Reviewerly/exHarmony huggingface dataset:
Role-Playing Technologies in the Format of Online Education at a University in the Framework of International Vocational-Oriented, Intercultural and Foreign-Language Cooperation

Our article is devoted to the problem of training qualified specialists for all sectors of the economy, industry, agriculture and service. The material and practical experience analyzed and studied by us make it possible to say that it is not enough for a university graduate to have knowledge only in his future specialty, but it is important in the modern conditions of the information space to master sufficiently foreign languages and the basics of intercultural communication. Today, the task has become even more urgent. The scientific and practical material we have studied has shown that for the successful process of developing linguistic, intercultural and professional competencies, interactive innovative technologies and techniques have recently become widely used. In our opinion, online tandem training is gaining great popularity. It not only complements traditional classical learning, but also extends its boundaries in many ways. We are convinced that in the framework of online tandem training, case-based role-playing technologies are developed and used in order to make students aware of a specific problem and be able to find the best ways to solve it. The practice of our classes shows that in the modern age of mobile changes, the person is independently able to realize his individual development trajectory in accordance with his abilities, capabilities and needs, and, responsibly, make decisions and effectively realize himself in the modern world. The methodology of mass open online courses developed by us and introduced into the educational process involves the widespread use of role-playing technologies in the framework of inter-subject cooperation.
Introduction

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Role of Affordance, Visual Density and Other HCC Criteria in Designing Virtual Learning Environments to Support STEM Learning for Autistic Students

This paper discusses the design of Virtual Learning Environments (VLEs) in helping students with Autism learn Science and Engineering concepts. The design of the environments was based on information centric principles. Further, human centered computing principles (HCC) were explored during the development of the VLEs. HCC principles such as affordance, visual density and cognitive load were taken into consideration during the design process. The VLEs were created for middle and high school students. An information-centric model was created to understand the process of designing and building the VLEs. Such information models based on engineering Enterprise Modeling Language (eEML) provided a structural foundation for the design and development of the VLEs. The learning environments were created using various interfaces and immersion levels; these included haptic based interfaces, fully immersive 3D environments and Augmented Reality (AR) based environments. These VLEs introduced students to concepts in assembly in the medical context and path-planning and navigation in the context of NASA's moon mission. Assessment activities were conducted to gain a better understanding of the impact of such VLEs on the learning of science and engineering concepts to students with Autism. The preliminary results of the assessment activities demonstrated the positive impact of such cyberlearning techniques and environments on the learning of students with Autism.
Trajectory Anomaly Detection based on Similarity Analysis

Automatic trajectory processing has multiple applications, mainly due to the wide availability of the data. Trajectory data have a significant practical value, making possible the modeling of various problems such as surveillance and tracking devices, detect anomaly trajectories, identifying illegal and adverse activity. In this study, we show a comparative analysis of the performance of two descriptors to detect anomaly trajectories. We define Wavelet and Fourier transforms as trajectory descriptors to generate characteristics and subsequently detect anomalies. The experiments emphasize performance in the description in the coefficient feature space. For that, we used unsupervised learning, specifically clustering techniques, to generate subsets and identify which are irregular. The implications of the study demonstrate that it is possible to use descriptors in trajectories for automatic anomaly detection and the use of unsupervised learning methods that automatically segment the required information. The performance and comparative analysis of our study are demonstrated through experiments and a case study considering synthetic and real data sets that leave evidence of our contribution.
Blockchain and Cryptocurrencies in Libya: a Study

Third-party problems are increasing in financial transactions and the dependence of trust on a specific central institution or system will not last for long. Moreover, there is a great need for solutions to provide the right ways to start the shift from centralized systems that control all aspects of transactions to distributed systems. Thus, this research explores Blockchain, which is one of the promising remised to centralized systems that keep tracking our information and violate our privacy. This research provides detailed information on Blockchain and cryptocurrencies regarding their history, architectures, applications, pros and cons. Further, a questioner has been conducted to examine the level of knowledge regarding Blockchain and cryptocurrencies. The questioner was handed to over 200 employees from the public and private sectors. The most important indication from the questioner was that over 80% of the employees in both sectors think Blockchain applications will make a big impact when used on securing, commercial transactions or other technical areas.
Enabling Large Scale Deep Learning on Smart Device by Exploiting Edge-Cloud Computational Paradigm

A deep neural network (DNN) has the ability to rival humans in performing wide variety of tasks. The exceptional ability of DNN comes with high computational, memory, and power cost. In order to enable DNN on edge devices and any realtime environments like self-driving car, many techniques have been developed. Most of the techniques use a single network and sacrifice huge amount of accuracy to reduce the cost. In our paper, we propose a hierarchical network to enable computation on edge devices with the help of a cloud to reduce the accuracy loss. The networks have to be able to detect uncertainty in their prediction which is not usually calibrated. This paper uses modern portfolio theory to calibrate uncertainty on the model prediction. The idea is to optimize the return of a horse race problem based on the doubling rate of gambling where a gambler needs to find the proportion between making a prediction and abstain when the network is not confident. For the high abstain score, the data will be forwarded to a deeper model for better prediction. We demonstrated that the proposed framework outperforms knowledge distillation and pruning algorithms. The superiority of our result enables a high-quality result on intelligence IoT applications.
DAN &amp; Danny: Combining Analog and Digital Self-Tracking Data to Manage Wellbeing

An uprising trend of Personal Informatics has leveraged mobile applications to help users track their wellbeing; however, these digital solutions focus on quantitative data, lacking the insights provided by qualitative data in paper notebooks. We propose to digitally augment a paper diary to allow both analogue and digital data, bridging the gap between qualitative and quantitative data tracking practices to support better awareness and reflection on health data. As a first case-study, we designed a self-tracking tool to help college students manage their wellbeing by increasing self-awareness and easing help-seeking behaviours. Next, we conducted a longitudinal study to validate the tool's effectiveness and analyse its acceptability. Results show that our approach helped students by allowing moments of self-reflection and self-awareness. Additionally, our findings suggest that qualitative data is most useful when important events and abrupt changes to wellbeing occur. Preference for paper or digital diaries is highly user-dependent; however, most participants favoured a digital-only tool with notetaking capabilities.
Validation of Calibration Method for Fish-eye Lens using Equiangular Markers : ∼ Aiming for Remote Car Driving ∼

Remote car control requires images of the area around the vehicle to be transmitted to the remote operator. Fish-eye cameras are often used to acquire full-dome images because of their wide viewing angle. Especially, the fish-eye lens of the equidistant projection model is suitable for developing the equirectangular cylindrical view image used in VR. However, inexpensive mass-productive fish-eye lenses raises projection errors due to lens distortion. It is necessary to project to ideal angle by correction method. Therefore, we have proposed a simple and tiny method for estimating and calibrating lens distortion using equiangular markers in a small equipment. In this paper, we evaluate the effectiveness of the proposed method by reprojecting the distorted image as an equidistant projection method using the estimated distortion parameters.
A machine learning approach for identifying an effective dimension in parametric dynamical systems

Abstract We consider dynamical systems including physical parameters. An operator maps a realisation of the parameters to a trajectory of a quantity of interest. The operator is approximated by an artificial neural network, i.e., an approach of machine learning. We arrange modified neural networks, which determine a kind of effective dimension in the parameter space.
Performance Evaluation of Systolic DCNN Accelerators

For the given deep convolutional neural network (DCNN) models, in this work, we carefully evaluate systolic DNN accelerators associated with different array configurations. In order to find the optimal array structure in terms of the processing efficiency, the open-source evaluation framework is adopted for investigating the relationships between the network architecture and the hardware-level options. We also present the selection criteria of array structure that leads to the efficient accelerator designs.
Protecting marginalized communities by mitigating discrimination in toxic language detection

As the harms of online toxic language become more apparent, countering online toxic behavior is an essential application of natural language processing. The first step in managing toxic language risk is identification, but algorithmic approaches have themselves demonstrated bias. Texts containing some demographic identity terms such as gay or Black are more likely to be labeled as toxic in existing toxic language detection datasets. In many machine learning models introduced for toxic language detection, non-toxic comments containing minority and marginalized community-specific identity terms were given unreasonably high toxicity scores. To address the challenge of bias in toxic language detection, we propose a two-step training approach. A pretrained language model with a multitask learning objective will mitigate biases in the toxicity classifier prediction. Experiments demonstrate that jointly training the pretrained language model with a multitask objective can effectively mitigate the impacts of unintended biases and is more robust to model bias towards commonly-attacked identity groups presented in datasets without significantly hurting the model’s generalizability.
Weight-Based Binary Channel Coding—A Machine Learning Approach

Channel encoding provides the ability of having a reliable error-free reception in connection with digital data transmission. The channel noise affects some of the information bits transmitted (i.e., the received and the transmitted will not be the same), and such affected bits are referred to as errors in the received. Channel coding attends such influences of additive channel noise on the information transmitted, through the redundancy provided for the information prior to transmission. This redundancy provides overheads for transmission, and these are responsible for error-detecting/-correcting features of the communication system. The error-detecting/-correcting ability of a particular channel coding scheme depends on its Hamming distance. In the present discussion, the encoding and decoding (machine learning approach) of single error-correcting binary channel coding scheme referred to as weight-based code is proposed. An $$^{\prime}n^{\prime}$$ bit code word will be transmitted in the place of $$^{\prime}k^{\prime}$$ bit Information word, where $$n = k + r$$ , (modulo-2 sum) is obtained by appending $$^{\prime}r^{\prime}$$ number of parity bits to $$^{\prime}k^{\prime}$$ bit Information word. Code efficiency is a measure of the useful number of bits in the total number of bits transmitted. Weight-based code is such $$\left( {n, k} \right)$$ binary coding scheme with a code efficiency of $$\frac{k}{n} \times 100\% = 50\%$$ and is a systematic single error-correcting code.
Models Distillation with Lifelong Deep Learning for Vietnamese Biomedical Named Entity Recognition

In realistic data, named entities may appear in a variety of rich contexts with unique characteristics and the performance of the named entity recognition (NER) task directly affects other NLP problems. Although both lifelong learning and deep learning have proven effective in many problems, including NER, the suitable combination of these two research directions is still limited. This paper describes a lifelong deep learning model for Vietnamese Biomedical NER based on model distillation mechanism. Our approach achieves potential results, helps to boost 2.16% compared to original deep learning model.
UP-GNN: Ensemble Graph Neural Network for Link Prediction via Uncertainty Learning and Positional Capturing

Link prediction is a basic task of network data mining, and has been greatly studied. Recently, graph neural networks (GNNs) has been proposed, and have achieved good prediction effects in many tasks. However, the existing graph neural network models have the following challenges in link prediction tasks: 1) They cannot capture the position of nodes in the broader context of the graph structure; 2) When learning the node's low-dimensional representation vector, the learned representation vector is difficult to approximate the real representation; 3) Although there are different solutions for different problems, there is no framework that can integrate the effects of different methods. Therefore, we propose an ensemble learning framework named UP-GNN. Specifically, this framework consists of three modules. The position-aware module captures position information by dividing the anchor-sets, the variational autoencoder module learns the Gaussian distribution of the node to capture uncertainty, and the attention mechanism module uses the attention mechanism to capture the attribute information of nodes. Each module learns the low-dimensional representation vector of each node, and then uses it for downstream tasks. Finally, according to the output of the three modules, using the voting method, get the final predicted labels. We evaluate our model and baselines on two real data sets, and the results show that our proposed model is better than the benchmark algorithms.
Protecting Deep Cerebrospinal Fluid Cell Image Processing Models with Backdoor and Semi-Distillation

Cerebrospinal fluid image is an informative source for the diagnosis of many diseases. Consequently, deep learning models for cerebrospinal fluid image processing turn out to be a promising computer-aided diagnosis technique. Current models can efficiently and correctly identify numerous categories of cells within an image of cerebrospinal fluid. Training a cerebrospinal fluid image processing model, especially a deep neural network, requires a vast amount of data and computation. Collecting necessary data for medical tasks is an expensive procedure, during which many experts, devices, and privacy concerns are involved. Therefore, it is crucial to protect these deep models from piracy and reselling. In this paper, we study the problem of intellectual property protection of deep cerebrospinal fluid image processing models. We adopt the backdoor-based watermark as the ownership evidence and propose a semi-distillation framework to embed the watermark into the model. The proposed scheme can verify the ownership of the genuine author, hence provide robust and unforgeable protection over deep cerebrospinal fluid image processing models.
On the 100<sup>th</sup> anniversary of the Vakhtangov Theatre in Moscow and the erecting of the Cultural Center in Vakhtangov’s house in Vladikavkaz

This article describes the celebratory events held at the state level from 2019 to 2021 dedicated to the centenary of the Vakhtangov Theatre in Moscow. The article also stresses a special role of Yevgeni Vakhangov’s home town Vladikavkaz in the history of the formation of the personality of the world famous theatre director and disciple of Konstantin Stanislavski. Vakhtangov was the best person to prove in practice the possibility of using the System in the development of many theatrical forms. Vladikavkaz is also a site where a cultural Centre will be opened in Yevgeni Vakhtangov’s reconstructed birth house in fall 2022. The Moscow period of Vakhtangov’s life described here emphasizes, on the one hand, the foundation of a drama studio which, by the end of Vakhtangov’s life, was transformed into The Third MAT Studio, and finally into the Vakhtangov Theatre. On the other hand, the article maintains the fact that the Vakhtangov Theatre in Moscow emerged from a drama school, which, in turn, is based on the collective work of Vakhtangov and his students during their practical studies and application of Stanislavski’s System in the drama studio. The results of that collective work are expressed in Vakhtangov’s use of fantastic realism (based on Stanislavski’s term of realism) on stage – a phenomenon he discovered while elaborating his unique method of directing.
Introduction and Background

In this monograph, we go on a journey through the state-of-the art of hardware-oriented symmetric-key cryptography using block ciphers and tweakable-block ciphers. Before we start the aforementioned journey, it is important to highlight what expectations the reader have besides the technical contributions. If a reader wants to leave this monograph with only one lesson learned, it is that hardware design and cryptography must go hand in hand, where the design of hardware-oriented cryptographic algorithms must be assisted by experimentation, measurements and practical implementations, as many assumptions on the performance that sound nice on paper usually fail in practice. At the same time, sacrificing qualitative or quantitative security considerations for more efficient schemes can sometimes lead to significant security issues.
Human Computer Interface using Eye Gazing with error fixation in Smooth and Saccadic Eye Movement

Human Computer Interface (HCI) requires proper coordination and definition of features that serve as input to the system. The parameters of a saccadic and smooth eye movement tracking are observed and a comparison is drawn for HCI. This methodology is further incorporated with Pupil, OpenCV and Microsoft Visual Studio for image processing to identify the position of the pupil and observe the pupil movement direction in real-time. Once the direction is identified, it is possible to determine the accurate cruise position which moves towards the target. To quantify the differences between the step-change tracking of saccadic eye movement and incremental tracking of smooth eye movement, the test was conducted on two users. With the help of incremental tracking of smooth eye movement, an accuracy of 90% is achieved. It is found that the incremental tracking requires an average time of 7.21s while the time for step change tracking is just 2.82s. Based on the observations, it is determined that, when compared to the saccadic eye movement tracking, the smooth eye movement tracking is over four times more accurate. Therefore, the smooth eye tracking was found to be more accurate, precise, reliable, and predictable to use with the mouse cursor than the saccadic eye movement tracking.
Comprehensive and Empirical Evaluation of Classical Annealing and Simulated Quantum Annealing in Approximation of Global Optima for Discrete Optimization Problems

The classical annealing and simulated quantum annealing are heuristic algorithms, which simulate the natural dynamics of physical systems to solve hard optimization problems. The former makes use of thermal fluctuations to arrive at the best solution, while the latter either partially or completely ignores classical dynamics and uses quantum fluctuations instead. The literature says quantum algorithms are superior to their classical counterparts in terms of convergence speed and optimal solutions. The classical effects are relatively easy to simulate on classical computing machines, but simulating quantum effects on classical machines proves hard because of their intrinsic parallel nature. To simulate quantum effects, one should make use of quantum Monte Carlo techniques borrowed from quantum physics. Most of the current literature available is focused on finding better algorithms to simulate quantum effects on classical machines, and not much research has been conducted to evaluate the performance of the existing algorithms on optimization problems. In this paper, we try to address the effectiveness of simulated quantum annealing algorithm in finding the global optima for a distinctive set of combinatorial optimization problems and also compare the solutions obtained from simulated quantum annealing with the solutions obtained from its classical counterpart, simulated annealing algorithm.
Ernest Hemingway's “Fiesta: The Sun Also Rises”: profiling lexical level

Based on the digitally tagged document, the original text of Ernest Hemingway's novel “Fiesta: The Sun Also Rises” has been processed. Then the text has been analyzed in order to determine the dominant features of the author's individual style. This gives us the unique possibility to statistically profile the original on the lexical level. The study of ours involves the quantitative analysis, which is the most effective for probabilistic and statistical data and for calculating the most frequent text elements. This method is highly important for determining the stylistic dominant in the work of fiction, as well as for studying the specific linguistic features of a particular author. Besides, quantitative analysis along with computer data processing opens up new possibilities for studying the problem of preserving the writer's individual style in translation.