Reviewerly/exHarmony huggingface dataset:
Efficient Branch-and-Bound Algorithm for Task Scheduling in Cloud Computing

To fully leverage the potential of cloud computing, it is essential to have efficient scheduling algorithms that can effectively assign tasks to the most appropriate resources for execution. This involves the process of mapping and scheduling tasks onto existing resources in a way that optimizes utilization and minimizes the total time required for completion, known as makespan. Utilization refers to the degree to which the network resources of the cloud are utilized. However, due to the dynamic and heterogeneous nature of cloud resources, task scheduling is a challenging NP-complete problem that requires the use of metaheuristics to identify the optimal scheduling approach. However, the use of an exact method in this context is rarely explored. The main objective of this paper is to optimize the task scheduling to minimize the makespan by using a new bounding operator that improves the scalability of the standard B&B algorithm. The proposed approach has been compared with the cuckoo search methaheuristic using the cloudsim simulator.
Teachers’ Perspectives on the Use of Virtual Reality in Collaborative Learning

The use of virtual reality for learning is a relatively recent trend in education. Hence, there are many understudied aspects. In this research, 16 secondary school teachers were invited to participate in symmetric and asymmetric collaborative learning in virtual reality using Class VR virtual reality case headset and a standalone Oculus Quest 2 headset. As results demonstrate, majority of teachers (68, 8%) believe that collaborative learning in virtual reality is significantly different from both computer-supported collaborative learning (CSCL) and traditional collaborative learning in classroom. While teachers believe that using virtual reality in distance and collaborative learning has strong potential in classroom, they also express concerns regarding technical, software and resource support and readiness of teachers themselves to use virtual reality.
Analysing the Demand and Forecasting the Number of Applicants at Ivan Franko National University of Lviv

The 2018-2022 admission campaigns at the Ivan Franko National University of Lviv were analyzed by faculty. In particular, the authors identified the most popular faculties among university entrants, estimated the number of applicants enrolled in the bachelor’s degree programs, and analyzed their competitive indicators. A data processing and visualization software package with the application of the log-normal distribution was used for the statistical analysis of the data. Data extrapolation was done using the information and numerical methods, which made it possible to predict the number of applicants to the selected faculties in 2023 and to assess the demand for certain majors among applicants.
Features of the Construction a Specialized Computer Remote Parametric Control System of a Floating Dock

The article presents an approach to automating the processes of controlling the parameters of floating docks at the local and remote level. The hierarchical functional structure and hardware and software of the remote parametric control system of a floating dock are described. The presented computer system performs decentralized processing of information between the SCADA system and cloud services.
Information Technology based on an Ontological Approach for Identification of the Recurrent Laryngeal Nerve During Operations on the Neck Organs

The article discusses the approach to the identification of the recurrent laryngeal nerve (RLN) during surgery on the thyroid gland. This technology consists of the following main stages: obtaining an information signal as a result of stimulation of the tissues of the surgical wound with electric current; signal segmentation - response to irritation; selection of the main spectral component of segments of the information signal; modeling of the distribution of the amplitudes of the main spectral components for the identification of RLN based on the use of an interval discrete model in the form of a difference scheme. The most problematic is the last stage, which is based on the construction of a mathematical model that reflects the electrophysiological properties of the area of the patient’s surgical wound. To configure this model, it is proposed to use an ontological approach. The ontological description of thyroid surgery makes it possible to set up a differential scheme for the identification of RLN taking into account the specifics of the patient’s disease and thus shortens the time for examining the surgical site.
A Method of Studying the Completeness of Interval Discrete Models Built on the Basis of Intelligent Computing

Mathematical models of complex objects in the form of interval differential equations have become widely used for decision-making. Such equations are built on the basis of the obtained experimental interval data within the limits of the inductive approach. At the same time, there is always the problem of completeness of the model, which consists in the fact that the model must reflect all the properties of the object that are significant from the point of view of the purpose of modeling, since the difference equation does not sufficiently interpret the physical properties of the object. The method proposed in the article makes it possible to construct a differential equation on the basis of its analog interval difference equation. The developed approach creates opportunities for the identification of interval models of complex objects based on the analysis of interval data according to a single universal scheme of intelligent calculations based on the behavior of a swarm of honey bees, or any other method of solving an optimization problem. On the other hand, the method makes it possible to ensure the completeness of the built model and to interpret the physical properties of the modeled object according to the classical scheme.
A Hybrid Extractive-Abstractive Framework with Pre &amp; Post-Processing Techniques To Enhance Text Summarization

The goal of this paper is to enhance text summarization using a hybrid methodology. The process of producing a condensed version of a text while keeping its essential details is known as text summarization. In this study, we have presented a method for training the T5 model on the SAMSum dataset with conversation sentences to increase its effectiveness in text summarization. To determine the impact of training the T5 model on the dataset, the model is assessed using the ROUGE metric both before and after training. ROUGE is a set of metrics used to evaluate the quality of automatic summaries by comparing them to reference summaries based on the overlap of n-grams, word sequences, and other linguistic units. In order to enhance the quality of the generated summary, our hybrid approach makes use of extractive and abstractive summarization techniques as well as pre-and post-processing techniques. There is an improvement in the ROUGE metrics of the model before and after training. ROUGE1 before training was observed to be 25.53 while ROUGE1 after training is calculated to be 45.17.
Sparse Generative Embeddings of Handwritten Digits

The process of unsupervised generative learning with visual data and the structure of informative low-dimensional sparse generative representations of images of handwritten digits were investigated. Learning models with the architecture of a sparse convolutional autoencoder with constraints to produce low-dimensional representations achieved successful learning demonstrated by training metrics and high accuracy of generation of images of digits. A well-defined, continuous and connected “stacked” structure of low-dimensional slices in the sparse latent space produced by activations of participating latent neurons was observed and described in detail. The conclusion is that structured informative representations obtained with unsupervised generative models can be an effective platform for investigation of the emergence of common types or “concepts” in sensory inputs in artificial and biological learning systems.
Construction of a Conceptual Model of the Accounting and Analytical System of Report Formation Under IFRS

as a result of the study, the mechanism of modeling the accounting process at the present stage is considered and a conceptual model of formation of accounting and management reporting in the context of transition to IFRS is built. The necessity of developing a mechanism for the formation of management accounting policy aimed at preparing information in accordance with IFRS requirements, which increases the degree of reliability of information and allows optimizing the process of making management decisions at various levels of management, is proved. The article analyzes the methods of obtaining IFRS reporting by Ukrainian enterprises and develops a model of automated processing of accounting information using the combined method of reporting. The proposed methodology will automate the preparation of financial and management reports of enterprises in the context of transition to international accounting standards. The practical significance of the obtained results lies in the possibility of using the developed scientific and methodological recommendations, models and methods, which, in accordance with the principles of international financial reporting standards, will help to improve the quality of information resources and increase the efficiency of economic resources of business entities.
Solving 2 by 2 Grid Sudoku Problem using Grover’s Algorithm with Intel Quantum SDK

Quantum technologies are moving towards enabling real world uses and as these technologies develop. The most important thing that differs Quantum from classical computers is that Quantum Computers can do “multiple” tasks at the same time as opposed to the classical computers doing a single task at a time. This phenomenon has led to the discovery of a few algorithms that are faster than regular computers. One such algorithm is the Grover’s Algorithm[l]. This algorithm has shown a few real-world applications such as Quantum Search Algorithm, Travelling salesman, etc[2]. In this paper, Grover’s algorithm is introduced, and a 2 x 2 Sudoku example is solved using Intel Quantum Software Development Kit[3]. The Intel Quantum SDK is quite new and in this paper we show how a simple Grover’s Problem can be implemented in the SDK. Sudoku is a popular number-based puzzle game played on a grid consisting of nine squares subdivided into smaller 3x3 grids. The objective is to fill the grid with numbers from 1 to 9, ensuring that each row, column, and 3x3 grid contains all numbers exactly once.
The Fault-Resistant Web Application Infrastructure Using Autoscaling

This paper is devoted to developing of a load balancing model between several databases with potential for their unlimited scaling.
Development of Virtual and Augmented Reality Applications with Realization Project-Based Learning

This research demonstrates the effectiveness of using project-based learning to develop virtual and augmented reality applications using available platforms such as Vuforia and Unity 3D. In project-based learning, students acquire knowledge and skills by researching and responding to interesting and complicated questions, problems or issues. The research involved 54 students from two universities. The students who participated in the research were separated in an experimental and a control group. Control work of the same level was carried out in both groups. In the post-test, 56% of the participants in the experimental group scored above 93, while in the control group only 36% of all participants scored above 80. The research showed that the knowledge and skills acquired by students in the experimental group in developing applications of virtual and augmented reality were higher than in the control group.
SEO-Optimization of Product Content on a Marketplace Platform

Internet marketing is an integral part of trade and promotion of goods and services in digital form, which has become especially popular due to the outbreak of the Covid-19 pandemic. The main element of the Internet marketing complex is the product. Search queries are used to promote the product. The goal of the content is to make the company known and highlight the value of the product for the customer. The article proposes a software module for automating the processes of filling in the description of product positions on the marketplace platform and selecting keywords. Such a software module will help automate the process of SEO optimization of the content of product positions on the marketplace and speed it up.
Design and Research of a Physical Aerobics Simulation System Based on Virtual Reality (VR) Technology

With the rapid advancement of modern science and technology, virtual reality (VR) technology has become increasingly popular due to its numerous advantages. One of the benefits of using advanced information technology and VR is the ability to capture, analyze, and store the movements of aerobics athletes, providing valuable data for improving training. Therefore, this paper aims to design a physical aerobics simulation system based on VR technology, and evaluate its performance. The study demonstrates that this system enhances the user's auditory and visual perception, and proves to be feasible. This paper utilizes state-of-the-art VR technology to design a physical aerobics simulation system that can accurately capture the movements of athletes. This system utilizes advanced algorithms to analyze and store the relevant movement data in detail, providing athletes with valuable feedback to improve their training. The system is able to simulate a wide variety of aerobics routines, and is designed to enhance the user's auditory and visual perception by providing a fully immersive experience. The results of this study demonstrate that the proposed system can effectively improve the user's perception and has a certain feasibility. However, there is still room for improvement in several areas. For example, future work can focus on enhancing the accuracy and sensitivity of the system's sensors to collect data, as well as improving the design of the experiment to collect more comprehensive and meaningful data. Additionally, further work can be done to improve the user's overall experience by incorporating feedback from users and implementing new features and functions to the system.
Research of Black-Box Adversarial Attack Detection Based on GAN

The abnormal behavior detector based on machine learning is vulnerable to adversarial attacks. Attackers achieve adversarial attacks with constructing counter samples on the detection algorithm, and get the expected output for the target model, by changing the attack behavior pattern and API call sequence. In order to improve the performance of black-box attack, this paper can generate adversarial sample to deceive discriminator by training the generator and discriminator through the black-box adversarial attack network based on GAN. Experiments on real world API attacks and benchmark data sets have proved that the anti-attack anomaly behavior detection system can well detect malware and anomaly behavior.
Network Layer Coding Genetic Algorithm to Train Convolutional Neural Networks

A coding method, network layer coding, is proposed to enable the parameters of convolutional neural networks to be encoded as chromosomes without loss of genetic effects. Using this coding approach, we try to train neural networks for image recognition by steady-state genetic methods and test the results on the cifar10 dataset to compare with traditional coding methods and to explore the feasibility of using genetic algorithms for neural network training.
Design of Integrated Monitoring System for Power Information Operation and Maintenance Based on Orthogonal Components

The task of the operation and maintenance overhaul of power information systems is becoming more and more complex and burdensome. At present, power information systems usually have problems with decentralized management, insufficient manpower, uneven resources, unlocatable faults and slow processing speed. The integrated operation and maintenance management of power information systems can reasonably and effectively utilize resources, promote the professional quality of operation and maintenance personnel, improve the quality of equipment operation and maintenance, reduce the consumption of human resources, and enhance production efficiency and quality. To address this, we comprehensively analyze the current situation and development direction of the power information monitoring system and propose an integrated monitoring system based on orthogonal components to build mutually decoupled orthogonal components through distributed architecture and web applications to realize centralized and unified monitoring systems distributed in various regions, real-time monitoring of equipment operation status, timely detection and warning of power equipment abnormalities, and data correlation and sharing between regions. The system assists operators to master the entire situation of the power system by collecting data from distributed areas, and therefore achieving centralized management of an intelligent power monitoring system through multiple orthogonal components, which effectively solves many difficult problems.
Application of Internet of Things and Artificial Intelligence Technology in Smart English Human-Computer Interaction Training System

The rapid development of artificial intelligence and Internet of Things (IoT) technology has brought about accelerated reform in China's education sector. The integration of various information-based teaching tools such as games, animations, micro-videos, and online teaching platforms has optimized the teaching process to meet the diverse learning needs of students, thereby improving the quality of teaching. In this context, this study aimed to construct a smart English classroom human-computer interaction training system based on artificial intelligence and IoT technology. The system facilitates a more interactive and immersive learning experience for students, leading to better teaching outcomes in the smart English classroom. Results from the study showed that the use of the human-computer interaction training system significantly improved the teaching effect, indicating the potential of the system to enhance the quality of English language education in China. Applying Internet of Things and artificial intelligence technology in designing human-computer interaction training system represents a promising direction for the development of smart English classrooms. The use of the human-computer interaction training system can lead to better teaching outcomes.
A Secure Access and Payment Scheme for V2X Base Station Alliance Based on Blockchain

Vehicular networking is an important application in 5G, and the coverage of mobile communication networks is critical for the development of vehicular networking. However, in areas with poor coverage of mobile communication networks, vehicular networking users still have networking requirements. Based on user attribute encryption and blockchain technology, this paper proposes an access scheme for Internet of Vehicles Base Station Alliance, which allows vehicular networking users access the vehicular networking base station alliance safely. The vehicular networking base station alliance including mobile communication networks, WLAN networks, and private networks, thus solving the communication problem of vehicular networking in coverage blind areas. At the same time, a payment verification method based on homomorphic encryption is proposed to ensure that the balance of vehicular users will not be leaked, and it can verify whether vehicular users have the ability to pay.
A Novel Hybrid Makeup Transfer Method Based on Latent Vector Manipulation

With the development of image processing technology and the improvement of people's living standards, there is a wide demand for makeup services in society. In this paper, we focus on style transfer and its application in automatic makeup generation, and propose a facial feature fusion recovery technique that utilizes an adaptive spatial feature fusion attention mechanism to solve the problem of inconsistent clarity between the reference image and the source image. By using the moving least squares method to calculate similarity on each sample image, the reference image set achieving the optimal makeup transfer effect can be efficiently selected. At the same time, the latent vector can be manipulated to achieve on-demand hybrid makeup generation and de-makeup image generation. By assigning different weights, the proportion of makeup in specific reference image in the final makeup effect can be controlled, and a new mixed makeup style can be obtained. The experiment shows that the proposed model can achieve state-of-the-art makeup transfer effect, and can also generate suitable makeup effects for low resolution input images.
Construction of English Teaching System Based on Deep Learning Models and Artificial Intelligence Technology

With the continuous development of artificial intelligence, big data, and information technology, it is now possible not only to provide high-quality teaching resources but also to create realistic foreign language teaching environments. This article introduces an English teaching system based on artificial intelligence technology. The system adopts deep learning models, including three modules: speech recognition, machine translation, and speech synthesis, which can provide personalized English learning experiences for students and improve learning efficiency and quality. The article provides detailed explanations on data preprocessing, system architecture, system implementation, and testing. The comprehensive testing results prove the superiority of the system in speech recognition, machine translation, and speech synthesis. The system has high practical value and teaching effectiveness, and can provide strong support for the development of English education. The system's superiority is also reflected in its ability to handle different types and sources of data, enabling accurate recognition, translation, and speech synthesis of student speech recordings and text information. The system has broad application prospects in the field of English education and can meet the different needs of students and teachers, providing new ideas and methods for the modernization and personalization of English education. The results show that this teaching mode can fully stimulate students' learning enthusiasm and initiative, thereby improving the quality of English teaching. The key words for this article are English education, deep learning, artificial intelligence technology, speech recognition, and speech synthesis.
Risks and Countermeasures of Artificial Intelligence for College Students' Growth

With the development of the times, artificial intelligence is also rapidly updating, bringing great convenience to people's lives, but also bringing many risks and challenges to college students. Currently, under the impact of artificial intelligence, college students are facing risks such as weakening their dominant position, obliterating value consensus, and reducing innovative thinking during their growth. The main reasons for these risks are insufficient teacher guidance, technical limitations, and insufficient government investment. In the future, we can explore Countermeasures based on the three major factors in education: educators, educational objects, and educational intermediaries, in order to better build an intelligent education ecosystem and cultivate creative talents required for the new era.