6,652 research outputs found
From Method Fragments to Method Services
In Method Engineering (ME) science, the key issue is the consideration of
information system development methods as fragments. Numerous ME approaches
have produced several definitions of method parts. Different in nature, these
fragments have nevertheless some common disadvantages: lack of implementation
tools, insufficient standardization effort, and so on. On the whole, the
observed drawbacks are related to the shortage of usage orientation. We have
proceeded to an in-depth analysis of existing method fragments within a
comparison framework in order to identify their drawbacks. We suggest
overcoming them by an improvement of the ?method service? concept. In this
paper, the method service is defined through the service paradigm applied to a
specific method fragment ? chunk. A discussion on the possibility to develop a
unique representation of method fragment completes our contribution
PF-OLA: A High-Performance Framework for Parallel On-Line Aggregation
Online aggregation provides estimates to the final result of a computation
during the actual processing. The user can stop the computation as soon as the
estimate is accurate enough, typically early in the execution. This allows for
the interactive data exploration of the largest datasets. In this paper we
introduce the first framework for parallel online aggregation in which the
estimation virtually does not incur any overhead on top of the actual
execution. We define a generic interface to express any estimation model that
abstracts completely the execution details. We design a novel estimator
specifically targeted at parallel online aggregation. When executed by the
framework over a massive TPC-H instance, the estimator provides
accurate confidence bounds early in the execution even when the cardinality of
the final result is seven orders of magnitude smaller than the dataset size and
without incurring overhead.Comment: 36 page
SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos
In this paper, we introduce SoccerNet, a benchmark for action spotting in
soccer videos. The dataset is composed of 500 complete soccer games from six
main European leagues, covering three seasons from 2014 to 2017 and a total
duration of 764 hours. A total of 6,637 temporal annotations are automatically
parsed from online match reports at a one minute resolution for three main
classes of events (Goal, Yellow/Red Card, and Substitution). As such, the
dataset is easily scalable. These annotations are manually refined to a one
second resolution by anchoring them at a single timestamp following
well-defined soccer rules. With an average of one event every 6.9 minutes, this
dataset focuses on the problem of localizing very sparse events within long
videos. We define the task of spotting as finding the anchors of soccer events
in a video. Making use of recent developments in the realm of generic action
recognition and detection in video, we provide strong baselines for detecting
soccer events. We show that our best model for classifying temporal segments of
length one minute reaches a mean Average Precision (mAP) of 67.8%. For the
spotting task, our baseline reaches an Average-mAP of 49.7% for tolerances
ranging from 5 to 60 seconds. Our dataset and models are available at
https://silviogiancola.github.io/SoccerNet.Comment: CVPR Workshop on Computer Vision in Sports 201
Drawing Elena Ferrante's Profile. Workshop Proceedings, Padova, 7 September 2017
Elena Ferrante is an internationally acclaimed Italian novelist whose real identity has been kept secret by E/O publishing house for more than 25 years. Owing to her popularity, major Italian and foreign newspapers have long tried to discover her real identity. However, only a few attempts have been made to foster a scientific debate on her work.
In 2016, Arjuna Tuzzi and Michele Cortelazzo led an Italian research team that conducted a preliminary study and collected a well-founded, large corpus of Italian novels comprising 150 works published in the last 30 years by 40 different authors. Moreover, they shared their data with a select group of international experts on authorship attribution, profiling, and analysis of textual data: Maciej Eder and Jan Rybicki (Poland), Patrick Juola (United States), Vittorio Loreto and his research team, Margherita Lalli and Francesca Tria (Italy), George Mikros (Greece), Pierre Ratinaud (France), and Jacques Savoy (Switzerland).
The chapters of this volume report the results of this endeavour that were first presented during the international workshop Drawing Elena Ferrante's Profile in Padua on 7 September 2017 as part of the 3rd IQLA-GIAT Summer School in Quantitative Analysis of Textual Data. The fascinating research findings suggest that Elena Ferrante\u2019s work definitely deserves \u201cmany hands\u201d as well as an extensive effort to understand her distinct writing style and the reasons for her worldwide success
A memory-based classification approach to marker-based EBMT
We describe a novel approach to example-based machine translation that makes use of marker-based chunks, in which the decoder is a memory-based classifier. The classifier is trained to map trigrams of source-language chunks onto trigrams of target-language chunks; then, in a second
decoding step, the predicted trigrams are rearranged according to their overlap. We present the first results of this method on a Dutch-to-English translation system
using Europarl data. Sparseness of the class space causes the results to lag behind a baseline phrase-based SMT system.
In a further comparison, we also
apply the method to a word-aligned version
of the same data, and report a smaller
difference with a word-based SMT system.
We explore the scaling abilities of the
memory-based approach, and observe linear
scaling behavior in training and classification
speed and memory costs, and loglinear
BLEU improvements in the amount
of training examples
Tracing program transformations with string origins
Program transformations play an important role in domain-specific languages and model-driven development. Tracing the execution of such transformations has well-known benefits for debugging, visualization and error reporting. In this paper we introduce string origins as a lightweight, generic and portable technique to establish a tracing relation between the textual fragments in the input and output of a program transformation. We discuss the semantics and the implementation of string origins using the Rascal meta programming language as an example. Furthermore, we illustrate the utility of string origins by presenting data structures and operations for tracing generated code, implementing protected regions, performing name resolution, and fixing inadvertent name capture in generated code
- …