Research in the area of Autonomic Networks is on the rise. Autonomicity-realized through control loops, is an enabler for advanced self-manageability of network nodes and devices. Therefore, the specification and design of autonomic behaviors is required for each of the management functions defined by the well established FCAPS network management framework (Fault-, Configuration-, Accounting-, Performanceand Security-Management). In the context of Autonomic FaultManagement, mechanisms and algorithms are required that enable efficient and scalable interactions among the FaultManagement processes defined by the TMN (Telecommunications Management Network) standard. TMN defines Fault-Detection, Fault-Isolation, and Fault-Removal as the processes involved in Fault-Management. Therefore, in Autonomic Networks, some capabilities of Fault-Isolation must be in-built into a node and into the whole fundamental network architecture (apart from those aspects handled in the management plane), and its results must be fed into the embedded automatic Fault-Removal mechanisms. This imposes some scalability requirements on the employed algorithms. In this paper, we propose a novel scalable Markov Chain based algorithm for on-line Fault-Isolation. Furthermore, we analyze its computational and space complexity and evaluate its fault identification capabilities, as well as scaling properties on potential issues within an IPv6 network.

Compliance governance in organizations has been recently gaining importance because of new regulations and the diversity of compliance sources. In this demo we will show an integrated solution for runtime compliance governance in Service-Oriented Architectures (SOAs). The proposed solution supports the whole cycle of compliance management and has been tested in a real world case study.

<b><i>Introduction:</i></b> The use of commercially available automatic speech recognition (ASR) software is challenged when dysarthria accompanies a physical disability. To overcome this issue, a mobile and personal speech assistant (mPASS) platform was developed, using a speaker-dependent ASR software. <b><i>Objective:</i></b> The aim of this study was to evaluate the performance of the proposed platform and to compare mPASS recognition accuracy to a commercial speaker-independent ASR software. In addition, secondary aims were to investigate the relationship between severity of dysarthria and accuracy and to explore people with dysarthria perceptions on the proposed platform. <b><i>Methods:</i></b> Fifteen individuals with dysarthric speech and 20 individuals with nondysarthric speech recorded 24 words and 5 sentences in a clinical environment. Differences in recognition accuracy between the two systems were evaluated. In addition, mPASS usability was assessed with a technology acceptance model (TAM) questionnaire. <b><i>Results:</i></b> In both groups, mean accuracy rates were significantly higher with mPASS compared to the commercial ASR for words and for sentences. mPASS reached good levels of usefulness and ease of use according to the TAM questionnaire. <b><i>Conclusions:</i></b> Practical applicability of this technology is realistic: the mPASS platform is accurate, and it could be easily used by individuals with dysarthria.

It is significant for companies to ensure their businesses conforming to relevant policies, laws, and regulations as the consequences of infringement can be serious. Unfortunately, the divergence and frequent changes of different compliance sources make it hard to systematically and quickly accommodate new compliance requirements due to the lack of an adequate methodology for system and compliance engineering. In addition, the difference of perception and expertise of multiple stakeholders involving in system and compliance engineering further complicates the analyzing, implementing, and assessing of compliance. For these reasons, in many cases, business compliance today is reached on a per-case basis by using ad hoc, hand-crafted solutions for specific rules to which they must comply. This leads in the long run to problems regarding complexity, understandability, and maintainability of compliance concerns in a SOA. To address the aforementioned challenges, we present in this invited paper a comprehensive SOA business compliance software framework that enables a business to express, implement, monitor, and govern compliance concerns.

Emerging technologies of autonomic networks impose demanding requirements on self-healing capabilities of networks. Fault management techniques based on the exploitation of fault propagation models (FPM) are a promising solution to conduct fault isolation and to infer the root cause of problems observed in the network. In this study, we investigate a fault propagation model developed for the needs of a unified architecture for resilience, survivability, and autonomic fault management in mobile ad hoc networks (MANETs). In particular, we propose a model to address the problem of frequent topology changes, a common scenario in mobile ad hoc networks. Simulations showed that in such environments the proposed model can detect multiple faults with high probability.