Large language models have recently gained popularity in various applications due to their ability to generate natural text for complex tasks. Recommendation systems, one of the frequently studied research topics, can be further improved using the ca...
Traffic flow prediction is one of the most important and attractive topics in geographical information science (GIS), traffic management, and logistics. Traffic flows exhibit significant complexity and dynamics, requiring a thorough understanding of...
Post-translational modifications (PTMs) regulate protein function, stability, and interactions, playing essential roles in cellular signaling, localization, and disease mechanisms. Computational approaches enable scalable PTM site prediction; however...
Traffic flow prediction can provide important reference data for managers to maintain traffic order, and can also be based on personal travel plans for optimal route selection. On account of the development of sensors and data collection technology,...
Background: Chronic diseases significantly burden healthcare systems due to the need for long-term treatment. Early diagnosis is critical for effective management and minimizing risk. The current traditional diagnostic approaches face various challen...
The rapid development of artificial intelligence technology is driving the intelligentization process across various fields, particularly in knowledge graph construction, where significant achievements have been made. However, research on hyper-relat...
Game commentary enhances viewer immersion and understanding, particularly in football video games, where dynamic gameplay offers ideal conditions for automated commentary. The existing methods often rely on predefined templates and game state inputs...
In light of the rapid expansion of maritime trade, the maritime transportation industry has experienced burgeoning growth and complexity. The deployment of trajectory prediction technology is paramount in safeguarding navigational safety. Due to limi...
The construction industry is undergoing a transformative shift through automation, with advancements in Generative AI (GenAI) and prompt engineering enhancing safety and efficiency, particularly in high-risk fields like underground construction, geot...
Automated essay scoring aims to evaluate the quality of an essay automatically. It is one of the main educational application in the field of natural language processing. Recently, Pre-training techniques have been used to improve performance on down...
Achieving high accuracy in Chinese event prediction remains challenging due to the prevalence of short-text events that lack sufficient background knowledge and semantic depth. Existing approaches predominantly rely on knowledge augmentation strategi...
Aspect-based sentiment quadruple prediction has important application value in the current information age. There are often implicit expressions and multi-level semantic relationships in sentences, making accurate prediction for existing methods stil...
Relation extraction tasks aim to predict the type of relationship between two entities from a given text. However, many existing methods fail to fully utilize the semantic information and the probability distribution of the output of pre-trained lang...
Survey data play a crucial role in various research fields, including economics, education, and healthcare, by providing insights into human behavior and opinions. However, item non-response, where respondents fail to answer specific questions, prese...
Aspect Sentiment Quad Prediction is a research topic of paramount significance and complexity within the Aspect-Based Sentiment Analysis task. Leveraging the generative paradigm of the T5 model, we achieve end-to-end extraction of aspect sentiment el...
In this work, the precipitation of a thin layer of a polymer solution was proposed to imitate the process of asymmetric membrane formation by a non-solvent induced phase separation (NIPS) technique. The phase inversion within the thin (<500 μm)...
Regarding urban flooding issues, applying Artificial Intelligence (AI) methodologies can provide a timely prediction of imminent incidences of flash floods. The study aims to develop and deploy an effective real-time pluvial flood forecasting AI plat...
Discrete prompts are the main method for interacting with Large Language Models (LLMs) due to their interpretability and cross-model compatibility. However, optimizing them for fine-grained tasks such as Aspect-Based Sentiment Analysis (ABSA) remains...
Named entity recognition (NER) in a few-shot setting is an extremely challenging task, and most existing methods fail to account for the gap between NER tasks and pre-trained language models. Although prompt learning has been successfully applied in...
Political perspective detection in news media—identifying political bias in news articles—is an essential but challenging low-resource task. Prompt-based learning (i.e., discrete prompting and prompt tuning) achieves promising results in...
Evaluating query-passage relevance is a crucial task in information retrieval (IR), where the performance of large language models (LLMs) greatly depends on the quality of prompts. Current prompt optimization methods typically require multiple candid...
Over half a century from the discovery of gamma-ray bursts (GRBs), the dominant radiation mechanism responsible for their bright and highly variable prompt emission remains poorly understood. Spectral information alone has proven insufficient for und...
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...
Generative models have shown excellent results in aspect-based sentiment analysis tasks by predicting quadruples by setting specific template formats. The existing research predicts sentiment elements and enhances the dependency between elements usin...
Recent advancements in planning prompting techniques for Large Language Models have improved their reasoning, planning, and action abilities. This paper develops a planning framework for Large Language Models using model predictive control that enabl...
Prompt learning has improved the performance of language models by reducing the gap in language model training methods of pre-training and downstream tasks. However, extending prompt learning in language models pre-trained with unimodal data to multi...
Test-time adaptation (TTA) has been proven to effectively improve the adaptability of deep learning semantic segmentation models facing continuous changeable scenes. However, most of the existing TTA algorithms lack an explicit exploration of domain...
With the continuous advancement of deep neural networks, salient object detection (SOD) in natural images has made significant progress. However, SOD in optical remote sensing images (ORSI-SOD) remains a challenging task due to the diversity of objec...
Recently, prompt-based methods have achieved a promising performance in many natural language processing benchmarks. Despite success in sentence-level classification tasks, prompt-based methods work poorly in token-level tasks, such as named entity r...
Insufficiently labeled samples and low-generalization performance have become significant natural language processing problems, drawing significant concern for few-shot text classification (FSTC). Advances in prompt learning have significantly improv...
Pre-trained language models with structured semantic knowledge have demonstrated remarkable performance in a variety of downstream natural language processing tasks. The typical methods of integrating knowledge are designing different pre-training ta...
The coal mining industry has accumulated a vast amount of knowledge on shearer accident analysis and handling during its development. Accurately identifying and extracting entity information related to shearer maintenance is crucial for advancing dow...
Text-guided image editing has been highlighted in the fields of computer vision and natural language processing in recent years. The approach takes an image and text prompt as input and aims to edit the image in accordance with the text prompt while...
Social communication skills, especially eye contact and joint attention, are frequently impaired in autism spectrum disorder (ASD) and predict functional outcomes. Applied behavior analysis is one of the most common evidence-based treatments for ASD,...
Many studies identified factors associated with vaccination intention and hesitancy, but factors associated with vaccination promptness and the effect of vaccination intention on vaccination promptness are unknown. This study identified factors assoc...
During group activities, instructors expect that students will ask each other questions. Therefore, in this study, we looked at the nature and role of peer-to-peer questions during an in-class activity. During the activity, students worked collaborat...
This study explores the potential of large language models (LLMs) in predicting medical diagnoses from Spanish-language clinical case descriptions, offering an alternative to traditional machine learning (ML) and deep learning (DL) techniques. Unlike...
With the increasing prevalence of cyber attacks and data breaches, the importance of strong passwords cannot be overstated. Password generating software has been widely used to generate complex passwords that are difficult to crack, but it has its li...
While the food environment has been implicated in diet-related health disparities, individuals’ ability to shape the food environment by limiting attention to a subset of products has not been studied. We examine the relationship between BMI category...
Background: the prognosis of patients with myocardial infarction with non-obstructive coronary arteries (MINOCA) is not benign; thus, prompting the need to validate prognostic scoring systems for this population. Aim: to evaluate and compare the prog...
Text classification is a machine learning technique employed to assign a given text to predefined categories, facilitating the automatic analysis and processing of textual data. However, an important problem is that the number of new text categories...
Prompt optimization through textual feedback has shown promising results in improving the performance of large language models (LLMs) on downstream tasks. However, existing approaches often rely on selecting prompt edits from a pool of candidate grad...
CCTV safety monitoring demands anomaly detectors combine reliable clip-level accuracy with predictable per-clip latency despite weak supervision. This work investigates compact vision–language models (VLMs) as practical detectors for this regim...
Online education review data have strong statistical and predictive power but lack efficient and accurate analysis methods. In this paper, we propose a multi-modal emotion analysis method to analyze the online education of college students based on e...
Competence in performing Building Performance Simulations (BPS) is a fundamental asset for an energy specialist. However, teaching BPS to new students can be challenging, often due to the overwhelming capabilities of the software. The consequences fo...
Universal image restoration (UIR) aims to accurately restore images with a variety of unknown degradation types and levels. Existing methods, including both learning-based and prior-based approaches, heavily rely on low-quality image features. Howeve...
Aspect-based sentiment classification (ABSC) is an important task in natural language processing (NLP) that aims to predict the sentiment polarity of different aspects in a sentence. The attention mechanism and pre-trained models are commonly used in...
Detecting mentions of green waste practices on social networks is a crucial tool for environmental monitoring and sustainability analytics. Social media serve as a valuable source of ecological information, enabling researchers to track trends, asses...
Background/Objectives: The integration of AI chatbots in nursing education, particularly in simulation-based learning, is advancing rapidly. However, there is a lack of structured evaluation models, especially to assess AI-generated simulations. This...
The purpose of this study is to explore Artificial Neural Networks (ANNs) to predict the compressive and tensile strengths of natural fibre-reinforced Compressed Earth Blocks (CEBs). To this end, a database was created by collecting data from the ava...
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