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

Pulmonary rehabilitation and cardiovascular risk in COPD: a systematic review

Introduction: Pulmonary Rehabilitation (PR) is an effective intervention in COPD however the value of PR in reducing cardiovascular risk in COPD (measured by aortic pulse wave velocity, aPWV) is unclear and there is no existing systematic review. Objectives: To conduct a systematic review examining whether PR results in alteration of CV risk in COPD (as measured by aPWV). Methods: An electronic systematic search concordant with PRISMA guidelines was conducted. The search was complete to the 27th of May 2017. Six databases were examined: Embase, Medline, AMED, Web of Science, Cochrane clinical trials, and CINAHL. Results: This study generated 767 initial matches, which were filtered using inclusion/exclusion criteria. Three studies (201 COPD participants) were included. Our analysis does not confirm that PR affects aPWV but studies were heterogeneous. Conclusion: There is currently insufficient information on the effect of PR on reducing CV risk in COPD. Therefore controversy remains, with the possibility that there might be some subjects who benefit and others who might experience an increase in CV risk in response to PR. These results will be of value to those interested in gaining a better understanding of the benefits of PR on CV risk in COPD

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

Pulmonary rehabilitation, physical activity and aortic stiffness in COPD

BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) have elevated cardiovascular risk, and cardiovascular disease is a major cause of death in COPD. The current literature indicates that changes in cardiovascular risk during pulmonary rehabilitation (assessed using aortic stiffness) are heterogeneous suggesting that there may be sub-groups of patients who do and do not benefit. OBJECTIVES: To investigate the characteristics of COPD patients who do and do not experience aortic stiffness reduction during pulmonary rehabilitation, examine how changes relate to physical activity and exercise capacity, and assess whether changes in aortic stiffness are maintained at 6 weeks following rehabilitation. METHODS: We prospectively measured arterial stiffness (aortic pulse-wave velocity), exercise capacity (Incremental Shuttle Walk Test) and physical activity (daily step count) in 92 COPD patients who started a six week pulmonary rehabilitation programme, 54 of whom completed rehabilitation, and 29 of whom were re-assessed six weeks later. RESULTS: Whilst on average there was no influence of pulmonary rehabilitation on aortic stiffness (pre- vs. post pulse-wave velocity 11.3 vs. 11.1 m/s p = 0.34), 56% patients responded with a significant reduction in aortic stiffness. Change in aortic stiffness (absolute and/or percentage) during rehabilitation was associated with both increased physical activity (rho = - 0.30, p = 0.042) and change in exercise capacity (rho = - 0.32, p = 0.02), but in multivariable analysis most closely with physical activity. 92% of the responders who attended maintained this response six weeks later. CONCLUSION: Elevated aortic stiffness in COPD is potentially modifiable in a subgroup of patients during pulmonary rehabilitation and is associated with increased physical activity. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03003208. Registered 26/12/ 2016

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