Electroencephalogram (EEG) has shown g reat potential in multi-modal emotion recognition (MER) due to its ability to directly capture emotional states. However, the nonstationarity of EEG signals leads to significant variations across subjects and sessions, posing challenges for subject-independent MER. While previous methods have made significant progress, they often fail to integrate multimodal signals into transfer learning frameworks effectively. To address this limitation, we propose a Multi-source Domain Adaptive Network (MSDA-Net) for MER, designed to mitigate cross-subject and cross-session distribution shifts and enhance recognition performance. Specifically, we first design a feature alignment module to integrate features from different modalities, generating cross-modal feature representations and extracting representative shared features. To further improve generalization, we incorporate domain-specific feature extractors to capture domain-invariant emotional representations. Additionally, we introduce an adapter module to adjust the feature representations between different modalities, aiming to capture inter-individual differences and cross-modal correlations better. Finally, we unify classification loss, discrepancy loss, and maximum mean discrepancy (MMD) loss into a joint optimization framework. Abundant experiments on the SEED and SEED-IV datasets demonstrate the superiority of MSDA-Net, highlighting its effectiveness in improving MER performance.

Cancer is characterized by complex subtypes and pronounced heterogeneity, which pose significant challenges for accurate identification and effective treatment. In response, multi-omics clustering has emerged as a powerful approach for integrating heterogeneous biological data to identify cancer subtypes, thereby playing a crucial role in early diagnosis and precision medicine. Despite promising progress, existing multi-omics clustering methods face two key limitations. First, most methods focus on mining the common information across omics but neglect the unique heterogeneity features of each omics. Second, representation learning and clustering are often decoupled, preventing joint optimization of feature representations and the clustering affinity matrix, ultimately leading to suboptimal performance. To tackle these difficulties, we propose a novel Structure-Aware Consensus Representation Learning with Dual-Channel Attention for Multi-Omics Cancer Subtype Clustering(SACR-DCA). SACR-DCA integrates two pivotal modules: (1) The multi-omics specific feature extraction and common representation fusion module, which uniquely captures both omics-specific characteristics and their shared information via a dual-channel attention fusion framework; (2) The clustering-oriented structure-aware representation learning and consensus enhancement module, which enhances consensus representations through structure-aware learning to boost clustering efficacy, leveraging a Cauchy-Schwarz (CS) divergence constraint for clustering adaptability. Performance experiments on ten real-world datasets fully demonstrate that our method outperforms existing methods.

Emotion recognition based on physiological signals plays a vital role in psychological health and human–computer interaction, particularly with the substantial advances in multimodal emotion recognition techniques. However, two key challenges remain unresolved: 1) how to effectively model the intra-modal long-range dependencies and inter-modal correlations in multimodal physiological emotion signals, and 2) how to address the performance limitations resulting from missing multimodal data. In this paper, we propose a multimodal bidirectional Mamba (BiMamba) network with test-time adaptation (TTA) for emotion recognition named BiM-TTA. Specifically, BiM-TTA consists of a multimodal BiMamba network and a multimodal TTA. The former includes intra-modal and inter-modal BiMamba modules, which model long-range dependencies along the time dimension and capture cross-modal correlations along the channel dimension, respectively. The latter (TTA) mitigates the amplified distribution shifts caused by missing multimodal data through two-level entropy-based sample filtering and mutual information sharing across modalities. By addressing these challenges, BiM-TTA achieves state-of-the-art results on two multimodal emotion datasets.

Multimodal emotion recognition (MER) has recently gained much attention since it can leverage information over multiple modalities. However, in real life, we often encounter the problem of missing modalities, as well as modeling the heterogeneity and correlation among multimodal data are challenges. To this end, we propose a unified model called cross-modal adaptive masked autoencoder (CMA-MAE) for incomplete multimodal learning. Our CMA-MAE model comprises a cross-modal adaptive fusion encoder (CMAFE) and a multiview adaptive encoder (MVAE) to capture and fuse the heterogeneity and correlation among multimodal features. Additionally, we design a convolutional decoder that progressive upsampling and fusion with the modality-invariant features to generate robust emotional features from partially observable data. To effectively utilize both data with complete and incomplete modalities for feature learning, we adopt an end-to-end approach that simultaneously optimizes classification and reconstruction tasks. Extensive testing on the DEAP and SEED-IV datasets is conducted to assess our model, with the findings demonstrating that our CMA-MAE model outperforms current leading approaches in both incomplete and complete multimodal learning scenarios.

In this article, we propose Img2CAD, the first approach to our knowledge that uses 2-D image inputs to generate computer-aided design (CAD) models with editable parameters. Unlike existing artificial intelligence (AI) methods for 3-D model generation using text or image inputs often rely on mesh-based representations, which are incompatible with CAD tools and lack editability and fine control, Img2CAD enables seamless integration between AI-based 3-D reconstruction and CAD software. We have identified an innovative intermediate representation called structured visual geometry, characterized by vectorized wireframes extracted from objects. This representation significantly enhances the performance of generating conditioned CAD models. In addition, we introduce two new datasets to further support research in this area:a big cad model dataset (ABC)-mono, the largest known dataset comprising over 200 000 3-D CAD models with rendered images, andKOCAD, the first dataset featuring real-world captured objects alongside their ground truth CAD models, supporting further research in conditioned CAD model generation.

The electroencephalogram (EEG) signal has become a highly effective decoding target for emotion recognition and has garnered significant attention from researchers. Its spatial topological and time-dependent characteristics make it crucial to explore both spatial information and temporal information for accurate emotion recognition. However, existing studies often focus on either spatial or temporal aspects of EEG signals, neglecting the joint consideration of both perspectives. To this end, this article proposes a hybrid network consisting of a dynamic graph convolution (DGC) module and temporal self-attention representation (TSAR) module, which concurrently incorporates the representative knowledge of spatial topology and temporal context into the EEG emotion recognition task. Specifically, the DGC module is designed to capture the spatial functional relationships within the brain by dynamically updating the adjacency matrix during the model training process. Simultaneously, the TSAR module is introduced to emphasize more valuable time segments and extract global temporal features from EEG signals. To fully exploit the interactivity between spatial and temporal information, the hierarchical cross-attention fusion (H-CAF) module is incorporated to fuse the complementary information from spatial and temporal features. Extensive experimental results on the DEAP, SEED, and SEED-IV datasets demonstrate that the proposed method outperforms other state-of-the-art methods.

Multi-modal signals are more powerful for emotion recognition since they can provide richer emotion-related information. However, the heterogeneity and correlation of multimodal inputs in emotion recognition are difficult to explain. Additionally, how to model the temporal-spectral-spatial characteristics of electroencephalogram (EEG) signals with brain electrode dynamics and asymmetry is a challenge. To this end, we propose a multi-modal network that makes use of spatio-temporal features and attention mechanisms. The multi-scale temporal and asymmetric spatial learning module extracts EEG temporalspectral and spatial asymmetry features, while the multi-view attention-enhanced convolutional learning module captures key image features from multiple perspectives and frameworks. Moreover, a cross-modal attention fusion module is designed to integrate the extracted EEG and image features, enabling collaborative emotion recognition. Extensive ablation and comparative experiments on the DEAP and SEED-IV datasets demonstrate the superiority of the proposed model in feature learning and multimodal fusion compared to existing state-of-the-art methods.

The common goal of the studies is to map any emotional states encoded from electroencephalogram (EEG) into 2-dimensional arousal-valance scores. It is still challenging due to each emotion having its specific spatial structure and dynamic dependence over the distinct time segments among EEG signals. This paper aims to model human dynamic emotional behavior by considering the location connectivity and context dependency of brain electrodes. Thus, we designed a hybrid EEG modeling method that mainly adopts the attention mechanism, combining a multi-domain spatial transformer (MST) module and a dynamic temporal transformer (DTT) module, named MSDTTs. Specifically, the MST module extracts single-domain and cross-domain features from different brain regions and fuses them into multi-domain spatial features. Meanwhile, the temporal dynamic excitation (TDE) is inserted into the multi-head convolutional transformer to form the DTT module. These two blocks work together to activate and extract the emotion-related dynamic temporal features within the DTT module. Furthermore, we place the convolutional mapping into the transformer structure to mine the static context features among the keyframes. Overall results show that high classification accuracy of 98.91%/0.14% was obtained by the $\beta$ frequency band of the DEAP dataset, and 97.52%/0.12% and 96.70%/0.26% were obtained by the $\gamma$ frequency band of SEED and SEED-IV datasets. Empirical experiments indicate that our proposed method can achieve remarkable results in comparison with state-of-the-art algorithms.

The dynamic uncertain relationship among each brain region is a necessary factor that limits EEG-based emotion recognition. It is a thought-provoking problem to availably employ time-varying spatial and temporal characteristics from multi-channel electroencephalogram (EEG) signals. Although deep learning has made remarkable achievements in emotion recognition, the biological topological information among brain regions does not fully exploit, which is vital for EEG-based emotion recognition. In response to this problem, we design a hybrid model called ST-GCLSTM, which comprises a spatial-graph convolutional network (SGCN) module and an attention-enhanced bi-directional Long Short-Term Memory (LSTM) module. The main advantage of ST-GCLSTM is that it can consider the biological topology information of each brain region to extract representative spatial-temporal features from multiple EEG channels. Specifically, we construct two layers SGCN by introducing adjacency matrices to adaptively learn the intrinsic connection among different EEG channels. Moreover, an attention-enhanced mechanism is placed into a bi-directional LSTM module to extract the crucial spatial-temporal features from sequential EEG data, and then these features serve as the input layer of the classifier to learn discriminative emotion-related features. Extensive experiments on the DEAP, SEED, and SEED-IV datasets demonstrate the effectiveness of the proposed ST-GCLSTM model, revealing that our model had an absolute performance improvement over state-of-the-art strategies.

Intrusion Detection System (IDS) is an important tool to ensure network security, which can detect and prevent malicious behavior in time. However, the noise and redundancy of data often reduce the detection performance of classifiers. The traditional model of intrusion detection system cannot effectively solve this problem. Therefore, in this paper, autoencoders (AEs) are firstly used to reduce the dimension of the original data, and a hybrid model combining particle swarm optimization (PSO) and gray wolf optimization (GWO) is proposed to optimize the support vector machine (SVM) parameters. This method combines the two optimization algorithms and selects the optimal parameter values according to the locally enhanced particles to train the classifier. In this paper, the NSL-KDD benchmark dataset and UNSW-NB15 dataset are used to evaluate the proposed model, and the model is compared with other classification methods separately. The experimental results show that our hybrid optimization model has better performance in detection accuracy and provides good detection rate and false alarm rate.

Emotion recognition plays a very important role nowadays. Emotional recognition of electroencephalogram (EEG) signals involves high-dimensional EEG data, which is still a challenging task now. This paper presents an emotion recognition framework based on EEG and peripheral signals, and the ReliefF algorithm is used to select features of EEG and peripheral signals. The DEAP data set is used to process multi-channel physiology signals. In this paper the human emotion is classified into two categories (happy, unhappy) and three categories (happy, neutral, unhappy), respectively. The performance of the proposed method is evaluated in conjunction with ReliefF algorithm. The best average accuracy of K-Nearest Neighbor (KNN) and random forest (RF) are 94.295% and 98.832% in the binary classification tasks, and 79.210% and 98.364% in the triple classification tasks, respectively. At the same time, we also have adopted F1-score as the metric in the binary classification, which are 93.944% and 98.835%. The evaluation on the DEAP data set shows that using the ReliefF algorithm for feature selection can achieve good results on the issue of physiological emotion recognition.

Traditional random forest has slow convergence in network intrusion detection and its learning performance is not perfect. In order to eliminate the redundant information in the original intrusion detection data, this paper proposes a random forest intrusion detection method based on the combination of information gain and ReliefF algorithm. The proposed method first uses the information gain to calculate the information gain value of each feature. Then, the ReliefF algorithm is used to calculate the weight of each feature. According to the information gain and the feature weight, the final feature subset is obtained. Finally, this paper uses a random forest classifier for classification. The experiment compares the three feature selection methods, including the proposed method, information gain based method and ReliefF-based method. The experimental results show that the precision, recall rate, and false positive rate of the proposed method are superior to those of the other two methods.

Data stream learning in non-stationary environments and skewed class distributions has been receiving more attention in machine learning communities. This paper proposes a novel ensemble classification method (ECSDS) for classifying data streams with skewed class distributions. In the proposed ensemble method, back-propagation neural network is selected as the base classifier. In order to demonstrate the effectiveness of our proposed method, we choose three baseline methods based on ECSDS and evaluate their overall performance on ten datasets from UCI machine learning repository. Moreover, the performance of incremental learning is also evaluated by these datasets. The experimental results show our proposed method can effectively deal with classification problems on non-stationary data streams with class imbalance.

The analysis of electroencephalogram (EEG) signal is a low-cost and effective technique to examine electrical activity of the brain and diagnose brain diseases in the Brain Computer Interface (BCI) applications. Classification of EEG signals is an important task in BCI applications. This paper investigates two common methods of feature extraction on EEG signals, autoregressive (AR) model and approximate entropy. AR coefficients of each segment of each channel are calculated by AR model and entropies of each channel are also calculated by approximate entropy. A combination strategy of feature extraction, where each feature vector consists of AR coefficients and approximate entropies, is proposed in this paper. Extreme learning machine is employed as a classifier for evaluating the classification performance. The classification of five different mental tasks is evaluated by the proposed method. It can be observed from experimental results that the proposed method can effectively improve the classification performance, and achieve a good compromise between classification accuracy and computational cost.

Summary With an explosive growth of the datacenter research, virtual machines migration aiming at optimization of virtual machines placement is a major technology of improving power efficiency and resource utilization in the datacenter. While recent studies have primarily focused on maximizing resource utilization or minimizing migration cost separately, there has been little attention on jointly taking these two objectives into account. In this paper, we present the optimization model taking minimum migration cost and maximum resource utilization with multi‐resources such as storage, bandwidth, CPU, and disk space into account. The optimization of our proposed model is non‐deterministic polynomial‐time hard. According to the model, we present an efficient approximate algorithm based on bin packing algorithm, called MinCost, to resolve our model and obtain a near‐optimal solution. Finally, the simulation results in this paper show that our model and algorithm is efficient. Copyright © 2015 John Wiley & Sons, Ltd.

In the reality, people use linguistic term rather than numerical information to express their evaluations or preferences in decision making problems. To deal with the qualitative information, we propose a linguistic decision making approach based on the ten-element linguistic-valued lattice implication algebra. In this paper, we discuss the properties of two important operations, i.e. ⊗ and ⊕, in ten-element linguistic lattice implication algebra. In the decision making approach proposed in this paper, we use the operation ⊗ to calculate the weighted criteria in view of its properties. The illustration example shows that the proposed approach seems more effective for decision making under a fuzzy environment with both comparable and incomparable linguistic truth values.

The problem of classifying imbalanced datasets has drawn a significant amount of interest from academia and industry. In this paper, we propose a modified support vector machine (SVM) approach using conformal kernel transformation to address the class imbalance problem. The proposed method first uses standard SVM algorithm to obtain an approximate hyperplane. And then, we give a kernel function and compute its parameters using the chi-square test. Finally, an experimental analysis is carried out with a wide range of highly imbalanced datasets over the proposal and several other methods. The results show that our proposal outperforms previously proposed methods.

SUMMARY The service‐oriented distributed network requires more stable and persistent services, but the services from dishonest or unstable nodes would damage the correctness and availability of services. To efficiently obtain the services from high dependable nodes, many trust management systems have been developed. However, the previous trust models in distributed networks ignore a fact that the entity itself could provide trust information for services also. In this paper, we present a novel trust management model based on the subjective logic trust for service‐oriented distributed networks. The proposed algorithm involves passive trust of entity and combines the direct trust and recommendation trust. We also propose a novel scheme called passive trust feedback to avoid the deceit of malicious nodes and unstable nodes, and to encourage honest nodes. Our proposed model expands the trust range of service resource via passive trust of entity, which generates a flexible access path to the service resources. Simulations show that the proposed trust management model can significantly improve the feasibility of trust management as well as effectively detect malicious entities. Copyright © 2012 John Wiley & Sons, Ltd.

The open and anonymous nature of peer-to-peer (P2P) networks makes it an ideal medium for attackers to spread malicious contents, which in turn leads to lower quality of network services due to lack of effective trust management mechanism. To improve the quality of services (or transactions), this paper proposes a novel trust and reputation model, named as GroupTrust, based on peer group and evaluation similarity degree in P2P networks. In the proposed model, trust relationships between peers are divided into three categories: trust relationship within a peer group, trust relationship between different groups, and trust relationship between a peer in a peer group with another peer out of this peer group. The model presents the evaluation similarity degree under different context of services and gives local and global reputation computation. Experimental results demonstrate that this model can get more real trust value and deal with the malicious attacks efficiently by comparison with existing models. © 2010 Wiley Periodicals, Inc.

Selfish behaviors significantly affect the overall performance of mobile ad hoc networks (MANETs). Reputation systems have been proved to be an efficient way to block such behaviors in MANETs. Several reputation models based on subjective logic have been proposed to improve the reputation mechanism, in which an uncertainty value is introduced for reputation computation when the local information is not sufficient. However, these reputation models fail to utilize the recommended opinions effectively and reduce the uncertainty value while these opinions are combined. In this paper, we propose a novel reputation computation model based on subjective logic to overcome the above deficiencies. We consider not only the recommenders' trustworthiness but also the familiarities among the recommended nodes during reputation computations. This familiarity is defined as a certainty value which is used to weight opinions in reputation computation. In our model, the recommendations of nodes with low trustworthiness or high uncertainty on the recommended nodes have little impact on the recommended nodes' reputations so that nodes can reach opinions with lower uncertainty value through reputation computations. We conduct simulations to evaluate our model on its performance. The simulation results show that the proposed model achieves about 40% improvement in the time of discovering and isolating selfish nodes compared with a previous model based on subjective logic and selfish nodes' success rate is further reduced by up to 10%.

Due to lack of effective trust management mechanism, there are a lot of deceptive behaviors in P2P networks, which seriously decrease the quality of network services. In order to improve the quality of services (or transactions), this paper proposes a novel trust and reputation model based on similarity evaluation in P2P environments. According to the different context of services, the model gives the similarity degree evaluation, and presents local reputation and global reputation computation. Experimental results demonstrate that this model can get more real trust value and deal with the malicious attacks.

ID3 algorithm is a decision tree induction algorithm, but its pruning method (EEP) is an ineffective method when the data sets are deficient, uncertain. In this paper we analyze and study the ID3 algorithm and its pruning methods, then improve on EEP algorithm, and put forward a new pruning method - IEEP which can prune more unknown nodes and can not fall algorithm accuracy rate. We present experimental results that show the method performs better than alternatives, especially when dealing with deficient data sets.