A5090776224- Topic Modeling
- IPv6, Mobility, Handover, Networks, Security
- Network Traffic and Congestion Control
- Advanced Graph Neural Networks
- Mobile Agent-Based Network Management
- Library Science and Information Systems
- Natural Language Processing Techniques
- Network Packet Processing and Optimization
- Cutaneous Melanoma Detection and Management
- Digital Rights Management and Security
- Nonmelanoma Skin Cancer Studies
- Advanced Image Processing Techniques
- Anomaly Detection Techniques and Applications
- Model Reduction and Neural Networks
Zhejiang University
2024
University of Sannio
2008
Recently, large language models (LLMs) have demonstrated superior capabilities in understanding and zero-shot learning on textual data, promising significant advances for many text-related domains. In the graph domain, various real-world scenarios also involve where tasks node features can be described by text. These text-attributed graphs (TAGs) broad applications social media, recommendation systems, etc. Thus, this paper explores how to utilize LLMs model TAGs. Previous methods TAG...
Abstract Artificial intelligence is fast-growing and applied in a wide range of industries nowadays, including the healthcare sector. Dermatology one areas where AI has big influence, particularly when it comes to dermoscopy-based skin lesion diagnosis. This paper aims develop useful techniques for disease image classification that make use deep learning machine techniques. Continuously, looking at making suggestions improvements raise model’s efficacy during training phases. The...
This paper aims to propose a session initiation protocol (SIP) automatic debugger tool. It is software instrument that will be used verify the compliance of voice over Internet (VoIP) devices, such as soft phones and VoIP gateways SIP specifications, test interoperability equipment produced by different manufacturers. Different tools are available on market conduct validation phase. However, they often have features limited packet capturing decoding, or simulation require complex developing...
Recently, large language models (LLMs) have demonstrated superior capabilities in understanding and zero-shot learning on textual data, promising significant advances for many text-related domains. In the graph domain, various real-world scenarios also involve where tasks node features can be described by text. These text-attributed graphs (TAGs) broad applications social media, recommendation systems, etc. Thus, this paper explores how to utilize LLMs model TAGs. Previous methods TAG...
Text-attributed Graphs (TAGs) are commonly found in the real world, such as social networks and citation networks, consist of nodes represented by textual descriptions. Currently, mainstream machine learning methods on TAGs involve a two-stage modeling approach: (1) unsupervised node feature extraction with pre-trained language models (PLMs); (2) supervised using Graph Neural Networks (GNNs). However, we observe that these representations, which have undergone large-scale pre-training, do...