Multimodal sentiment analysis aims to accurately identify sentiment orientations by integrating information from multiple modalities such as text, audio, and video. However, a key challenge in multimodal fusion is effectively balancing the sufficiency and necessity of information across modalities. Traditional models often fail to qualify and capture this balance due to the presence of noise and redundant information in multimodal data, leading to suboptimal performance in sentiment analysis. To address this issue, we propose a novel multimodal sentiment analysis method calledUCMIB-PNS, which is guided by information bottleneck and probabilistic causality. The method employs anUncertainCross-ModalInformationBottleneck(UCMIB)module to reduce redundant information within modalities and maximize discriminative information. The UCMIB utilizes codebooks to dynamically record the distributions of samples and employs random sampling to conduct uncertain modeling across different modalities. It integrates uncertainty-aware contrastive learning and KL divergence for dynamic comparison and compression of information from different modalities. Moreover, UCMIB-PNS uses differentiableProbability ofNecessity andSufficiency(PNS)estimators to estimate and re-weight the sufficiency and necessity of modalities by constructing several counterfactual scenarios through end-to-end learning. Experiments conducted on four publicly available multimodal sentiment analysis datasets demonstrate that UCMIB-PNS achieves optimal performance on both clean and noisy data. Extended experiments further validate the method's robustness under different types of noise.

The out-of-distribution (OOD) detection on graph-structured data is crucial for deploying graph neural networks securely in open-world scenarios. However, existing methods have overlooked the prevalent scenario of multi-label classification in real-world applications. In this work, we investigate the unexplored issue of OOD detection within multi-label node classification tasks. We propose ML-GOOD, a simple yet sufficient approach that utilizes an energy function to gauge the OOD score for each label. We further develop a strategy for amalgamating multiple label energies, allowing for the comprehensive utilization of label information to tackle the primary challenges encountered in multi-label scenarios. Extensive experimentation conducted on seven diverse sets of real-world multi-label graph datasets, encompassing cross-domain scenarios. The results show that the AUROC of ML-GOOD is improved by 5.26% in intra-domain and 6.54% in cross-domain compared to the previous methods. These empirical validations not only affirm the robustness of our methodology but also illuminate new avenues for further exploration within this burgeoning field of research.

Multimodal sentiment analysis leverages information from multiple sensors to achieve a comprehensive interpretation of emotions. However, different modalities do not always boost each other as expected. They compete with each other, leading to some modalities being under-optimized during the training process. To address this issue, we propose Adaptive Gradient Scaling with Sparse Mixture-of-Experts (AGS-SMoE) . We first discuss the issue of modal preemption in unified multimodal learning from the perspective of causal preemption. Driven by actual cause, we use the gradient norms from different encoders at two fusion stages as evidence, estimating the current modal preemption state using a parameter-free method. Then, based on the dynamic preemption factor, we design a gradient scaling method to balance optimization for different encoders. Furthermore, we use Mixture-of-Experts to sparsify and perceive multimodal tokens in different preemption states. As a result, our experiments on four multimodal sentiment analysis datasets have achieved state-of-the-art results. Moreover, our method improves modal representation learning at different stages. Extensive experiments confirm that our method can alleviate the modal preemption problem in a plug-and-play manner. Our code is available at https://github.com/TheShy-Dream/AGS-SMoE .

Deep learning (DL) based knowledge tracing (KT) models have challenges for uninterpretable prediction and parameter representation in educational applications, though they achieved remarkable outcomes in predicting the exercise performance of students. This paper proposes a novel knowledge tracing model of high precision and interpretability (namedXKT) for questions with multiple knowledge concepts based on cognitive learning theories and multidimensional item response theory (MIRT). TheXKTconsists of three differentiable network components: multi-feature embedding, cognition processing network, andMIRT-based neural predictor, which aim to provide an explainable prediction of student exercise performance. Specifically, inXKT, multi-feature embedding learns the rich semantic representation (e.g., knowledge distribution information) to enhance knowledge tracing using a cognition processing network. The cognition processing network performs selective perception, ability memory processing, and long-term knowledge memory processing to ensure the explainable factor representation for theMIRT-based neural predictor. Lastly, theMIRT-based neural predictor employs psychometric parameters to interpret student exercise predictions better. Extensive experiments on four real-world datasets show thatXKToutperforms existingKTmethods in predicting future learner responses. Moreover, ablation studies further show thatXKToffers good interpretability of student performance predictions with multiple knowledge concepts, indicating excellent potential in real-world educational applications.

Three-dimensional point cloud classification is fundamental but still challenging in 3-D vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and high-level intrinsic features together. These two levels of features are critical to improving classification accuracy. To this end, we propose a dual-graph attention convolution network (DGACN). The idea of DGACN is to use two types of graph attention convolution operations with a feedback graph feature fusion mechanism. Specifically, we exploit graph geometric attention convolution to capture low-level extrinsic features in 3-D space. Furthermore, we apply graph embedding attention convolution to learn multiscale low-level extrinsic and high-level intrinsic fused graph features together. Moreover, the points belonging to different parts in real-world 3-D point cloud objects are distinguished, which results in more robust performance for 3-D point cloud classification tasks than other competitive methods, in practice. Our extensive experimental results show that the proposed network achieves state-of-the-art performance on both the synthetic ModelNet40 and real-world ScanObjectNN datasets.

Learning approaches enhanced by digital technologies have gained considerable attention in higher education. Prior research has mainly focused on digital technology adoption in formal learning settings of higher education. Yet, empirical research into discerning what influences a student’s digital informal learning has not been well investigated. This paper proposes that individuals’ digital competence affects their digital informal learning (DIL) intention and actual behaviour. To understand better learners’ DIL and the effects of digital competence, we integrated digital competence into the decomposed theory of planned behaviour (DTPB) model and tested the model by using survey data from university students in Belgium. Here, we have explored different aspects of their DIL behaviours of cognitive, meta-cognitive, social and motivational learning. The findings showed both attitudinal factors in DTPB and digital competence adequately explained students’ DIL. Finally, the roles of digital competence and other DPTB factors are discussed in students’ DIL.

In the power industry, processing business big data from geographically distributed locations, such as online line-loss analysis, has emerged as an important application. How to achieve highly efficient big data storage to meet the requirements of low latency processing applications is quite challenging. In this paper, we propose a novel adaptive power storage replica management system, named PARMS, based on stochastic configuration networks (SCNs), in which the network traffic and the data center (DC) geodistribution are taken into consideration to improve data real-time processing. First, as a fast learning model with less computation burden and sound prediction performance, the SCN model is employed to estimate the traffic state of power data networks. Then, a series of data replica management algorithms is proposed to lower the effects of limited bandwidths and a fixed underlying infrastructure. Finally, the proposed PARMS is implemented using data-parallel computing frameworks (DCFs) for the power industry. Experiments are carried out in an electric power corporation of 230 million users, China Southern power grid, and the results show that our proposed solution can deal with power big data storage efficiently and the job completion times across geodistributed DCs are reduced by 12.19% on average.

This paper considers a problem of landmark point detection in clothes, which is important and valuable for clothing industry. A novel method for landmark localization has been proposed, which is based on a deep end-to-end architecture using prior of key point associations. With the estimated landmark points as input, a deep network has been proposed to predict clothing categories and attributes. A systematic design of the proposed detecting system is implemented by using deep learning techniques and a large-scale clothes dataset containing 145 000 upper-body clothing images with landmark annotations. Experimental results indicate that clothing categories and attributes can be well classified by using the detected landmark points, which are associated with regions of interest in clothes (e.g., the sleeves and the collars) and share robust learning representation property with respect to large variances of human poses, nonfrontal views, or occlusion. A comprehensive performance evaluation over two newly released datasets is carried out in this paper, showing that the proposed system with deep architecture for clothing landmark detection outperforms the state-of-the-art techniques.