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2022-jmlr-tell

5gdataset

In this work, we present a 5G trace dataset collected from a major Irish mobile operator. The dataset is generated from two mobility patterns (static and car), and across two application patterns(video streaming and file download). The dataset is composed of client-side cellular key performance indicators (KPIs) comprised of channel-related metrics, context-related metrics, cell-related metrics and throughput information. These metrics are generated from a well-known non-rooted Android network monitoring application, G-NetTrack Pro. To the best of our knowledge, this is the first publicly available dataset that contains throughput, channel and context information for 5G networks. To supplement our real-time 5G production network dataset, we also provide a 5G large scale multi-cell ns-3 simulation framework. The availability of the 5G/mmwave module for the ns-3 mmwave network simulator provides an opportunity to improve our understanding of the dynamic reasoning for adaptive clients in 5G multi-cell wireless scenarios. The purpose of our framework is to provide additional information (such as competing metrics for users connected to the same cell), thus providing otherwise unavailable information about the basestation (eNodeB or eNB) environment and scheduling principle, to end user. Our framework permits other researchers to investigate this interaction through the generation of their own synthetic datasets.

adv-signal-clfr

adversarial-intrusion-detection

adversarial-radioml

adversarialtraining-stochasticepsilon

akron

TBA

algforopt-notebooks

Jupyter notebooks associated with the Algorithms for Optimization textbook

amc-aml

atsd-experiments

azure-setup

b2agging

tbd

baggingvariance

confidence variance and new class estimation

beta_poisoning

Official implementation of 'The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers?' [Submitted to IJCNN 2021]

bigls2014-code

Codes for the BigLS Workshop (2014)

bio-course-materials

Course materials for a bioinformatics course.

biom-map-utils

Load biom and map files that are used in QIIME; however, there are no deps on QIIME or BIOM with this script.

biom2lefse

Convert Biom files to a LefSe compatible file format.

compsense.jl

conceptdriftdata

Generate synthetic data sets containing concept drift, or load one of two real-world concept drift benchmark data sets.

conceptdriftresources

A collection of resources for concept drift data and software

continual-learning-papers

Continual Learning papers list, curated by ContinualAI

controlsystemfigures

coresets

Coresets

cs-kernel-reconstruction

cyber-threat-detection

d2l-en

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.

data-poison-incremental-learning

datacollections

random collections of data

deep_mahalanobis_detector

Code for the paper "A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks".