Miracle-dz/UniFAR-FTUs huggingface dataset:
The Performance Analysis of Digital Signature Protocols for Enhancing Network Security

Comprehensive investigation the observation of virtual signature protocols and their position in making sure of community safety. This paper analyzes the performance of diverse digital signature protocols, including ECDSA, RSA, and DSA, and examines how they can be applied to decorate network safety. The paper discusses the blessings of virtual signatures over traditional techniques, including hash functions and symmetric ciphers. The paper also explores the privacy issues arising during virtual signing and proposes ways to mitigate their outcomes. The authors also gift algorithms for verifying virtual signatures. Furthermore, the paper examines numerous simulated environments to analyze the performance of virtual signature protocols. The outcomes display that digital signature protocols can be used to enhance the safety of networks and applications. Via leveraging virtual signature protocols, its miles possible to acquire a more stage of network safety in comparison to traditional methods
An Improved Dual Authentication Techniques for Secure Data Transmission in VANETs

Research related to user authentication techniques in vehicular ad hoc networks (VANETs) has received significant attention in the last decade. However, its security and privacy still become a central issue that is widely open to discussion. In 2016, Vijayakumar et al. published dual authentication and key management techniques for secure data transmission in VANETs. The scheme aims to provide a high level of security on the vehicle side to prevent unauthorized vehicles from entering the VANETs effectively. It relies on the fingerprint and hashes code HC for the authentication process. The authors claimed that the mechanism could withstand the replaying attack by appending it with an updated timestamp and the packet's transmission. However, this paper shows that their scheme suffered a denial of service (DoS) attack due to misplaced timestamps. So, we propose an improvement of their scheme to withstand the attacks.
Semantic-Oriented Resource Allocation for Multi-Modal UAV Semantic Communication Networks

Semantic communication is envisioned as a potential communication paradigm based on artificial intelligence that holds the promise of breaking the Shannon limit for future 6G networks. This paradigm offers a promising opportunity for Unmanned Aerial Vehicles (UAVs) to conserve communication resources and minimize latency by only transmitting relevant semantic information. Despite the promising potential of UAV semantic communication networks, resource allocation in this context remains largely unexplored, particularly regarding multi-modal communication that adjusts the types of transmitted information (image, text, video, etc.) according to the task objectives and the available resources. This paper addresses the semantic-oriented resource allocation for multi-modal semantic communication with a focus on the UAV image-sensing task-oriented scenario. Firstly, a multi-modal semantic communication for the original image-sensing tasks of UAVs is designed. Subsequently, a semantic-level resource allocation problem based on the approximate semantic entropy and the semantic rate is formulated in terms of the transmit power allocation, channel assignment, and the number of transmitted semantic symbols. To solve the problem formulated, which involves a hybrid discrete-continuous action space, a novel algorithm called Hybrid-Decision-Controlled Deep Reinforcement Learning-based Semantic Communication Allocation (HDCD-SC) is introduced. The simulation results demonstrate that the proposed HDCD-SC algorithm can dynamically adjust the transmission modal according to the available resources, and achieve better performance in terms of latency, amount of semantic information, and notable reductions in energy and bandwidth costs when compared to other benchmarks.
Decentralization of Identities Using Blockchain

Digital Identity Management has become an essential component of federal business units today. It involves the gathering and storing of an individual's personal information entrusted to identity providers. With an increase in the number of data breaches occurring every passing year, vulnerabilities posed by technology conglomerates and social identity providers' access to personal data represent a severe cause of alarm. Blockchain Technology, with its inherent ability to prevent manipulation and unauthorized alteration of recorded information, represents an excellent solution to identity ownership concerns. This paper seeks to provide an in-depth exploration of digital identities and the use of Blockchain Technology to create a completely decentralized system of managing identities.
Unveiling DAO Objectives: A Textual Analysis and Topic Modeling Approach

The present research utilizes Topic Modelling as a methodology to acquire a deeper understanding of the goals and operations of Decentralised Autonomous Organisations (DAOs). This is achieved by examining textual data derived from the proposals put forth by these organizations. The issue at hand pertains to comprehending the multitude of ideas inside Decentralised Autonomous Organisations (DAOs) and their alignment with the respective objectives of these entities. Through the application of Topic Modelling, we aim to investigate textual patterns, identify topics, and discern significant themes within the decentralized autonomous organization (DAO) ecosystem. This research endeavor seeks to address the existing research gaps pertaining to the alignment of proposals with organizational objectives. This research aims to fill these knowledge gaps by examining the unique thematic priorities of various decentralized autonomous organizations (DAOs), providing insights into their functions, and elucidating their involvement in investment, community, technological, and monetary issues. Through the utilization of experimental research, this research provides DAO stakeholders with the ability to make wellinformed judgements, prioritize ideas, and customize methods in order to more effectively match with their distinct missions and objectives. Consequently, this research contributes to the enhancement of operational efficiency and governance within DAOs.
Movie Recommendation Systems Using Improved Non-Negative Matrix Factorization

Currently, there exist effective strategies for providing practical recommendations crucial in many industries, this encompasses a range of online platforms, such as electronic commerce, social networking sites, and various web-based services, as a result of the big data explosion. There are numerous customized movie recommendation systems available today that use publicly accessible movie datasets (like MovieLens) and provide enhanced performance measures (such the Root-Mean-Square Error (RMSE)). The ability to scale and the practicality of usage feedback and verification are determined through real-world implementation and assessment, they are two important difficulties that movie recommendation systems currently struggle with. This research suggests a novel approach using non-negative matrix factorization that resolves these issues through similarity and matrix factorization. Finding two matrices, both consisting of non-negative values, and their resulting product when multiplied together closely resembles the original matrix is the goal of non-negative matrix factorization. Additionally, it places the latent elements under non-negative constraints. The suggested approach offers novel update procedures to discover the latent variables for rating prediction. In contrast to the majority of collaborative filtering techniques, the suggested technique can forecast all the unknowable ratings. Its computational complexity is quite low, and it is simple to implement. Empirical research using the MovieLens shows that the suggested approach is more forgiving of the sparsity and scalability issues and produces good results.
A Novel Approach for Balancing Accuracy and Interpretability in Neural Networks

Neural networks have transformed numerous domains, exhibiting unmatched performance when fitting dataset features to a single network or enhancing interpretability using distinct networks for each feature, known as Neural Additive Models (NAM). This paper presents the Grouped-NAM (GNAM) approach, aiming to find a balance between performance and interpretability by clustering features and allocating a unique network to each cluster. Our primary objective is to determine if GNAM can outperform NAM in model accuracy. Using k-means clustering for feature grouping, we evaluated GNAM to identify the ideal number of feature groups. The results highlight a discernible trade-off between accuracy and interpretability, influenced by factors such as feature significance and clarity in decision-making. The study pinpoints the optimal feature group count that outperforms NAM while preserving meaningful interpretability. These insights substantially contribute to the field of neural network modeling, emphasizing the importance of balancing performance and interpretability. GNAM presents itself as a promising framework that adeptly bridges this tradeoff.
Prototype Design & Development of Solar Based Electric Vehicle

we know that, air pollution has increased tremendously due to growth in population. Since because of the everyday increase in power demand, there is always a gap observed between energy generated and energy consumed. Due to generation constraints and to match the required load demand, methods of non-conventional energy sources have been attracted more attention amongst winds, solar, biomass, and tidal sources of producing electricity. The study and development of solar-based energy generation and its different applications mainly in transportation for urban and rural sectors have been wide attention. Looking to this fact in this paper, an effort has been made to study fundamental design aspects for solar-based electric vehicles to develop a prototype to judge its suitability as an alternative to electrical vehicles operating in another field like petrol diesel CNG.
A Photoplethysmography (PPG) Sensor based Stress Level Monitoring System for Undergraduate Students of Technical Education

With each passing day, an increasing number of students are found dealing with significant levels of mental stress, which are primarily induced by socio-academic factors. Of late this has become severe among students of technical education in India. Stress significantly impacts various cognitive processes in students’ minds, including thinking, learning, memory, awareness of their surroundings, and social intelligence. It holds great relevance in modern educational systems and the holistic development of students. Therefore, analyzing student stress levels becomes crucial in understanding their overall well-being and facilitating effective educational practices. In this work, we explore human physiological signal in form of Photoplethysmograph (PPG) for detection of mental stress levels in students. Our target participants are undergraduate students of technical institutions of national importance in India, aged between 17-23. PPG signals are used to generate heart rate data, collected using the Polar OH1+ device, an optical heart rate monitor worn as an armband, and paired with smartphone applications such as ‘Polar Flow’ and ‘Polar Beat’. The low-cost, affordable and non-intrusive nature of this device makes it a suitable candidate for usage by students. We identify relevant features can be extracted using PPG sensor and can be proven to be relevant in this problem. Finally a classification model is built on the identified features which enables stress level detection in students with an accuracy of over 86%, as evident from our experimental results.
Rational Text Augmentation Method with Korean Misspellings

With the development of deep learning, natural language processing models have been proposed, but they may not be able to perform fully on sentences with misspellings. From this perspective, we propose a text augmentation method using Korean misspellings from online comments. Unlike previous text augmentation methods, we focus on the fact that the original meaning of Korean words can be inferred even when misspellings occur. Furthermore, Korean has a variety of misspellings even within a single character, so we analyze the frequencies of them to create augmented sentences. The experimental results show that our method can achieve an average performance improvement of 4.5% p on the NSMC and Korean Hate Speech and is a more rational and efficient method compared to previously used text augmentation methods.
Performance Analysis of a Polymer Based Antenna for Cognitive Radio Applications

Microstrip patch antennas are popular due to their appealing characteristics such as low profile, light weight, low cost, ease of fabrication, and integration with RF devices. A narrowband, wide-beam antenna known as a microstrip or patches are created by etching the antenna element pattern in the metal traces that are adhered to insulating dielectric substrates. Most commonly used dielectric substrate is FR4, which has good mechanical and electrical properties but, it possesses very low thermal conductivity. At very low temperature, it becomes less flexible and rigid. Novel polymer substrate materials are proposed to design the patch antennas for cognitive radio applications in S band frequency. Polyethylene based patch antennas are designed using HFSS in three most popular shapes such as rectangular, circular and triangular. The triangular patch with polyethylene substrate achieves the maximum gain with a value of 7.46dB and a maximum directivity of 7.6dB, which is twice the gain from the ordinary FR4 substrate. Proposed materials are more flexible and has shown improvement in terms of gain, directivity and return loss. Further design optimization may be needed to reduce VSWR.
Convergence of Momentum-based Distributed Stochastic Approximation with RL Applications

We develop a novel proof strategy for deriving almost sure convergence of momentum-based distributed stochas-tic approximation (DSA) schemes. Popular momentum-based schemes such as Polyak's heavy-ball and Nesterov's Accelerated SGD can be analyzed using our template. Our technique enables us to do away with three restrictive assumptions of existing approaches. One, we do not need the communication matrix to be doubly stochastic. Two, we do not need the noise to be uniformly bounded. Lastly, our approach can handle cases where there are multiple or non-point attractors. As an application, we use our technique to derive convergence for momentum-based extensions of the multi-agent TD(O) algorithm, where the above restrictive assumptions do not hold.
Energy Saving in Cellular Wireless Networks via Transfer Deep Reinforcement Learning

With the increasing use of data-intensive mobile applications and the number of mobile users, the demand for wireless data services has been increasing exponentially in recent years. In order to address this demand, a large number of new cellular base stations are being deployed around the world, leading to a significant increase in energy consumption and greenhouse gas emission. Consequently, energy consumption has emerged as a key concern in the fifth-generation (5G) network era and beyond. Reinforcement learning (RL), which aims to learn a control policy via interacting with the environment, has been shown to be effective in addressing network optimization problems. However, for reinforcement learning, especially deep reinforcement learning, a large number of interactions with the environment are required. This often limits its applicability in the real world. In this work, to better deal with dynamic traffic scenarios and improve real-world applicability, we propose a transfer deep reinforcement learning framework for energy optimization in cellular communication networks. Specifically, we first pre-train a set of RL-based energy-saving policies on source base stations and then transfer the most suitable policy to the given target base station in an unsupervised learning manner. Experimental results demonstrate that base station energy consumption can be reduced significantly using this approach.
ROUGE Score Analysis and Performance Evaluation Between Google T5 and SpaCy for YouTube News Video Summarization

The project aims to compare the performance be-tween Google T5 and SpaCy for YouTube video textual summarization based on the ROUGE score. The project utilizes two distinct techniques: abstractive summarization using Google T5 and extractive summarization using SpaCy. The CNN/DailyMail dataset serves as the primary data source for training the model and the YouTube video description sections are used for evaluating the results. The project evaluates the effectiveness of each technique separately. The strengths and weaknesses of abstractive and extractive summarization approaches are discussed, providing valuable insights into their respective trade-offs and applicability for YouTube news video summarization. From the observations, it is found that Google T5 outperforms SpaCy every single time. The overall ROUGE-1 score for Google T5 is found to be 0.339 while the SpaCy scored 0.232. The findings demonstrate the potential of AI-based techniques for automating the summarization process for YouTube news videos.
A Literature Review on Cloud Computing Adoption in the Indian SME Manufacturing Sector and the Future Agenda

Cloud computing is a promising new standard for delivering computing services. Cloud computing delivery is gaining more popularity in the era of Industry 4.0. In emerging countries like India, cloud-computing adoption is still nascent. India's small and medium-scale enterprises (SME) sector contributes a significant portion to the Indian GDP at 8%, and the total manufacturing output of 45% is further strengthened by employing more than 80 million persons, which is only next to agriculture. Various studies have been conducted on cloud adoption in large organisations, and few studies are in the field of SMEs worldwide. However, very few studies, almost none, have identified the problems of SMEs in the Indian context. This literature review has identified and summarised the issues of Indian SMEs. This study also strives to review articles published in Cloud computing and SMEs worldwide and examine the factors influencing cloud computing adoption in SMEs in the Indian Context, where one fits size theory will not work due to the uniqueness of Country specific.
An Approach to Classify Ocular Toxoplasmosis Images using Deep Learning Models

Ocular Toxoplasmosis (OT) is a very well-known eye inflammation that is occurred from a parasite named Toxoplasma gondii (T. gondii) that can lead to several vision problems. Diagnosis traditionally involves clinical examinations and imaging, which can be complex and expensive, requiring specialized expertise. Recently, the interest of using the domain of artificial intelligence (AI) for diagnosing various eye diseases by analyzing fundus images has been increasing. To utilize this field, in this paper, a pre-trained model named VGG16 is built using deep learning (DL) algorithms. Basically, this is trying to use Artificial Neural Network (ANN) and Convolutional Neural(CNN) to classify the fundus images that have three categorical forms of OT. The dataset was collected with 355 images, and after augmentation, the data increased to around 700 images. Augmentation is done after rotating, flipping, shifting, shearing, and zooming. Data is also preprocessed to make the visions clear for the models to predict more accurately. Later, the performance of this proposed model is measured by some evaluating factors, such as the F1-score, precision, recall, loss, accuracy, and confusion matrix. In terms of overall performance among ANN and CNN models, ANN performed better with an accuracy of 98%, whereas to get acceptable accuracy, the CNN model required changing the images to grayscale, resulting in 95% accuracy.
Latency-Optimal Network Microservice Architecture Deployment in SDN

The Software-Defined Networking (SDN) paradigm enables network administrators to manage the behavior of the network thanks to a centralized control plane. By programming network-level applications, it is possible to determine how traffic flows must be handled programmatically. In the same manner that computing applications have evolved from monolithic to microservice-based architectures, network-level applications are expected to evolve into microservice-based SDN controllers, implementing each application as a replicable and individually deployable component of the SDN controller. In such a scenario, the Quality of Service (QoS) experienced by traffic flows depends on how these microservices are placed and deployed through the network topology. In this work, we provide a system to optimize the QoS of the traffic in microservice-based SDN networks by optimally placing and replicating the network-level microservices. Experimental results show the effectiveness of the proposed solution over a real network topology with varying traffic loads.
Development of An Intelligent Alert System for Ensuring Social Distancing in Public Waiting Spaces

Community involvement is critical in preventing virus outbreaks, notably through adherence to health measures such as physical distancing. This work proposes an automatic warning system for waiting seats designed to facilitate the implementation of physical distancing protocols in community activity centers. Manual monitoring techniques are insufficient for continually monitoring waiting seat occupancy, necessitating an automated solution. The suggested system detects objects on chairs by integrating load cells and ultrasonic sensors, while fuzzy logic algorithms efficiently select which seats can be filled. By accurately identifying the seat that is occupied first by the user, the system can display indicators to indicate which seats cannot be occupied. Experiment results demonstrate the system’s effectiveness, where the system has an error rate of 0.66%. Furthermore, the system promptly displays LED indicators for each chair, with an average delay of 0.93 seconds. Through the implementation of this automated warning system, community centers can effectively promote physical distancing, contributing to the prevention of virus transmission.
A Near Optimal Smart Door Security System Based on Internet of Things

This project aims to solve the security problem faced by digital door locking system. By integrating technologies such as computer vision, image processing, internet of things and deep learning various solutions can be developed for a digital to locking system. Most of the existing system fails to recognize the person who wears a mask aur covered in mud etc. This was due to minimum accuracy and reliability. In this regard the proposed approach uses deep learning method to recognize a face using mask or mud. The proposed approach also comes with a cost effective iot based system using principal component analysis. Modifications have been done in Eigen face algorithm which is used by proposed approach. This algorithm is used in the case of component images which are blurred and get clear face recognition automatically. During the comparison it has been identified that proposed approach performs well then the existing approaches.