Adversarial examples (AEs), which cause misclassification by adding subtle perturbations to input images, have been proposed as an attack method on image-classification systems using deep neural networks (DNNs). Physical AEs created by attaching stickers to traffic signs have been reported, which are a threat to traffic-sign-recognition DNNs used in advanced driver assistance systems. We previously proposed an attack method for generating a noise area on images by superimposing an electrical signal on the mobile industry processor interface and showed that it can generate a single adversarial mark that triggers a backdoor attack on the input image. Therefore, we propose a misclassification attack method n DNNs by creating AEs that include small perturbations to multiple places on the image by the fault injection. The perturbation position for AEs is pre-calculated in advance against the target traffic-sign image, which will be captured on future driving. With 5.2% to 5.5% of a specific image on the simulation, the perturbation that induces misclassification to the target label was calculated. As the experimental results, we confirmed that the traffic-sign-recognition DNN on a Raspberry Pi was successfully misclassified when the target traffic sign was captured with. In addition, we created robust AEs that cause misclassification of images with varying positions and size by adding a common perturbation. We propose a method to reduce the amount of robust AEs perturbation. Our results demonstrated successful misclassification of the captured image with a high attack success rate even if the position and size of the captured image are slightly changed.

Recent years have seen a decline in the art of analog IC design even though analog interface and analog signal processing remain just as essential as ever. While there are many contributing factors, four specific pressures which contribute the most to the loss of creativity and innovation within analog practice are examined: process evolution, risk aversion, digitally assisted analog, and corporate culture. Despite the potency of these forces, none are found to be insurmountable obstacles to reinvigorating the industry. A more creative future is within our reach.

Human skin visualization in the beauty industry with a smart-phone based on deep learning was discussed. Skin was photographed with a medical camera that could simultaneously capture RGB and UV images of the same area. Smartphone RGB images were converted into versions similar to medical RGB and UV images via a deep learning method called cycle-GAN, which was trained with the medical and the smartphone images. After converting the smartphone image into a version similar to a medical RGB image using cycle-GAN, the processed image was also converted into a pseudo-UV image via a deep learning method called U-NET. Hidden age spots were effectively visualized by this image. RGB and UV images similar to medical images can be captured with a smartphone. Provided the neural network on deep learning is trained, a medical camera is not required.

We study the evolution and the dominance of the service based functions and their distinguishing features. As the service industry matures, its functions bear many similarities with the software development processes, such as intense man-machine interaction, knowledge intensive activities, flexibility in the organization, control and execution of tasks. In this paper we discuss wide range of interconnected topics, emphasizing the multi-faceted nature of service functions. These include the evolution of service industry and their products, the consumerization of high tech products based on their large-scale adoption and the consequent creation of implicit requirements; the technology transfer processes; the error proneness due to intense and prolonged interaction with computers and some methods of mitigating error incidence. We argue that by proper 'humanization and personalization' of interactive systems and by the use of teams of computer supported professionals, we can prevent such errors. We discuss some useful team types, models of their behavior and their control aspects. As the cost of communications shrinks like due to the Internet, we conclude that a fully decentralized system control provides a flat, flexible, and fair and friction-free organization for large team based service systems.