Reviewerly/exHarmony huggingface dataset:
Monetary Stabilization in Cryptocurrencies - Design Approaches and Open Questions

The price volatility of cryptocurrencies is often cited as a major hindrance to their wide-scale adoption. Consequently, during the last two years, multiple so called stablecoins have surfaced---cryptocurrencies focused on maintaining stable exchange rates. In this paper, we systematically explore and analyze the stablecoin landscape. Based on a survey of 24 specific stablecoin projects, we go beyond individual coins for extracting general concepts and approaches. We combine our findings with learnings from classical monetary policy, resulting in a comprehensive taxonomy of cryptocurrency stabilization. We use our taxonomy to highlight the current state of development from different perspectives and show blank spots. For instance, while over 91% of projects promote 1-to-1 stabilization targets to external assets, monetary policy literature suggests that the smoothing of short term volatility is often a more sustainable alternative. Our taxonomy bridges computer science and economics, fostering the transfer of expertise. For example, we find that 38% of the reviewed projects use a combination of exchange rate targeting and specific stabilization techniques that can render them vulnerable to speculative economic attacks - an avoidable design flaw.
Expert System to Diagnose Extra Lung Tuberculosis Using Bayes Theorem: Expert System to Diagnose Extra Lung Tuberculosis Using Bayes Theorem

Tuberculosis is a common infectious disease, and in many cases is deadly. Tuberculosis usually attacks the lungs, but can also affect other body parts. Tuberculosis spreads through the air when someone with active TB infection coughs, sneezes, or spreads their saliva through the air. the limited medical staff in the puskesmas compared to the total population results in often delayed services to TB patients. This research focuses on making an expert system to diagnose tuberculosis using Bayes's Theorem which has been known that the method has been widely used to diagnose diseases in several cases
Impartial games with entailing moves

Combinatorial game theory has also been called “additive game theory” whenever the analysis involves sums of independent game components. Such disjunctive sums invoke comparison between games, which allows abstract values to be assigned to them. However, there are rulesets with entailing moves that break the alternating play axiom and/or restrict the other player’s options within the disjunctive sum components. These situations are exemplified in the literature by a ruleset such as nimstring, a normal play variation of the classical children’s game dots & boxes, and top entails, an elegant ruleset introduced in the classical work Winning Ways by Berlekamp, Conway, and Guy. Such rulesets fall outside the scope of the established normal play theory. Here we axiomatize normal play via two new terminating games, ∞(Left wins) and∞(Right wins), and achieve a more general theory. We define affine impartial, which extends classical impartial games, and we analyze their algebra by extending the established Sprague-Grundy theory with an accompanying minimum excluded rule. Solutions of nimstring and top entails are given to illustrate the theory.
Intelligent chatbot assistant: A study of Natural Language Processing and Artificial Intelligence

The development and research of Artificial Intelligence have had a recent surge in recent years, which includes the medical field. Despite the new technology and tools available, the staff is still ...
Zero-shot Transfer Learning for Gray-box Hyper-parameter Optimization

Zero-shot hyper-parameter optimization refers to the process of selecting hyper- parameter configurations that are expected to perform well for a given dataset upfront, without access to any observations of the losses of the target response. Existing zero-shot approaches are posed as initialization strategies for Bayesian Optimization and they often rely on engineered meta-features to measure dataset similarity, operating under the assumption that the responses of similar datasets behaves similarly with respect to the same hyper-parameters. Solutions for zero- shot HPO are embarrassingly parallelizable and thus can reduce vastly the required wallclock time of learning a single model. We propose a very simple HPO model called Gray-box Zero(0)-Shot Initialization (GROSI) as a conditional parametric surrogate that learns a universal response model by exploiting the relationship between the hyper-parameters and the dataset meta-features directly. In contrast to existing HPO solutions, we achieve transfer of knowledge without engineered meta- features, but rather through a shared model that is trained simultaneously across all datasets. We design and optimize a novel loss function that allows us to regress from the dataset/hyper-parameter pair unto the response. Experiments on 120 datasets demonstrate the strong performance of GROSI, compared to conventional initialization strategies. We also show that by fine-tuning GROSI to the target dataset, we can outperform state-of-the-art sequential HPO algorithms.
IMPLEMENTASI ALGORITMA FAST (FEATURES FROM ACCELERATED SEGMENT TEST) CORNER DETECTOR UNTUK PENGENALAN ALAT MUSIK TRADISIONAL KABUPATEN KUNINGAN BERBASIS AUGMENTED REALITY

One of the cultures of Kuningan Regency that still exists today is traditional musical instruments whose numbers and types, historical information and how to play are increasingly rare to be introduced in schools and studios. One of the efforts to preserve and introduce traditional musical instruments in Kuningan Regency to the community, is by holding annual traditional musical instrument festivals, but these efforts have not been able to increase public knowledge about information from traditional musical instruments in Kuningan Regency. Technology that is able to provide information on traditional musical instruments and is able to be attractive and interactive is absolutely necessary as an alternative effort. Augmented Reality application by implementing the FAST Corner Detector algorithm can be built as an alternative to the introduction of traditional musical instruments in Kuningan Regency. This application was built using the RUP (Rational Unified Process) system development method. Application of Augmented Reality Introduction to Traditional Musical Instruments in Kuningan Regency can provide information about the types of musical instruments as well as examples of sounds and videos on how to play traditional musical instruments as a means of media in an effort to preserve traditional musical instruments in Kuningan regency in society, especially the younger generation.Keywords : Augmented Reality, FAST Corner Detector, Rational Unified Process,Traditional musical instrumen
IMPROVING STUDENTSâ€™ SKILL IN WRITING RECOUNT TEXTS BY USING PEER REVIEW STRATEGY TO THE EIGHTH GRADE STUDENTS OF SMP KATOLIK BUDI MURNI 3 MEDAN

The aim of this study is to improve studentsâ€™ skill in writing recount texts and to find out the studentsâ€™ responses after being taught by using Peer Review Strategy. The subject of the study is the students of Class VIII A of SMP Budi Murni 3 Medan in the Academic Year of 2017/2018. Questionnaire, observation sheet, field notes, and writing tests were used as the instruments for collecting data. The results of the study showed the significant improvement of the studentsâ€™ skill in writing recount texts. It is proven by the studentsâ€™ mean score of each test: pre-test is 64,68, formative test is 70,75 and post-test is 75,86. The progress is about 11,18 based on the studentsâ€™ mean score. Moreover, the improvement could also be seen from the Minimum Mastery Criteria (Kriteria Ketuntasan Minimum (KKM)) which showed that the studentsâ€™ score of pre-test is 20,68%, formative test is 58,62%, and post-test is 72,41%. It can be concluded that there is a significant progress of 52% based on (Kriteria Ketuntasan Minimum (KKM)). The students also agreed with the application of Peer Review Strategy in writing recount texts. It can be seen from the result of questionnaire that there was 60% of strongly agree and 40% of agree. Therefore, the application of Peer Review Strategy is very suitable to improve studentsâ€™ skill in writing recount texts.
Library and publishing complex of university — union as fundament for raising of efficience

Presented is experience of Siberian Federal University in reforming organizational structure of educational supporting departments. History of creation, formation, reforming of the library and publishing complex of university is submitted. Considered is specificity of work of scientific library and publishing house of university. The article describes creation of the resource center as division of the library and publishing complex, responsible for automation and technical support of key business processes. The authors рresent approaches to improving activities of the scientific library and publishing house of university. It is indicated, that automated systems “University Book Provision”, “Publishing of Works of Authors of the Siberian Federal University” have been developed for coordination of activities between divisions. Library and publishing council functions to regulate and coordinate activities of library and publishing processes, integrate them into scientific and educational environment of university. It is noted, that automation of business processes made possible to ensure efficiency of division’s work with parallel optimization of the staffing table. It is emphasized, that unification of the library and the publishing house into one structure made it possible to resolve issue of obtaining information resources. Experience of Siberian Federal University proved to be successful, and that is proved by public recognition, victory of scientific library of Siberian Federal University at All-Russian competition of library innovations and use of practice of Siberian Federal University by other universities of the country.
Performance evaluation of virtual cloud labs using hypervisor and container

Objectives: To measure the performance of docker swarm technology in virtual labs.Methods : The virtual laboratory is developed as a group of four system machines (VMs) on the same host computer as a cluster.The simulation depends on Linux OS, VirtualBox, Docker Swarm, Nginx, and Redis tools.Visualizing the tracing process by using portainer.Findings: The performance analysis of building virtual labs and running six main educational services using docker swarm virtualization technology are explained in detail.The experimental results have shown that the maximum utilization of the central processing unit (CPU) has reached 13% only for the nodes, 11% for the services, and 1% for the container, which considered very efficient in terms of processing.Moreover, the results have proved the effectiveness of the docker swarm in terms of memory usage since the maximum memory usage of nodes reached 101 MB, 103 MB for Container, and only 2% for each service.Additionally, the maximum network transition has reached (941 Bps) for service.Novelty/Applications: Building Cloud Virtual Labs enable students to connect remotely to the virtual machine at anytime and anywhere.Also, these labs enable instructors to trace the students' progress and manage the evaluation process.
ADAPTATION SYSTEM FOR STUDENTS STUDYING WITH DISTANCE EDUCATIONAL TECHNOLOGIES

External factors determine the widespread dissemination of distance learning. The number of students studying using distance learning technologies is growing each year. To successfully master the programme the students should be ready to work with the distance learning system. It should also be considered that the methodological support of students is necessary at each stage of learning through various communication channels convenient for the student (telephone, e-mail, instant messengers, mail, consultation within the university).The adaptation system includes several stages. The main stage is to prepare a student for study. Activities at this stage are held before the start of the semester. Based on the developed system for adapting students to learning using distance learning technologies, a model was built that can help conduct a detailed analysis of the existing system. The aim of the study is to characterize each stage of the process of adapting students to learning using distance learning technologies. The subject of the research is the system of students' adaptation at the university. The results of the study were obtained using the methods of experiment, analysis and testing and can be used to build a student adaptation system in a higher educational institution. The developed model can be used to analyze existing student adaptation systems, to identify the weaknesses of systems, and to develop measures to improve the analyzed adaptation systems of students studying with distance educational technologies.
Analisa Kinerja pada Standalone Server dan Clustering Server Teknologi RAC (Real Application Clustering) dengan Algoritma DNS (Domain Name System) Round Robin Berbasis Oracle Linux 6.4 di Lingkungan Virtual

ABSTRACT –A standalone server is a server that runs applications and databases on the same computer or server. As a service provider, the technology on the server is required to continue to develop in line with the needs. Therefore, a server clustering architecture was developed to serve the needs of clients on a larger scale. One solution to this problem is by implementing the RAC (Real Application Clustering) technology provided by Oracle 11gR2. RAC technology can fulfill client needs by using Round Robin DNS (Domain Name Server) algorithm. Then, this technology can help add new servers and recently assigned servers as cluster members. Also, RAC has advantages of memory storage for database storage. Based on the results of tests that have been done, server clustering has better quality according to a standalone server. Testing is done using QOS (Quality of Service) testing which is carried out on the network and also on load tests using TPM (Transaction per Minute) which is carried out on the database.
Generalized Universal Approximation for Certified Networks

To certify safety and robustness of neural networks, researchers have successfully applied abstract interpretation, primarily using interval bound propagation. To understand the power of interval bounds, we present the abstract universal approximation (AUA) theorem, a generalization of the recent result by Baader et al. (2020) for ReLU networks to a large class of neural networks. The AUA theorem states that for any continuous function f, there exists a neural network that (1) approximates f (universal approximation) and (2) whose interval bounds are an arbitrarily close approximation of the set semantics of f. The network may be constructed using any activation function from a rich class of functions---sigmoid, tanh, ReLU, ELU, etc.---making our result quite general. The key implication of the AUA theorem is that there always exists certifiably robust neural networks, which can be constructed using a wide range of activation functions.
The fraud loss for selecting the model complexity in fraud detection

Statistical fraud detection consists in making a system that automatically selects a subset of all cases (insurance claims, financial transactions, etc.) that are the most interesting for further investigation. The reason why such a system is needed is that the total number of cases typically is much higher than one realistically could investigate manually and that fraud tends to be quite rare. Further, the investigator is typically limited to controlling a restricted number k of cases, due to limited resources. The most efficient manner of allocating these resources is then to try selecting the k cases with the highest probability of being fraudulent. The prediction model used for this purpose must normally be regularised to avoid overfitting and consequently bad prediction performance. A loss function, denoted the fraud loss, is proposed for selecting the model complexity via a tuning parameter. A simulation study is performed to find the optimal settings for validation. Further, the performance of the proposed procedure is compared to the most relevant competing procedure, based on the area under the receiver operating characteristic curve (AUC), in a set of simulations, as well as on a credit card default dataset. Choosing the complexity of the model by the fraud loss resulted in either comparable or better results in terms of the fraud loss than choosing it according to the AUC.
THE PREPARATION OF FUTURE TEACHERS: MODERN APPROACHES TO LEARNING

The article is devoted to the issue of approaches to learning used by prospective teachers. The key difference between two main approaches to learning — deep and surface — lies in the intention or absence of intention to understand the material. Deep approach to learning is likely to result in a high level of understanding and better learning outcomes while surface approach leads to ineffective learning. Understanding students’ approaches to learning helps adjust teaching strategies for more effective learning. The purpose of the article is to identify the approaches to learning used by prospective teachers and consider the implications for teacher trainers. The article reports findings of a survey study into the approaches to learning used by prospective teachers of the Ukrainian language and literature mastering English as a specialism at a Ukrainian university. The main research tool was a questionnaire consisting of twenty statements. Ten statements concerning deep approach include such aspects as deep approach per se, relating ideas, use of evidence, intrinsic motivation. The other ten statements relate to surface approach and include such subscales as surface approach proper, syllabus boundness, fear of failure, extrinsic motivation. The respondents were asked to express how much they agreed with the statements using Likert scale. The data were analysed to find out mean scores and standard deviation. The findings demonstrate that students employ both approaches almost equally intensely: the mean scores of the Deep Learning Approach and the Surface Learning Approach scales are 3.64 and 3.34 correspondingly. However, according to the evaluation criteria the figures fall into different categories: deep approach has a high level of prevalence while surface approach has a moderate one. Basing on the obtained results, the authors suggest that teacher trainers should pay more attention to promoting deep learning approach through applying effective instructional strategies which improve the quality of learning; fostering students’ personal interest in learning; changing the focus of the assessment system from reproducing to understanding
Kesiapan Pembelajaran Daring di Fakultas Teknologi Informasi Universitas Kristen Maranatha melalui Analisis Kuesioner

Abstract — Information technology develops very rapidly, including the use of information technology in education system. This study aims to measure the readiness of Maranatha Christian University to implement online learning or e-learning, especially using learning management system cls.maranatha.edu (CLS).The assessment of readiness is done by distributing questionnaires to civitas academic. As a study case, the civitas academic is limited to the faculty of information technology. There are three respondents, namely students, lecturers, and officials. The results of the questionnaires is the CLS should be improved and new features must be added to support a better education. Keywords— questionnaire, e-learning, learning management system.
Strategi Pembelajaran Ekstrakurikuler Karawitan Di SMA Negeri 1 Pemalang

The Learning strategies are learning components that must be applied in learning activities by teacher, including learning of karawitan extracurricular in High School 01 Pemalang. The purpose of this study is to describe the learning strategies used by teachers in karawitan extracurricular of High School 01 Pemalang, uses descriptive qualitative study methods. The data collection is obtained through observation, interviews, and documentation technique. While validity of the data was tested using source triangulation techniques, then data analysed by interactive analysis technique. The results showed that the learning strategies in karawitan extracurricular of High School 01 Pemalang was using a teacher-centered, that the teacher becomes the center of the learning which determines the material, methods, dan learning objectives. Then, the syntax (stage) of learning strategies are orientation, presentation, guided training, structured training, independent training, independet activities, and evalution.
Penerapan model pembelajaran koopertatif tipe talking stick untuk meningkatkan pemahaman konsep hak dan kewajiban sebagai warga masyarakat kelas IV sekolah dasar

&lt;em&gt;This study aims to improve the understanding of the rights and obligations of community members by applying the Talking Stick cooperative learning model. This research is a Classroom Action Research with two cycle. The subjects of this study were fourth grade student of Mangkuyudan elementary school number 02 Surakarta with total of 28 students. This study uses data collection technique in the form of observations, documentation, field notes, tests, and. And interviews uses qualitative and quantitative data analysis. The first cycle of the second meeting produced a percentage of 52,17% classically, and the study continued with the second cycle of the second meeting with percentage of 83,33% classically. Based on the results of the study, it can be concluded that the ability to improve the understanding of the concept of rights and obligatons of community members in grade 4 students of Mangkuyudan elementary school number 02 Surakarta in the academic year 2019/2020 can be improved through the cooperative learning model of the Talking Stick type.&lt;/em&gt;
Rationalizing Translation Elongation by Reinforcement Learning

Translation elongation is a crucial phase during protein biosynthesis. In this study, we develop a novel deep reinforcement learning based framework, named Riboexp, to model the uneven distribution of ribosomes on mRNA transcripts during translation elongation. In particular, Riboexp employs a policy network to perform a context-dependent feature selection for the prediction of ribosome density. Our extensive tests demonstrate that Riboexp can outperform other state-of-the-art methods in predicting ribosome density by up to 5.9% in per-gene Pearson correlation coefficient. Enabled by the reinforcement learning strategy, Riboexp can identify more relevant features for the prediction task than other commonly used attribution methods in deep learning. In-depth analyses also reveal the meaningful biological insights generated by the Riboexp framework. Moreover, the application of Riboexp in codon optimization results in an increase of protein production by around 31% over the previous state-of-the-art method that models ribosome density. These results have established Riboexp as a powerful and useful computational tool in the studies of translation dynamics and protein synthesis.