Electric network frequency (ENF) is an important criterion in audio forensic analysis. However, environmental uncertainties often introduce various types of noises, diminishing the number of useful ENF samples in audio recordings. This issue is even more challenging in short-duration recordings. To address this issue, we propose an adaptive-window-based harmonic recombination (AWHR) method, which can accurately estimate ENF from noisy audio. Initially, we identify noisy samples and use a metric called noise ratio (NR) to determine the optimal harmonic. Adaptive windows are selectively applied to the noisy samples of the optimal harmonic to mitigate frequency spikes, prevent distortions, and preserve signal quality. This step also reduces computational complexity by minimizing the number of samples requiring enhancement. Finally, via a proposed harmonic recombination mechanism, we improve the number of useful ENF samples, which reduces the NR. Given the lack of ENF datasets designed to evaluate considerably contaminated audio, we have also built an ENF noisy audio harmonic (ENF-NAH) dataset. Experiments on public ENF-WHU and our ENF-NAH datasets show that the proposed AWHR method is effective in handling varying levels of contamination and is applicable to both long and short audio recordings.

Federated learning (FL) is a technique that enables clients to collaboratively train a model by sharing local models instead of raw private data. However, existing reconstruction attacks can recover the sensitive training samples from the shared models. Additionally, the emerging poisoning attacks also pose severe threats to the security of FL. However, most existing Byzantine-robust privacy-preserving federated learning solutions either reduce the accuracy of aggregated models or introduce significant computation and communication overheads. In this paper, we propose a novelBlockchain-basedSecure andRobustFederatedLearning (BSR-FL) framework to mitigate reconstruction attacks and poisoning attacks. BSR-FL avoids accuracy loss while ensuring efficient privacy protection and Byzantine robustness. Specifically, we first construct a lightweight non-interactive functional encryption (NIFE) scheme to protect the privacy of local models while maintaining high communication performance. Then, we propose a privacy-preserving defensive aggregation strategy based on NIFE, which can resist encrypted poisoning attacks without compromising model privacy through secure cosine similarity and incentive-based Byzantine-tolerance aggregation. Finally, we utilize the blockchain system to assist in facilitating the processes of federated learning and the implementation of protocols. Extensive theoretical analysis and experiments demonstrate that our new BSR-FL has enhanced privacy security, robustness, and high efficiency.

With the transformation of education from the traditional classroom environment to online education and assessment, it is more and more important to accurately assess the difficulty of questions than ever. As teachers may not be able to follow the student’s performance and learning behavior closely, a well-defined method to measure the difficulty of questions to guide learning is necessary. In this paper, we explore the concept of question difficulty and provide our new Chinese DT-QDC dataset. This is currently the largest and only Chinese question dataset, and it also has enriched attributes and difficulty labels. Additional attributes such as keywords, chapter, and question type would allow models to understand questions more precisely. We proposed the MTMS-BERT and ORMS-BERT, which can improve the judgment of difficulty from different views. The proposed methods outperforms different baselines by 7.79% on F1-score and 15.92% on MAE, 28.26% on MSE on the new DT-QDC dataset, laying the foundation for the question difficulty comprehension task.