Rencent advancements in large language models (LLM) have shown impressive versatility across various tasks.Short text matching is one of the fundamental technologies in natural language processing.In previous studies, the common approach to applying them to Chinese is segmenting each sentence into words, and then taking these words as input.However, existing approaches have three limitations: 1) Some Chinese words are polysemous, and semantic information is not fully utilized.2) Some models suffer potential issues caused by word segmentation and incorrect recognition of negative words affects the semantic understanding of the whole sentence.3) Fuzzy negation words in ancient Chinese are difficult to recognize and match.In this work, we propose a novel adaptive Transformer for Chinese short text matching using Data Augmentation and Semantic Awareness (DASA), which can fully mine the information expressed in Chinese text to deal with word ambiguity.DASA is based on a Graph Attention Transformer Encoder that takes two word lattice graphs as input and integrates sense information from N-HowNet to moderate word ambiguity.Specially, we use an LLM to generate similar sentences for the optimal text representation.Experimental results show that the augmentation done using DASA can considerably boost the performance of our system and achieve significantly better results than previous state-of-theart methods on four available datasets, namely MNS, LCQMC, AFQMC, and BQ.

Recently, word enhancement has become very popular for Chinese Named Entity Recognition (NER), reducing segmentation errors and increasing the semantic and boundary information of Chinese words. However, these methods tend to ignore the semantic relationship before and after the sentence after integrating lexical information. Therefore, the regularity of word length information has not been fully explored in various word-character fusion methods. In this work, we propose a Lexicon-Attention and Data-Augmentation (LADA) method for Chinese NER. We discuss the challenges of using existing methods in incorporating word information for NER and show how our proposed methods could be leveraged to overcome those challenges. LADA is based on a Transformer Encoder that utilizes lexicon to construct a directed graph and fuses word information through updating the optimal edge of the graph. Specially, we introduce the advanced data augmentation method to obtain the optimal representation for the NER task. Experimental results show that the augmentation done using LADA can considerably boost the performance of our NER system and achieve significantly better results than previous state-of-the-art methods and variant models in the literature on four publicly available NER datasets, namely Resume, MSRA, Weibo, and OntoNotes v4. We also observe better generalization and application to a real-world setting from LADA on multi-source complex entities.

Supervisory Control and Data Acquisition (SCADA) systems are one of the most common industrial control systems (ICS). As the security threat of SCADA systems has been rising in recent years, intrusion detection has become indispensable. Among SCADA systems, there is a lack of research on intrusion detection of temporal and spatial characteristics, and the effectiveness of the existing intrusion detection approaches could be improved. This paper proposes an intrusion detection model based on spatio-temporal characteristics of SCADA systems, combining the attention mechanism, called STAM, allows a full understanding of the correlation between sensor and controller parameters. Experiments on three typical SCADA system datasets show that STAM proposed in this paper achieves state-of-the-art results and can be better applied to intrusion detection in SCADA systems. The effectiveness of STAM is evaluated by accuracy, precision, recall, and F1-score. The accuracy rates on new gas pipeline, water storage tank, and Secure Water Treatment (SWaT) datasets can reach 95.34%, 98.92% and 99.95% respectively.

The subtle and dynamic nature of insider threat makes it one of the most challenging problems in cyber security domain. Most of the existing studies model the problem from the perspective of user behavior, but the imbalance of data categories and the weak correlation between discrete behaviors are not considered simultaneously. To address these problems, we use reinforcement learning-based Generative Adversarial Network to synthesize high-quality minority class data, and use Word2Vec language model to learn the distance metric between different behaviors. In this paper, we propose a Resource Access Pattern Network (RAP-Net), which applies reinforcement learning-based Generative Adversarial Network, Word2Vec, Convolutional Neural Network, Recurrent Neural Network, and Attention Mechanism to insider threat detection. RAP-Net extracts user resource access pattern sequences from audit log files, and then performs data augmentation on the minority class sequences. After learning the distance metric of different tokens in sequences, feature vectors are sent to the classifier for anomaly detection. RAP-Net successfully addresses two major pain points in the current field, namely data imbalance and weak correlation of discrete behaviors. Intensive experimental results on the CMU-CERT r4.2 dataset demonstrate that RAP-Net outperforms state-of-the-art studies in the field.

The development of real-time road condition systems will better monitor road network operation status. However, the weak point of all these systems is their need for comprehensive and reliable data. For traffic data acquisition, two sources are currently available: 1) floating vehicles and 2) remote traffic microwave sensors (RTMS). The former consists of the use of mobile probe vehicles as mobile sensors, and the latter consists of a set of fixed point detectors installed in the roads. First, the structure of a three-layer BP neural network is designed to achieve the fusion of the floating car data (FCD) and the fixed detector data (FDD) efficiently. Second, in order to improve the accuracy of traffic speed estimation, a multi-source data fusion model that combines information from floating vehicles and microwave sensors, and that, by using GA-PSO-BP neural network is proposed. The proposed model has combined GA and PSO ingeniously. The hybrid model can not only overcome the difficulties of the traditional fusion model of its estimation inaccuracy, but also compensate the insufficiency of the traditional BP algorithm. Finally, this system has been tested and implemented on actual roads, and the simulation results show the accuracy of data has reached 98%.