Reviewerly/exHarmony huggingface dataset:
Error correction by Reed–Solomon codes using its automorphisms

The article explores the syndrome invariants of АГ-group of automorphisms of Reed–Solomon codes (RS-codes) that are a joint group of affine and cyclic permutations. The found real invariants are a set of norms of N Г-orbits that make up one or another АГ-orbit. The norms of Г-orbits are vectors with 2 1 Cδ− coordinates from the Galois field, that are determined by all kinds of pairs of components of the error syndromes. In this form, the invariants of the АГ-orbits were cumbersome and difficult to use. Therefore, their replacement by conditional partial invariants is proposed. These quasi-invariants are called norm-projections. Norm-projection uniquely identifies its АГ-orbit and therefore serves as an adequate way for formulating the error correction method by RS-codes based on АГ-orbits. The power of the АГ-orbits is estimated by the value of N2, equal to the square of the length of the RS-code. The search for error vectors in transmitted messages by a new method is reduced to parsing the АГ‑orbits, but actually their norm-projections, with the subsequent search for these errors within a particular АГ-orbit. Therefore, the proposed method works almost N2 times faster than traditional syndrome methods, operating on the basic of the “syndrome – error” principle, that boils down to parsing the entire set of error vectors until a specific vector is found.
Development Quantum Algorithms, Systems and Prevention or Elimination Quantum Types of Errors

The article proposes the structure of operators of quantum algorithms, its mathematical and schematic representation. The procedure for developing a quantum computing system or algorithm involves the presence of interference, quantum entanglement, and superposition. In a classical computer, the amount of data is measured in bits, and in a quantum computer, in qubits. A qubit is a quantum discharge or the smallest element for storing information in a quantum computer, as well as a quantum object that can be in a superposition of two states, that is, encode both a logical unit and zero at the same time. The general structure of the universal quantum algorithm is implemented as diagrams that reveal the basic elements, their properties, functions and place in the work of the quantum algorithm. The input value for the quantum algorithm is a binary function. A detailed decomposition of each block of the schematic diagram for the description of sequential processes and stages of quantum algorithms has been performed. A quantum block that runs n times is necessary for the sequential use of quantum operators and the subsequent measurement of the result of the entire computing process. Due to the probabilistic nature of quantum computing, the obtained basis vectors will contain some of the information necessary to solve a particular computational problem. Classical and quantum types of errors and methods for their elimination are described and developed. The main obstacles to protecting the channel from noise are the inability to copy data and information, the continuity of error and the destruction of quantum information during measurement (according to the principles of quantum computing, measuring a set of qubits without destroying the information encoded there is impossible). The bit and phase types of errors were corrected by modeling quantum circuits, three-qubit coding, and a set of quantum gates.
Accuracy of the MTseng<sub>v</sub> algorithm for on-line biometric signature classification

The first part of the paper describes the MTsengv algorithm for on-line handwritten signatures verification. MTsengv algorithm was created by modifying the Tseng algorithm, in order to increase the accuracy of signature verification. After that, an algorithm for determining the decision threshold is presented. The algorithm is based on False Positive and False Negative error analysis. The second part of the paper describes an experiment in which the performance of a MTseng algorithm was determined using True Positive, True Negative, False Positive and False Negative errors. The mentioned errors are shown tabularly. Finally, the performed comparative analysis of the results shows a higher accuracy of the MTsengv algorithm..
Introduction to optimization

This chapter introduces the fundamentals of algorithms in the context of data mining, optimization, and machine learning, including the feasibility, constraints, optimality, Lagrange multipliers, KKT conditions, and gradient-based techniques.
Click, validate, and reply

To participate in contemporary society, we increasingly need to interact with digital technologies. To do so efficiently, we need to adjust our sensorimotor skills and perceptual aptitudes, our need for social recognition, and our emotional orientations, among others, to the digital logic. These adjustments in turn present us with several paradoxes that we need to resolve. These adjustments and decisions prompt the development of a terminal self.
Metodologías de desarrollo de objetos de aprendizaje para el Aula virtual de la UTXJ

It is known that each human being is unrepeatable, a factor to take into account for the teaching of classes, therefore it is essential to adapt methodologies for an optimal learning of each student, the teacher can be very good as a professional, but as a teacher, not Throughout the education in Mexico the student usually has problems, with the different subjects, to avoid desertions it is necessary to look for learning objects so that the student can acquire the desired competence, the LMS are learning management systems, within which there are learning objects that allow constructing educational contents from pieces that can be assembled, the creations of those objects take as a guide the self-containment, usability, reusability, effectiveness, accessibility, portability, and the source of where the information, Important points in the creation of the elements are the design which should be based on the psychology of learning seeking to improve the understanding and teaching methods, for meaningful learning, the detection of the student's learning style should be taken into account, as well like these elements texts, images, videos, conceptual maps, diagrams etc. To develop learning object methodologies.
Atom–atom entanglement via coherent photons in a nonlinear optical cavity

In the present article, dynamics of entanglement between two different two-level atoms, as nonidentical qubits, via monochromatic coherent photons is explored. The atoms are embedded in an optical lossless cavity filled with a centrosymmetric dielectric. A conserved (Casimir) operator, i.e. a dynamic integral of motion, is introduced to decompose the matrix representation of the total atom–photon Hamiltonian into direct sum of irreducible blocks. Using the eigenvalues and eigenvectors of the blocks, the time evolution unitary operator of the system is computed. The time-dependent atomic density operator is then calculated by partial tracing of atom–field density operator over photonic states for both initially separable and maximally entangled atomic states. The partially transposed atomic density operator, with respect to one of the atomic subsystems, is finally used to determine the negativity as an entanglement quantifier. Our results indicate that the robust entanglement is observed when the initial maximally atomic entangled state is applied. Furthermore, as Kerr-type coupling (arising from the third-order susceptibility of the centrosymmetric dielectric) increases it is shown that the entanglement enhances. In addition, effects of the initial atomic state preparation, the atom–photon structure parameters as well as field intensity on the entanglement birth and death are also addressed.
Monitoring System Design for Intelligent Hydraulic Safety Based on the IoT

Abstract In order to improve the safety operation management of hydraulic structures in hydropower stations, this paper analyzes the status and shortcomings of hydraulic safety monitoring automation system, and puts forward the design idea and scheme based on IoT. The intelligent connection of hydraulic monitoring equipment is realized by optimizing the design of sensing collection, communication network, monitoring application and information security of the system. By coordinating the monitoring, configuration and management of each monitoring terminal equipment, and using cloud computing technology to calculate, analyze and evaluate the monitoring data in real time, the intelligent online monitoring of hydraulic structures is realized, so as to ensure the safe and efficient operation of hydropower station infrastructure.
Incorporating Task-Related Information in Dimensionality Reduction of Neural Population Using Autoencoders

Dimensionality reduction plays an important role in neural signal analysis. Most dimensionality reduction methods can effectively describe the majority of the variance of the data, such as principal component analysis (PCA) and locally linear embedding (LLE). However, they may not be able to capture useful information given a specific task, since these approaches are unsupervised. This study proposes an autoencoder-based approach that incorporates task-related information as strong guidance to the dimensionality reduction process, such that the low dimensional representations can better reflect information directly related to the task. Experimental results show that the proposed method is capable of finding task-related features of the neural population effectively.
TRAINING OF FOREIGN STUDENTS OF THE PREPARATORY DEPARTMENT INFORMATION TECHNOLOGY IN CONTEXT OF UNIVERSAL DISTANCE LEARNING

Objective of the article is to identify trends in the field of education in higher education in modern conditions, to characterize the applied methods of using the preparatory department in teaching foreign students. Applied methods : The use of information and communication methods makes it possible to carry out the educational process in a distance format and to carry out the effectiveness of their application. The u-learning concept offers a wide variety of tools and means, thus offering the most appropriate way in the learning process. Results . The effectiveness of educational technologies used in teaching, such as the availability, structuredness of information for the purpose of its determinism and visualization, has been developed. The analysis of progress is carried out on the example of foreign students of the preparatory department in the discipline of Information Technologies. Application of various forms of technology to improve student achievement. Conclusions . The article deals with a complex and controversial problem of the educational process in the context of a general transition to distance learning. The new reality requires awareness of both new conditions and the teaching methods themselves. The use of social networks within the framework of the teacher's choice of forms of education and their adaptation for a specific training course ensures purposefulness, orderliness and efficiency of the educational process.
A Method to Extract Knowledge Explanation Sentences from Conversations in Comics with Combination of Contents and Expressions

Comics are entertainment contents that conversations of characters drive stories. Since people can obtain new knowledge from conversations in real lives, the conversations in comics could be useful to acquire new knowledge. The conversations in comics contain various utterance sentences of characters. Some of the utterance sentences contain knowledge, and the others not. We define the utterance sentence as a knowledge explanation sentence. If the knowledge explanation sentences are extracted automatically, those can be used for knowledge acquisition with comics and comics recommendation.In this paper, we propose an extraction method of the knowledge explanation sentences from characters' conversations in comics. We assume that the knowledge explanation sentences include content words and expressions that are special for knowledge explanation. The proposed method represents a sentence as a combination vector of an averaged word vector and an expression vector. The averaged word vector is an average of each content word's embedding. The expression vector has factors of expression meanings that are used for knowledge explanation. The combination vectors are trained by Support Vector Machine to obtain a classifier. The classification outputs a set of knowledge explanation sentences that will be an extraction result.We tested the proposed method by comparing other methods with 4,611 sentences from five comics. The proposed method obtained 0.80 accuracy, 0.77 precision, and 0.88 recall that were the highest among the prepared methods.
Privacy Preserving and Resilient RPKI

Resource Public Key Infrastructure (RPKI) is vital to the security of inter-domain routing. However, RPKI enables Regional Internet Registries (RIRs) to unilaterally takedown IP prefixes - indeed, such attacks have been launched by nation-state adversaries. The threat of IP prefix takedowns is one of the factors hindering RPKI adoption.In this work, we propose the first distributed RPKI system, based on threshold signatures, that requires the coordination of a number of RIRs to make changes to RPKI objects; hence, preventing unilateral prefix takedown. We perform extensive evaluations using our implementation demonstrating the practicality of our solution. Furthermore, we show that our system is scalable and remains efficient even when RPKI is widely deployed.
Do Ugly Ducklings Become Gray Swans?

Abstract The Ugly Duckling fairy tale tells of an ugly duckling who grows into a beautiful swan. Many companies or plants have action items from many sources that are ugly. They have lingered a long time, they are poorly defined and/or they have little chances of being completed as written. This article explains steps to recognize action items that are ugly and what can be done to bring them to closure before they mature into a Gray Swan, an improbable, but still devastating event.
Advanced Method of Parametric Identification of Tolerance Estimations of the Interval Vector of Discrete Dynamic Models Parameters

The problem of parametric identification of interval discrete dynamic models is considered in the article. It is shown that to solve this problem it is necessary to solve an interval system of nonlinear algebraic equations, the solutions area of which is nonconvex and incoherent. An improved method for parametric identification of interval estimates of discrete dynamic models' parameters is proposed. It in contrast to the existing one, maximizes the volume of the detected subarea of solutions of the interval system of nonlinear algebraic equations in the parameter space. Carried out the comparison of the results of application of the proposed and existing method for identification of interval estimates of parameters of model.
Building a Domain-Specific Knowledge Graph for Business Networking Analysis

The emergence of graph learning supports the study of phenomenon that are best represented by knowledge graphs, using deep learning techniques. However, deep learning is mostly built around supervised learning which requires datasets for training and testing. This paper presents BusinessNet, a domain-specific knowledge graph to examine the business networking phenomenon and offers a novel dataset for the graph learning community. BusinessNet is built from unstructured news wire text data from which entities such as organizations, people, and places, as well as the relationships among them are extracted to build the graph. The resulting graph is a heterogeneous network of organizations, people, and places as nodes and that will include different types relations. The analysis of the resulting dataset shows interesting characteristics for graph learning models evaluations.
Chapter 5 Scottish mineral Geological Conservation Review sites – Hydrothermal veins and mineral assemblages

This fifth paper on Scottish mineral Geological Conservation Review (GCR) sites details those of highest conservation value for hydrothermal veins and associated mineral assemblages. The GCR sites are both large and small reflecting variations in past economic mining and extraction history. During the long mining histories, and after, the sites have yielded new mineral species and a new metal. From the largest ore field for lead, zinc and copper 80 or so minerals have been recognised, including nine new species, one of which was established as the first sulphite, scotlandite. Elsewhere gold and silver were also extracted. For some veins the country rocks were analysed to determine whether they provided the vein elements for primary and secondary minerals, and sometimes dated. Two sites exhibit polymetallic mineralisation, i.e., five elements in the mineral assemblages. In one site veins in close proximity to a sanukitoid pluton received signature elements from the intrusion. Veins in various areas formed in faults, fractures and shears which became conduits for ground waters reacting with vein minerals. Oxygen in the water reacted with lead, zinc and copper minerals and in the presence of CO2 and SO4 supergene mineral assemblages formed in appropriate alkalinities. It is therefore possible to establish paragenetic sequences and calculate stability fields for specific assemblages. Alkalinities and formation temperatures may also be obtained through fluid inclusions studies and isotope analysis.
MODERN FORMS AND METHODS OF BİBLİOGRAPHİC ACTİVİTY İN LİBRARİES

The article discusses modern forms and methods of bibliographic activity in libraries. The role of modern information and communication technologies is explained. The information society places new demands on all social and communication institutions, including the activation of the formation of electronic document space, libraries and bibliographies. It is noted that the information society, the activation of the formation of electronic document space, puts new demands on all social and communication institutions, including libraries and bibliographies. New information technologies allow to significantly expand, deepen and diversify all forms of information and bibliographic services, which makes them faster, more efficient and more convenient. At the same time, it is explained that with the application of digital technologies, the category of information and bibliographic services is becoming a daily information service used by any reader of any library. Electronic catalogs are becoming powerful information systems to support the work of libraries and perform large-scale tasks.
Facial Representation Extraction by Mutual Information Maximization and Correlation Minimization

Facial expression recognition and face recognition are two amusing and practical research orientations in computer vision. Multi-task joint learning can improve each other’s performance, which has rarely been studied in the past. This work proposes a joint learning framework to enhance emotional representation and identity representation extraction by incorporating a multi-loss training strategy. Specifically, we propose mutual information loss to ensure that the facial representation is unique and complete and offer correlation loss to extract identity representation using orthogonality constraints. Classification loss is used to learn emotional representation. As a result, we can obtain an unsupervised learning framework to reduce the identity annotation bottleneck using large-scale labelled emotional data for the face verification task. Our algorithm is verified on an artificially synthesized face database: Large-scale Synthesized Facial Expression Dataset (LSFED) and its variants. The identity representation obtained by the algorithm is used for face verification. The performance is comparable to some existing supervised face verification methods.
Fuzzy Container Orchestration for Self-adaptive Edge Architectures

The edge or edge computing refers to the computational infrastructure between sensors and Internet-of-Things world on the one side and centralised cloud data centres on the other. Edge clusters could consist of small single-board devices, which are widely used in different applications. This includes microcontrollers regulating an industrial process or controllers monitoring and managing traffic roadside. Although generally hardware capabilities of devices are growing, resources at the edge are often still limited, requiring an intelligent resource management. This can done through self-adaptive scaling mechanisms in these clusters allowing to scale components of the application in the cluster. We introduce here an auto-scalable cluster architecture for lightweight container-based edge devices. A serverless architecture forms the core of this architecture. The auto-scaling component is based on fuzzy logic in order to better manage uncertainty problems arising in such contexts. Our evaluations show that our platform architecture and the auto-scaling functionality satisfy the need of lightweightness in edge architecture.
Research on Performance of Sub-pixel Displacement Measurement Algorithm

N-R algorithm, surface fitting algorithm, gradient algorithm and IC-GN algorithm are the main sub-pixel displacement measurement algorithms in digital image correlation method to improve the displacement measurement accuracy. In this paper, the simulation speckle pattern is generated by computer numerical simulation to carry out experiments, and the calculation accuracy and efficiency of the above four algorithms are compared. The results show that the N-R algorithm has the lowest calculation efficiency and the surface fitting method has the lowest calculation accuracy. The calculation efficiency and accuracy of gradient algorithm, IC-GN method are between N-R algorithm and surface fitting method, which is more suitable for engineering applications.
Paralleling process of searching objects on cytological images by a template

Modern approaches to finding image elements are analyzed. An algorithm for searching micro-objects in histological and cytological images using a database is developed. A tiered-parallel form of parallelization of the process of micro-object pattern search is designed. Micro-object pattern search software is implemented. The obtained result show that the operating time of the software module with parallelization speeds up the processing on average by 20% for cytological images
Detección de anomalías en redes de sensores inalámbricos

Over time, wireless sensor networks (WSN) have been used for a variety of applications. Extensive work has been dedicated to various WSN applications. It is important to note that, due to their physical limitations, the sensors are prone to several types of faults. These restrictions can pose serious problems in event detection applications. Especially if the WSNs are deployed in hostile environments, such as the industrial or environmental sector. The detection of anomalies has recently attracted the attention of the scientific community, due to its relevance in real-world applications. The proposed solutions depend to a large extent on supervision and communication, using techniques based on tools such as Machine Learning and Neural Networks. In this context, we introduce the most commonly used anomaly detection techniques in WSN. Compiling and comparing the main methods applied in specific scenarios, we analyze the advantages and conveniences of using any of them.
Ukrainian dactyl alphabet gesture recognition using cross platform software and convolutional neural networks

The technology, which is implemented with cross platform tools, is proposed for modeling of gesture units of sign language, animation between states of gesture units with a combination of gestures (words). Implemented technology simulates sequence of gestures using virtual spatial hand model and performs recognition of dactyl items from camera input using trained on collected training dataset set convolutional neural network. With the cross platform means technology achieves the ability to run on multiple platforms without re-implementing for each platform