Reviewerly/exHarmony huggingface dataset:
Effects of external financial flows on income inequality in selected Southern African Development Community member states

Background: The developmental goals of various emerging markets are quantitative targets set to reduce income inequality, alleviate poverty, reduce unemployment and achieve continuous inclusive economic growth amongst other key economic performance indicators. The interest is mainly on what can be done on economic performance to fight escalating inequality, increase economic growth and maintain low inflation amongst other economic indicators. Aim: The study investigates the effects of external financial flows on income inequality in the Southern African Development Community (SADC) region. Setting: The study shows the long-run stable relationship between the set of variables. Methods: The study have used the panel cointegration, autoregressive distributed lag and causality techniques. Results: The findings are that in the long run, remittances can strongly reduce income inequality, foreign direct investment (FDI) and cross border bank lending have an increasing effect and foreign aid can weakly reduce inequality. In the short run, FDI and cross border bank lending can strongly explain income inequality, and negative remittances and foreign aid are insignificantly explaining income inequality. Furthermore, the evidence from panel causality confirms the bidirectional causality amongst remittances, cross border bank lending and income inequality, and unidirectional causality in other sets of variables. Conclusion: It can be concluded that external financial flows can play a vital role to reduce persistent income inequality in the SADC region. It is recommended that the SADC governments need to formulate policies on remittances as they have positive returns on human capital, strengthen foreign aid institutions and create conducive environment to attract FDI.
Identification of Lost Children using Face Aging with Conditional GAN

Face recognition technology is a big area that consists of the many features in it but it also comes with some of the factors which affect this technology, one of the factors is Face aging which makes face recognition more difficult. As in India, a large number of children go missing every year. Also just using a photograph is not enough for the process to proceed smoothly and it results in a huge percentage of the missing child cases remain untraced. This paper presents a novel use of face recognition with face aging to overcome the limitation of existing systems. The proposed system has a portal where the public can upload an image of a suspected child and also have a feature where searching for any lost child is possible. The proposed system has mainly concentrated on an Age Conditional generative adversarial network (C-GAN) algorithm for face aging and the FaceNet algorithm for face feature extraction and face recognition.
Requirement Gathering Techniques Widely Used in Global Software Engineering: A Comprehensive Study

The research paper is solely based upon conducting the study with respect to analysis of the recent work done in the field of Global Software Engineering (GSE). It is massively spreading within various organizations. GSD is significant with respect to cost reduction, efficiency in context of delivery of software and extending economic resources. The working model of GSE is a complex and there are certain threats that are directly targeting the audiences. The factors affecting are the differences in context of Culture and geographic distances that can directly affect the performance of a software process. One of the important aspects that is kept in consideration in Global Requirement engineering is particularly communication. The issues with respect to communication can be divided further into theteam and targeted clients. The study is highlighting the concept is to generate the best practices in Global software engineering (GSE) along with Lexicon model. The research has focused on more than one technique along with demonstrating their advantages to present a solution and a technique that can be adopted to generate requirements in GSE.
Improved design of automatic solar tracking system based on integration of accelerometer, compass sensor, and GPS

Nowadays, solar energy has been studied and applied widely.This study presents a design of a two-degree-of-freedom system that automatically controls the photovoltaic module's orientation according to the sun's apparent motion to improve energy efficiency.The system exploits the valuable information from the Global Positioning System (GPS) receiver, accelerometer, and compass sensor.It not only enhances energy efficiency significantly but also reduces the complexity of mechanical systems and controllers.
The Algorithm of a Game-Based System in the Relation between an Operator and a Technical Object in Management of E-Commerce Logistics Processes with the Use of Machine Learning

Machine learning (ML) is applied in various logistic processes utilizing innovative techniques (e.g., the use of drones for automated delivery in e-commerce). Early challenges showed the insufficient drones’ steering capacity and cognitive gap related to the lack of theoretical foundation for controlling algorithms. The aim of this paper is to present a game-based algorithm of controlling behaviours in the relation between an operator (OP) and a technical object (TO), based on the assumption that the game is logistics-oriented and the algorithm is to support ML applied in e-commerce optimization management. Algebraic methods, including matrices, Lagrange functions, systems of differential equations, and set-theoretic notation, have been used as the main tools. The outcome is a model of a game-based optimization process in a two-element logistics system and an algorithm applied to find optimal steering strategies. The algorithm has been initially verified with the use of simulation based on a Bayesian network (BN) and a structured set of possible strategies (OP/TO) calculated with the use of QGeNie Modeller, finally prepared for Python. It has been proved the algorithm at this stage has no deadlocks and unforeseen loops and is ready to be challenged with the original big set of learning data from a drone-operating company (as the next stage of the planned research).
The Utilization of Moodle-Based Learning Management System (LMS) in Learning Mathematics and Physics to Students’ Cognitive Learning Outcomes

This study aims to determined whether there was an increase in student cognitive learning outcomes through the use of a Moodle-based Learning Management System in mathematics and physics learning.. This type of research uses a pre experimental design. The population in this study were students of Babussalam SMKS, while the sample was class XI students of the Babussalam North Aceh Islamic Boarding School. Based on the results of the analysis and discussion of research data, The percentage of the number of students who met the completeness criteria in the physics class was 67% while in the mathematics class it was 91%. The average N-gain of cognitive learning outcomes for mathematics class was 0.6, higher than the N-gain average for Physics subjects, 0.475. This is because physics learning is not sufficiently taught through the delivery of material through Moodle, but must involve students in learning with a scientific approach. In this study, Moodle has not optimally integrated learning activities with a scientific approach so that cognitive learning outcomes have not reached the expected targets in physics subjects and learning is not sufficiently taught through the delivery of material through Moodle, but must involve students in learning with a scientific approach. In this study, Moodle has not optimally integrated learning activities with a scientific approach so that cognitive learning outcomes have not reached the expected targets in physics subjects.. In this study, Moodle has not optimally integrated learning activities with a scientific approach so that cognitive learning outcomes have not reached the expected targets in physics subjects.
Multi-objective Multi-label Feature Selection with an Aggregated Performance Metric and Dominance-based Initialisation

Feature selection (FS) is an essential pre-processing step that selects small and informative feature subsets. However, most FS methods focus on single-label classification, where each instance has one class label. Multi-label classification (MLC) is increasingly more common as instances possess a set of class labels. It is more challenging to perform FS for MLC since it needs to consider the interactions between labels. Furthermore, since the target output is a set of labels, there are various metrics for evaluating MLC performance, and each metric assesses the quality of a feature subset on various aspects. However, certain MLC metrics are shown to possess conflicting behaviour. To evolve high-quality feature subsets, it is essential to consider the trade-offs between metrics, making FS for MLC even more challenging. Evolutionary multi-objective optimisation (EMO) is a great technique to optimise multiple (potentially) conflicting metrics, representing different objectives. However, given a large number of objectives, EMO algorithms usually do not evolve a diverse set of good feature subsets, especially when there is a limited computational resource. This paper proposes a novel approach to reducing the number of objectives, which is expected to maintain or improve the evolved feature subsets over the original objectives. We also propose a new decomposition mechanism based on multiple reference points and a novel initialization mechanism to enhance the quality of the evolved feature subsets. The experimental results show that the proposed algorithm can evolve diverse feature subsets with better trade-off between multiple performance metrics than recent FS algorithms for MLC.
Employing Virtual Reality (VR) Technology with Experiential Learning Perspective to Enhance Students’ Learning Experience

The authors advocate to employ Virtual Reality (VR) with the application of experiential learning in order to enhance the students' learning experience.In modern world with diverse platforms for innovations and solutions, technology is shaping the future of education.New digital technology is used to improve the process of teaching and learning.Digital technology opens up a new space for learning where students are immerse through various forms of technological learning platforms, devices and applications.The application of digital technology in education can assist students to cope with different learning environment and improves students' learning efficiency.One of the innovative technologies for education is Virtual Reality (VR).VR has changed the way how students learn.VR allows us to bridge the gaps between educators and learners.VR can be used to stimulate a digital learning environment for educators and students to be together in the same room regardless of wherever they are physically and with digital representations of themselves.This paper will discuss the potential of integrating VR in education as one of the effective methods that can enhance students' learning experience.
Identifying elusive piercing points along the North American transform margin using mixture modeling of detrital zircon data from sedimentary units and their crystalline sources

The San Gabriel and Canton faults represent early stages in the development of the San Andreas fault system. However, questions of timing of initiation and magnitude of slip on these structures remain unresolved, with published estimates ranging from 42–75 km and likely starting in the Miocene. This uncertainty in slip history reflects an absence of appropriate piercing points. We attempt to better constrain the slip history on these faults by quantifying the changing proportions of source terranes contributing sediment to the Ventura Basin, California, through the Cenozoic, including refining data for a key piercing point. Ventura Basin sediments show an increase in detrital zircon U-Pb dates and mineral abundances associated with crystalline sources in the northern San Gabriel Mountains through time, which we interpret to record the basin’s northwest translation by dextral strike-slip faulting. In particular, an Oligocene unit mapped as part of the extra-regional Sespe Formation instead has greater affnity to the Vasquez Formation. Specifically, the presence of a unimodal population of ~1180 Ma zircon, high (57%) plagioclase content, and proximal alluvial fan facies indicate that the basin was adjacent to the San Gabriel anorthosite during deposition of the Vasquez Formation, requiring 35–60 km of slip on the San Gabriel-Canton fault system. Mixture modeling of detrital zircon data supported by automated mineralogy highlights the importance of this piercing point along the San Gabriel-Canton fault system and suggests that fault slip began during the late Oligocene to early Miocene, which is earlier than published models. These two lines of evidence disagree with recent models that estimate >60 km of offset, requiring a reappraisal of the slip history of an early strand of the San Andreas transform zone.
Probabilistic Models for Competence Assessment in Education

Probabilistic models of competence assessment join the benefits of automation with human judgment. We start this paper by replicating two preexisting probabilistic models of peer assessment (PG1-bias and PAAS). Despite the use that both make of probability theory, the approach of these models is radically different. While PG1-bias is purely Bayesian, PAAS models the evaluation process in a classroom as a multiagent system, where each actor relies on the judgment of others as long as their opinions coincide. To reconcile the benefits of Bayesian inference with the concept of trust posed in PAAS, we propose a third peer evaluation model that considers the correlations between any pair of peers who have evaluated someone in common: PG-bivariate. The rest of the paper is devoted to a comparison with synthetic data from these three models. We show that PG1-bias produces predictions with lower root mean squared error (RMSE) than PG-bivariate. However, both models display similar behaviors when assessing how to choose the next assignment to be graded by a peer, with an “RMSE decreasing policy” reporting better results than a random policy. Fair comparisons among the three models show that PG1-bias makes the lowest error in situations of scarce ground truths. Nevertheless, once nearly 20% of the teacher’s assessments are introduced, PAAS sometimes exceeds the quality of PG1-bias’ predictions by following an entropy minimization heuristic. PG-bivariate, our new proposal to reconcile PAAS’ trust-based approach with PG1-bias’ theoretical background, obtains a similar percentage of error values to those of the original models. Future work includes applying the models to real experimental data and exploring new heuristics to determine which teacher’s grade should be obtained next to minimize the overall error.
Need Identification on Android-Based Neighborhood Electric Vehicle Dashboard View Using The Kano Model

A customer is someone who repeatedly comes to the same place when he wants to buy an item or get service because he is satisfied with the goods or services. Companies must understand the needs and desires of customers in order to satisfy customer needs. Technology that is currently developing is electric cars, especially the Neighborhood Electric Vehicle (NEV) with the application of smartphones. Conventional dashboards can be replaced with smartphones Android-based. Fulfillment of customer satisfaction with smartphone dashboard products is identified by conducting a customer desires survey and categorized it using the Kano model. The survey was conducted on 14 attributes by producing three categories, such as one dimensional, attractive and indifferent. The Android dashboard is built based on survey results instruments. The use of Arduino nano and ESP32 as controllers that process vehicle instrument data sent using Wi-Fi in real time to Android-based smartphones has a maximum error of 5% for speed data.
Successive Image Generation from a Single Sentence

Through various examples in history such as the early man’s carving on caves, dependence on diagrammatic representations, the immense popularity of comic books we have seen that vision has a higher reach in communication than written words. In this paper, we analyse and propose a new task of transfer of information from text to image synthesis. Through this paper we aim to generate a story from a single sentence and convert our generated story into a sequence of images. We plan to use state of the art technology to implement this task. With the advent of Generative Adversarial Networks text to image synthesis have found a new awakening. We plan to take this task a step further, in order to automate the entire process. Our system generates a multi-lined story given a single sentence using a deep neural network. This story is then fed into our networks of multiple stage GANs inorder to produce a photorealistic image sequence.
Modernizing the Health Insurance Industry Using Blockchain and Smart Contracts

The Indian health insurance industry is expanding at a rapid rate and presently is the second-largest benefactor to the extensive insurance industry (after Automobile).It has seen a meteoric rise in the past decade, and the projections forecast that in a developing nation like India,the health insurance sector should percolate to each and every rural and urban household. This sector includes multiple stakeholders – hospitals,insurance organizations,third-party insurers,beneficiaries,pharmacies,insurance start-ups, and the government. To ameliorate the cross-pollinated and distributed insurance industry which relies heavily on passing of data between its stakeholders, we must look at blockchain infrastructure as a solution. It is imperative to develop a central and trustworthy blockchain network that wouldboost the exchange of important healthcare data between different nodes with security,completeness, and integrity. Blockchain technology provides a consummate way to store and execute data through all the different insurance industry participants and aid in the further penetration of health insurance in the country. Blockchain is a distributed,decentralized public ledger that records transactions after proper validation through the consensus algorithm. It can automate the process of collecting health data of the customer,outline the policy terms,negotiate tariffs and other required information, and then apply smart contracts based on predefined user agreed business rules in a pellucid and non-debatable manner. Blockchain systems have gained public advocacy in being deployed as a healthcare solution in many countries around the world. Adopting such a technology could revitalize and revolutionize our health insurance industry.
An action interface manager for ROSPlan

Task planning and task execution are two high-level robot control modules that often are working with representations of the scenario at different levels of abstraction. Thus, a further mapping module is required to connect the abstract planned actions to the robot-specific algorithms that must be called in order to execute these actions. We present a novel implementation of such a module that allows a user to define this mapping for all actions through a single configuration file. This greatly reduces the amount of effort that is required to integrate an automated planner with a robotic platform. This module has been integrated as an Action Interface of the automated task planning framework ROSPlan, and includes a Graphical User Interface though which the configuration file can be easily generated and updated. The use of the interface is demonstrated in two scenarios: with robot actors possessing only a single action, and a more complex scenario with multiple agents and types of actions.
Classification of uncertain data with a selection of relevant features based on similarities measures of Interval-Valued Fuzzy Sets

The article deals with the problem of selecting the most appropriate attributes for a given classification method with the use of inclusion and similarity measures for interval-valued fuzzy sets. These types of measures with uncertainty were introduced using partial or linear order. The article introduces a modified IV-Relief algorithm using the above-mentioned measures. The theoretical considerations were supported by the analysis of the effectiveness of the proposed algorithm on a well-known dataset on breast cancer diagnostics. The proposed methods make it possible to extend the recognized classification methods so that they operate on uncertain data.
Media and Information Literacy: A Critical Response to the Challenge of „Infodemic‟ in the Covid-19 Pandemic Era in Nigeria

Nigeria, the most populous country in Africa is not left out in the fight against the COVID-19 outbreak that continues to ravage the entire universe.The deadly virus as of 26th June 2021 has infected more than 181.3 million people and killed over 3.9 million people globally.In Nigeria alone, it has infected over 167 thousand people and killed 2,119 people between February 27 th , the day the first case was recorded and 26 th June 2021.Unfortunately, as the virus continues to spread worldwide, there is also a rapid increase in the rate of infodemic -information overload majority of which are fake, disinformation and misinformation -about the virus, its transmission and cure.Thus, this paper interrogates the present reality of the infodemic in Nigeria, especially in the present COVID-19 pandemic and the vision of media and information literacy.The problem concerned the extent to which infodemic could precariously engineer crisis, disgust, fear, hostility and panic which might degenerate to conflict, insecurity, stigmatisation and eventual death.Combining textual analysis with receptor oriented, the article critically examined the social media platform posts and activities in this domain.Major findings apart from revealing that the free and unlimited access to information on social media platforms have been the active driver of the current experience, also showed that the inability of people to discern the veracity and authenticity of information within the context of the COVID-19 pandemic have made many vulnerable.Thus, the present article concluded that media and information literacy is a necessity in fighting the challenge of infodemic in Nigeria and promoting healthy information in media and technological environments.Therefore, among others, the introduction of media and information literacy to both literate and illiterate sectors of society is recommended.
Indices on Cryptocurrencies: an Evaluation

Several cryptocurrency (CC) indices track the dynamics of the risingCC sector, and soon ETFs will be issued on them. We conduct a qualitativeand quantitative evaluation of the currently existing CC indices. As the CCsector is not yet consolidated, index issuers face the challenge of tracking thedynamics of a fast-growing sector that is under continuous transformation. Wepropose several criteria and various measures to compare the indices under review.Major di erences between the indices lie in their weighting schemes, theircoverage of CCs and the number of constituents, the level of transparency, andthus their accuracy in mapping the dynamics of the CC sector. Our analysisreveals that indices that adapt dynamically to this rising sector outperformtheir competitors. Interestingly, increasing the number of constituents doesnot automatically lead to a better t of the CC sector. All codes are availableon Quantlet.com