Reviewerly/exHarmony huggingface dataset:
Validation of the digital competence assessment tool for early childhood teachers

The purpose of this study was to verify the item quality, validity, and reliability of the assessment tool for diagnosing the digital competence for early childhood teachers. To this end, a Google survey was conducted on 250 teachers working in kindergartens and daycare centers in Seoul, Incheon, Gyeonggi, and Chungcheong. The research results are as follows: First, as a result of analyzing the item quality, it was found that the item average did not have an excessively low average, so the difficulty was not high, and there were no items with an excessively small standard deviation.&#x0D; Skewness and kurtosis met the criteria and assumed normality. Second, as a result of performing exploratory urine analysis to analyze construct validity, confirmatory factor analysis was performed with 28 competencies of 7 factors, excluding 4 items that were not suitable for factors. As a result, RMSEA, RMR, CFI, TLI, and IFI all met the acceptance criteria, so the model was fit. On the other hand, the standardized estimation coefficient for convergent validity, C.R. All of the values exceeded the acceptance criteria, and both the conceptual reliability and mean variance extract for each factor were more than the acceptance criteria. In addition, as a result of verifying the reliability of each factor, the reliability was found to be high, with Cronbach's α .89 to .94. Finally, 28 evaluation items with 7 factors were derived through validation and reliability of the digital competence assessment tool for early childhood teachers. Therefore, it is expected that it can be used as basic data to compose the contents of teacher education to strengthen digital competence.
Test environments for large‐scale software systems—An industrial study of intrinsic and extrinsic success factors

Abstract The characteristics of the test environment are of vital importance to its ability to support the organizations testing objectives. This paper seeks to address the need for a structured and reliable approach, which can be used by companies and other organizations to optimize their test environments in each individual case. The reported study included a series of interviews with 30 individuals, a series of focus groups with in total 31 individuals and a cross‐company workshop with 30 participants from five large‐scale companies, operating in different industry segments. The study resulted in a list of success factors, including not only characteristics and capabilities existing within a test environment (intrinsic success factors) but also properties not inherent to the test environment, but still vital for a successfully implemented test environment (extrinsic success factors). This distinction is important, as the root causes differ and as addressing them requires distinct approaches—not only of technology but also of organization, communication and collaboration. We find that successful implementations of test environments for large‐scale software systems depend primarily on how they support the company's business strategy, test organization and product testability (extrinsic success factors). Based on this, test environments can then be optimized to improve test environment capabilities, usability and stability (intrinsic success factors). The list of intrinsic and extrinsic success factors was well received by all five companies included in the study, supporting that the intrinsic and extrinsic success factors for test environments can be applied to a large segment of the software industry.
A secure and efficient multi-domain data sharing model on consortium chain

In a large-scale networking scenario with massive distribution of devices, data are independently generated and maintained by multiple domains. To solve the problem of isolated data islands in multi-domain, this paper designs a multi-domain data sharing model based on consortium chain. Aiming at the problems of low efficiency and lack of consideration of intra-domain consensus processes in traditional data sharing schemes, this paper optimizes the security and efficiency of data sharing from two parts: inter-domain chain and intra-domain chain. To ensure fine granularity, flexibility and security of the access control process in inter-domain chain, this model combines the Attribute-Based Access Control (ABAC) with smart contract, and a permission grading mechanism is designed to solve the problem of low retrieval efficiency in this access control model through reducing the retrieval range by grading and matching Policy set. Considering the problem of high delay of node consensus in the large-scale networking environment of intra-domain chain, a layered practical Byzantine fault tolerance optimization (LPBFT) algorithm with introducing a reputation model is designed, which greatly improves the efficiency and security of the intra-domain consensus. The consortium chain model is designed and implemented from the perspectives of inter-domain and intra-domain, which optimized the security and efficiency of the multi-domain data sharing model. Finally, a prototype system is implemented based on Hyperledger Fabric, and the validity of the model is verified from the aspects of inter-domain access control and intra-domain consensus algorithm.
Missing Person Identification

At present the Computer automated Face recognition systems are used for personal identification, but the Age variations of an individual poses a serious problem for it. Designing an appropriate feature representation and an effective matching framework for age invariant face recognition remains an open problem. To classify person age using faces author using combination of two CNN where one CNN will extract face features which can help in identify changes in face over time and second CNN helps in predicting/ classifying age. Face aging causes intra-subject variations (such as geometric changes during childhood &amp; adolescence, wrinkles and saggy skin in old age) which negatively affects the accuracy of face recognition systems. Therefore, this paper proposes a unified, multi-task framework to jointly handle these two tasks, termed MTLFace, which can learn age-invariant identity-related representation while achieving pleasing face synthesis. Specifically, we first decompose the mixed face features into two uncorrelated components—identity- and age-related features—through an attention mechanism, and then decorrelate these two components using multi-task training and continuous domain adaption. d. This system will decrease the crimes and ensure the security in our society.
A Brief Study on Human Action Recognition

Human action recognition is the process of labelling image sequences with action labels. Robust solutions to this problem have applications in domains such as visual surveillance, video retrieval and human–computer interaction. The task is challenging due to variations in motion performance, recording settings and inter-personal differences. In this survey, we explicitly address these challenges. We provide a detailed overview of current advances in the field. Image representations and the subsequent classification process are discussed separately to focus on the novelties of recent research. Moreover, we discuss limitations of the state of the art and outline promising directions of research.
An Optimized Machine Learning Based Rule Engine Architecture

Rules are segments of knowledge which are generally conveyed as, “when some conditions are evaluated as true, then perform some tasks". A rule engine is basically an advanced software system responsible for rules evaluation and execution. While it is easy to add rules, problems arise when their numbers tend to explode exponentially over time due to new business scenario needs. It eventually becomes more complex when an enterprise information system, having configuration model powered by a declarative rule engine, needs to be maintained. Performance significantly degrades when there are thousands of rules, since the engine must figure out every time which rule should be fired when large number of facts arrive for processing, thus rapidly choking up the system. Objective of this paper is to use machine learning techniques to optimize declarative business rules system when the number of rules increases creating performance degradation and complexity issues.
Cultural Representation to EFL according to English Textbooks for Twelfth Grades in Indonesia

The study aims to examine cultural content in the textbooks for Senior High School grades 12th and how represents the culture in textbooks through the material to be studied in the textbooks. This research applied a descriptive qualitative method, data obtained from English textbooks for 12th grades from Indonesia which are analyzed by grouping cultural elements. To collect the data, this research applies document review. The finding shows there are three elements of culture in this textbook. 1. Practice 2. Person 3. Product. These English textbooks introduce many various cultures around the world not only England or American culture but also, a lot of other countries including Indonesian culture.
A Novel Steganographic Approach for Secured Communication via Boustrophedon Transformation to Develop IoT-based Smart City

Steganography is a vast arena to provide security in digital media at the time of network-level transmission. This research is based on a pioneer steganographic approach introducing Image Authentication using Boustrophedon Transformation (IABT) to ensure secured communication. The research experiment is performed using ten benchmark images of 512 × 512 dimensions and the performance result of each of the images has been analyzed thoroughly. In order to implement this transformation technique to the cover images, each of the cover media is decomposed into 1 × 3 blocks. These decomposed spatial components are transformed into frequency components by implementing the Boustrophedon transformation. Confidential data in a bit stream format is embedded into each of the transformed coefficients. Furthermore, the Inverse Boustrophedon transformation is used for converting the transformed embedded coefficients back to the pixel components. This process is kept on repeating until the entire bit stream of the hidden information is inserted into the carrier media to produce a steganography image. Moreover, the performance factor of the proposed method is examined in terms of PSNR, MSE, IF, and Payload, which shows overall better performances of the IABT.
One-Way Quantum Computer

So far, weOne-way quantum computer have described a quantum computer technology that relies on gates and connecting wires to implement any algorithm. Such a paradigm is sometimes called the quantum circuit model. In this model, every quantum algorithm is implemented through a quantum circuit, and the basic building blocks of quantum circuits are quantum gates. Each quantum gate operates on one or two qubits, which are called single-qubit gates and two-qubit (possibly entangling) gates, respectively. An example of entangling two-qubit gates is the controlled-NOT gate (or controlled-Z), which is given in the computational basis by the matrix.
Efficiency of a Quantum Computer

To understand why the workings of a quantum computer is radically different from a classical computer—a difference that makes it far more powerful than any possible classical computer—some of the results obtained are summarized.
Quantum Superposition and Entanglement

Two properties of Hilbert space that are pivotal in making quantum algorithms faster than classical algorithms are superposition and entanglement, discussedEntanglement in Sects. 5.1 and 5.7. A few special casesSuperposition, discussed below, concretely illustrate the general principles of superposition and entanglement for quantum algorithms.
A Simple Model of Knowledge Scaffolding

We introduce a simple model of knowledge scaffolding, simulating the process of building a corpus of knowledge based on logic derivations starting from a set of “axioms”. The starting idea around which we developed the model is that each new contribution, still not present in the corpus of knowledge, can be accepted only if it is based on a given number of items already belonging to the corpus. When a new item is acquired by the corpus we impose a limit to the maximum growth of knowledge for every step that we call the “jump” in knowledge. We analyze the growth with time of the corpus and the maximum knowledge and analyzing the results of our simulations we managed to show that they both follow a power law. Another result is that the number of “holes” in the knowledge corpus always remains limited. Using an approach based on a death-birth Markov process we were able to derive some analytical approximation of it.
Introduction

Quantum mechanics describes and explains the workings of nature—and is by far the most successful theory of science. Although quantum mechanics has qualitatively changed our view of nature, a satisfactory understanding of it is still far from complete. The ‘enigmatic’ aspects of quantum mechanics—specially the pivotal and non-classical role that measurements plays in connecting the mathematical formalism with observations —are discussed in the context of quantum computers in the concluding Chapters. In fact, the workings of quantum computers throw new light on the mysteries and conundrums of quantum mechanics.
Shor’s Algorithm

The Shor algorithm is widely regarded as the first non-trivial quantum algorithmAlgorithmsquantum that shows a potential of an ‘exponential’ speeding-up over its equivalent classical algorithms.
Quantum Error Correction

Fault-tolerant quantum computation essentially relies on quantum error-correcting codes. By repeated measurements of the error syndromesError syndromes and the application of the corresponding correction operations, encoded states can be stored reliably for extended periods of time
Managing Information System Risk Audit

It is not a surprise anymore that e-Commerce has rapidly becoming a “normal” way of doing business as the world has become advanced with the technology. Now, buying and selling goods and services can be done online at any time and any place. However, there is still the concern towards the consumers, as some of them are oblivious on how their personal information will be handled and used. A total of 121 respondents were gathered to obtain inputs on how Bruneian perceived the risk of using e-commerce. The study provides an analysis of the perception of e-commerce customers in Brunei on the security and privacy issues. From the findings of the survey, managing risk will be constructed based on the perception of the local e-commerce customers.
Exploring Blockchain-Based Digital Marketing in the UAE

This chapter explores the possibilities of using blockchain technology in digital marketing practices in the UAE. The research also examines how blockchain may affect digital marketing's contemporary practices to provide recommendations to digital marketing practitioners on utilising blockchain technology. Secondary data analysis, an online survey, and a Delphi study were conducted to achieve the research objectives. This chapter corroborates a gap in the literature by analysing the secondary data. The online survey results depict users' frustrations, openness to advertisement incentives, transparent supply chains, and brand advocacy. Lastly, the Delphi study identified digital marketing frustrations and blockchain experts' insight. The research confirms that blockchain-based digital marketing could enhance user experiences and specific marketing objectives while facilitating market development or differentiation. It is recommended for marketers to study and remain open to the technology's adoption to improve their performance.
Shape Based Feature Extraction for Currency recognition and compare the accuracy and sensitivity with ORB algorithm

Aim: The aim of this research is to recognize the currency using image processing techniques. In this research novel shape based feature extraction method is proposed and the accuracy is compared with (Oriented FAST and Rotated BRIEF) ORB algorithm. Materials and Methods: The performance of the proposed system has been evaluated using a challenging dataset Github with a wide variety of conditions. The research includes two groups. Total sample size of 40 is taken for analysis. The Sample size was calculated using clinical.com by keeping alpha error-threshold by 0.05, 95 % confidence interval, power 80 % for control group (Shape based feature extraction <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{N}=20$</tex> ) and study group (ORB algorithm <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{N}=20$</tex> ). The analysis shows that the Shape based feature extraction method has an accuracy of 97.48 % and the accuracy of ORB algorithm is 89.85 %. The sensitivity of the Shape based feature extraction method is 97.20 % and the sensitivity of the ORB algorithm is 88.81 %. Significance value is 0.000 (p ¡ 0.05, 2-tailed). Conclusion: In this work it is found that the novel shape based feature extraction method performs significantly better than ORB algorithm in terms of accuracy and sensitivity.
Facial Expression Detection using Convolutional Neural Network

A face expression is a clear representation of an individual's affective state, cognitive activity, intention, personality, psychopathology, and it serves as a means of communication in interpersonal relationships. Automatic facial expression recognition is a critical segment of usual interface between human and a machine, and it must be used for behavioristic psychology therapeutic practice as well. Face identification and placement in a chaotic scene, facial feature extraction, and facial feature categorization are all tasks that an autonomous face features Recognition system must complete. Convolution Neural Networks are used to create a facial expression detection system. LeNet Architecture is the foundation of the CNN model. During this experiment, we have used the Kaggle facial features dataset in which we acquire seven types of face expressions which indicates our emotions swiftly. Kaggle facial feature data set with 7 face expressions labeled as (happy, sad, surprise, fear, rage, disgust, neutral) was taken for the research. Through this paper we are able to obtain 56.7% accuracy and 0.576 precision while testing the dataset.
Agile Lifestyle of Development

An iterative technique to software development and management that facilitates teams delivering value to their customers rapidly and with fewer problems. Agile method is extensively used in different corporate industry level projects as a light development strategy that aims to meet the changing needs. Short-term repetitions are best used for successful product development. Conventional software development processes do not seem to be very effective in managing rapid change of needs. Aside from the benefits of Agile, criticism of the agile approach claims that it fails to identify structural and aesthetic issues and is therefore hesitant to offer minor decisions throughout the design process. Here, in the middle of this research paper, we try to identify the effects of a much better approach in terms of efficiency and pace to s consistent software development procedure within an organizational and cultural framework.
Semi-supervised Regression under Federated Learning Framework Based on Partial Information Estimation

Nowadays, data becomes a valuable asset. More people even governments are increasing attention to data sharing and privacy protection. Federated Learning helps to protect privacy and break data island, which is favored by many scholars for its unique advantages. However, less research related to Federated Learning focused on regression problems, not to mention semi-supervised regression. In this paper, we propose the semi-supervised regression under Federated Learning framework based on Partial Information Estimation (Fed-PI), and the effectiveness of models are verified by Monte Carlo simulations. Through the use of Concrete dataset, we experimentally demonstrate that our algorithm performs better compare with the result of other models. These findings would encourage Federated Learning applied to more areas and increase interoperability.
Text-Based Emotion Detection and Applications: A Literature Review

Emotion Detection is the sentiment analysis process used to extract emotions from the text that best represent the author's mental state. In recent years, the emotion detection domain has become very popular due to its potential applications in artificial intelligence, human-computer interaction, psychology, and marketing. Despite its vast application, emotion detection is challenging in natural language processing. Detecting emotions from a text or image requires exhaustive knowledge and analysis. However, with machine learning, artificial intelligence, and data mining advancement, it has become possible to face this chal-lenge. Furthermore, the huge amount of textual data available online through social media, blogs, news, and articles helped the cause. For emotion detection, most of the studies have relied on machine learning and deep learning models and achieved good results. However, the researchers face some challenges that need to be addressed, such as the inability to extract the semantic information, the feature extraction process being time-consuming and inefficient, difficulty in identifying different emotions from non-standard language, imbalanced datasets, etc. This article aims to explore the existing approaches, methods, and evaluation measures used for emotion detection. The significant contribution, the methodology applied, and the results obtained by different researchers to gain the best possible results are also highlighted. Finally, the article highlights the limitations and provides the future direction that can be useful for research in emotion detection from text.
Real Time Object Detection

Introducing YOLO, another way to deal with object identification. Previous object detection function re-uses separators to create location. All things considered, we present the identification object as a repetitive problem in geographically separated jump boxes and related class opportunities. All acquisition pipes are an independent entity, they may be well developed to begin to finish directly on-site operations. Our integrated design is very fast. Our Consequences should be discarded photo rotations continuously at 45 edge every second. An additional moderate practice of the organization, YOLO, processes 155 shocking casings per second. Compared to the best class identification frameworks, YOLO makes many limitations yet very different to expect misleading sides on the basis. Finally, YOLO learns the typical presentation of articles. It overcomes other local techniques, including SSD and R-CNN, while summarizing from conventional images to as diverse as art.
Online Voting System

This analysis has shown that candidate selection often involves politics, and that whenever voters vote for their elections, they end up with a typical exploitation and manual approach. Manually selecting it can usually lead to incorrect behavior. Therefore, it is necessary to introduce an online voting system. Often used to extend technology from physical voting systems to digital voting systems. This particular analysis envisions implementing an online voting system with options such as: B. A system implemented by each party and supported by options that tend to participate in voting. The main reason to switch from a traditional voting system to an online voting system is to save time and allow you to vote online from anywhere. Completed by using PHP as the backend language, half of the frontend using web technologies (HTML, CSS, JS, Bootstrap) and Microsoft SQL Server as information storage.