Pd-Doped SnO 2

Methane (CH4), ethane (C2H6), ethylene (C2H4), and acetylene (C2C2) are important fault characteristic hydrocarbon gases dissolved in power transformer oil. Online monitoring these gaseous components and their generation rates can present the operational state of power transformer timely and effectively. Gas sensing technology is the most sticky and tricky point in online monitoring system. In this paper, pure and Pd-doped SnO2 nanoparticles were synthesized by hydrothermal method and characterized by X-ray powder diffraction, field-emission scanning electron microscopy, and energy dispersive X-ray spectroscopy, respectively. The gas sensors were fabricated by side-heated preparation, and their gas sensing properties against CH4, C2H6, C2H4, and C2H2 were measured. Pd doping increases the electric conductance of the prepared SnO2 sensors and improves their gas sensing performances to hydrocarbon gases. In addition based on the frontier molecular orbital theory, the highest occupied molecular orbital energy and the lowest unoccupied molecular orbital energy were calculated. Calculation results demonstrate that C2H4 has the highest occupied molecular orbital energy among CH4, C2H6, C2H4, and C2H2, which promotes charge transfer in gas sensing process, and SnO2 surfaces capture a relatively larger amount of electric charge from adsorbed C2H4

Towards Privacy-Preserving and Verifiable Federated Matrix Factorization

Recent years have witnessed the rapid growth of federated learning (FL), an emerging privacy-aware machine learning paradigm that allows collaborative learning over isolated datasets distributed across multiple participants. The salient feature of FL is that the participants can keep their private datasets local and only share model updates. Very recently, some research efforts have been initiated to explore the applicability of FL for matrix factorization (MF), a prevalent method used in modern recommendation systems and services. It has been shown that sharing the gradient updates in federated MF entails privacy risks on revealing users' personal ratings, posing a demand for protecting the shared gradients. Prior art is limited in that they incur notable accuracy loss, or rely on heavy cryptosystem, with a weak threat model assumed. In this paper, we propose VPFedMF, a new design aimed at privacy-preserving and verifiable federated MF. VPFedMF provides for federated MF guarantees on the confidentiality of individual gradient updates through lightweight and secure aggregation. Moreover, VPFedMF ambitiously and newly supports correctness verification of the aggregation results produced by the coordinating server in federated MF. Experiments on a real-world moving rating dataset demonstrate the practical performance of VPFedMF in terms of computation, communication, and accuracy