Reviewerly/exHarmony huggingface dataset:
Digital Competencies in Online Education

The chapter will explore some aspects of online education such as digital skills, digital competences, and innovations in education, which the COVID-19 pandemic put into the foreground of the processes of learning and education at universities. The ability to apply digital skills and develop them into digital competencies is a basic prerequisite for learners and educators. The author analyses and evaluates the current state and challenges posed by online education, especially at colleges and universities. The concepts and terminology in the field of education in cyberspace, models of digital competencies of educators, scientific and educational policy documents defining the requirements for university teachers in online education were considered. The investigation was supported by the results of a questionnaire survey. The author points out the possibilities and ultimate requirements of online education at universities in terms of students, teachers, innovations, evaluation, advantages, and barriers in online education.
Automated learning rate search using batch-level cross-validation

Deep learning researchers and practitioners have accumulated a significant amount of experience on training a wide variety of architectures on various datasets. However, given a network architecture and a dataset, obtaining the best model (i.e. the model giving the smallest test set error) while keeping the training time complexity low is still a challenging task. Hyper-parameters of deep neural networks, especially the learning rate and its (decay) schedule, highly affect the network's final performance. The general approach is to search the best learning rate and learning rate decay parameters within a cross-validation framework, a process that usually requires a significant amount of experimentation with extensive time cost. In classical cross-validation (CV), a random part of the dataset is reserved for the evaluation of model performance on unseen data. This technique is usually run multiple times to decide learning rate settings with random validation sets. In this paper, we explore batch-level cross-validation as an alternative to the classical dataset-level, hence macro, CV. The advantage of batch-level or micro CV methods is that the gradient computed during training is re-used to evaluate several different learning rates. We propose an algorithm based on micro CV and stochastic gradient descent with momentum, which produces a learning rate schedule during training by selecting a learning rate per epoch, automatically. In our algorithm, a random half of the current batch (of examples) is used for training and the other half is used for validating several different step sizes or learning rates. We conducted comprehensive experiments on three datasets (CIFAR10, SVHN and Adience) using three different network architectures (a custom CNN, ResNet and VGG) to compare the performances of our micro-CV algorithm and the widely used stochastic gradient descent with momentum in a early-stopping macro-CV setup. The results show that, our micro-CV algorithm achieves comparable test accuracy to macro-CV with a much lower computational cost.
Oil Pollution Assessment of Soils in Khabarovsk

The TPH-in-soil of three functional zones (industrial, residential, agro-residential) of Khabarovsk have been studied. Areas of acceptable, low, moderately hazardous, moderate, high and very high pollution levels have been identified. It has been found that the TPH-in-soil of different urban functional areas differs significantly. For statistical processing of experimental data, the Boxplots method ("box-and-whiskers diagram") has been used. It has been proposed to switch to the median value of the aggregate sample of the TPH-in-soil in residential and agro-residential areas as the baseline data.
COVID-19 and Social Science Research— Beyond Disruption and Digital Divide, a Digital Turn

This comment takes a bird’s eye view of the problems and changes in social sciences caused, amplified or accelerated by the pandemic. It further problematises the formulation of the impact of COVID-19 beyond the disruption-and-digital-divide framing to argue that the nature of certain realities has been digitalising already for a considerable amount of time in more complicated and non-linear ways than the criticism of ‘online education’ has captured. The digital transformation of social realities calls for an acknowledgement of a comprehensive digital turn in social science research.
Zoom Experiences in Higher Education: helping college students build data visualization capacity remotely

This Innovative Practice paper presents the redesign of an introductory data visualization college course to foster participation and engagement in an online setting. Four classroom exercises, referred to as “Zoom Experiences,” are described. Zoom Experiences varied depending on the topic being covered and were different each day to keep students' interest. Students were asked to indicate if they would recommend the “experience” (or not) using a 5-point Likert Scale ranging from: (1) I would definitely NOT recommend, to (5) I would definitely recommend the exercise. A total of 84 responses were analyzed. Analysis of responses indicate over 57% would recommend or highly recommend the activities, 35% would likely recommend, and 8% would not recommend the activities. The goal of the experiences was to help students stay engaged in an on-line element of a course originally designed for in-class participation.
A Survey of different video steganography approaches against man-in-the middle attacks

Steganography is necessary to encrypt data efficiently between different organizations. Steganography can have significant use in transmitting secret data through videos. However, this is susceptible to third party attacks such as video change, copyright change, security update changes etc. Numerous cryptographic algorithms have been proposed to be incorporated into data hiding techniques. As the number of sensitive data required is large, these models require powerful computer memory and high processing cycles to process. In that case, these cryptographic models could not be applied to large video data for integrity computation and encryption. In this paper, different video steganography approaches and their limitations are studied on the video databases.
I Am (Still) Here!

Students must be accounted for in a classroom environment, and there are several methods to ensure student checking. Traditionally, attendance can be done with paper-based solutions such as the signing of attendance sheets. However, this is unreliable, time-wasting, and not scalable. In this paper, we describe an automated, costless, portable, web-based platform that is resilient to fraud thanks to device fingerprinting techniques, retrieving indicators such as the user-agent, IP address, fonts installed, geolocation, and others. Various simulations tested our solution and proved that the platform could detect fraud using the fingerprinting mechanism and the geolocation method.
Attacks on Permissioned Blockchain for IoT

The growth of the Internet of Things (IoT) at a rapid pace and its integration with various other technologies like Artificial Intelligence and Blockchain has raised several security concerns. Considering the decentralized nature of Blockchain and growing number of IoT devices, integrating both can have several advantages and can improve security. However, this integration also gave rise to new attacks. In this paper, we provide a survey of various attacks that are applicable for an IoT infrastructure that is embedded with Permissioned blockchain. When compared with public blockchains, Permissioned blockchains have lesser attack vector. We considered those attacks that will be applicable for Permissioned blockchains. We classify the attacks into IoT device attacks, Blockchain attacks, Network attacks and Malware attacks.
Hybrid ensemble neural network approach for photovoltaic production forecast

With the main goal of saving the environment and reducing the amount of burnt fossil fuels, the penetration of renewable energy sources as a share of the electrical energy production is constantly increasing. However, this growth significantly jeopardizes electrical grid stability, since renewable sources highly depend on the meteorological conditions, which are stochastic by their nature. Therefore, careful planning of energy use is necessary, which is why a photovoltaic production forecaster model has been presented within this paper. The main focus was presenting a hybrid ensemble neural network approach which combines ensembling method with complex LSTM + CNN networks with the aim of improving forecasting performance. The approach has been tested using real-world year-long data from the town of Adeje in Tenerife and the results show an improvement in forecasting precision in comparison with the conventional ensemble model and with the hybrid approach on the test data, both state-of-the-art solutions.
Digital Video Image Preprocessing Algorithm Based on Embedded System

Abstract To solve the massive noise contained in the images acquired under low illumination, we designed a digital video image Preprocessing device with the denoising function. Based on the embedded CPU and operating system, video images are acquired by the camera. The noise contained in the video images is filtered by the improved median filtering algorithm and wavelet image denoising. Subsequently, the images are transmitted through USB and network interface, and the storage function of image files is implemented. The device can remove the noise contained in videos effectively, which is conducive to performing more advanced processing on the images.
Multi-class Similar Scene Retrieval in Soccer Videos: A Scene Confusion Reduction Method Based on Combination of Long and Short Frame Sequences

Similar scene retrieval in soccer videos has been drawing a lot of attention in recent years. In previous studies, long and unified frame sequences extracted from soccer videos are used to represent a soccer scene. However, it causes scene confusion that affects the retrieval performance. In this paper, we propose a scene confusion reduction method based on the combination of long and short frame sequences for similar scene retrieval in soccer videos. Our method preserves both the complete contextual information of the long frame sequences and the immediate state of the action represented by the short frame sequences. The experimental results show that the MAP@10 achieves 0.587 with our approach.
Secure Information Transfer Using Two Keyless Cryptography Methods

In the current paper, some methods of information security protocols based on physical layer security are considered. It is proved that well known Shamir’s protocol can be applied to RSA cryptosystem but not to Rabin, Mac-Ellice and trellis based cryptosystems. The main stream of this paper is a description of key sharing protocol on constant public and noiseless channels (like Internet). It is shown that it is able to provide a high reliability and control of security in terms of Shannon’s information providing nothing-additional requirements to communication channels and without any cryptographic assumptions.
Challenges, Opportunities, and Future Prospects

The central intention of this chapter is to discuss the primary security challenges in internet of things (IoT) environments with the main emphasis on the opportunities for deep learning for securing and maintaining the privacy of IoT-based systems.
Formation of ideas about end-to-end digital technologies among prospective speech therapists

According to the program Digital Economy of the Russian Federation adopted in 2017, the share of the population with digital skills should be 40%. Moreover, these skills include not only user skills, but also specific ones related to a specific professional activity. In this regard, higher educational institutions are faced with the task of training personnel who will be able to work effectively in the era of digital development of the economy, namely, to be able to work with end-to-end digital technologies. In order to cope with this task successfully, it is necessary to change the main professional educational programs and include work programs of disciplines in which end-to-end digital technologies become the object and / or means of study. This paper analyzes the problem of the readiness of prospective speech therapists majoring in 44.03.03 special (defectological) education to use end-to-end digital technologies in their professional activities. The authors characterize the course End-to-end digital technologies, which appeared in the 2021/2022 academic year at Samara State University of Social Sciences and Education among first-year students of the Faculty of Psychology and Special Education. Based on the results of the course approbation, the authors formulate conclusions about the advisability of supplementing the training of students with a workshop that allows them to master several applied software products based on end-to-end digital technologies at an in-depth level.
<scp>OpenNym</scp>: Privacy preserving recommending via pseudonymous group authentication

Abstract A user accessing an online recommender system typically has two choices: either agree to be uniquely identified and in return receive a personalized and rich experience, or try to use the service anonymously but receive a degraded non‐personalized service. In this paper, we offer a third option to this “all or nothing” paradigm, namely use a web service with a public group identity, that we refer to as an OpenNym identity, which provides users with a degree of anonymity while still allowing useful personalization of the web service. Our approach can be implemented as a browser shim that is backward compatible with existing services and as an example, we demonstrate operation with the Movielens online service. We exploit the fact that users can often be clustered into groups having similar preferences and in this way, increased privacy need not come at the cost of degraded service. Indeed use of the OpenNym approach with Movielens improves personalization performance.
On Mean Dependency Distance as a Metric of Translation Quality Assessment

This paper has adopted a quantitative approach to carry out a linguistic study, within the theoretical framework of dependency grammar. Translation is a process where source language and target language interact with each other. The present study aims at exploring the feasibility of mean dependency distance as a metric for automated translation quality assessment. The current research hypothesized that different levels of translation are significantly different in the aspect of mean dependency distance. Data of this study were based on the written translation in Parallel Corpus of Chinese EFL Learners which was composed of translations from Chinese EFL learners in various topic. The translations were human-scored to determine the levels of translation, according to which the translations were categorized. Our results indicated that: (1) senior students perform better in translation than junior students, and mean dependency distance of translations from senior group is significantly shorter than the junior; (2) high quality translations yield shorter mean dependency distance than the low quality translations; (3) mean dependency distance of translations is moderately correlated with the human score. The resultant implication suggests the potential for mean dependency distance in differentiating translations of different quality.
Adventures of a Government Researcher

Visualization, interactive computer graphics, and related topics have been a particularly dynamic area of computer science, producing advances that impact society. Working at times in a research laboratory and at times for two science funding agencies, I held positions at the level of an individual researcher, Director of a VR Laboratory, and funding agency Program Director. This article will discuss some of my experiences, including insight into how science programs in funding agencies are initiated.
On Finding Irredundant Association Rules in a Product of Partial Order

The task of searching association rules of a special type in partially ordered data is considered. To increase the counting speed in the analysis of nonbinary data, an improved version of the FP-Growth algorithm of search for maximum frequent elements in data and parallel calculations based on CUDA technology are used. The results of working the sequential and parallel algorithms are presented.
Cervical Cancer Classification Using Elman Recurrent Neural Network and Genetic Algorithm

Medical screening using Pap smear images is a standard method for cervical cancer diagnosis. However, this method still has many weaknesses, such as time-consuming and subjectivity in interpreting an image. This paper aims to develop a method used as a cervical cancer classification using pap-smear images. Five classes were used, namely: normal, CIN1, CIN2, CIN3, and malignant. The method work as follows: (i) pre-processes image using amoeba median filter and Gaussian filter (ii) nuclei detection, and segmentation (iii) extracts characteristics image using texture and shape analysis (iv) classify the image using Elman Recurrent Neural Network and Genetic Algorithm. Based on experiments conducted, the proposed method could detect and classify pap smear images with sensitivity, specificity, and accuracy of 96%, 98%, and 95%, respectively.
Opening the black box of mathematics for CG

Mathematics is recognized as common basis for CG technology, but sometimes used as a black box in a CG software tool. We expect better understanding of maths will conduce to not only better CG tools, but also innovative ideas for a future production pipeline. The goal of this course is to pull the trigger for the graphics people to know more about usefulness and fun of the maths behind the scenes.
Implementation of a Peer-to-Peer Network Using Blockchain to Manage and Secure Electronic Medical Records

An electronic medical record (EMR) is the digital medical data of a patient, and they are healthcare system's most valuable asset. In this paper, we introduce a decentralized network using blockchain technology and smart contracts as a solution to manage and secure medical records storing, and transactions between medical healthcare providers. Ethereum blockchain is employed to build the blockchain. Solidity object-oriented language was utilized to implement smart contracts to digitally facilitate and verify transactions across the network (creating records, access requests, permitting access, revoking access, rejecting access). This will mitigate prevailing issues of current systems and enhance their performance, since current EMRs are stored on a centralized database, which cannot guarantee data integrity and security, consequently making them susceptible to malicious attacks. Our proposed system approach is of vital importance considering that healthcare providers depend on various tests in making a decision about a patient's diagnosis, and the respective plan of treatment they will go through. These tests are not shared with other providers, while data is scattered on various systems, as a consequence of these ensuing scenarios, patients suffer of the resulting care provided. Moreover, blockchain can meliorate the motley serious challenges caused by future use of IoT devices that provide real-time data from patients. Therefore, integrating the two technologies will produce decentralized IoT based healthcare systems.
Database Semantics

For long-term upscaling, the computational reconstruction of a complex natural mechanism must be input-output equivalent with the prototype, i.e. the reconstruction must take the same input and produce the same output in the same processing order as the original. Accordingly, the modeling of natural language communication in Database Semantics (DBS) uses a time-linear derivation order for the speaker’s output and the hearer’s input. The language-dependent surfaces serving as the vehicle of content transfer from speaker to hearer are raw data without meaning or any grammatical properties whatsoever, but measurable by natural science.
Domain Adaptation for Agricultural Image Recognition and Segmentation Using Category Maps

Recognition accuracy obtained using deep learning drops precipitously when the training data are insufficient. This paper presents a data-expansion method for training of the transfer learning source domain. Using expanding images generated from weights on a category map as source data, we compared accuracies obtained from five derivative models and our previously reported method. Moreover, we obtained the result of domain adaptation between actual images and synthetic images using weights obtained during transfer learning. Based on those results, we verify whether the amount of training data can be expanded quantitatively and qualitatively. Experiment results obtained from two open benchmark datasets and our original benchmark dataset demonstrated that our proposed method outperforms the previous method under a guarantee of sufficient accuracy for the synthetic images.
Voting Booth Helper System Using Machine Learning

Elections are the fundamental defining characteristics of any democracy that is being governed by the people, where in people express their choices or articulate opinions through voting. The existing voting system uses EVM system at polling booths for voting and its main drawback is the manual validation of the voter. In the polling booths, the voting process is organized by few organizers having a count from 5 to 10 or even above. These people are assigned to perform certain tasks, one of such tasks is to validate the voter. With the raising population this consumes a lot of time, which in turn increases the man power and the human error. This project aims to provide an efficient solution to overcome the drawbacks of the existing voting system. We have developed a module using face recognition algorithm, to validate the voter accurately and efficiently within no time. It even reduces the man power, as it alone, performs all the tasks performed by the several organizers at the voting booths. The algorithm made use of, is the Multi-Task Cascaded Convolutional Neural Networks which is known for its accuracy and speed. The reduction of man power helps to control the rapid increase of covid cases, which is the most prevailing problem and helps the voters to vote with ease.
Large Scale Image Registration Utilizing Data-Tunneling in the MapReduce Cluster

Applications of image registration tasks are computation-intensive, memory-intensive, and communication-intensive. Robust efforts are required on error recovery and re-usability of both the data and the operations, along with performance optimization. Considering these, we explore various programming models aiming to minimize the folding operations (such as join and reduce) which are the primary candidates of data shuffling, concurrency bugs and expensive communication in a distributed cluster. Particularly, we analyze modular MapReduce execution of an image registration pipeline (IRP) with the external and internal data (data-tunneling) flow mechanism and compare them with the compact model. Experimental analyzes with the ComputeCanada cluster and a crop field data-sets containing 1000 images show that these design options are valuable for large-scale IRPs executed with a MapReduce cluster. Additionally, we present an effectiveness measurement metric to analyze the impact of a design model for the Big IRP, accumulating the error-recovery and re-usability metrics along with the data size and execution time. Our explored design models and their performance analysis can serve as a benchmark for the researchers and application developers who deploy large-scale image registration and other image processing tasks.