Jian Feng^*, Yifan Guo, Cailing Du

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5135-5151, 2025, DOI:10.32604/cmc.2025.059610 - 06 March 2025

Abstract Graph similarity learning aims to calculate the similarity between pairs of graphs. Existing unsupervised graph similarity learning methods based on contrastive learning encounter challenges related to random graph augmentation strategies, which can harm the semantic and structural information of graphs and overlook the rich structural information present in subgraphs. To address these issues, we propose a graph similarity learning model based on learnable augmentation and multi-level contrastive learning. First, to tackle the problem of random augmentation disrupting the semantics and structure of the graph, we design a learnable augmentation method to selectively choose nodes and… More >

Yi Zuo, Zhenping Chen^*, Jing Feng, Yunhao Fan

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4649-4667, 2025, DOI:10.32604/cmc.2025.059472 - 06 March 2025

Abstract Image classification is crucial for various applications, including digital construction, smart manufacturing, and medical imaging. Focusing on the inadequate model generalization and data privacy concerns in few-shot image classification, in this paper, we propose a federated learning approach that incorporates privacy-preserving techniques. First, we utilize contrastive learning to train on local few-shot image data and apply various data augmentation methods to expand the sample size, thereby enhancing the model’s generalization capabilities in few-shot contexts. Second, we introduce local differential privacy techniques and weight pruning methods to safeguard model parameters, perturbing the transmitted parameters to ensure More >

Jiyang Xu¹, Qi Wang^1,*, Xin Xiong², Weidong Min^1,3, Jiang Luo⁴, Di Gai¹, Qing Han^1,3

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3921-3941, 2025, DOI:10.32604/cmc.2024.058586 - 06 March 2025

Abstract The unsupervised vehicle re-identification task aims at identifying specific vehicles in surveillance videos without utilizing annotation information. Due to the higher similarity in appearance between vehicles compared to pedestrians, pseudo-labels generated through clustering are ineffective in mitigating the impact of noise, and the feature distance between inter-class and intra-class has not been adequately improved. To address the aforementioned issues, we design a dual contrastive learning method based on knowledge distillation. During each iteration, we utilize a teacher model to randomly partition the entire dataset into two sub-domains based on clustering pseudo-label categories. By conducting contrastive… More >

Yujiao Tang¹, Yadong Wu^1,*, Yuanmei He², Jilin Liu¹, Weihan Zhang¹

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2331-2352, 2025, DOI:10.32604/cmc.2024.059115 - 17 February 2025

Abstract Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion… More >

Hengyang Liu¹, Yang Yuan^1,*, Pengcheng Ren¹, Chengyun Song¹, Fen Luo²

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 543-560, 2025, DOI:10.32604/cmc.2024.056478 - 03 January 2025

Abstract Existing semi-supervised medical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch. However, current copy-paste methods have three limitations: (1) training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information; (2) low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data; (3) the segmentation performance in low-contrast and local regions is less than optimal. We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy (SADT), which enhances feature diversity and learns high-quality features to overcome these problems. To be more… More >

James Wysock^1,*, Jesse Persily^1,*, Angela Tong², Eli Rapoport¹, Ben Zaslavsky¹, Majlinda Tafa¹, Herbert Lepor¹

Canadian Journal of Urology, Vol.31, No.5, pp. 11986-11991, 2024

Abstract Introduction: Local disease recurrence following focal therapy (FT) for prostate cancer may be due to failure to eradicate focal disease or development of disease in the untreated prostate (in- and out-of-field recurrences). Several studies suggest in-field contrast enhancement (CE) on post treatment multi-parametric (mp) MRI between 6-12 months following FT indicates residual disease. The present study assesses the incidence and oncologic implications of early CE observed following primary partial gland cryoablation (PPGCA).
Material and methods: The surveillance protocol for men enrolled in our prospective outcomes study following PPGCA included mpMRI at 6-12 months, 2 years, 3.5 years,… More >

Victor Kozlov^1,*, Alsu Zimasova¹, Nikolai Kozlov²

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.12, pp. 2693-2707, 2024, DOI:10.32604/fdmp.2024.052398 - 23 December 2024

Abstract The behavior of a viscous liquid film on the wall of a rapidly rotating cylinder subjected to angular vibrations is experimentally studied. The cavity is filled with an immiscible low-viscosity liquid of lower density. In the absence of vibrations, the high viscosity liquid covers the inner surface of the cylinder with a relatively thin axisymmetric film; the low-viscosity liquid is located in the cavity interior. It is found that with an increase in the amplitude of rotational vibrations, the axisymmetric interphase boundary loses stability. An azimuthally periodic 2D “frozen wave” appears on the film surface… More >

Jian Feng^*, Tian Liu, Cailing Du

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2895-2909, 2024, DOI:10.32604/cmc.2024.056434 - 18 November 2024

Abstract Unsupervised learning methods such as graph contrastive learning have been used for dynamic graph representation learning to eliminate the dependence of labels. However, existing studies neglect positional information when learning discrete snapshots, resulting in insufficient network topology learning. At the same time, due to the lack of appropriate data augmentation methods, it is difficult to capture the evolving patterns of the network effectively. To address the above problems, a position-aware and subgraph enhanced dynamic graph contrastive learning method is proposed for discrete-time dynamic graphs. Firstly, the global snapshot is built based on the historical snapshots… More >

Xu Sun, Yinhui Yu^*, Qing Cheng

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4149-4171, 2024, DOI:10.32604/cmc.2024.052725 - 12 September 2024

Abstract Object detection in unmanned aerial vehicle (UAV) aerial images has become increasingly important in military and civil applications. General object detection models are not robust enough against interclass similarity and intraclass variability of small objects, and UAV-specific nuisances such as uncontrolled weather conditions. Unlike previous approaches focusing on high-level semantic information, we report the importance of underlying features to improve detection accuracy and robustness from the information-theoretic perspective. Specifically, we propose a robust and discriminative feature learning approach through mutual information maximization (RD-MIM), which can be integrated into numerous object detection methods for aerial images.… More >

Jingtao Sun^1,2, Fengling Zhang^1,2,*, Huanqi Liu^1,2, Wenyan Hou^1,2

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3173-3192, 2024, DOI:10.32604/cmc.2024.051705 - 15 August 2024

Abstract As image manipulation technology advances rapidly, the malicious use of image tampering has alarmingly escalated, posing a significant threat to social stability. In the realm of image tampering localization, accurately localizing limited samples, multiple types, and various sizes of regions remains a multitude of challenges. These issues impede the model’s universality and generalization capability and detrimentally affect its performance. To tackle these issues, we propose FL-MobileViT-an improved MobileViT model devised for image tampering localization. Our proposed model utilizes a dual-stream architecture that independently processes the RGB and noise domain, and captures richer traces of tampering… More >