Reviewerly/exHarmony huggingface dataset:
A Continuous Transform for Localized Ridgelets

We develop a new continuous wavelet-like transform for localized ridgelets. Contrary to the classical ridgelets (which are not local), this new dictionary exhibits desirable decay so that all the atoms lie in L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> ). Furthermore, each localized ridgelet atom is itself a superposition of continuously many classical ridgelets. Our construction hinges on a careful wavelet analysis in the Radon domain, different than the usual Radon-domain wavelet analysis found in the study of classical ridgelets. This is crucial in ensuring the locality of our new, localized ridgelet atoms. We prove a continuous transform and inversion formula for this new dictionary. Finally, due to the locality of these atoms, we conjecture that this new dictionary is better conditioned than the system of non-local ridge functions used ubiquitously in modern neural networks.
Towards Gaze-contingent Visualization of Real-time 3D Reconstructed Remote Scenes in Mixed Reality

Real-time 3D perception through RGB-D cameras and its rendering inside modern mixed reality (MR) devices have been shown to enhance a user’s sense of presence and immersion in a remote scene. In telepresence applications, it allows the operator to control a remote robot with full immersion. However, the streaming of such real-time 3D data from RGB-D cameras, especially real-time dense 3D reconstructed scenes, suffer from network latency and throughput limitations that can negatively impact user experience. The human visual system can serve as an inspiration to address this problem. Human eyes have their sharpest visual acuity at the center of their field-of-view, which falls off at the periphery. This research proposes a framework that utilizes this concept to intelligently sample, process, stream, and render of dense 3D reconstructed scenes in MR for immersive visualization. The proposed system, termed as Gaze-contingent Visualization System, shows significant reductions in latency and throughput needs, up to 80% in both. The innovative system improves the state-of-the-art in immersive visualization of remote real-time 3D reconstructed scenes.
Loan Requests Classification Using Artificial Intelligence

Loan requests classification is an important step that helps both customers and service providers. It allows for better responses to customer needs. AI can play an invaluable role in this process by providing accurate classification of incoming loan requests, enabling customers to know what to apply for. AI does not only help customers but also service providers to streamlines the process. This work aims to develop an AI-based model using customer basic data such as age, location, and other relevant basic information to suggest the most suitable loan type. This saves the customers from the hassle of researching and comparing different types and then selecting the most appropriate one. This leads to increase customers satisfaction as they are able to make well-informed decisions. Additionally, this AI-based solution helps the service providers to evaluate requests in a shorter period of time. This study evaluated several classification methods using a loan dataset from Saudi Social Development Bank (SDB) to suggest the most suitable loan for the customers. The experiments demonstrated that the proposed AI model with basic features exhibited accurate results with an F-score value of 99.4%.
Decentralized Federated Deep Learning Image Recognition Models

In the era of IoT, numerous frameworks and cutting-edge models have been introduced to enhance user experience and privacy and reduce the risk of data breaches. Over time, IoT device usage has grown tremendously, and a flood of data has been sent to servers for processing. Federated learning has been deployed for efficient decentralization while preserving privacy. Federated learning has been applied in various IoT-related applications such as image classification, object segmentation, object detection, and sensor analytics. Existing centralized image recognition models fall short of providing accurate image classification with acceptable processing time for real-time deployment while preserving privacy. In this paper, we designed two decentralized deep learning models using federated learning, the CNN-TFF and the VGG16-TFF. With around 250 training iterations, we achieved a high accuracy rate of up to 90% with a decrease in the loss value for the CIFAR-100 dataset using the VGG16-TFF model while maintaining data privacy using federated learning.
Semi-Automated Video Archiving Using Object Detection, Tracking and Ontological Annotation

In light of this unmistakable exponential data expansion, visual media archiving must be rethought. Human generated meta-data might not be sufficient for efficient data retrieval. Object detection and object recognition enable the generation of computer-generated meta data that improve the storage and retrieval of data. We present a semi-automated approach for preserving video footage that combines object identification and ontologies. This significantly cuts down on search time, data scrubbing by providing only the relevant information making for more efficient queries.
Efficient Drones-Birds Classification Using Transfer Learning

Transfer learning is one of the recent deep learning technologies that can saves most of the time consumed in training deep networks. It could be performed by using pre-trained deep networks. In this paper, we will propose a new model for drones-bird classification based on transfer learning. We modify and evaluate three popular pre-trained deep models using dataset consists of drones and birds. The performance of each pre-trained network is evaluated and compared. Results show that, these pretrained networks could be adopted efficiently in classification of the addressed dataset. Additionally, the accuracy of the ResNet18 outperforms other evaluated networks. The accuracy and F-Score of ResNet18 exceeds 98% in all cases. Also, the other models have an excellent performance. There are many important applications of the problem addressed especially in security, defense, and surveillance. So, accurate classification of drones and birds is very important.
Robot Controller Parameter Identification for Simulated Environments

Simulation of robotics systems presents many advantageous features as it can offer a safe, fast and controlled replica of real systems. Its main purpose is to test new applications or behaviours prior to deployment. It can also leverage the training of robotics systems. However, in order to achieve a reliable simulation, many physical parameters have to be appropriately chosen. Some of these parameters can be a difficult choice for the simulation designer. In this paper, we present a method to identify robot controller parameters in simulated environments. The presented methodology uses a few real-world trajectories, it iteratively minimizes the difference with virtual trajectories until convergence is achieved. The purpose of our work is not to find the optimal parameters but a suboptimal set which is realistic enough.
Machine Learning based on Air-writing Recognition System

With the development of the Internet of Things, new devices need more diversified input methods. Gesture recognition has become a hot research field, and air-writing [1] is a significant part of the field of gesture recognition. In our previous air writing recognition system, we proposed an input system that measures dynamic data based on an inertial measurement unit that uses a dynamic time warping (DTW) algorithm for recognition. In this paper, cDTWw algorithm, KNN & DTW classification algorithm and fastDTW algorithm are used for comparative experiments. In order to improve the actual recognition ability of the air writing system, save the recognition time as much as possible , and optimize the recognition accuracy. We found that the cDTW w algorithm has a faster recognition speed than the fastdtw algorithm in the air writing recognition system. KNN & DTW class fication algorithm significantly improves the recognition accuracy in the air writing recognition system.
Research Trends in Mobile Robots: A Comparative Analysis within the Periods before and after the Industry 4.0 Revolution

Mobile robots are at the forefront of studies in the field of robotics. Research topics such as path planning and task planning take place importantly in the field of mobile robots. In this study, papers in the field of mobile robots in the last 20 years are discussed. The abstracts of 13,404 papers in the Scopus database were analyzed in two 10-year periods between 2002-2011 and 2012-2021, before and after 2011, when the Industry 4.0 Revolution emerged. Analysis results were created by text mining method. According to the results of the analysis, the research topics named Path Planning Algorithm and Automated Guided Vehicles in Industry were among the topics that attracted attention. As a result of these analyses, changes in research areas with developing technologies were shared with both academic and industry stakeholders for future study.
A Quick Employment of Markov Decision Process (MDP) in Partially Unknown Three-dimensional Discrete Space

This project aims to develop a planning algorithm under partially unknown 3D discrete space. Value iteration is employed since MDP complies with the state transition and decision patterns here, with an adaption for changing transitional probabilities and rewards. Eventually, the robot conducts an optimal policy on a fully known map and reaches the destination in a partially unknown environment with less efficiency.
Facing Graph Classification Problems by a Multi-agent Information Granulation Approach

In this paper, we investigate an agent-based prototypical framework for graph classification by offering a comparison with a standard graph neural network on several heterogeneous datasets. The design of the algorithm is based on a swarm of agents orchestrated via evolutionary optimization in charge of finding meaningful substructures from unstructured training data. The algorithm is tailored to face pattern recognition problems by a suitable embedding from the graph domain to Euclidean space, also exploiting local metric learning. Results on five open-access datasets of fully labeled graphs show interesting performances in terms of accuracy, counterbalanced by relatively high computational complexity.
Behavioural Modelling of Digital Circuits in SystemVerilog Using Grammatical Evolution

Digital circuit design is a very complex and time consuming task which requires a great deal of skill. It has been greatly aided by the use of Hardware Description Languages (HDLs) and powerful logic simulators. HDLs permit circuit designers to design circuits at a very abstract level. These are then tested before being committed to hardware. We present Automatic Design of Digital Circuits (ADDC), a system that employs Grammatical Evolution, SystemVerilog (an HDL) and Icarus Verilog (simulator) to design conventional circuits. ADDC is easily configurable to use with different HDLs and logic simulators. ADDC designs circuits at a higher abstraction level compared with previous works due to the use of HDLs, which are very expressive. Constructs such as if-else, always procedural block, generate for-loop (or synthesizable for-loop) and switch-case are readily available aiding designers to behaviourally describe circuits. In addition, due to the expressiveness of HDLs, ADDC evolved solutions are quite human interpretable. ADDC is tested using three combinational and two sequential circuits. We show that ADDC is successful on all five benchmark problems. In addition, we show that the introduction of simple domain knowledge into grammars has a major impact on evolutionary performance. Furthermore, Probabilisitc Tree Creation 2 initialization routine performed better on most digital circuit benchmark problems but not all compared to Sensible Initialization.
Improving Simulation Realism in Developing a Fuzzy Modular Autonomous Driving System for Electric Boats

In this work an Autonomous Driving System is presented with the attempt to define a qualified standard for a specific class of electric boats. The design involved both Classical Computation and Computational Intelligence techniques (mainly Fuzzy Logic and Artificial Neural Networks) and several version of the same ADS modular architecture have obtained progressively replacing the classical modules with the intelligent ones. The modular architecture is organized in "vertical" control levels which establish a control hierarchy and "horizontal" layers that follow the direction of data processing (from real-time perception of the environment to propulsion control). Compared to what was done in our previous works, the architecture proposed for the Autonomous Driving System for a specific type of boat has been updated and it is more detailed here. Furthermore, the ad-hoc Simulator developed to test and validate the ADS was improved in realism degree and other simulations have been performed. Outcoming data and post-analyses are reported here together with our conclusions.
Distributed Neural Fault-Tolerant Supervisory Control Against Backlash-Like Hysteresis

Neural networks (NNs) and fuzzy logic systems (FLSs) (Wang 1993, 1994; Shen et al. 2019; Su et al. 2000), as two universal function approximations, are utilized widely in the control design of nonlinear systems. For the approximation theories of NNs and FLSs, it is under the necessary condition that they are able to approximate a continuous unknown function as precisely as possible. The necessary condition is that all the variables in the function must always be within a compact set. In fact, the boundedness of approximation error is guaranteed by the condition. Now, let’s take function f(y) as an example to illustrate clearly it, where y can be a scalar or vector. It can be approximated by NNs or FLSs. Without losing the generality, its estimate is denoted by $$\hat{f}(y)$$ . Obviously, approximation error can be described as $$\varepsilon (y)=f(y)-\hat{f}(y)$$ . There exists a compact set $$\varOmega _y$$ is a compact set related to y. For the function f(y), only if y or each element of y always belongs to the set $$\varOmega _y$$ , it can be accurately approximated by neural networks or fuzzy logic systems, which implies that $$\varepsilon (y) $$ is bounded on the set.
Cybersecurity in Medical Field: A Threat Looming Large

Dear Editor, Today’s society and way of life are inconceivable without technology and the internet. From young children to our elderly grandparents, everyone is addicted to the internet, and these amenities contribute to make our lives more convenient and comfortable. Even then, we all know that anything made simpler inevitably has a drawback. The same things take place in the cyberworld and with these internet resources. It would take a lot of time and energy to steal private information from hardcopy files, but now it is simpler and takes less time thanks to the internet. Cybersecurity in recent times has been in news for past several years specially with hacking of government portals and websites some for ransom and many just to de face the websites by external enemy elements.[1] It was unheard of in medical field. But the recent hack of All India Institute of Medical Sciences (AIIMS) Hospital, New Delhi website gave a reality check.[2] Hospitals are also exposed and these situations are more vulnerable. So, given the dependence of medical institutions nowadays, cybersecurity cannot be taken lightly. It has the potential to cripple the health services severely. Because hospital records are kept digitally, an attack might completely wipe out all the data.[3] The reasons for hacking can be multiple, but data security should be priority for all. Medical data for a person is confidential and non-negotiable. So, what all steps can be taken to negate the threat of data stealing and hacking. Time to time personal training in IT, Data usage control, monitoring of mobile and connected devices should be routinely done.[4] A weak IT system is very vulnerable for cyber threats. Criminals can ask for hefty ransom, sell the patient data for insurance frauds, can tamper with internet connected medical devices, etc.[5] People tried to make paperless work with total digitalization after installing technology in medical institutions, but following a cyberattack, institutions had to reconsider whether digital or paper files were preferable for data protection. We cannot go back to past paper file records but we can increase the security of our current system. So, instead of being sitting ducks lets pledge for better data security. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
A Worldwide Overview on the Information Security Posture of Online Public Services

The availability of public services through online platforms has improved the coverage and efficiency of essential services provided to citizens worldwide. These services also promote transparency and foster citizen participation in government processes. However, the increased online presence also exposes sensitive data exchanged between citizens and service providers to a wider range of security threats. Therefore, ensuring the security and trustworthiness of online services is crucial to Electronic Government (EGOV) initiatives' success. Hence, this work assesses the security posture of online platforms hosted in 3068 governmental domain names, across all UN Member States, in three dimensions: support for secure communication protocols; the trustworthiness of their digital certificate chains; and services' exposure to known vulnerabilities. The results indicate that despite its rapid development, the public sector still falls short in adopting international standards and best security practices in services and infrastructure management. This reality poses significant risks to citizens and services across all regions and income levels.
Investigating the Relationship Between Internet Deserts and Socio-Economic Determinants of Prosperity in the United States

Warnings of an America divided sounded by Robert Putnam in his seminal work, Bowling Alone: The Collapse and Revival of American Community, heed true twenty years since its publication. Putnam presented a critical thesis on American political disengagement, which over the past 30 years has been exacerbated by an inequitable distribution of digital access and inclusion. Despite significant scientific and social progress deriving from advances in communications technology, online connectivity, and digital spaces undertaken since the 1990s, many Americans are still bowling alone. This work examines the question: to what extent do discrepancies between urban and rural communities related to internet access (a) affect quality of life and (b) lead to the creation of internet and civic deserts downstream? Using k-means cluster analysis, this study identifies ‘Internet Deserts’ (ID) and ‘Internet Shangri-Las’ (IS) across 2016, 2018, and 2020. It then conducts multivariate linear regression analysis across the ten top ID and IS congressional districts. Quantitative results show that in both ID and IS, income-related internet subscription is the most statistically significant factor in levels of internet access and subscription rates. However, ID face structural issues of access (or lack thereof) that preclude income, demonstrating a need for policies to encourage domestic internet infrastructure investment.
Future Lawyers, Future Laws: Reimagining Legal Education for Sustainability in a World “Governing Through Goals”

Current cohorts of law students are preparing to enter the legal profession in a very, very different world to the one that their academic instructors, and earlier generations of lawyers, encountered.
Regulatory Compliance in the Digital Age: Leveraging Legal Tech for Enhanced Governance

This paper advocates for the adoption of legal technology to enhance regulatory compliance in Nigeria. It adopts a doctrinal approach, elucidating key concepts such as regulatory compliance, the digital age, and legal technology. Subsequently, the paper delves into the significance of regulatory compliance in Nigeria, seeking to discern its implications for companies.Furthermore, the research meticulously evaluates the various ways legal technology can be effectively deployed by legal and compliance officers and regulators to enhance regulatory compliance within Nigeria. It delves into specific technological solutions, tools, and methodologies that can streamline processes, boost efficiency, and ensure adherence to complex regulatory frameworks. Additionally, the paper diligently addresses the risks and drawbacks associated with the failure to incorporate technology into regulatory compliance within Nigeria. It explores potential challenges, such as resource inefficiency, increased compliance burdens, and diminished competitiveness, underscoring the importance of embracing technological advancements to stay at the forefront of regulatory compliance practices.Throughout the paper, the findings amassed through diligent research and analysis are succinctly summarised, drawing attention to key insights and noteworthy revelations. Based on these findings, the paper formulates practical and well-considered recommendations, providing guidance on effectively integrating legal technology into the regulatory compliance landscape of Nigeria. Ultimately, the paper concludes with a comprehensive summary of its key points and highlights the significance of embracing legal technology to foster a more efficient, compliant, and investor-friendly business environment in Nigeria. By presenting a strong case for the adoption of legal technology, the paper aims to inspire further discussions and action toward bolstering regulatory compliance in the digital age.
Foreign Direct Investment and Renewable Energy Developmentin Sub-Saharan Africa: Does Governance Quality Matter?

Existing studies have been separated, considering the foreign direct investment (FDI) and renewable energy development (RE) nexus and the governance quality- renewable energy development relationship. However, the study regarding the moderation of governance quality on the FDI-renewable energy nexus is quite scarce. To fill the gap in the literature, the study therefore examines the moderation of governance quality on the influence of FDI on (RE) in 37 sub-Saharan African economies over the period 1996-2020. To achieve this goal, the panel corrected standard errors (PCSE) estimation technique has been adopted. The results show that FDI has a positive and significant effect on RE, meaning that an increase in foreign direct investment could lead to a 0.05 increase in RE. Moreover, the results unveil that governance quality is positively and significantly associated with RE. This means that a unit increase in control of corruption, governance effectiveness, rule of law, and voice and accountability leads to a 9.64, 9.10, 10.10 and 9.08 increase unit in the renewable energy sector, respectively. Most importantly, the results indicate that the interaction between FDI and governance quality has a positive and significant effect on RE. Policy implications are discussed based on the findings revealed by this study.