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Label Enhancement (LE) strives to convert logical labels of instances into label distributions to provide data preparation for label distribution learning (LDL). Existing LE methods ordinarily neglect to consider original features and logical ...

• Unify features and logical labels as dual-view descriptions in a projection space.
• Dual contrastive learning is used to obtain high-level representations for LE.
• Consider label consistency to guide recovering label distributions.

Compared to traditional software defect prediction, Just-In-Time Software Defect Prediction (JIT-SDP) is a more fine-grained software defect prediction method used for defect prediction at the software change level. However, JIT software defect ...

The anal sphincter complex comprises the anal sphincter and the U-shaped deep and superficial puborectalis muscle. As an important supporting structure of the posterior pelvic floor, together with its surrounding tissues and muscles, the ...

Multi-view Representation Learning (MRL) has recently attracted widespread attention because it can integrate information from diverse data sources to achieve better performance. However, existing MRL methods still have two issues: (1) They ...

• A novel learning framework considering inter-view and inter-sample relationships.
• Extracted semantic and graph labels collaborate to guide representation learning.
• The pursuit of semantic consistency strengthens intrinsic ...

The adaptive neuro-fuzzy inference system (ANFIS) has shown promising performance in modeling nonlinear problems, leveraging the strengths of both neural networks and fuzzy inference systems. However, as the problem scale increases, the growing ...

• Hybrid models combining fuzzy inference and metaheuristics.
• Effective global optimization of ANFIS parameters.
• Robust generalization capability on nonlinear regression problems.
• Promising approach for complex optimization ...

Recent years have witnessed the success of deep networks in compressed sensing (CS), which allows for a significant reduction in sampling cost and has gained growing attention since its inception. In this paper, we propose a new practical and compact ...

The swift proliferation of multimodal rumors on social media, particularly those with manipulated images and complex intermodal interactions, significantly challenges current detection methods. In response, we utilize statistical image features, ...

Hyperspectral imaging plays a pivotal role across diverse applications, like remote sensing, medicine, and cytology. The utilization of 2D sensors to acquire 3D hyperspectral images (HSIs) via a coded aperture snapshot spectral imaging (CASSI) system has ...

The current face scanning era can quickly and conveniently attain identity authentication, but face images imply sensitive information simultaneously. Under such context, we introduce a novel cancelable face recognition methodology by using ...

• MRQSVD is introduced for pyramid representation of bimodal face images.
• Three-stream ConvNet with predefined filters is developed for features extraction.
• The proposed cancelable recognition scheme outperforms several existed ...

Pose-agnostic anomaly detection refers to the situation where the pose of test samples is inconsistent with the training dataset, allowing anomalies to appear at any position in any pose. We propose a novel method IGSPAD to address this challenge. ...

With the advent of virtual reality technology, omnidirectional image (ODI) rescaling techniques are increasingly embraced to reduce transmitted and stored file sizes while preserving high image quality. Despite this progress, current ODI rescaling ...

Clustering is fundamentally a subjective task: a single dataset can be validly clustered in various ways, and without further information, clustering systems cannot determine the appropriate clustering to perform. This underscores the importance of ...

In the domain of computer vision, Transformers have shown great promise, yet they face difficulties when trained from scratch on small datasets, often underperforming compared to convolutional neural networks (ConvNets). Our work highlights Vision ...

Class-incremental learning (CIL) has been widely applied in the real world due to its flexibility and scalability. Recent advancements in CIL have achieved outstanding performance. However, deep neural networks, including CIL models, face ...

Achieving automated vertebrae classification in spine images is a crucial yet challenging task due to the repetitive nature of adjacent vertebrae and limited fields of view (FoV). Different from previous methods that leverage the serial ...

An <inline-formula> <tex-math notation="LaTeX">$(n, k, d; r)_{q}$ </tex-math></inline-formula>-locally repairable code (LRC) is called a Singleton-optimal LRC if it achieves the Singleton-type bound. Analogous to the classical MDS conjecture, the maximal ...

By mapping iterative optimization algorithms into neural networks (NNs), deep unfolding networks (DUNs) exhibit well-defined and interpretable structures and achieve remarkable success in the field of compressive sensing (CS). However, most existing DUNs ...

Model Inversion (MI) attacks aim to reconstruct privacy-sensitive training data from released models by utilizing output information, raising extensive concerns about the security of Deep Neural Networks (DNNs). Recent advances in generative ...${}_{\mathrm{}}$

Transferable targeted adversarial attacks aim to mislead models into outputting adversary-specified predictions in black-box scenarios. Recent studies have introduced single-target attacks that train a generator for each target class to generate ...

In the healthcare field, cross-institutional collaboration can fasten medical research progress. Vertical federated learning (VFL) addresses data heterogeneity across multiple medical institutions while ensuring medical data privacy, thereby ...