Abstract: A setting method of in-memory computing simulator is presented. It involves an in-memory computing device performing various test combinations of neural network models and datasets, recording the corresponding first estimation indices. A processing device then uses these test combinations to execute a simulator with adjustable settings and records the corresponding second estimation indices. The processing device calculates a correlation sum according to the first estimation indices and second estimation indices, and performs an optimal algorithm to search an optimal parameter in a setting space to maximize the correlation sum.

Abstract: A system for verifying edited image includes: a producer terminal device configured to tile a source image for a plurality of smaller tiled images with individual source image hash values to accordingly calculate an integrated source image hash value, and to execute digitally signing to generate an image tag pair; an editor terminal device configured to receive the image tag pair, to divide the source image into these smaller tiled images according to a tile configuration, to edit part of the smaller tiled images, to include the rest part of these smaller tiled images to generate an edited integral image and further calculate an integrated edit image hash value, and to execute digitally signing to generate a zero-knowledge proof (ZKP) assurance; and, a user terminal device configured to receive the ZKP assurance to accordingly verify whether or not the edited integral image is generated by editing the source image.

Abstract: A method for analyzing an electrocardiography (ECG) signal is provided. The ECG signal is measured through a sensor. One or more specified waveforms in the ECG signal are detected. Multiple ECG features from each specified waveform are captured. The ECG features are input into multiple candidate models to obtain multiple candidate scores. Each candidate score is compared with a standard value to obtain an ideal score among the candidate scores. One of the candidate models corresponding to the ideal score is selected as a risk prediction model to predict disease risk through the risk prediction model.

Abstract: A setting method of in-memory computing simulator includes: performing a plurality of test combinations by an in-memory computing device and recording a plurality of first estimation indices corresponding to the plurality of test combinations respectively, wherein each of the plurality of test combinations includes one of a plurality of neural network models and one of a plurality of datasets, executing a simulator according to the plurality of test combinations by a processing device and recording a plurality of second estimation indices corresponding to the plurality of test combinations respectively, wherein the simulator has a plurality of adjustable settings; calculating a correlation sum according to the plurality of first estimation indices and the plurality of second estimation indices by the processing device, and performing an optimal algorithm to search an optimal parameter in the setting space constructed by the plurality of settings so that the correlation sum is maximal.