106,080 research outputs found
Analysing Human Mobility Patterns of Hiking Activities through Complex Network Theory
The exploitation of high volume of geolocalized data from social sport
tracking applications of outdoor activities can be useful for natural resource
planning and to understand the human mobility patterns during leisure
activities. This geolocalized data represents the selection of hike activities
according to subjective and objective factors such as personal goals, personal
abilities, trail conditions or weather conditions. In our approach, human
mobility patterns are analysed from trajectories which are generated by hikers.
We propose the generation of the trail network identifying special points in
the overlap of trajectories. Trail crossings and trailheads define our network
and shape topological features. We analyse the trail network of Balearic
Islands, as a case of study, using complex weighted network theory. The
analysis is divided into the four seasons of the year to observe the impact of
weather conditions on the network topology. The number of visited places does
not decrease despite the large difference in the number of samples of the two
seasons with larger and lower activity. It is in summer season where it is
produced the most significant variation in the frequency and localization of
activities from inland regions to coastal areas. Finally, we compare our model
with other related studies where the network possesses a different purpose. One
finding of our approach is the detection of regions with relevant importance
where landscape interventions can be applied in function of the communities.Comment: 20 pages, 9 figures, accepte
Detecting Missing Dependencies and Notifiers in Puppet Programs
Puppet is a popular computer system configuration management tool. It
provides abstractions that enable administrators to setup their computer
systems declaratively. Its use suffers from two potential pitfalls. First, if
ordering constraints are not specified whenever an abstraction depends on
another, the non-deterministic application of abstractions can lead to race
conditions. Second, if a service is not tied to its resources through
notification constructs, the system may operate in a stale state whenever a
resource gets modified. Such faults can degrade a computing infrastructure's
availability and functionality.
We have developed an approach that identifies these issues through the
analysis of a Puppet program and its system call trace. Specifically, we
present a formal model for traces, which allows us to capture the interactions
of Puppet abstractions with the file system. By analyzing these interactions we
identify (1) abstractions that are related to each other (e.g., operate on the
same file), and (2) abstractions that should act as notifiers so that changes
are correctly propagated. We then check the relationships from the trace's
analysis against the program's dependency graph: a representation containing
all the ordering constraints and notifications declared in the program. If a
mismatch is detected, our system reports a potential fault.
We have evaluated our method on a large set of Puppet modules, and discovered
57 previously unknown issues in 30 of them. Benchmarking further shows that our
approach can analyze in minutes real-world configurations with a magnitude
measured in thousands of lines and millions of system calls
Life is an Adventure! An agent-based reconciliation of narrative and scientific worldviews\ud
The scientific worldview is based on laws, which are supposed to be certain, objective, and independent of time and context. The narrative worldview found in literature, myth and religion, is based on stories, which relate the events experienced by a subject in a particular context with an uncertain outcome. This paper argues that the concept of “agent”, supported by the theories of evolution, cybernetics and complex adaptive systems, allows us to reconcile scientific and narrative perspectives. An agent follows a course of action through its environment with the aim of maximizing its fitness. Navigation along that course combines the strategies of regulation, exploitation and exploration, but needs to cope with often-unforeseen diversions. These can be positive (affordances, opportunities), negative (disturbances, dangers) or neutral (surprises). The resulting sequence of encounters and actions can be conceptualized as an adventure. Thus, the agent appears to play the role of the hero in a tale of challenge and mystery that is very similar to the "monomyth", the basic storyline that underlies all myths and fairy tales according to Campbell [1949]. This narrative dynamics is driven forward in particular by the alternation between prospect (the ability to foresee diversions) and mystery (the possibility of achieving an as yet absent prospect), two aspects of the environment that are particularly attractive to agents. This dynamics generalizes the scientific notion of a deterministic trajectory by introducing a variable “horizon of knowability”: the agent is never fully certain of its further course, but can anticipate depending on its degree of prospect
Mobile object location discovery in unpredictable environments
Emerging mobile and ubiquitous computing environments present hard challenges to software engineering. The use of mobile code has been suggested as a natural fit for simplifing software development for these environments. However, the task of discovering mobile code location becomes a problem in unpredictable environments when using existing strategies, designed with fixed and relatively stable networks in mind. This paper introduces AMOS, a mobile code platform augmented with a structured overlay network. We demonstrate how the location discovery strategy of AMOS has better reliability and scalability properties than existing approaches, with minimal communication overhead. Finally, we demonstrate how AMOS can provide autonomous distribution of effort fairly throughout a network using probabilistic methods that requires no global knowledge of host capabilities
backShift: Learning causal cyclic graphs from unknown shift interventions
We propose a simple method to learn linear causal cyclic models in the
presence of latent variables. The method relies on equilibrium data of the
model recorded under a specific kind of interventions ("shift interventions").
The location and strength of these interventions do not have to be known and
can be estimated from the data. Our method, called backShift, only uses second
moments of the data and performs simple joint matrix diagonalization, applied
to differences between covariance matrices. We give a sufficient and necessary
condition for identifiability of the system, which is fulfilled almost surely
under some quite general assumptions if and only if there are at least three
distinct experimental settings, one of which can be pure observational data. We
demonstrate the performance on some simulated data and applications in flow
cytometry and financial time series. The code is made available as R-package
backShift
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