Reviewerly/exHarmony huggingface dataset:
Developing Digital Skills In Preschoolers. Presentation Of The Project Erasmus+ “Blend”

This article presents recent research on digital skills in preschoolers, European educational policies presented in the Erasmus + program, the contents of the within the integrated activities of preschool education, products and results obtained in the implementation of the strategic cooperation project, action 2 (Ka2): "Building learning education new digital -BLEND" implemented in 36 months (2017-2020).We will address this topic through research on digital skills in preschoolers, through the specifics of Romanian preschool education, and through the European education and training program of the European Commission (2017), Erasmus +.The development of digital skills for the new generations is a stringent goal in the development plans of European educational institutions because it is not enough to be a digital native, it is important to develop your digital skills according to the five areas that correspond to the description: skills related to information and data, communication and collaboration, creating digital content, safety, problem-solving.The cooperation project "Building learning education new digital -BLEND" was implemented in the kindergarten of Vocational School "Tiberiu Morariu" Salva, Bistrita-Nasaud, Romania for 36 months (2017-2020), in collaboration with five educational institutions from France, the UK, Italy, and Spain.
Kernelized Deep Networks for Speech Signal Segmentation Using Clustering and Artificial Intelligence in Neural Networks

Speech signal segmentation is a key component of speech processing tasks, such as speaker recognition, speech recognition, and emotion recognition. Traditional approaches often have low accuracy and high computational complexity. To address this issue, we propose a novel approach using kernelized deep networks. The method utilizes Mel-Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) for feature extraction, with Spectral subtraction, wavelet denoising and Wiener filtering as preprocessing techniques. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are used for dimensionality reduction. Spectral clustering using the graph Laplacian and normalized Laplacian matrix is then employed to cluster the speech signals into different segments. The Lanczos algorithm, Implicitly Restarted Arnoldi Method and Krylov subspace is used to train the networks and classify the signals. Results on TIMIT and BURN corpus demonstrate that the proposed approach outperforms traditional techniques in terms of segmentation accuracy and reduces computational complexity. This research highlights the potential of kernelized deep networks for speech segmentation and contributes to the field of speech processing.
The Disaggregated Impact of Financial Development on the Effectiveness of Monetary Policy in Nigeria

The fast development in the financial system has attracted researchers’ interest in studying its implication on the economy, though empirical evidence is highly limited on disaggregated levels. This study is undertaken to examine the impact of disaggregated financial development on the effectiveness of monetary policy in Nigeria. The study used Autoregressive Distributed Lag Model to capture the data-generating process as well as both short-run and long-run relationships. The scope of analysis ranged from 2000 quarter 1 to 2021 quarter 4 to circumvent the effect of regime changes, as the chosen time horizon represents the period of the uninterrupted civilian regime in Nigeria. The data are sourced from the Central Bank of Nigeria’s and the International Monetary Fund’s statistical databases. Moreso, quarterly frequency data are used to reflect the short-run nature of the monetary policy. The finding reveals financial market development enhances the effectiveness of monetary policy in terms of achieving both its primary and secondary objectives while financial institutions development does not. Given the findings, it is recommended that the Government together with the Central Bank of Nigeria should design policies that would enhance the efficiency of the financial market, particularly markets infrastructure and technology-based products to reduce information asymmetry and transactional costs to ease the way of doing business.
Research on state evaluation method of secondary system of intelligent substation

The secondary system of the substation is an important part of the power system, which is of great significance to ensure the safe and stable operation of the whole station and even the power grid. Considering that the quality problems of secondary equipment caused by the lack of maintenance methods of substation equipment have brought potential safety hazards to the stable operation of the power grid, it is imperative to carry out the condition assessment for the secondary system of the substation to realize the condition based maintenance.As the basis of condition-based maintenance, the research on condition assessment of secondary system has theoretical and practical significance.
Role of ICT for entrepreneurship development in Nigeria

A large proportion of young graduates in most African countries exist without clear job prospects. Self-employment may be one potential answer to this issue. On the other side, such an approach necessitates the production of entrepreneurship skills. ICT should be seen as an ideal tool to accelerate the development process of Nigeria’s entrepreneurship programs. This article attempted to address the roles of ICT in an Entrepreneurship Development in Nigeria's entrepreneurial growth. It has also been discovered that restricting factors such as regular power loss, shortage of skilled technicians/repairers, availability of insurance, emergencies, and uncertainty are barriers that any potential entrepreneur will overcome. Among other issues, the article suggests that the government subsidizes these ICT resources imports to minimize the expense of such tools to enable them to remain in business and the government should be health insurance for these ICT businesspersons in the event of injuries, fraud and natural catastrophe.
Pengaruh Flashcard terhadap Hasil Belajar Materi Keragaman Tanaman Khas, Suku Bangsa, Tarian Indonesia di Sekolah Dasar

The purpose of this study is to investigate the impact of flashcard learning media on student learning outcomes in grade III SDN 02 Sungai Raya on the topic of a variety of characteristic flora, ethnic groups, and regional dances of Indonesia. Experiments, quasi-experimental design study formats, and experimental designs with non-equivalent control groups were utilized. This study's population consists entirely of grade III SDN 02 Sungai Raya students. This study's sample consisted of class III A (28 students) as an experimental class and class III B (28 students) as a control class. The data gathered is in the form of student learning outcomes in both classrooms, provided directly by students. The approach used for data gathering is a measuring technique with data collecting instruments in the form of multiple-choice test questions. In addition, the t-test will be used to do the analysis. Based on the analytical findings, (tcount3,786) &gt; (ttable1,674), implying that H0 is rejected. So there is a difference in the learning results of pupils taught using flashcard learning media and those taught without. This difference was subsequently examined using effect size, obtaining an ES of 0.90 with a high criterion. In grade III SDN 02 Sungai Raya, flashcard learning effects student learning results on the material of diversity of characteristic flora, ethnic groups, and regional dances of Indonesia.
CV Building Chatbot with Career Assistance

A professionally created resume is the foremost requirement to land your dream job. CV Building Chatbot with Career Assistance is a text-based chatbot designed and developed to help students in designing an effective and attractive CV and to also provide career assistance with course recommendation based on the skills they possess. The Chatbot is designed using Natural Language Processing (NLP) to ensure utmost efficiency of the chatbot. A usable system using the proposed algorithm will be implemented. A comparative analysis of all the algorithms was done to select an algorithm with the best efficiency. Ideas of chatbots with other domain are also considered in order to ensure that CV building chatbot with career assistance will have all the functionalities and provide the best service and performance. A unique feature of chatbot is that students can get access to different templates provided and can get career guidance based on the information they have provided.
Synthetic Data and Public Policy: supporting real-world policymakers with algorithmically generated data

Good policy is best developed by drawing on a wide array of high-quality evidence. The rapid growth of data science and the emergence of big datasets has materially advanced the supply and use of quantitative evidence. However, some key constraints remain, including that available datasets are still not big enough for some analytical purposes. There are also privacy and data security risks. Synthetic data is an emerging area of data science that can potentially support policy decision making through enabling research to work faster and with fewer errors while also ensuring privacy and security.
COMMON FIXED POINT THEOREM FOR WEAKLY COMPATIBLE MAPPINGS IN DIGITAL METRIC SPACE

In this paper, a common fixed point theorem for a pair of weakly compatible mappings in the setting of digital metric space is proved with a supportive example. Our result extends and improves the result of Ozgur Ege et al. [2].
Quantum Circuit Design for Integer Multiplication Based on Schönhage–Strassen Algorithm

Quantum arithmetic circuits have attracted extensive attention recently since it plays fundamental roles in many applications of quantum computing. Specifically, quantum circuits for integer multiplication are of great significance to various quantum algorithms, including Shor’s integer factorization and discrete logarithm algorithm. In this article, we design a family of quantum circuits for integer multiplication based on the famous classical integer multiplication algorithm, Schönhage–Strassen algorithm. We have made slight modifications to the algorithm to simplify its quantum circuit implementation. As a result, the quantum circuit we designed has gate depth <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\log ^{2} n)$ </tex-math></inline-formula> . To the best of our knowledge, this is the first poly-logarithmic depth quantum circuit for integer multiplication which keeps the circuit size and the number of ancillary qubits subquadratic. Our design has size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n\log n\log \log n)$ </tex-math></inline-formula> counted by elementary quantum gates which is the same as the time complexity of the Schönhage–Strassen algorithm, and it consumes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n\log n\log \log n)$ </tex-math></inline-formula> clean ancillary qubits. In addition, we also utilize a weaker version of Schönhage–Strassen algorithm to give a family of circuits which has depth at the same order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\log ^{2} n)$ </tex-math></inline-formula> but with significantly smaller constants, while still keeping the size and number of ancillary qubits subquadratic.
A Blockchain-Based Secure Searching Strategy for Metadata in Mobile Edge Computing

With intelligent devices’ prevalence, mobile edge computing (MEC) technology can effectively process the massive data in the Internet of Things (IoT). The metadata produced in MEC plays a role in colossal data management. However, it also includes lots of user privacy information. Traditional privacy protection solutions sacrifice the metadata’s availability or are incompatible with the distributed environment. To overcome this dilemma, we propose a blockchain-based secure searching strategy for metadata (BSSMeta) in MEC. By leveraging a lightweight proxy re-encryption scheme and a decentralized key generation method on the blockchain, BSSMeta keeps the security of the metadata searching tasks. Meanwhile, a main/secondary smart contract method and a buffer uploading strategy are proposed for the efficiency of the searching and uploading processes. As a byproduct, BSSMeta also supports searching metadata in a multiuser environment and gives users access control to metadata. Finally, we provide a security analysis of BSSMeta and implement a prototype on the Hyperledger Fabric platform. The results of experiments on various workloads show that BSSMeta is feasible and flexible in security and efficiency.
Deep Adaptive Quadruplet Hashing With Probability Sampling for Large-Scale Image Retrieval

With the preferable efficiency in storage and computation, hashing has shown potential application in large-scale multimedia retrieval. Compared with traditional hashing algorithms using hand-crafted characteristics, deep hashing inherits the representational capacity of deep neural networks to jointly learn semantic features and hash functions, encoding raw data into compact binary codes with significant discrimination. Generally, most of the current multi-wise hashing methods view the similarity margins between image pairs as constant values in training process. When the distance between sample pairs exceeds the fixed margin, the hashing network would not learn anything. Besides, available hashing methods commonly introduce the random sampling strategy to build training batches and ignore the sample distribution, which is harmful to parameter optimization. In this paper, we propose a novel Deep Adaptive Quadruplet Hashing with probability sampling (DAQH) for discriminative binary code learning. Specifically, with exploring the distribution relationship of raw samples, a non-uniform probability sampling strategy is proposed to build more informative and representative training batches, while maintaining the diversity of training samples. By introducing the prior similarity of sample pairs to calculate corresponding margins, an adaptive margin quadruplet loss is designed to dynamically preserve the underlying semantic relationships with its neighbors. To tune the attributes of binary codes, by combining quadruple regularization and orthogonality optimization, binary code constraint is developed to make the learned embedding with significant discrimination. Extensive experimental results on various benchmark datasets demonstrate our proposed DAQH framework achieves state-of-the-art visual similarity search performance.
Globalization and income inequality: A panel data analysis of 37 developing countries in Africa

The lack of economic facts on the effects of globalization and income inequality in Africa has made it difficult for an effective economic policy response to the problem of inequalities in the continent. Against this backdrop, this paper examined the nexus between globalization and income inequality by estimating Ordinary Least Square (OLS) and the Random model for Panel data of 37 developing countries in Africa over the period of 1970 to 2014. The relationship between income level and income inequality justifies the validity of Kuznets’ U hypothesis. The results showed a negative relationship between the total globalization index and income inequality after taxes and transfer for the (OLS). Results for the globalization sub-indicators such as economic globalization showed a robust positive significant relationship for both measures of income inequality measures (Gini net and Gini market), while political globalization showed a negative relationship with both income inequality measures. It is recommended that government in African countries should strengthen their institutions to promote policy frameworks that will increase income levels and the human capital capacity, while steps should be taken to control the ever-increasing population and age dependency ratio.
Learning as the best medicine: Proposal for SMART schooling for hospitalized children

<h2>Abstract</h2> This research paper explores learning alternatives for chronically ill students, considering the challenges they encounter in their learning processes due to prolonged or intermittent absences from school. International practices and recent research findings on this topic will be reviewed in the contexts of hospital schools, SMART hospitals, and SMART learning technologies to highlight their primary characteristics. The current situation of hospitalized students, particularly those in Dubai, will be discussed to propose an alternative education program to aid them: the Edu-Med Care Model. This model is built on SMART education and healthcare approaches to support the goal of helping students overcome barriers that prevent them from accessing conventional learning spaces. The strengths and limitations of the Edu-Med Care Model will be evaluated.
ConvGRU-CNN: Spatiotemporal Deep Learning for Real-World Anomaly Detection in Video Surveillance System

Video surveillance for real-world anomaly detection and prevention using deep learning is an important and difficult research area.It is imperative to detect and prevent anomalies to develop a nonviolent society.Realworld video surveillance cameras automate the detection of anomaly activities and enable the law enforcement systems for taking steps toward public safety.However, a human-monitored surveillance system is vulnerable to oversight anomaly activity.In this paper, an automated deep learning model is proposed in order to detect and prevent anomaly activities.The real-world video surveillance system is designed by implementing the ResNet-50, a Convolutional Neural Network (CNN) model, to extract the high-level features from input streams whereas temporal features are extracted by the Convolutional GRU (ConvGRU) from the ResNet-50 extracted features in the time-series dataset.The proposed deep learning video surveillance model (named ConvGRU-CNN) can efficiently detect anomaly activities.The UCF-Crime dataset is used to evaluate the proposed deep learning model.We classified normal and abnormal activities, thereby showing the ability of ConvGRU-CNN to find a correct category for each abnormal activity.With the UCF-Crime dataset for the video surveillance-based anomaly detection, ConvGRU-CNN achieved 82.22% accuracy.In addition, the proposed model outperformed the related deep learning models.
Can you notice my attention? A novel information vision enhancement method in MR remote collaborative assembly

In mixed reality (MR) remote collaborative assembly, remote experts can guide local users to complete the assembly of physical tasks by sharing user cues (eye gazes, gestures, etc.) and spatial visual cues (such as AR annotations, virtual replicas). At present, remote experts need to carry out complex operations to transfer information to local users, but the fusion of virtual and real information makes the display of information in the MR collaborative interaction interface appear messy and redundant, and local users sometimes find it difficult to pay attention to the focus of information transferred by experts. Our research aims to simplify the operation of remote experts in MR remote collaborative assembly and to enhance the expression of visual cues that reflect experts' attention, so as to promote the expression and communication of collaborative intention that user has and improve assembly efficiency. We developed a system (EaVAS) through a method that is based on the assembly semantic association model and the expert operation visual enhancement mechanism that integrates gesture, eye gaze, and spatial visual cues. EaVAS can give experts great freedom of operation in MR remote collaborative assembly, so that experts can strengthen the visual expression of the information they want to convey to local users. EaVAS was tested for the first time in an engine physical assembly task. The experimental results show that the EaVAS has better time performance, cognitive performance, and user experience than that of the traditional MR remote collaborative assembly method (3DGAM). Our research results have certain guiding significance for the research of user cognition in MR remote collaborative assembly, which expands the application of MR technology in collaborative assembly tasks.
Quantum Efficiency of Single Dibenzoterrylene Molecules in <i>p</i>-Dichlorobenzene at Cryogenic Temperatures

We measure the quantum efficiency (QE) of individual dibenzoterrylene (DBT) molecules embedded in p-dichlorobenzene at cryogenic temperatures. To achieve this, we combine two distinct methods based on the maximal photon emission and on the power required to saturate the zero-phonon line to compensate for uncertainties in some key system parameters. We find that the outcomes of the two approaches are in good agreement for reasonable values of the parameters involved, reporting a large fraction of molecules with QE values above 50%, with some exceeding 70%. Furthermore, we observe no correlation between the observed lower bound on the QE and the lifetime of the molecule, suggesting that most of the molecules have a QE exceeding the established lower bound. This confirms the suitability of DBT for quantum optics experiments. In light of previous reports of low QE values at ambient conditions, our results hint at the possibility of a strong temperature dependence of the QE.
The impact of knowledge sharing quality on e-supply chain management: The mediating role of e-trust

This study has tried to investigate the impact of knowledge sharing quality on E-supply chain management, with the mediating effect of E-trust. To attain the research purposes, a quantitative approach of research was applied. The needed data was collected from a sample consisting of 281 managers of Fast-Moving Consumer Goods (FMCG) companies in Jordan. A questionnaire was utilized to collect the data. Appropriate statistical analyses were conducted such as scale reliability and validity by using IBM SPSS 21 software. Structural model and hypothesis testing using AMOS software were then utilized. The results have shown that knowledge sharing quality with its main dimensions (usefulness, timeliness, accuracy, and relevance) has positive significant impacts on E-supply chain management. Besides that, E-trust has revealed a significant mediating impact between the studied independent and dependent factors. Other complementary future studies related to the practices of E-supply chain management practices, quality of knowledge sharing, and E-trust in other Jordanian organizations were also proposed.