SkillBot: Towards Data Augmentation using Transformer language model and linguistic evaluation

Creating accurate, closed-domain, and machine learning-based chatbots that perform language understanding (intent prediction/detection) and language generation (response generation) requires significant datasets derived from specific knowledge domains. The common challenge in developing a closed-domain chatbot application is the lack of a comprehensive dataset. Such scarcity of the dataset can be complemented by augmenting the dataset with the use of state- of-the-art technologies existing in the field of Natural Language Processing, called ‘Transformer Models’. Our applied computing project experimented with a ‘Generative Pre-trained Transformer’ model, a unidirectional transformer decoder model for augmenting an original dataset limited in size and manually authored. This model uses unidirectional contextual representation i.e., text input is processed from left to right while computing embeddings corresponding to the input sentences. The primary goal of the project was to leverage the potential of a pre-trained transformer-based language model in augmenting an existing, but limited dataset. Additionally, the idea for using the model for text generation and appending the generated embedding to the input embedding supplied was to preserve the intent for the augmented utterances as well as to find a different form of expressions for the same intent which could be expressed by the potential users in the future. Our experiment showed improved performance for understanding language and generation for the chatbot model trained on the augmented dataset indicating that a pre-trained language model can be beneficial for the effective working of natural language-based applications such as a chatbot model trained on the augmented dataset indicating that a pre-trained language model can be beneficial for the effective working of natural language-based applications such as a chatbo

Skillbot: A Conversational Chatbot based Data Mining and Sentiment Analysis

With the advent of technology, Artificial Intelligence is emerging exponentially. Using this advancement, chatbots are widely used in various sectors to accommodate users with their queries without waiting. In this study, work in the development, training, improvement, and chat sentiment analysis of Skillbot Chatbot is performed. First, the data was scrapped using tools like the GPT2 model from the Gov. UK website, and that data was used to train intents for the Skillbot model. After successful training, testing, and evaluation of Skillbot for better performance, conversations of users were analyzed deeply. Sentiment analysis was also performed as it is important to train the Skillbot to efficiently respond to users. Then, this project was deployed on Streamlit named Conversation Analyzer. Analysis was performed using different technologies like Natural Languages processing, Vader model for sentiment analysis, TextBlob for topic modeling of conversations, Streamlit for visualization, Rasa, Artificial Intelligence, and machine learning. Chatbot training with cleaned data and conversation analysis would be very beneficial for Skillbot to give users better services. The findings with massive data wrangling, model training for Skillbot, and chat analysis would provide results’ evaluations with successful and unsuccessful dialogues with insights to help warrant future research and Skillbot improvemen

The Future of Mobility with Connected and Autonomous Vehicles in Smart Cities

Cities around the world are being wrecked by the ever-increasing bur-den of traffic. Smart cities are a recent innovation perceived as a winning strategy to cope with some severe urban problems such as traffic, pollution, energy consumption, waste treatment. This concept is attracting significant interest in the world of technology and sensors. Governments can streamline the way cities are run, saving money and making them more efficient as a result. Rapid urban developments, sustainable transportation solutions are required to meet the increasing demands for mobility whilst mitigating the potentially negative social, economic, and environmental impacts. This study analyses the smart mobility initiatives and the challenges for smart cities with connected and autonomous vehicles (CAVs), it also highlights the literature that supports why CAVs are essential for smart maintainable development as part of the intelligent transportation system (ITS)

The Metaverse evolution: Toward Future Digital Twin Campuses

The term 'Metaverse' has been used to refer to the next generation Internet (NextG). New, developing, and recently innovation technologies have enabled the incorporation of digital twins into education's metaverse. This is a shared virtual area that combines all virtual worlds over the Internet. This will enable users represented by digital avatars to interact and cooperate as if they were in the physical world. While the metaverse may seem futuristic, it is accelerating because of emerging technologies such as AI and Extended Reality. This paper explores the technologies utilised to build the metaverse and practical applications on improving the educational experience and offer value by optimising how students are taught. Thus, we shall study in detail eight enabling technologies that are important for the Metaverse ecosystem: Virtual, Augmented, and Mixed Reality. Autonomy of Avatar, Computer Agent, and Digital Twin. Data Storage, Data sharing and Data interoperability. This article will discuss prospective metaverse technology and its difficulties. Finally, we have looked at societal acceptance, privacy, and security challenge

Effective Security as an ill-defined Problem in Vehicular Ad hoc Networks (VANETs)

As the application of computer technology continues to proliferate and diversify, vehicles are becoming increasingly intelligent and it is expected that in the near future they will be equipped with radio interfaces for short range communications. This will enable the formation of vehicular networks, commonly referred to as VANETs, an instance of mobile ad hoc networks with vehicles as mobile nodes. Vehicular networks are receiving a lot of attention due to the wide variety of services they can provide and are likely to be deployed commercially in coming years. Security is a fundamental issue because such networks will provide the necessary infrastructure for various applications that can help improve the safety of road traffic. Effective security of vehicular ad hoc network is an ill-defined problem as most existing security mechanisms available for VANET do not combine efficiency, security and traceability. They tend to score well in one or two qualities, but not all three because of the potential contradictions between some of their attributes. In this paper, we give an overview of VANETs and the security challenges related to their deployment. We identify and analyse current security limitations, then an effort is made to show that efficiency, security and traceability are the key qualities to consider while implementing an effective security mechanism. Therefore the most suitable way to achieve this goal is by identifying the intersection point connecting their attributes. © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Autonomous, Seamless and Resilience Carrier Cloud Brokerage Solution for Business Contingencies during Disaster Recovery

The challenge of disaster recovery management for cloud based services is constantly evolving. The costs of cloud service downtime in the event of disaster striking is the subject of much international research. The key issue to resolve is developing suitably resilient and seamless live/realtime mechanisms for disaster recovery. In this paper, we have implemented a proof of concept for an autonomous and fault tolerant carrier cloud brokerage solution with resilient provisioning of on-the-fly cloud resources. When a disaster strikes, the proposed solution will trigger the migration of an entire IaaS from one cloud to another without causing any disruption to the business. In the event of non-availability of hosts for the deployment of virtual network functions for different business processes, an on-the-fly host selection mechanism is proposed and implemented to locate other active compute hosts without any disruptions. In order to evaluate the performance of the proposed solution, we defined several usecase scenarios for each cloud service. This proposed solution will not only reduce the capital expenditure but also provides a reliable and efficient way to access the data during disaste

Feasibility of Serverless Cloud Services for Disaster Management Information Systems

Serverless is the new generation of cloud services that supports the pay-per-use policy in true spirit by charging only for the execution time of the hosted code. Amazon introduced serverless service of Lambda in 2014 and it is consider as the most popular serverless cloud service till date. This paper focuses on the serverless cloud services of Lambda and elaborates the importance of Lambda based serverless cloud services for hosting the disaster management information systems (DMIS). We have identified two repeatedly occurring phases of the life cycle of a DMIS viz. low activity phase and high activity phase. Our findings state that serverless cloud services are well-suited for both of these phases of a DMIS. Serverless reduces the operational cost during the low activity phase by detaching the code from running containers and it improves the scalability during the high activity phase by quickly assigning the already available containers from the container pool. However, this all comes with the price of reduced QoS (Quality of Service) for initial requests after specific idle duration and our experimental results report the QoS degradation with respect to idle time for Lambda service

User acceptance of information technology: A critical review of technology acceptance models and the decision to invest in Information Security

Abstract. In today’s fast changing world, technology is increasingly influencing and having a major impact on all aspect of our daily life. For decades, the user acceptance of technology has been a vital field of study. Despite numerous models been proposed to explain and predict the use of a system or assist in decision to invest in information security, the latest models and theories are still not been able to fully capture the complexity of the relationship between humans and technology. This paper provides a historical overview and a critical review of technology acceptance models (TAM) and theories. It also explores external variables influencing in-formation security investment. It is concluded that although TAM and associated theories are well-established concepts in the information systems community, further research will be required to capture other important elements influencing public acceptance of technology which are not currently represented in existing models

Measuring consumer behavioural intention to accept technology: Towards autonomous vehicles technology acceptance model (AVTAM)

The work presented in the paper aims at exploring information technology acceptance in the context of Autonomous Vehicles (AV) with the objectives of identifying and testing the constructs that will influence future AVs acceptance. Most models of technology acceptance focus on barriers of successful information technology implementation in organisations and technologies that have already been deployed. There is only a small number of studies conducted on emerging and disruptive technologies such as AV. We address this issue by deriving context-related determinants from an extensive literature analysis and further describing a technology acceptance modeling process to provide an explanation for drivers’ and factors influencing people behavioural intention to accept AV technology. Based on our evaluation we take the determinants self-efficacy, perceived safety, trust, anxiety and legal regulation into consideration. The outcome and main contribution of this paper is the proposal of a theoretical AV technology acceptance model (AVTAM). This study concluded that the performance of these AV technologies, their safety on the road and consumer’s trust for the AV manufacturers will play a very important role for mass AV adoption

Lightweight Computation to Robust Cloud Infrastructure for Future Technologies (Workshop Paper)

Hardware and software lightweight solutions became the mainstream for current and future emerging technologies. Container-based virtualization provides more efficient and faster solutions than traditional virtual machines, offering good scalability, flexibility, and multi-tenancy. They are capable of serving in a heterogeneous and dynamic environment across multiple domains, including IoT, cloud, fog, and multi-access edge computing. In this paper, we propose a lightweight solution for LCC (Live Container Cloud) that permits the user to access live/remote cloud resources faster. LCC can be embedded as a fog/edge node to permit the users to allocate and deallocate cloud resources. The performance of such a containerization technology is presented