Reviewerly/exHarmony huggingface dataset:
THE PHILOSOPHY OF EFFICACY OF FLIPPED LEARNING AS A NEW PEDAGOGICAL PARADIGM

The article traces the escalation of the search for strategies of ac- tive cognition in the era of “Phygital” education. It relates the main provisions of the pedagogical technology of inverted learning, the didactic presumption of which is the transfer of the initial stages of mastering a new material to prelimi- nary independent work while using the class time for practical application of the acquired knowledge and creative activity at higher levels of the cognitive process. The philosophical principles of inverted learning are most clearly manifest- ed in the teacher-student synergetic tandem in the mainstream of humanistic pedagogy, consonant with Kant’s transcendentalism, which flips the idea of a passive subject and accentuates the active construct of cognition. As research methods the author chooses the analysis of various practices of working with English-language media content; examples are used to trace the efficacy of applying the flipped learning modus in the course of professionally oriented English for students of economics. The approbation work with video lectures results in analyzing causes of success and failure in employing elements of the flipped teaching technology, together with pros and cons that it can bring to teachers and students. The author concludes that the addressed educational paradigm can entail a change in priorities – from studying the material to its active assimilation for continued use.
Steps towards a Dislocation Ontology for Crystalline Materials

The field of Materials Science is concerned with, e.g., properties and performance of materials. An important class of materials are crystalline materials that usually contain “dislocations” – a line-like defect type. Dislocation decisively determine many important materials properties. Over the past decades, significant effort was put into understanding dislocation behavior across different length scales both with experimental characterization techniques as well as with simulations. However, for describing such dislocation structures there is still alack of a common standard to represent and to connect dislocation domain knowledge across different but related communities. An ontology offers a common foundation to enable knowledge representation and data interoperability, which are important components to establish a “digital twin”. This paper outlines the first steps towards the design of an ontology in the dislocation domain and shows a connection with the already existing ontologies in the materials science and engineering domain.
Hybrid Model Using Scrum Methodology for Software Development System

Because since the mid-1990s, traditional project management techniques have been progressively being phased out and replaced with lightning experimental tools. This issue is mostly caused by the inadequacies of traditional techniques, which include a sluggish response time to positively influence business needs as well as a proclivity to go over expenditure and be late to a project's deadline. Both traditional and agile techniques are discussed in detail in this article, along with their features, strengths, and shortcomings. In addition, the four main stages and nine specialties of the Unified Method, as well as the common components of the Scrum process, are explained in this article. The article concludes with the suggestion of a new hybrid agile methodology that blends the Analytical Hierarchy Processes with the Scope statement to make use of the advantages of both methodologies while simultaneously suppressing their shortcomings. The hybrid approach may be used in the technology industry, and in particular in economic sectors that facilitate the exchange projects, to great effect.
From Letterpress to Screen

When we think about modularity, we often consider simple structures defined as single elements that exist to become part of a more complex structure. Modules join each other in a logical manner, following a set of given rules.
SYSTEMATIC ANALYSIS OF EDUCATION

Today, almost all regions of the world are governed by knowledge and intelligence. Today humanity is very advanced in science and education. In our information age, the prestige of intellectual professions is growing. Bringing a person to this potential, realizing his intellectual potential is the most important task of the education system. However, countries around the world see that economies with underdeveloped education are also underdeveloped. Today it is impossible to educate modern personnel without the introduction of a modern education system. The synergetic methodology is an important area that contributes to integrated education, which can become the basis of modern education based on a systems approach. The problem of a systems approach to modern education, promoted in the article, focuses on interdisciplinary integration, critical and logical thinking as part of synergistic education. This is an approach that corresponds to modern education, proving that it is constantly evolving, tirelessly working on itself and that stagnation in the learning process is unacceptable.
Deep Learning: Artificial Neural Network Retail Stores VIP Customers Prediction Study

Background/Objective : As recent studies have begun to shed a light on an artificial neural network deep learning based on machine learning techniques, attempts to apply this technology have been increasing in various industries such as production, manufacturing, quality control, marketing, social networks and so on. Methods/Statistical : In this study, this study analyzes sales data generated from retail stores in Gyeonggi-do, Korea (4 stores), and customer data (approximately 13,000 people) using the stores. In this study, the neural network algorithm was used to show better performance than the existing machine learning techniques. It also shows how to utilize retailer data in multiple data mining techniques using unsupervised learning. Statistical analysis/Findings : As a result of the analysis, cluster analysis was divided into four clusters, and the model showed the best performance of the neural network model. Of the four clusters, a cluster was created that represented the characteristics of VIP customers and that cluster was modeled as a target. The group's customers were found to be mainly using the store's mobile alarm service and delivery service.Improvements/Applications : The analysis makes it easy to identify your differentiated marketing and outlook.
On the Problem of Covering a Convex Set by Equal balls in a Three-dimensional Space with a non-Euclidean Metric

The paper considers the problem of covering a closed convex set with equal balls. To solve it, we proposed a new numerical algorithm based on the optical-geometric approach. The feature of the algorithm is to replace the distance between objects with the time of movement between them. This allows us to apply an approach based on the physical principles of light wave propagation. The proposed mathematical apparatus and software are useful for modeling since they allow us to consider some additional assumptions and restrictions. In particular, for the sensor location problem, we can describe the situation when the sensor operating area does not have the shape of a ball due to some reason. This can help improve the accuracy of the modeling and avoid the occurrence of blind spots. For the logistics facilities, the proposed approach allows you to assess their transport accessibility and attractiveness of the location more accurately. The results of the computational experiments are presented and discussed.
GM FASST: General Method for Labeling Augmented Sub-sampled Images from a Small Data Set for Transfer Learning

In this work, the use of transfer learning was explored to achieve the overall goal of classifying a small number (approx. 215 images) of Nonlinear Multiphoton Multimodal Microscopy Images of unstained oral cancer biopsies into three categories—healthy, inflammatory, and cancerous. This is achieved by first training a neural network model to detect basic histological components of human tissues, such as the stroma and mucosa, from a much larger (5000 images) Kaggle data set containing images of stained human colorectal cancer biopsies. Experiments were conducted to optimize the model’s architecture and hyperparameters, i.e., hidden layers, optimizers, and API callback functions used prior to retraining the model on a new and much smaller data set consisting of 215 Nonlinear Multiphoton Multimodal Microscopy Images. In addition to having different class labels, these images were acquired using an entirely different imaging and detection set up, and thus have different features than the Kaggle data set. In order to expand the limited size of the Nonlinear Multiphoton Multimodal Microscopy Images data set; tiling methods were used to sub-sample and augment the images though standard transforms such as rotation, scaling and mirroring. This research shows transfer learning and data set re-sampling can improve classification accuracy by 10% over training on the smaller data set alone.
College Libraries: Self-Quarantine Activities: Podcasts

Here are a few activities and resources to student, faculty and staff to cope while self-quarantining
The Impact of Social Media Applications on Predicting Stocks Prices and Exchange Volume: The Case of Jordan

Social media applications now a days are a primary source of many studies, also is primary information source for many users to make their decisions depending on the easily available information. The present study aims to examines the impact of social media applications on stock’s prices predicting and exchange volume for the three classifications in Amman Stock Exchange - ASE (first, second and third market) as one of developing countries exchange markets. By using a designed and developed questionnaire as a study primary tool and descriptive analytical approach, the questionnaire distributed on intermediation companies’ employees in Amman Stock Exchange (ASE), the results showed significant impact of social media applications on predicting stock’s prices and exchange volume of the first market, but intermediate impact of the second and third market. The study recommending the necessarily for management of intermediation companies to employ dedicated and qualified employees with technological predictive skills and train employees on technology not only for how to collect data, but also about analyzing and understanding to follow up the data workflow and its impact. In addition, the stock’s investors need to take into consideration the impact of social media users to affect and lead the decisions of buying or selling specific stocks.
COVID-19 is Accelerating the Digital Blending of Working and Learning

This articles outlines three key areas of promise in the increasingly digital post-pandemic economy and postsecondary online ecosystem.
Automated Essay Scoring Using Transformer Models.

Automated essay scoring (AES) is gaining increasing attention in the education sector as it significantly reduces the burden of manual scoring and allows ad hoc feedback for learners. Natural language processing based on machine learning has been shown to be particularly suitable for text classification and AES. While many machine-learning approaches for AES still rely on a bag-of-words (BOW) approach, we consider a transformer-based approach in this paper, compare its performance to a logistic regression model based on the BOW approach and discuss their differences. The analysis is based on 2,088 email responses to a problem-solving task, that were manually labeled in terms of politeness. Both transformer models considered in that analysis outperformed without any hyper-parameter tuning the regression-based model. We argue that for AES tasks such as politeness classification, the transformer-based approach has significant advantages, while a BOW approach suffers from not taking word order into account and reducing the words to their stem. Further, we show how such models can help increase the accuracy of human raters, and we provide a detailed instruction on how to implement transformer-based models for one's own purpose.
A Survey on Personality in Cyberspace Security

Cyberspace is a collection of all information systems, which refers to the information environment for human survival. Cyberspace security has expanded from physical and information domain security to human-centered social and cognitive domain security. The research on human security has become an inevitable trend of Cyberspace security. The characteristics of human are complex and changeable. The personality, as a stable psychological characteristic, is an appropriate breakthrough point for human security research. This paper investigates and untangles relevant personality research in Cyberspace security. The concepts of Cyberspace security and personality are introduced concisely. A research framework of personality in Cyberspace security is proposed, including theoretical research, technological research and technological application. The technological research mainly includes three parts: personality measurement, personality vulnerability and protection methods in Cyber security. Additionally, this paper discusses the current research status and problems of personality in Cyber security in detail. Finally, the future research directions and development trends of personality in Cyber security are explored.
EVALUASI PENGELOLAAN DANA BANTUAN OPERASIONAL SEKOLAH (BOS) SD NEGERI HARAPAN MAKMUR KABUPATEN MUSI RAWAS KECAMATAN MUARA LAKITAN

The School Operational Assistance Fund (BOS) is a government program which is basically for the provision of non-personnel operating costs for basic education units as the implementer of the compulsory education program. The analytical method used is descriptive qualitative analysis method. The results show that (1) Planning for BOS funds is in accordance with the 2019 BOS Technical Guidelines because it is seen from Government Regulation Number 3 of 2019 concerning education funding from BOS funds obtained by SD Negeri Harapan Makmur which comes from the State Budget (APBN) for 2019 is IDR 157,600,000 / year, - (2) Implementation of BOS funds, especially the allocation is in accordance with the 2019 Technical Guidelines for BOS, because in the allocation, filling in dapodikdasmen data (3) the distribution of BOS funds is in accordance with the 2019 technical guidelines, namely by going through two stages (4 ) The use of BOS funds is in accordance with the 2019 Technical Guidelines for BOS, in the form of 11 components that can be funded by BOS funds. (5) not all of the supervision has been carried out due to the lack of direct supervision from the school committee (6) Reporting the accountability of BOS funds is in accordance with the 2019 Technical Guidance, only there are no banners information, and for external reports it is in accordance with the reports made every quarter.
Applications of Signal Processing

Signal processing is an area of electrical engineering which emphasizes the analysis, synthesis, and modification of signals that can be in the form of voice, image, video, and many more. Artificial intelligence and machine learning approaches tend to increase the accuracy of the signal processing performed. In today’s computing world, machine learning is being utilized in many critical real-life applications, and it is also proving its worth in terms of reducing efforts, cost, and time in processing. In this chapter, the applications of signal processing where machine learning can be applied for increasing the performance and effectiveness of a system have been discussed. Specifically, a study on the applicability of machine learning approaches in audio signal processing, audio compression, digital image processing, video compression, digital communications, healthcare, seismology, speech recognition, computer vision, and economic forecasting has been made in this chapter. This study has revealed that applying machine learning approaches like deep learning, artificial neural network, deep neural network, or convolutional neural network certainly increases the performance of the system by reducing the effort, time, and cost.
Heterogeneous Information Network Representation Learning Framework Based on Graph Attention Network

Commonly used heterogeneous information networks include knowledge graphs and heterogeneous information networks with simple schemas. Their representation learning follows usually different methods. The similarities and differences between knowledge graphs and heterogeneous information networks with simple schemas are summarized, and a general heterogeneous information network representation learning framework is proposed. The proposed framework can be divided into three parts: the basic vector model, the graph attention network based propagation model, and the task model. The basic vector model is used to learn basic network vector; The propagation model learns the high-order neighbor features of the network by stacking attention layers. The replaceable task module is suitable for different application scenarios. Compared with the benchmark model, the proposed framework achieves relatively good results in the link prediction task of the knowledge graph and the node classification task of the heterogeneous information network.
Learning partial correlation graphs and graphical models by covariance queries

We study the problem of recovering the structure underlying large Gaussian graphical models or, more generally, partial correlation graphs. In high-dimensional problems it is often too costly to store the entire sample covariance matrix. We propose a new input model in which one can query single entries of the covariance matrix. We prove that it is possible to recover the support of the inverse covariance matrix with low query and computational complexity. Our algorithms work in a regime when this support is represented by tree-like graphs and, more generally, for graphs of small treewidth. Our results demonstrate that for large classes of graphs, the structure of the corresponding partial correlation graphs can be determined much faster than even computing the empirical covariance matrix.
Leg-wheel composite wheelchair based on parallelogram mechanism

Abstract with the aging society in China, the demand for wheelchairs is increasing for the elderly with mobility disabilities. At the same time, the number of people with physical disabilities caused by accidents and natural disasters is also rising. This paper designs a kind of electric wheelchair which can adapt to many kinds of terrain, which integrates climbing stairs, crossing obstacle function and safety and comfort to meet the living needs of specific people.
Psychological and Pedagogical Foundations for Improving Students’ Subjective Well-Being

The article deals with the psychological factors of promoting the subjective well-being of the students of Kazakh Innovative University of Humanities and Law in the conditions of the profession on the example of the activities of students of the pedagogical speciality. According to the results of the study, it is possible to include: meaning-life orientations, personality-regulatory properties. The most effective and, accordingly, subjectively successful in their professional activities were the subjects who were characterized by optimal combinations of life-meaning orientations and personal-regulatory properties. In particular, it was a combination of high levels of expression of a positive attitude towards oneself ( LK-I), others (LK-W), life activity (Process) in general, on the one hand, and programming and planning on the other. The zone of immediate development of such students is the disclosure of the reflexive and creative resource of the individual. The results of the correlation study indicated the presence of reliable relationships between life-meaning orientations, personal-regulatory properties and criteria for the effectiveness of professional activity of students of the registry of Kazakh Innovative University of Humanities and Law, which confirms the optimal combination of life-meaning orientations and personal-regulatory properties of the individual.
Gender and ethnic wage differentials inhibit growth: A shred of evidence

Racial, ethnic, and gender wage differentials, in particular those that are not explained by human capital differences between the respective groups, are fixtures of labor markets in almost all countries, developed and developing alike. Discriminatory wage differentials have detrimental social and economic effects. Gender differentials have larger distortional effects than other ethnic and racial differentials, and might call for different policies to address them. Measuring and documenting wage and employment differentials is an essential first step towards eliminating these differentials, which in turn is a very important economic as well as social policy goal akin to the Sustainable Development Goals set by the international community.
Entanglement witness on a programmable photonic processor

Multiphoton quantum interference is a one of the two technology platforms in which a quantum advantage has been demonstrated [1]. In these systems, a large-scale quantum state forms due to repeated effective interactions of photons (through the generalized Hong-Ou-Mandel effect) at the nodes of a photonic processor, resulting in a state that intrinsically possesses quantum complexity. A key question is how to validate the formation of a large-scale quantum state. One aspect of this is to measure whether the output quantum state is entangled – a necessary but not sufficient criterion for complexity. In this work, we derive and demonstrate an entanglement witness for large-scale optical quantum interference, and experimentally use this to demonstrate the presence of entanglement in our optical processor [2]. [1] Zhong et al., Science 370, 1460 – 1463 (2020) [2] Taballione et al., ArXiv 2012.05673 (2021)
Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd Counting

Datasets for training crowd counting deep networks are typically heavy-tailed in count distribution and exhibit discontinuities across the count range. As a result, the de facto statistical measures (MSE, MAE) exhibit large variance and tend to be unreliable indicators of performance across the count range. To address these concerns in a holistic manner, we revise processes at various stages of the standard crowd counting pipeline. To enable principled and balanced minibatch sampling, we propose a novel smoothed Bayesian sample stratification approach. We propose a novel cost function which can be readily incorporated into existing crowd counting deep networks to encourage strata-aware optimization. We analyze the performance of representative crowd counting approaches across standard datasets at per strata level and in aggregate. We analyze the performance of crowd counting approaches across standard datasets and demonstrate that our proposed modifications noticeably reduce error standard deviation. Our contributions represent a nuanced, statistically balanced and fine-grained characterization of performance for crowd counting approaches.