Time Series Classification (TSC) has achieved significant results in improving the efficiency and safety of life and production. This paper aims to enhance the accuracy of TSC by encoding one-dimensional time series into two-dimensional Recurrence Plots (RP) to obtain richer texture information. However, existing RPs face issues such as multi-scale issues, tendency confusion, and information redundancy, which hinder their application in TSC tasks. To address these problems, this paper proposes a Large Receptive Field Network (LRFN). LRFN advocates for extracting global information by enlarging the receptive field to overcome the multi-scale issues of RPs. It further addresses other problems by applying Multi-scale Signed RP (MSRP). The method encodes the time series as MSRP and then constructs multiple Global-Local Convolution (GLC) modules. These modules obtain medium, local, and global receptive fields through the Dilated Residual Module, SelfCalibrated Convolution, and Atrous Spatial Pyramid Pooling, respectively. By integrating three different qualities and sizes of receptive fields, comprehensive global information is acquired to address the multi-scale problem. Experimental results show that LRFN can further improve the accuracy of TSC tasks. LRFN demonstrated the best comprehensive performance in comparison to 13 baseline methods across 43 different domains in the UCR dataset. It performed optimally on 20 datasets, with an average accuracy of 91.3%. Additionally, receptive field visualization experiments further validate the effectiveness of LRFN.

Multivariate time series classification (MTSC) is a significant research topic in the realm of data mining, with broad applications in different industries, including healthcare, finance, meteorology, and traffic. While existing studies have designed many classifiers based on LSTMs, CNNs, and Transformer, the sophisticated architectures raise concerns regarding efficiency in computation. Additionally, most methods concentrate on a single dimension, typically temporal patterns, without fully considering multi-dimensional information such as the independence and interactions across variables that are essential in multivariate settings. To address these challenges, this article introduces FreConvNet, a lightweight convolutional network integrated with frequency adaptive learning. Inheriting the modular design paradigm of Transformer to achieve multi-view modeling of multivariate time series. FreConvNet consists of two key components: the frequency adaptive block (FAB) and the convolutional feed-forward network (ConvFFN). The FAB leverages the Fourier Transform in conjunction with adaptive filters to capture both long-term and short-term dependencies in the temporal dimension. Following that, ConvFFN captures cross-variable and cross-feature interactions by controlling inter-channel information flow through grouped pointwise convolutions, while introducing non-linearity to enhance representational capacity. Extensive experiments conducted on the well-known UEA archive validate that FreConvNet outperforms existing convolution-based, Transformer-based, and hybrid methods in classification performance and offers a computationally efficient solution.

Context-aware emotion recognition (CAER) leverages comprehensive scene information, including facial expressions, body postures, and contextual background. However, current studies predominantly rely on facial expressions, body postures, and global contextual features; the interaction between the agents (target individuals) and other objects in the scene is usually absent or incomplete. In this article, a three-dimensional view relationship-based CAER (TDRCer) method is proposed, which comprises two branches: the personal emotional branch (PEB) and the contextual emotional branch (CEB). First, PEB is designed for the extraction of facial expression features and body posture features from the agent. A vision transformer (ViT), pretrained by contrastive learning with a novel loss function combining Euclidean distance and cosine similarity, is applied to enhance the robustness of facial expression features. Meanwhile, the human body contour images extracted by semantic segmentation are fed into another ViT to extract body posture features. Second, CEB is constructed for the extraction of global contextual features and interactive relationships among objects in the scene. The images masked by the agents' bodies are fed into a ViT to extract global contextual features. By leveraging both the gaze angle and depth map, a three-dimensional view graph (3DVG) is constructed to represent the interactive relationships between agents and objects in the scene. Then, a graph convolutional network is employed to extract interactive relationship features from the 3DVG. Finally, the multiplicative fusion strategy is applied to fuse the features of two branches, and the fused features are utilized to classify the emotions. TDRCer achieves an accuracy of 89.90% on the CAER-S dataset and a mean average precision (mAP) of 36.02% on the EMOTIons in context (EMOTIC) dataset. The code can be accessed at https://github.com/mengTender/TDRCer.

The ability to detect aerial objects with limited annotation is pivotal to the development of real-world aerial intelligence systems. In this work, we focus on a demanding but practical sparsely annotated object detection (SAOD) in aerial images, which encompasses a wider variety of aerial scenes with the same number of annotated objects. Although most existing SAOD methods rely on fixed thresholding to filter pseudo-labels for enhancing detector performance, adapting to aerial objects proves challenging due to the imbalanced probabilities/confidences associated with predicted aerial objects. To address this problem, we propose a novel Progressive Exploration-Conformal Learning (PECL) framework to address the SAOD task, which can adaptively perform the selection of high-quality pseudo-labels in aerial images. Specifically, the pseudo-label exploration can be formulated as a decision-making paradigm by adopting a conformal pseudo-label explorer and a multi-clue selection evaluator. The conformal pseudo-label explorer learns an adaptive policy by maximizing the cumulative reward, which can decide how to select these high-quality candidates by leveraging their essential characteristics and inter-instance contextual information. The multi-clue selection evaluator is designed to evaluate the explorer-guided pseudo-label selections by providing an instructive feedback for policy optimization. Finally, the explored pseudo-labels can be adopted to guide the optimization of aerial object detector in a closed-looping progressive fashion. Comprehensive evaluations on two public datasets demonstrate the superiority of our PECL when compared with other state-of-the-art methods in the sparsely annotated aerial object detection task.

Major depressive disorder (MDD) is the most common psychological disorder that affects mental and physical health. To narrow the gap in real world mental healthcare and improve the effectiveness of MDD treatment, an increasing number of artificial intelligence (AI) methods have been proposed to explore electroencephalography (EEG) features, including traditional signal features and measures of brain functional connectivity network (BFCN), for the recognition of depression-related patterns. However, these methods fail to capture long-term dependencies and limit the modeling ability of information transmission dependencies in MDD brain regions. To address these issues, we propose a novel brain functional residual temporal convolution network (BFRTCN) method for MDD recognition. On one hand, this model directly focuses on the connectivity weights of BFCNs to model the information transmission between brain regions, allowing for better differentiation of the differences in information transmission patterns between MDD and normal control (NC). On the other hand, we introduce a residual temporal convolution network (ResiTCN) that utilizes temporal convolution layers to capture short-term changes in brain regions and establish residual connections to help maintain long-term dependencies for improving ability to capture disease variations. Experimental results on benchmark datasets validate the superior performance and time complexity of BFRTCN. Analysis shows that the Beta band MDD transmission mode is relatively stable. There are defects in the brain functional connections between the frontal and right temporal (RT) regions on Alpha and Gamma bands, which can serve as potential biomarkers for MDD recognition.

Confocal Laser Endomicroscopy (CLE) has shown great advantages in the diagnosis of gastrointestinal diseases. To solve the problems of time-consuming manual classification of CLE video information frames and insufficient labeled data, we proposed a Spatial-Temporal Fusion Pseudo-Labeling method (STFPL) based on semi-supervised learning. Firstly, the classification networks trained with limited labeled data are used to generate the predictions of unlabeled images and selected videos. Secondly, the predictions of images and videos are fused to obtain pseudo-labels. Thirdly, the unlabeled loss formed by predictions and pseudo-labels and the loss of labeled data are combined to update the classification networks. Finally, the experimental results demonstrated that STFPL outperforms other semi-supervised algorithms on the CLE video dataset. In addition, STFPL can achieve the effectiveness of supervised classification on a dataset for evaluating the quality of intestinal cleaning, Nerthus dataset.

With the development of human-computer interaction and mobile sensors, emotion recognition based on physiological signals has aroused a lively discussion among scholars. The main difficulty faced is the small amount of data which leads to poor training results. In this paper, we proposed a multimodal emotion recognition using CNN-SVM and data augmentation (CSDAMER). Electrocardiography (ECG), galvanic skin response (GSR) and respiration (RSP) are utilized as input data, which are less requiring on the collection environment and can be collected by mobile sensors. To improve the training effect of model, data augmentation is performed by transformations, such as inversion, recombination and noise injection. Moreover, the convolutional layer of the convolutional neural network (CNN) is leveraged to extract the high-level features of the physiological signals, and then the features are input into the support vector machine (SVM) classifier to obtain the recognition results. The experimental results show that CSDAMER achieves 80.7% and 79.92% accuracy in arousal and valance, respectively. Compared with CNN alone, the accuracy of arousal and valance is increased by 12.87% and 9.95%. Meanwhile, the addition of the data augmentation improves the accuracy in arousal and valance by 21.94% and 25.73%.

The classic active contour model raises a great promising solution to polygon-based object extraction with the progress of deep learning recently. Inspired by the physical vibration theory, we propose a contour vibration network (CVNet) for automatic building boundary delineation. Different from the previous contour models, the CVNet originally roots in the force and motion principle of contour string. Through the infinitesimal analysis and Newton's second law, we derive the spatial-temporal contour vibration model of object shapes, which is mathematically reduced to second-order differential equation. To concretize the dynamic model, we transform the vibration model into the space of image features, and reparameterize the equation coefficients as the learnable state from feature domain. The contour changes are finally evolved in a progressive mode through the computation of contour vibration equation. Both the polygon contour evolution and the model optimization are modulated to form a close-looping end-to-end network. Comprehensive experiments on three datasets demonstrate the effectiveness and superiority of our CVNet over other baselines and state-of-the-art methods for the polygon-based building extraction. The code is available at https://github.com/xzq-njust/CVNet.

Hashing techniques have attracted considerable attention owing to their advantages of efficient computation and economical storage. However, it is still a challenging problem to generate more compact binary codes for promising performance. In this paper, we propose a novel contrastive vision transformer hashing method, which seamlessly integrates contrastive learning and vision transformers (ViTs) with hash technology into a well-designed model to learn informative features and compact binary codes simultaneously. First, we modify the basic contrastive learning framework by designing several hash layers to meet the specific requirement of hash learning. In our hash network, ViTs are applied as backbones for feature learning, which is rarely performed in existing hash learning methods. Then, we design a multiobjective loss function, in which contrastive loss explores discriminative features by maximizing agreement between different augmented views from the same image, similarity preservation loss performs pairwise semantic preservation to enhance the representative capabilities of hash codes, and quantization loss controls the quantitative error. Hence, we can facilitate end-to-end joint training to improve the retrieval performance. The encouraging experimental results on three widely used benchmark databases demonstrate the superiority of our algorithm compared with several state-of-the-art hashing algorithms.

Due to the progressive complexity of traffic networks, the traffic pressure increases sharply and traffic accidents occur frequently, which is largely posed by imprecise traffic information service provided in existing Intelligent traffic service systems (ITSSs). To relieve traffic information trek encountered in complex traffic networks, the personalized traffic information recommendation based on click through rate (CTR) prediction has attracted extensive attention. However, the data sparsity and cold start problems in traditional recommendations hinder their real-time applications in ITSSs. In this paper, the multisource data with different types of context information are utilized to construct a personalized fine-grained recommendation method for intelligent traffic services (PF-ITS), which includes three components: a road condition optimization strategy (RCOS) to capture users' behavior preferences and traffic patterns, an encoder–decoder long short-term memory (LSTM) model to address the data sparsity and cold start problems with rich context information, and an improved DeepFM model based on attention mechanism (DeepAFM) to exert personalized fine-grained traffic information recommendation with embeddings of multisource data. More specifically, the RCOS is proposed to perform coarse-grained route recommendation based on path planning theory, in which the traffic data and drivers' preferences are fully utilized for comprehensive modeling. The encoder–decoder LSTM model is employed for representation learning, in which the traffic sequences and driving behaviors are mapped into dense distributed representations with rich semantic information. The DeepAFM is utilized to achieve effective driving safety guarantee according to an individual's requirements, in which the weighted low-order feature combinations and high-order feature interactions are incorporated for personalized fine-grained recommendation. We also conduct extensive experiments on public data sets and in real-world scenarios. The experimental results demonstrate that the PF-ITS method based on RCOS and DeepAFM outperforms the state-of-the-art baseline models in effectiveness and efficiency.

Emotion recognition has become a research focus in the brain–computer interface and cognitive neuroscience. Electroencephalogram (EEG) is employed for its advantages as accurate, objective, and noninvasive nature. However, many existing research only focus on extracting the time and frequency domain features of the EEG signals while failing to utilize the dynamic temporal changes and the positional relationships between different electrode channels. To fill this gap, we develop the dynamic differential entropy and brain connectivity features based EEG emotion recognition using linear graph convolutional network named DDELGCN. First, the dynamic differential entropy feature which represents the frequency domain feature as well as time domain feature is extracted based on the traditional differential entropy feature. Second, brain connectivity matrices are constructed by calculating the Pearson correlation coefficient, phase-locked value and transfer entropy, and then are used to denote the connectivity features of all electrode combinations. Finally, a linear graph convolutional network is customized and applied to aggregate the features from total electrode combinations and then classifies the emotional states, which consists of five layers, namely, an input layer, two linear graph convolutional layers, a fully connected layer, and a softmax layer. Extensive experiments show that the accuracies in the valence and arousal dimensions reach 90.88% and 91.13%, and the precision reaches 96.66% and 97.02% on the DEAP dataset, respectively. On the SEED dataset, the accuracy and precision reach 91.56% and 97.38%, respectively.

Depression is one of the most common mental disorders affecting people, but its recognition rate is low due to subjectivity and other factors. Mild depression, in particular, has milder and less recognizable symptoms. In this study, we used electroencephalography (EEG) and machine learning methods to identify mild depression. Our approach was to construct a new weighted combinatorial classifier model to distinguish patients with mild depression from normal controls. In this experiment, 10 mildly depressed patients and 10 normal controls watched different emotional facial pictures, recorded their EEG signals and preprocessed them, and then extracted linear and nonlinear features to construct feature vectors. K-nearest neighbor(KNN), support vector machine(SVM), logistic regression(LR), random forest(RF) and back propagation neural network(BPNN) were selected as five individual classifiers, and the differential evolution algorithm(DE) was used to optimize the weights to improve the overall performance of the recognition model. The experimental results showed that the classification accuracy of the proposed method was better than that of the individual classifiers, and the highest 99.09% was achieved when the number of iterations was 50, indicating that the fusion model had a higher recognition accuracy of mild depression than the single modes. At the same time, compared with other combination strategies, this method was also better than other strategies. This research may provide a means to identify mild depression.

Emotion recognition is a key technique of intelligent human-computer interaction (HCI) systems. In the current research on emotion recognition, there are several limitations such as inconsistent brain network scale and high time complexity of modal decomposition. To overcome these shortcomings, we propose a novel emotion recognition method incorporating MST-based brain network and FVMD-GAMPE. Firstly, electroencephalography (EEG) data is decomposed into four frequency bands $(\theta,\alpha,\beta,\gamma)$ by wavelet packet transform (WPT), and mutual information (MI) between channel pairs is calculated to construct the connectivity matrix. Secondly, the brain network based on the minimum spanning tree (MST) is constructed and seven features are extracted. Thirdly, fast variational modal decomposition (FVMD) and WPT are applied to process EEG data to obtain the variational mode functions (VMF) of different frequency bands. Then, the parameters of the multi-scale permutation entropy (MPE) are optimized with the genetic algorithm (GA), and then MPE features are extracted. Finally, the features extracted from MST-based brain network are fused with MPE features, and then fused features are fed to the random forest (RF) classifier to recognize emotional states. Experimental results on DEAP show that the best classification accuracy for valance and arousal are 89.58% and 88.54%, respectively. The result analysis demonstrates MST-based brain network in the negative emotional states has a more divergent topology. This means that brain regions are more active and have a faster exchange of information flow when the brain processes negative emotions. On the other hand, brain network of women is similar to a star-shaped structure, which indicates women’s brain activation is higher than man. This study provides theoretical support for research on negative bias.

Emotion recognition has become a research focus in the field of human-computer interaction (HCI). As an excellent physiological signal, electroencephalographic (EEG) is considered to be a favorable tool for emotion recognition. Most traditional methods focus on extracting features in time domain and frequency domain but the adjacent information and asymmetric information from adjacent and asymmetric channels are often ignored. Although several graph neural network (GNN) models are utilized to learn EEG features, most of the emotion recognition studies of GNN ignore the information existing between adjacent electrodes. In this paper, we propose an EEG emotion recognition method based on hierarchy graph convolution network (HGCN) named ERHGCN. Firstly, six different features including power spectral density (PSD), differential entropy (DE), differential asymmetry (DASM), rational asymmetry (RASM), asymmetry (ASM) and differential caudality (DCAU) from five frequency bands are extracted. Secondly, to improve graph convolution network (GCN) shortcoming of only extracting time and frequency features, HGCN is applied to extract deeper spatial feature by treating the longitudinal and transverse adjacent electrode pairs in different ways. Finally, six extracted features are fed into the HGCN model, then all features are integrated by two full connection layers. We conducted extensive experiments on DEAP dataset and experimental results show that the proposed method can obtain 90.56% and 88.79% recognition accuracies for valence and arousal classification tasks.

Emotion recognition has become an important component of human–computer interaction systems. Research on emotion recognition based on electroencephalogram (EEG) signals are mostly conducted by the analysis of all channels' EEG signals. Although some progresses are achieved, there are still several challenges such as high dimensions, correlation between different features and feature redundancy in the realistic experimental process. These challenges have hindered the applications of emotion recognition to portable human–computer interaction systems (or devices). This paper explores how to find out the most effective EEG features and channels for emotion recognition so as to only collect data as less as possible. First, discriminative features of EEG signals from different dimensionalities are extracted for emotion classification, including the first difference, multiscale permutation entropy, Higuchi fractal dimension, and discrete wavelet transform. Second, relief algorithm and floating generalized sequential backward selection algorithm are integrated as a novel channel selection method. Then, support vector machine is employed to classify the emotions for verifying the performance of the channel selection method and extracted features. At last, experimental results demonstrate that the optimal channel set, which are mostly located at the frontal, has extremely high similarity on the self-collected data set and the public data set and the average classification accuracy is achieved up to 91.31% with the selected 10-channel EEG signals. The findings are valuable for the practical EEG-based emotion recognition systems.

In recent years, automatic emotion recognition renders human–computer interaction systems intelligent and friendly. Emotion recognition based on electroencephalogram (EEG) has received widespread attention and many research results have emerged, but how to establish an integrated temporal and spatial feature fusion and classification method with improved convolutional neural networks (CNNs) and how to utilize the spatial information of different electrode channels to improve the accuracy of emotion recognition in the deep learning are two important challenges. This paper proposes an emotion recognition method based on three-dimensional (3D) feature maps and CNNs. First, EEG data are calibrated with 3 s baseline data and divided into segments with 6 s time window, and then the wavelet energy ratio, wavelet entropy of five rhythms, and approximate entropy are extracted from each segment. Second, the extracted features are arranged according to EEG channel mapping positions, and then each segment is converted into a 3D feature map, which is used to simulate the relative position of electrode channels on the scalp and provides spatial information for emotion recognition. Finally, a CNN framework is designed to learn local connections among electrode channels from 3D feature maps and to improve the accuracy of emotion recognition. The experiments on data set for emotion analysis using physiological signals data set were conducted and the average classification accuracy of 93.61% and 94.04% for valence and arousal was attained in subject-dependent experiments while 83.83% and 84.53% in subject-independent experiments. The experimental results demonstrate that the proposed method has better classification accuracy than the state-of-the-art methods.

Although more and more researchers pay attention to the emotion classification, traditional emotion classification methods can not embrace changes in the global and local areas of the human brain after being stimulated. We propose an emotion classification method based on SVM combining brain functional connectivity. Firstly, the nonlinear phase-locked value (PLV) is used to calculate the multiband brain functional connectivity network, which is then converted into a binary brain network, and seven features of binary brain network are calculated. Secondly, support vector machines (SVM) are used to classify positive and negative emotions at the valence dimension and arousal dimension in the multiband. Experimental results on DEAP show that the best emotion classification accuracy of the proposed method is 86.67% in the arousal dimension, and 84.44% in the valence dimension. The results demonstrate that the classification accuracy of the arousal dimension is better than the valence dimension and the Beta2 frequency band is more suitable for emotion classification. Finally, several findings on brain functional connectivity network is discussed. The left and right areas of brain functional connectivity network are unbalanced in the low frequency band, and the feature values of clustering coefficient, average shortest path length, global efficiency, local efficiency, node degree are positively correlated with the arousal degree in the arousal dimension. Humans emotions are suppressed in the low frequency band, and the brain functional connectivity network after emotional stimulation is strengthened in the high frequency band. Our findings on emotion classification are valuable and consistent with the study of neural mechanisms.

Electronic health records (EHRs) data plays an important role in the development of healthcare undertaking. There are many challenges in mining EHRs, such as temporality, irregularity, sparsity, bias, etc. Thus effective feature extraction and representation are key steps before any further applications. In this paper, we propose a multi-task deep representation learning method (MTDRL) with the objective of extracting the valuable clinical information from raw data and learning an effective and interpretable patient representation. Firstly, MTDRL utilizes Bidirectional Gated Recurrent Unit (BiGRU) as an encoder to learn the hidden state vectors which are used as inputs for the following networks. This encoding part can be seen as a shared network for all tasks. Secondly, patient's in-hospital mortality prediction and sequence reconstruction are simultaneously conducted based on the encoding network. Specifically, an attention mechanism and a fully-connected layer are incorporated in prediction task and BiGRU is implemented in the other task to reconstruct the visit sequences. Finally, we apply MTDRL to real EHRs data and the experimental results demonstrate that MTDRL is capable of learning more effective patient representation and has a significant improvement in the performance of patient's in-hospital mortality prediction. Meanwhile, the prediction results can be effectively interpreted with the attention mechanism and provide a clinically meaningful references.

Emotion plays an indispensable role in the process of human cognition and decision-making, but it is often neglected in human-computer interaction (HCI). Although many researchers have applied some decomposition algorithms to extract features from EEG, there are several shortcomings including the modal aliasing, a high computational cost and so on. In this paper, we introduce variational mode decomposition (VMD) and wavelet packet decomposition (WPD) algorithms and propose a multidimensional feature extraction method based on VMD and WPD for emotion recognition. Firstly, we apply VMD to decompose the EEG signal into a specific number of variational mode functions (VMF). Secondly, WPD is executed to generate an emotional frequency band. Then, we continue to extract the wavelet packet entropy (WPE), modified multi-scale sample entropy (MMSE), fractal dimension (FD) and first difference (1ST) of each emotional VMF to construct a new feature form. Finally, the random forest (RF) is utilized to classify the extracted emotional states. The experiment results demonstrate that the proposed method is more competitive and universal for emotion recognition, which provides a novel idea for the application of multidimensional feature extraction.

Summary There is a large amount of historical data of the patient's hospitalization named the electronic health records (EHRs), but the data are not fully utilized for great challenges as poor quality, high dimension, and so on. Previous studies have primarily used machine learning methods that rely heavily on manual extraction of features. Recently, many deep learning approaches are applied to predictive model of EHRs. Recurrent neural networks (RNN) are often used to model EHR data, but RNN performance degrades in the face of large sequence lengths. To solve these challenges, we develop a long short‐term memory with attention mechanism for mortality prediction. The dataset used in this article is the Medical Information Mart for Intensive Care III, which contains comprehensive clinical data for the patients. The experimental results demonstrate that the predicted results can be effectively interpreted using the attention mechanism. Compared with other baseline models, our model improves the accuracy of prediction, and helps doctors reduce the average diagnostic time.

Electronic health records are digital records of patients' medical history, diagnosis, medication, treatment plans. EHRs not only contain the patients' medical and treatment history, but also systematically collect patients' clinical data. Therefore, it is very valuable to improve the patient's health care management by mining the information in the EHRs. However, due to the irregularities and sparsity of EHRs, EHRs mining is very challenging. In this paper, the laboratory data, physiological indicators and diagnosis time during the patients' hospitalization period are extracted from the MIMIC-III database. Then, the extracted features are used to generate the patients' representation vector. Finally, we propose a prediction model based on BiLSTM and attention mechanism, which is called Bi-Attention. The BiLSTM is adopted to learn the forward and backward timing information in the patient's representation vectors and to predict the patient's disease by utilizing the specific clinical information in the timed medical record with the attention mechanism. The experimental results show that compared with other methods, the proposed model can effectively improve the prediction performance.

Summary Network virtualization is a promising technology, which is used to solve the existing shortcomings of the Internet architecture and in which virtual network (VN) embedding plays a vital role in resource allocation. However, current virtual network embedding algorithms mainly deal with static VN embedding; the allocations of network resources are constant and excessive in the VN requests' lifetime. In reality, the user's resource requirements usually change with time; hence, static VN embedding algorithms can lead to low utilization of substrate resources and reduction of the operators' revenues. To solve the aforementioned problem, we focus on the virtual network embedding considering dynamic demands in this paper. We apply supervised radial basis function (RBF) neural network to predict the changes of virtual requests and dynamically reallocate substrate resources by adjusting already allocated resources. Simulation experiments show that compared with the static algorithms, dynamic VN embedding schemes with supervised RBF can more accurately predict the demand changes of virtual networks, which adaptively adjusts resources allocation and management and therefore improves resource utilization of substrate networks.

Energy efficiency is a key issue for wireless sensor nodes, especially for wireless body area networks (WBANs) that operate near the human body or in the human body. Aiming at the problem that WBAN system still has too fast energy consumption, we propose a ZigBee star network model with multiple sensing nodes as end nodes, and design an adaptive transmission power correction control algorithm with adjustment factors to select the appropriate transmit power to reduce the energy consumption. Experiments show that the proposed power control algorithm reduces the overall energy consumption by reasonably controlling the transmission power in the ZigBee star network model.

Although the existing networks are more often deployed in the multidomain environment, most of existing researches focus on single-domain networks and there are no appropriate solutions for the multidomain virtual network mapping problem. In fact, most studies assume that the underlying network can operate without any interruption. However, physical networks cannot ensure the normal provision of network services for external reasons and traditional single-domain networks have difficulties to meet user needs, especially for the high security requirements of the network transmission. In order to solve the above problems, this paper proposes a survivable virtual network mapping algorithm (IntD-GRC-SVNE) that implements multidomain mapping in network virtualization. IntD-GRC-SVNE maps the virtual communication networks onto different domain networks and provides backup resources for virtual links which improve the survivability of the special networks. Simulation results show that IntD-GRC-SVNE can not only improve the survivability of multidomain communications network but also render the network load more balanced and greatly improve the network acceptance rate due to employment of GRC (global resource capacity).

To deal with the disadvantages of Group Search Optimizer (GSO) as slow convergence, easy entrapment in local optima and failure to use history information, a Self-adaptive Group Search Optimizer with Elitist strategy (SEGSO) is proposed in this paper. To maintain the group diversity, SEGSO employs a self-adaptive role assignment strategy, which determines whether a member is a scrounger or a ranger based on ConK consecutive iterations of the producer. On the other hand, scroungers are updated with elitist strategy based on simulated annealing by using history information to improve convergence and guarantee SEGSO to remain global search. Experimental results demonstrate that SEGSO outperform particle swarm optimizer and original GSO in convergence rate and escaping from local optima.

Character Modeling is becoming more and more difficult in animation industry today. Lots of designers are usually involved to cooperatively accomplish a character by computer networks or the Internet. This paper presents a Multi-Objective evaluation based Cooperative Character Modeling System(MOCMS), which can evolve various character models to generate creative ones based on multi-objective evaluations. The objectives are designed to embody different personalities, including qualitative and quantitative aspects. The former are given by different cooperative designers while the latter are calculated automatically by computers. This can incorporate qualitative and quantitative evaluation in a formal manner. Case study demonstrates that the proposed method can evolve character models according to the designers' intentions and shorten design time.

Multi-objective collaborative design synthesis and its evaluation is usually viewed as a multi-objective optimization problem (MOP). In this paper, it is formulated firstly and then, a hybrid Multi-objective Evolutionary Algorithm (h-MOEA) is proposed by introducing ideas from evolutionary computation, which is suitable for solving the MOP in design synthesis and its evaluation. Furthermore, a design synthesis and its evaluation method supporting multi-objective collaborative design, composed of many iterative steps, is developed and the h-MOEA is encapsulated as a black-box optimization tool to generate design solutions, namely the Pareto optimal set. And also, a Web-based experimental prototype is developed to verify the proposed method. Finally, a case study has been done to evaluate the effectiveness of proposed methods.

Lots of geographically distributed designers are usually involved to accomplish a complex design task and creativity and amenity of artifacts are more and more emphasized, so researches on cooperative creative conceptual design system are valuable both theoretically and practically. This paper firstly identifies some major requirements of cooperative creative conceptual design system. Then, creative genetic algorithm based on simple genetic algorithm is proposed to serve as creative mechanisms and methods. The design and development of prototype TripleCDS is described and some typical design images are shown, so the creative mechanisms and methods and prototype are validated