Hasil untuk "Computer software"

J. Biamonte, P. Wittek, Nicola Pancotti et al.

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

Daniel L. Chen, Daniel L. Chen, Martin Schonger et al.

oTree is an open-source and online software for implementing interactive experiments in the laboratory, online, the field or combinations thereof. oTree does not require installation of software on subjects’ devices; it can run on any device that has a web browser, be that a desktop computer, a tablet or a smartphone. Deployment can be internet-based without a shared local network, or local-network-based even without internet access. For coding, Python is used, a popular, open-source programming language. www.oTree.org provides the source code, a library of standard game templates and demo games which can be played by anyone.

G. Gibbs

Justin Kuczynski, Jesse Stombaugh, William Walters et al.

Owen R. Jones, Jinliang Wang

David Benavides, Sergio Segura, Antonio Ruiz-Cortés

Software product line engineering is about producing a set of related products that share more commonalities than variabilities. Feature models are widely used for variability and commonality management in software product lines. Feature models are information models where a set of products are represented as a set of features in a single model. The automated analysis of feature models deals with the computer-aided extraction of information from feature models. The literature on this topic has contributed with a set of operations, techniques, tools and empirical results which have not been surveyed until now. This paper provides a comprehensive literature review on the automated analysis of feature models 20 years after of their invention. This paper contributes by bringing together previously disparate streams of work to help shed light on this thriving area. We also present a conceptual framework to understand the different proposals as well as categorise future contributions. We finally discuss the different studies and propose some challenges to be faced in the future.

Cole Trapnell, L. Pachter, Steven Salzberg

Motivation: A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, generates millions of short sequence fragments in a single run. These fragments, or ‘reads’, can be used to measure levels of gene expression and to identify novel splice variants of genes. However, current software for aligning RNA-Seq data to a genome relies on known splice junctions and cannot identify novel ones. TopHat is an efficient read-mapping algorithm designed to align reads from an RNA-Seq experiment to a reference genome without relying on known splice sites. Results: We mapped the RNA-Seq reads from a recent mammalian RNA-Seq experiment and recovered more than 72% of the splice junctions reported by the annotation-based software from that study, along with nearly 20 000 previously unreported junctions. The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer. We describe several challenges unique to ab initio splice site discovery from RNA-Seq reads that will require further algorithm development. Availability: TopHat is free, open-source software available from http://tophat.cbcb.umd.edu Contact: cole@cs.umd.edu Supplementary information: Supplementary data are available at Bioinformatics online.

D. Drouin, A. R. Couture, Dany Joly et al.

W. Gropp, E. Lusk, Nathan E. Doss et al.

Nigel Gi lbert, K. G. Troitzsch

Zhengyou Zhang

A. Duchowski

S. K. Jenson, Julia O. Domingue

Kiam Heong Ang, G. Chong, Yun Li

Stephen D. Oliner, Daniel Sichel, Daniel Sichel

J. Dongarra

This report compares the performance of different computer systems in solving dense systems of linear equations. The comparison involves approximately a hundred computers, ranging from a CRAY Y-MP to scientific workstations such as the Apollo and Sun to IBM PCs.

Gerhard Fischer

I. Iervolino, C. Galasso, E. Cosenza

D. Ince, L. Hatton, John Graham-Cumming

Aurek Chattopadhyay, Nan Niu

Identifying the features to be released in the next version of software, from a pool of potential candidates, is a challenging problem. User feedback from app stores is frequently used by software vendors for the evolution of apps across releases. Privacy feedback, although smaller in volume, carries a larger impact influencing app's success. Multiple existing work has focused on summarizing privacy concerns at the app level and has also shown that developers utilize feedback to implement security and privacy-related changes in subsequent releases. However, the current literature offers little support for release managers and developers in identifying privacy concerns prior to release. This gap exists as user reviews are typically available in app stores only after new features of a software system is released. In this paper, we introduce Pre-PI, a novel approach that summarizes privacy concerns for to-be-released features. Our method first maps existing features to semantically similar privacy reviews to learn feature-privacy review relations. We then simulate feedback for candidate features and generate concise summaries of privacy concerns. We evaluate Pre-PI across three real-world apps, and compare it with Hark, a state-of-the-art method that relies on post-release user feedback to identify privacy concerns. Results show that Pre-PI generates additional valid privacy concerns and identifies these concerns earlier than Hark, allowing proactive mitigation prior to release.