Reviewerly/exHarmony huggingface dataset:
Formation of project competencies at two-level University education of students in the direction of “Library and information activities”

The article considers the structural and content aspect of University education for bachelors and masters of library and information activities in the field of project management and socio-cultural design. The article presents the universal, General professional and professional project competences formed at different levels of higher industry education, which are provided by the current Federal state educational standards and official projects of approximate basic educational programs. the basic indicators of their achievement are indicated. Characterized taught at the Moscow state Institute of culture bibliotekovedenie profile of the discipline “Project workshop” (BA) “Theory and methodology of socio-cultural design with the participation of the library” (master): objectified perspective and range of educational tasks allocated to the competence generated and designated indicators of evidence of their development. “Project workshop” is focused on the formation of the project culture of future bachelors of library and information activities, taking into account the current trends of socio-cultural design and the capabilities of libraries. The main types of contact work with students are practical classes, including individualized small groups. A large place is given to extracurricular independent work on the material of the training course. The master's course “Theory and methodology of socio-cultural design with the participation of the library” is aimed at further development of students' fundamental and applied aspects of project knowledge. The study of educational material by undergraduates is carried out through an optimal combination of problem lectures and thematic discussion seminars, which allow to give a coherent system of scientific knowledge in the discipline, to form a scientific approach to the students' project activities of libraries. An important place in the process of studying the course is occupied by group and individualized tasks in the framework of classroom and extracurricular (including field) practical classes, as well as active independent search activity of students. It is concluded that there is sufficient educational and methodological base for the formation of students in the framework of two-level training complex project competencies that allow effective professional activities, logical continuity of special project training of students studying in the bachelor's and master's degree in the direction of “Library and information activities”.
Myanmar News Headline Generation with Sequence-to-Sequence model

News Headline generation is one of the most valuable research recently in NLP area. Generation of News headline means by learning to map articles to headlines using Sequence-to-Sequence model. Headline Generator that used an encoder and a decoder designed using Long Short-Term Memory (LSTM) was applied in this work. In this paper, an automatic headline generation for Myanmar News article using Seq2Seq model is implemented. There are various ways to generate a headline for news. In this paper, headline was generated using Seq2Seq with one-hot encoding and described about the comparative analysis results. While constructing the model, there are some challenges such as vocabulary counting and find out unknown terms in word embedding. In order to get more meaningful results, used the error analysis to typical neural headline generation system and evaluated based on machine generated headlines and actual headlines using ROUGE evaluation metric. The experiments have been conducted on Myanmar News dataset of 7000 pairs of news articles and their corresponding headlines. According to the evaluation, Seq2Seq with one-hot encoding outperforms than other Seq2Seq with word embedding (GloVe) and Recursive Recurrent Neural Network (Recursive RNN).
SpectreCheck: An Approach to Detecting Speculative Execution Side Channels in Data Cache

Speculative execution has been widely used in modern CPU designs. This technique improves the CPU performance significantly. However, it may introduce the speculative execution side channels which can be exploited by attackers maliciously, such as the well-known Spectre attack. Although Spectre can expose the speculative execution side channels in data cache, it relies heavily on the training of branch predictors and timing analysis of the target physical processor. Thereby, it is difficult to predict if Spectre attack on processors that are under design in the early stage can succeed or not. For future white-box processors under design, how to identify the speculative execution side channels in data cache in the early stage is an important issue. To address this problem, we propose an approach to generating branch directions (including mis-predictions) of conditional branch instructions based on Instruction Set Architecture simulation. The predictions of the branch predictor in the processor under design will be guided by these branch directions to trigger the speculative execution side channels in data cache for detection. In our experiments, the RISC-V BOOM processor is used as a case study where the speculative execution side channel in data cache can be detected by our approach.
Managing COVID-19: Legal and Institutional Issues

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Active investigation and publishing of calculation web based applications for studying process

Abstract The article discusses STEM technologies used in technical specialties students’ teaching to conduct scientific and technical calculations and development of calculation applications. As the platform ecosystem Python, Jupyter Notebook and Jupyter Lab, Visual Studio Code are used. The use of the listed software tools allows not only to carry out multivariate calculations and calculative experiments but also to describe solved problems formulation, simply and quickly design the user interface and to visualize the calculation results. On the basis of the Virtual Box virtual machine an environment has been developed which allows to develop applications quickly, publish them using Plotly Dash, Voila, Streamlt technologies, control users, content and create electronic active content educational resources with moderate labor intensity.
The genesis of the “Homo responsibility”

The article reveals the essence of the concept of “responsible person” and the problem of its пenesis. Different theoretical and methodological approaches to its definition are shown. The phenomenon of social responsibility is considered as the basis of productive social creativity. The author analyzes the main trends and the nature of the process of formation of social responsibility of the individual in modern conditions. Particular attention is paid to increasing the role of personal responsibility in times of social instability and uncertainty. The accumulated experience of scientific understanding of the concept of «social responsibility» is revealed. Specific features of social responsibility and its interpretation in the works of F.Bastia, I. A. Ilyin, P. A. Kropotkin, I. I. Lapshin, N. O. Lossky, V. S. Solovyov, N. F. Fedorov, and others.
Transfer Learning Model for Image Segmentation by Integrating U-Net ++ and SE Block

In the image classification task, we only need to learn local features, but in the image segmentation task, we also need to learn positional information. Therefore, there is a difference between the image segmentation task and the image classification task in the features to be learned. In this study, we propose SE-U-Net++, which efficiently learns both local features and positional information by incorporating SE blocks, and a transfer learning algorithm that bridges the difference between the tasks by comparing parameters in the convolutional layer.
PrivyTRAC – Privacy and Security Preserving Contact Tracing System

Smartphone location-based methods have been proposed and implemented as an effective alternative to traditional labor intensive contact tracing methods. However, there are serious privacy and security concerns that may impede wide-spread adoption in many societies. Furthermore, these methods rely solely on proximity to patients, based on Bluetooth or GPS signal, ignoring lingering effects of virus, including COVID-19, present in the environment. This results in inaccurate risk assessment and incomplete contact tracing. A new system concept called PrivyTRAC preserves user privacy, increases security and improves accuracy of smartphone contact tracing. PrivyTRAC enhances users’ and patients’ privacy by letting users conduct self-evaluation based on the risk maps download to their smartphones. No user information is transmitted to external locations or devices, and no personally identifiable patient information is embedded in the risk maps as they are processed anonymized and aggregated locations of confirmed patients. The risk maps consider both spatial proximity and temporal effects to improve the accuracy of the infection risk estimation. Experiments conducted in the paper illustrate improvement of PrivyTRAC over proximity-based methods in terms of true and false positives. An approach to further improve infection risk estimation by incorporating both positive and negative local test results from contacts of confirmed cases is also described.
On Modelling Multi-Agent Path Finding as a Classical Planning Problem

Multi-Agent Path Finding (MAPF) deals with finding collision-free paths for a set of agents. It is a special case of a planning problem with only two actions - move and wait - and with one major constraint of no collision between the agents. The paper addresses the question of how to model MAPF as a classical sequential planning problem. Several models in the PDDL language are proposed and empirically compared.
Convolutional Neural Networks for opinion mining on Drug reviews

With the massive growth of Internet, users use to share experiences and to interact with other users in health communities such as medical forums about drugs, treatments and diagnosis feedback. In this context, the quality of drugs can be determined by patient reviews about drugs because their opinions are in general honest and felt. The process of opinion extraction is called opinion mining which is one of the most important research directions in natural language processing field. It is a system that combines NLP and machine learning techniques for determining opinions. In this paper, we investigate the use of Deep Learning (DL) methods for drug reviews opinion mining, we propose a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) algorithms. The results show that the proposed CNN model achieved the best results with an accuracy of 85.96%.
Sequence-To-One Neural Networks for Japanese Dialect Speech Classification

Automatic speech recognition (ASR) is usually constructed for recognizing standard language. Thus, when input speech includes dialect which is a variety of a language, performance of ASR is seriously degraded. To relax this problem, an approach is to use dialect-specific ASR recognizers by introducing a dialect speech classification module. In this situation, the performance of dialect-specific ASR depends on that of dialect speech classification. We propose a Japanese dialect speech classification method using sequence-to-one neural networks that are one of the successful methods in speech classification research fields. The experimental results showed that a classification system provided high classification accuracy.
A High Security Signature Algorithm Based on Kerberos for REST-style Cloud Storage Service

The Representational State Transfer (REST) is a distributed application architecture style which adopted on providing various network services. The identity authentication protocol Kerberos has been used to guarantee the security identity authentication of many service platforms. However, the deployment of Kerberos protocol is limited by the defects such as password guessing attacks, data tampering, and replay attacks. In this paper, an optimized Kerberos protocol is proposed and applied in a REST-style Cloud Storage Architecture. Firstly, we propose a Lately Used Newly (LUN) key replacement method to resist the password guessing attacks in Kerberos protocol. Secondly, we propose a formatted signature algorithm and a combination of signature string and time stamp method to cope with the problems of tampering and replay attacks which in deploying Kerberos. Finally, we build a security protection module using the optimized Kerberos protocol to guarantee a secure identity authentication and the reliable data communication between the client and the server. Analyses show that the module significantly improves the security of Kerberos protocol in REST-style cloud storage services.
A Survey Report on Hypernym Techniques for Text Classification

In this digital world, social media has become a communication platform for the entire world. It allows the users to express their views and opinions on various platforms. During this process, both structured and unstructured data is collected in a random manner. The exclusion of categorization causes the user to have difficulty in understanding or accessing information relating to those categories that they like. In the field of social network analysis, the automation procedure for inferring special interests from users is a challenging task. The solution for this is classification of text which inherently classifies with natural language against certain categories on text. Feature Expansion is one of the main aspects of designing an effective machine learning model for classifying texts. This technique has more relevance when unstructured data is in question. In this paper, a comparison study of various methods used for text classification is presented. The methods are broadly categorized into two major types. One is without feature expansion and the other with Hypernym-Hyponym based feature expansions. Different machine learning algorithms under both the categories are mentioned. The datasets, algorithms, results of evaluation of various algorithms are surveyed and tabulated.
GLIB: Ameliorated English Skills Development with Artificial Intelligence

In language learning, most of the learners can learn the theory and memorize the sound of a language. However, the ability to speak and learn a language properly requires good practice, experience and good learning strategies. This research is an approach to innovate and improve the language learning strategies along with practices that help to improve the language skills for non-native learners and children who are in the early stage of learning. In this research, the English language will be used to experiment with our solution as the source due to its demand and familiarity in the world. This research is focused on four main skills such as conversation skills, pronunciation skills, listening skills and grammatical skills. This research is done by analyzing the difficulties in each of the skills mentioned above and also discusses and provides details about the solution implemented to improve learning English in an efficient way. The implementation of this research is done by using technologies like natural language processing, machine learning, and deep learning approaches to come up with components to train the learner. The application of this research is delivered by using a cross-platform application called GLIB. The name GLIB is inspired by a library in C language which represents fluency. This mobile application provides facilities to improve all the English language skills mentioned above along with guides, tips, practices, and feedback based on an evaluation to improve the English language.
An Approach to the Analysis and Design of Fuzzy Control System

An approach to the analysis and design of continuous T-S fuzzy control system is proposed in this paper. The fuzzy logic controller has two consequents in each rule. They are numerator part and denominator part and are equivalent to any proper practical controller. It is shown that the overall closed-loop system behaves like an uncertain polytope of polynomials and the system stability can be checked by using some graphical robust stability criteria. The complex process on finding a common Lyapunov function to guarantee the system stability can be omitted. An illustrative example will be given to demonstrate the ability of the design procedure in the proposed approach.
Virtual Laboratories in Virtual Learning Environments

In this article, we present the structure and publication of a virtual learning environment through the PACIE methodology, incorporating resources such as virtual laboratories to carry out information technology practices by students, accessing from computers and devices such as smartphones and tablets. Because of the pandemic caused by the COVID-19 virus, students cannot enter the physical laboratories of the university. An analysis of the academic performance of students who received lessons in the previous semester is carried out in person with the academic performance of students who are receiving classes in this particular period online.
Design of seed paper coaster for sustainable engineering and environmental education in kaxabu niumian community

Abstract The consumption of natural resources in the world is increasing. People consume a lot of energy in daily products, such as air and water, cause soil pollution, consumption of natural resources, and the Kaxabu Niumian tribe from a natural pollution-free environment the current excessive development and disintegration of agricultural waste materials. Therefore, it is necessary to understand and promote the entire educational process of new environmental protection technologies. Through the learning of school science and technology education, everyone can inspire ideas, starting from the community and school’s environmental education, making products that do not harm the environment, and continue to operate the environmental technology market through technology to improve the living environment. The material used in this product’s design is made of natural bamboo or environmentally friendly PVA material shared in the community, so there is almost no burden on the environment. The principle of use will be made through diversified geometric shapes and high-pressure forming of customized shapes. Starting from elementary school education, the primary school students collect water drops on the coaster to become a small potted plant. The water resources are recycled, and the coaster has new functions. This product design principle is taught as a whole set of environmental protection and sustainable engineering methods. The ultimate goal of this research is to integrate technology into education to understand the use of environmentally friendly materials in life, achieve innovative and sustainable energy technologies, and promote environmental awareness.
Client profile prediction using convolutional neural networks for efficient recommendation systems in the context of smart factories

ABSTRACT In this work, we exploit a classifier based on convolutional neural network to predict users’ interests regarding products and services, according to their publications in social media. We aim at designing a customizable recommendation system to target social networks’ users as potential buyers. We evaluate the performance of training algorithms that use adaptive learning. The evaluation is performed in terms of the achieved accuracy rate to predict user’s interest. The selection result is backed up by the multi-criteria decision making. Two metrics are used to identify the real user interest profile of users based on publicly available photos.
Recaptured image detection through multi-resolution residual-based correlation coefficients

With the great development of image display technologies and the widespread use of various image acquisition device, recapturing high-quality images from high-fidelity LCD (liquid crystal display) screens becomes relatively convenient. These recaptured images pose serious threats on image forensic technologies and bio-authentication systems. In order to prevent the security loophole of image recapture attack, we propose a recaptured image detection method based on multi-resolution residual-based correlation coefficients. Specifically, we first classify the divided image blocks into three categories according to their content complexity. Then, for each classified block, sharpness degree is used as metric to select the local representative block. Finally, pixel-wise correlation coefficients in the residual of the local representative blocks are adopted as features for training and testing. Single database experiments demonstrate that our proposed method not only performs very close to the state-of-the-art methods on relative low-quality NTU-ROSE and BJTU-IIS databases, but also improves the performance on the most difficult-to-detect ICL-COMMSP database obviously, which verifies the effectiveness of the proposed multi-resolution strategy and the used residual-based correlation coefficients. Besides, mixed database experiments verify the superiority of the generalization ability of our proposed method. Moreover, it is robust to JPEG compression.
Iterative Nadaraya-Watson Distribution Transfer for Colour Grading

We propose a new method with Nadaraya-Watson that maps one N-dimensional distribution to another, taking into account available information about correspondences. We extend the 2D/3D problem to higher dimensions by encoding overlapping neighborhoods of data points and solve the high-dimensional problem in 1D space using an iterative projection approach. To show the potentials of this mapping, we apply it to colour transfer between two images that exhibit overlapped scenes. Experiments show quantitative and qualitative improvements over the previous state of the art colour transfer methods.
3D Object Visualization using Interactive Holographic Projection

The museum as a means of learning is actually not worth visiting, where 435 of the recorded museums are in poor condition according to the Directorate for the Preservation of Culture and Museum of the Ministry of Education and Culture. Therefore, an interactive holographic projection system was created to convey information effectively and interactively so that it was interesting to learn. The method used is the museum object displayed in the form of a hologram and can be moved and interacted by the user using the Leap Motion hand sensing sensor. Based on a questionnaire of 61 respondents, 47.50% of respondents strongly agreed that this system could help the learning of the development of human civilization and 68.9% agreed strongly that this system could support the development of museums and education in Indonesia.