Temporal Knowledge Graphs (TKGs) model dynamic events, and understanding temporal evolution is crucial for effective reasoning. While existing methods leverage Graph Neural Networks (GNNs) to model structural dependencies, they often rely on Recurrent Neural Networks (RNNs) to process sequence of graph structures. They struggle to (1) incorporate contextual information, (2) explicitly model long-term effects, and (3) handle in-equidistant data. To address these challenges, we propose Context-Aware Hawkes Graph Network (CoHN), a novel TKG reasoning approach based on the Hawkes process. CoHN features a tailored conditional intensity function that models TKG event occurrences. It is characterized by two additive terms: base intensity and historical influence, representing spontaneous tendency and influence of past events at in-equidistant time intervals. Firstly, we design a Contextual Encoder (CE) to encode contextual information for all entities and compute the base intensity. We then present an attention-based Evolutionary Encoder that captures local structural information and explicitly models long-term dependencies across the TKG. A self-exciting fusion module further aggregates historical evolutionary dependencies at all timestamps to quantify the final historical influence. Extensive experiments on common benchmarks demonstrate the superiority, robustness, and efficiency of our method.

ABSTRACT The differential evolution algorithm, as a simple yet effective random search algorithm, often faces challenges in terms of rapid convergence and a sharp decline in population diversity during the evolutionary process. To address this issue, an improved differential evolution algorithm, namely the multi‐population collaboration improved differential evolution (MPC‐DE) algorithm, is introduced in this article. The algorithm proposes a multi‐population collaboration mechanism and a two‐stage mutation operator. Through the multi‐population collaboration mechanism, the diversity of individuals involved in mutation is effectively controlled, enhancing the algorithm's global search capability. The two‐stage mutation operator efficiently balances the requirements of the exploration and exploitation stages. Additionally, a perturbation operator is introduced to enhance the algorithm's ability to escape local optima and improve stability. By conducting comprehensive comparisons with 15 well‐known optimization algorithms on CEC2005 and CEC2017 test functions, MPC‐DE is thoroughly evaluated in terms of solution accuracy, convergence, stability, and scalability. Furthermore, validation on 57 real‐world engineering optimization problems in CEC2020 demonstrates the robustness of the MPC‐DE. Experimental results reveal that, compared to other algorithms, MPC‐DE exhibits superior convergence accuracy and robustness in both constrained and unconstrained optimization problems. These research findings provide strong support for the widespread applicability of multi‐population collaboration in differential evolution algorithms for addressing practical engineering problems.

Abstract Graph Neural Networks (GNNs) are deep learning models specifically designed for analyzing graph-structured data, capturing complex relationships and structures to improve analysis and prediction. A common task in GNNs is node classification, where each node in the graph is assigned a predefined category. The Graph Attention Network (GAT) is a popular variant of GNNs known for its ability to capture complex dependencies by assigning importance weights to nodes during information aggregation. However, the GAT’s reliance on local attention mechanisms limits its effectiveness in capturing global information and long-range dependencies. To address this limitation, we propose a new attention mechanism called Global-Local Graph Attention (GLGA). Our mechanism enables the GAT to capture long-range dependencies and global graph structures while maintaining its ability to focus on local interactions. We evaluate our algorithm on three citation datasets (Cora, Citeseer, and Pubmed) using multiple metrics, demonstrating its superiority over other baseline models. The proposed GLGA mechanism has been proven to be an effective solution for improving node classification tasks.

Abstract Cloud removal algorithm is a crucial step of remote sensing image preprocessing. The current mainstream remote sensing image cloud removal algorithms are implemented based on deep learning, and most of them are supervised. A large number of data pairs are required for training to achieve cloud removal. However, real with/without cloud image pairs datasets are difficult to obtain in the real world, and the models obtained by training on synthetic datasets often need to generalize better to natural scenes. And the existing unsupervised thin cloud removal methods based on Cycle‐GAN framework with considerable model complexity and unstable training are not an excellent solution to the problem of lack of paired datasets. Based on this, in this paper, the authors propose an unsupervised remote sensing image thin cloud removal method based on contrastive learning—GAN‐UD. It is a network consisting of a frequency‐spatial attention generator and a discriminator. In addition, the authors introduce local contrastive loss and global content loss to constrain the content of the generated images to ensure that the generated cloud‐free images are consistent with the input cloud images in terms of image content. Experimental results show that the proposed method in this paper can still effectively remove thin clouds from remote sensing images without paired training datasets, outperforms current unsupervised cloud removal methods, and achieves comparable performance to supervised methods.

Abstract In order to perform emotion prediction in music quickly and accurately, we have proposed a muscular and tall neural network architecture for music emotion classification. Specifically, during the audio pre-processing stage, we converge mel-scale frequency cepstral coefficients features and residual phase features with weighting, enabling the extraction of more comprehensive music emotion characteristics. Additionally, to enhance the accuracy of predicting musical emotion while reducing computational complexity during training phase, we consolidate Long short term memory network with Broad learning system network. We employ long short term memory structure as the feature mapping node of broad learning system structure, leveraging the advantages of both network models. This novel Neural Network architecture, called BLNN (Broad-Long Neural Network), achieves higher prediction accuracy. i.e., 66.78%, than single network models and other benchmark with/without consolidation methods. Moreover, it achieves lower time complexity than other excellent models, i.e., 169.32 s of training time and 507.69 ms of inference time, and achieves the optimal balance between efficiency and performance. In short, the extensive experimental results demonstrate that the proposed BLNN architecture effectively predicts music emotion, surpassing other models in terms of accuracy while reducing computational demands. In addition, the detailed description of the related work, along with an analysis of its advantages and disadvantages, and its future prospects, can serve as a valuable reference for future researchers.

A large amount of data acquisition and annotation work is required to train a supervised machine learning model for open scenes. However, traditional manual approaches are inefficient. Here, a method is proposed for on-site image acquisition and semiautomatic annotation based on eye-tracking. This method uses the recognition capabilities and computational advantages of humans and machines to improve annotation efficiency, overcoming the bottleneck of AI-based approaches to the natural scenery understanding of field robots. The proposed method contains three advancements. First, we designed a head-mounted display with a built-in pose measurement module to achieve first-person teleoperation data acquisition, where a pseudoframe interpolation algorithm is designed to overcome the latency problem in immersive remote data transmission and to achieve efficient field data acquisition. Second, we propose an adaptive superpixel segmentation algorithm to reduce human–machine interactions based on eye-tracking. Third, since traditionally, the annotation process cannot provide feedback to the acquisition process and results in a low conversion rate. We proposed a new conversion rate index denoting the rate of transforming collected data into valid data to quantify the acquisition quality in real time. While achieving an annotation quality of 0.964 per the Dice index, which is approximately equal to that of the manual method, the proposed method improves the annotation efficiency by more than 3 times. Finally, the agricultural field experiments containing a real-life scene of robotic tomato-picking verified that the proposed method based on human–computer interaction can make full use of human perception and recognition intelligence.

Abstract The nuclear magnetic resonance (NMR) spectroscopy has fruitful applications in chemistry, biology and life sciences, but suffers from long acquisition time. Non‐uniform sampling is a typical fast NMR method by undersampling the time‐domain data of the spectrum but need to restore the fully sampled data with proper constraints. The state‐of‐the‐art method is to model the time‐domain data as the sum of exponential functions and reconstruct these data by enforcing the low rankness of Hankel matrix. However, this method is solved by minimizing the sum of singular values of the Hankel matrix, which leads to the distortion of low‐intensity spectral peaks. Here, a low rank Hankel matrix reconstruction approach with a partial sum of singular values is proposed to protect small singular values, which can faithfully reconstruct all peaks. Results on both synthetic and realistic NMR spectroscopy show that the proposed method can reconstruct a more consistent spectrum to the fully sampled one than other state‐of‐the‐art methods and have particular advantages on preserving low‐intensity peaks .

Generally, web services are provided with different QoS values, so they can be selected dynamically in service composition process. However, the conventional context free composition QoS model does not consider the changeability of QoS values and the context sensitive constraints during composition process. In this paper, we propose a rule based context sensitive QoS model to support the changeability of QoS values and the context sensitive constraints. By considering context in the QoS model, web service composition can be used widely and flexibly in the real world business.