11,628 research outputs found
Practical issues for the implementation of survivability and recovery techniques in optical networks
Model-based dependability analysis : state-of-the-art, challenges and future outlook
Abstract: Over the past two decades, the study of model-based dependability analysis has gathered significant research interest. Different approaches have been developed to automate and address various limitations of classical dependability techniques to contend with the increasing complexity and challenges of modern safety-critical system. Two leading paradigms have emerged, one which constructs predictive system failure models from component failure models compositionally using the topology of the system. The other utilizes design models - typically state automata - to explore system behaviour through fault injection. This paper reviews a number of prominent techniques under these two paradigms, and provides an insight into their working mechanism, applicability, strengths and challenges, as well as recent developments within these fields. We also discuss the emerging trends on integrated approaches and advanced analysis capabilities. Lastly, we outline the future outlook for model-based dependability analysis
SensorCloud: Towards the Interdisciplinary Development of a Trustworthy Platform for Globally Interconnected Sensors and Actuators
Although Cloud Computing promises to lower IT costs and increase users'
productivity in everyday life, the unattractive aspect of this new technology
is that the user no longer owns all the devices which process personal data. To
lower scepticism, the project SensorCloud investigates techniques to understand
and compensate these adoption barriers in a scenario consisting of cloud
applications that utilize sensors and actuators placed in private places. This
work provides an interdisciplinary overview of the social and technical core
research challenges for the trustworthy integration of sensor and actuator
devices with the Cloud Computing paradigm. Most importantly, these challenges
include i) ease of development, ii) security and privacy, and iii) social
dimensions of a cloud-based system which integrates into private life. When
these challenges are tackled in the development of future cloud systems, the
attractiveness of new use cases in a sensor-enabled world will considerably be
increased for users who currently do not trust the Cloud.Comment: 14 pages, 3 figures, published as technical report of the Department
of Computer Science of RWTH Aachen Universit
Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is
further complicated by many theoretical issues, such as the I-equivalence among
different structures. In this work, we focus on a specific subclass of BNs,
named Suppes-Bayes Causal Networks (SBCNs), which include specific structural
constraints based on Suppes' probabilistic causation to efficiently model
cumulative phenomena. Here we compare the performance, via extensive
simulations, of various state-of-the-art search strategies, such as local
search techniques and Genetic Algorithms, as well as of distinct regularization
methods. The assessment is performed on a large number of simulated datasets
from topologies with distinct levels of complexity, various sample size and
different rates of errors in the data. Among the main results, we show that the
introduction of Suppes' constraints dramatically improve the inference
accuracy, by reducing the solution space and providing a temporal ordering on
the variables. We also report on trade-offs among different search techniques
that can be efficiently employed in distinct experimental settings. This
manuscript is an extended version of the paper "Structural Learning of
Probabilistic Graphical Models of Cumulative Phenomena" presented at the 2018
International Conference on Computational Science
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