Reviewerly/exHarmony huggingface dataset:
Using Extended Reality to Support Cyber Security

Cyber security is a constantly evolving area of interest. Many solutions are currently available and new methods and technologies are emerging. Although some solutions already exist in extended reality, a lack of engagement and storytelling is available, with a consequence of decreasing the probability of dissemination and awareness of the risks involved in cybersecurity. This chapter gives an overview of an extended reality platform that can be potentially used for the simulation of security threats and that combines artificial intelligence and game design principles. The main goal of this research is to develop an extended reality solution to simulate a story involving virtual characters and objects for the entertainment industry, with possible applications in other sectors such as education and training. After an introduction to extended reality, the chapter focuses on an overview on the available extended reality technologies in the context of cybersecurity.
On the behaviour of permutation‐based variable importance measures in random forest clustering

Abstract Unsupervised random forest (RF) is a popular clustering method that can be implemented by artificially creating a two‐class problem. Variable importance measures (VIMs) can be used to determine which variables are relevant for defining the RF dissimilarity, but they have not received as much attention as the supervised case. Here, I show that sampling schemes used in generating the artificial data—including the original one—can influence the behaviour of the permutation importance in a way that can affect conclusions on variable relevance and also propose a solution. Generating the artificial data using a Bayesian bootstrap keeps the desirable properties of the permutation VIM.
Data Security for Cloud Datasets With Bloom Filters on Ciphertext Policy Attribute Based Encryption

Cloud data storage environments allow the data providers to store and share large amounts of datasets generated from various resources. However, outsourcing private data to a cloud server is insecure without an efficient access control strategy. Thus, it is important to protect the data and privacy of user with a fine-grained access control policy. In this article, a Bloom Filter-based Ciphertext-Policy Attribute-Based Encryption (BF-CP-ABE) technique is presented to provide data security to cloud datasets with a Linear Secret Sharing Structure (LSSS) access policy. This fine-grained access control scheme hides the whole attribute set in the ciphertext, whereas in previous CP-ABE methods, the attributes are partially hidden in the ciphertext which in turn leaks private information about the user. Since the attribute set of the BF-CP-ABE technique is hidden, bloom filters are used to identify the authorized users during data decryption. The BF-CP-ABE technique is designed to be selective secure under an Indistinguishable-Chosen Plaintext attack and the simulation results show that the communication overhead is significantly reduced with the adopted LSSS access policy.
Research on Methodology of Correlation Analysis of Sci-Tech Literature Based on Deep Learning Technology in the Big Data

In the big data era, it is a great challenge to identify high-level abstract features out of a flood of sci-tech literature to achieve in-depth analysis of data. The deep learning technology has developed rapidly and achieved applications in many fields, but has rarely been utilized in the research of sci-tech literature data. This article introduced the presentation method of vector space of terminologies in sci-tech literature based on the deep learning model. It explored and adopted a deep AE model to reduce the dimensionality of input word vector feature. Also put forward is the methodology of correlation analysis of sci-tech literature based on deep learning technology. The experimental results showed that the processing of sci-tech literature data could be simplified into the computation of vectors in the multi-dimensional vector space, and the similarity in vector space could be used to represent similarity in text semantics. The correlation analysis of subject contents between sci-tech literatures of the same or different types can be made using this method.
A Study of Event Detection Methods in Eye-Tracking Based on Its Properties

The eye is one of means of human and computer interaction. Naturally, someone will direct their gaze to interesting objects. Eye-tracking is a technology aimed at understanding the direction of human gaze. Event-detection is a process of detecting and classifying eye movements that are divided into several types. Various studies in the field of event detection play an essential role in improving the quality of eye-tracking technology. Discussion of research properties is important to see trends in event detection research and to introduce this research to new researchers. This paper reviews some properties used in eye-tracking research, especially in the field of event detection, including eye data, sampling frequency, types of video-based eye-trackers, types of stimulus, types of eye movements, and signal denoising. This paper also discusses the development of research over the past few years and the challenges faced in event detection research. This review paper can be a guide for new researchers who wish to continue research in the field of eye-tracking, especially event detection.
Integrated Development Environment on Cloud

In this era of Industry 4.0 with advancement in technology everything is migrating to the cloud, including software development tools and environments, making development work seamless and flexible. With this inspiration in mind, we are working on a project that will leverage the power of the cloud and make the entire software development process accessible via a standard web browser. No more hardware limitations delaying the work. Everything from setting up a development environment, to programming and testing will be just a few clicks away. Users will be given a web interface through which they can launch the IDE and their own workspace and begin coding. They will be provided computational resources suitable for their needs. All they need for this new way of development is an active internet connection and a standard web browser. In the backend, each user's workspace will run on an individual Docker container; the container will have a basic system configuration set that can be changed as needed. The entire workspace data and configuration will be stored on the cloud and accessible at any time and from any location. Users can also review these workspaces by sharing them with other collaborators. This project will also eliminate the need for expensive and resourceful traditional programming devices such as desktops or laptops because users can do the same with the help of our platform Cloud IDE, which requires only an active internet connection and a standard web browser, and is available on alternative devices such as smartphones, tablets, etc. The platform will be completely secure with security layers, prohibition of unauthorised access and data corruption. Hence the users can use this development environment (workspace) anywhere (giving them freedom of location and time) at anytime with just a web browser and active Internet connection.
Securing the Distributed Kalman Filter Against Curious Agents

Distributed filtering techniques have emerged as the dominant and most prolific class of filters used in modern monitoring and surveillance applications, such as smart grids. As these techniques rely on information sharing among agents, user privacy and information security have become a focus of concern. In this manuscript, a privacy-preserving distributed Kalman filter (PP-DKF) is derived that maintains privacy by decomposing the information into public and private substates, where only a perturbed version of the public substate is shared among neighbors. The derived PP-DKF provides privacy by restricting the amount of information exchanged with state decomposition and conceals private information by injecting a carefully designed perturbation sequence. A thorough analysis is performed to characterize the privacy-accuracy trade-offs involved in the distributed filter, with privacy defined as the mean squared estimation error of the private information at the honest-but-curious agent. The resulting PP-DKF improves the overall filtering performance and privacy of all agents compared to distributed Kalman filters employing contemporary privacy-preserving average consensus techniques. Several simulation examples corroborate the theoretical results.
Systems

There are numerous scientific disciplines: some are purely scientific, such as mathematics, physics, and biology, while others are applied, such as computer science and engineering [1]. In considering any discipline nevertheless, the notion of system is an important one [2]; in physics, they study physical systems; in biology, they study biosystems; in sociology, they study social systems; and so on. Hence, the development of the General Systems Theory has been inspired [3, 4], referred to as systemics. Systemics focuses on the characteristics of systems across the barriers between scientific disciplines. Such a perspective is considered important with regard to EIS since in approaching EIS, one would have to deal with social systems (because there are human entities, human behavior, and so on, in any enterprise) and also with technical systems (because there are technical devices, software applications, and so on, in any information system). Hence, both social systems and technical systems would not only need to be studies in isolation but it is also necessary to understand their interrelationship.
Best Features Selection for Biomedical Data Classification Using Seven Spot Ladybird Optimization Algorithm

This article presents a new adaptive algorithm called FS-SLOA (Feature Selection-Seven Spot Ladybird Optimization Algorithm) which is a meta-heuristic feature selection method based on the foraging behavior of a seven spot ladybird. The new efficient technique has been applied to find the best subset features, which achieves the highest accuracy in classification using three classifiers: the Naive Bayes (NB), the Nearest Neighbors (KNN) and the Support Vector Machine (SVM). The authors' proposed approach has been experimented on four well-known benchmark datasets (Wisconsin Breast cancer, Pima Diabetes, Mammographic Mass, and Dermatology datasets) taken from the UCI machine learning repository. Experimental results prove that the classification accuracy of FS-SLOA is the best performing for different datasets.
Problems in Cryptography and Cryptanalysis

The integer factorization problem used in the RSA cryptosystem, the discrete logarithm problem used in Diffie-Hellman Key Exchange protocol and the Elliptic Curve Discrete Logarithm problem used in Elliptic Curve Cryptography are traditionally considered the difficult problems and used extensively in the design of cryptographic algorithms. We provide a number of other computationally difficult problems in the areas of Cryptography and Cryptanalysis. A class of problems called the Search problems, Group membership problems, and the Discrete Optimization problems are examples of such problems. A number of computationally difficult problems in Cryptanalysis have also been identified including the Cryptanalysis of Block ciphers, Pseudo-Random Number Generators and Hash functions.
Transforms for image and video coding

As we saw in Chapter 3, most natural scenes captured using regular sampling will exhibit high levels of interpixel correlation. This means that the actual entropy of the pixels in a region of an image is likely to be significantly lower than that given by the first order entropy or, in other words, that the spatial representation is redundant. If our aim is to compress the image, it is therefore likely that an alternative representation may be available that is more amenable to reducing redundancy. The aim of image transformation is to create such a representation by decorrelating the pixels in an image or image region. This is achieved by mapping from the image domain to an alternative domain where the pixel energy is redistributed and concentrated into a small number of coefficients. This process is sometimes referred to as energy compaction. As we will see, this redistribution of energy does not result in data compression as the transform itself is a lossless operation. What we can achieve, however, is a concentration of energy into a small number of high-valued coefficients. These larger coefficients are likely to have a higher psychovisual significance than their low-valued counterparts and can be coded accordingly.
Facilitating Collaborative Engineering Analysis Problem Solving in Immersive Virtual Reality

Abstract This work addresses barriers to the adoption of immersive virtual reality within engineering education. A virtual classroom is described that affords users enhanced viewing and interaction with 3D objects, while retaining educational supports for drawing, writing, and collaboration with others in the same virtual space. A didactic example of this approach is provided for use in an engineering statics course. To evaluate this concept, two traditional problems were developed that involved the analysis of 3D objects presented with the problems. A pilot study involving 19 student participants, in pairs or individually, solved these problems, with one requiring physical measurement of problem variables, entirely within the virtual classroom. Analysis of the qualitative and quantitative data collected during the study suggests that this platform is a viable means to introduce 3D graphics and interaction to the engineering classroom. Participants were highly engaged by the approach, praised the introduction of 3D graphics, and were able to use the technology, despite being unfamiliar, within minutes. Though student performance was ostensibly worse that when solving ordinary textbook problems on paper, it was well within expectations due to the increased complexity of the approach, both technologically and pedagogically. If the platform and approach were used more comprehensively throughout such a course, particularly one already offered in a distance-education format, performance on these sorts of problems would likely improve. Technology improvements that are on the near horizon will support this evaluation and together may represent an may represent a significant inflection point in the cost-effectiveness curve of immersive virtual reality for engineering education leading to widespread adoption.
Firefly Algorithm

Firefly Algorithm (FA) is a meta-heuristic algorithm which is categorized as one of the fast-growing swarm intelligence algorithms. Based on the flashing pattern of light and their intelligent behaviour, FA can solve problem in all fields of optimization and is considered very useful for solving complex mathematical and engineering problems. This chapter describes converging nature of the firefly, rules for developing the firefly algorithm and FA models. Illustration of the implementation of the FA for a typical optimization problem considering a numerical equation, with twenty (20) fireflies showed best results fbest = 8.9936e-13 corresponding to nbest = [1.0000 2.0000]. This algorithm is useful for solving routing and clustering problems and generic optimization of process variables.
Cloud Computing Security Issues of Sensitive Data

Numerous organizations are using aspects of the cloud to store data, but as sensitive data is placed on the cloud, privacy and security become difficult to maintain. When users upload data to the cloud, they may become increasingly vulnerable to account hijacking, unauthorized access, and the data may become unavailable because of various technical reasons. Questions remain about the security of sensitive data in the cloud, and in this chapter, the authors perform an analysis of 36 peer reviewed publications describing 30 observations of cloud computing technology (2010-2017). In the articles, applications of cloud computing include, for instance, business (26%) and the internet of things (IoT; 2%), and the result suggests that some issues are unique to a particular domain (such as business, education, health) and some issues cross all domains. The results suggest that data integrity issues have the highest number of solutions whereas data breaches have the lowest number of solutions.
Deep Learning for Sentiment Analysis in Indonesian Novel Review

The rapid development of technology, especially in the internet field, is influencing the increasing number of texts available. In recent years, there has been an increase in research on the internet or social media to find out the sentiments in the review text. Sentiment analysis is a part of Natural Language Processing (NLP), which can help to show whether certain opinions tend to contain positive opinions or negative opinions. In this study, three sentiment polarities were studied using an Indonesian novel review dataset. Data was classified using the Long Short-Term Memory (LSTM) approach, one of the deep learning methods. To increase success rate, we used pre-trained word embedding to represent words into vectors. The analysis was performed by comparing the word embedding model using GloVe, Word2Vec i.e. Continuous Bag of Words and Skip-gram, and FastText i.e. Continuous Bag of Words and Skip-gram. The experimental results showed that sentiment analysis using the FastText Continuous Bag of Words model reached the highest accuracy of 80% while the Word2Vec Skip-gram model had the lowest accuracy of 78.3%. So, it can be concluded that the implementation of the FastText CBOW model is accurately used as a word representation to analyze sentiments on Indonesian novel review.
An Autonomous Drone and Optical Network Based Animal Chaser and Multi Purpose Highway Traffic Monitoring System

An autonomous drone assisted imagery system for animal chasing on national highway, along with accident detection and automated emergency service measure and road quality detection for repairing by image processing is the innovative objective of this paper. A dedicated optical network has been proposed to provide high-speed data communication to cameras and drones, which later can also be utilized for various public related mass information services to government and private sectors. The autonomous transport and traffic control system can also track rampant illegal vehicle parking on highways, detect over speeding and overloaded goods vehicle for safety and load violation. In future, on road Wi-Fi service can be provided for commercial and urgent services by incorporating WAP units at specific points. Earlier drone-based systems have been used for survey and surveillance, but this will be a unique concept to facilitate various needs and requirements solely based on highways related problems and common occurring events, which need to be controlled on regular basis.
Fuzzy logic and applications

This chapter discusses very comprehensively the principles of fuzzy logic and its applications in power electronic systems. Membership functions and the rule table are explained. Fuzzy inference system structures by the Mamdani, Lusing Larson, and Sugeno methods are explained. An example of a restaurant tipping system is given in the beginning for clarity. The example applications include speed control of induction motor vector drives, phase-controlled converter DC drives with nonlinearity compensation, efficiency optimization control of vector drives with pulsating torque compensation, control of wind generation systems by induction and synchronous machines, estimation of converter current waves, MRAC slip gain tuning control of induction motors, and estimation of machine stator resistance. The MATLAB-based Fuzzy Logic Toolbox is described with an example illustration. Simulink simulation with interface of a fuzzy system for induction motor drive is explained. Finally, advances and trends in fuzzy logic applications are summarized.
Field programmable Gate Array based Real Time Object Tracking using Partial Least Square Analysis

In this paper, we proposed an object tracking algorithm in real time implementation of moving object tracking system using Field programmable gate array (FPGA). Object tracking is considered as a binary classification problem and one of the approaches to this problem is that to extract appropriate features from the appearance of the object based on partial least square (PLS) analysis method, which is a low dimension reduction technique in the subspace. In this method, the adaptive appearance model integrated with PLS analysis is used for continuous update of the appearance change of the target over time. For robust and efficient tracking, particle filtering is used in between every two consecutive frames of the video. This has implemented using Cadence and Virtuoso software integrated environment with MATLAB. The experimental results are performed on challenging video sequences to show the performance of the proposed tracking algorithm using FPGA in real time.
TransRAM: A classification model of breast pathology images that imitates the diagnosis process of pathologists

Breast cancer, one of the most common health threats to females worldwide, has always been a crucial topic in the medical field. However, manual diagnosis suffers a series of challenges, such as considerable time cost and low diagnosis consistency. In this paper, we proposed TransRAM, which merited both Transformers and Recurrent Models of Visual Attention, as a strong alternative for digital pathological diagnosis of the breast. This model imitated the diagnosis process of pathologists, paying attention to a series of local features on the pathology image and summarizing related information to arrive at the final diagnosis. TransRAM was evaluated on the Bioimaging Challenge 2015 Breast Histology Dataset (BACH) and achieved a classification accuracy of 90.94%, which was better than related publications. In addition, we compared the model effects under different parameter settings and visualized the model decision-making processes.
Power Swapper: Approximate Functional Block Assisted Cryptosystem Security

The hardware security domain in the recent years has experienced a plethora of threats of which side-channel attacks (SCAs) has been one of the emerging threats. These SCAs function by exploiting the side-channels which invariably leak important data during an application’s execution. The information leaked through side-channels are inherent characteristics of the system and is often unintentional. This information can be microarchitectural or physical information such as power consumption, thermal maps, timing of the operation, acoustics, and cache-trace. Intercepting secret information based on the study of power signature is a subdivision of SCAs where power consumption information serves as a covert channel leaking crucial information about the executed operations. Such physical SCAs are known to be a significant threat to cryptosystems such as AES (Advances Encryption Standard) and can reveal the encryption key efficiently. To overcome such concerns and protect the data integrity, we introduce Power Swapper in this work. The proposed Power Swapper thwarts the attack by randomly choosing one of the multiple modules that perform the intended activity, but have power consumption different than a standard implementation and can lead to similar power consumption as one of the other modules that perform a different operation. To achieve this, we introduce carefully crafted swapping of the standby modules that are responsible for the AES operation thus deluding the attacker without hurting the crypto operations. To minimize the overheads, we design reconfigurable computing elements that can perform a given operation, but with different power consumption depending on the configuration. This methodology has been validated for the AES power analysis attack and the key information observed by the attacker is seen to be completely futile, indicating the success of the proposed method.