Introduction: Time series analysis plays a critical role in various applications, including sensor data monitoring, weather forecasting, economic predictions, and network traffic management. While traditional methods primarily focus on modeling time series data at a single temporal scale and achieve notable results, they often overlook dependencies across multiple scales. Furthermore, the intricate structure of multi-scale time series complicates the effective extraction of features at different temporal resolutions.Method: To address these limitations, we propose AMDCnet, a multi-scale-based time series decomposition and collaboration network designed to enhance the model's capacity for decomposing and integrating data across varying time scales. Specifically, AMDCnet transforms the original time series into multiple temporal resolutions and conducts multi-scale feature decomposition while preserving the overall temporal dynamics. By extracting features from downsampled sequences and integrating multi-resolution features through attention-gated co-training mechanisms, AMDCnet enables efficient modeling of complex time series data.Results: AMDCnet achieving 44 best results and 10 second-best results out of 64 cases. Experimental results on 8 benchmark datasets demonstrate that AMDCnet achieves state-of-the-art performance in time series forecasting.Discussion: Our research provides a robust baseline for the application of artificial intelligence in multivariate time series forecasting. This work leverages multi-scale time series decomposition and gated units for feature fusion, effectively capturing dependencies across different temporal scales. Future studies may further optimize the scale decomposition and fusion modules. Such efforts could enhance the representation of multi-scale information and help address key challenges in multivariate time series prediction.

Deep Neural Networks (DNN) have realized significant achievements across various application domains. There is no doubt that testing and enhancing a pre-trained DNN that has been deployed in an application scenario is crucial, because it can reduce the failures of the DNN. DNN-driven software testing and enhancement require large amounts of labeled data. The high cost and inefficiency caused by the large volume of data of manual labeling, and the time consumption of testing all cases in real scenarios are unacceptable. Therefore, test case selection technologies are proposed to reduce the time cost by selecting and only labeling representative test cases without compromising testing performance. Test case selection based on neuron coverage (NC) or uncertainty metrics has achieved significant success in Convolutional Neural Networks (CNN) testing. However, it is challenging to transfer these methods to Recurrent Neural Networks (RNN), which excel at text tasks, due to the mismatch in model output formats and the reliance on image-specific characteristics. What’s more, balancing the execution cost and performance of the algorithm is also indispensable. In this paper, we propose a state-vector aware test case selection method for RNN models, namely DeepVec, which reduces the cost of data labeling and saves computing resources and balances the execution cost and performance. DeepVec selects data using uncertainty metric based on the norm of the output vector at each time step (i.e., state-vector), and similarity metric based on the direction angle of the state-vector. Because test cases with smaller state-vector norms often possess greater information entropy and similar changes of state-vector direction angle indicate similar RNN internal states. These metrics can be calculated with just a single inference, which gives it strong bug detection and model improvement capabilities. We evaluate DeepVec on five popular datasets, containing images and texts as well as commonly used 3 RNN classification models, and compare it with NC-based, uncertainty-based, and other black-box methods. Experimental results demonstrate that DeepVec achieves an average relative improvement of 12.5%-118.22% over baseline methods in selecting fault-revealing test cases with time costs reduced to only 1% to 1‱. At the same time, we find that the absolute accuracy improvement after retraining outperforms baseline methods by 0.29%-24.01% when selecting 15% data to retrain.

Physiological multimodal emotion recognition (PMER) has become a key research direction for advancing human-computer interaction and affective computing. However, current PMER methods are affected by significant individual differences and the limited number of samples, making it challenging to capture complex emotional states comprehensively. To address aforementioned issues, this letter proposes a novel multimodal Few-Shot emotion recognition model, called Hierarchical Interactive Alignment Network (HIA-Net). Specifically, the Hierarchical Adaptive Interactive Attention (HAIA) module of HIA-Net is proposed to capture multidimensional emotional features and aggregate the cross-modal information effectively. Additionally, a cross-domain optimization strategy based on the maximum mean discrepancy is proposed to enhance the HIA-Net's adaptability across varying data distributions. Experimental results show that HIA-Net achieves state-of-the-art performance under Few-Shot experimental paradigms on the SEED and SEED-FRA datasets. Our codes are available at https://github.com/As1yk/HIA-Net.

Fatigued driving is a major societal risk factor in the road safety system, affecting the efficiency of the entire transportation network and public safety. Fatigue detection methods based on multimodal physiological signals can objectively reflect a driver’s neurocognitive state, offering higher monitoring reliability. However, existing methods face two main challenges: 1) the high cost of annotating high-quality physiological signals leads to challenges in learning with limited data; and 2) the signal-to-noise ratio of multimodal physiological signals is variable, leading to imbalances in the confidence in the decision between modalities. To address these issues, this article proposes the meta-evidential dual-stream network (MEDS-Net), which integrates a meta-learning framework with evidential deep learning to establish a solution for driver fatigue detection for the first time. Specifically, MEDS-Net employs a dual-stream evidence generation network to extract heterogeneous features from multimodal physiological signals and uses a Dirichlet distribution to quantify cognitive uncertainty for each modality. Additionally, MEDS-Net introduces a meta-learning framework to enhance the model’s ability to handle data scarcity through the design of task simulation mechanisms and a two-layer optimization strategy. Extensive experiments on the SEED-VIG dataset demonstrate that MEDS-Net outperforms current state-of-the-art methods in terms of effectiveness and generalizability. Furthermore, MEDS-Net can precisely quantify the importance of global features in explaining modal contributions and average activation patterns.

Credit card (CC) fraud detection within the realm of financial security faces challenges such as data imbalance, large-scale anonymized transaction datasets, and the need for system-specific model training. Past methods often fail to address these aforementioned issues simultaneously. Utilizing a single model results in a lack of zero-shot capability without adaptation for real-world scenarios. This article introduces LLM40FD, a novel framework that leverages a large language model (LLM) to overcome these obstacles in anonymous zero-shot fraud detection. LLM40FD addresses the aforementioned challenges in CC fraud detection by employing a distribution-based one-class function and the walking embedding, without reliance on labeled data or fine-tuning in downstream. Additionally, LLM40FD enhances the model’s ability to detect fraudulent patterns and define robust decision boundaries. This is achieved through a dual-augmentation strategy and implicit contrastive learning, which generate enriched positive and negative samples. Our experiments demonstrate that LLM40FD not only achieves state-of-the-art (SOTA) performance in the full-shot setting but also exhibits strong zero-shot capability even with limited training data. Furthermore, we conduct additional experiments to validate the effectiveness and working mechanism of LLM40FD.