In this paper, a control method of double-swing-arm tracked robot based on deep reinforcement learning is proposed to solve the problem of stable operation of the robot on uneven terrain. A control algorithm without complex kinematics analysis is designed, so that the robot can learn independently and keep balance on various irregular terrain. This paper mainly studies the stability of the robot when crossing the terrain, so as to reduce the damage to the robot hardware. The main contributions are as follows: (1) Combining the hierarchical control strategy with the curiosity module, and testing in the simulation environment has achieved good performance; (2) By adding stability design, the robot can pass through uneven terrain more smoothly; (3) Three kinds of terrain task scenes are developed in the simulation environment, and the effectiveness of the control algorithm is verified. The experimental results show that this method can effectively improve the robot’s ability to cross complex terrain while maintaining high stability.

Modern data mining applications require to perform incremental clustering over dynamic datasets by tracing temporal changes over the resulting clusters. In this paper, we propose A-Posteriori affinity Propagation (APP), an incremental extension of affinity propagation (AP) based on cluster consolidation and cluster stratification to achieve faithfulness and forgetfulness. APP enforces incremental clustering where i) new arriving objects are dynamically consolidated into previous clusters without the need to re-execute clustering over the entire dataset of objects, and ii) a faithful sequence of clustering results is produced and maintained over time, while allowing to forget obsolete clusters with decremental learning functionalities. Four popular labeled datasets are used to test the performance of APP with respect to benchmark clustering performances obtained by conventional AP and incremental affinity propagation based on nearest neighbor assignment algorithms. Experimental results show that APP achieves comparable clustering performance while enforcing scalability at the same time.

In few-shot learning, the model must first be trained using the same type of dataset, and its effectiveness may be compromised when identifying classes from different datasets. Therefore, recent research has proposed cross-domain few-shot learning, which aims to train a single-network model that can be applied across most datasets. Cross-domain few-shot learning typically involves two stages during training: (1) pre-training and (2) meta-training. In the pre-training stage, a feature extractor is trained using traditional supervised learning to extract features. In the meta-training stage, a classifier is trained using few-shot learning methods, whereby meta-learning algorithms classify the features extracted by the feature extractor. In this study, a dual attention mechanism that incorporates channel and spatial attention is introduced into the feature extractor in the pre-training stage, to enhance feature extraction capabilities. In the meta-training stage, samples obtained from Fourier-transform-based image corruption algorithms are introduced. The transformation algorithm scales the phase and amplitude of the Fourier transform output to change the texture and semantics of the image, allowing the classifier to learn more diverse images and improve its generalization ability. Finally, during the meta-training process, the prediction losses of the images before and after corruption are simultaneously calculated to maintain consistency in the prediction results and effectively mitigating the risk of overfitting. The experimental results demonstrate that the proposed method achieves better performance on the majority of the eight testing datasets, effectively reducing overfitting during training.

Various levels of feature maps extracted from the convolutional neural network models can capture multiple degrees of semantic cues in representation. However, learning the relationship between different semantic features across numerous layers can be challenging. Furthermore, existing representations cannot effectively capture the orientation cues. This paper proposes a representation method, the multi-layer orientation histogram, to address these problems. The main highlights are: (1) An iterative multi-layer integration method to combine the feature maps of various levels is suggested in this study. This method can provide discriminative characteristics based on spatial relationships. (2) An effective approach is suggested to apply Gabor filtering for detecting orientation cues. It can amplify the most dominant orientation cues and is convenient for efficiently using them in subsequent implementations. (3) The proposed representation directly captures orientation cues from each learned feature map. It can incorporate the learned deep features and orientation cues to create a more discriminative representation. Comparative experiments demonstrate that the proposed method exhibits a highly competitive performance on several benchmark datasets in terms of mean average precision. Moreover, this method can provide an effective architecture for learning discriminative global features.

Convolutional Neural Networks (CNNs) experience performance and training efficiency changes according to the selection of correct hyperparameters. The research presents WACSO which combines Crow Search Optimization with Grey Wolf Optimizer to improve Convolutional Neural Networks hyperparameter selection through a hybrid metaheuristic algorithm. The hybrid algorithm WACSO uses exploration parts from CSO together with GWO exploitation mechanics to obtain optimized performance. WACSO reaches higher classification accuracy than traditional optimization algorithms when performing tests on the MNIST and CIFAR-10 datasets along with Random Search and particle swarm optimization and genetic algorithms and standalone CSO and standalone GWO. The best classification results reached 98.9% accuracy levels on MNIST along with 91.5% accuracy levels on CIFAR-10. The final outcomes of this system depend on the combination of model structure along with dataset challenges and available computational power. The investigation demonstrates that mixing algorithms drawn from nature can lead to successful CNN hyperparameter optimization. The promising outcomes of WACSO depend on multiple variables including computation expenses and sensitive parameter adjustments and universal result adaptability between different datasets and network setups. Research into WACSO should expand to involve longer evaluations across multiple datasets and various models to confirm widespread usage.

Multimodal aspect-based sentiment analysis (MABSA) is an essential task in the field of sentiment analysis, which still confronts several critical challenges. The first challenge is how to effectively capture key information within both image and text features to enhance the recognition and understanding of complex sentiment expressions. The second challenge is how to achieve cross-modal alignment of multi-granularity text features and image features. The third challenge is how to narrow the semantic gap between image modality and text modality through effective cross-modal feature fusion. To address these issues, a framework that leverages external knowledge and multi-granularity image and text features (EKMG) is proposed. Firstly, an external knowledge enhanced semantic extraction module is introduced to fuse external knowledge with image features and text features, thereby capturing the key information from texts and images. Secondly, we design a multi-granularity image-text contrastive learning module. This module initially introduces a graph attention network and a novel cross-modal fusion mechanism to align image features and text features at multiple granularities. Additionally, the module employs an image-text contrastive learning strategy to narrow the semantic gap between different modalities. Experimental results on two public benchmark datasets demonstrate that EKMG achieves significant performance improvements compared to state-of-the-art baseline models.

Cross-lingual and cross-domain knowledge alignment without sufficient external resources is a fundamental and crucial task for fusing irregular message. Aiming to discover equivalent objects from different knowledge graphs (KGs), embedding-based entity alignment (EA) has been attracting great interest from industry and academic research recently. Most of related methods usually explore the correlation between entities and relations through neighbor nodes, structural information and external resources. However, the complex intrinsic interactions among triple elements and role information are rarely modeled, which leads to the inadequate illustration. In addition, external resources are unavailable in some scenarios especially cross-lingual and cross-domain applications, which reflects the weak scalability. To tackle the above insufficiency, a novel universal EA framework (OTIEA) based on ontology pair and role enhancement mechanism via triple-aware attention is proposed in this paper without introducing external resources. Specifically, an ontology-enhanced triple encoder is designed via mining intrinsic correlations and ontology pair information instead of independent elements. In addition, the EA-oriented representations can be obtained in triple-aware entity decoder by fusing role diversity. Finally, a bidirectional iterative alignment strategy is deployed to expand seed entity pairs. The experimental results on three real-world datasets show that our framework achieves a competitive performance compared with baselines.

In Lie group convolutional neural networks (LG-CNNs), the calculation and storage of Lie group distances have quadratic space complexity. In order to improve the memory utilization efficiency of LG-CNNs, a novel Lie group convolutional neural network called LieCConv is proposed. LieCConv utilizes an innovative sampling algorithm and a linear space complexity calculation and storage approach for Lie group distances, substantially enhancing network memory efficiency. Firstly, LieCConv employs a novel sampling algorithm called array-neighborhood sampling (ANS) in the downsampling stage. ANS only requires neighborhood information to obtain an excellent sample set with a low threshold of use. The sample set generated by ANS reflects the distribution of the original set. Then, LieCConv adopts a batch calculation and storage scheme for Lie group distances, which effectively declines the space complexity of calculating and storing Lie group distances from quadratic complexity to linear complexity, reducing the memory consumption during training. Finally, the contrast between ANS and farthest point sampling was presented, demonstrating that ANS better captures the distribution characteristics of the original dataset. The memory usage of LieCConv and LieConv was compared, revealing that LieCConv reduces the memory usage for calculating and storing Lie group distances to less than 500 MB. And the performance of LieCConv was evaluated on RotMNIST, RotFashionMNIST and TT100K, validating that LieCConv is universal and effective.

In this paper, a hybrid controller with a sampled data control is investigated to achieve finite-time master–slave synchronization of delayed fractional-order neural networks (DFONNs). A Lyapunov-Krasovskii functional is constructed to obtain the sufficient conditions that incorporate delay information. For the first time, the asymptotic stability of the error system is guaranteed in a finite-time using the inequality technique and a sampled-data hybrid controller. The obtained conditions are expressed via linear matrix inequality. Notably, the proposed approach outperforms existing methods, demonstrating improved results in a comparative analysis. An explicit formula is utilized to calculate the settling time, which is significantly influenced by the fractional order 0<β≤1. The superior performance of the proposed control method is evident, showcasing its effectiveness through numerical simulations and addressing the synchronization problem in DFONNs.

Classification models trained on imbalanced datasets tend to be biased towards the majority category, resulting in reduced accuracy for minority categories. A common approach to address this problem is to generate artificial data for underrepresented categories. The Synthetic Minority Over-sampling Technique (SMOTE) algorithm and its variants are widely used for this purpose. In this paper, we propose a modification to the data generation mechanism called Iteration-based SMOTE (ISMOTE). Unlike SMOTE, the ISMOTE algorithm trains the data for multiple iterations. In each iteration, the model generates new samples in the vicinity of appropriately misclassified data. These new samples are then fed into the classification model, thus improving classification accuracy over the course of multiple iterations. We compare the performance of ISMOTE with SMOTE and other commonly used oversampling algorithms. Our empirical results demonstrate that ISMOTE significantly improves the quality of the generated data compared to other oversampling methods. Additionally, we conduct experiments to verify the effect of parameters on the model and provide suggestions for choosing appropriate values to improve performance.