Reviewerly/exHarmony huggingface dataset:
Serious Game Development for Color and Object Name Recognition in Early Childhood Education (Case Study: TKK Mitra)

The concept of playing and learning in the classroom has begun to change from playing using physical toys to playing using a computer such as games. The use of serious games as a learning media can help the improvement of dopamine hormones in human body, which is responsible for bringing pleasure and joy to our mind. When that hormone is triggered, the children will be easier to understand the information provided through the game. TKK Mitra has several problems, such as (1) The old computer with a low spec that they had, so it would be difficult for them to download or to install the new game. (2) The lack of games that can be used for teaching the children, so they often get bored quickly. The purpose of this research is to design an educational PC game. The result of this research is an education game for color and object recognition in English for early childhood education.
Detecting Malicious URL using Machine Learning: A Survey

Malicious Uniform Resource Locator/malicious websites are a high threat to cyber security. Malicious URLs host uninvited content like malware, spam, drive by downloads phishing, etc. Users become victims of scams like financial loss, thieving of personal data, malware installation, and causes losses of millions of dollars every year. There is a need to detect those threats in a very efficient and timely manner. Several studies have examined different techniques to handle the problem; the foremost used approach remains blacklisting. The most obstacle to using blacklist is that the difficulties in maintaining an up-todate list of URLs. So here we proposed the Machine learning approach to detect the malicious URLs. We also discussed various methods for malicious URL detection, feature representations, and finally discussed various algorithms for the classification and feature extraction.
INSTITUTIONALIZATION OF SUCCESSFUL MARKETING PRACTICES OF DIGITAL UNIVERSITIES BASED ON QUALITY MANAGEMENT IN MODERN RUSSIA

The purpose of the paper is to substantiate the important role of quality management of provided educational services for success of marketing practices of a digital university and to develop recommendations for institutionalization of successful marketing practices of digital universities based on quality management in modern Russia.The authors use the case method and the methods of logical, comparative, trend, and regression analysis.The authors perform a critical analysis of marketing practices of digital universities in modern Russia and compare the consumer preferences as to the choice between the traditional and digital educational services in case of absence and in case of presence of experience of digital training.Also, growth of the indicators of quality of educational services, provided by digital universities, and growth of the indicators of efficiency of their marketing practices are compared.The research objects are top 10 digital universities of Russia (according to the 2019 rating).As a result, it is substantiated that results of implementation of marketing practices of digital universities in modern Russia depend on quality management of provided educational services.A restraining factor on the path of development of digital universities in modern Russia is their insufficient attention to the issues of quality management, which reduces the efficiency of marketing activities and causes their low competitiveness as suppliers of educational services, objects of investing, and employers.The offered recommendations will allow raising the quality and marketing activity of digital universities in Russia and ensuring institutionalization of their successful marketing practices, thus stimulating the increase of efficiency of marketing activities.
Local Contrast Enhancement based on Adaptive Logarithmic Mappings

This article presents an implementation of the local image enhancement technique proposed in [J.L. Lisani, Adaptive local image enhancement based on logarithmic mappings, IEEE International Conference on Image Processing (ICIP), 2018].The technique is based on a logarithmic mapping function that is adapted to the luminance characteristics of the neighborhood of each pixel.The method permits to simultaneously increase the luminance in dark regions and decrease it in bright regions.Variants of the originally published method are discussed in terms of contrast enhancement and creation of halo artifacts.
A Systematic Approach for Data Hiding Using Cryptography and Steganography

Information Security has always been a very substantial facet when it comes to hindering unauthorised access, destruction or inspection of confidential data.Today every field in the world makes use of multimedia information like image, video, or audio and the file transmitted.To serve data security cryptography and steganography are mainly used techniques, each of them had a problem.Cryptography is that the art of saving information by encrypting it into an obscure format.On the opposite hand, steganography is that the art and science of secret communication to send messages during a way which hides even the existence of the communication.This paper provides a mixed system design utilizing cryptography and steganography techniques to provide a reliable communication system capable of resisting attackers.The affine cipher is used in this paper to encrypt the hidden text message which provides an effective level of security.After that, combining the techniques of Discrete Cosine Transform (DCT) and Least Significant Bit (LSB) cryptography is utilized to mask encrypted messages inside the image.The achievement of the built structure is measured using the value of Mean Square Error (MSE), Peak signal to noise ratio (PSNR) value, and analysis of histograms.The results indicate the system designed offers a noble security level.
Students’ ecological culture: from everyday practices to environmental responsibility

Э. В. Бушкова-Шиклина, Т. А
A Cryptosystem for Database Security Based on TSFS Algorithm

Implementation of TSFS (Transposition, Substitution, Folding, and Shifting) algorithm as an encryption algorithm in database security had limitations in character set and the number of keys used. The proposed cryptosystem is based on making some enhancements on the phases of TSFS encryption algorithm by computing the determinant of the keys matrices which affects the implementation of the algorithm phases. These changes showed high security to the database against different types of security attacks by achieving both goals of confusion and diffusion.
Augmented fidelities for single-qubit gates

An average gate fidelity is a standard performance metric to quantify deviation between an ideal unitary gate transformation and its realistic experimental implementation. The average is taken with respect to states uniformly distributed over the full Hilbert space. We analytically (single-qubit) and numerically (two-qubit) show how this average changes if the uniform distribution condition is relaxed, replaced by parametrized distributions - polar cap and von Mises-Fisher distributions - and how the resulting fidelities can differentiate certain noise models. In particular, we demonstrate that Pauli channels with different noise rates along the three axes can be faithfully distinguished using these augmented fidelities.
Research on Artificial Intelligence Machine Learning Character Recognition Method Based on Feature Fusion

With the continuous development of artificial intelligence technology, people's research on it has become more in-depth and machine learning has become a new research core issue in artificial intelligence. Before artificial intelligence technology was mature, it was a very difficult task to realize automatic character recognition and input. At present, human beings have made some progress in intelligent robots, voice recognition and network search. The text recognition method based on machine learning is of great significance to the promotion of information technology. Text recognition based on machine learning, although it has formed relatively rich theoretical support in principle and skill, but still faces various factors during actual practice, making its recognition effect difficult to be reflected. Based on the feature fusion perspective, this paper combines the background and main identification methods of text recognition in artificial intelligence machine learning to analyze the purpose, which lays a foundation for future specific applications.
Evaluating the software quality non-functional requirement through a fuzzy logic-based model based on the ISO/IEC 25000 (SQuaRE) standard

Software quality measurement is an essential task in any software related project. Throughout the years many software quality models have been proposed defining a set of quality elements that have to be measured in order to evaluate the software quality. However currently, there is no standard to measure any of the non-functional requirements, instead there are models that provide a characterization for a particular non-functional requirement, including the software quality (ISOnEC 25000). Even if this model provides a common base for quality measurement, most of the defined characteristics are qualitative, thus their measurement are complicated, very often subjective, with a limited scope mostly particular, and not necessarily follow the measurement principles, which limiting the comparison. Additionally, uncertainty and lack of information are also present in software projects, making it harder to measure the overall software quality. Because of these issues, better methods for software quality evaluation have to be used. In this paper a fuzzy logic-based model is proposed for quality evaluation, aiming to handle in a formal way the uncertainty, the lack of information and enable the possibility to compare quantitatively the quality evaluation at distinct levels. The model was tested by measuring nine real life projects and obtaining conclusions about the quality of each project.
The Interaction between Teachers’ Professional Burnout and Professional Satisfaction

The world is facing with requirements, which are changing the way people and the environment interact, changing needs, and long-term challenges. School is no exception, where endless reforms cause a great deal of chaos that teachers, students, and their parents are tired of. The article discusses the extent to which professional burnout is expressed in Lithuanian teachers ’community as a whole, and its relation to teachers’ professional satisfaction. The aim of the research is to reveal the interaction between burnout and professional satisfaction of Lithuanian teachers in order to increase their professional satisfaction. The analysis of scientific literature was selected due to the theoretical explanation of the research problem. The quantitative research method was selected due to empirical research. The indicators on the questionnaire scales providedan opportunity to determine the links between Lithuanian teachers’ professional burnout and professional satisfaction according to different structural stress dimensions. Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS 23) for Windows. The survey included 398 teachers (a representative sample of the study was 381 respondents). The main results. Based on the results of theoretical and empirical research, the main factors influencing the interaction between professional burnout and professional satisfaction were identified. A significant negative interaction between professional satisfaction and professional burnout was also confirmed.
A Survey on Current Technologies for Web Development

Web-based application developers are presented with a dizzying variety of option formats, languages, frameworks and technological objects.We survey, identify and compare technologies that help the creation of Web apps.We conclude that, while the Web's connectivity problems have been largely solved, the cacophony of Web-based applications technologies represents the lack of a solid model tailored to this domain.
Evolutionary Algorithms: Multimodal Problems and Spatial Distribution

Over the last few decades, optimization problems have gained special attention in the world of computing, mainly because thanks to them, complex problems, which could only be addressed through approaches, now can be solved.In the wild, biodiversity is manifested by subtle differences in the individuals genetic code and consequently in the evolution of species.This approach is intended to apply to solving optimization problems through multimodal evolutionary algorithms.Standard evolutionary algorithms are not able to find more than a local optimum in the case of multimodal functions due to stochastic errors are committed (an individual randomly move one class to another) and that the population has a finite size (finite diversity).For this reason, in this work, a detailed study of the techniques of solving multimodal problems by using spatial evolutionary algorithms is done.In addition, the design details of new mechanisms for spatial evolutionary algorithms that allow us to reallocate the space of solutions are introduced.Thus, we will be able to deal with the resolution of complex problems with multiple local or global solutions.
Choreographic Development of Message-Passing Applications

Choreographic development envisages distributed coordination as determined by interactions that allow peer components to harmoniously realise a given task. Unlike in orchestration-based coordination, there is no special component directing the execution. Recently, choreographic approaches have become popular in industrial contexts where reliability and scalability are crucial factors. This tutorial reviews some recent ideas to harness choreographic development of message-passing software. The key features of the approach are showcased within , a toolchain which allows software architects to identify defects of message-passing applications at early stages of development.
Use of digital technologies in innovative educational environment (on the example of the mobile application Lecture Racing)

Problem and goal. Currently, there is active transformation of educational technologies, which explains the emergence of new educational needs, a change in the principles and mechanisms of access to knowledge and the need to summarize existing and implement new approaches to the formalization and systematization of data collection and storage, taking into account characteristics specific education and information technology [2- 4; 10; 12; 15]. The article reveals the concept of digital technologies in the educational environment, presents theoretical and practical materials containing knowledge about digital technologies. Methodology. The pilot study was conducted using the mobile app “Lecture Racing”, which allows to check the level of knowledge of students, among other things. This is a unique tool for conducting tests and surveys at lectures or classes, both pre-prepared and without preprepared tests, in real time [14]. Results. The stages of development of digital technologies are considered, and the stages of the experiment on the use of digital technologies in the educational process are described. Description of the results of the methods used to identify the level of motivation and performance. Conclusion. The use of digital technologies, on the example of the mobile application “Lecture Racing”, has a positive impact on the motivation of students, academic performance, and contributes to the self-development of the teacher in an innovative educational environment.
CO-WORD GRAPHS FOR DESIGN AND MANUFACTURE KNOWLEDGE MAPPING

Abstract Design &amp; Manufacture Knowledge Mapping is a critical activity in medium-to-large organisations supporting many organisational activities. However, techniques for effective mapping of knowledge often employ interviews, consultations and appraisals. Although invaluable in providing expert insight, the application of such methods is inherently intrusive and resource intensive. This paper presents word co-occurrence graphs as a means to automatically generate knowledge maps from technical documents and validates against expert generated knowledge maps.
Intention Progression under Uncertainty

A key problem in Belief-Desire-Intention agents is how an agent progresses its intentions, i.e., which plans should be selected and how the execution of these plans should be interleaved so as to achieve the agent’s goals. Previous approaches to the intention progression problem assume the agent has perfect information about the state of the environment. However, in many real-world applications, an agent may be uncertain about whether an environment condition holds, and hence whether a particular plan is applicable or an action is executable. In this paper, we propose SAU, a Monte-Carlo Tree Search (MCTS)-based scheduler for intention progression problems where the agent’s beliefs are uncertain. We evaluate the performance of our approach experimentally by varying the degree of uncertainty in the agent’s beliefs. The results suggest that SAU is able to successfully achieve the agent’s goals even in settings where there is significant uncertainty in the agent’s beliefs.
Do you have the right scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods

It has been a common approach to pre-train a language model on a large corpus and fine-tune it on task-specific data. In practice, we observe that fine-tuning a pre-trained model on a small dataset may lead to over- and/or under-estimate problem. In this paper, we propose MC-Tailor, a novel method to alleviate the above issue in text generation tasks by truncating and transferring the probability mass from over-estimated regions to under-estimated ones. Experiments on a variety of text generation datasets show that MC-Tailor consistently and significantly outperforms the fine-tuning approach.
CARP: Compression Through Adaptive Recursive Partitioning for Multi-Dimensional Images

Fast and effective image compression for multi-dimensional images has become increasingly important for efficient storage and transfer of massive amounts of high resolution images and videos. Desirable properties in compression methods include (1) high reconstruction quality at a wide range of compression rates while preserving key local details, (2) computational scalability, (3) applicability to a variety of different image/video types and of different dimensions, and (4) ease of tuning. We present such a method for multi-dimensional image compression called Compression via Adaptive Recursive Partitioning (CARP). CARP uses an optimal permutation of the image pixels inferred from a Bayesian probabilistic model on recursive partitions of the image to reduce its effective dimensionality, achieving a parsimonious representation that preserves information. CARP uses a multi-layer Bayesian hierarchical model to achieve self-tuning and regularization to avoid overfitting-- resulting in one single parameter to be specified by the user to achieve the desired compression rate. Extensive numerical experiments using a variety of datasets including 2D ImageNet, 3D medical image, and real-life YouTube and surveillance videos show that CARP dominates the state-of-the-art compression approaches-- including JPEG, JPEG2000, MPEG4, and a neural network-based method--for all of these different image types and often on nearly all of the individual images.
Difference based query strategies in active learning

In this paper two active learning methods are proposed in the machine learning literature, both of them based on difference calculation idea. One of the new methods is difference sampling query strategy. This strategy calculates a novel difference list and the elements of this list are then able to influence the uncertainty measure of the appropriate unlabelled instance. By taking the ratio of these measures a new informativeness metric is defined, and the aim of the difference sampling strategy is to minimize this ratio. Besides that, expected difference change query strategy was developed using a new metric, the global difference metric for each step. This strategy combines expected model change and uncertainty sampling strategies by taking the expectation of the difference of uncertainty values. The aim of this combined strategy is to query the instance that will most likely result the greatest change in global difference of the next step. The experimental results on image dataset show that both of the difference based sampling query strategies surpass the competitive methods in literature.
Perancangan Sistem Smart Resto Mobile Reservation Client Server Multiplatform

Waroeng Steak And Shake is one of the favorite restaurants of Bekasi residents which in serving and payment ordering services is still using the manual method by taking notes on paper. This method is very ineffective because it still uses a lot of paper to record orders and cause long customer queues just to order food or pay for orders. Customers cannot provide special notes directly to the chef so orders are made according to standards. To overcome the problem of the length of the queue and the use of a lot of paper and customers can provide notes directly to the chef, the authors propose to design a smart resto mobile multiplatform server client-server system with the waterfall method. The author hopes that this method can be implemented well in the system of ordering service records directly to the chef and of course reduce the queue at Waroeng Steak And Shake restaurant in Bekasi&#x0D;
Improved Traffic Sign Detection In Videos Through Reasoning Effective RoI Proposals

Traffic sign detection is an important task in assisted safety and autonomous driving. It is important to continuously detect the traffic signs emerged on the road. Currently, most object detection methods make independent detections based on single images. When we apply these methods directly to a video clip to detect traffic signs without taking into account temporal correlations among adjacent frames, missed detections or incorrect detections can frequently occur due to motion blur, size change, partial occlusion, and/or bad pose. In this paper, we fully exploit the temporal consistency of traffic sign detection in videos. More specifically, we incorporate information of adjacent frames with high confidence scores to enhance the discovery of potential objects in the missed or incorrect detected frames by “recovering” the missed RoI proposals or by “improving” the incorrect RoI proposals with low confidence scores. Our method can be regarded as a “detection-by-tracking” strategy, which results in a more robust detection performance in videos.
Parametric amplifier for Bell measurement in continuous-variable quantum state teleportation

A parametric amplifier is in essence a linear four-port device, which couples and linearly mixes two inputs before amplifying and sending them to two output ports. Here we show that for quadrature-phase amplitudes, a parametric amplifier can replace beam splitters to play the role of the mixer. We apply this idea to a continuous-variable quantum state teleportation scheme in which a parametric amplifier replaces a beam splitter in the Bell measurement. We show that this scheme is loss tolerant in the Bell measurement process and thus demonstrate the advantage of the parametric amplifier over the beam splitter in the applications in quantum measurement.