Multi-label text classification (MLTC) aims to assign appropriate labels to each document from a given set. Prior research has acknowledged the significance of label information, but its utilization remains insufficient. Existing approaches often foc...
The increasing complexity and scale of cyber threats demand advanced, automated methodologies for extracting actionable cyber threat intelligence (CTI). The automated extraction of Tactics, Techniques, and Procedures (TTPs) from unstructured threat r...
Plagiarism is an act of intellectual high treason that damages the whole scholarly endeavor. Many attempts have been undertaken in recent years to identify text document plagiarism. The effectiveness of researchers’ suggested strategies to iden...
Ontology-based data management and knowledge graphs have emerged in recent years as efficient approaches for managing and utilizing diverse and large data sets. In this regard, research on algorithms for automatic semantic labeling and modeling as a...
This study presents the development of an AI-powered chatbot designed to facilitate accurate and efficient retrieval of information from the FDA drug labeling documents. Leveraging OpenAI’s GPT-3.5-turbo model within a controlled, document-grou...
Visual–language tracking often faces challenges like target deformation and confusion caused by similar objects. These issues can disrupt the alignment between visual inputs and their textual descriptions, leading to cross-modal semantic drift...
Knowledge graph completion can make knowledge graphs more complete, which is a meaningful research topic. However, the existing methods do not make full use of entity semantic information. Another challenge is that a deep model requires large-scale m...
This paper addresses the challenges of sample scarcity and class imbalance in remote sensing image semantic segmentation by proposing a decoupled synthetic sample generation framework based on a latent diffusion model. The method consists of two stag...
Weakly Supervised Video Anomaly Detection (WSVAD) is a critical task in computer vision. It aims to localize and recognize abnormal behaviors using only video-level labels. Without frame-level annotations, it becomes significantly challenging to mode...
Recent vision-language pre-training models, like CLIP, have been shown to generalize well across a variety of multitask modalities. Nonetheless, their generalization for downstream tasks is limited. As a lightweight adaptation approach, prompt learni...
Few-shot object detection has attracted extensive attention due to the abomination of time-consuming or even impractical large-scale data labeling. Current studies attempted to employ prototype-matching approaches for object detection, constructing c...
Earth observation data serve as a fundamental resource in Earth system science. The rapid advancement of remote sensing and in situ measurement technologies has led to the generation of massive volumes of data, accompanied by a growing body of geogra...
Nowadays, financial institutions increasingly leverage artificial intelligence to enhance decision-making and optimize investment strategies. A specific application is the automatic analysis of large volumes of unstructured textual data to extract re...
Unsupervised sentence embedding, vital for numerous NLP tasks, struggles with the inherent asymmetry of semantic relationships within contrastive learning (CL). This paper proposes Label Smoothing-based Ranking Negative Sampling (LS-RNS), a novel fra...
Facing fast-increasing electronic documents in the Digital Media Age, the need to extract textual features of online texts for better communication is growing. Sentiment classification might be the key method to catch emotions of online communication...
Stance detection is a crucial task in natural language processing and social computing, focusing on classifying expressed attitudes towards specific targets based on the input text. Conventional methods predominantly view stance detection as a task o...
Raw image classification datasets generally maintain a long-tailed distribution in the real world. Standard classification algorithms face a substantial issue because many labels only relate to a few categories. The model learning processes will tend...
Personality characteristics represent the behavioral characteristics of a class of people. Social networking sites have a multitude of users, and the text messages generated by them convey a person’s feelings, thoughts, and emotions at a particular t...
Weakly supervised crack segmentation aims to create pixel-level crack masks with minimal human annotation, which often only differentiate between crack and normal no-crack patches. This task is crucial for assessing structural integrity and safety in...
Author Gender Identification (AGI) is an extensively studied subject owing to its significance in several domains, such as security and marketing. Recognizing an author’s gender may assist marketers in segmenting consumers more effectively and...
Customer complaints play an important role in the adjustment of business operations and improvement of services, particularly in the aviation industry. However, extracting adequate textual features to perform a multi-label classification of complaint...
Information privacy is a critical design feature for any exchange system, with privacy-preserving applications requiring, most of the time, the identification and labelling of sensitive information. However, privacy and the concept of “sensitiv...
The automatic extraction of biomedical events from the scientific literature has drawn keen interest in the last several years, recognizing complex and semantically rich graphical interactions otherwise buried in texts. However, very few works revolv...
In today’s world, mental health diseases have become highly prevalent, and depression is one of the mental health problems that has become widespread. According to WHO reports, depression is the second-leading cause of the global burden of dise...
Scientific creativity is a crucial indicator of adolescents’ potential in science and technology, and its automated evaluation plays a vital role in the early identification of innovative talent. To address challenges such as limited sample siz...
Although image recognition technologies are developing rapidly with deep learning, conventional recognition models trained by supervised learning with class labels do not work well when test inputs from untrained classes are given. For example, a rec...
Pesticide registration information is an essential part of the pesticide knowledge base. However, the large amount of unstructured text data that it contains pose significant challenges for knowledge storage, retrieval, and utilization. To address th...
The expansion of electronic health records (EHRs) has generated a large amount of unstructured textual data, such as clinical notes and medical reports, which contain diagnostic and prognostic information. Effective classification of these textual me...
Users of web or chat social networks typically use emojis (e.g., smilies, memes, hearts) to convey in their textual interactions the emotions underlying the context of the communication, aiming for better interpretability, especially for short polyse...
Open-vocabulary semantic segmentation aims to label each pixel of an image based on text descriptions provided at inference time. Recent approaches for this task are based on methods which require two stages: the first one uses a mask generator to ge...
Object detection in drone-captured imagery has attracted significant attention due to its wide range of real-world applications, including surveillance, disaster response, and environmental monitoring. Although the majority of existing methods are de...
Integrating information from heterogeneous data sources poses significant mathematical challenges, particularly in ensuring the reliability and reducing the uncertainty of predictive models. This paper introduces the Geometric Orthogonal Multimodal F...
Fine-grained feature extraction and affective semantic mapping remain significant challenges in product form analysis. To address these issues, this study proposes a contrastive learning-based cross-modal fusion approach for product form imagery reco...
Access Control Policies (ACPs) are essential for ensuring secure and authorized access to resources in IoT networks. Recognizing these policies involves identifying relevant statements within project documents expressed in natural language. While cur...
Data imbalance is a frequently occurring problem in classification tasks where the number of samples in one category exceeds the amount in others. Quite often, the minority class data is of great importance representing concepts of interest and is of...
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...
To address the lack of effective risk-identification methods during the commissioning of new power grid equipment, we propose a knowledge graph construction approach for both scheme generation and risk identification. First, a gated attention mechani...
Precise classification of unsound wheat grains is essential for crop yields and food security, yet most existing approaches rely on vision-only models that demand large labeled datasets, which is often impractical in real-world, data-scarce settings....
Values (why to conserve) and Attributes (what to conserve) are essential concepts of cultural heritage. Recent studies have been using social media to map values and attributes conveyed by the public to cultural heritage. However, it is rare to conne...
Automated waste classification is an essential step toward efficient recycling and waste management. Traditional deep learning models, such as convolutional neural networks, rely on extensive labeled datasets to achieve high accuracy. However, the an...
This article presents a new conceptual approach for the interpretative topic modeling problem. It uses sentences as basic units of analysis, instead of words or n-grams, which are commonly used in the standard approaches.The proposed approach’s speci...
Introduction: Due to the lack of labeled data, applying predictive maintenance algorithms for facility management is cumbersome. Most companies are unwilling to share data or do not have time for annotation. In addition, most available facility manag...
12 May 2022
The increasing expansion of biomedical documents has increased the number of natural language textual resources related to the current applications. Meanwhile, there has been a great interest in extracting useful information from meaningful coherent...
The explosive growth of the social media community has increased many kinds of misinformation and is attracting tremendous attention from the research community. One of the most prevalent ways of misleading news is cheapfakes. Cheapfakes utilize non-...
Aspect-based sentiment analysis (ABSA) is a task of fine-grained sentiment analysis that aims to determine the sentiment of a given target. With the increased prevalence of smart devices and social media, diverse data modalities have become more abun...
The lack of explicit negative labels issue is a prevalent challenge in numerous domains, including CV, NLP, and Recommender Systems (RSs). To address this challenge, many negative sample completion methods are proposed, such as optimizing sample dist...
Assigning predefined classes to natural language texts, based on their content, is a necessary component in many tasks in organizations. This task is carried out by classifying documents within a set of predefined categories using models and computat...
With the growing adoption of electric vehicles, optimizing the user experience of charging infrastructure has become critical. However, extracting actionable insights from the vast number of user reviews remains a significant challenge, impeding dema...
Large language models (LLMs) have rapidly advanced natural language processing, showcasing remarkable effectiveness as automated annotators across various applications. Despite their potential to significantly reduce annotation costs and expedite wor...
Generative Adversarial Networks have recently demonstrated the capability to synthesize photo-realistic real-world images. However, they still struggle to offer high controllability of the output image, even if several constraints are provided as inp...
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