Reviewerly/exHarmony huggingface dataset:
Introduction

We are now beginning to see the widespread use of Artificial Intelligence (AI) in all walks of life. From Alexa in the home to the promise of driverless cars in the future and even in the fight against combating some of the most challenging medical problems faced by humanity. Many aspects of AI have transitioned from a purely theoretical field to an applied one. Therefore, unlike traditional university courses, this book provides an apprenticeship to those wishing to pick up AI and apply it in solving real-world problems. Never before have we seen so many frameworks surrounding the application of AI. With many large organisations such as Google, Microsoft, IBM, Facebook and NVidia, the race is on to capture market share as we continue to see the uptake of AI. This means that anybody can use these tools to start developing their solutions to solve their unique problems. Therefore, this book is timely as its underlying goal is to bridge the gap between the well-supported frameworks that organisations provide and anyone with a desire to learn not just university undergraduates.
Fundamentals of Machine Learning

Machine Learning (ML) is a field of AI that utilises historical data and domain knowledge to automatically construct algorithms to solve a given problem. This is in contrast to other methods which construct algorithms through explicit programming and predefined rules. ML can be a lengthy and complex task and is regarded as an iterative process through model training and refinement. The training process is conducted using training data which can be either known or unknown depending on how the algorithm learns. Trained models are evaluated to measure their performance using unseen test data. Depending on the results it may be necessary to retrain algorithms with additional data to increase accuracy over time.
Sistem Absensi Kegiatan Ormawa Berbasis QR Code

The attendance system has an important role in everyday life, for example in schools, universities, factories, offices, hospitals, and other places that require attendance. Various efforts have been made so that the recording of attendance can be done easily and quickly. Efforts have been made, for example, by using fingerprints, RFID, and using QR codes. This study develops an attendance system for student organizations by using a QR code. There are two applications developed, namely a web-based system and a mobile-based system. Data management is carried out on a web-based system. Attendance input is done on a mobile-based system. Attendance input is done using a QR code. The database used is designed to maintain data integrity to minimize recording errors. The attendance system for ormawa activities has been successfully developed. The use of a QR code makes inputting timesheets easier and faster. Users simply scan the QR code to enter the timesheet.
Introduction to the Field of Information Security

Nowadays, millions of users are sharing their critical information over the internet using appllications that may use unsecured channels and It could threaten their interests and rights, and often risk their physiological or physical lives if the data is intentionally exposed or modified. We may prevent this by taking advantage of security mechanisms to incorporate security services in all aspects of our digital lives. Within this paper we discuss information security concepts within three main areas: elliptic curves, hash function, and symmetric encryption.
Cloud Platform for Autonomous Driving

Autonomous driving clouds provide essential services to support autonomous vehicles. Today these services include but not limited to distributed simulation tests for new algorithm deployment, offline deep learning model training, HD map generation, etc. These services require infrastructure support including distributed computing, distributed storage, as well as heterogeneous computing. In this chapter, we present the details of our implementation of a unified autonomous driving cloud infrastructure, and how we support these services on top of this infrastructure.
Detection of Facial Micro Expressions and Textual-Tracking for Paralyzed Using Computer Vision

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
‘MIND MAPPING GAME’ USING PICTURES TO IMPROVE STUDENTS VOCABULARY

This article aimed to describe about the use of mind mapping game using pictures to improve students’ vocabulary. The use of mind mapping game using pictures is important for students’ junior high school of suburban areas. Mind mapping’ game can make students have fun in learning. Visual or using picture the students can see the real of words. Vocabulary is important for students in the suburban school, because they never learn about English before. Then, the teacher have guide them how to write, read, translate and remember the vocabulary. Teacher want introduce English. In order that, students have progress in learning and students’ vocabulary. The research methodology uses Classroom Action Research (CAR).
I meccanismi di suffissazione relativi alla formazione dei verbi nella lingua russa di Internet

The present work investigates the role of derivation for the development of lexical system in the Russian network. Specifically, it focuses on suffixation mechanisms that form verbal neologisms motivated by nouns. It describes the productivity of the different derivational models, their behaviour and underlines the influence of new lexemes on the relation between semantics and structure of the phrase, highlighting some peculiarities of Russian language in the Internet.
A study of observation scales based on the FH dissimilarity measure

Hierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Guimarães et al. proposed a hierarchical graph based image segmentation (HGB) method based on the Felzenszwalb-Huttenlocher dissimilarity. This HGB method computes, for each edge of a graph, the minimum scale in a hierarchy at which two regions linked by this edge should merge according to the dissimilarity. In order to generalize this method, we first propose an algorithm to compute the intervals which contain all the observation scales at which the associated regions should merge. Then, following the current trend in mathematical morphology to study criteria which are not increasing on a hierarchy, we present various strategies to select a significant observation scale in these intervals. We use the BSDS dataset to assess our observation scale selection methods. The experiments show that some of these strategies lead to better segmentation results than the ones obtained with the original HGB method.
The Effect of Childcare Teachers’ Awareness of the Rights of Infants and Toddlers and Empowerment on the Interaction between Teachers and Infants and Toddlers

The study was conducted by surveying 230 childcare teachers in Gyeonggi-do. The collected data were analyzed by independent sample t-test and one-way ANOVA using SPSS 24.0. Scheffe's post hoc test was conducted to examine the differences among groups. The study results are as follows. First, the following three factors respectively showed significant differences in teachers’ social conditions: childcare teachers' awareness in the rights of infants and toddlers, empowerment, and interaction between the teachers and infants and toddlers. Second, there was a positive correlation between the awareness of the rights in infants and toddlers and the interaction, the empowerment and interaction, and the awareness of the rights of infants and toddlers and empowerment. Third, childcare teachers’ awareness of the rights and empowerment had a positive effect on the interaction. In conclusion, growing teachers’ awareness of the rights of infants and toddlers in order to have high quality interaction is essential. Also, raising teachers’ empowerment with the management of an organization related to the education of infants and toddlers is needed.
Information Assurance with special reference to the Security Content Automation Protocol (SCAP)—An Overview

Information Assurance, in short, is called as IA. This is responsible for securing informationsystems and computing. The term holds the highest degree of security related affairs. Ingenerally Computer Security considered as a branch and area of security but now apart fromthis, Information Security, IT Security, and Information Assurance considered as important.And among these, security related domain Information Assurance treated as broader andinterdisciplinary. Moreover, this Information Assurance holds all the areas and dealing of ITSecurity and Information Security but additionally, it is responsible for the designing,development of policies, regulation and guidelines of security related projects /proposal, etc.And among the administrative and protocol related affairs Security Content AutomationProtocol treated as important. In short, it is called as SCAP. It is a kind of method for usingspecific standards for the purpose of enabling vulnerability management systems in anautomated way for the measurement as well as policy compliance assessment regarding thesystems inbuilt in a company or institutions; that may include IT company or may not be.This is a conceptual paper, initially, it has discussed with the areas of Information Assurancebut gradually it has described about the Security Content Automation Protocol; including itsaim and objectives, versions, etc. paper mentioned all the areas in short and simple sense.
A Characterization of Dante Alighieri: An NLP approach to The Divine Comedy

The main goal of this research is to offer a new perspective of Dante Alighieri and his most famous work: The Divine Comedy based on natural language processing. We seek to provide rigorous evidence to enhance literary analysis on The Divine Comedy. We utilized sentiment analysis, text classification and topic modeling. We analyzed both the original Divine Comedy written in Italian, and the Divine Comedy translated to English. We also used natural language processing to compare the Divine Comedy to a variety of Shakespeare's plays and poems.
On the stability of optimization algorithms given by discretizations of the Euler-Lagrange ODE

We study discretizations of an Euler-Lagrange ODE which generate a large family of accelerated methods that converge with polynomial rate. Specifically, we address the question asking why a naive explicit-implicit Euler discretization of this ODE produces an unstable algorithm, even for a strongly convex objective function. We prove that for a strongly convex quadratic objective function and a sufficiently small step size, the naive discretization will exhibit stable behavior within a bounded number of iterations.
Aprendizado de Máquina para Segurança: Algoritmos e Aplicações

The Brazilian Symposium on Information and Computational Systems Security (SBSeg) is the country’s main forum for disseminating research results and relevant activities related to information, data protocols, and computational systems security. In this edition (2019), 13 short-course proposals were submitted, a significant number that demonstrates the relevance and the community interest in current subjects of the area. Four proposals were selected for publication and presentation, an acceptance rate of 30%. This book comprises the four chapters produced by the authors of the accepted short-course proposals. Chapter 1 addresses one of the main challenges for information security today: creating and maintaining a reliable digital identity for each user. With the advent of blockchains, new possibilities for assigning and controlling digital identities have emerged and this chapter presents to readers a theoretical discussion and practical activities on creation and managing decentralized identifiers, as well as authentication and revocation of credentials. Chapter 2 shows how the security area can benefit from the enormous advances made recently in data science and machine learning fields. The goal is to be able to discover security-relevant information that may be hidden in a large amount of non-suspicious data produced by security tools and operating systems. Key concepts in the field will be discussed and several practical examples will be demonstrated using free tools. Chapter 3 discusses blockchains in depth, as this tool is becoming increasingly used in several areas, including security. Among the various technologies adopted in blockchains, this chapter emphasizes the consensus mechanism, one of the main actors in the performance and security of the chain. The consensus mechanisms most used today require a great deal of computational power to operate satisfactorily and provide an acceptable level of security. In this context, the main types of blockchain and their consensus mechanisms will be presented and discussed, as well as experiments that illustrate in practice different aspects of security in current implementations. Concluding this book, Chapter 4 focuses on another face of security that has drawn a lot of attention from society: malicious applications that, after infecting a device, seek to hide in a variety of ways in order to escape detection by security mechanisms and forensic techniques, even when they are running. This chapter shows the application of reverse engineering techniques for analyzing malicious applications in the Linux environment, which are exemplified both in kernel and user modes based on their dynamic execution trace and in code debugging techniques.
Evaluating Partitioning Based Clustering Methods for Extended Non-negative Matrix Factorization (NMF)

Data is humongous today because of the extensive use of World Wide Web, Social Media and Intelligent Systems.This data can be very important and useful if it is harnessed carefully and correctly.Useful information can be extracted from this massive data using the Data Mining process.The information extracted can be used to make vital decisions in various industries.Clustering is a very popular Data Mining method which divides the data points into different groups such that all similar data points form a part of the same group.Clustering methods are of various types.Many parameters and indexes exist for the evaluation and comparison of these methods.In this paper, we have compared partitioning based methods K-Means, Fuzzy C-Means (FCM), Partitioning Around Medoids (PAM) and Clustering Large Application (CLARA) on secure perturbed data.Comparison and identification has been done for the method which performs better for analyzing the data perturbed using Extended NMF on the basis of the values of various indexes like Dunn Index, Silhouette Index, Xie-Beni Index and Davies-Bouldin Index.
Abnormal Crowd Behavior Detection Using Optimized Pyramidal Lucas-Kanade Technique

Abnormal behavior detection is challenging and one of the growing research areas in computer vision.The main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/uncertain events.In this work, Pyramidal Lucas Kanade algorithm is optimized using EME-HOs to achieve the objective.First stage, OPLKT-EMEHOs algorithm is used to generate the optical flow from MIIs.Second stage, the MIIs optical flow is applied as input to 3 layer CNN for detect the abnormal crowd behavior.University of Minnesota (UMN) dataset is used to evaluate the proposed system.The experimental result shows that the proposed method provides better classification accuracy by comparing with the existing methods.Proposed method provides 95.78% of precision, 90.67% of recall, 93.09% of f-measure and accuracy with 91.67%.
Defending Adversarial Examples by a Clipped Residual U-Net Model

Deep learning-based systems have succeeded in many computer vision tasks.However, it is found that the latest study indicates that these systems are in danger in the presence of adversarial attacks.These attacks can quickly spoil deep learning models, e.g., different convolutional neural networks (CNNs), used in various computer vision tasks from image classification to object detection.The adversarial examples are carefully designed by injecting a slight perturbation into the clean images.The proposed CRU-Net defense model is inspired by state-ofthe-art defense mechanisms such as MagNet defense, Generative Adversarial Network Defense, Deep Regret Analytic Generative Adversarial Networks Defense, Deep Denoising Sparse Autoencoder Defense, and Condtional Generattive Adversarial Network Defense.We have experimentally proved that our approach is better than previous defensive techniques.Our proposed CRU-Net model maps the adversarial image examples into clean images by eliminating the adversarial perturbation.The proposed defensive approach is based on residual and U-Net learning.Many experiments are done on the datasets MNIST and CIFAR10 to prove that our proposed CRU-Net defense model prevents adversarial example attacks in WhiteBox and BlackBox settings and improves the robustness of the deep learning algorithms especially in the computer vision field.We have also reported similarity (SSIM and PSNR) between the original and restored clean image examples by the proposed CRU-Net defense model.
Randomized MILP framework for Securing Virtual Machines from Malware Attacks

Cloud computing involves remote server deployments with public network infrastructures that allow clients to access computational resources.Virtual Machines (VMs) are supplied on requests and launched without interactions from service providers.Intruders can target these servers and establish malicious connections on VMs for carrying out attacks on other clustered VMs.The existing system has issues with execution time and false-positive rates.Hence, the overall system performance is degraded considerably.The proposed approach is designed to eliminate Cross-VM side attacks and VM escape and hide the server's position so that the opponent cannot track the target server beyond a certain point.Every request is passed from source to destination via one broadcast domain to confuse the opponent and avoid them from tracking the server's position.Allocation of SECURITY Resources accepts a safety game in a simple format as input and finds the best coverage vector for the opponent using a Stackelberg Equilibrium (SSE) technique.A Mixed Integer Linear Programming (MILP) framework is used in the algorithm.The VM challenge is reduced by a firewall-based controlling mechanism combining behavior-based detection and signature-based virus detection.The proposed method is focused on detecting malware attacks effectively and providing better security for the VMs.Finally, the experimental results indicate that the proposed security method is efficient.It consumes minimum execution time, better false positive rate, accuracy, and memory usage than the conventional approach.
An Efficient SDFRM Security System for Blockchain Based Internet of Things

Blockchain has recently sparked interest in both the technological and business firms.The Internet of Things's (IoT) core principle emerged due to the connectivity of several new technologies, including wireless technology, the Internet, embedded automation systems, and micro-electromechanical devices.Manufacturing environments and operations have been successfully converted by implementing recent advanced technology like Cloud Computing (CC), Cyber-Physical System (CSP), Information and Communication Technologies (ICT) and Enterprise Model, and other technological innovations into the fourth industrial revolution referred to as Industry 4.0.Data management is defined as the process of accumulation in order to make better business decisions, and process, secure and store information about a company.In the incipient model, there are interconnected contrivances and Machine-to-Machine (M2M) interactions, and transaction data are stored on the Blockchain.Security is a challenging aspect that must be punctiliously considered during the design and development phases of a CSP.In this research article, we proposed a Secure and Distributed Framework for Resource Management (SDFRM) in Industry 4.0 environments within a distributed and collaborative Industry 4.0 system, the dynamic and trust-based Distributed Management Framework (DMF) of shared resource access.Such issues are focused by taking into account of the traditional characteristics of IoT/Industrial Internet of Things' (IIoT)-predicated environments, an SDFRM in Industry 4.0 environments within a distributed and collaborative Industry 4.0 system.Also, to ensure strong privacy over the procedures associated with Access Control (AC), a privacy-preserving method is proposed and integrated into the DMF.The proposed DMF, based on blockchain technology and peer-to-peer networks, allows dynamic access management and system governance without using third parties who could be attacked.We worked hard to design and implement the proposal to demonstrate its viability and evaluate its performance.Our proposal outperforms the Multichain Blockchain in terms of successful storage transactions with an achieved average throughput of 98.15%.
A Novel Outlier Detection with Feature Selection Enabled Streaming Data Classification

Due to the advancements in information technologies, massive quantity of data is being produced by social media, smartphones, and sensor devices.The investigation of data stream by the use of machine learning (ML) approaches to address regression, prediction, and classification problems have received considerable interest.At the same time, the detection of anomalies or outliers and feature selection (FS) processes becomes important.This study develops an outlier detection with feature selection technique for streaming data classification, named ODFST-SDC technique.Initially, streaming data is pre-processed in two ways namely categorical encoding and null value removal.In addition, Local Correlation Integral (LOCI) is used which is significant in the detection and removal of outliers.Besides, red deer algorithm (RDA) based FS approach is employed to derive an optimal subset of features.Finally, kernel extreme learning machine (KELM) classifier is used for streaming data classification.The design of LOCI based outlier detection and RDA based FS shows the novelty of the work.In order to assess the classification outcomes of the ODFST-SDC technique, a series of simulations were performed using three benchmark datasets.The experimental results reported the promising outcomes of the ODFST-SDC technique over the recent approaches.