As a new paradigm in machine learning, self-supervised learning (SSL) is capable of
learning high-quality representations of complex data without relying on labels. In addition to …

This study conducts a thorough examination of malware detection using machine learning
techniques, focusing on the evaluation of various classification models using the Mal-API …

Twitter bot detection has become a crucial task in efforts to combat online misinformation,
mitigate election interference, and curb malicious propaganda. However, advanced Twitter …

Monitoring the health status of patients in the Intensive Care Unit (ICU) is a critical aspect of
providing superior care and treatment. The availability of large-scale electronic health …

Stroke prediction plays a crucial role in preventing and managing this debilitating condition.
In this study, we address the challenge of stroke prediction using a comprehensive dataset …

Textual backdoor attacks pose significant security threats. Current detection approaches,
typically relying on intermediate feature representation or reconstructing potential triggers …

As malicious actors employ increasingly advanced and widespread bots to disseminate
misinformation and manipulate public opinion, the detection of Twitter bots has become a …

In recent years, the proliferation of online communication platforms and social media has
given rise to a new wave of challenges, including the rapid spread of malicious bots. These …

The presence of a large number of bots on social media has adverse effects. The graph
neural network (GNN) can effectively leverage the social relationships between users and …

Inspired by the success of contrastive learning, we systematically examine recommendation
losses, including listwise (softmax), pairwise (BPR), and pointwise (MSE and CCL) losses. In …