Reviewerly/exHarmony huggingface dataset:
KONSTRUKCIJA I VALIDACIJA SKALE ZA PROCJENU SOCIO-KOMUNIKACIJSKIH KOMPETENCIJA ODGAJATELJA

This paper was based on the fact that educators’ social communicative competences are insufficiently discussed in professional literature, and that there is no research instrument testing them. Therefore, the goal of this paper was to develop and then empirically validate the Scale for the Assessment of Educators’ Social Communicative Competences. The first version of the instrument consisted of the following subscales: Nonviolent Communication, Self-Awareness, Awareness of Others, Self-Management-Emotion Regulation, Self-Esteem, Nonviolent Conflict Resolution. The initial version of the scale contained 70 items. The instrument was applied to a sample of 243 respondents, educators from all over Bosnia and Herzegovina, and psychometric validation was carried out, which included an item analysis, a factor analysis to check the validity and reliability of the measuring instrument. Eventually, an instrument consisting of 32 items was obtained. The following 6 factors were confirmed: Nonviolent Communication, Self-Awareness, Awareness of Others, Self-Management-Emotion Regulation, Self-esteem and Nonviolent Conflict Resolution. The reliability of all the subscales except for the Self-Awareness subscale was found to be satisfactory: Non-violent Communication (5 items; α=.697), Self-Awareness (6 items; α=.487), Awareness of Others (4 items; α=.804), Self-Management-Regulation of Emotions (5 items; α=.817), Self-Esteem (7 items; α=.701), Nonviolent Conflict Resolution (5 items; α=.771). It was concluded that the developed research instrument intended for the assessment of educators’ social communicative competences has satisfactory characteristics.
Linearized Reed-Solomon Codes with Support-Constrained Generator Matrix and Applications in Multi-Source Network Coding

Linearized Reed-Solomon (LRS) codes are evaluation codes based on skew polynomials. They achieve the Singleton bound in the sum-rank metric and therefore are known as maximum sum-rank distance (MSRD) codes. In this work, we give necessary and sufficient conditions for the existence of MSRD codes with a support-constrained generator matrix. These conditions are identical to those for MDS codes and MRD codes. The required field size for an $[n,k]_{q^m}$ LRS codes with support-constrained generator matrix is $q\geq \ell+1$ and $m\geq \max_{l\in[\ell]}\{k-1+\log_qk, n_l\}$, where $\ell$ is the number of blocks and $n_l$ is the size of the $l$-th block. The special cases of the result coincide with the known results for Reed-Solomon codes and Gabidulin codes. For the support constraints that do not satisfy the necessary conditions, we derive the maximum sum-rank distance of a code whose generator matrix fulfills the constraints. Such a code can be constructed from a subcode of an LRS code with a sufficiently large field size. Moreover, as an application in network coding, the conditions can be used as constraints in an integer programming problem to design distributed LRS codes for a distributed multi-source network.
Post Covid-19: Students’ Readiness towards Open Distance Learning in Higher Education

The outbreak of Covid-19 in 2019 has forced institutions of higher learning in Malaysia to switch to open distance learning (ODL).As the implementation was done abruptly, there were concerns raised on whether the students were actually ready to undergo the new learning system.This study examines the different factors that may influence students' readiness in undergoing ODL in a higher education setting.It also investigates the relationships between those factors and how they contribute towards the students' actual performance during ODL.These investigations are important as it provides an empirically-proven data and perspectives on how students actually perform in their studies during ODL; an area which the researchers believe is still lacking and requires further inquiries.The data for the study was gathered from a questionnaire distributed online to 111 students.The questionnaire consisted of items looking into the students' demographic data, their self-directedness, learning preference, study habits and equipment capabilities while undergoing ODL.The elicited data were then analysed using ordinal logistic regression model and empirical illustration to yield an in-depth analysis between the factors involved in the study.The major findings of this study show that students' readiness towards ODL are indeed influenced by their demographic factors such as their parents' education background, the telco services they use, and the availability of proper equipment as learning aids.Furthermore, the application of ordinal logistic regression in the current research shows that students who possess a high readiness in undergoing ODL tend to produce better results in their studies during the pandemic.Apart from that, the study also reported new findings that indicate students' study habits bore no effect on their readiness to go through ODL.This is rather odd as positive study habits are commonly associated with a high readiness to undergo a new learning system, which is not the case for the participants in this study.Another puzzling finding also reveals that the students' equipment capabilities have a negative relationship with their performance in ODL.This means that even if some participants do not have the necessary equipment to actively undergo ODL, they do not feel that their performance in their studies are negatively affected.These new findings are certainly inconsistent with the results shown in previous, similar studies.Hence, it is recommended that future researchers look into why students' typical study habits are not
Multi-VALUE: A Framework for Cross-Dialectal English NLP

Dialect differences caused by regional, social, and economic factors cause performance discrepancies for many groups of language technology users. Inclusive and equitable language technology must critically be dialect invariant, meaning that performance remains constant over dialectal shifts. Current systems often fall short of this ideal since they are designed and tested on a single dialect: Standard American English (SAE). We introduce a suite of resources for evaluating and achieving English dialect invariance. The resource is called Multi-VALUE, a controllable rule-based translation system spanning 50 English dialects and 189 unique linguistic features. Multi-VALUE maps SAE to synthetic forms of each dialect. First, we use this system to stress tests question answering, machine translation, and semantic parsing. Stress tests reveal significant performance disparities for leading models on non-standard dialects. Second, we use this system as a data augmentation technique to improve the dialect robustness of existing systems. Finally, we partner with native speakers of Chicano and Indian English to release new gold-standard variants of the popular CoQA task. To execute the transformation code, run model checkpoints, and download both synthetic and gold-standard dialectal benchmark datasets, see http://value-nlp.org.
Hamming Distributions of Popular Perceptual Hashing Techniques

Content-based file matching has been widely deployed for decades, largely for the detection of sources of copyright infringement, extremist materials, and abusive sexual media. Perceptual hashes, such as Microsoft's PhotoDNA, are one automated mechanism for facilitating detection, allowing for machines to approximately match visual features of an image or video in a robust manner. However, there does not appear to be much public evaluation of such approaches, particularly when it comes to how effective they are against content-preserving modifications to media files. In this paper, we present a million-image scale evaluation of several perceptual hashing archetypes for popular algorithms (including Facebook's PDQ, Apple's Neuralhash, and the popular pHash library) against seven image variants. The focal point is the distribution of Hamming distance scores between both unrelated images and image variants to better understand the problems faced by each approach.
Demonstration of machine-learning-enhanced Bayesian quantum state estimation

Machine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum foundations. Here, we experimentally realize an approach for defining custom prior distributions that are automatically tuned using ML for use with Bayesian quantum state estimation methods. Previously, researchers have looked to Bayesian quantum state tomography due to its unique advantages like natural uncertainty quantification, the return of reliable estimates under any measurement condition, and minimal mean-squared error. However, practical challenges related to long computation times and conceptual issues concerning how to incorporate prior knowledge most suitably can overshadow these benefits. Using both simulated and experimental measurement results, we demonstrate that ML-defined prior distributions reduce net convergence times and provide a natural way to incorporate both implicit and explicit information directly into the prior distribution. These results constitute a promising path toward practical implementations of Bayesian quantum state tomography.
1067. Predicting next 7-day Discharges of Hospitalized COVID-19 Patients Using Ensemble Learning

Abstract Background COVID-19 pandemic, especially during resurgences of cases in hard-hit areas, led to significant shortage of hospital beds. Such shortages may be alleviated through timely and effective forecasting of hospital discharges. The objective of this study is to predict next 7-day discharges of hospitalized COVID-19 patients using daily-based electronic health records (EHR) data. Methods Using EHR data of hospitalized COVID-19 patients from 03/2020-08/2021, we employed ensemble learning to predict next 7-day discharges of individual patients. We used both baseline and daily inpatient features for model training, validation, and test. Baseline features include demographic and clinical characteristics, and comorbidities. The daily inpatient features were vital signs, laboratory tests, medications administered, acute physiological scores, use of ventilator, and use of intensive care unit. 1832 hospitalized patients were identified (12,397 hospital days). Samples were randomly split at patient level (7:2:1) into training set (N=1,283 patients with 8,704 hospital days), validation set (N=366 patients with 2,524 days), and test/holdout set (patient N=183, and 1,169 days). Prediction models were trained on the training set and the validation set. We conducted the model training separately on the samples of admission day and the samples of days after admission day. The predictions were based on the ensemble learning from decision tree, XGBoost, logistic regression, and multilayer perceptron, long short-term memory (LSTM), bi-directional LSTM, and convolutional neural network. The combination of ensemble learning on the test/holdout set was used for final next 7-day predictions based on ‘hard’ voting (by majority). Where there was a tie, we used ‘soft’ voting (sum of probabilities) to break the tie. Figure 1.Data Processing PipelineFigure 2.Ensemble Model Architecture Results The overall average hospital length of stay was 8.7 (SD=10.5) days. The ensemble learning accuracies for admission-day samples and after-admission-day samples were 0.781 and 0.793, and the F1-scores for were 0.761 and 0.789, respectively. Conclusion EHR data of hospitalized COVID-19 patients can be used to predict next 7-day hospital discharges. Additional inpatient features and more advanced machine learning techniques are needed for prediction accuracy improvement. Disclosures All Authors: No reported disclosures.
Beyond Sight and Sound: The Role of Haptic Technology in Enhancing the Cinematic Virtual Reality Experience

Cinematic virtual reality (CVR) movies are a form of filmmaking gaining interest from Hollywood studios and media companies. It has been proven, that these movies offer an immersive and engaging experience for the audience, through the use of 360-degree views. The results of this study suggest that the haptic feedback in CVR movies offers the potential to enhance audience experience through feelings of connection, immersion, and interactivity.
The Effects of Reader’s Theatre on Students’ Reading Performance: A Systematic Review

Reading skills has always been emphasised in English language classrooms because reading skills is believed to be crucial in helping students become proficient writers and speakers in the process of second language acquisition.However, the "Programme for International Student Assessment" (PISA) 2018 results revealed that most of Malaysian students were still below international benchmark in reading.Therefore, the current systematic review focuses on the effectiveness of Reader's Theatre, a strategy to teach reading, in improving students' reading performance.Employing the "Preferred Reporting Items for Systematic Review and Meta-Analyses" (PRISMA) review methodology, a total of 15 eligible articles were selected from two databases, which were ERIC and Google Scholar.The identified articles ranged from the year 2013 to 2022.From the articles, it was found out that Reader's Theatre was effective in improving students' oral reading fluency and reading comprehension.Most of the studies in the primary school context focused on the components of oral reading fluency, such as pronunciation, word recognition and prosody; studies in the secondary school context focused more on vocabulary building and reading comprehension.The results also demonstrated the effects of Reader's Theatre in improving motivation among students.It is suggested that future studies focus more on investigating the effectiveness of Reader's Theatre with the integration of technology.
PERANCANGAN MOTION GRAPHIC VIDEO ANIMASI SEBAGAI SARANA IKLAN PROMOSI DAN INFORMASI

Advertising is a way to promote a product, brand or service to an audience to drive interest, engagement and sales. Social media can be utilized as a medium for spreading motion graphic content more widely and efficiently. This animation research aims to provide information on the effect of designing animation videos as a means of promotional advertising on Ucok Sticker and produce product advertising videos that can increase consumer appeal. From the research conducted obtained the results in the form of a 2D video animation product advertisements used as a promotional medium to attract consumer interest in Ucok Sticker products. This video is made using several software such as coreldraw, photohop and wondershare filmora x. It can be concluded that this research aims as a promotional advertisement and information to increase consumer attractiveness and sales. Researchers hope that this advertising video has better expectations for the future.
Pre-service Teachers’ Willingness of Teaching in Rural Areas

The strategic support of rural revitalization is the revitalization of education, and the revitalization of education depends on the revitalization of talents.In this study, questionnaire survey and semi-structured interview were used to investigate pre-service teachers' willingness of teaching in rural areas.In general, there are some problems, such as pre-service teachers have one-sided employment tendency, not active willingness of teaching in rural areas, weak service consciousness, low quality of hardship, insufficient career planning, incomplete understanding of employment situation, rural less practice experience, employment expectation gap, incomplete understanding of work content, less care about work policies.In addition, job stability, salary, working environment, public opinion, professional preference and career choice confidence have a positive impact on pre-service teachers' willingness of teaching in rural areas.Based on this, this paper put forward the promotion strategies of pre-service teachers' willingness of teaching in rural areas from the perspectives of individuals, universities and the government.Individuals should establish correct ideas and give
Topological Near Groups

Aims/ Objectives: The concept of topological group is a simple combination of the concepts of abstract group and topological space. The purpose of this paper is to combine the concepts of topological space and near groups to dene topological near groups on an nearness approximation space and study its properties.
W2V and Glove Embedding based Sentiment Analysis of Text Messages

Social media may be a capable communications medium, with a broad impact over cities as well as farther regions. Computerized change has not as it were affected businesses and made the world more available. It has had a long-lasting effect on the way individuals communicate and has become a necessary portion of their lives. For occurrence, WhatsApp has re-imagined the culture of IMs (moment informing) and taken it to a whole new level. The chat message will be a critical source of data to know the intention of an individual. In this consideration, it is being analyzed whether individuals are beneath stretch, and what almost their attitude towards others is. This could be surveyed utilizing W2V and Glove embedding, which can discover hidden semantic structures within the text body. The embedding learns the relationship between the words to build the representation. This is achieved by various methods like co-occurrence matrix, probabilistic modeling, and neural networks. The proposed framework clearly portrays the inserting of words, classification of extricated words, and giving an alarm to the concerned individual beneath crisis cases. It too gives nonstop monitoring of the client.
Pengaruh Model CIRC dan Teknik Close Reading Terhadap Membaca Kritis dalam Pembelajaran Bahasa Indonesia

This study was conducted to determine the effect of the CIRC model and close reading techniques on critical reading literacy in Indonesian language subjects for 5th graders at SDN Perumnas Tangerang. This research was conducted with a population of 171 students. The research sample was 42 students with an explanation of 21 students in the experimental class and 21 students in the control class. The research instrument used an observation sheet and a test sheet. Data analysis using T-test. From the results of the CIRC class hypothesis test, it was found that tcount (3.556) &gt; ttable (1.720). Hypothesis test results for the Close Reading class Sig. (2-tailed) &lt; 0.05 that is 0.04 and t count (3.146) &gt; t table (1.720). Anova test with sig = 0.000 &lt;0.05, Fcount (8.309) &gt; Ftable (4.325). The results of the analysis showed that there was an effect of the CIRC model and close reading technique on critical reading skills and the simultaneous effect of the CIRC model and close reading technique on students' critical reading ability.
Research on The Cultivation Path of Craftsman Spirit in Higher Vocational Education Based on Survey Data

With the development of China's economy and society, the importance of "craftsman's spirit" has become more and more prominent. As the main educational institution for training technical talents, higher vocational colleges vigorously promote the exploration of the cultivation path of craftsman spirit in higher vocational education, which provides new ideas and directions for the reform and development of higher vocational education, and is the fundamental need of the national innovation driven development strategy. Based on the questionnaire survey of vocational students in a certain range, this paper analyzes the problems existing in the cultivation path of craftsman spirit in Higher Vocational Education from multiple levels and the countermeasures.
A New Set of 3D Shifted Fractional-Order Gegenbauer Descriptors for Volumetric Image Representation

Volumetric images have a three-dimensional (3D) view, in which viewers can examine their characteristics from any angle. The more accurate the digital representation of volumetric images, the more precise and valuable the assessment of what these images represent. The representation of volumetric images is a significant area of study in pattern recognition and computer vision. Recently, volumetric image analysis using orthogonal moments with fractional order has opened up a new study pathway, which has led scholars to discover many real-life applications through investigating efficient algorithms to represent the features of 3D images. In this study, a new set of 3D shifted fractional-order Gegenbauer moments (FrGMs) for volumetric image representation is proposed. First, a mathematical description of the shifted Gegenbauer moments for 3D images is presented. Second, a fast, highly accurate method for calculating the fractional-order shifted Gegenbauer moments of 3D images is introduced. Finally, the efficiency of the proposed FrGMs is evaluated through various suitable experiments and compared with existing methods in terms of the reconstruction of 3D images, the invariability property, sensitivity to noise, and computation time. The experimental results clearly show that FrGMs outperform existing related algorithms.
Development of Android-Based Interactive Multimedia Non-Fiction Text Materials Containing Kediri Raya Local Wisdom and 4C Skills

The study aims to create interactive multimedia non-fiction text material based on Android with the content of Kediri Raya local wisdom and 4C skills and assess the validity of the developed interactive multimedia. This study used is development research methods with ADDIE model. The research subjects were fourth-grade elementary school students. The research instrument used a questionnaire. Qualitative descriptive data analysis in this study was used to process data sourced from comments and suggestions from validators contained in the validation questionnaire. Quantitative descriptive data analysis was used to analyze the data obtained from expert and user validation. The results showed that (1) First, performance and needs analysis. Second, the determination of interactive multimedia designs that are attractive, easy to use, and in accordance with learning. Third, the development of non-fiction text material containing Kediri Raya local wisdom, the development of questions containing 4C skills, and the development of Android-based interactive multimedia. Fourth, implementation, which is implemented in SD. Before being implemented in SD, it must go through the validation of material and media experts. Fifth, evaluation, which contains values and inputs given by material and media experts, is the basis for improving interactive multimedia and (2) the results of material expert validation get a score of 85.7% and media expert validation gets 97%. The product can be used in learning Indonesian in 4th-grade elementary school as a solution for online learning.