Reviewerly/exHarmony huggingface dataset:
CONTROL AND REGULATION OF THE ACTIVITIES OF REGIONAL UNIVERSITIES

The article explores the problematic issues of the development of the organization of external and internal control of the activities of universities on the basis of a historical approach to the analysis of the experience of the Soviet school and modern principles. The existing system of training and employment of graduates is not always ideal. The study identified clear problems that do not correspond to the needs of the labor market and trained personnel, including in individual regions, which indicates the need to improve the interaction between universities and employers. The systematization of forms of state regulation in the field of higher education made it possible to clearly imagine the directions of external control turned more to regulating the interaction of the three main groups of stakeholders (stakeholders) of the university: state authorities, students and employers. Research was carried out in order to substantiate the logical model of interaction of university stakeholders, which allows controlling their activities. In the developed model, the main unit regulating and monitoring the interaction of stakeholders is the National Council under the President of the Russian Federation. The interaction of the university and the National Council will make it possible to implement the requirements for the qualifications of graduates in the educational process of their training that meet the modern and future requirements of the labor market. It is proposed by the state authorities to determine indicators for monitoring the effectiveness (effectiveness) of state targeted programs for the employment of university graduates, and the educational institution itself to establish target values of control indicators that need preliminary monitoring. It was recommended that the strategic goals of the regional university be balanced in four aspects of activity (business processes of educational activities and resources, relations with stakeholders, finance, research activities), harmonized not only among themselves, but also in a space-time representation. It is proposed to monitor the implementation of tasks for each aspect of the activity on the basis of developed and presented actual and target values of control indicators of regional universities
Steady State Modeling of Highly Rotating and Viscous Flow using VOF Method for Rotary Glass Fiberization Process

Fiberglass is a very commonly used insulation and filtration media material that is manufactured using the rotary glass fiberization process.The spinner used for fiberglass manufacturing is the heart of the manufacturing process and the spinner is subjected to extreme temperatures and centrifugal stresses.It requires usage of special alloys and high precision manufacturing processes as the spinner has thousands of orifice holes.Experimental methods are very expensive, energy-intensive, and unsafe, hence the use of CFD based design and process optimization is very desirable.Numerical modelling involves the resolution of complex multiphase flow physics in a rotating domain having length scales varying from 500 microns to a few meters.The modelling challenges are further augmented due to the high viscosity of the molten glass in the range of 1000 [Poise] at 2000 [F], and large centrifugal accelerations in the order of 1000-1500g.A steady-state, CFD solution methodology is developed for efficiently simulating this process using the Volume-of-Fluid method.The solution developed for this application uses the pseudo-transient method along with several other numerical recipes to increase the solution accuracy and robustness with aggressive pseudo-time step sizes.The proposed modelling approach allows significantly faster solution as compared to a full transient approach.The case studies in this work are presented systematically following a step by step approach by decomposing the complex problem into sub-problems.In the first case study, a two-phase Poiseuille flow is validated for highly viscous fluids.The second case study involves validation of highly rotating and viscous flows in a closed domain.In the final case study, an analytical and numerical analysis is carried out for a representative fiberization spinner model.The free surface profiles, glass film thickness, mass flow rate, and pressure distribution are validated against the analytical solution in the final case study
How Student Characteristics Affect Economy Students’ Digital Competences: A latent profile study

The use of information and communication technologies (ICT) profoundly impacts in- dustry and work. It therefore is important for vocational education and training to em- phasize on the development of digital competences. However, we still know very little about how these skills are distributed among students in vocational education and train- ing (VET) in general, and the so-called ‘cooperative study programmes’ (CSP) in parti- cular. CSPs combine vocational education components with university coursework lead- ing up to a journeymen certificate and an academic degree simultaneously. This study analyses the digital competences of first-year students in a business-focussed CSP at the Baden-Wuerttemberg Cooperative State University (DHBW) in Germany, using a Ger- man version (the D21-Index) of the European framework for digital competences (DigComp) to assess students’ competence levels. A latent profile analysis revealed four competence profiles (reserved digitised, reserved appreciative, constructive digitised, and reflective digitised). Interestingly, our subsequent analysis showed that none of the student characteristics we assessed turned out to be distinctive for any of the four pro- files. Considerations of relevant inset points for VET institutions nationally and interna- tionally to develop tailored support of digital competence development of their students are integrated in the conclusions.
Algorithmic Information Disclosure by Regulators and Competition Authorities

Also in the digital age, markets work properly as long as consumers are well informed. What is peculiar of the digital age is that consumers have become very fragile, also because firms can extensively manipulate the information that they produce and distribute to markets. Antitrust authorities may find their way to prosecute business manipulative conduct, as some rulings suggest. However, the enforcement of antitrust law is subject to precise circumstances and requires cumbersome proceedings, especially when dominant firms are involved. Therefore, a simpler and more widespread intervention is needed. Although over the years traditional disclosure regulation has showed its limits, today algorithmic analysis gives room to more effective forms of disclosure regulation. Therefore, the paper maintains that both regulators and antitrust authorities can use these new forms of disclosure regulation to perform better their functions. Thanks to algorithmic analysis, (a) regulators can provide consumers with targeted co-regulated disclosures; (b) while competition authorities, using their advocacy powers, can provide trustworthy rankings and reviews about firms’ ability to comply with antitrust and consumer protection laws.
Demo: Railway Signalling Security Testbed

We replicate safety-critical railway signalling to test and evaluate a variety of cybersecurity controls in a realistic environment without risking to interrupt real-world railway operation. The testbed contains a signal box, several field elements and utilises railway standard protocols. We demonstrate the applicability of attacks and respective counter strategies.
REALME: An Approach for Handwritten Signature Verification based on Smart Wrist Sensor

Handwritten signature verification has a wide range of applications e.g., signing banker cheques or any legal documents. Most of the existing systems for handwritten signature verification uses specialized equipment like a smart pen and a tablet to acquire the signatures. Whereas in our proposed scheme, the signatures are captured on a plain sheet of paper using a low cost commercially available wrist-worn MetaWear smart sensor that is capable of recording motions using accelerometer and gyroscope. Smart sensor data from 10 subjects are acquired while performing real and fake signatures. Machine learning algorithm is trained using a known set of real and fake signatures and then using the test data, the algorithm identifies that whether the signatures of an individual are real or fake. A total of 12 trails are obtained from each user resulting in a total of 120 samples (10 users × 12 trails for each user) for genuine signatures and 120 samples for the fake signatures. Feature extraction is applied in time domain to extract sixteen features. Wrapper method for feature selection selects the optimum subset of features from the extracted features. Real and fake signatures are identified using BayesNet algorithm with an accuracy of 98% with a feature vector length of 5. It is evident from the results that the proposed scheme produces results which are comparable to the state-of-the-art methods available in the literature in terms of accuracy and has superior performance in terms of feature vector length.
CEREX@FIRE-2020: Overview of the Shared Task on Cause-effect Relation Extraction

Extraction of causal relations from text is an important problem in Natural Language Processing (NLP). The extracted relations play important roles in several downstream analytical and predictive tasks like identification of actionable items, question-answering and isolation of predictor variables for a predictive system. Curating causal relations from text documents can also help in automatically building causal networks which are also useful for reasoning tasks. The proposed CEREX track aims to find a suitable model for automatic detection of causal sentences and extraction of the exact cause, effect and the causal connectives from textual mentions.
Lightweight and real-time object detection model on edge devices with model quantization.

Abstract Environment perception is of vital importance in autonomous driving as it serves as autonomous car’s “eye” to perceive the surrounding environment. We propose a solution to achieve real-time environment perception on resource constrained edge devices like mobile phones, even though they have limited computation resources. We used float-to-int quantization based on TensorFlow Lite to quantilize the SSD model. TensorFlow Lite achieves accuracy lossless quantization with quantization-aware training by inserting fake quant operator during training, which imitates the loss introduced by quantization in feedforward training. Our experiment shows that superiority of our proposed method, which is highly speed, light weight and relatively high accuracy.
Five Free Expression Safeguards from a Facebook User's Perspective

Pressure is mounting on Facebook to moderate content more aggressively, risking collateral damage to free expression. Activists have responded by demanding new policies to protect expression rights but, until now, there has been no empirical evidence about public opinion of these safeguards.Using an original survey of a non-representative sample of US adults who post regularly on Facebook (n=496), I explore concerns about moderation and reactions to five proposed safeguards: increased transparency reporting of moderation decisions, the right to an explanation of those decisions, the right to appeal them, an independent oversight board, and a government audit. (Some are already partially implemented.)The findings are statistically significant (p < 0.05) and have implications for evidence-based policymaking. The right to appeal a moderation decision would ease concerns about overmoderation about as effectively as the moderation oversight board which Facebook is already building. I recommend that Facebook prioritize implementing the right to appeal, but also note that most respondents want more power ceded to the board. A US government audit of Facebook’s moderation would intensify concerns, so I recommend that potential government intervention be limited to mandated transparency.Challenging a dominant narrative, I find respondents are more concerned about overmoderation than undermoderation, and add this to the list of reasons that content moderation AI should not be tuned aggressively.Further, I compare the data against the power expression protection theory (Andsager, et al. 2004. Free expression and five democratic publics.), which claims people of more powerful demographics tend to value freedom of expression more. Finding no support in this context, I conclude it would be a mistake to stereotype those concerned about overmoderation as limited to privileged groups.
The Automatic option of inference rules for the fuzzy TF-IDF

For several NLP tasks, we use the Fuzzy Inference System (FIS) to develop a set of decisional systems. Still, the selection rules FIS process needs some modification to be more useful. In this paper, we use FIS to produce terms weight for electronic documents basing on the popular TF-IDF (term frequency-inverse term frequency) components. To insert rules for FIS manually remains a real issue in the text processing domains, where the size of the processed data is enormous. In our work, we suggest including the Association Models as automatic technics to produce the inference rules instead of the traditional manual way. Precisely, our approach process in five steps, i.e., preprocessing, limiting membership function for every input and output, nominal representation of the database, call for the Apriori and Filter association as association models to select the most important rules, and finally, post-processing of the received rules. Applying the new technique to produce fuzzy weight for a benchmarked database achieves a significant gain in time, precision rules, and weighting quality. Thus, running the new descriptor matrix with statistical classifiers, e.g., Bayesian Networks, improves accuracy compared with the traditional way and existing methods.
Graph Analytics to Reason Citations of Prophets in the Holy Quran

Graph Analytics is a recently emerging field that offers sophisticated mechanisms to model, analyse and visualise data in the form of graphs. Citations and valuable information about various prophets have been quoted in several chapters of the Holy Quran in textual form. In this contribution, we aim to model these citation data and information using a novel graph analytics oriented framework. This paper briefly reports our findings on formulating the proposed model, data collection and empirical analysis.
Enabling Smart Home Voice Control for Italian People with Dysarthria: Preliminary Analysis of Frame Rate Effect on Speech Recognition

Within the field of automatic speech recognition, the processing of dysarthric speech is a challenge because standard approaches are ineffective in presence of dysarthria. This paper presents preliminary evidence that the performance of speaker-dependent speech recognition systems trained for speakers with dysarthria may be substantially improved by tuning the size and shift of the spectral analysis window used to compute the initial short-time Fourier transform used in many speech front ends. Evidence for this comes from a set of experiments performed on a small collection of Italian speech (isolated words) from five different speakers suffering from different degrees of dysarthria. The experimental framework used in the paper constructs speaker-dependent GMM-HMM speech recognition models using the triphone Kaldi recipe and varying choices of the spectral analysis window size and shift. Results show a variable improvement (31% to 81%), according to the selected user with dysarthria.
Characteristic Declination—A Useful Concept for Accelerating 3D Solar Potential Calculations

The characteristic declination is the declination for the day on which the daily extraterrestrial irradiation on a horizontal surface is identical to its monthly average value. It was introduced as a means to determine monthly average values of irradiation. Herein, its potential usefulness to reduce computing time when mapping solar potential in complex urban areas is explored. This simplification reduces computing demand by a factor of 30× while introducing a +5% to +8% error in the annual monthly irradiation on a typical urban neighborhood for low and midlatitudes. Errors are larger (+10% to +12%) for high latitudes. The magnitude of the errors is comparable to other relevant uncertainties in solar mapping tools, associated with solar radiation modeling, the layout and details of the buildings, or the photovoltaic (PV) energy yield models.
CULTURAL AND LEISURE EVENTS OF UNIVERSAL SCIENTIFIC LIBRARIES IN SIBERIA

The decree of the President of the Russian Federation declared 2019 as the Year of Theatre. Libraries traditionally participate in promotion of theatre art, provide their places for plays, hold art meetings, organize theatre clubs, and use theatrical tools in club activities to hold public events. The article objective is to identify and characterize types, forms, orientation, and specifics of cultural, educational, and leisure activities implemented by the central universal scientific libraries of the Siberian Federal district (SFD) as part of the Year of Theater. 10 central libraries were chosen to be analyzed: Altai Regional Universal Scientific Library named after V. Y. Shishkov (Barnaul), Irkutsk Regional State Universal Scientific Library named after I. I. Molchanov- Sibirsky, State Scientific Library of the Kuzbas named after V. D. Fedorov (Kemerovo), Krasnoyarsk Regional State Universal Scientific Library, Novosibirsk State Regional Scientific Library, Omsk State Regional Scientific Library named after A. S. Pushkin, National Library named after M. V. Chevalkov (Gorno- Altaisk), National Library of the Tuva Republic named after A. S. Pushkin (Kizil), National Library named after N. G. Domozhakov (Abakan), Tomsk Regional Universal Scientific Library named after A. S. Pushkin. The source bases of this study were public plans and reports of libraries, news and events reports posted on the official libraries websites. The theatre topic reflects itself in preparing various libraries exhibitions, chronographs publications, organizing meetings with culture and arts figures, clubs work, organizing competitions, cultural and educational events and projects, holding festivals. Sociocultural event “Library Night 2019” aimed at supporting reading was a bright manifestation of the “Library + Theater” union. Libraries used the following forms of events: performances, concerts, lectures, master classes, exhibitions, excursions, quizzes, competitions, playgrounds. The events held during the Year of Theatre varied in content and combination of traditional and new forms; had strongly marked book and reading popularization; were oriented on different age groups, interactive; used many multimedia products. Although the chosen forms of the events are alike, the fact that each library has its unique content based on local history makes special cultural and leisure activities of every library.
Guarantees for Mix-flows in Inter-Datacenter WANs in Single and Federated Clouds

Inter-datacenter WANs connect geo-distributed datacenters and carry a considerable amount of traffic, generally a mix of flows. There has been little work done to provide custom guarantees depending on the nature of traffic requirements in such environments. In this paper we address this problem by proposing Vritti, an adaptable spatial-temporal system for traffic engineering in inter-datacenter WAN environments applicable to single and federated clouds. With Vritti, cloud providers can offer tailor-made guarantees to users with widely differing requirements (in terms of hard, soft or no deadlines) and traffic characteristics (in terms of known or unknown traffic volumes). We use linear programming to mathematically formulate the problem with the objective of maximizing utility. We propose two online algorithms to generate admission control, scheduling and routing decisions. Our simulations show that Vritti can effectively meet deadline transfers and provide fairness to non-deadline transfers in both single and federated cloud environments.
Online EM-Based Ensemble Classification With Correlated Agents

A binary ensemble classification method that sequentially processes the data collected from multiple decision agents in the presence of parameter uncertainties is proposed. Agents are assumed to form correlated groups whose decisions are modeled as multivariate Bernoulli random vectors. The prior probabilities of the binary hypotheses and the corresponding probabilities of the outcomes under each hypothesis are treated as unknown deterministic parameters. Cappé's online Expectation-Maximization algorithm is employed to estimate the parameter values, which are then fed into the ensemble classifier. The proposed technique is shown the reduce the computational complexity while delivering performance close to its offline counterpart, which requires multiple passes over the data. Numerical examples are presented to corroborate the results.
Load Balancing in Heterogeneous Distributed Systems Using Singleton Model

Load balancing is the process of improving the performance of the system by sharing of workload among the processors. The workload of a machine means the total processing time it requires to execute all the tasks assigned to it. Load balancing is one of the important factors to heighten the working performance of the cloud service provider. The benefits of distributing the workload include increased resource utilization ratio which further leads to enhancing the overall performance thereby achieving maximum client satisfaction. In this paper, we are demonstrating the use of the singleton model for load balancing.
Pre-Operative Auditory-Perceptual Voice Characteristics in Patients Undergoing Thyroid and/or Parathyroid Surgery

human voice is one of the most important tools in humaneveryday communication and the voice was probably importantto human communication even before language evolved. Also...
The Market for Bitcoin Transactions

Transaction fees in the bitcoin system work differently from those in conventional payment systems due to the design of the bitcoin mining algorithm. In particular, transaction fees and transaction volume in the bitcoin system increase whenever the network is congested, and our VAR results confirm that is indeed the case. To account for the empirical findings, we build a model where users and miners together determine transaction fees and transaction volume. Even though the mechanism of fluctuating transaction fees in bitcoin introduces an extra cost of uncertainty to users, a back-of-envelope calculation shows that the cost of using the bitcoin network for transactions is still smaller than the cost of using the current conventional payment system with a fixed transaction fee rate. However, this calculation may underestimate the cost due to the crowding-out effect on small transactions during the congested period.
Optimization Research and Defect Object Detection of Aeroengine Blade Boss Based on YOLOv4

Abstract This paper proposes an object detection algorithm based on YOLOv4 (You Only Look Once) aiming at detecting the defects of the blade boss inside the aeroengine. The algorithm uses transfer learning to load the weights of the pre-trained model trained on the coco public data set. In order to adapt to the characteristics of small targets and complex structures better in the detection of bosses, the size of the bounding boxes is adjusted through the cluster analysis. In the meantime, the original data set is enhanced through the Mosaic method. The experimental results show that the improved YOLOv4 model increased the detection accuracy by 15.85%, the recall rate by 21%, with an average intersection ratio of 0.75, and the detection performance is significantly better than the data applying the SSD object detection algorithm in the same data set.