International Journal of Web Information Systems: Volume 20 Issue 4

Click-through rate prediction model based on graph networks and feature squeeze-and-excitation mechanism

Zhongqin Bi, Susu Sun, Weina Zhang, Meijing Shan

Predicting a user’s click-through rate on an advertisement or item often uses deep learning methods to mine hidden information in data features, which can provide users with more…

GethReplayer: a smart contract testing method based on transaction replay

Xiaohong Shi, Ziyan Wang, Runlu Zhong, Liangliang Ma, Xiangping Chen, Peng Yang

Smart contracts are written in high-level programming languages, compiled into Ethereum Virtual Machine (EVM) bytecode, deployed onto blockchain systems and called with the…

Multiobjective flexible job-shop scheduling optimization for manufacturing servitization

Wei Wang, Jian Zhang, Yanhe Jia

With the development trend of China’s service-oriented manufacturing moving toward intelligence and personalization, the deep integration of manufacturing and service has become a…

Similarity search on social networks with incremental graph indexing based on probabilistic inference

Zhiwei Qi, Tong Lu, Kun Yue, Liang Duan

This paper aims to propose an incremental graph indexing method based on probabilistic inferences in Bayesian network (BN) for approximate nearest neighbor search (ANNS) that adds…

Large language models for automated Q&A involving legal documents: a survey on algorithms, frameworks and applications

Xiaoxian Yang, Zhifeng Wang, Qi Wang, Ke Wei, Kaiqi Zhang, Jiangang Shi

This study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports…

Graph structure estimation neural network-based service classification

Yanxinwen Li, Ziming Xie, Buqing Cao, Hua Lou

With the introduction of graph structure learning into service classification, more accurate graph structures can significantly improve the precision of service classification…

Web-aided data set expansion in deep learning: evaluating trainable activation functions in ResNet for improved image classification

Zhiqiang Zhang, Xiaoming Li, Xinyi Xu, Chengjie Lu, Yihe Yang, Zhiyong Shi

The purpose of this study is to explore the potential of trainable activation functions to enhance the performance of deep neural networks, specifically ResNet architectures, in…