospital were used. In the case of missing completely at random, in six situations, include full data and 10% to 50% missing data, AIC and BIC values of the models were compared. The results showed that the behavior of these two indicators was the same in comparing the models. Also, the choice of the appropriate imputation method for cured mixture models depends on the rate of missing data. In 10% of missing, due to the low number of missing individuals, the simple method such as mean was the best method. But for higher percentages, hot deck and regression methods performed better. Also, due to the skewed distribution of patients' age data, in cases with a missing of more than 40%, the median has shown better performance.
A Comparative Study of RSA and ELGAMAL Cryptosystems

Purpose: In this research, a comparative analysis of the RSA and ELGAMAL algorithms is presented using their encryption and decryption speed with different key sizes. ElGamal and RSA are two popular public-key cryptographic algorithms that depend on the mathematical difficulty of computing discrete logarithms in finite fields and factoring large integers, respectively.&#x0D; Methodology: In this study, the speed at which messages of various sizes can be encrypted and decrypted using various key sizes is used to gauge how well these algorithms work.&#x0D; Findings: RSA usually outperforms ElGamal in terms of encryption and decryption speed, according to the results. However, as the key sizes grow, the performance gap between the two algorithms narrows.&#x0D; Unique Contribution to Theory, Policy and Practice: These findings can aid in selecting the appropriate algorithm and key size for different applications that require secure communication and data encryption.
The Square-root Elastic Net With a New Calculating Method

The elastic net is a well-known algorithm and powerful in simultaneous estimation and variable selection under diverse conditions. In this paper, we proposed a novel version called square-root elastic net, which expands the application of the elastic net in some common cases that the noise distribution of data is not normal. We showed a calculating method based on proximal gradient descent combined with backtracking. Similar to the elastic net, the square-root elastic net can select more essential features than the sample size. Furthermore, the mean square error of the square-root elastic net is remarkably reduced when dealing with the abnormal noise compared to the elastic net.
Uji Performa Prediksi Gempa Bumi di Jawa Timur dengan Artificial Neural Network

East Java Province is an area directly adjacent to the Eurasian and Indo-Australian plate subduction zones, this has resulted in East Java province being an area prone to earthquakes. Predictions regarding the frequency of earthquake occurrences are very interesting to study. This needs to be done in order to increase our preparedness in an effort to reduce the risk of earthquakes. Research on earthquake prediction has been carried out, one of which is the artificial neural network method. The purpose of this study is to obtain the best network architecture that is applied to the data on the frequency of earthquake occurrences per month in East Java Province. Data on earthquake occurrences come from the BMKG Nganjuk Geophysics Station, which was recorded during the 2016-2021 period. The data was then grouped into the total frequency of events per month. The criteria for selecting the best network architecture are carried out by comparing each possible architecture's error values. The test method uses SSE (sum square error) criteria for each architectural model of the artificial neural network. The test results show that the input variation has a significant contribution and a greater correlation than the variation in the number of hidden neurons. The best prediction results are obtained in the model with 9-30-1 architecture with an error value of 0.1958.