Reviewerly/exHarmony huggingface dataset:
Transfer Learning as a Method to Reproduce High-Fidelity NLTE Opacities in Simulations

Simulations of high-energy density physics often need non-local thermodynamic equilibrium (NLTE) opacity data. This data, however, is expensive to produce at relatively low-fidelity. It is even more so at high-fidelity such that the opacity calculations can contribute ninety-five percent of the total computation time. This proportion can even reach large proportions. Neural networks can be used to replace the standard calculations of low-fidelity data, and the neural networks can be trained to reproduce artificial, high-fidelity opacity spectra. In this work, it is demonstrated that a novel neural network architecture trained to reproduce high-fidelity krypton spectra through transfer learning can be used in simulations. Further, it is demonstrated that this can be done while achieving a relative percent error of the peak radiative temperature of the hohlraum of approximately 1\% to 4\% while achieving a 19.4x speed up.
Entanglement polygon inequalities for pure states in qudit systems

Entanglement is one of the important resources in quantum tasks. Recently, Yang $et$ $al.$ [arXiv:2205.08801] proposed an entanglement polygon inequalities (EPI) in terms of some entanglement measures for $n$-qudit pure states. Here we continue to consider the entanglement polygon inequalities. Specifially, we show that the EPI is valid for $n$-qudit pure states in terms of geometric entanglement measure (GEM), then we study the residual entanglement in terms of GEM for pure states in three-qubit systems. At last, we present counterexamples showing that the EPI is invalid for higher dimensional systems in terms of negativity, we also present a class of states beyond qubits satisfy the EPI in terms of negativity.
Ancestor-to-Creole Transfer is Not a Walk in the Park

We aim to learn language models for Creole languages for which large volumes of data are not readily available, and therefore explore the potential transfer from ancestor languages (the 'Ancestry Transfer Hypothesis'). We find that standard transfer methods do not facilitate ancestry transfer. Surprisingly, different from other non-Creole languages, a very distinct two-phase pattern emerges for Creoles: As our training losses plateau, and language models begin to overfit on their source languages, perplexity on the Creoles drop. We explore if this compression phase can lead to practically useful language models (the 'Ancestry Bottleneck Hypothesis'), but also falsify this. Moreover, we show that Creoles even exhibit this two-phase pattern even when training on random, unrelated languages. Thus Creoles seem to be typological outliers and we speculate whether there is a link between the two observations.
VALUE ORIENTATIONS OF MODERN STUDENTS

The article deals with some problems of value orientations in students' life. The analysis of value orientations in students’ life in higher educational establishments of economy and pedagogy is provided.
Online team working with emerging technologies in a university math class

This work presents a new approach devoted to recreating a team working situation in a STEAM university mathematics class by using Engageli, an e-learning platform for real-time online higher education courses. A particular experience is considered, in order to understand the core tenets to construct a fruitful online group activity in mathematics. The success of such an activity depends on both the usage of an intuitive software user-friendly and the creation of suitable and stimulating learning paths. The analysis is based on the well-known SWOT analysis concerning the students’ comments, integrated by the answers of four multiple choice questions and the analytics provided by Engageli. The outcome of the work suggests that the online experience has potential, but further improvements are needed.
Mixture of von Mises-Fisher distribution with sparse prototypes

Mixtures of von Mises-Fisher distributions can be used to cluster data on the unit hypersphere. This is particularly adapted for high-dimensional directional data such as texts. We propose in this article to estimate a von Mises mixture using a l 1 penalized likelihood. This leads to sparse prototypes that improve clustering interpretability. We introduce an expectation-maximisation (EM) algorithm for this estimation and explore the trade-off between the sparsity term and the likelihood one with a path following algorithm. The model's behaviour is studied on simulated data and, we show the advantages of the approach on real data benchmark. We also introduce a new data set on financial reports and exhibit the benefits of our method for exploratory analysis.
Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data

Recently, Self-Supervised Representation Learning (SSRL) has attracted much attention in the field of computer vision, speech, natural language processing (NLP), and recently, with other types of modalities, including time series from sensors. The popularity of self-supervised learning is driven by the fact that traditional models typically require a huge amount of well-annotated data for training. Acquiring annotated data can be a difficult and costly process. Self-supervised methods have been introduced to improve the efficiency of training data through discriminative pre-training of models using supervisory signals that have been freely obtained from the raw data. Unlike existing reviews of SSRL that have pre-dominately focused upon methods in the fields of CV or NLP for a single modality, we aim to provide the first comprehensive review of multimodal self-supervised learning methods for temporal data. To this end, we 1) provide a comprehensive categorization of existing SSRL methods, 2) introduce a generic pipeline by defining the key components of a SSRL framework, 3) compare existing models in terms of their objective function, network architecture and potential applications, and 4) review existing multimodal techniques in each category and various modalities. Finally, we present existing weaknesses and future opportunities. We believe our work develops a perspective on the requirements of SSRL in domains that utilise multimodal and/or temporal data
Penerapan Metode Haversine Formula Pada Pencarian Lokasi Fasilitas Kesehatan Terdekat

Health facilities are places used to provide health services to the community. Types of public health facilities include hospitals, health centers, clinics, doctor's practices and pharmacies. Knowing the location of the nearest health facility is important in an emergency situation. One way to access health facility information quickly and easily is by using an application on a smartphone. This study builds a mobile application to locate the nearest health facility using the haversine formula. The use of the haversine formula allows users to search for the nearest health facility based on the user's current location. The application that was built can also display information in the form of a sequence of the nearest health facilities based on location data and the type of health facility selected. Testing on the functionality of the application interface was carried out using the black box method, and the test results were obtained based on user ratings of 82.38%, which means that the application built is in the very good category in terms of application interface functionality.
Enhancing grammar competence of vocational school students through the Omygram learning chart

Various methods and approaches have been used to support English learning. However, there is still a lack of information on how the grammar competence of vocational students can be improved in the Malaysian context. This study aims to build the Omygram learning chart and identify its effects on the achievement level of the students in Program Vokasional Menengah Atas (PVMA), or the upper-secondary vocational programme, on their English grammar competence. The study involved two secondary schools in Johor, Malaysia, employing a quasi-experimental design. A descriptive analysis from SPSS showed the increment in the mean score for the post-test of the treatment group of 5.58, higher than the control group (0.33) after the intervention was applied. The results of t-test showed no significant difference in achievement between pre-test and post-test for the control group [p = 0.504, (p 0.05)]. On the other hand, the result of the t-test for the treatment group showed a significant difference in achievement between the pre-test and post-test [p = 0.000, (p0.05)]. Students’ motivation after using the Omygram learning chart was also high. In conclusion, the development of the Omygram learning chart is proven to effectively increase students’ achievement in English. This confirms that developing learning aids improves students’ grammar competence and motivates them to learn the language better. As a result, the goal of producing a skilled workforce in TVET (Technical Education and Vocational Training) and possessing soft skills will be more achievable.
Implementasi Gerakan Literasi dalam Pembentukan Karakter Siswa di MI Muhammadiyah Losari Rawalo Banyumas

This study aims to examine how the implementation of literacy culture in the formation of student character at Madrasah Ibtidaiyah Muhammadiyah Losari Rawalo Banyumas for the 2020/2021 Academic Year. This research is a type of field research using a qualitative descriptive approach. The research subjects were the principal, fifth grade teacher, librarian, and fifth grade student. Data collection techniques were observation, interviews, and documentation. Data analysis techniques with data reduction, data presentation, and conclusions. The results showed that the implementation of the School Literacy Movement (GLS) in building student character at MI Muhammadiyah Losari Rawalo Banyumas for the 2020/2021 academic year was carried out by habituation and growing interest in reading. The habituation and growth of reading interest is carried out by reading activities 15 minutes before the KBM (Teaching and Learning Activity), the existence of a routine schedule of library visits, the use of literacy corners in every corner of the classroom, and the provision of motivational walls from student work. The implementation of the School Literacy Movement (GLS) has a positive impact on the formation of student character, namely religious character, discipline, love of reading, and responsibility.
Quantum density estimation with density matrices: Application to quantum anomaly detection

Density estimation is a central task in statistics and machine learning. This problem aims to determine the underlying probability density function that best aligns with an observed data set. Some of its applications include statistical inference, unsupervised learning, and anomaly detection. Despite its relevance, few works have explored the application of quantum computing to density estimation. In this article, we present a novel quantum-classical density matrix density estimation model, called Q-DEMDE, based on the expected values of density matrices and a novel quantum embedding called quantum Fourier features. The method uses quantum hardware to build probability distributions of training data via mixed quantum states. As a core subroutine, we propose a new algorithm to estimate the expected value of a mixed density matrix from its spectral decomposition on a quantum computer. In addition, we present an application of the method for quantum-classical anomaly detection. We evaluated the density estimation model with quantum random and quantum adaptive Fourier features on different data sets on a quantum simulator and a real quantum computer. An important result of this work is to show that it is possible to perform density estimation and anomaly detection with high performance on present-day quantum computers.
Several significant equalities on intuitionistic fuzzy operators

There are various operators and operations in intuitionistic fuzzy set theory. The roles of these operators and operations are very significant as they show a deeper interconnection between the two ordinary modal logic operators. It can be well noted that modal operators can change intuitionistic fuzzy sets into fuzzy sets easily. Considering all of these, we establish several equalities on intuitionistic fuzzy sets.
Students’ Strategies in Improving Their Reading Comprehension

The problem of this research was that there was no clear information about students’ strategies in improving their reading comprehension. Therefore, a descriptive quantitative method was used to find out the students’ strategies in improving their reading comprehension. A questionnaire consisting of 10 questions was distributed English Education Department students of IAIN Batusangkar to collect research data. The questionnaire was distributed to them, and there were 37 students who gave their responses. The data were analyzed by using descriptive statistical analysis. Based on the research results, it was found that there were three steps of reading strategies applied by students in improving their reading comprehension. In pre-reading stage, students applied six reading strategies: previewing, creating question, making prediction, writing the main points of what reader already know about the text, reading introductions and summaries, and creating a pre-reading outline strategy. The mostly applied strategy was previewing strategy, and the least one was reading introductions and summaries strategy. In whilst reading stage, they applied reading the selection text, visualizing, paraphrasing, elaborating, changing reading rate, inferring, note-taking, and rereading strategy. The mostly applied strategy was paraphrasing strategy, and the least one was rereading strategy. In the post-reading stage, students applied two reading strategies: summarizing and reviewing. Between both strategies, summarizing was dominantly applied.
Orthogonal Transform based Generative Adversarial Network for Image Dehazing

Image dehazing has become one of the crucial preprocessing steps for any computer vision task. Most of the dehazing methods try to estimate the transmission map along with the atmospheric light to get the dehazed image in the image domain. In this paper, we propose a novel end-to-end architecture that directly estimates dehazed image in Krawtchouk transform domain. For this a customized Krawtchouk Convolution Layer (KCL) in the architecture is added. KCL is constructed using Krawtchouk basis functions which converts the image from the spatial domain to the Krawtchouk transform domain. Another convolution layer is added at the end of the architecture named as Inverse Krawtchouk Convolution Layer (IKCL) which converts the image back to the spatial domain from the transform domain. It has been observed that the haze is mainly present in lower frequencies of hazy images, wherein the Krawtchouk transform helps to analyze the high and low frequencies of the images separately. We have divided our architecture into two branches, the upper branch deals with the higher frequencies while the lower branch deals with the lower frequencies of the image. The lower branch is made deeper in terms of the layers as compared to the upper branch to address the haze present in the lower frequencies. Using the proposed Orthogonal Transform based Generative Adversarial Network (OTGAN) architecture for image dehazing, we were able to achieve competitive results when compared to the present state-of-the-art methods.
Comparison of Supervised and Self-Supervised Deep Representations Trained on Histological Images

Self-supervised methods gain more and more attention, especially in the medical domain, where the number of labeled data is limited. They provide results on par or superior to their fully supervised competitors, yet the difference between information coded by both methods is unclear. This work introduces a novel comparison framework for explaining differences between supervised and self-supervised models using visual characteristics important to the human perceptual system. We apply this framework to models trained for Gleason score and conclude that self-supervised methods are more biased toward contrast and texture transformation than their supervised counterparts. At the same time, supervised methods code more information about the shape.
Wavelet Feature Maps Compression for Image-to-Image CNNs

Convolutional Neural Networks (CNNs) are known for requiring extensive computational resources, and quantization is among the best and most common methods for compressing them. While aggressive quantization (i.e., less than 4-bits) performs well for classification, it may cause severe performance degradation in image-to-image tasks such as semantic segmentation and depth estimation. In this paper, we propose Wavelet Compressed Convolution (WCC) -- a novel approach for high-resolution activation maps compression integrated with point-wise convolutions, which are the main computational cost of modern architectures. To this end, we use an efficient and hardware-friendly Haar-wavelet transform, known for its effectiveness in image compression, and define the convolution on the compressed activation map. We experiment with various tasks that benefit from high-resolution input. By combining WCC with light quantization, we achieve compression rates equivalent to 1-4bit activation quantization with relatively small and much more graceful degradation in performance. Our code is available at https://github.com/BGUCompSci/WaveletCompressedConvolution.
Tracking semantic relatedness: Numeral classifiers guide gaze to visual world objects

Directing visual attention towards items mentioned in utterances can optimize understanding the unfolding spoken language and preparing to behave appropriately in different world situations. In particular, numeral classifiers not only specify semantic classes of nouns but could function as reference trackers. Whereas all classifier types function to single out objects for reference in the real world and may assist attentional guidance, we propose that only sortal classifiers are inherently attached to the nouns’ semantics; whereas container classifiers are pragmatically attached and the default classifiers index a noun without specifying the semantics. Using eye tracking and the “visual world paradigm”, we found that classifier types affect the rapidity of spontaneously looking at the target objects on a screen. We had Chinese speakers (N=20) listen to sentences and we observed that they looked spontaneously within 150 ms after offset of the Sortal classifier. Shortly after 200 ms the same occurred for the container classifiers, but with the default only after 764 ms. These latencies were significantly different from each other indicating that the stronger the semantic relatedness between a classifier and its noun, the more efficient the deployment of overt attention.
Use of the Manufacturing Execution System in Practice: Understanding the end Users and the Acceptance of the System

Purpose – this paper aims to examine the factors that affect the acceptance and use of the Manufacturing Execution System from the point of view of end users, using the Unified Theory of Acceptance and Use of Technology (UTAUT) model.Theoretical framework - The technology acceptance model is based on principles adopted in the attitude paradigm of psychology, which specifies how to measure the relevant attitudinal components of behavior; distinguishes beliefs and attitudes and specific as external stimuli, the objective characteristics of an attitude object, are causally linked to beliefs, attitudes and behavior.Design/methodology/approach - Data were collected by the survey method, using the seven-point Likert scale. The approach was quantitative, using the Structural Equation Modeling technique, using SPSS V.25 software.Findings - The findings of this study showed that the Facilitating Conditions influence the Usage Behavior of users of the Manufacturing Execution System. The application of multi-group comparison analysis (MGA) showed that Effort Expectancy positively influences Behavioral Intention and Social Influence also positively affects Behavioral Intention.Research, Practical &amp; Social implications - The study brings relevant contributions in relation to aspects of understanding about the level of acceptance of MES end users in the operation phase.Originality/value - This paper makes a contribution to the literature due to the pioneering nature of the research, since no research was found in academic bases that address the use of UTAUT with the MES system. Another important contribution is that this study proposes a new model, based on UTAUT using the application of MGA.Keywords - Acceptance and Use. Technology. Manufacturing Execution System.
ORGANIZATION PRINCIPLES AND APPROACHES ANALYSIS TO IMPROVING THE PERSON RE-IDENTIFICATION ACCURACY IN DISTRIBUTED VIDEO SURVEILLANCE SYSTEMS

The paper presents a classification of existing re-identification systems according to such criteria as system type, requests number and type, and operating time. The general scheme is discussed, which reflects the basic operation principle of re-identification systems, and the main approaches and methods for solving this problem using convolutional neural networks are considered. The study ways existing to improve re-identification algorithms and systems accuracy has been carried out. The influence analysis hyperparameters choice in convolutional neural networks training on the efficiency and dynamics re-identification algorithm training is carried out.
Credit Card Fraud Detection System

Abstract: In today’s world everything is online which also increases the chances of fraud. There are approximately 36.4 percent fraud related to commercial cards which include credit card, debit card, etc. In 2022 there are 64 million people who uses credit card to initiate the transaction, therefore they are also prone to the card fraud. So, in this document we talk about the various machine learning algorithms to predict the credit card fraud based on the previous transaction pattern. Keywords: K nearest neighbour, Machine learning, Credit card, decision tree, random forest