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Handwriting analysis has traditionally been used to infer personality traits from the stylistic features of writing. With advances in machine learning, the accuracy and applicability of these analyses have significantly improved. This paper ...

Surgical triplets recognition aims to identify instruments, verbs, and targets in a single video frame, while establishing associations among these components. Since this task has severe imbalanced class distribution, precisely identifying tail ...

The binary cross-entropy (BCE) loss function is widely utilized in multi-label classification (MLC) tasks, treating each label independently. The log-sum-exp pairwise (LSEP) loss, which emphasizes higher logits for positive classes over negative ...

Similarity measures play an important role in data analysis, as the performance of different classification or clustering techniques relies on choosing an appropriate measure. Most formulas either increase values with “negative matches” or ...

Deep neural network models have demonstrated their effectiveness in classifying multi-label data from various domains. Typically, they employ a training mode that combines mini-batches with optimizers, where each sample is randomly selected with ...

Class imbalance significantly impacts the performance of multi-label classifiers. Oversampling is one of the most popular approaches, as it augments instances associated with less frequent labels to balance the class distribution. Existing ...

The stability of power systems is paramount to industrial operations. The deleterious inherent characteristics of false data injection attacks (FDIA) have drawn substantial interest due to their severe threats to power grids. Contemporary ...

• Developing more realistic measurement and topology attack models.
• Variational graph attention autoencoder for extracting variable topology information.
• Robust multi-label classification model for detecting hybrid attacks.

This paper serves as the first empirical study on self-supervised pre-training on partially supervised learning, an emerging yet unexplored learning paradigm with missing annotations. This is particularly important in the medical imaging domain, ...

• This is the first study of self-supervised pre-training for PSMLC.
• This is the first study of MTL in self-supervised pre-training for PSMLC.
• A pretext task based on VRM is proposed for PSMLC.

The identification of sexual activities in images can be helpful in detecting the level of content severity and can assist pornography detectors in filtering specific types of content. In this paper, we propose a Deep Learning-based framework, ...

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• Introduces the novel task named Human Sexual Activity Recognition (HSAR).
• DeepHSAR: novel semi-supervised framework for fine-grained image recognition.
• Presents SexualActs-150k dataset, manually labeled for 19 types of sexual ...

Multi-label classification studies a problem where each instance is associated with multiple relevant labels, which leads to the exponential growth of output space. To address this issue of high-dimensional label space, dimensionality reduction ...

With the development of the information age and the popularization of electronic documents, a large number of electronic documents are generated every day in government agencies, enterprises and institutions, and are used more and more frequently. ...

The integration of Unmanned Aerial Vehicles (UAVs) with artificial intelligence (AI) models for aerial imagery processing in disaster assessment, necessitates models that demonstrate exceptional accuracy, computational efficiency, and real-time ...

As a prominent manifestation of user-generated content (UGC), micro-video has emerged as a pivotal medium for individuals to document and disseminate their daily experiences. In particular, micro-videos generally encompass abundant content ...

Missing values are a common problem in data science and machine learning. Removing instances with missing values is a straightforward workaround, but this can significantly hinder subsequent data analysis, particularly when features outnumber ...

Label enhancement (LE) aims to enrich logical labels into their corresponding label distributions. But existing LE algorithms fail to fully leverage the structural information in the feature space to improve LE learning. To address this key issue,...

• Propose two algorithms, the label enhancement based on feature representation(LEFR) and the LE based on graph convolutional network(LE-GCN).
• The LEFR first apply manifold learning to map the relatedness between low-dimensional feature ...

Multi-label text classification refers to assigning multiple relevant category labels to each text, which has been widely applied in the real world. To enhance the performance of multi-label text classification, most existing methods only focus on ...

Protein-protein interaction (PPI) plays an important role in various biological processes of organisms, and is beneficial for the development of relevant drugs, the diagnosis of diseases, etc. On this account, an increasing number of studies on ...

• Propose to learn PPI representations jointly from both topology and consensus views.
• Introduce label signal to guide the learning process of PPI representations.
• Enforce a constraint to maintain consistency between topology and ...

Classification as an essential part of Machine Learning and Data Mining has significant roles in engineering, medicine, agriculture, military, etc. With the evolution of data collection tools and the unceasing efforts of researchers, new datasets ...

Skin cancer is one of the most serious threats to human health among skin lesions. Computer-aided diagnosis methods can assist patients in identifying and detecting skin lesion types early, thereby enabling corresponding treatments. In this paper,...

Smart contracts are decentralized applications that hold a pivotal role in blockchain-based systems. Smart contracts are composed of error-prone programming languages, so it is affected by many vulnerabilities (e.g., time dependence, outdated ...