Haotian Hu

Dongbo Wang, Chang Liu, Zhixiao Zhao et al.

In the context of the rapid development of large language models, we have meticulously trained and introduced the GujiBERT and GujiGPT language models, which are foundational models specifically designed for intelligent information processing of ancient texts. These models have been trained on an extensive dataset that encompasses both simplified and traditional Chinese characters, allowing them to effectively handle various natural language processing tasks related to ancient books, including but not limited to automatic sentence segmentation, punctuation, word segmentation, part-of-speech tagging, entity recognition, and automatic translation. Notably, these models have exhibited exceptional performance across a range of validation tasks using publicly available datasets. Our research findings highlight the efficacy of employing self-supervised methods to further train the models using classical text corpora, thus enhancing their capability to tackle downstream tasks. Moreover, it is worth emphasizing that the choice of font, the scale of the corpus, and the initial model selection all exert significant influence over the ultimate experimental outcomes. To cater to the diverse text processing preferences of researchers in digital humanities and linguistics, we have developed three distinct categories comprising a total of nine model variations. We believe that by sharing these foundational language models specialized in the domain of ancient texts, we can facilitate the intelligent processing and scholarly exploration of ancient literary works and, consequently, contribute to the global dissemination of China's rich and esteemed traditional culture in this new era.

Tong Zhu, Yan Meng, Haotian Hu et al.

Although the use of pay-per-click mechanisms stimulates the prosperity of the mobile advertisement network, fraudulent ad clicks result in huge financial losses for advertisers. Extensive studies identify click fraud according to click/traffic patterns based on dynamic analysis. However, in this study, we identify a novel click fraud, named humanoid attack, which can circumvent existing detection schemes by generating fraudulent clicks with similar patterns to normal clicks. We implement the first tool ClickScanner to detect humanoid attacks on Android apps based on static analysis and variational AutoEncoder (VAE) with limited knowledge of fraudulent examples. We define novel features to characterize the patterns of humanoid attacks in the apps' bytecode level. ClickScanner builds a data dependency graph (DDG) based on static analysis to extract these key features and form a feature vector. We then propose a classification model only trained on benign datasets to overcome the limited knowledge of humanoid attacks. We leverage ClickScanner to conduct the first large-scale measurement on app markets (i.e.,120,000 apps from Google Play and Huawei AppGallery) and reveal several unprecedented phenomena. First, even for the top-rated 20,000 apps, ClickScanner still identifies 157 apps as fraudulent, which shows the prevalence of humanoid attacks. Second, it is observed that the ad SDK-based attack (i.e., the fraudulent codes are in the third-party ad SDKs) is now a dominant attack approach. Third, the manner of attack is notably different across apps of various categories and popularities. Finally, we notice there are several existing variants of the humanoid attack. Additionally, our measurements demonstrate the proposed ClickScanner is accurate and time-efficient (i.e., the detection overhead is only 15.35% of those of existing schemes).