91 citations · 181 across the 16 of their papers we have counts for
16 papers
Multimodal Large Language Models: A Survey
Jiayang Wu, Wensheng Gan, Zefeng Chen +2
The exploration of multimodal language models integrates multiple data types, such as images, text, language, audio, and other heterogeneity. While the latest large language models…
Model-as-a-Service (MaaS): A Survey
Wensheng Gan, Shicheng Wan, Philip S. Yu
Due to the increased number of parameters and data in the pre-trained model exceeding a certain level, a foundation model (e.g., a large language model) can significantly improve d…
AI-Generated Content (AIGC): A Survey
Jiayang Wu, Wensheng Gan, Zefeng Chen +2
To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist…
Web 3.0: The Future of Internet
Wensheng Gan, Zhenqiang Ye, Shicheng Wan +1
With the rapid growth of the Internet, human daily life has become deeply bound to the Internet. To take advantage of massive amounts of data and information on the internet, the W…
Web3: The Next Internet Revolution
Shicheng Wan, Hong Lin, Wensheng Gan +2
Since the first appearance of the World Wide Web, people more rely on the Web for their cyber social activities. The second phase of World Wide Web, named Web 2.0, has been extensi…
MDL-based Compressing Sequential Rules
Xinhong Chen, Wensheng Gan, Shicheng Wan +1
Nowadays, with the rapid development of the Internet, the era of big data has come. The Internet generates huge amounts of data every day. However, extracting meaningful informatio…
Metaverse in Education: Vision, Opportunities, and Challenges
Hong Lin, Shicheng Wan, Wensheng Gan +2
Traditional education has been updated with the development of information technology in human history. Within big data and cyber-physical systems, the Metaverse has generated stro…
A Generic Algorithm for Top-K On-Shelf Utility Mining
Jiahui Chen, Xu Guo, Wensheng Gan +2
On-shelf utility mining (OSUM) is an emerging research direction in data mining. It aims to discover itemsets that have high relative utility in their selling time period. Compared…
Itemset Utility Maximization with Correlation Measure
Jiahui Chen, Yixin Xu, Shicheng Wan +2
As an important data mining technology, high utility itemset mining (HUIM) is used to find out interesting but hidden information (e.g., profit and risk). HUIM has been widely appl…
Temporal Fuzzy Utility Maximization with Remaining Measure
Shicheng Wan, Zhenqiang Ye, Wensheng Gan +1
High utility itemset mining approaches discover hidden patterns from large amounts of temporal data. However, an inescapable problem of high utility itemset mining is that its disc…
Towards Target High-Utility Itemsets
Jinbao Miao, Wensheng Gan, Shicheng Wan +2
For applied intelligence, utility-driven pattern discovery algorithms can identify insightful and useful patterns in databases. However, in these techniques for pattern discovery,…
Anomaly Rule Detection in Sequence Data
Wensheng Gan, Lili Chen, Shicheng Wan +2
Analyzing sequence data usually leads to the discovery of interesting patterns and then anomaly detection. In recent years, numerous frameworks and methods have been proposed to di…
TargetUM: Targeted High-Utility Itemset Querying
Jinbao Miao, Shicheng Wan, Wensheng Gan +2
Traditional high-utility itemset mining (HUIM) aims to determine all high-utility itemsets (HUIs) that satisfy the minimum utility threshold (\textit{minUtil}) in transaction datab…
FUIM: Fuzzy Utility Itemset Mining
Shicheng Wan, Wensheng Gan, Xu Guo +2
Because of usefulness and comprehensibility, fuzzy data mining has been extensively studied and is an emerging topic in recent years. Compared with utility-driven itemset mining te…
THUE: Discovering Top-K High Utility Episodes
Shicheng Wan, Jiahui Chen, Wensheng Gan +2
Episode discovery from an event is a popular framework for data mining tasks and has many real-world applications. An episode is a partially ordered set of objects (e.g., item, nod…
TOPIC: Top-k High-Utility Itemset Discovering
Jiahui Chen, Shicheng Wan, Wensheng Gan +2
Utility-driven itemset mining is widely applied in many real-world scenarios. However, most algorithms do not work for itemsets with negative utilities. Several efficient algorithm…