Reviewerly/exHarmony huggingface dataset:
Can you create? Visualising and modelling real-world mathematics with technologies in STEAM educational settings

In the past three decades, and especially accelerated during the Covid-19 pandemic, technology-assisted learning and teaching, and particularly in our case dynamic mathematics software in science-technology-engineering-arts-mathematics (STEAM) settings are becoming increasingly important. More recently, the development of more advanced mathematics-related technologies such as augmented reality, computer-aided design software, 3D printing, and global positioning system-enabled visualisations and modulations of mathematical concepts and the development of different behaviours in learning and teaching. These advanced technologies could enable students to more easily become creators, develop simulations, and utilise multiple representations of real-world or abstract objects by applying their mathematical and modelling skills. In addition, through such approaches, mathematical modelling and visualisations facilitated crossing subject boundaries in Science Technology Engineering and Mathematics (STEM) and more recently STEAM fields. Consequently, there is an increased amount of research connecting students' visualisations and modelling of their real-world environments and working on real-world problems with these technologies. In this paper, we reviewed promising research within the past two years on the uses of advanced technologies in mathematics, and in a broad sense STEAM education, circling around the question 'Can you create?' and how such novel approaches could impact behaviours of students and teachers in the current educational circumstances.
Real-time object detection for UAVs images based on improved YOLOv5

Object detection is the key task for many Unmanned Aerial Vehicles (UAVs) tasks. Compared with images captured from ground-based angles, images captured by UAVs are characterized by large differences in object scales, complex backgrounds, and dense objects. Common object detection models on UAVs platforms are not very accurate or effective. To solve the above problems, in our work, we propose a real-time object detector based on improved YOLOv5N for UAVs remote sensing images. Firstly, we introduce GhostNet to replace the standard convolutional layer in the feature extraction and feature fusion network of the original YOLOv5N, thus reducing the computational overhead of the detector. Secondly, the last layer of the backbone network is substituted by a Separable Visual Transformer (SepViT) block to enhance the connectivity of the backbone with global information. Finally, the Efficient Channel- Coordinate Attention (ECCA) module, realized by combining the Efficient Channel Attention (ECA) network and the Coordinate Attention (CA) module, has also been added to YOLOv5N, by combining channel information and spatial information to highlight information that helps detect small objects. The evaluation results based on the VisDrone2019-DET dataset show that the dataset is widely used for UAV remote sensing image evaluation, and the proposed model achieves 32.9% average accuracy (mAP50), which is 6.8% better than YOLOv5N, 15.6% reduction in model computation, 6.7% reduction in inference time, and both detection accuracy and detection speed, it is suitable for deployment on UAVs for real-time object detection tasks.
Predicting Price Direction of Bitcoin based on Hybrid Model of LSTM and Dense Neural Network Approach

Bitcoin is a rapidly growing but extremely risky cryptocurrency. It marks a watershed moment in the history of cash. These days, digital currency is preferred to actual money. Bitcoin has decentralized authority and placed it in the hands of its users. Many people are joining the largest and most well-known Bitcoin mining pools as the risk of working alone is too great. In order to enhance their chances of creating the next block in the Bitcoins blockchain and decrease the mining reward volatility, users can band together to form Bitcoin pools. This tendency toward consolidation may also be seen in the rise of large-scale mining farms equipped with powerful mining resources and speedy processing capability. Because of the risk of a 51% assault, this pattern shows that Bitcoin’s pure, decentralized protocol is moving toward greater centralization in its distribution network. Not to be overlooked is the resulting centralization of the bitcoin network as a result of cloud wallets making it simple for new users to join. Because of the easily hackable nature of Bitcoin technologies, this could lead to a wide range of security vulnerabilities. The proposed approach uses normalization and filling missing values in preprocessing, PCA for feature Extraction and finally training the model using LSTM-DNN Models. The proposed approach outperforms other two models such as CNN and DNN.
Cyber Threats in Cloud Computing Environment

Cloud computing has gotten to be a well-known worldview for giving on-demand get to shared computing assets. Be that as it may, as more information is being exchanged and put away within the cloud, concerns approximately information security and security have risen. Potential dangers to cloud computing include threats to data and inadequate security. Before organizations switch, security issues need to be properly evaluated. The goals of this study are to define cloud computing and cyber assaults against cloud infrastructure. In any case, servers and administrations are harmed by cryptography, conveyed refusal of benefit, and flooding attacks. In general, cloud security and cryptography play a basic part in guaranteeing the privacy, judgment, and accessibility of information within the cloud. As more companies go ahead with their information to the cloud, it is critical to proceed to create and actualize strong security measures to ensure against potential dangers and assaults. This study intends to illustrate cloud computing and risks & issues. The following research is to find solutions when the cloud security issues have been found.
Real-time Object Detection using Haar Cascade Classifier for Robot Cars

Ensuring accurate detection and identification of objects in the environment is crucial for the safe and efficient operation of autonomous vehicles in today’s world. The Haar Cascade Classifier, a machine learning algorithm for object detection, has gained significant popularity in recent years. This algorithm analyzes specific features of an object and has found diverse applications, including facial recognition. This research aims to investigate the application of the Haar Cascade Classifier in an autonomous robot car. The study encompasses the Haar-like features, which are fundamental building blocks of the algorithm, and the process for training and testing the dataset. The research also explores the actions that the robot car can take based on the type of object detected in its surroundings and delves into the practical implementation of the Haar Cascade Classifier in an autonomous vehicle. Furthermore, this work provides valuable insights into the effectiveness of the algorithm in detecting and identifying objects in an autonomous vehicle’s environment.
Blockchain Integrated Intelligent Monitoring System for Wireless Sensor Network

Security is a significant challenge in both wired and wireless networks. Data security has recently become a major concern in wireless sensor networks, particularly when data is sent to other systems. Any active node in a wireless sensor network is either collecting information from other active nodes or sending data to other active nodes. This research is primarily concerned with security based on authentication and authorization processes using blockchain technology. Blockchain-based security includes an intelligent monitoring system that monitors all active nodes in the network that are transferring data. This research study intends to design and develop a probabilistic mathematical model to monitor the functioning of each node based on the network activities. This is monitored by the blockchain enabled intelligent monitoring system. In this study, the proposed method measures network performance using four performance metrics: Packet Delivery Ratio (PDR), Network Lifetime (NLT), Residual Energy (RE), and Detection Accuracy (DA). This research work is entirely based on blockchain-enabled wireless sensor network (WSN) security.
Authentication in Cloud Computing: Open Problems

An authentication mechanism plays an essential role in access control. Authentication methods vary depending on the environment used, and many authentication methods are designed for cloud computing systems. This research paper examines the current authentication techniques employed in cloud computing systems and highlights unresolved issues and challenges that exist in this domain.
A Secure File Sharing and Audit Trail Tracking Platform with Advanced Encryption Standard for Cloud-Based Environments

In the digital era, organizations, especially financial institutions, place an increasing emphasis on data security and privacy. To maintain data confidentiality, availability, and integrity, financial auditing organizations need secure file sharing and audit trail tracking technologies. Financial auditing firms demand a cloud-based audit trail monitoring platform as well as a secure file exchange platform with high encryption standards. Users may submit and download data using a secure online interface. An administrative dashboard simplifies user registration and deactivation. The audit trail function allows the administrator to know who requested a file, when they requested it, and when the file was downloaded. This audit trail monitoring technology raises compliance responsibilities. The platform uses Advanced Encryption Standard (AES) encryption to secure data. The platform encrypts submitted files using a random key. The file owner gets a download request, which he or she may accept or deny. If the request is granted, the owner sends the user the AES key required to decode and download the file. On the platform, Amazon Web Services and Relational Database Service (RDS) hold massive files (RDS). The Amazon database is protected by login and DoS alarms. Login notifications for Amazon root and IAM users notify the administrator of the browser, IP address, date, and number of attempted logins. The administrator receives DoS attack notifications and database traffic statistics from a variety of sources. Administrators may use alerts to prevent security breaches. The solution facilitates secure and timely communication between financial auditing firms. Data is protected by AES encryption and Amazon S3 storage, while audit trail monitoring and alerts prevent data breaches.
Detection of Bone Fracture using Prewitt Edge Algorithm and Comparing with Laplacian Algorithm to Increase Accuracy and Sensitivity.

The purpose of the research was to is to compare accuracy and specificity in the bone fracture detection using novel modified Prewitt Edge Detection (PED) with Laplacian Edge Detection (LED). Two groups are compared, novel modified Prewitt Edge Detection (PED) (N=10) and Laplacian edge detection (LED) (N=10) The overall sample size was calculated using the G Power software with an alpha of 0.05, enrollment ratio of 0.1, confidence interval of 5%, and power of 80%. Using the SPSS statistical package, an independent sample t-test was used to compare the accuracy and specificity rate. Novel modified Prewitt edge detection (PED) algorithm found to be statistically significant when compared with the Laplacian edge detection (LED) classifier which gives accuracy p= 0.026, and specificity p=0.001(p<0.05) of bone fracture X-ray image. The Laplacian edge detection approach seems to be outperformed by a new modified Prewitt edge detection algorithm.
A Comparative Analysis of the Most Commonly Used Activation Functions in Deep Neural Network

Due to their effectiveness in many applications like neural language processing, image analysis, video analysis and healthcare and their ability to resolve challenging machine learning issues and complex problems, deep neural networks have recently gained more significance. A neural network may learn to simulate nonlinear relationships between the input and output variables by introducing nonlinearity through an activation function. As a result, it can recognize more intricate patterns and make more precise predictions. There are several activation functions, and it is used on a neural network determines how well it performs. The most important activation functions, are hyperbolic tangent (Tanh), sigmoid activation function, Rectified linear unit (ReLU), Exponential linear unit (ELU), softmax and a leaky ReLU activation function. They are analyzed and compared here. The conclusion of this research may be used in the comparison and to choose the activation functions according to the need.
Intelligent Home Surveillance System using Convolution Neural Network Algorithms

The creation of an automated security system aims to protect residences and workplaces by automating visitor entrance and enabling more flexibility in visitor record maintenance. Among all biometric authentications, face recognition is very secure because of unique facial features. There are two phases in authentication, face mask detection and face recognition. In first phase, Grassmann algorithm is used for face mask detection. If any mask is discovered, an alarm will sound for the user to remove the mask and in second phase face recognition is done through CNN. The CNN method is utilized to compare facial traits, and if an outsider is found, a warning message is then displayed to the user. Real time datasets are collected for training and testing the CNN model. The executed result gives 98.02% higher accuracy compared to existing method.
Haar Cascade Classifier-based Real-Time Face Recognition and Face Detection

Face recognition is one of the most promising applications of image analysis that has gained a lot of attention recently. One may say that a sizable amount of facial recognition tasks involves face detection. Depending on how powerful it is, it can concentrate processing resources on the area of a picture that contains a face. Human faces vary in attitude, expression, location, orientation, and skin tone, making it difficult to identify faces in pictures. spectacles wearers or people with facial hair, camera gain, illumination, and image resolution. The main objective of face detection algorithms is to determine whether a face is visible in an image. The sophistication and accuracy of face detection and recognition have recently been the subject of numerous studies, but the Real-Time Face Detector created by the Viola-Jones Algorithm, which can accurately recognize faces in real-time, has caused a revolution in this area. Numerous detecting problems, including object, face, emotion, and face identification, have been successfully solved using neural networks. It is efficient to recognize objects using cascade classifiers based on Haar features. Face, eye, and smile detection using the Haar cascade classifier and facial recognition using OpenCV will both be implemented in this study utilizing the LBPH algorithm. If the individual is known, the accuracy of the image is predicted as well as face, eye, and grin detection using bounding boxes in the camera stream. The results are then displayed according to whether the person is known or one in the dataset.
Development of Digital Competence Framework for Open Science

Open Science enables access to research by ensuring cooperation, transparency, availability of scientific research results and the use of open research data. In order to realize the full potential, it is necessary to improve the existing digital competences of the future stakeholders of Open Science. Two research objectives were set for this paper. The first objective was related to identifying and defining domains and digital competences for the framework. The framework consists of a domain of generic digital competences and a domain of professional competences. Nine of the generic digital competences were considered, while the domain of professional competences is under development and only certain digital competences are indicated. The second objective is related to the evaluation of the domain of generic digital competences and the application of this domain in determining the levels of digital competences. The research was conducted on a convenience sample of the student population in Croatia. Further guidelines for the development of the framework and digital competences are presented.
IoT Ontology Development Process for Well-Being, Aging and Health: Challenges and Opportunities

Ontology development processes are not trivial, given the inherently complex nature of knowledge capturing and management, as well as the need to provide structural and methodical approach on the process methodology itself in order for it to be adopted and usable. If aiming to develop an ontology for multidimensional concepts, such as well-being, aging and health, it is certain that knowledge from multiple domains have to be included, which only extends the time needed for ontology engineering. If such environments aim to be supported by the Internet of Things, than challenges rise even more. This paper provides a scoping analysis of existing well-known ontology development methodologies, with a note on the extent of their adoption and readiness to be used in a multi-domain circumstances. The approach to IoT ontology development process tailoring has been presented and elaborated, as well as the challenges specific to IoT ontology development for well-being, aging and health. Finally, research opportunities have been presented and future directions given on providing more comprehensive, more tailored and more usable ontology development methodologies.
Sentiment Analysis Using Bi-CARU with Recurrent CNN Models

For many natural language processing tasks, sentiment analysis has become increasingly important for extracting meaningful information from social media data. With the out-performance of neural network technology, the task of sentiment analysis can be addressed by advanced deep learning models. In this work, a combination model of Bidirectional-CARU (Bi-CARU) and Recurrent CNN is introduced to the sentiment analysis tasks. The proposed Bi-CAUR consists of three layers designed to obtain the main features of the input sequence, which can alleviate the long-term dependency problem and perform kernel information filtering from concrete to abstract, effectively improving the performance of the intermediate network on this problem. Next, the recursive structure of the CNN is connected to Bi-CARU to determine the sentiment analysis. The proposed Recurrent CNN implementation accepts features produced by its own previous convolution and pooling, which incorporates the performance of a CNN and requires only fewer parameters. Experimental results show that we are slightly more accurate, achieve faster convergence, and require fewer training parameters.
A context-aware multiple Blockchain architecture for managing low memory devices

Blockchain technology constitutes a paradigm shift in the way we conceive distributed architectures. A Blockchain system lets us build platforms where data are immutable and tamper-proof, with some constraints on the throughput and the amount of memory required to store the ledger. This paper aims to solve the issue of memory and performance requirements developing a multiple Blockchain architecture that mixes the benefits deriving from a public and a private Blockchain. This kind of approach enables small sensors - with memory and performance constraints - to join the network without worrying about the amount of data to store. The development is proposed following a context-aware approach, to make the architecture scalable and easy to use in different scenarios.
Federated Learning Support for Cybersecurity: Fundamentals, Applications, and Opportunities

The term “Federated Learning” (FL) refers to a modern and advanced intelligence system that uses data storage that is not centralised. Most industrialists are hesitant to use the Internet of Everything (IoT) technology since cyberattacks are common and occur in numerous real-time applications around the globe. To deal with this problem, FL can be used to prevent such cyberattacks by offering better cybersecurity. This research intends to fill in some of the gaps between where federated AI is now and where it can be widely used by doing a thorough investigation of FL’s security and privacy features. We provide an informative description of techniques and different implementation styles, while also examining the current difficulties in FL, and we establish a complete evaluation of security and privacy problems that must be taken into account. Our research shows that the privacy risks connected with FL are lower than the security risks. While inference-based attacks pose the greatest risk to FL’s privacy, transmission difficulties, toxicity, and backdoor breaches pose the greatest risk to security. In the final section of the paper, we outline key areas for future study that will help FL adapt to real-world settings.
Importance of Optical Metrics for IGP Configuration Change Prediction

We used Machine Learning (ML) algorithms on data provided by a client’s real network to identify the role of optical device metrics in detecting flapping links (links that go down multiple times a day) and Interior Gateway Protocol (IGP) configuration changes in the next five days based on data collected for the previous five days. Our prototype shows that we could predict upcoming IGP changes five days ahead with 95% precision and 75% recall. Adding optical data to the ML model increased the performance by about 9%. The importance of optical features like “OCH-OPTMAX”, “OCH-OPTMIN”, “OCH-DGDMAX”, “E-SES”, “E-UAS” and “E-ES” that account for optical minimum power, unavailable seconds, and errored seconds is confirmed using common feature importance wrapper methods. This ML approach is significantly better than existing manual methods followed by the operator.
On the Worst-Case Analysis of Cyclic Coordinate-Wise Algorithms on Smooth Convex Functions

We propose a unifying framework for the automated computer-assisted worst-case analysis of cyclic block coordinate algorithms (BCD) in the unconstrained smooth convex optimization setup. We compute exact worst-case bounds for the cyclic coordinate descent and the alternating minimization algorithms over the class of smooth convex functions, and provide sublinear upper and lower bounds on the worst-case rate for the standard class of functions with coordinate-wise Lipschitz gradients. We obtain in particular a new upper bound for cyclic coordinate descent that outperforms the best available ones by an order of magnitude. We also demonstrate the flexibility of our approach by providing new numerical bounds using simpler and more natural assumptions than those normally made for the analysis of block coordinate algorithms. Finally, we provide numerical evidence for the fact that a standard scheme that provably accelerates random coordinate descent to a ${O}(1/k^{2}$) complexity is actually inefficient when used in a (deterministic) cyclic algorithm.