Reviewerly/exHarmony huggingface dataset:
Introduction to Enterprise Ontology

The notion of Enterprise Ontology as adopted in this book comprises both system ontology (dynamics) and world ontology (statics). By applying the notion, one acquires an understanding of the essence of an enterprise that is comprehensive, coherent, consistent, and concise, thus allowing one to achieve intellectual manageability, which is one of the general goals of Enterprise Engineering. The philosophical background in which Enterprise Ontology is positioned, is characterised by being both constructivist and interpretive. In addition to the importance of the Enterprise Ontology for all participants in modern social and economic life, the urgency of adopting and spreading it is substantiated. The only prerequisite to achieve this is an open mind.
Embedding Advanced Persistent Threat in Steganographic Images

The main characteristic of Advanced Persistent Threats is the stealthy and long period of attack. These threats usually aim at stealing secure information and include six phases: (i) Reconnaissance, (ii) Delivery, (iii) Initial intrusion, (iv) Command and control, (v) Lateral movement, and (vi) Data exfiltration. This paper proposes an APT attack scheme that exploits image steganography, in which a malicious code is embedded into a digital image. The stego-image can be triggered by an extractor program. The most common methods of delivering malicious code to target include phishing, watering hole attack, removable drive, compromised control system, and provided system service; these can all be detected. In contrast, schemes using our steganography-based approach are less likely to be detected since there is no detecting system/software that analyzes pixel values of an image. The behavior and signature of the extractor program are very different with the other malwares. The steganography is always used to deliver secret message by building a covert channel, but this technique can also be used to hide malware. In our experiments, both stego-image analysis and extractor program are tested. We present the more efficient detection methods to countermeasure advanced persistent threat in this paper.
The Software Tool for Comparison and Configuration of Nonlinear Optimization Techniques in Orb-Slam

This paper presents the study on development of a software tool with a graphical user interface (GUI) to compare nonlinear optimization methods in ORB-SLAM library. It uses g2o graph optimization framework to solve five optimization problems while tracking SLAM: (a) Global Bundle Adjustment (GBA), (b) Local Bundle Adjustment (LBA), (c) Relative Simulation Optimization (SO), (d) Pose Graph Optimization over SO(3) Constraints, and (e) Essential Graph Optimization. By default, ORB-SLAM solves these problems using Levenberg- Marquardt method, but our tool allows to select optimization methods between Levenberg-Marquardt, Powell's Dogleg and Gauss-Newton algorithms to result in the best configuration solution when working with a video dataset. The developed application allows a user to configure the optimization tasks in ORB-SLAM and run an experiment to observe the performance evaluation of the current configuration. In addition, the ORBSLAM functionality has been extended using Ceres solver optimization framework. The tests of various datasets showed that the developed application is a convenient and effective tool for comparing different solutions and finding the fastest and most accurate ORB-SLAM configuration.
The Response of Criminal Justice System

This chapter analyses the response of various stakeholders like police, prosecutors, defence lawyers, judiciary and forensic laboratories while dealing with cyber economic crimeCyber economic crime with respect to their role in the entire process of investigation and prosecution. The chapter focuses on disposal rate, pendency percentagePendency percentage , detection and conviction rateConviction rate of cyber economic crimeCyber economic crime with case studies. Challenges and problems faced on various fronts by all stakeholders are also analysed.
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Fair Transfer of Multiple Style Attributes in Text

To preserve anonymity and obfuscate their identity on online platforms users may morph their text and portray themselves as a different gender or demographic. Similarly, a chatbot may need to customize its communication style to improve engagement with its audience. This manner of changing the style of written text has gained significant attention in recent years. Yet these past research works largely cater to the transfer of single style attributes. The disadvantage of focusing on a single style alone is that this often results in target text where other existing style attributes behave unpredictably or are unfairly dominated by the new style. To counteract this behavior, it would be nice to have a style transfer mechanism that can transfer or control multiple styles simultaneously and fairly. Through such an approach, one could obtain obfuscated or written text incorporated with a desired degree of multiple soft styles such as female-quality, politeness, or formalness. To the best of our knowledge this work is the first that shows and attempt to solve the issues related to multiple style transfer. We also demonstrate that the transfer of multiple styles cannot be achieved by sequentially performing multiple single-style transfers. This is because each single style-transfer step often reverses or dominates over the style incorporated by a previous transfer step. We then propose a neural network architecture for fairly transferring multiple style attributes in a given text. We test our architecture on the Yelp dataset to demonstrate our superior performance as compared to existing one-style transfer steps performed in a sequence.
Towards a Student Security Compliance Model (SSCM)

Users are considered the weakest link in ensuring information security (InfoSec). As a result, users' security behaviour remains crucial in many organizations. In response, InfoSec research has produced many behavioural theories targeted at explaining information security policy (ISP) compliance. Meanwhile, these theories mostly draw samples from employees often in developing countries. Such theories are not applicable to students in educational institutions since their psychological orientation with regards to InfoSec is different when compared with employees. Based on this premise, the chapter presents arguments founded on synthesis from existing literature. It proposes a students' security compliance model (SSCM) that attempts to explain predictive factors of students' ISP compliance intentions. The study encourages further research to confirm the proposed relationships using qualitative and quantitative techniques.
A Taxonomy of Methods for Handling Data Streams in Presence of Concepts Drifts

Concept drift is the scenario in online learning in which value of target variable changes with respect to time. The learning algorithms should be adaptive in nature in order to cater the changes imposed due to change in concept. This paper discusses about the adaptive algorithms that are used in learning from the evolving data with different changing patterns. The various applications owing concept drift that are major sources of digital data stream and other real-world problems addresses concept drift are also discussed in this paper.
Text Language Identification Using Letters (Frequency, Self-information, and Entropy) Analysis for English, French, and German Languages

People illustrate the world, convey stories, share ideas, and interconnect in over 6900 languages. Information on the Internet may appear unlimited. All over history, electrical and computer experts have built tools such as telephone, telegraph and internet router, which have helped people communicate. Computer software that can translate between languages stands for one of such tools. The first step of translating a text is to categorize its language. In this research, self-identification program of text language was designed and tested depending on text letters (frequency, self-information, and entropy of certain chosen letters) for the English, French and German languages. The research, trying to detect the original language, is successful of detecting these languages, after applied to randomly selected text files. The detection program was written using C++ programming language.
Pipelined Implementation of Millar-Rabin Primality Tester Using Altera FPGA Kit

Due to the demand for large prime numbers to be used by many public key cryptographic systems such as RSA and SSC (Schmidt-Samoa cryptosystem), this led for the development of fast and reliable methods for primality testing to determine whether a given integer is prime or composite. Many algorithms were proposed by to address the efficient method of testing the primality of the integer number. In this paper, we propose a pipelined reconfigurable FPGA implementation for the primality testing coprocessor using Millar-Rabin method by employing the maximum possible parallelism of the internal operations. The proposed design targeted the $$ {\text{ALTERA Cyclone }}\,{\text{IV FPGA}} $$ ( $$ {\text{EP}}4{\text{CGX}}22{\text{CF}}19{\text{C}}7) $$ along with $$ {\text{Quartus II}} $$ simulation package. The proposed design was evaluated in terms of the maximum operational frequency, the total path delay, the total design area and the total thermal power dissipation. The synthesized results revealed that the proposed parallel architecture implementation has recorded: critical path delay of $$ 22.65 \,{\text{ns}} $$ , maximum operational frequency of $$ 51.11\,{\text{MHz}} $$ , hardware design area (number of logic elements) of $$ 6184\,{\text{LEs}} $$ , and total thermal power dissipation estimated as 151.30 mW. Consequently, the proposed PT architecture can be efficiently employed by many public key cryptographic mechanisms.
Introduction of Reversible Data Hiding Schemes

The hiding techniques proposed and published recently that have particularly focused on using Reversible Data Hiding (RDH) methods because they satisfied many requirements simultaneously that they are safe and reversible. RDH method gained more attention in the past few years. Because of its increasing applications in the areas such as military, law enforcement, and health care. This study lists some recently RDH methods such as Difference Expansion scheme (DE), the Histogram Shifting scheme (HS), the Pixel-Value Ordering scheme (PVO), the Dual-image based scheme, and the Interpolation-based scheme. The readers who were interested in this field can comprehend data hiding methods especially RDH technique.
ArchNet: A data hiding design for distributed machine learning systems

Integrating idle embedded devices into cloud computing is a promising approach to support distributed machine learning. In this paper, we approach to address the data hiding problem in such distributed machine learning systems. For the purpose of the data encryption in the distributed machine learning systems, we propose the Tripartite Asymmetric Encryption theorem and give mathematical proof. Based on the theorem, we design a general image encryption scheme ArchNet.The scheme has been implemented on MNIST, Fashion-MNIST and Cifar-10 datasets to simulate real situation. We use different base models on the encrypted datasets and compare the results with the RC4 algorithm and differential privacy policy. Experiment results evaluated the efficiency of the proposed design. Specifically, our design can improve the accuracy on MNIST up to 97.26% compared with RC4.The accuracies on the datasets encrypted by ArchNet are 97.26%, 84.15% and 79.80%, and they are 97.31%, 82.31% and 80.22% on the original datasets, which shows that the encrypted accuracy of ArchNet has the same performance as the base model. It also shows that ArchNet can be deployed on the distributed system with embedded devices.
Brush-Style Transfer through Constrained Convolutional Sparse Coding

In this paper, a new technique to transfer the sketching style from one free-hand sketch to another is presented. Those sketches can be from different artists who used different brush styles while sketching. Given a unique brush-style from a sketch, our method transfers that style into another input sketch. Our brush-style transfer starts with an automatically constructed brush dictionary which proposes which sparse set of brushes are used at each part of the input sketch. After that, a oneto- one mapping is learned, between the unique brush sketch elements and the input sketch elements, by sparsely encoding input sketch with the brush dictionary. The quality of our brushstyle transfer is evaluated qualitatively by applying our technique on transferring multiple brush styles into input sketches. This is an addition to a quantitative evaluation through extensive subject case study were participants with 86% accuracy match a transferred sketch to the sketch they believe was used in the transfer.
The Necessary Link Between Security Culture and Data Protection in the European Union

Appropriate behavior is indeed a major factor in Security culture, along with the mechanisms available to supervise and sanction, when needed be, wrongful actions. The concept of Security culture was interlinked with Information Security, thus spawning a new concept of Information Security culture, that later evolved to Cybersecurity Culture, which is a concept that has been developed in CSS’s all over Europe and the world. Cybersecurity culture, as opposed to Security culture, is a term that can be applied not only to companies, organizations or institutions, but also to the general public of a given nation or even a region. CSS’s have been the instrument to explain and develop this term.
Multi-Target Tracking Using Electronic Warfare Receiver in Jamming Operating Environment

A tracking algorithm considering the characteristics of threat target tracking in a jamming operating environment is required to interfere and deceive the onboard radars of multiple threat targets(e.g., aircraft and missiles) during the jamming of army communications.This paper proposes a new algorithm for tracking multiple threat targets in a jamming operating environment.The proposed algorithm consists of two stages: 1) tracking of azimuth direction and elevation using an interactive multiple model and a joint probabilistic data association filter; and 2) adaptive adjustment of the noise covariance to solve the untraceable scenario problem for the threat targets.The performance of the proposed tracking algorithm is validated by the results obtained by modeling the simulator considering the threat target characteristics in a jamming operating environment.
The superhuman being manipulated: the digital and the real personality on social networks

The main objective of the article is to describe the basics of using data on the activity/passivity of social network users and gadget owners, as well as related challenges. Using the method of content analysis, we consider the profile in the social network as the profile of the identity of the owner. Through the prism of presence/absence of information, we can assume who is this or that user. Based on various criteria, we can determine to which category a particular user can be attributed. We consider the profile in the network as a place of confession for the user, and the gadget considered as “prosthesis” that everyone needs to achieve their goals. Therefore, the person and the gadget are considered holistic. We provide information on the development of the OCEAN method, which allows us to «measure» the human psyche based on auto-expression on a social network. We present examples of using users data by Cambridge Analytics for political purposes, such as the 2016 presidential election in the USA, Brexit, and examples showing global data collection, high data representativeness, automating data collection processes, processing information from social network users and their profiling. To demonstrate the level of representativeness of the data, we compared the number of Facebook users in the USA with a population of this country. For comparison purpose, we also analyzed the statistics of users of the Android system. Based on the methods of induction and deduction, as a result, we presented the problems associated with the use of information of gadgets owners (identity theft, using data for manipulation (political and consumer) purpose, threats to democratic elections and the loss of subjectivity in the decision-making process). We also noted the trend (disappointment of people who understand the situation in the political system as a whole), which will develop as a reaction to these processes. As a conclusion, we propose solutions that can reduce negative processes (improving legislation at the international and national level, promoting awareness). The significance of this study for science and society is to clarify the problems associated with big data, which often remain outside the scope of discussion.
Study and Analysis of Apriori and K-Means Algorithms for Web Mining

Emerging technologies such as cloud computing and data mining deal with issues such as scalability, security and efficiency. Web mining is broadly classified under data mining, refers to the resultant data combination obtained by assembling data available in the Web and information mining techniques. In general, mining can be defined as the process of extracting significant things from huge datasets. The applications of Web mining include the evaluation of the successful completion of a particular task, assessing the applicability of specific Web sites and comprehending client conduct. The proposed work is concerned with distributed networks and is aimed at improving the efficiency of data mining and cloud computing techniques. The main goal is to find a solution for generating various itemsets in each site. A well-known algorithm in data mining is the Apriori algorithm which discards infrequent items at the cost of useful data. A major limitation of this algorithm is its slowness, owing to increased transactions. The K-means segmentation algorithm is employed for increasing the efficiency by clustering the initial itemset.
Data Mining in Banking Sector Using Weighted Decision Jungle Method

Classification, as one of the most popular data mining techniques, has been used in the banking sector for different purposes, for example, for bank customer churn prediction, credit approval, fraud detection, bank failure estimation, and bank telemarketing prediction. However, traditional classification algorithms do not take into account the class distribution, which results into undesirable performance on imbalanced banking data. To solve this problem, this paper proposes an approach which improves the decision jungle (DJ) method with a class-based weighting mechanism. The experiments conducted on 17 real-world bank datasets show that the proposed approach outperforms the decision jungle method when handling imbalanced banking data.
PLEADER: A Fast and Area Efficient Hardware Implementation of Leader Algorithm

Leader algorithm is a clustering algorithm generally used as a pre-clustering step in various accelerated techniques for clustering large datasets. The algorithm while clustering large datasets can be computationally expensive because of its high convergence time. In this paper, a fast and area-efficient hardware implementation of leader algorithm is proposed which utilizes an efficient parallelism technique to speed up the algorithm without much resource consumption. It uses a master–slave architecture model where the slave processes after comparing a data with the existing clusters send the result to the master process which then updates the clusters. The algorithm is capable of automatically choosing the required number of slave processes and instead of increasing its number reutilizes them repeatedly making the process an area-efficient technique. Experiments are conducted with various datasets, and the result confirms that the proposed algorithm outperforms the original method in terms of speed.
Beyond Data Protection to Command and Control (C2) Sustainability in a Post-COVID19 World: Execution of U.S. Data Protection Act for U.S. Data Protection Agency

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Agent Technology for Data Analytics of Gene Expression Data: A Literature Review

Analytics of gene expression data is the prolonged research area of present situation. Analysis of gene expression data requires enormous amount of work and huge set of algorithms. Using agent computing we deal with complex systems which are discovered many opportunities for developing data mining systems in a different ways. Hence to create predictive models, there is a huge need for intelligent and autonomous software agents which can procure useful information from the large datasets of raw information. Predictive analytics models can be created from these datasets which can be further used for various applications in security, future prediction etc. This research paper gives an overall function of multi agent systems in analytics of gene expression data, in terms of characteristics, adaptability, reliability and robotics of agents. Analytics on gene expression data is one of the emerging research fields. A large set of methodology and algorithms are existing in the field but in the application of agent technology in the field of gene expression data is at the infant stage. So the aim of this review paper is to integrate agent technology in the gene expression data analytics.
Immersive Technology

This chapter presents a conceptual discussion on the roles of immersive technology relating to its past, present, and future. The underlying theories and assumptions pertinent to each stage of immersive technology are discussed by emphasizing the influences on pedagogical practices and assessment. An important focus of the chapter is to look at the function of present immersive technology in learning from the perspective of a technology taxonomy. Discussions on future immersive technologies are made by making a connection between immersive technology and other new technologies like artificial intelligence. The chapter concludes with suggestions for future research in immersive technology.
The Internet of Things

Abstract Apple iPhones do not use the standardized micro-Universal Serial Bus (USB) port but specialized ports for their proprietary Lightning connectors. However, in October 2018, Apple’s vice-president of hardware engineering John Ternus announced the switch to USB-C for the new generation of iPads from 2019. Apple iPhone is expected to follow suit. This marks the end to a long battle over port standardization for personal computers within the Institute of Electrical and Electronics Engineers (IEEE). The change came about due to a push from the European Commission for industry to agree on a universally proprietary free standard. This chapter documents how states have been increasingly involved in steering intellectual property rights (IPR) policies within standards-developing organization (SDO) fora. Civil society too has had a role to play from the stance of open source solutions to interoperability, albeit with less and varying degrees of success across the fora. The chapter focuses on how state pressure has counterbalanced historic corporate pressure for preserving patent protection.