Reviewerly/exHarmony huggingface dataset:
Rural schools: a model for achieving targets for personal development in a psychologically comfortable educational environment

Introduction. Regulatory documents in the field of education clearly indicate the requirement to build a developing educational environment in each educational organization. At the same time, psychological comfort is an important condition for its functioning. To study the formation and support of such an environment is an urgent problem for rural schools, especially in the context of achieving the goals of developing the actual qualities of the personality of the information world. The purpose of the research is to justify and develop a model for achieving targets for personal development in a psychologically comfortable educational environment in a rural school. Materials and methods of the research. The main method is the triadic method of information criteria. Additional methods are the analysis of literary works on the problem and modeling. Research results. The model for achieving targets for personal development in a psychologically comfortable educational environment in a rural school has scientific justification and textual and graphical expression (design). The model is presented in steps. It has 7 levels, which together determine options for forming of personality traits that are in demand by modern challenges and threats as a component of the “21st century skills” for students in rural schools. The model determines the total of probable forms of the educational environment, identified according to the criterion "the possibility of developing personality traits that are in demand in the modern world, subject to psychological comfort". It allows a) to identify possible combinations of these personal qualities that determine the creation of different variations-types of the educational environment; b) to analyze the directions and trajectories which can be used to form and develop the environment for the required personality parameters. The model is an analogue of a morphological box, which, taking into account the context of a particular rural school, allows to determine the most optimal way for developing personality traits in an educational environment “tuned” for the priority improvement of independence, initiative, involvement, subjectivity, reflexivity and confidence of all subjects. Significance of the research. The results of the study develop the pedagogical theory of the educational environment in the context of achieving the goals of modern education and ensuring the psychological well-being of subjects. The developed model can be applied not only in rural schools, but also in urban ones, adjusted for a specific context. As for the managerial aspect, the result in the form of a model is valuable knowledge for administrative workers of educational organizations of any type.
A digital footprint as a mechanism of individualizing a student’s educational trajectory (on the case of the "Digital technologies of self-education" course)

Introduction. Digital transformation of the education sector leads to accumulation of a significant amount of digital information about the educational process. Analysis of a student's digital footprint can become one of the means of individualizing his educational trajectory. To personalize the progress of mastering an educational program, it is optimal to use the resources available in the toolbox of the educational institution. The purpose of the paper is to describe a model of an electronic training course based on LMS Moodle, which allows you to build a trajectory for studying a subject based on a student's digital footprint, as well as to describe the results of this course testing. Materials and methods. Modeling of the e-course was carried out on the basis of the analysis of the basic principles of individualization of the educational trajectory, described in scientific and methodological works. Testing of the course "Digital Technologies of Self-Education" was carried out on the basis of the Samara State Transport University. Nonparametric methods of mathematical statistics were used to process the obtained results. To compare the control and experimental groups, the Mann-Whitney. Results. The study revealed efficiency of including the mechanisms of individualization of the educational trajectory into the educational process. 86% of students completed their studies on time with a positive result. The number of students who continued to complete the course assignments after receiving the minimum points was 27%. The research confirmed the quality of knowledge and skills obtained by students in the course of mastering an electronic training course with the support of an automated mechanism for forming and assessing students' competencies. The study proved the positive impact of the course "Digital Technologies of Self-Education", developed on the basis of the algorithmization of the process of achieving the goal, on mastering related subjects (p &lt; 0,01). Practical significance. The proposed model for developing a training course based on LMS Moodle using standard system tools is universal and allows you to individualize the trajectory of a student's mastering of an academic discipline by varying educational tasks depending on the level of individual skills. Conclusion. Algorithmization and automation of the process of collecting and processing educational results of students frees a teacher from routine tasks, solves the problem of strengthening thematic and interdisciplinary connections in the learning process, and also enables to make the learning process more flexible and personalized without losing the quality of competencies formation. Students' understanding of their educational route enhances their motivation for learning, allows them to realize their individual needs in developing professional skills.
Virtual laboratory work in physics at a technical university

Introduction. In the post-pandemic period, when all universities are switching to full–time education, the problem of the meaning and place of virtual laboratory work (hereinafter referred to as VLR) in physics at a technical university has arisen in a new way. The following questions have become relevant: about the frequency of the use of different types of VLR in universities in Russia and abroad; about the effectiveness of their use for the formation of practical and intellectual experimental skills; about methods and means of training that allow to increase the effectiveness of the formation of experimental skills when performing VLR in physics. Materials and methods. Our research is based on the methodology of conducting a physical experiment and the requirements of digital learning. The methods of analysis of modern sources on the problem of the use of VLR in physics, questioning of teachers and testing of students on the problem of the formation of experimental skills when conducting VLR in physics in technical universities were used. The questionnaire was attended by physics teachers of MGUPP and the National Research University "MPEI". Testing was conducted among students of 1-2 courses of the named universities. Results. The most common types of VLR are VLR animations and VLR videos and the frequency of their use in technical universities. It was found that the frequency of using VRL animations is 24%, and VRL videos – 53%. It is proved that VLR animations with an interactive component make it possible to effectively form the practical and intellectual skills of students in laboratory classes. To strengthen the independence of students when performing VLR, methods of using interactive tests and using Excel calculation tables are proposed. In the Moodle environment, tests for 15 VLRs have been developed, an example of a test for VLR "Light Interference" is given. 10 Excel calculation tables have been created to monitor the results of laboratory work by students. Such tables for the VLR "Ohm's Law" and "Determination of the Poisson ratio by the Reichardt method" are given in the article. Conclusion. The prospects for further research include the creation of electronic lecture notes and their inclusion together with interactive tests and calculation tables in a demonstration and information complex on general physics.
Peran Kampus Mengajar dalam Meningkatkan Literasi, Numerasi dan Adaptasi Teknologi Peserta Didik Sekolah Dasar di Sumatera Barat

This study aims to determine the role of the Kampus Mengajar in improving literacy, numeracy and technological adaptation of elementary school students in West Sumatra in order to improve the quality of education. This type of research uses a descriptive qualitative approach. This research was conducted for six months. The location of this study was carried out in three elementary schools in West Sumatra, namely SD Negeri 18 Anduring Padang, SD-IT Ar-Rahman Palangki, Sijunjung Regency and SD-IT Medina Lengayang Pesisir Selatan. Procedures, techniques and instruments of data collection using observation, interviews and documentation. So it can be concluded that the Kampus Mengajar in improving literacy, numeracy and technological adaptation has an important and successful role in running its program which is assisted by students and the support of the school and students.
The Tribes of Machine Learning and the Realm of Computer Architecture

Machine learning techniques have influenced the field of computer architecture like many other fields. This paper studies how the fundamental machine learning techniques can be applied towards computer architecture problems. We also provide a detailed survey of computer architecture research that employs different machine learning methods. Finally, we present some future opportunities and the outstanding challenges that need to be overcome to exploit full potential of machine learning for computer architecture.
High Resolution Solar Image Generation using Generative Adversarial Networks

We applied Deep Learning algorithm known as Generative Adversarial Networks (GANs) to perform solar image-to-image translation. That is, from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager(HMI) line of sight magnetogram images to SDO/Atmospheric Imaging Assembly(AIA) 0304-{\AA} images. The Ultraviolet(UV)/Extreme Ultraviolet(EUV) observations like the SDO/AIA0304-{\AA} images were only made available to scientists in the late 1990s even though the magenetic field observations like the SDO/HMI have been available since the 1970s. Therefore by leveraging Deep Learning algorithms like GANs we can give scientists access to complete datasets for analysis. For generating high resolution solar images we use the Pix2PixHD and Pix2Pix algorithms. The Pix2PixHD algorithm was specifically designed for high resolution image generation tasks, and the Pix2Pix algorithm is by far the most widely used image to image translation algorithm. For training and testing we used the data for the year 2012, 2013 and 2014. The results show that our deep learning models are capable of generating high resolution(1024 x 1024 pixels) AIA0304 images from HMI magnetograms. Specifically, the pixel-to-pixel Pearson Correlation Coefficient of the images generated by Pix2PixHD and original images is as high as 0.99. The number is 0.962 if Pix2Pix is used to generate images. The results we get for our Pix2PixHD model is better than the results obtained by previous works done by others to generate AIA0304 images. Thus, we can use these models to generate AIA0304 images when the AIA0304 data is not available which can be used for understanding space weather and giving researchers the capability to predict solar events such as Solar Flares and Coronal Mass Ejections. As far as we know, our work is the first attempt to leverage Pix2PixHD algorithm for SDO/HMI to SDO/AIA0304 image-to-image translation.
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes

This work proposes a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes. It is constructed such that unimodal models struggle and only multimodal models can succeed: difficult examples ("benign confounders") are added to the dataset to make it hard to rely on unimodal signals. The task requires subtle reasoning, yet is straightforward to evaluate as a binary classification problem. We provide baseline performance numbers for unimodal models, as well as for multimodal models with various degrees of sophistication. We find that state-of-the-art methods perform poorly compared to humans (64.73% vs. 84.7% accuracy), illustrating the difficulty of the task and highlighting the challenge that this important problem poses to the community.
An Algebraic Method to Fidelity-based Model Checking over Quantum Markov Chains

Fidelity is one of the most widely used quantities in quantum information that measure the distance of quantum states through a noisy channel. In this paper, we introduce a quantum analogy of computation tree logic (CTL) called QCTL, which concerns fidelity instead of probability in probabilistic CTL, over quantum Markov chains (QMCs). Noisy channels are modelled by super-operators, which are specified by QCTL formulas; the initial quantum states are modelled by density operators, which are left parametric in the given QMC. The problem is to compute the minimumfidelity over all initial states for conservation. We achieve it by a reduction to quantifier elimination in the existential theory of the reals. The method is absolutely exact, so that QCTL formulas are proven to be decidable in exponential time. Finally, we implement the proposed method and demonstrate its effectiveness via a quantum IPv4 protocol.
Optimizing Optimizers: Regret-optimal gradient descent algorithms

The need for fast and robust optimization algorithms are of critical importance in all areas of machine learning. This paper treats the task of designing optimization algorithms as an optimal control problem. Using regret as a metric for an algorithm's performance, we study the existence, uniqueness and consistency of regret-optimal algorithms. By providing first-order optimality conditions for the control problem, we show that regret-optimal algorithms must satisfy a specific structure in their dynamics which we show is equivalent to performing dual-preconditioned gradient descent on the value function generated by its regret. Using these optimal dynamics, we provide bounds on their rates of convergence to solutions of convex optimization problems. Though closed-form optimal dynamics cannot be obtained in general, we present fast numerical methods for approximating them, generating optimization algorithms which directly optimize their long-term regret. Lastly, these are benchmarked against commonly used optimization algorithms to demonstrate their effectiveness.
Pengajaran Kosakata Dasar Bahasa Inggris (English Basic Vocabulary) dengan Metode Drilling Untuk Anak-Anak Desa Kalangan Mulur Sukoharjo

The purpose of this community service activity is to teach and introduce English vocabulary through the drilling method so that it can improve the ability to memorize English vocabulary. The method of community service used is a demo. In carrying out this activity, teaching English vocabulary is carried out using the drilling or retreat method. There are several stages carried out, namely: scheduling the teaching of foreign language vocabulary, presenting English vocabulary material, introducing and using English vocabulary around, inviting children to interpret or define each new vocabulary taught, repeating English vocabulary material, and conducting tests to assess the level of achievement in learning English vocabulary using the drilling method. So that children can master vocabulary easily, fun learning is needed so that children are interested in memorizing English vocabulary. In its use, children must also be able to manage time well by using various drilling methods so that children do not feel bored quickly, enthusiastically, and enthusiastically learning English. Here, the task of educators is to determine and use appropriate English teaching techniques applied to children.
Predicting Backchannel Signaling in Child-Caregiver Multimodal Conversations

Conversation requires cooperative social interaction between interlocutors. In particular, active listening through backchannel signaling (hereafter BC) i.e., showing attention through verbal (short responses like ``Yeah'') and non-verbal behaviors (e.g. smiling or nodding) is crucial to managing the flow of a conversation and it requires sophisticated coordination skills. How does BC develop in childhood?Previous studies were either conducted in highly controlled experimental settings or relied on qualitative corpus analysis, which does not allow for a proper understanding of children's BC development, especially in terms of its collaborative/coordinated use. This paper aims at filling this gap using a machine learning model that learns to predict children's BC production based on the interlocutor's inviting cues in child-caregiver naturalistic conversations. By comparing BC predictability across children and adults, we found that, contrary to what has been suggested in previous in-lab studies, children between the ages of 6 and 12 can actually produce and respond to backchannel inviting cues as consistently as adults do, suggesting an adult-like form of coordination.
The Effect of Social Support and Interest in Online Learning on Student Achievement Motivation

Changes in the order of learning, which are all face-to-face to online, certainly have many consequences, one of which is the aspect of achievement motivation. This study determines how social support and interest in online learning influence student achievement motivation. This study uses a quantitative approach by distributing online questionnaires. The population of this study is 2200 students using the slovin formula (5%), so the sample in this study is 338 students. Data analysis techniques include multiple linear regression with t-test and F-test as hypothesis testing. The calculation results of the data obtained by the multiple linear regression equation are Y = 41.561 + 0.471 - 0.189, with a large influence on social support and online learning interest with the student achievement motivation variable, namely 53.6%. The results of the hypothesis test obtained are tcount for social support variable 10,600 ttable (1,967), count for online learning interest variable 5,161 ttable (1,967. Simultaneously, Fount 194,868 Ftable 3.02. Based on these results, it is stated that the variables of social support and interest are online learning has a positive and significant effect on students' achievement motivation variables.
Implementing a Ternary Decomposition of the Toffoli Gate on Fixed-FrequencyTransmon Qutrits

Quantum computation is conventionally performed using quantum operations acting on two-level quantum bits, or qubits. Qubits in modern quantum computers suffer from inevitable detrimental interactions with the environment that cause errors during computation, with multi-qubit operations often being a primary limitation. Most quantum devices naturally have multiple accessible energy levels beyond the lowest two traditionally used to define a qubit. Qudits offer a larger state space to store and process quantum information, reducing complexity of quantum circuits and improving efficiency of quantum algorithms. Here, we experimentally demonstrate a ternary decomposition of a multi-qubit operation on cloud-enabled fixed-frequency superconducting transmons. Specifically, we realize an order-preserving Toffoli gate consisting of four two-transmon operations, whereas the optimal order-preserving binary decomposition uses eight \texttt{CNOT}s on a linear transmon topology. Both decompositions are benchmarked via truth table fidelity where the ternary approach outperforms on most sets of transmons on \texttt{ibmq\_jakarta}, and is further benchmarked via quantum process tomography on one set of transmons to achieve an average gate fidelity of 78.00\% $\pm$ 1.93\%.
End to End Trainable Active Contours via Differentiable Rendering

We present an image segmentation method that iteratively evolves a polygon. At each iteration, the vertices of the polygon are displaced based on the local value of a 2D shift map that is inferred from the input image via an encoder-decoder architecture. The main training loss that is used is the difference between the polygon shape and the ground truth segmentation mask. The network employs a neural renderer to create the polygon from its vertices, making the process fully differentiable. We demonstrate that our method outperforms the state of the art segmentation networks and deep active contour solutions in a variety of benchmarks, including medical imaging and aerial images. Our code is available at https://github.com/shirgur/ACDRNet.
Implementing the Tangent Graeffe Root Finding Method

The tangent Graeffe method has been developed for the efficient computation of single roots of polynomials over finite fields with multiplicative groups of smooth order. It is a key ingredient of sparse interpolation using geometric progressions, in the case when blackbox evaluations are comparatively cheap. In this paper, we improve the complexity of the method by a constant factor and we report on a new implementation of the method and a first parallel implementation.
MFNet: Panoptic segmentation network based on multiscale feature weighted fusion and frequency domain attention mechanism

Existing panoptic segmentation networks based on multiscale methods do not distinguish shared features according to different scales, which leads to a loss of feature information and suboptimal results. To solve this problem, the authors improve on UPSNet (a unified panoptic segmentation network) by introducing a multiscale fusion module cascaded between layers into the semantic segmentation head of UPSNet. On the one hand, this enriches the feature information for differently scaled features by cascading the feature information of adjacent scales, and on the other hand, the feature information at each scale can be combined adaptively to achieve a better segmentation effect. Furthermore, a frequency domain attention mechanism is introduced into the backbone network of UPSNet to improve the feature extraction ability of the backbone network by learning more frequency domain feature information. The experimental results of the authors’ improved network on the Common Objects in Context (COCO) and Cityscapes data sets show that compared with UPSNet, the performance is significantly improved: the panoptic quality is improved by 0.9% and 1.6%, respectively.
Architecture using blockchain data privacy for healthcare data management

With the fast development of blockchain technology in latest years, its application in scenarios that require privacy, such as health area, became encouraged and widely discussed. This paper presents an architecture to ensure the privacy of health-related data, which are stored and shared within a blockchain network in a decentralized manner, through the use of encryption with the RSA, ECC and AES algorithms. Evaluation tests were performed to verify the impact of cryptography on the proposed architecture in terms of computational effort, memory usage and execution time. The results demonstrate an impact mainly on the execution time and on the increase in the computational effort for sending data to the blockchain, however, justifiable considering the privacy and security provided with the architecture and encryption.
Data Synthesis for Fairness Audits of Learning Analytics Algorithms

The purpose of methods of fairness auditing is to uncover to what extent Learning Analytics algorithms are fair. Fairness auditing methods often rely on pre-existing test data. In the context of Learning Analytics auditing, learning data is needed for testing. However, learning data might not be available (in large quantities) due to privacy concerns. Our poster shares our findings on how relational data for fairness audits of Learning Analytics systems can be synthesized from little pre-existing data, using the most promising available data synthesizers.