Reviewerly/exHarmony huggingface dataset:
Digital competence of Russian university teachers

This research paper presents the results of the author’s survey aimed at assessing the digital competence of modern university teachers. The relevance of digital competencies is becoming more and more obvious due to the transformations in the activities of educational organizations. These changes concern the use of digital technologies not only in university management, but also directly in the educational process. The development of distance learning and remote interaction formats is an objective reality in the modern world. The purpose of this article is to make an attempt of assessing a university teacher’s digital competence level based on the general perception of his/her abilities and the digital technologies used in his/her professional activities. The author analyses the teachers’ readiness to use digital technologies and their attitude to distance learning. The study showed that the majority of the interviewed teachers have a high level of basic and universal digital competencies. It was found that most of the respondents already have experience of taking online courses to improve their professional skills. The analysis makes it possible to conclude that the teachers who participated in the survey demonstrate a high readiness to use digital technologies in their professional activities. The author’s study is not supposed to give a complete picture of all Russian university teachers’ digital competence level, since this requires both greater coverage of respondents and the use of other channels (beyond digital) to collect data within the survey. At the same time, this research allows to formulate possible ideas for improving Russian university teachers’ digital competence level, which nowadays becomes the key to improving their effectiveness. The results of the study can be used when working out universities’ personnel development programs in order to ensure a higher level of the teachers’ digital competence.
CBD: A Deep-Learning-Based Scheme for Encrypted Traffic Classification with a General Pre-Training Method

With the rapid increase in encrypted traffic in the network environment and the increasing proportion of encrypted traffic, the study of encrypted traffic classification has become increasingly important as a part of traffic analysis. At present, in a closed environment, the classification of encrypted traffic has been fully studied, but these classification models are often only for labeled data and difficult to apply in real environments. To solve these problems, we propose a transferable model called CBD with generalization abilities for encrypted traffic classification in real environments. The overall structure of CBD can be generally described as a of one-dimension CNN and the encoder of Transformer. The model can be pre-trained with unlabeled data to understand the basic characteristics of encrypted traffic data, and be transferred to other datasets to complete the classification of encrypted traffic from the packet level and the flow level. The performance of the proposed model was evaluated on a public dataset. The results showed that the performance of the CBD model was better than the baseline methods, and the pre-training method can improve the classification ability of the model.
Behind-The-Scenes (BTS): Wiper-Occlusion Canceling for Advanced Driver Assistance Systems in Adverse Rain Environments

Driving in an adverse rain environment is a crucial challenge for vision-based advanced driver assistance systems (ADAS) in the automotive industry. The vehicle windshield wiper removes adherent raindrops that cause distorted images from in-vehicle frontal view cameras, but, additionally, it causes an occlusion that can hinder visibility at the same time. The wiper-occlusion causes erroneous judgments by vision-based applications and endangers safety. This study proposes behind-the-scenes (BTS) that detects and removes wiper-occlusion in real-time image inputs under rainy weather conditions. The pixel-wise wiper masks are detected by high-pass filtering to predict the optical flow of a sequential image pair. We fine-tuned a deep learning-based optical flow model with a synthesized dataset, which was generated with pseudo-ground truth wiper masks and flows using auto-labeling with acquired real rainy images. A typical optical flow dataset with static synthetic objects is synthesized with real fast-moving objects to enhance data diversity. We annotated wiper masks and scenes as detection ground truths from the collected real images for evaluation. BTS outperforms by achieving a 0.962 SSIM and 91.6% F1 score in wiper mask detection and 88.3% F1 score in wiper image detection. Consequently, BTS enhanced the performance of vision-based image restoration and object detection applications by canceling occlusions and demonstrated it potential role in improving ADAS under rainy weather conditions.
Achieving Sender Anonymity in Tor against the Global Passive Adversary

Tor is the de facto standard used for anonymous communication over the Internet. Despite its wide usage, Tor does not guarantee sender anonymity, even in a threat model in which the attacker passively observes the traffic at the first Tor router. In a more severe threat model, in which the adversary can perform traffic analysis on the first and last Tor routers, relationship anonymity is also broken. In this paper, we propose a new protocol extending Tor to achieve sender anonymity (and then relationship anonymity) in the most severe threat model, allowing a global passive adversary to monitor all of the traffic in the network. We compare our proposal with Tor through the lens of security in an incremental threat model. The experimental validation shows that the price we have to pay in terms of network performance is tolerable.
SQL Injection Detection Technology Based on BiLSTM-Attention

SQL injection is one of the most popular and serious threats to information security. By exploiting the database vulnerability, the attacker may gain access to sensitive data, or enable the compromised computer to carry out further network attacks. Our research focuses on the application of neural network method to identify the injection characteristics of SQL injection string. The depth sequence model with loss layer (long-term and short-term memory and gating cycle unit) is adopted, and multi string analysis and word2vec are used for query string vectorization. By adding Attention mechanism to improve the BiLSTM model, a BiLSTM-Attention detection model is designed to train and test the data. The experimental results show that the accuracy of the SQL injection detection model based on BiLSTM is 99.3% and the recall is 98.2% on the actual data set. Compared with traditional machine learning method and neural network method, this method can identify SQL injection attack more efficiently.
Topological graph states and quantum error-correction codes

Deciding if a given family of quantum states is topologically ordered is an important but nontrivial problem in condensed matter physics and quantum information theory. We derive necessary and sufficient conditions for a family of graph states to be in TQO-1, which is a class of quantum error-correction code states whose code distance scales macroscopically with the number of physical qubits. Using these criteria, we consider a number of specific graph families, including the star and complete graphs, and the line graphs of complete and completely bipartite graphs, and discuss which are topologically ordered and how to construct the codewords. The formalism is then employed to construct several codes with macroscopic distance, including a three-dimensional topological code generated by local stabilizers that also has a macroscopic number of encoded logical qubits. The results indicate that graph states provide a fruitful approach to the construction and characterization of topological stabilizer quantum error-correction codes.
Mesarovician Abstract Learning Systems

The solution methods used to realize artificial general intelligence (AGI) may not contain the formalism needed to adequately model and characterize AGI. In particular, current approaches to learning hold notions of problem domain and problem task as fundamental precepts, but it is hardly apparent that an AGI encountered in the wild will be discernable into a set of domain-task pairings. Nor is it apparent that the outcomes of AGI in a system can be well expressed in terms of domain and task, or as consequences thereof. Thus, there is both a practical and theoretical use for meta-theories of learning which do not express themselves explicitly in terms of solution methods. General systems theory offers such a meta-theory. Herein, Mesarovician abstract systems theory is used as a super-structure for learning. Abstract learning systems are formulated. Subsequent elaboration stratifies the assumptions of learning systems into a hierarchy and considers the hierarchy such stratification projects onto learning theory. The presented Mesarovician abstract learning systems theory calls back to the founding motivations of artificial intelligence research by focusing on the thinking participants directly, in this case, learning systems, in contrast to the contemporary focus on the problems thinking participants solve.
Analysis Sentiment Based on IMDB Aspects from Movie Reviews using SVM

A movie is a spectacle that can be done at a relaxed time. Currently, there are many movies that can be watched via the internet or cinema. Movies that are watched on the internet are sometimes charged to watch so that potential viewers before watching a movie will read comments from users who have watched the movie. The website that is often used to view movie comments today is IMDB. Movie comments are many and varied on the IMDB website, we can see comments based on the star rating aspect. This causes users to have difficulty analyzing other users' comments. So, this study aims to analyze the sentiment of opinions from several comments from IMDB website users using the star rating aspect and will be classified using the support vector machine method (SVM). Sentiment analysis is a classification process to understand the opinions, interactions, and emotions of a document or text. SVM is very efficient for many applications in science and engineering, especially for classification (pattern recognition) problems. In addition to the SVM method, the TF-IDF technique is also used to change the shape of the document into several words. The results obtained by applying the SVM model are 79% accuracy, 75% precision, and 87% recall. The SVM classification is also superior to other methods, namely logistic regression.
A Collaborative Dragonfly Algorithm with Novel Communication Strategy and Application for Multi-Thresholding Color Image Segmentation

&lt;p&gt;The Dragonfly Algorithm (DA) is a novel swarm intelligence algorithm with some positive applications in recent years. The algorithm simulates the basic survival ability of dragonflies to evade predators and capture prey in natural environment. The original DA algorithm converges too fast, and it is easy to fall into the local optimum, which causes the search to stagnate and the algorithm effect is not ideal. Based on above, a collaborative evolutionary dragonfly algorithm (CDA) with multi-group strategy is proposed in this paper. It uses multi-group strategy and Cauchy mutation to jointly improve the convergence speed and accuracy of the original algorithm. Image segmentation is an essential aspect of computer graphics and image processing. It has become increasingly important. This paper uses threshold technology based on the CDA algorithm to find the optimal index value under different threshold conditions. The experimental results have demonstrated that the CDA is highly competitive in terms of convergence speed and convergence accuracy DA algorithm, and CDA also performs excellent advantages in graphic segmentation experiments.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Detection of E-Mail Phishing Attacks – using Machine Learning and Deep Learning

Phishing is the most prominent cyber-crime that uses camouflaged e-mail as a weapon.In simple words, it is defined as the strategy adopted by fraudsters in-order-to get private details from persons by professing to be from wellknown channels like offices, bank, or a government organization.In this era of modernization, electronic mails are accustomed globally as communiqué channel for both private and professional purposes.The particulars exchanged over emails are often confidential and sensitive for example info of bank statements, payment bills, debit-credit reports, and authentication data.This makes e-mails precious for hackers because they can exploit these details for maleficent intends.The main goal of the attackers is to acquire personal details by deceiving the e-mail recipient to click noxious link or download the attachment under false pretences.In the last few years, there is an exponential rise in cyber threats including the major ones, phishing e-mails have result in huge monetary and identity losses.Several models have been developed to separate ham and phished e-mails but attackers are always trying new methods to invade the privacy of the people.Hence, there is dire need to perpetually develop new models or to upgrade the existing ones.The focus of the paper is to elaborate that specifically centers around on both machinelearning (ML) and deep-learning (DL) approaches for detecting phishing e-mails.It shows comparative analysis and assessment of various DL and ML models that were proposed in the last few decades to classify phishing e-mails at different stages of crime in a systematic manner.This paper discusses the problem's concept, its explication, and the anticipated future directions.
VPP: Visibility-Based Path Planning Heuristic for Monitoring Large Regions of Complex Terrain Using a UAV Onboard Camera

The use of unmanned aerial vehicles with multiple onboard sensors has grown significantly in tasks involving terrain coverage such as environmental and civil monitoring, disaster management, and forest fire fighting. Many of these tasks require a quick and early response, which makes maximizing the land covered from the flight path a challenging objective, especially when the area to be monitored is irregular, large and includes many blind spots. Accordingly, state-of-the-art total viewshed algorithms can be of great help to analyze large areas and find new paths providing maximum visibility. This article shows how the total viewshed computation is a valuable tool for generating paths that provide maximum visibility during a flight. We introduce a new heuristic called visibility-based path planning (VPP) that offers a different solution to the path planning problem. VPP identifies the hidden areas of the target territory to generate a path that provides the highest visual coverage. Simulation results show that VPP can cover up to 98.7% of the Montes de Malaga Natural Park and 94.5% of the Sierra de las Nieves National Park, both located within the province of Malaga (Spain) and chosen as regions of interest. In addition, a real flight test confirmed the high visibility achieved using VPP. Our methodology and analysis can be easily applied to enhance monitoring in other large outdoor areas.
Single Camera-Based Remote Physical Therapy: Verification on a Large Video Dataset

In recent years, several systems have been developed to capture human motion in real-time using common RGB cameras. This approach has great potential to become widespread among the general public as it allows the remote evaluation of exercise at no additional cost. The concept of using these systems in rehabilitation in the home environment has been discussed, but no work has addressed the practical problem of detecting basic body parts under different sensing conditions on a large scale. In this study, we evaluate the ability of the OpenPose pose estimation algorithm to perform keypoint detection of anatomical landmarks under different conditions. We infer the quality of detection based on the keypoint confidence values reported by the OpenPose. We used more than two thousand unique exercises for the evaluation. We focus on the influence of the camera view and the influence of the position of the trainees, which are essential in terms of the use for home exercise. Our results show that the position of the trainee has the greatest effect, in the following increasing order of suitability across all camera views: lying position, position on the knees, sitting position, and standing position. On the other hand, the effect of the camera view was only marginal, showing that the side view is having slightly worse results. The results might also indicate that the quality of detection of lower body joints is lower across all conditions than the quality of detection of upper body joints. In this practical overview, we present the possibilities and limitations of current camera-based systems in telerehabilitation.
Fusion of Multi-Intensity Image for Deep Learning-Based Human and Face Detection

For ordinary IR-illuminators in nighttime surveillance system, insufficient illumination may cause misdetection for faraway object while excessive illumination leads to over-exposure of nearby object. To overcome these two problems, we use the MI3 image dataset, which is established by multi-intensity IR-illumination (MIIR), as our benchmark dataset for modern object detection methods. We first provide complete annotations for the MI3 as its current ground-truth is incomplete. Then, we use these multi-intensity illuminated IR videos to evaluate several widely used object detectors, i.e., SSD, YOLO, Faster R-CNN, and Mask R-CNN, by analyzing the effective range of different illumination intensities. By including a tracking scheme, as well as developing of a new fusion method for different illumination intensities to improve the performance, the proposed approach may serve as a new benchmark of face and object detection for a wide range of distances. The new dataset (Dataset is available: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://ieee-dataport.org/documents/mi3</uri> ) with more complete annotations and source codes (Codes are available: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/thesuperorange/deepMI3</uri> ) is available online.
MetaEDL: Meta Evidential Learning For Uncertainty-Aware Cold-Start Recommendations

Recommender systems have been widely used to predict users’ interests and filter information from a large number of candidate items. However, accurately capturing the interests of users having limited interactions with a system remains a long-lasting challenge. Furthermore, existing recommender systems primarily focus on predicting user preferences without quantifying the prediction uncertainty. Uncertainty can help to quantify the model confidence when making a recommendation where low model confidence could serve as a more accurate indicator of a user’s cold-start level than simply using the number of interactions. We present a novel recommendation model that seamlessly integrates a meta-learning module with an evidential learning approach. The former module generalizes meta knowledge to tackle cold-start recommendations by exploiting fast adaptation. The latter quantifies both aleatoric and epistemic uncertainty without performing expensive posterior inference. Evidential learning achieves this by placing evidential priors and treating the output of the meta-learning module as evidence-based pseudo counts and learns a function to directly predict the evidence of a target interaction. Experiments on four benchmark datasets justify that our proposed model captures the uncertainty of users and demonstrates its superior performance over the state-of-the-art recommendation models.
Role of pedagogy and psychology in the development of self­-actualization and value orientations of medical residents

The article discusses topical issues of training future health care specialists at different stages of professionalization. Particular attention is paid to the stage of training in residency, when not only the formation and development of the activity professionalism, but also the personality professionalism of the future doctor is carried out. Important knowledge during training in residency and postgraduate institution is acquired by the need for self-­actualization and value orientations of trainees in the structure of an individual’s professionalism. The work emphasizes that in the preparation of future doctors, the resolution of indicated problem can successfully occur not only under the influence of central medical disciplines for training highly qualified personnel. Today, many researchers focus on the role of humanitarian disciplines, which have a significant impact on the personal and professional aspects of training. In this case, what it involves is the mastering of Pedagogy and Educational Psychology programs. The study found that the participation of residents in training in two educational humanitarian programs provides favorable transformation of value orientations, reflecting the need for self­-actualization, acceptance of medical practice values, and value attitude to the professionalism of a doctor. The data obtained indicate the need for purposeful use of pedagogy and psychology to improve the professionalism of future doctors during preparation for professional activity.
Optimized Support Vector Regression-Based Model for Solar Power Generation Forecasting on the Basis of Online Weather Reports

Increasing the forecasting accuracy of photovoltaic (PV)-generated power is currently an important topic, particularly in the maintenance of the stability and reliability of modern electric grid systems. In this study, a model based on a particle swarm optimization (PSO)-optimized support vector regression (SVR) is proposed for the accurate forecasting of PV output power. In the process, an SVR-based model is established based on the most influential historical experimental data collected from an actual PV power station. A PSO-based algorithm is adapted for the selection of dominant SVR-based model parameters and improvement of performance. Moreover, a novel data preparation algorithm is developed for the preparation of a solar irradiance pattern on the basis of weather conditions and the percentages of cloud cover collected from online weather forecast reports. Finally, the proposed model is experimentally verified by deploying it to three different PV systems (1875Wp, 2000Wp and 2700Wp). Analytical and experimental results indicate that the proposed forecasting model ensures improved accuracy. The nRMSE of the proposed forecasting model is 2.841%. The proposed model will be effective in forecasting PV output power in existing PV systems. A guideline for the accurately forecasting of PV output power in practical applications is presented.
Gamification as a trend in organizing professional education of sociologists in the context of distance learning: analysis of practices

The article identifies the essence of the phenomenon of gamification as a modern trend in distance education, as well as outlines its innovative potential based on the analysis of cases of introduction of gamification elements in the training of sociology students at Kryvyi Rih State Pedagogical University and Petro Mohyla Black Sea National University. Case studies were performed using an analytical model of gamification, which includes four interrelated elements: actors, content, effects, context.