Biblio

The objective of this study is to explore changes of trust by a situation where drivers need to intervene. Trust in automation is a key determinant for appropriate interaction between drivers and the system. System transparency and types of system failure influence shaping trust in a supervisory control. Subjective ratings of trust were collected to examine the impact of two factors: system transparency (Detailed vs. Less) and system failure (by Limits vs. Malfunction) in a driving simulator study in which drivers experienced a partially automated vehicle. We examined trust ratings at three points: before and after driver intervention in the automated vehicle, and after subsequent experience of flawless automated driving. Our result found that system transparency did not have significant impacts on trust change from before to after the intervention. System-malfunction led trust reduction compared to those of before the intervention, whilst system-limits did not influence trust. The subsequent experience recovered decreased trust, in addition, when the system-limit occurred to drivers who have detailed information about the system, trust prompted in spite of the intervention. The present finding has implications for automation design to achieve the appropriate level of trust.

Emerging trends that are shaping the future of the automotive industry include electrification, autonomous driving, sharing, and connectivity, and these trends keep changing annually. Thus, the automotive industry is shifting from mechanical devices to electronic control devices, and is not moving to Internet of Things devices connected to 5G networks. Owing to the convergence of automobile-information and communication technology (ICT), the safety and convenience features of automobiles have improved significantly. However, cyberattacks that occur in the existing ICT environment and can occur in the upcoming 5G network are being replicated in the automobile environment. In a hyper-connected society where 5G networks are commercially available, automotive security is extremely important, as vehicles become the center of vehicle to everything (V2X) communication connected to everything around them. Designing, developing, and deploying information security techniques for vehicles require a systematic security-risk-assessment and management process throughout the vehicle's lifecycle. To do this, a security risk analysis (SRA) must be performed, which requires an analysis of cyber threats on automotive vehicles. In this study, we introduce a cyber kill chain-based cyberattack analysis method to create a formal vulnerability-analysis system. We can also analyze car-hacking studies that were conducted on real cars to identify the characteristics of the attack stages of existing car-hacking techniques and propose the minimum but essential measures for defense. Finally, we propose an automotive common-vulnerabilities-and-exposure system to manage and share evolving vehicle-related cyberattacks, threats, and vulnerabilities.

RISC-V is free and open standard instruction set architecture following reduced instruction set computer principle. Because of its openness and scalability, RISC-V has been adapted not only for embedded CPUs such as mobile and IoT market, but also for heavy-workload CPUs such as the data center or super computing field. On top of it, Robotics is also a good application of RISC-V because security and reliability become crucial issues of robotics system. These problems could be solved by enthusiastic open source community members as they have shown on open source operating system. However, running RISC-V on local FPGA becomes harder than before because now RISC-V foundation are focusing on cloud-based FPGA environment. We have experienced that recently released OS and toolchains for RISC-V are not working well on the previous CPU image for local FPGA. In this paper we design the local FPGA platform for RISC-V processor and run the robotics application on mainstream Robot Operating System on top of the RISC-V processor. This platform allow us to explore the architecture space of RISC-V CPU for robotics application, and get the insight of the RISC-V CPU architecture for optimal performance and the secure system.

Web technology has evolved to offer 360-degree immersive browsing experiences. This new technology, called WebVR, enables virtual reality by rendering a three-dimensional world on an HTML canvas. Unfortunately, there exists no browser-supported way of sharing this canvas between different parties. As a result, third-party library providers with ill intent (e.g., stealing sensitive information from end-users) can easily distort the entire WebVR site. To mitigate the new threats posed in WebVR, we propose CanvasMirror, which allows publishers to specify the behaviors of third-party libraries and enforce this specification. We show that CanvasMirror effectively separates the third-party context from the host origin by leveraging the privilege separation technique and safely integrates VR contents on a shared canvas.

Recently, as the age of the Internet of Things is approaching, there are more and more devices that communicate data with each other by incorporating sensors and communication functions in various objects. If the IoT is miniaturized, it can be regarded as a sensor having only the sensing ability and the low performance communication ability. Low-performance sensors are difficult to use high-quality communication, and wireless security used in expensive wireless communication devices cannot be applied. Therefore, this paper proposes authentication and key Agreement that can be applied in sensor networks using communication with speed less than 1 Kbps and has limited performances.

In military operations, Commander's Intent describes the desired end state and purpose of the operation, expressed in a concise and clear manner. Command by intent is a paradigm that empowers subordinate units to exercise measured initiative to meet mission goals and accept prudent risk within commander's intent. It improves agility of military operations by allowing exploitation of local opportunities without an explicit directive from the commander to do so. This paper discusses what the paradigm entails in terms of architectural decisions for data fusion systems tasked with real-time information collection to satisfy operational mission goals. In our system, information needs of decisions are expressed at a high level, and shared among relevant nodes. The selected nodes, then, jointly operate to meet mission information needs by forwarding and caching relevant data without explicit directives regarding the objects to fetch and sources to contact. A preliminary evaluation of the system is presented using a target tracking application, set in the context of a NATO-based mission scenario, called Anglova. Evaluation results show that delegating some decision authority to the data fusion system (in terms of objects to fetch and sources to contact) allows it to save more network resources, while also increasing mission success rate. The system is therefore particularly well-suited to operation in partially denied or contested environments, where resource bottlenecks caused by adversarial activity impair one's ability to collect real-time information for mission-critical decision making.

In this paper, the well-known problem of codependence between inverse dynamics torque and contact force in bimanual object manipulation is addressed. The common contact constraint, namely rigid grasping, is exploited to decompose the set of dynamics equations into two orthogonally decoupled sets. Subsequently, the inverse dynamics control is formulated in a sub-manifold that is independent of the contact force, leading to analytically correct solutions that do not need to resort to common approximations for the aforementioned codependence problem. The contact force is also analytically computed and, therefore, can be optimally distributed using the torque redundancy. Relying on this prediction is most significant in situations where a force sensor at the end-effector is not present or is faulty. Even in the availability of sensory data, the predicted force may be used to correct typically noisy or delayed when filtered measurements, resulting in improved robustness. Simulation experiments on a planar bimanual manipulation model are presented.

Stochastic Computing (SC) is an alternative design paradigm particularly useful for applications where cost is critical. SC has been applied to neural networks, as neural networks are known for their high computational complexity. However previous work in this area has critical limitations such as the fully-parallel architecture assumption, which prevent them from being applicable to recent ones such as convolutional neural networks, or ConvNets. This paper presents the first SC architecture for ConvNets, shows its feasibility, with detailed analyses of implementation overheads. Our SC-ConvNet is a hybrid between SC and conventional binary design, which is a marked difference from earlier SC-based neural networks. Though this might seem like a compromise, it is a novel feature driven by the need to support modern ConvNets at scale, which commonly have many, large layers. Our proposed architecture also features hybrid layer composition, which helps achieve very high recognition accuracy. Our detailed evaluation results involving functional simulation and RTL synthesis suggest that SC-ConvNets are indeed competitive with conventional binary designs, even without considering inherent error resilience of SC.

Cloud Computing represents one of the most significant shifts in information technology and it enables to provide cloud-based security service such as Security-as-a-service (SECaaS). Improving of the cloud computing technologies, the traditional SIEM paradigm is able to shift to cloud-based security services. In this paper, we propose the SIEM architecture that can be deployed to the SECaaS platform which we have been developing for analyzing and recognizing intelligent cyber-threat based on virtualization technologies.