Reviewerly/exHarmony huggingface dataset:
Blockchain-Enabled IoT (B-IoT): Overview, Security, Scalability &amp; Challenges

In Internet of Things (IoT) several applications that call for the collecting and sharing of enormous amounts of data where the information security is a crucial problem. Blockchain technology is still being used in IoT applications, especially at the lower end of the computing spectrum. As a result, a number of issues and questions need to be resolved, and it is uncertain what the future will bring. The IoT operational architecture is centralized by design whereas blockchain has decentralized architecture, however, decentralized architecture enables scalable and effective communication between conventional and IoT equipment. Decentralization, distributed accounting, and point-to-point communication are among the characteristics of blockchain. Blockchain integration in IoT applications can enable services like availability, authentication, authorization and integrity. This study provides an overview of the literature on IoT and blockchain integration by looking at current research issues and trends in the use of blockchain-related methods and technologies in the context of security.
A Model to Measure Cyber Security Maturity at the National Level

Cyberspace is one of the important components of countries' national security, and the evaluation of the security of this space will identify the strengths and weaknesses of systems and networks. Using the results of the evaluations, countries can adopt the necessary strategies to improve cyber security. Considering the inefficiency and weakness of global and regional models for measuring Iran's cyber security maturity, the present research was conducted to obtain a national cyber security maturity measurement model with a mixed approach (qualitative and quantitative) and content analysis method. Further, after receiving experts' opinions about the importance and degree of influence of the components on each other, ten key components of cyber security were selected using the method of cross-effects analysis and Micmac software, and 30 criteria were used compiled to measure the key components. Finally, the level of maturity of Iran's cyber security has been determined based on the proposed maturity model, and the results of this model have been compared with other research models and its advantages have been stated.
Smart Traffic Light Using Raspberry Pi and Digital Image Processing

Congestion is one of the problems in traffic, especially at intersections with traffic lights. In this study, a traffic light management system was developed by utilizing image processing that can adjust the duration in the queue depending on road conditions. The system is developed using Raspberry Pi 4 as the controller. The system will capture the image of the traffic conditions via cameras and then perform the image processing using OpenCV and Tensorflow to calculate the number of vehicles. The number of vehicles will be sent to the webserver database to be processed as the input for traffic system settings. The testing shows that the system can set the timing of the traffic lights. When there are 3 vehicles in the queue with traffic light is red then the system set the timing for the green light to 10 seconds. Additional of 1 vehicle in the queue will increase the duration of the green light by 4 seconds.
Multiplayer Ludo Game Application Based on Mixed Reality Using UrhoSharp and RabbitMQ Message Broker

This paper describes the design and implementation of the Ludo game on Microsoft Hololens to create a mixed reality-based application with Hololens technology connected to many services. The Ludo game is made for two players, with player one playing on Hololens and player two sending and receiving player one's data via the console. The Ludo game with Hololens is one of the results of the development of Hololens technology and the RabbitMQ Messaging Server. The Ludo game is made on Visual Studio software using UrhoSharp Framework and RabbitMQ Messaging Server service with the AMQP protocol. As application frameworks, Hololens and UrhoSharp function as devices for visualization and running games. At the same time, the RabbitMQ Messaging Server and AMQP protocol serve as services that connect two players from the Hololens and console by sending the data needed by the application. The test results show that Hololens is running successfully and communicating with other console players. Thus, the results obtained are the purpose of this paper, which is to create a mixed reality-based application with Hololens technology connected to many services.
HW/SW implementation of RSA digital signature on a RISC-V-based System-on-Chip

A digital signature is a cryptographic technique used to generate the signature of a message and verify the signature of that particular message. This signature scheme can ensure the validation of the authenticity, integrity, and non-repudiation of a message. Nowadays, the public-key cryptosystem RSA is widely used to perform the digital signature by using a public/private key pair. This paper describes the software and hardware hybrid implementation of the RSA digital signature on a System-on-Chip (SoC) that uses a RISC-V processor as processing core. The key generation for the RSA has been done in software, and the most time-consuming mathematical operation behind the RSA algorithm, the modular exponentiation, has been implemented in hardware. The proposed approach has been validated using one Xilinx Kintex-7 FPGA on the Genesys-2 FPGA board. The acceleration factor has also been calculated by comparing the software versus hardware implementation. The designed RSA IP has the flexibility to be reconfigured with different key sizes (512, 1024, 2048 bits) as per the requirements of the security level. The proposed implementation is verified using the National Institute of Standards and Technology (NIST) test vectors.
Existing Tower Infrastructure Classification and Analysis for 5G Implementation in Jakarta Area Using Machine Learning Models

The Ministry of Communication and Informatics plans to construct infrastructure for fifth-generation network deployment in Indonesia. However, their readiness to support 5G systems should be evaluated. There is non-standalone architecture for 5G network related to the use of existing towers. The NSA architecture enables the use of towers that meet the 5G specification. Machine learning can be used to classify these towers quickly. Based on working frequency on the existing tower data, this research conduct tower classification using machine learning models to get the tower's quantity that are ready for the tower's non-standalone architecture and visualize the data on the open street map to evaluate the network needed in the area. The considered area for the tower data is Jakarta Area. The data shows 839 towers need to be upgraded to the fifth-generation frequency. The data also shows 1152 towers that are ready for the fifth-generation network. This result can benefit the operator in estimating the investment of the tower to be ready for the new technology and impact the CAPEX view to construct the new tower.
Spectral Privacy Detection on Black-box Graph Neural Networks

Graph neural networks (GNNs) have emerged as a promising approach for real-world applications such as the Internet of Vehicles (IoV) or social networks, yet the spectral properties of the GNN model are under-explored, especially from a privacy detection and protection perspective. This article proposes a general framework to distinguish black-box GNN based on spectral filtering performance. The framework offers a new perspective for spectral privacy detection with a generated graph sample set. Specifically, by introducing a set of graphs of different connectivity, we could access the spectral characteristics of black-box GNN. Furthermore, we could infer information about its underlying structure, such as message passing or positional embedding. Overall, our work sheds light on the spectral properties of GNNs and opens up new avenues for analyzing their behavior in real-world applications.
An approach for automatically solving handwritten equations using deep learning

The mathematical description of two things that are equal—one on each side of an "equals" sign—is called an equation. Equations help us address problems in our daily lives. Other fields that employ mathematical equations include medical, aerospace, traffic control, etc. In this paper, we have proposed a deep learning-based approach to solving handwritten equations in a digital system. We have only attempted to solve algebraic or arithmetic equations. The accuracy achieved while testing the application with expressions and equations was observed to be 90%. To solve equations received from the image scanned, we have proposed the use of a string extraction and manipulation method to convert the handwritten tedious equations into an expression that can be solved easily using a computer or digital system.
Single Axis Solar Tracking System Using 555 IC

Growing need for energy and worries about the environment have made renewable energy resources an increasingly important alternatives to conventional power resources. With considerable ecological and economic prospects, the topic of green energy is quickly becoming one that is promising for countries. In spite of its enormous potential, solar power, particularly in agricultural areas, performs a crucial role as a major power resource. The project is split into two phases, with the hardware and software implementation of a system that uses a 555 IC with light detecting resistor Sensors for continuous tracking of sunlight being the primary focus of each phase. The servo motor and sensors have been linked to achieve optimal efficiency by following the path of the sun. In this project,one axis of a solar tracking gadget has been created in order to capture more solar energy. A 555IC has been employed as the primary controlling component in this project. To precisely determine where the sun is in the sky, the project uses dual LDRs. The solar panel is subsequently turned in the appropriate direction using the servomotor. The PV panel and servo motor are mechanically connected. By implementing a single-axis solar tracking system, the system optimizes the daily sun tracking, thereby enhancing the efficiency of the solar panel.
Enhancing Data Engineering and Accelerating Learning through Intelligent Automation

The rapid growth of data and the increasing complexity of analytical tasks have necessitated the development of efficient data engineering processes and accelerated learning techniques. Intelligent data engineering and automated learning are two important fields in the domain of artificial intelligence. The aim of intelligent data engineering is to develop efficient and effective algorithms for processing large volumes of data. Automated learning, on the other hand, focuses on creating algorithms that can learn from data without being explicitly programmed. Both fields are closely related and have significantly contributed to the development of various applications such as machine learning, natural language processing, computer vision, and robotics. Through a comprehensive review of data engineering concepts, challenges, and automated learning techniques, this paper highlights the benefits and advantages of incorporating intelligent automation into data engineering workflows. It presents case studies and real-world examples showcasing successful implementations of intelligent automation in data engineering projects. In addition, the paper discusses performance evaluation metrics, challenges, ethical considerations, and potential intelligent automation risks. In addition, it discusses future research and development opportunities in the disciplines of intelligent data engineering and automated learning. The findings of this study shed light on the potential of intelligent automation in augmenting data engineering processes and accelerating learning, thereby revealing new avenues for efficient data utilization and knowledge extraction across a variety of domains.
Gesture Recognition Using Multiple mmWave FMCW Radars

Human-computer interaction using radar has gained a lot of attention. Gesture recognition with a single Frequency Modulated Continuous Wave (FMCW) radar has been investigated by many researchers, and several promising results have been achieved. However, the stability of such a system still needs to be improved. This paper proposes a signal-processing approach for gesture recognition based on multiple FMCW radars.VGG16 pre-trained model is employed to extract features from the dataset, after which gesture recognition is performed using a Support Vector Machine (SVM) classifier and an XGBoost classifier, respectively. Moreover, the effect of the incomplete dataset in case of failure of one of the radars on the accuracy of gesture recognition is also analysed. The experimental results show that the SVM classifier in the multiple radar scenario obtains up to 96.67% accuracy on the test set, which is 1.78% to 5.95% higher than the single radar scenario. In the multi-radar scenario, when the extracted dataset is incomplete, the SVM classifier achieves up to 95% recognition accuracy. This proves that multi-radar is more stable than the single radar scheme. This system can be applied in smart homes, in-car entertainment systems or smart factories.
Natural Feature Tracking Algorithm in Augmented Reality Recognition of Asmaul Husna

This study discusses using a natural feature-tracking algorithm in augmented reality (AR) technology to recognize Asmaul Husna. This study uses a natural feature-tracking algorithm to test the detection speed at different distances. The method used in this study is to extract natural features from the Asmaul Husna object to be recognized. Next, a natural feature tracking algorithm previously developed was used for the detection process. The study tested different distances from the object to be identified, namely 10 cm, 20 cm, and 30 cm. The study results show that the natural feature tracking algorithm used in AR technology provides high-speed detection. The average detection time was 0.017 seconds at 10 cm, 0.025 seconds at 20 cm, and 0.040 seconds at 30 cm. In conclusion, this study demonstrates that using a natural feature-tracking algorithm in AR technology can provide an effective and efficient solution for recognizing objects with breakneck detection speed. This can help in the development of AR technology for broader and more valuable applications, particularly in the field of religious education.
Nonvolatile Opto-Mechanical Memory in a Fast Switch via Buckling-based Multi-Stability

We demonstrate a near-continuously tunable non-volatile photonic memory in a MHz-rate piezo-actuated photonic cavity modulator. This functionality is achieved via buckling-based mechanical multi-stability arising from the interaction between intrinsic stresses and mechanical inhomogeneity of the actuators.
Face Verification System for Age-Invariance using Deep Convolution Neural Network(CNN)

There has been remarkable research progress made in the area of face recognition and it is been used in a wide range of applications from personal logins, security checks, attendance systems, etc., The progression of age reflects the facial features and this remains a great challenge while performing face recognition when matching is made with images of a large age gap. This paper proposes a robust face recognition technique invariant of the ages. The proposed system adapts the Deep Convolution Neural Networks (CNN) considering the matching of faces irrespective of the ages. Unlike other face recognition systems, the proposed system estimates the age and creates an Age-Invariant Face Representation using deep feature learning, where the features that are less affected by age-related changes like the texture Descriptors and the appearance-based features are extracted. This representation is used by the classification/matching step to perform recognition. The CACD data set is utilized to carry out the task of training and testing the algorithm. The system is verified to get robust results and when compared with the existing system provides an accuracy of 98.01% and the Rank-1 Accuracy of 88.21%.
Application of the Association Rule in Online Stores Using Apriori Algorithm for Product Recommendations at Promotional Events

People now regard online shopping to be a necessity. Many online shops fail because of fierce competition and poor sales strategies. This study uses Fawazz Shop transaction history data. This study aims to recommend goods for Fawazz store promotion events. The Association Rule of apriori methods on data mining was used in this research to determine the structure of links between goods, data processing using Ms. Excel, Rapid Miner 9.1 Software, and Testing using Python. This study's results show three association rule rules using minimum support of 10% and minimum confidence of 50%, namely a combination of iron ear match items and blackhead tweezers with a support value of 30.4%, a confidence value of 74.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , a combination of amplifier antenna items and digital antennas with a support value of 23.5%, a confidence value of 61.2%, and a combination of iron ear match items and digital antennas. The results of this model can be used to recommend goods at promotional events.
Implementation of Support Vector Machine for Recycled Plastic Bottles Detection

Societal and economic developments impact the level of consumption, which causes the addition of plastic waste. This study used the Support Vector Machine (SVM) method with Oriented Fast and Rotated Brief (ORB) feature extraction to detect recycled plastic bottles. The experiment was carried out using preprocessed datasets, and the results showed that the Support Vector Machine model with a polynomial kernel gave the best performance in classifying with an accuracy of 74%. In addition, the variation experiments on split data and kernel types show that the SVM model with polynomial kernels provides the best accuracy on the division of different training and test data. We recommend carefully selecting parameters according to the characteristics of the data. In future work, we will consider developing a more complex system by integrating recycled plastic bottle detection with Internet of Things (IoT) technology and developing better feature extraction to improve the accuracy of recycled plastic bottle detection.
Blockchain Revolution: Empowering the Electric Vehicle Industry through Integration and Case Study Analysis

In the rapidly evolving landscape of electric vehicles (EVs), blockchain technology emerges as a transformative force, empowering the industry with enhanced security, transparency, and efficiency in transactions and energy management. However, the successful integration of blockchain technology in the context of EV is still in its infancy. Thus, this work aims to provide a comprehensive study of this young field from a broader perspective. We first discuss the integration process of blockchain technology in the EV domain. Then we explore the potential applications of blockchain technology in enhancing the efficiency, reliability, and sustainability of EV infrastructure and discuss some of the promising research in each category. Finally, we provide a typical application scenario and its specific embodiment in the EV infrastructure and strive to shed light on all-inclusive future research directions, which may facilitate the integration of blockchain technology in the EV ecosystem from theory to practice.
Classification of Electronic Components Using MobileNetV2 Architecture-Based Convolutional Neural Network

Research on Convolutional Neural Networks (CNN) continues to be developed to increase efficiency and speed in processing data and reduce the need for large amounts of training data. The CNN network architecture used in this study is MobileNetV2. The MobileNetV2 architecture is used in the transfer learning process, which is a learning technique where the model that has been trained is used as a basis for learning other tasks, in this case, for image recognition. In the CNN algorithm training process, four layers are used in the electronic component image classification process. The layers used include flattened, dropout layer 0.5, dense 128 layers with Relu activation, and dense 15 layers with a softmax activation function. The image dataset used is 5.716 images divided into 15 different classes. The dataset that becomes the input data will be divided into training and test data. To produce the highest accuracy, the model will be developed parameters. The model parameters developed in this study are the dataset ratio, learning rate, and the number of epochs. The test results from the CNN MobileNetV2 model produce the highest accuracy value of 96.42 and the lowest loss value of 11.97. At this highest accuracy, the learning rate is 0.01 with 50 epochs and a dataset comparison of 80%:20%. In this 80%:20% dataset comparison, 143 batches are needed to complete one epoch.
Recognition of Punctured Convolutional Codes Based on Multi-scale CNN

Punctured convolutional codes are widely applied in satellite communication systems and mobile communication systems. Blind recognition of channel coding plays a significant role in wireless communication technologies such as cognitive radio and radio spectrum detection. This work proposes a deep multi-scale convolution neural network (CNN) which is composed of a multi-scale feature extractor and dilated convolution layers for punctured convolutional codes recognition. The multi-scale feature extractor can better extract the features of different punctured matrices from codeword sequence with convolution kernels of different sizes. The dilated convolution layers expand the receptive field by using different dilation factors. In addition, mixture of experts is adopted to improve model stability and increase classification accuracy. Experimental results demonstrate that the proposed model performs consistently better than existing models on punctured convolutional codes. The proposed multi-scale CNN also shows better performance on common convolutional codes with code rate R = 1/2 and diverse constraint lengths.
Online Directed Graph Estimation for Dynamic Network Topology Inference

Network topology inference based on collected observations plays a fundamental role in many smart applications such as internet of vehicles. However, identifying the variation of network topology may be challenged since the network may be directed, and the topology inference problem is usually modelled as a non-convex problem. Moreover, large amount of data may be required to correctly identify the change of network topology. In this paper, the network topology is characterized by directed graphs, and the estimation of topology variation is modelled as a convex problem based on graph filtering. Since signals filtering over graphs can be considered to be a model-based problem, much less observations are required, and the topology estimation can then be performed with online behaviours. The proposed algorithms are validated on synthetic and real datasets, and both of them demonstrate good estimation performances.