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Technical Perspective: Traffic Classification in the Era of Deep Learning
From Communications of the ACM

Technical Perspective: Traffic Classification in the Era of Deep Learning

"Traffic Classification in an Increasingly Encrypted Web," by Iman Akbari et al., does a great job in reviewing related work in the network traffic classification...

Traffic Classification in an Increasingly Encrypted Web
From Communications of the ACM

Traffic Classification in an Increasingly Encrypted Web

In this paper, we design a novel feature engineering approach used for encrypted Web protocols, and develop a neural network architecture based on stacked long...

Technical Perspective: Physical Layer Resilience through Deep Learning in Software Radios
From Communications of the ACM

Technical Perspective: Physical Layer Resilience through Deep Learning in Software Radios

"Polymorphic Wireless Receivers," by Francesco Restuccia and Tommaso Melodia, tackles the problem of physical layer resilience in wireless systems from a completely...

Polymorphic Wireless Receivers
From Communications of the ACM

Polymorphic Wireless Receivers

We introduce PolymoRF, a deep learning-based polymorphic receiver able to reconfigure itself in real time based on the inferred waveform parameters.

Technical Perspective: hXDP
From Communications of the ACM

Technical Perspective: hXDP: Light and Efficient Packet Processing Offload

In "hXDP: Efficient Software Packet Processing on FPGA NICs," the authors offer an interesting solution to bridging the performance gap between the CPU and the...

hXDP
From Communications of the ACM

hXDP: Efficient Software Packet Processing on FPGA NICs

We present hXDP, a solution to run on FPGAs software packet processing tasks described with the eBPF technology and targeting the Linux's eXpress Data Path.

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: The Compression Power of the BWT
From Communications of the ACM

Technical Perspective: The Compression Power of the BWT

"Resolution of the Burrows-Wheeler Transform Conjecture," by Dominik Kempa and Tomasz Kociumaka, finally settles the question of how well r in the BWT captures...

Resolution of the Burrows-Wheeler Transform Conjecture
From Communications of the ACM

Resolution of the Burrows-Wheeler Transform Conjecture

In this paper, we show that r = (z log2 n) holds for every text. This result has numerous implications for text indexing and data compression.

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.
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