Reviewerly/exHarmony huggingface dataset:
COURSES OF SOCIAL SUPPORT FOR STUDENTS IN US UNIVERSITIES

The article raises the topic of social support for students in US universities. It was found that the social support of students is the activity of the social service of the educational institution to help them solve pressing social, psychological and pedagogical problems that arise in the process of learning and life. It was found that the social support of higher education seekers is aimed at creating optimal conditions for learning, stimulating awareness of the nature of problems, preparation for their avoidance or independent solution. The directions of social support in American institutions of higher education are determined: work with freshmen, work with those who have learning difficulties, work on leadership development, work with those who need employment, work with students who came to study from other countries, work with military and military veterans, work with students with disabilities, work with those who use alcohol or drugs, work with lesbians, gays, bisexuals and transgender people, work with representatives of different religious communities. The idea that social support of students in US universities is carried out in the same areas as in Ukrainian universities is substantiated. Special attention is paid to the social support of servicemen, military veterans and their families, as one of the most popular areas of social support in US universities is working with servicemen and military veterans – the number of students in this category to student services is higher than others. The practical experience of social support of this category of students by the student service of Cornell University is summarized. Promising directions of scientific research in this field are offered.
EXPERIENCE OF APPLICATION OF REMOTE PSYCHODRAMA IN SOCIAL WORK DURING A PANDEMIC CRISIS

The article presents the experience of using psychodrama in an online format as a way of psychological assistance in a therapeutic group and social project during a pandemic. It is argued that in the conditions of force majeure, the timely provision of psychological assistance is of particular importance. The purpose of the article is to substantiate the use of remote psychodrama in social work during the pandemic crisis. In an online format, psychodrama is an effective way to provide psychological assistance. At the same time, differences from traditional psychodrama and the specifics of psychodrama online are revealed. The experiences of a person in quarantine are intensified, therefore, when conducting a psychodramatic game, ecological entry into the zoom space becomes important. The necessity of using the physical actions of the participants in a playful way to maintain the level of energy in the psychodramatic warm-up, which is a particularly important element in the work of an online psychodramatherapist, has been identified. Attention is focused on the fact that the main techniques of psychodrama, such as role reversal, duplication and mirror, take place not only in the usual format, but also remotely.It has been established that for group members an important part in the final phase of psychodrama is sharing, thanks to which the protagonist receives support and additional attention, and group members get the opportunity to relieve tension, emotional release, additional awareness and meaning. The following methods were used in the study: the method of psychotherapy and psychodrama in an online format. Results: the difficulties of adapting the psychodrama method to the virtual space are analyzed. As an example, psychodramatic games are described in the groups “Go through the crisis” and “Happiness in the palms”. Conclusions: remote psychodrama in the era of digital technologies occupies a worthy place among the methods of psychological assistance.
Context-aware Pseudo-true Video Interpolation at 6G Edge

In the 6G network, lots of edge devices facilitate the low-latency transmission of video. However, with limited processing and storage capabilities, the edge devices cannot afford to reconstruct the vast amount of video data. On the condition of edge computing in the 6G network, this article fuses a self-similarity-based context feature into Frame Rate Up-Conversion (FRUC) to generate the pseudo-true video sequences at high frame rate, and its core is the extraction of the context layer for each video frame. First, we extract the patch centered at each pixel and use the self-similarity descriptor to generate the correlation surface. Then, the expectation or skewness of the correlation surface in statistics is computed to represent its context feature. By attaching an expectation or a skewness to each pixel, the context layer is constructed and added to the video frame as a new channel. According to the context layer, we predict the motion vector field of the absent frame by using the bidirectional context match and finally produce the interpolated frame. From the experimental results, it can be seen that by deploying the proposed FRUC algorithm on edge devices, the output pseudo-true video sequences have satisfying objective and subjective qualities.
The Analysis of Digital Literacy of Teachers in PGRI 363 Junior High School in Pondok Petir

This PkM aims to socialize digital literacy as an effort to provide an understanding of the digital world that is directly related to learning used in online learning. Since the world was hit by the COVID-19 pandemic, almost all lines of life have changed. Slowly but surely, people must begin to get used to this situation, including the education. In online learning, teachers face various challenges that come from both teachers and students. The most perceived challenge by teachers is to motivate students in learning. This PkM was conducted by 5 lecturers and involved 5 students who were running a field practice program at SMP PGRI 363 Pondok Petir. This activity lasts for 3 days on November 9-11, 2021. This activity begins with observing the needs of teachers in terms of teaching and learning activities in the classroom. After making observations, it was found that teachers and students need a variety of activities that can reduce boredom when learning online. The activity was continued by socializing material about the importance of digital literacy to access information, then the next day the teacher was given the opportunity to develop various activities that involve student activities in digital learning.
BLOCKCHAIN: A Cyber Defense Force Multiplier

At its core, a blockchain is simply a database for storing digital information where each of time-stamped, published data is together with all preceding and subsequent blocks. What makes blockchain unique from traditional data storage is that data is not stored or controlled within a centralized database, but rather database management is shared by a decentralized network of trustless nodes that each maintain an identical copy of the blockchain. Before data can be published to the blockchain, it must first be validated by a majority of network nodes—typically referred to as or, depending on the consensus methodology used. This concept of “distributed consensus” makes the blockchain inherently tamper resistant and ensures that only verified transactions will be published to the blockchain.1 The two primary consensus methodologies used in blockchain technology today are , first popularized in 2008 by the founder(s) of the Bitcoin2 blockchain, and , which has been used extensively in the development of derivative blockchain projects to help solve the issues of scalabil- 1 Samantha Ravich, “Leveraging Blockchain Technology to Protect the National Security Industrial Base,” July 10, 2017, https://www.fdd.org/analysis/memos/2017/07/10/leveraging-block chain-technology-to-protect-the-national-security-industrial-base/ 2 Satoshi Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,” 2008, https://bicoin.org/ bitcoin.pdf ity and energy consumption associated with proof-of-workbased blockchains.
MORPHOLOGICAL ERROR ANALYSIS ON WORD CLASSES FOUND IN STUDENTS’ DESCRIPTIVE WRITING OF THE FIFTH SEMESTER ENGLISH DEPARTMENT AT NOMMENSEN HKBP UNIVERSITY (NHU) MEDAN

The writer did a research of fifth semester English department students at Faculty of Art and Nommensen HKBP University Medan. This research entitled : Morphological Error Analysis on Word Classes Found in Students’ Descriptive Writing of The Fifth Semester English Department at Nommensen HKBP University (NHU)- Medan The writer asked the students to write a descriptive text. When they did the test, they felt confuse to write words with correct morphology. The result of the analysis was found that they knew about morphology, but they are confused to apply that. After they finished to write a descriptive text, the writer analyzed them. The writer underlined and classified the errors into four word classes on Morphological error. There are four word classes of morphological Errors, they are noun morphological errors, verb morphological errors, adjective morphological errors and adverb morphological errors . The result of the analysis, the writer concluded that there are four word classes was found by the writer, namely noun morphological errors, verb morphological errors, adjective morphological errors, and adverb morphological error. The data of morphological erors are found 154 errors. The percentage of errors in noun morphological was 52 errors i.e, 33,8%, errors of verb morphological was 91 errors or, 59,0%, errors of adjective morphological was 7 errors i.e, 4.6%, and error of adverb morphological was 4 errors i.e, 2.6%. The result of the analysis was found that the students knew about morphology but they are still confused to write words based on the correct morphology, so they made the errors. Looking at the percentage it shows verb morphological was 91 errors or 59,0%, of 154 errors, it means that verb is mostly used in a text, to express the ideas or activities .&#x0D; &#x0D; Key words : Morphological Error , Word Classes , Descriptive Writing &#x0D;
Case-Based Inverse Reinforcement Learning Using Temporal Coherence

Providing expert trajectories in the context of Imitation Learning is often expensive and time-consuming. The goal must therefore be to create algorithms which require as little expert data as possible. In this paper we present an algorithm that imitates the higher-level strategy of the expert rather than just imitating the expert on action level, which we hypothesize requires less expert data and makes training more stable. As a prior, we assume that the higher-level strategy is to reach an unknown target state area, which we hypothesize is a valid prior for many domains in Reinforcement Learning. The target state area is unknown, but since the expert has demonstrated how to reach it, the agent tries to reach states similar to the expert. Building on the idea of Temporal Coherence, our algorithm trains a neural network to predict whether two states are similar, in the sense that they may occur close in time. During inference, the agent compares its current state with expert states from a Case Base for similarity. The results show that our approach can still learn a near-optimal policy in settings with very little expert data, where algorithms that try to imitate the expert at the action level can no longer do so.
Rice Plants Disease Identification Using Deep Learning with Convolutional Neural Network Method

Indonesia is an agricultural country where most of the population grows rice and most farmers cannot detect early if there is a pest attack on rice plants . This research discuss about deep learning implementation to classify or identify diseases in rice leaves using mobile application. This system will make users easily to diagnose diseases by displaying diagnostic results in the form of the name of the disease along with its taxonomy, disease description and drug recommendations for disease solutions. There are four classes of leaves used in this research, including healthy leaves, leaf blight, brown spot and potassium deficiency. The design of the model uses two approaches, one of them are modeling convolutional neural network from the scratch and modeling with transfer learning using inception v3 architecture. Both models will go through training process to produce a model that is ready to be used for classification. In application testing, a comparison is made between two models. From the tests that have been carried out, it is concluded that the system with model made using transfer learning approach, produce good accuracy with an accuracy of 90%. Meanwhile the System with the other model gain an accuracy of 62%. So when the data used in research are extremely low, it is best to use transfer learning as an approach to design a mode.
Edge Detection Of Potato Leaf Damage With Laplacian Of Gaussian Algorithm

The Potato plants are type young plant that easily attacked by pests and diseases, part of plant that often attacked by disease is leaves which can affect growth process and reduce crop yields. One way to determine if potato leaf is healthy or unhealthy is by using the edge detection method. Crop failure in potato plants can be detected through damage to leaves. The purpose of this study was to help facilitate identification type of damage to leaf margins of potato plants by applying the Laplacian of Gaussian algorithm. Based on results of testing on several research datasets sourced from the Agricultural Sector of the Karo Regency Government through an application of edge image detection on potato plant leaves through a grayscale, threshold and detection process with the Laplacian of Gaussian algorithm. It takes the longest time of 12.34 s with an error of 1.45 on the type of damage caused by aphids and at least 6.03 s with an error of 0.71 on the normal leaf edge detection results. Based on test results on 17 potato leaf images, the average test time is 8.45 s&#x0D;
Professional training mechanisms for educators of digital technologies

conducting an institutional audit of general secondary education institutions] : nakaz MON Ukrainy vid 09 sichnia 2019 r. № 17.
Contextualization of the Concept of Fraud Victimization for the Crime of Fraud in Trade Activities Indonesia – China (Victimization Study from the Perspective of Indonesian Exporters)

This study aims to examine fraud cases in Indonesia-China trade relations from the point of view of the Indonesian exporters as victims. Empirically and theoretically, the research focuses on gaps in trade relations that trigger the victimization of Indonesian exporters and seeks to uncover economic, political, legal, and cultural asymmetries in accordance with the stages and typology of fraud victimization initiated by Michael Levi (2009). This study contextualizes fraud victimization on the phenomenon of criminogenic asymmetries in Indonesia-China trade relations. The study uses a qualitative approach with an explanatory method, while the data analysis uses a triangulation technique. The results of the study reveal several essential things. First, the victimization cases of Indonesian exporters occur because of the existence of non-legal route options in trade with China that puts the exporters in the category of diffuse victimization. This option is available because it is a rational choice of cost-effective, duty-free, and with no licensing. It is even more dramatic when the government and law enforcement commit victim blaming, then see cases of default as negligence, not fraud. Second, there is a lack of coordination between international trade stakeholders in Indonesia, creating difficulties in assisting entrepreneurs’ processes, especially if the exporters become victims of fraud. Third, the role of groups of perpetrators of organized fraud exists by using the guise of business in Indonesia-China trade relations. Fourth, the fraud victimization experienced by Indonesian exporters is inseparable from the existence of asymmetric criminogenic factors, namely the legal system, technology, knowledge, and culture between the two countries. Fifth, there are empirical and theoretical differences and gaps related to the contextualization of Levi's concept of fraud victimization when placed within the framework of criminogenic asymmetries in the Indonesia-China trade case. This gap is due to the occurence of three export routes that significantly affect the concept of victimization typology.
Fractal Dimension and Perturbation Strength: A Local Optima Networks View

We study the effect of varying perturbation strength on the fractal dimensions of Quadratic Assignment Problem (QAP) fitness landscapes induced by iterated local search (ILS). Fitness landscapes are represented as Local Optima Networks (LONs), which are graphs mapping algorithm search connectivity in a landscape. LONs are constructed for QAP instances and fractal dimension measurements taken from the networks. Thereafter, the interplay between perturbation strength, LON fractal dimension, and algorithm difficulty on the underlying combinatorial problems is analysed. The results show that higher-perturbation LONs also have higher fractal dimensions. ILS algorithm performance prediction using fractal dimension features may benefit more from LONs formed using a high perturbation strength; this model configuration enjoyed excellent performance. Around half of variance in Robust Taboo Search performance on the data-set used could be explained with the aid of fractal dimension features.
Co-evolutionary Diversity Optimisation for the Traveling Thief Problem

Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem. Many studies have considered diversity 1) as a mean to explore niches in behavioural space (quality diversity) or 2) to increase the structural differences of solutions (evolutionary diversity optimisation). In this study, we introduce a co-evolutionary algorithm to simultaneously explore the two spaces for the multi-component traveling thief problem. The results show the capability of the co-evolutionary algorithm to achieve significantly higher diversity compared to the baseline evolutionary diversity algorithms from the literature.
Facial Mask Detection Using Image Processing with Deep Learning

Coronavirus disease 2019 (COVID-19) has a significant impact on human life. The novel pandemic forced humans to change their lifestyles. Scientists have broken through the vaccine in many countries, but the face mask is the only protection for public interaction. In this study, deep neural networks (DNN) have been employed to determine the persons wearing masks correctly. The faster region-based convolutional neural networks (RCNN) model has been used to train the data using graphics processing unit (GPU) device. To achieve our goals, we used a multiphase detection model: first, to label the face mask, and second to detect the edge and compute edge projection for the chosen face region within the face mask. The current findings revealed that faster RCNN was efficient and precise, giving 97% accuracy. The overall loss after 200,000 epochs is 0.0503, with a trend to decrease. While the loss is falling, we are getting more accurate results. As a result, the faster RCNN technique effectively identifies whether a person is wearing face masks or not, and the training period was decreased with better accuracy. In the future, Deep Neural Network (DNN) might be used first to train the data and then compress the dimensions of the input to run it on low-powered devices, resulting in a lower computational cost. Our proposed system can achieve high face detection accuracy and coarsely obtain face posture estimation based on the specified rule. The faster RCNN learning algorithm returns high precision, and the model’s lower computational cost is achieved on GPU. We use the “label-image” application to label the photographs extracted from the dataset and apply Inception V2 of faster RCNN for face mask detection and classification.
A New Fitness-Landscape-Driven Particle Swarm Optimization

Fitness landscape is an evolutionary mechanism and fitness landscape theory has developed considerably since it was proposed by Sewall Wright in the 1930s. In evolutionary algorithms, some characteristic information by analyzing the fitness landscape can be obtained to improve the optimization performance of algorithms. This paper introduces a new fitness-landscape-driven particle swarm optimization (FLDPSO). In the method, the correlation metric between fitness value and distance is obtained by charactering the fitness landscape of optimization problems. Then, two new proposed variants of particle swarm optimization (PSO) are developed to improve the optimization performance. Moreover, a selection mechanism based on this metric is introduced to select a fitter variant from these two variants. Finally, the experimental simulation is executed on 18 benchmark functions to assess the optimization performance of the proposed FLDPSO algorithm. The results show that FLDPSO can improve optimization accuracy and convergence very well.
Video annotation with metadata: A case study of soccer game video annotation with players

In recent years, the use of convolutional neural networks for video processing has become very attractive. The reason lies in the computational power for data processing which is available today. There are many well-defined research areas where neural networks have brought higher reliability than other conventional approaches; for example, traffic sign recognition and isolated number recognition. In this paper, we will describe the architecture and the implementation of the process of soccer game annotation. The game is annotated with data about players. The technology of convolutional neural networks is used for number recognition. The process runs in real-time on a streaming video. Content enriched with metadata is given to the user in parallel with the real-time video. In the paper, we will describe in some detail the following modules: Image binarization, shot localization, the selection and recognition of numbers on players` jerseys.
An Exploratory Study of Fiction Writing's Relationship to Additional-Language Narrative Performance and Ownership

Narrative competence, the ability to construct and comprehend especially fictional narratives, has long represented an advanced ability for language users (Pavlenko, 2006).This study explored whether additional language (AL) writers, who demonstrated narrative competence in English by composing fictional stories about imagined future scenarios, differed in degree of expressed Englishlanguage ownership (Hanauer & Dolan, 2014;Nicholes, 2017b;Olckers, 2013) from AL writers who performed statements about imagined futures.In this study, seventy (N = 70) AL writers were invited to write fictional accounts of their imagined futures and then report their perceived ownership of English.Writers were grouped by those who accomplished stories and those who composed statements imagining their futures.Mann-Whitney U tests of difference indicated that those AL writers who composed stories, with characters, description, setting, or other story elements, reported statistically significantly more ownership of English both pre-intervention (U = 429, p = .028,r = .26)and post-intervention (U = 403, p = .013,r = .30).Meanwhile, the writing experience did not itself relate to significant changes in reported ownership for the group of participants who wrote either stories or statements.This study indicates that narrative performance may predict or otherwise be associated with AL perceived ownership of English as an additional language and concludes with theoretical and pedagogical implications for fiction writing in the AL classroom.