Abstract Self-driving cars over the last decade have achieved significant progress like driving millions of miles without any human intervention. However, behavioral safety in applying deep-neural-network-based (DNN based) systems for self-driving cars could not be guaranteed. Several real-world accidents involving self-driving cars have already happened, some of which have led to fatal collisions. In this paper, we present a novel and automated technique for verifying steering angle safety for self-driving cars. The technique is based on deep learning verification (DLV), which is an automated verification framework for safety of image classification neural networks. We extend DLV by leveraging neuron coverage and slack relationship to solve the judgement problem of predicted behaviors, and thus, to achieve verification of steering angle safety for self-driving cars. We evaluate our technique on the NVIDIA’s end-to-end self-driving architecture, which is a crucial ingredient in many modern self-driving cars. Experimental results show that our technique can successfully find adversarial misclassifications (i.e., incorrect steering decisions) within given regions if they exist. Therefore, we can achieve safety verification (if no misclassification is found for all DNN layers, in which case the network can be said to be stable or reliable w.r.t. steering decisions) or falsification (in which case the adversarial examples can be used to fine-tune the network).

With the increasing popularity of smartphones, the mobile security issues have become serious, and more and more malware has been found. Android applications are often used to handle sensitive information, thus they have become the main targets of malware attacks. In order to efficiently detect Android malware, in this paper, we present a multi-strategy combination framework. We use five types of static features to characterize Android applications from multiple aspects. To improve the classification accuracy and reduce the overfitting of the framework, we use three filter-based feature selection methods to identify the most informative top-k features. Then we input the applications represented by the feature subsets into five classification algorithms to build classifiers. Finally, we predict the classification results by hard voting or soft voting. We have performed many experiments in a well-marked dataset consisting of 41,155 samples. The experimental results show that our approach can achieve over 98% in accuracy, precision, recall and F-score. Compared with other existing methods, our approach has the best malware detection rate of 98.75%.

Recently, there are an increasing number of developing wearable devices that intent to display various contents. As can be seen in sign languages and hand signals, hand gestures can express rich information with small motions, therefore, they are useful input for those devices. Especially, these gestures are less obtrusive actions than other actions such as speaking. It means that hand-gesture-based input interfaces are very suitable for public use, where a user can't use desktop devices such as a mouse or a keyboard. Although there are many studies on recognition of hand gestures, existing hand shape recognition methods have several problems to be applied for home automation. A wearable wrist contour sensor device has been proposed in previous studies. However discussions about how to design the protocols of hand gesture interface are insufficient. In this paper, we develop real time hand gesture interfaces of content browse for wearable or remote displays. Usability test using the developed interfaces reveals that pronation is to be assigned as a variable configurator and hand shape is to be assigned as an operation switcher.

In spite of IT innovation, people cannot get rid of non-creative tasks of searching daily-use objects at home. This paper presents a drawer-type storage system for supporting object search, “TansuBot”. By using this system and a smart device (e.g. smart phone), a user can review the photos of contents stored in the system. In addition, the system can present candidate drawers where the searching target object may be stored based on preliminary information such as usage histories. Concretely, LED blinking and pop-up actions (pushing drawers forward) are used for display. To realize these supports, a stacker crane type wall-moving robot is equipped at the backside of storage. The robot has a movable camera and mechanisms to push a drawer forward. For easy installation to a home, storage efficiency and cost reduction should be considered in the design of the instrument. Especially for cost reduction, this paper presents an approach to use wooden parts for main mechanisms. This approach also contributes to user-friendly presence and appearance of the instrument. This paper reports about the development of a prototype and an experiment to evaluate the functions for supporting object search. The results of the experiment prove the importance of the functions realized by the system; displaying contents' photos on a smart device and showing candidate drawers to investigate. The outcomes indicate that those functions have positive effects on reduction of searching time and mental burden.

Hierarchical State Transition Matrix (HSTM) is a table-based modeling language for developing designs of software systems. Although widely used and adopted by (particularly Japanese) software industry, there is still lack of mechanized formal verification supports for conducting rigorous and automatic analysis to improve reliability of HSTM designs. In this paper, we first present a formalization of HSTM designs as state transition systems. Consequentially, based on this formalization, we propose a symbolic encoding approach, through which correctness of a HSTM design with respect to LTL properties could be represented as Bounded Model Checking (BMC) problems that could be determined by Satisfiability Modulo Theories (SMT) solving. We have implemented our encoding approach in a tool called Garakabu2 with the state-of-the-art SMT solver CVC3 as its back-ended solver. Furthermore, in our preliminary experiments, a conceptually simple but steadily effective way of accelerating SMT solving for HSTM designs is investigated and reported.

In this paper, we describe a novel sensor device which recognizes hand shapes using wrist contours. Although hand shapes can express various meanings with small gestures, utilization of hand shapes as an interface is rare in domestic use. That is because a concise recognition method has not been established. To recognize hand shapes anywhere with no stress on the user, we developed a wearable wrist contour sensor device and a recognition system. In the system, features, such as sum of gaps, were extracted from wrist contours. We conducted a classification test of eight hand shapes, and realized approximately 70% classification rate.