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Cyril Leung

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17 papers
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17

JBHI Journal 2022 Journal Article

Dynamic Link Prediction for Discovery of New Impactful COVID-19 Research Approaches

  • Xiangyu Wang
  • Yuan Li
  • Taiyu Ban
  • Jiarun Zhu
  • Lyuzhou Chen
  • Muhammad Usman
  • Xin Wang
  • Huanhuan Chen

In fighting the COVID-19 pandemic, the main challenges include the lack of prior research and the urgency to find effective solutions. It is essential to accurately and rapidly summarize the relevant research work and explore potential solutions for diagnosis, treatment and prevention of COVID-19. It is a daunting task to summarize the numerous existing research works and to assess their effectiveness. This paper explores the discovery of new COVID-19 research approaches based on dynamic link prediction, which analyze the dynamic topological network of keywords to predict possible connections of research concepts. A dynamic link prediction method based on multi-granularity feature fusion is proposed. Firstly, a multi-granularity temporal feature fusion method is adopted to extract the temporal evolution of different order subgraphs. Secondly, a hierarchical feature weighting method is proposed to emphasize actively evolving nodes. Thirdly, a semantic repetition sampling mechanism is designed to avoid the negative effect of semantically equivalent medical entities on the real structure of the graph, and to capture the real topological structure features. Experiments are performed on the COVID-19 Open Research Dataset to assess the performance of the model. The results show that the proposed model performs significantly better than existing state-of-the-art models, thereby confirming the effectiveness of the proposed method for the discovery of new COVID-19 research approaches.

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IJCAI Conference 2020 Conference Paper

A Gamified Assessment Platform for Predicting the Risk of Dementia +Parkinson’s disease (DPD) Co-Morbidity

  • Zhiwei Zeng
  • Hongchao Jiang
  • Yanci Zhang
  • Zhiqi Shen
  • Jun Ji
  • Martin J. McKeown
  • Jing Jih Chin
  • Cyril Leung

Population aging is becoming an increasingly important issue around the world. As people live longer, they also tend to suffer from more challenging medical conditions. Currently, there is a lack of a holistic technology-powered solution for providing quality care at affordable cost to patients suffering from co-morbidity. In this paper, we demonstrate a novel AI-powered solution to provide early detection of the onset of Dementia + Parkinson's disease (DPD) co-morbidity, a condition which severely limits a senior's ability to live actively and independently. We investigate useful in-game behaviour markers which can support machine learning-based predictive analytics on seniors' risk of developing DPD co-morbidity.

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IJCAI Conference 2020 Conference Paper

A Testbed for Studying COVID-19 Spreading in Ride-Sharing Systems

  • Harrison Jun Yong Wong
  • Zichao Deng
  • Han Yu
  • Jianqiang Huang
  • Cyril Leung
  • Chunyan Miao

Order dispatch is an important area where artificial intelligence (AI) can benefit ride-sharing systems (e. g. , Grab, Uber), which has become an integral part of our public transport network. In this paper, we present a multi-agent testbed to study the spread of infectious diseases through such a system. It allows users to vary the parameters of the disease and behaviours to study the interaction effect between technology, disease and people's behaviours in such a complex environment.

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KR Conference 2020 System Paper

Explainable and Argumentation-based Decision Making with Qualitative Preferences for Diagnostics and Prognostics of Alzheimer's Disease

  • Zhiwei Zeng
  • Zhiqi Shen
  • Benny Toh Hsiang Tan
  • Jing Jih Chin
  • Cyril Leung
  • Yu Wang
  • Ying Chi
  • Chunyan Miao

Argumentation has gained traction as a formalism to make more transparent decisions and provide formal explanations recently. In this paper, we present an argumentation-based approach to decision making that can support modelling and automated reasoning about complex qualitative preferences and offer dialogical explanations for the decisions made. We first propose Qualitative Preference Decision Frameworks (QPDFs). In a QPDF, we use contextual priority to represent the relative importance of combinations of goals in different contexts and define associated strategies for deriving decision preferences based on prioritized goal combinations. To automate the decision computation, we map QPDFs to Assumption-based Argumentation (ABA) frameworks so that we can utilize existing ABA argumentative engines for our implementation. We implemented our approach for two tasks, diagnostics and prognostics of Alzheimer's Disease (AD), and evaluated it with real-world datasets. For each task, one of our models achieves the highest accuracy and good precision and recall for all classes compared to common machine learning models. Moreover, we study how to formalize argumentation dialogues that give contrastive, focused and selected explanations for the most preferred decisions selected in given contexts.

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AAAI Conference 2019 Short Paper

Computing Argumentative Explanations in Bipolar Argumentation Frameworks

  • Zhiwei Zeng
  • Chunyan Miao
  • Cyril Leung
  • Zhiqi Shen
  • Jing Jih Chin

The process of arguing is also the process of justifying and explaining. Here, we focus on argumentative explanations in Abstract Bipolar Argumentation. We propose new defence and acceptability semantics, which operates on both attack and support relations, and use them to formalize two types of explanations, concise and strong explanations. We also show how to compute the explanations with Bipolar Dispute Trees.

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IJCAI Conference 2019 Conference Paper

Intelligent Decision Support for Improving Power Management

  • Yongqing Zheng
  • Han Yu
  • Kun Zhang
  • Yuliang Shi
  • Cyril Leung
  • Chunyan Miao

With the development and adoption of the electricity information tracking system in China, real-time electricity consumption big data have become available to enable artificial intelligence (AI) to help power companies and the urban management departments to make demand side management decisions. We demonstrate the Power Intelligent Decision Support (PIDS) platform, which can generate Orderly Power Utilization (OPU) decision recommendations and perform Demand Response (DR) implementation management based on a short-term load forecasting model. It can also provide different users with query and application functions to facilitate explainable decision support.

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IJCAI Conference 2018 Conference Paper

Building Ethics into Artificial Intelligence

  • Han Yu
  • Zhiqi Shen
  • Chunyan Miao
  • Cyril Leung
  • Victor R. Lesser
  • Qiang Yang

As artificial intelligence (AI) systems become increasingly ubiquitous, the topic of AI governance for ethical decision-making by AI has captured public imagination. Within the AI research community, this topic remains less familiar to many researchers. In this paper, we complement existing surveys, which largely focused on the psychological, social and legal discussions of the topic, with an analysis of recent advances in technical solutions for AI governance. By reviewing publications in leading AI conferences including AAAI, AAMAS, ECAI and IJCAI, we propose a taxonomy which divides the field into four areas: 1) exploring ethical dilemmas; 2) individual ethical decision frameworks; 3) collective ethical decision frameworks; and 4) ethics in human-AI interactions. We highlight the intuitions and key techniques used in each approach, and discuss promising future research directions towards successful integration of ethical AI systems into human societies.

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AAAI Conference 2018 Short Paper

Building More Explainable Artificial Intelligence With Argumentation

  • Zhiwei Zeng
  • Chunyan Miao
  • Cyril Leung
  • Jing Jih Chin

Currently, much of machine learning is opaque, just like a “black box”. However, in order for humans to understand, trust and effectively manage the emerging AI systems, an AI needs to be able to explain its decisions and conclusions. In this paper, I propose an argumentation-based approach to explainable AI, which has the potential to generate more comprehensive explanations than existing approaches.

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AAMAS Conference 2018 Conference Paper

Context-based and Explainable Decision Making with Argumentation

  • Zhiwei Zeng
  • Xiuyi Fan
  • Chunyan Miao
  • Cyril Leung
  • Chin Jing Jih
  • Ong Yew Soon

Argumentation-based approaches to decision making have gained considerable research interest, due to their ability to select and justify decisions. In order to make better decisions, context is a key piece of information that needs to be considered. However, most existing argumentation-based models and frameworks have not modelled or reasoned with context explicitly. In this paper, we present a new argumentation-based approach for making context-based and explainable decisions. We propose a graphical representation for modelling decision problems involving varying contexts, Decision Graphs with Context (DGC), and a reasoning mechanism for making context-based decisions which relies on the Assumption-based Argumentation formalism. Based on these constructs, we introduce two types of explanations, argument explanation and context explanation, identifying the reasons for the decisions made from an argument-view and a context-view respectively.

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AAAI Conference 2018 Conference Paper

SmartHS: An AI Platform for Improving Government Service Provision

  • Yongqing Zheng
  • Han Yu
  • Lizhen Cui
  • Chunyan Miao
  • Cyril Leung
  • Qiang Yang

Over the years, government service provision in China has been plagued by inefficiencies. Previous attempts to address this challenge following a toolbox e-government system model in China were not effective. In this paper, we report on a successful experience in improving government service provision in the domain of social insurance in Shandong Province, China. Through standardization of service work- flows following the Complete Contract Theory (CCT) and the infusion of an artificial intelligence (AI) engine to maximize the expected quality of service while reducing waiting time, the Smart Human-resource Services (SmartHS) platform transcends organizational boundaries and improves system efficiency. Deployments in 3 cities involving 2, 000 participating civil servants and close to 3 million social insurance service cases over a 1 year period demonstrated that SmartHS significantly improves user experience with roughly a third of the original front desk staff. This new AI-enhanced mode of operation is useful for informing current policy discussions in many domains of government service provision.

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AAAI Conference 2017 Short Paper

A Computational Assessment Model for the Adaptive Level of Rehabilitation Exergames for the Elderly

  • Hao Zhang
  • Chunyan Miao
  • Han Yu
  • Cyril Leung

Rehabilitation exergames can engage the elderly in physical activities and help them recover part of their deteriorating capabilities. However, most existing exergames lack measures of how suitable they are to specific individuals. In this paper, we propose the Computational Person-Environment Fit model to evaluate the adaptability of the exergames to each individual elderly user.

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AAMAS Conference 2017 Conference Paper

Two Forms of Explanations in Computational Assumption-based Argumentation

  • Xiuyi Fan
  • Siyuan Liu
  • Huiguo Zhang
  • Chunyan Miao
  • Cyril Leung

Computational Assumption-based Argumentation (CABA) has been introduced to model argumentation with numerical data processing. To realize the “explanation power” of CABA, we study two forms of argumentative explanations, argument explanations and CU explanations representing diagnosis and repair, resp.

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AAAI Conference 2016 Conference Paper

Efficient Collaborative Crowdsourcing

  • Zhengxiang Pan
  • Han Yu
  • Chunyan Miao
  • Cyril Leung

We consider the problem of making efficient quality-timecost trade-offs in collaborative crowdsourcing systems in which different skills from multiple workers need to be combined to complete a task. We propose CrowdAsm - an approach which helps collaborative crowdsourcing systems determine how to combine the expertise of available workers to maximize the expected quality of results while minimizing the expected delays. Analysis proves that CrowdAsm can achieve close to optimal profit for workers in a given crowdsourcing system if they follow the recommendations.

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ECAI Conference 2016 Conference Paper

Explained Activity Recognition with Computational Assumption-Based Argumentation

  • Xiuyi Fan
  • Siyuan Liu 0003
  • Huiguo Zhang
  • Cyril Leung
  • Chunyan Miao

Activity recognition is a key problem in multi-sensor systems. In this work, we introduce Computational Assumption-based Argumentation, an argumentation approach that seamlessly combines sensor data processing with high-level inference. Our method gives classification results comparable to machine learning based approaches with reduced training time while also giving explanations.

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AAAI Conference 2016 Conference Paper

Productive Aging through Intelligent Personalized Crowdsourcing

  • Han Yu
  • Chunyan Miao
  • Siyuan Liu
  • Zhengxiang Pan
  • Nur Syahidah Khalid
  • Zhiqi Shen
  • Cyril Leung

The current generation of senior citizens are enjoying unparalleled levels of good health than previous generations. The need for personal fulfilment after retirement has driven many of them to participate in productive aging activities such as volunteering. This paper outlines the Silver Productive (SP) mobile app, a system powered by the RTS-P intelligent personalized task sub-delegation approach with dynamic worker effort pricing functions. It provides an algorithmic crowdsourcing platform to enable seniors to contribute their effort through productive aging activities and help organizations ef- ficiently utilize seniors’ collective productivity.

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AAAI Conference 2015 Conference Paper

Efficient Task Sub-Delegation for Crowdsourcing

  • Han Yu
  • Chunyan Miao
  • Zhiqi Shen
  • Cyril Leung
  • Yiqiang Chen
  • Qiang Yang

Reputation-based approaches allow a crowdsourcing system to identify reliable workers to whom tasks can be delegated. In crowdsourcing systems that can be modeled as multi-agent trust networks consist of resource constrained trustee agents (i. e. , workers), workers may need to further sub-delegate tasks to others if they determine that they cannot complete all pending tasks before the stipulated deadlines. Existing reputation-based decision-making models cannot help workers decide when and to whom to sub-delegate tasks. In this paper, we proposed a reputation aware task sub-delegation (RTS) approach to bridge this gap. By jointly considering a worker’s reputation, workload, the price of its effort and its trust relationships with others, RTS can be implemented as an intelligent agent to help workers make sub-delegation decisions in a distributed manner. The resulting task allocation maximizes social welfare through efficient utilization of the collective capacity of a crowd, and provides provable performance guarantees. Experimental comparisons with state-of-the-art approaches based on the Epinions trust network demonstrate significant advantages of RTS under high workload conditions.

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IJCAI Conference 2013 Conference Paper

A Reputation Management Approach for Resource Constrained Trustee Agents

  • Han Yu
  • Chunyan Miao
  • Bo An
  • Cyril Leung
  • Victor R. Lesser

Trust is an important mechanism enabling agents to self-police open and dynamic multi-agent systems (ODMASs). Trusters evaluate the reputation of trustees based on their past observed performance, and use this information to guide their future interaction decisions. Existing trust models tend to concentrate trusters’ interactions on a small number of highly reputable trustees to minimize risk exposure. When a trustee’s servicing capacity is limited, such an approach may cause long delays for trusters and subsequently damage the reputation of trustees. To mitigate this problem, we propose a reputation management approach for trustee agents based on distributed constraint optimization. It helps a trustee to make situation-aware decisions on which incoming requests to serve and prevent the resulting reputation score from being affected by factors out of the trustee’s control. The approach is evaluated through theoretical analysis and within a simulated, highly dynamic multi-agent environment. The results show that it can achieve close to optimally efficient utilization of the trustee agents’ collective capacity in an ODMAS, promotes fair treatment of trustee agents based on their behavior, and significantly outperforms related work in enhancing social welfare.

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