Biblio

Cyber-physical systems (CPS) can benefit by the use of learning enabled components (LECs) such as deep neural networks (DNNs) for perception and decision making tasks. However, DNNs are typically non-transparent making reasoning about their predictions very difficult, and hence their application to safety-critical systems is very challenging. LECs could be integrated easier into CPS if their predictions could be complemented with a confidence measure that quantifies how much we trust their output. The paper presents an approach for computing confidence bounds based on Inductive Conformal Prediction (ICP). We train a Triplet Network architecture to learn representations of the input data that can be used to estimate the similarity between test examples and examples in the training data set. Then, these representations are used to estimate the confidence of set predictions from a classifier that is based on the neural network architecture used in the triplet. The approach is evaluated using a robotic navigation benchmark and the results show that we can computed trusted confidence bounds efficiently in real-time.

Learning-based approaches often outperform hand-coded algorithmic solutions for many problems in robotics. However, learning long-horizon tasks on real robot hardware can be intractable, and transferring a learned policy from simulation to reality is still extremely challenging. We present a novel approach to model-free reinforcement learning that can leverage existing sub-optimal solutions as an algorithmic prior during training and deployment. During training, our gated fusion approach enables the prior to guide the initial stages of exploration, increasing sample-efficiency and enabling learning from sparse long-horizon reward signals. Importantly, the policy can learn to improve beyond the performance of the sub-optimal prior since the prior's influence is annealed gradually. During deployment, the policy's uncertainty provides a reliable strategy for transferring a simulation-trained policy to the real world by falling back to the prior controller in uncertain states. We show the efficacy of our Multiplicative Controller Fusion approach on the task of robot navigation and demonstrate safe transfer from simulation to the real world without any fine-tuning. The code for this project is made publicly available at https://sites.google.com/view/mcf-nav/home.

As any other infrastructure relying on cyber-physical systems (CPS), naval CPS are highly interconnected and collect considerable data streams, on which depend multiple command and navigation decisions. Being a data-driven decision system requiring optimized supervisory control on a permanent basis, it is critical to examine the CPS vulnerability to anomalies and their propagation. This paper presents an approach to detect CPS anomalies and estimate their propagation applying a quality assessed graph, which represents the CPS physical and digital subsystems, combined with system variables dependencies and a set of data and information quality measures vectors. Following the identification of variables dependencies and high-risk nodes in the CPS, data and information quality measures reveal how system variables are modified when an anomaly is detected, also indicating its propagation path. Taking as reference the normal state of a naval propulsion management system, four anomalies in the form of cyber-attacks - port scan, programmable logical controller stop, and man in the middle to change the motor speed and operation of a tank valve - were produced. Three anomalies were properly detected and their propagation path identified. These results suggest the feasibility of anomaly detection and estimation of propagation estimation in CPS, applying data and information quality analysis to a system graph.

To bring a uniform development platform which seamlessly combines hardware components and software architecture of various developers across the globe and reduce the complexity in producing robots which help people in their daily ergonomics. ROS has come out to be a game changer. It is disappointing to see the lack of penetration of technology in different verticals which involve protection, defense and security. By leveraging the power of ROS in the field of robotic automation and computer vision, this research will pave path for identification of suspicious activity with autonomously moving bots which run on ROS. The research paper proposes and validates a flow where ROS and computer vision algorithms like YOLO can fall in sync with each other to provide smarter and accurate methods for indoor and limited outdoor patrolling. Identification of age,`gender, weapons and other elements which can disturb public harmony will be an integral part of the research and development process. The simulation and testing reflects the efficiency and speed of the designed software architecture.

Robots operating alongside humans in field environments have the potential to greatly increase the situational awareness of their human teammates. A significant challenge, however, is the efficient conveyance of what the robot perceives to the human in order to achieve improved situational awareness. We believe augmented reality (AR), which allows a human to simultaneously perceive the real world and digital information situated virtually in the real world, has the potential to address this issue. Motivated by the emerging prevalence of practical human-wearable AR devices, we present a system that enables a robot to perform cooperative search with a human teammate, where the robot can both share search results and assist the human teammate in navigation to the search target. We demonstrate this ability in a search task in an uninstrumented environment where the robot identifies and localizes targets and provides navigation direction via AR to bring the human to the correct target.

With the development of new technologies in the world, governments have tendency to make a communications with people and business with the help of such technologies. Electronic government (e-government) is defined as utilizing information technologies such as electronic networks, Internet and mobile phones by organizations and state institutions in order to making wide communication between citizens, business and different state institutions. Development of e-government starts with making website in order to share information with users and is considered as the main infrastructure for further development. Website assessment is considered as a way for improving service quality. Different international researches have introduced various indexes for website assessment, they only see some dimensions of website in their research. In this paper, the most important indexes for website quality assessment based on accurate review of previous studies are "Web design", "navigation", services", "maintenance and Support", "Citizens Participation", "Information Quality", "Privacy and Security", "Responsiveness", "Usability". Considering mentioned indexes in designing the website facilitates user interaction with the e-government websites.

Blockchain is seen as a promising technology to provide reliable and secure services due to its decentralized characteristic. However, because of the limited throughput, current blockchain platforms can not meet the transaction demand in practical use. Though researchers proposed many new solutions, they suffered either decentralization or security issues. In this paper, using Directed Acyclic Graph (DAG) structure, we improve the linear structure of traditional blockchain protocol. In the new structure, blocks are organized in levels and width, which will generate into a compacted DAG structure (CoDAG). To make CoDAG more efficient and secure, we design algorithms and protocols to place the new-generated blocks appropriately. Compared with traditional blockchain protocols, CoDAG improves the security and transaction verification time, and enjoys the consistency and liveness properties of blockchain. Taking adversary parties into consideration, two possible attack strategies are presented in this paper, and we further prove that CoDAG is a secure and robust protocol to resist them. The experimental results show that CoDAG can achieve 394 transactions per second, which is 56 times of Bitcoin's throughput and 26 times of Ethereum's.

Multi-Agent Task Allocation is a pre-requisite for many autonomous, real-world systems because of the need for intelligent task assignment amongst a team for maximum efficiency. Similarly, agent failure, task, failure, and a lack of state information are inherent challenges when operating in complex environments. Many existing solutions make simplifying assumptions regarding the modeling of these factors, e.g., Markovian state information. However, it is not clear that this is always the appropriate approach or that results from these approaches are necessarily representative of performance in the natural world. In this work, we demonstrate that there exists a class of problems for which non-Markovian state modeling is beneficial. Furthermore, we present and characterize a novel heuristic for task allocation that incorporates realistic state and uncertainty modeling in order to improve performance. Our quantitative analysis, when tested in a simulated search and rescue (SAR) mission, shows a decrease in performance of more than 57% when a representative method with Markovian assumptions is tested in a non-Markovian setting. Our novel heuristic has shown an improvement in performance of 3-15%, in the same non-Markovian setting, by modeling probabilistic failure and making fewer assumptions.

Robots operating alongside humans in field environments have the potential to greatly increase the situational awareness of their human teammates. A significant challenge, however, is the efficient conveyance of what the robot perceives to the human in order to achieve improved situational awareness. We believe augmented reality (AR), which allows a human to simultaneously perceive the real world and digital information situated virtually in the real world, has the potential to address this issue. We propose to demonstrate that augmented reality can be used to enable human-robot cooperative search, where the robot can both share search results and assist the human teammate in navigating to a search target.

Fully automated vehicles will require new functionalities for perception, navigation and decision making -- an Autonomous Driving Intelligence (ADI). We consider architectural cases for such functionalities and investigate how they integrate with legacy platforms. The cases range from a robot replacing the driver -- with entire reuse of existing vehicle platforms, to a clean-slate design. Focusing on Heavy Commercial Vehicles (HCVs), we assess these cases from the perspectives of business, safety, dependability, verification, and realization. The original contributions of this paper are the classification of the architectural cases themselves and the analysis that follows. The analysis reveals that although full reuse of vehicle platforms is appealing, it will require explicitly dealing with the accidental complexity of the legacy platforms, including adding corresponding diagnostics and error handling to the ADI. The current fail-safe design of the platform will also tend to limit availability. Allowing changes to the platforms, will enable more optimized designs and fault-operational behaviour, but will require initial higher development cost and specific emphasis on partitioning and control to limit the influences of safety requirements. For all cases, the design and verification of the ADI will pose a grand challenge and relate to the evolution of the regulatory framework including safety standards.

Interactive visualization provides valuable support for exploring, analyzing, and understanding textual documents. Certain tasks, however, require that insights derived from visual abstractions are verified by a human expert perusing the source text. So far, this problem is typically solved by offering overview-detail techniques, which present different views with different levels of abstractions. This often leads to problems with visual continuity. Focus-context techniques, on the other hand, succeed in accentuating interesting subsections of large text documents but are normally not suited for integrating visual abstractions. With VarifocalReader we present a technique that helps to solve some of these approaches' problems by combining characteristics from both. In particular, our method simplifies working with large and potentially complex text documents by simultaneously offering abstract representations of varying detail, based on the inherent structure of the document, and access to the text itself. In addition, VarifocalReader supports intra-document exploration through advanced navigation concepts and facilitates visual analysis tasks. The approach enables users to apply machine learning techniques and search mechanisms as well as to assess and adapt these techniques. This helps to extract entities, concepts and other artifacts from texts. In combination with the automatic generation of intermediate text levels through topic segmentation for thematic orientation, users can test hypotheses or develop interesting new research questions. To illustrate the advantages of our approach, we provide usage examples from literature studies.

This paper presents a novel visual analytics technique developed to support exploratory search tasks for event data document collections. The technique supports discovery and exploration by clustering results and overlaying cluster summaries onto coordinated timeline and map views. Users can also explore and interact with search results by selecting clusters to filter and re-cluster the data with animation used to smooth the transition between views. The technique demonstrates a number of advantages over alternative methods for displaying and exploring geo-referenced search results and spatio-temporal data. Firstly, cluster summaries can be presented in a manner that makes them easy to read and scan. Listing representative events from each cluster also helps the process of discovery by preserving the diversity of results. Also, clicking on visual representations of geo-temporal clusters provides a quick and intuitive way to navigate across space and time simultaneously. This removes the need to overload users with the display of too many event labels at any one time. The technique was evaluated with a group of nineteen users and compared with an equivalent text based exploratory search engine.