Gesture recognition acts as a key enabler for user-friendly human-computer interfaces (HCI). To bridge the human-computer barrier, numerous efforts have been devoted to designing accurate fine-grained gesture recognition systems. Recent advances in wireless sensing hold promise for a ubiquitous, non-invasive and low-cost system with existing Wi-Fi infrastructures. In this paper, we propose DeepNum, which enables fine-grained finger gesture recognition with only a pair of commercial Wi-Fi devices. The key insight of DeepNum is to incorporate the quintessence of deep learning-based image processing so as to better depict the influence induced by subtle finger movements. In particular, we make multiple efforts to transfer sensitive Channel State Information (CSI) into depth radio images, including antenna selection, gesture segmentation and image construction, followed by noisy image purification using high-dimensional relations. To fulfill the restrictive size requirements of deep learning model, we propose a novel region-selection method to constrain the image size and select qualified regions with dominant color and texture features. Finally, a 7-layer Convolutional Neural Network (CNN) and SoftMax function are adopted to achieve automatic feature extraction and accurate gesture classification. Experimental results demonstrate the excellent performance of DeepNum, which recognizes 10 finger gestures with overall accuracy of 98% in three typical indoor scenarios.

Data-aware processes play an crucial role in various IT systems, including requirement elicitation, domain analysis, software design, and system execution. Due to frequent changes in business environments and continual internal adjustments of enterprises, data-aware processes are increasingly evolved into multiple process variants. The detection of differences between variants can be related to process mapping, process integration, or process substitution. A critical step of the procedure is to investigate the data-aware process consistency. Unfortunately, existing studies only provide a simple “yes” or “no” answer or look for an answer purely from the control flow perspective. The objective of this paper is to propose a systematic solution for effective measurement of consistency between data-aware processes. First, we identify essential activity constraints which reside in data-aware processes. Then, we introduce a novel concept of activity constraint graph (ACG) and propose an algorithm for constructing ACGs. Finally, we use ACGs to measure the data-aware process consistency on a scale from 0 to 1.0. Our technique has been implemented in a prototype tool, and extensive experiments using both real and synthetic datasets are conducted to evaluate the accuracy, distribution of consistency degrees, and capacity of difference detection of our approach. Results show that our approach is more accurate, generates a finer distribution of consistency degrees, and detects differences more effectively than other state-of-the-art approaches.

Digital twins (DTs) have seen widespread application across industries, enabling deep integration of cyber–physical systems. However, previous research has largely focused on domain-specific DTs and lacks a universal, cross-industry modeling framework, resulting in high development costs and low reusability. To address these challenges, this study proposes a DT modeling method based on hierarchical decoupling and topological connections. First, the system is decomposed top–down into three levels—system, subsystem, and component—through hierarchical functional decoupling, reducing system complexity and supporting independent component development. Second, a method for constructing component-level DTs using standardized information sets is introduced, employing the JSON-LD language to uniformly describe and encapsulate component information. Finally, a topological connection mechanism abstracts the relationships between components into an adjacency matrix and assembles components and subsystems bottom–up using graph theory, ultimately forming the system-level DT. The effectiveness of the proposed method was validated using a typical surface water purification system as a case study, where the system was decomposed into four functional subsystems and 12 types of components. Experimental results demonstrate that the method efficiently enables automated integration of DTs from standardized components to subsystems and the complete system. Compared with conventional monolithic modeling approaches, it significantly reduces system complexity, supports efficient component development, and accelerates system integration. For example, when the number of components exceeds 300, the proposed method generates topology connections 44.69% faster than direct information set traversal. Consequently, this approach provides a novel and effective solution to the challenges of low reusability and limited generality in DT models, laying a theoretical foundation and offering technical support for establishing a universal cross-industry DT modeling framework.

Severe software defects can lead to significant issues and even result in substantial financial losses. As a result, automated code defect detection has garnered widespread attention. To fix these defects as soon as possible, J ust- I n- T ime D efect P rediction (JIT-DP) and J ust- I n- T ime D efect L ocalization (JIT-DL) have been investigated. Specifically, JIT-DP aims to predict commit-level defects when a code change is submitted, and JIT-DL aims to perform line-level defect localization before they lead to failures. Several methods have been developed for JIT-DP and JIT-DL, with those using C ode P re- T rained M odels (CPTMs) leading to the best results. However, most previous studies concentrate on JIT-DP, even though JIT-DL is arguably more crucial. Identifying which specific lines of a commit are defective facilitates the prediction of whether the commit is defective. Therefore, we propose a M ulti- T ask L earning method (JIT-MTL) designed to address both JIT-DP and JIT-DL. Specifically, we train a CPTM to simultaneously identify defective commits and lines. The line-level predictions are directly used for JIT-DL, while the commit-level predictions for the same commit are merged to determine the results for JIT-DP. To enhance the accuracy of line-level predictions, we provide the CPTM with additional commit-level information, including commit-level defect code representation and expert features. Experimental results on the JIT-Defect4J dataset show that JIT-MTL outperforms the state-of-the-art baselines, with improvements of 14% in F1 in JIT-DP, 53.9% in Top-10 Accuracy, and 36.4% in Top-5 Accuracy in JIT-DL.