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Ruiqiang Zhang

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4 papers

1 author row

AAAI Conference 2026 Conference Paper

AuthSig: Safeguarding Scanned Signatures Against Unauthorized Reuse in Paperless Workflows

Ruiqiang Zhang
Zehua Ma
Guanjie Wang
Chang Liu
Hengyi Wang
Weiming Zhang

With the deepening trend of paperless workflows, signatures as a means of identity authentication are gradually shifting from traditional ink-on-paper to electronic formats. Despite the availability of dynamic pressure-sensitive and PKI-based digital signatures, static scanned signatures remain prevalent in practice due to their convenience. However, these static images, having almost lost their authentication attributes, cannot be reliably verified and are vulnerable to malicious copying and reuse. To address these issues, we propose AuthSig, a novel static electronic signature framework based on generative models and watermark, which binds authentication information to the signature image. Leveraging the human visual system’s insensitivity to subtle style variations, AuthSig finely modulates style embeddings during generation to implicitly encode watermark bits-enforcing a One Signature, One Use policy. To overcome the scarcity of handwritten signature data and the limitations of traditional augmentation methods, we introduce a keypoint-driven data augmentation strategy that effectively enhances style diversity to support robust watermark embedding. Experimental results show that AuthSig achieves over 98% extraction accuracy under both digital-domain distortions and signature-specific degradations, and remains effective even in print-scan scenarios.

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EAAI Journal 2025 Journal Article

A novel data-driven machine learning approach for improved strain rate control in thermomechanical testing of sheet metals

James Dear
Ruiqiang Zhang
Zhusheng Shi
Jianguo Lin

Thermomechanical tests on sheet metals are commonly conducted using Gleeble systems to investigate their viscoplastic behaviour. Accurate strain rate control is crucial in these tests to accurately determine the material's thermomechanical properties. However, the well-known temperature gradient along the gauge length makes accurate strain rate control using the conventional approach challenging. In this study, to improve strain rate control in thermomechanical testing, a novel data-driven Machine Learning (ML) approach has been developed, for the first time, based on the analysis of the characteristics of the experimental data obtained using the conventional approach. This novel approach utilises Long Short-Term Memory (LSTM) networks, with inputs of target deformation temperature and strain rate, and outputs of strain distributions along the gauge length throughout the deformation process. The output strain distributions are subsequently integrated to calculate the time-dependent displacement required to stretch the specimen under the target conditions. Uniaxial tensile tests on an aluminium alloy under hot stamping conditions were conducted using both conventional and novel approaches for strain rate control. The experimental data obtained using the conventional approach were adopted to train and test the novel approach. The results show that this novel approach can significantly improve strain rate control in thermomechanical tests. Compared to the conventional approach which results in a Percentage Error (PE) in strain rate of up to ∼770% and a Mean Absolute Percentage Error (MAPE) in strain rate of up to ∼120%, the novel approach significantly reduces both PE and MAPE values by over 88%. This novel ML approach provides an effective solution for controlling strain rate in thermomechanical testing, enabling accurate determination of thermomechanical properties of sheet metals.

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TIST Journal 2013 Journal Article

Improving recency ranking using twitter data

Yi Chang
Anlei Dong
Pranam Kolari
Ruiqiang Zhang
Yoshiyuki Inagaki
Fernanodo Diaz
Hongyuan Zha
Yan Liu

In Web search and vertical search, recency ranking refers to retrieving and ranking documents by both relevance and freshness. As impoverished in-links and click information is the the biggest challenge for recency ranking, we advocate the use of Twitter data to address the challenge in this article. We propose a method to utilize Twitter TinyURL to detect fresh and high-quality documents, and leverage Twitter data to generate novel and effective features for ranking. The empirical experiments demonstrate that the proposed approach effectively improves a commercial search engine for both Web search ranking and tweet vertical ranking.

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AAAI Conference 2011 Conference Paper

Detecting Multilingual and Multi-Regional Query Intent in Web Search

Yi Chang
Ruiqiang Zhang
Srihari Reddy
Yan Liu

With rapid growth of commercial search engines, detecting multilingual and multi-regional intent underlying search queries becomes a critical challenge to serve international users with diverse language and region requirements. We introduce a query intent probabilistic model, whose input is the number of clicks on documents from different regions and in different language, while the output of this model is a smoothed probabilistic distribution of multilingual and multi-regional query intent. Based on an editorial test to evaluate the accuracy of the intent classiﬁer, our probabilistic model could improve the accuracy of multilingual intent detection for 15%, and improve multi-regional intent detection for 18%. To improve web search quality, we propose a set of new ranking features to combine multilingual and multi-regional query intent with document language/region attributes, and apply different approaches in integrating intent information to directly affect ranking. The experiments show that the novel features could provide 2. 31% NDCG@1 improvement and 1. 81% NDCG@5 improvement.

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