Adversarial machine learning has been both a major concern and a hot topic recently, especially with the ubiquitous use of deep neural networks in the current landscape. Adversarial attacks and defenses are usually likened to a cat-and-mouse game in which defenders and attackers evolve over the time. On one hand, the goal is to develop strong and robust deep networks that are resistant to malicious actors. On the other hand, in order to achieve that, we need to devise even stronger adversarial attacks to challenge these defense models. Most of existing attacks employs a single $\ell_p$ distance (commonly, $p\in\{1,2,\infty\}$) to define the concept of closeness and performs steepest gradient ascent w.r.t. this $p$-norm to update all pixels in an adversarial example in the same way. These $\ell_p$ attacks each has its own pros and cons; and there is no single attack that can successfully break through defense models that are robust against multiple $\ell_p$ norms simultaneously. Motivated by these observations, we come up with a natural approach: combining various $\ell_p$ gradient projections on a pixel level to achieve a joint adversarial perturbation. Specifically, we learn how to perturb each pixel to maximize the attack performance, while maintaining the overall visual imperceptibility of adversarial examples. Finally, through various experiments with standardized benchmarks, we show that our method outperforms most current strong attacks across state-of-the-art defense mechanisms, while retaining its ability to remain clean visually.

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。

本文报道的研究通过应用计算机视觉技术将普通的垃圾桶转化为更聪明的垃圾箱。在传感器和执行器设备的支持下，垃圾桶可以自动对垃圾进行分类。特别是，垃圾箱上的摄像头拍摄垃圾的照片，然后进行中央处理单元分析，并决定将垃圾桶放入哪个垃圾箱中。我们的垃圾箱系统的准确性达到90％。此外，我们的模型已连接到Internet，以更新垃圾箱状态以进行进一步管理。开发了用于管理垃圾箱的移动应用程序。

算法追索权旨在推荐提供丰富的反馈，以推翻不利的机器学习决策。我们在本文中介绍了贝叶斯追索权，这是一种模型不足的追索权，可最大程度地减少后验概率比值比。此外，我们介绍了其最小的稳健对应物，目的是对抗机器学习模型参数的未来变化。强大的对应物明确考虑了使用最佳传输（Wasserstein）距离规定的高斯混合物中数据的扰动。我们表明，可以将最终的最差目标函数分解为求解一系列二维优化子问题，因此，最小值追索问题发现问题可用于梯度下降算法。与现有的生成健壮的回流的方法相反，可靠的贝叶斯追索不需要线性近似步骤。数值实验证明了我们提出的稳健贝叶斯追索权面临模型转移的有效性。我们的代码可在https://github.com/vinairesearch/robust-bayesian-recourse上找到。

拼写错误纠正是自然语言处理中具有很长历史的主题之一。虽然以前的研究取得了显着的结果，但仍然存在挑战。在越南语中，任务的最先进的方法从其相邻音节中介绍了一个音节的上下文。然而，该方法的准确性可能是不令人满意的，因为如果模型可能会失去上下文，如果两个（或更多）拼写错误彼此静置。在本文中，我们提出了一种纠正越南拼写错误的新方法。我们使用深入学习模型解决错误错误和拼写错误错误的问题。特别地，嵌入层由字节对编码技术提供支持。基于变压器架构的序列模型的序列使我们的方法与上一个问题不同于同一问题的方法。在实验中，我们用大型合成数据集训练模型，这是随机引入的拼写错误。我们使用现实数据集测试所提出的方法的性能。此数据集包含11,202个以9,341不同的越南句子中的人造拼写错误。实验结果表明，我们的方法达到了令人鼓舞的表现，检测到86.8％的误差，81.5％纠正，分别提高了最先进的方法5.6％和2.2％。

In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

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.

This study proposes an approach for establishing an optimal multihop ad-hoc network using multiple unmanned aerial vehicles (UAVs) to provide emergency communication in disaster areas. The approach includes two stages, one uses particle swarm optimization (PSO) to find optimal positions to deploy UAVs, and the other uses a behavior-based controller to navigate the UAVs to their assigned positions without colliding with obstacles in an unknown environment. Several constraints related to the UAVs' sensing and communication ranges have been imposed to ensure the applicability of the proposed approach in real-world scenarios. A number of simulation experiments with data loaded from real environments have been conducted. The results show that our proposed approach is not only successful in establishing multihop ad-hoc routes but also meets the requirements for real-time deployment of UAVs.

Self-Supervised Learning (SSL) is crucial for real-world applications, especially in data-hungry domains such as healthcare and self-driving cars. In addition to a lack of labeled data, these applications also suffer from distributional shifts. Therefore, an SSL method should provide robust generalization and uncertainty estimation in the test dataset to be considered a reliable model in such high-stakes domains. However, existing approaches often focus on generalization, without evaluating the model's uncertainty. The ability to compare SSL techniques for improving these estimates is therefore critical for research on the reliability of self-supervision models. In this paper, we explore variants of SSL methods, including Jigsaw Puzzles, Context, Rotation, Geometric Transformations Prediction for vision, as well as BERT and GPT for language tasks. We train SSL in auxiliary learning for vision and pre-training for language model, then evaluate the generalization (in-out classification accuracy) and uncertainty (expected calibration error) across different distribution covariate shift datasets, including MNIST-C, CIFAR-10-C, CIFAR-10.1, and MNLI. Our goal is to create a benchmark with outputs from experiments, providing a starting point for new SSL methods in Reliable Machine Learning. All source code to reproduce results is available at https://github.com/hamanhbui/reliable_ssl_baselines.

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the ``IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method.