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Hai Dong

RMIT University, School of Computing Technologies, Faculty Member

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Dr. Hai Dong is the founding director of the CloudTech-RMIT Green Cryptocurrency Joint Research Laboratory (GreenCryptoLab) and the leader of the Smart Sensing and Services Research Area at the School of Computing Technologies at RMIT University, Melbourne, Australia. Prior to this position, he served as a Vice-Chancellor’s Research Fellow at RMIT University and a Curtin Research Fellow at Curtin University, Australia. He received a PhD from Curtin University, a Bachelor’s degree from Northeastern University, China, and a Graduate Certificate in Learning and Teaching. He published a monograph and 150+ (100+ CORE-A*/A or JCR/SJR-Q1) research articles in international journals and conferences, including TIFS, TMC, TOSEM, TSC, TSE, AAAI, ACM MM, ASE, ESORICS, ICML, ICSOC, ICWS, etc. He received the Best Paper Award in ICSOC 2016 and IEEE ICBC 2025, as well as the 2023 RMIT Award for Research Engagement and Impact - Industry Engagement in Graduate Research. He secured 5 million+ research funding from government (i.e., ARC, CRC, and QNRF) and industry (i.e., ANZ, CloudTech, and Telstra) as a chief investigator. He currently chairs the IEEE Computational Intelligence Society Task Force on Deep Edge Intelligence. He has participated in the organising/program committees of 100+ international conferences, including AAAI, CIKM, ICDM, ICSOC, ICWS, WWW, etc. He has been invited to review for 50+ international journals, such as CSUR, TKDE, TOIT, TSC, etc. His primary research interests include Service-Oriented Computing, Edge Intelligence, Blockchain, AI Security, Cyber Security, Machine Learning, etc. He is a senior member of the IEEE.

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Papers by Hai Dong

Qualitative Economic Model for Long-Term IaaS Composition

Service-Oriented Computing, 2016

We propose a new qualitative economic model based optimization approach to compose an optimal set... more We propose a new qualitative economic model based optimization approach to compose an optimal set of infrastructure service requests over a long-term period. The economic model is represented as a temporal CP-Net to capture the provider's dynamic business strategies in qualitative service provisions. The multidimensional qualitative preferences are indexed in a k-d tree to compute the preference ranking of a set of incoming requests. We propose a heuristic based sequential optimization process to select the most preferred composition without the knowledge of historical request patterns. Experimental results prove the feasibility of the proposed approach.

IaaS Reservation Optimization for Multi-QoS Level based SaaS Provisioning

Aust. J. Intell. Inf. Process. Syst., 2019

We design an evolutionary computation based solution for SaaS providers to optimize the cost of r... more We design an evolutionary computation based solution for SaaS providers to optimize the cost of reserved IaaS instance subscription. The services provisioned by SaaS providers have multiple options of Quality of Service (QoS) levels. A Bayesian network model is designed to map between the QoS levels of SaaS instances and their actual resource consumption on IaaS instances. A genetic algorithm with heuristics is designed to optimize the reserved IaaS instance subscription. The proposed solution is evaluated via a set of simulated experiments.

Self-Adaptive Semantic Focused Crawler for Mining Services Information Discovery

IEEE Transactions on Industrial Informatics, 2014

It is well recognized that the Internet has become the largest marketplace in the world, and onli... more It is well recognized that the Internet has become the largest marketplace in the world, and online advertising is very popular with numerous industries, including the traditional mining service industry where mining service advertisements are effective carriers of mining service information. However, service users may encounter three major issues-heterogeneity, ubiquity, and ambiguity, when searching for mining service information over the Internet. In this paper, we present the framework of a novel self-adaptive semantic focused crawler-SASF crawler, with the purpose of precisely and efficiently discovering, formatting, and indexing mining service information over the Internet, by taking into account the three major issues. This framework incorporates the technologies of semantic focused crawling and ontology learning, in order to maintain the performance of this crawler, regardless of the variety in the Web environment. The innovations of this research lie in the design of an unsupervised framework for vocabulary-based ontology learning, and a hybrid algorithm for matching semantically relevant concepts and metadata. A series of experiments are conducted in order to evaluate the performance of this crawler. The conclusion and the direction of future work are given in the final section.

A Customized Semantic Service Retrieval Methodology for the Digital Ecosystems Environment

Ontology-Learning-Based Focused Crawling for Online Service Advertising Information Discovery and Classification

Lecture Notes in Computer Science, 2012

Online advertising has become increasingly popular among SMEs in service industries, and thousand... more Online advertising has become increasingly popular among SMEs in service industries, and thousands of service advertisements are published on the Internet every day. However, there is a huge barrier between service-provider-oriented service information publishing and service-customer-oriented service information discovery, which causes that service consumers hardly retrieve the published service advertising information from the Internet. This issue is partly resulted from the ubiquitous, heterogeneous, and ambiguous service advertising information and the open and shoreless Web environment. The existing research, nevertheless, rarely focuses on this research problem. In this paper, we propose an ontology-learning-based focused crawling approach, enabling Web-crawler-based online service advertising information discovery and classification in the Web environment, by taking into account the characteristics of service advertising information. This approach integrates an ontology-based focused crawling framework, a vocabulary-based ontology learning framework, and a hybrid mathematical model for service advertising information similarity computation.

Developing a conceptual framework of semantic search engine to promote the collaborations between SMEs in the digital ecosystems environment

2008 2nd IEEE International Conference on Digital Ecosystems and Technologies, 2008

Abstract The research with regards to digital ecosystems emerged in the end of last century, and ... more Abstract The research with regards to digital ecosystems emerged in the end of last century, and has developed in this century. Due to its immaturity, some fields within it are deemed to be blank. This research proposes to fill one of the blanks in the digital ecosystems research. By analyzing the issues in the current research, we propose to develop a semantic search engine, with the purpose of querying services provided by SMEs. The objective of this research is to build a reliable and trustworthy link between SMEs in the digital ecosystems ...

Contemporary Cultural Researches on Knowledge Sharing

2007 IEEE International Conference on Service Operations and Logistics, and Informatics, 2007

Knowledge sharing, as an essential component in the field of knowledge management research, attra... more Knowledge sharing, as an essential component in the field of knowledge management research, attracts increasing researchers into this discipline. Current researches on KS can be categorized as two independent viewsexploring from technical perspective or from cultural perspective. From technical perspective, researchers studied multiple methodologies implementing KS; from cultural perspective, scholars studied social issues which influence the style and the performance of KS. In this paper, we propose to compare and contrast the contemporary cultural researches on knowledge sharing from multiple perspectives. Conclusion and future work are made in the final section. I.

Project Track and Trace Methodology in the Environment of Digital Ecosystem

2007 Inaugural IEEE-IES Digital EcoSystems and Technologies Conference, 2007

Digital Ecosystems (DE) is an open-source network environment for Small and Medium Enterprises (S... more Digital Ecosystems (DE) is an open-source network environment for Small and Medium Enterprises (SMEs). By means of DE, project organisations are able to realize the distribution of organisational structure, to reduce internal cost and to achieve the biggest benefit. However, some issues exist in the process of project track and trace processes. In this paper, we analyze the daily project track and trace activities, including the organisational structure, the procedure of project planning and the scenarios of project distribution, by a case study in City of Melville Councila typical project organisation, to discover the issues in these activities. To solve the issues, we extend the theory of CCCI Metrics to the field of project management. By means of the extended theory, a Project Track and Trace Card is designed to assist the senior management to timely administrate the project completion status in organisations, and then to promote the knowledge sharing in project organisations.

Semantic Web Service matchmakers: state of the art and challenges

Concurrency and Computation: Practice and Experience, 2012

Web services provide a standard means for the interoperable operations between electronic devices... more Web services provide a standard means for the interoperable operations between electronic devices in a network. The mission of Web service discovery is to seek an appropriate Web service for a service requester on the basis of the service descriptions in Web service advertisements and the service requester's requirements. Nevertheless, the standard language used for encoding service descriptions does not have the capacity to specify the capabilities of a Web service, leading to the problem of ambiguity in the service discovery process. This brings up the vision of Semantic Web Services and Semantic Web Service discovery, which make use of the Semantic Web technologies to enrich the semantics of service descriptions for service discovery. Semantic Web Service matchmakers are the programs or frameworks designed to implement the task of Semantic Web Service discovery and have drawn a significant amount of attention from both academia and industry from the start of this century. In this paper, we conduct a survey of the contemporary Semantic Web Service matchmakers in order to obtain an overview of the state of the art in this research area. We summarize six technical dimensions from the past literature and analyze the typical Semantic Web Service matchmakers mostly developed during the past 4 or 5 years in terms of the six dimensions. By means of this analysis, we gain an understanding of the current research and summarize a series of potential issues to that would provide the foundation for future research in this area.

SOF: a semi-supervised ontology-learning-based focused crawler

Concurrency and Computation: Practice and Experience, 2012

The rapid increase in the volume of data available on the Internet makes it increasingly impracti... more The rapid increase in the volume of data available on the Internet makes it increasingly impractical for a crawler to index the whole Web. Instead, many intelligent crawlers, known as ontology-based semantic focused crawlers, have been designed by making use of Semantic Web technologies for topiccentered Web information crawling. Ontologies, however, have constraints of validity and time, which may influence the performance of the crawlers. Ontology-learning-based focused crawlers are therefore designed to automatically evolve ontologies by integrating ontology learning technologies. Nevertheless, surveys indicate that the existing ontology-learning-based focused crawlers do not have the capability to automatically enrich the content of ontologies, which makes these crawlers unreliable in the open and heterogeneous Web environment. Hence, in this paper, we propose a framework for a novel semi-supervised ontology-learning-based focused (SOF) crawler, the SOF crawler, which embodies a series of schemas for ontology generation and Web information formatting, a semi-supervised ontology learning framework, and a hybrid Web page classification approach aggregated by a group of support vector machine models. A series of tests are implemented to evaluate the technical feasibility of this proposed framework. The conclusion and the future work are summarized in the final section.

Heuristics Based Mosaic of Social-Sensor Services for Scene Reconstruction

Web Information Systems Engineering – WISE 2020, 2020

We propose a heuristics-based social-sensor cloud service selection and composition model to reco... more We propose a heuristics-based social-sensor cloud service selection and composition model to reconstruct mosaic scenes. The proposed approach leverages crowdsourced social media images to create an image mosaic to reconstruct a scene at a designated location and an interval of time. The novel approach relies on the set of features defined on the bases of the image metadata to determine the relevance and composability of services. Novel heuristics are developed to filter out non-relevant services. Multiple machine learning strategies are employed to produce smooth service composition resulting in a mosaic of relevant images indexed by geolocation and time. The preliminary analytical results prove the feasibility of the proposed composition model.

Distributed Machine Learning for Predictive Analytics in Mobile Edge Computing Based IoT Environments

2020 International Joint Conference on Neural Networks (IJCNN), 2020

Predictive analytics in Mobile Edge Computing (MEC) based Internet of Things (IoT) is becoming a ... more Predictive analytics in Mobile Edge Computing (MEC) based Internet of Things (IoT) is becoming a high demand in many real-world applications. A prediction problem in an MEC-based IoT environment typically corresponds to a collection of tasks with each task solved in a specific MEC environment based on the data accumulated locally, which can be regarded as a Multi-task Learning (MTL) problem. However, the heterogeneity of the data (non-IIDness) accumulated across different MEC environments challenges the application of general MTL techniques in such a setting. Federated MTL (FMTL) has recently emerged as an attempt to address this issue. Besides FMTL, there exists another powerful but under-exploited distributed machine learning technique, called Network Lasso (NL), which is inherently related to FMTL but has its own unique features. In this paper, we made an in-depth evaluation and comparison of these two techniques on three distinct IoT datasets representing real-world application scenarios. Experimental results revealed that NL outperformed FMTL in MEC-based IoT environments in terms of both accuracy and computational efficiency.

Subjective Metrics-based Cloud Market Performance Prediction

This paper explores an effective machine learning approach to predict cloud market performance fo... more This paper explores an effective machine learning approach to predict cloud market performance for cloud consumers, providers and investors based on social media. We identified a set of comprehensive subjective metrics that may affect cloud market performance via literature survey. We used a popular sentiment analysis technique to process customer reviews collected from social media. Cloud market revenue growth was selected as an indicator of cloud market performance. We considered the revenue growth of Amazon Web Services as the stakeholder of our experiments. Three machine learning models were selected: linear regression, artificial neural network, and support vector machine. These models were compared with a time series prediction model. We found that the set of subjective metrics is able to improve the prediction performance for all the models. The support vector machine showed the best prediction results compared to the other models.

ORPMS: An Ontology-based Real-time Project Monitoring System in the Cloud

J. Univers. Comput. Sci., 2011

Project monitoring plays a crucial role in project management, which is a part of every stage of ... more Project monitoring plays a crucial role in project management, which is a part of every stage of a project's life-cycle. Nevertheless, along with the increasing ratio of outsourcing in many companies' strategic plans, project monitoring has been challenged by geographically dispersed project teams and culturally diverse team members. Furthermore, because of the lack of a uniform standard, data exchange between various project monitoring software becomes an impossible mission. These factors together lead to the issue of ambiguity in project monitoring processes. Ontology is a form of knowledge representation with the purpose of disambiguation. Consequently, in this paper, we propose the framework of an ontology-based real-time project monitoring system (ORPSM), in order to, by means of ontologies, solve the ambiguity issue in project monitoring processes caused by multiple factors. The framework incorporates a series of ontologies for knowledge capture, storage, sharing and t...

Enabling Edge Cloud Intelligence for Activity Learning in Smart Home

ArXiv, 2020

We propose a novel activity learning framework based on Edge Cloud architecture for the purpose o... more We propose a novel activity learning framework based on Edge Cloud architecture for the purpose of recognizing and predicting human activities. Although activity recognition has been vastly studied by many researchers, the temporal features that constitute an activity, which can provide useful insights for activity models, have not been exploited to their full potentials by mining algorithms. In this paper, we utilize temporal features for activity recognition and prediction in a single smart home setting. We discover activity patterns and temporal relations such as the order of activities from real data to develop a prompting system. Analysis of real data collected from smart homes was used to validate the proposed method.

A Knowledge Graph Based Approach for Mobile Application Recommendation

Service-Oriented Computing, 2020

With the rapid prevalence of mobile devices and the dramatic proliferation of mobile applications... more With the rapid prevalence of mobile devices and the dramatic proliferation of mobile applications (apps), app recommendation becomes an emergent task that would benefit both app users and stockholders. How to effectively organize and make full use of rich side information of users and apps is a key challenge to address the sparsity issue for traditional approaches. To meet this challenge, we proposed a novel endto-end Knowledge Graph Convolutional Embedding Propagation Model (KGEP) for app recommendation. Specifically, we first designed a knowledge graph construction method to model the user and app side information, then adopted KG embedding techniques to capture the factual triplet-focused semantics of the side information related to the first-order structure of the KG, and finally proposed a relation-weighted convolutional embedding propagation model to capture the recommendationfocused semantics related to high-order structure of the KG. Extensive experiments conducted on a real-world dataset validate the effectiveness of the proposed approach compared to the state-of-the-art recommendation approaches.

Conflict Detection in IoT-based Smart Homes

2021 IEEE International Conference on Web Services (ICWS), 2021

We propose a novel framework that detects conflicts in IoT-based smart homes. Conflicts may arise... more We propose a novel framework that detects conflicts in IoT-based smart homes. Conflicts may arise during interactions between the resident and IoT services in smart homes. We propose a generic knowledge graph to represent the relations between IoT services and environment entities. We also profile a generic knowledge graph to a specific smart home setting based on the context information. We propose a conflict taxonomy to capture different types of conflicts in a single resident smart home setting. A conflict detection algorithm is proposed to identify potential conflicts using the profiled knowledge graph. We conduct a set of experiments on real datasets and synthesized datasets to validate the effectiveness and efficiency of our proposed approach.

NPS-AntiClone: Identity Cloning Detection based on Non-Privacy-Sensitive User Profile Data

2021 IEEE International Conference on Web Services (ICWS), 2021

Social sensing is a paradigm that allows crowdsourcing data from humans and devices. This sensed ... more Social sensing is a paradigm that allows crowdsourcing data from humans and devices. This sensed data (e.g. social network posts) can be hosted in social-sensor clouds (i.e. social networks) and delivered as social-sensor cloud services (SocSen services). These services can be identified by their providers' social network accounts. Attackers intrude socialsensor clouds by cloning SocSen service providers' user profiles to deceive social-sensor cloud users. We propose a novel unsupervised SocSen service provider identity cloning detection approach, NPS-AntiClone, to prevent the detrimental outcomes caused by such identity deception. This approach leverages non-privacy-sensitive user profile data gathered from social networks to perform cloned identity detection. It consists of three main components: 1) a multi-view account representation model, 2) an embedding learning model and 3) a prediction model. The multi-view account representation model forms three different views for a given identity, namely a post view, a network view and a profile attribute view. The embedding learning model learns a single embedding from the generated multi-view representation using Weighted Generalized Canonical Correlation Analysis. Finally, NPS-AntiClone calculates the cosine similarity between two accounts' embedding to predict whether these two accounts contain a cloned account and its victim. We evaluated our proposed approach using a real-world dataset. The results showed that NPS-AntiClone significantly outperforms the existing state-of-the-art identity cloning detection techniques and machine learning approaches.

Social Media Identity Deception Detection

ACM Computing Surveys, 2021

Social media have been growing rapidly and become essential elements of many people’s lives. Mean... more Social media have been growing rapidly and become essential elements of many people’s lives. Meanwhile, social media have also come to be a popular source for identity deception. Many social media identity deception cases have arisen over the past few years. Recent studies have been conducted to prevent and detect identity deception. This survey analyzes various identity deception attacks, which can be categorized into fake profile, identity theft, and identity cloning. This survey provides a detailed review of social media identity deception detection techniques. It also identifies primary research challenges and issues in the existing detection techniques. This article is expected to benefit both researchers and social media providers.

A work-in-progress platform for solving knowledge sharing issues in organizations

2008 6th International Symposium on Intelligent Systems and Informatics, 2008

In this paper, we propose a knowledge sharing platform based on the technology of ontology and ag... more In this paper, we propose a knowledge sharing platform based on the technology of ontology and agent, in order to solve the issues in the knowledge sharing activities within organizations. Based on the analysis of literature, the issues within current organizational knowledge sharing activities and knowledge sharing technologies are explored. By means of the literature analysis, we present a new definition of knowledge sharing in the context of organizational environment. Against these issues, our research objectives are delivered, and potential research effects are indicated. After that we choose a research approach and propose the conceptual model of our knowledge sharing platform. Finally we state the progress of our current research work, make conclusions and plan further works. I. Figure 3. Organizational knowledge sharing ontology in Protégéowl