ABSTRACTTime Series Anomaly Detection (TSAD) has demonstrated broad applicability across various industries, including manufacturing, healthcare, and finance. Its primary objective is to identify unusual deviations in the test set by capturing the typical behavioral patterns of timing data. Despite their strong detection capabilities when labeled data is not available, current reconstruction‐based approaches still struggle with anomalous interference and inadequate semantic information extraction at higher time series levels. To tackle these problems, we provide a multi‐scale dual‐domain patch attention contrast learning model (DMAP‐DDCL) that incorporates adaptive path selection and adaptive dynamic context‐aware masking. Dynamic context‐aware masks are specifically used by DMAP‐DDCL to improve the model's generalization ability and mitigate bias resulting from the influence of anomalous data during training. Multi‐scale patch segmentation and dual attention to the segmented patches are introduced to capture local details and global correlations as time dependencies. By enlarging the contrast between the two data perspectives, global and local, DMAP‐DDCL improves the capacity to differentiate between normal and abnormal patterns. In addition, we enhance the adaptive path of the multi‐scale bi‐domain attention network, which adapts the multi‐scale modeling process to the temporal dynamics of the inputs and enhances the model's accuracy. According to experimental results, DMAP‐DDCL performs better on five real datasets from various domains than eight state‐of‐the‐art baselines. Specifically, our model enhances F1 and R_AUC_ROC by an average of 7.5% and 16.67%.

ABSTRACTIn text classification, creating an adversarial example means subtly perturbing a few words in a sentence without changing its meaning, causing it to be misclassified by a classifier. A concerning observation is that a significant portion of adversarial examples generated by existing methods change only one word. This single‐word perturbation vulnerability represents a significant weakness in classifiers, which malicious users can exploit to efficiently create a multitude of adversarial examples. This paper studies this problem and makes the following key contributions: (1) We introduce a novel metric to quantitatively assess a classifier's robustness against single‐word perturbation. (2) We present the SP‐Attack, designed to exploit the single‐word perturbation vulnerability, achieving a higher attack success rate, better preserving sentence meaning, while reducing computation costs compared to state‐of‐the‐art adversarial methods. (3) We propose SP‐Defence, which aims to improve by applying data augmentation in learning. Experimental results on 4 datasets and 2 masked language models show that SP‐Defence improves by 14.6% and 13.9% and decreases the attack success rate of SP‐Attack by 30.4% and 21.2% on two classifiers respectively, and decreases the attack success rate of existing attack methods that involve multiple‐word perturbations.

ABSTRACTThis review paper provides a systematic overview of the metaverse markets security problems and solutions. The metaverse is an emerging digital space, bridging virtual, augmented and mixed reality environments. As the metaverse evolves, issues related to customer security have emerged, which include breaches of privacy, thefts of identity and cybercrimes, all of this compounded by insecurities in the decentralised structures. Through this systematic literature review, we analyse these challenges and assess current approaches to mitigate them, including encryption, decentralised identity and related regulatory frameworks, but highlight their limited capacity for dealing with immersive virtual spaces' unique risks. The review also explores some advanced solutions adopting artificial intelligence, blockchain and privacy enhancing technologies (PETs) for securing the metaverse and enhancing its privacy. Furthermore, the review also points out the gaps in the current literature, particularly a lack of customised customer protection structures and a poor analysis of the psychological effects. It also proposes future solutions such as quantum‐resistant security and zero‐trust architecture to fortify the security. This review highlights the necessity of partnership between the industries, as well as the setting of security protocols so as to safeguard customer trust and engagement in the growing digital space.

ABSTRACTDeep neural networks have exhibited preeminent performance in anomaly detection, but they struggle to effectively capture changes over time in multivariate time‐series data and suffer from resource consumption issues. Spiking neural networks address these limitations by capturing the change in time‐varying signals and decreasing resource consumption, but they sacrifice performance. This paper develops a novel spiking‐based hybrid model incorporated a temporal prediction network and a reconstruction network. It integrates a unique first‐spike frequency encoding scheme and a firing rate based anomaly score method. The encoding scheme enhances the event representation ability, while the anomaly score enables efficient anomaly identification. Our proposed model not only maintains low resource consumption but also improves the ability of anomaly detection. Experiments on publicly real‐world datasets confirmed that the proposed model acquires state‐of‐the‐art performance superior to existing approaches. Remarkably, it costs 5.04× lower energy consumption compared with the artificial neural network version.

ABSTRACTSatellite imagery offers rich information for land cover classification, but choosing an effective yet efficient feature extractor or backbone architecture remains challenging. In this study, I benchmark 25 vision‐transformers across 10 public land cover datasets to guide backbone selection for downstream classification tasks. The proposed approach encodes each satellite image into a fixed‐length feature vector via a pre‐trained transformer, then trains and tests a linear support‐vector classifier on these encodings to isolate the impact of the backbone alone. I report average classification accuracy and F1‐score over three random stratified splits per dataset, and I also measure training time to assess the computational cost. Results show that the image encoding performed using large‐receptive‐field transformers with advanced self‐attention—particularly deit3_base_patch16_224 and twins_svt_large—achieve the highest accuracies without incurring prohibitive training times. In contrast, encodings of the compact variants achieve faster training but incur notable performance drops around 7%–8%. These findings reveal a clear trade‐off between representational power and efficiency. Practitioners can leverage such rankings to select a transformer backbone that best balances accuracy and computational efficiency for satellite image‐based land cover classification tasks, accelerating the development of robust and resource‐aware systems.

ABSTRACTThe tourism sector is adopting smart solutions to offer visitors more personalised and sustainable experiences. By leveraging urban infrastructure and new technologies, tourist destinations aim to enhance the interaction between travellers and their environment. Artificial intelligence (AI) and natural language processing (NLP) play a key role in this transformation, particularly through chatbots. They are AI‐driven applications designed to simulate human‐like conversations, enabling users to interact with digital services through text or voice interfaces. In the tourism sector, they facilitate real‐time access to information and services, improving the visitors' experience. These applications typically rely on intent recognition APIs, which may be proprietary, requiring access fees and potentially leading to high implementation costs. This study explores the use of a domain‐specific language (DSL) dedicated to chatbot development for smart tourism. The first contribution comprises various research topics and emerging technologies used to improve smart tourism experiences and their impact on key tourism components such as attractions, accessibility, amenities, activities, available packages, and ancillary services. Second, this work aims to present the key concepts of model‐driven engineering involved in constructing a DSL and to introduce our approach to building a DSL, with a focus on presenting the DSL metamodel. Third, this study identifies the challenges and limitations of using DSLs in chatbot development.

ABSTRACTThe research on nested named entity recognition (NER) is conducive to providing richer semantic representations and capturing the nested structure among entities, which is crucial for the execution of downstream tasks. This paper aims to summarise the nested NER methods that have been combined with emerging technologies in recent years. We summarise the nested NER methods that are integrated with emerging technologies from three dimensions: model, framework, and data. Additionally, we explore the research progress of nested NER in two scenarios: cross‐lingual modality and multi‐modal in different modalities. Furthermore, we discuss the practical applications of NER technology in five fields: biomedicine, justice, finance, media, and e‐commerce. Through this review, we can clearly see the development trends of nested NER technology under emerging technologies and different modalities, as well as its broad application prospects in various fields. This provides a reference for future exploration directions in nested NER.

RETRACTION: D. R. J. Dolly, J. D. Peter, and D. J. Jagannath, “,” Expert Systems (Early View): https://doi.org/10.1111/exsy.12901. The above article, published online on 29 November 2021 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor‐in‐Chief, David Camacho; and John Wiley & Sons Ltd. The article was submitted as part of a guest‐edited special issue. Discussion, analysis, and research conducted in this manuscript are insufficiently described. Accordingly, the results are considered insufficiently supported and not reproducible.

RETRACTION: P. K. Jain, W. Quamer, and R. Pamula, “,” Expert Systems 40, no. (2023): , https://doi.org/10.1111/exsy.13247. The above article, published online on 14 February 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor‐in‐Chief, David Camacho; and John Wiley & Sons Ltd. The article was submitted as part of a guest‐edited special issue. Experiments and analysis do not support the research objectives sufficiently. Accordingly, the conclusions of this manuscript are considered unreliable.

ABSTRACT Artificial intelligence (AI) and explainable artificial intelligence (XAI) are advancing rapidly, with the potential to deliver significant benefits to modern society. The healthcare sector, in particular, has experienced transformative changes; overall, these technologies are helping to address numerous challenges, such as cancer cell detection, tumour zone identification in animal bodies, predictions of major and minor diseases, diagnosis, and more. This article provides an in‐depth and detailed overview of AI and XAI, focusing on recent trends and their implications for advancing Healthcare 5.0 applications. Initially, the study examines the key concepts and exceptional features of AI, XAI, and Healthcare 5.0. Additional emphasis is placed on state‐of‐the‐art practices currently being implemented in healthcare, particularly those involving AI and XAI. Subsequently, it establishes a coherent link between AI and XAI in Healthcare 5.0, grounded in contemporary advancements. Based on the findings, algorithms are recommended to address initial obstacles to integrating AI into the Healthcare 5.0 framework. Proposals for further enhancing Healthcare 5.0 performance through the integration of XAI and its unique features are discussed in detail. The work also provides in‐depth implementation strategies and highlights model‐specific trends within AI and XAI frameworks in Healthcare 5.0. Particular attention is given to AI model predictions in healthcare settings, emphasising their contributions to improved patient feedback and the delivery of more sophisticated care. Most importantly, this research highlights the potential for AI and XAI to support sustainable advancements in Healthcare 5.0 applications. Finally, significant issues are analysed, and an open discussion is presented on future guidelines for the blending of AI with XAI, and Healthcare 5.0 applications.