This paper presents a methodology for training Tacotron 2 for text-to-speech synthesis in the Kreole language, aiming to improve access to textual information for visually impaired individuals. This paper discusses Tacotron 2’s architecture, reviews relevant literature, and presents experimental implementations. By synthesizing current research trends serves as a valuable resource for TTS advancement. We trained the Tacotron 2 model to map input text sequences to Mel-spectrograms representing speech segments using a dataset of 25 audio recordings matched to their corresponding text transcripts. Segmenting audio data, using Google Colab for model training, and using HiFi-GAN as a vocoder for audio synthesis were all parts of the training process. The completeness, repeatability, and clarity of the synthesized speech were assessed in the listening assessments. Tacotron 2 performed admirably, generating intelligible sounds for 13 of 25 words despite the small sample. This study addresses the future and focuses Tacotron 2’s promise in low-resource language environments.
The emergence of fifth-generation technology (5G) has revolutionised telecommunication networks, offering enhanced mobile broadband, ultra-reliable (eMBB), ultra-low-latency communications (uRLLC), and massive machine-type communication (mMTC) service classes. This breakthrough has garnered significant attention and investment worldwide, driving innovation and growth in the digital era. However, the adoption of cloud-based 5G core (5GC) networks, while offering scalability and deployment flexibility, has posed challenges to meeting stringent latency requirements, particularly for uRLLC services specifically for robotic telesurgery. To address this problem, mobile network operators (MNOs) have turned to edge computing (EC), using the control and user plane separation (CUPS) architecture introduced in the third-generation partnership project (3GPP) release 14 specification. This architecture enables the deployment of the user plane function (UPF) closer to users, reducing latency, and improving quality of service (QoS). However, the deployment of the UPF as a standalone node on the edge of the network exposes the packet forwarding control protocol (PFCP) to cybersecurity attacks, which pose risks to telesurgery services and could even lead to loss of life. In the existing literature, only a few techniques focus on minimising these attacks when the UPF is deployed on the edge of the network far from the 5GC. Therefore, this paper reviews PFCP attacks and explores machine learning (ML) techniques to mitigate these security threats. This paper further provides recommendations and future research directions for mitigating these attacks.
With the increasingly busy lifestyles of people, advanced maintenance systems such as automated feeding has become of utmost importance to aquarium owners. Despite existing technologies, there remains a gap in smart feeding solutions tailored to the specific dietary requirements of different fish species and population sizes. This paper investigates smart fish feeding through automated detection, counting, classification and feeding of fishes within aquariums. As part of this paper, a smart population-driven aquarium maintenance system, named AquaTrack, is proposed that integrates Internet of Things (IoT), computer vision, machine learning, and sensor technologies. The system detects, classifies, and counts fish, adjusting feeding amounts based on species and population to mitigate the risks of overfeeding and underfeeding, which can significantly affect water quality. The system was evaluated through four research questions, where a general accuracy of 76.9% in fish species identification and 77.1% in fish counting were found. While promising outcomes were observed under specific conditions, variability and limitations in performance across different scenarios were also noted. Environmental factors such as lighting, reflections, shadows, and particulate matter affected the accuracies, particularly in scenarios involving smaller fish quantities. Based on these findings, the study recommends further refinement of the system to enhance its capabilities.
With the rise of the internet usage and web applications, Reflected Cross-Site Scripting (RXSS) attacks have become increasingly prevalent, accounting for over 90% of recent XSS incidents. This paper proposes a novel defense mechanism against RXSS through a browser extension, called RXSS Protect, integrated with a machine learning (ML) algorithm. This extension, compatible with Google Chrome, Microsoft Edge, and Mozilla Firefox, employs a Support Vector Machine (SVM) model to detect and block malicious scripts in real-time. The system’s architecture includes a Flask server for running the ML model, a browser extension for client-side operations, and an SQLite database for storing URL data. This approach aims to enhance web browsing security by providing an effective tool against RXSS attacks, with potential for future extensions to other types of cyber threats. The SVM model is trained on a dataset of benign and malicious URLs and XSS payloads. Evaluation focused on answering two key research questions, related to detection accuracy and performance across browsers. Results showed that RXSS Protect achieved a high accuracy of 97.53% in identifying RXSS payloads and relatively good overall performance across browsers.
In the modern contemporary world, technological advancements have revolutionized the transportation sector. The study begins with an in-depth analysis of the current traffic situation in Mauritius, and then examines existing carpooling systems in other countries. The aim of this research is to provide a more efficient and sustainable transportation solution that can ease traffic congestion in Mauritius. By utilizing state-of-the-art technology, the proposed carpooling system has the potential to significantly reduce the number of cars on the road, leading to reduced traffic congestion, decreased carbon emissions, and improved air quality. The study includes a thorough analysis of the current traffic situation in Mauritius, as well as an examination of existing carpooling systems in other countries. Additionally, the system will incorporate features such as registration, booking, SOS, user ratings, and Google Maps to ensure a positive user experience. On the implementation level of a carpooling system Flutter, Cloud Firebase, and Google Cloud Platform will be used. The proposed carpooling system will utilize real-time data to match drivers with passengers based on their location, destination, date and time. Overall, this dissertation provides a comprehensive framework for the development and implementation of a successful carpooling system in Mauritius.
