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Technical Perspective: Reconsidering the Design of User-Schedulable Languages
From Communications of the ACM

Technical Perspective: Reconsidering the Design of User-Schedulable Languages

The breakthrough of "Achieving High Performance the Functional Way," by Bastian Hagedorn et al., is in fundamentally rethinking the design of user-schedulable languages...

Achieving High Performance the Functional Way
From Communications of the ACM

Achieving High Performance the Functional Way: Expressing High-Performance Optimizations as Rewrite Strategies

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...

Technical Perspective: Beautiful Symbolic Abstractions for Safe and Secure Machine Learning
From Communications of the ACM

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...

Proving Data-Poisoning Robustness in Decision Trees
From Communications of the ACM

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...

Technical Perspective: The 'Art' of Automatic Benchmark Extraction
From Communications of the ACM

Technical Perspective: The 'Art' of Automatic Benchmark Extraction

"DIAMetrics," by Shaleen Deep et al., describes a versatile framework from Google for automatic extraction of benchmarks and their distributed execution and performance...

DIAMETRICS
From Communications of the ACM

DIAMETRICS: Benchmarking Query Engines at Scale

This paper introduces DIAMetrics: a novel framework for end-to-end benchmarking and performance monitoring of query engines.

Sampling Near Neighbors in Search for Fairness
From Communications of the ACM

Sampling Near Neighbors in Search for Fairness

We propose several efficient data structures for the exact and approximate variants of the fair near neighbor problem.

Technical Perspective: Can Data Structures Treat Us Fairly?
From Communications of the ACM

Technical Perspective: Can Data Structures Treat Us Fairly?

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...

Technical Perspective: Visualization Search: From Sketching to Natural Language
From Communications of the ACM

Technical Perspective: Visualization Search: From Sketching to Natural Language

"Expressive Querying for Accelerating Visual Analytics," by Tarique Siddiqui et al., provides a general abstraction, along with advanced interfaces, focusing on...

Expressive Querying for Accelerating Visual Analytics
From Communications of the ACM

Expressive Querying for Accelerating Visual Analytics

In this work, we introduce the problem of visualization search and highlight two underlying challenges of search enumeration and visualization matching.

Technical Perspective: Evaluating Sampled Metrics Is Challenging
From Communications of the ACM

Technical Perspective: Evaluating Sampled Metrics Is Challenging

"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...

On Sampled Metrics for Item Recommendation
From Communications of the ACM

On Sampled Metrics for Item Recommendation

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...

Technical Perspective: Balancing At All Loads
From Communications of the ACM

Technical Perspective: Balancing At All Loads

"Rateless Codes for Near-Perfect Load Balancing in Distributed Matrix-Vector Multiplication" addresses the problem of selecting code rates to optimize system performance...

Rateless Codes for Near-Perfect Load Balancing in Distributed Matrix-Vector Multiplication
From Communications of the ACM

Rateless Codes for Near-Perfect Load Balancing in Distributed Matrix-Vector Multiplication

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...

Technical Perspective: 'What Is the Ideal Operating System?'
From Communications of the ACM

Technical Perspective: 'What Is the Ideal Operating System?'

The authors of "Set the Configuration for the Heart of the OS" put a fresh view on the practicability of automatic kernel debloating.

Set the Configuration for the Heart of the OS
From Communications of the ACM

Set the Configuration for the Heart of the OS: On the Practicality of Operating System Kernel Debloating

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...

Technical Perspective: Leveraging Social Context for Fake News Detection
From Communications of the ACM

Technical Perspective: Leveraging Social Context for Fake News Detection

In "FANG," the authors focus on a strategy of automatically detecting disinformation campaigns on online media with a new graph-based, contextual technique for...

FANG
From Communications of the ACM

FANG: Leveraging Social Context for Fake News Detection Using Graph Representation

We propose Factual News Graph (FANG), a novel graphical social context representation and learning framework for fake news detection.

Technical Perspective: How Do Experts Learn New Programming Languages?
From Communications of the ACM

Technical Perspective: How Do Experts Learn New Programming Languages?

"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...

Here We Go Again
From Communications of the ACM

Here We Go Again: Why Is It Difficult for Developers to Learn Another Programming Language?

Our findings demonstrate that interference is a widespread phenomenon, forcing programmers to adopt suboptimal, opportunistic learning strategies.
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