Recently, prompt-based learning has shown impressive performance on various natural language processing tasks in few-shot scenarios. The previous study of knowledge probing showed that the success of prompt learning contributes to the implicit knowle...
In 3D segmentation with lesion-only annotations, lesion supervision defines voxel-wise foreground membership, whereas medical knowledge characterizes the relations between lesion states and relevant anatomical structures. How to integrate these two f...
Landslide detection using medium-resolution optical remote sensing imagery remains challenging in complex mountainous environments because of spectral ambiguity, vegetation cover, shadows, and background interference. Although recent deep learning me...
Background: Early detection of dementia is essential for timely intervention, yet existing diagnostic approaches remain costly, invasive, or dependent on specialized expertise. Electroencephalography (EEG) offers a non-invasive and accessible alterna...
Bearing fault diagnosis is crucial for stable operation and safe manufacturing as industry intelligence becomes increasingly advanced. However, under complicated non-linear vibration modes and multiple operating conditions, most of the current diagno...
Data-driven models have been successfully applied to flood prediction. However, the nonlinearity and uncertainty of the prediction process and the possible noise or outliers in the data set will lead to incorrect results. In addition, data-driven mod...
As satellite capabilities have evolved and new observation requirements have emerged, satellites have become essential tools in disaster relief, emergency monitoring, and other fields. However, the efficiency of satellite scheduling still needs to be...
To address the critical requirements for electromagnetic spectrum orchestration in complex ultra-dense communication environments, this paper proposes an Expert Knowledge-Guided improved NSGA-III framework to solve large-scale frequency assignment pr...
Vehicle-to-Vehicle (V2V) cooperative object detection enhances environmental perception capabilities in complex traffic scenarios by sharing sensory information among vehicles, but limited transmission bandwidth and wireless channel noise can signifi...
Reliable digital twins of complex nonlinear systems require not only high predictive accuracy but also physical consistency, robustness under degraded operating conditions, and explicit uncertainty handling. Purely data-driven models often suffer fro...
Accurately predicting crop yield and its spatiotemporal variability is crucial for precision agriculture. This study developed a prior knowledge-guided remote sensing yield estimation framework at Youyi Farm in China. Based on multi-source data from...
Online abnormal event detection of non-cooperative space targets based on space-based optical observations is an urgent requirement for ensuring space security, particularly for sudden state changes such as attitude and orbit maneuver events. However...
Energy-aware distributed manufacturing has become a key focus in modern production systems due to the growing demand for sustainable and efficient operations. This study investigates the energy-aware distributed flexible job shop scheduling problem w...
Optimization algorithms have a wide range of applications in symmetry problems, such as graphs, networks, and pattern recognition. In this paper, a dynamic periodic multi-depot multi-trip vehicle routing model for scheduling test samples is construct...
The rapid acquisition of deposit volume information and dynamic modeling, as well as the visualization of disaster scenes, have great significance for the sharing of landslide information and the management of emergency rescue. However, existing meth...
Prompt tuning visual-language models (VLMs) for specialized tasks often involves leveraging task-specific textual tokens, which can tailor the pre-existing, broad capabilities of a VLM to more narrowly focused applications. This approach, exemplified...
Academic knowledge graphs are essential resources and can be beneficial in widespread real-world applications. Most of the existing academic knowledge graphs are far from completion; thus, knowledge graph completion—the task of extending a know...
This paper introduces the Knowledge-Guided Genetic Algorithm (KGGA), a hybrid metaheuristic that reimagines crossover as a form of genetic engineering rather than random recombination. By embedding knowledge-guided exploitation principles directly in...
Semantic segmentation and height estimation tasks in remote sensing imagery exhibit distinctive characteristics, including scale sensitivity, category imbalance, and insufficient fine details. Recent approaches have leveraged multi-task learning meth...
In real-world applications, feature selection is crucial for enhancing the performance of data science and machine learning models. Typically, feature selection is a complex combinatorial optimization problem and a multi-objective optimization proble...
Identifying distributed photovoltaic generation, battery energy storage, and load-dominant behaviour remains challenging when transformer-area measurements are sparse, operating patterns overlap, and reference labels are incomplete. We develop a know...
Knowledge graph completion (KGC) involves inferring missing entities or relationships within a knowledge graph, playing a crucial role across various domains, including intelligent question answering, recommendation systems, and dialogue systems. Tra...
Distantly supervised relation extraction is the most popular technique for identifying semantic relation between two entities. Most prior models only focus on the supervision information present in training sentences. In addition to training sentence...
Building community discovery aims to identify spatially continuous and cognitively coherent groups of buildings that represent meaningful urban spatial structures. Existing methods mainly integrate geometric similarity, spatial proximity, and topolog...
Recently, fake news, such as low-quality news with intentionally false information, has threatened the authenticity of news information. However, existing detection methods are inefficient in modeling complicated data and leveraging external knowledg...
For practical maritime SAR image classification tasks with special imaging platforms, scenes to be classified are often different from those in the training sets. The quantity and diversity of the available training data can also be extremely limited...
Extreme low-light image enhancement presents persistent challenges due to compounded degradations involving underexposure, sensor noise, and structural detail loss. Traditional low-light image enhancement methods predominantly employ global adjustmen...
With the advent of large language models (LLMs), significant progress has been made in improving the fluency of machine translation (MT). However, hallucination remains a persistent challenge to translation accuracy, making Critical Error Detection (...
Unstructured geographic descriptions containing spatial relations, such as landmark references, directions, and relative distances, remain a major challenge for accurate geocoding and spatial analysis. Existing approaches based on string matching or...
Background: Single-target Alzheimer’s disease (AD) therapies have repeatedly failed to modify disease progression, highlighting a critical mismatch between multifactorial pathology and reductionist pharmacology. Methods: We developed a represen...
A hybrid modelling framework is proposed to predict endpoint sulphur content in the ladle furnace (LF) refining process by embedding metallurgical expert knowledge into interpretable machine learning (ML). Industrial process data were extracted from...
Cross-well lithology recognition is important for reservoir characterization, but its application to newly drilled wells is constrained by inter-well variations in logging responses and limited lithological labels. This study proposes a geoscience kn...
The graded management of structured sensitive data has become a key challenge in data security governance, particularly amid digital transformation in sectors such as government, finance, and healthcare. The existing methods suffer from limited gener...
The purpose of aspect-based sentiment analysis (ABSA) is to determine the sentiment polarity of aspects in a given sentence. Most historical works on sentiment analysis used complex and inefficient methods to integrate external knowledge. Furthermore...
Data-driven machine learning (ML) models forecast photovoltaic (PV) power accurately, but the conventional Random Forest (RF) samples candidate predictors uniformly and therefore ignores established physical knowledge about how meteorological variabl...
Informatization is an important trend in the field of mountain highway management, and the digital twin is an effective way to promote mountain highway information management due to the complex and diverse terrain of mountainous areas, the high compl...
Cyber threat intelligence summarization plays a critical role in enhancing threat awareness and operational response in cybersecurity. However, existing summarization models often fail to capture essential threat elements due to the unstructured natu...
With the continuous improvement of power system intelligence, multimodal data generated during distribution network maintenance have grown exponentially. However, existing power multimodal datasets commonly suffer from issues such as low sample quali...
Due to access to the source data during the transfer phase, conventional domain adaptation works have recently raised safety and privacy concerns. More research attention thus shifts to a more practical setting known as source-data-free domain adapta...
Particle cracking contributes to lithium-ion battery degradation, motivating modeling approaches that combine numerical adaptability with interpretable degradation knowledge. Mamdani fuzzy systems encode such knowledge through linguistic rules, but t...
Accurate JIC (Japanese Investigation Committee) classification of osteonecrosis of the femoral head (ONFH) is critical for collapse risk prediction and hip-preserving treatment. However, clinical classification faces challenges: indistinct lesion bou...
Some of the recent studies on drug sensitivity prediction have applied graph neural networks to leverage prior knowledge on the drug structure or gene network, and other studies have focused on the interpretability of the model to delineate the mecha...
Agricultural heritage landscapes (AHLs) are multidimensional cultural landscapes shaped by interconnected productive, ecological, hydraulic, infrastructural, and sociocultural relationships. However, these dimensions are often assessed separately, li...
Accurate prediction of key ocean elements (e.g., chlorophyll-a concentration, sea surface temperature, etc.) is imperative for maintaining marine ecological balance, responding to marine disaster pollution, and promoting the sustainable use of marine...
Background: Contrast-enhanced magnetic resonance imaging (MRI), including late gadolinium enhancement (LGE) and cerebral blood volume (CBV) maps, is essential for characterizing pathologies such as myocardial scars and brain tumors. However, acquirin...
Text classification aims to classify text according to pre-defined categories. Despite the success of existing methods based on the fine-tuning paradigm, there is a significant gap between fine-tuning and pre-training. Currently, prompt learning meth...
Accurate kidney segmentation in abdominal computed tomography (CT) images is important for quantitative analysis and computer-assisted clinical workflows, yet deploying deep learning models in practice remains challenging due to computational constra...
Syndrome differentiation and disease diagnosis are central to Traditional Asian Medicine (TAM) because they guide personalized treatment. Yet, most TAM courses give students few structured opportunities to practise these paired skills. We developed G...
Landslide susceptibility mapping (LSM) constitutes a valuable analytical instrument for estimating the likelihood of landslide occurrence, thereby furnishing a scientific foundation for the prevention of natural hazards, land-use planning, and econom...
Sentiment analysis is widely applied in the financial domain. However, financial documents, particularly those concerning the stock market, often contain complex and often ambiguous information, and their conclusions frequently deviate from actual ma...
of 96