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Upcoming Conferences
The International Conference on Software Maintenance and Evolution (ICSME) is the premier international forum for researchers and practitioners from academia, industry, and government to present, discuss, and debate the most recent ideas, experiences, and challenges in software maintenance and evolu...
29-04
Sep
Sep
We solicit original, unpublished research papers on computing technologies and visual languages for modeling, programming, communicating, and reasoning, which are easier to learn, use or understand by humans than the current state-of-the-art. Papers should focus on efforts to design, formalize, impl...
14-18
Oct
Oct
We solicit high-quality original research papers (and significant work-in-progress papers) in any aspect of Big Data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity), including the Big Data challenges in scientific and engineering, social, sensor/IoT/IoE, and multimedia (audio, v...
09-12
Dec
Dec
Trending from the Computer Society Digital Library
IEEE Software
Igor Steinmacher
Informatics, Computing, and Cyber Systems, Northern Arizona University, United States
Many community-based open source software (OSS) projects depend on a continuous influx of newcomers for their survival and continuity, yet newcomers face many barriers to contributing to a project. We provide guidelines based on our previous work for both OSS communities and newcomers to OSS project...
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Kaiming He
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of l...
IEEE Transactions on Pattern Analysis & Machine Intelligence
Tapas Kanungo
In k\hbox{-}{\rm{means}} clustering, we are given a set of n data points in d\hbox{-}{\rm{dimensional}} space {\bf{R}}^d and an integer k and the problem is to determine a set of k points in {\bf{R}}^d, called centers, so as to minimize the mean squared distance from each data point to its nearest c...