Reviewerly/exHarmony huggingface dataset:
Multiple Intellegences-based learning innovation towards Era 5.0

The development and advancement of technology at this time is very beneficial for human life, especially in the world of education where with the development of this technology students are expected to improve their performance in lessons. But mostly what happens at this time is the lack of teacher attention to the intelligence possessed by the child. Every child certainly has different intelligence, for that teachers are required to be able to understand and analyze the intelligence possessed by their students. With this multiple intelligence-based learning innovation, teachers can improve learning outcomes according to the abilities and intelligence possessed by each child. The purpose of this research is to find out how multiple intelligence-based learning is appropriate in the teaching and learning process. The method used from this research is quantitative method by using Google From and WhatsApps application. The results of this study indicate that multiple intelligence-based learning is very well applied in learning. The conclusion of this study explains that the application of multiple intelligence-based learning is very good for improving children's learning outcomes so that children's achievement can increase from the previous one. The limitation of this research is that researchers do not make how many percent of educators who have been able to apply multiple intelligence in learning and the extent of development that has been achieved by educators, for that it is hoped that further researchers can conduct more complete research
THEORETICAL FOUNDATIONS OF EDUCATION QUALITY MANAGEMENT

The article analyzes the importance of the quality of higher education at the present stage. The basic concepts of quality, quality of education are considered. The conclusions allow us to talk about the increasing influence of the quality of education of university graduates and employers in assessing the quality of education.
Discrete-time semiclassical Szegedy quantum walks

Quantum walks are promising tools based on classical random walks, with plenty of applications such as many variants of optimization. Here we introduce the semiclassical walks in discrete time, which are algorithms that combines classical and quantum dynamics. Specifically, a semiclassical walk can be understood as a classical walk where the transition matrix encodes the quantum evolution. We have applied this algorithm to Szegedy's quantum walk, which can be applied to any arbitrary weighted graph. We first have solved the problem analytically on regular 1D cycles to show the performance of the semiclassical walks. Next, we have simulated our algorithm in a general inhomogeneous symmetric graph, finding that the inhomogeneity drives a symmetry breaking on the graph. Moreover, we show that this phenomenon is useful for the problem of ranking nodes in symmetric graphs, where the classical PageRank fails. We have demonstrated experimentally that the semiclassical walks can be applied on real quantum computers using the platform IBM Quantum.
Satisfiability of Arbitrary Public Announcement Logic with Common Knowledge is Σ^1_1-hard

Arbitrary Public Announcement Logic with Common Knowledge (APALC) is an extension of Public Announcement Logic with common knowledge modality and quantifiers over announcements. We show that the satisfiability problem of APALC on S5-models, as well as that of two other related logics with quantification and common knowledge, is $\Sigma^1_1$-hard. This implies that neither the validities nor the satisfiable formulas of APALC are recursively enumerable. Which, in turn, implies that APALC is not finitely axiomatisable.
Solving nonstationary Markov decision processes via contextual decomposition: A military air battle management application

Reinforcement learning for nonstationary problems is a subject of widespread research given that most realistic problems do not exist within static environments. Approaching these problems can require significant effort in feature engineering to provide a learning algorithm with enough useful information about the state space to uncover complex system dynamics. As an alternative for problems with sufficient data describing the nonstationary environment, we propose the contextual decomposition Markov decision process (CDMDP) as a collection of stationary sub-problems intended to approximate nonstationary problem dynamics using a linear combination of value functions. We demonstrate the effectiveness of the CDMDP approach with an application in military air battle management. We use a designed computational experiment and analysis of variance to show that a complex, nonstationary learning problem can be effectively approximated with a small set of stationary sub-problems, and that the CDMDP solution significantly improves solution quality over a baseline approach without the need for additional feature engineering. If a researcher suspects that a complex and continuously varying environment can be approximated by a small number of stationary contexts, the CDMDP framework may save significant computational resources and yield decision policies that are much easier to visualize and implement.
Benchmarking HTAP databases for performance isolation and real-time analytics

Hybrid Transactional/Analytical Processing (HTAP) databases are designed to execute real-time analytics and provide performance isolation for online transactions and analytical queries. Real-time analytics emphasize analyzing the fresh data generated by online transactions. And performance isolation depicts the performance interference between concurrently executing online transactions and analytical queries. However, HTAP databases are extreme lack micro-benchmarks to accurately measure data freshness. Despite the abundance of HTAP databases and benchmarks, there needs to be more thorough research on the performance isolation and real-time analytics capabilities of HTAP databases. This paper focuses on the critical designs of mainstream HTAP databases and the state-of-the-art and state-of-the-practice HTAP benchmarks. First, we systematically introduce the advanced technologies adopted by HTAP databases for real-time analytics and performance isolation capabilities. Then, we summarize the pros and cons of the state-of-the-art and state-of-the-practice HTAP benchmarks. Next, we design and implement a micro-benchmark for HTAP databases, which can precisely control the rate of fresh data generation and the granularity of fresh data access. Finally, we devise experiments to evaluate the performance isolation and real-time analytics capabilities of the state-of-the-art HTAP database.
Precise makespan optimization via hybrid genetic algorithm for scientific workflow scheduling problem

Abstract Task scheduling in scientific workflows represents an NP-hard problem due to the number of interdependent tasks, data transfers, and the possible execution infrastructure assignments in cloud computing. For this reason, metaheuristics are one of the most widely applied optimisation techniques. Makespan is one of the main objectives in this problem. However, this metric needs to be complemented with a quality measure with respect to the actual execution time in order to avoid incurring more costs than expected by using an over-optimistic approximation. This research applies a new enhanced disk-network-computing evaluation model, that takes into account the communication among the storage devices involved, which plays an important role in actual schedules. The model is implemented in a genetic algorithm and the well-known heuristic HEFT. We propose different hybridisation metaheuristics in conjunction with a new accuracy metric to measure the difference between the makespan approximations and the real one. The new evaluation model is able to improve accuracy with respect to the standard model, and the proposed hybrid methods significantly improve makespan in the case of heterogeneous infrastructures.
Implementation of Object-oriented Design Technology and Deep Learning Algorithm in Interior Design CAD System

In order to study the identification of abnormal financial commodities in e-business, this article proposes an algorithm for identifying financial risks in ebusiness commodity transactions based on DL and CAD models.Unsupervised learning is trained by using the model gradient descent method of random automatic learning.At the same time, the algorithm uses automated distributed tables for data distribution, so as to identify the abnormal behavior in e-business transactions.The simulation results show that the accuracy of this algorithm can reach more than 90%, and the highest can reach about 95%.Its accuracy is 9.14% higher than that of traditional NN (Neural network) algorithm and 5.17% higher than that of BPNN (Back Propagation Neural Network) algorithm.Using the model constructed in this article to classify and identify abnormal behaviors in ebusiness transactions has good results, which can realize rapid and accurate identification requirements.It provides an effective and reliable method for identifying abnormal financial commodities of e-business.
Efficient Real-time Video Conferencing with Adaptive Frame Delivery

Real-time video conferencing systems have recently become indispensable tools. However, existing commodity video conferencing systems often fail to deliver a satisfactory quality of experience (QoE) due to discrepancies between the actual network throughput and the average bitrate of encoded videos. In this paper, we present a system called Tyrus that aims to achieve efficient real-time video conferencing. Tyrus adapts its delivery of B-frames based on the real network bandwidth and the average bitrate of encoded videos, effectively addressing the QoE loss caused by mismatch issues. This system can be easily implemented on top of prevalent video conferencing systems that utilize standard video encoders. Unlike traditional approaches that treat all frame types equally, Tyrus enables adaptive delivery of B-frames, prioritizing the allocation of bandwidth to other crucial video frames. By doing so, it reduces end-to-end conferencing latency, especially when the network throughput experiences significant fluctuations. Additionally, Tyrus defines the playing deadline for B-frames by considering factors such as delivery time, buffering time, and the progress of preceding frames. It accurately classifies frame types according to video coding standards and estimates bandwidth allocation on a frame-by-frame basis. Moreover, Tyrus proactively handles potential errors in B-frame playing deadline estimation, minimizing their impact on video conferencing performance. Our evaluation results, obtained through real implementation on top of WebRTC, demonstrate the effectiveness of Tyrus. On average, it reduces per-frame latency by 19.03%, video stalls by 22.36%, and improves bandwidth utilization by at least 10.29%.
Domain‐adapted driving scene understanding with uncertainty‐aware and diversified generative adversarial networks

Abstract Autonomous vehicles are required to operate in an uncertain environment. Recent advances in computational intelligence techniques make it possible to understand driving scenes in various environments by using a semantic segmentation neural network, which assigns a class label to each pixel. It requires massive pixel‐level labelled data to optimise the network. However, it is challenging to collect sufficient data and labels in the real world. An alternative solution is to obtain synthetic dense pixel‐level labelled data from a driving simulator. Although the use of synthetic data is a promising way to alleviate the labelling problem, models trained with virtual data cannot generalise well to realistic data due to the domain shift. To fill this gap, the authors propose a novel uncertainty‐aware generative ensemble method. In particular, ensembles are obtained from different optimisation objectives, training iterations, and network initialisation so that they are complementary to each other to produce reliable predictions. Moreover, an uncertainty‐aware ensemble scheme is developed to derive fused prediction by considering the uncertainty from ensembles. Such a design can make better use of the strengths of ensembles to enhance adapted segmentation performance. Experimental results demonstrate the effectiveness of our method on three large‐scale datasets.
Characterization of AGM Belief Contraction in Terms of Conditionals

We provide a semantic characterization of AGM belief contraction based on frames consisting of a Kripke belief relation and a Stalnaker-Lewis selection function. The central idea is as follows. Let K be the initial belief set and K-A be the contraction of K by the formula A; then B belongs to the set K-A if and only if, at the actual state, the agent believes B and believes that if not-A is (were) the case then B is (would be) the case.
Uncovering Epistemic and Social Knowledge Structures That Academic Developers Valorize in the Age of Technology-Mediated Curriculum Development

Global trends suggest that blended learning is increasingly adopted as a mode of delivery in higher education institutions (HEIs). This push for increased use of technology is, amongst others, driven by the ability to create more flexible learning opportunities, to enrich the learning experience of students and to respond to higher education directives in terms of student access and success. The recent COVID-19 pandemic has influenced the higher educational sector to such an extent that resisting the integrated use of digital technology in learning, teaching and assessment practices, deems impossible. This rise in the integration of digital technology in higher education practices inevitably also highlights the impact on curriculum planning, academic professional learning and the role of academic developers in this complex setting. As in many cases, academic staff are mostly disciplinary experts and do not necessarily have the appropriate curriculum development and digital technology expertise. Faculties and academics are, therefore, increasingly reliant on academic developers to assist them in the development of a technology-mediated curriculum. The evolving nature of HEIs and the increased focus on digital technology in learning and teaching practices require a close examination of the practices academic developers (ADs) are prioritizing and value in their quest to assist teaching academics. In an attempt to address above-mentioned challenges, this chapter proposes an approach to investigate the knowledge practices valued by academic developers that specialize in the use of digital technologies in the curriculum. The aim is to gain deeper insights into the epistemic and social knowledge practices appropriate to succeed in supporting teaching academics in the process of digital technology integration in the curriculum.
MIREAHMATY—Three-Dimensional Chess in the System of Virtual Reality VR

The rules of three-dimensional chess are being developed. Some variants of the rule of moves and capture for each piece are proposed. A unique description of the game situation is analyzed. Various options for placing check, checkmate and stalemate are considered.
A Method for Collecting and Consolidating Big Data on the Requirements of Employers for the Competencies of Specialists to Actualize Educational Programs and Resources

The article deals with the search, collection and analysis of big data on the requirements of employers for the competencies of specialists in the digital economy, taking into account regional characteristics. A method is proposed for extracting and integrating competency descriptions from offered vacancies in open Internet sources using search crawlers. Descriptions of competencies are presented as vector models of keywords. The method makes it possible to formalize descriptions of a set of demanded vacancies for subsequent clustering, geospatial analysis, comparative analysis with vector models of competencies descriptions from educational programs offered by educational institutions in the regions. The method of clustering vector models is synthesized using the apparatus of fuzzy logic and neural networks. A methodology has been developed for evaluating educational programs and resources in educational institutions of the region in order to make decisions about the need for their modernization and actualizating. The structure of the system for collecting, consolidating and intellectual analysis of formalized descriptions of the requirements of employers from the real sector of the regional economy is proposed. Consolidated data is used to actualize specialist training programs and modernize educational resources, to set up a personalized educational environment. The system is an intellectual component of the information and educational environment of a higher educational institution in the region. #CSOC1120.
Quality in Use Evaluation Model Based on ISO/IEC 25022 for Human Talent Management Information Systems

Quality in use has become a point of interest in recent years for the software development industry. As a result, the need has arisen to verify that a software product reaches an acceptable level of quality. There are currently ISO standards related to software quality for the different stages of the life cycle. In order to evaluate the quality in use, ISO 25022 is used, in which the metrics to be applied are declared. This research work seeks, through a case study, to propose a quality assessment model in use based on ISO 25022 following the process of the engineering cycle. With the proposed model, a human talent management information system was evaluated, obtaining results of the characteristics in the range: Satisfactory (Effectiveness 88.75%), while the others are in the range of Fair (Efficiency 59.21% and Satisfaction 66.74%). These results allow us to identify the level of quality in use by characteristics and in general to help in the decision making of those responsible for software development. With this, it is concluded that it is possible to evaluate the quality in use of a Human Talent Management Information System using the model designed in this research.
Locust Swarm Optimization Algorithm: A Bio-Heuristic for Global Optimization Problem

The article proposes a bioheuristic algorithm that simulates a combination of patterns of individual and swarm behavior of locusts to solve optimization problems for multidimensional multiextremal functions. The locust is a representative example of insects that can combine swarming and solitary behavior, which is implemented by various attractive operators. Two locusts in individual behavior do not get closer if there is a small distance between them, and vice versa. In swarming behavior, locusts rapidly concentrate around individuals that have found food sources. This makes it possible not only to model the cooperative behavior of a locust colony more realistically, but also to enable a computational mechanism that avoids such shortcomings of many popular bioheuristics as premature con-vergence, maintains a balance between the rate of convergence of the algorithm and the diversification of the solution search space. We experimentally tested the algorithm on three multivariate test functions. We compared the results with competing particle swarm and differential evolution algorithms. The proposed algorithm provides the best results for all considered functions. Checking the statistical significance of the results got using the Wilcoxon signed-rank test for independent samples showed that the results of the algorithm are statistically significant. #CSOC1120.
Edge Detection Algorithms to Improve the Control of Robotic Hands

This paper improves the remote control of a robotic hand through a comparison of two artificial vision algorithms, by using the “Edge Detection” and “Find Hand” respectively, for both cases the programming uses the Python language. In addition, the system features a wireless connection with the robotic hand’s servo motor controller, making it easy to manipulate the hand from a far point with Kalman modified, Butterworth filters, and control movement prediction with two feedbacks loops. In response to the tests carried out, this article determined an optimal operation for the automatic and remote-control of the hand and fingers, with high accuracy, with the precision of 78.7% to 94.44%, and F1 measurement from 77.38% to 92.30%; with an improvement to reduce the noise and mechanical influence in the electrical circuit.
Information Security Enhancements of the University’s Automated Information System

The article is aimed at studying means and methods for improving the information security of the automated information system of a modern university. The authors consider the Russian educational system as part of a modern society that reproduces its structure as a centralized, hierarchical management system, which, in turn, makes it possible to incorporate central development planning mechanisms into the management process. At the same time, the authors believe that this makes it possible to apply the same integrated approaches in the process of automating university management as in corporate security systems, including organizational terms. The article specifies the regulations to be taken into account when developing and building an automated university information system and ensuring its information security. Analyzing the options for developing the information system of the university, the authors conclude that the best way to proceed is to develop one’s own design, highlighting its most important aspects. As key means of ensuring the information security of the automated information system of the university, the authors propose its segmentation and virtualization of user workplaces. #CSOC1120.
The Information Field as an Integral Model

The article explores the phenomenon of the information field. The study hypothesizes that the information field is an integral reflection of the surrounding world. The mechanism of information field formation as a reflection of reality and the formation of an integrated information model are described. The generalized object of the information field is described. The main properties of information field objects are described. It is proved that in the information field there are more relations between models than relations between real objects. The meaning of information units is described. The systematics of types of information field objects is given. Their main properties from the position of semiotics are described. It is shown that the tasks of the information field are the acquisition of knowledge and the formation of a picture of the world. #CSOC1120.
Systematic Review on Bisonets for Linking Two Domains

There is a growing rate of online content creation and therefore new and exciting ways to filter data and still deliver relevant information to the user is important for user experience. Intelligent computing systems and dynamic methodologies are required to efficiently access this vast information and knowledge. Merging new and often conflicting information or domains is necessary for information retrieval so that information that is returned. Papers from the year 2012 to 2022 were reviewed to find common trends and implementation strategies. We have discovered that random forest, logistic regression, SVM, Naive Bayes and neural networks are the most used algorithms for outlier analysis. Logistic regression has been the mostly used algorithm and has the highest accuracy of 99,49%, however random forest although it is the second highest implemented algorithm, it has the highest accuracy score of 99.72%. This study has highlighted basic Bisonet annotations and has summarized the process of Bisonet creation in four easy steps namely; text Mining, Linking Nodes, endowing links with weights and following strong links. There is not enough research that that focuses on Bisonet therefore it is important to find a simplified framework for the implementation of Bisonet projects.