Reviewerly/exHarmony huggingface dataset:
Distributed Fusion of Optimally Quantized Local Tracker Estimates for Underwater Wireless Sensor Networks

Multi-sensor underwater surveillance has been a significant research problem for civilian and naval applications. Due to limited bandwidth considerations, the underwater wireless sensor networks (UWSNs) use measurement quantization to transmit information from individual sensors to the fusion center to perform centralized tracking/fusion. However, at the measurement level, quantization of azimuth information is complex due to its non-linear behavior. To address this problem, this paper proposes to perform the distributed tracking and quantizing the local estimates (state and covariance) to provide improved bandwidth and reduce computational load. The local tracker estimates the updated state and covariance of a target’s time-varying dynamics in the given surveillance from the obtained measurements using extended Kalman filter (EKF) and global nearest neighbor (GNN) data association. The measurement model contains both detections of target and false alarms. This paper uses optimal quantization rather than linear quantization owing to its minimal bandwidth requirement. Once the quantized local tracks are obtained at the fusion center, these tracks are quantified using track-to-track association (T2TA) in the S-D assignment framework. The associated tracks are fused using correlation-free fusion algorithms like covariance intersect (CI), sampling covariance intersects (SCI), ellipsoidal intersect (EI), and arithmetic average (AA) algorithms to achieve the global track. The position root mean square error (PRMSE), bandwidth, and error ellipses are used to quantify the performance of the proposed framework. The simulation results show that the PRMSE of the optimally quantized fusion estimates yields good agreement with the unquantized method. Simulation results further reveals that, optimal quantization utilizes lower bandwidth compared to linear quantization. In addition, optimally quantized local estimates accomplishes promising covariance regions at the fusion center.
A new approach to analyze the independence of statistical tests of randomness

One of the fundamental aspects when working with batteries of statistic tests is that they should be as efficient as possible, i.e. that they should check the properties and do so in a reasonable computational time. This assumes that there are no tests that are checking the same properties, i.e. that they are not correlated. One of the most commonly used measures to verify the interrelation between variables is the Pearson's correlation. In this case, linear dependencies are checked, but it may be interesting to verify other types of non-linear relationships between variables. For this purpose, mutual information has recently been proposed, which measures how much information, on average, one random variable provides to another. In this work we analyze some well-known batteries by using correlation analysis and mutual information approaches.
Eigen-Spectrum Estimation and Source Detection in a Massive Sensor Array Based on Quantum Assisted Hamiltonian Simulation Framework

In this work, we propose quantum assisted eigenvalue estimation and target detection algorithms for a large sensor array via Hamiltonian simulation. Quantum algorithms provide complexity advantage of a certain class of problems on a quantum computer with fewer physical resources as compared to their classical counterparts. The proposed algorithms make use of the quantum phase estimation (QPE) as its core computing component. We have introduced an analytical quantum framework to map from classical to quantum in the context of target detection. Target detection involves an appropriate choice of threshold based on the probability of detection or false alarm. We exploited the massive sensor array structure and invoked the random matrix theory to propose an optimal threshold. It also takes into account the quantum measurement noise in the framework. Numerical simulations are performed to ascertain the efficacy of the proposed framework. The results suggest near term applications of the quantum algorithm for large-scale linear systems.
Users’ Psychopathologies: Impact on Cybercrime Vulnerabilities and Cybersecurity Behavior

The internet and digital technologies have become an integral part of people's daily lives. The online world provides many benefits to billions of users globally. However, it also brings risks too, because it is easy for criminals to reach their victims and exploit their online behavior. Nevertheless, users often perform risky security behaviors for convenience and usability, and because of their inadequate security awareness. With around 25% of the world's population experiencing mental and/or neurological disorders, it is important to understand how users' psychopathologies manifest themselves in the context of cybersecurity. This chapter has reviewed the symptoms of several mental disorders while considering the online benefits and risks, and these symptoms have been applied to evaluate users' vulnerability to cybercrimes and cybersecurity practices. The findings reveal how the complexity of each mental disorder influences users' online engagement and susceptibility to cybercrimes, and how uniquely, to varying degrees, they affect different cybersecurity behaviors.
An Intelligent Application for Preventing the Counterfeit Medicines Through a Distributed Blockchain

Recently, one of the most challenging problems has been considered about the purity of drug or drug safety. This issue has a significant impact in various fields related to medicine, especially in the pharmaceutical industry's commercial area. It takes thousands of lives per year for fake drugs to be delivered and consumed. There are no successful approaches to handling the network of the phony medicine syndicate globally, and the stakeholders in the drug industry have to act under authentication among them. This paper focuses on explaining an approach to resolve the issue of counterfeit drugs by using Blockchain technology. Further, Blockchain is a distributed system where each node holds the same data and aligns with other nodes without worrying about losing data. The suggested approach is based on storing the drug distribution criteria from medicine manufacturing to the consumer on the Blockchain network. The effect of a medicine on a patient will be recorded in a database for future statistics. For keeping transaction records, an approved Blockchain will be applied, and only trusted people will be able to access the system and push data to the Blockchain.
Energy-Efficient Massive MIMO for Serving Multiple Federated Learning Groups

With its privacy preservation and communication efficiency, federated learning (FL) has emerged as a learning framework that suits beyond 5G and towards 6G systems. This work looks into a future scenario in which there are multiple groups with different learning purposes and participating in different FL processes. We give energy-efficient solutions to demonstrate that this scenario can be realistic. First, to ensure a stable operation of multiple FL processes over wireless channels, we propose to use a massive multiple-input multiple-output network to support the local and global FL training updates, and let the iterations of these FL processes be executed within the same large-scale coherence time. Then, we develop asynchronous and synchronous transmission protocols where these iterations are asynchronously and synchronously executed, respectively, using the downlink unicasting and conventional uplink transmission schemes. Zero-forcing processing is utilized for both uplink and downlink transmissions. Finally, we propose an algorithm that optimally allocates power and computation resources to save energy at both base station and user sides, while guaranteeing a given maximum execution time threshold of each FL iteration. Compared to the baseline schemes, the proposed algorithm significantly reduces the energy consumption, especially when the number of base station antennas is large.
Blockchain for Decarbonization

Industry 4.0 has ushered in a new era of connectivity and communication within various industries. Motivated by the United Nation’s Sustainable Development Goals (SDGs) of achieving net zero emissions by 2050, long-term energy sustainability plans encourage decentralized/distributed technologies such as blockchain to take center stage, alongside Internet of Things (IoT) devices, smart sensors, and smart contracts. Blockchain is posited to be impactful in decarbonizing various industries as it is an immutable, secure, and transparent ledger that incentivizes industrial adoption through reduced costs and increased efficiency. Therefore, in this book chapter, we will delve into the motivation of blockchain applications in the following industries: 1) energy markets, 2) chemical and manufacturing industries, and 3) carbon trading markets. Various case studies and current blockchain practices in each respective industry will also be reviewed and we will discuss the potential of blockchain towards decarbonization.
Differential Privacy-Based Permissioned Blockchain for Private Data Sharing in Industrial IoT

Permissioned blockchain such as Hyperledger fabric enables a secure supply chain model in Industrial Internet of Things (IIoT) through multichannel and private data collection mechanisms. However, the existing data sharing and querying mechanism in Hyperledger fabric is not suitable for supply chain environment in IIoT because the queries are evaluated on actual data stored on ledger which consists of sensitive information such as business secrets, and special discounts offered to retailers and individuals. To solve this problem, we propose a differential privacy-based permissioned blockchain using Hyperledger fabric to enable private data sharing in supply chain in IIoT (DH-IIoT). We integrate differential privacy into the chaindcode (smart contract) of Hyperledger fabric to achieve privacy preservation. As a result, the query response consists of perturbed data which protects the sensitive information in the ledger. We evaluate and compare our differential privacy integrated chaincode of Hyperledger fabric with the default chaincode setting of Hyperledger fabric for supply chain scenario. The results confirm that the proposed work maintains 96.15% of accuracy in the shared data while guarantees the protection of sensitive ledger’s data.
A Modified U-Net Based Framework for Automated Segmentation of Hippocampus Region in Brain MRI

An accurate localization of the brain anatomical structure for correct and reliable diagnostic strategies is of great concern in many bio-medical applications. Towards this end, manual or semi-automated delineation methods used are found to be time consuming. Herein, to address this problem, we present an enhanced model for automated segmentation of two neighboring small structures of the brain in the Hippocampus region i.e., anterior and posterior. Our aim is to improve the segmentation performance, where the proposed architecture captures contextual information in encoding path and enables precise localization by utilizing the decoding path in a symmetric way. In particular, our proposed methodology enhances the original U-Net architecture with 3-dimensional (3D) data processing and employs spatial elastic deformation. Further, we evaluated the segmentation performance using recursive U-Net for comparison. The effectiveness of different optimization strategies are evaluated on a publicly available data comprising of 3D magnetic resonance imaging volumes from mono-modal hippocampus region. Our experimental results demonstrate the robustness of the proposed model by using patch-based augmentation technique for hippocampal segmentation.
Undergraduate's Perspective on Being an Effective Online Student During Lockdown due to COVID-19 Pandemic: An Educational Data Mining Study

The mandatory implementation of online education due to the COVID-19 outbreak has drawn research about this modality - analyzing students' perspectives about course designs, the instructors' performance, and the institution's services. In contrast, little is known about what an undergraduate should do to perform well academically, a factor that can even impact the retention of students who face this modality for the first time, according to self-determination theory and student-centered learning. This work was guided by the question: What are the effective strategies for excellent school performance in online education according to the undergraduates themselves? We performed qualitative data analysis of thirty-four letters written by second-year engineering students, with one semester of experience in online education at a private university in Mexico using an educational data mining methodology. The qualitative analysis used text and sentiment analysis tools. The texts contained academic and wellness recommendations for other learners who would start their education in this modality. The research showed that, in addition to the actions generally valid for academic success in face-to-face courses, participants advised the relevance of taking notes, leveraging digital tools, and defining a specific study space at home when learning in the online modality. In terms of wellness, participants recommended looking at the positive side of things, having a good diet, sleeping well, and resting from the computer. Higher education is a complex system in which different stakeholders participate; however, beyond excellent content, professors, technologies, and learning platforms, students consolidate learning through their actions.
Geeks and Newbies: Investigating the Criminal Expertise of Online Sex Offenders

While online sex offenders use a wide range of strategies to try to avoid police detection, attempts to avoid detection of child sexual exploitation materials (CSEM) and online sexual solicitation of children have received very little attention. This study aims to understand online sex offenders’ behaviors by modeling the factors associated with their use of technological data protection and anonymity preservation strategies. The data is based on a sample of 199 men involved in crimes related to the use of child pornography or sexual solicitation of minors online. The analytical strategy based on the use of an artificial neural network (ANN), a machine-learning system, identified two trends. First, those who displayed problematic substance use and sexual thoughts and fantasies as well as behaviors reported to be preoccupying did not use specific strategies to avoid police detection. Second, two combinations of factors predict use of police anti-detection strategy, suggesting that the criminal expertise of online sex offenders is manifested in two different patterns: those building on existing knowledge, and those learning skills through previous judicial experience.
LCornerIoU: An Improved IoU-based Loss Function for Accurate Bounding Box Regression

Bounding box regression is an important step in the process of object detection, which has a direct impact on the accuracy of model location. The $\ell_{n}$ loss is widely used in bounding box regression, but the criterion of locational accuracy is Intersection over Union (IOU), and there is no strong correlation between them. Although IoU-based loss functions have been proposed successively, such as IoU, GIoU, DIoU and CIoU, which make location loss directly related to evaluation criteria, there are still problems such as slow training convergence speed and inaccurate model location. Based on the IoU loss function, we add normalized corner distance and parameterized width height difference to form a new loss function: LCornerIoU loss. The results of bounding box regression simulation show that LCornerIoU loss is superior to other IoU-based loss functions in terms of convergence speed and positioning accuracy. Experiments on COCO benchmark show that the performance of the Faster R-CNN, RetinaNet, and YOLO-v5s models combined with LCornerIoU loss have consistent performance improvement compared with IoU loss.
Application of artificial intelligence and machine learning in blockchain technology

Blockchain technology is a distributed, decentralized, immutable ledger. It is used to store encrypted data; on the other hand, in artificial intelligence, human natural intelligence can be simulated. All the characteristics of the human brain may be programmed with artificial intelligence. Blockchain technology may be thought of as a body and artificial intelligence as its brain. In blockchain technology, if artificial intelligence approaches are used, then data collection, data enabling, data analytic, and decision-making of data collection can be improved. With both these technologies, we will be able to affect and enact upon data in different ways. Also, they may be proven as game-changers in the exploitation of data. At the same time, machine learning approaches may enhance the architecture of blockchain technology. Blockchain technology can make artificial intelligence further rational and comprehensible. We can trace the trail of decisions and determine why a particular decision was taken by a machine learning algorithm and on the other hand, blockchain technology records data and variables that go through in the decision-making process in machine learning. In this chapter, we have discussed key concepts of blockchain technology and applications of artificial intelligence and machine learning in the blockchain. This chapter also investigated edge computing as a potential use case of blockchain technology.
Energy Management System for Small-Scale Hybrid Wind Solar Battery-Based Microgrid

In this paper an effective energy the executives framework for a small-scale crossbreed wind-sun-oriented battery-based miniature matrix is proposed. Change frameworks for the breeze and sun-oriented energy and battery stockpiling framework are created alongside regulators to test the activity of mixture miniature lattice, power electronic converters, and control calculations. An energy the executives framework keeps up the force balance for the varieties of environmentally friendly power age and furthermore for the heap request varieties. This works in independent mode. Consequently, it gives a testing stage to various control calculations, energy the executives frameworks, and test conditions.
A support system for formal college students: A case study of a community-based app augmented with a chatbot

The rapid adoption and advancement of technology has improved communication methods in many facets of life, including education. The affordance of an effective virtual communication channel can support college students' sense of belonging and mitigate their anxiety, which in turn can help them reach their maximum academic potential and greater well-being. In this study, we explored the common problems faced by college students—in particular, students enrolled in the Faculty of Computing and Information Technology at King Abdulaziz University—when they seek help, ask questions, or find out about current and upcoming events in the college. Moreover, we explored their knowledge of chatbots and their perceived effectiveness in answering frequently asked questions. The results revealed 1) the absence of a single platform that helps them communicate and share mutual benefits in an effective, easy, and fast manner, and 2) that less than half of the surveyed participants had previously interacted with a chatbot. Thus, the paper presents the development of a unified communication channel by building a mobile application that brings college students together to communicate and exchange common experiences and interests while being supported by a digital assistant technology (chatbot), which can answer common questions and inquiries. Users in a pilot test showed a high satisfaction level with the app.
Research on Data Security Sharing in Blockchain: Challenges and Prospects

With the rapid development of blockchain technology, data sharing has been widely studied in academia and industry in recent years, and a series of important achievements have been made. At present, there is a security problem of data sharing in blockchain technology. Firstly, it summarizes the data security sharing. Then, the data security sharing is divided into three aspects: confidentiality, integrity and availability, and the existing challenges and corresponding solutions are analyzed. Finally, the development status and trend of data security sharing in blockchain are summarized, and the important research directions in the future are prospected.
Path planning of mobile robots based on improved RRT algorithm

Abstract Aiming at the shortcomings of RRT (Rapidly-exploring Research Tree) algorithm such as long time cost and low utilization of sampling points, an improved RRT algorithm is proposed. By adopting the sampling strategy based on dynamic probability, the robot is prevented from falling into a local minimum during the sampling process. At the same time, the adopt of variable step strategy as the random tree expands reduces the number of sampling points. Finally, the initial path is optimized to make it more suitable for the robot to walk. The improved RRT algorithm is compared with the RRT algorithm and the Goal-bias RRT algorithm in both simulations on MATLB and experiments on robot based on ROS (Robot Operating System). And the results show that improved RRT algorithm increases the speed of path planning, reduces the length of path, and the planned path is smoother and more suitable for the real robot to move.
Weight enumerators, intersection enumerators and Jacobi polynomials II

In the present paper, we introduce the concepts of Jacobi polynomials and intersection enumerators of codes over Fq and Zk for arbitrary genus g. We also discuss the interrelation among them. Finally, we give the MacWilliams type identities for Jacobi polynomials.
Anti-Forensic Attacks Using Generative Adversarial Networks

Abstract The rise of deep learning has led to rapid advances in multimedia forensics. Algorithms based on deep neural networks are able to automatically learn forensic traces, detect complex forgeries, and localize falsified content with increasingly greater accuracy. At the same time, deep learning has expanded the capabilities of anti-forensic attackers. New anti-forensic attacks have emerged, including those discussed in Chap. 10.1007/978-981-16-7621-5_14 based on adversarial examples, and those based on generative adversarial networks (GANs). In this chapter, we discuss the emerging threat posed by GAN-based anti-forensic attacks. GANs are a powerful machine learning framework that can be used to create realistic, but completely synthetic data. Researchers have recently shown that anti-forensic attacks can be built by using GANs to create synthetic forensic traces. While only a small number of GAN-based anti-forensic attacks currently exist, results show these early attacks are both effective at fooling forensic algorithms and introduce very little distortion into attacked images.
Integrated Digital Twins and Blockchain Framework for Privacy and Security of Applications

Digital twin technology has moved beyond simulating only small and light systems into creating virtual replicas of more complex systems that depend on a hybrid of emerging technologies such as the Internet of Things (IoT), Extended Reality (XR), Artificial intelligence (AI), and blockchain. A growing body of literature investigates the importance of integrating digital Twin and Blockchain technologies in multiple applications. This chapter investigates the power of blockchain to process and secure digital twin models. Moreover, it discussed the application of integrating Blockchain and the digital Twin in a set of recent domains, supply chain and logistics, public utilities, and healthcare. Finally, the chapter discussed a set of open research areas that promise more applications of Blockchain and the digital Twin soon.