How to produce a convolutional neural net that is small enough to run on a mobile device, and accurate enough to be worth using? The strategies in "Enabling AI...David Alexander Forsyth From Communications of the ACM | December 2020
We present a novel approach to running state-of-the-art AI algorithms in edge devices, and propose two efficient approximations to standard convolutional neural...Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, Ali Farhadi From Communications of the ACM | December 2020
What is the right leak oracle that can precisely capture the behavior of leaks in Web applications? "BLeak: Automatically Debugging Memory Leaks in Web Applications...Harry Xu From Communications of the ACM | November 2020
This paper introduces BLeak (Browser Leak debugger), the first system for automatically debugging memory leaks in web applications.
John Vilk, Emery D. Berger From Communications of the ACM | November 2020
The key insight of the "Generative Adversarial Networks," by Ian Goodfellow et al., is to learn a generative model's loss function at the same time as learning...Alexei A. Efros, Aaron Hertzmann From Communications of the ACM | November 2020
In this overview paper, we describe one particular approach to unsupervised learning via generative modeling called generative adversarial networks. We briefly...Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio From Communications of the ACM | November 2020
"MadMax: Analyzing the Out-of-Gas World of Smart Contracts," by Neville Grech et al., effectively discovers a new smart contract vulnerability, and proposes a detection...Benjamin Livshits From Communications of the ACM | October 2020
We identify gas-focused vulnerabilities and present MadMax: a static program analysis technique that automatically detects gas-focused vulnerabilities with very...Neville Grech, Michael Kong, Anton Jurisevic, Lexi Brent, Bernhard Scholz, Yannis Smaragdakis From Communications of the ACM | October 2020
"Lower Bounds for External Memory Integer Sorting via Network Coding" proves a remarkable connection between how efficiently computers can perform sorting and transmitting...Paul Beame From Communications of the ACM | October 2020
In this paper, we present a tight conditional lower bound on the complexity of external memory sorting of integers.
Alireza Farhadi, Mohammad Taghi Hajiaghayi, Kasper Green Larsen, Elaine Shi From Communications of the ACM | October 2020
There are few algorithms for multi-flow graphs beyond flow accumulation. The authors of "Flood-Risk Analysis on Terrains" take a big step to fill this knowledge...Shashi Shekhar From Communications of the ACM | September 2020
In this paper, we study a number of flood-risk related problems, give an overview of efficient algorithms for them, as well as explore the efficacy and efficiency...Aaron Lowe, Pankaj K. Agarwal, Mathias Rav From Communications of the ACM | September 2020
"Computing Value of Spatiotemporal Information," by Heba Aly et al., describes a technique for computing the monetary value of a person's location data for a potential...Cyrus Shahabi From Communications of the ACM | September 2020
We investigate the intrinsic value of location data in the context of strong privacy, where location information is only available from end users via purchase.
...Heba Aly, John Krumm, Gireeja Ranade, Eric Horvitz From Communications of the ACM | September 2020
We show that by making just a few changes to a parallel/distributed relational database system, such a system can become a competitive platform for scalable linear...Shangyu Luo, Zekai J. Gao, Michael Gubanov, Luis L. Perez, Dimitrije Jankov, Christopher Jermaine From Communications of the ACM | August 2020
Magellan's key insight is that a successful entity matching system must offer a versatile system building paradigm for entity matching that can be easily adapted...Wang-Chiew Tan From Communications of the ACM | August 2020
Entity matching can be viewed as a special class of data science problems and thus can benefit from system building ideas in data science.
AnHai Doan, Pradap Konda, Paul Suganthan G. C., Yash Govind, Derek Paulsen, Kaushik Chandrasekhar, Philip Martinkus, Matthew Christie From Communications of the ACM | August 2020
Can we build purpose-built, warehouse-scale datacenters customized for large-scale arrays of ASIC accelerators or, to use a term coined in the paper by Michael...Parthasarathy Ranganathan From Communications of the ACM | July 2020
This paper distills lessons from Bitcoin ASIC Clouds and applies them to other large scale workloads, showing superior TCO (total cost of ownership) versus CPU...Michael Bedford Taylor, Luis Vega, Moein Khazraee, Ikuo Magaki, Scott Davidson, Dustin Richmond From Communications of the ACM | July 2020