Advances in metagenomic sequencing have increasingly implicated gut microbiome dysbiosis in numerous complex diseases, yet its application for precise differential diagnosis remains a major challenge. Existing computational approaches often show limited predictive performance and insufficient robustness when applied to large-scale, imbalanced microbiome datasets, and they typically lack mechanisms to effectively capture microbial community-level or functional guild interactions. To address these limitations, we developed AR-CDT Net, a novel deep learning framework that integrates a Multi-Scale Deformable Convolution (MS-DConv) module with a Channel-wise Dynamic Tanh (CD-Tanh) activation function to achieve more accurate and robust classification of host disease states. Evaluated on a large-scale cohort comprising over 8000 samples spanning eight disease phenotypes, AR-CDT Net demonstrated highly competitive within-cohort performance, outperforming nine representative models across the majority of classification tasks. Importantly, in a stringent cross-dataset generalization test, the model was trained on the highly imbalanced primary multi-disease cohort and validated on relatively balanced independent external cohorts. It achieved a statistically significant AUC of 0.7921 on the highly heterogeneous external T2D cohort, confirming that AR-CDT captures transferable biological signals rather than dataset-specific artifacts. Furthermore, by combining dimensionality reduction with SHAP-based interpretation of our One-vs-Rest (OvR) classifiers, AR-CDT disentangles disease-specific pathogenic signatures from the shared dysbiotic background among clinically distinct yet microbially similar diseases.

RF-based wireless sensing is a promising technology for enabling applications such as human activity recognition, intelligent healthcare, and robotics. However, the growing concern in data privacy and also the complexity in modeling and keeping track of the statistical heterogeneity of RF signal hinders its wide application, especially in large-scale wireless networking systems. In this paper, we introduce a hierarchical clustering-based federated learning framework, called Uniform Manifold Clustering Federated Learning (UMCFL), that has the potential to address the above challenges. UMCFL first divides all the RF signal receivers into different clusters according to the similarity of their data distributions and then construct an individual model for receivers within each cluster. To measure the data distribution similarity between receivers in a computationally efficient way, UMCFL first adopts a uniform manifold approximation and projection (UMAP)-based solution to convert data samples at each receiver into a low-dimensional representation and then use maximum mean discrepancy (MMD) to calculate the similarity score between datasets of different receivers. We prove the convergence of UMCFL and perform extensive experiments to evaluate its performance. Experimental results show that UMCFL achieves up to 63% improvement in sensing accuracy, compared to the traditional FedAvg-based wireless sensing solution.

Accurate models ensure the efficient and safe operations of industrial processes. However, modeling of industrial processes with non-stationary characteristic is challenging. In this study, an interval type-2 fuzzy neural network (IT2FNN) based on active semi-supervised learning (ASSL-IT2FNN) is proposed for such industrial processes. First, an IT2FNN with adaptive fuzzy membership function (FMF) and hierarchical learning method is utilized to achieve considerable modeling accuracy and efficiency. Second, to better tackle with the non-stationary characteristic, an unsupervised distribution detection method is proposed to identify the occurrence moments of concept drift actively from the perspective of probability and spatial projection. Then, by an active semi-supervised learning method, the concept drift samples with partial labels are used to build a subnetwork, guaranteeing the performance of the constructed IT2FNN models with incomplete data in non-stationary environments. Finally, the proposed ASSL-IT2FNN is verified by benchmark simulations and real industrial data, and the experimental results demonstrate its outperformance.Note to Practitioners—Concept drift is an important indicator of a non-stationary environment. It manifests as changes in the distribution of samples over time, which may reduce the accuracy of models constructed based on historical samples. This study aims to alleviate the impact of concept drift on model performance in non-stationary industrial processes while reduce the cost of sample labeling. By using the adaptive FMF and hierarchical learning method, the designed IT2FNN can effectively deal with the inherent nonlinearity of complicated industrial processes. The samples representing concept drift are actively identified by the Kullback-Leibler (KL) divergence and maximum mean difference (MMD), enabling timely detections of concept drift moments in unsupervised situations. The active semi-supervised learning is used to adapt the constructed model to handle the concept drift, wherein a subnetwork is designed based on concept drift samples and similar historical samples.

Semantic communication is a novel communication paradigm that focuses on recognizing and delivering the desired meaning of messages to the destination users. Most existing works in this area focus on delivering explicit semantics, labels or signal features that can be directly identified from the source signals. In this paper, we consider the implicit semantic communication problem in which hidden relations and closely related semantic terms that cannot be recognized from the source signals need to also be delivered to the destination user. We develop a novel adversarial learning-based implicit semantic-aware communication (iSAC) architecture in which the source user, instead of maximizing the total amount of information transmitted to the channel, aims to help the recipient learn an inference rule that can automatically generate implicit semantics based on limited clue information. We prove that by applying iSAC, the destination user can always learn an inference rule that matches the true inference rule of the source messages. Experimental results show that the proposed iSAC can offer up to a 19.69 dB improvement over existing non-inferential communication solutions, in terms of symbol error rate at the destination user.

Semantic communication has recently attracted significant interest from both industry and academia due to its potential to transform the existing data-focused communication architecture towards a more generally intelligent and goal-oriented semantic-aware networking system. Despite its promising potential, semantic communications and semantic-aware networking are still in their infancy. Most existing works focus on transporting and delivering the explicit semantic information, e.g., labels or features of objects, that can be directly identified from the source signal. The original definition of semantics as well as recent results in cognitive neuroscience suggest that it is the implicit semantic information, in particular the hidden relations connecting different concepts and feature items that play the fundamental role in recognizing, communicating, and delivering the real semantic meanings of messages. Motivated by this observation, we propose a novel reasoning-based implicit semantic-aware communication network architecture that allows destination users to directly learn a reasoning mechanism that can automatically generate complex implicit semantic information based on a limited clue information sent by the source users. Our proposed architecture can be implemented in a multi-tier cloud/edge computing networks in which multiple tiers of cloud data center (CDC) and edge servers can collaborate and support efficient semantic encoding, decoding, and implicit semantic interpretation for multiple end-users. We introduce a new multi-layer representation of semantic information taking into consideration both the hierarchical structure of implicit semantics as well as the personalized inference preference of individual users. We model the semantic reasoning process as a reinforcement learning process and then propose an imitation-based semantic reasoning mechanism learning (iRML) solution to learning a reasoning policy that imitates the inference behavior of the source user. A federated graph convolutional network (GCN)-based collaborative reasoning solution is proposed to allow multiple edge servers to jointly construct a shared semantic interpretation model based on decentralized semantic message samples. Extensive experiments have been conducted based on real-world datasets to evaluate the performance of our proposed architecture. Numerical results confirm that iRML offers up to 25.8 dB improvement on the semantic symbol error rate, compared to the semantic-irrelevant communication solutions.

Currently, recommending collaboration plans of dynamic multiple workflow processes under security requirements or constraints has become a hot topic, as it not only can reduce the risk during execution, but also can avoid enormous financial loss caused by security breaches. However, the existing methods cannot solve the parametric problem caused by dynamic relations of multiple workflow processes or recommend an optimal collaboration plan because of erroneous information. In this article, we propose a new approach to recommend security-aware collaboration plans for multiple workflow processes with dynamic relations. First, we develop three basic computing patterns of dynamic relations and construct a parametric sprouting graph for multiple workflow processes with dynamic relations. Second, we present the procedure of recommending an optimal secure collaboration plan that satisfy security requirements. By comparison with the existing methods via two experiments, our approach not only can solve the parametric problem caused by dynamic relations of multiple workflow processes, but also can recommend an optimal secure collaboration plan under security requirements.

The accurate and timely prediction of nitrogen oxides (NOx) emissions ensures eco-friendly and efficient operations for municipal solid waste incineration (MSWI) plants. Due to the high nonlinearity and uncertainty in MSWI processes, constructing an efficient prediction model remains challenging. This article proposes a comprehensively improved interval type-2 fuzzy neural network (CI-IT2FNN) for NOx emissions prediction. First, the neighborhood rough set is introduced to determine the structure of this fuzzy neural network automatically, including the number of fuzzy rules and their corresponding consequent parameters. Second, an adaptive shape factor is added to the fuzzy membership function to better cope with the uncertainty, which can help to improve the generalization ability of network. Furthermore, to reduce the computational complexity, the Begian–Melek–Mendel method is utilized as the defuzzification method in this article. Then, by integrating the linear least square estimation and gradient decent, a hierarchical learning algorithm is applied to adjust the network parameters to further enhance the learning efficiency and accuracy. Finally, after being evaluated by a benchmark simulation, the proposed CI-IT2FNN demonstrates its effectiveness and superiority on NOx emissions prediction.