Deep learning (DL) finds rich applications in the wireless domain to improve spectrum awareness. Typically, DL models are either randomly initialized following a statistical distribution or pretrained on tasks from other domains in the form of transfer learning without accounting for the unique characteristics of wireless signals. Self-supervised learning (SSL) enables the learning of useful representations from Radio Frequency (RF) signals themselves even when only limited training data samples with labels are available. We present a self-supervised RF signal representation learning method and apply it to the automatic modulation recognition (AMR) task by specifically formulating a set of transformations to capture the wireless signal characteristics. We show that the sample efficiency (the number of labeled samples needed to achieve a certain performance) of AMR can be significantly increased (almost an order of magnitude) by learning signal representations with SSL. This translates to substantial time and cost savings. Furthermore, SSL increases the model accuracy compared to the state-of-the-art DL methods and maintains high accuracy when limited training data is available.
A Streamlit-based Artificial Intelligence Trust Platform for Next-Generation Wireless Networks
Old Dominion University（奥多明尼昂大学）；University of Stavanger（斯塔万格大学）；Virginia Commonwealth University（弗吉尼亚联邦大学）；Norwegian University of Science and Technology（挪威科技大学）；eKare, Inc.（eKare公司）
随着人工智能（AI）方法在下一代网络（NextG）中的快速发展和集成，AI算法在频谱使用、带宽、延迟和安全性方面为NextG提供了显著优势。NextG的一个关键特征是AI的集成，即，基于自监督算法的自学习架构，以提高网络的性能。一个安全的人工智能驱动的结构也有望保护NextG网络免受网络攻击。然而，AI本身可能会受到攻击，即，模型中毒是攻击者的目标，它会导致网络安全违规。本文提出了一个使用Streamlit for NextG网络的AI信任平台，允许研究人员评估，防御，认证和验证他们的AI模型和应用程序，以抵御规避，中毒，提取和干扰等对抗性威胁。
University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯大学医院及医学院）；National Center for Tumor Diseases (NCT), University Hospital Heidelberg（海德堡大学医院国家肿瘤疾病中心）；Leeds Institute of Medical Research at St James’s, University of Leeds（利兹大学圣詹姆斯利兹医学研究所）
Self Supervised Learning for Few Shot Hyperspectral Image Classification
German Aerospace Center (DLR)（德国航空航天中心）；Technical University of Munich (TUM)（慕尼黑工业大学）；Inria（法国国家信息与自动化研究所）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔国立理工学院）
Glasgow College, University of Electronic Science and Technology of China（电子科技大学格拉斯哥学院）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；School of Foreign Languages, University of Electronic Science and Technology of China（电子科技大学外国语学院）
Self-supervised Learning for Sonar Image Classification
German Research Center for Artificial Intelligence（德国人工智能研究中心）；Bonn-Rhein-Sieg University of Applied Sciences（波恩-莱茵-锡格应用科学大学）；University of Groningen（格罗宁根大学）
DisenSemi: Semi-supervised Graph Classification via Disentangled Representation Learning
University of International Business and Economics（对外经济贸易大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Terminus Group（特美刻集团）；Peking University（北京大学）；Sichuan University（四川大学）
Semi-supervised Nonnegative Matrix Factorization for Document Classification
Harvey Mudd College（哈维穆德学院）；Colorado State Univ.（科罗拉多州立大学）；Univ. of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Cal. State Univ. Channel Islands（加州州立大学海峡群岛分校）；Wofford College（沃福德学院）；New York Univ.（纽约大学）；Univ. of Alabama（阿拉巴马大学）；Univ. of Cal., Los Angeles（加州大学洛杉矶分校）；Boston Medical Cent.（波士顿医疗中心）；Southern Methodist Univ.（南卫理公会大学）；Occidental College（西方学院）
