Adapting specifications for reactive controllers

For systems to respond to scenarios that were unforeseen at design time, they must be capable of safely adapting, at runtime, the assumptions they make about the environment, the goals they are expected to achieve, and the strategy that guarantees the goals are fulfilled if the assumptions hold. Such adaptation often involves the system degrading its functionality, by weakening its environment assumptions and/or the goals it aims to meet, ideally in a graceful manner. However, finding weaker assumptions that account for the unanticipated behaviour and of goals that are achievable in the new environment in a systematic and safe way remains an open challenge. In this paper, we propose a novel framework that supports assumption and, if necessary, goal degradation to allow systems to cope with runtime assumption violations. The framework, which integrates into the MORPH reference architecture, combines symbolic learning and reactive synthesis to compute implementable controllers that may be deployed safely. We describe and implement an algorithm that illustrates the working of this framework. We further demonstrate in our evaluation its effectiveness and applicability to a series of benchmarks from the literature. The results show that the algorithm successfully learns realizable specifications that accommodate previously violating environment behaviour in almost all cases. Exceptions are discussed in the evaluation

Risk-driven revision of requirements models

© 2016 ACM.Requirements incompleteness is often the result of unanticipated adverse conditions which prevent the software and its environment from behaving as expected. These conditions represent risks that can cause severe software failures. The identification and resolution of such risks is therefore a crucial step towards requirements completeness. Obstacle analysis is a goal-driven form of risk analysis that aims at detecting missing conditions that can obstruct goals from being satisfied in a given domain, and resolving them. This paper proposes an approach for automatically revising goals that may be under-specified or (partially) wrong to resolve obstructions in a given domain. The approach deploys a learning-based revision methodology in which obstructed goals in a goal model are iteratively revised from traces exemplifying obstruction and non-obstruction occurrences. Our revision methodology computes domain-consistent, obstruction-free revisions that are automatically propagated to other goals in the model in order to preserve the correctness of goal models whilst guaranteeing minimal change to the original model. We present the formal foundations of our learning-based approach, and show that it preserves the properties of our formal framework. We validate it against the benchmarking case study of the London Ambulance Service

Computational alignment of goals and scenarios for complex systems

The purpose of requirements validation is to determine whether a large requirements set will lead to the achievement of system-related goals under different conditions - a task that needs automation if it is to be performed quickly and accurately. One reason for the current lack of software tools to undertake such validation is the absence of the computational mechanisms needed to associate scenario, system specification and goal analysis tools. Therefore, in this paper, we report first research experiments in developing these new capabilities, and demonstrate them with a non-trivial example associated with a Rolls Royce aircraft engine software component

Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks

[EN] Intrusion detection system (IDS) is regarded as the second line of defense against network anomalies and threats. IDS plays an important role in network security. There are many techniques which are used to design IDSs for specific scenario and applications. Artificial intelligence techniques are widely used for threats detection. This paper presents a critical study on genetic algorithm, artificial immune, and artificial neural network (ANN) based IDSs techniques used in wireless sensor network (WSN)The authors extend their appreciation to the Distinguished Scientist Fellowship Program(DSFP) at King Saud University for funding this research.Alrajeh, NA.; Lloret, J. (2013). Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks. International Journal of Distributed Sensor Networks. 2013(351047):1-6. https://doi.org/10.1155/2013/351047S16201335104

Global Current Practices of Ventilatory Support Management in COVID-19 Patients: An International Survey

Background: As the global outbreak of COVID-19 continues to ravage the world, it is important to understand how frontline clinicians manage ventilatory support and the various limiting factors. / Methods: An online survey composed of 32 questions was developed and validated by an international expert panel. / Results: Overall, 502 respondents from 40 countries across six continents completed the survey. The mean number (±SD) of ICU beds was 64 ± 84. The most popular initial diagnostic tools used for treatment initiation were arterial blood gas (48%) and clinical presentation (37.5%), while the national COVID-19 guidelines were the most used (61.2%). High flow nasal cannula (HFNC) (53.8%), non-invasive ventilation (NIV) (47%), and invasive mechanical ventilation (IMV) (92%) were mostly used for mild, moderate, and severe COVID-19 cases, respectively. However, only 38.8%, 56.6% and 82.9% of the respondents had standard protocols for HFNC, NIV, and IMV, respectively. The most frequently used modes of IMV and NIV were volume control (VC) (36.1%) and continuous positive airway pressure/pressure support (CPAP/PS) (40.6%). About 54% of the respondents did not adhere to the recommended, regular ventilator check interval. The majority of the respondents (85.7%) used proning with IMV, with 48.4% using it for 12– 16 hours, and 46.2% had tried awake proning in combination with HFNC or NIV. Increased staff workload (45.02%), lack of trained staff (44.22%) and shortage of personal protective equipment (PPE) (42.63%) were the main barriers to COVID-19 management. / Conclusion: Our results show that general clinical practices involving ventilatory support were highly heterogeneous, with limited use of standard protocols and most frontline clinicians depending on isolated and varied management guidelines. We found increased staff workload, lack of trained staff and shortage of PPE to be the main limiting factors affecting global COVID-19 ventilatory support management

Socially and biologically inspired computing for self-organizing communications networks

The design and development of future communications networks call for a careful examination of biological and social systems. New technological developments like self-driving cars, wireless sensor networks, drones swarm, Internet of Things, Big Data, and Blockchain are promoting an integration process that will bring together all those technologies in a large-scale heterogeneous network. Most of the challenges related to these new developments cannot be faced using traditional approaches, and require to explore novel paradigms for building computational mechanisms that allow us to deal with the emergent complexity of these new applications. In this article, we show that it is possible to use biologically and socially inspired computing for designing and implementing self-organizing communication systems. We argue that an abstract analysis of biological and social phenomena can be made to develop computational models that provide a suitable conceptual framework for building new networking technologies: biologically inspired computing for achieving efficient and scalable networking under uncertain environments; socially inspired computing for increasing the capacity of a system for solving problems through collective actions. We aim to enhance the state-of-the-art of these approaches and encourage other researchers to use these models in their future work

Automated Support for Diagnosis and Repair

Model checking and logic-based learning together deliver automated support, especially in adaptive and autonomous systems.Fil: Alrajeh, Dalal . Imperial College London; Reino UnidoFil: Russo, Alessandra. Imperial College London; Reino UnidoFil: Kramer, Jeff . Imperial College London; Reino UnidoFil: Uchitel, Sebastian. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Learning neural search policies for classical planning

Heuristic forward search is currently the dominant paradigmin classical planning. Forward search algorithms typicallyrely on a single, relatively simple variation of best-first searchand remain fixed throughout the process of solving a plan-ning problem. Existing work combining multiple search tech-niques usually aims at supporting best-first search with anadditional exploratory mechanism, triggered using a hand-crafted criterion. A notable exception is very recent workwhich combines various search techniques using a trainablepolicy. That approach, however, is confined to a discrete ac-tion space comprising several fixed subroutines.In this paper, we introduce a parametrized search algorithmtemplate which combines various search techniques withina single routine. The template’s parameter space defines aninfinite space of search algorithms, including, among others,BFS, local and random search. We then propose a neural ar-chitecture for designating the values of the search parametersgiven the state of the search. This enables expressing neuralsearch policies that change the values of the parameters asthe search progresses. The policies can be learned automat-ically, with the objective of maximizing the planner’s per-formance on a given distribution of planning problems. Weconsider a training setting based on a stochastic optimizationalgorithm known as thecross-entropy method(CEM). Exper-imental evaluation of our approach shows that it is capable offinding effective distribution-specific search policies, outper-forming the relevant baselines