EduChat (https://www.educhat.top/) is a large-scale language model (LLM)-based chatbot system in the education domain. Its goal is to support personalized, fair, and compassionate intelligent education, serving teachers, students, and parents. Guided by theories from psychology and education, it further strengthens educational functions such as open question answering, essay assessment, Socratic teaching, and emotional support based on the existing basic LLMs. Particularly, we learn domain-specific knowledge by pre-training on the educational corpus and stimulate various skills with tool use by fine-tuning on designed system prompts and instructions. Currently, EduChat is available online as an open-source project, with its code, data, and model parameters available on platforms (e.g., GitHub https://github.com/icalk-nlp/EduChat, Hugging Face https://huggingface.co/ecnu-icalk ). We also prepare a demonstration of its capabilities online (https://vimeo.com/851004454). This initiative aims to promote research and applications of LLMs for intelligent education.
In the Satellite-integrated Internet of Things (SIoT), typical multi-access schemes, i.e., Contention Resolution Diversity Slotted ALOHA scheme (CRDSA), face with the obvious challenge of heavily conflicting packets regarding high channel traffic, which is not well addressed by the methods of Successive Interference Cancellation (SIC) due to the stubborn loop issues. In this work, therefore, we develop a Generalized Deduplication (GD) based Contention Resolution Diversity Slotted ALOHA scheme with a Compulsory Divorce mechanism (CD-CRDSA) by considering the correlative properties among the accessing data from the users. In particular, the proposed mechanism could effectively separate individual packets from conflicting slots to maintain the sustainability of SIC process, thus achieve better throughput. Moreover, we make the theoretical analysis on the throughput performance with the compression gain of the proposed mechanism. The simulation results indicate that compared to the typical CRDSA protocol and the Non-Orthogonal Multiple Access (NOMA) scheme, the proposed CDCRDSA significantly reduces the amounts of un-resolved slots and improves throughput performance, especially in high-load areas.
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