Zhaohui Wu

TIST Journal 2022 Journal Article

Jointly Optimizing Expressional and Residual Models for 3D Facial Expression Removal

• Qian Zheng
• Yueming Wang
• Zhenfang Hu
• Xiaobo Zhang
• Zhaohui Wu
• Gang Pan

This article proposes a facial expression removal method to recover a 3D neutral face from a single 3D expressional or non-neutral face. We treat a 3D non-neutral face as the sum of its neutral one and the residual. This can be satisfied if the correspondence between 3D vertices of expressional faces and those of neutral faces is established. We propose a non-rigid deformation method to establish the correspondence between 3D faces. Then, according to algebra inequality, the minimization of a neutral face model can be replaced by the minimization of its upper bound, i.e., the errors of an expressional face model and a residual model. Thus, we co-optimize the representation errors of the latter two models and build the relationship between the representation coefficients of the two models. Given an expressional face as the input, its corresponding neutral face can be inferred by the associative representation parameters in these two models. In the testing stage, we use an iterative joint fitting scheme to obtain a more accurate recovery. Extensive experiments are conducted to evaluate our method. The results show that our method obtains considerably better performance than existing methods in terms of average root mean square errors and recognition rates, and also better visual effects.

TIST Journal 2020 Journal Article

Forecasting Price Trend of Bulk Commodities Leveraging Cross-domain Open Data Fusion

• Binbin Zhou
• Sha Zhao
• Longbiao Chen
• Shijian Li
• Zhaohui Wu
• Gang Pan

Forecasting price trend of bulk commodities is important in international trade, not only for markets participants to schedule production and marketing plans but also for government administrators to adjust policies. Previous studies cannot support accurate fine-grained short-term prediction, since they mainly focus on coarse-grained long-term prediction using historical data. Recently, cross-domain open data provides possibilities to conduct fine-grained price forecasting, since they can be leveraged to extract various direct and indirect factors of the price. In this article, we predict the price trend over upcoming days, by leveraging cross-domain open data fusion. More specifically, we formulate the price trend into three classes (rise, slight-change, and fall), and then we predict the specific class in which the price trend of the future day lies. We take three factors into consideration: (1) supply factor considering sources providing bulk commodities,<?brk?> (2) demand factor focusing on vessel transportation with reflection of short time needs, and (3) expectation factor encompassing indirect features (e.g., air quality) with latent influences. A hybrid classification framework is proposed for the price trend forecasting. Evaluation conducted on nine real-world cross-domain open datasets shows that our framework can forecast the price trend accurately, outperforming multiple state-of-the-art baselines.

IJCAI Conference 2018 Conference Paper

Jointly Learning Network Connections and Link Weights in Spiking Neural Networks

• Yu Qi
• Jiangrong Shen
• Yueming Wang
• Huajin Tang
• Hang Yu
• Zhaohui Wu
• Gang Pan

Spiking neural networks (SNNs) are considered to be biologically plausible and power-efficient on neuromorphic hardware. However, unlike the brain mechanisms, most existing SNN algorithms have fixed network topologies and connection relationships. This paper proposes a method to jointly learn network connections and link weights simultaneously. The connection structures are optimized by the spike-timing-dependent plasticity (STDP) rule with timing information, and the link weights are optimized by a supervised algorithm. The connection structures and the weights are learned alternately until a termination condition is satisfied. Experiments are carried out using four benchmark datasets. Our approach outperforms classical learning methods such as STDP, Tempotron, SpikeProp, and a state-of-the-art supervised algorithm. In addition, the learned structures effectively reduce the number of connections by about 24%, thus facilitate the computational efficiency of the network.

AAAI Conference 2016 Conference Paper

BBookX: Building Online Open Books for Personalized Learning

• Chen Liang
• Shuting Wang
• Zhaohui Wu
• Kyle Williams
• Bart Pursel
• Benjamin Brautigam
• Sherwyn Saul
• Hannah Williams

We demonstrate BBookX1, a novel system that automatically builds in collaboration with a user online open books by searching open educational resources (OER). This system explores the use of retrieval technologies to dynamically generate zero-cost materials such as textbooks for personalized learning.

IS Journal 2016 Journal Article

Cyborg Intelligence: Recent Progress and Future Directions

• Zhaohui Wu
• Yongdi Zhou
• Zhongzhi Shi
• Changshui Zhang
• Guanglin Li
• Xiaoxiang Zheng
• Nenggan Zheng
• Gang Pan

The combination of biological and artificial intelligence is a promising methodology to construct a novel intelligent modality, proposed as cyborg intelligence. The hierarchical conceptual framework is based on the interaction and combination of comparable components of biological cognitive units and computing intelligent units. The authors extend the previous conceptual framework and focus on sensorimotor circuits to explore the representation and integration of sensation. They then present a cognitive computing model for brain-computer integration and design efficient machine-learning algorithms for neural signal decoding. They also propose biological reconstruction methods for sensorimotor circuits to not only restore but enhance functionalities with AI. They develop a series of demonstrating systems to validate the conceptual framework of cyborg intelligence and possibly herald bright prospects and high values in diversified aspects of theoretical research, engineering techniques, and clinical applications. Finally, the authors summarize the latest research trends and challenges, which they believe will further boost new scientific frontiers in cyborg intelligence.

AAAI Conference 2016 Conference Paper

Exploring Multiple Feature Spaces for Novel Entity Discovery

• Zhaohui Wu
• Yang Song
• C. Giles

Continuously discovering novel entities in news and Web data is important for Knowledge Base (KB) maintenance. One of the key challenges is to decide whether an entity mention refers to an in-KB or out-of-KB entity. We propose a principled approach that learns a novel entity classifier by modeling mention and entity representation into multiple feature spaces, including contextual, topical, lexical, neural embedding and query spaces. Different from most previous studies that address novel entity discovery as a submodule of entity linking systems, our model is more a generalized approach and can be applied as a pre-filtering step of novel entities for any entity linking systems. Experiments on three real-world datasets show that our method significantly outperforms existing methods on identifying novel entities.

IS Journal 2015 Journal Article

A Cauchy-Based State-Space Model for Seizure Detection in EEG Monitoring Systems

• Yueming Wang
• Yu Qi
• Junming Zhu
• Jianmin Zhang
• Yiwen Wang
• Gang Pan
• Xiaoxiang Zheng
• Zhaohui Wu

This article proposes a state-space model with Cauchy observation noise (SSMC) to detect seizure onset in a long-term EEG monitoring system. Facing the challenge of high false detection rates (FDRs) in many existing methods caused by impulsive EOG/EMG artifacts, the SSMC uses a nonlinear state-space model to encode the gradual changes of epileptic seizure signals and reject abrupt changes brought by artifacts. The Cauchy distribution is proposed to model impulsive observation noises because this heavy-tailed distribution is better at capturing abrupt changes in noise than Gaussian, thus reducing false alarms. Experiments are carried out on a dataset collected from an EEG-monitoring brain-machine interface system that contains 10 patients and 367 hours of EEG data. The authors' method achieves a high sensitivity of 100 percent with a low FDR of 0. 08 per hour and a median time delay of 8. 10 seconds, demonstrating the method's effectiveness.

AAAI Conference 2015 Conference Paper

A Neural Probabilistic Model for Context Based Citation Recommendation

• Wenyi Huang
• Zhaohui Wu
• Chen Liang
• Prasenjit Mitra
• C. Giles

Automatic citation recommendation can be very useful for authoring a paper and is an AI-complete problem due to the challenge of bridging the semantic gap between citation context and the cited paper. It is not always easy for knowledgeable researchers to give an accurate citation context for a cited paper or to find the right paper to cite given context. To help with this problem, we propose a novel neural probabilistic model that jointly learns the semantic representations of citation contexts and cited papers. The probability of citing a paper given a citation context is estimated by training a multi-layer neural network. We implement and evaluate our model on the entire CiteSeer dataset, which at the time of this work consists of 10, 760, 318 citation contexts from 1, 017, 457 papers. We show that the proposed model significantly outperforms other stateof-the-art models in recall, MAP, MRR, and nDCG.

TIST Journal 2015 Journal Article

City-Scale Social Event Detection and Evaluation with Taxi Traces

• Wangsheng Zhang
• Guande Qi
• Gang Pan
• Hua Lu
• Shijian Li
• Zhaohui Wu

A social event is an occurrence that involves lots of people and is accompanied by an obvious rise in human flow. Analysis of social events has real-world importance because events bring about impacts on many aspects of city life. Traditionally, detection and impact measurement of social events rely on social investigation, which involves considerable human effort. Recently, by analyzing messages in social networks, researchers can also detect and evaluate country-scale events. Nevertheless, the analysis of city-scale events has not been explored. In this article, we use human flow dynamics, which reflect the social activeness of a region, to detect social events and measure their impacts. We first extract human flow dynamics from taxi traces. Second, we propose a method that can not only discover the happening time and venue of events from abnormal social activeness, but also measure the scale of events through changes in such activeness. Third, we extract traffic congestion information from traces and use its change during social events to measure their impact. The results of experiments validate the effectiveness of both the event detection and impact measurement methods.

EAAI Journal 2015 Journal Article

Efficient web service QoS prediction using local neighborhood matrix factorization

• Wei Lo
• Jianwei Yin
• Ying Li
• Zhaohui Wu

In the era of Big Data, companies worldwide are actively deploying web services in both intranet and internet environments. Quality-of-Service (QoS), the fundamental aspect of web service has thus attracted numerous attention in industry and academia. The study on sufficient QoS data keeps advancing the state in Service-Oriented Computing (SOC) area. To collect a large amount of resource in practice, QoS prediction applications are designed and built. Nevertheless, how to generate accurate results in high productivity is still a main challenge to existing frameworks. In this paper, we propose LoNMF, a Local Neighborhood Matrix Factorization application that incorporates domain knowledge in modern Artificial Intelligence (AI) technique to tackle this challenge. LoNMF first proposes a two-level selection mechanism that can identify a set of highly relevant local neighbors for target user. And then, it integrates the geographical information to build up an extended Matrix Factorization (MF) approach for personalized QoS prediction. Finally, it iteratively generates results by utilizing hints from previous round computations, a gradient boosting strategy that directly accelerates solving process. Experimental evidence on large-scale real-world QoS data shows that LoNMF is scalable, and consistently outperforming other state-of-the-art applications in prediction accuracy and efficiency.

AAAI Conference 2015 Conference Paper

Sense-Aaware Semantic Analysis: A Multi-Prototype Word Representation Model Using Wikipedia

• Zhaohui Wu
• C. Giles

Human languages are naturally ambiguous, which makes it difficult to automatically understand the semantics of text. Most vector space models (VSM) treat all occurrences of a word as the same and build a single vector to represent the meaning of a word, which fails to capture any ambiguity. We present sense-aware semantic analysis (SaSA), a multi-prototype VSM for word representation based on Wikipedia, which could account for homonymy and polysemy. The “sense-specific” prototypes of a word are produced by clustering Wikipedia pages based on both local and global contexts of the word in Wikipedia. Experimental evaluation on semantic relatedness for both isolated words and words in sentential contexts and word sense induction demonstrate its effectiveness.

IS Journal 2014 Journal Article

A Computational Model of the Hybrid Bio-Machine MPMS for Ratbots Navigation

• Lijuan Su
• Nenggan Zhang
• Min Yao
• Zhaohui Wu

As a typical cyborg intelligent system, ratbots possess not only their own biological brain but machine visual sensation, memory, and computation. Electrodes implanted in the medial forebrain bundle (MFB) connect the rat's biological brain with the computer, which presents a hybrid bio-machine parallel memory system in the ratbot. For the novel multiple parallel memory system (MPMS) with real-time MFB stimuli, a computational model is proposed to explain the learning and memory processes underlying the enhanced performance of the ratbots in maze navigation tasks. It's shown that the proposed computational model can predict the finish trial number of the maze learning task, which matches well with behavioral experiments. This work will be helpful to understand the memory and learning mechanisms of cyborg intelligent systems and has the potential significance of optimizing the cognitive performance of these systems as well.

IS Journal 2014 Journal Article

Cyborg Intelligence: Towards Bio-Machine Intelligent Systems

• Zhaohui Wu
• Gang Pan
• Jose C. Principe
• Andrzej Cichocki

The guest editors present a special issue on cyborg intelligence.

IS Journal 2014 Journal Article

From Artificial Intelligence to Cyborg Intelligence

• Daniel Zeng
• Zhaohui Wu

How has cyborg intelligence combined the best of machine and biological intelligence, and what is yet to come? Here, some of the key advances of this new research paradigm are considered.

IS Journal 2014 Journal Article

Pervasive Service Bus: Smart SOA Infrastructure for Ambient Intelligence

• Gang Pan
• Li Zhang
• Zhaohui Wu
• Shijian Li
• Laurence Yang
• Man Lin
• Yuanchun Shi

Ambient intelligence (AmI) aims to make our everyday environments intelligent--that is, sensitive, adaptive, and responsive to the presence of people--in a transparent manner. Several challenges exist to building an efficient infrastructure for AmI, including interoperation of heterogeneous systems, intelligence for anticipatory user assistance, adaptability to dynamic environments for good user experience, and scalability to additional users and spaces. Here, the authors propose Pervasive Service Bus (PSB), a smart service-oriented architecture (SOA) framework for AmI spaces that models all computing activities as unified pervasive services. They present an online planning algorithm to adapt service flows to contexts and user tasks. PSB employs a sub-bus-based layout to maintain efficiency in large-scale service interactions. They also discuss their results in evaluating PSB's performance in a Smart Home testbed.

IJCAI Conference 2013 Conference Paper

Online Community Detection for Large Complex Networks

• Wangsheng Zhang
• Gang Pan
• Zhaohui Wu
• Shijian Li

Complex networks describe a wide range of systems in nature and society. To understand the complex networks, it is crucial to investigate their internal structure. In this paper, we propose an online community detection method for large complex networks, which make it possible to process networks edge-by-edge in a serial fashion. We investigate the generative mechanism of complex networks and propose a split mechanism based on the degree of the nodes to create new community. Our method has linear time complexity. The method has been applied to six real-world network datasets and the experimental results show that it is comparable to existing methods in modularity with much less running time.

IS Journal 2013 Journal Article

The Convergence of Machine and Biological Intelligence

• Zhaohui Wu
• Raj Reddy
• Gang Pan
• Nenggan Zheng
• Paul F.M.J. Verschure
• Qiaosheng Zhang
• Xiaoxiang Zheng
• Jose C. Principe

To explore the exciting new domain of brain informatics, we invited several well-known experts to discuss the state of the art, the challenges, the opportunities, and the trends. In "Creating Human-Level AI by Educating a Child Machine, " Raj Reddy proposes an architecture for a "child machine" that can learn and is teachable. In "Cyborg Intelligence, " Zhaohui Wu, Gang Pan, and Nenggan Zheng describe a biological-machine system consisting of both an organic and a computing part. In "Formal Minds and Biological Brains II: From the Mirage of Intelligence to a Science and Engineering of Consciousness, " Paul F. M. J. Verschure discusses human-like cognitive architectures and describes the Distributed Adaptive Control (DAC) architecture for perception, cognition, and action. In "The Challenges of Closed-Loop Invasive Brain-Machine Interfaces, " Qiaosheng Zhang and Xiaoxiang Zheng discuss the challenges and trends in closed-loop brain-machine interfaces. In "Neural Signal Processing in Brain-Machine Interfaces, " Jose C. Principe takes a critical look at the challenges and opportunities of performing computation with pulses, as neurons do. In "Neuroprosthesis Control via a Noninvasive Hybrid Brain-Computer Interface, " Alex Kreilinger, Martin Rohm, Vera Kaiser, Robert Leeb, Rüdiger Rupp, and Gernot R. Müller-Putz describe an example of the convergence of biological intelligence and machine intelligence in a hand-elbow neuroprosthesis control unit.

IJCAI Conference 2011 Conference Paper

Locality-Constrained Concept Factorization

• Haifeng Liu
• Zheng Yang
• Zhaohui Wu

Matrix factorization based techniques, such as nonnegative matrix factorization (NMF) and concept factorization (CF), have attracted great attention in dimension reduction and data clustering. Both of them are linear learning problems and lead to a sparse representation of the data. However, the sparsity obtained by these methods does not always satisfy locality conditions, thus the obtained data representation is not the best. This paper introduces a locality-constrained concept factorization method which imposes a locality constraint onto the traditional concept factorization. By requiring the concepts (basis vectors) to be as close to the original data points as possible, each data can be represented by a linear combination of only a few basis concepts. Thus our method is able to achieve sparsity and locality at the same time. We demonstrate the effectiveness of this novel algorithm through a set of evaluations on real world applications.

IS Journal 2011 Journal Article

TaskShadow: Toward Seamless Task Migration across Smart Environments

• Gang Pan
• Yuqiong Xu
• Zhaohui Wu
• Shijian Li
• Laurence Yang
• Man Lin
• Zhong Liu

The OSGi-based platform TaskShadow supports seamless task migration across smart environments using a task-to-service mapping algorithm to semantically search for suitable low-level services that achieve high-level tasks.

AAAI Conference 2010 Conference Paper

Learning to Surface Deep Web Content

• Zhaohui Wu
• Lu Jiang
• Qinghua Zheng
• Jun Liu

We propose a novel deep web crawling framework based on reinforcement learning. The crawler is regarded as an agent and deep web database as the environment. The agent perceives its current state and submits a selected action (query) to the environment according to Q-value. Based on the framework we develop an adaptive crawling method. Experimental results show that it outperforms the state of art methods in crawling capability and breaks through the assumption of full-text search implied by existing methods.

AAAI Conference 2010 Conference Paper

Non-Negative Matrix Factorization with Constraints

• Haifeng Liu
• Zhaohui Wu

Non-negative matrix factorization (NMF), as a useful decomposition method for multivariate data, has been widely used in pattern recognition, information retrieval and computer vision. NMF is an effective algorithm to find the latent structure of the data and leads to a parts-based representation. However, NMF is essentially an unsupervised method and can not make use of label information. In this paper, we propose a novel semi-supervised matrix decomposition method, called Constrained Non-negative Matrix Factorization, which takes the label information as additional constraints. Specifically, we require that the data points sharing the same label have the same coordinate in the new representation space. This way, the learned representations can have more discriminating power. We demonstrate the effectiveness of this novel algorithm through a set of evaluations on real world applications.

AIIM Journal 2007 Journal Article

Integrative mining of traditional Chinese medicine literature and MEDLINE for functional gene networks

• Xuezhong Zhou
• Baoyan Liu
• Zhaohui Wu
• Yi Feng

Objective The amount of biomedical data in different disciplines is growing at an exponential rate. Integrating these significant knowledge sources to generate novel hypotheses for systems biology research is difficult. Traditional Chinese medicine (TCM) is a completely different discipline, and is a complementary knowledge system to modern biomedical science. This paper uses a significant TCM bibliographic literature database in China, together with MEDLINE, to help discover novel gene functional knowledge. Materials and methods We present an integrative mining approach to uncover the functional gene relationships from MEDLINE and TCM bibliographic literature. This paper introduces TCM literature (about 50, 000 records) as one knowledge source for constructing literature-based gene networks. We use the TCM diagnosis, TCM syndrome, to automatically congregate the related genes. The syndrome–gene relationships are discovered based on the syndrome–disease relationships extracted from TCM literature and the disease–gene relationships in MEDLINE. Based on the bubble-bootstrapping and relation weight computing methods, we have developed a prototype system called MeDisco/3S, which has name entity and relation extraction, and online analytical processing (OLAP) capabilities, to perform the integrative mining process. Results We have got about 200, 000 syndrome–gene relations, which could help generate syndrome-based gene networks, and help analyze the functional knowledge of genes from syndrome perspective. We take the gene network of Kidney–Yang Deficiency syndrome (KYD syndrome) and the functional analysis of some genes, such as CRH (corticotropin releasing hormone), PTH (parathyroid hormone), PRL (prolactin), BRCA1 (breast cancer 1, early onset) and BRCA2 (breast cancer 2, early onset), to demonstrate the preliminary results. The underlying hypothesis is that the related genes of the same syndrome will have some biological functional relationships, and will constitute a functional network. Conclusion This paper presents an approach to integrate TCM literature and modern biomedical data to discover novel gene networks and functional knowledge of genes. The preliminary results show that the novel gene functional knowledge and gene networks, which are worthy of further investigation, could be generated by integrating the two complementary biomedical data sources. It will be a promising research field through integrative mining of TCM and modern life science literature.

AAMAS Conference 2006 Conference Paper

A Composite Trust Model for Semantic Web

• Xiaoqing Zheng
• Zhaohui Wu
• Huajun Cheng
• Yuxin Mao

AIIM Journal 2006 Journal Article

Knowledge discovery in traditional Chinese medicine: State of the art and perspectives

• Yi Feng
• Zhaohui Wu
• Xuezhong Zhou
• Zhongmei Zhou
• Weiyu Fan

Objective As a complementary medical system to Western medicine, traditional Chinese medicine (TCM) provides a unique theoretical and practical approach to the treatment of diseases over thousands of years. Confronted with the increasing popularity of TCM and the huge volume of TCM data, historically accumulated and recently obtained, there is an urgent need to explore these resources effectively by the techniques of knowledge discovery in database (KDD). This paper aims at providing an overview of recent KDD studies in TCM field. Methods A literature search was conducted in both English and Chinese publications, and major studies of knowledge discovery in TCM (KDTCM) reported in these materials were identified. Based on an introduction to the state of the art of TCM data resources, a review of four subfields of KDTCM research was presented, including KDD for the research of Chinese medical formula, KDD for the research of Chinese herbal medicine, KDD for TCM syndrome research, and KDD for TCM clinical diagnosis. Furthermore, the current state and main problems in each subfield were summarized based on a discussion of existing studies, and future directions for each subfield were also proposed accordingly. Results A series of KDD methods are used in existing KDTCM researches, ranging from conventional frequent itemset mining to state of the art latent structure model. Considerable interesting discoveries are obtained by these methods, such as novel TCM paired drugs discovered by frequent itemset analysis, functional community of related genes discovered under syndrome perspective by text mining, the high proportion of toxic plants in the botanical family Ranunculaceae disclosed by statistical analysis, the association between M-cholinoceptor blocking drug and Solanaceae revealed by association rule mining, etc. It is particularly inspiring to see some studies connecting TCM with biomedicine, which provide a novel top–down view for functional genomics research. However, further developments of KDD methods are still expected to better adapt to the features of TCM. Conclusions Existing studies demonstrate that KDTCM is effective in obtaining medical discoveries. However, much more work needs to be done in order to discover real diamonds from TCM domain. The usage and development of KDTCM in the future will substantially contribute to the TCM community, as well as modern life science.

IS Journal 2005 Journal Article

DartGrid II: a semantic grid platform for ITS

• Zhaohui Wu
• Shuiguang Deng
• Jian Wu
• Huajun Chen
• Shuming Tang
• Haijun Gao

Intelligent transportation systems offer an alternative approach to solving many problems by implementing advances in information, Internet, communication, and cybernetics technologies. Grid computing can support traffic data semantization, resource sharing, ITS subsystem cooperation, and global-scale distributed computing that connects all kinds of resources. We are currently using grid technology to build DartGrid II, a semantic ITS platform to support resource sharing, service flow management, and cross-domain cooperation.

AIIM Journal 2004 Journal Article

Ontology development for unified traditional Chinese medical language system

• Xuezhong Zhou
• Zhaohui Wu
• Aining Yin
• Lancheng Wu
• Weiyu Fan
• Ruen Zhang

Traditional Chinese medicine (TCM) as a complete knowledge system researches into human health conditions via a different approach compared to orthodox medicine. We are developing a unified traditional Chinese medical language system (UTCMLS) through an ontology approach that will support TCM language knowledge storage, concept-based information retrieval and information integration. UTCMLS is a huge knowledge project, which is a broad collaboration of 16 distributed groups, most of them with no prior experience of formal ontology development. Therefore, the cooperative and comprehensive ontology engineering is crucial. We use Protégé 2000 for ontology development of concepts and relationships that represent the domain and that will permit storage of TCM knowledge. This paper focuses on the methodology, design and development of ontology for UTCMLS.

ECAI Conference 1992 Conference Paper

A Summary of Coupled Expert Systems Building Toolkit ZIPE

• Zhaohui Wu
• Tong Huang
• Jing Ying
• Zhijun He 0001