Reviewerly/exHarmony huggingface dataset:
Studying the Effectiveness of Teacher Training in Organization of Open Education

The modern concept of open education modifies the traditional learning format through advanced information and communications technology. This fact imposes special requirements for all participants in the learning process and makes it necessary to reconsider the tasks and functions of students and teachers. This is relevant, therefore, to find a way of making a gradual transition to the model of open education. Based on an experiment with introductory training, the authors perform an empirical assessment of teachers’ preparedness to organize open education and use open educational resources (OER). The effectiveness of the introductory training of teachers was checked by means of interviewing and testing respondents. The research results showed that there were significant changes in the training criteria: according to the motivational and value-based criterion, virtually all teachers displayed a positive shift in their attitude to organization of open education; in terms of the cognitive criterion, the teachers acquired thorough knowledge of open education tools; according to the operational criterion, the teachers acquired the necessary skills in developing and introducing OER. In the process of observation, we recorded a rise in the number of teachers with a sufficient (by 60.5% compared with the verification stage) and high (by 16.6%) level of preparedness. The introductory training, therefore, is a principal component in the stable system of open education.
ONLINE TRANSPORTATION SURVEILLANCE SYSTEM FOR IMPROVING DRIVING BEHAVIOUR

Traffic is a problem in every nation. Indonesia is one of the most populous countries in Indonesia, certainly has such one of the biggest problems in the world, especially in big cities such as Jakarta. This is mostly contributed by the number of vehicles in Jakarta. Combine with current trends of Online transportation; the problem is getting bigger. Online transportation is on the rise in Indonesia. Sometimes seen as providing more benefits to people of Indonesia, online transportation also has its side-effects. As the number of online transportations rises, so is the level of traffic in big cities such as Jakarta. This traffic is also getting worse due to the bad driving behavior of Indonesian people, especially the online transportation driver. To better relieve traffic, a system to improve the drivers’ behavior can be developed to ensure safety, increasing driving behavior awareness, and hopefully decrease the traffic problem as a whole.
Improving Digital Literacy Competence for Teachers through Bimtek for Making Media and Evaluating Online Learning for Elementary Schools

Online learning is a demand in the current 4.0 era. This is because the development of technology and information so that learning must be carried out using technology. In addition, the Covid-19 outbreak gave birth to a learning implementation policy which was carried out at home using the online method. For this reason, teachers are required to have modern digital literacy competencies in order to be able to do online learning. However, the problem that exists today is that there are still many teachers who do not understand digital technology, namely 97.5% of teachers. This is as is known from the results of interviews and observations in Ranah Pesisir District, Pesisir Selatan Regency, West Sumatra, that there are still many teachers who have not been able to carry out learning using digital technology. And there are still many teachers who have not been able to carry out online learning. There are still many teachers who have not been able to do online learning evaluations. The solution to overcome this problem is to transfer knowledge through online learning guidance for teachers. The first stage carried out is to test the digital literacy competence of teachers through a questionnaire. After that, provide an understanding to the teacher about online learning. Then the next stage is conducting technical guidance on making innovative and interactive learning media for learning. Furthermore, technical guidance is carried out on the implementation of online learning evaluation for teachers. The last stage, evaluating the digital literacy competence of teachers on the implementation and evaluation of online learning using a questionnaire.
A Sub-linear Time Algorithm for Approximating k-Nearest-Neighbor with Full Quality Guarantee

In this paper we propose an algorithm for the approximate k-Nearest-Neighbors problem. According to the existing researches, there are two kinds of approximation criteria. One is the distance criterion, and the other is the recall criterion. All former algorithms suffer the problem that there are no theoretical guarantees for the two approximation criteria. The algorithm proposed in this paper unifies the two kinds of approximation criteria, and has full theoretical guarantees. Furthermore, the query time of the algorithm is sub-linear. As far as we know, it is the first algorithm that achieves both sub-linear query time and full theoretical approximation guarantees.
Analysis of the role-play “Ethics of life. Ancient philosophers’ experience”

The article is aimed at studying the use of philosophical practices in the educational process and everyday life. The author proceeds from the idea that ancient philosophy provides rich material for understanding a person’s lifestyle, on the basis of which anyone can create a set of spiritual exercises, philosophical practices for self-knowledge and selfdevelopment. In the research, the author describes the experience of role-playing, participating, in which students independently developed philosophical practices, based on the ideas and value principles of ancient schools. The author conducts a detailed analysis of the effectiveness of using these practices for the processes of self-knowledge and self-development.
Water productivity simulation for irrigated farmlands in the Brantas River Basin

Simulation of land and water management for the irrigated farmlands has become an interest to answer how agricultural productivity is related to the water use in a river basin. This research was undertaken in the Brantas River Basin in Indonesia, involving a modelling process with the virtual water concept as a tool to analyze water productivity. The research simulated the agricultural water demand and provided the results of the virtual water content of paddy and maize, based on the available farming land, water availability, cropping pattern and climatological conditions of the respective hydrological years of 1997 and 2011 as a benchmark. After which projections were made based on the low rainfall scenario for the years 2021 to 2025. The research concluded that the presence of excess irrigation in the tropical climates hinders the increase in agricultural productivity, and therefore, the irrigation in the Brantas River Basin needs to be improved by, among others, selecting plants with higher water productivity, developing water-sensitive cropping patterns, and water conveyance savings.
A Block Chain Approach for Privacy Preserving and Data Security in Internet of Things

Internet of Things has been increasing its usage and recognition in vast sectors like Defence, Business, Industries, and Hospitals. The data disruption is strictly unacceptable in a number of these sectors because it could end up in serious Loss or Damages to the entire system. As of now, IOT is using a central cloud storage system for information storage and transactions. However, some examples already verified that Central cloud storage information might be hacked and changed by the specialists. This paper presents an IoT system having localized block chain storage which works on real time data and manipulates with narrowness of data interruption and modification and its recovery.
STUDENTS’ LEARNING OUTCOMES OF BLENDED LEARNING IMPLEMENTATION IN WRITING LESSON

Writing requires specific skills in brainstorming, organizing, editing, and proofreading. There are varieties of research conducted in finding which approach is the most impactful to teach writing and one of the approaches emphasized is blended learning, widely acceptable in education field. Nevertheless, its effectiveness on writing is still questionable and the effort to integrate blended learning in teaching writing needs a thorough consideration. Therefore, the purpose of the study is to researcrh the adequacy of blended learning approach on students’ writing performance. Employing an explanatory research type, an experimental research design was applied. A mixed-method method was executed to collect, analyse and interpret data. For quantitative, a quasi-experimental design was carried out to 60 respondents who were selected through purposive samplings and distributed to control and experimental groups. The study used pretest and posttest for both groups. The result shows that there is no significant difference of blended learning and students’ writing performance between the experimental group and control group in the pretest and posttest. Overall, the study shows that both instructions have place in writing. Hence, educators have to become eclectic practitioners who are able to apply suitable approach that can contribute to the growth of teaching and learning writing.
On Batch Normalisation for Approximate Bayesian Inference

We study batch normalisation in the context of variational inference methods in Bayesian neural networks, such as mean-field or MC Dropout. We show that batch-normalisation does not affect the optimum of the evidence lower bound (ELBO). Furthermore, we study the Monte Carlo Batch Normalisation (MCBN) algorithm, proposed as an approximate inference technique parallel to MC Dropout, and show that for larger batch sizes, MCBN fails to capture epistemic uncertainty. Finally, we provide insights into what is required to fix this failure, namely having to view the mini-batch size as a variational parameter in MCBN. We comment on the asymptotics of the ELBO with respect to this variational parameter, showing that as dataset size increases towards infinity, the batch-size must increase towards infinity as well for MCBN to be a valid approximate inference technique.
Using a Markov cyberattack model for evaluation of security metrics

This paper presents a description of a Markov model of cyberattacks by which two security metrics are constructed. An algorithm is given for estimating the input parameters of the model based on a limited number of empirical data. An example that illustrates the use of the proposed security metrics is considered.
DIAGNOSA KEADAAN GIZI ANAK MENGGUNAKAN METODE CASE BASED REASONING

Nutrition can occur in every phase of life, beginning in the womb until old age. This nutritional issue is also one of the main issues in human development. Implementation of Case Based Reasoning can help diagnose a child's nutritional status. Case-based reasoning (CBR) is a problem-solving method that uses the knowledge of past experience to solve new problems. From the results of research methods Case Based Reasoning can be used to diagnose the nutritional status of children. Where the application of Case-Based Reasoning (CBR) method for case studies of children's nutritional status diagnosis has achieved accuracy of more than 90%.
Всеукраїнська олімпіада з математики для вступників до КПІ ім. Ігоря Сікорського: аналіз результатів і майбутні перспективи

Analysis of All-Ukrainian Mathematical Olympiads for entrants of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" that was held in 2017 and 2018 is studied in the paper. Organizational aspects and application of computer technologies for conducting Mathematical Olympiads for University entrants, geography of the Olympiad participants has been analyzed. The subjects of the tasks, the form of their presentation at different rounds of the Olympiad, the methodology for conducting the first and second rounds, time framework foreach stage, specifics of the Olympiad work assessment using capabilities of the Moodle educational platform were studied. Peculiarities of online distance round of the Olympiad on the basis of Moodle platform are studied in detail. Quality of the taken extramural test was analyzed using classical test theory. On the grounds of this analysis a table and a histogram of interval frequency distribution of individual scores were built. Strong and weak sides of the use of computer technologies are revealedon example of the organized Olympiads. Outlook and possible directions of further development of Mathematical Olympiads for University entrants in nowadays conditions are outlined.
Employment of Machine Learning in Cyber Crimes

Machine Learning is the application of AI with the capability to learn autometically without being explicitly programmed. Machine Learning is one of the most booming technologies these days that one would have ever come across. As name reflects, it builts computer intelligence same as human intelligence with its  ability to learn from experiences and available data in the system. Machine learning is vigorously used today in every field one would expect. Cyber Crime is the activity that involves a computer, networked device or a network to harm someone or something. While most cybercrimes are carried out in order to generate profit for the cybercriminals, some cybercrimes are carried out against computers or devices to damage or disable them, while others use computers or networks to spread malware, illegal and unwanted information and other materials.In this whole scenerio Machine Learning proved as a booster for the cyber criminals as it is implemented in every tiny step in the cyber crime from initiation till automation of the activity by cyber criminals.
ANALYSIS OF UNIVERSITIES' ROLE IN REGIONAL EDUCATION DEVELOPMENT PROGRAMS

Success of the socio-economic development of a region is largely determined by the development level of its regional education system, primarily the higher education system. The current state of the regional economy and the need to implement the innovative scenario of economic development determine the requirements to improve the efficiency of personnel training. Current state programs for the development of education in the regions are mainly focused on the development of preschool, school and secondary vocational education. These programs do not take into account the leading role of higher educational institutions in creating and promoting innovations, in training highly qualified specialists. The need to ensure closer interaction between higher educational institutions located in the regions and the regional education system determines the relevance of the research topic. The aim of the study is to identify the peculiarities of interaction between regional programs for the development of education and higher educational institutions located in these regions. The scientific novelty of the research lies in the fact that the authors proposed new elements to the mechanism of interaction between regional development programs of education and higher education system of the region. The article analyzes the programs of education development in the regions of the Volga Federal District. Particular attention is paid to the role of the higher education system in ensuring the socio-economic development of the region, the priority of which is the need to implement the innovative development scenario.
The Development of Learning Media Using Interactive Jobsheets for Basic Subjects in Electricity and Electronics Using PSIM in Tamansiswa Vocational High School

This study aims to develop learning devices for the subjects of Elementary Electricity and Electronics in class X. As long as learning in class does not provide practical activities, it is necessary to develop learning devices equipped with practical activities as soon as possible.This study uses a 4-D model development model. This study consisted of 2 classes with 1 non-experimental class and 1 experimental class.The rating results show the percentage of 96% so that it can be concluded that the learning device meets eligibility. The results of sample t-test correlate results obtained are 39.480, the value of t table is 2.045. The rating results indicate the percentage of 88% so that it can be concluded that the learning tool helps students carry out learning activities and practicum. The results of t-test on 2 independent samples obtained 4.420, the value of t table is 2.045.
IMPLEMENTASI METODE SAW (SIMPLE ADDITIVE WEIGHTING) SEBAGAI PENDUKUNG KEPUTUSAN UNTUK REKOMENDASI HABITAT KELINCI BERBASIS IoT (INTERNET OF THINGS)

Rabbits are mammals from the family Leporidae (green plant eaters) which can be found in many parts of the earth. Rabbits are very susceptible to temperature changes. Temperature greatly affects the rabbit's body metabolism. The temperature ideal for rabbits is in the range of 60 – 650F or equal to 15.5 - 18.30C temperature is known as "comfort zone" for rabbits. The level of heat stress in rabbits is very high in the tropics thereby reducing rabbit productivity. With a system that researchers can make it easier to determine the suitable place to serve as a rabbit habitat with the Internet of Things system network, and reduce the failure rate in keeping rabbits as well as the implementation of the node-ed as a platform to display the results of the calculation method of the decision.&#x0D; The system is applied using the SAW (Simple Additive Weight) ranking method or commonly called this weighted method by using three parameters including temperature; humidity; light. However, the data must go through a calculation that generates a value of each location then normalize to get a decision by the sum of the multiplication of normalization with weights.&#x0D; Implementation Methods of SAW (Simple Additive weighting) as Decision Support Habitat For Rabbits Recommendation Based IoT (Internet Of Things) has been successfully applied. So that it can rank rabbit habitat locations based on parameters of temperature, humidity, light. However, although the system is built based on IOT but has not publicly accessible or still based on the localhost.
MalariaNet: A Computationally Efficient Convolutional Neural Network Architecture for Automated Malaria Detection

Despite much progress in detection and treatment, malaria remains one of the most prevalent diseases on earth, both in terms of incidence and death rate.Multiple studies have shown that early detection of malaria is paramount to preventing fatal outcomes; however, current testing methods have notable issues involving cost and accessibility.As a result, deep learning algorithms have been developed for malaria detection and have achieved state of the art results in rapid diagnosis; however, it has been noted that the computational expense of running elaborate models makes deep learning based detection methods inaccessible in remote areas of the world.We develop a computationally efficient, relatively shallow neural network architecture that can diagnose malaria from cell images obtained from thin blood smear slides.Specifically, our algorithm, dubbed MalariaNet, is a 7-layer convolutional neural network trained using the Adaptive Moment Estimation algorithm on the open source NIH malaria dataset, containing 27,588 images of parasitized and uninfected cells.We report that MalariaNet achieves an accuracy of 0.968, F1 score of 0.955, precision of 0.946, and recall of 0.974.We hope that our computationally considerate model inspires more research in producing accessible artificial intelligence solutions for disease detection tasks.
Pendampingan pengolahan data nilai siswa berbasis web

Reporting of learning outcomes is a collection of the final grades of all subjects taken by students in a particular academic year semester. The processing of grade reports at SD Muhammadiyah Ambarketawang 3 in handling the administration of reporting learning outcomes is currently not effective, where data collection and processing takes several weeks to get the final result. This is because the data obtained by data collection has not been centralized and stored offline. This community service is to provide solutions to problems by implementing the E-rapot application which is an online learning outcome data management application so that it can be processed and monitored from anywhere. The method of activity is providing training to teachers for assistance in web-based value processing. The results of community service activities have increased the understanding and skills of teachers to manage student learning outcomes data online. It is hoped that the increase in understanding is expected in the future to make reports on students' online learning outcomes more effective and with maximum results. In addition, in the era of the COVID 19 pandemic, teacher reports have become easier for school principals to monitor.
Self-supervised self-supervision by combining deep learning and probabilistic logic

Labeling training examples at scale is a perennial challenge in machine learning. Self-supervision methods compensate for the lack of direct supervision by leveraging prior knowledge to automatically generate noisy labeled examples. Deep probabilistic logic (DPL) is a unifying framework for self-supervised learning that represents unknown labels as latent variables and incorporates diverse self-supervision using probabilistic logic to train a deep neural network end-to-end using variational EM. While DPL is successful at combining pre-specified self-supervision, manually crafting self-supervision to attain high accuracy may still be tedious and challenging. In this paper, we propose Self-Supervised Self-Supervision (S4), which adds to DPL the capability to learn new self-supervision automatically. Starting from an initial "seed," S4 iteratively uses the deep neural network to propose new self supervision. These are either added directly (a form of structured self-training) or verified by a human expert (as in feature-based active learning). Experiments show that S4 is able to automatically propose accurate self-supervision and can often nearly match the accuracy of supervised methods with a tiny fraction of the human effort.
Random-Receiver Quantum Communication

We introduce the task of random-receiver quantum communication, in which a sender transmits a quantum message to a receiver chosen from a list of n spatially separated parties. The choice of receiver is unknown to the sender, but is known by the n parties, who coordinate their actions by exchanging classical messages. In normal conditions, random-receiver quantum communication requires a noiseless quantum communication channel from the sender to each of the n receivers. In contrast, we show that random-receiver quantum communication can take place through entanglement-breaking channels if the order of such channels is controlled by a quantum bit that is accessible through quantum measurements. Notably, this phenomenon cannot be mimicked by allowing free quantum communication between the sender and any subset of k<n parties.