DEPARTMENT:
From the President
To the Members of ACM
This is an update on the efforts of the ACM Executive Committee and Council to draft a longer-term strategic plan for our organization.
Yannis Ioannidis
Page 5
DEPARTMENT:
Cerf's Up
On QR Codes and Safety
The QR code, a convenient mechanism for delivering digital information to a reader, does not come with any human-readable way to ascertain safety. You have no way to tell whether it is potentially malicious.
Vinton G. Cerf
Page 7
DEPARTMENT:
Letters to the Editor
Neighborhood Watch
Vinton G. Cerf wonders "whether there is any possibility of establishing 'watcher networks'" in his October 2022 Communications "Cerf's Up" column. Philip K. Dick describes this problem in his story The Minority Report.
CACM Staff
Pages 8-11
DEPARTMENT:
[email protected]
What is Data Science?
Koby Mike and Orit Hazzan consider why multiple definitions are needed to pin down data science.
Koby Mike, Orit Hazzan
Pages 12-13
COLUMN:
News
Post-Quantum Cryptography
Cryptographers seek algorithms quantum computers cannot break.
Don Monroe
Pages 15-17
Computational Linguistics Finds Its Voice
Advances in artificial intelligence permit computers to converse with humans in seemingly realistic ways.
Samuel Greengard
Pages 18-20
Can AI Demonstrate Creativity?
When fed a sufficient amount of training data, artificial intelligence techniques can be used to generate new ideas in several different ways. Is that creativity?
Keith Kirkpatrick
Pages 21-23
SECTION:
Education
Four Ways to Add Active Learning to Computing Courses
How active-learning techniques can benefit students in computing courses.
Barbara Ericson
Pages 26-29
COLUMN:
Kode Vicious
The Elephant in the Room
It is time to get the POSIX elephant off our necks.
George V. Neville-Neil
Pages 30-31
COLUMN:
Computing Ethics
Ethical AI is Not about AI
The equation Ethics + AI = Ethical AI is questionable.
Deborah G. Johnson, Mario Verdicchio
Pages 32-34
COLUMN:
Viewpoint
Software Engineering of Machine Learning Systems
Seeking to make machine learning more dependable.
Charles Isbell, Michael L. Littman, Peter Norvig
Pages 35-37
Building Machine Learning Models Like Open Source Software
Proposing a community-based system for model development.
Colin Raffel
Pages 38-40
The Premature Obituary of Programming
Why deep learning will not replace programming.
Daniel M. Yellin
Pages 41-44
An Analysis of Black Faculty in CS Research Departments
Exploring Black faculty at computer science research departments where Ph.D. programs exist.
Juan E. Gilbert, Jeremy A. Magruder Waisome, Simone Smarr
Pages 45-47
SECTION:
Practice
From Zero to 100
Demystifying zero trust and its implications on enterprise people, process, and technology.
Matthew Bush, Atefeh Mashatan
Pages 48-55
The Arrival of Zero Trust: What Does it Mean?
A discussion of zero-trust enterprise efforts in cybersecurity.
Michael Loftus, Andrew Vezina, Rick Doten, Atefeh Mashatan
Pages 56-62
SECTION:
Contributed Articles
Extracting the Essential Simplicity of the Internet
Looking past inessential complexities to explain the Internet's simple yet daring design.
James Mccauley, Scott Shenker, George Varghese
Pages 64-74
(Re)Use of Research Results (Is Rampant)
Prior pessimism about reuse in software engineering research may have been a result of using the wrong methods to measure the wrong things.
Maria Teresa Baldassarre, Neil Ernst, Ben Hermann, Tim Menzies, Rahul Yedida
Pages 75-81
HPC Forecast: Cloudy and Uncertain
An examination of how the technology landscape has changed and possible future directions for HPC operations and innovation.
Daniel Reed, Dennis Gannon, Jack Dongarra
Pages 82-90
SECTION:
Review Articles
The Lean Data Scientist: Recent Advances Toward Overcoming the Data Bottleneck
A taxonomy of the methods used to obtain quality datasets enhances existing resources.
Chen Shani, Jonathan Zarecki, Dafna Shahaf
Pages 92-102
SECTION:
Research Highlights
Technical Perspective: Beautiful Symbolic Abstractions for Safe and Secure Machine Learning
"Proving Data-Poisoning Robustness in Decision Trees," by Samuel Drews et al., addresses the challenge of processing an intractably large set of trained models when enumeration is infeasible in a clean, beautiful, and elegant …
Martin Vechev
Page 104
Proving Data-Poisoning Robustness in Decision Trees
We present a sound verification technique based on abstract interpretation and implement it in a tool called Antidote, which abstractly trains decision trees for an intractably large space of possible poisoned datasets.
Samuel Drews, Aws Albarghouthi, Loris D'Antoni
Pages 105-113
COLUMN:
Last Byte
Aftermath Impact
An ancient Roman dispatched to find the greatest technological advances of the time may lose something of far greater importance.
William Sims Bainbridge
Pages 116-ff