Resilience markers for safer systems and organisations

If computer systems are to be designed to foster resilient performance it is important to be able to identify contributors to resilience. The emerging practice of Resilience Engineering has identified that people are still a primary source of resilience, and that the design of distributed systems should provide ways of helping people and organisations to cope with complexity. Although resilience has been identified as a desired property, researchers and practitioners do not have a clear understanding of what manifestations of resilience look like. This paper discusses some examples of strategies that people can adopt that improve the resilience of a system. Critically, analysis reveals that the generation of these strategies is only possible if the system facilitates them. As an example, this paper discusses practices, such as reflection, that are known to encourage resilient behavior in people. Reflection allows systems to better prepare for oncoming demands. We show that contributors to the practice of reflection manifest themselves at different levels of abstraction: from individual strategies to practices in, for example, control room environments. The analysis of interaction at these levels enables resilient properties of a system to be ‘seen’, so that systems can be designed to explicitly support them. We then present an analysis of resilience at an organisational level within the nuclear domain. This highlights some of the challenges facing the Resilience Engineering approach and the need for using a collective language to articulate knowledge of resilient practices across domains

The LOFAR Transients Pipeline

Current and future astronomical survey facilities provide a remarkably rich opportunity for transient astronomy, combining unprecedented fields of view with high sensitivity and the ability to access previously unexplored wavelength regimes. This is particularly true of LOFAR, a recently-commissioned, low-frequency radio interferometer, based in the Netherlands and with stations across Europe. The identification of and response to transients is one of LOFAR's key science goals. However, the large data volumes which LOFAR produces, combined with the scientific requirement for rapid response, make automation essential. To support this, we have developed the LOFAR Transients Pipeline, or TraP. The TraP ingests multi-frequency image data from LOFAR or other instruments and searches it for transients and variables, providing automatic alerts of significant detections and populating a lightcurve database for further analysis by astronomers. Here, we discuss the scientific goals of the TraP and how it has been designed to meet them. We describe its implementation, including both the algorithms adopted to maximize performance as well as the development methodology used to ensure it is robust and reliable, particularly in the presence of artefacts typical of radio astronomy imaging. Finally, we report on a series of tests of the pipeline carried out using simulated LOFAR observations with a known population of transients.Comment: 30 pages, 11 figures; Accepted for publication in Astronomy & Computing; Code at https://github.com/transientskp/tk

Dublin City University video track experiments for TREC 2002

Dublin City University participated in the Feature Extraction task and the Search task of the TREC-2002 Video Track. In the Feature Extraction task, we submitted 3 features: Face, Speech, and Music. In the Search task, we developed an interactive video retrieval system, which incorporated the 40 hours of the video search test collection and supported user searching using our own feature extraction data along with the donated feature data and ASR transcript from other Video Track groups. This video retrieval system allows a user to specify a query based on the 10 features and ASR transcript, and the query result is a ranked list of videos that can be further browsed at the shot level. To evaluate the usefulness of the feature-based query, we have developed a second system interface that provides only ASR transcript-based querying, and we conducted an experiment with 12 test users to compare these 2 systems. Results were submitted to NIST and we are currently conducting further analysis of user performance with these 2 systems

An Evolutionary Algorithm to Optimize Log/Restore Operations within Optimistic Simulation Platforms

In this work we address state recoverability in advanced optimistic simulation systems by proposing an evolutionary algorithm to optimize at run-time the parameters associated with state log/restore activities. Optimization takes place by adaptively selecting for each simulation object both (i) the best suited log mode (incremental vs non-incremental) and (ii) the corresponding optimal value of the log interval. Our performance optimization approach allows to indirectly cope with hidden effects (e.g., locality) as well as cross-object effects due to the variation of log/restore parameters for different simulation objects (e.g., rollback thrashing). Both of them are not captured by literature solutions based on analytical models of the overhead associated with log/restore tasks. More in detail, our evolutionary algorithm dynamically adjusts the log/restore parameters of distinct simulation objects as a whole, towards a well suited configuration. In such a way, we prevent negative effects on performance due to the biasing of the optimization towards individual simulation objects, which may cause reduced gains (or even decrease) in performance just due to the aforementioned hidden and/or cross-object phenomena. We also present an application-transparent implementation of the evolutionary algorithm within the ROme OpTimistic Simulator (ROOT-Sim), namely an open source, general purpose simulation environment designed according to the optimistic synchronization paradigm

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

The impact of cockpit automation on crew coordination and communication. Volume 1: Overview, LOFT evaluations, error severity, and questionnaire data

The purpose was to examine, jointly, cockpit automation and social processes. Automation was varied by the choice of two radically different versions of the DC-9 series aircraft, the traditional DC-9-30, and the glass cockpit derivative, the MD-88. Airline pilot volunteers flew a mission in the simulator for these aircraft. Results show that the performance differences between the crews of the two aircraft were generally small, but where there were differences, they favored the DC-9. There were no criteria on which the MD-88 crews performed better than the DC-9 crews. Furthermore, DC-9 crews rated their own workload as lower than did the MD-88 pilots. There were no significant differences between the two aircraft types with respect to the severity of errors committed during the Line-Oriented Flight Training (LOFT) flight. The attitude questionnaires provided some interesting insights, but failed to distinguish between DC-9 and MD-88 crews