Brain-computer Interface (BCI) technology enables direct communication between the brain and external devices via non-invasive methods and holds significant potential in neu roengineering, rehabilitation, and human-computer interaction. However, decoding motor imagery (MI) from electroencephalo gram (EEG) signals remains challenging due to these signals' non-stationary characteristics and the limited interpretability of existing deep learning models. In this paper, we propose a novel Hierarchical Collaborative Fuzzy Network (HCFN) for interpretable EEG-based MI decoding. We introduce an Adaptive Fuzzy Temporal Convolutional Network (AFTCN) that employs dynamic fuzzy kernels within causal convolutions to extract robust temporal features from EEG signals. Additionally, we design a fuzzy attention-guided Takagi–Sugeno–Kang (TSK) architecture that achieves a tighter integration between feature extraction and fuzzy inference through a novel fuzzy feedback loop, thereby improving the discriminability of extracted features. Extensive experiments on the BCI Competition IV-2a, IV-2b and OpenBMI datasets, under both subject-dependent and cross subject evaluation paradigms, demonstrate that the proposed model outperforms state-of-the-art methods in classification ac curacy and Cohen's kappa. Furthermore, we provide multi-level interpretability analyses, from macro to micro perspectives, elucidating the model's decision-making processes and highlighting the advantages of our collaborative reasoning framework over conventional cascaded approaches. The code is available at https://github.com/Pitiless-Quinn/HCFN.

edge intelligence | internet of autonomous unmanned vehicles | software systemsWith the development of the embedded systems, navigation, sensors, robots, and big data analytics, the automobile vehicles industry has been one major economic sector recently, and its economical and societal impacts continue to expand.To extend the capabilities of automobile vehicles, the Internet of Autonomous Unmanned Vehicles (IAUV) has been proposed to form a global network of sensors, robots and unmanned vehicles, improving cooperation between heterogeneous communication systems to provide reliable Internet services in civil applications such as environmental monitoring, video surveillance, network provisioning, wireless power transfer, and emergency or disaster assistance.However, these powerful applications always require the support from automobile, transportation, wireless communications, networking, resource management, intelligent computing, security, and robotics, and so on.Due to diverse and interdisciplinary nature of these challenges, architectures, algorithms and developmental software systems proposed by networking, robotics, transportation, cognitive and artificial intelligence research communities will need to be utilized.Fortunately, the abilities of IAUV systems can be greatly realized with high performance of low latency and high reliability by applying artificial intelligence-enabled computing, communication, edge service deployment and flexible resource schedule of unmanned vehicles, robots and sensors appropriate and ---------------

With the development of sensors, more and more multimodal data are accumulated, especially in biomedical and bioinformatics fields. Therefore, multimodal data analysis becomes very important and urgent. In this study, we combine multi-kernel learning and transfer learning, and propose a feature-level multi-modality fusion model with insufficient training samples. To be specific, we firstly extend kernel Ridge regression to its multi-kernel version under the lp-norm constraint to explore complementary patterns contained in multimodal data. Then we use marginal probability distribution adaption to minimize the distribution differences between the source domain and the target domain to solve the problem of insufficient training samples. Based on epilepsy EEG data provided by the University of Bonn, we construct 12 multi-modality & transfer scenarios to evaluate our model. Experimental results show that compared with baselines, our model performs better on most scenarios.

The safety monitoring system of intelligent transportation provides driving fatigue warning and risk control. Electroencephalogram (EEG) signals can directly reflect the neuronal activity of the brain. The detection and early warning of driving fatigue using EEG signals has important practical significance. However, because of the non-stationarity and timeliness of EEG signals, the single feature detection method is significantly impacted by data distribution differences. In this paper, in the framework of multi-input multi-output (MIMO) Takagi-Sugeno-Kang (TSK) fuzzy system, transferable TSK fuzzy classifier with multi-views (T-TSK-MV) is developed for EEG-based driving fatigue recognition in intelligent transportation. First, in view-specific consequent parameter learning, the view-specific consequent regularizer is designed based on technologies of ridge regression, maximum mean discrepancy (MMD), and manifold regularization, which becomes the bridge to transfer the discriminative information from the related domain to the target domain. In addition, the$\ell _{2,1} $-norm sparse constraint on consequent parameters is used to simplify fuzzy rules. Then multi-view learning is integrated into the consequent parameter learning, in which T-TSK-MV explores the view-shared consequent regularizer and adaptively assigns weights to each view. The$\ell _{2,1} $-norm sparse constraint on view-shared consequent regularizer can effectively exploit the local structure of multi-view data. Finally, the fuzzy classifier is constructed on view-specific regularizers and view weights. The experiment on real-word datasets shows that the proposed fuzzy classifier can significantly improve the driving fatigue recognition performance.

Text sentiment classification is an important technology for natural language processing. A fuzzy system is a strong tool for processing imprecise or ambiguous data, and it can be used for text sentiment analysis. This article proposes a new formulation of a multi-task Takagi-Sugeno-Kang fuzzy system (TSK FS) modeling, which can be used for text sentiment image classification. Using a novel multi-task fuzzy c-means clustering algorithm, the common (public) information among all tasks and the individual (private) information for each task are extracted. The information about clustering, for example, cluster centers, can be used to learn the antecedent parameters of multi-task TSK fuzzy systems. With the common and individual antecedent parameters obtained, a corresponding multi-task learning mechanism for learning consequent parameters is devised. Accordingly, a multi-task fuzzy clustering–based multi-task TSK fuzzy system (MTFCM-MT-TSK-FS) is proposed. When the proposed model is built, the information conveyed by the fuzzy rules formed is two-fold, including (1) common fuzzy rules representing the inter-task correlation information and (2) individual fuzzy rules depicting the independent information of each task. The experimental results on several text sentiment datasets demonstrate the validity of the proposed model.

N6-methyladenosine (m6A) has been shown to play crucial roles in RNA metabolism, physiology, and pathological processes. However, the specific regulatory mechanisms of most methylation sites remain uncharted due to the complexity of life processes. Biological experimental methods are costly to solve this problem, and computational methods are relatively lacking. The discovery of local co-methylation patterns (LCPs) of m6A epi-transcriptome data can benefit to solve the above problems. Based on this, we propose a novel biclustering algorithm based on the beta distribution (BDBB), which realizes the mining of LCPs of m6A epi-transcriptome data. BDBB employs the Gibbs sampling method to complete parameter estimation. In the process of modeling, LCPs are recognized as sharp beta distributions compared to the background distribution. Simulation study showed BDBB can extract all the three actual LCPs implanted in the background data and the overlap conditions between them with considerable accuracy (almost close to 100%). On MeRIP-Seq data of 69,446 methylation sites under 32 experimental conditions from 10 human cell lines, BDBB unveiled two LCPs, and Gene Ontology (GO) enrichment analysis showed that they were enriched in histone modification and embryo development, etc. important biological processes respectively. The GOE_Score scoring indicated that the biclustering results of BDBB in the m6A epi-transcriptome data are more biologically meaningful than the results of other biclustering algorithms.

No abstract available.

The seven papers in this special section focus on advanced machine learning methods for brain machine interfacing. Particular emphasis is on novel theories and methods using transfer learning and deep learning proposed for Brain-Machine Interfacing (BMI) or Brain-Computer Interfacing (BCI). Our purpose is to review the new progress and achievements on transfer learning, deep learning, and their applications in BMI or BCI in recent years.

Conventional classification models for epileptic EEG signal recognition need sufficient labeled samples as training dataset. In addition, when training and testing EEG signal samples are collected from different distributions, for example, due to differences in patient groups or acquisition devices, such methods generally cannot perform well. In this paper, a cross-domain classification model with knowledge utilization maximization called CDC-KUM is presented, which takes advantage of the data global structure provided by the labeled samples in the related domain and unlabeled samples in the current domain. Through mapping the data into kernel space, the pairwise constraint regularization term is combined together the predictive differences of the labeled data in the source domain. Meanwhile, the soft clustering regularization term using quadratic weights and Gini-Simpson diversity is applied to exploit the distribution information of unlabeled data in the target domain. Experimental results show that CDC-KUM model outperformed several traditional non-transfer and transfer classification methods for recognition of epileptic EEG signals.

Person reidentification, as one of the most important areas in the video surveillance field, is a crucial task in computer vision. It has attracted more and more attention in both academic research and industry due to its extremely high application value. However, the recognition accuracy of person reidentification is subjected to factors, such as illumination, pose, occlusion, and viewpoint. To alleviate the effect of such factors, the multiscale Retinex with color restoration (MSRSC) algorithm is adopted to preprocess the original images so that the color information can be restored and the illumination condition can be improved. To obtain pose-invariant features (PIFs), the convolution pose machine that can generate the body joint points of pedestrians is applied to divide the body into seven parts, the pose transform network is then used to align the body parts, and finally, the PIFs can be obtained from a designed pseudo-Siamese network by using the original and aligned images as the training samples. To further improve recognition accuracy, a reranking method based on bidirectional k-nearest neighbors (KNN) is presented to optimize the ranking list. Experimentally, the proposed method is implemented on three data sets: viewpoint invariant pedestrian recognition (VIPeR), CUHK03, and Market1501. The results demonstrate our method outperforms the other methods, as a result of both the representation of a more discriminative feature descriptor and the introduction of a reranking method.

Industry 4.0 places special emphasis on the use of intelligent models to discover patterns in data. In this article, we propose a novel Takagi-Sugeno-Kang (TSK) fuzzy system with low model complexity for multiview data pattern discovery. Compared with the classic TSK fuzzy systems, the proposed one has three merits: First, we introduce a transformation matrix to relax the strict binary label matrix of the training set so that the margins between classes become more discriminative. Second, we introduce two kinds of sparsity regularizations, i.e., cross-rule and cross-view, to reduce indiscriminative fuzzy rules and consequent parameters so that the model complexity is significantly reduced. Third, we introduce the alternating direction method of multipliers to optimize the objective function so that we have compact closed-form solutions in each iteration. Extensive experiments on different kinds of multiview image datasets indicate the promising performance for data pattern discovery with low model complexity.

Facial expression is one of the important characteristics of drivers during driving. It is very useful in safe driving detection. Recognizing drivers' expressions by the facial images can be solved with machine learning classification strategies. To obtain a reliable reorganization performance, most of approaches assume that the facial images in the training and testing datasets are independently and identically distributed. However, for real time drivers' facial expression recognition, due to vehicle motion, changes in illumination, noise and head movement, the features displayed for the training dataset may be not valid for the testing dataset. To solve this problem, a novel approach is proposed for cross-dataset transfer driver expression recognition via global discriminative and local structure knowledge exploitation in shared projection subspace (GD-LS-SS). By leaning a shared common subspace, GD-LS-SS utilizes the local geometrical structure of data by exploiting the knowledge of graph topology, meanwhile exploiting the global discriminative information by using the pairwise constrained knowledge between the source and labeled target data. Taking advantage of kernel trick, the kernel version of GD-LS-SS is proposed to learn the kernel projection for handling nonlinear cross-dataset transfer and to further promote the recognition accuracy. Experiments on the KMU-FED dataset show that the satisfactory recognition performance of GD-LS-SS outperforms several traditional non-transfer and related transfer approaches.

Behavioral decision systems may suffer from poor performance due to the failure in capturing the vibrations of environmental information. To better capture such vibrations and then make more accurate predictions, a parallel deep neural network based on dual fusions including feature and decision is proposed, called DFFD-Net. DFFD-NET is composed of two parts, the feature fusion network and the driving data network. The feature fusion model adopts two different operations, deconvolution and linear weighting, to fuse local features and global features, respectively. Deconvolution is applied between the convolutional layers, while linear weighting is operated among the outputs of SPP and LSTM. To further improve the accuracy of the prediction, the decisions generated from both networks are further weighed to get the final decision. Experimentally, DFFD-NET is implemented in the benchmarks BDDV and TORCS, and the results show that the final performance is benefited from both feature fusion and decision fusion. From the comparison, DFFD-NET can get state-of-the-art results on both perplexity and precision by only using the images captured from the front-facing camera as well as a few sensing data.

With the continuous exploration of genetic research, gradually exposed privacy issues become the bottleneck that limits its development. DNA motif finding is an important study to understand the regulation of gene expression; however, the existing methods generally ignore the potential sensitive information that may be exposed in the process. In this work, we utilize the -differential privacy model to provide provable privacy guarantees which is independent of attackers’ background knowledge. Our method makes use of sample databases to prune the generated candidate motifs to lower the magnitude of added noise. Furthermore, to improve the utility of mining results, a strategy of threshold modification is designed to reduce the propagation and random sampling errors in the mining process. Extensive experiments on actual DNA databases confirm that our approach can privately find DNA motifs with high utility and efficiency.

Morphological classification of human sperm heads is a key technology for diagnosing male infertility. Due to its sparse representation and learning capability, dictionary learning has shown remarkable performance in human sperm head classification. To promote the discriminability of the classification model, a novel local constraint and label embedding multi-layer dictionary learning model called LCLM-MDL is proposed in this study. Based on the multi-layer dictionary learning framework, two dictionaries are built on the basis of Laplacian regularized constraint and label embedding term in each layer, and the two dictionaries are approximated to each other as much as possible, so as to well exploit the nonlinear structure and discriminability features of the morphology of human sperm heads. In addition, to promote the robustness of the model, the asymmetric Huber loss is adopted in the last layer of LCLM-MDL, which approximates the misclassification error by using the absolute error function. Finally, the experimental results on HuSHeM dataset demonstrate the validity of the LCLM-MDL.

Clustering is an important issue in brain medical image segmentation. Original medical images used for clinical diagnosis are often insufficient for clustering in the current domain. As there are sufficient medical images in the related domains, transfer clustering can improve the clustering performance of the current domain by transferring knowledge across the related domains. In this article, we propose a novel shared hidden space transfer fuzzy c- means (FCM) clustering called SHST-FCM for cross-domain brain computed tomography (CT) image segmentation. SHST-FCM projects both the data samples of the source domain and target domain into the shared hidden space, such that the distributions of the two domains are as close as possible. In the learned shared subspace, the data samples of the source domain serve as the auxiliary knowledge to aid the clustering process in the target domain. Extensive experiments on brain CT medical image datasets indicate the effectiveness of the proposed method.

In the field of medical imaging research, renal segmentation is an important task which is tedious and error-prone when performed manually. Deep learning methods have been successfully applied to feature learning in medical applications. In this paper, We focused on the high accuracy of the classification task because of its effect on the accuracy of a better segmentation, and a Renal Segmentation Algorithm Combined Low-level Features with Deep Coding Feature from medicine images is proposed. Firstly, we use the advantage of Stacked auto-encoder networks to automatically learn the high-level features that capture the structured information and semantic context in the image. Several low-level features are extracted, which can effectively capture contrast and spatial information in the renal regions, and incorporated to compensate with the learned high-level features at the output of the very last fully connected layer. The concatenated feature vector is further fed into a Least squares SVM detector with Morlwet kernel to obtain classification results. We trained the deep network on medicine data set and experimentally shows that our proposed method has high classification accuracy and can speed up the clinical task to segment the renal.

Electroencephalogram (EEG) data processing applications have become routine tasks in both bioscience and neuroscience research, which are usually highly compute and data intensive. In this paper, we present a parallel method to analyze the huge EEG data with a Beowulf cluster. Through an example of the synchronization measurement of multiple neuronal populations, the procedure of exploiting the parallelism of EEG data processing applications to achieve speed-up has been detailed. The experimental results indicate that the execution efficiency of EEG data processing can be improved dramatically using parallel and distributed computing techniques even with inexpensive computing platform.