Training multiple-layered deep neural networks (DNNs) is difficult. The standard practice of using a large number of samples for training often does not improve the performance of a DNN to a satisfactory level. Thus, a systematic training approach is needed. To address this need, we introduce an innovative approach of using metamorphic relations (MRs) as "knowledge carriers" to train DNNs. Based on the concept of metamorphic testing and MRs (which play the role of a test oracle in software testing), we make use of the notion of metamorphic group of inputs as concrete instances of MRs (which are abstractions of knowledge) to train a DNN in a systematic and effective manner. To verify the viability of our training approach, we have conducted a preliminary experiment to compare the performance of two DNNs: one trained with MRs and the other trained without MRs. We found that the DNN trained with MRs has delivered a better performance, thereby confirming that our approach of using MRs as knowledge carriers to train DNNs is promising. More work and studies, however, are needed to solidify and leverage this approach to generate widespread impact on effective DNN training.
Using Metamorphic Relations to Verify and Enhance Artcode Classification
University of Cambridge（剑桥大学）；University of Nottingham Ningbo China（宁波诺丁汉大学）；University of Nottingham（诺丁汉大学）；University of Wollongong（伍伦贡大学）；Swinburne University of Technology（斯威本科技大学）
Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocation
Beijing University of Posts and Telecommunications（北京邮电大学）；North China Electric Power University（华北电力大学）；Commonwealth Science and Industrial Research Organisation（联邦科学与工业研究组织）
Robust Generalization of Graph Neural Networks for Carrier Scheduling
RISE Computer Science（RISE计算机科学研究院）；KTH Royal Institute of Technology（皇家理工学院）；Digital Futures（数字未来研究院）；Karolinska Institutet（卡罗林斯卡学院）；MedTechLabs（医疗技术实验室）；Uppsala University（乌普萨拉大学）
Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks
Kyoto University（京都大学）
去年，IEEE 802.11极高吞吐量研究组（EHT Study Group）成立，以发起对IEEE 802.11新特性的讨论。EHT研究小组讨论了无线局域网（WLAN）中接入点（AP）的协调控制方法。本研究提出了一种使用图卷积网络（GCN）的基于深度强化学习的信道分配方案。作为一种深度强化学习方法，我们使用了一种著名的方法双深度Q网络。在密集部署的无线局域网中，AP的可用拓扑结构的数量是非常高的，因此，我们提取的拓扑结构的特征的基础上GCN。我们将GCN应用到竞争图中，其中在其载波侦听范围内的AP被连接以提取载波侦听关系的特征。此外，为了提高学习速度，特别是在学习的早期阶段，我们采用了基于博弈论的方法来收集独立于神经网络模型的训练数据。仿真结果表明，与现有的方法相比，该方法可以适当地控制信道。
