Reviewerly/exHarmony huggingface dataset:
Gamification in the competence-based approach

The rapid and widespread introduction of education based on competencies is the creation of a competent approach to learning, where the main goal is to train specialists who can skillfully respond to a rapidly changing world. The competence-based approach forces us to change the usual pedagogical beliefs that occur in the educational process: methods of leaving are replaced by more modern methods and techniques. The purpose of study is to describe a new method "Gamification" in the system of competence-based approach. The scientific novelty lies in the fact that the described method is only being introduced into the educational environment. As an example, the article describes a new form of education – gamification, which is widely used in modern educational environment.
Two Novel Deep Learning Models for Short-term Forecasting of Solar Radiation Using Meteorological Variables

In addition to machine learning methods, deep learning is increasingly being used for solar radiation prediction. In this study, two new deep learning models with attention mechanisms are proposed, and the influence of the temporal representation on the short-term results is evaluated. To address this question, the two networks were trained on an irradiation dataset from a network of 80 meteorological stations in Galicia, Spain, with different representation intervals being simulated. The results show that, although the proposed models achieve better results in comparison to other popular models trained for the same task, the transformation performed on the data degrades the prediction ability of the models as the representation interval increases.
Is Blockchain the solution to the challenges of reliable interoperability in the healthcare ecosystem?

The incorporation of technology to the healthcare ecosystem is identified as a potential strategy to improve people’s quality of life. The health ecosystem includes several interested and interconnected parties with different needs, entailing a high degree of information exchange between the actors involved. However, this information is highly fragmented and distributed, making continuous access difficult. Health care requires a permanent flow of such information, where health systems interoperate to have a global view of the historical data of a patient. However, there are several problems that prevent this objective to be met, this work is focused on identifying such problems. In this sense, Blockchain technology has a great opportunity to support the health sector, since it has multiple qualities that make it ideal for supporting the interoperability of health management systems. This document shows some of the most critical and challenging issues facing the interoperability of health systems, such as: health standards and smart contracts. Challenges for the interoperability of healthcare systems using blockchain technology are also identified and a solution from a software engineering perspective is shown. By doing this, our research extends and complements existing blockchain research in healthcare.
Impact of Artificial Intelligence in Basic General Education in Ecuador

The information and knowledge society leads to urgent changes in the world's educational systems. These changes must go hand in hand with new technologies and intangible services, education must focus their education for the jobs of the future. Education is one of the fundamental pillars in the advancement and progress of people, according to UNESCO, education can and should contribute to a new vision of sustainable world development. Artificial Intelligence in education currently offers a range of possibilities to both the teacher and the student in the teaching-learning process. According to UNESCO, Artificial Intelligence has the power to transform education. This article makes a study of Artificial Intelligence and how it is related to education; an analysis of the basic general education curriculum in Ecuador, as well as the importance of the use of tools based on Artificial Intelligence that contribute to strengthening the teaching-learning process.
A New Approach for Image Thinning

In this paper we presented a novel, general purpose thinning method with emphasis on symmetry. The proposed algorithm is of iterative character. We achieved better skeleton structure by splitting the method into 3 stages, each with a different thinning strength. We introduced an image designed for testing the thinning algorithms and the results of various skeletonization methodologies. Our tests show the proposed algorithm outperforms the current state-of-the-art methods in accuracy and symmetry. We provided implementation details and showed how the performance of the method was increased. Thinning is a basic stage in image segmentation and hence feature extraction for object recognition. Both the test image and the implementation of our method will be made publicly available.
VSC®-8000/HS- A Robust Tool for Differentiation of Overlapped Black Pen Inks in Fraudulent Documents

Contemporary advancement and digitalization of technology has made analysis of ink extremely challenging. Selecting the best technique for ink analysis is the need of the hour. The latest version of Video spectral comparator i.e. VSC®- 8000/HS has a vast horizon of features such as multi-spectral light sources, database of currencies and passports of different countries and even micro-spectrometer for composition analysis. The present study focuses on determining the efficacy and capability of light sources of varied spectrum and spectrometer for the examination of black ball, gel and fountain pen inks particularly in questioned under scrutiny written with hand. VSC®-8000/HS have demonstrated to be substantially efficient, robust and an exhaustive nondestructive technique for analysis of documents serving as evidence which have been manipulated including obliterations, additions or overlapping of two or more black ball or fountain pen inks. However, the technique did not provide effective results for black gel pen inks in contrast to black ball and fountain pen inks. Nonetheless, it can effectively harmonize with the standard workflow of a forensic document examiner to ensure qualitative and quantitative facts and figures for the expert testimony.
Determination of dengue hemorrhagic fever disease factors using neural network and genetic algorithms

The Aedes aegypti mosquito and the Aedes albopictus mosquito are carriers of the virus that causes Dengue Hemorrhagic Fever (DHF). In Indonesia, the spread of DHF disease has taken place for 41 years. Within this period, there was an increase in the number of spreading areas by 97% and an increase in the number of cases by 99%. Based on the data from previous studies, further information is needed related to the factors that have the most influence on the level of DHF infection in a region. Based on the initial study conducted, there are 6 factors that have the potential to influence the number of DHF cases in an area, namely temperature (X1), rainfall (X2), population density (X3), altitude (X4), distribution of males (X5), and distribution of education level (X6). In this study, the problem of determination dengue disease factors was modeled using a neural network. The activation function in this neural network model then estimated using genetic algorithms. Determination of the best factor is carried out in a genetic algorithm by combining several parameters of the crossover probability (Pc) and mutation probability (Pm). This experiment show that the main factors that influence the spread of DHF in Bandung area are temperature, altitude, distribution of gender, and distribution of education levels. The best accuracy system obtained in this study using these 4 factors reached 72%.
Zakat Management System with Allocation Prediction Using Case-Based Reasoning

Zakat has become one of the vital opportunity to be given to the poor and needy. However, there are problems faced by the institution of zakat with the inefficiency and inaccurate issue, especially in the zakat allocation and distribution aspects. Moreover, the zakat allocation and distribution process is time consuming due to the variety of the criteria to be considered, especially when it involves an educational institution. Since the problem usually originates from the organization of zakat itself, it is essential to minimize the difficulties so that zakat can be distributed in a proper way to the qualified person with a suitable allocation. Therefore, the purpose of this project is to develop a web-based Zakat Management and Allocation Prediction System using Case-based Reasoning(CBR) technique. The proposed method consists of two components: (1) Web-based zakat management system which aims to properly manage all related data of the zakat applicant, and (2) Zakat allocation module using CBR to suggests the allocation amount of zakat by finding the similarities between the previous cases and the new cases. For the prediction purposes, the significant main features are identified and suitable weightage is assigned to be able the CBR engine to produce a suggestion. Experimental results using real data collected from UiTM(Perak) Tapah Campus show that our proposed model achieves a significant improvement in the efficiency of managing and allocating the amount of zakat.
The Modern Origins &amp; Sources of China's Techtransfer

The research identified the key element on P. R. China’s incentives in modern history on techtransfer practices. With reviewing on the state funding surrogacy in the natural sciences, the author identified the key militant coercive contracting clauses in the document of the National Natural Science Foundation of China. With its combined workings with the statutory &amp; martial laws, the analysis takes a comparative culture approach that partially counteracts the work of the “United Front Working Group of the CPC”, which constitutes as the major cyber espionage organization that manifests upon the communication &amp; information technologies. The “United Front”’s work has substabtially undermined the premices of market economy and eroded the markets’ self-organizing &amp; self-regulating functionalities. With heterogenous semantic networks taken into consideration, the psychosociological elements were taken into consideration even with the psychological data codings in the semantic networks of P. R. China.
Clean and Learn: Improving Robustness to Spurious Solutions in API Question Answering

The development of a question answering (QA) system for application programming interface (API) documentation can greatly facilitate developers in API-related tasks. However, when applying deep learning technology, API QA systems suffer from the spurious solution problem. That is, the answer can literally appear in multiple positions (i.e. start-end indices) in the API documentation, though only one of them (called golden solution) correctly solves the question given its context. The other incorrect candidates (called spurious solutions) hinder the neural network model to learn reasonable solutions or correct answers. In this work, we propose Clean-and-Learn, an effective and robust method for API QA over documents. In order to reduce the spuriousness of candidate solutions used for training, we design several scoring functions to rank the candidate occurrences (clean). Only high-quality (top-[Formula: see text]) candidate solutions are involved in training. Then, we perform multi-task learning by weighing the losses computed from the top-k occurrences (learn). We evaluate our method on the constructed APIQASet dataset. The experiment results show that Clean-and-Learn achieves a ROUGE-L score of 75.8 and accuracy of 70.5% in API QA, which significantly outperforms state-of-the-art approaches.
Understanding and Mitigating Data Contamination in Deep Anomaly Detection: A Kernel-based Approach

Deep anomaly detection has become popular for its capability of handling complex data. However, training a deep detector is fragile to data contamination due to overfitting. In this work, we study the performance of the anomaly detectors under data contamination and construct a data-efficient countermeasure against data contamination. We show that training a deep anomaly detector induces an implicit kernel machine. We then derive an information-theoretic bound of performance degradation with respect to the data contamination ratio. To mitigate the degradation, we propose a contradicting training approach. Apart from learning normality on the contaminated dataset, our approach discourages learning an additional small auxiliary dataset of labeled anomalies. Our approach is much more affordable than constructing a completely clean training dataset. Experiments on public datasets show that our approach significantly improves anomaly detection in the presence of contamination and outperforms some recently proposed detectors.
Rethinking how distributed applications are built

In our more and more connected world where people are used to managing their lives via digital services, it has become mandatory for a successful company to build applications that can scale with the popularity of the company's services. Scalability is not the only requirement but similarly important is that modern applications are highly available and fast because users are not willing to wait in our ever faster moving world. Due to this, we have seen a shift from the classic monolith towards micro service architectures which promise to be more easily scalable. The emergence of serverless functions further strengthened this trend more recently. By implementing a micro service architecture, application developers are all of a sudden exposed to the realm of distributed applications with its seemingly limitless scalability but also its pitfalls nobody tells you about upfront. So instead of solving business domain problems, developers find themselves fighting with race conditions, distributed failures, inconsistencies and in general a drastically increased complexity. In order to solve some of these problems, people introduce endless retries, timeouts, sagas and distributed transactions. These band aids can quickly result in a not so scalable system that is brittle and hard to maintain. The underlying problem is that developers are responsible for ensuring reliable communication and consistent state changes. Having a system that takes care of these aspects could drastically reduce the complexity of developing scalable distributed applications. By inverting the traditional control-flow from application-to-database to database-to-application, we can put the database in charge of ensuring reliable communication and consistent state changes and, thus, freeing the developer to think about it. In this keynote, I want to explore the idea of putting the database in charge of driving the application logic using the example of Stateful Functions, a library built on top of Apache Flink that follows this idea. I will explain how Stateful Functions achieves scalability and consistency but also what its limitations are. Based on these results, I would like to sketch the requirements for a runtime that can truly realise the full potential of Stateful Functions and discuss with you ideas how it could be implemented.
Tagging Fake Profiles in Twitter Using Machine Learning Approach

Today, social media data spread more swiftly, which can be beneficial or destructive in various circumstances. Because of their widespread use, OSNs have become a platform for spammers to distribute undesirable content. Many security breaches have been noticed with the increasing frequency of cyber-attacks. Cyber-attacks are making news, and consumers are turning their trust in online social networks against them. These attacks have far-reaching consequences. This necessitates identifying such fictitious users to keep the trust of the social networks’ users intact. In this paper, a method based on machine learning classification algorithms for specifically tagging fake Twitter profiles has been proposed. Fake users are spam profiles that appear to be actual users and are responsible for tarnishing the reputation of legitimate users. Data from about 8 K users have been used to validate the suggested system for this purpose. The accuracy of the proposed technique is around 83.5% with logistic regression classifier.
A data-owner centric privacy model with blockchain and adapted attribute-based encryption for internet-of-things and cloud environment

Advances in internet of things (IoT) and cloud computing technologies have led to the emergence of new applications such as in e-health domain bringing convenience for both physicians and patients. However, the development of these new technologies makes users' privacy vulnerable. The threats on private data may arise from service providers themselves voluntarily or by inadvertence. As a result, the data owner would like to ensure that the collected data are securely stored and accessed only by authorised users. In this paper, we propose a novel data-owner centric privacy model in IoT/cloud environment. Our model combines two promising paradigms for data privacy, which are attribute-based encryption (ABE) and blockchain, to strengthen the data-owner privacy protection. We propose a new scheme of ABE that is, in one hand, suitable to resource-constrained devices by externalising the computing capabilities, thanks to fog computing paradigm and, in the other hand, combined with a blockchain-based protocol to overcome a single point of trust and to enhance data-owner access control.
ANALISIS KESESUAIAN MATERI AJAR DENGAN TES HASIL BELAJAR MAHASISWA DI LINGKUNGAN FKIP UHN MEDAN.

This research is a type of evaluation research conducted using qualitative methods. This research will be conducted at the Teaching and Education Faculty of the University of HKBP Nommensen. The time for conducting this research is the odd semester of the 2019/2020 school year. Plans that will participate in this research are Lecturers of FKIP UHN Medan. The sample chosen in this study was one lecturer who worked on each odd semester course at FKIP UHN by taking a sample with a random sampling technique. This study aimed to explain the topic and the suitability of the material submitted with material given to students by lecturers of Mathematics Education Study Program UHN Medan FKIP 2019/2020 Academic Year. The results showed that: 1) Overall syllabus conformity with teaching materials approved by lecturers from each study program at FKIP UHN was 99,428. It can be concluded that the learning objectives based on PBM carried out by lecturers in FKIP UNH are already "Very Good". 2) Overall, the suitability between the minutes of lectures and the questions tested by lecturers from each study program at FKIP UHN for UTS is 98.86% and UAS is 100%. So it can be concluded that the learning objectives to be achieved which have been programmed before have "Very Good".
Normal No More: The Future of Tourism Planning Post-Pandemic

COVID-19 literally stopped the recent unprecedented tourism growth in its tracks with airline groundings, restaurant closures, business suspensions, hotel evacuations, and mandatory travel bans. What has come in the aftermath is a realization that tourism will see shifts in employment styles and function, pricing, customer behaviors, and successful products due to new desires among travelers for alternate tourism experiences that provide authentic, low-density settings while ensuring public health and safety. This is of particular importance to Caribbean Small Island Developing States (SIDS) due to their high dependence on tourism for economic stimulation and quality of life. This chapter presents findings from a survey of 30 resort tourism sites across the United States to highlight the impacts of COVID-19 on general operations, employee and customer relations, and coping strategies during the pandemic. Additionally, this chapter draws on other recent COVID-19 research to summarize significant impacts to tourism during the pandemic with a particular focus on how this will impact SIDS. Finally, this chapter presents a series of new navigational aids for SIDS to provide guidance for tourism planning and management among these unique and sensitive island nations.
The Effect of Biology Inquiry Program using Metaverse Platform on the Affective Field of Elementary Science Gifted Student

Objectives The purpose of this study was a development the biology inquiry program using a metaverse platform and to check the effect on the affective domain of elementary science gifted students. Methods The researcher developed a biology inquiry program in the virtual world by investigating prior studies. Also, 17 students participated in the pre and post-test of learning motivation and task commitment. Results The biology inquiry program developed using a metaverse platform could use a virtual world and mirror world, and the learning process and method were drawn by preceding research analysis. Also, the researcher tested the improvement and effect on the learning motivation and task commitment of science gifted students. Conclusions The biology inquiry program using the metaverse platform could encourage a positive effect on the affective domain of elementary science gifted students. And it would lead to reducing Zoom fatigue by providing positive recognition of online learning through the expansion of social interaction. &#x0D;
Content System and Teaching/Learning Case Study for Systematic Convergence of Artificial Intelligence and Science Subjects

Objectives The purpose of this study is to propose a content system for systematic linkage, integration, and convergence of artificial intelligenc and science subjects and teaching/learning cases based on the content system. Methods First, a draft of the content system was devised by analyzing and extracting content elements for each area of artificial intelligence and science subjects for AI-SCIENCE convergence education through literature research. Second, the validity of the AI-SCIENCE convergence education content system was verified through expert Delphi. Third, based on the derived content system, teaching/learning cases for AI-SCIENCE convergence education were proposed. Fourth, the validity of the teaching and learning case for AI-SCIENCE convergence education was verified through expert Delphi. Fifth, the implications of the Delphi test results were derived and the AI-SCIENCE convergence education content system-based teaching/learning case was systematized. Results First, as the knowledge of AI-SCIENCE convergence education, learning hierarchies were established by grades 3-4, 5-6, and middle school student development stages and content levels. 2 areas and 4 core concepts of artificial intelligence education, 15 areas and 35 core concepts of science education were selected. Second, based on the analyzed and extracted content system, teaching and learning cases were presented under the theme of ‘planet’. Third, to verify the validity of the content system and teaching/learning cases, expert Delphi was conducted and opinions were collected. As a result, the field composition of AI-SCIENCE convergence education met the conditions of content validity in terms of suitability and importance. Conclusions By analyzing and extracting the content system related to the knowledge and understanding of AI-SCIENCE convergence education, it is possible to facilitate the connection, integration, and convergence of the learning content in the AI field with science in terms of subject characteristics. In addition, subjects other than science subjects will also be able to utilize the presented content system in connection with the content system of each subject.&#x0D;