Large language models (LLMs) have substantially pushed artificial intelligence (AI) research and applications in the last few years. They are currently able to achieve high effectiveness in different natural language processing (NLP) tasks, such as machine translation, named entity recognition, text classification, question answering, or text summarization. Recently, significant attention has been

Future connected and autonomous vehicles (CAVs) must be secured against cyberattacks for their everyday functions on the road so that safety of passengers and vehicles can be ensured. This article presents a holistic review of cybersecurity attacks on sensors and threats regarding multi-modal sensor fusion. A comprehensive review of cyberattacks on intra-vehicle and inter-vehicle communications is

Smart cities are one of the consequences of digital transformation, and there have been many attempts to assess the smartness of cities with various frameworks. Among these frameworks, smart city maturity models (SCMMs) evaluate the existing conditions of cities and provide guidelines for progressing through the subsequent stages of maturity. However, most maturity models follow the instructions of

Federated learning (FL) is a decentralized machine learning (ML) technique that enables multiple clients to collaboratively train a common ML model without them having to share their raw data with each other. A typical FL process involves (1) FL client(s) selection, (2) global model distribution, (3) local training, and (4) aggregation. As such FL clients are heterogeneous edge devices (i.e., mobile

Information that can encapsulate a person's daily life and its different aspects provides insightful knowledge. This knowledge can prove to be more useful than general knowledge for improving personalized tasks. When it comes to storing such knowledge, personal knowledge graphs (PKGs) come in as handy saviors. PKGs are knowledge graphs which store details that are pertinent to a user but not, in general

A filter bubble refers to the phenomenon where Internet customization effectively isolates individuals from diverse opinions or materials, resulting in their exposure to only a select set of content. This can lead to the reinforcement of existing attitudes, beliefs, or conditions. In this study, our primary focus is to investigate the impact of filter bubbles in recommender systems (RSs). This pioneering

To achieve scientific progress in terms of building a cumulative body of knowledge, careful attention to benchmarking is of the utmost importance, requiring that proposals of new methods are extensively and carefully compared with their best predecessors, and existing methods subjected to neutral comparison studies. Answers to benchmarking questions should be evidence-based, with the relevant evidence

Digital twins, succinctly described as the digital representation of a physical object, is a concept that has emerged relatively recently with increasing application in the manufacturing industry. This article proposes the application of this concept to the healthcare domain to provide enhanced clinical decision support and enable more patient-centric, and simultaneously more precise and individualized

A review of the deep learning based image steganography techniques is presented in this paper. For completeness, the recent traditional steganography techniques are also discussed briefly. The three key parameters (security, embedding capacity, and invisibility) for measuring the quality of an image steganographic technique are described. Various steganography techniques, with emphasis on the above

In Wang et al. (2019), a co-author of this article was affiliated with Curtin University at the time the research, writing, and publication of the article took place. Curtin University was not made aware of this co-author's participation in the research or the article at the time it was published and did not grant this individual author ethics or consent approval for the research, a university requirement