The knowledge tracing (KT) aims to track changes in students' knowledge status and predict their future answers based on their historical answer records. Current research on KT modeling focuses on predicting student' future performance based on existing, unupdated records of student learning interactions. However, these approaches ignore the distractors (such as slipping and guessing) in the answering process and overlook that static cognitive representations are temporary and limited. Most of them assume that there are no distractors in the answering process and that the record representations fully represent the students' level of understanding and proficiency in knowledge. In this case, it may lead to many lack of synergy and incoordination issue in the original records. Therefore we propose a cognitive representation optimization for KT (CRO-KT) model, which utilizes a dynamic programming algorithm to optimize structure of cognitive representations. This ensures that the structure matches the students' cognitive patterns in terms of the difficulty of the exercises. Furthermore, we use the co-optimization algorithm to optimize the cognitive representations of the subtarget exercises in terms of the overall situation of exercises responses by considering all the exercises with co-relationships as a single goal. Meanwhile, the CRO-KT model fuses the learned relational embeddings from the bipartite graph with the optimized record representations in a weighted manner, enhancing the expression of students' cognition. Finally, experiments are conducted on three publicly available datasets respectively to validate the effectiveness of the proposed cognitive representation optimization model. The source code of CRDP-KT is available at https://github.com/bigdata-graph/CRO-KT.

Graph Neural Networks (GNNs) have emerged as powerful tools for graph learning, and one key challenge arising in GNNs is the development of effective pooling operations for learning meaningful graph representations. In this paper, we propose a novel Edge-Node Attention-based Hierarchical Pooling (ENAHPool) operation for GNNs. Unlike existing cluster-based pooling methods that suffer from ambiguous node assignments and uniform edge-node information aggregation, ENAHPool assigns each node exclusively to a cluster and employs attention mechanisms to perform weighted aggregation of both node features within clusters and edge connectivity strengths between clusters, resulting in more informative hierarchical representations. To further enhance the model performance, we introduce a Multi-Distance Message Passing Neural Network (MD-MPNN) that utilizes edge connectivity strength information to enable direct and selective message propagation across multiple distances, effectively mitigating the over-squashing problem in classical MPNNs. Experimental results demonstrate the effectiveness of the proposed method.

Complicated background and small object issues are the primary challenges currently confronting remote sensing object detection (RSOD). To tackle the aforementioned issues in RSOD, we propose an axis-squeeze and multirouting scale-adaptive fusion network (AMSFNet). In this network, a unidirectional multiscale coupling module (UMCM) is designed to enhance the object feature extraction capabilities and improve the accuracy of small object detection. Furthermore, utilizing the axis-squeeze and detail enhancement approach, we construct a squeeze-enhanced axial attention module that specifically targets background disruption, which can efficiently aggregate global and local information and reduce the impact of intricate background noise on the object. To improve the synergy between features of varying sizes and ensure that the model can allow for a compromise between detecting small and large targets simultaneously, a multirouting SAF approach is proposed, which combines fine-grained features with high-level features using multiple feed-forward connections. The proposed approach has been shown to be efficient by ablation and comparison experiments performed on three public benchmark datasets: RSOD, VisDrone, and DIOR. AMSFNet achieves a mean average precision (mAP) of 95.4%, 88.7%, and 36.6% on the RSOD, DIOR, and VisDrone datasets, respectively.

To fully leverage contextual information for the precise segmentation of objects in remote sensing images, while addressing the challenges associated with substantial object scale variations and complex backgrounds, we propose a lightweight cross-domain coupling network (LCCN) tailored for semantic segmentation of high-resolution remote sensing images (HRSIs). To standardize feature selection and fusion procedures, the LCCN incorporates an innovative Encoder-Coupler-Decoder architecture designed to facilitate key feature extraction and optimization. A cross-domain coupling module (CDCM) is created in the Coupler to conduct preliminary features screening of spaces and dimensions based on channel and spatial attention. It performs multi-scale feature extraction and global information modeling through the feature grouping and loop aggregation. This helps to extract key features while reducing the computational overhead. To further decrease the interference from complex backgrounds, a secondary optimization of the key features is carried out: a lightweight fully-featured mapping attention module (LFMAM) is designed within the Decoder. LFMAM utilizes an interactive fusion strategy and a lightweight linear self-attention mechanism, comprehensively considering all interactions between global-to-global, global-to-local, local-to-local, and local-to-global processes. By capturing the effective correlations and variances among features to further refine them, it enables the network to further optimize the crucial information while ensuring light weight. We have conducted extensive comparison experiments and ablation experiments on the ISPRS Vaihingen and ISPRS Potsdam datasets. The extensive experimental results demonstrate that our proposed LCCN can obtain superior performance compared to other advanced semantic segmentation models.

The application of contrastive learning (CL) to collaborative filtering (CF) in recommender systems has achieved remarkable success. CL-based recommendation models mainly focus on creating multiple augmented views by employing different graph augmentation methods and utilizing these views for self-supervised learning. However, current CL methods for recommender systems usually struggle to fully address the problem of noisy data. To address this problem, we propose the G raph A ugmentation E mpowered C ontrastive L earning (GAECL) for recommendation framework, which uses graph augmentation based on topological and semantic dual adaptation and global co-modeling via structural optimization to co-create contrasting views for better augmentation of the CF paradigm. Specifically, we strictly filter out unimportant topologies by reconstructing the adjacency matrix and mask unimportant attributes in nodes according to the PageRank centrality principle to generate an augmented view that filters out noisy data. Additionally, GAECL achieves global collaborative modeling through structural optimization and generates another augmented view based on the PageRank centrality principle. This helps to filter the noisy data while preserving the original semantics of the data for more effective data augmentation. Extensive experiments are conducted on five datasets to demonstrate the superior performance of our model over various recommendation models.

While highly influential in deep learning, especially in natural language processing, the Transformer model has not exhibited competitive performance in unsupervised graph representation learning (UGRL). Conventional approaches, which focus on local substructures on the graph, offer simplicity but often fall short in encapsulating comprehensive structural information of the graph. This deficiency leads to suboptimal generalization performance. To address this, we proposed the GraKerformer model, a variant of the standard Transformer architecture, to mitigate the shortfall in structural information representation and enhance the performance in UGRL. By leveraging the shortest-path graph kernel (SPGK) to weight attention scores and combining graph neural networks, the GraKerformer effectively encodes the nuanced structural information of graphs. We conducted evaluations on the benchmark datasets for graph classification to validate the superior performance of our approach.

In recent years, the results of view-based 3D shape recognition methods have saturated, and models with excellent performance cannot be deployed on memory-limited devices due to their huge size of parameters. To address this problem, we introduce a compression method based on knowledge distillation for this field, which largely reduces the number of parameters while preserving model performance as much as possible. Specifically, to enhance the capabilities of smaller models, we design a high-performing large model called Group Multi-view Vision Transformer (GMViT). In GMViT, the view-level ViT first establishes relationships between view-level features. Additionally, to capture deeper features, we employ the grouping module to enhance view-level features into group-level features. Finally, the group-level ViT aggregates group-level features into complete, well-formed 3D shape descriptors. Notably, in both ViTs, we introduce spatial encoding of camera coordinates as innovative position embeddings. Furthermore, we propose two compressed versions based on GMViT, namely GMViT-simple and GMViT-mini. To enhance the training effectiveness of the small models, we introduce a knowledge distillation method throughout the GMViT process, where the key outputs of each GMViT component serve as distillation targets. Extensive experiments demonstrate the efficacy of the proposed method. The large model GMViT achieves excellent 3D classification and retrieval results on the benchmark datasets ModelNet, ShapeNetCore55, and MCB. The smaller models, GMViT-simple and GMViT-mini, reduce the parameter size by 8 and 17.6 times, respectively, and improve shape recognition speed by 1.5 times on average, while preserving at least 90% of the recognition performance.

Both point cloud-based and view-based deep learning methods for 3D shape recognition have achieved relatively remarkable results in recent years. However, there are few methods to jointly represent 3D shapes from both point cloud and multi-view modal data. Therefore, we propose a guidance enhancement network (GENet) for 3D shape recognition based on multimodal data. On the one hand, the point cloud is encoded with features from both explicit and implicit aspects, and on the other hand, all views are encoded and constructed as a graph. In the multilayer guidance enhancement module, graph convolutional neural network (GCN) enhances each view feature, and then temporary high-level features (initially point cloud global feature) guide multiple low-level view features to obtain correlation coefficients, through which the views with higher importance are filtered as inputs for the next layer of the structure and the view features in the current layer are weighted and aggregated. The aggregated view features are then connected to the high-level features with residuals to form the enhanced high-level features. The 3D shape descriptor is finally obtained after several guidance and enhancements. The proposed GENet achieves state-of-the-art results on the 3D benchmark dataset ModelNet.

Both point cloud-based and multi-view-based methods have achieved remarkable results in 3D shape recognition, yet there are few methods that combine the two types of data. In this paper, a novel Global-Local Complementary Network (GLCNet) based on multimodal data is proposed. The network obtains more powerful shape descriptors by stacking multiple layers of Global-Local Complementary Module (GLC Module). More specifically, the Global-Local Relation Score Module is first used to obtain the relationship between view features and global feature. The relationship is then utilized to facilitate the aggregation of view features and to filter out the more important ones. Finally, the aggregated view features are fused with the global features to form a stronger global feature. GLCNet enables the characteristics of various data to be fully utilized and achieves a true sense of complementarity of strengths and weaknesses. Extensive experiments on the benchmark dataset ModelNet show that GLCNet achieves state-of-the-art results in 3D shape classification and retrieval.

This paper mainly studies graph representation learning in unsupervised scenarios combined with Transformer models. Transformer network models have been widely used in many fields of machine learning and deep learning, and the application of transformer architectures to graph data has been very popular recently. For graph data, the field of graph representation learning has recently attracted a lot of attention. Graph-level representation is widely used in the real world, such as drug molecule design and disease classification in biochemistry. Traditional graph kernel methods, which design different graph kernels for different substructures, are simple but have poor generalization performance. Recently methods based on language models, such as graph2vec, use a particular substructure as the graph representation, which is also similar to the hand-crafted approach and also leads to poor generalization ability. In this paper, we propose the UGTransformer model, which builds on the standard Transformer architecture. We introduce several simple and effective structural encoding methods in order to encode the structural information of the graph into the model efficiently. The unsupervised representation of graphs is learned through a multi-headed attention mechanism and by using powerful aggregation functions. We conducted experiments on a benchmark date set for graph classification, and the experimental results validate the effectiveness of our proposed model.

Network representations are powerful tools to modeling the dynamic time-varying financial complex systems consisting of multiple co-evolving financial time series, e.g., stock prices. In this work, we develop a novel framework to compute the kernel-based similarity measure between dynamic time-varying financial networks. Specifically, we explore whether the proposed kernel can be employed to understand the structural evolution of the financial networks with time associated with standard kernel machines. For a set of time-varying financial networks with each vertex representing the individual time series of a different stock and each edge between a pair of time series representing the absolute value of their Pearson correlation, our start point is to compute the commute time (CT) matrix associated with the weighted adjacency matrix of the network structures, where each element of the matrix can be seen as the enhanced correlation value between pairwise stocks. For each network, we show how the CT matrix allows us to identify a reliable set of dominant correlated time series as well as an associated dominant probability distribution of the stock belonging to this set. Furthermore, we represent each original network as a discrete dominant Shannon entropy time series computed from the dominant probability distribution. With the dominant entropy time series for each pair of financial networks to hand, we develop an entropic dynamic time warping kernels through the classical dynamic time warping framework, for analyzing the financial time-varying networks. We show that the proposed kernel bridges the gap between graph kernels and the classical dynamic time warping framework for multiple financial time series analysis. Experiments on time-varying networks extracted through New York Stock Exchange (NYSE) database demonstrate that the effectiveness of the proposed method.

We proposed an innovative reversible data hiding technique that is formulated on histogram shifting by using multilayer localized n-bit truncation image (LBPTI), namely, generated form 8-bit plane by means of efficient lossless compression. After selecting the reference point from the block, the neighbor topmost points are used to attain the data embedding without modifying the peak point; in addition, the key information regarding peak point is not mandatory in extraction end to extract the secret information. In order to make the embedded cover-image similar to the histogram of original cover-image, we exploited the localization with efficient lossless compression on lower block level to increase the embedding capacity while controlling extra bit to expand additional embedding capacity on optimum level besides sustaining the quality of cover-image.