Reviewerly/exHarmony huggingface dataset:
An Overview of Big Data Security With Hadoop Framework

Big Data is a very huge volume of data which is beyond the storage capacity and processing capability of traditional system. The volume of data is increasing at exponential rate. Therefore, there is the need of such mechanism to store and process such high volume of data. The impressiveness of the Big data lies with its major applicability to almost all industries. Therefore, it represents both, the tremendous opportunities and complex challenges. Such omnipotent eminence leads to the privacy and security related challenges to the big data. Nowadays, security of big data is mainly focused by every organization because it contains a lot of sensitive data and useful information for taking decisions. The hostile nature of digital data itself has certain inherited security challenges. The aim of Big data security is to identify security issues and to find the better solution for handling security challenges. The observation and analysis of different security mechanism related to the issues of big data and their solutions are focused in this chapter.
Arbitrary Scene Text Detection with Bezier Proposal

Scene text detection is widely studied in natural language processing since 2016, in which arbitrary scene text detection is always the difficulty. At present, to deal with the problem of how to detect arbitrary shape text, the semantic segmentation-based methods are widely used, but the post-processing and label generation operations are complex. Sparse R-CNN is a novel object detection framework with simple process and high accuracy, which can simplify post-processing by bipartite graph matching loss. Therefore, an arbitrary shape text detect method without any post-process based on Bezier proposal with Sparse R-CNN is proposed. Firstly, the feature pyramid network with attention mechanism is used to extract features, and then the processed features go into the Sparse R-CNN detection head to get the score and coordinates, and finally the detection results are visualized according to the score. The results on ICDAR2015 and CTW1500 datasets show that our method can detect arbitrary text effectively, and our method have higher accuracy and higher speed than other methods.
Autonomous mobile robot nonlinear trajectory tracking controller design and implementation on ROS-MATLAB system

The aim of this paper is solving the problem of an autonomous mobile robot indoor trajectory tracking by nonlinear adaptive controller. Similar with a human driver, the adaptive control system optimizes the mobile robot movement based on Lyapunov direct method. Therefore, it can help to rapid development for self-driving industry regarding cost-effectiveness. In addition, faced with the expensive motion capture equipment, this paper introduces an cheaper experiment system with ROSMATLAB framework and low-cost embedded hardware. The controller implementation task was finished on a practical mobile robot, Turtlebot2. In future, the technology can be applied to the self-driving car in industry, and improves the feasibility of outdoor automatic logistics.
Imaging Methods

The methods presented before in Chaps. 2 , 3 , and 5 concentrate on form deviations, topography, roughness, and waviness. In principle, also dimensional deviations, position deviations and defect inspection can be accomplished by these methods. More common is to use imaging and image processing as the main processes for measurement and evaluation for these more coarse deviations. This chapter comprises the following imaging methods:
Dovetailing Belief Base Revision with Truth Approximation

Gustavo Cevolani et al. (Erkenntnis 75(2):183–202, 2011) have shown that their account of verisimilitude of ‘conjunctive theories’ of a finite propositional language can be nicely linked to a variant of AGM belief set revision, viz. belief base revision, in the sense that the latter kind of revision is functional for truth approximation according to the conjunctive account. In the present chapter I offer a generalization of these ideas to the case of approaching any divide of a (finite or infinite) universe, allowing several interpretations, besides true (false) atomic propositions, notably nomic states (not) in equilibrium, nomic (im-)possibilities, (non-)instantiated ‘Q-predicates’ of a monadic language. It shows how and why approximation of ‘the true boundary’ takes place by belief base revision guided by evidence. In the nomic (im-)possibilities interpretation this chapter essentially deals with a belief base revision perspective on basic and quantitative nomic truth approximation of two-sided theories in the sense of Chaps. 4 and 5 . The previous chapter dealt with a belief set revision (i.e., AGM-) perspective on basic and refined nomic truth approximation of (one-sided) exclusion theories. This chapter will leave the challenge open of a belief base revision perspective on refined nomic truth approximation of two-sided theories, and, more generally, a belief base perspective on refined approximation of ‘the true boundary’ belonging to whatever interpretation.
Optimized Resource Provisioning in Cloud Using Multi-Stage Stochastic Programming

Cloud computing is an enabling technology with convenient, network on-demand access, ubiquitous to a pool of computing sources that are shared and configured. In the cloud, resource provisioning is a significant aspect in Infrastructure as a Service, and it faces problems like uncertainty in price, heterogeneity, availability, and consideration of demand. To overcome the issues, stochastic programming and approximate dynamic programming (SPDP) in cloud computing is proposed, which enhances the uncertainties in the cloud framework namely price, heterogeneity, demand, and availability. SPDP is a multi-stage framework with dynamic programming that considers uncertainty in inherent probability space and a tree is constructed to minimize the space. The minimization in space enhances the exploration, resource, over and under-provisioning, and substantial enrichment in profitability effectively. The proposed technique illustrates the sensitive exploration of prices that is beneficial for providers and users. From the investigation of results, it is identified that the SPDP has promising outcomes when compared with other available algorithms.
ESS: An Efficient Storage Scheme for Improving the Scalability of Bitcoin System

With the continuous development of blockchain technology, Bitcoin as the first cryptocurrency has drawn massive attention from various sectors. A full node on Bitcoin network has to store over 320GB of data as of March 9, 2021. According to the growth rate of Bitcoin transactions, it is estimated that the size of Bitcoin data on a full node will exceed 400GB within two years. The storage problem makes it difficult for common users to easily store all of Bitcoin data, which weakens the decentralization capability of Bitcoin network. In this work, we propose an efficient storage scheme (ESS) based on the distribution characteristics of the unspent transaction outputs in Bitcoin system. It can prune the storage of the transactions for those low query frequency blocks. When a new block is generated, a node using ESS scheme can quickly verify most of the transaction inputs. Only a small amount of transaction outputs of older blocks need to be retrieved from the full node for payment verification. Experimental results demonstrated that our scheme can reduce the size of a normal node in current Bitcoin system by about 85% at a low communication cost.
EPIDEMIOLOGICAL PROFILE, HEALTH COSTS AND HOSPITAL MORTALITY DUE TO SYSTEMIC DISEASES OF THE CONNECTIVE TISSUE IN THE STATE OF CEARA IN THE PERIOD 2008-2018: RESULTS OF A STUDY GROUP IN RHEUMATOLOGY OF BRASILIA.

Systemic Connective Tissue Diseases (SCTD) are important cause not only of pain, physical disability, decreased quality of life and years of life lost, but of high costs with hospitalizations and medications per patient. Recent studies have shown that the proportional economic impact on SCTD can often be greater than cardiovascular and oncologic. With evolution and improvement in the treatment of SCTD, the prevalence and, consequently, the costs with great impact in public and private health systems have increased. The objective of the present study is to describe the epidemiological profile, costs recorded and the hospital mortality coefficient by SCTD in the state of Ceará.
Tablets in Primary Schools

This article presents the results of a short project in which tablets were used for teaching the respiratory, circulatory, and digestive systems to 162 sixth-grade primary school students who were divided into three groups. The first group was taught conventionally using a textbook, while in the second a constructivist teaching method was applied and the teachers actively participated in the process. The third group was also taught using the same constructivist teaching method, but the instruction was technologically enhanced with tablets and an application with augmented reality features. Data was collected by means of evaluation sheets and a questionnaire. The results indicated that students in the third group outperformed students in the other two groups. As for students' misconceptions, the only notable difference was between the third and first group. These results can be attributed to students' strong positive attitude towards the use of tablets, motivation, and enjoyment, as well as to the teaching method. The study's implications are also discussed.
Classification of File Data Based on Confidentiality in Cloud Computing Using K-NN Classifier

Cloud computing is a new paradigm model that offers different services to its customers. The increasing number of users for cloud services i.e. software, platform or infrastructure is one of the major reasons for security threats for customers' data. Some major security issues are highlighted in data storage service in the literature. Data of thousands of users are stored on a single centralized place where the possibility of data threat is high. There are many techniques discussed in the literature to keep data secure in the cloud, such as data encryption, private cloud and multiple clouds concepts. Data encryption is used to encrypt the data or change the format of the data into the unreadable format that unauthorized users could not understand even if they succeed to get access of the data. Data encryption is very expensive technique, it takes time to encrypt and decrypt the data. Deciding the security approach for data security without understanding the security needs of the data is a technically not a valid approach. It is a basic requirement that one should understand the security level of data before applying data encryption security approach. To discover the data security level of the data, the authors used machine learning approach in the cloud. In this paper, a data classification approach is proposed for the cloud and is implemented in a virtual machine named as Master Virtual Machine (Vmm). Other Vms are the slave virtual machines which will receive from Vmm the classified information for further processing in cloud. In this study the authors used three (3) virtual machines, one master Vmm and two slaves Vms. The master Vmm is responsible for finding the classes of the data based on its confidentiality level. The data is classified into two classes, confidential (sensitive) and non-confidential (non-sensitive/public) data using K-NN algorithm. After classification phase, the security phase (encryption phase) shall encrypt only the confidential (sensitive) data. The confidentiality based data classification is using K-NN in cloud virtual environment as the method to encrypt efficiently the only confidential data. The proposed approach is efficient and memory space friendly and these are the major findings of this work.
Complex Scene Tracking Algorithm Based on Multi-feature Fusion

At present, natural disasters of coalmine in China are still serious, and coalmine safety guarantee capabilities are still relatively low. In recent years, a lot of manpower and material resources have been implemented to upgrade the coalmine monitoring system, but manual operation has limited their applications and effects, the efficiency is low and the error rate is high. The application of computers for intelligent tracking and monitoring of dynamic object can greatly reduce the waste of manpower, improve monitoring efficiency, and provide more reliable and concise safety early warning and system linkage than manual operation. In addition, object tracking can provide information support for behavior understanding, event detection, object classification, its importance is self-evident. The object tracking algorithm has achieved good results in open scenes with rich features and sufficient illumination. However, in confined spaces such as coalmine and tunnels, due to unfavorable factors such as coal dust, lack of illumination, scale changes, and image blur-ring, etc. The stability and robustness of tracking are greatly affected. In this paper, the deep network model is used to extract the depth features of the tracking target, the depth features and the results of the HOG feature location filter are weighted and merged to obtain the final target location. The algorithm has better tracking stability and robustness in the case of insufficient illumination and scarce feature points in coalmines. The dynamic object tracking experiment is carried out on the coalmine monitoring video. The experimental results show that the algorithm is more robust than the comparison algorithm and can meet the needs of object tracking in complex scenes in coalmine.
Introduction

This chapter provides a brief introduction to multilingual phone recognition. The AFs for multilingual phone recognition, approaches for multilingual phone recognition, and code-switched phone recognition are briefly introduced. The significance of AFs in improving the performance of multilingual phone recognition systems is highlighted. Eventually, the objective and scope of the present work are presented.
A comparative study of text mining in big data analytics using deep learning and other machine learning algorithms

Text mining has become very important in modern day and has various applications, e.g., spam email classification, similar news item clustering, etc. There have been many scenarios where regression is accompanied with text mining, e.g., for predicting sales of a product of any store the product description also plays an important role. In this paper we have tried to solve text mining problems in big data analytics using deep learning methods. Deep learning on the other hand known to be strongest supervised learning method. Here we can make use of back propagation concept to harness the power. We can also use gradient descent learning method to reduce the cost function and settle to global minima. We will also do a comparative analysis of other machine learning algorithms with deep learning methods. We will also construct an rXs contingency table popularly known as Crosstab.
Trusted Cloud- and Femtocell-Based Biometric Authentication for Mobile Networks

Authentication procedures are conducted in order to control and stop illegitimate access of such valuable data. This chapter discusses the biometric authentication inside the cloud. The authors describe how biometric information of a user can be securely transmitted and then stored inside the user database maintained in the trusted cloud. Femtocell, a recent development in mobile network using which secures biometric data transmission from the mobile device to the cloud, is discussed in this chapter.
Donation Model Development Based on the Methodologies of Blockchain

In order to solve the problems occurring in the current donation systems, the methodologies of blockchain include decentralized data storage, transaction hash, digital signature, blocks connection within the chain are adopted to the donation model, called blockchain donation model (BDM). In BDM, all donation transactions are recorded in detail and stored permanently; they are not allowed to change, modify, or delete. In addition, donors could indicate the specific groups for donating directly because of the peer-to-peer transmission method. All the information is sealed through the secure mechanism; only donors and donees could decrypt it with their own keys. BDM records transaction flows starting from receiving to spending the donation in detail. BDM is not only a place for listing details of donations but also a secured transaction storage. The blocks are connected together with a strong chain which is maintained by all nodes in the blockchain network. Thus, BDM is a platform dealing with the donation issue under a well secure mechanism, no intermediaries, and a permanent storage.
Ubiquitous Teachers' Training and Lessons Learned With the uProf! Model

This chapter illustrates features and outcomes of the experiences with uProf!, a model for teachers' professional training in ubiquitous learning by tablets and quick response (QR) codes. The model was designed, implemented, and successfully validated in 2012 within a full-immersion, learner-centered, and metacognitive course for 80 Italian high school teachers who are part of a school network for the enhancement of curriculum continuity from middle into high school. Later on, uProf! was used to provide continuous professional training and tailored in-class tutoring during these teachers' first implementations of technology-enhanced learning experiences.
LU decomposition

Abstract This chapter discusses parallel dense matrix computations, in particular the solution of linear systems by LU decomposition with partial row pivoting. It first presents a general Cartesian scheme for the distribution of matrices. Based on BSP cost analysis, the square cyclic distribution is proposed as particularly suitable for matrix computations such as LU decomposition and Gaussian elimination. The chapter introduces two-phase broadcasting of vectors, which is a useful collective-communication method for sending copies of matrix rows or columns to a group of processors. It also discusses how to achieve high performance by delaying rank-1 matrix updates to create a multiple-rank update, which can be carried out by multiplying tall-and-skinny matrices in a cache-friendly manner. The high-performance parallel LU decomposition is tested on a top-ranking supercomputer, and its performance is analysed with respect to computation, communication, and synchronization.
PRBS Generator using FinFET

To design a PRBS (Pseudo Random Binary Sequence) Generator using FinFET modules to obtain better performance of the system with reduced power consumption and less current dissipation in the circuit. The 45nm-FinFET modules are used for simulation purposes and are being downloaded from the standard modules of PTM. PRBS generator circuits constructed by using FinFET modules are simulated using the LTSPICE tool platform and the outputs are studied in the digital format that provides a random sequence of output bits for the provided inputs. In the current system PRBS generators are constructed with the use of CMOS technology that has more power consumption and high current dissipation through the circuit. This can be overcome by FinFET modules that have a Double-Gate structure that would reduce the leakage current through the circuit as both the gates can be controlled independently. Simulation by using FinFET modules also has a low power consumption due to the reduction of transistors in the circuit design than the current CMOS technology. The areas of Cryptography and Data Encryption/Decryption are extemporized by the implementation of the PRBS generator using FinFET devices. It is essentially provided as input for testing VLSI systems.
Application of Intelligent Fingerprint Punch Card Technology in College Student Affairs Management

Student information management is an important link in college students' work. With the continuous advancement of university education reform, student information presents the characteristics of wide source, fast change and difficult maintenance. This paper mainly studies the application analysis of artificial intelligence technology in university student affairs management. In system building, not only business thinking should be separated, but also the process of business analysis should be transformed into the process of system building. By sorting out the data items of student information, the data model is established. By establishing and expanding the business association between data items, the standardization and knowledge-based management of student information can be realized. On this basis, relevant artificial intelligence technology is introduced to help student staff analyze data, arrange work, and find problems and so on.
An Insight into State-of-the-Art Techniques for Big Data Classification

This article describes how classification algorithms have emerged as strong meta-learning techniques to accurately and efficiently analyze the masses of data generated from the widespread use of internet and other sources. In particular, there is need of some mechanism which classifies unstructured data into some organized form. Classification techniques over big transactional database may provide required data to the users from large datasets in a more simplified way. With the intention of organizing and clearly representing the current state of classification algorithms for big data, present paper discusses various concepts and algorithms, and also an exhaustive review of existing classification algorithms over big data classification frameworks and other novel frameworks. The paper provides a comprehensive comparison, both from a theoretical as well as an empirical perspective. The effectiveness of the candidate classification algorithms is measured through a number of performance metrics such as implementation technique, data source validation, and scalability etc.
Mastering Technology-Enhanced Language Learning, Computer-Assisted Language Learning, and Mobile-Assisted Language Learning

This chapter explains the overview of Technology-Enhanced Language Learning (TELL); the overview of Computer-Assisted Language Learning (CALL); the relationship between Computer-Mediated Communication (CMC) and language learning; the overview of Mobile-Assisted Language Learning (MALL); and the technological utilization for language learning in the digital age. TELL, CALL, and MALL significantly deal with the impact of technology on teaching and learning the second language or foreign language. TELL, CALL, and MALL are the utilization of the advanced devices as the technological innovation to display multimedia as the modern language learning methods in the digital age. TELL, CALL, and MALL effectively improve learning motivation and develop better attitudes in students and language learners toward language learning. The chapter argues that encouraging the applications of TELL, CALL, and MALL has the potential to improve language learning performance and reach strategic goals in the modern language learning environments.