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Automatic text classification, in which textual data is categorized into specified categories based on its content, is a classic issue in the science of Natural Language Processing (NLP). These models have proven useful when applied to data with ...

Practitioners have emphasized the importance of employing sentiment analysis techniques in decision-making. The data utilized in this process is typically gathered from social media, making it somewhat unreliable for decision-making. To address ...

In the Semi-Supervised Text Classification (SSTC) task, the performance of the SSTC-based models heavily rely on the accuracy of the pseudo-labels for unlabeled data, which is not practical in real-world scenarios. Prompt-learning has recently ...

Multi-label text classification (MLTC) is a challenging task in natural language processing. Improving the performance of MLTC through building label dependencies remains a focus of current research. Previous researches used label tree structure ...

Text comparison is a process of comparing and matching two or more texts to determine their similarities or differences. By calculating the similarity between two texts, tasks such as classification, clustering, retrieval, and comparison can be ...

Answer selection aims to find the most appropriate answer from a set of candidate answers, playing an increasingly important role in Community-based Question Answering. However, existing studies overlook the correlation among historical answers of ...

Extractive Question Answering (EQA) tasks have gained intensive attention in recent years, while Pre-trained Language Models (PLMs) have been widely adopted for encoding purposes. Yet, PLMs typically take as initial input token embeddings and rely ...

The core and hard part of Mathematical Information Retrieval (MathIR) is formula retrieval. The datasets used for formula retrieval are usually scientific documents containing formulas. However, there is a lack of labeled datasets specifically for ...

Online Social Networks (OSNs) such as Twitter, Sina Weibo, and Facebook play an important role in our daily life recently. The influence diffusion between users is a common phenomenon on OSNs, which has been applied in numerous applications such ...

This paper examines the impact of true news on the propagation of false news in social media networks. Due to the unavailability of real-world data, we present our methodological approach for collecting Twitter data using the Twitter API. Our ...

In this paper, we model social media with graph stream and propose an efficient event detection algorithm that costs only linear time and space. Different from existing work, we propose an LIS (longest increasing subsequence)-based edge weight to ...

Mergers and acquisitions are pivotal strategies employed by companies to maintain competitiveness, leading to enhanced production efficiency, scale, and market dominance. Due to their significant financial implications, predicting these operations ...

Predicting trust in Online Social Networks (OSNs) is essential for a range of applications including online marketing and decision-making. Traditional methods, while effective in some scenarios, encounter difficulties when attempting to handle the ...

Insider threats involving authorised individuals exploiting their access privileges within an organisation can yield substantial damage compared to external threats. Conventional detection approaches analyse user behaviours from logs, using binary ...

With the situation of cyber security becoming more and more complex, the mining and analysis of Cyber Threat Intelligence (CTI) have become a prominent focus in the field of cyber security. Social media platforms like Twitter, due to their ...

Vehicular networking technology is advancing rapidly, and one promising area of research is blockchain-based vehicular edge computing to enhance resource allocation and data security. This paper aims to optimize resource allocation and data ...

Privacy inference imposes a serious threat to user privacy in Online Social Networks (OSNs) as the vast amount of personal data and relationships in OSNs can be used not only to infer user privacy but also to enrich the training set of inference ...

Data anonymization is a fundamental and practical privacy-preserving data publication (PPDP) method, while searching for the optimal anonymization scheme using traditional methods has been proven to be NP-hard. Some recent studies have introduced ...

With the exponential growth of personal data use for machine learning models, significant privacy challenges arise. Anonymisation and federated learning can protect privacy-sensitive data at the cost of accuracy but there is lack of research on ...

Feature selection is a crucial process in data science that involves selecting the most effective subset of features. Evolutionary computation (EC) is one of the most commonly-used feature selection techniques and has demonstrated good performance,...

The recommendation system based on federated learning has become one of the most popular distributed machine learning technologies, which to some extent protects the privacy and security of users. However, personal privacy information can still be ...

With the increasing number of software vulnerabilities being disclosed each year, prioritizing them becomes essential as it is challenging to patch all of them promptly. Exploitability prediction plays a crucial role in assessing the severity of ...

Criminal investigation plays a vital role nowadays where the law enforcement agencies (LEAs) carry out this critical mission thoroughly and competently. However, such complicated mission involves a broad spectrum of tasks including collecting ...