60,487 research outputs found
A Fast Compiler for NetKAT
High-level programming languages play a key role in a growing number of
networking platforms, streamlining application development and enabling precise
formal reasoning about network behavior. Unfortunately, current compilers only
handle "local" programs that specify behavior in terms of hop-by-hop forwarding
behavior, or modest extensions such as simple paths. To encode richer "global"
behaviors, programmers must add extra state -- something that is tricky to get
right and makes programs harder to write and maintain. Making matters worse,
existing compilers can take tens of minutes to generate the forwarding state
for the network, even on relatively small inputs. This forces programmers to
waste time working around performance issues or even revert to using
hardware-level APIs.
This paper presents a new compiler for the NetKAT language that handles rich
features including regular paths and virtual networks, and yet is several
orders of magnitude faster than previous compilers. The compiler uses symbolic
automata to calculate the extra state needed to implement "global" programs,
and an intermediate representation based on binary decision diagrams to
dramatically improve performance. We describe the design and implementation of
three essential compiler stages: from virtual programs (which specify behavior
in terms of virtual topologies) to global programs (which specify network-wide
behavior in terms of physical topologies), from global programs to local
programs (which specify behavior in terms of single-switch behavior), and from
local programs to hardware-level forwarding tables. We present results from
experiments on real-world benchmarks that quantify performance in terms of
compilation time and forwarding table size
An overview of decision table literature 1982-1995.
This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.
Rule-based Machine Learning Methods for Functional Prediction
We describe a machine learning method for predicting the value of a
real-valued function, given the values of multiple input variables. The method
induces solutions from samples in the form of ordered disjunctive normal form
(DNF) decision rules. A central objective of the method and representation is
the induction of compact, easily interpretable solutions. This rule-based
decision model can be extended to search efficiently for similar cases prior to
approximating function values. Experimental results on real-world data
demonstrate that the new techniques are competitive with existing machine
learning and statistical methods and can sometimes yield superior regression
performance.Comment: See http://www.jair.org/ for any accompanying file
Unsupervised Extraction of Representative Concepts from Scientific Literature
This paper studies the automated categorization and extraction of scientific
concepts from titles of scientific articles, in order to gain a deeper
understanding of their key contributions and facilitate the construction of a
generic academic knowledgebase. Towards this goal, we propose an unsupervised,
domain-independent, and scalable two-phase algorithm to type and extract key
concept mentions into aspects of interest (e.g., Techniques, Applications,
etc.). In the first phase of our algorithm we propose PhraseType, a
probabilistic generative model which exploits textual features and limited POS
tags to broadly segment text snippets into aspect-typed phrases. We extend this
model to simultaneously learn aspect-specific features and identify academic
domains in multi-domain corpora, since the two tasks mutually enhance each
other. In the second phase, we propose an approach based on adaptor grammars to
extract fine grained concept mentions from the aspect-typed phrases without the
need for any external resources or human effort, in a purely data-driven
manner. We apply our technique to study literature from diverse scientific
domains and show significant gains over state-of-the-art concept extraction
techniques. We also present a qualitative analysis of the results obtained.Comment: Published as a conference paper at CIKM 201
Determinants of Long-term Economic Development: An Empirical Cross-country Study Involving Rough Sets Theory and Rule Induction
Empirical findings on determinants of long-term economic growth are numerous, sometimes inconsistent, highly exciting and still incomplete. The empirical analysis was almost exclusively carried out by standard econometrics. This study compares results gained by cross-country regressions as reported in the literature with those gained by the rough sets theory and rule induction. The main advantages of using rough sets are being able to classify classes and to discretize. Thus, we do not have to deal with distributional, independence, (log-)linearity, and many other assumptions, but can keep the data as they are. The main difference between regression results and rough sets is that most education and human capital indicators can be labeled as robust attributes. In addition, we find that political indicators enter in a non-linear fashion with respect to growth.Economic growth, Rough sets, Rule induction
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