Comprehensive cognitive-affective profiling for precision ASD assessment is hindered by inadequate computational modeling of emotion-behavior dynamics. This gap persists due to limited methods for reconciling EEG, eye-tracking, and facial expression modalities-critical for capturing ASD heterogeneity drivers. This study proposes the Cognitive-Affective Ability Assessment (CA3) framework, focusing on attention control in conjunction with valence-arousal, to address these limitations through: (1)Ability-Specific Neural Disentanglementextracting attention- and emotion-specific representations from EEG using ET and facial expression recordings as anchors; (2)Symmetrical Cross-Ability Alignmentmodeling contextual dependencies via a symmetric cross-attention mechanism; and (3)Uncertainty-Aware Ability Integrationfusing per-ability predictions using Dirichlet modeling and Dempster-Shafer theory while quantifying confidence. Extensive experiments have been conducted to evaluate CA$^{3}$vs. state-of-the-arts counterparts on the multimodel datasets (EEG, ET and facial expression recordings) with CCNU (27 ASD vs. 30 typically developing (TD) children) and BNU (49 ASD vs. 48 TD), and the results demonstrate that: 1) In the ASD cognitive-affective abilities assessment task, accuracy is improved by 2.7% for attention control and 2.5% for emotion perception, and by 3.5% for cognitive-affective abilities assessment, compared with current state-of-the-art methods. 2) For attention control and valence-arousal, composite ability gaps are measured between ASD and typically developing children: 15.6% and 19.9% for mild ASD, 28.2% and 39.1% for moderate ASD, and 44.4% and 61.3% for severe ASD, respectively. Overall, the framework effectively bridges computational assessment with clinically actionable insights, enabling robust decision-making through personalized cognitive-affective ASD profiles.

Discovering biomarkers for Autism Spectrum Disorder (ASD) is essential for elucidating its etiology, enabling early diagnosis, and refining treatment strategies. Electroencephalogram (EEG) microstates reflect the brain's overall dynamic changes, aiding in exploring differences in brain function patterns between ASD and Typically Developing (TD) groups. To this end, this study proposes an adaptive EEG microstate analysis approach based on Hidden Markov Models (HMMs) for the discovery of ASD biomarkers. Specifically, the proposed method, within the HMM framework, adaptively extracts millisecondscale transient brain microstate patterns that recur over time and models microstates using a multivariate Gaussian distribution rather than static topological structures. Resting-state EEG from 178 children aged 3 to 6 are used to validate the proposed approach. The analysis of the four microstates (#1, #2, #3, and #4) reveals significant differences between ASD and TD groups. Temporally, the ASD group shows difficulty in microstate transitions, primarily between microstates #2 and #3. In the frequency and spatial domains, TD individuals exhibit stronger brain region activation and interaction in microstates #2 and #4, whereas the ASD group shows reduced activity. Notably, during microstate #3, the ASD group demonstrates higher spectral power and channel coherence. Additionally, Ttests on intergroup feature differences and the results of the ASD discrimination task (accuracy: 88.89%) further confirm the potential of microstate features in assessing ASD tendencies. Overall, this study holds the potential to reveal novel insights into the neural mechanisms underlying ASD and identify valuable biomarkers for clinical assessment and diagnosis.

Accurate early detection of obstructive sleep apnea (OSA) is critical for enabling timely auto-adjusting positive airway pressure (APAP) interventions. However, existing methods largely rely on binary classification (normal vs. apnea), failing to capture the transitional state preceding OSA onset and inducing therapy delays-hampered by open issues of ambiguous biomarkers and signal temporal misalignment. To address this, this study redefines sleep physiology into three distinct states: normal breathing, pre-apnea transitional (30 s pre-onset), and apnea. This study further proposes DREAM-OSA, a dual-modal transformer framework that specifically targets the transitional states, providing APAP with a sufficient advance response window (up to 10 s) to enable true early prediction of OSA events. It synergizes the complementary electroencephalogram (EEG) and respiratory signals through: 1) Modality-Specific Tokenization: EEG (decomposed into$\delta, \theta, \alpha, \beta, \gamma$bands) and respiratory signals are segmented into 1 s patches, encoded via dedicated 1DCNNs while preserving temporal-spectral structural information through learnable embeddings; and 2) Hierarchical Attention: Intra-modal self-attention captures temporal-spectral dynamics within each modality, while inter-modal cross-attention models bidirectional EEG-respiratory interactions. Evaluated on the MASS-SS1 dataset vs. the state-of-the-art methods towards real-time OSA early warning, DREAM-OSA achieves: overall accuracy up to 95.0%, and per-class F1-scores reaching 91.9% (normal), 94.9% (transitional), and 97.0% (apnea), demonstrating significantly more reliable detection of the transitional states, whereas its counterparts face performance bottleneck.

Electroencephalogram (EEG) artifact removal has been investigated for decades with the goal of reconstructing the clean signals for the subsequent EEG analysis. However, existing denoising methods still have limited capabilities to handle the highly mixed artifacts and the fine-grained temporal dependency of artifact-free EEG without a priori knowledge of the artifacts. To address the challenges, this study proposes a CNN-Transformer-based dual-stage collaborative ensemble learning framework (namely CT-DCENet) in the form of three modules: 1) randomized collaboration module initially utilizes four individual learners to reveal multi-group morphological characteristics of the denoised EEG, 2) linear ensemble module integrates the outputs of four individual learners via weighted linear combination to preliminarily estimate the denoised EEG, 3) information complementation module takes in the residual between the contaminated EEG and the above estimated EEG, and critically applies CNN-Transformer-based feature extractor and denoising head to learn the detailed characteristics of the denoised EEG. CT-DCENet is conducted in a dual-stage training manner to derive the morphological characteristics & the detailed characteristics of the artifact-free EEG successively. The experimental results on the public EEG datasets indicate that 1) CT-DCENet significantly outperforms the state-of-the-art counterparts (e.g., DuoCL, GCTNet) under the conditions of various artifacts and noise intensities, where the increases of SNR & PCC are 0.79 dB, 0.6% and the decrease of RRMSE is 1.9% for the removal of EMG, ECG, EOG mixed artifacts, 2) the reconstructed EEG by CT-DCENet can well fit the clean EEG with a low error achieved, especially for the peak amplitude, the high-frequency area and the boundary area of the EEG waveform, providing promising EEG data for the downstream task-oriented EEG analysis.

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 qualitative predictions. However, this automation still significantly lags behind the requirements in clinical practices. This study addresses this issue by predicting the probability of GS induction under the joint restriction of a patient's EEG (electroencephalogram) and the stimulus parameters, sustained by a two-stage learning model (namely ECTnet): 1) Temporal-Spatial Feature Learning. Channel-wise convolution via multiple convolution kernels first learns the deep features of the EEG, followed by a "ConvLSTM" constructing the temporal-spatial features aided with the enforced convolution operations at the LSTM gates; 2) GS Prediction. The probability of seizure induction is predicted based on the EEG features fused with stimulus parameters, through which the optimal parameter setting(s) may be obtained by minimizing the stimulus charge while ensuring the probability above a threshold. Experiments have been conducted on EEG data from 96 subjects with mental disorders to examine the performance and design of ECTnet. These experiments indicate that ECTnet can effectively automate the selection of optimal stimulus parameters: 1) an AUC of 0.746, F1-score of 0.90, a precision of 89% and a recall of 93% in the prediction of seizure induction have been achieved, outperforming the state-of-the-art counterpart, and 2) inclusion of parameter features increases the F1-score by 0.054.

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 discrimination of neurological disorders like Parkinson's Disease (PD) and Attention Deficit and Hyperactivity Disorder (ADHD). However, the existing approaches are often subject to performance bottlenecks (e.g., conventional feature extraction and deep learning based feature construction), as these can lose the structural information that correlates multiple data dimensions or/and demands excessive empirical and application-specific settings. This study proposes a Deep Factor Learning model on a Hilbert Basis tensor (namely, HB-DFL) to automatically derive latent low-dimensional and concise factors of tensors. This is achieved through the application of multiple Convolutional Neural Networks (CNNs) in a non-linear manner along all possible dimensions with no assumed a priori knowledge. HB-DFL leverages the Hilbert basis tensor to enhance the stability of the solution by regularizing the core tensor to allow any component in a certain domain to interact with any component in the other dimensions. The final multi-domain features are handled through another multi-branch CNN to achieve reliable classification, exemplified here using MRI discrimination as a typical case. A case study of MRI discrimination has been performed on public MRI datasets for discrimination of PD and ADHD. Results indicate that 1) HB-DFL outperforms the counterparts in terms of FIT, mSIR and stability (mSC and umSC) of factor learning; 2) HB-DFL identifies PD and ADHD with an accuracy significantly higher than state-of-the-art methods do. Overall, HB-DFL has significant potentials for neuroimaging data analysis applications with its stability of automatic construction of structural features.

Self-supervised pre-training has become the priory choice to establish reliable neural networks for automated recognition of massive biomedical microscopy 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 representations in an unsupervised manner from unlabeled biomedical microscopy images of low diversity in samples? and (2) how to obtain the most significant representations demanded by a high-quality segmentation? Aiming at these issues, this study proposes a knowledge-based learning framework (TOWER) towards enhanced recognition of biomedical microscopy images, which works in three phases by synergizing contrastive learning and generative learning methods: (1) Sample Space Diversification: Reconstructive proxy tasks have been enabled to embed a priori knowledge with context highlighted to diversify the expanded sample space; (2) Enhanced Representation Learning: Informative noise-contrastive estimation loss regularizes the encoder to enhance representation learning of annotation-free images; (3) Correlated Optimization: Optimization operations in pre-training the encoder and the decoder have been correlated via image restoration from proxy tasks, targeting the need for semantic segmentation. Experiments have been conducted on public datasets of biomedical microscopy images against the state-of-the-art counterparts (e.g., SimCLR and BYOL), and results demonstrate that: TOWER statistically excels in all self-supervised methods, achieving a Dice improvement of 1.38 percentage points over SimCLR. TOWER also has potential in multi-modality medical image analysis and enables label-efficient semi-supervised learning, e.g., reducing the annotation cost by up to 99% in pathological classification.

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 neuroengineering scenarios, especially those centering on consumer EEG devices. Current superresolution (SR) methods generally do not suffice in the reconstruction of high-resolution (HR) EEG as it remains a grand challenge to properly handle the connection relationship amongst EEG electrodes (channels) and the intensive individuality of subjects. This study proposes a deep EEG SR framework correlating brain structural and functional connectivities (Deep-EEGSR), which consists of a compact convolutional network and an auxiliary fully connected network for filter generation (FGN). Deep-EEGSR applies graph convolution adapting to the structural connectivity amongst EEG channels when coding SR EEG. Sample-specific dynamic convolution is designed with filter parameters adjusted by FGN conforming to functional connectivity of intensive subject individuality. Overall, Deep-EEGSR operates on low-resolution (LR) EEG and reconstructs the corresponding HR acquisitions through an end-to-end SR course. The experimental results on three EEG datasets (autism spectrum disorder, emotion, and motor imagery) indicate that: 1) Deep-EEGSR significantly outperforms the state-of-the-art counterparts with normalized mean squared error (NMSE) decreased by 1%-6% and the improvement of signal-to-noise ratio (SNR) up to 1.2 dB and 2) the SR EEG manifests superiority to the LR alternative in ASD discrimination and spatial localization of typical ASD EEG characteristics, and this superiority even increases with the scale of SR.

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 scenarios without a priori knowledge of artifacts. This study proposes an approach (namely DuoCL) to deep artifact removal with a dual-scale CNN (Convolutional Neural Network)-LSTM (Long Short-Term Memory) model, operating on the raw EEG in three phases: 1) Morphological Feature Extraction, a dual-branch CNN utilizes convolution kernels of two different scales to learn morphological features (individual sample); 2) Feature Reinforcement, the dual-scale features are then reinforced with temporal dependencies (inter-sample) captured by LSTM; and 3) EEG Reconstruction, the resulting feature vectors are finally aggregated to reconstruct the artifact-free EEG via a terminal fully connected layer. Extensive experiments have been performed to compare DuoCL to six state-of-the-art counterparts (e.g., 1D-ResCNN and NovelCNN). DuoCL can reconstruct more accurate waveforms and achieve the highest ${\mathsf{SNR}}$ & correlation (${\mathsf{CC}}$) as well as the lowest error (${\mathsf{RRMSE}}_{\mathsf{t}}$ & ${\mathsf{RRMSE}}_{\mathsf{f}}$). In particular, DuoCL holds potentials in providing a high-quality removal of unknown and hybrid artifacts.

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 correlating various data attributes. However, research challenges remain in the derivation of factors due to highly scattered data distribution of Mv-TSD and intensive interferences/outliers embedded in the source data. The proposed Enhanced Bayesian Factorization approach (Enhanced-BF) addresses the challenges in three phases: (1) variant scale partitioning applies to Mv-TSD according to degree of amplitude and obtains the blocks of variant scales; (2) hierarchical Bayesian model for tensor factorization automatically derives the factors of each block with interferences suppressed; (3) Bayesian unification model merges those block factors to construct the final structural features.Enhanced-BFhas been evaluated using a case study of brain data engineering with multivariate electroencephalogram (EEG). Experimental results indicate that the proposed method manifests robustness to the interferences and outperforms the counterparts in terms of operation efficiency and error when factorizing EEG tensor. Besides,Enhanced-BFexcels in factorization-based analysis of ongoing autism spectrum disorder (ASD) EEG: 3 times speed-up in factorization and$87.35\%$accuracy in ASD discrimination. The latent factors (“biomarkers”) can distinctly interpret the typical EEG characteristics of ASD subjects.

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 sensors, but also for the need to simultaneously estimate the noise and interference. We present a generative model with an augmented leadfield matrix to simultaneously estimate brain source activity and sensor noise statistics in electromagnetic brain imaging (EBI). We then derive three Bayesian inference algorithms for this generative model (expectation-maximization (EBI-EM), convex bounding (EBI-Convex) and fixed-point (EBI-Mackay)) to simultaneously estimate the hyperparameters of the prior distribution for brain source activity and sensor noise. A comprehensive performance evaluation for these three algorithms is performed. Simulations consistently show that the performance of EBI-Convex and EBI-Mackay updates is superior to that of EBI-EM. In contrast to the EBI-EM algorithm, both EBI-Convex and EBI-Mackay updates are quite robust to initialization, and are computationally efficient with fast convergence in the presence of both Gaussian and real brain noise. We also demonstrate that EBI-Convex and EBI-Mackay update algorithms can reconstruct complex brain activity with only a few trials of sensor data, and for resting-state data, achieving significant improvement in source reconstruction and noise learning for electromagnetic brain imaging.

It is desirable to maintain both high accuracy and runtime efficiency in lane detection. State-of-the-art methods mainly address the efficiency problem by direct compression of high-dimensional features. These methods usually suffer from information loss and cannot achieve satisfactory accuracy performance. To ensure the diversity of features and subsequently maintain information as much as possible, we introduce multi-frequency analysis into lane detection. Specifically, we propose a multi-spectral feature compressor (MSFC) based on two-dimensional (2D) discrete cosine transform (DCT) to compress features while preserving diversity information. We group features and associate each group with an individual frequency component, which incurs only 1/7 overhead of one-dimensional convolution operation but preserves more information. Moreover, to further enhance the discriminability of features, we design a multi-spectral lane feature aggregator (MSFA) based on one-dimensional (1D) DCT to aggregate features from each lane according to their corresponding frequency components. The proposed method outperforms the state-of-the-art methods (including LaneATT and UFLD) on TuSimple, CULane, and LLAMAS benchmarks. For example, our method achieves 76.32% F1 at 237 FPS and 76.98% F1 at 164 FPS on CULane, which is 1.23% and 0.30% higher than LaneATT. Our code and models are available at https://github.com/harrylin-hyl/MSLD.

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 enormously complicated interdependencies amongst data elements. However, grand challenges still remain: (1) to incrementally derive the multi-mode factors of the augmenting big data and (2) to achieve this goal under the circumstance of insufficient a priori knowledge. This study develops an incrementally parallel factorization solution (namely I-PARAFAC) for huge augmenting tensors (multi-way arrays) consisting of three phases over a cutting-edge GPU cluster: in the “giant-step” phase, a variational Bayesian inference (VBI) model estimates the distribution of the close neighborhood of each factor in a high confidence level without the need for a priori knowledge of the tensor or problem domain; in the “baby-step” phase, a massively parallel Fast-HALS algorithm (namely G-HALS) has been developed to derive the accurate subfactors of each subtensor on the basis of the initial factors; in the final fusion phase, I-PARAFAC fuses the known factors of the original tensor and those accurate subfactors of the “increment” to achieve the final full factors. Experimental results indicate that: (1) the VBI model enables a blind factor approximation, where the distribution of the close neighborhood of each final factor can be quickly derived (10 iterations for the test case). As a result, the model of a low time complexity significantly accelerates the derivation of the final accurate factors and lowers the risks of errors; (2) I-PARAFAC significantly outperforms even the latest high performance counterpart when handling augmenting tensors, e.g., the increased overhead is only proportional to the increment while the latter has to repeatedly factorize the whole tensor, and the overhead in fusing subfactors is always minimal; (3) I-PARAFAC can factorize a huge tensor (volume up to 500 TB over 50 nodes) as a whole with the capability several magnitudes higher than conventional methods, and the runtime is in the order of 1/n to the number of compute nodes; (4) I-PARAFAC supports correct factorization-based analysis of a real 4-order EEG dataset captured from a variety of epilepsy patients. Overall, it should also be noted that counterpart methods have to derive the whole tensor from the scratch if the tensor is augmented in any dimension; as a contrast, the I-PARAFAC framework only needs to incrementally compute the full factors of the huge augmented tensor.

Summary 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 intensive noises and the intrinsic nonstationarity determined by the evolution of brain states; and (2) the lack of a user‐friendly computing platform to sustain the complicated analytics. This study presents the design of an online EEG classification system aided by Cloud centering on a lightweight Convolutional Neural Network (CNN). The system incrementally trains the CNN on Cloud and enables hot deployment of the trained classifier without the need to restart the gateway to adapt to the users' needs. The classifier maintains a High Convolutional Layer to gain the ability of processing high‐dimensional EEG segments. The number of hidden layers is minimized to ensure the efficiency of training. The lightweight CNN adopts an “hourglass” block of fully connected layers to reduce the number of neurons quickly toward the output end. A case study of depression evaluation has been performed against raw EEG datasets to distinguish between (1) Healthy and Major Depression Disorder with an accuracy, sensitivity, and specificity of [98.59% ± 0.28%], [97.77% ± 0.63%], and [99.51% ± 0.19%], respectively; and (2) Effective and Noneffective treatment outcome with an accuracy, sensitivity, and specificity of [99.53% ± 0.002%], [99.50% ± 0.01%], and [99.58% ± 0.02%], respectively. The results show that the classification can be completed several magnitudes faster when EEG is collected on the gateway (several milliseconds vs. 4 seconds).

Electromagnetic brain imaging is the reconstruction of brain activity from non-invasive recordings of the 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, we introduce a novel robust empirical Bayesian algorithm that enables better reconstruction of distributed brain source activity with two key ideas: kernel smoothing and hyperparameter tiling. Since the proposed algorithm builds upon many of the performance features of the sparse source reconstruction algorithm - Champagne and we refer to this algorithm as Smooth Champagne. Smooth Champagne is robust to the effects of high levels of noise, interference, and highly correlated brain source activity. Simulations demonstrate excellent performance of Smooth Champagne when compared to benchmark algorithms in accurately determining the spatial extent of distributed source activity. Smooth Champagne also accurately reconstructs real MEG and EEG data.

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, DNN) largely remain static, while brain states of diseases are highly dynamic and exhibits significant individuality; and (2) EEG analytics are too complicated and have to be sustained by advanced computing services. This study adopts an automatic machine learning method to construct a dual-CNN (convolutional neural network) of high performance in terms of both accuracy and efficiency. The model can optimize its hyperparameters continuously on its own initiative. Experimental results in the evaluation of depression using real EEG datasets indicate that (1) the proposed method executes 3.5 times faster compared with a conventional counterpart; (2) the dual-CNN gains a significant performance improvement (versus CapsuleNet and Resnet-16) in identifying Major Depression Disorder (MDD) with accuracy, sensitivity, and specificity up to 98.81, 98.36, and 99.31 percent respectively; and those for treatment outcome are 99.52, 99.63, and 99.37 percent respectively, and (3) classification can be completed several hundred times faster than EEG being collected upon a COTS computer.

Video-based person re-identification (re-id) has attracted a lot of research interest. When facing 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-consuming task, which will limit the application of these methods in the real environment. Therefore, it is valuable and necessary to investigate how to learn a discriminative re-id model by using limited labeled training pedestrian videos. In this paper, we propose a semi-supervised cross-view projection-based dictionary learning (SCPDL) approach for video-based person re-id. Specifically, SCPDL jointly learns a pair of feature projection matrices and a pair of dictionaries by integrating the information contained in labeled and unlabeled pedestrian videos. With the learned feature projection matrices, the influence of variations within each video to the re-id can be reduced. With the learned dictionary pair, pedestrian videos from two different cameras can be converted into coding coefficients in a common representation space, such that the differences between different cameras can be bridged. In the learning process, the labeled pedestrian videos are used to ensure that the learned dictionaries have favorable discriminability; the large quantities of unlabeled pedestrian videos are used to ensure that SCPDL can better capture the variations between pedestrian videos, such that the learned dictionaries can own stronger representative capability. Experiments on two public pedestrian sequence data sets (iLIDS-VID and PRID 2011) demonstrate the effectiveness of the proposed approach.

Summary 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 brain functions and malfunctions. Brain data are routinely non‐linear and non‐stationary in nature, and existing algorithms and approaches to neural data processing are generally complicated in order to characterize the non‐linearity and non‐stationarity. Brain big data processing has pressing needs for appropriate computing technologies to address three grand challenges: (1) efficiency, (2) scalability and (3) reliability. Recent advances of computing technologies are making non‐linear methods viable in sophisticated applications of massive brain data processing. General‐purpose Computing on the Graphics Processing Unit (GPGPU) technology fosters an ideal environment for this purpose, which benefits from the tremendous computing power of modern graphics processing units in massively parallel architecture that is frequently an order of magnitude larger than the modern multi‐core CPUs. This article first recaps significant speed‐ups of existing algorithms aided by GPGPU in neuroimaging and processing electroencephalogram (EEG), functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG) and etc. The article then demonstrates a series of successful approaches to processing EEG data in various dimensions and scales in a massively parallel manner: (1)decomposition:a massively parallel Ensemble Local Mean Decomposition (ELMD) algorithm aided by GPGPU can decompose EEG series, which forms the basis of further time‐frequency transformation, in real‐time without sacrificing the precision of processing; (2)synchronization measurement:a parallelized Nonlinear Interdependence (NLI) method for global synchronization measurement of multivariate EEG with speed‐up of more than 1000 times, and it was successful in localization of epileptic focus; and (3)dimensionality reduction:a large‐scale Parallel Factor Analysis which excels in run‐time performance and scales far better by hundreds of times than conventional approach does, and it supports fast factorization of EEG with more than 1000 channels. Through these practices, the massively parallel computing technology manifests great potentials in addressing the grand challenges of brain big data processing. Copyright © 2016 John Wiley & Sons, Ltd.

Software systems for data-centric smart city applicationsThe smart city is the key technology for efficient management, sustainable development, and efficient governance in the current worldwide urbanization process.It incorporates the latest information technologies particularly data-driven trends to improve support for the everyday life of people, especially in security, transportation, and social services.The smart city paradigm can be specified as a large-scale distributed system in which the massive data generated by smart electronic devices, smart environments, and Internet of Things (IoT) can be organized, managed, and analyzed.The design of scalable software applications, frameworks, and packages is important in forming approaches which integrate smart city infrastructures (environment, physical ICT infrastructure), public processes, and services.Recently, software system development for data-centric smart city applications has become a very active area of research in academia and has attracted significant interest from industry.Since computing issues for smart cities are highly interdisciplinary and cover various topics, a special issue of Software: Practice and Experience provides the ideal forum for presenting and discussing the latest research.The goal of this special issue is to present outstanding research results in regard to software systems for data-centric smart city applications.We received 23 manuscript submissions in total; of these, 7 papers were accepted after several rounds of very constructive and deep reviews.Large-scale wireless communication is the fundamental infrastructure needed to ensure the operation of smart city, cloud computing, the IoT, etc.A relay network can provide an efficient solution to reliable transmission of large amounts of data.However, when base stations are densely populated, energy consumption becomes a critical problem.To solve this problem, Lam et al propose a software system for robust power management taking uncertain channel gains into consideration.The system relies on a distributed power allocation algorithm to reduce the overhead of extra information exchange while guaranteeing performance with respect to energy savings and robustness in a dynamic communications environment.Smart city uses many data sources.To manage the massive volume of data, data compression is essential, especially for surveillance videos.Since lossy compression methods cause information loss, a high compression ratio negatively affects data analysis.Xiao et al tackled this problem by proposing a sensitive-object-oriented compression method for surveillance videos.Regions with sensitive objects critical to the analysis of surveillance videos are detected prior to compression.Higher bit rates are assigned to these regions to enable a high compression ratio for the whole video stream without sacrificing the performance of data analysis centered on the sensitive objects.Data analysis is at the very core of smart city applications.Four interesting topics are included in this special issue, covering cloud-based mining for traffic data, low-cost computing for face tracking, fast processing of forensic data, and accurate analysis of human emotions: 1. Yu et al propose a SPARK-based software framework for trajectory pattern mining and trajectory clustering for taxi journeys.The proposed algorithms adopt in-memory computation and load the trajectory sequences into resilient distribution datasets to overcome high I/O overhead and communication overhead.High efficiency and scalability were achieved for the whole distributed framework.2. Fast processing of forensic data in heterogeneous distributed system is demonstrated by Quick et al.Their work gives digital forensic specialists a method and framework to review and analyze media in a timely manner through digital forensic data reduction.

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 tensors while introducing no significant distortions in the factorization procedure and (2) handle influences of the noises in sophisticated applications. A hierarchical parallel processing framework over a GPU cluster, namely H-PARAFAC, has been developed to enable scalable factorization of large tensors upon a “divide-and-conquer” theory for Parallel Factor Analysis (PARAFAC). The H-PARAFAC framework incorporates a coarse-grained model for coordinating the processing of sub-tensors and a fine-grained parallel model for computing each sub-tensor and fusing sub-factors. Experimental results indicate that (1) the proposed method breaks the limitation on the scale of multidimensional data to be factorized and dramatically outperforms the traditional counterparts in terms of both scalability and efficiency, e.g., the runtime increases in the order of n2 when the data volume increases in the order of n3, (2) H-PARAFAC has potentials in refraining the influences of significant noises, and (3) H-PARAFAC is far superior to the conventional window-based counterparts in preserving the features of multiple modes of large tensors.

It has long been an important issue in various disciplines to examine massive multi-dimensional data by extracting the embedded multi-way factors. With the quick increases in both scales and dimensions of data under analysis, research challenges arise in order to reflect the dynamics of large-scale tensors while introducing no significant distortions in the factorization procedure in sophisticated applications. A massively parallel computing framework, namely H-PARAFAC, has been developed to enable Parallel Factor Analysis (PARAFAC) of massive tensors upon a "divide-and-conquer" theory (a modified alternating least squares approach). The hierarchical framework incorporates a coarse-grained model for coordinating the processing of sub tensors and a fine-grained parallel model for computing each sub tensor and fusing sub-factors. Experiments have been performed on a GPU cluster, and the results indicate that (1) the proposed method breaks the limitation on the size of data to be factorized, and (2) it dramatically outperforms the traditional counterparts in terms of both scalability and efficiency, e.g., The runtime increases linearly with the data volume increases in the order of n3.

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. Moreover, the approach must be able to quickly analyze the neural data of exponentially growing scales and sizes, in tens or even hundreds of channels, so that timely conclusions and decisions may be made. A salient approach to multi-way data analysis is the parallel factor analysis (PARAFAC) that manifests its effectiveness in the decomposition of the electroencephalography (EEG). However, the conventional PARAFAC is only suited for offline data analysis due to the high complexity, which computes to be$O(n^{2})$with the increasing data size. In this study, a large-scale PARAFAC method has been developed, which is supported by general-purpose computing on the graphics processing unit (GPGPU). Comparing to the PARAFAC running on conventional CPU-based platform, the new approach dramatically excels by${>}360$times in run-time performance, and effectively scales by${>}400$times in all dimensions. Moreover, the proposed approach forms the basis of a model for the analysis of electrocochleography (ECoG) recordings obtained from epilepsy patients, which proves to be effective in the epilepsy state detection. The time evolutions of the proposed model are well correlated with the clinical observations. Moreover, the frequency signature is stable and high in the ictal phase. Furthermore, the spatial signature explicitly identifies the propagation of neural activities among various brain regions. The model supports real-time analysis of ECoG in${>}1{,}000$channels on an inexpensive and available cyber-infrastructure.

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 individual level due to the ultrahigh complexity of these scenarios. In this study, a simulation framework for general evacuation scenarios has been developed. Each individual in the scenario is modeled as an adaptable and autonomous agent driven by a weight-based decision-making mechanism. The simulation is intended to characterize the individuals' adaptable behaviors, the interactions among individuals, among small groups of individuals, and between the individuals and the environment. To handle the second challenge, this study adopts GPGPU to sustain massively parallel modeling and simulation of an evacuation scenario. An efficient scheme has been proposed to minimize the overhead to access the global system state of the simulation process maintained by the GPU platform. The simulation results indicate that the “adaptability” in individual behaviors has a significant influence on the evacuation procedure. The experimental results also exhibit the proposed approach's capability to sustain complex scenarios involving a huge crowd consisting of tens of thousands of individuals.

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-tree, namely CKDB-tree. A cell-tree indexing approach has been developed on the basis of the CKDB-tree that supports dynamic continuous queries. The approach significantly reduces the space costs and scales well with the increasing data size. Towards providing a scalable solution to filtering massive steaming data, this study has explored the feasibility to utilize the contemporary general-purpose computing on the graphics processing unit (GPGPU). The CKDB-tree-based approach has been extended to operate on both the CPU (host) and the GPU (device). The GPGPU-aided approach performs query indexing on the host while perform streaming data filtering on the device in a massively parallel manner. The two heterogeneous tasks execute in parallel and the latency of streaming data transfer between the host and the device is hidden. The experimental results indicate that (1) CKDB-tree can reduce the space cost comparing to the cell-based indexing structure by 60 percent on average, (2) the approach upon the CKDB-tree outperforms the traditional counterparts upon the KDB-tree by 66, 75 and 79 percent in average for uniform, skewed and hyper-skewed data in terms of update costs, and (3) the GPGPU-aided approach greatly improves the approach upon the CKDB-tree with the support of only a single Kepler GPU, and it provides real-time filtering of streaming data with 2.5M data tuples per second. The massively parallel computing technology exhibits great potentials in streaming data monitoring.

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 parallel file system for remote sensing applications and implement it with the OrangeFS file system. It provides application-aware data layout policies, together with RS data object based data I/O interfaces, for efficient support of various data access patterns of RS applications from the server side. With the prior knowledge of the desired RS data access patterns, HPGFS could offer relevant space-filling curves to organize the sliced 3-D data bricks and distribute them over I/O servers. In this way, data layouts consistent with expected data access patterns could be created to explore data locality and achieve performance improvement. Moreover, the multi-band RS data with complex structured geographical metadata could be accessed and managed as a single data object. Through experiments on remote sensing applications with different access patterns, we have achieved performance improvement of about 30 percent for I/O and 20 percent overall.

How to improve social communication skills for children, especially those with social communication difficulties such as attention deficit/hyperactivity disorder, 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 improve their social communication skills. We have established an intelligent system, inside which a child can freely play interactive social skills games with virtual characters. The virtual characters can adjust their own behaviors by adapting to the child's cognitive state (e.g., focus of attention) and affective state (e.g., happiness or surprise). The child's behavior is identified in real-time by recognition of multimodal sensory information, which includes head pose and eye gaze estimation, gesture detection, and affective state detection supported by a series of algorithms proposed in this study. Furthermore, this intelligent system has been enabled in a nonintrusive manner using a novel approach of multicamera surveillance to provide the child with natural interaction with the system. Experimental results show the system can estimate a user's attention and affective states with correctness rates of 93% and 91.3%, respectively. The results obtained suggest that the methods have strong potential as alternative methods for sensing human behavior and providing appropriate support.

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 capabilities and also the difficulties inherent in organizing these enormous tasks and RS image data. We propose a task-tree based mosaicking for remote sensed imageries at large scale with dynamic DAG scheduling. It expresses large scale mosaicking as a data-driven task tree with minimal height. And also a critical path based dynamical DAG scheduling solution with status queue named CPDS-SQ is provided to offer an optimized schedule on multi-core cluster with minimal completion time. All the individual dependent tasks are run by a core parallel mosaicking program implemented with MPI to perform mosaicking on different pairs of images. Eventually, an effective but easier approach is offered to improve the large-scale processing capability by decoupling the dependence relationships among tasks from the complex parallel processing procedure. Through experiments on large-scale mosaicking, we confirmed that our approach were efficient and scalable.

Scheduling in traditional distributed systems has been mainly studied for system performance parameters without data transmission requirements. With the emergence of Data Grids (DGs) and Data Centers, data-aware scheduling has become a major research issue. In this work we present two implementations of classical genetic-based data-aware schedulers of independent tasks submitted to the grid environment. The results of a simple. empirical analysis confirm the high effectiveness of the genetic algorithms in solving very complex data intensive combinatorial optimization problems.

SUMMARY Data centers are experiencing a remarkable growth in the number of interconnected servers. Being one of the foremost data center design concerns, network infrastructure plays a pivotal role in the initial capital investment and ascertaining the performance parameters for the data center. Legacy data center network (DCN) infrastructure lacks the inherent capability to meet the data centers growth trend and aggregate bandwidth demands. Deployment of even the highest‐end enterprise network equipment only delivers around 50% of the aggregate bandwidth at the edge of network. The vital challenges faced by the legacy DCN architecture trigger the need for new DCN architectures, to accommodate the growing demands of the ‘cloud computing’ paradigm. We have implemented and simulated the state of the art DCN models in this paper, namely: (a) legacy DCN architecture, (b) switch‐based, and (c) hybrid models, and compared their effectiveness by monitoring the network: (a) throughput and (b) average packet delay. The presented analysis may be perceived as a background benchmarking study for the further research on the simulation and implementation of the DCN‐customized topologies and customized addressing protocols in the large‐scale data centers. We have performed extensive simulations under various network traffic patterns to ascertain the strengths and inadequacies of the different DCN architectures. Moreover, we provide a firm foundation for further research and enhancement in DCN architectures. Copyright © 2012 John Wiley & Sons, Ltd.

SUMMARY Earth observation applications are now facing the challenges of managing and processing massive data sets from multiple sources from large‐scale distributed data centers (DCs). To solve this research problem, this paper presents an infrastructure of multiple data centers (MDC) for managing and processing massive remote sensing images. The proposed system is built on both groups of distributed DCs/clusters, which are equipped with DC or cluster resource manager. Access security and information service are introduced to support this architecture of MDC. We collaboratively organized the algorithm, and data belonged to the MDC in the manner of workflow. In practice, we succeeded in working out the concrete problems regarding procedures in processing applications collaboratively and transfer the massive remote sensing dataset fast and with stable cross‐MDC. On the basis of the previously mentioned research work, we will investigate the platform integration of MDC. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Wireless sensor networks (WSNs) are emerging as useful technology for information extraction from the surrounding environment by using numerous small‐sized sensor nodes that are mostly deployed in sensitive, unattended, and (sometimes) hostile territories. Traditional cryptographic approaches are widely used to provide security in WSN. However, because of unattended and insecure deployment, a sensor node may be physically captured by an adversary who may acquire the underlying secret keys, or a subset thereof, to access the critical data and/or other nodes present in the network. Moreover, a node may not properly operate because of insufficient resources or problems in the network link. In recent years, the basic ideas of trust and reputation have been applied to WSNs to monitor the changing behaviors of nodes in a network. Several trust and reputation monitoring (TRM) systems have been proposed, to integrate the concepts of trust in networks as an additional security measure, and various surveys are conducted on the aforementioned system. However, the existing surveys lack a comprehensive discussion on trust application specific to the WSNs. This survey attempts to provide a thorough understanding of trust and reputation as well as their applications in the context of WSNs. The survey discusses the components required to build a TRM and the trust computation phases explained with a study of various security attacks. The study investigates the recent advances in TRMs and includes a concise comparison of various TRMs. Finally, a discussion on open issues and challenges in the implementation of trust‐based systems is also presented. Copyright © 2012 John Wiley & Sons, Ltd.

Data Centers (DCs) are experiencing a tremendous growth in the number of hosted servers. Aggregate bandwidth requirement is a major bottleneck to data center performance. New Data Center Network (DCN) architectures are proposed to handle different challenges faced by current DCN architecture. In this paper we have implemented and simulated two promising DCN architectural models, namely switch-based and hybrid models, and compared their effectiveness by monitoring the network throughputs and average packet latencies. The presented analysis may be a background for the further studies on the simulation and implementation of the DCN customized topologies, and customized addressing protocols in the large-scale data centers.

In a Delay Tolerant Network (DTN), the nodes have intermittent connectivity and complete path(s) between the source and destination may not exist. The communication takes place opportunistically when any two nodes enter the effective range. One of the major challenges in DTNs is message forwarding when a sender must select a best neighbor that has the highest probability of forwarding the message to the actual destination. However, finding an appropriate route remains an NP-hard problem. This paper presents a concept of Checkpoint (CP) based message forwarding in DTNs. The CPs are autonomous high-end wireless devices with large buffer storage and are responsible for temporarily storing the messages to be forwarded. The CPs are deployed at various places within the city parameter that are covered by bus routes and where human meeting frequencies are higher. For the simulative analysis a synthetic human mobility model in ONE simulator is constructed for the city of Fargo, ND, USA. The model is tested over various DTN routing protocols and the results indicate that using CP overlay over the existing DTN architecture significantly decreases message delivery time as well as buffer usage.

Analysis of neural signals like electroencephalogram (EEG) is one of the key technologies in detecting and diagnosing various brain disorders. As neural signals are non-stationary and non-linear in nature, it is almost impossible to understand their true physical dynamics until the recent advent of the Ensemble Empirical Mode Decomposition (EEMD) algorithm. The neural signal processing with EEMD is highly compute-intensive due to the high complexity of the EEMD algorithm. It is also data intensive because 1) EEG signals contain massive data sets 2) EEMD has to introduce a large number of trials in processing to ensure precision. The Map Reduce programming mode is a promising parallel computing paradigm for data intensive computing. To increase the efficiency and performance of the neural signal analysis, this research develops parallel EEMD neural signal processing with Map Reduce. In this paper, we implement the parallel EEMD with Hadoop in a modern cyber infrastructure. Test results and performance evaluation show that parallel EEMD can significantly improve the performance of neural signal processing.

The exiting covariance matching method is not suited for real-time applications due to its demand for exhaustive search. Aiming at this problem, we developed a novel approach based on fuzzy genetic algorithm (GA) to boost the computing efficiency of covariance matching. The approach employs GA in searching for optimal solution in a large image region. To avoid premature convergence or local optimum which often occur in traditional GAs, we use a fuzzy inference system to adaptively estimate the crossover and mutation probabilities to gain convergence in a much higher speed than using a conventional GA. Experimental results show that the proposed approach can significantly improve the processing speed of covariance matching, while keeping the matching results almost unchanged. The runtime performance of the proposed approach is faster than its counterparts using exhaustive search with eight times and more.

Grid computing uses a job submission model that requires the users to perform a set of interactive operations for executing an application on the Grid. Grid users are therefore burdened with the tasks of understanding the basic concept of Grid computing and the details of job management. Cloud computing, on the other hand, applies a utility model that allows the user to access the underlying platform via Web services. This work brings the Cloud concept to the Grid with a result of replacing the job submission model with a service infrastructure. In this case, Grid applications are presented as Web services that can be executed on the Grid automatically without user interactions. All issues related to job management are performed by the system. The service infrastructure significantly simplifies the users' task in accessing the Grid.

A Grid simulation infrastructure can facilitate a simulation comprising models of different grains and/or models of various types in nature to investigate large and complicated problems. On top of the infrastructure, a simulation of evacuating thousands of pedestrians in a large urban area has been constructed. A number of agent-based and computational models residing at two administrative domains operate together, which successfully presents the dynamics of the complex scenario at scales of both individual and crowd levels. Experimental results indicate that the proposed approach can effectively cope with the size and complexity of a scenario involving a large crowd.

In peacekeeping, domestic or combat operations, unanticipated crowd confrontations can occur. As a highly dynamic social group, human crowd in confrontation is a fascinating phenomenon. This study proposes a novel method based on the concept of vector field to formulate the way in which external stimuli may affect the behaviors of individuals in a crowd. Our approach represents each individual as an autonomous agent whose actions are guided by the vector field model. Furthermore, the concept of information entropy has been adopted to describe the connection between individuals' behaviors and the potential of disorder of the whole crowd. A quantitative analysis on intangible dynamics of a crowd in confrontation is then enabled, which is significant in designing crowd control tactics.

It has been widely known that various benefits can be achieved by reducing energy consumption for high end computing. This paper aims to develop power aware scheduling heuristics for parallel tasks in a cluster with the DVFS technique. In this paper, formal models are presented for precedence-constrained parallel tasks, DVFS enabled clusters, and energy consumption. This paper studies the slack time for non-critical jobs, extends their execution time and reduces the energy consumption without increasing the task's execution time as a whole. This paper develops a power aware task clustering algorithm for parallel task scheduling Simulation results justify the design and implementation of proposed energy aware scheduling heuristics in the paper.

In wireless sensor networks, how to efficiently use the energy of the nodes while assigning global unique ID to each node is a challenging problem. By analyzing the communication cost of the clustering and topological features of a sensor network, we present a distributed scheme of Energy Efficient Clustering with Self-organized ID Assignment (EECSIA). In the context of EECSIA, a network first selects the nodes in the high-density areas as cluster heads, and then assigns an unique ID to each node based on local information. In addition, EECSIA periodically updates cluster heads according to the nodes' residual energy and density. The method is independent of time synchronization, and it does not rely on the nodes' geographic locations either. Simulation results show that the scheme performs well in terms of cluster scale, and number of nodes alive over rounds.

Ensemble empirical-mode decomposition (EEMD) is a novel adaptive time-frequency analysis method, which is particularly suitable for extracting useful information from noisy nonlinear or nonstationary data. Unfortunately, since the EEMD is highly compute-intensive, the method does not apply in real-time applications on top of commercial-off-the-shelf computers. Aiming at this problem, a parallelized EEMD method has been developed using general-purpose computing on the graphics processing unit (GPGPU), namely, G-EEMD. A spectral entropy facilitated by G-EEMD was, therefore, proposed to analyze the EEG data for estimating the depth of anesthesia (DoA) in a real-time manner. In terms of EEG data analysis, G-EEMD has dramatically improved the run-time performance by more than 140 times compared to the original serial EEMD implementation. G-EEMD also performs far better than another parallelized implementation of EEMD bases on conventional CPU-based distributed computing technology despite the latter utilizes 16 high-end computing nodes for the same computing task. Furthermore, the results obtained from a pharmacokinetics/pharmacodynamic (PK/PD) model analysis indicate that the EEMD method is slightly more effective than its precedent alternative method (EMD) in estimating DoA, the coefficient of determination R(2) by EEMD is significantly higher than that by EMD (p < 0.05, paired t-test) and the prediction probability P(k) by EEMD is also slighter higher than that by EMD (p < 0.2, paired t-test).

Distributed virtual machines can help to build scalable, manageable, and efficient grid infrastructures. The work proposed in this paper focuses on employing virtual machines for grid computing. In order to efficiently run grid applications, virtual machine resource information should be provided. This paper first discusses the system architecture of virtual machine pools and the process of information retrieval from virtual machines. Based on the characterization of the system model, this paper presents the work on how to retrieve resource information from Xen/VMware virtual machines via VMware Common Information Model Software Development Kit and lightweight Java agents. The resource information is integrated into a grid information service. The work is implemented in a test bed with Xen/VMware virtual machines and the Globus Toolkit. With a performance evaluation and discussion on a real test bed, it is declared that the design and implementation of information services for virtual-machine-based grid systems are feasible, efficient, and scalable.

The last decade has witnessed an explosion of interest in technology of large simulation with the rapid growth of both the complexity and the scale of problem domains. A variety of techniques have been developed for fostering large simulations over the Internet. Along this direction, existing work normally only suit for coarse-grained models. In this paper, we present a hybrid grid infrastructure which applies to both coarse-grained and fine-grained models. A gateway approach has been proposed to present simulation models of an individual administrative domain for fine-grained problems, and it also bridges simulation models operating in multiple administrative domains to form large simulations for studying problems of different grains. A prototype infrastructure has been realized with the support of federated simulation technology.

Abstract Human crowd is a fascinating social phenomenon in nature. This paper presents our work on designing behavior model for virtual humans in a crowd simulation under normal‐life and emergency situations. Our model adopts an agent‐based approach and employs a layered framework to reflect the natural pattern of human‐like decision making process, which generally involves a person's awareness of the situation and consequent changes on the internal attributes. The social group and crowd‐related behaviors are modeled according to the findings and theories observed from social psychology (e.g., social attachment theory). By integrating our model into an agent execution process, each individual agent can response differently to the perceived environment and make realistic behavioral decisions based on various physiological, emotional, and social group attributes. To demonstrate the effectiveness of our model, a case study has been conducted, which shows that realistic human behaviors can be generated at both individual and group level. Copyright © 2008 John Wiley & Sons, Ltd.

Efficiently simulating discrete-event models in a parallel and distributed manner is a challenging endeavour. On one hand, various factors, such as hardware infrastructure or model characteristics, have to be considered. On the other hand, there is a wide variety of algorithms which address subproblems of parallel and distributed simulation and whose performance depends on the application at hand. We illustrate the resulting difficulties with respect to the development of parallel and distributed simulation systems and argue that the simulation of distributed simulation systems is a feasible approach to alleviate them. To underpin this, we introduce SIMSIM, a sequential simulator for parallel and distributed simulation systems. SIMSIM's pertinency is illustrated by the development of a load balancing algorithm for PDEVS. The algorithm's performance is analysed using SIMSIM and the predicted performance is compared to the performance of its implementation in the simulation system JAMES II

In previous work we presented the DTRD algorithm, an optimistic synchronisation algorithm for parallel discrete event simulation of multi-agent systems, and showed that it outperforms time warp and time windows on range of test cases. DTRD uses a decision theoretic model of rollback to derive an optimal time to delay read event so as to maximise the rate of LVT progression. The algorithm assumes that the inter-arrival times (both virtual and real) of events are normally distributed. In this paper we present a more detailed evaluation of the DTRD algorithm, and specifically how the performance of the algorithm is affected when the inter-arrival times do not follow the assumed distributions. Our analysis suggests that the performance of the algorithm is relatively insensitive to events whose inter-arrival times are not normally distributed. However as the variance of the input events increases its performance degrades to that of Time Warp. Our approach to evaluation is general, and we outline how the analysis may be applied to other decision theoretic algorithms

In this paper we present a throttling mechanism for optimistic simulations of multi-agent systems, which delays read accesses to the shared simulation state that are likely to be rolled back. We develop a decision-theoretic model of rollback and show how this can be used to derive the optimal time to delay a read event so as to minimize the expected overall execution time of the simulation. We briefly describe an implementation of this approach in ASSK, a distributed simulation kernel developed to investigate synchronization mechanisms for MAS simulation, and report the results of preliminary experiments to evaluate the effectiveness of our approach.

The paper presents a load management mechanism for distributed simulations of multi-agent systems. The mechanism minimizes the cost of accessing the shared state in the distributed simulation by dynamically redistributing shared state variables according to the access pattern of the simulation model. To evaluate the effectiveness and performance of the mechanism, a series of benchmark experiments were performed using the PDES-MAS framework for distributed simulation of multi-agent systems. Although preliminary, the results indicate that the proposed mechanism significantly reduces the overall access cost of the system.

Distributed simulation cloning technology is designed to analyze alternative scenarios of a distributed simulation concurrently within the same simulation execution session. One important goal of the technology is to optimize execution by avoiding repeated computation amongst independent scenarios. Our research is concerned with the cloning of High Level Architecture (HLA) based distributed simulations. A decoupled federate architecture is designed to support correct federate cloning at runtime. A federate may spawn clones to explore different scenarios at a decision point. To address the complexity of the overall cloning-enabled distributed simulation due to increasing scenario spawning, we have devised an efficient and precise scheme to identify and partition scenarios. It is desirable to use an incremental cloning mechanism to replicate only those federates whose states will be affected while the rest remain intact and are shared amongst the original and new scenarios. Our incremental cloning mechanism ensures accurate sharing and initiates cloning only when strictly necessary.