In the rapidly advancing field of software development, the demand for practical code translation tools has surged, driven by the need for interoperability across different programming environments. Existing learning-based approaches often need help with low-resource programming languages that lack sufficient parallel code corpora for training. To address these limitations, we propose a novel training framework that begins with monolingual seed corpora, generating parallel datasets via back-translation and incorporating compiler feedback to optimize the translation model.As a case study, we apply our method to train a code translation model for a new-born low-resource programming language, Cangjie. We also construct a parallel test dataset forJava-to-Cangjietranslation and test cases to evaluate the effectiveness of our approach. Experimental results demonstrate that compiler feedback greatly enhances syntactical correctness, semantic accuracy, and test pass rates of the translatedCangjiecode. These findings highlight the potential of our method to support code translation in low-resource settings, expanding the capabilities of learning-based models for programming languages with limited data availability.

Massive multiple-input-multiple-output (MIMO) systems provide high spatial resolution of the antenna array and the angle reciprocity of the massive MIMO channel holds in both time division duplex (TDD) and frequency division duplex (FDD) systems. In this paper, we propose a new secure strategy towards transmissions in massive MIMO systems. Each coherent time is divided into two stages. The angle signature of the uplink (UL) channel is estimated in the first stage and the downlink (DL) angle signature can be obtained using angle reciprocity. In the second stage, the angle signature of the DL channel is adjusted by spatial rotation according to the data to be transformed. Using the independent distribution of angle signatures of different channels, security performance can be enhanced.