数十年来，计算机系统持有大量个人数据。一方面，这种数据丰度允许在人工智能（AI），尤其是机器学习（ML）模型中突破。另一方面，它可能威胁用户的隐私并削弱人类与人工智能之间的信任。最近的法规要求，可以从一般情况下从计算机系统中删除有关用户的私人信息，特别是根据要求从ML模型中删除（例如，“被遗忘的权利”）。虽然从后端数据库中删除数据应该很简单，但在AI上下文中，它不够，因为ML模型经常“记住”旧数据。现有的对抗攻击证明，我们可以从训练有素的模型中学习私人会员或培训数据的属性。这种现象要求采用新的范式，即机器学习，以使ML模型忘记了特定的数据。事实证明，由于缺乏共同的框架和资源，最近在机器上学习的工作无法完全解决问题。在本调查文件中，我们试图在其定义，场景，机制和应用中对机器进行彻底的研究。具体而言，作为最先进的研究的类别集合，我们希望为那些寻求机器未学习的入门及其各种表述，设计要求，删除请求，算法和用途的人提供广泛的参考。 ML申请。此外，我们希望概述范式中的关键发现和趋势，并突出显示尚未看到机器无法使用的新研究领域，但仍可以受益匪浅。我们希望这项调查为ML研究人员以及寻求创新隐私技术的研究人员提供宝贵的参考。我们的资源是在https://github.com/tamlhp/awesome-machine-unlearning上。

药物误解是可能导致对患者造成不可预测后果的风险之一。为了减轻这种风险，我们开发了一个自动系统，该系统可以正确识别移动图像中的药丸的处方。具体来说，我们定义了所谓的药丸匹配任务，该任务试图匹配处方药中药丸所拍摄的药丸的图像。然后，我们提出了PIMA，这是一种使用图神经网络（GNN）和对比度学习来解决目标问题的新方法。特别是，GNN用于学习处方中文本框之间的空间相关性，从而突出显示带有药丸名称的文本框。此外，采用对比度学习来促进药丸名称的文本表示与药丸图像的视觉表示之间的跨模式相似性的建模。我们进行了广泛的实验，并证明PIMA在我们构建的药丸和处方图像的现实数据集上优于基线模型。具体而言，与其他基线相比，PIMA的准确性从19.09％提高到46.95％。我们认为，我们的工作可以为建立新的临床应用并改善药物安全和患者护理提供新的机会。

无线传感器网络由随机分布的传感器节点组成，用于监视目标或感兴趣的区域。由于每个传感器的电池容量有限，因此维持连续监视的网络是一个挑战。无线电源传输技术正在作为可靠的解决方案，用于通过部署移动充电器（MC）为传感器充电传感器。但是，由于网络中出现不确定性，为MC设计最佳的充电路径是具有挑战性的。由于网络拓扑的不可预测的变化，例如节点故障，传感器的能耗率可能会显着波动。这些变化也导致每个传感器的重要性变化，在现有作品中通常被认为是相同的。我们在本文中提出了一种使用深度强化学习（DRL）方法提出新颖的自适应充电方案，以解决这些挑战。具体来说，我们赋予MC采用充电策略，该策略确定了下一个在网络当前状态上充电条件的传感器。然后，我们使用深层神经网络来参数这项收费策略，该策略将通过强化学习技术进行培训。我们的模型可以适应网络拓扑的自发变化。经验结果表明，所提出的算法的表现优于现有的按需算法的大幅度边缘。

跨不同边缘设备（客户）局部数据的分布不均匀，导致模型训练缓慢，并降低了联合学习的准确性。幼稚的联合学习（FL）策略和大多数替代解决方案试图通过加权跨客户的深度学习模型来实现更多公平。这项工作介绍了在现实世界数据集中遇到的一种新颖的非IID类型，即集群键，其中客户组具有具有相似分布的本地数据，从而导致全局模型收敛到过度拟合的解决方案。为了处理非IID数据，尤其是群集串数据的数据，我们提出了FedDrl，这是一种新型的FL模型，它采用了深厚的强化学习来适应每个客户的影响因素（将用作聚合过程中的权重）。在一组联合数据集上进行了广泛的实验证实，拟议的FEDDR可以根据CIFAR-100数据集的平均平均为FedAvg和FedProx方法提高了有利的改进，例如，高达4.05％和2.17％。

鉴于在各种条件和背景下捕获的图像的识别药物已经变得越来越重要。已经致力于利用基于深度学习的方法来解决文献中的药丸识别问题。但是，由于药丸的外观之间的相似性很高，因此经常发生错误识别，因此识别药丸是一个挑战。为此，在本文中，我们介绍了一种名为Pika的新颖方法，该方法利用外部知识来增强药丸识别精度。具体来说，我们解决了一种实用的情况（我们称之为上下文药丸识别），旨在在患者药丸摄入量的情况下识别药丸。首先，我们提出了一种新的方法，用于建模在存在外部数据源的情况下，在这种情况下，在存在外部处方的情况下，药丸之间的隐式关联。其次，我们提出了一个基于步行的图形嵌入模型，该模型从图形空间转换为矢量空间，并提取药丸的凝结关系。第三，提供了最终框架，该框架利用基于图像的视觉和基于图的关系特征来完成药丸识别任务。在此框架内，每种药丸的视觉表示形式都映射到图形嵌入空间，然后用来通过图表执行注意力，从而产生了有助于最终分类的语义丰富的上下文矢量。据我们所知，这是第一项使用外部处方数据来建立药物之间的关联并使用此帮助信息对其进行分类的研究。皮卡（Pika）的体系结构轻巧，并且具有将识别骨架纳入任何识别骨架的灵活性。实验结果表明，通过利用外部知识图，与基线相比，PIKA可以将识别精度从4.8％提高到34.1％。

在过去的几十年中，由于其在广泛的应用中，现场文本认可从学术界和实际用户获得了全世界的关注。尽管在光学字符识别方面取得了成就，但由于诸如扭曲或不规则布局等固有问题，现场文本识别仍然具有挑战性。大多数现有方法主要利用基于复发或卷积的神经网络。然而，虽然经常性的神经网络（RNN）通常由于顺序计算而遭受慢的训练速度，并且遇到消失的梯度或瓶颈，但CNN在复杂性和性能之间衡量折衷。在本文中，我们介绍了SAFL，一种基于自我关注的神经网络模型，具有场景文本识别的焦点损失，克服现有方法的限制。使用焦损而不是负值对数似然有助于模型更多地关注低频样本训练。此外，为应对扭曲和不规则文本，我们在传递到识别网络之前，我们利用空间变换（STN）来纠正文本。我们执行实验以比较拟议模型的性能与七个基准。数值结果表明，我们的模型实现了最佳性能。

Machine Reading Comprehension has become one of the most advanced and popular research topics in the fields of Natural Language Processing in recent years. The classification of answerability questions is a relatively significant sub-task in machine reading comprehension; however, there haven't been many studies. Retro-Reader is one of the studies that has solved this problem effectively. However, the encoders of most traditional machine reading comprehension models in general and Retro-Reader, in particular, have not been able to exploit the contextual semantic information of the context completely. Inspired by SemBERT, we use semantic role labels from the SRL task to add semantics to pre-trained language models such as mBERT, XLM-R, PhoBERT. This experiment was conducted to compare the influence of semantics on the classification of answerability for the Vietnamese machine reading comprehension. Additionally, we hope this experiment will enhance the encoder for the Retro-Reader model's Sketchy Reading Module. The improved Retro-Reader model's encoder with semantics was first applied to the Vietnamese Machine Reading Comprehension task and obtained positive results.

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

We introduce an approach for the answer-aware question generation problem. Instead of only relying on the capability of strong pre-trained language models, we observe that the information of answers and questions can be found in some relevant sentences in the context. Based on that, we design a model which includes two modules: a selector and a generator. The selector forces the model to more focus on relevant sentences regarding an answer to provide implicit local information. The generator generates questions by implicitly combining local information from the selector and global information from the whole context encoded by the encoder. The model is trained jointly to take advantage of latent interactions between the two modules. Experimental results on two benchmark datasets show that our model is better than strong pre-trained models for the question generation task. The code is also available (shorturl.at/lV567).

In the era of Internet of Things (IoT), network-wide anomaly detection is a crucial part of monitoring IoT networks due to the inherent security vulnerabilities of most IoT devices. Principal Components Analysis (PCA) has been proposed to separate network traffics into two disjoint subspaces corresponding to normal and malicious behaviors for anomaly detection. However, the privacy concerns and limitations of devices' computing resources compromise the practical effectiveness of PCA. We propose a federated PCA-based Grassmannian optimization framework that coordinates IoT devices to aggregate a joint profile of normal network behaviors for anomaly detection. First, we introduce a privacy-preserving federated PCA framework to simultaneously capture the profile of various IoT devices' traffic. Then, we investigate the alternating direction method of multipliers gradient-based learning on the Grassmann manifold to guarantee fast training and the absence of detecting latency using limited computational resources. Empirical results on the NSL-KDD dataset demonstrate that our method outperforms baseline approaches. Finally, we show that the Grassmann manifold algorithm is highly adapted for IoT anomaly detection, which permits drastically reducing the analysis time of the system. To the best of our knowledge, this is the first federated PCA algorithm for anomaly detection meeting the requirements of IoT networks.