We present a dataset of histopathology images from OSCC patients treated at Sun Yat-sen Memorial Hospital (2015–2022). Each case includes two tissue sections (core and boundary), with six images per patient captured at ×200, ×400, and ×1000 magnifications (2592×1944 pixels). Key histopathological features—such as cancer cells, nests, keratin pearls, nuclear atypia, and necrosis—are included. The study was approved by the Ethics Committee with a waiver of informed consent, and patient-level diagnosis and prognosis annotations were obtained from electronic records.

Artificial Intelligence (AI) has increasingly influenced modern society, recently in particular through significant advancements in Large Language Models (LLMs). However, high computational and storage demands of LLMs still limit their deployment in resource-constrained environments. Knowledge distillation addresses this challenge by training a smaller language model (student) from a larger one (teacher). Previous research has introduced several distillation methods for both generating training data and training the student model.

• This dataset comprises license plate information captured by Automatic Number Plate Recognition (ANPR) devices as vehicles either entered or left the smart village area of Alpujarra, which encompasses the towns of Pampaneira, Capileira, and Bubión.

The growing adoption of declarative software specification languages, coupled with their inherent difficulty in debugging, has underscored the need for effective and automated repair techniques applicable to such languages. Researchers have recently explored various methods to automatically repair declarative software specifications, such as template-based repair, feedback-driven iterative repair, and bounded exhaustive approaches. The latest developments in Large Language Models (LLMs) provide new opportunities for the automatic repair of declarative specifications.