As one of the most critical tasks in legal artificial intelligence, legal judgment prediction (LJP) has garnered growing attention, especially in the civil law system. However, current methods often overlook the challenge of imbalanced label distribu...
Few-shot relation extraction constitutes a critical task in natural language processing. Its aim is to train a model using a limited number of labeled samples when labeled data are scarce, thereby enabling the model to rapidly learn and accurately id...
Traditional machine learning-based entity extraction methods rely heavily on feature engineering by experts, and the generalization ability of the model is poor. Prototype networks, on the other hand, can effectively use a small amount of labeled dat...
Prototype-Based Networks (PBNs) are inherently interpretable architectures that facilitate understanding of model outputs by analyzing the activation of specific neurons—referred to as prototypes—during the forward pass. The learned proto...
Supervised learning methods excel in traditional relation extraction tasks. However, the quality and scale of the training data heavily influence their performance. Few-shot relation extraction is gradually becoming a research hotspot whose objective...
Deep learning has become an effective method for hyperspectral image classification. However, the high band correlation and data volume associated with airborne hyperspectral images, and the insufficiency of training samples, present challenges to th...
Few-shot relation extraction (FSRE) constitutes a critical task in natural language processing (NLP), involving learning relationship characteristics from limited instances to enable the accurate classification of new relations. The existing research...
Objective and continuous monitoring of Parkinson’s disease (PD) tremor in free-living conditions could benefit both individual patient care and clinical trials, by overcoming the snapshot nature of clinical assessments. To enable robust detecti...
Few-shot hyperspectral image classification aims to develop the ability of classifying image pixels by using relatively few labeled pixels per class. However, due to the inaccuracy of the localization system and the bias of the ground survey, the pot...
Existing few-shot learning models leverage vision-language pre-trained models to alleviate the data scarcity problem. However, such models usually process visual and text information separately, which causes still inherent disparities between cross-m...
Few-shot text classification aims to recognize new classes with only a few labeled text instances. Previous studies mainly utilized text semantic features to model the instance-level relation among partial samples. However, the single relation inform...
The Nielsen Report points out that credit card fraud caused business losses of USD 28.65 billion globally in 2019, with the US accounting for more than one-third of the high share, and that insufficient identification of credit card fraud has brought...
Named entity recognition, a fundamental task in natural language processing, faces challenges related to the sequence labeling framework widely used when dealing with nested entities. The span-based method transforms nested named entity recognition i...
With the development of unmanned aerial vehicle (UAV), obtaining high-resolution aerial images has become easier. Identifying and locating specific crops from aerial images is a valuable task. The location and quantity of crops are important for agri...
Farmland shelterbelt plays an important role in protecting farmland and ensuring stable crop yields, and it is mainly distributed in the form of bands and patches; different forms of distribution have different impacts on farmland, which is an import...
Methods for fault diagnosis based on metric learning, in which a query sample is classified by picking the closest prototype from the support set based on their feature similarities, have been the subject of many studies. In real-world applications o...
Automatic modulation classification (AMC) based on data-driven deep learning (DL) can achieve excellent classification performance. However, in the field of electronic countermeasures, it is difficult to extract salient features from wireless communi...
Inverse synthetic aperture radar (ISAR) is a significant means of detection in space of non-cooperative targets, which means that the imaging geometry and associated parameters between the ISAR platform and the detection targets are unknown. In this...
Much research has focused on classification within a closed set of emitters, while emitters outside this closed set are misclassified. This paper proposes an open-set recognition model based on prototypical networks and extreme value theory to solve...
Research on few-shot action recognition has received widespread attention recently. However, there are some blind spots in the current research: (1) The prevailing practice in many models is to assign uniform weights to all samples; nevertheless, suc...
Achieving deep learning-based bearing fault diagnosis heavily relies on large labeled training samples. However, in real industry applications, labeled data are scarce or even impossible to obtain. In this study, we addressed a challenging few-shot b...
Anomaly detection systems based on artificial intelligence (AI) have demonstrated high performance and efficiency in a wide range of applications such as power plants and smart factories. However, due to the inherent reliance of AI systems on the qua...
It is well known that road traffic safety is one of the crucial topics in the field of automotive safety in assisted driving. In the face of complex traffic scenarios, there are still a large number of unsolved problems in the identification of vehic...
Few-shot learning has achieved great success in computer vision. However, when applied to Synthetic Aperture Radar Automatic Target Recognition (SAR-ATR), it tends to demonstrate a bad performance due to the ignorance of the differences between SAR i...
Continual learning (CL), also known as lifelong learning, is an emerging research topic that has been attracting increasing interest in the field of machine learning. With human activity recognition (HAR) playing a key role in enabling numerous real-...
The rapid serial visual presentation-based brain–computer interface (RSVP-BCI) system achieves the recognition of target images by extracting event-related potential (ERP) features from electroencephalogram (EEG) signals and then building targe...
At present, synthetic aperture radar (SAR) automatic target recognition (ATR) has been deeply researched and widely used in military and civilian fields. SAR images are very sensitive to the azimuth aspect of the imaging geomety; the same target at d...
Meta-learning has demonstrated significant advantages in small-sample tasks and has attracted considerable attention in wind turbine fault diagnosis. However, due to extreme operating conditions and equipment aging, the monitoring data of wind turbin...
Few-shot relation extraction aims to identify and extract semantic relations between entity pairs using only a small number of annotated instances. Many recently proposed prototype-based methods have shown excellent performance. However, existing pro...
A power grid is a network that carries electrical energy from power plants to customer premises. One existing power grid is going through a massive and revolutionary transformation process. It is envisioned to achieve the true meaning of technology a...
This paper investigates the feasibility of cross-domain recognition for human activities captured using low-resolution 8 × 8 infrared sensors in indoor environments. To achieve this, a novel prototype recurrent convolutional network (PRCN) was...
Feature extraction is an important process for the automatic recognition of synthetic aperture radar targets, but the rising complexity of the recognition network means that the features are abstractly implied in the network parameters and the perfor...
In few-shot fault diagnosis tasks in which the effective label samples are scarce, the existing semi-supervised learning (SSL)-based methods have obtained impressive results. However, in industry, some low-quality label samples are hidden in the coll...
The purpose of few-shot relation extraction (RE) is to recognize the relationship between specific entity pairs in text when there are a limited number of labeled samples. A few-shot RE method based on a prototype network, which constructs relation p...
Individual identification of Holstein cattle is crucial for the intelligent management of farms. The existing closed-set identification models are inadequate for breeding scenarios where new individuals continually join, and they are highly sensitive...
The pine wood nematode (PWN; Bursaphelenchus xylophilus) is a major invasive species in China, causing huge economic and ecological damage to the country due to the absence of natural enemies and the extremely rapid rate of infection and spread. Accu...
Synthetic aperture radar (SAR) image classification under limited data conditions faces two major challenges: inter-class similarity, where distinct radar targets (e.g., tanks and armored trucks) have nearly identical scattering characteristics, and...
In recent years, deep-learning-based WiFi fingerprinting has been intensively studied as a promising technology for providing accurate indoor location services. However, it still demands a time-consuming and labor-intensive site survey and suffers fr...
Volkswagen Technical Development (TE) is responsible for all prototype development and prototype production for the Volkswagen brand and has its own logistics department (TE-Logistics). In the logistics of prototype parts in the automotive industry,...
Hiking and cycling have gained popularity as ways of promoting well-being and physical activity. This has not gone unnoticed by Portuguese authorities, who have invested in infrastructure to support these activities and to boost sustainable and natur...
With the population aging globally, cataracts have become one of the main causes of vision impairment. Early diagnosis and treatment of cataracts are crucial for preventing blindness. However, the use of deep learning models for assisting in the diag...
Humanity’s way of life has been irreversibly transformed by new technological advancements during the past decades. Although such technological innovations have been gradually transposed into architecture, their full integration is not yet achi...
Background: Yupingfeng San (YPFS) is a classic formula for treating allergic rhinitis (AR), which is composed of Astragalus mongholicus Bunge (AST), Atractylodes macrocephala Koidz (AMR), and Saposhni-kovia divaricata (Turcz.) Schischk (SR) at a rati...
Traditional malware-classification methods reliant on large pre-labeled datasets falter when encountering new or evolving malware types, particularly when only a few samples are available. And most current models utilize a fixed architecture; however...
A vibration scale training model for converter transformers is proposed by combining attention modules with convolutional neural networks to solve the nonlinear problem of converter transformers in similar processes. Firstly, according to the structu...
It is a well-established practice to build a robust system for sound event detection by training supervised deep learning models on large datasets, but audio data collection and labeling are often challenging and require large amounts of effort. This...
As Advanced Persistent Threats (APTs) continue to evolve, constructing a dynamic cybersecurity knowledge graph requires precise extraction of entity–relationship triples from unstructured threat intelligence. Existing approaches, however, face...
Conventional food fraud detection using hyperspectral imaging (HSI) relies on the discriminative power of machine learning. However, these approaches often assume a balanced class distribution in an ideal laboratory environment, which is impractical...
Endangered species detection plays an important role in biodiversity conservation and is significant in maintaining ecological balance. Existing deep learning-based object detection methods are overly dependent on a large number of supervised samples...
This study focuses on the construction of a prototype series of pumps. The technological capabilities of the entire series of gear pumps with a three-poly-involute outline were determined. We developed neural networks to analyze the dimensional toler...
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