# New Horizons in Web Search, Web Data Mining, and Web-Based Applications
## 2 An Overview of Published Articles
\"Link Prediction with Hypergraphs via Network Embedding\" by [PERSON], [PERSON], and [PERSON] was published in December 2022 and introduced a new link prediction method using hypergraphs and network embedding (HNE), demonstrating technological progress in the field of network analysis and providing a new perspective for studying complex relationships. Hypergraphs provide a natural way to represent complex higher-order relationships. The findings of this paper have broad implications, proposing potential applications in different fields such as online social network recommendations and bioinformatics by integrating hypergraphs and network embedding methods. \"Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder\" by [PERSON], [PERSON], and [PERSON] was published in December 2022, and it proposed a new unsupervised domain adaptation method that significantly improves domain adaptation technology by using the Stacked Convolutional Sparse Autoencoder (SCSA). It obtains higher-level representations for unsupervised domain adaptation by performing layer projection from the original data. SCSA effectively addresses the challenges of performance degradation caused by ineffective optimization and data redundancy in deep neural networks. Compared with existing methods, it shows superior classification accuracy of up to 89.3%. This research effectively improves the efficiency of using unsupervised methods to transfer knowledge in different domains. \"Development of a Web Application for the Detection of Coronary Artery Calcium from Computed Tomography\" by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] was published in November 2022, and it introduced a novel web application that uses Agaston technology for semiautomatic quantification of coronary artery calcium (CAC). This study makes an important advancement in cardiovascular disease analysis. The innovative approach in the system provides accessibility to any device through internet connectivity, which significantly simplifies the processes of healthcare professionals and improves the practicality and efficiency of cardiovascular risk assessment. This system not only simplifies the workflow of cardiologists but may also help with the early detection and management of cardiovascular diseases. \"Fuzzy MLKNN in Credit User Portrait\" by [PERSON], [PERSON], and [PERSON] was published in November 2022, and it proposed an improved fuzzy MLKNN multi-label learning algorithm. The new algorithm solves the subjectivity problem caused by the discretization of credit data and provides more dimensional portraits for credit users. It weakens the subjectivity of credit data after discretization by introducing intuitionistic fuzzy numbers and better realizes the multi-label portrait of credit users by using the corresponding fuzzy Euclidean distance. Compared with traditional MLKNN algorithms, it significantly improves performance, especially in reducing one error. The method creatively combines fuzzy set theory with multi-label learning, paving the way for more sophisticated credit data analysis and potentially aiding in more accurate credit risk assessments. \"Prompt Tuning for Multi-Label Text Classification: How to Link Exercises to Knowledge Concepts?\" by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] was published in October 2022, and it proposed a novel multi-label text classification prompt adjustment method (PTMLTC). The proposed method automatically links exercises with knowledge concepts in educational environments. Specifically, the relevance scores of exercise content and knowledge concepts are learned by a prompt tuning model with a unified template, and then the multiple associated knowledge concepts are selected with a threshold. It solves the cost and time challenges of requiring a large amount of training data in traditional multi-label text classification methods and performs significantly better than existing methods in terms of efficiency and accuracy on the self-constructed Exercises-Concepts datasetof the Data Structure course.