游国恩的楚辞研究

游国恩是海内外名的楚辞研究大家，本着重评述其在楚辞研究方面的成就及治学方法与特点，指出，他在历史眼光与考据功力，宏观探索与微观考证，广搜博采与扎实献学功夫三个方面的结合，体现了与众不同的独特风格，取得了卓越的成就。     

数学素质教育中的创新能力培养

根据21世纪所需人才特点，结合高等教育的教学原则，简述了在《高等数学》教学中，如何培养当代社会所需的大学生创新能力，并以Lagrange中值定理及其相关命题为例加以阐述。     

Encountering Pedagogy through Relational Art Practices

Two artists involved in ‘socially engaged art’ practice were invited to work with art education teacher candidates and instructors in an effort to rethink notions of teaching, learning and art. We initiated this residency, which we called ‘The Summerhill Residency’, to examine how learning encounters might create environments for meaningful exchanges between the ways in which artists and secondary art education teacher candidates learn to think about pedagogy and the nature of artistic learning. Drawing upon Bourriaud's theory of relational aesthetics, we consider, yet trouble, the relational aspects of the processes and products of the artist residency, and examine the crisis of imagination that permeated teacher candidates' experiences. Throughout the project, a/r/tography offered a rich form of living enquiry that opened up possibilities for learning within a community of enquirers.     

Fair and Explainable Depression Detection in Social Media

Detection at an early stage is vital for the diagnosis of the majority of critical illnesses and is the same for identifying people suffering from depression. Nowadays, a number of researches have been done successfully to identify depressed persons based on their social media postings. However, an unexpected bias has been observed in these studies, which can be due to various factors like unequal data distribution. In this paper, the imbalance found in terms of participation in the various age groups and demographics is normalized using the one-shot decision approach. Further, we present an ensemble model combining SVM and KNN with the intrinsic explainability in conjunction with noisy label correction approaches, offering an innovative solution to the problem of distinguishing between depression symptoms and suicidal ideas. We achieved a final classification accuracy of 98.05%, with the proposed ensemble model ensuring that the data classification is not biased in any manner.