Reviewerly/exHarmony huggingface dataset:
ANALOGIC NON-PROPERLY PREPARED TEACHERS VERSUS NOISY CONTAMINATED OPTICAL CHARACTER RECOGNITION REGARDING STUDENTS’ ACADEMIC PERFORMANCE, ADOPTING ARTIFICIAL NEURAL NETWORKS’ MODELING

This Research papers tackles an important and interestingly complex, and a challenging educational problematic phenomenon. Specifically, it addresses two analogously interrelated issues namely: the non-properly prepared teachers that characterized by undesirable impact on students' academic achievement inside classrooms. Additionally, Herein, this issue shown to be analogous to recognition process of noisy contaminated Optical Character Recognition (OCR). Briefly, this comparative study objectively illustrates analogous relationship between contaminated noisy information provided by non-properly prepared teaching process versus noisy contaminated (OCR) process By more details, various noisy power level values which changed in learning environment, results in considerable correspondence with different learning rate values. The unfavorable amount of teacher’s improperness is mapped similar to well-known communication technology term namely signal to noise (S/N) ratio. Which quantitatively measures the clarity degree related to received desired learning / teaching signal across the educational communication channel. In other words, it illustrates simulated outcome presented as percentage of lessons’ focusing degree versus # Neurons for different learning rate values. More properly. Performance of non-properly prepared teacher results in noisy information submitted to children’s brain in classrooms. Accordingly. it observed annoyance of learning environment and negatively affects the quality of children’s learning performance. Herein, this research work illustrates specifically the analogy between learning under noisy data environment in Artificial Neural Networks (ANNS) models versus the effect of physical environment on quality of education in classrooms. The observed non-properly prepared teachers' phenomenon in classrooms observed to have negatively undesired effect on the evaluated educational process performance. Analogously, the observed effect of additively contaminating noise power on any of map size made with the resolution of (3x3) pixels. These pixels were associated to diverse three English clear characters (T&amp;L, or H) which originally written over (3x3) binary (black &amp; white) digitized retina. Herein; obtained interesting findings shaded light over more complex challenging research directions towards in future more elaborated investigational study for such interdisciplinary observed educational phenomena.
End-to-End Residual Network for Light Field Reconstruction on Raw Images and View Image Stacks

Light field (LF) technology has become a focus of great interest (due to its use in many applications), especially since the introduction of the consumer LF camera, which facilitated the acquisition of dense LF images. Obtaining densely sampled LF images is costly due to the trade-off between spatial and angular resolutions. Accordingly, in this research, we suggest a learning-based solution to this challenging problem, reconstructing dense, high-quality LF images. Instead of training our model with several images of the same scene, we used raw LF images (lenslet images). The raw LF format enables the encoding of several images of the same scene into one image. Consequently, it helps the network to understand and simulate the relationship between different images, resulting in higher quality images. We divided our model into two successive modules: LFR and LF augmentation (LFA). Each module is represented using a convolutional neural network-based residual network (CNN). We trained our network to lessen the absolute error between the novel and reference views. Experimental findings on real-world datasets show that our suggested method has excellent performance and superiority over state-of-the-art approaches.
Formation of Communicative Competence of Foreign Students in Conditions of Distance Learning

The article addresses to the issue of communicative competence formation of foreign students in the conditions of distance learning. The number of foreign students has been determined, which currently are studying in Ukrainian universities. The introduction of distance learning is emphasized to be one of the ways to modernize higher education. The concepts of “distance learning” and “competence” has been clarified. The problems which arose at the beginning of the widespread implementation of distance learning are noted. One of the adaptation advantages of distance learning is holding frequent large-scale online meetings. Articles of domestic and foreign scientists on distance education and communicative competence formation to foreign students are covered. It was found that the main factors influencing the communicative competence formation to students are professional orientation, humanistic style of communication and focus on the cultural aspect. The requirements for teacher’s of higher educational institutions have been defined in order to form communicative competence in foreign students. The relevance of distance learning implementation in the educational process has been emphasizes. The scientist, who first started the research on distance learning, found out the differences between online and traditional learning. Emphasis is placed on synchronous and asynchronous modes of distance learning. The essence of complex learning has been also revealed. The advantages and disadvantages of distance learning are investigated. The process of organizing educational activities online is presented – which is a foreign language learning. Educational platforms for the distance learning organization are presented. The advantages of working on the platform “Moodle” and “Coursera” are revealed. Conclusions are made regarding the raised issue.
A Systems Approach to the Development of Educational Standards for Fostering Personal Values

Abstract This article proposes to consider the education system as a factor in the formation of people’s personal values. We examine students’ perceptions of the degree to which existing educational standards are oriented towards the formation and development of personal values, as well as formulate proposals for the implementation of a systems approach to the development of educational standards as a factor in improving the existing complex of personal values. The methodology applied included: desk analysis by comparison, deduction, and induction; a questionnaire study; variance, discriminant, factor and morphological analysis. Respondents were segmented according to the criteria “presence of groups of values” — “conjugation of groups of values with variables.” University students, i.e., the part of society that is considered the most active and progressive, were found to have a low level of moral and ethical values. After considering the place of education in the macro-environment of the country was substantiated, we propose a number of measures to address the problem of raising the level of values among all participants in the educational process, in order to reduce the gap between the pace of technical advancement and humanitarian development of society.
Kegiatan Membaca Sesuai Tempo dan Ritme Perkembangan Anak Usia Dini

This study used qualitative descriptive reseach method with a literature review approach or literature review The puspose of language development for young children is able to communicate with others. Communication can be done with reading activity. Through a literature review. This research can be a reference for parents and teacher in providing reading activities to early childhood during their development and development. The result of the literature review show that reading activities can be stimulated by paying attention to the tempo in the development of early childhood language.
Expression Identification and Emotional Classification of Students in Job Interviews Based on Image Processing

The performance of college students in job interviews can be significantly promoted, if they are guided properly to identify and regulate negative emotions. However, the existing automatic expression identification algorithms cannot recognize expressions ideally, due to the small sample set, and the lack of diverse storage forms. To solve the problem, this paper explores the expression identification and emotional classification of students in job interviews based on image processing. Firstly, the ideas of interview emotion identification were expounded based on computer technology and image processing technology, and the college students’ interview emotion regulation process was modeled. Then, the histogram of oriented gradients (HOG) was adopted to extract the local textures and edges from the expression images of students in job interviews, and the face expressions were identified for the analysis on interview emotions. Based on the graph neural network (GNN) and representation learning, a job interview expression identification algorithm was designed for college students, which effectively suppresses the uncertainty of these images in the real-world unconstrained environments. The proposed algorithm was proved effective through experiments.
Development of a Full Scale Moth Hydrofoil Control System Test Rig

The design, construction and evaluation of a test platform to test the International Moth class in real world conditions was undertaken with the aim to investigate the effects of altering the value of the proportional control coefficient on flight modes to provide data for further numerical simulations.Through consultation with industry, technical experts and reviews of literature, a design was produced that allowed foils to be tested over a range of velocities, foil configurations and control systems that would be free to move in pitch and heave, however constrained in roll, yaw, sway and surge.The rig with the degrees of freedom can be seen below in Figure 1.Improvement of an existing electronic ride control system (ERCS) allowed the test moth to be able to fly at a range of depth to chord ratios along with the capability to change the respective flight modes with varying amplitudes.Ultimately, it was concluded that in a realworld environment the differences in drag between the range of values tested resulted in no serious measurable performance gains despite significant motion variations.However, it was apparent that small relationships formed and that it is essential for more research to be conducted in order to validate the data.The rig developed provides an easily accessible method for testing control algorithms in a real-world environment without the need for complex sailing configurations.The rig also allows cheap ways of tuning a system that is ripe for full on-water implementation.
Prompting to Distill: Boosting Data-Free Knowledge Distillation via Reinforced Prompt

Data-free knowledge distillation (DFKD) conducts knowledge distillation via eliminating the dependence of original training data, and has recently achieved impressive results in accelerating pre-trained language models. At the heart of DFKD is to reconstruct a synthetic dataset by inverting the parameters of the uncompressed model. Prior DFKD approaches, however, have largely relied on hand-crafted priors of the target data distribution for the reconstruction, which can be inevitably biased and often incompetent to capture the intrinsic distributions. To address this problem, we propose a prompt-based method, termed as PromptDFD, that allows us to take advantage of learned language priors, which effectively harmonizes the synthetic sentences to be semantically and grammatically correct. Specifically, PromptDFD leverages a pre-trained generative model to provide language priors and introduces a reinforced topic prompter to control data synthesis, making the generated samples thematically relevant and semantically plausible, and thus friendly to downstream tasks. As shown in our experiments, the proposed method substantially improves the synthesis quality and achieves considerable improvements on distillation performance. In some cases, PromptDFD even gives rise to results on par with those from the data-driven knowledge distillation with access to the original training data.
AI-Based Media Coding Standards

Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) is the first standards organization to develop data coding standards that have artificial intelligence (AI) as their core technology. MPAI believes that universally accessible standards for AI-based data coding can have the same positive effects on AI as standards had on digital media. Elementary components of MPAI standards&#x2013;AI modules (AIMs)&#x2013;expose standard interfaces for operation in a standard AI framework (AIF). As their performance may depend on the technologies used, MPAI expects that competing developers providing AIMs will promote horizontal markets of AI solutions that build on and further promote AI innovation. Finally, the MPAI framework licences (FWLs) provide guidelines to intellectual property right (IPR) holders facilitating the availability of compatible licenses to standard users.
Pre-trained CNNs as Feature-Extraction Modules for Image Captioning

In this work, we present a thorough experimental study about feature extraction using Convolutional NeuralNetworks (CNNs) for the task of image captioning in the context of deep learning. We perform a set of 72experiments on 12 image classification CNNs pre-trained on the ImageNet [29] dataset. The features areextracted from the last layer after removing the fully connected layer and fed into the captioning model. We usea unified captioning model with a fixed vocabulary size across all the experiments to study the effect of changingthe CNN feature extractor on image captioning quality. The scores are calculated using the standard metrics inimage captioning. We find a strong relationship between the model structure and the image captioning datasetand prove that VGG models give the least quality for image captioning feature extraction among the testedCNNs. In the end, we recommend a set of pre-trained CNNs for each of the image captioning evaluation metricswe want to optimise, and show the connection between our results and previous works. To our knowledge, thiswork is the most comprehensive comparison between feature extractors for image captioning.
The Cultivation Quality Evaluation of International Talents Based on Deep Learning

The average quality of Chinese talents, especially international talents, is falling behind the international level. For the introduction of high-level international talents, the evaluation of the cultivation quality of high-level international talents is an important factor, and this work is a basis of the said matter. However, most of the existing works related to this topic are just about the definitions of high-level international talents, their features, and importance, or discussions of other aspects. This paper aims to study the evaluation of the cultivation quality of international talents based on deep learning. At first, the paper gave a framework for the ideas of international talent cultivation, established an Evaluation Index System (EIS) containing 4 basic elements for such evaluation, and emphasized the importance of the training and testing of professional business English. At first, the paper gave a framework for the ideas of international talent cultivation, and established an Evaluation Index System (EIS) containing 4 basic elements for such evaluation. Then, this paper proposed the evaluation flow, and adopted an Empirical Mode Decomposition (EMD) algorithm to the analysis of the quality change during the cultivation process of international talents. After that, for the change trend of the cultivation quality of international talents at different time scales, this paper applied a Long Short Term Memory (LSTM) network for training and obtained the corresponding predicted quality value. At last, experimental results verified the effectiveness of the proposed model in predicting the cultivation quality evaluation of international talents.
Research on the Development of Localized Music Curriculum System Based on the Theory of Multiple Intelligences

The integration and development of music curriculum signals have attracted the attention of researchers in real teaching scenarios. Based on the theory of multiple intelligences algorithm, this paper constructs a music curriculum integration and development model. This paper establishes a nonnegative matrix decomposition scheme, adds a constraint term that can reflect the smoothness of the time-varying gain matrix to the divergence-based objective function, and iteratively solves the problem of curriculum integration and development through the minimum optimization algorithm obtained by constructing the auxiliary function. In the process of simulation experiment, the optimal solution of each factor matrix was designed, the source music signal was reconstructed, and the sufficiently sparse source music signal was separated. The experimental results show that, compared with the parameter estimation without preprocessing, the parameter estimation with preprocessing is more accurate, the accuracy of the improved algorithm reaches 97.1%, the signal compression ratio reaches 0.656, and the enhanced signal can obtain at most 6 dB. The signal-to-noise ratio is improved, and the convergence speed is fast and asymptotically reaches the lower bound. It is suitable for parameter estimation of low-order and high-order autoregressive processes and effectively promotes the smooth development of music course signals.
A Model for Teaching Geometry to Schoolchildren Using Smartphone App

Smartphones are popular among children of all age groups and have the potential to make learning process interactive as well as engaging. Educational smartphone apps for children should counterbalance the limited abilities of children. An iterative model for developing educational apps for schoolchildren has been proposed and substantiated by designing an app to teach about triangles in the present study. We conducted a pretest-posttest control group study. The app developed by us along with traditional methods was used to make 26 children in the experimental group aware of the concept and properties of triangles while the control group with equal number of children was taught about triangles using only chalk and blackboard. The performance of children in answering questions related to triangles was then assessed in the two groups. The study did not notice any significant difference in the performance of the children in the two groups. However, the feedback of the children in the experimental group was found to be positive in terms of the overall environment created by learning through the app in the classroom. Meticulously planned use of geometry apps may augment classroom teaching and stimulate children to be actively involved. Parents, practitioners and teachers need to be aware of the requirements of children and facilitate to provide conducive, age appropriate smartphone apps for learning geometry to them.
The impact of Artificial Intelligence (AI) and Engines on Boardgames (Chess and Go)

The intended purpose of this paper is to inform readers of the advancement of Artificial Intelligence (AI) in recent decades and show how Chess/Go engines are representative of the improvements made in the field of AI.Also, the positive and negative implications of advancements in chess engine have been discussed.Moreover, the structural and efficiency improvements of Chess/Go engines have been detailed.Chess, Go, and other perfect knowledge 2 player games are just a measuring stick of the improvement of artificial intelligence.The objective of AI development is to generalize an algorithm which is applicable in the field of sciences.This can then help tackle environmental issues such as pollution, issues in the field of medicine such as drug production and also come up with an effective treatment for cancer.Due to AI's accelerated learning ability, it can come up with creative solutions to problems communities face today.
Strategic alliance between business processes and enterprise resource planning towards performances: an empirical study

In the era of Industry 4.0, business firms are facing more and more competitive environment than ever before. Success here is no longer a matter of analysing only the individual firm, but chain of firms. Instead of new start, firms are now exploring new ways to integrate business processes under information-based systems so as to make best return from the market. But integration in any business is critical as well as complex. Here, an attempt has been made to propose a model featuring business process integration and hypothesise constructs that significantly impact firm performance under information systems application. Primary data are collected through questionnaire and the relationship proposed is tested by developing structural equation modelling. Considering scenario of Indian business where integrated business processes are still in nascent stage; survey results highlight empirical evidence of beneficial impact of strategic alliance in between business process and enterprise resource planning towards performances.
A Comparative Study of Various Machine Learning Algorithms in Fog Computing

Internet of Things is the reality of the future.It encompasses a vast prototype.The applications are limitless and the research potential is huge.Several unanswered questions still stand in the way.One of these challenging questions is the Network layer implementation of IoT.Cloud offers one perspective solution.Cloud services face the down side of connectivity and latency.Fog computing, as a complementing technology for Cloud answers the issues of latency and connectivity in the Cloud.Being novel idea in itself, Fog computing offers promising future growth.A major concept in Fog networks is its ability to learn and adopt to changing environment.This ability of the Fog devices to self-learn and improve itself is the core of machine-learning.Hence, machine-learning algorithms occupy central place in visualizing robust and effective Fog networks.This paper presents a comparative study of various machine-learning algorithms and their applications in the Fog network.The paper also projects the undiscovered research paradigms and future directions
Heterogeneous System MMPP/GI(2)/∞ with Random Customers Capacities

A heterogeneous queuing system with an infinite number of servers is considered in this paper.Customers arrive in the system according to a Markov Modulated Poisson Process.The type of incoming customer is defined as i-type with probability pi (i = 1, 2).Each customer carries a random quantity of work (capacity of the customer).In this study service time does not depend on the customers capacities.It is shown that the joint probability distribution of the customers number and total capacities in the system is multidimensional Gaussian distribution under the asymptotic condition of an infinitely growing service time.Simulation results allow us to determine an applicability area of the asymptotic result.