The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing. In this paper, we provide a review of the state-of-the-art in ML for computational science and engineering. We discuss ways of using ML to speed up or improve the quality of simulation techniques such as computational fluid dynamics, molecular dynamics, and structural analysis. We explore the ability of ML to produce computationally efficient surrogate models of physical applications that circumvent the need for the more expensive simulation techniques entirely. We also discuss how ML can be used to process large amounts of data, using as examples many different scientific fields, such as engineering, medicine, astronomy and computing. Finally, we review how ML has been used to create more realistic and responsive virtual reality applications.

The number of Internet of Things (IoT) devices has increased considerably in the past few years, resulting in a large growth of cyber attacks on IoT infrastructure. As part of a defense in depth approach to cybersecurity, intrusion detection systems (IDSs) have acquired a key role in attempting to detect malicious activities efficiently. Most modern approaches to IDS in IoT are based on machine learning (ML) techniques. The majority of these are centralized, which implies the sharing of data from source devices to a central server for classification. This presents potentially crucial issues related to privacy of user data as well as challenges in data transfers due to their volumes. In this article, we evaluate the use of federated learning (FL) as a method to implement intrusion detection in IoT environments. FL is an alternative, distributed method to centralized ML models, which has seen a surge of interest in IoT intrusion detection recently. In our implementation, we evaluate FL using a shallow artificial neural network (ANN) as the shared model and federated averaging (FedAvg) as the aggregation algorithm. The experiments are completed on the ToN_IoT and CICIDS2017 datasets in binary and multiclass classification. Classification is performed by the distributed devices using their own data. No sharing of data occurs among participants, maintaining data privacy. When compared against a centralized approach, results have shown that a collaborative FL IDS can be an efficient alternative, in terms of accuracy, precision, recall and F1-score, making it a viable option as an IoT IDS. Additionally, with these results as baseline, we have evaluated alternative aggregation algorithms, namely FedAvgM, FedAdam and FedAdagrad, in the same setting by using the Flower FL framework. The results from the evaluation show that, in our scenario, FedAvg and FedAvgM tend to perform better compared to the two adaptive algorithms, FedAdam and FedAdagrad.

Medical education is a dynamic field that witnesses continuous evolution and development. The employment of Virtual Reality (VR) based visualization and training environments in the delivery of anatomy teaching transfers the learning experience from one that involves memorising the structures without a true understanding of the 3-Dimensional (3D) relations, to a process that involves a thorough understanding of the structure based on visualisation rather than memorising, which makes the learning process more efficient and enjoyable, and less time consuming. This paper describes the development of a Virtual Reality and 3D visualisation system for anatomy teaching. The developed system offers a real-time 3D representation of the heart in an interactive VR environment that provides self-directed learning and assessment tools through a variety of interfaces and functionalities. To ensure the accuracy and precision of the developed system it was evaluated by a group of medical professionals.

This paper presents an evaluation of a three-dimensional (3D) semi-immersive Virtual Reality (VR) system for online luxury product visualization and customization service. The paper’s objective is to present the results of the survey-based analysis using Technology Acceptance Model with ease ofuse, usefulness, perceived experience value and perceived presence as independent variables, in testing the attitudes towards the system. The impact of product customization and personalization features on the perceived experience value and the attitudes towards the system were also tested. The result shows that the perceived presence, usefulness, ease of use and the perceived experience value have a significant positive effect on the attitudes towards the 3D VR semiimmersive system. The result also revealed that utilizing the advantage of 3D VR systems with regards to real-time manipulation of the product and the flexibility in customizing the 3D models’ features in real time has elevated the attitudes towards the aforementioned emerging technology within this specific context.