Reviewerly/exHarmony huggingface dataset:
A K-NN based Data Reduction Technique in String Space via Space Separation

The two most frequent representations in Pattern Recognition are statistical coding which characterize objects as vectors and structural depictions encoding components as symbolic high-level data structures like strings, trees or graphs. The two most frequent representations in Pattern Recognition are statistical codifications and structural representations. Although the overwhelming majority of classifiers can handle statistical spaces, only a few techniques can handle structural representations. The kNN Classifier, which is a neural network classifier, is one of the few techniques for both statistical and structural areas. Because it is based on the calculation of differences between all samples in the set, this approach is very versatile, but also inefficient. One of the solutions to this problem is to utilise prototype creation. These methods produce a condensed version of the original dataset by transforming and aggregating the detain the original collection. While these generating methods are very reliant on the data format, they are not well suited for structural data production. The generation- based reduction method was utilized in this case. In this article, we show how string data may be reduced to a more manageable size using homogenous clusters. This method divides space into class- homogeneous clusters, after which the median of each group is a representative prototype. The first step to address this problem is to find the median element in a string collection. According to our comprehensive test results, our method exceeds competitors in both statistical and string-based fields. We were able to demonstrate the effectiveness of our strategy using our technology to exhibit competitive differences in the categorization rate and the data reduction.
Electronic educational resources as a means of students’ information competence development in Geography at a modern school

The paper discusses the relevance of the use of electronic educational resources (EER) in educational activities as a means of students information competence development. A variety of electronic educational resources, widely represented in the modern educational space, are actively used at Geography lessons and contribute to the development of students ability to work competently with information: to search for it in various sources; apply computer technology and use information devices to solve educational problems; independently select and analyze data; establish causal relationships; present information in a structured form using maps, presentations, virtual objects, tables, diagrams, etc. In order to study various aspects of the use of EER, a questionnaire survey was launched for Geography teachers at secondary schools in the south of the Tyumen Region. The paper contains a number of conclusions based on the analysis of the study results. Teachers note significant experience and high activity in the use of EER due to both the implementation of projects digitizing the educational environment and a change in the education format because of the pandemic. For solving various educational problems preference is given to electronic educational platforms, electronic textbooks and teaching aids, as well as Internet resources for preparing for the Basic State Exam and the Unified State Exam. Electronic educational resources have become an integral component of the educational process, significantly expanding educational opportunities for students skills and abilities development when they work with information.
Music and mathematics in Tom Johnson’s work: the composer’s view

Tom Johnson is an interesting case of interdisciplinarity in that he makes music and mathematics coincide in a very obvious and literal way. He was led to this approach in the context of the American avant-garde of the 1970s, in which many artists tried to set artistic creation in the field of the impersonal. In using mathematics, Tom Johnson is in search for something that might allow the music to compose itself automatically. He establishes sequences of numbers and translates them literally into melodies, harmonies or rhythms. In doing so, he manages to make the mathematical background clearly appear. He seems to think of his music as a way of reflecting mathematics in the form of audible phenomena, and ultimately, he attaches some kind of mystical dimension to this experience. Mathematical truths, as he calls them, are unquestionable. Their laws do not depend on human will, they are deeply inscribed in nature, and a composition strictly based on them is necessarily linked to the absolute.
Research on Construction Method of Knowledge Graph in Electric Power Operation and Maintenance Field Based on Few-shot

With the application of artificial intelligence technology in electric power industry, knowledge graph will play an important role in power grid dispatching, intelligent operation inspection, customer service question and answer, etc. The problem of low resources is very common in the construction of knowledge graph in the field of electric power operation and maintenance in real application scenarios. How to extract effective information from large-scale, small or even unlabeled power data, fully explore the value of field data, promote the efficient use of information resources, and build a knowledge graph in the power field with high efficiency and low cost is the main concern in the field of knowledge graph in the power field. Based on the knowledge graph construction method in the field of power operation and maintenance with a small sample, this paper proposes a FLAT-based entity relationship joint extraction model and an active sampling strategy based on meta-learning. The problem of knowledge graph construction in the sample domain is to realize the automatic construction and dynamic update of the knowledge graph construction in the power operation and maintenance domain.
Using Hyperledger Fabric Blockchain to Improve Information Assurance of IoT Devices for AI Model Development

This chapter aims to show that our research using the Hyperledger Fabric blockchain for Navy logistics assets can be applied to data supporting artificial intelligence (AI) and software development in terms of system safety and timely data acquisition. Data-driven AI/machine learning (ML) requires trusted data for their use in AI functions and requires significant amounts of training data from diverse sources including Internet of things (IoT) devices/sensors. Unauthorized alterations to data supporting AI/ML could go unnoticed within the AI function build process but surface during operation in hazards affecting unwanted human death or resource destruction. AI/ML controlling hardware usually falls into the two highest software control categories: Levels 1 and 2, risk of death, disability, or resource destroyed. We show how trust can be implemented through distributed consensus to ensure that only authorized people can modify data and that the modification is traceable and transparent. Distributed ledgers provide system safety through BC provenance, immutability, and policy enforcement through smart contracts. We also explain how Hyperledger Fabric could be used to provide data to researchers in a timely manner through “smart repositories” while ensuring provenance and integrity.
An Effective and Reliable Cross-Blockchain Data Migration Approach

As blockchain is widely applied, various decentralized applications would inevitably encounter data migration problems, for reasons, such as the multilevel blockchain scenarios, the exhaustion of blockchain disk space and the swap of the rapidly evolving blockchain engines. In order to proceed the applications smoothly, it is necessary to migrate original blockchain data to a new blockchain instance, which is the cross-blockchain data migration. However, ensuring the reliability of data provenance and the data consistency, and balancing migration efficiency and historical state granularity, introduce unique challenges over cross-blockchain data migration. This paper proposes an effective and reliable cross-blockchain data migration approach to coping with these challenges. To ensure the reliability, a collective mechanism of controlling, executing and storing procedures is proposed to assort migration transactions between blockchains. Furthermore, we propose two migration schemes in order to adapt decentralized application scenarios. Extensive experiments are conducted to demonstrate the effectiveness of the proposed approach.
Predictive SEO for Tourism Websites Through Transformer Keyword Identification

The selection of appropriate keywords is a basic task to any search engine optimization (SEO) and search engine marketing (SEM) effort. Keyword selection can be influenced by several factors and is always a dynamic process that needs to be reiterated frequently. In this paper we present an innovative keyword selection technique by defining a novel category of long tail keywords that we call “transformer keywords”. We introduce the concept of transformer keywords as a way to enable travel websites to get the chance to appear in top search engine results through SEO or SEM quite fast. Our concept is especially beneficial to new websites or websites that experience low domain authority. We show that these keywords are of great importance especially to the travel industry and businesses that operate in seasonal destinations. After presenting the theoretical framework of transformer keywords, we analyze a six-step algorithm/procedure for identifying them with the use of Google Trends. Finally, we use Google Keyword Planner to measure and quantify the benefits of using transformer keywords. Our research case study is based on real data from the well-known tourist destination of Kefalonia in the Ionian Islands, Greece. We conclude that the use of transformer keywords can be especially beneficial if targeted correctly during the appropriate timeframes that we propose. All our key findings are formulated into a straightforward transformer keyword usage best practice guide for the travel industry.
Survey on Capsule Network’s Depth Scalability and Learned Feature Spatial Relationships Retention

In 2017, the first working architecture of capsule networks called Capsule Network with Dynamic Routing (CapsNet) was introduced and shown to perform better than Convolutional Neural Networks (CNNs) in the MNIST dataset. It was also speculated that capsule networks’ ability to retain spatial relationships among learned features are better than CNNs. Ever since then, much research has been devoted to improving capsule network architectures. However, most proposed architectures are relatively shallow and not scalable depth-wise. Also, some works have shown that the said retention ability is not as good as speculated. In this survey, we reviewed research works on capsule networks that focus on depth scaling and the said retention ability. We found that the routing algorithm used in a capsule network plays a crucial role in both the depth scalability and the said retention ability. We believe that future research on capsule networks should be focused on improving the routing algorithm to ensure the success of capsule networks.
Counterdiabatic driving for periodically driven open quantum systems

We discuss dynamics of periodically driven open quantum systems. The time evolution of the quantum state is described by the quantum master equation, and the form of the dissipator is chosen so that the instantaneous stationary state is given by the Gibbs distribution. We find that the correlation between the population part and the coherence part of the density operator is induced by an adiabatic gauge potential. Although the introduction of the counterdiabatic term eliminates the correlation, additional correlations prevent convergence to the Gibbs distribution. We study the performance of the control by the counterdiabatic term. The system has three different scales and the performance strongly depends on the relations between their magnitudes. This article is part of the theme issue 'Shortcuts to adiabaticity: theoretical, experimental and interdisciplinary perspectives'.
Turning News Texts into Business Sentiment

This paper describes a demonstration system for our project on news-based business sentiment nowcast. Compared to traditional business sentiment indices which rely on a time-consuming survey and are announced only monthly or quarterly, our system takes advantage of news articles continually published on the Web and updates the estimate of business sentiment as the latest news come in. Additionally, it provides functionality to search any keyword and temporally visualize how much it influenced business sentiment, which can be a useful analytical tool for policymakers and economists. The codes and demo system are available at https://github.com/kazuhiro-seki/sapir-web .
Automation of Detection and Fault Management Response of Common Last-Mile Loss-Of-Connectivity Outages Within the Access Network

The article to assesses whether it may be possible to recommend a solution to enable automation of the process of detection and fault management of common conclusive loss-of-connectivity last-mile outages, within the access network. To ascertain the utility of the research, UK based MPLS VPN managed service providers, their fault management staff and their business customers, were surveyed using online questionnaires for their views. UK public Internet users were additionally surveyed via five UK Internet forums. UK communication providers offering MPLS VPN solutions were characterised. Access network connectivity technologies and fault management functions were compared, contrasted and analysed. An aspiration for the solution to be beneficial to the largest potential population, meant that current non-proprietary Internet Standard technologies were selected, justified and identified which could be recommended for use. It was found that of the participating survey respondents, two-thirds were in favour of automation. Many current communication provider processes were found to be mostly automated. The article concludes with recommendations of how an automated solution could potentially be enabled. This involves further use of business-to-business interfacing between communication providers, automation of their Fault Management Systems and introducing Bi-Directional forwarding for detection between last-mile active network elements.
Product of Two R-Calculi

Product and sum are basic algebraical constructions. Given two algebras $$\mathbf{A}$$ and $$\mathbf{B},$$ we have algebras $$\mathbf{A}\times \mathbf{A}$$ and $$\mathbf{A}+\mathbf{B}$$ , For logics, two value algebras have product and sum [3, 6], which induce the product and sum of the logics based on value algebras; Gentzen deduction systems are product and sum of the ones based on value algebras [4, 5]; and R-calculi are the product and sum of R-calculi based on value algebras. In the chapter we discuss the product of two value domains, two tableau proof systems and two R-calculi. In next chapter we discuss the sum.
Robust Image Matching for Information Systems Using Randomly Uniform Distributed SURF Features

Detection of similar images taken in different perspectives is a big concern in digital image processing. Fast and robust methods have been proposed in this area. In this chapter, a novel image matching approach is proposed by using speeded-up robust features (SURF). SURF is a local feature detector and descriptor that can be used for tasks such as object recognition or registration or classification or 3D reconstruction. Successful detection of the images is achieved by finding and matching corresponding interest points using SURF features. The task of finding correspondences between two images is performed through using a novel brute-force method which uniformly generates random pairs for matching similarity. Experimental results show that the proposed method yields better results than conventional brute force methods in which at least 5% accuracy increment is obtained.
Application of Neuro-Fuzzy Logic on the Newsvendor Inventory Model

Challenges arise in inventory management and control when there is uncertainty involved. In the newspaper inventory model where demand levels are stochastic, ensuring the optimal quantity of inventory for each time period is crucial in optimizing profit. In this paper, neuro-fuzzy logic is utilized to evaluate this optimal quantity of inventory through the use of the software Matlab. The input variables are purchase cost, selling price, salvage value, mean demand, and standard deviation demand while the output variable is quantity. Iterations of the experiment were done with the objective of minimizing error and maximizing correlation values. The best iteration of the experiment achieved a training error value of 0.0012437 with a coefficient determination value of ~1 for both the training data and testing data.
From guidelines to culturally competent artificial intelligence

This chapter introduces cultural knowledge representation: how to encode the knowledge required to interact appropriately with people who belong to different cultures in a computer program. First, a review is provided of the most popular solutions adopted by roboticists to encode knowledge in a formalism that robots can automatically process. Second, a discussion follows on how guidelines provided by experts in transcultural nursing can be translated into a computer ontology and how artificial intelligence programs can automatically process cultural knowledge to produce an engaging verbal interaction with people. Third, we show how tools for probabilistic reasoning may be used to learn more about a person during the interaction to cope with individual differences and avoid stereotyped representations of people's cultures.
Collective Classes in a Group of Different Ages Based on the Integration of the Content of Different Academic Subjects

The article proposes to consider the initial stage of the transition to such a form of training organization as collective classes in different-age&#x0D; groups of younger schoolchildren according to the methodology developed by the talented teacher A.G. Rivin. They are held at the Moscow&#x0D; secondary school named after Ivan Yarygin of the Krasnoyarsk region on the integrated content of academic subjects “Literary Reading”, “Fine Arts”, “Technology”. The specificity of the texts compiled by teachers for use in the process of paragraph-by-paragraph elaboration of the text using the technology of A.G. Rivina. Particular attention is paid to the issues of including first-graders (some of whom do not yet know how to read and write) in classes where younger schoolchildren of an older age participate. In this regard, multilevel algorithms of educational interaction are described. It highlights the difficulties that may arise with this form of training, as well as ways to overcome them.
Design and Implementation of a Microblog-Oriented Network Data Acquisition and Analysis System

In order to design a data collection and analysis system that creates multiple crawling methods based on different data types, by selecting Sina Weibo user information, using Python crawling technology, CRF algorithm and PageRank algorithm, etc., a single blogger’s Weibo key is generated Word cloud, interactive quantity chart including reposting, commenting, likes, etc., original reposting quantity ratio chart, obtained the user's Weibo traffic data within a limited time, and analyzed the user's area of interest and the degree of Weibo spread. The research results of this subject have certain reference significance for promoting the analysis and actual development of microblog data.
Application of Modern Educational Technology in Teaching Chinese as a Foreign Language: take mobile phone software Zoom platform as an example

In 2020, due to the spread of the global COVID-19, the teaching of Chinese as a foreign language had been greatly affected. Many Chinese and foreign educational institutions at home and abroad or Confucius institutes have chosen to make full use of modern educational technology to choose the mode of online Chinese teaching. This paper mainly studies the application of modern educational technology in Chinese teaching during the epidemic situation, analyzes the mode of teaching Chinese as a foreign language during the COVID-19 situation, analyzes the mobile phone software zoom platform used by Chinese education institutions or Confucius Institutes at home and abroad, and summarizes the application's value, problems and solutions of online teaching of Chinese as a foreign language, and in the process of practicing the relevant teaching design, and it further studies how to make full use of the technical support of zoom platform and relevant media to make up for the lack of interaction in distance Chinese teaching, in order to better promote the development of Chinese as a foreign language and promote the internationalization of Chinese.
Evaluating Performance of NBA Players with Sentiment Analysis on Twitter Messages

Traditionally, we conduct polls to obtain people's opinions on certain subjects, but now as social media prevails, scientists can harvest people's opinions from the great amount of data generated from social media users. This paper performs sentiment analysis on the Twitter comments regarding NBA games to obtain public opinions on the NBA players as a new way of player-performance evaluation, instead of adopting the traditional way to assess players according to their statistics in the games or the poll results by the audience. The Twitter messages regarding 5 games during the 2019 NBA playoff finals are collected, and three types of sentiments (absolute, objective, and subjective sentiments) are extracted from these messages. This work explores which type of sentiment has the strongest correlation with the player performance and thus makes the best value to evaluate the player performance. Keywords are also extracted from the messages. Our findings suggest that subjective sentiment is the best value among the three types of sentiments.
A deepfake video detection method based on multi-modal deep learning method

Recently, most deepfake video classification tasks depend on frame-level features and try to train deep neural networks to characterize fake videos. Although this kind of methods can achieve good results, they also waste the audio information and timing information of the deepfake video dataset. Therefore, to make better use of the audio information, we propose a multi-modal method to detect deepfake videos. The principle of our method is based on the mismatch between audio information and visual information, such as the inconsistency of mouth shape and voice. We calculate the modality dissonance score(MDS score) of videos to classify true/false videos. Extensive experiments reach 84.4% accuracy on the DFDC dataset, which demonstrates the effectiveness of our method.