Reviewerly/exHarmony huggingface dataset:
Transformation Of Higher Education In Digital Economy

The work is devoted to the issues of transformation of the higher education system in the digital economy. Challenges facing Russian universities are discussed: technology, the rhythm of life, business activity have accelerated so much that the set of competencies acquired at a university “burns out” within three to four years, and you need to be prepared for the fact that the individual often has to radically change his profession or range of duties. The data from specialized statistical collections are presented, which show the situation with the provision of qualified personnel in the Russian Federation in the digital economy. The internal environment of the university has a number of questions that include: how do they see the process of digital changes employees of educational organizations, what role do digital technologies play in the transformation of traditional teaching practices. It is assumed that the digital university model should consist of four blocks: a university management information system, online support of the educational process, key competencies of the digital economy and educational process management on the basis of an individual educational path. In the face of uncertainty, the only thing that will help to quickly reorient and train specialists for the tasks of a particular moment is high-quality secondary and higher education, which will train not narrowly-focused specialists who are in demand right now, but will form a universal basis, based on which people will be able to quickly re-train in accordance with the future requirements that are yet unknown.
WEB-BASED EQUIPMENT INVENTORY INFORMATION SYSTEM IN THE SERVICE DIVISION OF PT ARISTA SUKSES MANDIRI JAKARTA

Inventory management of work tools at vehicle after sales service division PT. Arista Sukses Mandiri Jakarta still done manually using printed form (for dailycheck) and handwriting book (which record every tool that in and/or out from tool room), because of that staffs need more time to do them. Managing the tools inventory must be correct and precise both data and reality in the ground, but done manually the risk of data error is higher than if the staff do with computerized system. Beside that using computerized system in the scope of inventory of tools can make its process faster, so the time efficiency can be reached. Inventory system of tools based web is developed by the writer (also developer) to solve or at least reduce those problems. The method used in developing of this web based application is modified waterfall with whirlpools for the beginner as suggested by Powel, et al. This method consists of problem definition, requirement analytic, prototyping design, implementation, integration/testing, and release/maintenance. The final product of the research and development on this paper is web based application for work tools inventory management that can make the process of managing faster, more precise, and more efficiency.
On Designing Application of Finding Nearby Islamic Boarding Schools in West Java using Haversine Formula and Euclidean Distance Algorithms

Islamic Boarding School is one of the alternative schools that are of interest to Indonesians.Finding the most appropriate boarding school is one of the initial strategies in determining schools.There are 25,938 pesantren in Indonesia, 8,264 of them are in West Java.The search for Islamic boarding schools is important considering the large number of them.This paper aims to tell the design of the search application for the nearest boarding school using Haversine Formula and Euclidean Distance algorithm.Applications that will be made compare the two methods so that they get the best results by using Longitude and Latitude calculations.The results showed that the application of the Haversine formula algorithm and the Euclidean distance on the nearest location search application was successfully built and could be used to find Islamic boarding schools in West Java.Haversine Formula and Euclidean Distance algorithms are able to provide distance accuracy to the location.But the Euclidean distance algorithm is more accurate than the haversine formula with a difference of 0.07 KM.
MAKING OF DIGITAL FORENSIC READINESS INDEX (DiFRI) MODELS TO MALWARE ATTACKS

The increasing number of malware spread in the world today, then there will be more opportunities to commit crime, so readiness is needed for every internet user in dealing with these crimes. The readiness to handle crime is called digital forensic readiness. Therefore, we need a specific digital forensic readiness model to measure the level of readiness of internet users or institutions in achieving malware attacks. This model has the main components used to determine or calculate the level of readiness of internet users or institutions, the main components are the strategy component, the policy &amp; procedure component, the technology &amp; security component, the digital forensic response component, the control &amp; legality component. The calculation method used in this study is a Likert Scale, with this method the results will be obtained that are closer to the real situation. The value / index of readiness level obtained will provide recommendations to internet users and these recommendations can be used to make improvements properly and on target.
WATER CONSUMPTION PREDICTION USING FUZZY TIME SERIES - A CASE STUDY IN PRIVATE COMPANY OF TANGERANG DISTRICT INDONESIA

Consumption of water in the Tangerang Regency continuously increases from year to year due to the increasing population and birth rates an average increase of 3% every year. So, the water demand prediction to be important to meet customer or community needs. The private water utility company needs to use a new method for predicting future monthly water consumption values and improves accuracy when forecasting time series using a visibility graph and presents to make more accurate predictions. In this study, we aim to measure the trend analysis volume of water consumption prediction by Fuzzy Time Series versus actual usage volume. Fuzzy Time Series (FTS) is a concept plan method that uses fuzzy logic that is able to provide predictions (estimates) of time series data analysis for the next several periods. Mean Absolute Percentage Error (MAPE) is obtained for different configurations of the input sets and of the FTS model structure. From the results of the average value error accuracy was only 4.5% using FTS Chen Method and included in the low category and water consumption actual versus prediction with the FTS Chen method shown related stable.
Media Development Learning Game Al-Qur’an Interactive Multimedia Based

Madrasah Tsanawiyah (MTs) Al-Ihsan Babakanmanjeti are mostly simple learning methods such as writing on the board, reading through books, and lecture methods, especially in the method of learning to read Al-Qur'an like tajwid.The use of learning media that the authors make, namely Al-Qur'an learning games can be an alternative means for students to increase their interest in learning.The presence of Interactive multimedia-based Al-Qur'an reading media is expected to be able to attract the interest of students and the public to learn to focus more on the material being studied especially reading the Qur'an.The system development method used in making Al-Qur'an learning game applications is to use the MDLC method (Multimedia Development Life Cycle).Questionnaires were given to teachers and students who had tried using the Al-Qur'an learning game, the questionnaire was made aimed at knowing the responses of teachers and students to the learning game.Based on the survey results, teachers and students gave a positive response to the Al-Qur'an Media Learning Game application with an average percentage of 88.9% and by teachers an average percentage of 86.8%.
EDUCATION DILEMMAS IN THE DIGITAL ERA: PHILOSOPHICAL APPROACH

The article presents philosophical and critical exploration of education in the framework of intensive informatization of modern society. The use of digital technology is nowadays a key feature of educational practices in the world. Yet despite its prominence, digital technologies in education continue to be an issue that rarely receives sustained critical attention and thought. Tackling the wider picture, addressing philosophical, cultural, economic aspects of education in digital age, the study offers to make sense what happens and what doesn’t happen, when the digital and educational come together. Both positive and negative consequences of the spread of e-learning systems and technologies are analyzed. Examining contemporary education in terms of social justice, equality and meaningfulness the authors formulate the key tasks facing the philosophy of education in the modern digital era. The authors conclude that it is necessary to supplement electronic educational technologies with traditional educational practices. In particular they examine the trends and prospects of cognitive research and biotechnologies in the light of their influence on the human ideal that characterizes contemporary education. The authors argue that a serious and fruitful comprehension of education in the digital age requires a revision of the classic opposition of the subject and object, spirit and body, man and animal.
Towards Fog-Assisted Virtual Reality MMOG with Ultra-Low Latency

In this paper, we propose a method to realize a virtual reality MMOG (Massively Multiplayer Online Video Game) with ultra-low latency. The basic idea of the proposed method is to introduce a layer consisting of several fog nodes between clients and cloud server to offload a part of the rendering task which is conducted by the cloud server in conventional cloud games. We examine three techniques to reduce the latency in such a fog-assisted cloud game: 1) To maintain the consistency of the virtual game space, collision detection of virtual objects is conducted by the cloud server in a centralized manner; 2) To reflect subtle changes of the line of sight to the 3D game view, each client is assigned to a fog node and the head motion of the player acquired through HMD (Head-Mounted Display) is directly sent to the corresponding fog node; and 3) To offload a part of the rendering task, we separate the rendering of the background view from that of the foreground view, and migrate the former to other nodes including the cloud server. The performance of the proposed method is evaluated by experiments with an AWS-based prototype system. It is confirmed that the proposed techniques achieve the latency of 32.3 ms, which is 66 % faster than the conventional systems.
Determining the Smart University Infrastructure Development Level Based on Data Models

The paper presents applied research in the framework of solving determining the smart university infrastructure development level problem. Today, all digital transformation and transition to the digital economy concept and smart society development issues are very important. Education is the intellectual core of the digital transformation, the knowledge base, and the training center for the digital economy. Therefore, more attention should be paid to the creation and development of the smart university infrastructure. The purpose of the study is to develop criteria and models for determining the level of smart university infrastructure development. To describe the management of infrastructure level assessment, we propose to use adapted mathematical methods, structural analysis, and mathematical statistics methods. The study examines trends in the development of smart education, highlights the main elements of the smart university infrastructure (technological and organizational), and examines approaches to determining the level of its development based on data models. Taking into account the diversity and volume of quantitative and qualitative indicators of the smart university infrastructure, we propose to measure the level of the infrastructure using data models and Educational Data Mining tools. Universities can use got models when forming a development strategy or selecting funding directions, to reduce the time of digital transformation and transition to the optimal state of a smart university, to prepare for accreditation or to align the smart infrastructure of individual departments.
EVALUASI KEPUASAN PENGGUNA E-LEARNING PADA PROSES PEMBELAJARAN DARING DI INSTITUT MANAJEMEN WIYATA INDONESIA

The research aims to obtain an overview of the (1) The quality of e-learning systems, (2) The quality of e-learning information, (3) The quality of e-learning services, and (4) e-Learning user satisfaction in the online learning process at the Institut Manajemen Wiyata Indonesia. This research uses the descriptive method of weighted average evaluative. Data collection is implemented by spreading the questionnaire. The results showed: (1) The quality of E-learning systems in the online learning process is categorized (well), (2) The quality of E-learning information on the online learning process is categorized (well), (3) The quality of E-learning services on the category of online learning is not good, (4) The satisfaction of E-learning users in the online learning process is categorized well.
Artificial Intelligence in Practice – Real-World Examples and Emerging Business Models

There are everyday examples of Artificial Intelligence (AI) in different areas. Some of the prominent AI applications are virtual assistants, robots, AI applications related to computer vision and those used in medicine. This paper attempts to examine the recent trend of the real-world applications of AI and also identify the business models for these. The business models are then examined to see if these are existing business models that are used to enhance businesses using AI or if new AI-driven business models have emerged. The emerging AIdriven business models are Federated learning, the triangular partnership model and the use of Emotion AI to come up with new business models. The existing ones enhanced by AI are the freemium model, Rent to Buy model, leverage customer data and the land and expand model.
Abracadabra Law-Making and Accountability to Parliament for the Coronavirus Regulations

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Neolithic ceramic figurines. Several approaches to analytical study of the ceramic artefacts perceived as cultural heritage Neolitické keramické plastiky. Několik možností analytického studia památkově cenných keramických artefaktů

The article aims to present the results of the analysis of eight Neolithic ceramic figurines from the Lengyel culture settlement Těšetice-Kyjovice – Sutny, Czech Republic. We indicated the possibilities for acquiring analytical data, although destructive methods were unacceptable. We included methods neglected to date, but widely available in the common archaeological laboratory. Information on the composition of the ceramic matrix, the provenance of inclusions, the techniques used for shaping, firing, operating and discarding are all of key importance for advancing the discussion on the nterpretational potential of ceramic anthropomorphic figurines from typological and religionist discourses to analytically-focused discussions supported by hard data. Two serious factors have thus far prevented the widespread use of analytical methods: the destructive nature of the evidential analyses, which is contradictory to heritage protection and general awareness of the value and rarity of such finds. The second factor relates to the prohibitive cost of analyses when applied to a statistically robust number of samples. On the other hand, in the absence of broad mapping of measurable data, we will remain dependent on only individually published analyses of isolated samples, typically from a different culture, period or geographical territory. We believe the use of non- and semi-destructive methods offers a solution. With the aim of achieving the lowest destructive impact, we conducted an analysis of eight ceramic figurines by use-wear analysis, high resolution 3D analysis, portable-X-ray fluorescence (p-XRF) measurements and microscopy of polished sections.
Form and Meaning Representation of Chinese Construction Fundamental Issues in The Practice of Constructicography

This paper gives an introduction to a Digital Humanities project at the Department of Languages and Cultures (Ghent University), the Database of Medieval Chinese Texts (DMCT), a collaborative project with several international partners.The structure of the DB is multi-modular, consisting of reference modules in the form of XML marked-up medieval non-canonical Chinese Buddhist texts, as well as analytical modules such as the Variants, Syntax, and Sentence Analysis modules.The architecture is 'open' and modules can be added, modified, and interlinked based on specific research requirements.The DB is multifunctional and not only provides information on key texts and their linguistic features, but also constitutes a research tool (featuring sophisticated online input masks and analytical tools) with which researchers can input and process data.In addition to its function in a research environment, it is also used in advanced master classes, in the framework of master thesis and PhD projects, as well as for internships.The DB has also an important 'socio-institutional' function, being situated at the intersection of Buddhological and historical linguistic studies, two of the main fields of research at the department.
Methodological Aspects of the Use of Software in the Teaching of Engineering Disciplines: Tasks, Problems and Prospects

The main directions of improving training that can be achieved as part of the introduction of new software in the teaching of engineering disciplines are considered. The necessity of ensuring the continuity of training within the framework of a unified concept of using software tools is noted. The analysis of needs in the formation of new competencies based on the results of a survey of graduate students of BMSTU, and the views of employers, which showed the closeness of the representations of students, teachers and employers. It is noted that the developed software application technologies should guarantee their inextricable link. New approaches to the teaching of engineering disciplines based on computer modeling, which are used in world practice, are considered. Trends in the application of modeling technologies, including the use of integrated software products, are noted. The main modern trends are active training and teamwork, group training. These computer technologies have taken the project approach to learning to a new stage. New software products allow not only the formation of special competencies, but also provide the opportunity to acquire important communication skills. New opportunities for student motivation are opening up. Access to an open electronic educational environment, including open packages of applied engineering design programs allows you to create self-education skills, without which it is impossible for an engineer to work in rapidly changing conditions. The active role of the teacher in effective and competent self-education is noted.
Explainable deep learning models for dementia identification via magnetic resonance imaging

Abstract Background Today, to diagnose dementia, clinicians evaluate cognitive tests performed by patients and briefly analyze brain imaging data to look for biomarkers. While valuable information is present in MRI scans, these latter remain challenging to analyze and interpret. Artificial intelligence models have shown promising results to improve the current practice by supporting practitioners in the evaluation of imaging data. Nonetheless, the majority of developed statistical models are more often than not black‐box systems that issue predictions without any clear interpretability, hindering their practical applications. Methods We propose an interpretable method based on deep learning that works on minimally preprocessed T1‐weighted 3D scans of the brain. Relying on FullGrad [1], we can dissect the predictions of the model given an input scan. Once the model is trained, it can not only give an automated diagnostic but also generate a heatmap highlighting the regions of the brain that our model points to be responsible for its prediction of dementia. To ensure practicality, we integrate our model in a convenient app that can smoothly be run from a browser, as shown in the attached screenshot. Results We trained and evaluated our model on the OASIS dataset [2]. The specific explanation obtained by our model points at well‐known biomarkers, notably by highlighting the voxels of the hippocampus of patients with dementia. Interestingly, as it can be seen in the second annex, we notice that across individuals, our model focuses more on the voxels located in the right hippocampus. Conclusions In this study, we show how machine learning can identify dementia patients using MRI images while ensuring interpretable decisions of the models. Our tools, including the bespoke "explainer" viewer overlaid on each patient's brain, will enable the development of better and more reliable machine‐learning based diagnostics and nurture the trust of practitioners in computer‐aided diagnostics. Furthermore, this will help to discover currently unknown biomarkers and thus lead to a better understanding of the disease. References: 1) Full‐Gradient Representation for Neural Network Visualization, Suraj Srinivas, Francois Fleuret, 2) OASIS‐3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease, Pamela J LaMontagne.
Three-Dimensional Mirror Surface Measurement Based on Local Blur Analysis of Phase Measuring Deflectometry System

Despite the marked progress in recent years, structured light-based three-dimensional (3D) measurement techniques still have difficulty in capturing mirror surface reflection. The accuracy of 3D reconstruction for mirror objects should be further improved to adapt to the high reflectivity and curvature of such objects. To improve the stripe definition and reconstruction accuracy of highly reflective mirror objects, this paper analyzes the local blur of defocus stripes in phase measuring deflectometry (PMD) system, and presents a method to analyze the spatially varying defocusing and de-blurring, with the aid of a 3D block matching algorithm, thereby focusing on defocus stripes. Experimental results show that the proposed method can achieve micron-level reconstruction accuracy of standard flat mirrors, and detect the defects on highly reflective mirror objects at a high precision.
Prediction of Life Expectancy in Maluku Province Using Backpropagation Artificial Neural Networks

Life Expectancy at Birth (LE) is defined as the average estimated number of years a person can live to since their birth. The purpose of LE is to represent the health rate of a community. Backpropagation is an algorithm in artificial neural networks (ANN) used to predict or forecast data. This study aims to predict Life Expectancy in Moluccas. Based on the results of the analysis obtained an average forecasting success of 99.65% with the smallest error MAPE = 0,0035. Forecasting for the next 5 years shows that the Life Expectancy value tends to increase over the next 5 years from 2019-2023 at 65.7828 (2019) increasing to 66.6632 (2023).