Articles due for review: CLOSED
Publication date: August 2019
Computer plans an August 2019 special issue on data science and machine learning across the stack, which aims to recognize the deep penetration that data science and machine-learning (ML) techniques are making across the computing stack. Due to the prevalence of massive amounts of data, traditional computer science problems in areas as diverse as operating systems, databases, networking, workflow management, and HCI are now being revisited in terms of ML formulations.
For this special issue of Computer, we seek articles that identify the promise of new techniques, paying careful attention to the realm of the possible and limitations of these techniques. Articles should explain complex technical issues at a level conducive to Computer’s broad readership and how lessons learned from their projects can translate to other domains. The issue aims for a diversity of application areas; example topics include but are not restricted to:
- Data science solutions to classical computer science problems. A range of conferences (for example, SysML) have sprung up to reflect the growing interest in the intersection of machine learning and systems research. Prominent research has revisited classical problems using the new techniques of machine learning (for example, “learned index structures”). What is the potential for ML and data science to upend conventional wisdom and where are the areas where we can see significant improvement?
- New ML programming models and abstractions. As ML permeates more and more domains, programmers need significantly expressive tools to capture problem needs and specify solution strategies. What are the latest approaches to support the new generation of ML programmers and what software abstractions are available?
- Customized hardware solutions to ML problems. GPUs and GPU-based computing usher in a quantum leap in ML capability, and new customized hardware solutions are rapidly being proposed to address the growing demand. What are the latest system architectures to support the next generation of ML applications?
- Human-in-the-loop ML and mining: How can we accelerate human-in-the-loop ML and offer entirely new paradigms of HCI? How can techniques like crowdsourcing coexist with mining massive data?
Only submissions that describe previously unpublished, original, state-of-the-art research and that are not currently under review by a conference or journal will be considered. Extended versions of conference papers must be at least 30 percent different from the original conference work.
There is a strict 6,000-word limit (figures and tables are equivalent to 300 words each) for final manuscripts. Computer also caps references at 20. Authors should be aware that Computer cannot accept or process papers that exceed word count or reference limits.
Articles should be understandable by a broad audience of computer science and engineering professionals, avoiding a focus on theory, mathematics, jargon, and abstract concepts.
All manuscripts are subject to peer review on both technical merit and relevance to Computer’s readership.
Accepted papers must be well written and understandable, as the level of editing will be a light copyedit. For accepted papers, authors will be required to provide electronic files for each figure according to the following guidelines: for graphs and charts, authors must submit them in their original editable source format (PDF, Visio, Excel, Word, PowerPoint, etc.); for screenshots or photographs, authors must submit high-resolution files (300 dpi or higher at the largest possible dimensions) in JPEG or TIFF formats.
Please direct any questions about submissions to the guest editors (email@example.com):
For author guidelines and information on how to submit a manuscript electronically, visit www.computer.org/web/peer-review/magazines.