26,310 research outputs found
Analysing Data-To-Text Generation Benchmarks
Recently, several data-sets associating data to text have been created to
train data-to-text surface realisers. It is unclear however to what extent the
surface realisation task exercised by these data-sets is linguistically
challenging. Do these data-sets provide enough variety to encourage the
development of generic, high-quality data-to-text surface realisers ? In this
paper, we argue that these data-sets have important drawbacks. We back up our
claim using statistics, metrics and manual evaluation. We conclude by eliciting
a set of criteria for the creation of a data-to-text benchmark which could help
better support the development, evaluation and comparison of linguistically
sophisticated data-to-text surface realisers
Breaking the habit: measuring and predicting departures from routine in individual human mobility
Researchers studying daily life mobility patterns have recently shown that humans are typically highly predictable in their movements. However, no existing work has examined the boundaries of this predictability, where human behaviour transitions temporarily from routine patterns to highly unpredictable states. To address this shortcoming, we tackle two interrelated challenges. First, we develop a novel information-theoretic metric, called instantaneous entropy, to analyse an individual’s mobility patterns and identify temporary departures from routine. Second, to predict such departures in the future, we propose the first Bayesian framework that explicitly models breaks from routine, showing that it outperforms current state-of-the-art predictor
Garbage collection auto-tuning for Java MapReduce on Multi-Cores
MapReduce has been widely accepted as a simple programming pattern that can form the basis for efficient, large-scale, distributed data processing. The success of the MapReduce pattern has led to a variety of implementations for different computational scenarios. In this paper we present MRJ, a MapReduce Java framework for multi-core architectures. We evaluate its scalability on a four-core, hyperthreaded Intel Core i7 processor, using a set of standard MapReduce benchmarks. We investigate the significant impact that Java runtime garbage collection has on the performance and scalability of MRJ. We propose the use of memory management auto-tuning techniques based on machine learning. With our auto-tuning approach, we are able to achieve MRJ performance within 10% of optimal on 75% of our benchmark tests
Designing a CPU model: from a pseudo-formal document to fast code
For validating low level embedded software, engineers use simulators that
take the real binary as input. Like the real hardware, these full-system
simulators are organized as a set of components. The main component is the CPU
simulator (ISS), because it is the usual bottleneck for the simulation speed,
and its development is a long and repetitive task. Previous work showed that an
ISS can be generated from an Architecture Description Language (ADL). In the
work reported in this paper, we generate a CPU simulator directly from the
pseudo-formal descriptions of the reference manual. For each instruction, we
extract the information describing its behavior, its binary encoding, and its
assembly syntax. Next, after automatically applying many optimizations on the
extracted information, we generate a SystemC/TLM ISS. We also generate tests
for the decoder and a formal specification in Coq. Experiments show that the
generated ISS is as fast and stable as our previous hand-written ISS.Comment: 3rd Workshop on: Rapid Simulation and Performance Evaluation: Methods
and Tools (2011
FraudDroid: Automated Ad Fraud Detection for Android Apps
Although mobile ad frauds have been widespread, state-of-the-art approaches
in the literature have mainly focused on detecting the so-called static
placement frauds, where only a single UI state is involved and can be
identified based on static information such as the size or location of ad
views. Other types of fraud exist that involve multiple UI states and are
performed dynamically while users interact with the app. Such dynamic
interaction frauds, although now widely spread in apps, have not yet been
explored nor addressed in the literature. In this work, we investigate a wide
range of mobile ad frauds to provide a comprehensive taxonomy to the research
community. We then propose, FraudDroid, a novel hybrid approach to detect ad
frauds in mobile Android apps. FraudDroid analyses apps dynamically to build UI
state transition graphs and collects their associated runtime network traffics,
which are then leveraged to check against a set of heuristic-based rules for
identifying ad fraudulent behaviours. We show empirically that FraudDroid
detects ad frauds with a high precision (93%) and recall (92%). Experimental
results further show that FraudDroid is capable of detecting ad frauds across
the spectrum of fraud types. By analysing 12,000 ad-supported Android apps,
FraudDroid identified 335 cases of fraud associated with 20 ad networks that
are further confirmed to be true positive results and are shared with our
fellow researchers to promote advanced ad fraud detectionComment: 12 pages, 10 figure
An extensible benchmark and tooling for comparing reverse engineering approaches
Various tools exist to reverse engineer software source code and generate design information, such as UML projections. Each has specific strengths and weaknesses, however no standardised benchmark exists that can be used to evaluate and compare their performance and effectiveness in a systematic manner. To facilitate such comparison in this paper we introduce the Reverse Engineering to Design Benchmark (RED-BM), which consists of a comprehensive set of Java-based targets for reverse engineering and a formal set of performance measures with which tools and approaches can be analysed and ranked. When used to evaluate 12 industry standard tools performance figures range from 8.82\% to 100\% demonstrating the ability of the benchmark to differentiate between tools. To aid the comparison, analysis and further use of reverse engineering XMI output we have developed a parser which can interpret the XMI output format of the most commonly used reverse engineering applications, and is used in a number of tools
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