11,350 research outputs found
OpenML Benchmarking Suites
Machine learning research depends on objectively interpretable, comparable,
and reproducible algorithm benchmarks. Therefore, we advocate the use of
curated, comprehensive suites of machine learning tasks to standardize the
setup, execution, and reporting of benchmarks. We enable this through software
tools that help to create and leverage these benchmarking suites. These are
seamlessly integrated into the OpenML platform, and accessible through
interfaces in Python, Java, and R. OpenML benchmarking suites are (a) easy to
use through standardized data formats, APIs, and client libraries; (b)
machine-readable, with extensive meta-information on the included datasets; and
(c) allow benchmarks to be shared and reused in future studies. We also present
a first, carefully curated and practical benchmarking suite for classification:
the OpenML Curated Classification benchmarking suite 2018 (OpenML-CC18)
Dagstuhl Reports : Volume 1, Issue 2, February 2011
Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-HĂŒbner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro PezzĂ©, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn
Instant restore after a media failure
Media failures usually leave database systems unavailable for several hours
until recovery is complete, especially in applications with large devices and
high transaction volume. Previous work introduced a technique called
single-pass restore, which increases restore bandwidth and thus substantially
decreases time to repair. Instant restore goes further as it permits read/write
access to any data on a device undergoing restore--even data not yet
restored--by restoring individual data segments on demand. Thus, the restore
process is guided primarily by the needs of applications, and the observed mean
time to repair is effectively reduced from several hours to a few seconds.
This paper presents an implementation and evaluation of instant restore. The
technique is incrementally implemented on a system starting with the
traditional ARIES design for logging and recovery. Experiments show that the
transaction latency perceived after a media failure can be cut down to less
than a second and that the overhead imposed by the technique on normal
processing is minimal. The net effect is that a few "nines" of availability are
added to the system using simple and low-overhead software techniques
ChimpCheck: Property-Based Randomized Test Generation for Interactive Apps
We consider the problem of generating relevant execution traces to test rich
interactive applications. Rich interactive applications, such as apps on mobile
platforms, are complex stateful and often distributed systems where
sufficiently exercising the app with user-interaction (UI) event sequences to
expose defects is both hard and time-consuming. In particular, there is a
fundamental tension between brute-force random UI exercising tools, which are
fully-automated but offer low relevance, and UI test scripts, which are manual
but offer high relevance. In this paper, we consider a middle way---enabling a
seamless fusion of scripted and randomized UI testing. This fusion is
prototyped in a testing tool called ChimpCheck for programming, generating, and
executing property-based randomized test cases for Android apps. Our approach
realizes this fusion by offering a high-level, embedded domain-specific
language for defining custom generators of simulated user-interaction event
sequences. What follows is a combinator library built on industrial strength
frameworks for property-based testing (ScalaCheck) and Android testing (Android
JUnit and Espresso) to implement property-based randomized testing for Android
development. Driven by real, reported issues in open source Android apps, we
show, through case studies, how ChimpCheck enables expressing effective testing
patterns in a compact manner.Comment: 20 pages, 21 figures, Symposium on New ideas, New Paradigms, and
Reflections on Programming and Software (Onward!2017
A Deep Learning-based approach for Fault Detection of Power Lines
Master's thesis in Information- and communication technology (IKT590)A transmission network is the most crucial part of modern infrastructure. However, it requires an extensive amount of power line inspection each year to maintain, and with an increased interest in replacing large helicopters with drones for this process, the possibility of including AI is equally compelling. This thesis goes into the second part by taking a deep learning-based approach in the interest of fault detection. A literature review illustrates that earlier research has some to none understanding of the complexity re-quired for inspection. Due to the advancement in object detection and classification, this thesis has identified and implemented an applicable model capable of giving state-of-the-art accuracy in electrical pole and component detection by dividing the process into multiple layers. This thesis takes as well and proposes a new method that presented great result in assuring more reliable fault detection and is a way to improve the quality of images taken by drones. The pole detection layer gave 97.7 mAP, the component detection layer reached 95.6mAP, the fault classifier delivered an accuracy of 93%, and the proposed quality classifier had an accuracy of 93% as well. The presented approach illustrates the possibility of phasing the physical inspection out. The amount of component labeled that must be available for algorithmic training to surpass a human expert is not readily available. Nevertheless, the presented approach is a sufficient tool for assisting the inspector
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