Communications of the ACM
Latest Research
From Communications of the ACM
Technical Perspective: On Abstractions and Embedded Networks
"Symbol-Synchronous Buses," by Jonathan Oostvogels et al., conceives a notion of a symbol-synchronous bus, which effectively makes a multi-hop wireless network...
From Communications of the ACM
Symbol-Synchronous Buses: Deterministic, Low-Latency Wireless Mesh Networking with LEDs
We describe a novel networking paradigm that aims to enable a new class of latency-sensitive applications by systematically breaking networking abstractions.
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...
From Communications of the ACM
Technical Perspective: The Power of Low-Power GPS Receivers for Nanosats
The work explored in "Hummingbird," by Sujay Narayana et al., focuses on the energy consumption of a typical GPS receiver and its operational challenges in a nanosat...
From Communications of the ACM
Hummingbird: An Energy-Efficient GPS Receiver for Small Satellites
In this work, we elucidate the design of a low-cost, low-power GPS receiver for small satellites.
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...
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...
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...
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.
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...
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.
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...
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...
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...
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.
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...
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...
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.
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...
From Communications of the ACM