Xing Ma, Yangjie Zhou, Wu Sun, Zihan Liu, Jingwen Leng, Yun Lin, Shixuan Sun, Minyi Guo, Jin Song Dong. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

EEG signals exhibit non-stationary characteristics, particularly across different subjects, which presents significant challenges in the precise classification of mental workload levels when applying a trained model to new subjects. Domain adaptation techniques have shown effectiveness in enhancing the accuracy of cross-subject classification. However, current state-of-the-art methods for cross-subject mental workload classification primarily focus on global domain adaptation, which may lack fine-grained information and result in ambiguous classification boundaries. We proposed a novel approach called deep subdomain adaptation network with class confusion loss (DSAN-CCL) to enhance the performance of cross-subject mental workload classification. DSAN-CCL utilizes the local maximum mean discrepancy to align the feature distributions between the source domain and the target domain for each mental workload category. Moreover, the class confusion matrix was constructed by the product of the weighted class probabilities (class probabilities predicted by the label classifier) and the transpose of the class probabilities. The loss for maximizing diagonal elements and minimizing non-diagonal elements of the class confusion matrix was added to increase the credibility of pseudo-labels, thus improving the transfer performance. The proposed DSAN-CCL method was validated on two datasets, and the results indicate a significant improvement of 3∼10 percentage points compared to state-of-the-art domain adaptation methods. In addition, our proposed method is not dependent on a specific feature extractor. It can be replaced by any other feature extractor to fit new applications. This makes our approach universal to cross-domain classification problems.

This article addresses the nonsingular practical fixed-time adaptive fuzzy control problem of nonstrict-feedback multiagent systems (MASs) with actuator faults. By utilizing fuzzy-logic systems (FLSs), the identification of unknown nonlinearities in MASs is achieved while avoiding the algebraic loop problem. The singularities in fixed-time control are prevented by incorporating an adding power integration technique into the control scheme. Furthermore, we design an adaptive update law to mitigate the effects of unknown actuator faults and external disturbances. An adaptive fuzzy practical fixed-time MASs consistency control algorithm is proposed by combining these techniques with backstepping control. Based on stability analysis, the designed control scheme ensures that all MAS signals remain bounded within a fixed time and that the convergence time to achieve consistency does not depend on the initial states of the MASs. Finally, the validity of the proposed nonsingular practical fixed-time consensus control scheme is verified through simulation examples.

Taking incompatible multiple drugs together may cause adverse interactions and side effects on the body. Accurate prediction of drug-drug interaction (DDI) events is essential for avoiding this issue. Recently, various artificial intelligence-based approaches have been proposed for predicting DDI events. However, DDI events are associated with complex relationships and mechanisms among drugs, targets, enzymes, transporters, molecular structures, etc. Existing approaches either partially or loosely consider these relationships and mechanisms by a non-end-to-end learning framework, resulting in sub-optimal feature extractions and fusions for prediction. Different from them, this paper proposes a Multimodal Knowledge Graph Fused End-to-end Neural Network (MKGFENN) that consists of two main parts: multimodal knowledge graph (MKG) and fused end-to-end neural network (FENN). First, MKG is constructed by comprehensively exploiting DDI events-associated relationships and mechanisms from four knowledge graphs of drugs-chemical entities, drug-substructures, drugs-drugs, and molecular structures. Correspondingly, a four channels graph neural network is designed to extract high-order and semantic features from MKG. Second, FENN designs a multi-layer perceptron to fuse the extracted features by end-to-end learning. With such designs, the feature extractions and fusions of DDI events are guaranteed to be comprehensive and optimal for prediction. Through extensive experiments on real drug datasets, we demonstrate that MKG-FENN exhibits high accuracy and significantly outperforms state-of-the-art models in predicting DDI events. The source code and supplementary file of this article are available on: https://github.com/wudi1989/MKG-FENN.

Cross-subject classification is of great practical value in the mental monitoring. The trained model on a person can be transferred to another person without retraining. To date, it is achieved by global domain adaptation without considering differences in subdomain distributions. In this case, there is a lack of sensitivity to specific information associated with each category. To solve this problem, we proposed a deep subdomain adaptation network (DSAN) to estimate mental workload levels across different persons. In the proposed DSAN, the first temporal and spatial layers were designed as a feature extractor. The features extracted by the feature extractor were aligned between the source samples and target samples in each subdomain separately. The alignment loss calculated by local maximum mean discrepancy (LMMD) was back-propagated to update the weights of the feature extractor to enhance the feature extraction performance. Subdomain adaptation was achieved over iterations during the model training. The proposed subdomain adaptation is not specialized for a particular feature extractor, as shown in this paper. It is universal and can be applied after any feature extractors. Two datasets (Dataset MATB and Dataset SFE) were used to evaluate the proposed DSAN. The results showed that the proposed DSAN outperformed the compared methods in terms of classification accuracy, showing an elevation of $3 \% \sim 7 \%$. This study provides an effective solution for the cross-subject mental workload classification and will promote practical applications of mental workload monitoring.

Heterogeneous feature transfer (HeFT) learning can leverage the semantically related source domain from a different feature space for modeling the target domain with insufficient information. Although HeFT learning has made significant progress, it still faces two major challenges: weak interpretability of the transfer process and underutilization of the hidden information of the heterogeneous source and target domains. To address these two challenges, a framework called heterogeneous feature transfer using fuzzy inference rules (HeFT-FIR) is proposed. The HeFT-FIR framework has two parts: First, design of Takagi–Sugeno–Kang fuzzy systems (TSK-FSs) for the source and target domains, respectively, to achieve HeFT and enhance the interpretability of the transfer process; and second, integration of the HeFT learning mechanism with fuzzy inference rules to optimize the parameters of TSK-FSs and mine the hidden information of the two domains. Based on the framework, a TSK-FS-based heterogeneous feature transfer learning method is then developed with three fuzzy feature space-based learning mechanisms for joint distribution adaptation, local geometric property preservation, and heterogeneous discriminant information extraction, respectively. The mechanisms reduce the difference in distribution between the heterogeneous source and target domains in a common feature subspace, preserve the local geometric properties of two domains, and extract the global discriminant information of them. Extensive analyses are conducted to verify the superiority of the proposed framework and method.

Global warming and other environmental problems are increasing the demand for high-performance buildings. The development of high-performance computers and building information models has a significant impact on buildings’ energy simulation. High-performance building design needs to comprehensively consider geography, climate, material, cost and other factors which is a highly complex multidisciplinary research problem. Therefore, it is urgent to use advanced modeling and simulation technology to realize it, involving Building Information Modelling (BIM), parametric design and cloud platform. Comprehensive simulation of building performance refers to a multidisciplinary collaborative design, and the correlation between research objects and parameters should be achieved by complex programming design. This study integrates BIM, computer, cloud computing and other technologies to simulate BIM-based building energy consumption performance. Based on project information, geometry information and physical properties exhibited by materials stored in BIM model, the energy analysis model is created. Revit–Dynamo API functions are employed to generate a novel BIM model in Revit after automatically changing and transferring user-defined parameters. BIM energy consumption model is converted into Green Building eXtended Markup Language (GBXML) file and uploaded to Green Building Studio (GBS) cloud server. The optimal project solution is yielded by retrieving the energy consumption simulation results of BIM models with a range of parameters. The case study shows that building volume, glass material, window-wall ratio and window height have significant influence on energy consumption targets of buildings. In hot-summer and cold-winter areas, the total energy consumption of glass materials with high insulation and reflection coefficient is small. The window size slightly impacts the annual lighting energy consumption, but it has significant influence on the annual air conditioning energy consumption, with a maximum increase of about 22%. Finally, the application advantages and limitations of the framework in high-performance building design and its application prospects in energy-saving building design are discussed.