Reviewerly/exHarmony huggingface dataset:
Using the subject ontology of the unified digital space of scientific knowledge in scientometric tasks

The Formation of a Common digital space of scientific&#x0D; knowledge (CDSSK) is one of the priorities of the modern period and involves the use of unified approaches to the selection of information, its presentation, navigation, and terminological content. When constructing an subject ontology of the scientific direction — «Microbiology» — we applied a method of statistical calculation of the frequency distribution of keywords (KW) in information arrays formed as a result of thematic search for information on Microbiology. We assume that the frequency&#x0D; of KW occurrence can be considered as indicator of the scientific topic development intensity. The results of the study show that the size of the pool of keywords in categories can serve as an indicator that shows the breadth of the spectrum of research diversity, methods and objects: the more keywords in a category, the greater the diversity of research directions.
Deep Spatio-Temporal Attention Model for Grain Storage Temperature Forecasting

Temperature is one of the major ecological factors that affect the safe storage of grain. In this paper, we propose a deep spatio-temporal attention mode to predict stored grain temperature, which exploits the historical temperature data of stored grain and the meteorological data of the region. In this proposed model, we use the Sobel operator to extract the local spatial factors, and leverage the attention mechanism to obtain the global spatial factors of grain temperature data and temporal information. In addition, a convolutional neural network (CNN) is used to learn features of external meteorological factors. Finally, the spatial factors of grain pile and external meteorological factors are combined to predict future grain temperature using long short-term memory (LSTM) based encoder and decoder models. Experiment results show that the proposed model achieves higher predication accuracy compared with the traditional methods.
Research on Image Super-resolution Reconstruction Based on Deep Learning

Abstract Image super-resolution processing technology refers to the technology of reconstructing high-resolution images by using low-resolution images with mutual displacement of multiple frames of the same scene. At present, image super-resolution processing technology is widely used in image reconstruction in various fields, such as: medical images, remote sensing images, robot vision, etc. In recent years, deep learning has also been widely used in various fields, especially in computer vision and speech processing. Since image super-resolution reconstruction the method of deep learning is introduced into the technology. Due to the good performance advantages of the deep learning method, it has become an important method for the research of image superresolution reconstruction technology. In this paper, the current status of domestic and foreign research on image super-resolution reconstruction is first simplified. Then, the theory of deep learning was explained, mainly the artificial neural network theory, the related theory of super-resolution reconstruction based on deep learning, and the method of image super-resolution reconstruction based on deep learning.
DPlis: Boosting Utility of Differentially Private Deep Learning via Randomized Smoothing

Deep learning techniques have achieved remarkable performance in wide-ranging tasks. However, when trained on privacy-sensitive datasets, the model parameters may expose private information in training data. Prior attempts for differentially private training, although offering rigorous privacy guarantees, lead to much lower model performance than the non-private ones. Besides, different runs of the same training algorithm produce models with large performance variance. To address these issues, we propose DPlis--Differentially Private Learning wIth Smoothing. The core idea of DPlis is to construct a smooth loss function that favors noise-resilient models lying in large flat regions of the loss landscape. We provide theoretical justification for the utility improvements of DPlis. Extensive experiments also demonstrate that DPlis can effectively boost model quality and training stability under a given privacy budget.
Large region inpainting by re-weighted regularized methods

&lt;p style='text-indent:20px;'&gt;In the development of imaging science and image processing request in our daily life, inpainting large regions always plays an important role. However, the existing local regularized models and some patch manifold based non-local models are often not effective in restoring the features and patterns in the large missing regions. In this paper, we will apply a strategy of inpainting from outside to inside and propose a re-weighted matching algorithm by closest patch (RWCP), contributing to further enhancing the features in the missing large regions. Additionally, we propose another re-weighted matching algorithm by distance-based weighted average (RWWA), leading to a result with higher PSNR value in some cases. Numerical simulations will demonstrate that for large region inpainting, the proposed method is more applicable than most canonical methods. Moreover, combined with image denoising methods, the proposed model is also applicable for noisy image restoration with large missing regions.&lt;/p&gt;
crank: An Open-Source Software for Nonparallel Voice Conversion Based on Vector-Quantized Variational Autoencoder

In this paper, we present an open-source software for developing a nonparallel voice conversion (VC) system named crank. Although we have released an open-source VC software based on the Gaussian mixture model named sprocket in the last VC Challenge, it is not straightforward to apply any speech corpus because it is necessary to prepare parallel utterances of source and target speakers to model a statistical conversion function. To address this issue, in this study, we developed a new open-source VC software that enables users to model the conversion function by using only a nonparallel speech corpus. For implementing the VC software, we used a vector-quantized variational autoencoder (VQVAE). To rapidly examine the effectiveness of recent technologies developed in this research field, crank also supports several representative works for autoencoder-based VC methods such as the use of hierarchical architectures, cyclic architectures, generative adversarial networks, speaker adversarial training, and neural vocoders. Moreover, it is possible to automatically estimate objective measures such as mel-cepstrum distortion and pseudo mean opinion score based on MOSNet. In this paper, we describe representative functions developed in crank and make brief comparisons by objective evaluations.
PARAMETER ESTIMATION OF STOCHASTIC CHAOTIC SYSTEMS

A standard way of formulating stochastic differential equation systems is to additively extend the drift of a deterministic system with a random diffusion part, but an increasing trend in applications, such as meteorology, is to perturb deterministic models in multiplicative and highly nonlinear ways, that escape the standard framework. This work aims to present a Bayesian method that enables estimating the parameters of such systems. The approach is well suited for situations were the observation times are irregular with large gaps between, so that the use of usual prediction-based filtering methods is excluded. The key idea is to construct a likelihood that is based on feature vectors that characterize the variability of the system. We illustrate the capability of the method in different scenarios that are both chaotic and stochastic using the classical Lorenz system as the demonstration example.
Socio-psychological Characteristics of the Innovative Potential of Students as Indicators of Professional Elitism in a Digital Society

The digital society and, in particular, the digital economy require the employee to have certain competencies that aren’t limited to computer literacy, information competencies, etc. The so-called soft skills become very popular, among which an important place is occupied by effective thinking skills, self-management skills, communication and managerial skills [2]. Soft skills are considered as a style of behavior that combines the parameters of social flexibility and individual activity. In turn, the ability to build business communications in the business environment, teamwork skills, leadership, and behavioral flexibility are decisive for innovative personality behavior, which is an integral indicator [6] of professional elitism of the personality and determines the person’s ability to be adaptive to changes, generate changes, new forms of behavior and activities, change yourself, successfully carry out activities aimed at developing, implementing and disseminating new ideas, products, and technologies [3–5]. The article presents the results of a study of the socio-psychological characteristics of students’ innovative potential, including value orientations, professional motivation, socio-psychological qualities, as a condition for further professional implementation [1]. It’s proved that students with different levels of development of innovative potential prefer to work in companies with different types of organizational values (adochocratic, market, clan, hierarchical types of organizational culture); students with a high level of innovative potential to a much greater degree show leadership qualities, are focused on the role of entrepreneur and senior manager.
Radio Regulatory Knowledge Graph Framework and Its Application

The explosive growth of data in radio monitoring and spectrum management brings an urgent demand for intelligent radio regulatory. However, due to the existence of a large amount of heterogeneous multi-dimensional data, the knowledge graph in radio regulatory domain has not been constructed. In this paper, an efficient radio regulatory knowledge graph framework was proposed. First, we extracted radio regulatory related work to ontologies, entities, relations, and built the knowledge graph. Then the radio transmission equipment type approval knowledge graph was constructed as an application. The rich relationships among entities, properties were revealed. It proves that the proposed framework has much theoretical and practical value.
Digital signal modulation recognition method based on high-order cumulants and wavelet transform

In view of the current situation that the recognition rate of digital signal modulation recognition method is unsatisfactory at low Signal-to-Noise Ratio(SNR), a recognition method based on high-order cumulants and wavelet transform is proposed to realize the automatic modulation recognition of 8 kinds of digital signals such as 2ASK, 4ASK, 8ASK, 2PSK, 4PSK, 8PSK, 16QAM and 32QAM. Based on the high-order cumulants principle and wavelet transform theory, the characteristic parameters f1∼f5 are constructed by the elaborate analysis of the characteristic extraction of these signals. Through simulation experiments, the characteristic parameter changes of different types of modulation signals at different SNR are obtained, and design the classifier of Back Propagation (BP) neural network to classify the signals. The simulation results show that this method can improve the average correct recognition rates of 8 digital modulation signals reaching up to above 97% when the SNR is higher than 0dB, which greatly improves the signal recognition performance at low SNR.
Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation

While deep neural networks provide good performance for a range of challenging tasks, calibration and uncertainty estimation remain major challenges, especially under distribution shift. In this paper, we propose the amortized conditional normalized maximum likelihood (ACNML) method as a scalable general-purpose approach for uncertainty estimation, calibration, and out-of-distribution robustness with deep networks. Our algorithm builds on the conditional normalized maximum likelihood (CNML) coding scheme, which has minimax optimal properties according to the minimum description length principle, but is computationally intractable to evaluate exactly for all but the simplest of model classes. We propose to use approximate Bayesian inference technqiues to produce a tractable approximation to the CNML distribution. Our approach can be combined with any approximate inference algorithm that provides tractable posterior densities over model parameters. We demonstrate that ACNML compares favorably to a number of prior techniques for uncertainty estimation in terms of calibration on out-of-distribution inputs.
Feasibility, acceptability and challenges of phone reminder system implementation for tuberculosis pill refilling and medication in Northwest Ethiopia

Abstract Background: Mobile health (mHealth) technologies showed a promising effect on improving adherence to Tuberculosis (TB) treatment. However, its implementation was not well studied specifically in low-income settings. This study aimed to fill this evidence gap by exploring the feasibility, acceptability and challenges of phone-based TB medication and pill refilling reminder system implementation during continuation phase. Methods: The study used phenomenological study design to explore participants’ lived experiences on feasibility, acceptability and implementation challenges of the reminder system. This study was part of the Randomised Controlled Trial (RCT) that deployed the reminder system in Northwest Ethiopia. We purposively selected 15 TB patients from eight districts in Central Gondar Zone and Gondar Town Administration who were enrolled in the interventional group of the RCT. The study was guided by the simplified Consolidated Framework for Implementation Research (CFIR). Data was collected using an in-depth interview and also captured events by observation throughout the intervention period. Message delivery reports generated from Ethio-telecom database. Interpretation and analysis were conducted using thematic analysis. Results: This study has shown that the local telecommunication infrastructure has supported the deployment of the new intervention and more than eight of ten reminder messages were delivered to the participants. All participants that received the reminder messages replied as the system was useful and many of them suggested for wider scale-up. We identified challenges related to the inner and outer setting, implementation process and characteristics of the intervention. Conclusion: Phone-based reminder systems could have promising feasibility and acceptability to support TB treatment and care in low-income settings with modifiable challenges throughout the implementation process. More implementation science studies are required to improve its effectiveness in more complex contexts.
Existence and Cardinality of k-Normal Elements in Finite Fields

Normal bases in finite fields constitute a vast topic of large theoretical and practical interest. Recently, k-normal elements were introduced as a natural extension of normal elements. The existence and the number of k-normal elements in a fixed extension of a finite field are both open problems in full generality, and comprise a promising research avenue. In this paper, we first formulate a general lower bound for the number of k-normal elements, assuming that they exist. We further derive a new existence condition for k-normal elements using the general factorization of the polynomial \(x^m-1\) into cyclotomic polynomials. Finally, we provide an existence condition for normal elements in \({\mathbb {F}_{q^m}}\) with a non-maximal but high multiplicative order in the group of units of the finite field.
A cognitive approach to the decision to trust or distrust phishing emails

Abstract When interacting with computers or digital artifacts, individuals tend to replicate interpersonal trust and distrust mechanisms to calibrate their trust. Such mechanisms involve cognitive processes that individuals rely on before making a decision to trust or distrust. With the worldwide increase in email traffic, both the academic literature and professionals warn of insider threats, that is, coming from inside an organization, in particular those created by legitimate users who have decided to trust a phishing email. This article offers a cognitive approach to the decision whether to trust a phishing email. After reviewing the literature on decision making concerning a cognitive perspective, interpretation, trust, distrust, online deception, and insider threats, we present a study conducted on 249 participants designed to ascertain how they interpreted phishing emails and decided whether or not to trust them. We noted that certain elements eliciting trust or distrust remained invariable regardless of the participant. We show examples of phishing emails designed to maximize (or minimize) the decision to trust (or distrust), and lastly consider the limitations and ethical questions raised by this research.
A Classification-Based Approach to Automating Human-Robot Dialogue

We present a dialogue system based on statistical classification which was used to automate human-robot dialogue in a collaborative navigation domain. The classifier was trained on a small corpus of multi-floor Wizard-of-Oz dialogue including two wizards: one standing in for dialogue capabilities and another for navigation. Below, we describe the implementation details of the classifier and show how it was used to automate the dialogue wizard. We evaluate our system on several sets of source data from the corpus and find that response accuracy is generally high, even with very limited training data. Another contribution of this work is the novel demonstration of a dialogue manager that uses the classifier to engage in multi-floor dialogue with two different human roles. Overall, this approach is useful for enabling spoken dialogue systems to produce robust and accurate responses to natural language input, and for robots that need to interact with humans in a team setting.
Development of lesson plan learning cycle 7E models integrated web formative assessment and self efficacy

One of the science learning models that provide opportunities for students to be actively involved is LC 7E model. The LC 7E model can be integrated with formative assessment at each stage. Formative assessment is a form of an assessment carried out during the learning process to improve the learning process. The application of formative assessment provides feedback that functions to evaluate learning activities, so that students’ understanding of concepts increases. Quick and precise feedback can help students improve understanding of concepts. In addition, it can increase students’ self- efficacy. Increasing student self-efficacy has an impact on increasing understanding of concepts. Giving feedback quickly and accurately is hampered by the number of students who are too many and the learning time is short, so that technology in the form of a web is needed to provide feedback quickly and accurately. The purpose of this research is to produce a product in the form of an integrated web based formative assessment LC 7E learning tool. This study is research and development with the ADDIE model. The analysis stage is carried out to identify problems related to the material, assessment system and implementation of learning. At the design stage, it aims to make an initial design of learning device products. The develop stage aims to produce valid and appropriate learning tools through the validation stage. The learning tools developed to meet the very feasible criteria. The average result of the validation of learning tools by science lecturers was 96.2% and the result of the readability test was 97.8%. The results of the empirical test of concept understanding questions with 56 valid questions, the reliability coefficient of 0.925 is with very good criteria. Thus, it can be concluded that the integrated web-based formative assessment learning tool is valid and suitable for use in learning.
Training teachers of mathematics under the conditions of introduction of the professional standard

This article belongs to the field of methodological science that analyzes the interaction of school mathematics with the teaching of mathematical disciplines in higher educational institutions, in particular, to students of pedagogical directions. The article presents two extreme points of view on modern education. The first of them perceives education as a kind of value, completely ignoring the relevance of this or that educational program. The second focuses exclusively on the market conditions of the educational program, perceiving an educational institution as an enterprise in the service sector. The authors of the article criticize both extremes, considering the educational process as a phenomenon that combines the features of both value and market phenomena. Further, in the presented work, an attempt is made to answer the question, is knowledge of only the school curriculum of mathematics sufficient for high-quality professional activity in school as a teacher? The authors of the article unequivocally answer this question - no, not enough. A number of examples are given when the knowledge of university special mathematical disciplines not only contributes to a more perfect knowledge of the taught subject in the future teacher, but is also fundamentally necessary for the formation of specific skills and abilities of the future teacher of mathematics. As examples, such mathematical sections as complex numbers, the theory of limits, the construction of counterexamples, projective geometry, etc. The work also provides links to the professional standard of a pedagogue, adopted in 2019.
Computer vision with deep learning for ship draft reading

The draft is a measurement of the vertical distance between the waterline and the bottom of the ship hull. The displacement tonnage of a ship then can be calculated by the observed draft. The current draft survey is done by surveyors, which is subject to human errors. We propose to use computer vision with deep learning for draft reading from images. First, mask R-CNN is used to segment the region of interest—draft marks and water—from images. Then UNet is used to refine the waterline detection. The detection of marks is based on the computer vision methods and the content of marks is recognized by ResNet. Finally, we can infer the draft of a ship based on the extracted visual information. Experimental results on a realistic dataset have shown that the proposed method can perform the task of draft reading on a par with humans.
Formation of cognitive activity of technical university students using elements of blended learning in the study of quantum physics

The study aims to analyze the expediency of using the capabilities of LMS Moodle for the implementation of blended learning in physics at a technical university in the study of quantum physics. The opportunities presented by the Moodle online environment are analyzed. It is demonstrated that online learning combined with in-person learning greatly improves learning outcomes. An instrument for e-learning in quantum physics in the Moodle environment is described and its educational capabilities are determined. The article examines the method of creating computer models using Easy Gif Animator. Modeling is examined as a means of promoting the formation of students’ cognitive activity. The use of modeling and thought experiments contributes to enhancing students’ understanding of real-life experiments and theories in physics. The study results support the hypothesis that the introduction of an electronic learning component in teaching quantum physics will increase students’ cognitive activity levels.