Qiyu Wu

Steel products play an important role in daily production and life as a common production material. Currently, the quality of steel products is judged by manual experience. However, various inspection criteria employed by human operators and complex factors and mechanisms in the steelmaking process may lead to inaccuracies. To address these issues, we propose a learning-based method for steel quality prediction, which is named DRBoost,based on multiple machine learning techniques, including Decision tree, Random forest, and the LSBoost algorithm. In our method, the decision tree clearly captures the nonlinear relationships between features and serves as a solid baseline for making preliminary predictions. Random forest enhances the model’s robustness and avoids overfitting by aggregating multiple decision trees. LSBoost uses gradient descent training to assign contribution coefficients to different kinds of raw materials to obtain more accurate predictions. Five key chemical elements, including carbon, silicon, manganese, phosphorus, and sulfur, which significantly influence the major performance characteristics of steel products, are selected. Steel quality prediction is conducted by predicting the contents of these chemical elements. Multiple models are constructed to predict the contents of five key chemical elements in steel products. These models are symmetrically complementary, meeting the requirements of different production scenarios and forming a more accurate and universal method for predicting the steel product’s quality. In addition, the prediction method provides a symmetric quality control system for steel product production. Experimental evaluations are conducted based on a dataset of 2012 samples from a steel plant in Liaoning Province, China. The input variables include various raw material usages, while the outputs are the content of five key chemical elements that influence the quality of steel products. The experimental results show that the models demonstrate their advantages in different performance metrics and are applicable to practical steelmaking scenarios.

Qiyu Wu, Masaaki Nagata, Yoshimasa Tsuruoka. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Location data have great value for facility location selection. Due to the privacy issues of both location data and user identities, a location service provider can not hand over the private location data to a business or a third party for analysis or reveal the location data for jointly running data analysis with a business. In this paper, we propose a newly constructed PSI filter that can help the two parties privately find the data corresponding to the items in the intersection without any computations and, subsequently, we give the PSI filter generation protocol. We utilize it to construct three types of aggregate protocols for facility location selection with confidentiality. Then we propose a ciphertext matrix compressing method, making one block of cipher contain lots of plaintext data while keeping the homomorphic property valid. This method can efficiently further reduce the computation/communication cost of the query process—the improved query protocol utilizing the ciphertext matrix compressing method is given followed. We show the correctness and privacy of the proposed query protocols. The theoretical analysis of computation/communication overhead shows that our proposed query protocols are efficient both in computation and communication and the experimental results of the efficiency tests show the practicality of the protocols.

Qiyu Wu, Chongyang Tao, Tao Shen, Can Xu, Xiubo Geng, Daxin Jiang. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.

As edge computing attains tremendous popularity, IoT devices always outsource their data to nearby edge servers for storing and pre-processing, which improves the efficiency of data processing and reduces the required network resources. For privacy-preserving, sensitive data is mostly encrypted before outsourcing. Nevertheless, large volumes of data in edge computing usually comes from multiple data sources, which means that they are encrypted with different secret keys, making it difficult for edge server to query and process. Existing solutions are mostly proposed for this problem in cloud computing, but they do not take into account that the limitations of computing and storage capabilities of edge devices will prevent them from performing computationally expensive operations. In this paper, we propose a lightweight privacy-preserving equality query scheme (LPEQ) in edge computing for the first time, which allows authorized users to perform equality query efficiently and privately on the encrypted data outsourced by multiple IoT devices. We also introduce a formal security model and prove that the LPEQ meets secure requirements against curious entities under this model. Meanwhile, our theoretical analyses and experimental evaluations demonstrate that the LPEQ performs better efficiency in terms of computation and communication while retaining privacy-preserving properties. Therefore, it is practical for applications in edge computing.