26,659 research outputs found
kLog: A Language for Logical and Relational Learning with Kernels
We introduce kLog, a novel approach to statistical relational learning.
Unlike standard approaches, kLog does not represent a probability distribution
directly. It is rather a language to perform kernel-based learning on
expressive logical and relational representations. kLog allows users to specify
learning problems declaratively. It builds on simple but powerful concepts:
learning from interpretations, entity/relationship data modeling, logic
programming, and deductive databases. Access by the kernel to the rich
representation is mediated by a technique we call graphicalization: the
relational representation is first transformed into a graph --- in particular,
a grounded entity/relationship diagram. Subsequently, a choice of graph kernel
defines the feature space. kLog supports mixed numerical and symbolic data, as
well as background knowledge in the form of Prolog or Datalog programs as in
inductive logic programming systems. The kLog framework can be applied to
tackle the same range of tasks that has made statistical relational learning so
popular, including classification, regression, multitask learning, and
collective classification. We also report about empirical comparisons, showing
that kLog can be either more accurate, or much faster at the same level of
accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at
http://klog.dinfo.unifi.it along with tutorials
Context2Name: A Deep Learning-Based Approach to Infer Natural Variable Names from Usage Contexts
Most of the JavaScript code deployed in the wild has been minified, a process
in which identifier names are replaced with short, arbitrary and meaningless
names. Minified code occupies less space, but also makes the code extremely
difficult to manually inspect and understand. This paper presents Context2Name,
a deep learningbased technique that partially reverses the effect of
minification by predicting natural identifier names for minified names. The
core idea is to predict from the usage context of a variable a name that
captures the meaning of the variable. The approach combines a lightweight,
token-based static analysis with an auto-encoder neural network that summarizes
usage contexts and a recurrent neural network that predict natural names for a
given usage context. We evaluate Context2Name with a large corpus of real-world
JavaScript code and show that it successfully predicts 47.5% of all minified
identifiers while taking only 2.9 milliseconds on average to predict a name. A
comparison with the state-of-the-art tools JSNice and JSNaughty shows that our
approach performs comparably in terms of accuracy while improving in terms of
efficiency. Moreover, Context2Name complements the state-of-the-art by
predicting 5.3% additional identifiers that are missed by both existing tools
A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees
FASILL (acronym of "Fuzzy Aggregators and Similarity Into a Logic Language")
is a fuzzy logic programming language with implicit/explicit truth degree
annotations, a great variety of connectives and unification by similarity.
FASILL integrates and extends features coming from MALP (Multi-Adjoint Logic
Programming, a fuzzy logic language with explicitly annotated rules) and
Bousi~Prolog (which uses a weak unification algorithm and is well suited for
flexible query answering). Hence, it properly manages similarity and truth
degrees in a single framework combining the expressive benefits of both
languages. This paper presents the main features and implementations details of
FASILL. Along the paper we describe its syntax and operational semantics and we
give clues of the implementation of the lattice module and the similarity
module, two of the main building blocks of the new programming environment
which enriches the FLOPER system developed in our research group.Comment: In Proceedings PROLE 2014, arXiv:1501.0169
Poseidon: a 2-tier Anomaly-based Network Intrusion Detection System
We present Poseidon, a new anomaly based intrusion detection system. Poseidon is payload-based, and presents a two-tier architecture: the first stage consists of a Self-Organizing Map, while the second one is a modified PAYL system. Our benchmarks on the 1999 DARPA data set show a higher detection rate and lower number of false positives than PAYL and PHAD
Poseidon: a 2-tier Anomaly-based Intrusion Detection System
We present Poseidon, a new anomaly based intrusion detection system. Poseidon
is payload-based, and presents a two-tier architecture: the first stage
consists of a Self-Organizing Map, while the second one is a modified PAYL
system. Our benchmarks on the 1999 DARPA data set show a higher detection rate
and lower number of false positives than PAYL and PHAD
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