Information visualization analysis on Advances in Psychological Science:1983-2014

Objective： To describe the revolution and research status of Advances in Psychological Science. Methods： A total of 3060 articles published in Advances in Psychological Science from 1983 to 2014 were analyzed with the information visualization method using Citespace software from the aspects of pub- lications, cited frequency and downloads, funding, organizations, authors and keywords. Results： The results showed that the amount of literature published annually had an upward tendency, and 49.4% of the papers were supported by national or provincial projects. Institutions such as the Chinese Academy of Sciences （CAS） and the normal universities were rated in the forefront of the sci- entific research output. Xiting Huang, Hong Li and Yuejia Luo were at the top of the list of prolific authors. Conclusions： A new pattern of cooperative development of the theory and application in the field of psychological research is forming.

Empowering beginners in bioinformatics with ChatGPT

The impressive conversational and programming abilities of ChatGPT make it an attractive tool for facilitating the education of bioinformatics data analysis for beginners.In this study,we proposed an iterative model to fine-tune instructions for guiding a chatbot in generating code for bioinformatics data analysis tasks.We demonstrated the feasibility of the model by applying it to various bioinformatics topics.Additionally,we discussed practical considerations and limitations regarding the use of the model in chatbot-aided bioinformatics education.

Measuring Similarity of Academic Articles with Semantic Profile and Joint Word Embedding

Long-document semantic measurement has great significance in many applications such as semantic searchs, plagiarism detection, and automatic technical surveys. However, research efforts have mainly focused on the semantic similarity of short texts. Document-level semantic measurement remains an open issue due to problems such as the omission of background knowledge and topic transition. In this paper, we propose a novel semantic matching method for long documents in the academic domain. To accurately represent the general meaning of an academic article, we construct a semantic profile in which key semantic elements such as the research purpose, methodology, and domain are included and enriched. As such, we can obtain the overall semantic similarity of two papers by computing the distance between their profiles. The distances between the concepts of two different semantic profiles are measured by word vectors. To improve the semantic representation quality of word vectors, we propose a joint word-embedding model for incorporating a domain-specific semantic relation constraint into the traditional context constraint. Our experimental results demonstrate that, in the measurement of document semantic similarity, our approach achieves substantial improvement over state-of-the-art methods, and our joint word-embedding model produces significantly better word representations than traditional word-embedding models.