66,586 research outputs found
Compositional Analysis of Real-Time Embedded Systems
This tutorial is concerned with various aspects of component-based design and compositional analysis of real-time embedded systems. It will first give an overview of component-based frameworks and their underlying principles. It will then go in-depth into abstraction methods for real-time components and techniques for computing their optimal interfaces, for both systems implemented on uniprocessor and multiprocessor platforms, as well as extensions to multi-mode systems. Besides theoretical aspects, the tutorial will also present an implementation of the compositional analysis framework on Xen virtualization and a demonstration of the CARTS toolset with several examples seeing the techniques in action. It will also include two case studies highlighting the utility of the framework, including the ARINC-653 avionics software and a smart-phone application. We will conclude the tutorial with a number of open challenges and research opportunities in this domain
Engineering failure analysis and design optimisation with HiP-HOPS
The scale and complexity of computer-based safety critical systems, like those used in the transport and manufacturing industries, pose significant challenges for failure analysis. Over the last decade, research has focused on automating this task. In one approach, predictive models of system failure are constructed from the topology of the system and local component failure models using a process of composition. An alternative approach employs model-checking of state automata to study the effects of failure and verify system safety properties. In this paper, we discuss these two approaches to failure analysis. We then focus on Hierarchically Performed Hazard Origin & Propagation Studies (HiP-HOPS) - one of the more advanced compositional approaches - and discuss its capabilities for automatic synthesis of fault trees, combinatorial Failure Modes and Effects Analyses, and reliability versus cost optimisation of systems via application of automatic model transformations. We summarise these contributions and demonstrate the application of HiP-HOPS on a simplified fuel oil system for a ship engine. In light of this example, we discuss strengths and limitations of the method in relation to other state-of-the-art techniques. In particular, because HiP-HOPS is deductive in nature, relating system failures back to their causes, it is less prone to combinatorial explosion and can more readily be iterated. For this reason, it enables exhaustive assessment of combinations of failures and design optimisation using computationally expensive meta-heuristics. (C) 2010 Elsevier Ltd. All rights reserved
Rich Interfaces for Dependability: Compositional Methods for Dynamic Fault Trees and Arcade models
This paper discusses two behavioural interfaces for reliability analysis: dynamic fault trees, which model the system reliability in terms of the reliability of its components and Arcade, which models the system reliability at an architectural level. For both formalisms, the reliability is analyzed by transforming the DFT or Arcade model to a set of input-output Markov Chains. By using compositional aggregation techniques based on weak bisimilarity, significant reductions in the state space can be obtained
Compositional Falsification of Cyber-Physical Systems with Machine Learning Components
Cyber-physical systems (CPS), such as automotive systems, are starting to
include sophisticated machine learning (ML) components. Their correctness,
therefore, depends on properties of the inner ML modules. While learning
algorithms aim to generalize from examples, they are only as good as the
examples provided, and recent efforts have shown that they can produce
inconsistent output under small adversarial perturbations. This raises the
question: can the output from learning components can lead to a failure of the
entire CPS? In this work, we address this question by formulating it as a
problem of falsifying signal temporal logic (STL) specifications for CPS with
ML components. We propose a compositional falsification framework where a
temporal logic falsifier and a machine learning analyzer cooperate with the aim
of finding falsifying executions of the considered model. The efficacy of the
proposed technique is shown on an automatic emergency braking system model with
a perception component based on deep neural networks
SOTER: A Runtime Assurance Framework for Programming Safe Robotics Systems
The recent drive towards achieving greater autonomy and intelligence in
robotics has led to high levels of complexity. Autonomous robots increasingly
depend on third party off-the-shelf components and complex machine-learning
techniques. This trend makes it challenging to provide strong design-time
certification of correct operation.
To address these challenges, we present SOTER, a robotics programming
framework with two key components: (1) a programming language for implementing
and testing high-level reactive robotics software and (2) an integrated runtime
assurance (RTA) system that helps enable the use of uncertified components,
while still providing safety guarantees. SOTER provides language primitives to
declaratively construct a RTA module consisting of an advanced,
high-performance controller (uncertified), a safe, lower-performance controller
(certified), and the desired safety specification. The framework provides a
formal guarantee that a well-formed RTA module always satisfies the safety
specification, without completely sacrificing performance by using higher
performance uncertified components whenever safe. SOTER allows the complex
robotics software stack to be constructed as a composition of RTA modules,
where each uncertified component is protected using a RTA module.
To demonstrate the efficacy of our framework, we consider a real-world
case-study of building a safe drone surveillance system. Our experiments both
in simulation and on actual drones show that the SOTER-enabled RTA ensures the
safety of the system, including when untrusted third-party components have bugs
or deviate from the desired behavior
Multilingual Models for Compositional Distributed Semantics
We present a novel technique for learning semantic representations, which
extends the distributional hypothesis to multilingual data and joint-space
embeddings. Our models leverage parallel data and learn to strongly align the
embeddings of semantically equivalent sentences, while maintaining sufficient
distance between those of dissimilar sentences. The models do not rely on word
alignments or any syntactic information and are successfully applied to a
number of diverse languages. We extend our approach to learn semantic
representations at the document level, too. We evaluate these models on two
cross-lingual document classification tasks, outperforming the prior state of
the art. Through qualitative analysis and the study of pivoting effects we
demonstrate that our representations are semantically plausible and can capture
semantic relationships across languages without parallel data.Comment: Proceedings of ACL 2014 (Long papers
- âŠ