Reviewerly/exHarmony huggingface dataset:
Museum practice in the developing of applied artists’ professional competencies in the context of digitalization of education

The article examines the modern approach in the pedagogical science of training future decorative and applied art artists, the search for new forms of practice, especially museum practice. The issue of forming future specialists’ professional competencies is always acute, but today, in connection with the issues of the pandemic and the partial transition to distance learning, it has become particularly relevant and topical, including the field of art education. The authors are tasked to analyze the effectiveness of the discipline «Museum Practice» in a remote form, the development of professional competencies by bachelors. In the standard situation, this practice is partially conducted in the city museums where the university is located and also off-premise, by personal attendance, but in the 2019-2020 academic year, changes were made to the curriculum of the field of education «Decorative and Applied Arts and Folk Crafts» at the Southern Federal University, and the practice was held in an online form. The article considers the issue of remote dialogue between teacher-student-museum, identifies the positive and negative aspects of this form of communication for the development of professional competencies by future applied artists. The use of interactive, multimedia and visual technologies in the synthesis with independent work of the student gives a positive effect for the development of the necessary professional skills, even in a non-standard situation of practical training.
Transit-Guard: An OS-based Defense Mechanism Against Transient Execution Attacks

Transient attacks manipulate speculative execution to alter the control flow path in an application program and modify microarchitectural state. These state changes are not captured by the existing Instruction Set Architectures (ISAs). In this paper, we propose a novel OS-level detection-based mitigation mechanism, called Transit-Guard, that uses machine learning and real-time behavioral data of concurrent processes to detect and subsequently mitigate these attacks at run-time.
Robot Mapping and Navigation System Based on Multi-sensor Fusion

To deal with the problem of low accuracy of two-dimensional grid map and obstacle avoidance in dynamic environment of mobile robot with single 2D LiDAR, based on Turtlebot3 robot and Jetson TX2 development board, combining Bayesian estimation data fusion method, improved A* algorithm and dynamic window algorithm, this paper develops a robot mapping and navigation system. This system can fuse the data of LiDAR and RGB-D camera to enhance the mapping exactness, and realize dynamic environment navigation. Experimental results in real environment show that the mobile robot's exactness of two-dimensional grid map is enhanced, the path planning efficiency is improved, and the terminal positioning error is competent for the mobile robot's mapping and navigation tasks.
Vision-Based Finger Tapping Detection Without Fingertip Observation

A machine learning approach is investigated in this study to detect a finger tapping on a handheld surface, where the movement of the surface is observed visually; however, the tapping finger is not directly visible. A feature vector extracted from consecutive frames captured by a high-speed camera that observes a surface patch is input to a convolutional neural network to provide a prediction label indicating whether the surface is tapped within the sequence of consecutive frames (“tap”), the surface is still (“still”), or the surface is moved by hand (“move”). Receiver operating characteristics analysis on a binary discrimination of “tap” from the other two labels shows that true positive rates exceeding 97% are achieved when the false positive rate is fixed at 3%, although the generalization performance against different tapped objects or different ways of tapping is not satisfactory. An informal test where a heuristic post-processing filter is introduced suggests that the use of temporal history information should be considered for further improvements.
A Novel Blockchain-Based Revocable Permission Management on CP-ABE Scheme

Relying on the characteristics of decentralized, tamper-proof, and traceable, blockchain technology has begun to be widely used in permission management. It can be used for access control by using smart contracts to distribute tokens. But also because of the immutability of the blockchain, it cannot dynamically revoke permission. This paper proposed an on-chain user permission management mechanism that could support automatic and active permission revocation. This model proposed an extended CP-ABE chain user permission management model, which realized the permission grant based on user attributes by smart contracts. Furthermore, on this basis, an extended CP-ABE permission revocation mechanism was proposed. The timestamp technology was used to realize the automatic revocation of permission, and the subset coverage algorithm was used to realize the active revocation of permission. A blockchain-based permission distribution model was built, and the permission revocation scheme was introduced into it to conduct experiments. Its performance was tested and analyzed. The experimental results showed that when the number of permission distributions is 1000, the delay is about 5 s. At the same time, the permission change is above 130TPS, which met actual application requirements.
Distance teaching: opportunities, challenges and prospects

Russian universities were forced to switch to distance teaching format in the spring of 2020 because of the Covid-19 pandemic. Such way of teaching has become a specific testing ground for the development and testing the effectiveness of new information and digital interactive technologies and online teaching methods. Detailed descriptions of the methods and technologies for conducting all types of classes remotely in various disciplines are not freely accessible. Hence, the transition to distance teaching demanded from teachers to develop new methods and forms of teaching and to establish remote monitoring of knowledge acquisition by students. The purpose of the article is to analyze and summarize the results of approbation of methods to organize and to conduct all classes types and their monitoring in a distance teaching format in the discipline “Physics” at the Don State Technical University. The article contains general information and specific recommendations on the methodology for conducting and monitoring the level of mastering competencies by students. The results of the exams for intramural and extramural students confirmed the effectiveness of the methods used. The analysis of such experiences can contribute to creating the optimal online teaching methods.
Prognostication of Acute Lymphocytic Leukemia (ALL) using Capsule Network Algorithm

Abstract A type of cancer that affects the blood-forming tissues in the body including lymphatic system and bone marrow is Leukemia. The second most commonly occurring acute leukemia is the acute lymphoblastic leukemia or acute lymphocytic leukemia (ALL). Around 25% of the cases are observed to be due to malignant T-cell precursors while the remaining 75% of cases is due to precursors of B-cell lineage. In general, response to chemotherapy, white blood cell count and age are the clinical factors that contribute towards risk stratification. However, in recent years it has been identified that genetic alterations have enabled between individual prognosis and recovery. Despite advancement in technology, chemotherapy using anthracycline, corticosteroids and vincristine serves to be the backbone therapy to treat this disease. In this proposed work, we have used a deep convolutional neural network to detect the presence of ALL accurately and based on the image screened, it is further categorized into one of the 4 subclasses. Using Capsule network algorithm (CapsNet), we have established 100% average sensitivity for ALL detection with a highest specificity of 99.56%, precision of 99.82% and accuracy of 99.36%. When compared with other similar methodologies, we have been able to accomplish higher accuracy without microscopic image segmentation using capsule network algorithm.
Vision-Based Path Guidance to Achieve Dies-Free Roller Hemming Process

Due to its high production flexibility, roller hemming has become the mainstream process for forming and joining metal sheets in the automotive industry. The traditional roller hemming process requires specific dies to support sheet metal parts and repeated offline manual adjustment of hemming routes, resulting in high die costs, high time consumption, and excessive labor inputs. The universal platform presented in this paper could replace specific dies to effectively reduce costs and expand production flexibility. To reach this objective, a vision-based automatic compensation path to achieve a dies-free roller hemming process is proposed and investigated in this paper. Hand–eye sensor modules assisted by multi-coordinate synchronization calibration for the roller hemming were designed to reconstruct three-dimensional (3-D) shape data of the incoming materials. Results from the proposed system were validated with experimental measurements for the sheet offset and the compensation of the arm hemming position, showing that the single-axis error can be reduced to ≤0.1 mm.
High-dimensional methods for quantum homodyne tomography

We provide optimized recursion relations for homodyne tomography. We improve previous methods by mitigating the divergences intrinsic in the calculation of the pattern functions used previously, and detail how to implement the data analysis through Monte Carlo simulations. Our refinements are necessary for the reconstruction of excited quantum states which populate a high-dimensional subspace of the electromagnetic field Hilbert space. We also present a Julia package for the analysis and the reconstruction method.
A Proposal for a Hybrid Syllabus Search Tool that Combines Keyword Search and Content Based Classification

A syllabus is one of the most important clues in the analysis of the educational activities. Our previous works reported that the course syllabi of computer science (CS) curricula from about 47 universities can disclose the interesting structures in the CS curricula. However, the course syllabi were collected manually. Therefore, it was difficult to increase the number of syllabi largely, and semi-automatic crawling of massive course syllabi is needed for further analysis. We have been studying to collect syllabus information based on the contents of a large number of web pages downloaded from the university's website by using a general-purpose web crawler. We discovered the structures of the syllabus pages to some extent automatically by using the linear support vector machine (linear SVM). We used the top page of the target department educating bachelor's degree in CS field as a start page of crawling for each university. To look for such a department's page, we sometimes used Google search. Google Custom Search API <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ,(Google API) is expected to provide an efficient way to gather syllabus information while saving computation time, storage, and other resources. In this study, we propose a hybrid method which combines Google API as a general keyword search engine and linear SVM as content-based classification models. We developed a system to support the syllabus collection process. The system consists of three subsystems: Crawler, Classifier, and Database. Crawler is the combination of Google API and general-purpose web crawler. We can search syllabus-related web pages from university websites using Google API with syllabus-related search keywords and domain names of the websites. Classifier ranks pages related to CS syllabus from a large number of web pages according to the confidence scores of the linear SVM. We trained the decision model of linear SVM using the syllabus pages we collected in the former studies. Using the pages obtained from Google API and linear SVM, we can find a list of CS syllabus pages from more universities than using each method alone. Combining the top nine of Google API results and the top two of linear SVM's decision model, we obtained the CS syllabus pages from more than 96.6% of the 58 universities.
A specific emitter identification method based on full bispectrum and deep residual shrinkage network

To solve the problem of specific emitter identification (SEI) under the Rice channel, an SEI method based on full bispectrum and deep residual shrinkage networks (DRSN) is proposed. The method uses phase noise to model individual differences and utilizes non-parametric methods to extract the full bispectrum of the signal. Then the full bispectrum is used as the input of convolutional neural networks (CNN) and the model learns signal features and completes the classification task through multi-layer cascaded DRSN subsequently. The simulation experiment models 3 different radiation sources, and the modulation mode is set to BPSK, MSK, OQPSK. Experimental results show that the method we proposed can effectively identify different radiation sources, and also has a high recognition performance under low SNRs. Compared with other methods that use reduced-dimensional bispectrum features as input, the recognition accuracy is effectively improved.
Harvesting Faces from Social Media Photos for Biometric Analysis

The accuracy of automatic face recognition has increased significantly over the last decade. Technology developers actively try to improve their tools and algorithms; for this to occur, there is a need for high-quality datasets with a large number of images to test and develop new techniques. Online social networks provide a vast digital media resource, given the volume of traffic that goes through its infrastructure. The content within it varies but is predominately flooded by images. In this era where selfies are the norm, we examine a collection method employed to harvest face data from the subject's images via the web. We then show how it can be processed and organized so that it is useful for biometric applications. In addition, this experiment demonstrates how restrictions put in place by social media platforms are inadequate in the protection of their user's data.
Social Robotic Platform to Strengthen Literacy Skills

This work presents a robotic platform with mechanical movements of the head, torso, and arms interact in real-time through the “Tití app” that integrates game sessions to improve literacy skills, relating the development of a new learning scenario. The functionality of the app is based on the reading and writing errors proposed by the T.A.L.E test. The prototype developed for the dynamic interaction with the app, is controlled by an Arduino board, which manages the servo-motors and the Bluetooth communication to establish a wireless connection with a Tablet or mobile device that runs the app. The mechanical design and the control software development allow the execution of operations between the robotic prototype Titíbot and the Tití app. Its design form, function and context were considered to define each of its functions and customize the interaction to reinforce the scenario and context of application.
18‐3: Free Viewpoint Teleconferencing Using Cameras Behind Screen

Natural interaction in videoconferences can be made by correcting gaze/perspective, scale, and position by using cameras placed behind a partially transparent front emitting OLED panel and frame interpolation of deep neural networks. The diffraction artifacts from through‐screen imaging are removed by a deconvolution method.
A Fuzzy Near Neighbors Approach for Arabic Text Categorization Based on Web Mining Technique

Nowadays, the quantity of textual content available online has experienced such a colossal increase. Hence, the need for a system to investigate this content data is mandatory. In this concern, Text Categorization (TC) highlights many performance methods and techniques to analyze, explore and classify various types of documents. This study consists of two main steps. First, we extract terms from text documents using Fuzzy Near Neighbors (FNN) with web-based mining techniques algorithm. Second, we identify documents according to a particular form of similarity based on combining all Arabic encyclopedic dictionaries using clustering algorithms. In this article, Fuzzy C-Means (FCM) as a clustering algorithm is used to perform the precision of documents’ classification. This work suggests Arabic TC based on a multilingual encyclopedic dictionary (Arabic WordNet, OMW, Wikipedia, OmegaWiki, Wictionary, and Wikidata). To evaluate the efficacy of TC approach with FNN and FCM, an experimental study using a real-world dataset is carried out. The results of the present study indicate that proposed approach outperforms the traditional one and produces good results.
Neural Machine Translation for Turkish to English Using Deep Learning

Machine translation is one of the challenging Artificial Intelligence areas due to the complexity of the languages. Models that use Recurrent Neural Networks (RNN) such as Sequence-to-Sequence model have become popular on many Natural Language Processing (NLP) problems including machine translation. However, these models underperform on long sentences due to fixed length vector design. In this paper, we deployed a RNN based Sequence-to-Sequence model and Long Short-Term Memory (LSTM) with attention trained for Machine Translation (MT). We evaluated our model on a Turkish to English dataset. In addition, we have created a dataset with words from Ottoman literature which we plan to use in our future studies. We believe that this dataset will be a very valuable data source as not many NLP studies have been done with Ottoman literature. The effect of hyperparameters is also examined.
Volunteer movement in institutions of secondary vocational education

Volunteer activity is gaining popularity among students of professional educational organizations as one of the forms of social activity of the younger generation.Today, in many educational organizations, not only higher, but also secondary vocational education, volunteer associations operate on a permanent basis. The number of young people in Russia engaged in volunteer activities is constantly growing.Volunteering today is a driving force that helps to effectively solve many educational problems. Involving young people in volunteer activities is of great importance: by taking part in certain events or social projects, children gain invaluable practical experience; help specific people. And communication with people of different ages, social status, assistance in solving their problems, experience contribute to the moral development of young people. Based on modern scientific literature, of Internet sources identified the key characteristics of volunteering: independence, versatility, cohesion in certain situations of a team of like-minded people, creativity, interest, decision-making, socialization of volunteers. The movement of volunteering and its dynamics in modern Russia are considered on the example of the Elets Technical School of Railway Transport — branch of the Rostov State Transport University.
Human visual system deep learning enhancement algorithm based on optimization of physical information similarity

The performance of the imaging system under low light intensity will be affected by shot noise, and the shot noise will become stronger as the power of the light source decreases. Aiming at the impact of shot noise, this paper applies the principle of deep learning to low-light image enhancement. To improve the generalization ability of deep neural networks in different scenarios, a block matching solution based on BM3D is proposed to optimize the data of the Retinex network model. In the training process of the network, the consistency of the reflection component and the smoothness of the illumination component of the low-light image and the normallight image are used to constrain, without the need for real data of the reflection component and the illumination component. Experimental results show that this method can obtain a satisfactory low-light enhancement effect, and can significantly improve the reconstruction results of low-light images affected by noise.
TransExplain: Using neural networks to find suitable explanations for Chinese phrases

When reading an article, especially a professional article, we often encounter words or phrases that we don't recognize. They may be specific domain terms or emerging entities. When we can't guess their meaning from the article, we generally refer to the terminology dictionary or search for related content on the Internet to understand them. Some researchers have used natural language generation (NLG) models to explain these unknown phrases in recent years automatically. Still, current NLG models have difficulties generating long sentences with good coherence, and they are difficult to generate multiple sentences that describe unknown phrases from different angles. Therefore, this paper proposes a model that can judge whether an existing sentence can explain a certain phrase, called TransExplain. TransExplain can use LSTM, convolutional neural network, and attention mechanism to extract multiple sentence features and map them to a fixed-dimensional semantic feature vector. By calculating the cosine similarity between the semantic feature vector and the unknown phrase vector, it is judged whether the sentence can explain the semantics of the unknown phrase. And a loss function called positive and negative means square error is introduced to improve the model's ability that distinguishes negative examples. For this task, we provided a Chinese dataset containing phrases and explanation pairs in 7 important domains. On this dataset, TransExplain can achieve better results than previous similar tasks.
32‐3: Realization of a New MR System Using Display Integrated with Cameras

We present a new MR system using display integrated with cameras that we call DCam. It is supported by a powerful driver IC, in which there are ISP and image processing algorithm IP. By making use of DCam, the new MR system contributes to reduce weight of all‐in‐one device to 150g, provide a large FOV of 90, and save image transmission and processing time of 2 frame.
Quantum communication with structured photons

There has been rising interest in structured photons as physical realizations of high-dimensional quantum states and their impact on quantum communication. Enlarged Hilbert spaces offer several advantages like higher information capacity, better noise tolerance and the ability to benefit from advanced quantum communication protocols. We describe several of these benefits demonstrated for or inspired by structured photons and discuss the technological progress to generate, detect and modulate high-dimensional quantum information. We further outline the required components for a global quantum network using structured photons including various entanglement sources, quantum channels, repeaters, interfaces, and routers. Thus, we give an introduction and overview over the broad field of quantum communications with structured photons.