The breakthrough of "Achieving High Performance the Functional Way," by Bastian Hagedorn et al., is in fundamentally rethinking the design of user-schedulable languages...Jonathan Ragan-Kelley From Communications of the ACM | March 2023
We show how to employ functional programming techniques to solve with elegance the challenge of using a high-level language to describe functionality and a separate...Bastian Hagedorn, Johannes Lenfers, Thomas Kœhler, Xueying Qin, Sergei Gorlatch, Michel Steuwer From Communications of the ACM | March 2023
"Proving Data-Poisoning Robustness in Decision Trees," by Samuel Drews et al., addresses the challenge of processing an intractably large set of trained models...Martin Vechev From Communications of the ACM | February 2023
We present a sound verification technique based on abstract interpretation and implement it in a tool called Antidote, which abstractly trains decision trees for...Samuel Drews, Aws Albarghouthi, Loris D'Antoni From Communications of the ACM | February 2023
"DIAMetrics," by Shaleen Deep et al., describes a versatile framework from Google for automatic extraction of benchmarks and their distributed execution and performance...Peter Boncz From Communications of the ACM | December 2022
This paper introduces DIAMetrics: a novel framework for end-to-end benchmarking and performance monitoring of query engines.
Shaleen Deep, Anja Gruenheid, Kruthi Nagaraj, Hiro Naito, Jeff Naughton, Stratis Viglas From Communications of the ACM | December 2022
We propose several efficient data structures for the exact and approximate variants of the fair near neighbor problem.
Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri From Communications of the ACM | August 2022
In "Sampling Near Neighbors in Search for Fairness," Aumüller et al. investigate a basic problem in similarity search called near neighbor in the context of fair...Qin Zhang From Communications of the ACM | August 2022
"Expressive Querying for Accelerating Visual Analytics," by Tarique Siddiqui et al., provides a general abstraction, along with advanced interfaces, focusing on...Bill Howe From Communications of the ACM | July 2022
In this work, we introduce the problem of visualization search and highlight two underlying challenges of search enumeration and visualization matching.
Tarique Siddiqui, Paul Luh, Zesheng Wang, Karrie Karahalios, Aditya G. Parameswaran From Communications of the ACM | July 2022
"On Sampled Metrics for Item Recommendation," by Walid Krichene and Steffen Rendle, exposes a crucial aspect for the evaluation of algorithms and tools: the impact...Fabio Vandin From Communications of the ACM | July 2022
This paper investigates sampled metrics and shows that it is possible to improve the quality of sampled metrics by applying a correction, obtained by minimizing...Walid Krichene, Steffen Rendle From Communications of the ACM | July 2022
"Rateless Codes for Near-Perfect Load Balancing in Distributed Matrix-Vector Multiplication" addresses the problem of selecting code rates to optimize system performance...Emina Soljanin From Communications of the ACM | May 2022
We propose a rateless fountain coding strategy and prove that its latency is asymptotically equal to ideal load balancing, and it performs asymptotically zero redundant...Ankur Mallick, Malhar Chaudhari, Utsav Sheth, Ganesh Palanikumar, Gauri Joshi From Communications of the ACM | May 2022
This paper presents a study on the practicality of operating system kernel debloating, that is, reducing kernel code that is not needed by the target applications...Hsuan-Chi Kuo, Jianyan Chen, Sibin Mohan, Tianyin Xu From Communications of the ACM | May 2022
In "FANG," the authors focus on a strategy of automatically detecting disinformation campaigns on online media with a new graph-based, contextual technique for...Philippe Cudré-Mauroux From Communications of the ACM | April 2022
We propose Factual News Graph (FANG), a novel graphical social context representation and learning framework for fake news detection.
Van-Hoang Nguyen, Kazunari Sugiyama, Preslav Nakov, Min-Yen Kan From Communications of the ACM | April 2022
"Here We Go Again: Why Is It Difficult for Developers to Learn Another Programming Language?" by Shrestha et al. provides insight into the difficulty of learning...Jonathan Aldrich From Communications of the ACM | March 2022
Our findings demonstrate that interference is a widespread phenomenon, forcing programmers to adopt suboptimal, opportunistic learning strategies.
Nischal Shrestha, Colton Botta, Titus Barik, Chris Parnin From Communications of the ACM | March 2022