Dan Chen

Dan Chen
Wuhan University | WHU ·  College of Computer Science

Ph.D.

About

164
Publications
57,163
Reads
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4,706
Citations
Introduction
Dan Chen is a professor with School of Computer Science, Wuhan University, China. His research interests include Neuro-engineering, Data Science & Engineering, and AI.
Additional affiliations
August 2001 - August 2004
Singapore Institute of Manufacturing Technology
Position
  • Research Associate
Description
  • Modelling and Simulation of complex systems
September 2014 - present
Wuhan University
Position
  • Professor (Full)
August 2004 - December 2010
University of Birmingham
Position
  • Science City Research Alliance Fellow
Education
February 2002 - May 2005
Nanyang Technological University
Field of study
  • Modelling and Simulation of Complex Systems

Publications

Publications (164)
Article
Full-text available
Extracting the latent factors of big time series data is an important means to examine the dynamic complex systems under observation. These low-dimensional and "small" representations reveal the key insights to the overall mechanisms, which can otherwise be obscured by the notoriously high dimensionality and scale of big data as well as the enormou...
Article
Online electroencephalograph (EEG) classification is a core service of recently booming brain e-health, but its performance often becomes unstable because (1) conventional end-to-end models (e.g., deep neural network, DN N) largely remain static, while brain states of diseases are highly dynamic and exhibits significant individuality; and (2) EEG a...
Article
Full-text available
The past century has witnessed the grand success of brain imaging technologies, such as electroencephalography and magnetic resonance imaging, in probing cognitive states and pathological brain dynamics for neuroscience research and neurology practices. Human brain is “the most complex object in the universe,” and brain imaging data ( BID ) are rou...
Article
Multivariate time series data (Mv-TSD) portray the evolving processes of the system(s) under examination in a multi-view manner. Factorization methods are salient for Mv-TSD analysis with the potentials of structural feature construction. However, research challenges remain in the derivation of factors due to scattered data distribution of Mv-TSD a...
Article
Artifact removal has been an open critical issue for decades in tasks centering on EEG analysis. Recent deep learning methods mark a leap forward from the conventional signal processing routines; however, those in general still suffer from insufficient capabilities 1) to capture potential temporal dependencies embedded in EEG and 2) to adapt to sce...
Article
The risk of adverse effects in Electroconvulsive Therapy (ECT), such as cognitive impairment, can be high if an excessive stimulus is applied to induce the necessary generalized seizure (GS); Conversely, inadequate stimulus results in failure. Recent efforts to automate this task can facilitate statistical analyses on individual parameters or quali...
Article
Full-text available
This study introduces a Deep Reinforcement Learning approach (DRL-MD) aimed at optimizing the deployment of mitigations to minimize redundancy while ensuring effective defense against cyberattacks. DRL-MD initially enhances ATT &CK (Adversarial Tactics, Techniques, and Common Knowledge) to underscore the formal relationships between attacks and def...
Article
Full-text available
Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and t...
Article
Measurement of brain functional connectivity has become a dominant approach to explore the interaction dynamics between brain regions of subjects under examination. Conventional functional connectivity measures largely originate from deterministic models on empirical analysis, usually demanding application-specific settings (e.g., Pearson's Correla...
Article
Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics and probing brain structures. Neuroimaging data are multi-featured and non-linear by nature, and it is a natural way to organise these data as tensors prior to performing automated analyses such as dis...
Preprint
Self-supervised pre-training has become the priory choice to establish reliable models for automated recognition of massive medical images, which are routinely annotation-free, without semantics, and without guarantee of quality. Note that this paradigm is still at its infancy and limited by closely related open issues: 1) how to learn robust repre...
Article
Simultaneously estimating brain source activity and noise has long been a challenging task in electromagnetic brain imaging using magneto- and electroencephalography. The problem is challenging not only in terms of solving the NP-hard inverse problem of reconstructing unknown brain activity across thousands of voxels from a limited number of sensor...
Article
Autism Spectrum Disorder (ASD) has been identified as one of the most challenging and intriguing problems in neurodevelopment of children. Recent research suggest that conventional assessment on the basis of explicit behavior observations can be well complemented by evaluation of intrinsic neurophysiological states via analyses of brain imaging dat...
Article
Electroencephalogram (EEG) excels in portraying rapid neural dynamics at the level of milliseconds, but its spatial resolution has often been lagging behind the increasing demands in neuroscience research or subject to limitations imposed by emerging neuro-engineering scenarios, especially those centering on consumer EEG devices. Current super-reso...
Article
Finding appropriate cluster centers and determining the scope of influence explicitly associated with each center is at the very core of a successful clustering process, which has long been particularly difficult and important when handling bio-signals such as electroencephalography (EEG). Considering exploratory EEG analysis as a typical case, thi...
Article
Feature selection aims to explore the characteristics of a problem that is under investigation instead of focusing on extracting (deep) features or classification tasks. The pending issues being explored are as follows: 1) to minimize the interference of uncertain irrelevant features and 2) to construct the (full) set of relevant features as an ind...
Article
Detection of sleep spindles, a special type of burst brainwaves recordable with electroencephalography (EEG), is critical in examining sleep-related brain functions from memory consolidation to cortical development. It has long been an onerous and highly professional task to visually position individual sleep spindles and label their onset & offset...
Article
Full-text available
Recent advances in neuroscience indicate that analysis of bio-signals such as rest state electroencephalogram (EEG) and eye-tracking data can provide more reliable evaluation of children autism spectrum disorder (ASD) than traditional methods of behavior measurement relying on scales do. However, the effectiveness of the new approaches still lags b...
Article
Full-text available
It is critical to determine whether the brain state of an epilepsy patient is indicative of a possible seizure onset; thus, appropriate therapy or alarm may be delivered in time. Successful seizure prediction relies on the capability of accurately separating the preictal stage from the interictal stage of ictal electroencephalography (EEG). With th...
Article
Recent Electroencephalogram (EEG) analysis in connection with brain disorders has been tremendously benefiting from the (Deep) Neural Network technology in neuroscience research and neuro-engineering practices. However, the performance of existing hand-crafted models, such as the stability, has largely been refrained. This is the case especially in...
Article
With the rise of concerns over security and privacy in the cloud, the “security-on-demand” service mode dynamically provides cloud customers with trusted computing environments according to their specific security needs. Major challenges, however, remain to achieve this goal: (1) integrating an auditable, tamper-resistant trust-management mechanism...
Article
Examination of dysfunctional brain dynamics often needs to tackle the difficulties of analyzing neural data of multiple modalities as recordings from a single source (such as EEG) do not always suffice in characterizing the brain states of interest. A typical example is sleep scoring from Polysomnography (PSG) for evaluation of sleep disorder, whic...
Article
Recent years have witnessed the booming of big data analytical applications (BDAAs). This trend provides unrivaled opportunities to reveal the latent patterns and correlations embedded in the data, and thus productive decisions may be made. This was previously a grand challenge due to the notoriously high dimensionality and scale of big data, where...
Chapter
How to improve social interaction ability for children with autism spectrum disorder (ASD) has long been a challenge faced by researchers and therapists. Recent research indicates that computer-assisted approaches may be effective in addressing this issue. This study aimed to understand children’s behaviors and then provide appropriate support to i...
Article
Electromagnetic brain imaging is the reconstruction of brain activity from non-invasive recordings of magnetic fields and electric potentials. An enduring challenge in this imaging modality is estimating the number, location, and time course of sources, especially for the reconstruction of distributed brain sources with complex spatial extent. Here...
Chapter
EEG synchronization is an essential tool to understand mechanisms of communication between brain regions. Despite numerous successes along this direction, grand challenges still remain: (1) to establish the relation between treatment outcome and the synchronization patterns amongst brain regions and (2) to correctly quantify the synchronization amo...
Article
Full-text available
Protocol Reverse Engineering (PRE) is crucial for information security of Internet-of-Things (IoT), and message clustering determines the effectiveness of PRE. However, the quality of services still lags behind the strict requirement of IoT applications as the results of message clustering are often coarse-grained with the intrinsic type informatio...
Article
Brain healthcare, when supported by Internet of Things, can perform online and accurate analysis of brain big data for the classification of multivariate Electroencephalogram (EEG), which is a prerequisite for the recent boom in neurofeedback applications and clinical practices. However, it remains a grand research challenge due to (1) the embedded...
Conference Paper
A long-standing issue in the field of neuroscience is identifying evolving patterns from multivariate electroencephalography (EEG) signals superimposed with intensive noise. With insufficient prior knowledge, it becomes even more important to (1) accurately detect synchronization dynamics among data channels and (2) adaptively classify evolving pat...
Article
Full-text available
Brain big data empowered by intelligent analysis provide an unrivalled opportunity to probe the dynamics of the brain in disorder. A typical example is to identify evolving synchronization patterns from multivariate electroencephalography (EEG) routinely superimposed with intensive noise in epilepsy research and practice. Under the circumstance of...
Article
Surveillance video analysis plays a vital role in the daily operations of smart cities, which increasingly relies on person re-identification technology to sustain smart security applications. However, research challenges of re-identification remain especially in terms of recognizing the different appearances of the same person in a harsh real-worl...
Article
Full-text available
Video-based person re-id has attracted a lot of research interest. When facing with a dramatic growth in new pedestrian videos, existing video-based person re-id methods usually need large quantities of labeled pedestrian videos to train a discriminative model. In practice, labeling large quantities of pedestrian videos is a costly and time consumi...
Article
Full-text available
Voluntary vaccination reflects how individuals weigh the risk of infection and the cost of vaccination against the spread of vaccine-preventable diseases, such as smallpox and measles. In a homogeneously mixing population, the infection risk of an individual depends largely on the proportion of vaccinated individuals due to the effects of herd immu...
Article
Full-text available
With the booming of social media and health informatics, there exists a pressing need for a powerful tool to sustain comprehensive analysis of public and personal health information. In particular, it should be able (1) to maximize the discovery of association rules amongst data items and (2) to handle the rapid growing data scale. The FP-Growth al...
Article
It has long been an important issue in various disciplines to examine massive multidimensional data superimposed by a high level of noises and interferences by extracting the embedded multi-way factors. With the quick increases of data scales and dimensions in the big data era, research challenges arise in order to (1) reflect the dynamics of large...
Article
Full-text available
As neural data are generally noisy, artifact rejection is crucial for data preprocessing. It has long been a grand research challenge for an approach which is able: 1) to remove the artifacts and 2) to avoid loss or disruption of the structural information at the same time, thus the risk of introducing bias to data interpretation may be minimized....
Article
Brain data processing has been embracing the big data era driven by the rapid advances of neuroscience as well as the experimental techniques for recording neuronal activities. Processing of massive brain data has become a constant in neuroscience research and practice, which is vital in revealing the hidden information to better understand the bra...
Article
Remote sensing applications in Digital Earth are overwhelmed with vast quantities of remote sensing (RS) image data. The intolerable I/O burden introduced by the massive amounts of RS data and the irregular RS data access patterns has made the traditional cluster based parallel I/O systems no longer applicable. We propose a RS data object-based par...
Chapter
Data centers constitute the foundations of cloud computing. Increased adoption of cloud computing in various sectors of life necessitates the data center growth. Cloud providers are facing numerous challenges in data centers design and operation. This chapter presents the modeling and simulation of the major data center network architectures using...
Article
A landslide susceptibility evaluation is vital for disaster management and development planning in the Yangtze River Three Gorges Reservoir Area. In this study, with the support of remote sensing and Geographic Information System, 4 factor groups comprising 10 separate subfactors of landslide-related data layers were selected to establish a suscept...
Article
Full-text available
Analysis of neural data with multiple modes and high density has recently become a trend with the advances in neuroscience research and practices. There exists a pressing need for an approach to accurately and uniquely capture the features without loss or destruction of the interactions amongst the modes (typically) of space, time, and frequency. M...
Article
Full-text available
Simulation study on evacuation scenarios has gained tremendous attention in recent years. Two major research challenges remain along this direction: (1) how to portray the effect of individuals' adaptive behaviors under various situations in the evacuation procedures and (2) how to simulate complex evacuation scenarios involving huge crowds at the...
Article
In this survey, we review different text mining techniques to discover various textual patterns from the social networking sites. Social network applications create opportunities to establish interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, comments, and discussion boards. Data in social networkin...
Article
With the increasing complexity of both data structures and computer architectures, the performance of applications needs fine tuning in order to achieve the expected runtime execution time. Performance tuning is traditionally based on the analysis of performance data. The analysis results may not be accurate, depending on the quality of the data an...
Article
Due to the quick advances in the scale of problem domain of complex systems under investigation, the complexity of multi-input component models used to construct logical processes (LP) has significantly increased. High-performance computing technologies have therefore been extensively used to enable parallel simulation execution. However, the tradi...
Article
Full-text available
As the very large scale integration (VLSI) technology enters the nanoscale regime, VLSI design is increasingly sensitive to variations on process, voltage, and temperature. Layer assignment technology plays a crucial role in industrial VLSI design flow. However, existing layer assignment approaches have largely ignored these variations, which can l...
Article
Remote sensed imagery mosaicking at large scale has been receiving increasing attentions in regional to global research. However, when scaling to large areas, image mosaicking becomes extremely challenging for the dependency relationships among a large collection of tasks which give rise to ordering constraint, the demand of significant processing...
Article
Full-text available
Natural disasters occur unexpectedly and usually result in huge losses of life and property. How to effectively make contingency plans is an intriguing question constantly faced by governments and experts. Human rescue operations are the most critical issue in contingency planning. A natural disaster scenario is, in general, highly complicated and...
Article
The Contaminant Source Characterization (CSC) problem in a Water Distributed System (WDS) exhibits a compute-intensive challenge that requires highly reliable and high performance computing resources in order to achieve near real-time processing. Traditional solution to the CSC problem with MPI via Grid/cluster computing cannot fulfill CSC's QoS re...
Article
Full-text available
The estimation of synchronization amongst multiple brain regions is a critical issue in understanding brain functions. There is a lack of an appropriate approach which is capable of (1) measuring the direction and strength of synchronization of activities of multiple brain regions, and (2) adapting to the quickly increasing sizes and scales of neur...
Article
More and more real-time applications need to handle dynamic continuous queries over streaming data of high density. Conventional data and query indexing approaches generally do not apply for excessive costs in either maintenance or space. Aiming at these problems, this study first proposes a new indexing structure by fusing an adaptive cell and KDB...
Article
The real-time estimation of coherence amongst neural signals from different brain areas is a critical issue in understanding brain functions. The wavelet coherence based on Monte Carlo method (MC-WTC) is effective in measuring the time-frequency coherence of neural signals, but it generates large intermediate data and could not be applied in real-t...
Article
Full-text available
The social computing, such as social networking services (SNSs) and social Networking Platforms (SNPs) provide a coherent medium through which people can be interactive and socialize. The SNP is a Web-based social space, specifically designed for end user-driven applications that facilitate communication, collaboration and sharing of the knowledge...
Article
There are several use cases that involve the need to transfer data between datacenters when processing large-scale data sets. Data transmission in a multidatacenter computing system has new characteristics that are hard to express and handle using traditional methods. Thus, it is necessary for a new transmission strategy to be developed for users o...
Conference Paper
Full-text available
Simulation is an important approach to the study of scenarios of confrontation among antagonistic groups. It remains a research issue to explore the influence of the crowd size and imbalance between groupsspi0215e sizes on the process of confrontation. In this study, a multi-agent simulation system has been developed with functional programming mod...
Conference Paper
The study of the correlations that may exist between neural signals generated by different brain regions is a critical issue in understanding brain functions. There is a lack of an appropriate approach which is capable of (1) estimating the correlation between neural signals, and (2) adapting to the quickly increasing scales and sizes of neural sig...
Article
Full-text available
Gene Expression Programming (GEP) significantly surpasses traditional evolutionary approaches to solving symbolic regression problems. However, existing GEP algorithms still suffer from premature convergence and slow evolution in anaphase. Aiming at these pitfalls, we designed a novel evolutionary algorithm, namely Uniform Design-Aided Gene Express...
Article
Full-text available
The wireless sensor network (WSN) technology has applied in monitoring natural disasters for more than one decade. Disasters can be closely monitored by augmenting a variety of sensors, and WSN has merits in (1) low cost, (2) quick response, and (3) salability and flexibility. Natural disaster monitoring with WSN is a well-known data intensive appl...