Reviewerly/exHarmony huggingface dataset:
Delayed Recognition: A Co-Citation Perspective

A Sleeping Beauty is a publication that is apparently unrecognized for some period of time before experiencing sudden recognition by citation. Various reasons, including resistance to new ideas, have been attributed to such delayed recognition. We examine this phenomenon in the special case of co-citations, which represent new ideas generated through the combination of existing ones. Using relatively stringent selection criteria derived from the work of others, we analyze a very large dataset of over 940 million unique co-cited article pairs, and identified 1,196 cases of delayed co-citations. We further classify these 1,196 cases with respect to amplitude, rate of citation, and disciplinary origin and discuss alternative approaches towards identifying such instances.
Image Based Fractal Analysis for Detection of Cancer Cells

Early detection of cancer is crucial to treatment. In this paper, we investigated an image based fractal analysis method for detecting cancer cells. Cancer cells typically display abnormalities including unregulated cell growth. Fractal analysis can be used to analyze irregularly shaped figures and measure morphological complexity. Experiments with images of human breast cancer cells were investigated. Changes in the fractal dimension between cancer cells and healthy cells were analyzed and compared. Our preliminary results show that the image based fractal analysis technique is able to detect breast cancer cells. It has great potential for yielding insight into the morphological characterization of tumor growth and may serve as an indicator for cancer detection and cancer treatment efficiency.
Virtual Reality and Innovative Educational Environment

The article discusses the problems associated with introduction and implementation of modern innovative methodologies in the process of teaching the humanities at higher school. Scientific originality of the study lies in the fact that the author proposes theoretical models to develop the sphere of education where there is an increasing tendency towards integration of scientific disciplines, modules. The research findings are as follows: the author proves that virtual educational environment encompasses virtual labs (workshops), lecture spaces, virtual models and network educational institutions.
Lectures as a symbiosis of scientific-methodological and practice-oriented personal development technologies

The model of a modern University is characterized by the formalization of its activities within the framework of implementing specific educational programs through the educational process and focusing on the final result in the form of a competitive specialist. As a result, the universal values, the spirit of participation in the acquisition of new knowledge, which were present in previous generations of universities, are leveled. However, the students’ needs in their personal development remain at the present time. This leads to the search for new forms and the use of new technologies to meet this need. Based on the research, the authors propose a Lectures model as one of these forms, which includes scientific and methodological, practice-oriented and personal-development components. This is where the spirit of the University as an element of Higher education, the tasks of the University as an educational organization to train competitive specialists and the needs of the individual in self-development can be realized. Considering the development of information and educational technologies, it is necessary to apply them actively in the already available instruments of the Higher School. The educational process should become interesting, interactive, covering various aspects of the studied disciplines as much as possible. But at the same time, the educational process should not lose its main purpose - the formation of new knowledge and the upbringing of a socially active and positive personality. Taking into account the development of science and technology it is these two components that the Lectures are directed towards.
A Hybrid Inversive Congruential Pseudorandom Number Generator with High Period

Though generating a sequence of pseudorandom numbers by linear methods (Lehmer generator) displays acceptable behavior under some conditions of the parameters, it also has undesirableÂ features, which makes the sequence unusable for various stochastic simulations. An extension which showed promise for such applications is a generator obtained by using a first-order recurrence based upon the inversive modulo a prime or a prime power, called inversive congruential generator (ICG). A lot of work has been dedicated to investigate the periods (under some conditions of the parameters), the lattice test passing, discrepancyÂ and other statistical properties of such a generator. Here, we propose a new method, which we call hybrid inversive congruential generator (HICG), based upon a second order recurrence using the inversive modulo M, a power of 2. We investigate the period of thisÂ pseudorandom numbers generator (PRNG) and give necessary and sufficient conditions for our PRNG to have periods M (thereby doubling the period of the classical ICG) and M/2 (matching the one of the ICG). Moreover, we show that the lattice test complexity for a binary sequence associated to (a full period) HICG is precisely M/2.
SGD Generalizes Better Than GD (And Regularization Doesn't Help)

We give a new separation result between the generalization performance of stochastic gradient descent (SGD) and of full-batch gradient descent (GD) in the fundamental stochastic convex optimization model. While for SGD it is well-known that $O(1/\epsilon^2)$ iterations suffice for obtaining a solution with $\epsilon$ excess expected risk, we show that with the same number of steps GD may overfit and emit a solution with $\Omega(1)$ generalization error. Moreover, we show that in fact $\Omega(1/\epsilon^4)$ iterations are necessary for GD to match the generalization performance of SGD, which is also tight due to recent work by Bassily et al. (2020). We further discuss how regularizing the empirical risk minimized by GD essentially does not change the above result, and revisit the concepts of stability, implicit bias and the role of the learning algorithm in generalization.
Loading Initial Data into the Quantum Register

In this paper, we examine a simple algorithm for loading initial data into the quantum register. In order to perform the algorithm standard two input gates are used. The algorithm is tested for the Gaussian and sine wave states. In the Appendix full PyQuil code of the algorithm is attached.
Support of Scientific Research as Libraries’ Field of Activity

Support of scientific research as a field of activity is one of the leading topics discussed in the professional library, especially foreign, periodicals. In 2020, there was conducted the analysis of research support services provided by research libraries of domestic and foreign universities. Based on the experience of the 2017’ study, the authors divided all research support services into two groups for the analysis of 2020: information and library services and general scientific services. Information and library services include all those that the library traditionally provides to its readers and users, taking into account the development of modern information technologies. General scientific services are essentially a certain continuation of the work with information that researchers have already received in the course of their work. For the analysis of research universities, the authors selected Russian universities that were included in the Shanghai ranking of world universities for 2020 (there are nine of them), followed by the same number of foreign universities. Comparison of the results of the 2017’ and 2020’ studies showed that university libraries have found stable, relevant directions and forms of communication with scientists. They can be grouped as follows: preparation and maintenance of special resources that are a priori useful to researchers for obtaining information necessary for conducting, promoting and organizing scientific research; assistance in preparing publications; assistance in using tools for scientific communication; assistance in working with research data; assistance in working with scientometric data; comprehensive support for scientific research. The article indicates that the core of this area of library and information activity has been formed over the past four years. The only significant difference in the activities of research libraries of foreign and domestic universities is that the latter do not offer data management services. The authors draw attention to the appearance in library activities of such type of services as “help”/assistance on the websites of libraries of domestic and foreign universities. The authors propose to consider it in the “help — consulting — support” paradigm, which reflects the competence level of the librarian.
Fast Classification Learning with Neural Networks and Conceptors for Speech Recognition and Car Driving Maneuvers

Recurrent neural networks are a powerful means in diverse applications. We show that, together with so-called conceptors, they also allow fast learning, in contrast to other deep learning methods. In addition, a relatively small number of examples suffices to train neural networks with high accuracy. We demonstrate this with two applications, namely speech recognition and detecting car driving maneuvers. We improve the state of the art by application-specific preparation techniques: For speech recognition, we use mel frequency cepstral coefficients leading to a compact representation of the frequency spectra, and detecting car driving maneuvers can be done without the commonly used polynomial interpolation, as our evaluation suggests.
Modelling Human-Computer Interactions based on Cognitive Styles within Collective Decision-Making

The article proposes an approach to evaluate human-computer interaction in the collective decision-making model.It is believed that all team members interact with each other through a distributed information system.The approach involves considering, when modelling, the personality characteristics of perception, each member of the team as a set of cognitive styles.Within the scope of the proposed technique, it is believed that information flows are interconnected with the processes of collective decision-making, which makes it possible to model the process of collective decision-making, monitor and analyse the effectiveness of the collective's activities.Experimental studies accomplished with statistical data processing were carried out and discussed.
Semantic interoperability based on the European Materials and Modelling Ontology and its ontological paradigm: Mereosemiotics

The European Materials and Modelling Ontology (EMMO) has recently been advanced in the computational molecular engineering and multiscale modelling communities as a top-level ontology, aiming to support semantic interoperability and data integration solutions, e.g., for research data infrastructures. The present work explores how top-level ontologies that are based on the same paradigm - the same set of fundamental postulates - as the EMMO can be applied to models of physical systems and their use in computational engineering practice. This paradigm, which combines mereology (in its extension as mereotopology) and semiotics (following Peirce's approach), is here referred to as mereosemiotics. Multiple conceivable ways of implementing mereosemiotics are compared, and the design space consisting of the possible types of top-level ontologies following this paradigm is characterized.
Research on the application of target detection based on deep learning technology in power grid operation inspection

-External damage to power facilities caused by crane, excavator and other construction operations increases year by year, which will seriously threaten the safe operation of power system. It is an important measure to ensure the safe and reliable operation of power system to implement intelligent monitoring and early warning of power external breakdown through video and other non-contact observation means. The video data of power mainly comes from the fixed monitoring of helicopters, uavs and transformation poles and towers, which is characterized by large amount of data, complex scenes and serious environmental interference. The traditional target detection method usually selects the candidate area first, and then makes judgment based on the characteristics of human construction. The detection speed is slow and the accuracy is low, which makes it impossible to monitor the video data in real time, so as to make timely and accurate early warning and intervention fbr external damage. The target detection method based on deep learning optimizes or even eliminates the selection of candidate regions, which greatly speeds up the detection speed. By learning a lot of target samples through the deep neural network, the characteristics of high robustness are gradually fitted to make the target judgment more accurate. There are three key problems in introducing the target detection method based on deep learning into the power video detection: Firstly, the target detection method based on deep learning has a large amount of calculation and many parameters. In order to realize in-place operation on terminals with limited computing and storage capacity, it is necessary to find a practical method to simplify the network and reduce the amount of operational data in the detection process, which is the key to realize in-place operation and terminal operation of deep neural network. Secondly, for specific application scenarios, the effect of different target detection algorithms varies greatly, and there is a strong particularity of power video. Finding an effective target detection method is the key to improve the detection speed and accuracy. Finally, with the continuous development of deep learning, the structure of deep neural network changes with each passing day, and each has its own characteristics, which network structure is used as the feature extraction layer of target detection algorithm is the focus of research.
MIROSTAT: A NEURAL TEXT DECODING ALGORITHM THAT DIRECTLY CONTROLS PERPLEXITY

Neural text decoding algorithms strongly influence the quality of texts generated using language models, but popular algorithms like top-k, top-p (nucleus), and temperature-based sampling may yield texts that have objectionable repetition or incoherence. Although these methods generate high-quality text after ad hoc parameter tuning that depends on the language model and the length of generated text, not much is known about the control they provide over the statistics of the output. This is important, however, since recent reports show that humans prefer when perplexity is neither too much nor too little and since we experimentally show that cross-entropy (log of perplexity) has a near-linear relation with repetition. First, we provide a theoretical analysis of perplexity in top-k, top-p, and temperature sampling, under Zipfian statistics. Then, we use this analysis to design a feedback-based adaptive top-k text decoding algorithm called mirostat that generates text (of any length) with a predetermined target value of perplexity without any tuning. Experiments show that for low values of k and p, perplexity drops significantly with generated text length and leads to excessive repetitions (the boredom trap). Contrarily, for large values of k and p, perplexity increases with generated text length and leads to incoherence (confusion trap). Mirostat avoids both traps. Specifically, we show that setting target perplexity value beyond a threshold yields negligible sentence-level repetitions. Experiments with human raters for fluency, coherence, and quality further verify our findings.
GLOBAL TRENDS OF CRYPTOCURRENCY MARKET FORMATION AND REGULATION

The article describes current trends in the global financial market development as well as the formation of fundamentally new, innovative financial resources within the peering economy, such as cryptocurrencies. Its capitalization reached almost 1/100 of global gross domestic product. The purpose of the study is to analyze both the status of cryptocurrencies in different countries, including the United States of America, Japan, People’s Republic of China, and the European Union and the peculiarities of the cryptocurrency circulation development, as well as the money functions they are able to perform. The author has formulated the dilemma of this market regulation at the national and global levels. One should consider that the long-term role of crypto units depends upon the resolution of such a dilemma. And there is no unified approach of states and international organizations to the status and prospects of cryptocurrency regulation in our days. This creates a unique field of opportunities for further development of such instruments as the big economic and market movements undulate in big swings that were due to a sequence of actions and reactions by policymakers, investors, business owners, and workers. Given that state control over the emission of fiat money in the world can only be ensured to the extent that they control the banking system, the study provides an analysis of the legal status of such financial engineering products as cryptocurrencies. The author also accents on the three-tier blockchain technology. Finally, the domestic component is thoroughly analyzed, i.e. the regulation field of such instruments in Ukraine. The draft Concept of State Regulation of Cryptocurrency Transactions is also explained. Keywords: cryptocurrency; blockchain; status of crypto units; financial regulation; functions of money.
Introduction of a new dataset and method for location predicting based on deep learning in wargame

It is a challenge for existing artificial intelligence algorithms to deal with incomplete information of computer tactical wargames in military research, and one effective method is to take advantage of game replays based on data mining or supervised learning. However, the open source datasets of wargame replays are extremely rare, which obstruct the development of research on computer wargames. In this paper, a data set of wargame replays is opened for predicting algorithm on the condition of incomplete information, to be specific, we propose the dataset processing method for deep learning and an network model for enemy locations predicting. We first introduce the criteria and methods of data preprocessing, parsing and feature extraction, then the training set and test set for deep learning are predefined. Furthermore, we have designed a newly specific network model for enemy locations predicting, including multi-head input, multi-head output, CNN and GRU layers to deal with the multi-agent and long-term memory problems. The experimental results demonstrate that our method achieves good performance of 84.9% on top-50 accuracy. Finally, we open source the data set and methods on https://github.com/daman043/AAGWS-Wargame-master.
Introducing Swish and Parallelized Blind Removal Improves the Performance of a Convolutional Neural Network in Denoising MR Images

To improve the performance of a denoising convolutional neural network (DnCNN) and to make it applicable to images with inhomogeneous noise, a refinement involving an activation function (AF) and an application of the refined method for inhomogeneous-noise images was examined in combination with parallelized image denoising.Improvements in the DnCNN were performed by three approaches. One is refinement of the AF of each neural network that constructs the DnCNN. Swish was used in the DnCNN instead of rectifier linear unit. Second, blind noise removal was introduced to the DnCNN in order to adapt spatially variant noises. Third, blind noise removal was applied to parallelized image denoising, referred to herein as ParBID. The ParBID procedure is as follows: (1) adjacent 2D slice images are linearly combined to obtained higher peak SNR (PSNR) images, (2) combined images with different weight coefficients are denoised using the blind DnCNN, and (3) denoised combined images are separated into original position images by algebraic calculation.Experimental studies showed that the PSNR and the structural similarity index (SSIM) were improved by using Swish for all noise levels, from 2.5% to 7.5%, as compared to the conventional DnCNN. It was also shown that a well-trained CNN could remove spatially variant noises superimposed on images. Experimental studies with ParBID showed that the greatest PSNR and SSIM improvements were obtained at the middle slice when three slice images were used for linear image combination. More fine structures of images and image contrast remained when the proposed ParBID procedure was used.Swish can improve the denoising performance of the DnCNN, and the denoising performance and effectiveness were further improved by ParBID.
Initial Classifier Weights Replay for Memoryless Class Incremental Learning

Incremental Learning (IL) is useful when artificial systems need to deal with streams of data and do not have access to all data at all times. The most challenging setting requires a constant complexity of the deep model and an incremental model update without access to a bounded memory of past data. Then, the representations of past classes are strongly affected by catastrophic forgetting. To mitigate its negative effect, an adapted fine tuning which includes knowledge distillation is usually deployed. We propose a different approach based on a vanilla fine tuning backbone. It leverages initial classifier weights which provide a strong representation of past classes because they are trained with all class data. However, the magnitude of classifiers learned in different states varies and normalization is needed for a fair handling of all classes. Normalization is performed by standardizing the initial classifier weights, which are assumed to be normally distributed. In addition, a calibration of prediction scores is done by using state level statistics to further improve classification fairness. We conduct a thorough evaluation with four public datasets in a memoryless incremental learning setting. Results show that our method outperforms existing techniques by a large margin for large-scale datasets.
Measuring diagram quality through semiotic morphisms

Abstract This paper outlines a method to assess the effectiveness of diagrams, from semiotic foundations. In doing so, we explore the Peircian notion of signification, as applied to diagrammatic representations. We review a history of diagrams, with particular emphasis on schematics used for representing systems, and uncover the neglect of semiotic analysis of diagrammatic representations. Through application of category theory to the Peircian triadic model, we propose a set of quantitative quality measures for diagrams, and a framework for their assessment, based on the properties of their encoding, pragmatic and perceptual morphisms. These measures include diagram complexity, utility, aesthetics and expert assessment of semiotic content, together with qualitative feedback. We consider the diagrams as an aid to cognitive processes, rather than a purely communication media. This utility-focused perspective on diagram quality dimensions allows for fresh insights into the creation of effective diagrams.