Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Monitoring and Failure Recovery of Cloud-Managed Digital Signage

Digitaal signage kasutatakse laialdaselt erinevates valdkondades, nagu näiteks transpordisüsteemid, turustusvõimalused, meelelahutus ja teised, et kuvada teavet piltide, videote ja teksti kujul. Nende ressursside usaldusväärsus, vajalike teenuste kättesaadavus ja turvameetmed on selliste süsteemide vastuvõtmisel võtmeroll. Digitaalse märgistussüsteemi tõhus haldamine on teenusepakkujatele keeruline ülesanne. Selle süsteemi rikkeid võib põhjustada mitmeid põhjuseid, nagu näiteks vigased kuvarid, võrgu-, riist- või tarkvaraprobleemid, mis on üsna korduvad. Traditsiooniline protsess sellistest ebaõnnestumistest taastumisel hõlmab sageli tüütuid ja tülikaid diagnoose. Paljudel juhtudel peavad tehnikud kohale füüsiliselt külastama, suurendades seeläbi hoolduskulusid ja taastumisaega.Selles väites pakume lahendust, mis jälgib, diagnoosib ja taandub tuntud tõrgetest, ühendades kuvarid pilvega. Pilvepõhine kaug- ja autonoomne server konfigureerib kaugseadete sisu ja uuendab neid dünaamiliselt. Iga kuva jälgib jooksvat protsessi ja saadab trace’i, logib süstemisse perioodiliselt. Negatiivide puhul analüüsitakse neid serverisse salvestatud logisid, mis optimaalselt kasutavad kohandatud logijuhtimismoodulit. Lisaks näitavad ekraanid ebaõnnestumistega toimetulemiseks enesetäitmise protseduure, kui nad ei suuda pilvega ühendust luua. Kavandatud lahendus viiakse läbi Linuxi süsteemis ja seda hinnatakse serveri kasutuselevõtuga Amazon Web Service (AWS) pilves. Peamisteks tulemusteks on meetodite kogum, mis võimaldavad kaugjuhtimisega kuvariprobleemide lahendamist.Digital signage is widely used in various fields such as transport systems, trading outlets, entertainment, and others, to display information in the form of images, videos, and text. The reliability of these resources, availability of required services and security measures play a key role in the adoption of such systems. Efficient management of the digital signage system is a challenging task to the service providers. There could be many reasons that lead to the malfunctioning of this system such as faulty displays, network, hardware or software failures that are quite repetitive. The traditional process of recovering from such failures often involves tedious and cumbersome diagnosis. In many cases, technicians need to physically visit the site, thereby increasing the maintenance costs and the recovery time. In this thesis, we propose a solution that monitors, diagnoses and recovers from known failures by connecting the displays to a cloud. A cloud-based remote and autonomous server configures the content of remote displays and updates them dynamically. Each display tracks the running process and sends the trace and system logs to the server periodically. These logs, stored at the server optimally using a customized log management module, are analysed for failures. In addition, the displays incorporate self-recovery procedures to deal with failures, when they are unable to create connection to the cloud. The proposed solution is implemented on a Linux system and evaluated by deploying the server on the Amazon Web Service (AWS) cloud. The main result of the thesis is a collection of techniques for resolving the display system failures remotely

Cyber-physikalische Systeme:Herausforderung des 21. Jahrhunderts

Cyber-physical systems and the Internet of Things will be omnipresent in the near future. These systems will be tightly integrated in and interacting with our environment to support us in our daily tasks and in achieving our personal goals. However, to achieve this vision, we have to tackle various challenges

06031 Abstracts Collection -- Organic Computing -- Controlled Emergence

Organic Computing has emerged recently as a challenging vision for future information processing systems, based on the insight that we will soon be surrounded by large collections of autonomous systems equipped with sensors and actuators to be aware of their environment, to communicate freely, and to organize themselves in order to perform the actions and services required. Organic Computing Systems will adapt dynamically to the current conditions of its environment, they will be self-organizing, self-configuring, self-healing, self-protecting, self-explaining, and context-aware. From 15.01.06 to 20.01.06, the Dagstuhl Seminar 06031 ``Organic Computing -- Controlled Emergence\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. The seminar was characterized by the very constructive search for common ground between engineering and natural sciences, between informatics on the one hand and biology, neuroscience, and chemistry on the other. The common denominator was the objective to build practically usable self-organizing and emergent systems or their components. An indicator for the practical orientation of the seminar was the large number of OC application systems, envisioned or already under implementation, such as the Internet, robotics, wireless sensor networks, traffic control, computer vision, organic systems on chip, an adaptive and self-organizing room with intelligent sensors or reconfigurable guiding systems for smart office buildings. The application orientation was also apparent by the large number of methods and tools presented during the seminar, which might be used as building blocks for OC systems, such as an evolutionary design methodology, OC architectures, especially several implementations of observer/controller structures, measures and measurement tools for emergence and complexity, assertion-based methods to control self-organization, wrappings, a software methodology to build reflective systems, and components for OC middleware. Organic Computing is clearly oriented towards applications but is augmented at the same time by more theoretical bio-inspired and nature-inspired work, such as chemical computing, theory of complex systems and non-linear dynamics, control mechanisms in insect swarms, homeostatic mechanisms in the brain, a quantitative approach to robustness, abstraction and instantiation as a central metaphor for understanding complex systems. Compared to its beginnings, Organic Computing is coming of age. The OC vision is increasingly padded with meaningful applications and usable tools, but the path towards full OC systems is still complex. There is progress in a more scientific understanding of emergent processes. In the future, we must understand more clearly how to open the configuration space of technical systems for on-line modification. Finally, we must make sure that the human user remains in full control while allowing the systems to optimize

A framework and methods for on-board network level fault diagnostics in automobiles

A significant number of electronic control units (ECUs) are nowadays networked in automotive vehicles to help achieve advanced vehicle control and eliminate bulky electrical wiring. This, however, inevitably leads to increased complexity in vehicle fault diagnostics. Traditional off-board fault diagnostics and repair at service centres, by using only diagnostic trouble codes logged by conventional onboard diagnostics, can become unwieldy especially when dealing with intermittent faults in complex networked electronic systems. This can result in inaccurate and time consuming diagnostics due to lack of real-time fault information of the interaction among ECUs in the network-wide perspective. This thesis proposes a new framework for on-board knowledge-based diagnostics focusing on network level faults, and presents an implementation of a real-time in-vehicle network diagnostic system, using case-based reasoning. A newly developed fault detection technique and the results from several practical experiments with the diagnostic system using a network simulation tool, a hardware- in-the- loop simulator, a disturbance simulator, simulated ECUs and real ECUs networked on a test rig are also presented. The results show that the new vehicle diagnostics scheme, based on the proposed new framework, can provide more real-time network level diagnostic data, and more detailed and self-explanatory diagnostic outcomes. This new system can provide increased diagnostic capability when compared with conventional diagnostic methods in terms of detecting message communication faults. In particular, the underlying incipient network problems that are ignored by the conventional on-board diagnostics are picked up for thorough fault diagnostics and prognostics which can be carried out by a whole-vehicle fault management system, contributing to the further development of intelligent and fault-tolerant vehicles

Proceedings of the First NASA Formal Methods Symposium

Topics covered include: Model Checking - My 27-Year Quest to Overcome the State Explosion Problem; Applying Formal Methods to NASA Projects: Transition from Research to Practice; TLA+: Whence, Wherefore, and Whither; Formal Methods Applications in Air Transportation; Theorem Proving in Intel Hardware Design; Building a Formal Model of a Human-Interactive System: Insights into the Integration of Formal Methods and Human Factors Engineering; Model Checking for Autonomic Systems Specified with ASSL; A Game-Theoretic Approach to Branching Time Abstract-Check-Refine Process; Software Model Checking Without Source Code; Generalized Abstract Symbolic Summaries; A Comparative Study of Randomized Constraint Solvers for Random-Symbolic Testing; Component-Oriented Behavior Extraction for Autonomic System Design; Automated Verification of Design Patterns with LePUS3; A Module Language for Typing by Contracts; From Goal-Oriented Requirements to Event-B Specifications; Introduction of Virtualization Technology to Multi-Process Model Checking; Comparing Techniques for Certified Static Analysis; Towards a Framework for Generating Tests to Satisfy Complex Code Coverage in Java Pathfinder; jFuzz: A Concolic Whitebox Fuzzer for Java; Machine-Checkable Timed CSP; Stochastic Formal Correctness of Numerical Algorithms; Deductive Verification of Cryptographic Software; Coloured Petri Net Refinement Specification and Correctness Proof with Coq; Modeling Guidelines for Code Generation in the Railway Signaling Context; Tactical Synthesis Of Efficient Global Search Algorithms; Towards Co-Engineering Communicating Autonomous Cyber-Physical Systems; and Formal Methods for Automated Diagnosis of Autosub 6000

RAMARL: Robustness Analysis with Multi-Agent Reinforcement Learning - Robust Reasoning in Autonomous Cyber-Physical Systems

A key driver to offering smart services is an infrastructure of Cyber-Physical systems (CPS)s. By definition, CPSs are intertwined physical and computational components that integrate physical behaviour with computation. The reason is to autonomously execute a task or a set of tasks providing a service or a list of end-users services. In real-life applications, CPSs operate in dynamically changing surroundings characterized by unexpected or unpredictable situations. Such operations involve complex interactions between multiple intelligent agents in a highly non-stationary environment. For safety reasons, a CPS should withstand a certain amount of disruption and exert the operations in a stable and robust manner when performing complex tasks. Recent advances in reinforcement learning have proven suitable for enabling multi-agents to robustly adapt to their environment, yet they often depend on a massive amount of training data and experiences. In these cases, robustness analysis outlines necessary components and specifications in a framework, ensuring reliable and stable behaviour while considering the dynamicity of the environment. This paper presents a combination of multi-agent reinforcement learning with robustness analysis shaping a cyber-physical system infrastructure that reasons robustly in a dynamically changing environment. The combination strengthens the reinforcement learning, increasing the reliability and flexibility of the system by applying robustness analysis. Robustness analysis identifies vulnerability issues when the system interacts within a dynamically changing environment. Based on this identification, when incorporated into the system, robustness analysis suggests robust solutions and actions rather than optimal ones provided by reinforcement learning alone. Results from the combination show that this infrastructure can enable reliable operations with the flexibility to adapt to the changing environment dynamics.publishedVersio