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.

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. • A novel data-driven machine learning approach is developed to improve strain rate control. • Novel approach improves strain rate control, reducing PE and MAPE by over 88 %. • Novel approach reduces measured stress by up to 30 % due to improved strain rate control.

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.

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.

In web search, recency ranking refers to ranking documents by relevance which takes freshness into account. In this paper, we propose a retrieval system which automatically detects and responds to recency sensitive queries. The system detects recency sensitive queries using a high precision classifier. The system responds to recency sensitive queries by using a machine learned ranking model trained for such queries. We use multiple recency features to provide temporal evidence which effectively represents document recency. Furthermore, we propose several training methodologies important for training recency sensitive rankers. Finally, we develop new evaluation metrics for recency sensitive queries. Our experiments demonstrate the efficacy of the proposed approaches.

Realtime web search refers to the retrieval of very fresh content which is in high demand. An effective portal web search engine must support a variety of search needs, including realtime web search. However, supporting realtime web search introduces two challenges not encountered in non-realtime web search: quickly crawling relevant content and ranking documents with impoverished link and click information. In this paper, we advocate the use of realtime micro-blogging data for addressing both of these problems. We propose a method to use the micro-blogging data stream to detect fresh URLs. We also use micro-blogging data to compute novel and effective features for ranking fresh URLs. We demonstrate these methods improve effective of the portal web search engine for realtime web search.

No abstract available.

We intended to demonstrate the effect of using N-best speech recognition hypotheses for improving speech translation performance. A log-linear model, which integrated features from speech recognition and statistical machine translation, was used to rescore the translation candidates. Model parameters were estimated by optimizing an objectively measurable but subjectively relevant translation quality metric. Experimental results have shown that the proposed N-best approach improved translation quality over the conventional single-best approach. The improvements were confirmed consistently by several automatic translation evaluation metrics. 1.

Applying natural language processing technique to language modeling is a key problem in speech recognition. This paper describes a maximum entropy-based approach to language modeling in which both words together with syntactic and semantic tags in the long history are used as a basis for complex linguistic questions. These questions are integrated with a standard trigram language model or a standard trigram language model combined with long history word triggers and the resulting language model is used to rescore the N-best hypotheses output of the ATRSPREC speech recognition system. The technique removed 24% of the correctable error of the recognition system.

Much recent research has demonstrated that the correlation between a language model's perplexity and its effect on the word error rate of a speech recognition system is not as strong as was once thought.This represents a major problem for those involved in developing language models.This paper describes the development of new measures of language model quality.These measures retain the ease of computation and task independence that are perplexity's strengths, yet are considerably better correlated with word error rate.This paper also shows that mixture-based language models are improved by applying interpolation weights which are optimised with respect to these new measures, rather than a maximum likelihood criterion.