Reviewerly/exHarmony huggingface dataset:
History of private libraries of the Tambov Governorate in the research literature

We actualize the problem of studying private book collections of the estate libraries of the Tambov Governorate of the late 18th - early of the 20th centuries as a cultural environment elements. In the context of the special value of the Russia’s possessory libraries, the novelty of the work is determined by a comprehensive study of pre-revolutionary, Soviet and contemporary historiography. Interest in the book collections of the Tambov Governorate noble libraries is due to their importance as the book culture monuments of the 18th-19th centuries, their importance for verifying the private manors history. We show that among the works of pre-revolutionary historiography the history of personal libraries was considered a specific problem. Authors of that time were interested in questions of the book trade, the organization of free typographies, the largest libraries history. As part of the literature of this period analysis, we consider the works of N.A. Rubakin, I.I. Dubasov, K. Bogoyavlensky, I. Dobrotvorsky, G. Speransky, O.S. Lavrov, V. Simonov. Consideration by Soviet historiography the problem of private libraries formation made it possible to reveal the dominance of ideological attitudes in the cultural life assessments of the Russian province. We study the works of P.N. Chermensky, M. Belokrys, A.S. Chernov, N.I. Romakh. We reveal the contribution of Soviet authors to the study of the private libraries activities, the private collections reconstruction, the problems of reading culture of individual owners. Consideration of the contemporary period works is based on the latest results analysis of the identification of book monuments. We analyze the work of the Tambov Regional Universal Scientific Library named after A.S. Pushkin staff, who studies the works collection of L.A. Voeikov, G.R. Derzhavin, A.D. Khvoshchinsky and A.N. Nortsov.
We Have Outgrown IP Authentication

User authentication to licensed, networked electronic resources have often been a pain point for librarians, library staff, and especially library users since the 1990s. The experience of accessing online library resources from an off-campus or remote site often leaves users confused and frustrated. The author provides a thorough review of the user authentication landscape for e-resources in libraries over the past 25 years and discusses options to improve authentication methods based on a system of federated identity management, including proposals emerging from the RA21 (Resource Access for the 21st Century) initiative.
Short Paper: Secure Offline Payments in Bitcoin

Double-spending attacks on fast payments are one of the fatal architectural problems in Cryptocurrencies. Dmitrienko et al. proposed an offline fast payment scheme that relies on tamper-proof wallets produced by trustworthy manufacturers. With the wallets, the payee can immediately trust the transactions generated by the wallets without waiting for their registration to the blockchain. Secure coin-preloading to the wallet is important, while illegal coin-preloading can cause over/double-spending by the trusted wallets. For this, they proposed an interesting protocol that makes use of a fragment of the main blockchain to prove to the wallets the legitimacy of preloaded coins. One drawback is that, in proving that the fragment are from honest miners, their protocol requires a trusted online time-stamp server so that the wallets can verify the timestamps to see if the blocks in the fragment is mined with sufficiently large amount of computing resources. Otherwise, it sacrifices usability. In order to eliminate such an online trustee, in this paper we took the opposite approach that the payee (not the wallets) verifies the legitimacy of preloaded coins at the time of offline payment. As a consequence, our result shows that, with light-weight tamper-proof wallets, completely decentralized offline payment is possible without any modification to the existing Bitcoin network.
Security enhancement for touch panel based user authentication on smartphones

With the rapid spread of smartphones, user authentication for privacy protection is becoming increasingly important. Pattern lock is one of the most typical user authentication methods using a touch panel on smartphones. However, despite its high usability, it is vulnerable to shoulder surfing and smudge attacks. Therefore, to improve security of touch panel based user authentication on smartphones while maintaining usability, we propose a method that combines the function of pattern lock and handwritten biometrics and demonstrate its effectiveness through simulation experiments.
Accuracy study of smartglasses/smartphone AR systems for percutaneous needle interventions

Purpose: This study aims to investigate the accuracy of a cross platform augmented reality (AR) system for percutaneous needle interventions irrespective of operator error. In particular, we study the effect of the relative position and orientation of the AR device and the marker, the location of the target, and the angle of needle on the overlay accuracy. Method: A needle guidance AR platform developed using Unity and Vuforia SDK platforms was used to display a planned needle trajectory for targets via mobile and wearable devices. To evaluate the system accuracy, a custom phantom embedded with metal fiducial markers and an adjustable needle guide was designed to mimic different relative position and orientation scenarios of the smart device and the marker. After segmenting images of CT-visible fiducial markers as well as different needle trajectories, error was defined by comparing them to the corresponding augmented target/needle trajectory projected by smartphone and smartglasses devices. Results: The augmentation error for targets and needle trajectories were reported as a function of marker position and orientation, as well as the location of the targets. Overall, the image overlay error for needle trajectory was 0.28&plusmn;0.32&deg; (Max = 0.856&deg;) and 0.41&plusmn;0.23&deg; (Max = 0.805&deg;) using the iPhone and HoloLens glasses, respectively. The overall image overlay error for targets was 1.75&plusmn;0.59 mm for iPhone, and 1.74&plusmn;0.86 mm for HoloLens. Conclusions: The image overlay error caused by different sources can be quantified for different AR devices.
Adaptive Density Peak Clustering for Determinging Cluster Center

Density peak clustering algorithm is a new algorithm to achieve fast clustering by finding density peak. It has the advantages of simple implementation, less parameters required, processing non convex data and good clustering effect. However, it also relies on the truncation distance and can not automatically identify the cluster center. In this paper, a density peak clustering algorithm is proposed to adaptively determine the clustering center. First, the new local density is determined by the new neighborhood relationship, and then the clustering center is determined by the original algorithm to complete the clustering. Finally, the accuracy is verified by experiments.
The influence of using parts of personal learning environment on student performance and learning motivation

The article discusses the possibilities of creating and developing a personal educational environment (PLE) as a tool that allows students to improve their academic performance in universities. The study aims are to identify the links between the use of PLE parts and student performance indicators. Methods of mathematical statistics were used to investigate the results of a survey of 348 students about the use of information resources in the educational process. The analysis of the use of individual elements of the PLE for training and communication of students with each other and with teachers was held. Students using a personal learning environment mostly have a higher average mark, pass intermediate grades more successfully, and less often resort to retakes. The positive impact of using social networks and personal accounts for communication with teachers on performance indicators was revealed. One of the results of the study is the confirmation of the possibility of using PLE in an educational institution as a unified system that helps students in learning and improves average performance, due to the fact that a balanced set of elements of PLE involves in the work both successful and lagging students.
On subfields of the second generalization of the GK maximal function field

The second generalized GK function fields Kn are a recently found family of maximal function fields over the finite field with q2n elements, where q is a prime power and n≥1 an odd integer. In this paper we construct many new maximal function fields by determining various Galois subfields of Kn. In case gcd⁡(q+1,n)=1 and either q is even or q≡1(mod4), we find a complete list of Galois subfields of Kn. Our construction adds several previously unknown genera to the genus spectrum of maximal curves.
Two-Party State Channels with Assertions

An empirical case study to evaluate state channels as a scaling solution for cryptocurrencies demonstrated that providing an application’s full state during the dispute process for a state channel is financially costly (i.e. $0.24 to $8.83 for a battleship game) which can hamper their real-world use. To overcome this issue, we present State Assertion Channels, the first state channel to guarantee an honest party is always refunded the cost if it becomes necessary to send an application’s full state during the dispute process. Furthermore it ensures an honest party will pay an approximate fixed cost to continue an application’s execution via the dispute process. We provide a proof of concept implementation in Ethereum which demonstrates it costs approximately $0.02 to submit evidence regardless of the smart contract’s application.
Measuring Trends in Online Hate Speech Victimisation and Exposure, and Attitudes in New Zealand

Government agencies in New Zealand are not required to systematically collect data on online hate speech, thus, there is a lack of longitudinal evidence regarding this phenomenon. This report presents trends in personal experiences of and exposure to online hate speech among adult New Zealanders based on nationally representative data. The findings from this study are also compared with results from a similar research study conducted in 2018. In addition, this report explores people’s perceptions about other issues related to hate speech. The goal of this study is to fill some knowledge gaps regarding the extent and nature of hate speech in New Zealand. It also seeks to provide reliable and robust evidence to inform public conversation on the topic and the implementation of strategies and activities to ensure all New Zealanders, no matter what their characteristics, are safe online to benefit from the multiple opportunities of the digital environment.
Electrical Control Technology and PLC Course Teaching Reform Strategy

With the development of science and technology and the advancement of society, the demand for talents by institutions has been continuously improved, and new demands have been placed on the technical capabilities of talents. The higher vocational colleges are the most cradle for cultivating a new generation of electrical talents. The teaching strategy must be synchronized with the development of the times. The pursuit of the overall ability of the students should be improved, and the teaching mode of teaching, learning and integration should be realized. Practice is the sole criterion for testing students. In turn, the teaching results of higher vocational colleges will be improved, and the trainees will be trained in all aspects, improve their own professional knowledge and practical ability, and open a new chapter in electric power teaching.
In Search of Hidden Treasures

In this lesson, students learn to aurally identify chromatic harmonies and modulations as they listen to a full movement by singing simple, improvised figurations built on the guide-tone line. While tracking the movement’s tonal plan, students discover a surprising harmonic twist that raises significant interpretive questions about its overall design. As students explore these questions, they discover important musical relationships that span the entire movement and reveal the hidden treasures it contains.
Encryption performance and security of certain wide block ciphers

In a context of enhancing the performance of block encryption algorithms there are two well-known approaches. The first one relates to the construction of block cipher modes of operation. This approach is effective, but it requires a specific proof of security. The second approach deals with the construction of a cryptographic algorithm itself. In this paper, we introduce a novel family of schemes with a block size ranging from 256 up to 1056 bits. We call these algorithms Wide Block Ciphers (WBCs). The round transformation of the considered WBCs is based on a shift register with several feedbacks (the design is similar to the generalized Feistel networks). For the encryption transformation, i.e. the composition of round transformations, we study the properties of confusion, diffusion and mixing by methods of the Matrix-Graph Approach. We present a technique for evaluating these properties, which allows us to choose the algorithm parameters (block size, number of feedbacks, feedback locations) and propose the schemes reaching rational compromise between the encryption performance and the security level. The considered algorithms show better performance than other similar algorithms implemented by the original Feistel network with the same number of rounds. Without loss of generality, we focus on 256-3 version (256-bit block and three feedbacks). Developers can use the proposed schemes as building blocks of the algorithms for ensuring information confidentiality and integrity.
Generating high resolution digital mammogram from digitized film mammogram with conditional generative adversarial network

Deep-learning based application for digital mammography screening is limited due to lack of labeled data. Generating digital mammogram (DM) from existing labeled digitized screen-film mammogram (DFM) dataset is one approach that may alleviate the problem. Generating high resolution DMs from DFMs is a challenge due to the limitations of network capacity and lack of GPU memory. In this study, we developed a deep learning framework, Cycle-HDDM, with which high resolution DMs were generated from DFMs. Our Cycle-HDDM model first used a sliding window to crop DFMs and DMs into patches of 256 by 256 in size. Then, we divided the patches into three categories (breast, background and boundary) using breast masks. We paired patches from the DFM and DMdatasets for training with the constraint that these paired patches should be sampled from the same category of the two different image sets. We used U-Net as the generators and modified the discriminators so that the outputs of the discriminators were a two-channel image, one channel for distinguishing real and synthesized DMs, and the other for representing a probability map for breast mask. We designed a study to evaluate the usefulness of Cycle-HDDM in a segmentation task, the objective of which was to estimate the percentage of breast density (PD) on DMs using deep neural network (DNN). With IRB approval, 1651 DFMs and 813 DMs were collected. Both DFMs and DMs were normalized to a pixel size of 100&mu;m &times; 100&mu;m for the experiments. The results show that the synthesized DMs by Cycle-HDDM could significantly improve (p &lt; 0.001) the DNN-based mammographic density segmentation.
Aspect Phrase Extraction in Sentiment Analysis with Deep Learning

This paper deals with aspect phrase extraction and classification in sentiment analysis. We summarize current approaches and datasets from the domain of aspect-based sentiment analysis. This domain detects sentiments expressed for individual aspects in unstructured text data. So far, mainly commercial user reviews for products or services such as restaurants were investigated. We here present our dataset consisting of German physician reviews, a sensitive and linguistically complex field. Furthermore, we describe the annotation process of a dataset for supervised learning with neural networks. Moreover, we introduce our model for extracting and classifying aspect phrases in one step, which obtains an F1-score of 80%. By applying it to a more complex domain, our approach and results outperform previous approaches.
Applications of Deep Learning in Robot Vision

Deep learning techniques developed in robot soccer can therefore be transferred to other resource-constrained platforms, such as smartphones and Unmanned Aerial Vehicles. Deep learning is a hot topic in the pattern recognition, machine learning, and computer vision research communities. Deep learning has already attracted the attention of the robot vision community. The chapter is intended to be a guide for developers of robot vision systems and discusses requirements for robot vision applications. It describes relevant papers related to robot vision and also discusses current and future challenges of deep learning in robotic applications. The chapter explores advances based on deep learning related to robot vision and future challenges. Unsupervised learning is another relevant area of future development for deep learning-based vision systems. Biological learning is largely unsupervised; animals and humans discover the structure of the world by exploring and observing it.
Current and expected parameters of building digital competencies among the working population

This article is devoted to issues associated with developing a digital economy in the Russian Federation, given a context where digital technologies are finding their way into the work process. The current discussions on “digitalization” increasingly emphasize there not being any alternative to mastering these technologies. With it being a condition of employment, the use of various digital products demands that workers immerse themselves into the digital environment, which, in turn, sets the course for developing digital competencies. For starters, based on the authors examining a sample of job openings, the digital competencies that are currently in demand on the labor market were determined, with them having to correspond with workplace equipment status. All devices and technologies currently used by Russian enterprises and organizations are presented as digital means of labor, while being divided into classes and groups. Together they constitute certain parameters which the authors used as a foundation for their research on the digital competencies of workers or their digital career capital. Then analysis was conducted of data from a telephone survey of working residents of Saint-Petersburg, in order to determine their motivations and priorities when it comes to developing digital competencies. The results attained from the study suggest that building digital capital is demanded by the labor market, and that it can (and should) be focused on the needs of Russia’s economy. In the meantime digital competencies among the working population mostly depend on workers’ qualification, their age and abundance of digital technology in their everyday life. The latter is just as critical a factor as the work environment when it comes to defining the algorithms of familiarizing oneself with the products of the digital revolution. However, the population does not have a clear understanding on what digital knowledge and skills need to be acquired, while lacking the motivation to improve their skill set in this field. Employees often fail to see the opportunities offered by developing digital capital in the workplace. Regardless, most of them rightfully understand that changes in technology are inevitable and desirable, and that they need to be able to keep up. Hence this article analyzes, on one hand, the demand for digital competencies, shaped by the current situation on the labor market, and on the other hand – the motivations and expectations of the employees themselves in terms of possibilities for their use and further development. The results of the study allowed its authors to paint a comprehensive picture of the processes which bring the working population to enhance their digital competencies.
Enhancing Product Configuration and Sales Processes with Extended Reality

The advent of extended reality (XR) technologies is opening new doors for augmenting customer experience and enhancing sales processes. XR is promising not only for enhancing new product visualizations, and supporting sales processes by fostering discussions, but also for placing an emotional and tangible connect to future product designs. In this paper, state of the art and challenges of XR-based group product configuration for industrial applications are discussed. A solution concept and prototype for virtual and augmented reality is presented, with group configuration features for collaboration. Evaluation concepts and design insights are drawn out.
Distance learning as an innovative model of teaching mathematics in higher education

In the XXI century distance learning has become one of the most popular educational formats in higher education. The main driver of distance learning is the new needs of the labor market, requiring employees who are ready to adapt to changing conditions of the market and are included in the process of continuous self-improvement. Until now, the major part of the achievements of distance learning lay in the field of the humanities or special technical disciplines and was almost not integrated into the teaching of Mathematics in universities. We have developed and implemented a model of distance learning, adapted to the existing conditions for the teaching of mathematical disciplines in universities. The model, which was empirically tested during experiments conducted in the period from 2004 to 2018 at Southwestern State University and the Russian State Agrarian University – Moscow Timiryazev Agricultural Academy, demonstrated its effectiveness.
An Improved Apriori Algorithm Based on Matrix

This paper analyzes the limitations of the Apriori algorithm for mining frequent itemsets with low time and space efficiency, and proposes a frequent itemset mining algorithm for matrix, itemset count and item index list. It only needs to scan the database once. According to the prior nature of frequent itemsets, the size of the data scan is reduced by compressing the matrix, and then the bitwise and operation is performed on the compressed matrix row vectors. The itemset count and prefix index list are used to generate frequent itemsets. No candidate set is generated during the mining process. This algorithm improves the efficiency of the original algorithm.