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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.

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: 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: Model Structure Takes Guesswork Out of State Estimation
From Communications of the ACM

Technical Perspective: Model Structure Takes Guesswork Out of State Estimation

"Worst-Case Topological Entropy and Minimal Data Rate for State Estimation of Switched Linear Systems" gives a method for computing the topological entropy of a...

Worst-Case Topological Entropy and Minimal Data Rate for State Estimation of Switched Linear Systems
From Communications of the ACM

Worst-Case Topological Entropy and Minimal Data Rate for State Estimation of Switched Linear Systems

In this paper, we study the problem of estimating the state of a switched linear system when the observation of the system is subject to communication constraints...

Technical Perspective: Neural Radiance Fields Explode on the Scene
From Communications of the ACM

Technical Perspective: Neural Radiance Fields Explode on the Scene

Neural volume rendering exploded onto the scene in 2020, triggered by "NeRF," the impressive paper by Ben Mildenhall et al., on Neural Radiance Fields.

NeRF
From Communications of the ACM

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene...

Technical Perspective: The Importance of WINOGRANDE
From Communications of the ACM

Technical Perspective: The Importance of WINOGRANDE

"WINOGRANDE" explores new methods of dataset development and adversarial filtering, expressly designed to prevent AI systems from making claims of smashing through...

WinoGrande
From Communications of the ACM

WinoGrande: An Adversarial Winograd Schema Challenge at Scale

We introduce WinoGrande, a large-scale dataset of 44k problems, inspired by the original Winograd Schema Challenge, but adjusted to improve both the scale and the...

Technical Perspective: The Quest for Optimal Multi-Item Auctions
From Communications of the ACM

Technical Perspective: The Quest for Optimal Multi-Item Auctions

"Optimal Auctions Through Deep Learning," by Paul Dütting et al., contributes a very interesting and forward-looking new take on the optimal multi-item mechanism...

Optimal Auctions Through Deep Learning
From Communications of the ACM

Optimal Auctions Through Deep Learning

We overview recent research results that show how tools from deep learning are shaping up to become a powerful tool for the automated design of near-optimal auctions...

Technical Perspective: An Elegant Model for Deriving Equations
From Communications of the ACM

Technical Perspective: An Elegant Model for Deriving Equations

"Deriving Equations from Sensor Data Using Dimensional Function Synthesis," by Vasileios Tsoutsouras, et al., addresses the key problem of discovering relationships...

Deriving Equations from Sensor Data Using Dimensional Function Synthesis
From Communications of the ACM

Deriving Equations from Sensor Data Using Dimensional Function Synthesis

We present a new method, which we call dimensional function synthesis, for deriving functions that model the relationship between multiple signals in a physical...
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