Deep multi-view clustering aims to exploit the rich semantic information contained in heterogeneous multi-view data to uncover the underlying relationships among samples. However, existing deep multi-view clustering models often overlook inter-cluster separability and the effective integration of semantic information across views, resulting in insufficient feature discriminability and consequently limited clustering performance. To address the above issues, this paper proposes a novel deep multi-view clustering method via cluster-semantic guidance. We separate clusters to enhance inter-cluster discriminability, while incorporating a knowledge distillation mechanism to ensure cluster stability and facilitate the learning of clustering-friendly representations. Furthermore, by aggregating sample-level semantic information, the model is guided to follow a cluster-oriented learning strategy that promotes the extraction of discriminative features, thereby strengthening the sample representation capability. Our method effectively learns discriminative and clustering-friendly representations, guiding the model to acquire distinctive feature embeddings from a cluster-oriented perspective. Our comprehensive experiments across datasets of varying scales confirm the model's effectiveness, showing superior clustering performance over existing state-of-the-art methods.

With the rapid advancement of virtual reality, dynamic gesture recognition technology has become an indispensable and critical technique for users to achieve human–computer interaction in virtual environments. The recognition of dynamic gestures is a challenging task due to the high degree of freedom and the influence of individual differences and the change of gesture space. To solve the problem of low recognition accuracy of existing networks, an improved dynamic gesture recognition algorithm based on ResNeXt architecture is proposed. The algorithm employs three-dimensional convolution techniques to effectively capture the spatiotemporal features intrinsic to dynamic gestures. Additionally, to enhance the model’s focus and improve its accuracy in identifying dynamic gestures, a lightweight convolutional attention mechanism is introduced. This mechanism not only augments the model’s precision but also facilitates faster convergence during the training phase. In order to further optimize the performance of the model, a deep attention submodule is added to the convolutional attention mechanism module to strengthen the network’s capability in temporal feature extraction. Empirical evaluations on EgoGesture and NvGesture datasets show that the accuracy of the proposed model in dynamic gesture recognition reaches 95.03% and 86.21%, respectively. When operating in RGB mode, the accuracy reached 93.49% and 80.22%, respectively. These results underscore the effectiveness of the proposed algorithm in recognizing dynamic gestures with high accuracy, showcasing its potential for applications in advanced human–computer interaction systems.

Graph convolutional networks (GCNs) have been the predominant methods in skeleton-based human action recognition, including human-human interaction recognition. However, when dealing with interaction sequences, current GCN-based methods simply split the two-person skeleton into two discrete graphs and perform graph convolution separately as done for single-person action classification. Such operations ignore rich interactive information and hinder effective spatial inter-body relationship modeling. To overcome the above shortcoming, we introduce a novel unified two-person graph to represent inter-body and intra-body correlations between joints. Experimental results show accuracy improvements in recognizing both interactions and individual actions when utilizing the proposed two-person graph topology. In addition, several graph labeling strategies are designed to supervise the model to learn discriminant spatial-temporal interactive features. Finally, we propose a two-person graph convolutional network (2P-GCN). Our model outperforms state-of-the-art methods on four benchmarks of three interaction datasets: SBU, interaction subsets of NTU-RGB+D and NTU-RGB+D 120.

Due to the problem of severe deformation, occlusion, diversified scenarios, general object detection methods cannot achieve satisfactory results in underwater object detection tasks. In this paper, we propose a two-stage Underwater Small Target Detection (USTD) network. In the proposed USTD, the Deformable Convolutional Pyramid(DCP) is proposed to deal with the problems of deformation, occlusion, and various object sizes effectively. Besides, we also propose a strategy of domain generalization based on curriculum learning to improve generalization in multi-domain environments, which is named as Phased Learning. Afterward, we construct an underwater target detection set (UTDS) to evaluate the accuracy of our method in underwater target detection tasks. Our method shows superior detection performance in experiments and reaches state-of-the-art for underwater target detection. Finally, in the 2020 China Underwater Robot Professional Contest (URPC), our method reached third place in terms of accuracy.

Vibration signal always performs poorly in the fault diagnosis of low-speed rolling bearings. The fact that rolling bearings running under different speed conditions further increases the difficulty of fault diagnosis on low-speed bearing. To address the above problems, this article proposes a fault diagnosis method for low-speed rolling bearings based on acoustic emission (AE) signal and subspace embedded feature distribution alignment (SADA). First, the AE signal of low-speed rolling bearing is collected and the spectral dataset is constructed. Second, subspace alignment is used to align the basis vectors for both domains in order to prevent feature distortion. Then, a base classifier is trained to predict the pseudolabels of the target domain, which is used to quantitatively estimate the weight of the edge distribution and conditional distribution of the two domains for adaption. Finally, following the structural risk minimization (SRM) framework, a kernel function is constructed to establish the classifier f, which iteratively updates the pseudolabels in the target domain and obtains the coefficient matrix of the final framework to complete the identification task. The feasibility and effectiveness of the proposed method are verified by two AE datasets of low-speed rolling bearing.

Functional MRI based on blood oxygenation level-dependent (BOLD) contrast is well established as a neuroimaging technique for detecting neural activity in the cortex of the human brain. Recent studies have shown that variations of BOLD signals in white matter are also related to neural activities both in resting state and under functional loading. We develop a comprehensive framework of detecting task-specific fiber pathways. We not only study fiber tracts as open curves with different physical features (shape, scale, orientation and position), but also incorporate the BOLD signals associated with them to find stimulus-relevant pathways. Specifically, we propose a novel Riemannian metric, which is a weighted sum of distances in product space of shapes and functions. This metric provides both a cost function for registration and a proper distance for comparison. Experimental results on real data have shown that we can cluster fiber pathways correctly by evaluating correlations between BOLD signals and stimuli, temporal variations and power spectra of them.

Image fingerprinting is a technique that summarizes the perceptual characteristics of a digital image into an invariant digest, and it is one of the most effective solutions for digital rights management. Most conventional fingerprinting algorithms were developed by assembling manually designed feature extractor and quantizer, which requires extensive expert knowledge and may not capture the intrinsic or abstract visual characteristics of the digital image. Focusing on content identification related applications, we propose a data-driven image fingerprinting algorithm in this paper, where neural network is trained to automatically discover the optimal mapping from image to fingerprint. To ameliorate the difficulty of training, we start by training the fingerprint-computation network in a layer-wise manner to progressively improve its robustness against content-preserving distortions. Initialized by the states learned by layer-wise training, the network is then re-trained as a holistic unit, with the objective of maximizing its content identification accuracy. Moreover, we also develop a key-dependent version of the neural network-based fingerprinting algorithm. By quantifying its security using information-theoretic metrics, we have proved that the hierarchical architecture of neural network is beneficial to the security of fingerprinting algorithm. The experimental results on a large testing database show that the proposed work exhibits much higher content identification accuracy than state-of-the-art algorithms, and its execution speed is in the millisecond time scale.

High utility pattern mining (HUPM) has become a key issue in knowledge discovery since it provides retailers and managers with useful information for making decisions efficiently. However, previous studies most focused on mining the high-utility patterns (HUPs) from a single database. In this paper, we present a framework to incorporate the weighted model for parallel synthesis of the discovered HUPs from various databases. The pre-large concept was also used as a buffer here in order to provide more prospective HUPs, thus providing higher accuracy of the synthesized patterns. From our experiments, the developed model exceeds existing works, in particular the designed model has increased precision and recall on knowledge synthesization compared to the previous works.

A novel MDC algorithm based on discrete cosine transform (DCT) and the set partition in hierarchical trees (SPIHT) is proposed in this paper. Different from the commonly used DCT algorithm, all the transformed coefficients are reshaped into the wavelet decomposition structure to facilitate the use of the SPIHT algorithm. Then the direction-based information is used to form the three different channels. By using different bit rates to encode the information from three different orientations, i.e., vertical, horizontal and diagonal directions, the redundancy is introduced into the three channels. Every channel contains the hybrid information from three different directions. Experimental results show the advantages of this novel algorithm and the theoretical analysis has also been studied.