Symbol recovery in reverse engineering is crucial for restoring variable and data structure information in compiled binaries. While learning-based methods have shown promise in recovering both semantic information (names and types) and syntactic information (shapes), they require comprehensive datasets where expressions in binary code are precisely aligned with their source code equivalents. Current techniques for generating such alignments struggle with complex data access patterns, resulting in incomplete training data and consequently hampering model performance and recovery accuracy. We present AST-Align, a novel technique unifying alignment of variables and struct access expressions across multiple architectures (x86 and ARM) and languages (C/C++/Rust). AST-Align significantly improves the number of generated ground truths, capturing four times more struct fields than previous methods. Using this algorithm, we develop R3-Bench, a metadata-rich, extensible dataset with explicit project inclusion criteria and reproducible processing pipeline, comprising over 10 million functions across multiple architectures. Our evaluation establishes baseline performance by testing various approaches from n-gram models to Large Language Models. The results show that while general LLMs initially perform poorly, their effectiveness dramatically improves with proper demonstration. R3-Bench provides a robust foundation for assessing model capabilities and serves as a valuable reference for future symbol recovery research.

With the trend of cloud computing, the cloud log service is becoming increasingly important, as it plays a critical role in tasks such as root cause analysis, service monitoring and security audition. To meet these needs, we provide Tencent Cloud Log Service (TencentCLS), a one-stop solution for log collection, storage, analysis and dumping. It currently hosts more than a million tenants, of which the largest ones can generate up to PB-level logs per day. The most important challenge that TencentCLS faces is to support both low-latency and resource-efficient queries on such large quantities of log data. To address that challenge, we propose a novel search engine based upon Lucene. The system features a novel procedure for querying logs within a time range, an indexing technique for the time field, as well as optimized query algorithms dedicated to multiple critical and common query types. As a result, the search engine at TencentCLS gains significant performance improvements against Lucene. It achieves 20x performance increase with standard queries, and 10x performance increase with histogram queries in massive log query scenarios. In addition, TencentCLS also supports storing and querying with microsecond-level time precision, as well as the microsecond-level time order preservation capability.

Random forest is widely exploited as an ensemble learning method. In many practical applications, however, there is still a significant challenge to learn from imbalanced data. To alleviate this limitation, we propose a deep dynamic boosted forest (DDBF), a novel ensemble algorithm that incorporates the notion of hard example mining into random forest. Specifically, we propose to measure the quality of each leaf node of every decision tree in the random forest to determine hard examples. By iteratively training and then removing easy examples from training data, we evolve the random forest to focus on hard examples dynamically so as to balance the proportion of samples and learn decision boundaries better. Data can be cascaded through these random forests learned in each iteration in sequence to generate more accurate predictions. Our DDBF outperforms random forest on 5 UCI datasets, MNIST and SATIMAGE, and achieved state-of-the-art results compared to other deep models. Moreover, we show that DDBF is also a new way of sampling and can be very useful and efficient when learning from imbalanced data.