Reviewerly/exHarmony huggingface dataset:
Building a Maturity Framework for Information Security Governance Through an Empirical Study in Organizations

There is a dearth of academic research literature on the practices and commitments of information security governance in organizations. Despite the existence of referential and standards of the security governance, the research literature remains limited regarding the practices of organizations and, on the other hand, the lack of a strategy and practical model to follow in adopting an effective information security governance. This chapter aims to explore the engagement processes and the practices of organizations involved in a strategy of information security governance via a statistical and econometric analysis of data from a survey of 1000 participants (with a participation rate of 83.67%) from large and medium companies belonging to various industries. Based on the results of the survey regarding practices of information security management and governance, a practical maturity framework for the information security governance and management in organizations is presented.
Steganography Using Biometrics

In this smart age, smart gadgets with internet connectivity have become a necessity. While enjoying these facilities, one must count the security of their private or confidential information. With due time, a lot of cryptographic methods have been developed for enforcing security. On the other hand, with the advancement of technologies, the intruders and hackers have also developed their skills and tools. Therefore, many times we fail to protect our information. To get rid of this situation, the developers have to focus on some other method besides cryptography. Steganography can be considered as the solution to overcome this problem, as it is the technique which conceals the existence of any secret information in a usual media file. Moreover, inclusion of biometric with steganography enhances the security level, as biometric systems are dominating the field of authentication. Here, various techniques of steganography, biometrics, and steganography using biometrics will be discussed. Finally, the present scenario of steganography using biometrics will be demonstrated.
Development of an interaction-activated communication model with eye-gaze in voice communication

In human communication, nonverbal behaviors such as facial expression, body movement, eye-gaze, and pupil response play an important role in realizing activated interaction and communication. In our previous study, an activated interaction through avatars was analyzed, and an estimation model for the activated interaction was developed by introducing the concept of heat-transfer engineering. This model estimates the amount of heat in the communication field as the degree of the activated interaction based on an assumption that each talker's heat is transmitted to the communication field. However, it is hard to estimate interactions including the conveyance of eye-gaze because the model has no parameter of the eye-gaze. It is important to share the conveyance of mutual eye-gaze for producing the activated interaction in voice communication. Therefore, it is expected to develop a model that estimates the conveyance of eye-gaze. In this paper, focusing on eye-gaze which plays an important role in producing the activated interaction, we developed a model that estimates the degree of conveyance by the talker’s eye-gaze in voice communication. This model estimates the degree of the interaction-activated communication as heat transfer by combining the speech input and the eye-gaze input.
Representation

RepresentationRepresentation is important in machine-based pattern recognitionPattern recognition, AIArtificial Intelligence (AI), and machine learning. We need to represent states and state transitions appropriately in AI-based problem-solving. Similarly, in clusteringClustering and classificationClassification, we need to represent the data points, clusters, and classes.
An Image Forgery Detection Approach Based on Camera's Intrinsic Noise Properties

Digital images are found everywhere from cell phones to the pages of online news sites. With the rapid growth of the Internet and the popularity of digital image capturing devices, images have become major source of information. Now-a-days fudge of images has become easy due to powerful advanced photo-editing software and high-resolution cameras. In this article, the authors present a method for detecting forgery, which is detected by estimating camera's intrinsic noise properties. Differences in noise parameters of the image are used as evidence of Image tampering. The method works in two steps. In the first step, the given image is classified as forge or non-forge. In the second step, the forged region in the image is detected. Results show that the proposed method outperforms the previous methods and shows a detection accuracy of 85.76%.
Innovative Formalism for Biological Data Analysis

Modern medical devices involve information technology (IT) based on electronic structures for data and signals sensing and gathering, data and signals transmission, as well as data and signals processing in order to assist and help the medical staff to diagnose, cure, and to monitor the evolution of patients. By focusing on biological signals processing we may notice that numerical processing of information delivered by sensors has a significant importance for a fair and optimum design and manufacture of modern medical devices. The authors consider for this approach fuzzy set as a formalism of analysis of biological signals processing and they propose to accomplish this goal by developing fuzzy operators for filtering the noise of biological signals measurement. The authors exemplify this approach on neurological measurements performed with an electro-encephalograph (EEG).
SD Case Study: More Security Requirements

Security design principles introduced in this chapter are applied to the Address Book Application (ABA) and Personal Finances case studies. Various design models are used to express the security design in each case study. Sample code is used to describe some implementation details.
A Modified Spearman’s Rank Correlation Coefficient for an Efficient Method of Similarity Calculation in Collaborative Filtering-Based Recommendation

Collaborative Filtering is among the very popular filtering approaches used widely in e-commerce applications for the recommendation. This technique recommends items to the target user based on similarity of users. Similarity metrics play a vital role in the accuracy of collaborative filtering. An inaccurate top-n similar neighbors of the target user creates low prediction accuracy in collaborative filtering-based recommendation systems. Hence, in this paper, we have proposed a modified Spearman's Rank Correlation Coefficient which calculates the similarity between users and find the similar neighbors of the target user. In the comparative result analysis of similarity metrics, we deliberately insert the sparsity with varying magnitudes (10 and 20%) by omitting given ratings in pre-existing dataset. The omitted ratings are then predicted by using different similarity metrics. The comparison of the proposed algorithm and the traditional similarity metrics is tested on the MovieLens dataset using performance metric MAE.
Construction of College English Ecological Teaching Model based on Wisdom Classroom in the Era of Artificial Intelligence

In the era of artificial intelligence, college English teaching is reforming to implement from the mobile Internet ecological perspective. The rapid development of information technology has brought new opportunities and challenges to college English teaching. This paper, based on the Eco-linguistics Theory, explores the concept of “wisdom education” and the college English ecological teaching model. Relying on the data mining technology, we can realize the individual analysis and modeling portraits of language learners, and implement the intelligent classroom of individualization of autonomous learning, cooperation of learning activities, virtualization of learning environment and automation of educational management. Finally, the situation of harmonious development of various niches in language learning is formed.
Virtually Alone

For most learners, appropriate learning climates within spaces selected for self-study (e.g., libraries) are helpful in sustaining self-regulated learning efforts. In order to provide learners with appropriate self-study spaces, we examined an immersive virtual environment (IVE) for English phonemes acquisition practice. Social settings of a learning environment sometimes yield positive effects, but sometimes not. To assess the influences of social interactivity against learner’s effort towards his/her persistent skill development, we have conducted exploratory studies. In this paper, we examined self-regulated learning in IVEs that provided little social interactivity. We compared two “alone” environments: a literally solo space and a shared space devoid of communication capability. Participants in the shared space with a silent other recorded quite better scores compared to those in the solo space at the beginning of the experimental tasks.
Alpha Divergence based Siamese Network for Object Tracking

Object Tracking is a foundation in the field of vision, with powerful application capabilities. Discriminant-related tracking uses the technology of diagonalization of the circulant matrix to further improve the speed and accuracy of tracking. in the deep convolutional neural network based tracking, there is no clear explanation for the uncertainty of the target labeling. Relying heavily on manual labeling and the selection of loss function. We proposed alpha divergence-based Siamese network Tracking (alphaTK) to solve this problem. We minimized the alpha divergence between the conditional probability density output by the network and the conditional probability of annotations of the samples, void to choose the loss function. We modeled the noise generated by the label from the probabilistic perspective, and interpreting the uncertainty of the annotations. The Tracker is trained on a large number of data sets, and achieved excellent results on the OTB and UAV benchmark data sets.
Clustering Based on Two Layers for Abnormal Event Detection in Video Surveillance

Abnormal event detection has attracted great research attention in video surveillance. In this paper, the authors presented a robust method of trajectories clustering for abnormal event detection. This method is based on two layers and benefits from two well-known clustering algorithms: the agglomerative hierarchical clustering and the k-means clustering. Facing to the challenges related to the trajectories, e.g., different sizes, the authors introduce a preprocessing step to unify their sizes and reduce their dimensionality. The experimental results show the performance and accuracy of their proposed method.
Face Processing

Categorization and recognition of faces at first sight is extremely important for social species such as humans. This automated process is so efficient that people generally do not consider the intricacy of this task or the complicated neural bases that underlie this ability, which is so crucial for day-to-day social interaction. Research dating back to the 1960s has shown that newborn infants demonstrate a visual preference for human faces over other complex stimuli and more recent work has demonstrated that the face-processing system develops rapidly during the first year of life. The system continues to mature and develop into adulthood with changes occurring throughout infancy, childhood, and adolescence. Behavioral, neurophysiological, and clinical studies conducted over the past 50 years have contributed to our understanding of the development of our face-processing system.
Cryptocurrency Returns

One of the most significant innovations in the world of finance has been the creation and evolvement of cryptocurrencies. These digital means of exchange have been the focus of extensive news coverage, especially the Bitcoin, with a primary focus on the tremendous potential return and the high level of accompanying risk. In this chapter, we examine the risk-return pattern for an array of cryptocurrencies, contrasting the pattern with those of conventional currency and equity investments. We find the measures of cryptocurrency returns and risk to be a very high multiple of those of conventional investments, and the pattern is determined to be robust relative to the time frame. Consequently, cryptocurrencies are determined to provide an alternative to investors that involves tremendously high risk and return.
Introduction to MATLAB

This chapter contains a tutorial-style introduction to MATLAB, covering the setup on the computer (Sect. 2.2), the MATLAB syntax (Sects. 2.3 and 2.4), and visualization (Sect. 2.5). Advanced sections are also included on generating M-files to recreate graphics (Sect. 2.6), and on publishing and sharing MATLAB code (Sect. 2.7). The acquired knowledge will then be used in the exercises at the end of this chapter (Sect. 2.8).
Variogram Models

Here we look at model fitting. The structural functions mainly used for model fitting are introduced. The main tool for model fitting is the linear model of coregionalisation, but the application of the MAF transformation to build a linear model of coregionalisation is also demonstrated.
Defining degree of semantic similarity using description logic tools

Establishing the semantic similarity of information is an integral part of the process of solving any information retrieval tasks, including tasks related to big data processing, discovery of semantic web services, categorization and classification of information, etc. The special functions to determine quantitative indicators of degree of se­mantic similarity of the information allow ranking the found information on its semantic proximity to the pur­po­se or search request/template. Forming such measures should take into account many aspects from the mea­nings of the matched concepts to the specifics of the business-task in which it is done. Usually, to construct such si­milarity functions, semantic ap­proaches are combined with structural ones, which provide syntactic comparison of concepts descriptions. This allows to do descriptions of the concepts more detail, and the impact of syntactic matching can be significantly reduced by using more expressive descriptive logics to represent information and by moving the focus to semantic properties. Today, DL-ontologies are the most developed tools for representing semantics, and the mechanisms of reasoning of descriptive logics (DL) provide the possibility of logical inference. Most of the estimates presented in this paper are based on basic DLs that support only the intersection constructor, but the described approaches can be applied to any DL that provides basic reasoning services. This article contains the analysis of existing approaches, models and measures based on descriptive logics. Classification of the estimation methods both on the levels of defining similarity and the matching types is proposed. The main attention is paid to establishing the similarity between concepts (conceptual level models). The task of establishing the value of similarity between instances and between concept and instance consists of finding the most specific concept for the instance / instances and evaluating the similarity between the concepts. The term of existential similarity is introduced. In this paper the examples of applying certain types of measures to evaluate the degree of semantic similarity of notions and/or knowledge based on the geometry ontology is demonstrated.
Improving Logging Prediction on Imbalanced Datasets

Logging is an important yet tough decision for OSS developers. Machine-learning models are useful in improving several steps of OSS development, including logging. Several recent studies propose machine-learning models to predict logged code construct. The prediction performances of these models are limited due to the class-imbalance problem since the number of logged code constructs is small as compared to non-logged code constructs. No previous study analyzes the class-imbalance problem for logged code construct prediction. The authors first analyze the performances of J48, RF, and SVM classifiers for catch-blocks and if-blocks logged code constructs prediction on imbalanced datasets. Second, the authors propose LogIm, an ensemble and threshold-based machine-learning model. Third, the authors evaluate the performance of LogIm on three open-source projects. On average, LogIm model improves the performance of baseline classifiers, J48, RF, and SVM, by 7.38%, 9.24%, and 4.6% for catch-blocks, and 12.11%, 14.95%, and 19.13% for if-blocks logging prediction.
Computer Simulation for Quantum Tomography

A numerical method for modeling the reconstruction of quantum states of photons using homodyne tomography is presented. The difference between discretized and quantized sine functions is considered as a model of reconstruction of the quantum state of the photon of the harmonic of light using homodyne tomography. The obtained graphical images numerically confirmed the similarity with the known experimentally obtained squeezed quantum amplitudes, phase and vacuum states, as well as vacuum and coherent (shifted) quantum states of light. The parameters of numerical modeling of quantum states are determined and evaluated. Their graphic images are presented. Further use of the obtained results in quantum tomography, computer modeling of quantum reconstruction of a local photon and tomography of the quantum state of a photon using homodyne tomography is discussed. A new direction of development of space-time computer modeling of the quantum state with the help of homodyne network structures of information technologies and artificial intelligence in the spheres of human life is established.
Making Sense of the Original Writings Rule in the Digital Age

Few provisions contained within the Rules of Evidence have generated as much confusion among judges and practitioners as the original writings rule (Rule), miscast as the “best evidence rule.” The Rule is often misunderstood both as to its purpose and its proper application. Further, obfuscation caused by the Rule seems to quadruple when layering in problems created by advances in technology and the digital age. This commentary briefly reviews the origin and scope of the Rule and provides a general framework for approaching issues arising from it. The piece then addresses how the Rule might apply to technological advances in the digital age. For example, how does the Rule apply to computer-generated databases? More specifically, what limits a prosecutor’s use of information contained in a computer database to prove a defendant’s prior convictions? This very question arose in a recent case before the North Carolina Supreme Court. In that case, a fractured court employed differing – and, indeed, contradictory - analyses in seeking to resolve the issue. While the court did not put to rest tensions underlying the Rule, it did indicate that an old proposition continues to apply within this domain: “perfect is the enemy of the good,” as in, expect ambiguity when advancing technology is at the core of a problem.
SOHO Users' Perceptions of Reliability and Continuity of Cloud-Based Services

The adaptation of cloud computing services is continually growing because of its popularity, its ubiquitous, ease-of-use, and inexpensive nature. Small office/home office (SOHO) businesses are joining large organizations and purchasing cloud services to help with the continuity of their business services. However, cloud computing and its effect in business continuity and information security by SOHO users is not well understood. A qualitative case study was conducted to examine the perspectives of SOHO users of cloud services during Hurricane Sandy in the states of New York and New Jersey. SOHO cloud users were questioned about their understanding of cloud services for business continuity, the services provided by cloud vendors, and their perceptions about cloud data management and security services. The results of this study demonstrated that SOHO users gravitate to the cloud because it of its ubiquitous nature, however, they lack understanding of the business continuity and disaster recovery features and their impact in data security and, their business endurance.
Integration of Videoconferencing in ESP and EAP Courses

This chapter is devoted to the use of videoconferencing technology and other social media tools for creating a community of practice communication within which authentic tasks and activities in multicultural ESP and EAP classes can be carried out. It describes theoretical background for effective use of these media based on the results of INVITE project and other research in the area, it mentions the importance of international institutional cooperation and the chapter shares examples of good practice pointing at their advantages. Samples of end-of-course feedback reflecting students´ worries at the beginning of the course, their personal and professional achievements at the end of the course are included as well.
Social Media Crime Investigation and Forensic Analysis

Social Network Service (SNS) has changed our life tremendously. The world is more connected as it was never before. Though it has “bright side” it has “dark side” also. Social media is one of the most powerful platforms which has a huge impact to influence the thoughts of individuals, groups and communities. Misuse of such powerful platform can be devastating for the society. We are witnessing the weaponization of social media platform. Abusive use of social media to spread fake news, paid campaign and propaganda is major threat of concern for the society. How the social media platform is used for influencing the voters in national election is no more a secret? The world has witnessed the strategic use of social media platforms by the terrorist organizations to radicalize the youth to join their group. The rapid proliferation of social media network has gone to the level where it is no more in the control of individual or organization. It won’t be wrong to say that SNS is powerful enough to remotely control society. The objective of this chapter is to focus on how social media platform is misused and how to investigate social media crime. The chapter discusses in detail about the support provided by social media service providers to the law enforcement agencies and the legal constraints. [218]
Management Approach of Risk Analysis in Information Security

This article describes how the ease of access to information and communication technologies is practically a prerequisite for the functioning of modern society. Taking the competitive market into consideration, the protection of the information infrastructure for a company, could mean that company has a competitive advantage. This article is relevant to better understand how the actors involved in information and communication technologies could develop new models of information systems and risk management strategies. The results of this research show that each manager must handle threats, because otherwise the organization's objectives cannot be met. Given that uncertainty is a fact of life, then the uncertainty response should become a permanent managerial concern.