14,117 research outputs found
Using regular expressions to express bowing patterns for string players
The study of bowing is critically important for string players. Traditional bowing annotations are a valuable part of orchestral and individual documentation, but they do not help the performer to search a piece for other passages that should be bowed the same way, or to identify alternative bowing styles. We introduce a notation based on regular expressions that describes patterns of notes in the music, as well as the bowing to be applied to the pattern. These expressions support complex bowings, and not just single annotations without musical context. The notation is simpler than general tools for regular expressions used in some software, and is suitable for use by students and musicians. We have developed a music editor that implements the notation and edits documents in Lilypond. The approach has been evaluated by experimenting with the editor on six violin sonatas by Mozart. The experiments demonstrate that the regular expression notation
is successful at finding passages and inserting the bowings; that the patterns occur a number of times; and the bowings can be inserted automatically and consistently
Compressed Text Indexes:From Theory to Practice!
A compressed full-text self-index represents a text in a compressed form and
still answers queries efficiently. This technology represents a breakthrough
over the text indexing techniques of the previous decade, whose indexes
required several times the size of the text. Although it is relatively new,
this technology has matured up to a point where theoretical research is giving
way to practical developments. Nonetheless this requires significant
programming skills, a deep engineering effort, and a strong algorithmic
background to dig into the research results. To date only isolated
implementations and focused comparisons of compressed indexes have been
reported, and they missed a common API, which prevented their re-use or
deployment within other applications.
The goal of this paper is to fill this gap. First, we present the existing
implementations of compressed indexes from a practitioner's point of view.
Second, we introduce the Pizza&Chili site, which offers tuned implementations
and a standardized API for the most successful compressed full-text
self-indexes, together with effective testbeds and scripts for their automatic
validation and test. Third, we show the results of our extensive experiments on
these codes with the aim of demonstrating the practical relevance of this novel
and exciting technology
Designing optimal- and fast-on-average pattern matching algorithms
Given a pattern and a text , the speed of a pattern matching algorithm
over with regard to , is the ratio of the length of to the number of
text accesses performed to search into . We first propose a general
method for computing the limit of the expected speed of pattern matching
algorithms, with regard to , over iid texts. Next, we show how to determine
the greatest speed which can be achieved among a large class of algorithms,
altogether with an algorithm running this speed. Since the complexity of this
determination make it impossible to deal with patterns of length greater than
4, we propose a polynomial heuristic. Finally, our approaches are compared with
9 pre-existing pattern matching algorithms from both a theoretical and a
practical point of view, i.e. both in terms of limit expected speed on iid
texts, and in terms of observed average speed on real data. In all cases, the
pre-existing algorithms are outperformed
Abstracting object interactions using composition filters
It is generally claimed that object-based models are very suitable for building distributed system architectures since object interactions follow the client-server model. To cope with the complexity of today's distributed systems, however, we think that high-level linguistic mechanisms are needed to effectively structure, abstract and reuse object interactions. For example, the conventional object-oriented model does not provide high-level language mechanisms to model layered system architectures. Moreover, we consider the message passing model of the conventional object-oriented model as being too low-level because it can only specify object interactions that involve two partner objects at a time and its semantics cannot be extended easily. This paper introduces Abstract Communication Types (ACTs), which are objects that abstract interactions among objects. ACTs make it easier to model layered communication architectures, to enforce the invariant behavior among objects, to reduce the complexity of programs by hiding the interaction details in separate modules and to improve reusability through the application of object-oriented principles to ACT classes. We illustrate the concept of ACTs using the composition filters model
Perceptually Motivated Shape Context Which Uses Shape Interiors
In this paper, we identify some of the limitations of current-day shape
matching techniques. We provide examples of how contour-based shape matching
techniques cannot provide a good match for certain visually similar shapes. To
overcome this limitation, we propose a perceptually motivated variant of the
well-known shape context descriptor. We identify that the interior properties
of the shape play an important role in object recognition and develop a
descriptor that captures these interior properties. We show that our method can
easily be augmented with any other shape matching algorithm. We also show from
our experiments that the use of our descriptor can significantly improve the
retrieval rates
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Detecting and removing noisy instances from concept descriptions
Several published results show that instance-based learning algorithms record high classification accuracies and low storage requirements when applied to supervised learning tasks. However, these learning algorithms are highly sensitive to training set noise. This paper describes a simple extension of instance-based learning algorithms for detecting and removing noisy instances from concept descriptions. The extension requires evidence that saved instances be significantly good classifiers before it allows them to be used for subsequent classification tasks. We show that this extension's performance degrades more slowly in the presence of noise, improves classification accuracies, and further reduces storage requirements in several artificial and real-world databases
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