Reviewerly/exHarmony huggingface dataset:
Multi-authority ABE for Non-monotonic Access Structures

Attribute-Based Encryption (ABE) is a cryptographic primitive which supports fine-grained access control on encrypted data, making it an appealing building block for many applications. Multi-Authority Attribute-Based Encryption (MA-ABE) is a generalization of ABE where the central authority is distributed across several independent parties. We provide the first MA-ABE scheme from asymmetric prime-order pairings where no trusted setup is needed and where the attribute universe of each authority is unbounded. Moreover, it is the first to handle non-monotonic access structures. These features broaden the applicability and improve the efficiency of our scheme. Our construction makes a modular use of Functional Encryption schemes with fine-grained access control.
An Analysis of Recent Developments in Healthcare Technology from the Standpoints of Artificial Intelligence, the Internet of Things, and Remote Health Monitoring

During the last ten years, the healthcare market has been propelled by technological advancements in disease diagnosis, prevention, and treatment.This would not have been possible without the rapid development of AI-driven technology and also the digitization of healthcare operations in response to worsening global conditions and the increasing demand for rapid and high-quality medical assistance.In this paper we researched the most crucial technologies that can revolutionize healthcare sector.We delved into the technological developments that are pushing the healthcare sector forward into digital transformation especially from the perspective of artificial intelligence, internet of things and remote patient monitoring.
Successful Data Mining: With Dimension Reduction

The technique of collecting important knowledge characteristics from a dataset in order to further transform it into usable information is known as data mining.With the combined use of statistics and machine learning, data mining has grown in popularity for a variety of applications, including better decision-making, revenue and operation optimization, cost reduction, anomaly detection, and many more.Despite recognising patterns, One of the most challenging jobs in machine learning is clustering.It is difficult to build an acceptable number of clusters because doing so could decrease the effectiveness of training and assessment.In engineering and scientific applications, the clustering value has steadily increased during the past few years.Many clustering techniques have low classification accuracy, and if we utilise a lot of data with larger dimensions, it will affect the performance and required storage of the algorithms.To reduce this we have to reduce the diamensions of dataset so that clustering algorithm performance will increases and required space will decreases so in this approach we are going to reduce the dimensions of dataset on AFRBFNN [23] algorithm.It is predicated on RL ideas.It categorises every pattern that the two techniques discussed earlier were unable to categorize [23].Its misclassification rate has decreased.When compared to the other techniques, this model delivers the best classification accuracy.This approach is also quick and has less overlapping.So that we added PCA (Principal Component Analysis) to reduce the dimensions of the dataset and comparing the results with [23] before applying PCA and after applying PCA.
Influential factor analysis for cloud computing technology service provider

Nowadays, computer processes and applications are run on remote Internet servers providing many cloud services for their users. The increasing number of services, whose features are continually being refreshed, make it challenging to choose the most appropriate option. Evaluation of CCT (Cloud Computing Technology) service providers requires identifying important criteria and their impact on users' decision-making. This paper provides a detailed literature review of these criteria. The method is based on Multi-Criteria Decision-Making (MCDM) since unfavorable and beneficial criteria must be evaluated together to ensure that decision-making is efficient and comprehensive. The process is handled by gathering linguistic expert evaluations on different CCT service providers using a group decision-making (GDM) approach. The 2-Tuple DEMATEL method is applied to reveal the relationships between criteria and dimensions. The weights of the evaluation criteria are determined based on the findings of the empirical study. The impacts of each evaluation criterion are also obtained. The proposed framework enables IT managers and/or related managers to better evaluate CCT services. The empirical findings may produce a guide for users to find out about the system components of CCT. The research results will help companies assess and select the most suitable CCT using linguistic evaluation. The originality of this paper comes from its comprehensive strategical analysis of key factors of CCT to determine standards for service provider assessment. In addition, the researchers recommend the linguistic DEMATEL application, in which decision-makers (DMs) use the linguistic set with which they are comfortable and familiar to assess the relationship between evaluation dimensions and criteria.
Model-Free Formation Control of Autonomous Underwater Vehicles: A Broad Learning-Based Solution

Dear Editor, We develop a broad learning-based algorithm to enforce the formation control of AUVs. Compared with the deep learning (DL) based formation solutions, our solution employs the broad learning system (BLS) to remodel the learning framework without a retraining process, such that the learning structure can be simplified and the training time can be reduced. Finally, simulation and experimental studies are both performed to verify the effectiveness.
Security Ontology OntoSecRPA for Robotic Process Automation Domain

Robotic process automation (RPA)* based on the use of software robots has proven to be one of the most demanded technologies to emerge in recent years used for automating daily IT routines in many sectors, such as banking and finance. As with any new technology, RPA has a number of potential cyber security weaknesses, caused either by fundamental logical mistakes in the approach or by cyber-human mistakes made during the implementation, configuration, and operation phases. It is important to have an extensive understanding of the related risks before RPA integration into enterprise IT infrastructure. The main asset operated by RPA is confidential enterprise data. Data leakage and theft are the two main threats. The wide application of RPA technology in information security-sensitive sectors makes the protection of RPA against cyber-attacks an important task. Still, this topic is not yet adequately investigated in the scientific press and existing articles mainly concentrate on stating the RPA security importance and describing some threats. In this article, we present a flexible tool, security-oriented ontology OntoSecRPA*, which systematically describes RPA-specific assets, risks, security, threats, vulnerabilities, and countermeasures. To the best of our knowledge, there are currently no ontologies available that are specific to the RPA domain, and existing security ontologies lack RPA-related features. In the future, the proposed ontology can be updated and used in different ways, for example, as a checklist for risk management tasks in RPA solutions and a source of information for an expert system or a concentrated domain-specific source of information, which indicates its wide practical application. The proposed ontology was formally verified by applying ontology completeness assessment and used for risk assessment in a sample scenario.
From Immersive Experiences to the Metaverse: How Can We Engage More Users?

research-article Share on From Immersive Experiences to the Metaverse: How Can We Engage More Users? Author: Jie Li EPAM EPAMView Profile Authors Info & Claims InteractionsVolume 30Issue 3May + June 2023pp 48–53https://doi.org/10.1145/3589134Published:01 May 2023Publication History 0citation226DownloadsMetricsTotal Citations0Total Downloads226Last 12 Months226Last 6 weeks226 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Training Digital Competencies in Future Primary School Teachers: A Systematic Review

Technology proliferated in our daily lives, including in the educational system. To make good use of it, it is so important that teachers have the best training in its use and possibilities. The main objective of this study was to analyze the different methods of training for future primary school teachers and assess the efficacy of the methods. We used SCOPUS, and Web of Science databases to carry out this systematic review and we followed the PRISMA statement. Two authors independently conducted the literature search, and then, they reached a consensus. A total of 13 relevant articles were selected to be analyzed in detail. Our results showed that the training programs carried out improved the digital competencies of students of primary education degrees. Moreover, they improved the knowledge of theoretical content, improved linguistic competence and fostered cooperative work and creativity. In conclusion, we encourage teachers, not only future teachers but also current teachers, to take these types of training programs in order to improve their digital competency, which is a necessity in current society.
A Software Framework for Jetson Nano to Detect Anomalies in CAN Data

The current pace of development of cyber-physical systems requires the elaboration of fast methods for analyzing data circulating in them. Anomalies are patterns of data that do not conform to the concept of normal (expected) behavior. The data available on vehicular CAN bus reflects the current state of a vehicle, and it is the product of the engine control system and various sensors. In the present paper, we present software to process data from the CAN bus with the goal to detect anomalies in it. Often the data circulated in a vehicle is vendor-specific, in addition, we consider various methods for finding anomalies, therefore, it is advisable to design extensible software in the form of a software framework. The work is intended for the Jetson Nano platform, but can be run on another embedded Linux platform with restrictions on detection methods. We discuss hardware and software methods to obtain information on current state of the vehicle in real time, and then we briefly study how to implement anomaly analysis methods on the received data. Evaluation of detection methods is not included in the goals of the work; we mainly provide infrastructural methods for receiving data from the bus, decoding it and passing it to an anomaly predictor. Software was implemented in C++ with the ability to run Python code for the prediction, tests were carried out on a Mazda 6 first generation car and its ECU.
Segmentation of Prostate Cancer on TRUS Images Using ML

Medical research has made tremendous progress in detecting various pathologies in the human body. There is still the problem of the speed of the process, and the lack of a sufficient number of trained professionals in this field. Detection of prostate cancer, in particular, without surgery is a very labor- intensive process. A neural network-based machine learning algorithm has been proposed to solve this problem, making it possible to see suspected areas of lesions in the organ. In this study, a comprehensive analysis of TRUS image processing approaches was carried out, and an algorithm architecture was developed to segment the affected areas. Based on this analysis, we have developed a system for automatic detection and segmentation of prostate cancer.
Uncertainty Estimation in Machine Learning

Most machine learning techniques are based upon statistical learning theory, often simplified for the sake of computing speed. This paper is focused on the uncertainty aspect of mathematical modeling in machine learning. Regression analysis is chosen to further investigate the evaluation aspect of uncertainty in model coefficients and, more importantly, in the output feature value predictions. A survey demonstrates major stages in the conventional least squares approach to the creation of the regression model, along with its uncertainty estimation. On the other hand, it is shown that in machine learning the model complexity and severe nonlinearity become serious obstacles to uncertainty evaluation. Furthermore, the process of machine model training demands high computing power, not available at the level of personal computers. This is why so-called pre-trained models are widely used in such areas of machine learning as natural language processing. The latest example of a pre-trained model is the Generative Pre-trained Transformer 3 with hundreds of billions of parameters and a half-terabyte training dataset. Similarly, mathematical models built from real data are growing in complexity which is accompanied by the growing amount of training data. However, when machine models and their predictions are used in decision-making, one needs to estimate uncertainty and evaluate accompanying risks. This problem could be resolved with non-parametric techniques at the expense of greater demand for computing power, which can be offered by modern supercomputers available, including those utilizing graphical and tensor processing units along with the conventional central processors.
Development and Application of an Application with Augmented Reality Technology for Training Future Aircraft Designers

This article discusses the creation of an application with augmented reality technology, in order to improve the process of training skilled professionals in the field of aircraft engineering among students of higher education institutions. The application allows students to study and test their knowledge about the main aircraft units located in the aircraft class of the Department of Aircraft Construction and Design. This project is an Android-based application with the recognition of details on real objects (aircraft units) arranged in the class. The following tools and technologies have been used to create the application: Blender 3D for modeling objects, Unity 3D for creating the application interface and Vuforia Engine as a developer's toolkit.
Blockchain Architecture for Secure Storage of IoT Data

The article proposes a new approach to the transfer and storage of data, including from IoT devices, for medical systems to improve the information security of these systems. The proposed approach uses the blockchain methodology for storing batch and streaming information from various wearable devices designed to take medical indicators (blood pressure, pulse, temperature, etc.) in real time. For this, a blockchain from blockchains is used, which allows considering the peculiarity of biomedical data when they are presented in a distributed information storage. The article presents the storage architecture, describes data storage structures, and proposes pseudocode for creating a blockchain and adding information to it. A prototype of the system is presented, including services for receiving and processing information, as well as information retrieval. Computational experiments were performed to evaluate the performance of the system prototype on a test data set. Their results showed that the creation of new blockchains can be more time-consuming compared to traditional data storage. However, the proposed architecture, along with ensuring data protection, allows you to not lose the search speed.
Integrative Approach to Creation of Information Systems and Entropy Analysis of Linguistic Information

An integrative approach is presented as a particular dialectical method for building information-analytical and expert systems (IAS, ES). This method is based on the complex use of expert assessments and automated analysis of text objects. The fundamental basis of the method was the classical entropy analysis of sign systems of information theory in combination with modern elements of digital and cognitive expert linguistic analytics. The method is relevant for the formation of information and analytical projects and a formalized model of linguistic analysis of texts.
Detection of Lung Disease using an Efficient Image Classification Technique

Image classification is an area of research and exploration. Many researchers and scientists have been trying to find better and more efficient methods for various respective fields. But still, a lot of work is being done and yet to be done in this vast area. We have proposed a technique that uses the two points, i.e., feature subset selection and classification. An embedded model is proposed having a feature subset selection strategy and classification strategy. A lung cancer data set is attained from the UCI repository for analyzing the results. The first size of the initial feature set is reduced using feature selection techniques and then a classification strategy is applied to the attained reduced feature set to achieve good performance.
Artificial Intelligence Based Handwriting Digit Recognition (HDR) - A Technical Review

The ability of a computer to recognise and interpret handwritten input is referred to as "handwritten text recognition," proving an intangible ability for artificial intelligence (AI). The documents could be in the form of a scanned handwritten text or a photograph of a handwritten note that has accuracy issues from the start of the handwriting recognition process. The major problem is the availability of a wide range of handwriting that may be good or bad. The computer needs to be trained with enough examples, but when it comes to accuracy, it becomes challenging due to characters with similar looks. Further, if the letters are all connected, it also makes it hard for a computer to recognise accurately. In the case of a photograph of handwriting, if the angle is changed, it would result in obscure characters that would be harder for a computer to recognise. There is a lot to learn for a computer, so artificial intelligence comes into play, which allows the computer to learn over time and adapt its output. The computer can recognise hastily scribbled text and normal handwritten letters, which greatly improves the accuracy and ability of the system.
Steerable Pyramid-based Multi-Scale Fusion Algorithm for Single Image Dehazing

Multi-Scale Fusion Technique employs different versions of the image to be dehazed to extract haze-relevant parameters that aid in image dehazing. The paper proposes a variant of the technique using a Steerable Pyramid has been proposed. The proposed method extracts steer-coefficient matrices from the original hazy image and incorporates them into the multi-scale fusion algorithm. The proposed technique aims to reduce the halo effect observed in image-dehazing applications and related works. Moreover, this paper aims at the quality improvement of the output image by the novel algorithm it proposes. Quantitative, as well as a visual display of results, using the I-HAZE dataset, give an accurate interpretation of the effectiveness of the proposed work.
A Systematic Literature Review: Approach Toward Blockchain Future Research Trends

A decentralised peer-to-peer technology called blockchain offers security services based on the following four core ideas: authentication, confidentiality, integrity, and authorisation. The purpose of this paper is to provide an overview of the blockchain’s mechanism, architecture, and various components. It also compares different blockchain types based on various parameters and their applicability. This paper also emphasises the general security of blockchain. We evaluate the blockchain in particular to create thorough categories for blockchain security risks, examine actual attacks by bugs against the blockchain, and list recently created security measures for the technology. This paper also demonstrates the significance of various blockchain applications in the fields of healthcare, IoT, smart grid, governance, defence, and military. Overall, this paper provides an overview of various blockchain applications, their characteristics, challenges, and potential future.
An innovative wheelchair for quadreplegic patient using IoT

There is a common type of paralysis in which the body below neck, legs and in some cases its arm, unable to move. Such condition is referred as quadriplegic and mainly occur due to spinal injury and as result the connecting nerve between brain and spinal cord get damaged. This Paper is based on an IoT enabled wheelchair for disabled persons. This discloses an innovative wheelchair for quadriplegia patient that controlled by hand gestures, motion. Self-mobility is achieved with the smart wheelchair and provides mechanical movement according to preprogrammed algorithm. Researchers all over the world are focusing at developing smart wheelchairs to solve problems faced by Quadriplegia patient. Other than normal functionality our research focuses on Inbuilt Alert system, health regulation system and Artificial intelligence (AI), machine learning (ML) based path prediction system which makes this smart wheelchair design more Efficient and Safer.
Time Series Anomaly Detection System with Linear Neural Network and Autoencoder

The methodical compliances of time series data have been acclimated for digitalization in ultramodern industrial processes. Artificial implanted detectors produce unknown streaming data which is available for dimensional reduction. The proposed reduction step exploits autoencoders infrequently combined with direct neural networks. The decentralized machine learning based monitoring detectors are equipped to capture bitsy changes in the product schedule. The monitoring detectors descry multi-dimensional changes in the product outfit of N-dimensional time series. The traditional anomaly detection methods are changeable in real-time industrial-based time series data. We propose a multidimensional, effective, and decentralized machine learning based anomaly detection system for industrial high dimensional product schedules in this paper. The experimental setup is designed to map multidimensional reduction from time series data. The proposed decentralized system improves calculation time and effective anomaly detection can be employed for processing real- time industrial data.
Based on RDWT and SVD Arnold Transform: A Strong and Secure Image Watermarking Method

Originally, encryption techniques were used for owner authentication. Recently, digital image watermarking (DIW) presents a better scheme for copyright protection. This work presents a watermarking system based on singular value decomposition and hybrid redundant discrete wavelet transform. The technique includes fragmentation of cover image in four sub-bands. Arnold transform is used as a scrambling method which gives security to the proposed method. Watermarked image is undergoing through several attacks to find out the robustness. Experimental results show enhanced performance and better robustness of proposed scheme than existing scheme.