联合学习（FL）是一个分散的学习范式，其中多个客户在不集中其本地数据的情况下进行培训深度学习模型，因此保留数据隐私。现实世界中的应用程序通常涉及在不同客户端的数据集上进行分发转换，这损害了客户从各自的数据分布中看不见样本的概括能力。在这项工作中，我们解决了最近提出的功能转移问题，其中客户具有不同的功能分布，而标签分布相同。我们建议联邦代表性扩大（FRAUG）来解决这个实用且具有挑战性的问题。我们的方法在嵌入空间中生成合成客户端特定的样本，以增加通常小客户端数据集。为此，我们训练一个共享的生成模型，以融合客户从其不同功能分布中学习的知识。该发电机合成了客户端 - 不合时式嵌入，然后通过表示转换网络（RTNET）将其局部转换为特定于客户端的嵌入。通过将知识转移到客户端，生成的嵌入式作为客户模型的正常化程序，并减少对本地原始数据集的过度拟合，从而改善了概括。我们对公共基准和现实医学数据集的经验评估证明了该方法的有效性，该方法在包括Partialfed和FedBN在内的非IID特征的当前最新FL方法大大优于最新的FL方法。

当在涉及强大域移动的现实情况下应用时，可以推广到看不见的域的机器学习模型至关重要。我们解决了具有挑战性的域概括（DG）问题，其中在一组源域上训练的模型预计将在看不见的域中很好地概括，而无需接触其数据。 DG的主要挑战是，从源域中学到的功能不一定存在于看不见的目标域中，从而导致性能恶化。我们假设学习一组更丰富的功能对于改善向更广泛的未知域的转移至关重要。因此，我们提出了哥伦布，该方法通过对数据的最相关输入和多级表示的有针对性损坏来强制实施新功能发现。我们进行了广泛的经验评估，以证明所提出的方法的有效性，该方法通过在域床框架中的多个DG基准数据集上优于18个DG算法来实现新的最新结果。

The performance of the Deep Learning (DL) models depends on the quality of labels. In some areas, the involvement of human annotators may lead to noise in the data. When these corrupted labels are blindly regarded as the ground truth (GT), DL models suffer from performance deficiency. This paper presents a method that aims to learn a confident model in the presence of noisy labels. This is done in conjunction with estimating the uncertainty of multiple annotators. We robustly estimate the predictions given only the noisy labels by adding entropy or information-based regularizer to the classifier network. We conduct our experiments on a noisy version of MNIST, CIFAR-10, and FMNIST datasets. Our empirical results demonstrate the robustness of our method as it outperforms or performs comparably to other state-of-the-art (SOTA) methods. In addition, we evaluated the proposed method on the curated dataset, where the noise type and level of various annotators depend on the input image style. We show that our approach performs well and is adept at learning annotators' confusion. Moreover, we demonstrate how our model is more confident in predicting GT than other baselines. Finally, we assess our approach for segmentation problem and showcase its effectiveness with experiments.

Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains challenging. To address this issue, various machine learning controllers have been developed to better predict movement intent. As these controllers become more intelligent and take on more autonomy in the system, the traditional approach of representing the human-machine interface as a human controlling a tool becomes limiting. One possible approach to improve the understanding of these interfaces is to model them as collaborative, multi-agent systems through the lens of joint action. The field of joint action has been commonly applied to two human partners who are trying to work jointly together to achieve a task, such as singing or moving a table together, by effecting coordinated change in their shared environment. In this work, we compare different prosthesis controllers (proportional electromyography with sequential switching, pattern recognition, and adaptive switching) in terms of how they present the hallmarks of joint action. The results of the comparison lead to a new perspective for understanding how existing myoelectric systems relate to each other, along with recommendations for how to improve these systems by increasing the collaborative communication between each partner.

Nowadays, the current neural network models of dialogue generation(chatbots) show great promise for generating answers for chatty agents. But they are short-sighted in that they predict utterances one at a time while disregarding their impact on future outcomes. Modelling a dialogue's future direction is critical for generating coherent, interesting dialogues, a need that has led traditional NLP dialogue models that rely on reinforcement learning. In this article, we explain how to combine these objectives by using deep reinforcement learning to predict future rewards in chatbot dialogue. The model simulates conversations between two virtual agents, with policy gradient methods used to reward sequences that exhibit three useful conversational characteristics: the flow of informality, coherence, and simplicity of response (related to forward-looking function). We assess our model based on its diversity, length, and complexity with regard to humans. In dialogue simulation, evaluations demonstrated that the proposed model generates more interactive responses and encourages a more sustained successful conversation. This work commemorates a preliminary step toward developing a neural conversational model based on the long-term success of dialogues.

In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph. HNN derives multiple embeddings per node in the hypergraph where each embedding for a node is dependent on a specific hyperedge of that node. Notably, HNN is accurate, data-efficient, flexible with many interchangeable components, and useful for a wide range of hypergraph learning tasks. We evaluate the effectiveness of the HNN framework for hyperedge prediction and hypergraph node classification. We find that HNN achieves an overall mean gain of 7.72% and 11.37% across all baseline models and graphs for hyperedge prediction and hypergraph node classification, respectively.

A "heart attack" or myocardial infarction (MI), occurs when an artery supplying blood to the heart is abruptly occluded. The "gold standard" method for imaging MI is Cardiovascular Magnetic Resonance Imaging (MRI), with intravenously administered gadolinium-based contrast (late gadolinium enhancement). However, no "gold standard" fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. This has the potential to reduce the uncertainty due to the technical variability across labs and inherent problems of the data and labels. Our system consists of four processing stages designed to maintain the flow of information across scales. First, features from raw MRI images are generated using feature extractors built on ResNet and MoblieNet architectures. This is followed by the Atrous Spatial Pyramid Pooling (ASPP) to produce spatial information at different scales to preserve more image context. High-level features from ASPP and initial low-level features are concatenated at the third stage and then passed to the fourth stage where spatial information is recovered via up-sampling to produce final image segmentation output into: i) background, ii) heart muscle, iii) blood and iv) scar areas. New models were compared with state-of-art models and manual quantification. Our models showed favorable performance in global segmentation and scar tissue detection relative to state-of-the-art work, including a four-fold better performance in matching scar pixels to contours produced by clinicians.

Increasing popularity of deep-learning-powered applications raises the issue of vulnerability of neural networks to adversarial attacks. In other words, hardly perceptible changes in input data lead to the output error in neural network hindering their utilization in applications that involve decisions with security risks. A number of previous works have already thoroughly evaluated the most commonly used configuration - Convolutional Neural Networks (CNNs) against different types of adversarial attacks. Moreover, recent works demonstrated transferability of the some adversarial examples across different neural network models. This paper studied robustness of the new emerging models such as SpinalNet-based neural networks and Compact Convolutional Transformers (CCT) on image classification problem of CIFAR-10 dataset. Each architecture was tested against four White-box attacks and three Black-box attacks. Unlike VGG and SpinalNet models, attention-based CCT configuration demonstrated large span between strong robustness and vulnerability to adversarial examples. Eventually, the study of transferability between VGG, VGG-inspired SpinalNet and pretrained CCT 7/3x1 models was conducted. It was shown that despite high effectiveness of the attack on the certain individual model, this does not guarantee the transferability to other models.

Human Activity Recognition (HAR) is an emerging technology with several applications in surveillance, security, and healthcare sectors. Noninvasive HAR systems based on Wi-Fi Channel State Information (CSI) signals can be developed leveraging the quick growth of ubiquitous Wi-Fi technologies, and the correlation between CSI dynamics and body motions. In this paper, we propose Principal Component-based Wavelet Convolutional Neural Network (or PCWCNN) -- a novel approach that offers robustness and efficiency for practical real-time applications. Our proposed method incorporates two efficient preprocessing algorithms -- the Principal Component Analysis (PCA) and the Discrete Wavelet Transform (DWT). We employ an adaptive activity segmentation algorithm that is accurate and computationally light. Additionally, we used the Wavelet CNN for classification, which is a deep convolutional network analogous to the well-studied ResNet and DenseNet networks. We empirically show that our proposed PCWCNN model performs very well on a real dataset, outperforming existing approaches.

Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level by either modifying the graph structure or objective function without taking into account the local neighborhood of a node. In this work, we formally introduce the notion of neighborhood fairness and develop a computational framework for learning such locally fair embeddings. We argue that the notion of neighborhood fairness is more appropriate since GNN-based models operate at the local neighborhood level of a node. Our neighborhood fairness framework has two main components that are flexible for learning fair graph representations from arbitrary data: the first aims to construct fair neighborhoods for any arbitrary node in a graph and the second enables adaption of these fair neighborhoods to better capture certain application or data-dependent constraints, such as allowing neighborhoods to be more biased towards certain attributes or neighbors in the graph.Furthermore, while link prediction has been extensively studied, we are the first to investigate the graph representation learning task of fair link classification. We demonstrate the effectiveness of the proposed neighborhood fairness framework for a variety of graph machine learning tasks including fair link prediction, link classification, and learning fair graph embeddings. Notably, our approach achieves not only better fairness but also increases the accuracy in the majority of cases across a wide variety of graphs, problem settings, and metrics.