Federated learning has emerged as an effective paradigm for training machine learning models across distributed clients without sharing raw data. In many real-world applications, sequential data are inherently multimodal, involving heterogeneous streams such as audio, visual, and temporal signals. However, most existing federated approaches rely on deterministic neural networks, which often struggle to capture predictive uncertainty under heterogeneous data distributions, cross-modal inconsistencies, and dynamic client participation. In this paper, we propose FedBayesMamba , a Bayesian federated learning framework for multimodal sequential data modeling based on selective state space models. The proposed approach introduces Bayesian parameterization into the Mamba architecture to enable uncertainty-aware sequence modeling while preserving the computational efficiency of state space models. To effectively integrate uncertainty across distributed clients, we further develop a posterior aggregation strategy that combines client-level posterior distributions in a principled probabilistic manner. Extensive experiments on multiple benchmark datasets demonstrate that the proposed framework achieves competitive predictive performance and improved uncertainty estimation under Non-IID federated settings. The results also indicate that FedBayesMamba exhibits strong robustness and stability in challenging federated scenarios. These findings highlight the potential of combining Bayesian learning with state space models for multimodal temporal modeling, particularly in audio-visual perception and cross-modal sequence understanding tasks.

Multimodal emotion analysis is pivotal in decoding complex human affect by integrating diverse data sources such as text, audio, and visual signals. In this article, we introduce contextual interaction-based multimodal emotion analysis with enhanced semantic information (CIME), a novel spatio-temporal interaction network that significantly improves emotion recognition accuracy and robustness. CIME employs a text-centric cross-modal attention mechanism to refine semantic representations, while simultaneously leveraging a graph convolutional network to model contextual dialog information by capturing both intraspeaker and interspeaker relationships. This dual approach enables the effective fusion of modality-specific cues and the mining of latent emotional associations across modalities. Extensive experiments conducted on benchmark datasets—including IEMOCAP and MOSEI—demonstrate that CIME consistently outperforms existing state-of-the-art methods in terms of overall classification accuracy and weighted F1-scores. Furthermore, detailed ablation studies underscore the critical contributions of both the cross-modal attention and graph-based contextual modules.

With the rapid development of 5G communication technology, the data in the Internet of Medical Things (IoMT) application systems exhibits complex characteristics such as large volume, high dimensionality, nonlinearity, and diversity, which significantly affect the efficiency and detection performance of anomaly detection tasks. How to efficiently extract nonlinear features from high-dimensional data in the context of the IoMT while minimizing information distortion in data objects are challenging problems in recent academic research. A novel adaptive nonlinear feature extraction method via fruit fly olfactory neural network (Fly dimension expansion projection and remain main components by PCA, FDEPCA) is proposed, where 1) the data are mean-centered; 2) a binary sparse random projection matrix is used for dimension expansion projection; and 3) PCA is used to extract principal component information. The proposed method overcomes the problems of present nonlinear feature extraction in the face of high-dimensional outliers where the intrinsic geometric structure of the data is severely distorted and computationally expensive. The dataset after nonlinear feature extraction by the FDEPCA algorithm is applied to specific anomaly detection models, using ROC curves and AUC as evaluation metrics for classification performance. Extensive comparison experiments are conducted on eight publicly available datasets, and experimental results show that compared with the popular nonlinear feature extraction algorithms, the FDEPCA algorithm has better classification performance and projection time advantage. When applied to proximity-based, probability-based, and ensemble-based different anomaly detection models respectively, the FDEPCA algorithm exhibits strong applicability in different types of anomaly detection classifiers.

Palmprint-based biometric recognition has gained widespread attention due to its rich features, contactless acquisition, and low invasiveness. However, most existing methods neglect image quality, making them less effective for low-quality, noisy palmprint images. In this paper, we propose a palm intrinsic features learning selective state space model (PalmMamba) for palmprint image denoising, which consists of shallow feature representation, noise-insensitive palmprint-specific feature learning, and sharp palmprint image restoration modules. First, we convert the degraded noisy palmprint image into a high-dimensional shallow feature representation through a single-layer convolution backbone. Then, we develop parallel learning branches, including a second-order attention-based selective state space model and a mixed difference convolution module, to exploit diverse palmprint-specific features with both global and local details. Finally, we map the fine-grained palmprint-intrinsic feature map into the identity-preserved sharp palmprint image via a commonly used convolution layer. Extensive experimental results on five public palmprint databases demonstrate the encouraging performance of the proposed PalmMamba in palmprint image denoising.

With ever-increasing dataset size and data storage capacity, there is a strong need to build systems that can effectively utilize these vast datasets to extract valuable information. Large datasets often exhibit sparsity and pose cold start problems, necessitating the development of responsible recommender systems. Knowledge graphs have utility in responsibly representing information related to recommendation scenarios. However, many studies overlook explicitly encoding contextual information, which is crucial for reducing the bias of multi-layer propagation. Additionally, existing methods stack multiple layers to encode high-order neighbor information while disregarding the relational information between items and entities. This oversight hampers their ability to capture the collaborative signal latent in user-item interactions. This is particularly important in health informatics, where knowledge graphs consist of various entities connected to items through different relations. Ignoring the relational information renders them insufficient for modeling user preferences. This work presents an end-to-end recommendation framework named KGCAN (Knowledge Graph Enhanced Contextualized Attention-Based Network), which explicitly encodes both relational and contextual information of entities to preserve the original entity information. Furthermore, a user-specific attention mechanism is employed to capture personalized recommendations. The proposed model is validated on three benchmark datasets through extensive experiments. The experimental results demonstrate that KGCAN outperforms existing knowledge graph based recommendation models. Additionally, a case study from the healthcare domain is discussed, highlighting the importance of attention mechanisms and high-order connectivity in the responsible recommendation system for health informatics.

Recent advances in artificial intelligence have led to deepfake images, enabling users to replace a real face with a genuine one. deepfake images have recently been used to malign public figures, politicians, and even average citizens. deepfake but realistic images have been used to stir political dissatisfaction, blackmail, propagate false news, and even carry out bogus terrorist attacks. Thus, identifying real images from fakes has got more challenging. To avoid these issues, this study employs transfer learning and data augmentation technique to classify deepfake images. For experimentation, 190,335 RGB-resolution deepfake and real images and image augmentation methods are used to prepare the dataset. The experiments use the deep learning models: convolutional neural network (CNN), Inception V3, visual geometry group (VGG19), and VGG16 with a transfer learning approach. Essential evaluation metrics (accuracy, precision, recall, F1-score, confusion matrix, and AUC-ROC curve score) are used to test the efficacy of the proposed approach. Results revealed that the proposed approach achieves an accuracy, recall, F1-score and AUC-ROC score of 90% and 91% precision, with our fine-tuned VGG16 model outperforming other DL models in recognizing real and deepfakes.

Palmprint has attracted increasing attention for biometric recognition in recent years due to its outstanding reliability, user-friendliness and hygiene. However, existing palmprint recognition methods usually require high-quality palmprint images with clear texture and line patterns; however, in practical applications palmprint images are usually of low quality. In this study, we propose a dense hybrid attention (DHA) network for palmprint image super-resolution (SR) by recovering the clear palmprint-specific characteristics. The proposed DHA network first obtains the high-dimensional shallow representation via a single convolution layer, and then jointly learns the local and global palmprint-specific features via parallel convolutional neural network (CNN)-and transformer-based branches. Particularly, we develop two enhanced spatial and channel attention (CA) modules to adaptively emphasize the local position-specific characteristics of palmprints, such that the SR palmprint images can be well recovered with clear texture and edge characteristics. Experimental results on three publicly used palmprint databases clearly show the effectiveness of the proposed method for palmprint image SR.

In this study, using High-Density Surface Electromyography (HD-sEMG) signals and extracted features, we propose a Machine Learning (ML) architecture for predicting active aging through motor functional age (MFA) estimation, a new concept introduced by Prof. S. Boudaoud and Prof. K. Kinugawa in EIT-Health CHRONOS project. In fact, MFA should be equal to Chronological Age (CA) for active subjects. For this purpose, several time and frequency features have been tested, coupled to classical ML classifiers, to predict active aging through MFA estimation. The classifiers have been trained on the CHRONOS database composed by 79 active subjects (International Physical Activity Questionnaire (IPAQ): 2–3, from 25 to 74 yrs) and we tested, as a preliminary study, 4 sedentary subjects (IPAQ: 1, 45–54 yrs). The results show that the best classifier gave promising results (Accuracy = 71.6% ± 3.86) for confirming active aging (MFA=CA) for active tested subjects and predicting sedentary aging (MFA>CA) for some sedentary subjects. This preliminary results have to be confirmed in future studies on larger cohort.

Heterogeneous palmprint recognition has attracted considerable research attention in recent years because it has the potential to greatly improve the recognition performance for personal authentication. In this article, we propose a simultaneous heterogeneous palmprint feature learning and encoding method for heterogeneous palmprint recognition. Unlike existing hand-crafted palmprint descriptors that usually extract features from raw pixels and require strong prior knowledge to design them, the proposed method automatically learns the discriminant binary codes from the informative direction convolution difference vectors of palmprint images. Differing from most heterogeneous palmprint descriptors that individually extract palmprint features from each modality, our method jointly learns the discriminant features from heterogeneous palmprint images so that the specific discriminant properties of different modalities can be better exploited. Furthermore, we present a general heterogeneous palmprint discriminative feature learning model to make the proposed method suitable for multiple heterogeneous palmprint recognition. Experimental results on the widely used PolyU multispectral palmprint database clearly demonstrate the effectiveness of the proposed method.

Accident analyses have revealed that pilots can fail to process auditory stimuli such as alarms, a phenomenon known as inattentional deafness. The motivation of this research is to develop a passive brain computer interface that can predict the occurence of this critical phenomenon during real flight conditions. Ten volunteers, equipped with a dry-EEG system, had to fly a challenging flight scenario while responding to auditory alarms by button press. The behavioral results disclosed that the pilots missed 36% of the auditory alarms. ERP analyses confirm that this phenomenon affects auditory processing at an early (N100) and late (P300) stages as the consequence of a potential attentional bottleneck mechanism. Intersubject classification was carried out over frequency features extracted three second epochs before the alarms’ onset using sparse representation for classification (SRC), sparse and dense representation (SDR) and more conventional approach such as linear discriminant analysis (LDA), shrinkage LDA and nearest neighbor (1NN). In the best case, SRC and SDR gave respectively a performance of 66.9% and 65.4% of correct mean classification rate to predict the occurrence of inattentional deafness, outperforming LDA (60.6%), sLDA (60%) and 1 NN (59.6%). These results open promising perspectives for the implementation of neuroadaptive automation with as ultimate goal to enhance alarm stimulation delivery so that it is perceived and acted upon.

Palmprint recognition is an important and widely used biometric modality with high reliability, stability and user acceptability. In this paper we propose a simple and effective ensemble learning method for palmprint identification based on Random Subspace Sampling (RSS). To achieve it, we rely on 2D-PCA to build the random subspaces. As 2D-PCA is an unsurpevised technique, features are extracted in each subspace using 2D-LDA. A simple 1-Nearest Neighbor classifier is associated to each subspace, the final decision rule being obtained by majority voting rule. The experimental results on multispectral and PolyU palmprint datasets show very encouraging performances compared to state-of-the-art techniques.

Gait recognition is an emerging biometric technology that identifies people through the analysis of the way they walk. The challenge of model-free based gait recognition is to cope with various intra-class variations such as clothing variations, carrying conditions and angle variations that adversely affect the recognition performance. This paper proposes a method to select the most discriminative human body part based on group Lasso of motion to reduce the intra-class variation so as to improve the recognition performance. The proposed method is evaluated using CASIA Gait Dataset B. Experimental results demonstrate that the proposed technique gives promising results.

Chord represents the back-bone of occidental music genre as it contains rich harmonic information which is useful for various music applications such as music genre classification or music retrieval. Hence, chord recognition or transcription is of importance for music representation. In this paper we focus on chord recognition and especially investigate different features representation used in such a system: classical features as well as a new type of feature we propose are explored. We evaluate their usefulness through a multi-class chord classification problem.