Reviewerly/exHarmony huggingface dataset:
The Effect of Teachers' Digital Literacy on Innovative Teaching Behavior with ICT: The Mediating Effect of Teacher Autonomy and Engagement

본 연구는 교사의 ICT 수업 혁신행동에 영향을 미치는 교사의 개인적 특성인 교사의 디지털 리터러시, 교사 몰입 요인과 학교조직 환경의 특성에 해당하는 교사 자율성의 구조적 관계를 파악하고자 하였다. 이를 위해 국내 초·중·고 재직 교사 639명의 데이터를 수집하고 구조방정식 모형을 적용하여 이중매개효과를 분석하였다. 연구결과, 교사의 디지털 리터러시가 교사 몰입, 교사 자율성, ICT 수업 혁신행동에 정적 영향을, 교사 자율성이 교사 몰입에 정적 영향을, 교사 몰입이 ICT 수업 혁신행동에 정적 영향을 미치는 것을 확인하였다. 또한, 교사의 디지털 리터러시와 ICT 수업 혁신행동의 관계에서 교사 자율성, 교사 몰입은 부분 매개하였고 교사의 디지털 리터러시와 교사 몰입의 관계에서 교사 자율성의 간접효과를 확인하였으며, 교사 자율성과 ICT 수업 혁신행동의 관계에서 교사 몰입이 완전매개하였다. 교사 자율성이 ICT 수업 혁신행동에 직접적인 영향은 없었으나 교사 몰입을 매개로 ICT 수업 혁신행동을 증가시킨다는 점에서 교사 몰입의 중요성을 확인하였다. 연구결과를 토대로 교사의 ICT 수업 혁신행동에 영향을 주는 교사의 디지털 리터러시, 교사 몰입, 교사 자율성 요인을 고려하여 교사 개인 차원, 학교조직의 환경 차원에서 실천적 시사점을 제시하였다.This study aims to identify the structural relationship between teachers’ digital literacy, teacher engagement, and teacher autonomy, which are expected to affect innovative teaching behavior with ICT. The data of 639 elementary, middle, and high schools teachers in Korea were collected and the dual mediating effects was analyzed by applying a structural equation model. As a research result, teachers' digital literacy had a positive effect on teacher engagement, teacher autonomy, and innovative teaching behavior with ICT. And teacher autonomy had a positive effect on teacher engagement. It was confirmed that teacher engagement had a positive effect on teachers' innovative teaching behavior with ICT. In addition, teacher autonomy and teacher engagement were partially mediated in the relationship between teachers' digital literacy and Teachers' innovative teaching behavior with ICT. In the relationship between teacher's autonomy and innovative teaching behavior with ICT, teacher engagement was fully mediated. Based on these research results, practical implications were proposed focusing on the individual teachers and school organizational dimensions.
Novel optimization of quantum search algorithm to minimize complexity

Grover's quantum search algorithm promises quadratic time reduction for searching an item in unstructured databases. The novel optimization is achieved by transforming the standard diffusion operator, which is used as a flipping operator in the algorithm, to an efficient diffusion operator utilizing fewer quantum gates and having reduced circuit depth. The comparative results in terms of gate count and circuit depth reveal reductions of 40% and 32% for 3, 4, and 5-qubit implementations of standard and optimized implementation. Further, investigations of decomposed quantum circuits for optimized and standard implementations indicate an improvement of 12% in accuracy and 21% in execution time of the novel optimization of Grover's quantum search algorithm.
Holistic Thinking in the Implementation of Digital Learning Activities, with Due Regard to Implementation of a Learning Management System and Digital Learning Course: A Field Report

Digital learning (DL) has enjoyed a huge amount of hype over the past two years. This is especially the case among businesses which just 5 years ago were convinced that DL could never replace in-person training. Looking back at the developments of the past years in the area of learning, the rollout of DL among companies, including the IT infrastructure that goes with it, has not always been without its issues. For this and other reasons, efforts are currently underway to rethink and revise the DL decisions of the last few years. Yet if there is so much to rethink, why not think holistically from the outset? In this respect, we might compare DL within an organization to the circulatory system in the human body: It reaches all areas of the organizations, supplying employees with important knowledge that is necessary for the survival of the organization. The present moment offers an opportunity to rethink what has been achieved so far. In order to ensure the success of this holistic redesign, this paper describes 7 steps for developing a DL approach, with tips from fieldwork which also cover the different areas of a company, such as learning strategy, roles and responsibilities.
Evaluation of the sailent features in Indian currency notes

Security features are topographies invisible to the naked eye, such as micro-printing or features visible in ultraviolet, transmitted, and oblique light. The present paper focuses on the examination of Indian currency notes such as 2000, 500, 200, 100, 50, 20, and10 in different light sources and with the help of a compound microscope. This kind of examination is very useful when it comes to distinguishing counterfeit currency from genuine one. Crime is increasing day by day, and so is the making of false currency – this study discusses the numerous security or hidden features which are invisible to the naked eye, and therefore may very well be missed by the potential perpetrator. It is the responsibility of the government along with the issuing authority to incorporate these structures into all travel and security documents, including passports, voter-id cards, and many others. These features are implemented either during the manufacturing of the paper itself (e.g., fluorescent fibres) or at the time of printing (e.g., microprinting, watermarks, and other fluorescent features).
The Epoch-State Filter, Revisited

The epoch-state filter is a recursive sequential estimator that maintains its state estimate and associated covariance at a fixed time known as the epoch, avoiding the need to propagate the covariance between measurements. Instead, the filter maps measurement innovations from the measurement time to the epoch time, in a fashion similar to a batch estimator. When process noise is present, the epoch time must follow all but at most the penultimate measurement. Revisiting the topic, this paper contributes several variations, including a factorized form, a factorized information form, and sigma-point versions, and provides a number of clarifying illustrative examples.
Saudi Arabia Corporate Firms are Hesitant to Embrace Artificial Intelligence as of 2020 Despite the Numerous Benefits

The modern world has experienced numerous advancements, particularly in the Artificial Intelligence (AI) sector which is slowly shaping the mode of business in Saudi Arabia. This research paper focuses on why there has been hesitation in the establishment of AI in Saudi Arabia firms despite the numerous potential advantages of AI. Saudi Arabia has, in recent times, undergone diversification in its economy. Its AI is experienced in the sectors such as robotics, industry, finance, and banking, especially in forecasting and improvement of business processes alongside provisions of solutions to complicated functionalities. However, AI is halfway implemented in the country due to various constraints explored in this publication. This research article examines companies hesitant to invest in AI and elaborates on its deployment in companies in Saudi Arabia to utilize such technologies in entire sectors. Moreover, our research also analyzes the total investment associated with AI to realize the desired change in the organizational operating activities. Our primary research involved over 2000 senior executives regarding the adoption of AI, the firm’s prospects for deployment, and its effects on the markets, governments, and persons. The results of this study showcase that the new AI generation is primarily based on the digitalization platform. Lastly, it is noted that AI deployment and acquisitions require a large number of funds hence forcing struggling firms to evade its adoption.
Rejuvenating and regenerating on-campus education. Why particular forms of pedagogical life matter

The pandemic implied an acceleration of the impending devastation of various forms of public pedagogical life attached to the campus, changing the ecology of study and affecting the sense-ability and response-ability of the university as an ‘association for/to study’ (‘universitas studii’). This contribution sketches two developments that play a role in this weakening of pedagogical life: the establishment and expansion of a hyper-modern learning factory and the creation of the figure of the independent learner. It is suggested that the rejuvenation and regeneration of pedagogical life can be supported by a critical pedagogy that cultivates the art of distinction in order to describe and indicate the distinctions that matter to various forms of pedagogical life and what they make happen. This is then exemplified through a brief discussion of the lecture and the seminar engendering ‘spoken science’. Finally, it is indicated how a rejuvenation and regeneration implicates students and scholars.
A novel metaheuristic algorithm inspired by COVID-19 for real-parameter optimization

In this modern world, we are encountered with numerous complex and emerging problems. The metaheuristic optimization science plays a key role in many fields from medicine to engineering, design, etc. Metaheuristic algorithms inspired by nature are among the most effective and fastest optimization methods utilized to optimize different objective functions to minimize or maximize one or more specific objectives. The use of metaheuristic algorithms and their modified versions is expanding every day. However, due to the abundance and complexity of various problems in the real world, it is always necessary to select the most proper metaheuristic method; hence, there is a strong need to create new algorithms to achieve our desired goal. In this paper, a new and powerful metaheuristic algorithm, called the coronavirus metamorphosis optimization algorithm (CMOA), is proposed based on metabolism and transformation under various conditions. The proposed CMOA algorithm has been tested and implemented on the comprehensive and complex CEC2014 benchmark functions, which are functions based on real-world problems. The results of the experiments in a comparative study under the same conditions show that the CMOA is superior to the newly-developed metaheuristic algorithms including AIDO, ITGO, RFOA, SCA, CSA, CS, SOS, GWO, WOA, MFO, PSO, Jaya, CMA-ES, GSA, RW-GWO, mTLBO, MG-SCA, TOGPEAe, m-SCA, EEO and OB-L-EO, indicating the effectiveness and robustness of the CMOA algorithm as a powerful algorithm. As it was observed from the results, the CMOA provides more suitable and optimized solutions than its competitors for the problems studied. The CMOA preserves the diversity of the population and prevents trapping in local optima. The CMOA is also applied to three engineering problems including optimal design of a welded beam, a three-bar truss and a pressure vessel, showing its high potential in solving such practical problems and effectiveness in finding global optima. According to the obtained results, the CMOA is superior to its counterparts in terms of providing a more acceptable solution. Several statistical indicators are also tested using the CMOA, which demonstrates its efficiency compared to the rest of the methods. This is also highlighted that the CMOA is a stable and reliable method when employed for expert systems.
Parallel Feature Subset Selection Wrappers Using k-means Classifier

In a world where the volume of data is constantly increasing, the implementation time of various processes increases significantly. Therefore, the proper management and the effort to reduce the dimensions of the datasets are considered imperative. Feature selection can reduce the size of the datasets by keeping a smaller subset, while improving the accuracy of the classification. The main purpose of this paper is to propose and examine the efficiency of parallel feature selection wrappers based on k-means classifier. The simple kmeans algorithm and a parallel version of it are used. Different parallelization variants of feature subset selection (fss) are presented and their accuracy and computation time are also evaluated on four different datasets. The comparison is performed among different parallelization variations and the serial implementation of fss with the k-means clustering algorithm. Finally, the results of the research are presented, highlighting the importance of parallelization in reducing the execution time of the proposed algorithms.
Carleman linearization approach for chemical kinetics integration toward quantum computation

The Harrow, Hassidim, Lloyd (HHL) algorithm, known as the pioneering algorithm for solving linear equations in quantum computers, is expected to accelerate solving large-scale linear ordinary differential equations (ODEs). To efficiently combine classical and quantum computers for high-cost chemical problems, non-linear ODEs (e.g., chemical reactions) must be linearized to the highest possible accuracy. However, the linearization approach has not been fully established yet. In this study, Carleman linearization was examined to transform nonlinear first-order ODEs of chemical reactions into linear ODEs. Although this linearization theoretically requires the generation of an infinite matrix, the original nonlinear equations can be reconstructed. For the practical use, the linearized system should be truncated with finite size and the extent of the truncation determines analysis precision. Matrix should be sufficiently large so that the precision is satisfied because quantum computers can treat such huge matrix. Our method was applied to a one-variable nonlinear [Formula: see text] system to investigate the effect of truncation orders and time step sizes on the computational error. Subsequently, two zero-dimensional homogeneous ignition problems for H2-air and CH4-air gas mixtures were solved. The results revealed that the proposed method could accurately reproduce reference data. Furthermore, an increase in the truncation order improved accuracy with large time-step sizes. Thus, our approach can provide accurate numerical simulations rapidly for complex combustion systems.
Multilevel histogram shape‐based image watermarking invariant to geometric attacks

Abstract In this paper, a geometrically invariant image watermarking scheme is proposed by exploiting multilevel histogram shapes. The embedding procedure starts by decomposing the host image with the first level Haar wavelet. After that, histograms are extracted from the approximation subband via several rounds, which are used to embed watermark bits. Each round of embedding first extracts a histogram at a specified level. Then the histogram is split into fragments, into which a number of watermark bits can be embedded. In this way, a considerable watermarking capacity is available. Besides, a histogram adjustment in the first embedding round is suggested to guarantee good population of histogram bins. Experimental results support its robustness against various common attacks and geometric attacks. Moreover, the scheme can embed multiple watermark sequences with various robustness and capacity profiles, which enriches its practical applications.
EXPERIENCE IN DEVELOPING OF THE UNIVERSITY INFORMATION SYSTEM

The process of development and functionality of the university information system “Digital Profile &amp; Electronic Portfolio Management System for Employees, Teachers and Students (E-TUTOR)” – the programme designed for organisational, administrative, technical and informational support of the educational organisation activities when implementing basic professional educational programmes in accordance with the federal state educational standards of higher education – is described in the article. The choice of design methodology and approaches to the selection of the development team is presented with the description of the development tool as well as the system functionality; the ways of the system development are outlined.
SMONE: A Session-based Recommendation Model Based on Neighbor Sessions with Similar Probabilistic Intentions

A session-based recommendation system (SRS) tries to predict the next possible choice of anonymous users. In recent years, graph neural network (GNN) models have been successfully applied to SRSs and have achieved great success. Using GNN models in SRSs, each session graph is processed successively to obtain the embedding of the node (i.e, each action on an item), which is then imported into the prediction module to generate recommendation results. However, solely depending on the session graph to obtain the node embeddings is not sufficient because each session only involves a few items. Therefore, neighbor sessions have been used to extend the session graph to learn more informative node representations. In this paper, we introduce a S ession-based recommendation MO del based on N eighbor sessions with similar probabilistic int E ntions(SMONE). SMONE models the intentions behind sessions in a probabilistic way and retrieves the neighbor sessions with similar intentions. After the neighbor sessions are found, the target session and its neighbor sessions are modeled as a hypyergraph to learn the contextualized embeddings, which are combined with item embeddings through GNN to produce the final item recommendations. Experiments on real-world datasets prove the effectiveness and superiority of SMONE.
Role of Cloud Computing in Education

The ever evolving and dynamic field of education constantly hunts for options which are cost efficient and can optimize the progress and performance of learners. The data centers of Cloud Computing can be further segmented into multiple servers or VMs (Virtual Machines). By doing so, these problems can be effectively tackled as it leads to need based access to computing services via the cloud module. In addition, this form of cloud computing finds utility in the field of research across countless platforms as well. These days, the world of academics cannot function without automation and computing. And, in this respect, one can easily implement the available software servers to accomplish this easily. This research paper sheds light on the role of cloud computing in the academic setups. Almost every organization, including the academic industry seeks ways and means to effectively manage their available manpower and other resources. In this respect, the dynamic and newly trending field of Cloud computing which opens up a plethora of avenues relating to computing services. The aim of this paper is to highlight how cloud computing has found its way into educational institutions and the key elements which make it lucrative for academicians to integrate it into their working model.
Design of news recommendation model based on sub-attention news encoder

To extract finer-grained segment features from news and represent users accurately and exhaustively, this article develops a news recommendation (NR) model based on a sub-attention news encoder. First, by using convolutional neural network (CNN) and sub-attention mechanism, this model extracts a rich feature matrix from the news text. Then, from the perspective of image position and channel, the granular image data is retrieved. Next, the user's news browsing history is injected with a multi-head self-attention mechanism, and time series prediction is applied to the user's interests. Finally, the experimental results show that the proposed model performs well on the indicators: mean reciprocal rank (MRR), Normalized Discounted Cumulative Gain (NDCG) and area under the curve (AUC), with an average increase of 4.18%, 5.63% and 6.55%, respectively. The comparative results demonstrate that the model performs best on a variety of datasets and has fastest convergence speed in all cases. The proposed model may provide guidance for the design of the news recommendation system in the future.
Efficient Processing of k-Hop Reachability Queries on Directed Graphs

Given a directed graph, a k-hop reachability query, u→?kv, is used to check for the existence of a directed path from u to v that has a length of at most k. Addressing k-hop reachability queries is a fundamental task in graph theory and has been extensively investigated. However, existing algorithms can be inefficient when answering queries because they require costly graph traversal operations. To improve query performance, we propose an approach based on a vertex cover. We construct an index that covers all reachability information using a small set of vertices from the input graph. This allows us to answer k-hop reachability queries without performing graph traversal. We propose a linear-time algorithm to quickly compute a vertex cover, S, which we use to develop a novel labeling scheme and two algorithms for efficient query answering. The experimental results demonstrate that our approach significantly outperforms the existing approaches in terms of query response time.
On Selecting, Ranking, and Quantifying Features for Building a Liver CT Diagnosis Aiding Computational Intelligence Method

The liver is one of the most common locations for incidental findings during abdominal CT scans. There are multiple types of disease that can arise within the liver and many of them are nodular. The ultimate goal of our research is to develop an expert knowledge-based system using fuzzy signatures, to support decisions during diagnosis of the most frequent of these nodular lesions. Since the literature contains limited information about the graphical properties of CT images that must be taken into consideration and their relationship to one another, in this paper we focused on selecting and ranking the input parameters using expert knowledge and determining their importance. Six visual attributes of lesions (size, shape, density, homogeneity contour, and other features) were selected based on textbooks of radiology and expert opinion. The importance of these attributes was ranked by radiologist experts using questionnaires and a pairwise comparison technique. The most important feature was found to be the density of the lesion on the various CT phases, and the least important was the size, the order of the other attributes was other features, contour, homogeneity, and shape, with a Kendall concordance coefficient of 0.612. Weights for the attributes, to be used in the future fuzzy signatures, were also determined. As a last step, several statistical parameter-based quantities were generated to represent the above abstract attributes and evaluated by comparing them to expert opinions.
Research on Recognition and Analysis of Teacher–Student Behavior Based on a Blended Synchronous Classroom

Due to the impact of the COVID-19 pandemic, many students are unable to attend face-to-face courses, Therefore, in this case, distance education should be promoted to replace face-to-face education. However, because of the imbalance of education in different regions, such as the imbalance of education resources between rural and urban areas, the quality of distance education may not be guaranteed. Therefore, in China and some regions, there have been efforts made to carry out blended synchronous classroom attempts. In hybrid synchronous classroom situations, teachers’ workloads have increased, and it is difficult to fully understand students’ learning efficiency and class participation. We use deep learning to identify the behaviors of teachers and students in a blended synchronous classroom-based situation, aiming to automate the analysis of classroom videos, which can help teachers in classroom reflection and summary in a blended synchronous classroom or face-to-face classroom. In the behavior recognition of students and teachers, we combine the head, hand, and body posture information of teachers and students and add the feature pyramid (FPN) and convolutional block attention module (CBAM) for comparative experiments. Finally, S–T (student–teacher) analysis and engagement analysis were carried out on the identification results.