ScholarGPS: Prof. Cao as Highly Ranked Scholar – Top 0.05%
ScholarGPS listed Prof. Cao as Highly Ranked Scholars in Data Mining in 2022
ScholarGPS ranked Prof. Longbing Cao as #34 Highly Ranked Scholarship – Lifetime in Data Mining, which makes him the No. 1 in Australia.
ScholarGPS selects top 0.05% scholars in each discipline or specialty as their Highly Ranked Scholars. Their ranking integrates the performance in Productivity, Quality, and Impact of 30M scholars across all disciplines.
ARC LP2023: Ethical Enterprise Representations for Personalised Sustainable Finance
2023 ARC Linkage Project: LP230201022
Ethical Enterprise Representations for Personalised Sustainable Finance
Professor Longbing Cao, Associate Professor Yin Liao, Associate Professor Di Bu, Professor Dr Vito Mollica, Dr Xuhui Fan, Professor Alberto Rossi (PI), Ms Jing Sun (PI).
The rapidly evolving field of sustainable finance requires responsible services, satisfying environmental, social and governance (ESG) criteria. This requires disruptive FinTech innovations – ethical enterprise learning from whole-of-business financial data, however the corresponding valid theories and industrial solutions are unavailable. We aim to develop forward-looking ESG-integrated enterprise learning theories and tools to represent and analyse entire businesses and data and develop novel ESG ratings and ESG-efficient investment solutions. These will advance knowledge and capabilities in enterprise AI and sustainable finance, transform financial services, and enhance Australia’s leadership in FinTech research and innovation.
Access the relevant information on at the ARC grant outcome announcement webpage.
Book: Global COVID-19 Research and Modeling
To answer the big questions like ‘how have global scientists responded to tackling COVID-19?’ and ‘how has COVID-19 been quantified?’, our team explored 1M publications in English affiliated with 194 countries and 2M authors across 26 subjects and conducted series of research on COVID-19 modeling in the past 3.5 years.
This book provides answers to fundamental and challenging questions regarding the global response to COVID-19. It creates a historical record of COVID-19 research conducted over the four years of the pandemic, with a focus on how researchers have responded, quantified, and modeled COVID-19 problems. Since mid-2021, we have diligently monitored and analyzed global scientific efforts in tackling COVID-19. Our comprehensive global endeavor involves collecting, processing, analyzing, and discovering COVID-19 related scientific literature in English since January 2020. This provides insights into how scientists across disciplines and almost every country and regions have fought against COVID-19. Additionally, we explore the quantification of COVID-19 problems and impacts through mathematics, AI, machine learning, data science, epidemiology, and domain knowledge. The book reports findings on publication quantities, impacts, collaborations, and correlations with the economy and infections globally, regionally, and country-wide. These results represent the first and only holistic and systematic studies aimed at scientifically understanding, quantifying, and containing the pandemic. We hope this comprehensive analysis will contribute to better preparedness, response, and management of future emergencies and inspire further research in infectious diseases. The book also serves as a valuable resource for research policy, funding management authorities, researchers, policy makers, and funding bodies involved in infectious disease management, public health, and emergency resilience.
Humanoid AI: A new era of AI and robotics & Ameca
AI and robotics are ushering in a new era – humanoid AI.
Humanoid AI is emerging as the next major advancement in humanlike and humanlevel AI, paving the way for both artificial general intelligence and artificial narrow intelligence.
Longbing Cao. AI Robots and Humanoid AI: Review, Perspectives and Directions, 1-37, 19 March, 2024.
AI-powered humanoids synergize the advancements in large language models (LLMs), large multimodal models (LMMs), generative AI, and human-level AI with humanoid robotics, omniverse, and decentralized AI, transitioning from human-looking to humane humanoids and fostering human-like robotics, a new area of AI: humanoid AI.
Humanoid AI has emerged into a human-AI-robotics-web-integrative ecosystem, revolutionizing the landscape of the intelligent digital economy, societies, and cultures. While only a limited number of humanoids are currently empowered by LLMs or driven by generative AI, humanoid AI is emerging and driving fast-paced development of real-time, interactive, and humane humanoids, with revolutionary advancements and possibilities:
- Synergizing generative to humanlevel AI into humanoids
- Evolving paradigm shift from humanlooking to humane and humanlike humanoids
- Interacting between AI and robotics and between human systems and intelligence systems
- Enabling humane and humanlevel humanoids
Our humanoid AI Ameca – real-time, interactive and multimodal humanoid robot driven by generative AI, LLMs and LMMs
https://youtu.be/OUDPcn_7pts
Many techniques are required to enable humane and humanlevel robots, such as mechanical, material, biomedical, electrical and anthropomorphic designs. Intelligent techniques to enable humane and humanlevel robots include: (1) humanizing robots toward humane and humanlevel features, structures, functions and moral traits; (2) digitizing human features in robotics; and (3) intelligentizing robots with human intelligence in complex decentralized, distributed, or even virtualized applications and environments. Essential studies include:
- building mind-to-action mindful and actionable humanoids
- learning general humanoid intelligence
- supporting omnimodal perception-to-behavior humanoid learning
- advancing humanlike humanoids with humanlevel AI
- hybridizing humanoids with humanoid animation, imitation, digital twins, metaverse and mixed reality
- enabling humanoids with decentralized AI for decentralized humanoids: on-humanoid, edge and cloud humanoid systems
- developing humanoid AI hardware, software and applications
Fig. 3. Decentralized humanoids: On-humanoid, edge and cloud humanoid AI framework, synergizing humans, humanoids, edge and cloud devices, algorithms and services including LLMs.
ARC DP24: Data Complexity and Uncertainty-Resilient Deep Variational Learning
2024 ARC Discovery Project DP240102050
Data Complexity and Uncertainty-Resilient Deep Variational Learning
Professor Longbing Cao and Professor Joao Gama (Partner Investigator).
Enterprise data present increasingly significant characteristics and complexities, such as multi-aspect, heterogeneous and hierarchical features and interactions, and evolving dependencies and multi-distributions. They continue to significantly challenge the state-of-the-art probabilistic and neural learning systems with limited to insufficient capabilities and capacity. This research aims to develop a theory of flexible deep variational learning transforming new deep probabilistic models with flexible variational neural mechanisms for analytically explainable, complexity-resilient analytics of real-life data. The outcomes are expected to fill important knowledge gaps and lift critical innovation competencies in wide domains.
Access the relevant information on at the ARC grant outcome announcement webpage.
ARC LIEF24: Federated Omniverse Facilities for Smart Digital Futures
2024 ARC Linkage Infrastructure, Equipment and Facilities (LIEF) Project: LE240100131
Federated Omniverse Facilities for Smart Digital Futures
Professor Longbing Cao; Professor Patricia Davidson; Professor Vijay Varadharajan; Professor Jinman Kim; Professor Ping Yu; Professor Amin Beheshti; Associate Professor Quang Vinh Nguyen; Dr Sankalp Khanna (PI).
A world-first trans-disciplinary, -domain, and -institutional smart 3D omniverse R&D ecosystem AuVerse will be built in NSW, affiliated with Queensland, and accessible to academia and industry. AuVerse will support cloud-based, reality-virtuality-fused, immersive, interactive and secure future-oriented digital design, development, training and society. In the new era of digital innovation and paradigm shift, AuVerse will substantially boost Australia’s pivotal research leadership and business competitiveness in nurturing new-generation, collaborative and transformative digital R&D and talent pipeline. It will enable large-scale strategic business innovation and transformation including smart manufacturing and Industry 4.0.
Access the relevant information on at the ARC grant outcome announcement webpage.
TNNLS: Explicit and Implicit Pattern Relation Analysis for Discovering Actionable Negative Sequences
Explicit and Implicit Pattern Relation Analysis for Discovering Actionable Negative Sequences.
Wei Wang, Longbing Cao. IEEE Trans Neural Netw Learn Syst, vol. 35, no. 4, pp. 5183-5197, 2024.
Access the paper at the TNNLS website.
Real-life events, behaviors, and interactions produce sequential data. An important but rarely explored problem is to analyze those nonoccurring (also called negative) yet important sequences, forming negative sequence analysis (NSA). A typical NSA area is to discover negative sequential patterns (NSPs) consisting of important nonoccurring and occurring elements and patterns. The limited existing work on NSP mining relies on frequentist and downward closure property-based pattern selection, producing large and highly redundant NSPs, nonactionable for business decision-making. This work makes the first attempt for actionable NSP discovery. It builds an NSP graph representation, quantifies both explicit occurrence and implicit nonoccurrence-based element and pattern relations, and then discovers significant, diverse, and informative NSPs in the NSP graph to represent the entire NSP set for discovering actionable NSPs. A DPP-based NSP representation and actionable NSP discovery method, EINSP, introduces novel and significant contributions to NSA and sequence analysis: 1) it represents NSPs by a determinantal point process (DPP)-based graph; 2) it quantifies actionable NSPs in terms of their statistical significance, diversity, and strength of explicit/implicit element/pattern relations; and 3) it models and measures both explicit and implicit element/pattern relations in the DPP-based NSP graph to represent direct and indirect couplings between NSP items, elements, and patterns. We substantially analyze the effectiveness of EINSP in terms of various theoretical and empirical aspects, including complexity, item/pattern coverage, pattern size and diversity, implicit pattern relation strength, and data factors.
TNNLS: eVAE: Evolutionary variational autoencoder
eVAE: Evolutionary variational autoencoder
Zhangkai Wu, Longbing Cao and Lei Qi. IEEE Trans Neural Netw Learn Syst, 2024.
Access the paper at the arXiv website.
The surrogate loss of variational autoencoders (VAEs) poses various challenges to their training, inducing the imbalance between task fitting and representation inference. To avert this, the existing strategies for VAEs focus on adjusting the tradeoff by introducing hyperparameters, deriving a tighter bound under some mild assumptions, or decomposing the loss components per certain neural settings. VAEs still suffer from uncertain tradeoff learning.We propose a novel evolutionary variational autoencoder (eVAE) building on the variational information bottleneck (VIB) theory and integrative evolutionary neural learning. eVAE integrates a variational genetic algorithm into VAE with variational evolutionary operators including variational mutation, crossover, and evolution. Its inner-outer-joint training mechanism synergistically and dynamically generates and updates the uncertain tradeoff learning in the evidence lower bound (ELBO) without additional constraints. Apart from learning a lossy compression and representation of data under the VIB assumption, eVAE presents an evolutionary paradigm to tune critical factors of VAEs and deep neural networks and addresses the premature convergence and random search problem by integrating evolutionary optimization into deep learning. Experiments show that eVAE addresses the KL-vanishing problem for text generation with low reconstruction loss, generates all disentangled factors with sharp images, and improves the image generation quality, respectively. eVAE achieves better reconstruction loss, disentanglement, and generation-inference balance than its competitors.
AAAI24: Frequency Spectrum is More Effective for Multimodal Representation and Fusion
Frequency Spectrum is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detector
An Lao, Qi Zhang, Chongyang Shi, Longbing Cao, Kun Yi, Liang Hu, Duoqian Miao. AAAI 2024.
Access the paper at the arXiv website.
Multimodal content, such as mixing text with images, presents significant challenges to rumor detection in social media. Existing multimodal rumor detection has focused on mixing tokens among spatial and sequential locations for unimodal representation or fusing clues of rumor veracity across modalities. However, they suffer from less discriminative unimodal representation and are vulnerable to intricate location dependencies in the time-consuming fusion of spatial and sequential tokens. This work makes the first attempt at multimodal rumor detection in the frequency domain, which efficiently transforms spatial features into the frequency spectrum and obtains highly discriminative spectrum features for multimodal representation and fusion. A novel Frequency Spectrum Representation and fUsion network (FSRU) with dual contrastive learning reveals the frequency spectrum is more effective for multimodal representation and fusion, extracting the informative components for rumor detection. FSRU involves three novel mechanisms: utilizing the Fourier transform to convert features in the spatial domain to the frequency domain, the unimodal spectrum compression, and the cross-modal spectrum co-selection module in the frequency domain. Substantial experiments show that FSRU achieves satisfactory multimodal rumor detection performance.
Global scientists responses to COVID-19 & our work
More on COVID-19 modeling, please refer to here.
Global COVID-19 Research and Modeling: A Historical Record
To answer the big questions like ‘how have global scientists responded to tackling COVID-19?’ and ‘how has COVID-19 been quantified?’, our team explored 1M publications in English affiliated with 194 countries and 2M authors across 26 subjects and conducted series of research on COVID-19 modeling in the past 3.5 years.
This book provides answers to fundamental and challenging questions regarding the global response to COVID-19. It creates a historical record of COVID-19 research conducted over the four years of the pandemic, with a focus on how researchers have responded, quantified, and modeled COVID-19 problems. Since mid-2021, we have diligently monitored and analyzed global scientific efforts in tackling COVID-19. Our comprehensive global endeavor involves collecting, processing, analyzing, and discovering COVID-19 related scientific literature in English since January 2020. This provides insights into how scientists across disciplines and almost every country and regions have fought against COVID-19. Additionally, we explore the quantification of COVID-19 problems and impacts through mathematics, AI, machine learning, data science, epidemiology, and domain knowledge. The book reports findings on publication quantities, impacts, collaborations, and correlations with the economy and infections globally, regionally, and country-wide. These results represent the first and only holistic and systematic studies aimed at scientifically understanding, quantifying, and containing the pandemic. We hope this comprehensive analysis will contribute to better preparedness, response, and management of future emergencies and inspire further research in infectious diseases. The book also serves as a valuable resource for research policy, funding management authorities, researchers, policy makers, and funding bodies involved in infectious disease management, public health, and emergency resilience.
Differentiating behavioral impact with or without vaccination certification under mass vaccination and non-pharmaceutical interventions on mitigating COVID-19
Hu Cao and Longbing Cao. Scientific Reports, 14, 707 (2024). https://doi.org/10.1038/s41598-023-50421-9.
As COVID-19 vaccines became widely available worldwide, many countries implemented vaccination certification, also known as a “green pass”, to promote and expedite vaccination on containing virus spread from the latter half of 2021. This policy allowed those vaccinated to have more freedom in public activities compared to more constraints on the unvaccinated in addition to existing non-pharmaceutical interventions (NPIs). Accordingly, the vaccination certification also induced heterogeneous behaviors of unvaccinated and vaccinated groups. This makes it essential yet challenging to model the behavioral impact of vaccination certification on the two groups and the transmission dynamics of COVID-19 within and between the groups. Very limited quantitative work is available for addressing these purposes. Here we propose an extended epidemiological model SEIQRD^2 to effectively distinguish the behavioral impact of vaccination certification on unvaccinated and vaccinated groups through incorporating two contrastive transmission chains. SEIQRD^2 also quantifies the impact of the green pass policy. With the resurgence of COVID-19 in Greece, Austria, and Israel in 2021, our simulation results indicate that their implementation of vaccination certification brought about more than a 14-fold decrease in the total number of infections and deaths as compared to a scenario with no such a policy. Additionally, a green pass policy may offer a reasonable practical solution to strike the balance between public health and individual’s freedom during the pandemic.
How Have Global Scientists Responded to Tackling COVID-19?
Longbing Cao and Wenfeng Hou. Full technical report, pp. 1-125, University of Technology Sydney, 2022. medRxiv: https://doi.org/10.1101/2022.08.16.22278871
Since the outbreak of COVID-19, the global scientific communities across almost all countries have made urgent, intensive, and continuous effort on understanding, fighting and modeling the COVID-19 pandemic. COVID-19 research turns out to be the first overwhelming global scientific reaction to significant global crises and threats. This literature analysis report collects and summarizes the profiles, trends, quality and impact of this global scientific response. It collects and analyzes 346,267 scientific references in English, involving researchers from 189 countries and regions in 27 subject areas. The report generates a picture of how global scientists have responded to COVID-19 between Jan 2020 and Mar 2022 in terms of their publication quantity, impact, focused major problems, and research areas and methods over country/region, discipline and time and collaboratively. The report also captures broad-reaching distributions and trends of modeling COVID-19 by AI, data science, analytics, shallow and deep machine learning, epidemic modeling, applied mathematics, and social science methods, etc. We further show the correlations between publication quality and quantity and economic status and COVID-19 infections globally and in major countries and regions. The literature analysis results of this global scientific response to COVID-19 present a comprehensive global, regional and subject-specific picture of the significant cross-disciplinary, cross-country, cross-problem, and cross-technique profiles and differences of the COVID-19 publication quantity and quality. The report also discloses significant imbalances in the COVID-19 research across countries/regions, subject areas, problems, topics, methods, research collaborations, and economic statuses. We share the source and analytical data of this global literature analysis for further research on this unprecedented and future crises.
Access the COVID-19 global scientist response dataset and results at Kaggle.
COVID-19 Modeling: A Review
Longbing Cao and Qing Liu. doi: https://doi.org/10.1101/2022.08.22.22279022, 1-103, 2022 (new version).
The SARS-CoV-2 virus and COVID-19 disease have posed unprecedented and overwhelming demand, challenges and opportunities to domain, model and data driven modeling. This paper makes a comprehensive review of the challenges, tasks, methods, progresses, gaps and opportunities of modeling COVID-19 problems, data and objectives. It constructs a research landscape of the COVID-19 modeling tasks and methods, and further categorizes, summarizes, compares and discusses the related methods and progresses of modeling COVID-19 epidemic transmission processes and dynamics, case identification and tracing, infection diagnosis and medical treatments, non-pharmaceutical interventions and their effects, drug and vaccine development, psychological, economic and social influence and impact, and misinformation, etc. The modeling methods involve mathematical and statistical models, domain-driven modeling by epidemiological compartmental models, medical and biomedical analysis, data-driven learning by shallow and deep machine learning, simulation modeling, social science methods, and hybrid modeling.
AI in Combating the COVID-19 Pandemic.
Longbing Cao. IEEE Intell. Syst. 37(2): pp. 3-13, (2022). BibTeX
The SARS-CoV-2 virus, the COVID-19 disease, and the resulting pandemic have reshaped the entire world in an unprecedented manner. Massive efforts have been made by AI communities to combat the pandemic. What roles has AI played in tackling COVID-19? How has AI performed in the battle against COVID-19? Where are the gaps and opportunities? What lessons can we learn to enhance the ability of AI to battle future pandemics? These questions, despite being fundamental, are yet to be answered in full or systematically. They need to be addressed by AI communities as a priority despite the easing of the omicron infectiousness and threat. This article reviews these issues with reflections on global AI research and the literature on tackling COVID-19. It is envisaged that the demand and priority of developing “pandemic AI” will increase over time, with smart global epidemic early warning systems to be developed by a global collaborative AI effort.
How control and relaxation interventions and virus mutations influence the resurgence of COVID-19
Longbing Cao, Qing Liu. medRxiv, doi: https://doi.org/10.1101/2021.08.31.21262897, 1-29, 2021.
After a year of the unprecedented COVID-19 pandemic in 2020, the world has been overwhelmed by COVID-19 resurgences in 2021. Resurgences usually cause longer, broader and higher waves of infection, with greater threat to societies and economies compared to first waves. They may be caused by late implementation or early relaxation of non-pharmaceutical interventions (NPIs) such as social restrictions, ineffective interventions, or virus mutations. Here we provide quantitative evidence to characterize epidemiological differences between waves, evaluate possible causes, and predict potential trends under virus mutations. We use an event-driven dynamic epidemiological model embedded with time-dependent intervention interactions to compare two waves of COVID-19 outbreaks, and we quantify the impacts of control or relaxation interventions (called events) on wave patterns. We show the second waves in late 2020 in Germany, France and Italy could have been better contained by either carrying forward the effective interventions from their first waves or implementing better controls and timing. We also obtain the quantitative effects of enforcing or relaxing interventions on various transmissibility levels of coronavirus mutants (like delta or lambda) in the second waves and in the next 30 days. Comprehensive analyses at four levels – vertical (between waves), horizontal (across countries), what-if (scenario simulations on second waves) and future (30-day trend) – in the two 2020 waves in Germany, France and Italy show that (1) intervention fatigue (government and community reluctance to interventions), early relaxations and lagging interventions may be common reasons for the resurgences observed in many countries; (2) timely strong interventions such as full lockdown will contain resurgence; and (3) in the absence of sufficient vaccination, herd immunity and effective antiviral pharmaceutical treatments and with more infectious mutations, the widespread early or fast relaxation of interventions including public activity restrictions will result in a COVID-19 resurgence.
Modeling COVID-19 uncertainties evolving over time and density-dependent social reinforcement and asymptomatic infections
Qing Liu and Longbing Cao, Sci Rep, 12(1):5891, 1-14, 2022.
The novel coronavirus disease 2019 (COVID-19) presents unique and unknown problem complexities and modeling challenges, where an imperative task is to model both its process and data uncertainties, represented in implicit and high-proportional undocumented infections, asymptomatic contagion, social reinforcement of infections, and various quality issues in the reported data. These uncertainties become even more phenomenal in the overwhelming mutation-dominated resurgences with vaccinated but still susceptible populations. Here we introduce a novel hybrid approach to (1) characterizing and distinguishing Undocumented (U) and Documented (D) infections commonly seen during COVID-19 incubation periods and asymptomatic infections by expanding the foundational compartmental epidemic Susceptible-Infected-Recovered (SIR) model with two compartments, resulting in a new Susceptible-Undocumented infected-Documented infected-Recovered (SUDR) model; (2) characterizing the probabilistic density of infections by empowering SUDR to capture exogenous processes like clustering contagion interactions, superspreading and social reinforcement; and (3) approximating the density likelihood of COVID-19 prevalence over time by incorporating Bayesian inference into SUDR. Different from existing COVID-19 models, SUDR characterizes the undocumented infections during unknown transmission processes. To capture the uncertainties of temporal transmission and social reinforcement during the COVID-19 contagion, the transmission rate is modeled by a time-varying density function of undocumented infectious cases. We solve the modeling by sampling from the mean-field posterior distribution with reasonable priors, making SUDR suitable to handle the randomness, noise and sparsity of COVID-19 observations widely seen in the public COVID-19 case data.
Quantifying COVID-19: Modeling and Evaluation
Qing Liu, PhD thesis, Feb 2022, UTS
The coronavirus disease 2019 (COVID-19) has evolved to a global pandemic and poses significant demands and challenges in modeling its complex epidemic transmission, infection, and contagion. Moreover, it has shown to be vastly different
from known epidemics. To address the COVID-19 pandemic, significant efforts have been made to model COVID-19 transmission, diagnoses, interventions, and pathological and influence analysis, etc. However, due to the unique and unknown problem and data complexity, the related studies of COVID-19 still face numerous challenges, including undocumented infections, asymptomatic contagion, uncertainty and quality issues in the reported data, flexible external non-pharmaceutical interventions, unknown resurgence patterns or periodicity, and multiple mutations.
This thesis aims to understand COVID-19 concerning the COVID-19 research landscape, transmission complexity, non-pharmaceutical interventions, and COVID-19 resurgence. Focusing on the COVID-19 challenges, this thesis first compares the key characteristics of COVID-19 disease with several known epidemics, and it summarizes the COVID-19 modeling complexities caused by these attributes. Starting from this basic knowledge, this thesis further explores COVID-19 modeling, which results in the following four contributions. (1) This thesis tracks the current COVID-19 modeling progress with natural language techniques and statistically summarizes the major facts of COVID- 19 disease and COVID-19 modelling. This work structures a transdisciplinary research landscape and provides a holistic picture of COVID-19 modeling. (2) It infers the possible quantity of undocumented infections in the early stage of the COVID-19 outbreak with the proposed density-based Bayesian probabilistic compartmental model. This work examines the COVID-19 transmission complexities, in other words, undocumented infections, contagion reinforcement, and the imperfect conditions existing in COVID-19 reported data, that is, noise, sparsity, and uncertainty. (3)With the proposed event-driven generalized Susceptible-Exposed-Infectious-Recovered compartmental model, this thesis studies the impact of external interventions and activities in the dynamic COVID-19 evolving process and quantifies the efficacy of control policies and relaxation measures. (4) This thesis compares the differences between multiple COVID-19 waves, including the epidemiological attributes and the countermeasures, and it simulates the possible scenarios with different interventions and virus mutations. This exploration illustrates the possible reasons for COVID-19 resurgence and provides reliable guidance for society resuscitation.
Extensive experiments, including mean-field Bayesian inference, backward-looking empirical evaluations, forward-looking simulations, and short-term forecast, demonstrate the effectiveness of the proposed methods for modeling the COVID-19 complexities aforementioned. The findings and quantitative results in this thesis indicate clues, evidence, and guidance for governments and policymakers to appropriately manage and mitigate the COVID-19 pandemic.