Zhiming Li

Tianlin Li, Yunxiang Wei, Zhiming Li et al.

Recent advances in code large language models (CodeLLMs) have made them indispensable tools in modern software engineering. However, these models occasionally produce outputs that contain proprietary or sensitive code snippets, raising concerns about potential non-compliant use of training data, and posing risks to privacy and intellectual property. To ensure responsible and compliant deployment of CodeLLMs, training data detection (TDD) has become a critical task. While recent TDD methods have shown promise in natural language settings, their effectiveness on code data remains largely underexplored. This gap is particularly important given code's structured syntax and distinct similarity criteria compared to natural language. To address this, we conduct a comprehensive empirical study of seven state-of-the-art TDD methods on source code data, evaluating their performance across eight CodeLLMs. To support this evaluation, we introduce CodeSnitch, a function-level benchmark dataset comprising 9,000 code samples in three programming languages, each explicitly labeled as either included or excluded from CodeLLM training. Beyond evaluation on the original CodeSnitch, we design targeted mutation strategies to test the robustness of TDD methods under three distinct settings. These mutation strategies are grounded in the well-established Type-1 to Type-4 code clone detection taxonomy. Our study provides a systematic assessment of current TDD techniques for code and offers insights to guide the development of more effective and robust detection methods in the future.

Chenjie Zhou MD, Jianhua Ma Ph. D, Xiaoping Xu MD et al.

Objectives: Pancreatic cancer is a lethal disease, hard to diagnose and usually results in poor prognosis and high mortality. Developing an artificial intelligence (AI) algorithm to accurately and universally predict the early cancer risk of all kinds of pancreatic cancer is extremely important. We propose an ensemble AI algorithm to predict universally cancer risk of all kinds of pancreatic lesions with noncontrast CT. Methods: Our algorithm combines the radiomics method and a support tensor machine (STM) by the evidence reasoning (ER) technique to construct a binary classifier, called RadSTM-ER. RadSTM-ER takes advantage of the handcrafted features used in radiomics and learning features learned automatically by the STM from the CTs for presenting better characteristics of lesions. The patient cohort consisted of 135 patients with pathological diagnosis results where 97 patients had malignant lesions. Twenty-seven patients were randomly selected as independent test samples, and the remaining patients were used in a 5-fold cross validation experiment to confirm the hyperparameters, select optimal handcrafted features and train the model. Results: RadSTM-ER achieved independent test results: an area under the receiver operating characteristic curve of 0.8951, an accuracy of 85.19%, a sensitivity of 88.89%, a specificity of 77.78%, a positive predictive value of 88.89% and a negative predictive value of 77.78%. Conclusions: These results are better than the diagnostic performance of the five experimental radiologists, four conventional AI algorithms, which initially demonstrate the potential of noncontrast CT-based RadSTM-ER in cancer risk prediction for all kinds of pancreatic lesions.