Synthetic data generation plays a crucial role in many areas where data is scarce and privacy/confidentiality is a significant concern. Generative Adversarial Networks (GANs), arguably one of the most widely used data synthesis techniques, allow for the training of a model (i.e., generator) that can generate real-looking data by playing a min-max game with a discriminator model. When multiple organizations are reluctant to share their sensitive data, GANs models can be trained in a federated manner, commonly with the use of differential privacy (DP). In order to achieve a reasonable level of model utility, DP trades privacy exhibiting vulnerability to various attacks (e.g., membership inference attack). In this paper, we propose a hybrid solution, PP-FedGAN, to the asynchronous federated, privacy-preserving training of GANs models by combining the CKKS homomorphic encryption (HE) scheme with differential privacy. The addition of HE results in around 10 seconds of overhead on the client side per round and 115 seconds on the entire training procedure. We also analyze the security of PP-FedGAN under the honest-but-curious security model. Where stronger security guarantees are required, our proposal presents a better alternative to solutions that only employ DP.

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SRAM Physical Unclonable Functions (PUFs) are among other things today commercially used for secure primitives such as key generation and authentication. The quality of the PUFs and hence the security primitives, depends on intrinsic variations which are technology dependent. Therefore, to sustain the commercial usage of PUFs for cutting-edge technologies, it is important to properly model and evaluate their reliability. In this work, we evaluate the SRAM PUF reliability using within class Hamming distance (WCHD) for 16nm, 14nm, and 7nm using simulations and silicon validation for both low-power and high-performance designs. The results show that our simulation models and expectations match with the silicon measurements. From the experiments, we conclude the following: (1) SRAM PUF is reliable in advanced FinFET technology nodes, i.e., the noise is low in 16nm, 14nm, and 7nm, (2) temperature variations have a marginal impact on the reliability, and (3) both low-power and high-performance SRAMs can be used as a PUF without excessive need of error correcting codes (ECCs).@en

This paper presents an experimental investigation on the dynamic mechanical performance of S30408 austenitic stainless steel (ASS) under elevated temperatures, which is essential for determining the behaviour of structures made with this type of steel subjected to the coupled fire and impact/explosion. For this purpose, the quasi-static and dynamic compression tests using Split Hopkinson Pressure Bar (SHPB) were conducted under temperatures of 20–600 °C and strain rates from 0.001 to 3000 s⁻¹. In addition, the corresponding microstructures of tested samples were observed. The stress–strain responses, strain rate and temperature effects as well as the microstructural evolutions were analyzed. Test results show that the stress–strain responses are sensitive to the strain rate and temperature. The strain-rate sensitivity coefficient increases as the strain rate and temperature rise. The microstructural observation reveals that the grain dimension declines with an increment of strain rate or a decreasing temperature. Finally, the dynamic compressive stress–strain models for S30408 ASS under 20–600 °C were suggested on the basis of the Johnson-Cook (J-C) model and have been proved to give a reasonable prediction.

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The global spread of COVID-19 has caused a huge number of confirmed cases and deaths, which in return leads to a plethora of mental disorders across the world. In order to address citizens’ psychological problems, government agencies in many countries have employed AI-based chatbots to provide mental health services. However, there is a limited understanding of the determinants affecting citizens’ user experience and user satisfaction when mental health services supported by chatbots are provided. Thus, based on the Theory of Consumption Values (TCV), this study proposes an analytical framework to investigate the factors that are important to citizens’ user experience and user satisfaction when they interact with mental health chatbots. Analysis of data collected from 295 chatbot users in Wuhan and Chongqing reveals that personalization, enjoyment, learning, and condition are positively related to user experience and user satisfaction. However, voice interaction fails to devote to citizens’ user experience and user satisfaction. Thus, government agencies and their AI service contractors should enhance the functions and systems of mental health chatbots to ensure citizens’ user experience and user satisfaction. Also, they should more positively promote the use of mental health chatbots during the public health emergency.

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As a member of the aluminium composite, GLARE (GLAss fibre/epoxy REinforced aluminium laminates) was used in the upper fuselage of Airbus A380 because of its superior mechanical properties over monolithic aluminium alloys. Thermal processing is a potential method for materials recycling and reuse from GLARE scrap with the aim of environmental protection and economic benefits. Thermal delamination is a crucial pre-treatment step for GLARE recycling. Differential scanning calorimetry (DSC) and Thermogravimetric analysis (TGA) tests have been used to identify the decomposition temperature range of epoxy resins under non-isothermal condition in our previous work [1]. To obtain an appropriate solution for GLARE thermal delamination, the thermal degradation behaviour of epoxy resins in GLARE under isothermal conditions were investigated and isothermal decomposition kinetic models were built up based on DSC and thermogravimetric analysis TGA. The thermal delamination process of GLARE is determined based on thermal analysis results and experimental optimization.@en