Shijia Liu, David A. Smith. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

The emergence of digital twin technology offers a promising solution to address the limitations of traditional methods on early diagnosis and accurate propagation analysis of flight ground service delays. However, the application of digital twin technology in the civil aviation domain still stays at the lower maturity of the L2 level, which focuses on physical assets, operational data, and maintenance planning at airports, and failed to achieve the integration of flight ground operation mechanism and real‐time data, making it difficult to realize timely delay diagnosis. The simulation model is also limited to the offline simulation technology, which cannot connect to real‐time data for simulation from intermediate processes. In this work, we developed an advanced L3‐level airport digital twin system for flight ground service processes delay diagnosis and propagation, which focused on real‐time data‐driven simulation models and machine learning applications to meet the timely and precision requirements. First, we used the Unity3D platform to construct static three‐dimensional models of flight ground service objects on the airport cloud server. By parsing these behavioral state interfaces and mapping real‐time dynamic data from the airport sensing and business systems, we achieved accurate visualization of the airport’s dynamic operational processes. Then, a vehicle delay tree–based Bayesian diagnostic model was proposed in the digital twin system to analyze the relationships between multiple flights and service processes, which enables proactive diagnosis of the operation status and provides delay warning information. To improve the accuracy of propagation analysis, we proposed a “breakpoint” simulation method that enables real‐time simulation starting from an intermediate moment, facilitating the inference of flight ground service delays since the early warning moment. In addition, two delay tracing and propagation algorithms were proposed to identify delays and investigate propagation paths. Leveraging real‐time operational information, our approach provides valuable feedback for decision‐making, empowering the airport manager to formulate precise optimization strategies. Experiments on real‐world airport data have validated the effectiveness of our proposed method and provided practical recommendations for airport managers to reduce aircraft delays and improve airport operation efficiency.

Unlike conventional IT systems, industrial control systems (ICSs) requires tailored attack detection methods due to its unique communication protocols. Existing attack detection methods lack the ability to consider both detection accuracy and time performance, particularly for highly stealthy fake data injection attacks (FDIAs). To address these challenges, this work proposes an online parallel attack detection method for ICS based on multibandpass filter. By building multiple adaptive filters based on energy equilibrium and time–frequency domain data transformation, we implement multifrequency band data segmentation. Hierarchical temporal memory (HTM) models are employed to parallelly fit the segmented data and detect anomalies. Simulation experiments demonstrate that our method outperforms the state-of-the-art Numenta method, achieving a 9% higher detection accuracy while reducing detection time to just 1/14 of Numenta’s. These results highlight the significant advantages of our method in striking a balance between detection accuracy and time performance. Our proposed method fills the gap in ICS attack detection and offers substantial improvements over existing techniques.

Current wireless cross-domain solutions are limited to cross-one-factor tasks, requiring target domain data participation for training or position-independent feature extraction using multiple transceivers. Therefore, this paper aims to demonstrate cross-domain wireless sensing in a more challenging domain generalization (DG) setting without multiple transceivers or target domain data. Specifically, we propose a style-randomized cross-domain wireless sensing model called WiSR, which extracts domain-invariant features from multiple source domains. It quantifies Channel State Information (CSI) differences in the subcarrier dimensions as subcarrier-domain styles and instructs the feature extractor to gradually bias the gesture signals by randomizing the subcarrier-domain styles at the feature level. Meanwhile, a domain classifier that shares the same feature extractor is instructed to gradually bias the domain signals by randomizing the gesture features. Then, the adversarial training framework enables the domain classifier to reduce the influence of domain signals on the feature extractor. Extensive experiments have been performed on three gesture datasets with varying amounts of subcarriers from devices with different NICs, including cross-one-factor (such as room, user, location, and orientation) and cross-multi-factor sensing tasks. The results demonstrate that our method considerably increases performance on wireless DG tasks. Our code is available at:https://github.com/LiuSjia/WiSR.

Cross-domain wireless sensing has always been challenging due to the sensitivity of wireless signals to various environmental factors, which we refer to as subdomains. However, current efforts are limited to cross-one-subdomain tasks requiring target domain data for model training or multiple receivers for data collection. Taking common gesture recognition as an application example, we attempt to demonstrate the feasibility of cross-multiple-subdomain wireless sensing in the more challenging domain generalization (DG) setting. It is possible to extract domain-invariant features from one or several source domain(s), thereby avoiding the need for multiple receivers or target domain data. We also propose an intelligent wireless data augmentation technique based on subdomain-guided perturbations, named WiSGP. Specifically, the independent domain model generates perturbations in the direction of the largest subdomain variations. Then, these subdomain-guided perturbations augment the gesture model's input to enable better domain-invariant feature extraction, even when various subdomains interact. Similarly, gesture-guided perturbations augment the domain model's input, resulting in more accurate subdomain-guided perturbations and minimal gesture label changes. Extensive experiments have been conducted on three datasets collected from various NICs. In terms of room, location, orientation, and user subdomains, WiSGP exhibits excellent accuracy, generalizability, and portability for both cross-one-subdomain and cross-multiple-subdomain tasks.

Currently, available contactless continuous authentication (CA) techniques depend on physiological biometrics to identify individuals at long intervals through complex feature extraction, resulting in poor accuracy, high computation costs, and security vacuums during lengthy intervals. To address these issues, we propose WiPT, a WiFi-based contactless CA system that utilizes contextual features and behavioral biometrics to optimize contactless CA technology. Specifically, we designed a low-computation two-step user state detection (TUSD) mechanism that continuously monitors user states in real-time. It locks the system when the registered user leaves and allows user authentication only when the departing user returns. Therefore, it eliminates pointless periodic re-authentication and results in considerably shorter monitoring intervals while significantly reducing computation. Subsequently, benefiting from contextual features, WiPT can identify individuals using more detectable behavioral biometrics. We built a one-class classification model based on the Convolutional Autoencoder to automatically extract rich representations of WiFi signals associated with postural transition movements, resulting in lower authentication delay, higher accuracy, and anti-interference. WiPT was implemented by the widely available 802.11n devices and has been extensively evaluated with typical sit-to-stand postural transitions. WiPT achieves an average accuracy of 96.63% in authentication and 99.78% in defense across 30 subjects with an authentication delay of 5.59 milliseconds and a monitoring interval of 2 seconds. They are 4.87% and 5.03% more accurate and dozens of times less time-consuming than existing WiFi-based CA solutions.

Code-switching has long interested linguists, with computational work in particular focusing on speech and social media data (Sitaram et al., 2019).This paper contrasts these informal instances of code-switching to its appearance in more formal registers, by examining the mixture of languages in the Deutsches Textarchiv (DTA), a corpus of 1406 primarily German books from the 17th to 19th centuries.We automatically annotate and manually inspect spans of six embedded languages (Latin, French, English, Italian, Spanish, and Greek) in the corpus.We quantitatively analyze the differences between code-switching patterns in these books and those in more typically studied speech and social media corpora.Furthermore, we address the practical task of predicting code-switching from features of the matrix language alone in the DTA corpus.Such classifiers can help reduce errors when optical character recognition or speech transcription is applied to a large corpus with rare embedded languages.

A grammatical gender system divides a lexicon into a small number of relatively fixed grammatical categories.How similar are these gender systems across languages?To quantify the similarity, we define gender systems extensionally, thereby reducing the problem of comparisons between languages' gender systems to cluster evaluation.We borrow a rich inventory of statistical tools for cluster evaluation from the field of community detection (Driver and Kroeber, 1932;Cattell, 1945), that enable us to craft novel information-theoretic metrics for measuring similarity between gender systems.We first validate our metrics, then use them to measure gender system similarity in 20 languages.Finally, we ask whether our gender system similarities alone are sufficient to reconstruct historical relationships between languages.Towards this end, we make phylogenetic predictions on the popular, but thorny, problem from historical linguistics of inducing a phylogenetic tree over extant Indo-European languages.Languages on the same branch of our phylogenetic tree are notably similar, whereas languages from separate branches are no more similar than chance.