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Jiming Liu

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30 papers
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30

JAAMAS Journal 2026 Journal Article

Behavioral Self-Organization in Lifelike Synthetic Agents

  • Jiming Liu
  • Hong Qin

Abstract Modern computer graphics technology has enjoyed rapid development in recent years, attracting researchers and practitioners to explore a wide spectrum of applications ranging from computer-aided graphical design to artificial life and virtual reality. This paper is concerned with the animation-based entertainment use of computer graphics, i. e. , to create digitally synthetic agents that can self-animate themselves, adapt to their virtual environments, and learn new behaviors to attain some specific goals. Here we propose a synthetic agent computational architecture called inter-threaded motif-based behavioral self-organization architecture, in which one motif acquires a conditioned association from the presently sensed state of the environment to the requirement of a desired motion as well as a plausible behavioral pattern to enable such a motion, whereas another computes the optimal parameters for the identified behavior in fulfilling the motion requirement. This architecture will enable animated behaviors to be automatically programmed based on the concurrent self-organization of individual motifs as well as their crisscrossing interactions.

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AAAI Conference 2026 Conference Paper

Gated Variational Graph Autoencoders as Experts with Competition and Consensus for Multi-view Clustering

  • Zhaoliang Chen
  • William K. Cheung
  • Hong-Ning Dai
  • Byron Choi
  • Jiming Liu

Multi-view clustering has been found useful to leverage diverse data sources for accurate and robust underlying data representations. It typically relies on effectively integrating the latent features from different views through allocating weights while simultaneously mining their specificity and consensus information. However, it remains open how to achieve a more fine-grained sample-level weight allocation for promoting view-specific information fusion and view-shared consensus. To address this problem, we propose a novel multi-expert learning framework named Gated Variational Graph AutoEncoder with Competition and Consensus (GVGAE-C2). In particular, it employs multiple view-specific Variational Graph AutoEncoders (VGAEs) as experts to capture the latent features from their own views. Furthermore, we design a fine-grained structure-aware gating network, which dynamically computes sample-level weights based on the proposed structure-aware quality evaluation on each expert, thus facilitating competition among experts. Meanwhile, each expert is trained not only to study its assigned view's specificity features, but also explicitly encouraged to learn consensus-aware features across views. Extensive multi-view clustering experiments on benchmark datasets reveal that GVGAE-C2 significantly outperforms state-of-the-art methods.

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AAAI Conference 2021 Conference Paper

Cost-aware Graph Generation: A Deep Bayesian Optimization Approach

  • Jiaxu Cui
  • Bo Yang
  • Bingyi Sun
  • Jiming Liu

Graph-structured data is ubiquitous throughout the natural and social sciences, ranging from complex drug molecules to artificial neural networks. Evaluating their functional properties, e. g. , drug effectiveness and prediction accuracy, is usually costly in terms of time, money, energy, or environment, becoming a bottleneck for the graph generation task. In this work, from the perspective of saving cost, we propose a novel Cost-Aware Graph Generation (CAGG) framework to generate graphs with optimal properties at as low cost as possible. By introducing a robust Bayesian graph neural network as the surrogate model and a goal-oriented training scheme for the generation model, the CAGG can approach the real expensive evaluation function and generate search space close to the optimal property, to avoid unnecessary evaluations. Intensive experiments conducted on two challenging real-world applications, including molecular discovery and neural architecture search, demonstrate its effectiveness and applicability. The results show that it can generate the optimal graphs and reduce the evaluation costs significantly compared to the state-of-the-art.

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JBHI Journal 2021 Journal Article

Medication Combination Prediction Using Temporal Attention Mechanism and Simple Graph Convolution

  • Haiqiang Wang
  • Yinying Wu
  • Chao Gao
  • Yue Deng
  • Fan Zhang
  • Jiajin Huang
  • Jiming Liu

Medication combination prediction can be applied to the clinical treatment for critical patients with multi-morbidity. The suitable medication combination can help cure patients and keep the treatment medication safe. However, the complexity and uncertainty of clinical circumstances limit the predictive accuracy of medication combination. Thus, this paper proposes a new medication combination prediction model based on the temporal attention mechanism (TAM) and the simple graph convolution (SGC), named as TAMSGC. More specifically, the TAM can capture the temporal sequence information in the medical records, and the SGC is implemented to acquire the medication knowledge from the complicated medication combination. Experiments in a real dataset show that TAMSGC surpasses the baseline models on the predictive accuracy of medication combination.

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AAAI Conference 2019 Short Paper

EWGAN: Entropy-Based Wasserstein GAN for Imbalanced Learning

  • Jinfu Ren
  • Yang Liu
  • Jiming Liu

In this paper, we propose a novel oversampling strategy dubbed Entropy-based Wasserstein Generative Adversarial Network (EWGAN) to generate data samples for minority classes in imbalanced learning. First, we construct an entropyweighted label vector for each class to characterize the data imbalance in different classes. Then we concatenate this entropyweighted label vector with the original feature vector of each data sample, and feed it into the WGAN model to train the generator. After the generator is trained, we concatenate the entropy-weighted label vector with random noise feature vectors, and feed them into the generator to generate data samples for minority classes. Experimental results on two benchmark datasets show that the samples generated by the proposed oversampling strategy can help to improve the classification performance when the data are highly imbalanced. Furthermore, the proposed strategy outperforms other state-of-the-art oversampling algorithms in terms of the classification accuracy.

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TIST Journal 2018 Journal Article

Automatic Extraction of Behavioral Patterns for Elderly Mobility and Daily Routine Analysis

  • Chen Li
  • William K. Cheung
  • Jiming Liu
  • Joseph K. Ng

The elderly living in smart homes can have their daily movement recorded and analyzed. As different elders can have their own living habits, a methodology that can automatically identify their daily activities and discover their daily routines will be useful for better elderly care and support. In this article, we focus on automatic detection of behavioral patterns from the trajectory data of an individual for activity identification as well as daily routine discovery. The underlying challenges lie in the need to consider longer-range dependency of the sensor triggering events and spatiotemporal variations of the behavioral patterns exhibited by humans. We propose to represent the trajectory data using a behavior-aware flow graph that is a probabilistic finite state automaton with its nodes and edges attributed with some local behavior-aware features. We identify the underlying subflows as the behavioral patterns using the kernel k -means algorithm. Given the identified activities, we propose a novel nominal matrix factorization method under a Bayesian framework with Lasso to extract highly interpretable daily routines. For empirical evaluation, the proposed methodology has been compared with a number of existing methods based on both synthetic and publicly available real smart home datasets with promising results obtained. We also discuss how the proposed unsupervised methodology can be used to support exploratory behavior analysis for elderly care.

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AAAI Conference 2018 Short Paper

Bayesian Network Structure Learning: The Two-Step Clustering-Based Algorithm

  • Yikun Zhang
  • Jiming Liu
  • Yang Liu

In this paper we introduce a two-step clustering-based strategy, which can automatically generate prior information from data in order to further improve the accuracy and time ef- ficiency of state-of-the-art algorithms for Bayesian network structure learning. Our clustering-based strategy is composed of two steps. In the first step, we divide the potential nodes into several groups via clustering analysis and apply Bayesian network structure learning to obtain some pre-existing arcs within each cluster. In the second step, with all the withincluster arcs being well preserved, we learn the betweencluster structure of the given network. Experimental results on benchmark datasets show that a wide range of structure learning algorithms benefit from the proposed clusteringbased strategy in terms of both accuracy and efficiency.

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AAAI Conference 2018 Conference Paper

Group Sparse Bayesian Learning for Active Surveillance on Epidemic Dynamics

  • Hongbin Pei
  • Bo Yang
  • Jiming Liu
  • Lei Dong

Predicting epidemic dynamics is of great value in understanding and controlling diffusion processes, such as infectious disease spread and information propagation. This task is intractable, especially when surveillance resources are very limited. To address the challenge, we study the problem of active surveillance, i. e. , how to identify a small portion of system components as sentinels to effect monitoring, such that the epidemic dynamics of an entire system can be readily predicted from the partial data collected by such sentinels. We propose a novel measure, the γ value, to identify the sentinels by modeling a sentinel network with row sparsity structure. We design a flexible group sparse Bayesian learning algorithm to mine the sentinel network suitable for handling both linear and non-linear dynamical systems by using the expectation maximization method and variational approximation. The efficacy of the proposed algorithm is theoretically analyzed and empirically validated using both synthetic and real-world data.

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IJCAI Conference 2016 Conference Paper

Inferring Motif-Based Diffusion Models for Social Networks

  • Qing Bao
  • William K. Cheung
  • Jiming Liu

Existing diffusion models for social networks often assume that the activation of a node depends independently on their parents' activations. Some recent work showed that incorporating the structural and behavioral dependency among the parent nodes allows more accurate diffusion models to be inferred. In this paper, we postulate that the latent temporal activation patterns (or motifs) of nodes of different social roles form the underlying information diffusion mechanisms generating the information cascades observed over a social network. We formulate the inference of the temporal activation motifs and a corresponding motif-based diffusion model under a unified probabilistic framework. A two-level EM algorithm is derived so as to infer the diffusion-specific motifs and the diffusion probabilities simultaneously. We applied the proposed model to several real-world datasets with significant improvement on modelling accuracy. We also illustrate how the inferred motifs can be interpreted as the underlying mechanisms causing the diffusion process to happen in different social networks.

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AAAI Conference 2014 Conference Paper

Modeling and Mining Spatiotemporal Patterns of Infection Risk from Heterogeneous Data for Active Surveillance Planning

  • Bo Yang
  • Hua Guo
  • Yi Yang
  • Benyun Shi
  • Xiaonong Zhou
  • Jiming Liu

Active surveillance is a desirable way to prevent the spread of infectious diseases in that it aims to timely discover individual incidences through an active searching for patients. However, in practice active surveillance is difficult to implement especially when monitoring space is large but available resources are limited. Therefore, it is extremely important for public health authorities to know how to distribute their very sparse resources to high-priority regions so as to maximize the outcomes of active surveillance. In this paper, we raise the problem of active surveillance planning and provide an effective method to address it via modeling and mining spatiotemporal patterns of infection risks from heterogeneous data sources. Taking malaria as an example, we perform an empirical study on real-world data to validate our method and provide our new findings.

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IS Journal 2014 Journal Article

Social Intelligence and Technology

  • Christopher C. Yang
  • John Yen
  • Jiming Liu

In this special issue on social intelligence and technology, the guest editors discuss social media's evolution, including societal problems that have arisen or been solved as a result of this technology. They also introduce articles that offer novel solutions in the field.

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IS Journal 2014 Journal Article

WaaS: Wisdom as a Service

  • Jianhui Chen
  • Jianhua Ma
  • Ning Zhong
  • Yiyu Yao
  • Jiming Liu
  • Runhe Huang
  • Wenbin Li
  • Zhisheng Huang

An emerging hyper-world encompasses all human activities in a social-cyber-physical space. Its power derives from the Wisdom Web of Things (W2T) cycle, namely, "from things to data, information, knowledge, wisdom, services, humans, and then back to things. "' The W2T cycle leads to a harmonious symbiosis among humans, computers, and things, which can be constructed by large-scale converging of intelligent information technology applications with an open and interoperable architecture. The recent advances in cloud computing, the Internet of Things, Web of Things, Big Data, and other research fields have provided just such an open system architecture with resource sharing and services. The next step is to develop an open and interoperable content architecture with intelligent sharing and services for the organization and transformation in the data, information, knowledge, and wisdom (DIKW) hierarchy. This article introduces wisdom as a service (WaaS), a content architecture based on the pay-as-you-go IT trend. The WaaS infrastructure and the main challenges in WaaS research and applications are discussed. A case study is also described. Relying on cloud computing and big data, WaaS provides a practical approach to realize the W2T cycle in the hyper-world for the coming age of ubiquitous intelligent IT applications.

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IJCAI Conference 2013 Conference Paper

Social Collaborative Filtering by Trust

  • Bo Yang
  • Yu Lei
  • Dayou Liu
  • Jiming Liu

To accurately and actively provide users with their potentially interested information or services is the main task of a recommender system. Collaborative filtering is one of the most widely adopted recommender algorithms, whereas it is suffering the issues of data sparsity and cold start that will severely degrade quality of recommendations. To address such issues, this article proposes a novel method, trying to improve the performance of collaborative filtering recommendation by means of elaborately integrating twofold sparse information, the conventional rating data given by users and the social trust network among the same users. It is a model-based method adopting matrix factorization technique to map users into low-dimensional latent feature spaces in terms of their trust relationship, aiming to reflect users’ reciprocal influence on their own opinions more reasonably. The validations against a real-world dataset show that the proposed method performs much better than state-of-the-art recommendation algorithms for social collaborative filtering by trust.

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TIST Journal 2012 Journal Article

Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine

  • Clement H. C. Leung
  • Alice W. S. Chan
  • Alfredo Milani
  • Jiming Liu
  • Yuanxi Li

Effective sharing of diverse social media is often inhibited by limitations in their search and discovery mechanisms, which are particularly restrictive for media that do not lend themselves to automatic processing or indexing. Here, we present the structure and mechanism of an adaptive search engine which is designed to overcome such limitations. The basic framework of the adaptive search engine is to capture human judgment in the course of normal usage from user queries in order to develop semantic indexes which link search terms to media objects semantics. This approach is particularly effective for the retrieval of multimedia objects, such as images, sounds, and videos, where a direct analysis of the object features does not allow them to be linked to search terms, for example, nontextual/icon-based search, deep semantic search, or when search terms are unknown at the time the media repository is built. An adaptive search architecture is presented to enable the index to evolve with respect to user feedback, while a randomized query-processing technique guarantees avoiding local minima and allows the meaningful indexing of new media objects and new terms. The present adaptive search engine allows for the efficient community creation and updating of social media indexes, which is able to instill and propagate deep knowledge into social media concerning the advanced search and usage of media resources. Experiments with various relevance distribution settings have shown efficient convergence of such indexes, which enable intelligent search and sharing of social media resources that are otherwise hard to discover.

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IS Journal 2011 Journal Article

Brain Informatics

  • Ning Zhong
  • Jeffrey M. Bradshaw
  • Jiming Liu
  • John G. Taylor

Brain informatics (BI) is an emerging interdisciplinary and multidisciplinary research field that focuses on studying the mechanisms underlying the human information processing system. BI investigates the essential functions of the brain, ranging from perception to thinking, and encompassing such areas as multiperception, attention, emotion, memory, language, computation, heuristic search, reasoning, planning, decision making, problem solving, learning, discovery, and creativity. This special issue presents some of the best works being developed worldwide that deal with the new challenges of BI from an intelligent systems perspective.

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JAAMAS Journal 2009 Journal Article

An autonomy-oriented computing approach to community mining in distributed and dynamic networks

  • Bo Yang
  • Jiming Liu
  • Dayou Liu

Abstract A network community refers to a special type of network structure that contains a group of nodes connected based on certain relationships or similar properties. Our ability to mine communities hidden inside networks will readily enable us to effectively understand and exploit such networks. So far, various methods and algorithms have been developed to perform the task of community mining, where it is often required that the networks are processed in a centralized manner, and their structures will not dynamically change. However, in the real world, many applications involve distributed and dynamically evolving networks, in which resources and controls are not only decentralized but also updated frequently. It would be difficult for the existing methods to deal with these types of networks since their global topological representations are either not available or too hard to obtain due to their huge size, decentralization, and/or dynamic updates. The aim of our work is to address the problem of mining communities from a distributed and dynamic network. It differs from the previous ones in that here we introduce the notion of self-organizing agent networks, and provide an autonomy-oriented computing (AOC) approach to distributed and incremental mining of network communities. The AOC-based method utilizes reactive agents that can collectively detect and update community structures in a distributed and dynamically evolving network, based only on their local views and interactions. While providing detailed formulations, we present the results of our systematic validations using real-world benchmark networks as well as synthetic networks that include a distributed intelligent Portable Digital Assistant (iPDA) network example.

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IS Journal 2003 Journal Article

An adaptive user interface based on personalized learning

  • Jiming Liu
  • Chi Kuen Wong
  • Ka Keung Hui

This adaptive user interface provides individualized, just-in-time assistance to users by recording user interface events and frequencies, organizing them into episodes, and automatically deriving patterns. It also builds, maintains, and makes suggestions based on user profiles.

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