Reviewerly/exHarmony huggingface dataset:
A Qualitative Investigation of Selected Infrared Flow Visualization Image Processing Techniques

The deployment and integration of high-sensitivity infrared cameras in a transonic wind tunnel testenvironment has resulted in a unique capability to image aerodynamic phenomena in real-time. Multi-camera infrared flow visualization data systems are now routinely utilized at the NASA Ames Unitary Plan Wind Tunnel. The small flow-induced temperature gradients on the surface of the wind tunnel test article coupled with the high bit-depth of the infrared camera sensor makes the processing of the image data critically important. An image processing routine must enhance features of interest with minimal artifacts. Additionally, the production wind tunnel test environment demands that these processed images are made available in a real-time, automatic fashion. Therefore, any image processing routine must be computationally economical and enhance the image data with minimal input from a human operator. The following seeks to qualitatively explore selected image processing techniques by assessing their effectiveness to resolve flow features on a wind tunnel test article. A multi-scale contrast enhancement technique is discussed as well as a new implementation of a multi-scale, non-interpolated adaptive histogram equalization. Finally, a novel method is introduced that demonstrates the ability to resolve flow features imaged on bare-steel test articles possessing low emissivity.This method makes use of dynamic mode decomposition and discrete-time filtering to separate the background reflections that dominate low emissivity surfaces from the aerodynamic driven surface temperature gradients.This process will be shown to resolve the onset of boundary layer transition on a bare metal wing as well as identify and resolve hidden features in the image data. While the implementation of this technique is very preliminary it demonstrates the potential to extend the application of infrared flow-visualization within the wind tunnel test environment.
Needs Analysis of English Materials for Islamic Economics Students

This study aims to find out the needs of English material for Islamic economics students. The method employed in this research is descriptive. Forty students and ten stakeholders were asked to complete the questionnaire, and ten stakeholders were interviewed. The result of the research: 1) the highest themes or topics needed based on students' perception is the relevance of Islamic economics, meanwhile based stakeholders' perception is the methodology of Islamic economics; 2) the highest discourse texts needed based on students' perception is Islam as a way of life, whereas based on stakeholders' perception is facts and theories of Islamic economics; and 3) the highest English Skills needed to be based on students and stakeholders' perception is speaking skill. In sum, the researchers concluded that speaking is an important skill needed by the Islamic economics study program of the Muhammadiyah University of Bengkulu.
An Effective Tool for Assessing the Composite Vulnerability of Multifactor Authentication Technologies

While multifactor authentication technologies continue to advance and adoption rates for those technologies increase, there exists a need to characterize the composite vulnerability score for complete authentication solutions. To meet this need, we propose an extension to the Common Vulnerability Scoring System (CVSS) v3 calculator to provide an aggregate score for any metric category, enabling organizations and researchers to succinctly determine the composite vulnerability impact of authentication factor multiplicity. This chapter has presented a novel mathematical approach and demonstrated the approach through a real-world application which is a comparative study on the composite vulnerability of two different multifactor authentication technologies.
CHARACTERISTICS OF EMOTIONAL BURNOUT OF TEACHERS WHO WORK WITH CHILDREN WITH SPECIAL EDUCATIONAL NEEDS

The article is devoted to an empirical study of the characteristics of emotional burnout of teachers who work with children with special educational needs. The problem is exacerbated by the need to meet high professional and personal standards, to provide educational services at a high level, to maintain their own mental health and the course of activities in special pedagogical conditions. All the selected factors of influence and compliance put the modern special educator in new conditions that significantly complicate his/her already intense activities. High stress of pedagogical activity creates risks of formation of emotional burnout. The conducted empirical research revealed the features of emotional burnout of teachers who work with children with special educational needs; it also identified areas of positive manifestations through the activation of which it is possible to carry out psychoprophylactic actions. It was found that a special role in the formation of emotional burnout belongs to resistance, which can perform the function of "psychological resistance" of the teacher to the conditions and specifics of his/her professional life; it allows him/her to perform professional duties. In the structure of emotional burnout of teachers who work with children with special educational needs, it is resistance that plays a leading role, allows without leaving the profession to carry out a complex procedure of psychological and pedagogical support of special children. The special role in prevention of emotional burnout of age and pedagogical experience of the specialist is shown. Life experience, personal maturity and stability of the value sphere contribute to greater resistance to the negative factors of intense psycho-emotional activity of the teacher; they eliminate the manifestations of many indicators of emotional burnout. Prospects for further research are to clarify and verify the identified trends, in particular, the empirical study of those features of emotional burnout of teachers who work with children with special educational needs related to professional experience, but may be "masked" by age.
Categorical Analysis of Logical Networks in Application to Intelligent Radar Systems

The aim of the paper is to present developed algorithms of logical networks and their application to the actual tasks of artificial intelligence modeling in the technology of radar detection of low-visible air objects. The object of research - categorical models of logical networks in intelligent radar systems. Modified and predicate categories, methods of construction of logical networks are chosen by the method of this research. The aim is also to develop mathematical methods for presenting information based on categorical models of logical networks. It is necessary to establish a connection between the theory of categories and modeling of systems based on artificial intelligence using an intermediate field of knowledge - the algebra of finite predicates, by means of which a predicate interpretation of the category can be built. Based on this interpretation, elements of the theory of the modified category are developed for the modeling processes of the artificial intelligence functions.
Education in Humanistic Pedagogy

Introduction. The article examines the problem of education, based on the methodology of a humanistic approach to the formation of the individual. Comparative analysis made it possible to identify and characterize the features that are inherent in both communist and modern humanist education. Particular attention is paid to the principles of humanistic pedagogy, which determine the direction and activities of educational organizations. The importance of the problem in question is that a certain understanding of it is the basis of the formation of the spiritual culture of the individual and society. Materials and Methods. The theoretical and methodological strategy of this study was a comparative-comparative approach, which allowed to identify and characterize those features that are inherent in both communist and modern humanistic education. To this end, the following methods were used: theoretical modeling, abstraction, generalization, comparative analysis. Results of the study and discussion. Approaches to education under the influence of communist and humanistic (anthropocentric humanism) education have been revealed and critically understood. The role of Orthodox humanism in the formation of human personality is shown as the antithesis of anthropocentric humanism. Conclusion. The results of the study show that humanistic pedagogy, striving to be independent, develops its approaches to education. However, she uses the educational material that was criticized for her traditional pedagogical science and practice. Its main difference from the previous pedagogy is that the center of the pedagogical space is occupied by a separate personality. Education is understood as an activity aimed at creating the necessary conditions for its self-fulfillment, internal self-expression.
Improving Text Matching with Semantic Dependency Graph via Message Passing Neural Network

Text matching is a core natural language processing research problem. Deep semantic alignment and comparison between two text sequences lie in the core of text matching. While the attention-based model achieves high accuracy through word-level or char-lever alignment, they ignore the deep semantic relations between words and have poor generalization performance. This paper presents a neural approach to leveraging the Chinese Semantic Dependency Graph for text matching. This model uses Message Passing neural network to encode the semantic relation between word and use these semantic associations to assist semantic alignment and comparison. Experimental results demonstrate that our method substantially achieves state-of-the-art performance compare to the strong baseline model. The further discussion shows that our model can improve the text alignment process and have better robustness and comprehensibility.
An Experimental Approach to Conceal Confidential Data using LSB Embedding Technique

Security of our data is very important in current time, so to provide the security of our data should be on first priority. To conceal confidential information is very important in the present era. Steganography provides the features to conceal our confidential information into the cover medium. In this paper I used the LSB embedding technique to hide the data and also try to improve the quality of stego image with increased payload capacity. Here I am trying to provide authenticity, confidentiality, and integrity.
Near-body Interaction Enhancement with Distance Perception Matching in Immersive Virtual Environment

Recent virtual reality (VR) researches focus on developing a technology that provides users with a highquality virtual presence that is difficult to distinguish from reality.As a model for explaining the presence of users [1], there is a need for an interface device that delivers the same feedback as the real environment to the human sensory organ as a requirement to achieve an ideal level of presence.However, current head-mounted display (HMD) devices representing fully immersive virtual environments still present visually uneasy feedback with to the real world.The monocular image quality of HMDs is being developed from 2K high definition (HD) resolution to prototypes with 4K ultra high definition (UHD) or higher image quality.However, to cope with the highest density region (i.e., the macula of retina) of human vision, a realtime output technology capable of updating the image information of 8K UHD or higher in a time of several milliseconds or less is needed.Real-time focus change control technology that actively responds to accommodation, essential human depthsensing parameters at near-body space distance within a few meters [2], has not yet been universally applied to HMDs.Therefore, the commercial HMDs based on a binocular stereoscopic virtual imaging structure could not intrinsically solve the convergence and accommodation conflict problem [3].Since the conflict problem causes interaction errors in the virtual environment, it is necessary to reduce the errors by adjusting hardware or implementing software.This paper proposes a compensation method to enhance distance perception to cope with the problem that the sense of distance in the real space does not match the virtual space when the user engages in the virtual environment wearing HMDs.We implement virtual environments that generalize two interactive scenarios: whole-body movement and direct manipulation and analyze correlation with distance perception and interaction behavior.We find the distance perception parameters matched by experimenting with the two interaction scenarios.And then, we show that the interaction is effective in adjusting the virtual environment. II.
A MOEA-D-Based Service Quality Matching Optimization Algorithm in Electric Power Communication Network

In the smart grid scenario, this article proposes a blockchain-based smart grid service system based on the alliance chain established by power plants, distribution network operators, energy storage operators, and power companies as nodes. Each node can flexibly change its role according to its own needs, and can be used as a service provider or as a user requesting service. In this service system, users and service providers store business data and transaction data through the blockchain, and match services through smart contracts. On this basis, this paper establishes a mathematical model of power business service quality, abstracts the user matching power business process as a service composition problem under the constraint of service quality, and uses the multi-objective optimization algorithm MOEA/D as the basis, based on price, time delay, Service quality indicators such as bandwidth are analyzed and optimized for optimization goals. On the one hand, in the smart grid scenario, it has achieved decentralization, improved power data security, and user privacy security. On the other hand, multi-objective optimization algorithms are used to optimize the different needs of users to improve the service quality of the smart grid system. The results of calculation examples verify the validity and rationality of the proposed framework.
Modeling Indonesian COVID-19 Contact Tracing using Social Network Analysis

As coronavirus SARS-CoV-2 emerged around the world, researchers are looking for the best method to decrease the spread. Testing, Tracing, Treatment, or 3T is a rule to control the pandemic COVID-19. However, 3T in Indonesia is still poor, testing capacity is still low as well as the tracing rate. This research aims to model the Indonesian Corona Virus spread from a small cluster in society. As the difficulty rises in acquiring real data, the data are synthetically generated, as well as its relationship. This paper applied Social Network Analysis with Network X, a Python library. The modeling method started with creating the graph and its community graph, then calculate the betweenness centrality to generate Page Rank based graph. This paper shows that the top 3 of the highest Page Rank is LUP with the value of 0.012356, MIH with 0.012035 points, and WAGP with 0.011824. The relationship between people impacts contacts tracing in the graph. The higher rank of a person, the higher chance he or she transmitted the virus or got infected by the virus.
Genetic Analysis for Breast Cancer Prediction and Diagnosis

Breast cancer signifies one of the diseases which contributes to a huge number of deaths each year. It is the most usual kind of cancer and also one of the main reasons for women's deaths globally. In the medical domain, data mining methods and Artificial Intelligence algorithms are used in early prognosis to make decisions. In this paper, we have highlighted the root cause of Breast cancer with the imbalance of Genomes. Also, we have described some of the existing predictions using various algorithms for breast cancer data. Our proposed architecture involves a combination of Artificial Intelligence algorithms and genetic algorithms as we have focused not only on the prediction of Breast cancer but also, included the genetic algorithm for genome sequencing promptly to the identified people, to avoid the stages of Breast cancer with respect to the time of identification. We have predicted the breast cancer genome dataset using some machine learning algorithms, where the Support Vector Machine algorithm gives a prediction accuracy of 61.55%. We are researching and working on the results for identifying the most accurate genetic algorithm to complete the design of a cost-effective model for genome sequencing.
Economic growth dynamics of the East African region

The article is devoted to the description of the dynamics of the economic development of the East African states. The paper gives a description of the current economic situation of the East African countries, lists the states included in the East African region, gives the economic and geographical characteristics of the studied region. The author also analyses the evolution of the sectoral composition of gross domestic product (GDP) of the East African countries, considers the economic factors that influence the economic growth of the states of the region. The study analyses the problem of employment and unemployment in the East African countries. The paper considers the ways of improving the economic development of the region, in particular, economic diversification, inclusive growth and structural transformation, as well as structural and institutional measures that would encourage productive investment in the economy.
Effect of phase and spatial distinguishability of photon pairs on the entanglement fidelity

Polarization entanglement based cryptography uses the photon pairs generated in a spontaneous paramet- ric down-conversion process and guarantees the security through the violation of Bell’s inequality. Certain experimental parameters affects the entanglement fidelity and leads to a possible information leakage. The optical path difference of photons born at different crystals contributes to reduced fidelity, due to the extra birefringence of the nonlinear crystal. Although previous studies suggested methods to erase the distinguishability of photons by introducing compensation crystals, the phase difference, which is due to the lateral ray distribution is not studied. We used two commercially available collection optics; an aspheric and an achromatic lens. With these collection optics, the effect of collection optics on the entanglement fidelity is studied. We developed a simulation of such a system and found that aspheric collection optics is more suitable to achieve high fidelity.
Review of Collaborative Learning Communities in Middle School Literacy Education: Increasing Student Engagement With Authentic Literacy by Jolene T. Malavasic

Creating a collaborative learning environment where adolescents feel valued as co-constructors of knowledge can improve literacy learning outcomes for youth.This concept is the basis for the book, Collaborative Learning Communities in Middle School Literacy Education: Increasing Student Engagement With Authentic Literacy, by Jolene T. Malavasic (2020).She describes a framework for engaging middle schoolers in an after-school literacy program as part of a graduate practicum course.The program is a powerful model for teachers, teacher educators, and administrators not only regarding literacy instruction but also for building supportive relationships among teachers, students, and peers.Interwoven throughout the book's seven chapters are descriptions of a graduate teacher preparation course, an afterschool literacy program for middle school students, and theoretical foundations for the creation of both the graduate course and literacy program.While reading this book, as a high school special educator and literacy graduate student, my internal dialogue was afire with connections to adolescents, relationship building, collaborative learning, and the literacy strategies described by Malavasic.
JomGames: Creating a Motivating Learning Environment

Games have played an impactful life in our current generation brimming with technological advancement. Not only has it become the main source of entertainment for people to enjoy, but it has also transitioned to becoming a learning experience for others. With our current repetitive standards and methods of teaching and learning in schools, the mind of many students has dulled their creativities as well as developing the extrinsic motivation to study at all. However, the implementation of game learning can be developed into modern-day teaching and be used as a tool for students to grasp their potentials and be motivated to learn level more using games. From the mix mode research conducted, many tertiary students would rather replace their conventional studying methods with a game learning experience. The player will be able to understand, develop listening skills and verbal communication skills towards other people to understand when it is necessary to be assertive in an environment. Moreover, this game will tackle the understanding of real-life decision-making skills that mimic real-life situations for players to adapt to. With that said, JomGear Grind Games aims to help students learn various skills such as creative thinking and interpersonal skills.
ChoiceNet: CNN learning through choice of multiple feature map representations

Abstract We introduce a new architecture called ChoiceNet where each layer of the network is highly connected with skip connections and channelwise concatenations. This enables the network to alleviate the problem of vanishing gradients, reduces the number of parameters without sacrificing performance and encourages feature reuse. We evaluate our proposed architecture on three independent tasks: classification, segmentation and facial landmark localisation. For this, we use benchmark datasets such as ImageNet, CIFAR-10, CIFAR-100, SVHN CamVid and 300W.
Chinese Text Summarization Generation Algorithm based on ERNIE-GEN with Few-shot Learning

Abstract In view of the problems of exposure bias and insufficient annotation data in the existing Chinese short text summary algorithms, a new pre-training short text summary algorithm named ERNIE-GEN-CTS based on ERNIE-GEN is proposed. This paper uses pre-training language model to realize the double attention of word level and word level for short text, and fills the sub block with semantic information to optimize the exposure bias problem. After the pre-training model of large-scale Chinese corpus, the results are improved in LCSTS data set after the task tuning of the downstream summary, and the validity and stability of the model are verified.
Training Temporal and NLP Features via Extremely Randomised Trees for Educational Level Classification

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. These platforms also bring incredible diversity of learners in terms of their traits. A research area called Author Profiling (AP in general; here, Learner Profiling (LP)), is to identify such traits about learners, which is vital in MOOCs for, e.g., preventing plagiarism, or eligibility for course certification. Identifying a learner’s trait in a MOOC is notoriously hard to do from textual content alone. We argue that to predict a learner’s academic level, we need to also be using other features stemming from MOOC platforms, such as derived from learners’ actions on the platform. In this study, we specifically examine time stamps, quizzes, and discussions. Our novel approach for the task achieves a high accuracy (90% in average) even with a simple shallow classifier, irrespective of data size, outperforming the state of the art.