Xiaohui Tao

• Doctor of Philosophy
• Professor (Full) at University of Southern Queensland

Cold-start bundle recommendation focuses on modeling new bundles with insufficient information to provide recommendations. Advanced bundle recommendation models usually learn bundle representations from multiple views (e.g., interaction view) at both the bundle and item levels. Consequently, the cold-start problem for bundles is more challenging th...

The journal retracts and remove the article Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning Techniques [...]

The expansion of Graph Neural Networks (GNNs) has highlighted the importance of evaluating their performance in real-world scenarios. However, existing evaluation frameworks often overlook the integration of causality, a critical component that is essential for more robust evaluation of GNNs. To address this gap, we present a benchmark study that s...

The growing demand for data privacy in Machine Learning (ML) applications has seen Machine Unlearning (MU) emerge as a critical area of research. As the `right to be forgotten' becomes regulated globally, it is increasingly important to develop mechanisms that delete user data from AI systems while maintaining performance and scalability of these s...

With the rising volume of public and consumer engagement on social media platforms, the field of aspect-based sentiment analysis (ABSA) has garnered substantial attention. ABSA contains the systematic extraction of aspects, the analysis of associated sentiments, and the temporal evolution of these sentiments. Researchers have responded to the burge...

Aspect-based sentiment analysis (ABSA) involves identifying sentiment toward specific aspect terms in a sentence and allows us to uncover people’s nuanced perspectives and attitudes on particular aspects of a product, service, or topic. However, the scarcity of labeled data poses a significant challenge to training high-quality models. To address t...

Multi-type Legal Question Answering(MLQA) aims to automatically respond to legal questions presented in natural language. Current MLQA models generally include a text reading component and an answer prediction component. However, these models often prioritize handling lengthy legal documents over closely analyzing the given question, which can lead...

Machine unlearning (MU) is gaining increasing attention due to the need to remove or modify predictions made by machine learning (ML) models. While training models have become more efficient and accurate, the importance of unlearning previously learned information has become increasingly significant in fields such as privacy, security, and ethics....

In recent years, bundle recommendation systems have gained significant attention in both academia and industry due to their ability to enhance user experience and increase sales by recommending a set of items as a bundle rather than individual items. This survey provides a comprehensive review on bundle recommendation, beginning by a taxonomy for e...

Machine Unlearning, a pivotal field addressing data privacy in machine learning, necessitates efficient methods for the removal of private or irrelevant data. In this context, significant challenges arise, particularly in maintaining privacy and ensuring model efficiency when managing outdated, private, and irrelevant data. Such data not only compr...

Aspect-based sentiment analysis (ABSA) involves identifying sentiment towards specific aspect terms in a sentence and allows us to uncover nuanced perspectives and attitudes on particular aspects of a product, service, or topic. However, the scarcity of labeled data poses a significant challenge to training high-quality models. To address this issu...

As cognitive-inspired computation approaches, deep neural networks or deep learning (DL) models have played important roles in allowing machines to reach human-like performances in various complex cognitive tasks such as cognitive computation and sentiment analysis. This paper offers a thorough examination of the rapidly developing topic of DL-assi...

Reinforcement learning (RL) is renowned for its proficiency in modeling sequential tasks and adaptively learning latent data patterns. Deep learning models have been extensively explored and adopted in regression and classification tasks. However, deep learning has limitations, such as the assumption of equally spaced and ordered data, and the inab...

As a transformative technology across various industries, the metaverse has emerged to connect the physical world with the virtual world. During this process, the Internet of Things (IoT) has played a critical role in achieving effective cyber-physical interaction. However, its prevalent centralized interconnection architectures encounter challenge...

The introduction of blockchain technology has brought about significant transformation in the realm of digital transactions, providing a secure and transparent platform for peer-to-peer interactions that cannot be tampered with. The decentralised and distributed nature of blockchains guarantees the integrity and authenticity of the data, eliminatin...

This study introduced a multi-criteria decision-making methodology leveraging text mining and analytic hierarchy process (AHP) for online course quality evaluation based on students’ feedback texts. First, a hierarchical structure of online course evaluation criteria was formulated by integrating topics (sub-criteria) identified through topic model...

Recommender Systems are mainly used in various e-commerce applications, especially online stores threatening users’ privacy. The privacy issues can be overcome by using security solutions, which include blockchain technology for privacy applications. The fusion of the Internet of Things and blockchain technology has fully improved modern distribute...

The surge in text data has driven extensive research into developing diverse automatic summarization approaches to effectively handle vast textual information. There are several reviews on this topic, yet no large‐scale analysis based on quantitative approaches has been conducted. To provide a comprehensive overview of the field, this study conduct...

Exploring the nature of human intelligence and behavior is a longstanding pursuit in cognitive neuroscience, driven by the accumulation of knowledge, information, and data across various studies. However, achieving a unified and transparent interpretation of findings presents formidable challenges. In response, an explainable brain computing framew...

Advancements in artificial intelligence (AI) have driven extensive research into developing diverse multimodal data analysis approaches for smart healthcare. There is a scarcity of large-scale analysis of literature in this field based on quantitative approaches. This study performed a bibliometric and topic modeling examination on 683 articles fro...

Food computing, as a newly emerging topic, is closely linked to human life through computational methodologies. Meal recommendation, a food-related study about human health, aims to provide users a meal with courses constrained from specific categories (e.g., appetizers, main dishes) that can be enjoyed as a service. Historical interaction data, as...

Personalized clinical decision support systems are increasingly being adopted due to the emergence of data-driven technologies, with this approach now gaining recognition in critical care. The task of incorporating diverse patient conditions and treatment procedures into critical care decision-making can be challenging due to the heterogeneous natu...

Biometric recognition is a widely used technology for user authentication. In the application of this technology, biometric security and recognition accuracy are two important issues that should be considered. In terms of biometric security, cancellable biometrics is an effective technique for protecting biometric data. Regarding recognition accura...

Sequential Crime Prediction (SCP) aims to analyze future criminal intents within historical event transitions and predict next crime event. A problem lies in the correlations among different event features (e.g., time, locations, and categories), posing challenges to capture a comprehensive criminal intent. Most existing methods are hard to fully e...

Information from online platforms is vast, with health related data remaining largely unexplored for the purpose of developing a sentiment-based recommendation model. Though state-of-the-art models such as transformers are being researched in this domain, the model configuration has not been diligently investigated, particularly for deriving qualit...

Meal recommender system, as an application of bundle recommendation, aims to provide courses from specific categories (e.g., appetizer, main dish) that are enjoyed as a meal for a user. Existing bundle recommendation methods work on learning user preferences from user-bundle interactions to satisfy users’ information need. However, users in food sc...

Finding patterns among risk factors and chronic illness can suggest similar causes, provide guidance to improve healthy lifestyles, and give clues for possible treatments for outliers. Prior studies have typically isolated data challenges from single-disease datasets. However, the predictive power of multiple diseases is more helpful in establishin...

Sequential prediction has great value for resource allocation due to its capability in analyzing intents for next prediction. A fundamental challenge arises from real-world interaction dynamics where similar sequences involving multiple intents may exhibit different next items. More importantly, the character of volume candidate items in sequential...

Massive Open Online Courses (MOOCs) have gradually become a dominant trend in online education. However, due to the large number of learners participating in MOOCs, teachers usually cannot accurately know the learning outcomes of each MOOC user. In addition, many learners did not take the corresponding quiz after watching the MOOCs’ videos, and som...

Vehicle-cargo matching is a key task in freight O2O platform, which involves the complex interactions of drivers, vehicles, cargos, cargo owners and environmental context. Many existing works mainly study the matching of vehicle routing problems, the matching based on the credit evaluation of both drivers and cargo owners, and the matching based on...

The COVID-19 pandemic has had far-reaching effects on society, the economy, and mental health. The service industry, particularly the hotel sector, has been severely impacted, leading to job insecurity and negative mental health outcomes, especially among women who face heightened uncertainty and increased unemployment concerns. This study aims to...

Nonlinear patterns are challenging to interpret, validate, and are resource-intensive for deep learning (DL) and machine learning (ML) algorithms to predict chronic illness. Transformation of nonlinear features to a linear representation enables the human understanding of AI results and traditional and proven ML algorithms. We propose the counts of...

Current sentence-level evidence extraction based methods may lose the discourse coherence of legal articles since they tend to make the extracted sentences scattered over the article. To solve the problem, this paper proposes a Cascaded Answer-guided key segment learning framework for long Legal article Question Answering, namely CALQA. The framewo...

Multimodal medical data fusion has emerged as a transformative approach in smart healthcare, enabling a comprehensive understanding of patient health and personalized treatment plans. In this paper, a journey from data, information, and knowledge to wisdom (DIKW) is explored through multimodal fusion for smart healthcare. A comprehensive review of...

The convergence of systems neuroscience and open science arouses great interest in the current brain big data era, highlighting the thinking capability of intelligent agents in handling multi-source knowledge, information and data across various levels of granularity. To realize such thinking-inspired brain computing during a brain investigation pr...

Machine unlearning (MU) is a field that is gaining increasing attention due to the need to remove or modify predictions made by machine learning (ML) models. While training models have become more efficient and accurate, the importance of unlearning previously learned information has become increasingly significant in fields such as privacy, securi...

Informatics paradigms for brain and mental health research have seen significant advances in recent years. These developments can largely be attributed to the emergence of new technologies such as machine learning, deep learning, and artificial intelligence. Data-driven methods have the potential to support mental health care by providing more prec...

Evidences in Legal Question Answering (LQA) help infer accurate answers. Current sentence-level evidence extraction based methods may lose the discourse coherence of legal articles since they tend to make the extracted sentences scattered over an article. To this end, this paper proposes a cascaded key segment learning enhanced framework for \({\te...

Trip recommendation (TripRec) seeks to recommend a trip that consists of an ordered sequence of points-of-interest (POIs) for a tourist through a user-specific query. Recent neural TripRec methods with sequence-to-sequence models have achieved remarkable performance. However, alongside the exposure bias in general recommender systems, the selection...

Bundle recommendation aims to accurately predict the probabilities of user interactions with bundles. Most existing effective methods learn the embeddings of users and bundles from user-bundle interaction view and user-item-bundle interaction view. However, they seldom leverage the recommendation difference caused by the distinct learning trends of...

Researchers have been aware that emotion is not one-hot encoded in emotion-relevant classification tasks, and multiple emotions can coexist in a given sentence. Recently, several works have focused on leveraging a distribution label or a grayscale label of emotions in the classification model, which can enhance the one-hot label with additional inf...

Objective: Many Computer Aided Prognostic (CAP) systems based on machine learning techniques have been proposed in the field of oncology. The objective of this systematic review was to assess and critically appraise the methodologies and approaches used in predicting the prognosis of gynecological cancers using CAPs. Methods: Electronic database...

Sentiment analysis AKA opinion mining is one of the most widely used NLP applications to identify human intentions from their reviews. In the education sector, opinion mining is used to listen to student opinions and enhance their learning-teaching practices pedagogically. With advancements in sentiment annotation techniques and AI methodologies, s...

Mental healthcare is one of the prominent parts of the healthcare industry with alarming concerns related to patients depression, stress leading to self-harm and threat to fellow patients and medical staff. To provide a therapeutic environment for both patients and staff, aggressive or agitated patients need to be monitored remotely and track their...

Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI impact on education outli...

The adoption of artificial intelligence (AI) in healthcare is growing rapidly. Remote patient monitoring (RPM) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in-home care, and even hospitalized patients. The reliability of manual patient monitori...

The adoption of artificial intelligence (AI) in healthcare is growing rapidly. Remote patient monitoring (RPM) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in‐home care, and even hospitalized patients. The reliability of manual patient monitori...

Online learning and teaching increased in 2020, driven by the COVID-19 pandemic. As many researchers attempted to understand the impact stress had on the emotional behaviours and academic performance of students, most studies explored these pre- and during-COVID behaviours in the context of brick and mortar institutions transitioning to online deli...

Blockchain-enabled smart contracts have revolutionized the insurance industry due to their potential to streamline backend operations, mitigate fraudulent claims, and enhance data security and transparency. Guided by the design science methodology, the authors propose two specific smart contract frameworks to enhance insurance claims processing rel...

The field of Natural Language Processing (NLP) has evolved with, and as well as influenced, recent advances in Artificial Intelligence (AI) and computing technologies, opening up new applications and novel interactions with humans. Modern NLP involves machines’ interaction with human languages for the study of patterns and obtaining meaningful insi...

Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and s...

Sentiment analysis AKA opinion mining is one of the most widely used NLP applications to identify human intentions from their reviews. In the education sector, opinion mining is used to listen to student opinions and enhance their learning-teaching practices pedagogically. With advancements in sentiment annotation techniques and AI methodologies, s...

This research seeks to optimize the process of identifying correlations in common and high severity diseases via the fusion of knowledge graphs and deep learning artificial intelligence. Knowledge graphs can be complicated to construct and resource-intensive, alternatively, knowledge graphs can be seen to legitimize correlation incidence and better...

Machine translation is a popular automation approach for translating texts between different languages. Although traditionally it has a strong focus on natural language, images can potentially provide an additional source of information in machine translation. However, there are presently two challenges: (i) the lack of an effective fusion method t...

Recent advances in remote patient monitoring (RPM) systems can recognize various human activities to measure vital signs, including subtle motions from superficial vessels. There is a growing interest in applying artificial intelligence (AI) to this area of healthcare by addressing known limitations and challenges such as predicting and classifying...

Recent advances in remote patient monitoring (RPM) systems can recognize various human activities to measure vital signs, including subtle motions from superficial vessels. There is a growing interest in applying artificial intelligence (AI) to this area of healthcare by addressing known limitations and challenges such as predicting and classifying...

Secure deduplication aims to efficiently eliminate redundant data in cloud storage system, where convergent encryption (CE) is widely-used to provide the data confidentiality. As the number of convergent keys (CKs) in CE will increase dramatically with enlarging data, there is a critical issue that how to safely manage the CKs. Previous works usual...

Graph-based multi-view clustering aims to take advantage of multiple view graph information to provide clustering solutions. The consistency constraint of multiple views is the key of multi-view graph clustering. Most existing studies generate fusion graphs and constrain multi-view consistency by clustering loss. We argue that local pair-view consi...

Evaluating the high-effect factors of citizens' happiness is beneficial to a wide range of policy-making for economics and politics in most countries. Benefiting from the high-efficiency of regression models, previous efforts by sociology scholars have analyzed the effect of happiness factors with high interpretability. However, restricted to their...

Triaging of medical referrals can be completed using various machine learning techniques, but trained models with historical datasets may not be relevant as the clinical criteria for triaging are regularly updated and changed. This paper proposes the use of machine learning techniques coupled with the clinical prioritisation criteria (CPC) of Queen...

Bundle recommendation systems aim to recommend a bundle of items for a user to consider as a whole. They have become a norm in modern life and have been applied to many real-world settings, such as product bundle recommendation, music playlist recommendation and travel package recommendation. However, compared to studies of bundle recommendation ap...