Abacus Contest Manager

Brought to you by: bmerry, jkroon

Downloads: 0 This Week

Last Update: 2019-10-26

Browse Code

Add to Preferred Sources

Linux BSD

Abacus is a Contest Mangement system for ACM-like contests written in C++, with an interactive web interface.

We no longer maintain the code on SF - please be sure to use the code from github.

Project Activity

See All Activity >

License

GNU General Public License version 2.0 (GPLv2)

Follow Abacus Contest Manager

Abacus Contest Manager Web Site

Other Useful Business Software

$300 Free Credits to Build on Google Cloud Icon

$300 Free Credits to Build on Google Cloud

New customers can spin up VMs, build with AI, and query data at no cost.

Put your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.

Start Free

Rate This Project

Login To Rate This Project

User Reviews

Be the first to post a review of Abacus Contest Manager!

Additional Project Details

Operating Systems

Languages

English

Intended Audience

Education

User Interface

Qt, Web-based

Programming Language

C++, Java, JavaScript

Database Environment

MySQL

Registered

2005-11-07

Report inappropriate content

$300 Free Credits to Build on Google Cloud

New customers can spin up VMs, build with AI, and query data at no cost.

Put your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.

Start Free

Recommended Projects

Abacus software
Software to control and analyze Tausand Abacus coincidence devices
DOMjudge programming contest jury system
DOMjudge is an automated judge system for programming contests.
KACTL
KTH algorithm competition template library
Abacus
A tool for extracting and preprocessing spectral count data for label-free quantitative proteomic analysis. This program is designed to be used with the Trans-Proteomic Pipeline (TPP).
Influence Maximization via Martingales
This is the code used in the experiments of the following paper: Youze Tang, Yanchen Shi, and Xiaokui Xiao. "Influence Maximization in Near-Linear Time: A Martingale Approach." Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM, 2015. Please cite the paper if you choose to use the code.