Reviewerly/exHarmony huggingface dataset:
Motivating Employees and Organizations to Adopt a Cybersecurity-Focused Culture

We explore the reality of cyber risk faced by organizations, along with the lack of preparedness that continues to become apparent with each successful cyber-attack. Sources project continued growth and evolution of cyber-attacks; they will become more frequent and more sophisticated. To prepare for and mitigate these risks, we propose creating a cybersecurity-focused culture throughout all organizations. The greatest vulnerability, the employees, can also become the greatest defense when properly trained and informed. We review numerous studies that provide outcomes focused on enhancing cybersecurity culture to provide a list of recommended activities for organizations looking to strengthen their cybersecurity posture.
Feed-Forward Neural Networks

Feed-forward neural networks were the earliest implementations within deep learning. These networks are called feed-forward because the information within them moves only in one direction (forward)—that is, from the input nodes (units) towards the output units. In this chapter, we will cover some key concepts around feed-forward neural networks that serve as a foundation for various topics within deep learning. We will start by looking at the structure of a neural network, followed by how they are trained and used for making predictions. We will also take a brief look at the loss functions that should be used in different settings, the activation functions used within a neuron, and the different types of optimizers that could be used for training. Finally, we will stitch together each of these smaller components into a full-fledged feed-forward neural network with PyTorch.
Understanding Blockchain

One of the hottest technologies of late is Blockchain. But what exactly is a blockchain? And how does it actually work? In this chapter, we will explore the concept of blockchain, how the concept was conceived, and what problems it aimed to solve. By the end of this chapter, the idea and motivation behind blockchain would be crystal clear.
Net Learning

Graph neural networks, which generalize deep learning to graph-structured data, have achieved significant improvements in numerous graph-related tasks. Petri nets (PNs), on the other hand, are mainly used for the modeling and analysis of various event-driven systems from the perspective of prior knowledge, mechanisms, and tasks. Compared with graph data, net data can simulate the dynamic behavioral features of systems and are more suitable for representing real-world problems. However, the problem of large-scale data analysis has been puzzling the PN field for decades, and thus, limited its universal applicability. In this article, a framework of net learning (NL) is proposed. NL contains the advantages of PN modeling and analysis with the advantages of graph learning computation. Then, two kinds of NL algorithms are designed for performance analysis of stochastic PNs, and more specifically, the hidden feature information of the PN is obtained by mapping net information to the low-dimensional feature space. Experiments demonstrate the effectiveness of the proposed model and algorithms on the performance analysis of stochastic PNs.
Enterprise blockchain: an applications-based comprehensive literature review

Blockchain application beyond cryptocurrencies is popularly known as enterprise blockchain (EBC). This paper evaluates the value-adding potential of EBC by analysing applications from a theoretical perspective and evaluating the current pilot projects and startups from a practice perspective. The extent of truthfulness in exceptional hype around EBC is examined. EBC applications are comprehensively reviewed for the manufacturing industry, service industry, and the government sector. Current EBC pilot projects and startups are analysed through an industry level classification framework. Practitioners need to closely follow the EBC pilot projects and full-scale adoption in their industry to remain competitive and sustain in the market. The review exposes practitioners to the industries and organisations aiming to gain the first-mover advantage in EBC adoption. Practitioners are informed about the cautionary approach to be exercised while deciding to adopt EBC. Marketers may gain insights into the relationship between the number of pilot projects and startups.
Image error correction of hockey players' step-by-step pull shooting based on Bayesian classification

The target recognition accuracy of the traditional motion image error correction method is low, which leads to its poor application effect. In order to solve this problem, this paper proposes a new image error correction method based on Bayesian classification for hockey players' step-by-step pull shooting action. The SLIC method is used to distinguish the hockey players' step-by-step pull shooting action from the image background area, to obtain the proportion of the hockey players' step-by-step pull shooting action in the super pixel area. The hockey players' step-by-step pull shooting action with incomplete Naive Bayesian classification model, and to correct the image error with interpolation method. The experimental results show that the accuracy of the method for the hockey players' step-by-step pull shooting action is higher than 98%, and the image quality is high after the error correction.
Intelligent Medical Diagnostic Systems for Arthritis

Arthritis is a chronic, an autoimmune disease which may affect in many critical health problems in patients. From the past few years, the number of patients suffering from arthritis are swiftly escalating. Until now there is no accurate treatment discovered for this rare disease. The paper discusses the different machine learning techniques in early detection of Arthritis so that, early diagnosis can help the patients to detect/ cure the disease.
A Hybrid Enhanced Real-Time Face Recognition Model using Machine Learning Method with Dimension Reduction

Face recognition techniques play a crucial role in numerous disciplines of data security, verification, and authentication. The face recognition algorithm selects a face attribute from an image datasets. Recognize identification is an authentication device for verification as well as validation having both data analysis and feasible significance. The facerecognizing centered authentication framework can further be considered an AI technology implementation for instantly identifying a particular image. In this research, we are presenting a hybrid face recognition model (HFRM) using machine learning methods with “Speed Up Robust Features” (SURF), “scale-invariant feature transform” (SIFT), Locality Preserving Projections (LPP) &amp;Principal component analysis (PCA) method. In the proposed HFRM model SURF method mainly detects the local feature efficiently. SIFT method mainly utilizes to detect the local features and recognize them. LPP retains the local framework of facial feature area which is generally quite meaningful than on the sequence kept by a ‘principal component analysis (PCA) as well as “linear discriminate analysis” (LDA). The proposed HFRM method is compared with the existing (H. Zaaraoui et al., 2020) method and the experimental result clearly shows the outstanding performance in terms of detection rate and accuracy % over existing methods.
Support Vector Machines

In this chapter, we discuss the support vector machine (SVM), an approach for classification that was developed in the computer science community in the 1990s and that has grown in popularity since then.
Frost forecast - a practice of machine learning from data

Among the efforts in frost forecast using machine learning techniques, a well-adopted method is to first apply time series forecast for the lowest temperature at future time points, such as the next a few days, and then apply predictive model to predict the event of frost at these time points using corresponding temperature forecasted. According to the domain understanding, there exists some 'cause-effect' between environment factors, including temperature and others, and the occurrence of frost in a few hours' period. A new modelling concept has been proposed by Ding et al. to capture such cause-effect. Preliminary experiments showed encouraging results with a sample of minute-level sensor data collected in Ikuta campus of Meiji University. In this article, as a continuation of the work, we shall further discuss methods of modelling, including causal models and associative models, and propose a framework of hybrid system in supporting frost forecast of short-term (e.g., a few hours) as well as that of relatively longer periods (e.g., a few days). More experiments are provided, and the issues of performance evaluation are discussed.
Design and Implementation Web-Based System for Course Files Management by using WEBML Methodology: A Higher Education Perspective (King Khalid University)

Online System course file management aims to keep a full archive of the course file as it can be used in educational institutions or colleges for the quality of the educational service and its development later, this system depends on the standards of quality of national and international educational institutions, also the proposed system allows to retain The course file can be tracked and the decision can be adopted by the quality unit. The role of the Quality Unit comes by reviewing the course file and ensuring that its contents are complete in accordance with the standards of quality of national and international educational institutions, and accordingly the course file is approved or returned due to incomplete file. This research was based on the WEBML methodology, which aims to develop web-based information systems, and has used many tools to implement this methodology, including UML, in order to build analysis and design schemes for the proposed system as well as design the interfaces of the proposed system.
Analysis of breast cancer using grey level co-occurrence matrix and random forest classifier

This paper introduces two features of neighbourhood structural similarity (NSS) with grey level co-occurrence matrix (GLCM) proposed for the feature extraction of mammographic masses. Random forest (RF) classifier is used for classification whether the extracted masses are benign or malignant. NSS describes the equivalence in the midst of proximate regions of masses by combining two new features NSS-I and NSS-II. Benign masses are analogous and have systematic patterns. Malignant masses contain indiscriminate patterns because of their miscellaneous attributes. For benign-malignant mass classification, a number of texture features is proposed namely correlation, contrast, energy and homogeneity; it quantifies neighbouring pixels relationship and is unable to capture structural similarity within proximate regions. The performance of the features is evaluated using the images from the mini-MIAS and DDSM datasets and the random forest classifier does the recognition. This involves proper classification of masses with high accuracy.
K-Nearest Neighbor Based URL Identification Model for Phishing Attack Detection

Phishing causes many problems in business industry. The electronic commerce and electronic banking such as mobile banking involves a number of online transaction. In such online transactions, we have to discriminate features related to legitimate and phishing websites in order to ensure security of the online transaction. In this study, we have collected data form phish tank public data repository and proposed K-Nearest Neighbors (KNN) based model for phishing attack detection. The proposed model detects phishing attack through URL classification. The performance of the proposed model is tested empirically and result is analyzed. Experimental result on test set reveals that the model is efficient on phishing attack detection. Furthermore, the K value that gives better accuracy is determined to achieve better performance on phishing attack detection. Overall, the average accuracy of the proposed model is 85.08%.
On Complexity of Adaptive Splines

The paper discusses various methods of adaptive spline approximations for the flow of function values. It is considered an adaptive compression algorithm, which, for a priori given , has the properties 1) the complexity of the algorithm is proportional to the length of the original flow, 2) by the piecewise linear interpolation of the compression result, it is possible to restore the original flow with an accuracy of 3) the compression result is close to optimal and has 0(M) of arithmetic operations. The effectiveness of this approach is demonstrated on rapidly changing initial flows of numerical information in the digital experiment . In addition, the paper presents an exact two-sided estimate for the number 0(M2 ) of arithmetic operations for the optimal solution of the problem of compressing an informational numerical flow of length M with the possibility of recovering this flow with a predetermined accuracy. Provided that the original flow is convex, a compression algorithm is developed with an accurate twosided estimate of the number 0(Mlog2M) and with the possibility of recovery with a prescribed accuracy.
LAHAR-CNN: human activity recognition from one image using convolutional neural network learning approach

The problem of human action recognition has attracted the interest of several researchers due to its significant use in many applications. With the great success of deep learning methods in most areas, researchers decided to switch from traditional methods-based hand-crafted feature extractors to recent deep learning-based techniques to recognise the action. In the present research work, we propose a learning approach for human activity recognition in the elderly based on convolutional neural network (LAHAR-CNN). The CNN model is used to extract features from the dataset, then, a multilayer perceptron (MLP) classifier is used for action classification. It has been widely admitted that features learned using a CNN model on a large dataset can be successfully transferred to an action recognition task with a small training dataset. The proposed method is evaluated on the well-known MSRDailyActivity 3D dataset. It has shown impressive results that exceed the performances obtained in the state of the art using the same dataset, thus reaching 99.4%. Furthermore, our proposed approach predicts human activity (HA) from one single frame sample which justifies its robustness. Hence, the proposed model is ranked at the top of the list of space-time techniques.
Knowledge Management and Organizational Performance: A Research On Systematic Literature

Studies that investigate the KM and organizational performance are few. The purpose of this study is to review the literature and review the articles related to the effect of knowledge management (KM) on organizational performance. A systematic literature review was conducted, and three screenings were performed to refine the articles. Article were extracted from Scopus and Web of Science (WoS). A total of 29 articles were considered to meet the selection criteria was reviewed, analyzed, and important information was extracted. A frequency analysis was conducted on these articles. The findings indicated that majority of the articles were extracted from WoS and published between 2015-2017 in Malaysia and Taiwan. The reviewed studies were conducted on manufacturing and technological industries using quantitative method. Sample size in most studies is less than 200 and data was analyzed using first generation of data analysis. Findings were discussed, and it was recommended for future studies to increase the sample size and use more frequently structural equation modeling
Content based Image Retrieval System using Combination of Color and Shape Features, and Siamese Neural Network

With an advent of technologya huge collection of digital images is formed as repositories on world wide web (WWW). The task of searching for similar images in the repository is difficult. In this paper, retrieval of similar images from www is demonstrated with the help of combination of image features as color and shape and then using Siamese neural network which is constructed to the requirement as a novel approach. Here, one-shot learning technique is used to test the Siamese Neural Network model for retrieval performance. Various experiments are conducted with both the methods and results obtained are tabulated. The performance of the system is evaluated with precision parameter and which is found to be high.Also, relative study is made with existing works.
Object detection

Object detection is one of the most important tasks in computer vision. It not only classifies the category of objects but also determines the location of them on the image. In this chapter, we discuss state-of-the-art CNN-based object detection methods. Then, we present an example program of building, training, and testing the YOLO-v3 model on the PASCAL VOC 2007 dataset.
Coverage Optimization of Multi-Camera for Deformable Contour Shapes

Visual coverage of multi-camera is an important problem in industry, and most existing results focus on the static and exact accurate object model. However, in some cases, the object model is changing or not exactly known, which mostly can be represented by deformable contour shapes. In this paper, the outer and inner boundaries of the deformable contour shape set are obtained by the so-called alpha shapes method firstly. The overall cost function of the deformable contours is then constructed on two boundary contours. The optimization process is simplified by using the multi-camera to cover the two boundary contours. The coverage performance can be guaranteed if those two boundary contours are covered. The effectiveness of this approach is illustrated via a numerical example.
Cryptocurrency shocks

Abstract In this paper, we use a bivariate structural VAR to investigate risk spillovers from the cryptocurrency market to standard financial markets. We investigate the effects of cryptocurrency shocks on key financial markets, including the stock, bond, gold and foreign exchange markets. The results show that cryptocurrency shocks do not have statistically significant effects on standard financial markets except for the bond market. This is consistent with most of the existing literature that argues that cryptocurrencies are mostly a new and different asset class, not related to standard factors.
An Optimal Lempel Ziv Markov Based Microarray Image Compression Algorithm

In the recent years, microarray technology gained attention for concurrent monitoring of numerous microarray images.It remains a major challenge to process, store and transmit such huge volumes of microarray images.So, image compression techniques are used in the reduction of number of bits so that it can be stored and the images can be shared easily.Various techniques have been proposed in the past with applications in different domains.The current research paper presents a novel image compression technique i.e., optimized Linde-Buzo-Gray (OLBG) with Lempel Ziv Markov Algorithm (LZMA) coding technique called OLBG-LZMA for compressing microarray images without any loss of quality.LBG model is generally used in designing a local optimal codebook for image compression.Codebook construction is treated as an optimization issue and can be resolved with the help of Grey Wolf Optimization (GWO) algorithm.Once the codebook is constructed by LBG-GWO algorithm, LZMA is employed for the compression of index table and raise its compression efficiency additionally.Experiments were performed on high resolution Tissue Microarray (TMA) image dataset of 50 prostate tissue samples collected from prostate cancer patients.The compression performance of the proposed coding esd compared with recently proposed techniques.The simulation results infer that OLBG-LZMA coding achieved a significant compression performance compared to other techniques.
Blockchain and the European Union’s General Data Protection Regulation: from Conflict to “Peaceful” Coexistence?

This article examines briefly regulatory challenges in the context of blockchain technology from the perspective of data protection law. Hence, a great attention will be given to the key provisions of the new EU General Data Protection Regulation (GDPR). The aim of this paper is to discuss the main tensions between data protection and the increasing use of blockchain technology. The study first describes some of the fundamental aspects and core elements of the blockchain technology and consequently provides a critical analysis of the often exaggerated benefits of blockchain technology. The paper outlines the GDPR’s application challenges within the decentralized network to reveal the data protection concerns that may arise through the blockchains’ application. In this regard, legal and technological issues raised by the blockchain-based system are discussed. The conclusion summarizes the main findings and underlines the important issues for further policy development in the area of data protection law triggered by the rapid technological development.
An efficient data hiding technique in image using binary Hamming code along with particle swarm optimisation

An effective spatial domain steganographic scheme is proposed in this paper. Particle swarm optimisation (PSO) can be used to identify the most efficient pixel position in the greyscale cover image, then secret information embedded using linear block code - binary Hamming code which enhances embedding efficiency. The visual quality of the stego image is the primary element taken into consideration in formulating the objective function of the PSO. The efficiency of the proposed method was analysed through measures like peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) to validate the visual quality of the image. Chi-square, histogram, and regular singular (RS) attacks are the steganalysis techniques used in this paper that confirm the security offered for the secret information. The results are compared with various standard spatial domain embedding techniques for hiding data; binary Hamming code along with PSO provides the best stego image quality and it can sustain various noises during the transmission.
Bayesian Networks

Bayesian networks (or Bayes nets) are a notation for expressing the joint distribution of probabilities over a number of variables. Variables in a Bayesian network can be continuous or discrete (Lauritzen SL, Graphical models. Oxford University Press, New York, 1996), however, when all variables are discrete, all calculations can be represented as a series of sums and products. As such, Bayes nets provide a notation for expressing a wide variety of cognitive diagnostic models, including ones described in other chapters of this book. Several commercial software packages are available for supporting Bayesian networks including HUGIN (Andersen SK, Olesen KG, Jensen FV, Jensen F, Hugin—a shell for building Bayesian belief universes for expert systems. In: IJCAI’89, Detroit, MI, 1989. Reprinted in Shafer and Pearl 1990), Netica (Norsys, Inc., Netica [Computer software manual], 2004. Retrieved from http://www.norsys.com ), Genie (BayesFusion, LLC, Genie, 2017. Retrieved from http://bayesfusion.com (Bayesian network Computer Software)) and BayesiaLab (Bayesia, Bayesialab, 2017. Retrieved from http://www.bayesia.com (Bayesian Network Computer Software)) as well as a number of free software packages.
Introduction

This, introductory paper for Knowledge Socialism. The Rise of Peer Production: Collegiality, Collaboration, and Collective Intelligence, provides a brief overview of the concepts of knowledge economy and knowledge capitalism. It introduces the history and genealogy of the concept of knowledge socialism and explores its relationships to similar concepts such as knowledge cultures and radical openness. Finally, it offers some initial ideas about knowledge socialism in 2020 and provides a brief overview of Knowledge Socialism. The Rise of Peer Production: Collegiality, Collaboration, and Collective Intelligence.