Time series classification (TSC) is a critical area with broad applications. In the field of evidence theory, quantum evidence theory (QET) offers a promising framework for onedimensional TSC tasks, leveraging the capabilities of quantum basic probability amplitude (QBPA) to capture two-dimensional uncertainty. However, as the first step for the application of QET to TSC, how to construct QBPA still remains an open issue. In this paper, a novel approach to generate QBPA is devised. Specifically, we first apply the discrete Fourier transform (DFT) to the original data, extracting two-dimensional features embedded in the magnitude and phase from the frequency domain based on the front-few multi-frequency components, achieved by setting a threshold frequency index (TFI) to limit the frequencies considered. Next, we introduce the complex dual gaussian fuzzy number (CDGFN) as a carrier for QBPA, effectively representing two-dimensional uncertainty in the data. A CDGFN-based multisource information fusion (CDGFN-MSIF) algorithm for decision-making is proposed to combine information from different frequency components. Finally, the decisionmaking algorithm is validated on multiple time series datasets. Experimental results highlight the superior performance of the proposed approach over other state-of-the-art models, demonstrating its effectiveness and enhanced classification accuracy.

Real-time emotion recognition provides promising applications for mental healthcare monitoring and human-computer interaction design. Electroencephalography (EEG) emotion recognition has become a hot topic in the field of affective computing and intelligent brain-computer interface (BCI), and it is a feasible solution for achieving real-time emotion recognition. However, due to the uncertainty and individual specificity of emotional cognition, there are still some challenges in achieving efficient online emotion decoding applications. To address this, in this work, we propose an online emotion decoding method named DMSGL (Real-Time EEG Emotion Recognition from Dynamic Mixed Spatiotemporal Graph Learning). Specifically, in the DMSGL, we propose to explore the latent emotion-related graph features from EEG with cognition-inspired and data-driven learning strategies, and the temporal analysis with attention learning is utilized to further extract the robust spatiotemporal graph patterns for efficient EEG emotion decoding. Both simulated online emotion decoding and real-time emotion monitoring experimental results have consistently indicated that the proposed DMSGL can effectively satisfy the application requirements of real-time emotion decoding and achieves an accuracy of 68.35% in real-world online scenarios. Compared with other baseline methods, the proposed DMSGL has improved by 2-5% in the scenario of real-time emotion recognition. In conclusion, the proposed DMSGL provides a promising solution for realizing real-time emotion recognition and further exploring related applications. Our code is released on https://github.com/UESTC-BAC/DMSGL.

This paper explores silent speech decoding in active brain-computer interface (BCI) systems, which offer more natural and flexible communication than traditional BCI applications. We collected a new silent speech dataset of over 120 hours of electroencephalogram (EEG) recordings from 12 subjects, capturing 24 commonly used English words for language model pretraining and decoding. Following the recent success of pretraining large models with self-supervised paradigms to enhance EEG classification performance, we propose Large Brain Language Model (LBLM) pretrained to decode silent speech for active BCI. To pretrain LBLM, we propose Future Spectro-Temporal Prediction (FSTP) pretraining paradigm to learn effective representations from unlabeled EEG data. Unlike existing EEG pretraining methods that mainly follow a masked-reconstruction paradigm, our proposed FSTP method employs autoregressive modeling in temporal and frequency domains to capture both temporal and spectral dependencies from EEG signals. After pretraining, we finetune our LBLM on downstream tasks, including word-level and semantic-level classification. Extensive experiments demonstrate significant performance gains of the LBLM over fully-supervised and pretrained baseline models. For instance, in the difficult cross-session setting, our model achieves 47.2% accuracy on semantic-level classification and 42.3% in word-level classification, outperforming baseline methods substantially. Our research advances silent speech decoding in active BCI systems, offering an innovative solution for EEG language model pretraining and a new dataset for fundamental research.

This paper presents MindSpeak, a real-time brain-computer interface (BCI) system for recording, processing, and decoding silent speech to enable online multimodal communication between the human brain and a computer, involving both noninvasive multichannel EEG signals and text output. To enable hand-free and brain-only control, our system incorporates steady-state visual evoked potential (SSVEP) for users to select incomplete sentences from a predefined pool and confirm the correctness of decoded words. An intuitive graphical interface is designed for natural communication. We evaluate the effectiveness of our real-time BCI system, which achieves 77.3% accuracy in decoding silent speech and 98.9% accuracy in SSVEP-based selection and confirmation of correct sentences. Unlike existing BCI systems, the presented MindSpeak system significantly expands the application scope of existing BCI systems by enabling users to express complete thoughts through a fully BCI-controlled interactive interface. Our demonstration video is on: https://youtu.be/B1wt1dmCCrg.

Off-policy evaluation (OPE) is a crucial problem in reinforcement learning (RL), where the goal is to estimate the long-term cumulative reward of a target policy using historical data generated by a potentially different behaviour policy. In many real-world applications, such as precision medicine and recommendation systems, unobserved confounders may influence the action, reward, and state transition dynamics, which leads to biased estimates if not properly addressed. While existing methods for handling unobserved confounders in OPE focus on single-action settings, they are less effective in multi-action scenarios commonly found in practical applications, where an agent can take multiple actions simultaneously. In this paper, we propose a novel auxiliary variable-aided method for OPE in multi-action settings with unobserved confounders. Our approach overcomes the limitations of traditional auxiliary variable methods for multi-action scenarios by requiring only a single auxiliary variable, relaxing the need for as many auxiliary variables as the actions. Through theoretical analysis, we prove that our method provides an unbiased estimation of the target policy value. Empirical evaluations demonstrate that our estimator achieves better performance compared to existing baseline methods, highlighting its effectiveness and reliability in addressing unobserved confounders in multi-action OPE settings.

The dependency on extensive expert knowledge for defining subgoals in hierarchical reinforcement learning (HRL) restricts the training efficiency and adaptability of HRL agents in complex, dynamic environments. Inspired by human-guided causal discovery skills, we proposed a novel method, Human Causal Perception and Inference-driven Hierarchical Reinforcement Learning (HCPI-HRL), designed to infer diverse, effective subgoal structures as intrinsic rewards and incorporate critical objects from dynamic environmental states using stable causal relationships. The HCPI-HRL method is supposed to guide an agent's exploration direction and promote the reuse of learned subgoal structures across different tasks. Our designed HCPI-HRL comprises two levels: the top level operates as a meta controller, assigning subgoals discovered based on human-driven causal critical object perception and causal structure inference; the bottom level employs the Proximal Policy Optimisation (PPO) algorithm to accomplish the assigned subgoals. Experiments conducted across discrete and continuous control environments demonstrated that HCPI-HRL outperforms benchmark methods such as hierarchical and adjacency PPO in terms of training efficiency, exploration capability, and transferability. Our research extends the potential of HRL methods incorporating human-guided causal modelling to infer the effective relationships across subgoals, enhancing the agent's capability to learn efficient policies in dynamic environments with sparse reward signals.

Recent research has consistently indicated that the fusion of electroencephalography (EEG) features from multiple modalities can integrate cognitive state expressions across diverse dimensions, resulting in a substantial increase in emotion recognition accuracy. However, redundant information within the fused multimodal features could lead to the curse of dimensionality and overfitting of the learning model. In this work, we propose a multiscale EEG feature fusion and representation strategy for EEG emotion recognition named manifold of multiscale information fusion (MMIF), in which the optimal manifold of the multiscale fusion of local and global brain activation patterns can be automatically learned to realize an efficient representation of emotional EEG signals. To evaluate the performance, in this work, both off- and online EEG emotion recognition experiments were conducted, and the experimental results consistently verified the effectiveness and feasibility of the MMIF applied in real-time emotion decoding systems. Furthermore, the analytical experiments confirmed the discriminative capabilities and cognitive interpretability of the MMIF. In summary, the proposed MMIF model may provide an efficient avenue for exploring representations and enhancing the discrimination of multimodal fusion features, which may also provide a promising solution for designing online affective braincomputer interaction systems.

Major depressive disorder (MDD) is a serious psychiatric disorder characterized by persistent feelings of sadness, hopelessness, and lack of interest or pleasure in daily activities. Yet, reliable diagnostic tools for this brain disorder remain lacking. Functional near-infrared spectroscopy (fNIRS), an optical brain imaging technique, offers a promising approach for monitoring cerebral hemodynamic activity associated with MDD. In this study, we propose a novel algorithm based on wavelet coherence and a state-pathology separation network (WCSN) to automatically detect MDD using a dual-channel fNIRS system. The fNIRS signals were first preprocessed and transformed into two-dimensional feature maps using a wavelet coherence method. Following this, a wrapped exhaustive search was applied to select the optimal subset of feature maps, which was then utilized to reconstruct the dataset. Finally, samples were classified using the state-pathology separation network that employed a dual-encoder convolutional autoencoder (DCoAE) module to separate the feature maps into state features and pathology features, while a Transformer module distinguished MDD patients from healthy controls based solely on pathology features. The WCSN algorithm achieved exceptional performance with an accuracy of 0.923 ± 0.068 and a subject accuracy of 0.918 ± 0.076. Our result highlights the WCSN algorithm's ability to isolate pure pathology features, enhancing classification robustness and generalizability under dual-channel data conditions. Taken together, the proposed WCSN algorithm is well-suited for home-based MDD screening applications.

Accurate segmentation of brain structures is crucial for analyzing longitudinal changes in children's brains. However, existing methods are mostly based on models established at a single time-point due to difficulty in obtaining annotated data and dynamic variation of tissue intensity. The main problem with such approaches is that, when conducting longitudinal analysis, images from different time points are segmented by different models, leading to significant variation in estimating development trends. In this paper, we propose a novel unified model with co-registration framework to segment children's brain images covering neonates to preschoolers, which is formulated as two stages. First, to overcome the shortage of annotated data, we propose building gold-standard segmentation with co-registration framework guided by longitudinal data. Second, we construct a unified segmentation model tailored to brain images at 0-6 years old through the introduction of a convolutional network (named SE-VB-Net), which combines our previously proposed VB-Net with Squeeze-and-Excitation (SE) block. Moreover, different from existing methods that only require both T1- and T2-weighted MR images as inputs, our designed model also allows a single T1-weighted MR image as input. The proposed method is evaluated on the main dataset (320 longitudinal subjects with average 2 time-points) and two external datasets (10 cases with 6-month-old and 40 cases with 20-45 weeks, respectively). Results demonstrate that our proposed method achieves a high performance (>92%), even over a single time-point. This means that it is suitable for brain image analysis with large appearance variation, and largely broadens the application scenarios.

It is challenging to train an efficient learning procedure with multiagent reinforcement learning (MARL) when the number of agents increases as the observation space exponentially expands, especially in large-scale multiagent systems. In this article, we proposed a scalable attentive transfer framework (SATF) for efficient MARL, which achieved goals faster and more accurately in homogeneous and heterogeneous combat tasks by transferring learned knowledge from a small number of agents (4) to a large number of agents (up to 64). To reduce and align the dimensionality of the observed state variations caused by increasing numbers of agents, the proposed SATF deployed a novel state representation network with a self-attention mechanism, known as dynamic observation representation network (DorNet), to extract the dominant observed information with excellent cost-effectiveness. The experiments on the MAgent platform showed that the SATF outperformed the distributed MARL (independent Q-learning (IQL) and A2C) in task sequences from 8 to 64 agents. The experiments on StarCraft II showed that the SATF demonstrated superior performance relative to the centralized training with decentralized execution MARL (QMIX) by presenting shorter training steps, achieving a desired win rate of up to approximately 90% when increasing the number of agents from 4 to 32. The findings of our study showed the great potential for enhancing the efficiency of MARL training in large-scale agent combat missions.

Emotion recognition based on electroencephalogram (EEG) is fundamentally associated with human-like intelligence system. However, due to the noise-sensitive characteristics of EEGs and the individual variability of emotions, it is very challenging to extract inherent emotion dependent patterns from emotional EEG signals. In this work, we propose a L1-norm space defined discriminative brain network manifold learning model (L1-SGL), in which the EEG noise outliers can be effectively separated and the pseudolabeled samples caused by subjective feelings can be automatically corrected. Off-line experimental results consistently indicate that the L1-SGL can effectively suppress the influence of noise and achieve an incomparable superiority performance over other existing methods in EEG emotion recognition. Besides, benefiting from the time efficiency of the L1-SGL, an online emotion monitoring and regulation system is further implemented in this work. On-line emotion decoding experimental results (86.30%) of 25 participants prove that the L1-SGL can effectively satisfy the real-time requirements of on-line emotional monitoring applications, and the significant negative emotion regulation experimental results ( $p \lt 0.001$ ) further confirm the feasibility and effectiveness of L1-SGL model in real-time emotion regulation and interactive applications. Overall, the L1-SGL provides a promising solution for the real-time online affective brain-computer interfaces (aBCIs) and the intelligent clinical closed-loop treatments.

Benefiting from the high-temporal resolution of electroencephalogram (EEG), EEG-based emotion recognition has become one of the hotspots of affective computing. For EEG-based emotion recognition systems, it is crucial to utilize state-of-the-art learning strategies to automatically learn emotion-related brain cognitive patterns from emotional EEG signals, and the learned stable cognitive patterns effectively ensure the robustness of the emotion recognition system. In this work, to realize the efficient decoding of emotional EEG, we propose a graph learning system [Graph Convolutional Network framework with Brain network initial inspiration and Fused attention mechanism (BF-GCN)] inspired by the brain cognitive mechanism to automatically learn graph patterns from emotional EEG and improve the performance of EEG emotion recognition. In the proposed BF-GCN, three graph branches, i.e., cognition-inspired functional graph branch, data-driven graph branch, and fused common graph branch, are first elaborately designed to automatically learn emotional cognitive graph patterns from emotional EEG signals. And then, the attention mechanism is adopted to further capture the brain activation graph patterns that are related to emotion cognition to achieve an efficient representation of emotional EEG signals. Essentially, the proposed BF-CGN model is a cognition-inspired graph learning neural network model, which utilizes the spectral graph filtering theory in the automatic learning and extracting of emotional EEG graph patterns. To evaluate the performance of the BF-GCN graph learning system, we conducted subject-dependent and subject-independent experiments on two public datasets, i.e., SEED and SEED-IV. The proposed BF-GCN graph learning system has achieved 97.44% (SEED) and 89.55% (SEED-IV) in subject-dependent experiments, and the results in subject-independent experiments have achieved 92.72% (SEED) and 82.03% (SEED-IV), respectively. The state-of-the-art performance indicates that the proposed BF-GCN graph learning system has a robust performance in EEG-based emotion recognition, which provides a promising direction for affective computing.

Traffic oscillations present significant challenges to road transportation systems, resulting in reduced fuel efficiency, heightened crash risks, and severe congestion. Recently emerging Augmented Intelligence of Things (AIoT) technology holds promise for enhancing traffic flow through vehicle-road cooperation. A representative application involves using deep reinforcement learning (DRL) techniques to control connected autonomous vehicle (CAV) platoons to alleviate traffic oscillations. However, uncertainties in human-driven vehicles (HDVs) driving behavior and the random distribution of CAVs make it challenging to achieve effective traffic oscillation alleviation in the Internet of Things environment. Existing DRL-based mixed vehicular platoon control strategies underutilize downstream traffic data, impairing CAVs’ ability to predict and mitigate traffic oscillations, leading to inefficient speed adjustments and discomfort. This article proposes a dense communication cooperative RL policy for mixed vehicular platoons to address these challenges. It employs a parameter-sharing structure and a dense information flow topology, enabling CAVs to proactively respond to traffic oscillations while accommodating arbitrary vehicle distributions and communication failures. Experimental results demonstrate superior performance of the proposed strategy in driving efficiency, comfort, and safety, particularly in scenarios involving multivehicle cut-ins or cut-outs and communication failures.

Few-Shot Class-Incremental Learning (FSCIL)presents an extension of the Class Incremental Learning (CIL)problem where a model is faced with the problem of data scarcity while addressing the Catastrophic Forgetting (CF)problem. This problem remains an open problem because all recent works are built upon the Convolutional Neural Networks (CNNs)performing sub-optimally compared to the transformer approaches. Our paper presents Robust Transformer Approach (ROBUSTA)built upon the Compact Convolutional Transformer (CCT). The issue of overfitting due to few samples is overcome with the notion of the stochastic classifier, where the classifier's weights are sampled from a distribution with mean and variance vectors, thus increasing the likelihood of correct classifications, and the batch-norm layer to stabilize the training process. The issue of CFis dealt with the idea of delta parameters, small task-specific trainable parameters while keeping the backbone networks frozen. A non-parametric approach is developed to infer the delta parameters for the model's predictions. The prototype rectification approach is applied to avoid biased prototype calculations due to the issue of data scarcity. The advantage of ROBUSTAis demonstrated through a series of experiments in the benchmark problems where it is capable of outperforming prior arts with big margins without any data augmentation protocols.

Most few-shot learning works rely on the same domain assumption between the base and the target tasks, hindering their practical applications. This paper proposes an adaptive transformer network (ADAPTER), a simple but effective solution for cross-domain few-shot learning where there exist large domain shifts between the base task and the target task. ADAPTER is built upon the idea of bidirectional cross-attention to learn transferable features between the two domains. The proposed architecture is trained with DINO to produce diverse, and less biased features to avoid the supervision collapse problem. Furthermore, the label smoothing approach is proposed to improve the consistency and reliability of the predictions by also considering the predicted labels of the close samples in the embedding space. The performance of ADAPTER is rigorously evaluated in the BSCD-FSL benchmarks in which it outperforms prior arts with significant margins.

• This paper investigates the capacity of AI models for airway modelling on national datasets with paired clinical metadata. • We evaluated AI models against unharmonised, noisy, and out-of-distribution data, as well as the prognostication for FLD. • We found a new biomarker for mortality prediction, outperforming existing clinical measurements (FVC% and fibrosis scores). • In-depth analysis of AI models on airway modelling and prognosis, highlighting challenges and future research directions. Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway structures remains prohibitively time-consuming. While significant efforts have been made towards enhancing automatic airway modelling, current public-available datasets predominantly concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023′ (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for mortality prediction, a strong airway-derived biomarker (Hazard ratio>1.5, p < 0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.

Uncertainty modeling and reasoning in intelligent systems are crucial for effective decision-making, such as complex evidence theory (CET) being particularly promising in dynamic information processing. Within CET, the complex basic belief assignment (CBBA) can model uncertainty accurately, while the complex rule of combination can effectively reason uncertainty with multiple sources of information, reaching a consensus. However, determining CBBA, as the key component of CET, remains an open issue. To mitigate this issue, we propose a novel method for generating CBBA using high-level features extracted from Box–Cox transformation and discrete Fourier transform (DFT). Specifically, our method deploys complex Gaussian fuzzy number (CGFN) to generate CBBA, which provides a more accurate representation of information. The proposed method is applied to pattern classification tasks through a multisource information fusion algorithm, and it is compared with several well-known methods to demonstrate its effectiveness. Experimental results indicate that our proposed CGFN-based method outperforms existing methods, by achieving the highest average classification rate in multisource information fusion for pattern classification tasks. We found the Box–Cox transformation contributes significantly to CGFN by formatting data in a normal distribution, and DFT can effectively extract high-level features. Our method offers a practical approach for generating CBBA in CET, precisely representing uncertainty and enhancing decision-making in uncertain scenarios.

Deep reinforcement learning (DRL) algorithms often face challenges in achieving stability and efficiency due to significant policy gradient variance and inaccurate reward function estimation in complex scenarios. This study addresses these issues in the context of multi-objective car-following control tasks with time lag in traffic oscillations. We propose an expert demonstration reinforcement learning (EDRL) approach that aims to stabilize training, accelerate learning, and enhance car-following performance. The key idea is to leverage expert demonstrations, which represent superior car-following control experiences, to improve the DRL policy. Our method involves two sequential steps. In the first step, expert demonstrations are obtained during offline pretraining by utilizing prior traffic knowledge, including car-following trajectories from an empirical database and classic car-following models. In the second step, expert demonstrations are obtained during online training, where the agent interacts with the car-following environment. The EDRL agents are trained through supervised regression on the expert demonstrations using the behavioral cloning technique. Experimental results conducted in various traffic oscillation scenarios demonstrate that our proposed method significantly enhances training stability, learning speed, and rewards compared to baseline algorithms.

Multisource information fusion is a comprehensive and interdisciplinary subject. Dempster-Shafer (D-S) evidence theory copes with uncertain information effectively. Pattern classification is the core research content of pattern recognition, and multisource information fusion based on D-S evidence theory can be effectively applied to pattern classification problems. However, in D-S evidence theory, highly-conflicting evidence may cause counterintuitive fusion results. Belief divergence theory is one of the theories that are proposed to address problems of highly-conflicting evidence. Although belief divergence can deal with conflict between evidence, none of the existing belief divergence methods has considered how to effectively measure the discrepancy between two pieces of evidence with time evolutionary. In this study, a novel fractal belief Rényi (FBR) divergence is proposed to handle this problem. We assume that it is the first divergence that extends the concept of fractal to R/'enyi divergence. The advantage is measuring the discrepancy between two pieces of evidence with time evolution, which satisfies several properties and is flexible and practical in various circumstances. Furthermore, a novel algorithm for multisource information fusion based on FBR divergence, namely FBReD-based weighted multisource information fusion, is developed. Ultimately, the proposed multisource information fusion algorithm is applied to a series of experiments for pattern classification based on real datasets, where our proposed algorithm achieved superior performance.

Granger causality-based effective brain connectivity provides a powerful tool to probe the neural mechanism for information processing and the potential features for brain computer interfaces. However, in real applications, traditional Granger causality is prone to the influence of outliers, such as inevitable ocular artifacts, resulting in unreasonable brain linkages and the failure to decipher inherent cognition states. In this work, motivated by constructing the sparse causality brain networks under the strong physiological outlier noise conditions, we proposed a dual Laplacian Granger causality analysis (DLap-GCA) by imposing Laplacian distributions on both model parameters and residuals. In essence, the first Laplacian assumption on residuals will resist the influence of outliers in electroencephalogram (EEG) on causality inference, and the second Laplacian assumption on model parameters will sparsely characterize the intrinsic interactions among multiple brain regions. Through simulation study, we quantitatively verified its effectiveness in suppressing the influence of complex outliers, the stable capacity for model estimation, and sparse network inference. The application to motor-imagery (MI) EEG further reveals that our method can effectively capture the inherent hemispheric lateralization of MI tasks with sparse patterns even under strong noise conditions. The MI classification based on the network features derived from the proposed approach shows higher accuracy than other existing traditional approaches, which is attributed to the discriminative network structures being captured in a timely manner by DLap-GCA even under the single-trial online condition. Basically, these results consistently show its robustness to the influence of complex outliers and the capability of characterizing representative brain networks for cognition information processing, which has the potential to offer reliable network structures for both cognitive studies and future brain-computer interface (BCI) realization.

Multichannel electroencephalogram (EEG) is an array signal that represents brain neural networks and can be applied to characterize information propagation patterns for different emotional states. To reveal these inherent spatial graph features and increase the stability of emotion recognition, we propose an effective emotion recognition model that performs multicategory emotion recognition with multiple emotion-related spatial network topology patterns (MESNPs) by learning discriminative graph topologies in EEG brain networks. To evaluate the performance of our proposed MESNP model, we conducted single-subject and multisubject four-class classification experiments on two public datasets, MAHNOB-HCI and DEAP. Compared with existing feature extraction methods, the MESNP model significantly enhances the multiclass emotional classification performance in the single-subject and multisubject conditions. To evaluate the online version of the proposed MESNP model, we designed an online emotion monitoring system. We recruited 14 participants to conduct the online emotion decoding experiments. The average online experimental accuracy of the 14 participants was 84.56%, indicating that our model can be applied in affective brain-computer interface (aBCI) systems. The offline and online experimental results demonstrate that the proposed MESNP model effectively captures discriminative graph topology patterns and significantly improves emotion classification performance. Moreover, the proposed MESNP model provides a new scheme for extracting features from strongly coupled array signals.

Electroencephalogram (EEG) brain network embodies the brain’s coordination and interaction mechanism, and the transformations of emotional states are usually accompanied with changes in brain network spatial topologies. To effectively characterize emotions, in this work, we propose a cognition-inspired graph embedding model in the L1-norm space (L1-CGE) to learn an optimal low-dimensional embedded manifold for emotional brain networks. In the L1-CGE, the original brain networks are first encoded in the affinity space with the proposed cognition-inspired metric to construct the latent geometry manifold structure of emotional brain networks, and then the graph learning objective function is defined in the L1-norm space to obtain the optimal low-dimensional representations of brain networks. Essentially, the modularized community structures of emotional brain networks can be effectively emphasized by the L1-CGE to realize an effective depiction for emotions. Compared with existing methods, the L1-CGE model has achieved state-of-the-art performance on three public emotional EEG datasets in off-line conditions. Besides, the robust real-time experimental results have been achieved with the on-line emotion decoding system designed with L1-CGE. Both off- and on-line experimental results consistently demonstrate that the proposed L1-CGE is promising to provide a potential solution for the real-time affective brain-computer interface (aBCI) system.

Multisource data fusion analysis, particularly in decision-level fusion strategies, is emerging for application in real-life scenarios. The Dempster–Shafer evidence theory (DSET) is a prevalent approach that has significant importance in managing the fusion tasks. However, existing fusion approaches have limitations in dealing with redundant information and computational complexity associated with the fusion procedure. Though conflict management has been thoroughly studied, other limitations have not been well addressed. In this article, we propose a novel approach for evidential multisource data fusion based on game-theoretic analysis. The introduction of the Shapley function considers the interaction effect of focal elements, mitigating the negative influence of redundant evidence. Additionally, the computational complexity of the fusion procedure is reduced to the same level as the approximate Bayesian update model. We provide a numerical example with conflicting and redundant evidence to show that the proposed approach outperforms current advanced weighted average-based fusion methods. Moreover, a simulation experiment demonstrates the practicality and effectiveness of the proposed approach in identifying driver fatigue states based on electroencephalography (EEG) signals.

Information can be quantified and expressed by uncertainty, and improving the decision level of uncertain information is vital in modeling and processing uncertain information. Dempster-Shafer evidence theory can model and process uncertain information effectively. However, the Dempster combination rule may provide counter-intuitive results when dealing with highly conflicting information, leading to a decline in decision level. Thus, measuring conflict is significant in the improvement of decision level. Motivated by this issue, this paper proposes a novel method to measure the discrepancy between bodies of evidence. First, the model of dynamic fractal probability transformation is proposed to effectively obtain more information about the non-specificity of basic belief assignments (BBAs). Then, we propose the higher-order fractal belief Rényi divergence (HOFBReD). HOFBReD can effectively measure the discrepancy between BBAs. Moreover, it is the first belief Rényi divergence that can measure the discrepancy between BBAs with dynamic fractal probability transformation. HoFBReD has several properties in terms of probability transformation as well as measurement. When the dynamic fractal probability transformation ends, HoFBReD is equivalent to measuring the Rényi divergence between the pignistic probability transformations of BBAs. When the BBAs degenerate to the probability distributions, HoFBReD will also degenerate to or be related to several well-known divergences. In addition, based on HoFBReD, a novel multisource information fusion algorithm is proposed. A pattern classification experiment with real-world datasets is presented to compare the proposed algorithm with other methods. The experiment results indicate that the proposed algorithm has a higher average pattern recognition accuracy with all datasets than other methods. The proposed discrepancy measurement method and multisource information algorithm contribute to the improvement of decision level.

Complex evidence theory (CET) is an effective method for uncertainty reasoning in knowledge-based systems with good interpretability that has recently attracted much attention. However, approaches to improve the performance of uncertainty reasoning in CET-based expert systems remains an open issue. One key to performance improvement is the adequate management of conflict from multisource information. In this paper, a generalized correlation coefficient, namely, the complex evidential correlation coefficient (CECC), is proposed for the complex mass functions or complex basic belief assignments (CBBAs) in CET. On this basis, a complex conflict coefficient is proposed to measure the conflict between CBBAs; when CBBAs turn into classic BBAs, the complex correlation and conflict coefficients will degrade into traditional coefficients. The complex conflict coefficient satisfies nonnegativity, symmetry, boundedness, extreme consistency, and insensitivity to refinement properties, which are desirable for conflict measurement. Several numerical examples validate through comparisons the superiority of the complex conflict coefficient. In this context, a weighted discounting multisource information fusion algorithm, which is called the CECC-WDMSIF, is designed based on the CECC to improve the performance of CET-based expert systems. By applying the CECC-WDMSIF method to the pattern classification of diverse real-world datasets, it is demonstrated that the proposed CECC-WDMSIF outperforms well-known related approaches with higher classification accuracy and robustness.

In this study, we investigate the use of global information to speed up the learning process and increase the cumulative rewards of reinforcement learning (RL) in competition tasks. Within the framework of actor-critic RL, we introduce multiple cooperative critics from two levels of a hierarchy and propose an RL from the hierarchical critics (RLHC) algorithm. In our approach, each agent receives value information from local and global critics regarding a competition task and accesses multiple cooperative critics in a top-down hierarchy. Thus, each agent not only receives low-level details, but also considers coordination from higher levels, thereby obtaining global information to improve the training performance. Then, we test the proposed RLHC algorithm against a benchmark algorithm, that is, proximal policy optimization (PPO), under four experimental scenarios consisting of tennis, soccer, banana collection, and crawler competitions within the Unity environment. The results show that RLHC outperforms the benchmark on these four competitive tasks.

Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess a high similarity correlation that is critical prior knowledge. However, existing methods cannot effectively learn a synthesis model for high-dimensional DT from a small number of training samples. In this article, we propose a novel DT synthesis method, which makes full use of similarity as prior knowledge to address this issue. Our method is based on the proposed kernel similarity embedding, which can not only mitigate the high dimensionality and small sample issues, but also has the advantage of modeling nonlinear feature relationships. Specifically, we first put forward two hypotheses that are essential for the DT model to generate new frames using similarity correlations. Then, we integrate kernel learning and the extreme learning machine into a unified synthesis model to learn kernel similarity embeddings for representing DTs. Extensive experiments on DT videos collected from the Internet and two benchmark datasets, i.e., Gatech Graphcut Textures and Dyntex, demonstrate that the learned kernel similarity embeddings can provide discriminative representations for DTs. Further, our method can preserve the long-term temporal continuity of the synthesized DT sequences with excellent sustainability and generalization. Meanwhile, it effectively generates realistic DT videos with higher speed and lower computation than the current state-of-the-art methods. The code and more synthesis videos are available at our project pagehttps://shiming-chen.github.io/Similarity-page/Similarit.html.

Recently, distributed semisupervised learning (DSSL) algorithms have shown their effectiveness in leveraging unlabeled samples over interconnected networks, where agents cannot share their original data with each other and can only communicate nonsensitive information with their neighbors. However, existing DSSL algorithms cannot cope with data uncertainties and may suffer from high computation and communication overhead problems. To handle these issues, we propose a distributed semisupervised fuzzy regression (DSFR) model with fuzzy if-then rules and interpolation consistency regularization (ICR). The ICR, which was proposed recently for semisupervised problem, can force decision boundaries to pass through sparse data areas, thus increasing model robustness. However, its application in distributed scenarios has not been considered yet. In this work, we proposed a distributed fuzzy C-means (DFCM) method and a distributed interpolation consistency regularization (DICR) built on the well-known alternating direction method of multipliers to respectively locate parameters in antecedent and consequent components of DSFR. Notably, the DSFR model converges very fast since it does not involve back-propagation procedure and is scalable to large-scale datasets benefiting from the utilization of DFCM and DICR. Experiments results on both artificial and real-world datasets show that the proposed DSFR model can achieve much better performance than the state-of-the-art DSSL algorithm in terms of both loss value and computational cost. Our code is available online.1

The simulated driving environment provides a low cost and time-saving platform to test the performance of the autonomous vehicle by linkage with existing machine learning approaches. However, most of existing simulated driving environments focus on building flat roads in urban areas. Still, they neglected to endeavour the tough steep, curvy hill roads, such as mountain paths around suburban areas. In this study, by deploying in Unity engine, we developed the first complex mountain driving simulated environment with characterizing continuous curves and up/downhill. Then, two state-of-art reinforcement learning (RL) algorithms are used to train a vehicle agent and test the performance of autonomous vehicles in our developed simulated environment. Also, we set 5 different levels of vehicle's speeds and observe the cumulative rewards during the vehicle agent training. Our demonstration presents the developed environment supports for complex mountain scenario configurations and RL-based autonomous vehicles, and our findings show that the vehicle agent could achieve high cumulative rewards during the training stage, suggesting that our work is a potential new simulation environment for autonomous vehicles research. The demonstration video can be viewed via the link: https://youtu.be/0wSqGeCn-NU.

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment.Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications.EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas.In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years.Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals.Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications.Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.!

Generative adversarial networks (GANs) have been a popular deep generative model for real-world applications. Despite many recent efforts on GANs that have been contributed, mode collapse and instability of GANs are still open problems caused by their adversarial optimization difficulties. In this article, motivated by the cooperative co-evolutionary algorithm, we propose a cooperative dual evolution-based GAN (CDE-GAN) to circumvent these drawbacks. In essence, CDE-GAN incorporates dual evolution with respect to the generator(s) and discriminators into a unified evolutionary adversarial framework to conduct effective adversarial multiobjective optimization. Thus, it exploits the complementary properties and injects dual mutation diversity into the training, to steadily diversify the estimated density in capturing multimodes and improve generative performance. Specifically, CDE-GAN decomposes the complex adversarial optimization problem into two subproblems (generation and discrimination), and each subproblem is solved with a separated subpopulation (E-GeneratorsandE-Discriminators), evolved by its own evolutionary algorithm. Additionally, we further propose aSoft Mechanismto balance the tradeoff between E-Generators and E-Discriminators to conduct steady training for CDE-GAN. Extensive experiments on one synthetic dataset and three real-world benchmark image datasets demonstrate that the proposed CDE-GAN achieves a competitive and superior performance in generating good quality and diverse samples over baselines. The code and more generated results are available at our project homepagehttps://shiming-chen.github.io/CDE-GAN-website/CDE-GAN.html.

This special issue explores foundation methodologies, the latest directions, and emerging applications in a new generation of fuzzy systems related to human-explainable AI. The presented studies offer a snapshot of the latest advances in human-centric intelligence and interdisciplinary research, including computational behavioral science, learning methods for interpretable models, theoretical approaches and knowledge representation to explainability, decision support systems, and fuzzy control systems to explain robot behavior. They provided comprehensible explanations of AI decisions that enhanced the training performance on the benchmarks and the transparency through an interface to the end-users and engineers.

Mobile robot navigation is an essential problem in robotics. We propose a method for constructing and training fuzzy logic controllers (FLCs) to coordinate a robotic team performing collision-free navigation and arriving simultaneously at a target location in an unknown environment. Our FLCs are organized in a multilayered architecture to reduce the number of tunable parameters and improve the scalability of the solution. In addition, in contrast to simple traditional switching mechanisms between target seeking and obstacle avoidance, we develop a novel rule-embedded FLC to improve the navigation performance. Moreover, we design a grouping and merging mechanism to obtain transparent fuzzy sets and integrate this mechanism into the training process for all FLCs, thus increasing the interpretability of the fuzzy models. We train the proposed FLCs using a novel multiobjective hybrid approach combining a genetic algorithm and particle swarm optimization. Simulation results demonstrate the effectiveness of our algorithms in reliably solving the proposed navigation problem.

As big data often contains a significant amount of uncertain, unstructured, and imprecise data that are structurally complex and incomplete, traditional attribute reduction methods are less effective when applied to large-scale incomplete information systems to extract knowledge. Multigranular computing provides a powerful tool for use in big data analysis conducted at different levels of information granularity. In this article, we present a novel multigranulation supertrust fuzzy-rough set-based attribute reduction (MSFAR) algorithm to support the formation of hierarchies of information granules of higher types and higher orders, which addresses newly emerging data mining problems in big data analysis. First, a multigranulation supertrust model based on the valued tolerance relation is constructed to identify the fuzzy similarity of the changing knowledge granularity with multimodality attributes. Second, an ensemble consensus compensatory scheme was adopted to calculate the multigranular trust degree based on the reputation at different granularities to create reasonable subproblems with different granulation levels. Third, an equilibrium method of multigranular coevolution is employed to ensure a wide range of balancing of exploration and exploitation, and this strategy can classify super elitists' preferences and detect noncooperative behaviors with a global convergence ability and high search accuracy. The experimental results demonstrate that the MSFAR algorithm achieves a high performance in addressing uncertain and fuzzy attribute reduction problems with a large number of multigranularity variables.

Dempster-Shafer evidence (DSE) theory, which allows combining pieces of evidence from different data sources to derive a degree of belief function that is a type of fuzzy measure, is a general framework for reasoning with uncertainty. In this framework, how to optimally manage the conflicts of multiple pieces of evidence in DSE remains an open issue to support decision making. The existing conflict measurement approaches can achieve acceptable outcomes but do not fully consider the optimization at the decision-making level using the novel measurement of conflicts. In this article, we propose a novel evidential correlation coefficient (ECC) for belief functions by measuring the conflict between two pieces of evidence in decision making. Then, we investigate the properties of our proposed evidential correlation and conflict coefficients, which are all proven to satisfy the desirable properties for conflict measurement, including nonnegativity, symmetry, boundedness, extreme consistency, and insensitivity to refinement. We also present several examples and comparisons to demonstrate the superiority of our proposed ECC method. Finally, we apply the proposed ECC in a decision-making application of motor rotor fault diagnosis, which verifies the practicability and effectiveness of our proposed novel measurement.

The application of the Internet of Things (IoT) in medical field has brought unprecedented convenience to human beings. However, attackers can use device configuration vulnerabilities to hijack devices, control services, steal medical data, or make devices operate illegally. These restrictions have led to huge security risks for IoT, and have challenged the management of critical infrastructure services. Based on these problems, this article proposes an anomaly detection system for detecting illegal behavior (DIB) in medical IoT environment.The DIB system can analyze data packets transmitted by medical IoT devices, learn operation rules by itself, and remind management personnel that the device is in an abnormal operation state to ensure the safety of control service. We further propose a model that is based on rough set theory and fuzzy core vector machine (FCVM) to improve the accuracy of DIB classification anomalies. Experimental results show that the R-FCVM is effective.

Brain states are patterns of neuronal synchrony, and the electroencephalogram (EEG) microstate provides a promising tool to characterize and analyze the synchronous neural firing. However, the topographical spectral information for each predominate microstate is still unclear during the switch of consciousness, such as sedation, and the practical usage of the EEG microstate is worth probing. Also, the mechanism behind the anesthetic-induced alternations of brain states remains poorly understood. In this study, an advanced EEG microstate spectral analysis was utilized using multivariate empirical mode decomposition in Hilbert-Huang transform. The practicability was further investigated in scalp EEG recordings during the propofol-induced transition of consciousness. The process of transition from the awake baseline to moderate sedation was accompanied by apparent increases in microstate (A, B, and F) energy, especially in the whole-brain delta band, frontal alpha band and beta band. In comparison to other effective EEG-based parameters that commonly used to measure anesthetic depth, using the selected spectral features reached better performance (80% sensitivity, 90% accuracy) to estimate the brain states during sedation. The changes in microstate energy also exhibited high correlations with individual behavioral data during sedation. In a nutshell, the EEG microstate spectral analysis is an effective method to estimate brain states during propofol-induced sedation, giving great insights into the underlying mechanism. The generated spectral features can be promising markers to dynamically assess the consciousness level.

The study aims to evaluate the performance of the transfer learning algorithm to enhance the transferability of a deep reinforcement learning-based variable speed limits (VSL) control. The Double Deep Q Network (DDQN)-based VSL control strategy is proposed for reducing total time spent (TTS) on freeways. A real merging bottleneck is developed in the simulation and considered for the VSL control as the source scenario. Three types of target scenarios are considered, including the overspeed scenarios, adverse weather scenarios, and diverse capacity drop scenarios. A stable testing demand and a fluctuating testing demand are adopted to evaluate the effects of VSL control. The results show that by updating the neural networks, the transfer learning in the DDQN-based VSL control agent successfully transfers knowledge learned in the source scenario to other target scenarios. With the transfer learning, the entire training process is shortened by 32.3% to 69.8%, while keeping a similar maximum reward level, as compared to the VSL control with full learning from scratch. With the transferred DDQN-based VSL strategy, the TTS is reduced by 26.02% to 67.37% with the stable testing demand and 21.31% to 69.98% with the fluctuating testing demand in various scenarios, respectively. The results also show that when the task similarity between the source scenario and target scenario is relatively low, the transfer learning could lead to local optimum and may not achieve the global optimal control effects.

EEG inverse problem is underdetermined, which poses a long standing challenge in Neuroimaging. The combination of source-imaging and analysis of cortical directional networks enables us to noninvasively explore the underlying neural processes. However, existing EEG source imaging approaches mainly focus on performing the direct inverse operation for source estimation, which will be inevitably influenced by noise and the strategy used to find the inverse solution. Here, we develop a new source imaging technique, Deep Brain Neural Network (DeepBraiNNet), for robust sparse spatiotemporal EEG source estimation. In DeepBraiNNet, considering that Recurrent Neural Network (RNN) are usually "deep" in temporal dimension and thus suitable for time sequence modelling, the RNN with Long Short-Term Memory (LSTM) is utilized to approximate the inverse operation for the lead field matrix instead of performing the direct inverse operation, which avoids the possible effect of the direct inverse operation on the underdetermined lead field matrix prone to be influenced by noise. Simulations on various source patterns and noise conditions confirmed that the proposed approach could actually recover the spatiotemporal sources well, outperforming existing state of-the-art methods. DeepBraiNNet also estimated sparse MI related activation patterns when it was applied to a real Motor Imagery dataset, consistent with other findings based on EEG and fMRI. Based on the spatiotemporal sources estimated from DeepBraiNNet, we constructed MI related cortical neural networks, which clearly exhibited strong contralateral network patterns for the two MI tasks. Consequently, DeepBraiNNet may provide an alternative way different from the conventional approaches for spatiotemporal EEG source imaging.

The current reward learning from human preferences could be used to resolve complex reinforcement learning (RL) tasks without access to a reward function by defining a single fixed preference between pairs of trajectory segments. However, the judgment of preferences between trajectories is not dynamic and still requires human input over thousands of iterations. In this study, we proposed a weak human preference supervision framework, for which we developed a human preference scaling model that naturally reflects the human perception of the degree of weak choices between trajectories and established a human-demonstration estimator through supervised learning to generate the predicted preferences for reducing the number of human inputs. The proposed weak human preference supervision framework can effectively solve complex RL tasks and achieve higher cumulative rewards in simulated robot locomotion-MuJoCo games-relative to the single fixed human preferences. Furthermore, our established human-demonstration estimator requires human feedback only for less than 0.01% of the agent's interactions with the environment and significantly reduces the cost of human inputs by up to 30% compared with the existing approaches. To present the flexibility of our approach, we released a video (https://youtu.be/jQPe1OILT0M) showing comparisons of the behaviors of agents trained on different types of human input. We believe that our naturally inspired human preferences with weakly supervised learning are beneficial for precise reward learning and can be applied to state-of-the-art RL systems, such as human-autonomy teaming systems.

The vehicle platoon will be the most dominant driving mode on future roads. To the best of our knowledge, few reinforcement learning (RL) algorithms have been applied in vehicle platoon control, which has large-scale action and state spaces. Some RL-based methods were applied to solve single-agent problems. If we need to tackle multiagent problems, we will use multiagent RL algorithms since the parameters space grows exponentially with the increasing number of agents involved. Previous multiagent RL algorithms generally may provide redundant information to agents, indicating a large amount of useless or unrelated information, which may cause to be difficult for convergence training and pattern extractions from shared information. Also, random actions usually contribute to crashes, especially at the beginning of training. In this study, a communication proximal policy optimization (CommPPO) algorithm was proposed to tackle the above issues. In specific, the CommPPO model adopts a parameter-sharing structure to allow the dynamic variation of agent numbers, which can well handle various platoon dynamics, including splitting and merging. The communication protocol of the CommPPO consists of two parts. In the state part, the widely used predecessor-leader follower typology in the platoon is adopted to transmit global and local state information to agents. In the reward part, a new reward communication channel is proposed to solve the spurious reward and "lazy agent" problems in some existing multiagent RLs. Moreover, a curriculum learning approach is adopted to reduce crashes and speed up training. To validate the proposed strategy for platoon control, two existing multiagent RLs and a traditional platoon control strategy were applied in the same scenarios for comparison. Results showed that the CommPPO algorithm gained more rewards and achieved the largest fuel consumption reduction (11.6%).

Time-series medical images are an important type of medical data that contain rich temporal and spatial information. As a state-of-the-art, computer-aided diagnosis (CAD) algorithms are usually used on these image sequences to improve analysis accuracy. However, such CAD algorithms are often required to upload medical images to honest-but-curious servers, which introduces severe privacy concerns. To preserve privacy, the existing CAD algorithms support analysis on each encrypted image but not on the whole encrypted image sequences, which leads to the loss of important temporal information among frames. To meet this challenge, a convolutional-LSTM network, named HE-CLSTM, is proposed for analyzing time-series medical images encrypted by a fully homomorphic encryption mechanism. Specifically, several convolutional blocks are constructed to extract discriminative spatial features, and LSTM-based sequence analysis layers (HE-LSTM) are leveraged to encode temporal information from the encrypted image sequences. Moreover, a weighted unit and a sequence voting layer are designed to incorporate both spatial and temporal features with different weights to improve performance while reducing the missed diagnosis rate. The experimental results on two challenging benchmarks (a Cervigram dataset and the BreaKHis public dataset) provide strong evidence that our framework can encode visual representations and sequential dynamics from encrypted medical image sequences; our method achieved AUCs above 0.94 both on the Cervigram and BreaKHis datasets, constituting a significant margin of statistical improvement compared with several competing methods.

Multicriteria decision analysis evaluates multiple conflicting criteria in decision making, but conflicting criteria are typical in evaluating options. As the existing ordering operations involved in multicriteria decision making cannot easily be implemented with intervals, we assume that scalar representative values with intervals can effectively avoid this issue. To deal with interval-valued criteria, we propose a generalized golden rule representative value approach, which involves the sigmoid function of backpropagation neural networks to tune parameters. Our approach considers the uncertainties and side effects of the interval variables to improve individual scalar representative values. Based on numerical examples, we address the effectiveness of the proposed approach, and we provide a specific application concerning multicriteria decision making with interval criteria satisfaction.

SDN has provided significant convenience for network providers and operators in cloud computing. Such a great advantage is extending to the Internet of Things network. However, it also increases the risk if the security of an SDN network is compromised. For example, if the network operator's permission is illegally obtained by a hacker, he/she can control the entry of the SDN network. Therefore, an effective authentication scheme is needed to fit various application scenarios with high-security requirements. In this article, we design, implement, and evaluate a new authentication scheme called the hidden pattern (THP), which combines graphics password and digital challenge value to prevent multiple types of authentication attacks at the same time. We examined THP in the perspectives of both security and usability, with a total number of 694 participants in 63 days. Our evaluation shows that THP can provide better performance than the existing schemes in terms of security and usability.

The Semantic Internet of Things (SIoT) is the extension of the Internet of Things (IoT) and the Semantic Web, which aims to build an interoperable collaborative system to solve the heterogeneous problems in the IoT. However, the SIoT has the characteristics of both the IoT and the Semantic Web environment, and the corresponding semantic data present many new data features. In this article, we analyze the characteristics of semantic data and propose the concept of a uniform knowledge graph (UKG), allowing us to be applied to the environment of the SIoT better. Here, we design a semantic collaboration method based on a UKG. It can take the UKG as the form of knowledge organization and representation, and provide a useful data basis for semantic collaboration by constructing the semantic links to complete semantic relation between different data sets, to achieve the semantic collaboration in the SIoT. Our experiments show that the proposed method can analyze and understand the semantics of user requirements better and provide more satisfactory outcomes.

Inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity reflecting the robustness of brain systems. In this study, we present a novel application of multiscale relative inherent fuzzy entropy using repetitive steady-state visual evoked potentials (SSVEPs) to investigate EEG complexity change between two migraine phases, i.e., interictal (baseline) and preictal (before migraine attacks) phases. We used a wearable headband EEG device with O1, Oz, O2, and Fpz electrodes to collect EEG signals from 80 participants [40 migraine patients and 40 healthy controls (HCs)] under the following two conditions: During resting state and SSVEPs with five 15-Hz photic stimuli. We found a significant enhancement in occipital EEG entropy with increasing stimulus times in both HCs and patients in the interictal phase, but a reverse trend in patients in the preictal phase. In the 1st SSVEP, occipital EEG entropy of the HCs was significantly lower than that of patents in the preictal phase (FDRadjustedp <; 0.05). Regarding the transitional variance of EEG entropy between the 1st and 5th SSVEPs, patients in the preictal phase exhibited significantly lower values than patients in the interictal phase (FDR-adjustedp <; 0.05). Furthermore, in the classification model, the AdaBoost ensemble learning showed an accuracy of 81 ± 6% and area under the curve of 0.87 for classifying interictal and preictal phases. In contrast, there were no differences in EEG entropy among groups or sessions by using other competing entropy models, including approximate entropy, sample entropy, and fuzzy entropy on the same dataset. In conclusion, inherent fuzzy entropy offers novel applications in visual stimulus environments and may have the potential to provide a preictal alert to migraine patients.

Forecasting time series is an emerging topic in operational research. Existing time-series models have limited prediction accuracy when faced with the characteristics of nonlinearity and nonstationarity in complex situations related to energy and finance. To enhance overall prediction capabilities and improve forecasting accuracy, in this article we propose a fuzzy interval time-series forecasting model on the basis of network-based multiple time-frequency spaces and the induced-ordered weighted averaging aggregation (IOWA) operation. Specifically, a time-series signal is decomposed into ensemble empirical modes and then reconstructed as various time-frequency spaces, which are transformed into visibility graphs. Then, forecasting intervals in different spaces can be collected after the local random walker link prediction model is adopted. Furthermore, a rule-based representation value function inspired by Yager's golden rule approach is defined, and an appropriate representation value is calculated. Finally, after IOWA is used to aggregate the forecasting outcomes in different time-frequency spaces, the final forecast value can be obtained from the fuzzy forecasting interval. Considering that energy issues are of widespread interest in nature and the social economy, two cases, based on a hydrological time series from the Biliuhe River in China and two well-known sets of financial time-series data, Taiwan Stock Exchange Capitalization Weighted Stock Index and Hang Seng Index, are studied to test the performance of the proposed approach in comparison with existing models. Our results show that the proposed approach can achieve better performance than well-developed models.

The massive amount of video data produced by surveillance networks in industries instigate various challenges in exploring these videos for many applications, such as video summarization (VS), analysis, indexing, and retrieval. The task of multiview video summarization (MVS) is very challenging due to the gigantic size of data, redundancy, overlapping in views, light variations, and interview correlations. To address these challenges, various low-level features and clustering-based soft computing techniques are proposed that cannot fully exploit MVS. In this article, we achieve MVS by integrating deep neural network based soft computing techniques in a two-tier framework. The first online tier performs target-appearance-based shots segmentation and stores them in a lookup table that is transmitted to cloud for further processing. The second tier extracts deep features from each frame of a sequence in the lookup table and pass them to deep bidirectional long short-term memory (DB-LSTM) to acquire probabilities of informativeness and generates a summary. Experimental evaluation on benchmark dataset and industrial surveillance data from YouTube confirms the better performance of our system compared to the state-of-the-art MVS methods.

In virtue of advances in smart networks and the cloud computing paradigm, smart healthcare is transforming. However, there are still challenges, such as storing sensitive data in untrusted and controlled infrastructure and ensuring the secure transmission of medical data, among others. The rapid development of watermarking provides opportunities for smart healthcare. In this article, we propose a new data-sharing framework and a data access control mechanism. The applications are submitted by the doctors, and the data is processed in the medical data center of the hospital, stored in semi-trusted servers to support the selective sharing of electronic medical records from different medical institutions between different doctors. Our approach ensures that privacy concerns are taken into account when processing requests for access to patients’ medical information. For accountability, after data is modified or leaked, both patients and doctors must add digital watermarks associated with their identification when uploading data. Extensive analytical and experimental results are presented that show the security and efficiency of our proposed scheme.

Training gene expression data with supervised learning approaches can provide an alarm sign for early treatment of lung cancer to decrease death rates. However, the samples of gene features involve lots of noises in a realistic environment. In this study, we present a random forest with self-paced learning bootstrap for improvement of lung cancer classification and prognosis based on gene expression data. To be specific, we propose an ensemble learning with random forest approach to improving the model classification performance by selecting multi-classifiers. Then, we investigate the sampling strategy by gradually embedding from high- to low-quality samples by self-paced learning. The experimental results based on five public lung cancer datasets show that our proposed method could select significant genes exactly, which improves classification performance compared to that of existing approaches. We believe that our proposed method has the potential to assist doctors in gene selections and lung cancer prognosis.

Prior studies have proposed methods to recover multi-channel electroencephalography (EEG) signal ensembles from their partially sampled entries. These methods depend on spatial scenarios, yet few approaches aiming to a temporal reconstruction with lower loss. The goal of this study is to retrieve the temporal EEG signals independently which was overlooked in data pre-processing. We considered EEG signals are impinging on tensor-based approach, named nonlinear Canonical Polyadic Decomposition (CPD). In this study, we collected EEG signals during a resting-state task. Then, we defined that the source signals are original EEG signals and the generated tensor is perturbed by Gaussian noise with a signal-to-noise ratio of 0 dB. The sources are separated using a basic nonnegative CPD and the relative errors on the estimates of the factor matrices. Comparing the similarities between the source signals and their recovered versions, the results showed significantly high correlation over 95%. Our findings reveal the possibility of recoverable temporal signals in EEG applications.

Molecular descriptor selection is an essential procedure to improve a predictive quantitative structure–activity relationship (QSAR) model. However, within the QSAR model, there are a number of redundant, noisy and irrelevant descriptors. In this study, we propose a novel descriptor selection framework using self-paced learning (SPL) via sparse logistic regression (LR) with Logsum penalty (SPL-Logsum), which can simultaneously adaptively identify the simple and complex samples and avoid over-fitting. SPL is inspired by the learning process of humans or animals gradually learned from simple and complex samples to train models, and the Logsum penalized LR helps to select a small subset of significant molecular descriptors for improving the QSAR models. Experimental results on some simulations and three public QSAR datasets show that our proposed SPL-Logsum framework outperforms other existing sparse methods regarding the area under the curve, sensitivity, specificity, accuracy, and [Formula: see text]-values.

Graph clustering aims to identify clusters that feature tighter connections between internal nodes than external nodes. We noted that conventional clustering approaches based on a single vertex or edge cannot meet the requirements of clustering in a higher-order mixed structure formed by multiple nodes in a complex network. Considering the above limitation, we are aware of the fact that a clustering coefficient can measure the degree to which nodes in a graph tend to cluster, even if only a small area of the graph is given. In this paper, we introduce a new cluster quality score, i.e., the local motif rate, which can effectively respond to the density of clusters in a higher-order graph. We also propose a motif-based local expansion and optimization algorithm (MLEO) to improve local higher-order graph clustering. This algorithm is a purely local algorithm and can be applied directly to higher-order graphs without conversion to a weighted graph, thus avoiding distortion of the transform. In addition, we propose a new seed-processing strategy in a higher-order graph. The experimental results show that our proposed strategy can achieve better performance than the existing approaches when using a quadrangle as the motif in the LFR network and the value of the mixing parameter $\mu $ exceeds 0.6.

Attribute reduction with many patterns and indicators has been regarded as an important approach for large-scale data mining and machine learning tasks. However, it is extremely difficult for researchers to inadequately extract knowledge and insights from multiple overlapping and interdependent fuzzy datasets from the current changing and interconnected big data sources. This paper proposes a deep neuro-cognitive co-evolution for fuzzy attribute reduction (DNCFAR) that contains a combination of quantum leaping particle swarm optimization with nearest-neighbor memeplexes. A key element of DNCFAR resides in its deep neuro-cognitive cooperative co-evolution structure, which is explicitly permitted to identify interdependent variables and adaptively decompose them in the same neuro-subpopulation, with minimizing the complexity and nonseparability of interdependent variables among different fuzzy attribute subsets. Next DNCFAR formalizes to the different types of quantum leaping particles with nearest-neighbor memeplexes to share their respective solutions and deeply cooperate to evolve the assigned fuzzy attribute subsets. The experimental results demonstrate that DNCFAR can achieve competitive performance in terms of average computational efficiency and classification accuracy while reinforcing noise tolerance. Furthermore, it can be well applied to clearly identify different longitudinal surfaces of infant cerebrum regions, which indicates its great potential for brain disorder prediction based on fMRI.

The unprecedented increase in data volume has become a severe challenge for conventional patterns of data mining and learning systems tasked with handling big data. The recently introduced Spark platform is a new processing method for big data analysis and related learning systems, which has attracted increasing attention from both the scientific community and industry. In this paper, we propose a shared nearest-neighbor quantum game-based attribute reduction (SNNQGAR) algorithm that incorporates the hierarchical coevolutionary Spark model. We first present a shared coevolutionary nearest-neighbor hierarchy with self-evolving compensation that considers the features of nearest-neighborhood attribute subsets and calculates the similarity between attribute subsets according to the shared neighbor information of attribute sample points. We then present a novel attribute weight tensor model to generate ranking vectors of attributes and apply them to balance the relative contributions of different neighborhood attribute subsets. To optimize the model, we propose an embedded quantum equilibrium game paradigm (QEGP) to ensure that noisy attributes do not degrade the big data reduction results. A combination of the hierarchical coevolutionary Spark model and an improved MapReduce framework is then constructed that it can better parallelize the SNNQGAR to efficiently determine the preferred reduction solutions of the distributed attribute subsets. The experimental comparisons demonstrate the superior performance of the SNNQGAR, which outperforms most of the state-of-the-art attribute reduction algorithms. Moreover, the results indicate that the SNNQGAR can be successfully applied to segment overlapping and interdependent fuzzy cerebral tissues, and it exhibits a stable and consistent segmentation performance for neonatal cerebral cortical surfaces.

In recent years, the concept of entropy has been widely used to measure the dynamic complexity of signals. Since the state of complexity of human beings is significantly affected by their health state, developing accurate complexity evaluation algorithms is a crucial and urgent area of study. This paper proposes using inherent fuzzy entropy (Inherent FuzzyEn) and its multiscale version, which employs empirical mode decomposition and fuzzy membership function (exponential function) to address the dynamic complexity in electroencephalogram (EEG) data. In the literature, the reliability of entropy-based complexity evaluations has been limited by superimposed trends in signals and a lack of multiple time scales. Our proposed method represents the first attempt to use the Inherent FuzzyEn algorithm to increase the reliability of complexity evaluation in realistic EEG applications. We recorded the EEG signals of several subjects under resting condition, and the EEG complexity was evaluated using approximate entropy, sample entropy, FuzzyEn, and Inherent FuzzyEn, respectively. The results indicate that Inherent FuzzyEn is superior to other competing models regardless of the use of fuzzy or nonfuzzy structures, and has the most stable complexity and smallest root mean square deviation.

The main challenge of attribute reduction in large data applications is to develop a new algorithm to deal with large, noisy, and uncertain large data linking multiple relevant data sources, structured or unstructured. This paper proposes a new and efficient layered-coevolution-based attribute-boosted reduction algorithm (LCQ-ABR*) using adaptive quantum-behavior particle swarm optimization (PSO). First, the quantum rotation angle of an evolutionary particle is updated by a dynamic change of self-adapting step size. Second, a self-adaptive partitioning strategy is employed to group particles into different memeplexes, and the quantum-behavior mechanism with the particles' states depicted by the wave function cooperates to achieve superior performance in their respective memeplexes. Third, a new layered coevolutionary model with multiagent interaction is constructed to decompose a complex attribute set, and it can self-adapt the attribute sizes among different layers and produce the reasonable decompositions by exploiting any interdependence among multiple relevant attribute subsets. Fourth, the decomposed attribute subsets are evolved to compute the positive region and discernibility matrix by using their best quantum particles, and the global optimal reduction set is induced successfully. Finally, extensive comparative experiments are provided to illustrate that LCQ-ABR*has better feasibility and effectiveness of attribute reduction on large-scale and uncertain dataset problems with complex noise as compared with representative algorithms. Moreover, LCQ-ABR*can be successfully applied in the consistent segmentation for neonatal brain three-dimensional MRI, and the consistent segmentation results further demonstrate its stronger applicability.

This study considers the dynamic changes of complexity feature by fuzzy entropy measurement and repetitive steady-state visual evoked potential (SSVEP) stimulus. Since brain complexity reflects the ability of the brain to adapt to changing situations, we suppose such adaptation is closely related to the habituation, a form of learning in which an organism decreases or increases to respond to a stimulus after repeated presentations. By a wearable electroencephalograph (EEG) with Fpz and Oz electrodes, EEG signals were collected from 20 healthy participants in one resting and five-times 15 Hz SSVEP sessions. Moreover, EEG complexity feature was extracted by multi-scale Inherent Fuzzy Entropy (IFE) algorithm, and relative complexity (RC) was defined the difference between resting and SSVEP. Our results showed the enhanced frontal and occipital RC was accompanied with increased stimulus times. Compared with the 1st SSVEP session, the RC was significantly higher than the 5th SSVEP session at frontal and occipital areas (p <; 0.05). It suggested that brain has adapted to changes in stimulus influence, and possibly connected with the habituation. In conclusion, effective evaluation of IFE has a potential EEG signature of complexity in the SSEVP-based experiment.

Migraine is a chronic neurological disease characterized by recurrent moderate to severe headaches during a period like one month often in association with symptoms in human brain and autonomic nervous system. Normally, migraine symptoms can be categorized into four different stages: inter-ictal, pre-ictal, ictal, and post-ictal stages. Since migraine patients are difficulty knowing when they will suffer migraine attacks, therefore, early detection becomes an important issue, especially for low-frequency migraine patients who have less than 5 times attacks per month. The main goal of this study is to develop a migraine-stage classification system based on migraineurs' resting-state EEG power. We collect migraineurs' O1 and O2 EEG activities during closing eyes from occipital lobe to identify pre-ictal and non-pre-ictal stages. Self-Constructing Neural Fuzzy Inference Network (SONFIN) is adopted as the classifier in the migraine stages classification which can reach the better classification accuracy (66%) in comparison with other classifiers. The proposed system is helpful for migraineurs to obtain better treatment at the right time.σ