Reviewerly/exHarmony huggingface dataset:
Handwriting features definition methods and algorithms

Abstract The research relevance is conditioned upon the lack of assistance tooling for forensics experts and graphologists that allow to justify examination and analysis results. It is a non-trivial task to develop such toolset. The goal of the research is to formalize handwriting features extraction process in order to validate an authorship of a given Russian handwritten text. Existing approaches analysis and a mathematical model of handwriting features extraction from handwritten documents are provided along with the choice of implementation approach. Software toolset that allows the model’s practical application is developed.
Combining Natural Techniques to Achieve Seamless Locomotion in Consumer VR Spaces

We introduce a seamless locomotion concept designed for a commercial VR game. The locomotion concept was designed with the following goal: The whole game can be explored entirely without teleportation or joystick steering but only by natural locomotion. This should create a seamless fusion of exploration and discovery and, therefore, maximize presence and accessibility. Therefore, our game leverages a mix of different techniques: 1) Redirected Walking techniques such as rotation gains and impossible spaces, 2) Passive travel like lifts, moving platforms, vehicles, etc., 3) Indirect movement such as climbing, pulling a rope to move a raft, etc. Furthermore, these techniques are extended by or coupled with physical interactions or movements like crawling, jumping or sliding around corners, to distract player during the manipulation. The seamless integration of all these techniques enables the player to walk around freely in the recommended room-scale play space of 2x2 meters, while exploring a potentially infinite virtual space.
Geospatial Management and Analysis of Microstructural Data from San Andreas Fault Observatory at Depth (SAFOD) Core Samples

Core samples obtained from scientific drilling could provide large volumes of direct microstructural and compositional data, but generating results via the traditional treatment of such data is often time-consuming and inefficient. Unifying microstructural data within a spatially referenced Geographic Information System (GIS) environment provides an opportunity to readily locate, visualize, correlate, and apply remote sensing techniques to the data. Using 26 core billet samples from the San Andreas Fault Observatory at Depth (SAFOD), this study developed GIS-based procedures for: 1. Spatially referenced visualization and storage of various microstructural data from core billets; 2. 3D modeling of billets and thin section positions within each billet, which serve as a digital record after irreversible fragmentation of the physical billets; and 3. Vector feature creation and unsupervised classification of a multi-generation calcite vein network from cathodluminescence (CL) imagery. Building on existing work which is predominantly limited to the 2D space of single thin sections, our results indicate that a GIS can facilitate spatial treatment of data even at centimeter to nanometer scales, but also revealed challenges involving intensive 3D representations and complex matrix transformations required to create geographically translated forms of the within-billet coordinate systems, which are suggested for consideration in future studies.
Sparse Graph Based Sketching for Fast Numerical Linear Algebra

In recent years, a variety of randomized constructions of sketching matrices have been devised, that have been used in fast algorithms for numerical linear algebra problems, such as least squares regression, low-rank approximation, and the approximation of leverage scores. A key property of sketching matrices is that of subspace embedding. In this paper, we study sketching matrices that are obtained from bipartite graphs that are sparse, i.e., have left degree s that is small. In particular, we explore two popular classes of sparse graphs, namely, expander graphs and magical graphs. For a given subspace ${\mathcal{U}} \subseteq {{\mathbb{R}}^n}$ of dimension k, we show that the magical graph with left degree s = 2 yields a (1 ± ϵ) ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> -subspace embedding for ${\mathcal{U}}$, if the number of right vertices (the sketch size) $m = {\mathcal{O}}\left( {{k^2}/{\varepsilon ^2}} \right)$. The expander graph with $s = {\mathcal{O}}(\log k/\varepsilon )$ yields a subspace embedding for $m = {\mathcal{O}}\left( {k\log k/{\varepsilon ^2}} \right)$. We also discuss the construction of sparse sketching matrices with reduced randomness using expanders based on error-correcting codes. Empirical results on various synthetic and real datasets show that these sparse graph sketching matrices work very well in practice.
AI Enhanced Blockchain (I)

The emergence of blockchain has attracted many researchers from various fields to use its distinctive features to solve different problems. One of the issues that can be mitigated is network security. Blockchain is a decentralized ledger without the control of a central authority. It tracks all transactions and registers them on the digital block. These blocks are immutable, immutable and distributed. This paper introduces the challenges facing network security and the opportunities for network security using blockchain.
HVS-Based Perceptual Color Compression of Image Data

In perceptual image coding applications, the main objective is to decrease, as much as possible, Bits Per Pixel (BPP) while avoiding noticeable distortions in the reconstructed image. In this paper, we propose a novel perceptual image coding technique, named Perceptual Color Compression (PCC). PCC is based on a novel model related to Human Visual System (HVS) spectral sensitivity and CIELAB Just Noticeable Color Difference (JNCD). We utilize this modeling to capitalize on the inability of the HVS to perceptually differentiate photons in very similar wavelength bands (e.g., distinguishing very similar shades of a particular color or different colors that look similar). The proposed PCC technique can be used with RGB (4:4:4) image data of various bit depths and spatial resolutions. In the evaluations, we compare the proposed PCC technique with a set of reference methods including Versatile Video Coding (VVC) and High Efficiency Video Coding (HEVC) in addition to two other recently proposed algorithms. Our PCC method attains considerable BPP reductions compared with all four reference techniques including, on average, 52.6% BPP reductions compared with VVC (VVC in All Intra still image coding mode). Regarding image perceptual reconstruction quality, PCC achieves a score of SSIM ≥ 0.99 in all tests in addition to a score of MS-SSIM ≥ 0.99 in all but one test. Moreover, MOS = 5 is attained in 75% of subjective evaluation assessments conducted.
Extrapolation accuracy underestimates rule learning: Evidence from the function-learning paradigm

Understanding the development of non-linear processes such as economic or population growth is an important prerequisite for informed decisions in those areas. In the function-learning paradigm, people's understanding of the function rule that underlies the to-be predicted process is typically measured by means of extrapolation accuracy. Here we argue, however, that even though accurate extrapolation necessitates rule-learning, the reverse does not necessarily hold: Inaccurate extrapolation does not exclude rule-learning. Experiment 1 shows that more than one third of participants who would be classified as “exemplar-based learners” based on their extrapolation accuracy were able to identify the correct function shape and slope in a rule-selection paradigm, demonstrating accurate understanding of the function rule. Experiment 2 shows that higher proportions of rule learning than ruleapplication in the function-learning paradigm is not due to (i) higher a priori probabilities to guess the correct rule in the rule-selection paradigm; nor is it due to (ii) a lack of simultaneous access to all function values in the function-learning paradigm. We conclude that rule application is not tantamount to rule-learning, and that assessing rule xlearning via extrapolation accuracy underestimates the proportion of rule learners in function-learning experiments.
An Empirical Measurement Tool for Overall Listening Experience of Immersive Audio

Even while immersive audio applications in consumer gadgets are flourishing, there are no empirical measurement tools available for measuring the Overall Listening Experience (OLE) of immersive audio. This work aims at developing a subjective method to evaluate immersive experience using questionnaire- Overall Immersive Audio eXperience (OIAX) and compare immersive spatial audio with stereo. The questionnaire measures six subscales of immersive audio attributes - Presence, Immersion, Emotion, Clarity, Depth and Readability. The tool provides the firsthand experience of immersive audio products with good reliability. It is handy for designers to create attractive virtual auditory scenes integral to consumer multimedia products.
Mathematical Model of Particle Swarm Optimization: Numerical Optimization Problems

The Particle Swarm Optimization (PSO) algorithm was put forth by Kennedy and Eberhart in the year 1995. It is widely known for the ease with which it can be implemented and its simple approach. It is a multi-agent parallel search metaheuristic technique aimed at global optimization for numerical optimization problems. It has roots in artificial life techniques like swarm intelligence, fish schooling, etc. This chapter aims to introduce the mathematical bases for the algorithm and illustrates a few pictorial aids to understand the technique better. It is intended to serve as an introduction to spark the interest of the reader. Readers wishing to learn more about the applications of PSO and its variants to multi-objective, constrained, dynamic optimization problems and other advanced topics are recommended to consider the various references at the end of the chapter.
Disentangle, Assemble, and Synthesize: Unsupervised Learning to Disentangle Appearance and Location

The next step for the generative adversarial networks (GAN) is to learn representations that allow us to control only a certain factor in the image explicitly. Since such a representation of the factor is independent of other factors, the controllability obtained from these representations leads to interpretability by identifying the variation of the synthesized image and the transferability for downstream tasks by inference. However, since it is difficult to identify and strictly define latent factors, the annotation is laborious. Moreover, learning such representations by a GAN is challenging due to the complex generation process. Therefore, we resolve this limitation using a novel generative model that can disentangle latent space into the appearance, the x-axis, and the y-axis of the object, and reassemble these components in an unsupervised manner. Specifically, based on the concept of packing the appearance and location in each position of the feature map, we introduce a novel structural constraint technique that prevents these representations from interacting with each other. The proposed structural constraint promotes the disentanglement of these factors. In experiments, we found that the proposed method is simple but effective for controllability and allows us to control the appearance and location via latent space without supervision, as compared with the conditional GAN.
Dog Image Generation using Deep Convolutional Generative Adversarial Networks

Generative Adversarial Networks (GAN) serve as an important position of the data generation models, providing possibility for generating nonexistent images, style transfer, back-ground masking, alternative faces, etc. However, the generated images are becoming more and more realistic, which has raised the concern of people's privacy. In this paper, we implemented a Deep Convolutional Generative Adversarial Network (DCGAN) to show how to generate novel dog images from noise. We improved the performance of the basic DCGAN by applying different tricks, including adding noise to the training images, excute input normalization and batch normalization, comparing different activation functions, and using soft labels. The purpose of all these tricks is to synchronize the learning process between generator and discriminator as well as introduce stochasticity. The performance evaluation is based on Memorization-informed Frechet Inception Distance (MiFID) and results show that the MiFID value of our model reached outstanding performance, which is 95.85.
Implementing insider defenses

How to avoid insider cyber-attacks by creating a corporate culture that infuses trust.
Lateral chromatic aberration correction method and lens design feedback for dynamic optical tilt

A method of generating lateral chromatic aberration correction factor (CACF) during lens development is presented. This CACF will be able to feedback to lens designer whether the design will work when tilt Optical Image Stabilization (OIS) compensation is needed in real application. CACF will be used to correct any chromatic aberration caused by the OIS tilt during image capture. No post-processing or minimum effort is required to correct any significant chromatic aberration. The output image from the OIS tilt will not show color fringes artifact. It also simulates and predict the allowable tilt angle during design stage, thus reduce the amount of cost and resources to build prototype to characterize the camera
Video Recorder as an Object of Forensic Expert Analysis

The article addresses the issues related to stationary video recorders: their types, purpose, principles of functioning, design characteristics, key components installed in these devices. The author analyses the procedure for using the video recordings from the surveillance cameras in court proceedings. He also justifies the need for comprehensive examinations when investigating stationary video recorders and points out the cases when the appointment of an examination is necessary and when a specialist inspection is enough. The paper presents a research sequence for a stationary video recorder as a computer forensics’ object. Possible questions to the expert, methods, and approaches are listed; difficulties arising during the examination are considered. The author analyzes a case study of a stationary video recorder examination from the expert practice of the laboratory of computer forensics of the Russian Federal Centre of Forensic Science of the Russian Ministry of Justice.
Accurate Background Subtraction Using Dynamic Object Presence Probability in Sports Scenes

Foreground segmentation technologies play an important role in applications such as free-viewpoint video (FVV) and sports video analysis. In this situation, we propose a new method that achieves accurate foreground silhouette extraction method using dynamic object presence probability (DOPP). Our main contributions are as follows. 1) Object presence probability for each pixel is calculated from the object recognition results based on deep learning. After that, background subtraction is implemented by changing the threshold and the update rate of the background model in response to the object presence probability. Parameter tuning of background subtraction is executed by using the object recognition results to improve the silhouette extraction quality. 2) To calculate more accurate silhouette images, parameters of background subtraction are adjusted by monitoring optical flows between consecutive frames. The object presence probability of the current frame is dynamically updated by using the object presence probability of the previous frame with optical flows. In the experiments, we confirmed that the proposed method achieved more accurate silhouette extraction than conventional methods in three sports sequences.
Snake Model Improvement Based on Bilateral Filter

In view of the limitation of the extraction of the target object's edge contour by the classic Snake model, an improved algorithm of Snake model based on bilateral filter was proposed in this paper. This algorithm uses a bilateral filter instead of the Gaussian filter in the classic Snake model to preprocess the target image. Through comprehensive consideration of the spatial domain of the target image noise point and the gray value of adjacent pixels, the target image is more accurate and effective pretreatment, and then the target image obtained at this time after the pre-processing is finished, the subsequent edge contour extraction processing and operation are performed again, and the Matlab R2015b experimental platform and its own standard experimental image are used for experimental simulation. The experimental results show that the improved model in this paper is significantly better than classic Snake model in extracting the edge contour of the target object, which can not only better protect the edge of the target image, but also have a good optimization effect on the overall definition of the target image. During the experiment, the improved Snake model was used to observe and analyze the extraction effect of the edge contour of the same target object to verify the accuracy and effectiveness of the improved model proposed in this paper.
Automatic Student Network Search for Knowledge Distillation

Pre-trained language models (PLMs), such as BERT, have achieved outstanding performance on multiple natural language processing (NLP) tasks. However, such pre-trained models usually contain a huge number of parameters and are computationally expensive. The high resource demand hinders their application on resource-restricted devices like mobile phones. Knowledge distillation (KD) is an effective compression approach, aiming at encouraging a light-weight student network to imitate the teacher network, and accordingly latent knowledge is transferred from the teacher to student. However, the great majority of student networks in previous KD methods are manually designed, normally a subnetwork of the teacher network. Transformer is generally utilized as the student for compressing BERT but still contains masses of parameters. Motivated by this, we propose a novel approach named NAS-KD, which automatically generates an optimal student network using neural architecture search (NAS) to enhance the distillation for BERT. Experiment on 7 classification tasks in NLP domain demonstrates that NAS-KD can substantially reduce the size of BERT without much performance sacrifice.
The methodologist staff in central libraries of the Russian Federation subjects

The relevance of the paper topic is due to the need to study the personnel situation in the methodological departments of the central libraries (CB) of the constituent entities of the Russian Federation (RF) in the context of sociocultural transformations and active development of digital technologies. The article objective is to present to the library community the results of the study ”Personnel of methodologists in the central libraries of the constituent entities of the Russian Federation” and subsequent observations of the work of the methodological departments of the regions. It reflects the analysis results of quantitative, job, age composition of the personnel, the educational level of specialists, functional changes in methodological units, the system of advanced training of methodologists, and problems of demanded educational activities. The staffing potential level is quite high in most libraries, because methodologists belong to a promising age group of 55 years with higher education, broad competences, sufficient work experience, regularly updating their knowledge. At the same time, the author concerns the staff small number and further reduction in its number, the lack of opportunities to fill vacancies, the uneven workload of methodologists by a number of supervised libraries, the lack of standards for staffing special structural units and methodology to determine them, as well as the optimal structure of units. Methodists perform a wide range of responsibilities, being universal specialists, competent on various issues. There is a complication and increase in the requirements for methodical activities. 97,9 % of methodologists have higher education, but their large part does not have a special library education. Methodists receive regular training, but there are virtually no special training courses for methodologists in the training system. The identified problems require solution at coordinating the efforts of the methodological centers and state support.
Repair of a Cracked Historic Maryan Bell by Gas Welding

In this article, the range of works connected with the repair of a historical Maryan bell from 1639 are presented. The first attempts to repair damaged bells occurred in the 1930s in Poland. However, this process was stopped because of extensive technological difficulties. Welding and soldering-welding were the basic methods. There is one difference between these two methods—connecting surfaces are melted during the welding process but only heated until the melting temperature of the material added to the connection (that is the solder) during the soldering-welding process. It was important to heat the bell to the proper temperature during welding. Uneven heating causes the enlargement of existing cracks or the appearance of new ones, or even the complete destruction of the bell. Nowadays, a method of even heating using a special heating mat has been devised. Thanks to this method it is possible to control the heating and cooling process. The most important task during the whole operation of bell welding was obtaining the original sound. During this research, the chemical composition was examined to prepare a welding rod with a suitable chemical composition. After the repair process, an analysis of the sound of the bell was conducted. It was shown that the repair of bells is possible when correct thermal parameters are used. The most highly recommended technique for repairing bells is gas welding.
Fine-grained vehicle recognition method based on improved ResNet

Traditional image recognition algorithms generally extract features manually and use classifiers for classification. Aiming at the problems of general recognition effect and limited generalization ability, an improved ResNet-based fine-grained vehicle recognition method is proposed. The basic network adopts the residual structure, which improves the flow of information by introducing jump connections and can effectively alleviate over-fitting. And add the Inception module to the ResNet model, It uses different sizes of convolution kernels and has different sizes of receptive fields, which can further improve the recognition accuracy. The optimization algorithm uses Adam optimizer, the experiment uses Stanford University model database stanford cars as the data source, and compares experiments with other methods. The results show that the accuracy of this method on the test set reaches 91%, which effectively improves the recognition accuracy.
Toward a Unified Framework for Visualization Design Guidelines

Visualizations are now widely adopted across disciplines, providing effective means to understand and communicate data. However, people still frequently create misleading visualizations that distort the underlying data and ultimately misinform the audience. While design guidelines exist, they are currently scattered across different sources and devised by different people, often missing design trade-offs in different contexts and providing inconsistent and conflicting design knowledge to visualization practitioners. Our goal in this work is to investigate the ontology of visualization design guidelines and derive a unified framework for structuring the guidelines. We collected existing guidelines on the web and analyzed them using the grounded theory approach. We describe the current landscape of the available guidelines and propose a structured template for describing visualization design guidelines.