Reviewerly/exHarmony huggingface dataset:
Steps towards a post-nonclassical methodology of higher education (logical-semantic approach)

Post-nonclassical nature of changes in education actualizes the need for transforming its methodological grounds. The goal of this article is to offer a new perspective of interpretation of the methodology of education, as well as trace the steps towards its post-nonclassical transformation. The research leans on the theoretical-methodological provisions of the logical-semantic approach, which focuses on the laws of conceptualization. The article explores the objective and subjective logical-semantic patterns of the work of mind characteristic to classical and post-nonclassical worldview. Congruence with the objective logic of the work of mind of methodological grounds in educational practice of the higher school is confirmed by examples. The examples show the limitations of their methodological capabilities in solving modern problems of knowledge and practice. The limitations of their methodological capabilities in solution of the relevant cognitive tasks and practice are indicated. The article suggest and analyzes the methodological principle of the subject, which determines the human-sizedness and communicative alignment of methodological system of education, which ensures its compliance with the post-nonclassical methodology of science. The author demonstrates a number of changes within the education system, which are methodologically substantiated by the principle of the subject. The highlighted logical-semantic patterns of the work of mind can be used as an instrument for understanding and explaining the components of educational process, as well as their design and management. The author proposes a nonlinear structure of the content of education. It can underlie the creation of learning model, which would consider the individual characteristics of students and fulfill the educational potential of the traditional and innovative teaching systems.
Mining Big Data and Streams

Mining big data is getting a lot of attention currently because businesses need more complex information in order to increase their revenue and gain competitive advantage. Therefore, mining the huge amount of data as well as mining real-time data needs to be done by new data mining techniques/approaches. This chapter will discuss big data volume, variety, and velocity, data mining techniques, and open source tools for handling very large datasets. Moreover, the chapter will focus on two industrial areas telecommunications and healthcare and lessons learned from them.
Permutation Tests for Experimental Data

This paper surveys the use of nonparametric permutation tests for analyzing experimental data. The permutation approach, which involves randomizing or permuting features of the observed data, is a flexible and convenient way to draw statistical inferences in many common settings. It is particularly valuable when few independent observations are available, as is often the case for controlled experiments in economics and other social sciences. When viewed as a framework, the permutation method constitutes a comprehensive approach to statistical inference. In two-treatment testing, permutation concepts underlie popular rank-based tests, like the Wilcoxon and Mann-Whitney tests. But permutation reasoning is not limited to ordinal contexts. Analogous tests are easily constructed for the permutation of continuous measurements, and we argue that these non-ranked alternatives should often be preferred when working with continuous data. Permutation tests can also be used with multiple treatments, with ordered hypothesized effects, and with complex data structures, such as hypothesis testing in the presence of nuisance variables. Drawing examples from the experimental literature, this paper illustrates how permutation testing solves common data analysis challenges. Our aim is to help experimenters move beyond the handful of overused tests in play today, and to show how permutation testing constitutes a general framework for conducting statistical inference with experimental data.
Optimized S-box and Mix-Columns for AES Architecture for live IP video Encryption and Decryption by Key Generation Through face Recognition

This paper proposes an AES based Encryption and Decryption of a live IP video used for security in surveillance systems. Here, the key is generated based on neural networks techniques for facial recognition. Principal component analysis and Eigen vector algorithms are used to extract biometric facial features which are used to train the neural network. At the receiver side, the original video plays only if the user is authenticated or else it plays an encrypted video. This work proposes an AES architecture based on optimizing timing in terms of adding inner and outer pipeline registers for each round and Key Expansions. Further by optimizing the Crypto Multiplication for Mix columns via LUT based approach aid in further optimization in terms of timing. LUT and Pipelined based implementation techniques are optimal for FPGA based implementations. ROM table and pipelining are the two techniques used to implement AES. Result indicates that with the combination of pipelined architecture and Distributed/Split LUT-Pipelined techniques, the encryption has higher throughput and speed.
Personalisation of 3D Virtual Spaces for Enhanced Ubiquitous Learning

This study explores, using experiments, the effects of different architectural design features of 3D virtual educational buildings on higher-education learners during online e-learning sessions. Architectural features tested include shape, lighting, dimensions, colours and textures. Learners are divided into three groups: under-graduates, post-graduates, and adult learners. Results are demonstrated comprising charts and statistics capturing the extent of learners' enjoyment, information retention, and participation from being inside different 3D virtual spaces with different design characteristics. Consequently, design characteristics causing highest student retention, participation and contentment are established for design of a better 3D virtual learning environment (VLE). These provide guidelines for customised design practices inside 3DVLEs to create 3D virtual educational spaces best suited for ubiquitous “any-time” “any-place” e-learning of each individual student. This will aid in guiding the otherwise current ad-hoc design approach to building educational facilities in 3DVLEs.
The adoption and deployment of cloud computing: a comparative study between China and the US

At the beginning of this article reviewed the related literature about cloud computing technology, and then use the PEST method, from the political, economic, social, technology, four aspects, comparing the similarities and differences between the two countries in the field of cloud computing, and starting from the trend of investment and technology, the two countries at present and the future market development to make analysis, summed up the experience of cloud computing technology, Suggest feasible solutions to the problems in the development of China's cloud computing industry. Then, through the analysis of the background and development of cloud Guizhou, Oracle and TikTok, this paper points out the common security problems faced by the cloud computing industry in China and the United States. Finally, this paper discussed three ideas about combining cloud computing and 5G, the future of cloud computing, and the security part of cloud computing.
Occlusion Face Image Inpainting Method Based on Multi-scale and Contextual Attention

In order to solve the common issues of unclear repaired images and incoherent semantics in occluded face image inpainting, we propose a two-stage generation network that combines multi-scale joint and contextual attention. The first stage of the network uses a multi-scale parallel encoder-decoder to obtain preliminary inpainting results, and the second stage uses the context attention module to refine the inpainting details. Based on the idea of generating adversarial networks (GAN), we introduced a global&local discriminator to maintain the consistency of global and local content. Apart from the adversarial loss caused by GAN, our method also combines spatial attenuation reconstruction loss and a similar Markov random fields loss for training to further enhance the quality of the produced images. A large number of experiments on the CelebA and CelebA-HQ face datasets show that our method can obtain a relatively good inpainting effect.
Classification and Implementation of Relevance Data Based on Address Text

Tasks based on address text are commonly seen in real life, such as location-based services based on geographic information systems, rapid localization and positioning systems for emergencies, and alignment of different address information systems. This article introduces the solutions to the classification problems of address text by comparing different models of different parameters, such as pre-training + nezha + rdropout and pre-training + nezha + swa + rdropout, in the classification of address information, obtaining different model effects. After using rdropout, it also shows a faster training speed and convergence speed.
Overview of Big Data-Intensive Storage and its Technologies for Cloud and Fog Computing

Computing systems are becoming increasingly data-intensive because of the explosion of data and the needs for processing the data, and subsequently storage management is critical to application performance in such data-intensive computing systems. However, if existing resource management frameworks in these systems lack the support for storage management, this would cause unpredictable performance degradation when applications are under input/output (I/O) contention. Storage management of data-intensive systems is a challenge. Big Data plays a most major role in storage systems for data-intensive computing. This article deals with these difficulties along with discussion of High Performance Computing (HPC) systems, background for storage systems for data-intensive applications, storage patterns and storage mechanisms for Big Data, the Top 10 Cloud Storage Systems for data-intensive computing in today's world, and the interface between Big Data Intensive Storage and Cloud/Fog Computing. Big Data storage and its server statistics and usage distributions for the Top 500 Supercomputers in the world are also presented graphically and discussed as data-intensive storage components that can be interfaced with Fog-to-cloud interactions and enabling protocols.
Introduction

This chapter gives a short introduction into the field of cryptography. After defining the protection goals of cryptography, we briefly describe the main cryptographic primitives used to achieve these goals. We describe why the currently used public key cryptosystems become insecure in the presence of quantum computers and thereby motivate the field of post-quantum cryptography. We discuss current research activities in this field and introduce the main families of post-quantum cryptosystems.
Cyber Criminal Profiling

New computing and networking technologies have not only changed the way traditional crimes are committed but also introduced completely brand new “cyber” crimes. Cyber crime investigation and forensics is relatively a new field that can benefit from methods and tools from its predecessor, the traditional counterpart. This chapter explains the problem of cyber criminal profiling and why it differs from ordinary criminal profiling. It tries to provide an overview of the problem and the current approaches combined with a suggested solution. It also discusses some serious challenges that should be addressed to be able to produce reliable results and it finally presents some ideas for the future work.
<i>The Remarkable Platinum Minerals of the Konder Massif</i> by Alexander G. Mochalov. Mineralogical Almanac, vol. 23, issue 3. Mineralogical Almanac, Ltd., Moscow, Russia; www.minbook.com. In the U.S. order from Terry Huizing, tehuizing@fuse.net. 119 pages; 2019; $45 plus shipping and handling (softbound)

"The Remarkable Platinum Minerals of the Konder Massif by Alexander G. Mochalov. Mineralogical Almanac, vol. 23, issue 3. Mineralogical Almanac, Ltd., Moscow, Russia; www.minbook.com. In the U.S. order from Terry Huizing, tehuizing@fuse.net. 119 pages; 2019; $45 plus shipping and handling (softbound)." Rocks & Minerals, 95(6), p. 574
Development of an Information System for Storing Digitized Works of the Almaty Academgorodok Research Institutes

The present article describes the architecture of the integrated distributed information system created for storing digitized works of employees of Almaty Akademgorodok research institutes (Kazakhstan) and providing access to them using Web technology. Comparative analysis of two data storage systems for storing digitized works, Ceph and GlusterFS, is provided. The description of the software part of the information system is provided which consists of four subsystems: repository of digital objects, subsystem for managing current research information, subsystem of integration of distributed information resources, subsystem of access to distributed information resources based on Web technologies. The relation between the subsystems and their integration is described. The work defines the requirements to the repository of digital objects. The requirements for the repository of digital objects are defined; a comparative analysis of open source software used for these purposes is made.
Models of Artificial Higher Order Neural Networks

This chapter introduces the background of the higher order neural network (HONN) model developing history and overviews 24 applied artificial higher order neural network models. This chapter provides 24 HONN models and uses a single uniform HONN architecture for all 24 HONN models. This chapter also uses a uniform learning algorithm for all 24 HONN models and uses uniform weight update formulae for all 24 HONN models. In this chapter, polynomial HONN, Trigonometric HONN, Sigmoid HONN, SINC HONN, and Ultra High Frequency HONN structure and models are overviewed too.
Recommender Technologies and Emerging Applications

This chapter presents a brief overview of the field of recommender technologies and their emerging application domains. The authors explain the current major recommender system approaches within a unifying model, discuss emerging applications of recommender systems beyond traditional e-commerce, and outline emerging trends and future research topics, along with additional readings in the area of recommender technologies and applications. They believe that personalized recommender technologies will continue to advance and be applied in a variety of traditional and emerging application domains to assist users in the age of information overload.
Using Mobile Technologies With Young Language Learners to Support and Promote Oral Language Production

The paper examines how the use of mobile technologies such as tablets and handheld MP3 players can support and promote oral language production among young language learners. It explores how the use of these mobile technologies in the language classroom supports pedagogical practices anchored in socioconstructivist theories of SLA that emphasize the role of dialogue and social interaction among young language learners. The paper is based on a collaborative action research project involving French Immersion teachers and their students in primary schools in a western province of Canada. Findings show that the affordances of mobile technologies contribute to the creation of innovative learning environments and authentic language learning experiences that support and promote the production of oral language among young language learners. The inquiry demonstrates the adoption of second language pedagogical approaches anchored in socioconstructivist theories of SLA that promote autonomy and a sense of agency among language learners.
Research Structure and Paradigms

The hypothetico-deductive model pioneered by Galileo is commonly received as the ideal research standard. But, in many situations it may not be feasible to implement every aspect of that approach. This chapter surveys the objectives of research and the various approaches that might be pursued to answer specific questions in pursuit of research objectives. This chapter considers the role of statistics, causality, risk, alternative models, Neyman–Pearson hypothesis testing, and other factors in pursuit of publishable research.
The Role Of Sampling In Inferential Statistics

Using numerous examples with real data, this textbook closely integrates the learning of statistics with the learning of R. It is suitable for introductory-level learners, allows for curriculum flexibility, and includes, as an online resource, R-code script files for all examples and figures included in each chapter, for students to learn from and adapt and use in their future data analytic work. Other unique features created specifically for this textbook include an online R tutorial that introduces readers to data frames and other basic elements of the R architecture, and a CRAN library of datasets and functions that is used throughout the book. Essential topics often overlooked in other introductory texts, such as data management, are covered. The textbook includes online solutions to all end-of-chapter exercises and PowerPoint slides for all chapters as additional resources, and is suitable for those who do not have a strong background in mathematics.
Genetic-Fuzzy Programming Based Linkage Rule Miner (GFPLR-Miner) for Entity Linking in Semantic Web

This article describes how semantic web data sources follow linked data principles to facilitate efficient information retrieval and knowledge sharing. These data sources may provide complementary, overlapping or contradicting information. In order to integrate these data sources, the authors perform entity linking. Entity linking is an important task of identifying and linking entities across data sources that refer to the same real-world entities. In this work, they have proposed a genetic fuzzy approach to learn linkage rules for entity linking. This method is domain independent, automatic and scalable. Their approach uses fuzzy logic to adapt mutation and crossover rates of genetic programming to ensure guided convergence. The authors' experimental evaluation demonstrates that our approach is competitive and make significant improvements over state of the art methods.
Using the Random Tree Classifier to Improve the Project’s Cost Predictability in the Earned Value Management: An Empirical Study

This paper proposes the selection of historical cost performance data of processes using the Random Tree classifier and the application of the calculation proposed by [20], to improve the predictability of projects cost. The proposed technique was evaluated through an empirical study, which evaluated the implementation of the proposed technique in 23 software development projects. The proposed technique has been applied in real projects with the aim of evaluating the accuracy and variation of the CPI Accum and consequently the EAC. Then it was compared to the EVM traditional technique. Hypotheses tests with 95% significance level were performed, and the proposed technique was more accurate and more stable (less variation) than the traditional technique for calculating the Cost Performance Index – CPI.
Development of a Sign Language Dialogue System for a Healing Dialogue Robot

With the development of human-computer communication technology, it has become feasible to use chat bots for psychotherapy and psychological counseling. However, most of the chat bots are currently geared towards healthy people who can communicate using their voice. For people with Hearing or Speech Disorder (PHSD), it is not convenient to use their voice to communicate, and because of the complexity of sign languages, the current development of chat bots for sign languages are insufficient. Therefore, we propose a new design of a chat bot system for PHSD with Leap Motion gesture recognition. The Leap Motion is used as an information reading device, and then converts the obtained sign language information into text information. To pass the information smoothly, based on the defined processing, the chat bot expresses response behaviors through audio, text, and the facial expressions to the PHSD. This research will have a positive impact on the communication between PHSD and healthy people, and bring more innovative lines of research for robotics industries, sign language interpretation industries and psychotherapy industries.
Privacy Preserving Classification of Biomedical Data With Secure Removing of Duplicate Records

Classifying data is to automatically assign predefined classes to data. It is one of the main applications of data mining. Having complete access to all data is critical for building accurate models. Data can be highly sensitive, such as biomedical data, which cannot be disclosed or shared with third party, because it can harm individuals and organizations. The challenge is how to preserve privacy and usefulness of data. Privacy preserving classification addresses this problem. Collaborative models are constructed over networks without violating the data owners' privacy. In this article, the authors address two problems: privacy records deduplication of the same records and privacy-preserving classification. They propose a randomized hash technic for deduplication and an enhanced privacy preserving classification of biomedical data over horizontally distributed data based on two homomorphic encryptions. No private, intermediate or final results are disclosed. Experimentations show that their solution is efficient and secure without loss of accuracy.
A novel mechanism for dynamic multifarious and disturbed human face recognition using Advanced Stance Coalition (ASC)

Multifarious face recognition is a technique of identifying or verifying the identity of an individual using several significant features of the face. Many of the existing algorithms use least significant features and hence provide lesser amount of accuracy. In this paper, a novel scheme named Advanced Stance Coalition (ASC) is proposed to classify faces even in the most challenging picture frames. In addition to providing maximum accuracy, the ASC algorithm also reduces the calculation time. The images are feature enhanced by employing median filtering scheme followed by MD5 hashing scheme to preserve its originality. The block dioptry distribution along with naive Bayes is used for feature classification. AR, Yale, and ORL databases are used to test the efficiency of the proposed scheme. From the results, it can be seen that the proposed scheme is more efficient even with noisy images.
Design and Research of Web Crawler Based on Distributed Architecture

Internet data is abundance in content and diverse in organization. In order to automatically complete the process of collecting, analyzing, and storing large amounts of data and information on the web, a web crawler technology based on Hadoop distributed clusters is proposed. Using Nutch crawler framework, Hadoop distributed technology, and Zookeeper distributed coordination service framework, through the construction of distributed clusters, and high-performance Key-Value database Redis to store data, it verifies the feasibility of distributed crawlers. Through the analysis of data collection experiments, the comparison of multiple sets of experimental data between the distributed crawler based on Nutch and the traditional crawler shows that the crawler design of the distributed architecture is superior to the traditional crawler in terms of collection speed and efficiency.