Reviewerly/exHarmony huggingface dataset:
Economic development and Foreign Direct Investment: Evidence from OECD and selected African Countries

This study analyses the impact of foreign direct investments on economic performance of African and OECD countries, to elicit explanations for the economic development disparities between home economies of foreign direct investors and their host counterparts. Design/methodology/approach – Employing annual observations spanning from 1980 to 2018 of 43 African countries and 28 OECD countries, this study estimates a panel cointegration model based on dynamic OLS technique. Findings – The analysis found that foreign direct investment exerts strong predictive influence on the direction of economic growth, while controlling for the impact of financial development, human capital development, economic institutions, and physical capital stock. It was found that economic institutions and physical capital stock drive stimulative effect on economic growth across economies in Africa and OECD countries, while financial development and human capital exert influences on one or more of the sampled countries. Originality/value – The originality of this study lies in the application of the panel cointegration model based on dynamic OLS technique to analyse the cross-regional dynamics in the interaction between foreign direct investment and economic growth. Practical implications – Policy makers should ensure the sustenance of a conducive economic and political environment to the attract more foreign investors as increase in foreign investments drive positive trends in national productivity and enhances capital mobilization, job creation and social improvement. Keywords: African countries, OECD, FDI, Economic performance, Economic growth, Economic institutions, Physical capital stock, financial development, Economic freedom index, Human capital index, Real GDP, DOLS model.
IMPLEMENTASI EXPERT ADVISOR UNTUK TRADING OTOMATIS FOREX MENGGUNAKAN INDIKATOR RSI DAN MA DENGAN METODE MARTINGALE DI PLATFORM METATRADER 4

Among the many digital businesses, forex trading is one of them. To trade forex, various types of software are used which are tools for traders, such as Metatrader 4. The core business of forex trading is to buy or sell consistently to trade forex, making a profit. In order to get the desired profit, it is important to adjust the forecast towards a growing market. Therefore, we need the right forex indicators to predict prices that are not wrong. However, traders who follow predictive indicators signals often fail to execute trades on time and therefore are unable to make consistent profits. The solution for traders to find timely trading opportunities based on indicators is to create an automated forex trading system or Expert Advisor. Expert Advisor (EA) is a program/system that has the ability to appear in the terminal on the instructions of a trader without direct involvement. All tasks that the Expert Advisor performs automatically. Expert Advisors can work automatically without the need to monitor price movements for 24 hours. EA is designed to analyze the market based on 2 indicators RSI (Relative Strength Index) and MA (Moving Average) as a reference for prices that will occur and also as a reference for trading using the martingale method.
OPTIMASI KLASIFIKASI DETEKSI SENJATA API PADA ANIMASI BLACK LAGOON DENGAN ALGORITMA DEEPSORT DAN KALMAN FILTER

As technology develops, especially in the field of artificial intelligence, many new systems and programs are created with artificial intelligence, one of which is the field of computer vision, namely the detection of objects ranging from transportation, humans, masks, animals, and even firearms detection systems. However, this system still has deficiencies in the level of accuracy when complex conditions such as lighting and others. Currently firearms detection systems use datasets in the form of photos of real weapons, rarely or even no one uses other datasets such as games or animations to determine the performance of the detection system. This study aims to determine the performance of the firearms detection and classification system by experimenting with objects from an animation with a public dataset from the website www.imfdb.org/Black_Lagoon as well as several scenes from other animations and also an animation entitled Black Lagoon, then optimization is carried out.with the DeepSORT algorithm and Kalman Filter assisted by the YOLOv3 detection system, several percentage values were obtained such as a mAP value of 82.13%, 81.19% precision, and 78.53% recall in the training dataset using 100 iterations of epochs, and in the training model got a loss value of 8.27% and 6.67% for the val_loss value using 50 iterations of epochs.
BEHAVIOURAL FINANCE AND INVESTMENT DECISION MAKING

The study has been undertaken to critically examine if behaviour and biases of an individual investor have any impact on the selection of alternatives that are available to them in terms of the investments that they make. A brief analysis of the various theories available in terms of the above topic are also examined for their validity. The conclusion drawn based on the reviews of published papers is that behaviour, biases affect decision making and as such an investor has to be mindful of these biases and make sure that he doesn’t lose out on opportunities because of this.
IMPROVING STUDENTS’ READING COMPREHENSION THROUGH SQ5R METHODS: A DISTANCE LEARNING DURING THE PANDEMIC COVID-19

The entry of the Covid-19 virus in Indonesia has had a major impact on the education sector. The teacher could use several strategies during the learning process to be able to achieve the learning objectives. This study aimed to optimize the use of the Survey, Question, Read, Recite, Record, Review, Reflect (SQ5R) method in teaching reading during online learning at SMPN 3 Lamongan. The researcher used action research with direct observation during the learning process. The results showed that the SQ5R method could improve students' reading comprehension. The minimum score obtained by students in the initial test was 45. This value increased up to 65 in the final test, which indicated that all students had reached the minimum passing criteria standard that had been set. Meanwhile, the observations showed that students were able to analyze visual information, predict content and improve their notes. Overall, it was evident that the SQ5R approach has a positive impact on students' reading comprehension.
DESIGNING READING-WRITING TEACHING STRATEGY FOR THE ACADEMIC WRITING COURSE

The concept of integrating reading and writing in the language instruction has been portrayed through an increasing number of research studies. Nevertheless, many areas remain to be explored. Even though previous research studies often portrayed the beneficial impacts of integrating reading and writing, yet they do not suggest how such impacts affect the students in a specific course. Hence, the present research study aims to propose a design of reading-writing teaching strategy that needs to be addressed within a specific course especially the academic writing course. This research study employed the adapted model of ADDIE (Dick et al., 2015) to develop the proposed teaching strategy. The process of need analysis was carried out through analysing the introduction section of research articles written by the undergraduate students using Swales and Feak’s CARS model (2004) and examining the syllabus of reading and writing courses. Meanwhile, as the primary frameworks for the teaching strategy development, the rhetorical reading strategies and academic writing were adopted. The results of need analysis show that, mostly, undergraduate students’ research articles lack 1 step in Move 1, 2 steps in Move 2, and 3 steps in Move 3 particularly in the introduction section. The omission of steps in the CARS model by these student-authors seems to happen due to two major factors, namely the scarcity of (1) control over the writing process and (2) linguistic resources. Hence, suggesting the reading-writing teaching strategy for academic writing courses is considered essential.
INVESTIGATING THE COGNITIVE PROCESSES IN POST-EDITING ACTIVITY USING THINKING ALOUD PROTOCOLS

The study aims to investigate the cognitive processes and meta-cognitive strategies used by the post-editor when doing the post-editing activity. It examines the way the post editor reads, thinks, speculates, and makes decision to the machine translation results and correcting the problems founds from the translated text. Thinking Aloud Protocols (TAPs) is used as the approach of the study to record the meta-cognitive strategies applied by the post editor. Several instruments such as video recording tools, machine-translated text, and timer are operated during the data collection. From the study, it is shown that reasoning and problem solving become the most running strategy from the post-editor. It occurs since the post-editor mostly looks for the problems and tries to find a way of correcting mistakes from the translation. The result also shows that the post-editor tries to comprehend the problems and decides the accurate decision when song the post-editing. The cognitive process and meta-cognitive strategies are quite different between the translator and the post-editor.&#x0D;
Vehicle Ownership, Leasing, and Rental Blockchain Protocol VOLR BP

Blockchain technology has emerged as a disruptive force in recent years, holding the potential to revolutionize various industries. One sector that stands to greatly benefit from blockchain is the automotive industry. In 2021, the global automotive market size reached USD 2.73 trillion and is projected to reach USD 3.27 trillion by 2028, exhibiting a Compound Annual Growth Rate (CAGR) of 3.01%. This article introduces a protocol called Vehicle Ownership, Leasing, and Rental Blockchain Protocol (VOLR BP), which utilizes blockchain and Non-Fungible Tokens (NFTs) to create digital replicas of vehicles. These digital twins enable the secure and transparent transfer of ownership, leasing, and rental transactions. The VOLR BP aims to address existing challenges in vehicle ownership and streamline the processes involved in vehicle leasing and rental, ultimately fostering increased trust, efficiency and decreased administrative overhead.
Edge analytics on resource constrained devices

Camera sensors can measure our environment at high precision, providing the basis for detecting more complex phenomena in comparison to other sensors, e.g., temperature or humidity. Using benchmarks, this work evaluates object classification on resource constrained devices, focusing on video feeds from IoT cameras. The models that have been used in this research include MobileNetV1, MobileNetV2 and faster R-CNN that can be combined with regression models for precise object localisation. We compare the models by using their accuracy for classifying objects and the demand they impose on the computational resources of a Raspberry Pi. We conclude that the faster R-CNN model that is configured with the InceptionV2 regression model has the highest accuracy. However, this is at the cost of additional computational resources. We found that the best model to use for object detection functionality on the Raspberry Pi is the MobileNetV2 model paired with the SSDLite regression model.
Image steganography using Fresnelet transformations, stated coefficients and a pre-processed message

The method of concealing confidential information inside an image is known as image steganography. In this work, the image steganography technique is developed. The framework is made up of three components: preprocessing of secret messages, selecting coefficients for the hiding process, and the hiring process. The secret message is organized as a two dimentional array, then processed via mathematical equations, which provide more security because the result is an encrypted message. The paper suggests a set of steps to hide the message efficiently. The experimental results show that the suggested method provides a high steganographic quality of the cover image and a high capacity for embedding information, which are two characteristics of a successful steganography technique.
X-ELM: A Fast Explainability Approach for Extreme Learning Machines

In recent years, Explainable Artificial Intelligence (XAI) has emerged as one of the key specializations in Machine Learning (ML) research. XAI has gained significant interest in the last decade due in part to the reluctance of sensitive domains to adopt the use of “black box” models (i.e., models that use ambiguous or obfuscated reasoning). This motivation has led to the rediscovery of Shapley values [1], a method originally applied to coalitional game theory to optimally distribute the “payout” (i.e., importance) of the “players” (i.e., features) of a model. More recently, Lundberg and Lee developed a sophisticated methodology to approximate Shapley values by computing SHAP (SHapley Additive exPlanations) [2] values. SHAP uses the coefficients from local linear models created for every sample in a test set, providing robust sample-level, model-agnostic explainability. Calculating global SHAP values is computationally expensive, requiring models to be built for every sample in a test or validation set. To address these tractability concerns, we propose eXplainable Extreme Learning Machine (X-ELM) values, which can be computed using coefficients of ELM parameters to wholistically evaluate the global importance of each feature in a dataset using a single ELM ensemble model. We compare the extracted ELM coefficients to values extracted using SHAP methods to show that our approach yields values comparable to the state-of-the-art (SOTA) game theoretic approaches at a dramatically lower computational cost.
Fresh Products Application Information Management System

There has been a pronounced increase in online shopping since the start of the COVID-19 pandemic. Online supermarkets are one of the critical topics of research today. Today there is a wide range of online supermarket apps available nationally and internationally, and good progress has been made in transporting goods. However, there are still research gaps in managing food waste and fresh produce. Therefore, the research theme of this paper is to establish an information management system for a new production software. The methodology of this paper is as follows: firstly, the author analyses the functional requirements of the system, secondly the structural framework of the system is designed based on the requirements analysis, then the ER diagram is completed based on the understood data requirements, processing requirements, security and integrity requirements of the users and administrators, the database is created, tables are created, data is inserted, and finally the desired query functions are achieved through MySQL. Finally, the web design for the background management of product additions, user registration accounts, and login accounts was conducted by compiling HTML and CSS in VScode software. This paper has achieved the target query function. However, the query database steps are still slightly complicated, not convenient and fast enough, and the operational requirements of the administrator are also relatively high.
How open are repositories in re3data?

Openness may sound self-evident, but in fact it can mean different things, even within the Open Science community (Pasquetto, Sands &amp;amp; Borgman, 2015). To align understanding and to ensure common goals in the transition to Open Science, clear definitions are needed.
Reviewing the subject classification in re3data

When looking for a suitable repository, the discipline(s) a repository caters to is one of the most important pieces of information. The re3data Metadata Schema reflects this priority by requiring the element subject for all indexed repositories.
Using the re3data API

re3data offers detailed descriptions of more than 2600 repositories. These descriptions are based on the re3data Metadata Schema and can be accessed via the re3data API. There are many conceivable use cases for re3data metadata. We set up a GitHub repository where we provide some examples for using the re3data API.
Building a video classifier to improve the accuracy of depth-aware frame interpolation

With the current growth in video technology, 4K resolution and sixty frames per second are becoming the industry standards for live, pre-recorded, and animated footage. Although many old movies have a lower frame rate because of insufficient technology, the larger issue lies in the processing of videos. As videos are streamed online, the frame rate can be significantly impaired. So, if we can computationally increase the frame rate, we can dramatically reduce the amount of data being sent to the user. With the recent rising popularity, efficiency, and effectiveness of artificial intelligence, deep learning is undoubtedly the most plausible solution to this problem. Multiple projects have used neural networks to interpolate videos and improve their frame rate. However, since different categories of videos, such as two-dimensional and three-dimensional, can have drastically different color schemes and motion paths, training a single model to handle all of them leads to overfitting, which can be seen in current interpolation algorithms that are specialized to interpolate certain categories of videos. To combat this issue, we researched whether it would be possible to find a single method to perform frame interpolation invariant to the type of video inputted. In this process, we found several pre-existing models that performed well with either 2D or 3D footage, but not both; therefore, we hypothesized that building a video classifier to categorize the input video’s dimensionality would thus improve their accuracy. After integrating the classifier with two depth-aware frame interpolation models, we improved the average accuracy to 97.2%.
A comparative analysis of machine learning approaches for prediction of breast cancer

One of the most dreadful diseases for women and their health is breast cancer. Breast cancer death rates are higher than those for any other cancer, aside from lung cancer. Machine learning and deep learning techniques can be used to predict the early onset of breast cancer. The main objective of this analysis was to determine whether machine learning algorithms can be used to predict the onset of breast cancer with more than 90% accuracy. Based on research with supervised machine learning algorithms, Gaussian Naïve Bayes, K Nearest Algorithm, Random Forest, and Logistic Regression were considered because they offer a wide variety of classification methods and also provide high accuracy and performance. We hypothesized that all these algorithms would provide accurate results, and Random Forest and Logistic Regression would provide better accuracy and performance than Naïve Bayes and K Nearest Neighbor. The Wisconsin Breast Cancer dataset from the UC Irvine repository was used to perform a comparison between the supervised machine learning algorithms of Gaussian Naïve Bayes, K Nearest Neighbor, Random Forest, and Logistic Regression. Based on the results, the Random Forest algorithm performed best among the four algorithms in malignant prediction (accuracy = 98%), and Logistic Regression algorithm performed best among the four algorithms in benign prediction (accuracy = 99%). All the algorithms performed well in the prediction of benign versus malignant cancer, with more than 90% accuracy based on their F1-score. The study results can be used for further research in prediction of cancer using machine learning algorithms.
Comparison of Perception of 2020 Election Security Threats Between Young and Old Voters

Elections constitute the bedrock of a democratically governed society. Due to the long time it has historically taken to ensure equal social participation, the integrity of elections needs to be particularly protected to make every vote count. The democratic process itself has been subject to manipulation through widespread illegal practices in many municipal areas like New York or Chicago that have earned in the past a notoriety for ballot stuffing or stealing votes through impersonation. In the current American social climate of the 2020 presidential election, the same concerns come up to the forefront, compounded by threats of cybersecurity. Our research gauged types and extent of concernment among two age groups of voters: college students and senior citizens. We explored the correlations using the baseline survey and re-examined them after applying information frames through a paired comparison. We found that opinions about voter ID laws and cybersecurity have a strong association to age. Our original hypothesis was that seniors will unchangeably perceive voter identification as essential to election safety while young people will emphasize cybersecurity but will be open-minded. Contrary to that hypothesis, seniors expressed an equal concern over voter ID and cybersecurity and seemed to be more susceptible to the influence of new information. Comparably, college students overwhelmingly confirmed their preoccupation with cybersecurity, marginalizing voter ID, but unexpectedly did not show inclination to easily change their views. The age of voters plays a role in how they perceive the concerns and how they react to information about them.
The Effect of Varying Training on Neural Network Weights and Visualizations

Neural networks are used throughout modern society to solve many problems commonly thought of as impossible for computers. As their use becomes more widespread, an issue of measurement rises. In order to create metrics with which to measure the ability of neural networks, new techniques must be constantly developed. The purpose of this research is to determine whether varying training produces measurable and consistent patterns in the visualizations and weights of Convolutional Neural Networks. In order to carry out this investigation, a convolutional neural network was designed and run with varying levels of training to see if consistent, accurate, and precise changes or patterns could be observed. To determine if such a change or pattern existed, the weights and filters were analyzed through visualizations, both qualitatively and quantitatively. Several patterns were discovered in the visualizations, but they were inconsistent across layers and the quantitative models were only consistent in specific circumstances. This indicated that while training introduced and strengthened patterns in the weights and visualizations, the patterns observed may not be consistent between all neural networks.