Reviewerly/exHarmony huggingface dataset:
AI4U: A Tool for Game Reinforcement Learning Experiments

Reinforcement Learning is a promising approach to the design of Non-Player Characters (NPCs). It is challenging, however, to design games enabled to support reinforcement learning because, in addition to specifying the environment and the agent that controls the character, there is the challenge of modeling a significant reward function for the expected behavior from a virtual character. To alleviate the challenges of this problem, we have developed a tool that allows one to specify, in an integrated way, the environment, the agent, and the reward functions. The tool provides a visual and declarative specification of the environment, providing a graphic language consistent with game events. Besides, it supports the specification of non-Markovian reward functions and is integrated with a game development platform that makes it possible to specify complex and interesting environments. An environment modeled with this tool supports the implementation of most current state-of-the-art reinforcement learning algorithms, such as Proximal Policy Optimization and Soft Actor-Critic algorithms. The objective of the developed tool is to facilitate the experimentation of learning in games, taking advantage of the existing ecosystem around modern game development platforms. Applications developed with the support of this tool show the potential for specifying game environments to experiment with reinforcement learning algorithms.
The Development of E-Book on Flood Mitigation to Increase Students’ Metacognitive Skill

The aims of this study were to analyse the flood mitigation in the Musi Sub Watershed of Ujan Mas Kepahiang Sub-District and to describe the design, characteristic and feasibility of the physics e-book on integrated fluid material of developed disaster mitigation. In addition, the results of the e-book application of the integrated physics material on flood disaster mitigation are analysed in improving students’ HOTS on the dimension of metacognitive knowledge. This study applies a 4-D development model consisting of four stages which are Define, Design, Develop, and Disseminate. The analysis results of observations of flood-affected areas using a spatial zoning approach based on elevation, distance from the river, topography of the area, and the use of GIS applications are used to create flood-prone maps. The results of the flood mitigation study are used to design the contents of the fluid physics e-book. Characteristics of developed e-book include missions, competencies, basic theories, basic activities, material resources, and assessment characters. The sample used as a trial for implementing the e-book was the students at SMK 1 Rejang Lebong and selected by using the Purposive Sampling Technique. According to the expert validation, the e-book is highly feasible to be applied as teacher's teaching material with an average percentage score of 95.86%. Students’ HOTS ability on the metacognitive dimension of classical knowledge is 6.67% very good; 26.66% good; 46.67% adequate; 13.33% less; and 6.67% very less. The e-book of integrated fluid on mitigation flood for Class X SMA/MAK in the concentration subject of Technology and Engineering was able to improve students' metacognitive abilities with an N gain value of 0.65 (moderate).
Post Quantum Secure Command and Control of Mobile Agents Inserting quantum-resistant encryption schemes in the Secure Robot Operating System

The secure command & control (C&C) of mobile agents arises in various settings including unmanned aerial vehicles, single pilot operations in commercial settings, and mobile robots to name a few. As more and more of these applications get integrated into aerospace and defense use cases, the security of the communication channel between the ground station and the mobile agent is of increasing importance. The development of quantum computing devices poses a unique threat to secure communications due to the vulnerability of asymmetric ciphers to Shor's algorithm. Given the active development of new quantum resistant encryption techniques, we report the first integration of post-quantum secure encryption schemes with robotic operating system (ROS) and C&C of mobile agents, in general. We integrate these schemes in the application and network layers, and study the performance of these methods by comparing them to present-day security schemes such as the widely used RSA algorithm.
Hypothetical tensor-based multi-criteria recommender system for new users with partial preferences

Multi-Criteria Recommender Systems (MCRSs) have been developed to improve the accuracy of single-criterion rating-based recommender systems that could not express and reflect users? fine-grained rating behaviors. In most MCRSs, new users are asked to express their preferences on multi-criteria of items, to address the cold-start problem. However, some of the users? preferences collected are usually not complete due to users? cognitive limitation and/or unfamiliarity on item domains, which is called ?partial preferences?. The fundamental challenge and then negatively affects to accurately recommend items according to users? preferences through MCRSs. In this paper, we propose a Hypothetical Tensor Model (HTM) to leverage auxiliary data complemented through three intuitive rules dealing with user?s unfamiliarity. First, we find four patterns of partial preferences that are caused by users? unfamiliarity. And then the rules are defined by considering relationships between multi-criteria. Lastly, complemented preferences are modeled by a tensor to maintain an inherent structure of and correlations between the multi-criteria. Experiments on a TripAdvisor dataset showed that HTM improves MSE performances from 40 to 47% by comparing with other baseline methods. In particular, effectivenesses of each rule regarding multi-criteria on HTM are clearly revealed.
Edmodo use in ESP writing: The perceptions and barriers of sociology students

The teachers are expected to integrate technology into the classroom because today is the world of a computer. Edmodo is one of the educational websites that can facilitate the specific needs of Sociology students in writing. This research aimed to investigate the second-semester Sociology students perceive the use of Edmodo as a medium in writing descriptive text and how Edmodo helps the students in developing their writing skills. Qualitative research was used by researchers through documentation, observation, and interviews with twelve Sociology students as participants at Islamic University of Balitar. This study took eight months to complete the analysis which included data reduction, display of the data, and analysis of Sociology studentsâ€™ data. The result showed that Edmodo was helpful in ESP class (writing class) because Edmodo enhanced Sociology studentsâ€™ writing, developed interaction, communication, and it promoted Sociology studentsâ€™ creativity in solving the writing task. The analysis of the result suggested that further researchers research motivation or behavior stages because this research only focused on the perception stage. Further researchers also suggested that further researchers revise the research instruments.
All-words Word Sense Disambiguation for Russian Using Automatically Generated Text Collection

Abstract The limited amount of the sense annotated data is a big challenge for the word sense disambiguation task. As a solution to this problem, we propose an algorithm of automatic generation and labelling of the training collections based on the monosemous relatives concept. In this article we explore the limits of this algorithm: we employ it to harvest training collections for all ambiguous nouns, verbs and adjectives presented in RuWordNet thesaurus and then evaluate the quality of the obtained collections. We demonstrate that our approach can create high-quality labelled collections with almost full-coverage of the RuWordNet polysemous words. Furthermore, we show that our method can be applied to the Word-in-Context task.
DIGITALIZATION OF HIGHER EDUCATION AS A NEW SOCIAL REALITY

The article substantiates the thesis that digitalization of higher education leads to an essential change of educational paradigms, transformation of the value model of the professional community. The contradiction in the value attitudes of university staff, in particular, teachers and administrators, gives rise to many conflict situations and is the cornerstone of failure, a brake on the reform of higher education (digital mimicry). The purpose of the article is to identify the causes and consequences of institutional changes in higher education in the context of the introduction of digital technologies from the standpoint of a sociological explanatory model. The subject of the research is the processes of digitalization of higher education. As a result of theoretical and empirical studies, the authors proved that conflicts in the university community lead to the emergence of a new social reality – to the division of the academic community into rationalists and traditionalists, and the formation of a value model of professional rationality. And if network technologies do not seriously affect the everyday life of modern students and schoolchildren, for teachers they create a new, unstable socio-cultural environment that has a convergent, mixed nature of social interactions. The prestige of the teaching profession now directly depends on the ability to respond to the requests and needs of the network personality.
A criterion for comparing two sets of steganalytical methods with a determined reliability

The paper is devoted to comparing two sets of steganalytical methods. It is assumed that one of the sets has been studied, i.e. it is known whether the results of the methods included in it are correlated. Checking the method's set for correlation is a complex computational task, so it makes sense to compare the sets as a whole. If it turns out that the sets are close, then the unknown set can be considered studied. The comparison is carried out with a determined reliability because it is important for using the results in the steganalytical forensics.
PUBLISHING PRODUCTS OF THE NAS OF UKRAINE IN THE GLOBAL SCIENTIFIC COMMUNICATION SYSTEMS: STATE, PROBLEMS AND THE WAYS OF REALIZATION

Paper devoted to the topical issues of scientific periodicals functioning in Ukraine in the world of the global communication system development. The important feature of the world informational space development current stage is the unification of scientific periodicals work principle. The publishing platforms are providing such journals with virtual editions, worldwide open-access materials use licenses, general declaration on the scientific ethics. Such processes demand scientific periodicals to consolidate their efforts, understand the common goal and define the specific challenges to address the constant exhaustive work.
Cognitive-Conceptual Model for Developing Foreign Language Communicative Competence in Non-Linguistic University Students

This study is devoted to the foreign language communicative competence development among non-linguistic universities under-graduate students. This research covered the issue of foreign language education based on the cognitive-conceptual model for teaching English to non-linguistic university students. As the main research method, pedagogical modelling was chosen. The experiment was conducted at Russian State Vocational Pedagogical University, Ural Federal University named after the first President of Russia B.N. Yeltsin, and Ural Institute of State Fire Service of EMERCOM of Russia among 72 undergraduate students. The results of several curriculum-based tests showed that the percentage of correct determination of expressions with the context available increased from 54% to 93.2% in EG1, and from 41.9 to 85.5% in EG2. The overall students’ understanding of lexical units increased by 39.2% and 43.6%, respectively. The ratio of misunderstanding/understanding of speech utterances increased from 15/7 to 6/16, while the proportion of students who do not use/use various interpretation strategies changed from 8/14 to 3/19. The developed cognitive-conceptual methodology effectiveness for teaching foreign language communicative competence was proven. It can be applied to train students of various specialisations at different university degree levels.
Managing Vertical Memory Elasticity in Containers

The adoption of container technology to deploy a diverse variety of applications in clusters, cloud data centers, and even cloudlets at the edge has steadily increased. Efficient resource utilization and throughput maximization are just two important objectives for service providers trying to reduce operating costs. While containers consume CPU, memory, and I/O resources elastically, orchestration frameworks must still allocate containers according to resource availability and limit the amount of resources that each can use to avoid interference. While the practice of reserving the maximum amount of required memory for the entire execution of a container is prevalent, this paper investigates the benefits of managing container memory allocations dynamically. By frequently adjusting the amount of memory reserved for each container during execution, this autonomous approach aims to increase the average number of containers that can be hosted on a server. Results show that through careful adjustments of container limits, manipulation of pages between memory and swap, and container preemption, improvements in memory utilization, cloud costs, and job throughput can be achieved without prejudicing container performance.
Deep Reinforcement Learning

In the last chapter, we studied the various aspects of the brain-academy architecture of the ML Agents Toolkit and understood certain scripts that are very important for the agent to make a decision according to a policy. In this chapter, we will be looking into the core concepts of deep reinforcement learning (RL) through Python and its interaction with the C# scripts of the brain-academy architecture. We have had a glimpse of a part of deep RL when we briefly discussed the deep Q-learning algorithm using the OpenAI Gym environment (CartPole) and also when we were discussing the Baselines library of OpenAI. Through the course of training the ML Agents in Tensorflow through external brain, we have also used the proximal policy optimization (PPO) algorithm with the default hyperparameters present in the trainer_config.yaml file. We will be discussing these algorithms in depth along with several other algorithms from the actor critic paradigm. However, to fully understand this chapter, we have to understand how to build deep learning networks using Tensorflow and the Keras module. We also have to understand the basic concepts of deep learning and why it is required in the current context. Through this chapter we will also create neural network models for computer vision methods, which will be extremely important when we will be studying the GridWorld environment. Since we primarily have ray and camera sensors that provide the observation space to the agent, in most of the models, we will have two variants of policies: multi-layered perceptron (MLP-based networks) and convolution neural networks (CNN-2D-based networks). We will also be looking into other simulations and games that are created using the ML Agents Toolkit and will also try to train our models based on the Baseline implementations by OpenAI. However, let us first understand the fundamentals of generic neural network models in deep learning.
Evaluation of multivariate transductive neuro-fuzzy inference system for multivariate time-series analysis and modelling

Multivariate Transductive Neuro-Fuzzy Inference System model, named the mTNFI is a previously proposed conceptual transductive approach designed for analysis and modelling of multivariate time-series data. In this study, we revisit, implement and evaluate the mTNFI model potential for patterns of relationship extraction from a collection of interrelated time-series data. Results of conducted assessment confirm the mTNFI capability in recognizing patterns of relationship and then to model them in human-readable form, i.e. fuzzy rules. Additionally, a comparative analysis also show the superiority of the mTNFI model in comparison to other widely known time-series forecasting techniques when being used to predict future values.
Mining Power Misestimation in PoW Blockchain

In blockchain and cryptocurrency, a miner's estimation of its computational power capability (relative to the overall mining network's) is critical since it is used for profit estimation and mining control. We study the impact on the miner's power estimation in the presence of other rational miners, such as those launching selfish mining and block withholding. Such rational mining underestimates the total network proof-of-work rate and thus overestimates the miner's relative power. This paper introduces the novel mining power misestimation problem and analyzes its impact. Our analyses show that the incorrect estimation of the mining power yields the profit overestimation and drives the miner to actively mine when the actual expected profit is negative and that the problem/impact grows as there are greater rational miners withholding or delaying blocks. This paper informs the blockchain communities of the mining power misestimation and discusses the potential countermeasures for future work.
Quantum entanglement in coherent feedback system based on the cascaded four wave mixing processes

Based on the cascaded four wave mixing processes, a coherent-feedback control system is constructed by utilizing a linear beam splitter as the feedback controller. Considering the loss of optical propagation in the coherent feedback loop and the absorption effect of Rb vapor cells to beams, we theoretically investigate the entanglement properties of this system under different feedback ratio, gain and phase by calculating the covariance matrix of system and applying the positivity under partial transpose (PPT) criterion to all possible bipartitions. The result shows that the genuine tripartite entanglement exists in the coherent feedback control system, but the entanglement structure of system will be destroyed by the excessive feedback. In addition, when the phase is &lt;i&gt;π&lt;/i&gt;, we find that the tripartite entanglement can be enhanced by changing the gains and the reflectivity of the beam splitter in the range of 0.1 to 0.4. The results pave the way for manipulating multipartite entanglement by coherent feedback control and have potential application in quantum communication.
Utilization of Apriori Algorithm for Book Layout Design in UNTAR Library

Abstract The library is an important tool in supporting students in teaching and learning activities at various levels of education. The library provides various sources and information that serve as guidelines in the distribution of knowledge that can be applied in social life. Not only for students, library is also beneficial for the wider community as a learning tool that can be useful for daily life. This is not immune from the proper application of book literature. Placement of book literature which is used as a guide for information sources greatly influences the application of these sources. Along with the rapid development of technology, it facilitates activities and jobs that can support human life, one of which is library facilities. The development of technology in placing book literature which is commonly used as a guideline for information sources is based on a combination of book literature which is often used. Placement of book literature utilizes methods that can help determine the right combination of book literature such as the Apriori Algorithm. The use of Apriori Algorithm in determining the right combination of book literature will facilitate the design of book layout in the UNTAR Library. The combined results of the book literature from the Apriori Algorithm serve as a guide in designing book layouts in the UNTAR Library. Apriori algorithm produces 100% confidence value. This proves that the use of Apriori Algorithm is very effective in designing the layout of books in the UNTAR Library.
Volctrans Parallel Corpus Filtering System for WMT 2020

In this paper, we describe our submissions to the WMT20 shared task on parallel corpus filtering and alignment for low-resource conditions. The task requires the participants to align potential parallel sentence pairs out of the given document pairs, and score them so that low-quality pairs can be filtered. Our system, Volctrans, is made of two modules, i.e., a mining module and a scoring module. Based on the word alignment model, the mining mod- ule adopts an iterative mining strategy to extract latent parallel sentences. In the scoring module, an XLM-based scorer provides scores, followed by reranking mechanisms and ensemble. Our submissions outperform the baseline by 3.x/2.x and 2.x/2.x for km-en and ps-en on From Scratch/Fine-Tune conditions.
Color correction of the document owner’s photograph image during recognition on mobile device

The growing popularity of mobile services increases the risks of financial and other losses from fraudulent user actions. To reduce the number of illegal actions and comply with the law when using mobile services, it is often scheme that user presents it’s identity document. In case of remote access via a mobile device, this means receiving and analyzing a video of a document image. One of the criteria for the authenticity of the captured security document is the presence of security optically variable devices (kinegrams, holograms). Reliable determination of the presence or absence of such security elements from video stream frames is greatly complicated by changes in color between frames. The paper discusses the possibility of using a priori information about the monochrome photograph of the document owner to compensate changes in color between frames. Color distributions are investigated on the example of black-and-white photographs. A new method for automatic white balance correction is proposed. The results of the method are tested on real data obtained with a mobile device.
LEARNING TASKS IN ARTIFICIAL INTELLIGENCE EDUCATION

Modern trends in the development of digital technologies are directly related to Artificial Intelligence and its application in all spheres of life. This determines the need to study this discipline not only in universities but also in schools. In the last few years, various schools in Bulgaria have introduced the study of elements of Artificial Intelligence as an innovation in specialized, professional and innovative schools, as well as in clubs of interest. The Ministry of Education and Science has approved a sample curriculum. Textbooks and teaching aids are being created. The report aims to present a classification of learning tasks in artificial intelligence training in different levels and forms of training. Some basic key examples will be considered, through which the basic concepts and rules in classical artificial intelligence are introduced, related to both the basic algorithms for solving problems through search and the presentation of knowledge through logical rules. The authors will share their experience in the study of Artificial Intelligence and will present some opportunities to increase the effectiveness of training.