Abstract Multimodal sentiment analysis utilizes different types of information to improve sentiment recognition, driving research at the intersection of natural language processing and computer vision. However, practical applications often encounter missing image modal data due to collection restrictions, privacy protection, and other issues, which reduces the accuracy of sentiment analysis. Recently, diffusion models, as an emerging generative model, have demonstrated remarkable capabilities in handling modality completion and generation tasks. To this end, this paper presents an expert feature-aligned diffusion model (EFADM) integrated with curriculum learning, aiming to efficiently solve multimodal sentiment analysis tasks by dynamically generating missing modality data and optimizing feature alignment between modalities. EFADM consists of three primary modules: the Adaptive Noise Suppression Module (ANSM), the Expert Alignment Module (EAM), and a progressive generation framework based on curriculum learning. First, ANSM dynamically adjusts noise suppression during the diffusion process to enhance the quality of generated features. EAM strengthens semantic consistency between generated and existing modalities by aligning expert features. Additionally, the progressive generation strategy utilizing curriculum learning gradually increases task difficulty, thereby optimizing the model’s performance across different noise levels. Compared with existing methods, the proposed approach achieves leading performance on the publicly available MVSA series datasets.

The breakthroughs of next-generation information technologies have accelerated the advancement of industrial cyber-physical intelligence (ICPI), particularly in system intelligence and applications. However, this progress has also brought challenges in ensuring operational reliability and system intelligence. Anomaly detection, a critical component of fault-tolerant and intelligent ICPI, is usually addressed by treating it as a one-class classification and location problem. While autoencoder frameworks have shown promise in addressing this challenge, most existing methods usual struggle with precise anomaly identification or require resource-intensive region-based training. Furthermore, the dynamic nature of anomalies and the scarcity of labeled training data complicate the development and evaluation of anomaly detection models. In this article, an innovative tensor-based knowledge distillation approach (TKDA) is introduced, which integrates a pretrained teacher network, a tensor-decomposed student network, and a denoising module into a unified framework. Anomalies are identified and localized by analyzing differences in intermediate activation values between teacher and student networks during data processing. Extensive experiments demonstrate that TKDA addresses the limitations of low accuracy in anomaly location and inefficiency in computational processes, achieving significant improvements across diverse datasets, including F-MNIST, MNIST, CIFAR-10, MVTecAD, Retinal-OCT, and two medical datasets.

In currency-based updates, processing a query to a materialized view has to satisfy a currency constraint which specifies the maximum time lag of the view data with respect to a transaction database. Currency-based update policies are more general than periodical, deferred, and immediate updates; they provide additional opportunities for optimization and allow updating a materialized view from other materialized views. Algorithms for determining the source and timing of view updates and validating the resulting cost savings through simulation results are presented.>