Cooperative vehicle platooning significantly improves highway safety and fuel efficiency. In this model, a set of vehicles move in line formation and coordinate functions such as acceleration, braking, and steering using a combination of physical sensing and vehicle-to-vehicle (V2V) messaging. The authenticity and integrity of the V2V messages are paramount to highway safety. For this reason, recent V2V and V2X standards support the integration of a PKI. However, a PKI cannot bind a vehicle's digital identity to the vehicle's physical state (location, heading, velocity, etc.). As a result, a vehicle with valid cryptographic credentials can impact the platoon by creating "ghost" vehicles and injecting false state information. In this paper, we seek to provide the missing link between the physical and the digital world in the context of verifying a vehicle's platoon membership. We focus on the property of following, where vehicles follow each other in a close and coordinated manner. We aim at developing a Proof-of-Following (PoF) protocol that enables a candidate vehicle to prove that it follows a verifier within the typical platooning distance. The main idea of the proposed PoF protocol is to draw security from the common, but constantly changing environment experienced by the closely traveling vehicles. We use the large-scale fading effect of ambient RF signals as a common source of randomness to construct a PoF primitive. The correlation of large-scale fading is an ideal candidate for the mobile outdoor environment because it exponentially decays with distance and time. We evaluate our PoF protocol on an experimental platoon of two vehicles in freeway, highway, and urban driving conditions. In such realistic conditions, we demonstrate that the PoF withstands both the pre-recording and following attacks with overwhelming probability.
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Integration：Integration, the VLSI Journal。
Explanation：集成，VLSI雜志。
Publisher：Elsevier。
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The future of innovation processes is anticipated to be more data-driven and empowered by the ubiquitous digitalization, increasing data accessibility and rapid advances in machine learning, artificial intelligence, and computing technologies. While the data-driven innovation (DDI) paradigm is emerging, it has yet been formally defined and theorized and often confused with several other data-related phenomena. This paper defines and crystalizes "data-driven innovation" as a formal innovation process paradigm, dissects its value creation, and distinguishes it from data-driven optimization (DDO), data-based innovation (DBI), and the traditional innovation processes that purely rely on human intelligence. With real-world examples and theoretical framing, I elucidate what DDI entails and how it addresses uncertainty and enhance creativity in the innovation process and present a process-based taxonomy of different data-driven innovation approaches. On this basis, I recommend the strategies and actions for innovators, companies, R&D organizations, and governments to enact data-driven innovation.
A real-time motion training system for skydiving is proposed. Aerial maneuvers are performed by changing the body posture and thus deflecting the surrounding airflow. The natural learning process is extremely slow due to unfamiliar free-fall dynamics, stress induced blocking of kinesthetic feedback, and complexity of the required movements. The key idea is to augment the learner with an automatic control system that would be able to perform the trained activity if it had direct access to the learner's body as an actuator. The aiding system will supply the following visual cues to the learner: 1. Feedback of the current body posture; 2. The body posture that would bring the body to perform the desired maneuver; 3. Prediction of the future inertial position and orientation if the body retains its present posture. The system will enable novices to maintain stability in free-fall and perceive the unfamiliar environmental dynamics, thus accelerating the initial stages of skill acquisition. This paper presents results of a Proof-of-Concept experiment, whereby humans controlled a virtual skydiver free-falling in a computer simulation, by the means of their bodies. This task was impossible without the aiding system, enabling all participants to complete the task at the first attempt.
Bitcoin is the most secure blockchain in the world, supported by the immense hash power of its Proof-of-Work miners, but consumes huge amount of energy. Proof-of-Stake chains are energy-efficient, have fast finality and accountability, but face several fundamental security issues: susceptibility to non-slashable long-range safety attacks, non-slashable transaction censorship and stalling attacks and difficulty to bootstrap new PoS chains from low token valuation. We propose Babylon, a blockchain platform which combines the best of both worlds by reusing the immense Bitcoin hash power to enhance the security of PoS chains. Babylon provides a data-available timestamping service, securing PoS chains by allowing them to timestamp data-available block checkpoints, fraud proofs and censored transactions on Babylon. Babylon miners merge mine with Bitcoin and thus the platform has zero additional energy cost. The security of a Babylon-enhanced PoS protocol is formalized by a cryptoeconomic security theorem which shows slashable safety and liveness guarantees.
An informative measurement is the most efficient way to gain information about an unknown state. We give a first-principles derivation of a general-purpose dynamic programming algorithm that returns an optimal sequence of informative measurements by sequentially maximizing the entropy of possible measurement outcomes. This algorithm can be used by an autonomous agent or robot to decide where best to measure next, planning a path corresponding to an optimal sequence of informative measurements. The algorithm is applicable to states and controls that are continuous or discrete, and agent dynamics that is either stochastic or deterministic; including Markov decision processes and Gaussian processes. Recent results from approximate dynamic programming and reinforcement learning, including on-line approximations such as rollout and Monte Carlo tree search, allow the measurement task to be solved in real-time. The resulting solutions include non-myopic paths and measurement sequences that can generally outperform, sometimes substantially, commonly used greedy approaches. This is demonstrated for a global search problem, where on-line planning with an extended local search is found to reduce the number of measurements in the search by approximately half. A variant of the algorithm is derived for Gaussian processes for active sensing.
The complexity of cyberattacks in Cyber-Physical Systems (CPSs) calls for a mechanism that can evaluate critical infrastructures' operational behaviour and security without affecting the operation of live systems. In this regard, Digital Twins (DTs) provide actionable insights through monitoring, simulating, predicting, and optimizing the state of CPSs. Through the use cases, including system testing and training, detecting system misconfigurations, and security testing, DTs strengthen the security of CPSs throughout the product lifecycle. However, such benefits of DTs depend on an assumption about data integrity and security. Data trustworthiness becomes more critical while integrating multiple components among different DTs owned by various stakeholders to provide an aggregated view of the complex physical system. This article envisions a blockchain-based DT framework as Trusted Twins for Securing Cyber-Physical Systems (TTS-CPS). With the automotive industry as a CPS use case, we demonstrate the viability of the TTS-CPS framework in a proof of concept. To utilize reliable system specification data for building the process knowledge of DTs, we ensure the trustworthiness of data-generating sources through integrity checking mechanisms. Additionally, the safety and security rules evaluated during simulation are stored and retrieved from the blockchain, thereby establishing more understanding and confidence in the decisions made by the underlying systems. Finally, we perform formal verification of the TTS-CPS.
The core issue of cyberspace detecting and mapping is to accurately identify and dynamically track devices. However, with the development of anonymization technology, devices can have multiple IP addresses and MAC addresses, and it is difficult to map multiple virtual attributes to the same physical device by existing detecting and mapping technologies. In this paper, we propose a detailed PUF-based detecting and mapping framework which can actively detect physical resources in cyberspace, construct resource portraits based on physical fingerprints, and dynamically track devices. We present a new method to implement a rowhammer DRAM PUF on a general PC equipped with DDR4 memory. The PUF performance evaluation shows that the extracted rowhammer PUF response is unique and reliable on PC, which can be treated as the device's unique physical fingerprint. The results of detecting and mapping experiments show that the framework we proposed can accurately identify the target devices. Even if the device modifies its MAC address, IP address, and operating system, by constructing a physical fingerprint database for device matching, the identification accuracy is close to the ideal value of 100%.
Group testing can help maintain a widespread testing program using fewer resources amid a pandemic. In group testing, we are given $n$ samples, one per individual. These samples are arranged into $m < n$ pooled samples, where each pool is obtained by mixing a subset of the $n$ individual samples. Infected individuals are then identified using a group testing algorithm. In this paper, we use side information (SI) collected from contact tracing (CT) within nonadaptive/single-stage group testing algorithms. We generate CT SI data by incorporating characteristics of disease spread between individuals. These data are fed into two signal and measurement models for group testing, and numerical results show that our algorithms provide improved sensitivity and specificity. We also show how to incorporate CT SI into the design of the pooling matrix. That said, our numerical results suggest that the utilization of SI in the pooling matrix design based on the minimization of a weighted coherence measure does not yield significant performance gains beyond the incorporation of SI in the group testing algorithm.
Over the last several years, the field of natural language processing has been propelled forward by an explosion in the use of deep learning models. This survey provides a brief introduction to the field and a quick overview of deep learning architectures and methods. It then sifts through the plethora of recent studies and summarizes a large assortment of relevant contributions. Analyzed research areas include several core linguistic processing issues in addition to a number of applications of computational linguistics. A discussion of the current state of the art is then provided along with recommendations for future research in the field.
3D vehicle detection and tracking from a monocular camera requires detecting and associating vehicles, and estimating their locations and extents together. It is challenging because vehicles are in constant motion and it is practically impossible to recover the 3D positions from a single image. In this paper, we propose a novel framework that jointly detects and tracks 3D vehicle bounding boxes. Our approach leverages 3D pose estimation to learn 2D patch association overtime and uses temporal information from tracking to obtain stable 3D estimation. Our method also leverages 3D box depth ordering and motion to link together the tracks of occluded objects. We train our system on realistic 3D virtual environments, collecting a new diverse, large-scale and densely annotated dataset with accurate 3D trajectory annotations. Our experiments demonstrate that our method benefits from inferring 3D for both data association and tracking robustness, leveraging our dynamic 3D tracking dataset.
We present an end-to-end framework for solving the Vehicle Routing Problem (VRP) using reinforcement learning. In this approach, we train a single model that finds near-optimal solutions for problem instances sampled from a given distribution, only by observing the reward signals and following feasibility rules. Our model represents a parameterized stochastic policy, and by applying a policy gradient algorithm to optimize its parameters, the trained model produces the solution as a sequence of consecutive actions in real time, without the need to re-train for every new problem instance. On capacitated VRP, our approach outperforms classical heuristics and Google's OR-Tools on medium-sized instances in solution quality with comparable computation time (after training). We demonstrate how our approach can handle problems with split delivery and explore the effect of such deliveries on the solution quality. Our proposed framework can be applied to other variants of the VRP such as the stochastic VRP, and has the potential to be applied more generally to combinatorial optimization problems.
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