Reviewerly/exHarmony huggingface dataset:
Performance Comparison of Hot-Deck Imputation, K-Nearest Neighbor Imputation, and Predictive Mean Matching in Missing Value Handling, Case Study: March 2019 SUSENAS Kor Dataset

Missing value can cause bias and makes the dataset not represent the actual situation. The selection of methods for handling missing values is important because it will affect the estimated value generated. Therefore, this study aims to compare three imputation methods to handle missing values—Hot-Deck Imputation, K-Nearest Neighbor Imputation (KNNI), and Predictive Mean Matching (PMM). The difference in the way the three methods work causes the estimation results to be different. The criteria used to compare the three methods are the Root Mean Squared Error (RMSE), Unsupervised Classification Error (UCE), Supervised Classification Error (SCE), and the time used to run the algorithm. This study uses two pieces of analysis, comparison analysis, and scoring analysis. The comparative analysis applying a simulation that pays attention to the mechanism of missing value. The mechanism of the missing value used in the simulation is Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR). Then, scoring analysis aims to narrow down the results of comparative analysis by giving a score on the results of the imputation of the three methods. The result suggests Hot-Deck Imputation is the most excellent in dealing with a missing value based on the score.
Optimization of Product Design Mode of Art Industry Based on Multiterminal Selection Algorithm of Internet of Things

This article has first presented an overview and a brief design to the system, and then analyzed the system design goal and design principle. The role of the resource representation model in the Internet of Things is more important than the significance of the database schema for the web information system. In the current "vertical" application model, the device resources of the perception layer and the application entities of the business layer are in a tightly coupled relationship. In the process of system operation, it changes flexibly as the situation changes. This article will combine the characteristics of resources in the Internet of Things, from the perspectives of resources and services, and refer to the basic structure of the traditional Internet resource reference model OWL-S to appropriately expand and tailor it, and describe the relationship between Internet of Things device resources and application services. Therefore, the resource representation model oriented to the Internet of Things service in this article is proposed and simulated and verified. To a certain extent, this model can provide support for the unified description of IoT resources and services, so as to better adapt to the development needs of diversified IoT services. Based on the application and service in the Internet of Things environment restricted by time and space correlation and limited resources, the article discusses and proposes a multiterminal selection algorithm for Internet of Things business, and converts it into a mathematical combination optimization problem. Based on the ant colony optimization algorithm, a solution is given, which can realize the optimization of equipment combination scheme under resource-constrained conditions to maximize the total benefit value of the service, and can effectively improve the utilization rate of terminal resources. In the research on the analysis of the development strategy of China's industrial design industry in the era of intelligent interconnection, the analysis of the development strategy of the industrial design industry in the era of intelligent interconnection through analysis tools of industrial strategy reveals the new trend of industrial design in the era of intelligent interconnection. The analysis and formulation of industrial strategy are complicated, and it is a systematic project involving all aspects. The rational use of corresponding strategic analysis tools can further enhance the scientificity and rationality of industrial design strategy.
INNOVATION STRATEGIES AND MARKET ORIENTATION IN SELECTED NIGERIAN BANKS

In an attempt at improving market orientation, management of organisations employ innovation strategies and practices that reflect this area of operation. Thus, this study investigated innovation strategies and market orientation in selected Nigerian banks. It specifically examined the effect of process, product, marketing and organisational innovation on market orientation among selected Nigerian banks. It is a field survey within a cross sectional time frame of 2021. A total of 399 management and senior staff of five deposit money banks with international affiliation constituted the sample size. Data was gotten from the primary source and generated through the administration of copies of questionnaire to respondents. Data was analysed using descriptive and inferential statistics. The outcomes of the study showed that process, marketing and organisational innovation have positive significant effect on market orientation among selected Nigerian banks. This study therefore recommends that deposit money banks should implement process, product, marketing and organisational innovation by putting in place the effective and efficient utilization of technologies in the areas of operations and service delivery. It also recommends that they cover wide areas of the market in terms of segmentation, market share, promotional activities, advertisement and other marketing areas so that they can attract new customers and new businesses so as to increase the demand and visibility of their products thus enhancing their customers’ satisfaction. Also, the study recommends constant engagement in organisational innovation in a bid to meet the changing competitive nature of the banking industry and the set international standard to enhance their market orientation vis-à-vis their overall performance.
PENERAPAN MEDIA PEMBELAJARAN WONDERSHARE FILMORA PADA PEMBELAJARAN MATEMATIKA

The purpose of the study was to determine whether there was a difference between the average mathematics learning outcomes of students using the online learning model assisted by Wondershare Filmora learning media and the average mathematics learning outcomes of students using the online learning model without the aid of Wondershare Filmora learning media. This type of research is a comparative quantitative research. The population in this study were students of class IX SMP Negeri 1 Kalirejo. Data analysis was processed by two-party t-test and one-party t-test with the prerequisites for normality and homogeneity tests. The results showed that there was a difference in the average mathematics learning outcomes of students who used the online learning model assisted by Wondershare Filmora learning media and the average mathematics learning outcomes of students who used the online learning model without the aid of Wondershare Filmora learning media. One-sided t-test shows that the average mathematics learning outcomes of students who use the online learning model assisted by the Wondershare Filmora learning media are better than the average mathematics learning outcomes of students who use the online learning model without the aid of Wondershare Filmora learning media.
Impact of Unbalanced Classification on the Performance of Software Defect Prediction Models

Objectives:To propose a suitable imbalanced data classification model to split the dataset into two new datasets and to test the created imbalanced dataset by the prediction models.Methods: The imbalance defect data sets are taken from the PROMISE library and used for the performance evaluation.The results clearly demonstrate that the performance of three existing prediction classifier models, K-Nearest Neighbor (KNN), Naive Bayes (NB), and Back Propagation (BPN), is very susceptible in terms of unbalance of classification, while Support Vector Machine (SVM) and Extreme Learning Machine (ELM) are more stable.Findings: The outcome of this research reveals that applied SVM and ELM machine learning models improves the performance in defect prediction and records 29% more than KNN, and 19% more than NB and BPN.Novelty: According to the findings of a comprehensive study, the proposed machine learning new classification imbalance impact analysis method outperforms the existing ones in order to transform the original imbalance data set into a new data set with an increasing imbalance rate and be able to select models to evaluate different predictions on the new data set.
A signature-based data security and authentication framework for internet of things applications

&lt;span&gt;Internet of things (IoT) is the next big revolution in modernized network technologies connecting a massive number of heterogeneous smart appliances and physical objects. Owing to these technologies' novelty, various issues are characterized by security concerns are the most prioritized issue. A review of existing security approaches highlights that they are very particular about the solution towards a specific attack and cannot resist any unknown attacker. Therefore, this manuscript presents a novel computational model that introduces a unique authentication process using a simplified encryption strategy. The simulated study outcome shows that the proposed system offers efficient security and efficient data transmission performance in the presence of an unknown adversary. Hence, the study outcome exhibits better effects than frequently used security solutions when implemented in a vulnerable IoT environment.&lt;/span&gt;
Data-Driven Joint Beam Selection and Power Allocation for Multiple Target Tracking

For the problem of joint beam selection and power allocation (JBSPA) for multiple target tracking (MTT), existing works tend to allocate resources only considering the MTT performance at the current tracking time instant. However, in this way, it cannot guarantee the long-term MTT performance in the future. If the JBSPA not only considers the tracking performance at the current tracking time instant but also at the future tracking time instant, the allocation results are theoretically able to enhance the long-term tracking performance and the robustness of tracking. Motivated by this, the JBSPA is formulated as a model-free Markov decision process (MDP) problem, and solved with a data-driven method in this article, i.e., deep reinforcement learning (DRL). With DRL, the optimal policy is given by learning from the massive interacting data of the DRL agent and environment. In addition, in order to ensure the information prediction performance of target state in maneuvering target scenarios, a data-driven method is developed based on Long-short term memory (LSTM) incorporating the Gaussian mixture model (GMM), which is called LSTM-GMM for short. This method can realize the state prediction by learning the regularity of nonlinear state transitions of maneuvering targets, where the GMM is used to describe the target motion uncertainty in LSTM. Simulation results have shown the effectiveness of the proposed method.
Can AI learn to forget?

Specialized techniques may make it possible to induce selective 'amnesia' in machine learning models.
Virtual social capital: features of network and gaming resources in the development of cognitive and educational activities of high school students

Introduction. In the context of digitalization of education, the information and educational environment is actively developing, including formal corporate information resources and communication networks, as well as informal (non-corporate) interaction networks, whose function is performed by popular social networks and instant messengers. The digitalization of education is becoming an important factor in the formation of virtual interaction between the main parties of the educational process, namely, students, their parents and teachers. Virtual social capital is being formed as one of the ways to maintain, develop and structure human social ties in the information society. The purpose of this article is to study the features of the use of social network resources for solving cognitive and educational problems by high school students. Materials and methods: 1) handout survey for 358 students of grades 9-11 of secondary schools, lyceums and gymnasiums in the city of Kirov (Russian Federation); 2) secondary analysis of data from empirical studies on this issue (descriptive analysis). Results. The high involvement of teachers and high school students in popular social networks has been shown: up to 98% of high school students have their personal accounts, every fifth teacher uses social networks for work. Every day, teenagers spend 1 to 3 hours on social networks, regardless of their grade (χ2=3.5, df=6, р=0.74), 1 to 2 hours playing online games. 70% of high school students are subscribed to public pages that help to do homework, one third, to popular science public pages. High school students noted that thanks to computer games they improve their skills in mastering a foreign language, knowledge of basic subjects, and in every tenth case, they developed a desire for self-knowledge. Conclusion. The study showed that virtual social networks and online games perform important auxiliary functions, complementing the information and educational environment of the student: in addition to the communicative and leisure functions, they also perform organizational and information search functions. Social networks are becoming an important part of the virtual social capital of contemporary schoolchildren, as well as other parties to the educational process.
GLOBAL CRISES AND CYBERSECURITY ATACKS – AN ANALYSIS DURING THE COVID-19 PANDEMIC

The COVID-19 pandemic has affected all nations in various ways, both economically and especially socially. The new normality, as social behavior dictated by the restrictions imposed, sustained online interaction, a factor that allowed an increase in cyberattacks. This paper intends to analyze the cyberattacks conducted during the COVID-19 pandemic, starting from a series of international events such as the crisis in Venezuela or Ukraine. A part of this study is a quantitative analysis of the results obtained from consulting the Scopus database. In this part, in addition to the most important authors and key words used, we aimed to find if the countries that are the main targets of cyberattacks are also involved in researching cybersecurity during pandemic. Cyberattacks are analyzed in terms of motivation and targets, aiming to identify possible solutions based on the modus operandi of attackers and what will be the next steps in the research.
RAUM-VO: Rotational Adjusted Unsupervised Monocular Visual Odometry

Unsupervised learning for monocular camera motion and 3D scene understanding has gained popularity over traditional methods, which rely on epipolar geometry or non-linear optimization. Notably, deep learning can overcome many issues of monocular vision, such as perceptual aliasing, low-textured areas, scale drift, and degenerate motions. In addition, concerning supervised learning, we can fully leverage video stream data without the need for depth or motion labels. However, in this work, we note that rotational motion can limit the accuracy of the unsupervised pose networks more than the translational component. Therefore, we present RAUM-VO, an approach based on a model-free epipolar constraint for frame-to-frame motion estimation (F2F) to adjust the rotation during training and online inference. To this end, we match 2D keypoints between consecutive frames using pre-trained deep networks, Superpoint and Superglue, while training a network for depth and pose estimation using an unsupervised training protocol. Then, we adjust the predicted rotation with the motion estimated by F2F using the 2D matches and initializing the solver with the pose network prediction. Ultimately, RAUM-VO shows a considerable accuracy improvement compared to other unsupervised pose networks on the KITTI dataset, while reducing the complexity of other hybrid or traditional approaches and achieving comparable state-of-the-art results.
Quantum Readout of Imperfect Classical Data

The encoding of classical data in a physical support can be done up to some level of accuracy due to errors and the imperfection of the writing process. Moreover, some degradation of the stored data can happen over time because of physical or chemical instability of the system. Any readout strategy should take into account this natural degree of uncertainty and minimize its effect. An example are optical digital memories, where the information is encoded in two values of reflectance of a collection of cells. Quantum reading using entanglement, has been shown to enhances the readout of an ideal optical memory, where the two level are perfectly characterized. In this work, we analyse the case of imperfect construction of the memory and propose an optimized quantum sensing protocol to maximize the readout accuracy in presence of imprecise writing. The proposed strategy is feasible with current technology and is relatively robust to detection and optical losses. Beside optical memories, this work have implications for identification of pattern in biological system, in spectrophotometry, and whenever the information can be extracted from a transmission/reflection optical measurement.
effect of inbound marketing on online customer engagement

In today's digital world, marketers are facing challenges that make them care about customer engagement more than ever. The technological developments and the wide spread of the Internet have enhanced the role that customers play in creating value for the marketer, which has gone beyond buying to online interaction and engagement behaviors. Therefore, the study sought to examine the effect of inbound marketing as an approach described as the most effective in the digital age on encouraging online customer engagement. Therefore, the study sought to examine and diagnose the nature of the influence of inbound marketing as an approach described as the most effective in the digital age to encouraging online customer engagement. In the field axis, the study relied on a quantitative approach by targeting retail stores in Basra Governorate – Iraq, A sample of (237) retailers was surveyed using the random sampling method. The results showed that there is a narrow interest of retailers in the inbound marketing approach in enhancing online customer engagement, which focuses mainly on the presence of those retailers on social media networks.
Distance education and innovative teaching methods in higher education of Ukraine during the coronavirus pandemic

Abstract: The spread of COVID‑19 coronavirus disease in Ukraine in early 2020 contributed to global changes in the education system and intensive implementation of distance learning in all areas of the educational process. When choosing a model for the implementation of distance education, the teacher must take into account all its advantages and disadvantages in compliance with the requirements of discipline and organization of education in such a way as to ensure an adequate high level of knowledge of educational applicants. To ensure the quality of students’ education and to improve it, it is necessary to introduce various innovative technologies in the pedagogical process in accordance with modern trends. The introduction of innovative methods in pedagogical activities for the purpose of modernization, development and use of educational innovative and information technologies of distance learning, promotes comprehensive training of a specialist for professional activities.The structure of the innovative educational process and the model of implementation of distance education depends on the already known or own development of an interactive form and method of learning using pedagogical technologies, the volume of program material, the availability of electronic sources and their availability, the educational platform on the basis of which training is carried out, as well as on the readiness of teachers and students of education to changes that meet the conditions of a rapidly changing information society.An effective combination of interactive teaching methods and systems of electronic educational platforms to ensure proper distance learning enables students to gain practical knowledge and skills as much as possible, to promote the development of communicative competencies, to ensure a high level of thinking and acquisition of common and integrated competencies, to obtain theoretical knowledge and turn them into professional practical skills and abilities.The article discusses the development and formation, main advantages and actual disadvantages of distance education, taking into account training in the process of rapid implementation, modernization and adaptation of innovative teaching methods in the educational processes of Ukraine during the global COVID‑19 pandemic.
A Scientometric Analysis of Twenty Years Trends in Mobile Learning, Blended Learning, Online Learning, E-Learning and Dental Education Research

The main objective of this study is to determine a scientometric analysis of twenty years of trends in Mobile Learning, Blended Learning, Online Learning, E-Learning, Electronic Learning, and Dental Education research.Papers published on Mobile Learning, Blended Learning, Online Learning, E-Learning, Electronic Learning, and Dental Education research topics were extracted and analyzed using the Web of Science database.VOSViewer software was used to identify and visualize key trends, influential authors, and journals.The 374 filtered documents were selected based on three main criteria which are (i) Topics on Mobile Learning or Blended Learning or Online Learning or E-Learning or Electronic Learning and Dental Education research, (ii) Type of documents on 'Article', and (iii) Year Published within 2000 to 2020.We conducted several types of analyses on the body of research using VOSViewer which are (i) Co-authorship analysis, (ii) Co-occurrence analysis, (iii) Citation analysis, and (iv) Co-citation analysis.The main contribution and motivation for this study is in the form of a conceptual framework of types of Dental Health Education based on stakeholders' orientation in guiding future research in Dental Health Education with interdisciplinary research in Mobile Learning, Blended Learning, Online Learning, or E-Learning and support the UN Sustainable Development Goals agenda on Quality Education.There are five major keyword theme clusters concerning stakeholders-oriented clusters that we had determined based on the clusters which are (i) program provider, (ii) curriculum designer, (iii) dental students, (iv) dental hygiene education provider' and (v) interprofessional education provider themes.