463 research outputs found
Robust Online Monitoring of Signal Temporal Logic
Signal Temporal Logic (STL) is a formalism used to rigorously specify
requirements of cyberphysical systems (CPS), i.e., systems mixing digital or
discrete components in interaction with a continuous environment or analog com-
ponents. STL is naturally equipped with a quantitative semantics which can be
used for various purposes: from assessing the robustness of a specification to
guiding searches over the input and parameter space with the goal of falsifying
the given property over system behaviors. Algorithms have been proposed and
implemented for offline computation of such quantitative semantics, but only
few methods exist for an online setting, where one would want to monitor the
satisfaction of a formula during simulation. In this paper, we formalize a
semantics for robust online monitoring of partial traces, i.e., traces for
which there might not be enough data to decide the Boolean satisfaction (and to
compute its quantitative counterpart). We propose an efficient algorithm to
compute it and demonstrate its usage on two large scale real-world case studies
coming from the automotive domain and from CPS education in a Massively Open
Online Course (MOOC) setting. We show that savings in computationally expensive
simulations far outweigh any overheads incurred by an online approach
Multiple verification in computational modeling of bone pathologies
We introduce a model checking approach to diagnose the emerging of bone
pathologies. The implementation of a new model of bone remodeling in PRISM has
led to an interesting characterization of osteoporosis as a defective bone
remodeling dynamics with respect to other bone pathologies. Our approach allows
to derive three types of model checking-based diagnostic estimators. The first
diagnostic measure focuses on the level of bone mineral density, which is
currently used in medical practice. In addition, we have introduced a novel
diagnostic estimator which uses the full patient clinical record, here
simulated using the modeling framework. This estimator detects rapid (months)
negative changes in bone mineral density. Independently of the actual bone
mineral density, when the decrease occurs rapidly it is important to alarm the
patient and monitor him/her more closely to detect insurgence of other bone
co-morbidities. A third estimator takes into account the variance of the bone
density, which could address the investigation of metabolic syndromes, diabetes
and cancer. Our implementation could make use of different logical combinations
of these statistical estimators and could incorporate other biomarkers for
other systemic co-morbidities (for example diabetes and thalassemia). We are
delighted to report that the combination of stochastic modeling with formal
methods motivate new diagnostic framework for complex pathologies. In
particular our approach takes into consideration important properties of
biosystems such as multiscale and self-adaptiveness. The multi-diagnosis could
be further expanded, inching towards the complexity of human diseases. Finally,
we briefly introduce self-adaptiveness in formal methods which is a key
property in the regulative mechanisms of biological systems and well known in
other mathematical and engineering areas.Comment: In Proceedings CompMod 2011, arXiv:1109.104
Building a MultiAgent System from a User Workflow Specification
This paper provides a methodology to build
a MultiAgent System (MAS) described in terms of interactive
components from a domain-specic User Workow
Specication (UWS). We use a Petri nets-based notation
to describe workow specications. This, besides using a
familiar and well-studied notation, guarantees an highlevel
of description and independence with more concrete
vendor-specic process denition languages. In order to
bridge the gap between workow specications and MASs,
we exploit other intermediate Petri nets-based notations.
Transformation rules are given to translate a notation to
another. The generated agent-based application implements
the original workow specication. Run-time support is
provided by a middleware suitable for the execution of the
generated code
BISM: Bytecode-Level Instrumentation for Software Monitoring
BISM (Bytecode-Level Instrumentation for Software Monitoring) is a
lightweight bytecode instrumentation tool that features an expressive
high-level control-flow-aware instrumentation language. The language follows
the aspect-oriented programming paradigm by adopting the joinpoint model,
advice inlining, and separate instrumentation mechanisms. BISM provides
joinpoints ranging from bytecode instruction to method execution, access to
comprehensive static and dynamic context information, and instrumentation
methods. BISM runs in two instrumentation modes: build-time and load-time. We
demonstrate BISM effectiveness using two experiments: a security scenario and a
general runtime verification case. The results show that BISM instrumentation
incurs low runtime and memory overheads
Model driven design and implementation of activity-based applications in Hermes
Hermes is an agent-based middleware structured
as a component-based and 3-layered software architecture.
Hermes provides an integrated, exible programming
environment for design and execution of activity-based
applications in distributed environments. By using workow
technology, it supports even a non expert user programmer
in the model driven design and implementation of a domain
specic application. In this paper, after a description of
Hermes software architecture, we provide a simple demo
in biological domain and we show some real case studies in
which Hermes has been validated
Signal Convolution Logic
We introduce a new logic called Signal Convolution Logic (SCL) that combines temporal logic with convolutional filters from digital signal processing. SCL enables to reason about the percentage of time a formula is satisfied in a bounded interval. We demonstrate that this new logic is a suitable formalism to effectively express non-functional requirements in Cyber-Physical Systems displaying noisy and irregular behaviours. We define both a qualitative and quantitative semantics for it, providing an efficient monitoring procedure. Finally, we prove SCL at work to monitor the artificial pancreas controllers that are employed to automate the delivery of insulin for patients with type-1 diabetes
Efficient Large-scale Trace Checking Using MapReduce
The problem of checking a logged event trace against a temporal logic
specification arises in many practical cases. Unfortunately, known algorithms
for an expressive logic like MTL (Metric Temporal Logic) do not scale with
respect to two crucial dimensions: the length of the trace and the size of the
time interval for which logged events must be buffered to check satisfaction of
the specification. The former issue can be addressed by distributed and
parallel trace checking algorithms that can take advantage of modern cloud
computing and programming frameworks like MapReduce. Still, the latter issue
remains open with current state-of-the-art approaches.
In this paper we address this memory scalability issue by proposing a new
semantics for MTL, called lazy semantics. This semantics can evaluate temporal
formulae and boolean combinations of temporal-only formulae at any arbitrary
time instant. We prove that lazy semantics is more expressive than standard
point-based semantics and that it can be used as a basis for a correct
parametric decomposition of any MTL formula into an equivalent one with
smaller, bounded time intervals. We use lazy semantics to extend our previous
distributed trace checking algorithm for MTL. We evaluate the proposed
algorithm in terms of memory scalability and time/memory tradeoffs.Comment: 13 pages, 8 figure
MoonLight: a lightweight tool for monitoring spatio-temporal properties
We present MoonLight, a tool for monitoring temporal and spatio-temporal properties of mobile, spatially distributed, and interacting entities such as biological and cyber-physical systems. In MoonLight the space is represented as a weighted graph describing the topological configuration in which the single entities are arranged. Both nodes and edges have attributes modeling physical quantities and logical states of the system evolving in time. MoonLight is implemented in Java and supports the monitoring of Spatio-Temporal Reach and Escape Logic (STREL). MoonLight can be used as a standalone command line tool, such as Java API, or via MatlabTM and Python interfaces. We provide here the description of the tool, its interfaces, and its scripting language using a sensor network and a bike sharing example. We evaluate the tool performances both by comparing it with other tools specialized in monitoring only temporal properties and by monitoring spatio-temporal requirements considering different sizes of dynamical and spatial graphs
The Cost of Monitoring Alone
We compare the succinctness of two monitoring systems for properties of
infinite traces, namely parallel and regular monitors. Although a parallel
monitor can be turned into an equivalent regular monitor, the cost of this
transformation is a double-exponential blowup in the syntactic size of the
monitors, and a triple-exponential blowup when the goal is a deterministic
monitor. We show that these bounds are tight and that they also hold for
translations between corresponding fragments of Hennessy-Milner logic with
recursion over infinite traces.Comment: 22 page
Clinical usefulness of splanchnic oxygenation in predicting necrotizing enterocolitis in extremely preterm infants:a cohort study
Background: Impaired intestinal microcirculation seems to play an important role in the pathogenesis of necrotizing enterocolitis (NEC). A previous study showed that a SrSO2 < 30% is associated with an increased risk of developing of NEC. We aimed to determine the clinical usefulness of the cut off < 30% for SrSO2 in predicting NEC in extremely preterm neonates.Methods: This is a combined cohort observational study. We added a second cohort from another university hospital to the previous cohort of extremely preterm infants. SrSO2 was measured for 1–2 h at days 2–6 after birth. To determine clinical usefulness we assessed sensitivity, specificity, positive and negative predictive values for mean SrSO2 < 30. Odds ratio to develop NEC was assessed with generalized linear model analysis, adjusting for center.Results: We included 86 extremely preterm infants, median gestational age 26.3 weeks (range 23.0-27.9). Seventeen infants developed NEC. A mean SrSO2 < 30% was found in 70.5% of infants who developed NEC compared to 33.3% of those who did not (p = 0.01). Positive and negative predictive values were 0.33 CI (0.24–0.44) and 0.90 CI (0.83–0.96), respectively. The odds of developing NEC were 4.5 (95% CI 1.4–14.3) times higher in infants with SrSO2 < 30% compared to those with SrSO2 ≥ 30%.Conclusions: A mean SrSO2 cut off ≥ 30% in extremely preterm infants between days 2–6 after birth may be useful in identifying infants who will not develop NEC.</p
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