Executable Papers - improving the article format in computer science

Through the Article of the Future, Elsevier’s ongoing program to improve the online article format, publications on ScienceDirect benefit from improved presentation, useful contextual information, and better support for digital content. The Executable Paper builds on recent advances to online articles by providing a solution that improves the reproducibility of computational work.

The executable paper was conceived in 2011, when Elsevier launched the Executable Papers Grand Challenge, looking to the computer science community to address the question of how to reproduce computational results within the confines of the research article. The winner: the Collage Authoring Environment* is now launching a pilot special issue on 3D Object Retrieval with the journal Computers & Graphics, showcasing executable research results in articles.

In an executable paper, authors have the ability to embed chunks of executable code and data into their papers, and readers may execute that code within the framework of the research article. This creates a seamless user experience that allows readers to explore the computational elements while reading the article.

Our video explains how to use and access executable papers:

Special Issue on 3D Object Retrieval – features the Collage Authoring Environment

Computers & GraphicsGUEST EDITORS

Michela Spagnuolo, CNR-IMATI, Geneva, Italy
Remco Veltkamp, Utrecht University, Netherlands

As a theme for this first special issue showcasing Collage, we selected 3D Object Retrieval. Content based 3D retrieval is poised to become crucial in many modern computer graphics applications, such as computer-aided design, game development and film production. Several approaches have been proposed which adopt different methods to evaluate similarity and matching a query shape against objects in the database. Similarity evaluation is a challenging computational task, whose complexity ranges from defining global descriptors to establishing correspondences among parts of different objects. In this context, we invited contributions in 3D shape analysis, 3D object classification and indexing, 3D object matching, query interfaces and search modalities.

Landmark Transfer with Minimal Graph
By Vasyl Mykhalchuk, Frederic Cordier, Hyewon Seo

Evaluating 3D Spatial Pyramids for Classifying 3D Shapes
By Roberto J. Lopez-Sastre, Alberto García-Fuertes, Carolina Redondo-Cabrera, Francisco J Acevedo-Rodríguez, Saturnino Maldonado-Bascón

Data-aware 3D Partitioning for Generic Shape Retrieval
By Ivan Sipiran, Benjamin Bustos, Tobias Schreck

A Flexible and Extensible Approach to Automated CAD/CAM Format Classification
By Vincent A Cicirello, William C Regli

An Interactive Analysis of Harmonic and Diffusion Equations on Discrete 3D Shapes
By Giuseppe Patane, Michela Spagnuolo

Efficient 3D object recognition using foveated point clouds
By Rafael Beserra, Bruno Silva, Lourena Rocha, Rafael Aroca, Luiz Velho, Luiz M Gonçalves

Matching 3D Face Scans using Interest Points and Local Histogram Descriptors
By Stefano Berretti, Naoufel Werghi, Alberto del Bimbo, Pietro Pala

Tell us what you think

To help us make improvements to the Executable Paper, we would really appreciate your feedback. If you can spare five minutes, please take part in our survey on the Collage Executable Paper. We appreciate your help in advance!


*The Collage Authoring Environment enables authors to embed chunks of executable code and data into their papers; and allows the readers to execute that code on the underlying computing infrastructure. It combines the narrative of the computational experiment with embedded artifacts that run code and datasets, producing verified results. To learn more, visit: https://collage.elsevier.com/.