Reviewerly/exHarmony huggingface dataset:
About an Estimation Problem of a Linear System with Delay of Information

The problem of guaranteed estimation with geometrically bounded initial states and integrally limited disturbances is considered under delay information in the measurement equation. At the additional assumptions the problem is reduced to the creation of the reachable set of a special system. A discrete multistage system is specified for which the information set converges in Hausdorff’s metric to the corresponding information set of the continuous system when the diameter of partition is reduced. A numerical example is given.
Backend Systems for Mini-ICAL

The India-based Neutrino Observatory (INO) is a multi-institutional mega science project supported by DAE and DST, with the aim of experimental neutrino physics besides other topics. INO proposes a large magnetized Iron Calorimeter (ICAL) comprising of about 50KT of magnetized iron and 28800 RPCs as active elements. The mini-ICAL is a scaled-down prototype of the proposed ICAL, and it comprises of 20 RPCs with $$\sim $$ 85T of magnetized iron as an absorber. All the proposed design schemes for the ICAL are implemented in the mini-ICAL, which is currently operating and producing physics data, located in the transit campus at Madurai. “Backend systems” refers to the set of servers and applications that receive, record, visualize and act upon various kinds of data that are pushed/pulled from the Frontend of the detector. “Frontend” refers to the set of all devices that generate data—these include the RPCs that generate physics data, devices/gauges that generate various time-series data and also various active devices that put out informational log lines. The RPCs and the Backend systems are connected up in the form of a Local Area Network (LAN) using standard Ethernet hardware and IP protocols. Some software applications are custom designed and some are instances of free and open-source software (FOSS).
Heuristic algorithm for obtaining permutations with given cryptographic properties using a generalized construction

In this paper, we study a generalized construction of (2m, 2m)-functions using monomial and arbitrary m-bit permutations as constituent elements. We investigate the possibility of constructing bijective vectorial Boolean functions (permutations) with specified cryptographic properties that ensure the resistance of encryption algorithms to linear and differential methods of cryptographic analysis. We propose a heuristic algorithm for obtaining permutations with the given nonlinearity and differential uniformity based on the generalized construction. For this purpose, we look for auxiliary permutations of a lower dimension using the ideas of the genetic algorithm, spectral-linear, and spectral-difference methods. In the case of m = 4, the proposed algorithm consists of iterative multiplication of the initial randomly generated 4-bit permutations by transposition, selecting the best ones in nonlinearity, the differential uniformity, and the corresponding values in the linear and differential spectra among the obtained 8-bit permutations. We show how to optimize the calculation of cryptographic properties at each iteration of the algorithm. Experimental studies of the most interesting, from a practical point of view, 8-bit permutations have shown that it is possible to construct 6-uniform permutations with nonlinearity 108.
Identifying Sentiment from Crowd Audio

Oftentimes the voices of a few drown out the voices of many. We interpret this problem literally as we investigate the ability of supervised machine learning models to predict sentiment directly from crowd audio in which multiple speakers are speaking simultaneously. Using a dataset of one second audio recordings of individuals saying “yes” or “no” (n=4753), we mixed together different voices speaking at the same time to create a large dataset (n=150k) of crowd audio responses. Each audio mixture was annotated with the specific proportion of the crowd response. On different mixture sizes ranging from 1 to 10 constituent voices, we trained BLSTM models using raw audio to predict the average sentiment of the crowd response. We were able to predict the average crowd sentiment for up to 10 speakers speaking simultaneously (Pearson’s r=0.70), suggesting the feasibility of a deep-learning approach to approximate simple sentiment directly from audio recordings with overlapping speakers. We discuss our results in the context of the need for machine listening systems that make can make equitable real-time determinations about crowd sentiment, so that no voice goes unheard.
Analysis of Relationships between Common Distributions Based on Computing Science

Random variables are the most fundamental concept in probability theory and mathematical statistics.Each random variable can have a distribution, different random variables can have different distributions, and different random variables can have the same distribution.There are many random variables, and there are many distributions that can be formed, but there are not many commonly used distributions.In general probability theory and mathematical statistics textbooks, each distribution is explained separately, and the relationship between distributions is occasionally discussed, and there is no detailed description.Establishing the correspondence between distributions plays an important role in mastering the theory and relationship of common distributions.This paper studies the relationship between distributions, and establishes some common distribution diagrams on the basis of distribution relationships, so as to illustrate the connections between some distributions with distribution diagrams.
Research on Teaching Exploration of Children’s Songs in Preschool Education Majors

The level of playing and singing of preschool teachers is directly related to children's music enlightenment and the quality of children's music education.Children's song playing and singing skills are the basic teaching skills that preschool teachers must master.The requirements for playing and singing of children's songs in preschool education are different from those in music professional colleges, and it must be targeted when cultivating students' playing and singing skills.At present, the children's song playing and singing ability of students majoring in preschool education is generally relatively weak.In order to effectively carry out the teaching of children's song playing and singing, the subject research has carried out in-depth explorations on this, in order to improve the teaching effect of teachers, so as to promote the improvement of students' level of children's song playing and singing.Build a solid foundation for future work.
Penerapan Metode Daring Sebagai Optimalisasi Media Pembelajaran Bahasa Inggris Masyarakat Milenial Di Masa Pandemi Covid-19

The level of awareness about education in general and the ability to speak English is still low, especially in the millennial community, so it is necessary to make innovations in learning English. In order to support the implementation of English language learning in millennial communities during the COVID-19, it is necessary to apply an online method. During the pandemic, the government's efforts, one of which is face-to-face learning, cannot be done because it is to reduce and prevent the spread of COVID-19. Based on this explanation, our team held a devotion activity by the title "Implementation of Online Methods as Optimizing English Learning for Millennials in the COVID-19 Pandemic". The results of these activities ran well. It is hoped that this activity can improve the language skills of the millennial community by being much practice, especially English.
3D FACE RECONSTRUCTION FROM HARD BLENDED EDGES

3D face reconstruction from 2D images is an important research topic because it supports a wide range of applications, such as face recognition, animations, games, and AR/VR systems.3D face reconstruction from contour features is a challenging task, because traditional edge detection algorithms produce a lot of noises, which are prone to making the reconstruction model trapped in a local optimum or even being degraded.With the development of deep learning, a lot of researcher introduce neural network into contour detection, which can extract relatively clear contours compared with previous methods.In this article, we employ a hard blended face contour feature from neural network and canny edge extractor for face reconstruction.Our method not only improves the 3D face model reconstruction accuracy on synthesis images, but performs more accurately and robustly on in-the-wild images under blurriness, makeup, occlusion and ill-illumination conditions.
Speaker Recognition System Based on Mel Frequency Cepstral Coefficient and Four Features

Biometrics signs are the most important factor in the human recognition field and considered an effective technique for person authentication systems. Voice recognition is a popular method to use due to its ease of implementation and acceptable effectiveness. This research paper will introduce a speaker recognition system that consists of preprocessing techniques to eliminate noise and make the sound smoother. For the feature extraction stage, the method Mel Frequency Cepstral Coefficient (MFCC) is used, and in the second step, the four features (FF) Mean, Standard Division, Zero-Cross and Amplitude, which added to (MFCC) to improve the results. For data representation, vector quantization has been used. The evaluation method (k-fold cross-validation) has been used. Supervised machine learning (SML) is proposed using Quadratic Discriminant Analysis (QDA) classification algorithms. And the results obtained by the algorithm (QDA) varied between 98 percent and 98.43 percent, depending on the way of features extraction that was used. These results are satisfactory and reliable. Index Terms— SML, QDA, Voice Recognition, MFCC, FF.
The extent of the awareness of Al-Taif University students towards the digital moral values

The study aimed at identifying the extent of awareness of Al-Taif University students towards the digital moral values according to their viewpoints. It also aimed at providing some proposed mechanisms for enhancing such values. The study employed the descriptive analytical approach. To verify its objectives, the study designed a questionnaire which was conducted on a sample of (476) students (Males and females) from Al-Taif University. Some of the most important findings that the study found were : the extent of awareness of Al-Taif University student’s towards the digital moral values was high. The average of the agreement of the sample individuals on the extent of awareness towards the digital moral values was (4.13%). The findings also showed the absence of statistically significant differences in the extent of awareness towards the digital moral values among the sample individuals, attributed to the gender variable. While there were differences according to the variable of the education stage in favor of the sample individuals from those who have Bachelor’s degree. There are also differences attributed to the variable of the type of the name used in the general digital processes in favor of those who use the real name rather than the nickname. Furthermore, there are differences between the individuals of the sample attributed to the variable of accessing the combatting the public informatics crimes system in favor those who access such a system. Some of the most important proposed mechanisms for enhancing the digital moral values are: the necessity of inclusion of (the docu-ment of the rights and obligations of the university student) the punishments resulting from the moral breaches in using the technology, setting out a guide (standards of the digital values) for promoting the digital behaviour. The study recommends that the moral dimension of technology must be included in the study plans of the academic programmes in universities.
THE RESPONSIBILITY TO CONTINUE TRADITIONS IN THE NEW MILLENNIUM – 20TH NATIONAL SCIENTIFIC CONFERENCE WITH INTERNATIONAL PARTICIPATION „KNOWLEDGE SOCIETY AND 21ST CENTURY HUMANISM“

„Knowledge society and 21st century humanism“ is a national scientific conference with international participation held annually on 1st November at the University of Library Studies and Information Technologies and dedicated to the Day of the leaders of the Bulgarian Revival. The aim is, along with the celebration of the Day of the leaders of the National Revival, to give the scientific community the opportunity to present and get acquainted with the achievements in various thematic areas of knowledge. The idea for holding this forum came from Assoc. Prof. Dr. Petar Parijkov, lecturer and head of the Department of Book Distribution and General Education Disciplines at the then College of Librarianship in 2003. The concept is to initiate an international scientific conference entitled „The Book: Future Time in the Past?!“ within the traditional Sofia Book Fair. After the death of Petar Parijkov in 2007 the conference was organized by Prof. Dr. Kristina Varbanova–Dencheva and the Department of Book and Society. Since 2019, the team has been organizing the conference includes lecturers from the Department of Comprehensive Studies and all departments of the two faculties at ULSIT and is headed by Assoc. Prof. Dr. Lubomira Parijkova (Department of Library Sciences). The report presents various stages of the organization of the conference, describing the mandatory elements, innovations over the years, following the traditions established over time.
Ex-vivo Porcine Model for Generating an Internal Surface in Biological Subjects using 3D-GAN

Robotic automation in the medical industry is complex because of the biological nature of the processed materials and the risks to the patient. Everyone is different, which means that each individual has their own characteristics; different size, bone structure, joint positions, etc. This makes it hard for robots to autonomously operate on humans. RGB-D consumer devices and computing power are revolutionising the way robots interact with the environment. With the camera’s intrinsic parameters and the depth frame, a point cloud, i.e., a set of points in a Cartesian space $\mathrm{IR}^{3}$, can be generated giving more complete information to the application in real-time. This work investigates a GAN (Generative Adversarial Network) to generate the internal surface of an ex-vivo porcine left ham, as a precursor to consideration of human models. That is important as a prediction of the internal structure. A good internal ham surface could be generated even with a small dataset. The complexity of the shapes in the generated data are shown and structures like the ball-joint attachment can be seen.
THE WORLD AND THE TRANSFORMATION OF THE EDUCATION SYSTEM: A STUDY OF THE QUESTION

Today's world is "unstable" and "superfast. Under these conditions, society programs its future through the educational system. Education becomes a way to restore the balance with the global systems of the Earth. A new view on the functionality of the educational system and the process of transformation of educational models is being formed. The gap between the existing educational models and the needs of society is widening. This suggests the need for a change in the educational paradigm. Consequently, a new vision of education becomes an urgent scientific problem. The article analyzes the state of the educational system. Education is becoming convergent. The main principles of convergent education are characterized: education of understanding, not memorization; nurturing the ability to constantly change; forming the skills of information verification; teaching the ability to learn and withstand stress; forming the ability to live in a digital world and preserve humanity. The results of the study of the principles of convergent education were analyzed and summarized. The results are compared with the results of the survey of students. The diversity and commonality of positions are revealed. Urgent problems of educational process transformation are formulated. The necessity of a new format of learning - "learning on the full cycle of human life" is justified. This requires research on the aging human being; the creation of an innovative learning environment for age-appropriate learners. The inevitability of constant transformation of information and communication learning environment for new opportunities has been revealed; the need to modernize the system of indicators for evaluating and tracking the achievements of students. New ways and methods of evaluating creativity, intelligence, ability to co-create are needed. Their development is possible through interaction between students, employers and professional associations. The relevance of working out the technology of blended learning is shown. For this purpose it is necessary to investigate the methodological, psychological problems and consequences of blended learning for the individual. The changes described above force us to rethink the essence of the educational organization. The problem of increasing the effectiveness of educational management comes to the fore. This is possible through the implementation of a scientific approach to organization management. The administrator becomes a scientist; the head of the unit must have knowledge of educational management and self-management. The study showed that the improvement of the norms of civilizational development goes through the global renewal of educational models. As a result, a human-dimensional society is created, human-dimensionality of education and science system is realized.
ICEVis: Interactive Clustering Exploration for tumor sub-region analysis in multiparametric cancer imaging

Tumor tissue characteristics derived from imaging data are gaining importance in clinical research. Tumor sub-regions may play a critical role in defining tumor types and may hold essential information about tumor aggressiveness. Depending on the tumor’s location within the body, such sub-regions can be easily identified and determined by physiology, but these sub-regions are not readily visible to others. Regions within a tumor are currently explored by comparing the image sequences and analyzing the tissue heterogeneity present. To improve the exploration of such tumor sub-regions, we propose a visual analytics tool called ICEVis. ICEVis supports the identification of tumor sub-regions and corresponding features combined with cluster visualizations highlighting cluster validity. It is often difficult to estimate the optimal number of clusters; we provide rich facilities to support this task, incorporating various statistical measures and interactive exploration of the results. We evaluated our tool with three clinical researchers to show the potential of our approach.
Dimension independent excess risk by stochastic gradient descent

One classical canon of statistics is that large models are prone to overfitting, and model selection procedures are necessary for high dimensional data. However, many overparameterized models, such as neural networks, perform very well in practice, although they are often trained with simple online methods and regularization. The empirical success of overparameterized models, which is often known as benign overfitting, motivates us to have a new look at the statistical generalization theory for online optimization. In particular, we present a general theory on the excess risk of stochastic gradient descent (SGD) solutions for both convex and locally non-convex loss functions. We further discuss data and model conditions that lead to a “low effective dimension”. Under these conditions, we show that the excess risk either does not depend on the ambient dimension p or depends on p via a poly-logarithmic factor. We also demonstrate that in several widely used statistical models, the “low effective dimension” arises naturally in overparameterized settings. The studied statistical applications include both convex models such as linear regression and logistic regression and non-convex models such as M-estimator and two-layer neural networks.
Remote learning and its perspectives in higher education in the Brazilian Context

Lacs Sichel, D., Gomes, J., & de Cassia Claudiano Cabral Pinto, R. (2022). Remote learning and its perspectives in higher education in the Brazilian context. In J. Dyczkowska (Ed.), Accounting and business in a sustainable post-Covid world: New perspectives and challenges (pp. 185-194). Wroclaw: Publishing House of Wroclaw University of Economics and Business.
Exploring Retriever-Reader Approaches in Question-Answering on Scientific Documents

As readers of scientific articles often read to answer specific questions, the task of Question-Answering (QA) in academic papers was proposed to evaluate the ability of intelligent systems to answer questions in long scientific documents. Due to the large contexts in the questions, this task poses many challenges to state-of-the-art QA models. This paper explores the retriever-reader approaches widely used in open-domain QA and their impact when adapting to QA on long scientific documents. By treating one scientific article as the corpus for retrieval, we propose a retriever-reader method to extract the answer from the relevant parts of the document and an effective sliding window technique that improves the pipeline by splitting the articles into disjoint text blocks of fixed size. Experiments on QASPER, a dataset for QA in Natural Language Processing papers, showed that our method outperforms all state-of-the-art models and establishes a new state-of-the-art in the extractive questions subset with 30.43% F1 $$^{1}$$ The code and processed data are available at https://github.com/lekhang4497/qasper-retriever-reader .
A Study on the Evaluation Index System of Non-Authority Power of Kindergarten Directors

Economic development and social changes have been driving reforms in the field of preschool education, and people have realized that good leaders can enhance the quality of preschool education.The establishment of a non-authority power evaluation index system for kindergarten directors, as an aspect of the assessment of kindergarten directors, aims to provide a feasible practical basis for the improvement of kindergarten management.This study uses documentary study, interview method, open-ended questionnaire method, and expert evaluation methods to establish a non-authority power index system for kindergarten directors.The index system consists of four primary indicators including character, talent, knowledge and emotion, eight secondary indicators, and fourteen tertiary indicators of.Due to the diversified evaluation subjects and different evaluation standards, the evaluation behavior needs to remain dynamic and objective in order to maintain the continuous and stable development of preschool education quality.