Network protocols are the foundation of modern communication, yet their implementations often contain semantic vulnerabilities stemming from inadequate understanding of specification semantics. Existing gray-box and black-box testing approaches lack semantic modeling of protocols, making it difficult to precisely express testing intent and cover boundary conditions. Moreover, they typically rely on coarse-grained oracles such as crashes, which are inadequate for identifying deep semantic vulnerabilities. To address these limitations, we present a semantics-aware fuzzing framework, SemFuzz. The framework leverages large language models to extract structured semantic rules from RFC documents and generates test cases that intentionally violate these rules to encode specific testing intents. It then detects deep semantic vulnerabilities by comparing the observed responses with the expected ones. Evaluation on seven widely deployed protocol implementations shows that SemFuzz identified sixteen potential vulnerabilities, ten of which have been confirmed. Among the confirmed vulnerabilities, five were previously unknown and four have been assigned CVEs. These results demonstrate the effectiveness of SemFuzz in detecting semantic vulnerabilities.

Aspect sentiment triplet extraction (ASTE), which aims to identify aspects from review sentences along with their corresponding opinion expressions and sentiments, is an emerging task in fine-grained opinion mining. Since ASTE consists of multiple subtasks, including opinion entity extraction, relation detection, and sentiment classification, it is critical and challenging to appropriately capture and utilize the associations among them. In this paper, we transform ASTE task into a multi-turn machine reading comprehension (MTMRC) task and propose a bidirectional MRC (BMRC) framework to address this challenge. Specifically, we devise three types of queries, including non-restrictive extraction queries, restrictive extraction queries and sentiment classification queries, to build the associations among different subtasks. Furthermore, considering that an aspect sentiment triplet can derive from either an aspect or an opinion expression, we design a bidirectional MRC structure. One direction sequentially recognizes aspects, opinion expressions, and sentiments to obtain triplets, while the other direction identifies opinion expressions first, then aspects, and at last sentiments. By making the two directions complement each other, our framework can identify triplets more comprehensively. To verify the effectiveness of our approach, we conduct extensive experiments on four benchmark datasets. The experimental results demonstrate that BMRC achieves state-of-the-art performances.

Grammatical error correction (GEC) is an important application aspect of natural language processing techniques, and GEC system is a kind of very important intelligent system that has long been explored both in academic and industrial communities. The past decade has witnessed significant progress achieved in GEC for the sake of increasing popularity of machine learning and deep learning. However, there is not a survey that untangles the large amount of research works and progress in this field. We present the first survey in GEC for a comprehensive retrospective of the literature in this area. We first give the definition of GEC task and introduce the public datasets and data annotation schema. After that, we discuss six kinds of basic approaches, six commonly applied performance boosting techniques for GEC systems, and three data augmentation methods. Since GEC is typically viewed as a sister task of Machine Translation (MT), we put more emphasis on the statistical machine translation (SMT)-based approaches and neural machine translation (NMT)-based approaches for the sake of their importance. Similarly, some performance-boosting techniques are adapted from MT and are successfully combined with GEC systems for enhancement on the final performance. More importantly, after the introduction of the evaluation in GEC, we make an in-depth analysis based on empirical results in aspects of GEC approaches and GEC systems for a clearer pattern of progress in GEC, where error type analysis and system recapitulation are clearly presented. Finally, we discuss five prospective directions for future GEC researches.

Considerable progress in silicon pressure sensors has been made in recent years. This paper discusses three types of vacuum-sealed silicon micromachined pressure sensors that represent the present state of the art in this important area. The devices are a capacitive vacuum sensor, a surface-micromachined microdiaphragm pressure sensor, and a resonant pressure sensor. Vacuum sealing for these devices is accomplished using anodic bonding, films deposited using low-pressure chemical vapor deposition, and thermal out-diffusion of hydrogen, respectively. These sensors emphasize high sensitivity, small size, and excellent stability, respectively. The silicon-diaphragm vacuum sensor uses electrostatic force balancing to achieve a wide pressure measurement range.