2015 ACM SIG Elections

The 2015 ACM SIG Elections are being conducted by Election Services Corporation (ESC) for the Special Interest Groups SIGACCESS, SIGACT, SIGAPP, SIGBED, SIGBio, SIGCHI, SIGDA, SIGecom, SIGEVO, ACM SIGGRAPH, SIGITE, SIGMETRICS, SIGMIS, SIGOPS, SIGPLAN, SIGSOFT, and SIGWEB.

On 2 April 2015, members of the following SIGs (who were in good standing as of 12 March 2015) were sent voting information from ESC: SIGACCESS, SIGACT, SIGAPP, SIGBED, SIGBio and SIGITE.

On 13 April 2015, members of the following SIGs (who were in good standing as of 24 March 2015) were sent voting information from ESC: SIGecom, SIGEVO, ACM SIGGRAPH, SIGMETRICS, SIGMIS, and SIGOPS.

On 30 April 2015, members of the following SIGs (who were in good standing as of 9 April 2015) were sent voting information from ESC: SIGCHI, SIGDA, SIGPLAN, SIGSOFT, and SIGWEB.

If you have not received email from Election Services Corporation, please contact acmsighelp@electionservicescorp.com.

Volunteer with SocialCoder

You can use your technical skills for social good and offer volunteer support on software development projects to organizations who could not otherwise afford it. SocialCoder connects volunteer programmers/software developers with registered charities and helps match them to suitable projects based on their skills, experience, and the causes they care about. Learn more about ACM’s new partnership with SocialCoder, and how you can get involved.

Prediction-Serving Systems

ACM Queue’s “Research for Practice” is your number one resource for keeping up with emerging developments in the world of theory and applying them to the challenges you face on a daily basis. In this installment, Dan Crankshaw and Joey Gonzalez provide an overview of machine learning server systems. What happens when we wish to actually deploy a machine learning model to production, and how do we serve predictions with high accuracy and high computational efficiency? Dan and Joey’s curated research selection presents cutting-edge techniques spanning database-level integration, video processing, and prediction middleware. Given the explosion of interest in machine learning and its increasing impact on seemingly every application vertical, it's possible that systems such as these will become as commonplace as relational databases are today.