Reviewerly/exHarmony huggingface dataset:
Blockchain Applications for Export Control Compliance and Global Supply Chain Integrity

Export controls are an essential tool of the international security architecture and play an important role in protecting the supply chain of sensitive items. Governments and industry face important challenges implementing export controls, but there are also incentives for exporters compliance, which include avoiding reputational damage and protecting intellectual property. Blockchain is already being used to protect the supply chain integrity in specific industries. Three potential applications in the field of export controls are: sensitive data management; detection and prevention of falsified documents and validation of identities; and traceability of sensitive items. This technology can support effective implementation of export controls and help government and industry benefit in this process.
Youth Preferences in the Choice of Information Resources on the Internet

The article analyzes the results of a questionnaire survey of students of Moscow Technical University of Communications and Informatics, aimed at studying the role of electronic resources in youth communication. The results of empirical research aimed at attitudes of students to various forms of interaction in the Web are presented.&#x0D; The preferences of students in the choice of the optimal mass media, programs for the rapid exchange of information are indicated. The motives and peculiarities of the attitude to the Internet are analyzed and explained. In this regard, it has been determined to what extent university students are involved in the information and communication environment of the Internet, ready for the perception of educational and other information on the Web. Separately, the amount of time that students devote to communication in social networks is characterized. In addition, the questionnaire survey focuses on the normative aspect of communication on the Internet, reveals the attitude of young people to the violation of legislation on the World Wide Web.
Improvement of Style Transfer Algorithm based on Neural Network

In recent years, the application of style transfer has become more and more widespread. Traditional deep learning-based style transfer networks often have problems such as image distortion, loss of detailed information, partial content disappearance, and transfer errors. The style transfer network based on deep learning that we propose in this article is aimed at dealing with these problems. Our method uses image edge information fusion and semantic segmentation technology to constrain the image structure before and after the migration, so that the converted image maintains structural consistency and integrity. We have verified that this method can successfully suppress image conversion distortion in most scenarios, and can generate good results.
Quantum Entanglement, Metaphysics of Relations, Dispositionalism and Е. J. Lowe's Ontology

The paper aims to make а fair supplement to the concept of "metaphysics of relations" (bу М. Esfeld) with а coгrect coгresponding interpretation of the dispositional natшe of characteristics within Е. J. Lowe's ontology. А reasoning from science to philosophy leads М. Esfeld to the conclusion that "quantum entanglement understood in terms of non-separability of states speaks for the metaphysics of relations that denies the presence of intrinsic characteristics of the related systems". The same naturalistic argument provides rationale for the Е. J. Lowe's claim that "the attribution of а characteristics to а given kind could bе metaphysically contingent" for "the laws of nature can bе exactly what they are revealed to us, only because there are no other substantial kinds that could instantiate objects". As а heuristics for the intentional interpretation ofE. J. Lowe's dispositionalism we deal with the requirements of "time-relativeness" and "relevance to causality" from W. Malzkorn's list of criteria of satisfactory representation of dispositions.
Achieving the Stakeholders Equilibrium in Vocational Education for the Digital Economy

This chapter is aimed at searching for a sophisticated equilibrium in vocational education in the digital economy times between its key stakeholders or, in other words, its gear wheels, which are the government, enterprise, university, and the people. The authors focus on the programs designed in the light of the digital economy. As emerging economies face bigger challenges, the chapter is about such countries, in particular about Russia as one of them which has a big state's stake. First, the chapter lays the groundwork to the instrumentalist vision of education for the stakeholders' relations and covers a historical background to the political economy in Russia. Secondly, the authors discuss the CVET key stakeholders' motives, capacities, and barriers. The authors demonstrate the spots where these wheels mismatch. Third, online learning is presented as a possible solution for at least some of the mismatches, such as the lack of the specialists and practice-based constituents in the learning program, the system bureaucracy, obsolete teaching approach, and rigid learning trajectories.
No Silver Bullet: Optimized Montgomery Multiplication on Various 64-Bit ARM Platforms

In this paper, we firstly presented optimized implementations of Montgomery multiplication on 64-bit ARM processors by taking advantages of Karatsuba algorithm and efficient multiplication instruction sets for ARM64 architectures. The implementation of Montgomery multiplication can improve the performance of (pre-quantum and post-quantum) public key cryptography (e.g. CSIDH, ECC, and RSA) implementations on ARM64 architectures, directly. Last but not least, the performance of Karatsuba algorithm does not ensure the fastest speed record on various ARM architectures, while it is determined by the clock cycles per multiplication instruction of target ARM architectures. In particular, recent Apple processors based on ARM64 architecture show lower cycles per instruction of multiplication than that of ARM Cortex-A series. For this reason, the schoolbook method shows much better performance than the sophisticated Karatsuba algorithm on Apple processors. With this observation, we can determine the proper approach for multiplication of cryptography library (e.g. Microsoft-SIDH) on Apple processors and ARM Cortex-A processors.
Analysis of the sound for recognition of keyboard operations

The paper presents the problem of identifying the pressed buttons of a typical computer keyboard using audio signals. During the experiments, signals were recorded by a digital recorder while pressing A, M, P, Backspace, and Space keyboard keys. For identification, there were used feature extraction methods and classifiers. The key recognition efficiency for the random forests method was equal 97.5%. There were also implemented disturbance software to prevent proper classification. The conducted research confirms that the developed solution effectively prevents acoustic cryptanalysis.
Learning Structural Coherence Via Generative Adversarial Network for Single Image Super-resolution

Among the major remaining challenges for single image super-resolution (SISR) is the capacity to recover coherent images with global shapes and local details conforming to human vision system. Recent generative adversarial network (GAN) based SISR methods have yielded overall realistic SR images, however, there are always unpleasant textures accompanied with structural distortions in local regions. To target these issues, we introduce the gradient branch into the generator to preserve structural information by restoring high-resolution gradient maps in SR process. In addition, we utilize a U-net based discriminator to consider both the whole image and the detailed per-pixel authenticity, which could encourage the generator to maintain overall coherence of the reconstructed images. Moreover, we have studied objective functions and LPIPS perceptual loss is added to generate more realistic and natural details. Experimental results show that our proposed method outperforms state-of-the-art perceptual-driven SR methods in perception index (PI), and obtains more geometrically consistent and visually pleasing textures in natural image restoration.
Anomalies in Punjabi Language WordNet: An IndoWordNet Evaluation

The IndoWordNet is a multilingual WordNet for eighteen Indian languages. Punjabi language WordNet is a part of the IndoWordNet. The efforts made for the development of Punjabi language WordNet are undoubtedly appreciable, but various anomalies exist in this language resource. These anomalies were detected in Punjabi language WordNet, while using it for text processing. This paper presents the results of the evaluation, discusses the possible causes behind identified issues, and recommends necessary modifications. In this evaluation, the anomalies were identified by analyzing the IndoWordNet text files manually with the help of various word processors. To verify the results, these anomalies were also tested with online versions of the IndoWordNet available at www.cfilt.iitb.ac.in/indowordnet/ and www.tdil-dc.in/indowordnet/ . Although, these web applications behave differently for similar inputs, but the existence of anomalies was verified by these online versions. The IndoWordNet is a structured language resource, but these anomalies violate the rules of its structure. It was tested for the purpose of improving its data accuracy.
Deep learning-based 3D shape reconstruction with multi-frequency projection fringes

Three dimensional (3D) shape reconstruction based on structured light technique is one of the most crucial and attractive techniques in the field of optical metrology and measurements due to the nature of non-contact and high-precision. Acquiring high-quality 3D shape data of objects with complex surface is an issue that is difficult to solve by single-frequency method. However, 3D shape data of objects with complex surface can be obtained only at a limited accuracy by classical multi-frequency approach. In this paper, we propose a new robust deep learning shape reconstruction (DLSR) method based on the structured light technique, where we accurately extract shape information of objects with complex surface from three fringe patterns with different frequencies. In the proposed DLSR method, the input of the network is three deformed fringe patterns, and the output is the corresponding 3D shape data. Compared with traditional approach, the DLSR method is pretty simple without using any geometric information and complicated triangulation computation. The experimental results demonstrate that the proposed DLSR method can effectively achieve robust, high-precision 3D shape reconstruction for objects with complex surface.
Transition to Open Science

Abstract Many initiatives addressing different types of problems of the practice of science and research have been described or cited in this book. Some were one-issue local actions, some took a broader approach at the national and some at EU level. Some stayed on, others faded after a few years. Many of the issues addressed by these movements and initiatives were part of the system of science and appeared to be systemically interdependent. This is how they converged and precipitated in the movement of Open Science, somewhere at the beginning of the second decade of this century. I discuss the major move that was made since 2015 in the EU to embrace the Open Science practice as the way science and research are being done in Europe. This elicited tensions at first foremost relate to uncertainty regarding scholarly publishing, of how and where we publish open access. But also, with respect to what immediate sharing of data and results in daily practice of researchers means, how we value and give credit for papers and published data sets. It thus poses the question of how, if at all, we must compare incomparable academic work, how we get credit and build reputations in this new open practice of science. It is indeed believed that Open Science with its practice of responsible science will be a major contribution to address the dominant problems in science that we have analysed thus far, or at least will help to mitigate them. Open Science holds a promise to take science to the next phase as outlined in the previous chapters. That is not a romantic naive longing for the science that once was. It will be a truly novel way, but realistic way of doing scientific inquiry according to the pragmatic narrative pointed out. The Transition to Open Science as can be anticipated from the analyses above will not be trivial. The recent discussions have already shown that the transition to Open Science, even between EU member states, is a very different thing because of specific national, societal and academic contexts. I will conclude this chapter reporting some of my first-hand experiences, in Brussels and during visits to several EU member states in the course of a Mutual Learning Exercise, but also encounters in North America, South East Asia and South Africa where we in the past years have discussed Open Science. Although we know science and scholarship have many forms and flavours and that wherever you go, there is not one scientific community. For me discussing the Transition to Open Science in the past four years was really a Learning Exercise, an amazing, mostly encouraging, but many times quite shocking, even saddening adventure.
INFORMATION FLOWS FORMALIZATION FOR BSD FAMILY OPERATING SYSTEMS SECURITY AGAINST UNAUTHORIZED INVESTIGATION

Today there is an increase in the number and complexity of cyberattacks on critical infrastructure. This has led to the actualization of the security systems that are critical to national security. Software, including operating systems, is considered a resource of critical information infrastructure of the state, which is usually built on secure operating systems (UNIX, BSD family, Linux). But any operating systems and user software have flaws and security issues at different levels. It is important to model information flows in the operating systems, which will more effectively identify threats to information security, implement preventive and countermeasures. From these positions, the analysis of modern research in the direction of operating systems security and user software was carried out, which allowed to identify several basic areas, including the study of the impact of malware on operating systems and user software; vulnerability analysis; threat and risk research. The analysis showed that the issues related to the peculiarities of construction and information processes of a particular operating systems, as well as the lack of adequate mathematical models that can be applied to different security systems to obtain quantitative characteristics to compare the parameters of security systems. Also, structural and analytical models of information flows of the BSD family of operating systems were developed, which makes it possible to formalize the information processes of the studied operating system and develop effective preventive and countermeasures. In addition, the mathematical model of quantitative evaluation of software systems for information security operating in user mode has been improved. This model will be useful both for comparison of existing software information security systems, and for the analysis of changes in security algorithms of software information security systems
Images of Giftedness and Creativity

The authors investigate the psychological aspect of creativity through the giftedness phenomenon and its manifestations in real activity. The article presents the content analysis results of students’ ideas about giftedness and creativity, obtained by the associative experiment method. According to the authors, implicit notions and the giftedness theory have a significant impact on attitudes towards gifted people. The methodology of hierarchical person’s image structure (HPIS) was used as a research tool. The purpose of this study is to identify the image of a gifted person based on implicit ideas of students of various specialities and universities. As a result of the study, it was revealed that the basis of the structure and content of the gifted person image is made up of intellectual characteristics, with the acquisitive characteristics being the most unpopular in the respondents’ answers. In the second place, in terms of occurrence frequency, there were social characteristics, which are more typical for the students of humanitarian specialities. This group is also characterized by a more frequent indication of the negative qualities of a gifted person, while students of the technical specialities used positively colored words more often. When describing a gifted person, names and qualities of activity were more often used in the group of technical students. The data obtained by the authors confirm the special extraordinary, creative nature of a gifted person in the implicit representations of people.
Get better 1 pixel PCK: ladder scales correspondence flow networks for remote sensing image matching in higher resolution

Recently, remote sensing image matching by deep learning reaches competitive performance evaluated by Probability of Correct Keypoints(PCK). The percentage of image size is often used as the threshold of PCK. Even though it can achieve a good 1% PCK in high resolution by regression of transformer parameters, the value will be reduced by using the absolute 1 pixel as threshold in the higher resolution. Inspired by the flow-based methods used in natural image matching tasks, we convert the transformer to correspondence flow and propose ladder scales correspondence flow networks(LSCFN) to get better 1 pixel PCK in higher resolution. Input images are resized to multi scales and then sent to network backbone to generate multi feature pyramids. These pyramids are linked and effectively pull up the highest resolution of original backbone just like a ladder when the global correlation scale is fixed. LSCFN regress correspondence flow in ladder scales in a dense cascade way. We build LSCFN-b and LSCFN-s based on the degree of semantic change between compared images. One with only global correlation is used for the big change, another with global and local correlation is used for the opposite one. The proposed LSCFN achieve state-of-the-art performance evaluated by 1% of image size PCK and absolute 1 pixel PCK on google earth dataset[25].
Delving deep into light-weight salient object detection

Salient object detection is still a hot topic at the moment. Based on the pixel-by-pixel prediction of the image, the salient object detection model often has a large number of parameters, which brings problems such as high latency, large-scale computing and storage difficulties. In this article, we try to explore the application of the idea of structural reparameterization in real-time salient object detection, trying to alleviate the contradiction between the above three problems and accuracy and focusing on the important feature of optimizing the reasoning speed of the network. Based on the above ideas, we propose a plug-and-play flexible convolution with a new structure. The convolution module can be converted to a normal 3x3 convolution layer in the inference stage, so the inference speed will be greatly improved. Using the plug-and-play convolution module and pooling together, we construct a model with high inference speed.
LS-HDIB: A Large Scale Handwritten Document Image Binarization Dataset

Handwritten document image binarization is challenging due to high variability in the written content and complex background attributes such as page style, paper quality, stains, shadow gradients, and non-uniform illumination. While the traditional thresholding methods do not effectively generalize on such challenging real-world scenarios, deep learning-based methods have performed relatively well when provided with sufficient training data. However, the existing datasets are limited in size and diversity. This work proposes LS-HDIB - a large-scale handwritten document image binarization dataset containing over a million document images that span numerous real-world scenarios. Additionally, we introduce a novel technique that uses a combination of adaptive thresholding and seamless cloning methods to create the dataset with accurate ground truths. Through an extensive quantitative and qualitative evaluation over eight different deep learning based models, we demonstrate the enhancement in the performance of these models when trained on the LS-HDIB dataset and tested on unseen images.
Jammer To Detect And Avoid Hidden Camera Using EMP Waves

Hidden cameras has become major issues nowadays, especially in the case of women and children. Security becomes great threatening due to un lawful recordings using hidden cameras.  The security is achieved in such cases by using EMP (Electro-Magnetic Pulse) which plays the important role in the application of security project. Jamming required range of frequency of electronic devices. The EMP are most powerful methods which destroys any electronic devices by blocking its signal to a desired range .The EMP waves encounters the desired range of frequencies of electronic devices and stops working of it. Placing of components like EMP will provides security and avoids leaks of secret matters. This can be used in many other applications like trail rooms in textile shops, hotel rooms, military warfare, smart cities and soon.
Implementation of a multi-layer hamming network in problems for recognition dry areas of agricultural crops

Abstract The article discusses the possibilities of using the mathematical and instrumental enhancement of the multilayer Hamming network in the tasks of recognizing aerial photographs, in particular, the advantages of the neuromodel for implementing further research to solve the problems of increasing crop yield based on monitoring and segmentation of dry crop areas are highlighted.
The Implementation of Enhanced K-Strange Points Clustering Method in Classifying Undergraduate Thesis Titles

Clustering deals with the grouping together of data items which are similar amongst themselves and differ to a greater extent in terms of proximity to items of other groups. The problem is that in most institutions, undergraduate thesis titles are not grouped based on similarity and it is time-consuming for research students to search for a thesis report based on similarity or research papers which have similar topics since the titles are just stored sequentially in the database. The low score of Silhouette coefficient using k-means as a clustering algorithm on text clustering motivated to exploit the potentiality of the Enhanced K-Strange points clustering algorithm to obtain better results. The objective of this paper is to group the undergraduate thesis titles using the Enhanced K-Strange points clustering algorithm. The Silhouette coefficient is used to test the cluster quality. The result of the research is a method that can process the titles of the undergraduate thesis and group them into different groups using a clustering technique.Madeira, Malcolm AndrewJacob, Teslin