Reviewerly/exHarmony huggingface dataset:
A Novel Approach For Generating Customizable Light Field Datasets for Machine Learning

To train deep learning models, which often outperform traditional approaches, large datasets of a specified medium, e.g., images, are used in numerous areas. However, for light field-specific machine learning tasks, there is a lack of such available datasets. Therefore, we create our own light field datasets, which have great potential for a variety of applications due to the abundance of information in light fields compared to singular images. Using the Unity and C# frameworks, we develop a novel approach for generating large, scalable, and reproducible light field datasets based on customizable hardware configurations to accelerate light field deep learning research.
Cyberbullying detection and classification with improved IG and BiLSTM

Twitter is a microblogging and social networking service platform; users can post what they feel and think about to share with others. Although it facilitates users' social behaviour, a high degree of freedom of speech also leads to cyberbullying. The statement released by UNICEF showed that 36.5% of middle and high school students experienced cyberbullying, and 87% observed cyberbullying. Cyberbullying has greatly affected people's daily lives. We conduct this study to detect whether online comments contain cyberbullying behaviours and classify cyberbullying to alleviate this problem. This paper uses an improved information gain algorithm for feature selection, and the bidirectional LSTM neural network is used for classification. On the premise that the information gain threshold is limited to 0.0004, the precision on the test set can reach 95.15%.
Nephritis Prediction with Long-Short Term Memory and Mathematical Statistical Analysis

Acute nephritis prediction aims to predict whether patients are likely to suffer from acute nephritis based on a variety of clinical testing indicators. It can give early warning to potential patients for timely prevention and achieve the aim of reducing the mortality rate of the disease. However, the high dimensional and the strong time-sequential characteristics of medical information make it challenging to accurately predict acute nephritis. This paper used the Long-short Term Memory (LSTM) model to predict the occurrence of acute nephritis given the above problems. First, the data is checked for integrity and split into a training set and test set. Then, LSTM layers and dense layers are used to build up the basic framework of the model. After that, we establish the model and improve the accuracy by selecting appropriate loss, optimizer, and metrics parameters. To better measure the predictive effect of the model, we introduce the precision rate, recall rate, and F1 value. To further check the quality of the model, loss and accuracy graphs and two ROC curves are also drawn. The values of these parameters indicate that the model has a good predictive ability. In the end, we were able to predict whether patients in the test set would develop acute nephritis. The experimental results show that LSTM is good at processing long time series data, capturing long-term dependence and nonlinear dynamic changes.
DLNA: Deep Lightening Network with Attention for Low-light Image Enhancement

Recently, Low-light image enhancement has received much attention. Benefiting from the development of convolutional neural networks (CNN), this paper proposes an improved deep illumination network (DLNA) consisting of three modules, LBP, FA, and CBAM. The attention module CBAM, which fuses channels and spaces, is introduced to refine and highlight key features. A histogram loss function is also added to enhance the color texture to address visual distortion. The results indicate that our algorithm is better than others, whether qualitative or quantitative.
Music Creation with Computer Music Production Software

With the advent of the era of Internet technology, the global village has brought more information equality, and the development of information has allowed artists from all over the world to stand on the same starting line; the development of computers under Moore's Law has exploded, and artistic creation under the digital background has also Unprecedented prosperity, and the accompanying theoretical innovation, aesthetic interpretation, and cultural orientation have formed a brand-new artistic territory. The main purpose of this paper is to explore and analyze the practice of computer music software in music creation. This paper revolves around all the technical elaboration and creation case analysis, and believes that the electronic music sound creation promoted by digital technology presents a new sound landscape. Experiments show that the appropriate learning rate is 0.01~0.001 at the beginning of training. After a certain number of rounds: Gradually slow down. Near the end of training: The learning rate should have a decay starting value above 100x.
The advance of event relation extraction based on deep learning

With the development of machine learning and natural language processing, events and event relations, as indispensable elements of communication, have been paid more and more attention. The early use of machine learning for event extraction is based on vocabulary and context features. With the introduction of an inference mechanism and neural network, the performance of event extraction is greatly improved. Complete events contain many internal components (such as people, time, and place) and external relationships (such as timing, cause, and effect). Event relation extraction for many events can train the model to predict and realize a deeper understanding of text information. At the same time, event relationship extraction plays an essential role in many fields such as medical treatment, business, and law. The current event extraction model is mainly based on predicate recognition. However, it is far from achieving the target of complete event recognition. Besides, the accuracy of event extraction and prediction based on multiple-choice is not good. This paper reviews the main methods of event relation extraction. Furthermore, this paper discusses the prospect and challenge of event relation extraction.
Comparing of feature selection algorithms

Feature selection plays a key role in data preprocessing in machine learning, and has an important impact on the accuracy and performance of algorithm prediction. This paper introduces the framework of feature selection and the generation of feature subsets. The feature selection methods are classified according to the correlation between feature selection and algorithm. Finally, the performance and accuracy of the feature generation algorithm are verified by experiments, and the corresponding conclusions are given.
A Hilbert and Gauss Fitting Peak Searching Algorithm Combined with Wavelet Transform

Aiming at the interference noise and inherent electronic noise in the demodulation system of FBG sensor network, a new threshold wavelet denoising algorithm is proposed in this paper. Aiming at the demodulation problem after denoising, because the traditional Gaussian fitting algorithm is seriously affected by the spectrum type, the wavelength location of the spectrum adopts a Hilbert transform combined with Gaussian fitting peak finding algorithm, which is verified by Matlab simulation, and the method can reduce the influence of external factors on the spectrum. Compared with other similar algorithms, it can guarantee good measurement accuracy in dealing with asymmetric spectrum and multimodal spectrum.
A Kernel-Based Local Mean k-Nearest Centroid Neighbor Method for Classification

K-nearest neighbor criterion has a broad research and application background in pattern recognition, machine learning, data mining and other fields. In recent years, a series of extended methods have been proposed, such as k-nearest centroid neighbor(KNCN), local mean-based k-nearest centroid neighbor (LMKNCN), etc. This is because more scholars have studied k-nearest neighbor (KNN) method. Some scholars have proposed kernel-k-nearest neighbor(kernel-KNN) method by combining the support vector machine (SVM) and KNN method. Compared with the traditional KNN method, these methods have a better classification effect. However, the classification effect of LMKNCN method is inferior to that of regular sample distribution in the case of the random sample distribution. And kernel method can effectively solve indivisible linear problems by transforming the problem into high-dimensional space. In this paper, the kernel method is combined with LMKNCN. A new method, kernel-local mean-based k-nearest centroid neighbor (kernel-LMKNCN), is obtained by nucleating the original LMKNCN methods. Experimental results show that the proposed kernel-LMKNCN methods are superior to traditional KNCN, LMKNCN and kernel-KNN methods in classification accuracy.
Prerequisites from Logic and Probability Theory

This chapter lays the probabilistic groundwork for the rest of the book. We introduce standard probability theory. We call the elements A of the σ-algebra “propositions” instead of the more common “events.” We reserve the word “event” for the elements of the probability space Ω.
Improbability and Novelty of Descriptions

In this chapter, we define the information of an event A ∈ Σ, or in our terminology, the novelty of a proposition A as −log2 p(A). We further define the important new concept of a description and extend the definition of novelty from events to descriptions. Finally, we introduce the notions of completeness and directedness of descriptions and thereby the distinction between surprise and information, which are opposite special cases of novelty.
MapReduce based convolutional graph neural networks: A comprehensive review

Deep learning has brought a lot of changes in the digital era. Big data and deep learning models have broad research scope for future researchers since data is represented in different forms like audio, video, image, text and graph, etc. Out of which, handling Graph data is the cumbersome approach, as the hidden patterns are in plenty. Capturing hidden patterns in graph data is a challenging issue these days. This review provides a global view of convolutional graph neural networks using different machine learning models, and map reduce based neural graph networks. We discuss different state-of-art learning approaches for handling graph data. We further discuss the limitations of few existing models in handling massive data called BigGraph. We also identify the applications and challenges for Convolutional Graph neural Networks (ConvGNN).
A Novel Tensor-Based Hyperspectral Image Restoration Method With Low-Rank Modeling in Gradient Domains

The hyperspectral image (HSI) is easily contaminated by various kinds of mixed noise (such as Gaussian noise, impulse noise, stripes, and deadlines) during the process of data acquisition and conversion, which significantly affect the quality and applications of HSI. As an important and effective scheme for the quality improvement of HSI, the HSI restoration problem aims to recover a clean HSI from the noisy HSI with mixed noise. Thus, based on the tensor modeling of HSI, we propose a novel tensor-based HSI restoration model with low-rank modeling in gradient domains in a unified tensor representation framework in this article. First, for the spectral low-rank modeling of HSI in spectral gradient domain, we particularly exploit the low-rank property of spectral gradient, and propose the spectral gradient-based weighted nuclear norm low-rank prior term. Second, for the spatial-mode low-rank modeling of HSI in spatial gradient domain, we particularly exploit the low-rank property of spatial gradient tensors via the discrete Fourier transform, and propose the spatial gradient-based tensor nuclear norm low-rank prior term. Then, we use the alternative direction method of multipliers to solve the proposed model. Finally, the restoration results on both the simulated and real HSI datasets demonstrate that the proposed method is superior to many state-of-the-art methods in the aspects of visual and quantitative comparisons.
How to Grant Anonymous Access

In this paper, we propose three protocols to share, among a set of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> competing entities, the responsibility to grant anonymous access to a resource. The protocols we propose vary in their settings to take into account central or distributed registration. We prove that any subset of guardian authorities can neither tamper with, nor forge, new access-key tokens. Besides, two of the methods we propose are resistant to the eventual appearance of quantum computers. The protocols we propose permit new approaches for cryptographic applications such as electronic voting or blockchain access.
Quantum Complexity for Vector Domination Problem

In this paper we investigate quantum query complexity of two vector problems: vector domination and minimum inner product. We believe that these problems are interesting because they are closely related to more complex 1-dimensional dynamic programming problems. For the general case, the quantum complexity of vector domination is $$\varTheta (n^{1-o(1)})$$ , similarly to the more known orthogonal vector problem (OV). We prove a $$\tilde{O}(n^{2/3})$$ upper bound and a $$\varOmega (n^{2/3})$$ lower bound for special case of vector domination where vectors are from $$\{1,\dots ,W\}^d$$ and number of dimensions d is a constant and $$W \in O(\textit{poly } n)$$ . We also prove a $$\varOmega (n^{2/3})$$ lower bound for minimum inner product with the same constraints. To prove bounds we use reductions from the element distinctness problem as well as a classical data structure - Fenwick trees.
INSIDE

The field of electronic games is a great experimental area that contributes to the implementation of new technologies in a variety of applications. This article explores the field of mixed reality through the use of physical computing for the development of the electronic game Inside. In such an interactive environment, the physical and the virtual worlds coexist and interact with each other. A user can alter the virtual world's characteristics through a special controller designed for the game that contains electronic components that can sense real-world properties. The attention of the user is not focused only on the screen but also on the controller through which a two-way interaction is achieved. A virtual environment created by a game engine can receive messages from the controller and vice versa. Changes on the properties of the virtual world can be communicated to the controller aiming at the alteration of the characteristics of the physical world. As a result, the user experiences an immersion in an environment in which real and virtual objects coexist.
Verifying Adversarial Robustness of 3D Object Detectors for Autonomous Vehicles

Leading 3D object detectors for automated vehicles, such as PIXOR, do not robustly account for noise and are vulnerable to adversarial attacks. Existing attack methods do not accurately simulate naturally occurring noise, as they attempt to continuously on a discrete input space. In this paper, we propose a novel attack method, which maximizes loss by making gradient-informed, discrete changes. A subset of points within an image move based on a percentage change between the original and new gradient. We measure the validity of an attack based on its visual similarity to the original point cloud and numeric metrics.
Evolution of Cloud Computing With Blockchain and IoT

Blockchain is presently bringing a revolution in numerous industries like finance, healthcare, supply chain, insurance, registry, and the internet of things. Numerous industries integrate blockchain with their frameworks for the advantages of the blockchain. The growing demand for health has led to development of hospitals and medical institutions further leading to an increase in the medical data and the need for managing it. Thus, the authors discuss blockchain technology in association with the medical sector. Health records including personal details of the patients are to be kept confidential. These records are saved in the medical clouds, but they can be misused by the other parties deleting the original data or modifying it. The problems related to the healthcare industry have been discussed.
Edge AI-Based Crowd Counting Application for Public Transport Stops

Recently, the evolution of artificial intelligence has caused the emergence of smart systems exhibiting intelligent behavior like the human brain. Specifically, as a class of artificial intelligence methods, computer vision empowered with deep learning has tremendous promise for the accurate detection of crowds in real-time. In addition, the edge artificial intelligence approach allows for the development and deployment of artificial intelligence methods outside of the cloud. This study introduces the deep learning-based computer vision implementation to monitor public transport stops. The main aim is to determine the count of passengers through edge computing. The experimental study is realized with the popular YOLO object detector model on the Maixduino board developed for edge-based artificial intelligence (AI) applications with the internet of things (IoT). The experiments' results show that the obtained accuracy of crowd counting was found to be satisfactory.
Cryptocurrencies and Blockchain Applications

New technology and revolution in digital tools can be considered as the enablers to promote the performance of the global supply chain by integrating the actors. Among these technologies is the blockchain, first and foremost a technology for storing and transmitting information. This technology offers high standards of transparency and security in a decentralized environment. More concretely, the blockchain allows its users—connected in a network—to share data without an intermediary. The cryptocurrencies are new electronic currencies that use blockchain technology to transfer the ownership of cryptos between their different owners. The application of blockchains in cryptocurrency reduces the risk of sharing information and simplifies transaction processing thanks to the secured decentralized environment. The objective of this chapter is to provide a literature review that synthesizes the knowledge of using the blockchains in cryptocurrency. The results of the review can be used by academics and practitioners.
A comprehensive investigation on attack graphs

In the last few decades, the influence of the internet on human civilization is worth remembering. Today, starting from an individual to a big organization, everyone is dependent on the internet. The rise in numbers of computers, sensor devices, IoT devices, and the expansion of the network has made the internet and internet users prone to cyber-attacks. This chapter discusses attack Graph, a graph-based analysis of vulnerabilities and security in a network to discover the possible attack path that an intruder could take, and suggest preventive measures. Various attack graph models have been studied throughout the chapter; also the complexities and scalability are being discussed.
A Literature Review on the Use of Three-Dimensional Virtual Worlds in Higher Education

Three-dimensional virtual worlds (3DVW) have been substantially adopted in teaching and learning worldwide. The current study conducted a literature review of the published research relevant to the application of 3DVWs in higher education. A literature search was performed on nine scientific databases, and following scrutiny according to inclusion criteria, 176 papers were selected for review. The literature review process was summarized, reviews undertaken by the authors, and results about the applicability of 3DVWs in higher education were extracted. A wide variety of application areas for 3DVWs in higher education were found and classified into five main categories. Various 3DVW platforms and virtual environments used for educational goals were also identified. This study found that a wide range of virtual environments and tools have been implemented by 3DVW technology and applied for teaching and learning in higher education.
Deep learning approach for physical-layer security in Gaussian multiple access wiretap channel

Deep learning (DL) has exhibited great potential in communication systems. Recent advances in DL-based physical-layer techniques have shown that the communication system can be modeled as an autoencoder (AE), which performs end-to-end learning tasks. In this article, we investigate an AE-based deep learning framework for physical-layer security where multiple transmitters send their own data to a common receiver under an eavesdropping scenario (i.e., Gaussian multiple access wiretap channel). We have newly designed an integrated loss function with respect to secrecy performance in terms of symbol error rate among multiple users. Further, we verify that our training approach based on the proposed loss function can achieve better secrecy performance compared with the conventional training one.
English Competencies and Challenges for Data Science and Cyber Security Students at Al Istiqlal University

This present research aims to determine the growing needs and challenges faced by Data Science and Cyber Security students at Al Istiqlal University's Faculty of Information Technology when learning the English language. It also tries to ascertain whether gender and level of competence affected their requirements and difficulties in mastering the English language. 35 cadets who are specializing in Data Science and Cyber Security make up the sample. The researcher gave out 39 questionnaire items divided into eight domains. The results show that cadets in Data Science and Cyber Security did not undergo any guidance regarding how to utilize English in the discipline while engaging in the analysis of data or cyber security keywords. Additionally, the study demonstrates that cadets majoring in data science and cyber security did not receive any guidance on how to learn to communicate in English, and the teaching activities in the English programs they had taken did not match their notions of the standards for expert English. Moreover, English proficiency requirements for cadets enrolling in Data Science and Cyber Security courses should be taken into consideration. Additionally, no statically meaningful differences in the demands for key competencies and barriers faced by Data Science and Cyber Security cadets are found when gender and competency traits are taken into consideration.
Processing large graphs with an alternative representation

This chapter investigates the processing of large graphs with a novel graph representation scheme, the centered subgraph data matrix (CESDAM). The graph is represented in multiple CESDAMs, each containing subgraphs of the graph. Processing time and memory requirement are the two major issues while dealing with the processing of large graphs. The representation of graph is crucial for handling both these issues. The efficiency of CESDAM scheme in terms of time requirement is analyzed by incorporating the breadth-first searching, which is the backbone of most graph processing algorithms. The analysis shows, breadth-first searching on CESDAM requires time O(n × B × m2), where n is the number of nodes in the graph, B is the branching factor of the meta-graph prepared on top of CESDAM ids, and m is the size of each CESDAM. The widely used adjacency matrix is becoming inefficient with enormously growing sizes of modern-day graphs, which requires very large amount of space for such graphs. Representation of graph in parts with CESDAM reduces the memory requirement significantly in contrast to adjacency matrix, which requires space O(k × m2) only, where k is the number of CESDAMs.