Reviewerly/exHarmony huggingface dataset:
Three-Dimensional Reconstruction of Light Field Based on Phase Similarity

Light field imaging plays an increasingly important role in the field of three-dimensional (3D) reconstruction because of its ability to quickly obtain four-dimensional information (angle and space) of the scene. In this paper, a 3D reconstruction method of light field based on phase similarity is proposed to increase the accuracy of depth estimation and the scope of applicability of epipolar plane image (EPI). The calibration method of the light field camera was used to obtain the relationship between disparity and depth, and the projector calibration was removed to make the experimental procedure more flexible. Then, the disparity estimation algorithm based on phase similarity was designed to effectively improve the reliability and accuracy of disparity calculation, in which the phase information was used instead of the structure tensor, and the morphological processing method was used to denoise and optimize the disparity map. Finally, 3D reconstruction of the light field was realized by combining disparity information with the calibrated relationship. The experimental results showed that the reconstruction standard deviation of the two objects was 0.3179 mm and 0.3865 mm compared with the ground truth of the measured objects, respectively. Compared with the traditional EPI method, our method can not only make EPI perform well in a single scene or blurred texture situations but also maintain good reconstruction accuracy.
Multiway p-spectral graph cuts on Grassmann manifolds

Nonlinear reformulations of the spectral clustering method have gained a lot of recent attention due to their increased numerical benefits and their solid mathematical background. We present a novel direct multiway spectral clustering algorithm in the p-norm, for p∈(1,2] . The problem of computing multiple eigenvectors of the graph p-Laplacian, a nonlinear generalization of the standard graph Laplacian, is recasted as an unconstrained minimization problem on a Grassmann manifold. The value of p is reduced in a pseudocontinuous manner, promoting sparser solution vectors that correspond to optimal graph cuts as p approaches one. Monitoring the monotonic decrease of the balanced graph cuts guarantees that we obtain the best available solution from the p-levels considered. We demonstrate the effectiveness and accuracy of our algorithm in various artificial test-cases. Our numerical examples and comparative results with various state-of-the-art clustering methods indicate that the proposed method obtains high quality clusters both in terms of balanced graph cut metrics and in terms of the accuracy of the labelling assignment. Furthermore, we conduct studies for the classification of facial images and handwritten characters to demonstrate the applicability in real-world datasets.
Quaternion-Valued Convolutional Neural Network Applied for Acute Lymphoblastic Leukemia Diagnosis

The field of neural networks has seen significant advances in recent years with the development of deep and convolutional neural networks. Although many of the current works address real-valued models, recent studies reveal that neural networks with hypercomplex-valued parameters can better capture, generalize, and represent the complexity of multidimensional data. This paper explores the quaternion-valued convolutional neural network application for a pattern recognition task from medicine, namely, the diagnosis of acute lymphoblastic leukemia. Precisely, we compare the performance of real-valued and quaternion-valued convolutional neural networks to classify lymphoblasts from the peripheral blood smear microscopic images. The quaternion-valued convolutional neural network achieved better or similar performance than its corresponding real-valued network but using only 34% of its parameters. This result confirms that quaternion algebra allows capturing and extracting information from a color image with fewer parameters.
The development of digital educational technologies in Russia before the pandemic: History and peculiarities of the EdTech industry

The Covid-19 pandemic in March 2020 forced the restructuring of all educational processes and, like never before, use modern technologies so as not to stop the learning process at different levels of education. However, the trend towards digitalization of education emerged much earlier, and the technologies that were used during the pandemic have a long history. Scientific research most often looks at the application and effectiveness of digital technologies in the educational process of public educational institutions, but the EdTech industry is not limited to the field of formal education. Corporate training, additional and language education are some of the most digitalized market segments. The study considered in the article analyzed the development of educational technologies in Russia before the pandemic. The key stages of digitalization of education in Russia, the features of the introduction of educational technologies in educational institutions of the country are highlighted. An analysis of the development of the EdTech market in Russia before the pandemic is carried out, a comparison of the evolution of the market of educational technologies in Russia and abroad is carried out. It is concluded that the high-tech, but rather young and rapidly developing industry of educational technologies in Russia has a high potential for long-term development and investment attraction. Several areas of education were identified that were most digitalized even before the pandemic, and the positive impact of government initiatives and incentives for the introduction of educational technologies on the EdTech industry as a whole was revealed.
Dimensions of cybersecurity performance and crisis response in critical infrastructure organisations: an intellectual capital perspective

Purpose This research addresses the relationships between the current, dynamic organisational cyber risk climate, organisational cybersecurity performance and changes in cybersecurity investments, with an aim to address the hostile epistemic climate for intellectual capital management presented by the dynamics of cybersecurity as a phenomenon. Design/methodology/approach Expanding on the views of digital security and resilience as a knowledge problem, the research looks at cybersecurity as a critical capability within organisations, particularly relevant in critical infrastructure sectors. The problem is studied from the perspective of 400 C-level executives from critical infrastructure sectors across the UK. Data collected at the peak of the coronavirus disease 2019 (COVID-19) pandemic, a time when critical infrastructure organisations have been under a significant strain due to an increase in cybersecurity incidents, were analysed using partial least square structural equation modelling. Findings The research found a significant correlation between the board's perception of a change in their cybersecurity risk climate and patterns of both the development of cybersecurity management capabilities and cybersecurity investments. The authors also found that a positive correlation exists between the efforts placed by critical infrastructure organisations in cybersecurity training and the changes in investment in their cybersecurity, particularly in relation to their intellectual capital development efforts. Originality/value To the best of the authors’ knowledge, this is the first paper that explores the board's perception of cybersecurity in critical infrastructure organisations both from the intellectual capital perspective and in the dynamic cyber risk climate derived from the COVID-19 crisis. The authors’ findings expand on the growing perception of cybersecurity as a knowledge problem, and thus inform future research and practice in the domain of intellectual capital management and its role in supporting the cybersecurity and digital resilience of business and society.
Atlas: Grammar-based Procedural Generation of Data Visualizations

We present Atlas, a procedural grammar for constructing data visualizations. Unlike most visualization grammars which use declarative specifications to describe visualization components, Atlas exposes the generative process of a visualization through a set of concatenated high-level production rules. Each of these rules describes how an input graphical object is created, transformed, or joined with abstract data to derive an output object. The visualization state can thus be inspected throughout the generative process. We demonstrate Atlas’ expressivity through a catalog of visualization designs, and discuss the trade-offs in its design by comparing it to state-of-the-art grammars.
Developing a COVID-19 module for the European Social Survey

Abstract This short report describes the development of a COVID-19 module for Round 10 of the European Social Survey (ESS). The module is formed of 20 items, including 10 agreed with ESS National Coordinators and 10 developed by two academic teams following an external call for content. The majority of ESS participating countries will field the module for Round 10 fieldwork in 2020–2021. This report provides the rationale for the module, presents challenges of developing such a module for ESS, and gives an overview of the question development and testing process. The two external sub-modules— Government authority and legitimacy in the age of a pandemic and COVID-19 conspiracy beliefs and government rule compliance —are also introduced.
Parabolic Detection Algorithm of Tennis Serve Based on Video Image Analysis Technology

At present, the existing algorithm for detecting the parabola of tennis serves neglects the pre-estimation of the global motion information of tennis balls, which leads to great error and low recognition rate. Therefore, a new algorithm for detecting the parabola of tennis service based on video image analysis is proposed. The global motion information is estimated in advance, and the motion feature of the target is extracted. A tennis appearance model is established by sparse representation, and the data of high-resolution tennis flight appearance model are processed by data fusion technology to track the parabolic trajectory. Based on the analysis of the characteristics of the serve mechanics, according to the nonlinear transformation of the parabolic trajectory state vector, the parabolic trajectory starting point is determined, the parabolic trajectory is obtained, and the detection algorithm of the parabolic service is designed. Experimental results show that compared with the other two algorithms, the algorithm designed in this paper can recognize the trajectory of the parabola at different stages, and the detection accuracy of the parabola is higher in the three-dimensional space of the tennis service.
Threshold ECDSA with an Offline Recovery Party

A (t, n)-threshold signature scheme enables distributed signing among n players such that any subset of size at least t can sign, whereas any subset with fewer players cannot. Our goal is to produce digital signatures that are compatible with an existing centralized signature scheme: the key-generation and signature algorithms are replaced by a communication protocol between the players, but the verification algorithm remains identical to that of a signature issued using the centralized algorithm. Starting from the threshold scheme for the ECDSA signature due to Gennaro and Goldfeder, we present the first protocol that supports multiparty signatures with an offline participant during the key-generation phase and that does not rely on a trusted third party. Under standard assumptions on the underlying algebraic and geometric problems (e.g. the Discrete Logarithm Problem for an elliptic curve and the computation of $$e\mathrm{th}$$ root on semi-prime residue rings), we prove our scheme secure against adaptive malicious adversaries.
Participation of Central Libraries of the Subjects of the Russian Federation in the Organization of Scientific Activity

activities (R&amp;D) carried out by the central libraries (CL) of the subjects of the Russian Federation. The author carried out the study basing on the questionnaire survey conducted by the National Library of Russia on the assignment of the Ministry of Culture of the Russian Federation in accordance with the decision of the Annual Meeting of the Heads of Federal and Central Regional Libraries of Russia in 2020. Central libraries, in accordance with their functions and priority goals, are the basis of the country’s library system. Their role as scientific, methodological and coordinating centres of the library system of the region is established at the level of federal and regional library legislation. The questionnaire survey revealed that most libraries have regulatory and legal grounds for conducting R&amp;D. Employees of the central libraries are actively engaged in scientific-research work on a wide range of topics. At the same time, there should be distinguished two main areas of scientific activity of the central libraries: scientific support for the activities of libraries and historical and cultural research of regional nature based on library collections. Along with that, it is necessary to develop common approaches to solving organizational issues of R&amp;D and its support from methodological centres and management bodies. One of the problems is to bring the results of scientific work to a wide range of users. The paper notes that it is expedient to do this by including electronic copies of scientific publications of the central libraries in the section “To Library Professionals” of the National Electronic Library; by compliance with the federal legislation in the area of legal deposit and legal deposit of printed publications in electronic form; by including scientific publications of the central libraries in the system of the Russian Scientific Citation Index with placement in the Scientific Electronic Library (eLibrary). The use of the scientific potential of the central libraries is a significant condition for achieving the goals set out in the “Strategy for the Development of Librarianship in the Russian Federation for the period up to 2030”.
Video Captioning Based on Both Egocentric and Exocentric Views of Robot Vision for Human-Robot Interaction

Abstract Robot vision provides the most important information to robots so that they can read the context and interact with human partners successfully. Moreover, to allow humans recognize the robot’s visual understanding during human-robot interaction (HRI), the best way is for the robot to provide an explanation of its understanding in natural language. In this paper, we propose a new approach by which to interpret robot vision from an egocentric standpoint and generate descriptions to explain egocentric videos particularly for HRI. Because robot vision equals to egocentric video on the robot’s side, it contains as much egocentric view information as exocentric view information. Thus, we propose a new dataset, referred to as the global, action, and interaction (GAI) dataset, which consists of egocentric video clips and GAI descriptions in natural language to represent both egocentric and exocentric information. The encoder-decoder based deep learning model is trained based on the GAI dataset and its performance on description generation assessments is evaluated. We also conduct experiments in actual environments to verify whether the GAI dataset and the trained deep learning model can improve a robot vision system
Quantum image watermarking technology based on quantum wavelet transform

As one of the important branches of quantum image processing, quantum image watermarking technology has been studied by many related personnel. However, most of them are performed in the time domain, and there are relatively few algorithms for quantum image watermarking in the transform domain. This paper proposes a quantum image watermarking technology based on quantum D4 wavelet transform, which specifically includes several aspects. Combining quantum wavelet transform and controlled-rotating gate, this paper proposed a quantum image watermarking algorithm for the modified flexible representation of quantum images (FRQIM) and the normal arbitrary superposition state (NASS). Firstly, the wavelet coefficients of the image are obtained by quantum wavelet transform, then the rotating gate is designed by the information of the watermark image, then the embedding position of the watermark image information is controlled by the controlled gate, and finally the watermark information is embedded in the carrier image by the controlled-rotating gate. This scheme makes full use of the parallelism of quantum computing and can operate on two images at the same time. The simulation results show that for different image combinations, the embedded image is not significantly different from the original image. Compared with the quantum image watermarking algorithm that uses the quantum Fourier transform, the image distortion after watermarking is smaller, and there are more options for embedding the image.
Deep Learning Triplet Ordinal Relation Preserving Binary Code for Remote Sensing Image Retrieval Task

As satellite observation technology rapidly develops, the number of remote sensing (RS) images dramatically increases, and this leads RS image retrieval tasks to be more challenging in terms of speed and accuracy. Recently, an increasing number of researchers have turned their attention to this issue, as well as hashing algorithms, which map real-valued data onto a low-dimensional Hamming space and have been widely utilized to respond quickly to large-scale RS image search tasks. However, most existing hashing algorithms only emphasize preserving point-wise or pair-wise similarity, which may lead to an inferior approximate nearest neighbor (ANN) search result. To fix this problem, we propose a novel triplet ordinal cross entropy hashing (TOCEH). In TOCEH, to enhance the ability of preserving the ranking orders in different spaces, we establish a tensor graph representing the Euclidean triplet ordinal relationship among RS images and minimize the cross entropy between the probability distribution of the established Euclidean similarity graph and that of the Hamming triplet ordinal relation with the given binary code. During the training process, to avoid the non-deterministic polynomial (NP) hard problem, we utilize a continuous function instead of the discrete encoding process. Furthermore, we design a quantization objective function based on the principle of preserving triplet ordinal relation to minimize the loss caused by the continuous relaxation procedure. The comparative RS image retrieval experiments are conducted on three publicly available datasets, including UC Merced Land Use Dataset (UCMD), SAT-4 and SAT-6. The experimental results show that the proposed TOCEH algorithm outperforms many existing hashing algorithms in RS image retrieval tasks.
Investigation of Enhanced Ambient Contrast Ratio in Novel Micro/Mini-LED Displays

In recent years, ambient contrast ratio (ACR) has become very critical for advanced outdoor displays, including transparent displays, portable displays, and so on. In this work, the ACR of typical flat panel displays was introduced, while LED-based displays showed distinctive advantages. Micro-LED displays with a different pitch of 10 μm, 15 μm, 30 μm, and 60 μm were fabricated and characterized. Various mini-LED and micro-LED panels were systematically investigated in the aspect of brightness, reflection phenomenon, and ACR to reveal their enormous potential for outdoor applications. Through a series of experiments and comparisons, three methods were proposed to further improve the ACR of LED-based panels, including optical method, antireflection coating, and structure optimization.
M-Commerce Offline Payment

During the ongoing COVID-19 Pandemic we have seen a surge in online transactions. The current wallet payment architecture requires online connectivity during transactions. It is observed that there is a possibility of not getting connected to the Internet due to various reasons. We are working on a solution to enable offline transactions due to any internet failure. We identify three main scenarios where fully offline transaction capability is considered to be beneficial for both merchants and consumers. It includes making purchases in locations without online connectivity, when a reliable connection is not guaranteed, and when it is cheaper to carry out offline transactions due to higher communication/payment processing costs involved in online approvals. Our work aims to address the challenge of providing secure offline transaction capability when there is no online connectivity keeping in mind to not compromise any security features in our M-Commerce offline wallet payment application.
Self Care Nursing: Teleassessment Nursing with Chatbot Application the Coronavirus Disease-19 Pandemic Period in North Sumatra

AIM: Patients and families can independently identify health conditions through the chatbot teleassessment nursing application. METHODS: Design descriptive with a cross-sectional approach. The sampling technique used in this study was a purposive sampling technique. Setting at African Social Research Initiative (ASRI) Wound Care Pancing Medan, North Sumatra. One hundred and forty-seven at ASRI wound care clinic and 107 samples with characteristics that all people who have Android and have a telegram application. Data were processed and carried out by descriptive statistical tests. Tested the use of chatbots on outpatients at the ASRI wound care clinic. Participants are first taught how to use the application. This was carried out for 5 months from March to July by testing the use of a chatbot on outpatients at the ASRI wound care clinic. RESULTS: The results of research on teleassessment nursing found that 73 respondents (68%) could do it independently and 34 respondents (32%) could not do it due to their first experience of using chatbots, unstable internet networks, not yet proficient in using applications due to age and low educational background. The study also identified the respondent’s ability to make decisions about using health services. CONCLUSION: The telassessment nursing chatbot application allows patients and families to assess general conditions, danger signs and make decisions to use health services. TRIAL REGISTRATION TRIAL: Nursing Health Research Ethics Commission University of North Sumatera with number 2165/VI/SP/2020.