Lei Zhang

Li Peng, Jiayao Zhang, Yihang Wu et al.

The explosive growth of data has highlighted its critical role in driving economic growth through data marketplaces, which enable extensive data sharing and access to high-quality datasets. To support effective trading, signaling mechanisms provide participants with information about data products before transactions, enabling informed decisions and facilitating trading. However, due to the inherent free-duplication nature of data, commonly practiced signaling methods face a dilemma between privacy and reliability, undermining the effectiveness of signals in guiding decision-making. To address this, this paper explores the benefits and develops a non-TCP-based construction for a desirable signaling mechanism that simultaneously ensures privacy and reliability. We begin by formally defining the desirable utility signaling mechanism and proving its ability to prevent suboptimal decisions for both participants and facilitate informed data trading. To design a protocol to realize its functionality, we propose leveraging maliciously secure multi-party computation (MPC) to ensure the privacy and robustness of signal computation and introduce an MPC-based hash verification scheme to ensure input reliability. In multi-seller scenarios requiring fair data valuation, we further explore the design and optimization of the MPC-based KNN-Shapley method with improved efficiency. Rigorous experiments demonstrate the efficiency and practicality of our approach.

Zhan Zhang, Qin Zhang, Yang Jiao et al.

AI-aided clinical diagnosis is desired in medical care. Existing deep learning models lack explainability and mainly focus on image analysis. The recently developed Dynamic Uncertain Causality Graph (DUCG) approach is causality-driven, explainable, and invariant across different application scenarios, without problems of data collection, labeling, fitting, privacy, bias, generalization, high cost and high energy consumption. Through close collaboration between clinical experts and DUCG technicians, 46 DUCG models covering 54 chief complaints were constructed. Over 1,000 diseases can be diagnosed without triage. Before being applied in real-world, the 46 DUCG models were retrospectively verified by third-party hospitals. The verified diagnostic precisions were no less than 95%, in which the diagnostic precision for every disease including uncommon ones was no less than 80%. After verifications, the 46 DUCG models were applied in the real-world in China. Over one million real diagnosis cases have been performed, with only 17 incorrect diagnoses identified. Due to DUCG's transparency, the mistakes causing the incorrect diagnoses were found and corrected. The diagnostic abilities of the clinicians who applied DUCG frequently were improved significantly. Following the introduction to the earlier presented DUCG methodology, the recommendation algorithm for potential medical checks is presented and the key idea of DUCG is extracted.

Lei Zhang, David Zhang

This paper addresses an important issue, known as sensor drift that behaves a nonlinear dynamic property in electronic nose (E-nose), from the viewpoint of machine learning. Traditional methods for drift compensation are laborious and costly due to the frequent acquisition and labeling process for gases samples recalibration. Extreme learning machines (ELMs) have been confirmed to be efficient and effective learning techniques for pattern recognition and regression. However, ELMs primarily focus on the supervised, semi-supervised and unsupervised learning problems in single domain (i.e. source domain). To our best knowledge, ELM with cross-domain learning capability has never been studied. This paper proposes a unified framework, referred to as Domain Adaptation Extreme Learning Machine (DAELM), which learns a robust classifier by leveraging a limited number of labeled data from target domain for drift compensation as well as gases recognition in E-nose systems, without loss of the computational efficiency and learning ability of traditional ELM. In the unified framework, two algorithms called DAELM-S and DAELM-T are proposed for the purpose of this paper, respectively. In order to percept the differences among ELM, DAELM-S and DAELM-T, two remarks are provided. Experiments on the popular sensor drift data with multiple batches collected by E-nose system clearly demonstrate that the proposed DAELM significantly outperforms existing drift compensation methods without cumbersome measures, and also bring new perspectives for ELM.