Reviewerly/exHarmony huggingface dataset:
QEE: VR GAME THAT AIMS TO TEST AND ENHANCE THE PLAYERS KNOWLEDGE OF TAJWEED

This paper aims to provide an effective computer game which, in addition to entertaining players, helps improving their knowledge of Tajweed.Tajweed is an Islamic science that studies the correct recitation of the Qur'an words and letters.It has been proven that learning at an early age gives more reliable and long-lasting knowledge.In addition, teenagers are more capable of controlling their pronunciation and modifying them.For these reasons, Qee game targets the ages between 13 and 18.As teenagers nowadays are exposed to a variety of game technologies, which raised their expectations and satisfaction level, a 3D VR game was introduced as an attractive modern solution.A prototype of two levels was developed and tested by 20 participants.The testing session resulted in a successful outcome with some suggestions for future modifications.
PENGEMBANGAN PERPUSTAKAAN PERGURUAN TINGGI YANG BERORIENTASI PADA CIVITAS AKADEMIKA DAN PERKEMBANGAN TEKNOLOGI INFORMASI

Librarian competence as a library manager and the development of information technology influences the development of higher education libraries. The current development of information technology influences the increasingly high demands of users to be able to get increasingly high-quality library services. Another thing that is required so that a university library can provide quality services is the availability of librarians who have good competence in managing a library. The existence of a library that can provide quality services will be able to increase the satisfaction of users in using services in the library.
Technology, Theatre and Co-design: Impact and Design Considerations

The paper documents the co-design methodology followed by Northern Stage theatrical company for the design of a theatrical production, rich in digital elements. Drawing on our data from fieldwork, interviews and questionnaires, we initially report on the co-design activities, then using thematic analysis, we review the impact of technology in the co-design activities, the dynamics of using digital technologies in a performance and the limitations for small-medium theatrical companies. Our work extends research and practice on co-design and participatory design in creative industries and their experimentation with technology. More specifically, we contribute by casting light on the nature of activities and the level of digital maturity and readiness. The paper concludes with considerations of co-design and HCI work in attracting a new generation of performers and audiences for the digital era.
Literary Plagiarism and Copyright Protection: A Study in Academic Ethics and Copyright Law

With technological development and technological information, access to information and notions has become very easy. This revolution of technology assists and encourages further enhancement and innovation in various fields, yet it might be utilized in a wrong way or it might not be used in the exact context, in which it is wrong by itself. For instance, internet and computer have facilitated the process of obtaining information from E-books and researches, at the same time it has helped to be used in wrong ways through writing books and researches. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;Although technological development has helped to obtain information easily, and it has helped to gain information in a wrong way, yet through some technological programs we can ensure whether somebody has used another person’s notion or not. For instance, a program known as – Turnitin-&nbsp; would give authenticity to each part of the research, displays how each part of research has been transferred, and whether plagiarism has reached to a certain percentage or not. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; In the absence of a lucid and binding intellectual property rules, it would encourage people to plagiarize idea from others, or (else) it would encourage them to avoid plagiarism.&nbsp; Hence, having intellectual property binding rules- in particular copyright rules- reduces plagiarism. These rules would make researchers, particularly whom are novice, to follow the rules and avoid plagiarism when they quote other’s concept. The matter in which need to be clarified is pertaining to protection which exists in copyright law. This law protects forms of expression an idea not the idea itself which refer us to origins of scientific research and scientific integrity in quoting idea and information. We dedicate this research to elaborate this protection of law through two main sections. First section explores concept, sorts, and causes of plagiarism, and second section explores copyright of authors and protecting them from some kinds of plagiarism.
Public-private partnership in education: Features and prospects

Effective and constructive interaction of the state and business is an essential condition for the country's socio-economic development.One of the tools to ensure the growth of the financial, innovation, technological potential of the territories is the implementation of public-private partnership projects (hereinafter -PPP).The modern market system creates the conditions for the organization of cooperation of market entities in all sectors and areas, including in the field of education.Within this article, a comprehensive review of the PPP typology, structured on the basis of the multidimensionality of the classifications proposed by the authors, was conducted.Special attention is paid to the typology of partnership in the field of education, which has become widespread and has been actively implemented in Russia for the last five years.Based on the review of typologies of public-private partnerships in education, the main criteria were identified, by which the types of partnerships were classified, the main distinguishing features of PPP in education were formulated, the basic principles characteristic of PPP projects were identified, and the prospects and benefits that the sphere education from participation in public-private partnership.
A New High Accurate Estimation Method for Evaluating the Daily Solar Energy by Nested Percentiles Algorithm

Background and Objective: Owing to the wide use of Weibull distribution in solar energy analysis, researchers are constantly looking for easy and applicable methods for estimating its parameters with the smallest margin of error.This study was aimed to find a new algorithm that computes the Weibull parameters easily and accurately.Materials and Methods: Weibull distribution was performed to model the daily solar energy and corresponding maximum temperature in Queensland, Australia over a year.A new method, called nested percentiles algorithm was suggested for estimating the parameters.Anderson-Darling test was applied to measure the fitness of the suggested estimator.Results: Nested percentile algorithm gave high significant results as indicated by Anderson-Darling test.The parameters estimated by the new algorithm provided the best fit of the used datasets over than other results produced by EasyFit program.Conclusion: Nested Percentile Algorithm made two-parameter Weibull distribution more accurate than three-parameter distributions such as; modified Weibull distribution.It also gave more accurate results than EasyFit program.
PERANCANGAN TATA KELOLA SISTEM INFORMASI MANAJEMEN RUMAH SAKIT (SIMRS) BERBASIS ITIL V3 &amp; SERVICE DESK STANDART (STUDI KASUS : RSUD KOTA KEDIRI)

Minister of Health Regulation No. 82 of 2013 Chapter IV related to IT Governance recommends implementing IT governance for SIMRS. This study aims to design IT governance for SIMRS in The Kediri City Hospital. The design of IT governance is carried out after analyzing the gap of the current capability level with the target RSUD. The results of the IT governance design, 1 IT organizational structure, 11 IT processes, and 20 IT documents, were successfully created and approved by The Kediri City Hospital.
Adaptive Navigation Support: A Literature Review of Its Application and Implication in Learning Process

The use of technology has been widely used for many people in every aspect of life and become part of daily activities in recent years. One of technology application is in the learning and education process. Adaptive Navigation Support (ANS) is a method and part of a hypermedia study that can boost the performance of students by using the benefit of technology. With ANS, students are able to make the learning process more interesting as the learning material relatively corresponds to their knowledge and preferences. Furthermore, ANS can guide students in finding their paths and ways in hyperspace based on student requirements, goals, and characteristics. Therefore, ANS has caught many researchers attention in their studies to improve student skills and performance in the learning process. Many papers, discussed in ANS, have been published over the last decade, however, no one has studied the ANS in a comprehensive way. This paper is a literature review in ANS and its application as well as its implication in the learning process. There are fourty-nine papers as its source of analysis. Our findings show that the applications of ANS can be classified into four dimensions, such as platform, use case, impact, and process. These classifications are based on the study of ANS applications and implications over the last decade.
Parallel Computing Mode in Homomorphic Encryption Using GPUs Acceleration in Cloud

As an important encryption algorithm of performing computations directly on ciphertext without any need of decryption and compromising privacy, homomorphic encryption is an increasingly popular research topic of protecting privacy of the data in cloud security research.However, it will be a heavy workload for resources as the high computational complexity of homomorphic encryption.Therefore, GPU acceleration is employed to speed up homomorphic encryption.Motivated by this observation, we utilize parallel computing mode in DGHV algorithm with GPUs acceleration based on CPU-GPUs hybrid system.Our main contribution is to present a parallel computing mode for large-scale data encryption based on CPU-GPUs hybrid system as fast as possible.Specifically, we applies parallel computing mode in DGHV with GPUs acceleration to reduce the time duration and provide a comparative performance study.We further design pipeline architecture of processing stream to accelerate the speed of DGHV algorithm.Furthermore, experimental results validate that different parallelism has the corresponding granularity in parallel computing mode.Experimental results show that our method gains more than 84% improvement (run time), 67% improvement (run time), and 80% improvement (run time) compared to the sequential data encryption, sequential homomorphic addition, and sequential homomorphic multiplication in DGHV algorithm respectively.
rollmatch: An R Package for Rolling Entry Matching

The gold standard of experimental research is the randomized control trial.However, interventions are often implemented without a randomized control group for practical or ethical reasons.Propensity score matching (PSM) is a popular method for minimizing the effects of a randomized experiment from observational data by matching members of a treatment group to similar candidates that did not receive the intervention.Traditional PSM is not designed for studies that enroll participants on a rolling basis and does not provide a solution for interventions in which the baseline and intervention period are undefined in the comparison group.Rolling Entry Matching (REM) is a new matching method that addresses both issues.REM selects comparison members who are similar to intervention members with respect to both static (e.g., race) and dynamic (e.g., health conditions) characteristics.This paper will discuss the key components of REM and introduce the rollmatch R package.
Supervised and Nonlinear Alignment of Two Embedding Spaces for Dictionary Induction in Low Resourced Languages

Enabling cross-lingual NLP tasks by leveraging multilingual word embedding has recently attracted much attention.An important motivation is to support lower resourced languages, however, most efforts focus on demonstrating the effectiveness of the techniques using embeddings derived from similar languages to English with large parallel content.In this study, we present a noise tolerant piecewise linear technique to learn a non-linear mapping between two monolingual word embedding vector spaces.We evaluate our approach on inferring bilingual dictionaries.We show that our technique outperforms the state of the art in lower resourced settings with an average improvement of 3.7% for precision @10 across 14 mostly low resourced languages.
GLOBAL MEGATRENDS OF HUMANITY, FOURTH INDUSTRIAL REVOLUTION AND DEVELOPMENTS OF THE FUTURE ECONOMY. BIG DATA

The article deals with the main features of Big Data, the area of their application, the connection with machine learning. It is described that a self-studying program receives an arbitrary set of data units that conform to a certain pattern and after studying this set, the program is a model of this pattern, that is, a set of criteria by which the algorithm can determine whether a specific unit of data is the desired pattern or not. In the future, applying this model to a new and random data set, the program can find in it the units of data in which the pattern is executed. Professional tools for working with Big Data, such as software products and distributed computing (cloud computing) are considered. The means for working with Big Data, examples of targeted advertising based on their analysis are described. The effectiveness of targeted advertising application based on the use of analysis of character traits using the most popular personality model "Big Five" known also as OCEAN (openness to experience, conscientiousness, extraversion, agreeableness, neuroticism), surface characteristics and human way of life are estimated. The effectiveness of personalized advertising on the basis of the results of psychometric analysis using Big Data on an example of an election campaign is investigated. It is shown how the electorate's research was conducted, through simulation, division into different segments, with each of them being actively interacted with ethereal and targeted television advertising, as well as through advertising on the Internet. An estimation of perspectives and difficulties of using Big Data principles on the basis of application examples in various transport systems, resource savings, logistic complexes and systems, elevator building and medical industry.
An ICMetric Security System for Intelligent Wheelchairs Based on Triple MEMS Sensors

Embedded systems are increasing in many areas, some examples of an embedded system are mobile phones, video game consoles, and industrial monitoring. Currently, interest with embedded systems is concerned with the issues of the providing safety, privacy and security. Security techniques rely on keeping the keys hidden, system can be exposed when the keys are described. We propose a system based on ICMetric that exploits the features of an embedded device to produce the identification of device. This paper proves that unique device features can be utilized to present an identity to a device which can be consumed for the providing security. The ICMetric technology has the facility to protect the systems by exploiting features of device. The proposed system uses the embedded MEMS magnetometer, gyroscope and accelerometer in the myAHRS_plus sensor to create a device ICMetric. An intelligent wheelchair is required which is equipped with the MEMS sensors. The proposed system is built on the features which have been produced by bias values of magnetometer, gyroscope and accelerometer. Reading generated from sensors are analyzed statistically to generate a triple ICMetric numbers used for device identification. The ICMetric number is not stored on the system and can be reproduced when necessary. If the system is attacked, there will be no theft because the ICMetric number is non-store. The proposal system proves that using MEMS sensors to generate an ICMetric number which using for device identification increase authentication and security of embedded devices.
Stabilize IoT Blockchain using Smart Rewarding Mechanism: Incremental Block Reward

Bitcoin's block reward is dependent on miner hardware profiles such as computation capacity and mining hardware identities, therefore, incentivizing miners to upgrade mining rigs in order to mine more blocks and maximize the payoff.As a result, miners who refuse to upgrade mining hardware suffer decreasing payoff because the mining difficulty increases automatically as the computation power of the entire network grows.However, this phenomenon is highly counter-productive in Internet-of-Things (IoT) networks, where CPUs are embedded into various devices and hardware upgrading require non-trivial efforts, in addition to the constrained computational and power capabilities.As a result, we propose the Incremental Block Reward (IBR) to encourage miners to reuse mining hardware, thereby helping stabilize the network, reduce energy consumption, and encourage new miners (i.e. more devices) to join.The simulation results show IBR facilitates even wealth distribution and promotes more fair and long-term mining establishment.
Augmented Visual-Semantic Embeddings for Image and Sentence Matching

The task of image and sentence matching has witnessed significant progress recently, but it is still challenging arising from the tremendous semantic gap between a pixel-level image and its matched sentences. Due to limited training data, it is rather challenging to optimize the visual-semantic embeddings. In this work, we propose to augment visual-semantic embeddings via enlarging the training dataset. With more data, models can learn discriminative features with high-quality semantic concepts. More specifically, we augment data by generating sentences for given images. Our method consists of two steps. At first, to enlarge the training dataset, given an image, we perform image captioning. Instead of introducing redundancy to our augmented dataset, we hope that our generated sentences are in diverse style and maintain its fidelity at the same time. Therefore, we consult to generative adversarial networks (GANs) which can produce more flexible expressions compared to methods based on the maximum likelihood principle. Then, we augment visual-semantic embeddings with the augmented training dataset and obtain the model for the task of image and sentence matching. Experiments on the popular benchmark demonstrate the effectiveness of our method by achieving superior results compared to our baseline.
Incorporating Figure Captions and Descriptive Text in MeSH Term Indexing

The goal of text classification is to automatically assign categories to documents. Deep learning automatically learns effective features from data instead of adopting human-designed features. In this paper, we focus specifically on biomedical document classification using a deep learning approach. We present a novel multichannel TextCNN model for MeSH term indexing. Beyond the normal use of the text from the abstract and title for model training, we also consider figure and table captions, as well as paragraphs associated with the figures and tables. We demonstrate that these latter text sources are important feature sources for our method. A new dataset consisting of these text segments curated from 257,590 full text articles together with the articles’ MEDLINE/PubMed MeSH terms is publicly available.
Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond

Computing approximate nearest neighbors in high dimensional spaces is a central problem in large-scale data mining with a wide range of applications in machine learning and data science. A popular and effective technique in computing nearest neighbors approximately is the locality-sensitive hashing (LSH) scheme. In this paper, we aim to develop LSH schemes for distance functions that measure the distance between two probability distributions, particularly for f-divergences as well as a generalization to capture mutual information loss. First, we provide a general framework to design LHS schemes for f-divergence distance functions and develop LSH schemes for the generalized Jensen-Shannon divergence and triangular discrimination in this framework. We show a two-sided approximation result for approximation of the generalized Jensen-Shannon divergence by the Hellinger distance, which may be of independent interest. Next, we show a general method of reducing the problem of designing an LSH scheme for a Krein kernel (which can be expressed as the difference of two positive definite kernels) to the problem of maximum inner product search. We exemplify this method by applying it to the mutual information loss, due to its several important applications such as model compression.
Information System for the Support of the Work Program of the Discipline

This work is devoted to the development of information systems to support educational and methodological complexes of the discipline. The question of automating and typing a procedure for drawing up a work program for an educational institution is considered. The task of scientific research is to improve the quality of compilation and reduce the time spent on drawing up a work program for educational institutions, as well as to increase the convenience of updating the already created work programs. The object of the study is an automated information system to automate the compilation of the work program of the discipline. Subject of research – methods of unification of documents, methods for assessing the quality of the RP. The purpose of the work is to automate the procedure for drawing up a work program for educational directions for educational institutions and to simplify its maintenance and updating. As a research method, a modeling method was chosen. Modeling is a method for creating and researching models. The study of the model allows you to get new knowledge, new holistic information about the object. The first part of the work is devoted to the presentation of the results of the study of the subject area. In the course of the work, an analysis of the literature on the following topics was carried out: unification and assessment of the quality of documents; criteria for assessing the quality of documents; features of drawing up a work program discipline according to GOST. Highlights the main problems encountered in the preparation of the RP disciplines. The second part is devoted to the development of methods for assessing the quality of the work program document, and an analysis of the developed model has been conducted. The third part of the work includes the development of the basic elements of an information system and their description, as well as the development of system requirements, as well as its direct implementation. As a result of the study, an information system was designed and implemented, which allows to automate the process of creating a work program for the discipline by providing a partially completed template, as well as checking the compliance of hours with the work plan.