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Internet of Things (IoT) devices and applications are being deployed in our homes and workplaces and in our daily lives. These devices often rely on continuous data collection and machine learning models for analytics and actuations. However, this approach introduces a number of privacy and efficiency challenges, as the service operator can perform arbitrary inferences on the available data. Recently, advances in edge processing have paved the way for more efficient, and private, data processing at the source for simple tasks and lighter models, though they remain a challenge for larger, and more complicated models. In this paper, we present a hybrid approach for breaking down large, complex deep neural networks for cooperative, privacy-preserving analytics. To this end, instead of performing the whole operation on the cloud, we let an IoT device to run the initial layers of the neural network, and then send the output to the cloud to feed the remaining layers and produce the final result. We manipulate the model with Siamese fine-tuning and propose a noise addition mechanism to ensure that the output of the user's device contains no extra information except what is necessary for the main task, preventing any secondary inference on the data. We then evaluate the privacy benefits of this approach based on the information exposed to the cloud service. We also asses the local inference cost of different layers on a modern handset. Our evaluations show that by using Siamese fine-tuning and at a small processing cost, we can greatly reduce the level of unnecessary, potentially sensitive information in the personal data, and thus achieving the desired trade-off between utility, privacy and performance.

The hot deformation behavior of nickel-base superalloy UDIMET 720 in solution-treated conditions, simulating the forging process of the alloy, was studied using hot compression experiments. Specimens were deformed in the temperature range of 1000 °C to 1175 °C with strain rates of 10 Ϫ3 to 1 s Ϫ1 and total strain of 0.8. Below 1100 °C, all specimens showed flow localization as shear band through the diagonal direction, with more severity at higher strain rates. A uniform deformation was observed when testing between 1100 °C and 1150 °C with dynamic recrystallization as the major flow softening mechanism above 1125 °C. Deformation above ␥Ј solvus temperature was accompanied with grain boundary separation. The hot working window was determined to be in the interval 1100 °C to 1150 °C. Thermomechanical behavior of the material was modeled using the power-law, the Sellars-Tegart, and an empirical equation. The flow stress values showed a nonlinear dependence of strain rate sensitivity to strain rate. The analysis indicated that the empirical method provides a better constitutive equation for process modeling of this alloy. The apparent activation energy for deformation was calculated and its variations with strain rate and temperature are discussed.

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The dynamic strain aging behaviour of low carbon steel wire rod was examined at room temperature to 450'C using tensile testing at strain rates of I [0][1][2][3][4]