在不断增长的互联网世界中，获取关键数据（例如密码和登录凭据以及敏感的个人信息）的多种方法已扩大。页面模仿（通常称为网络钓鱼）是获取此类宝贵信息的一种方法。网络钓鱼是黑客最直接的网络攻击形式之一，也是受害者最简单的网络攻击形式之一。它还可以为黑客提供访问目标的个人和公司帐户所需的一切。这样的网站不提供服务，而是从用户那里收集个人信息。在本文中，我们在使用经常性神经网络检测恶意URL方面达到了最先进的准确性。与以前查看在线内容，URL和流量编号的研究不同，我们只是查看URL中的文本，这使其更快并捕获了零日的攻击。该网络已被优化，可用于移动器等小设备，而没有牺牲推理时间。

Applying Machine learning to domains like Earth Sciences is impeded by the lack of labeled data, despite a large corpus of raw data available in such domains. For instance, training a wildfire classifier on satellite imagery requires curating a massive and diverse dataset, which is an expensive and time-consuming process that can span from weeks to months. Searching for relevant examples in over 40 petabytes of unlabelled data requires researchers to manually hunt for such images, much like finding a needle in a haystack. We present a no-code end-to-end pipeline, Curator, which dramatically minimizes the time taken to curate an exhaustive labeled dataset. Curator is able to search massive amounts of unlabelled data by combining self-supervision, scalable nearest neighbor search, and active learning to learn and differentiate image representations. The pipeline can also be readily applied to solve problems across different domains. Overall, the pipeline makes it practical for researchers to go from just one reference image to a comprehensive dataset in a diminutive span of time.

We propose an end-to-end inverse rendering pipeline called SupeRVol that allows us to recover 3D shape and material parameters from a set of color images in a super-resolution manner. To this end, we represent both the bidirectional reflectance distribution function (BRDF) and the signed distance function (SDF) by multi-layer perceptrons. In order to obtain both the surface shape and its reflectance properties, we revert to a differentiable volume renderer with a physically based illumination model that allows us to decouple reflectance and lighting. This physical model takes into account the effect of the camera's point spread function thereby enabling a reconstruction of shape and material in a super-resolution quality. Experimental validation confirms that SupeRVol achieves state of the art performance in terms of inverse rendering quality. It generates reconstructions that are sharper than the individual input images, making this method ideally suited for 3D modeling from low-resolution imagery.

For many years, Evolutionary Algorithms (EAs) have been applied to improve Neural Networks (NNs) architectures. They have been used for solving different problems, such as training the networks (adjusting the weights), designing network topology, optimizing global parameters, and selecting features. Here, we provide a systematic brief survey about applications of the EAs on the specific domain of the recurrent NNs named Reservoir Computing (RC). At the beginning of the 2000s, the RC paradigm appeared as a good option for employing recurrent NNs without dealing with the inconveniences of the training algorithms. RC models use a nonlinear dynamic system, with fixed recurrent neural network named the \textit{reservoir}, and learning process is restricted to adjusting a linear parametric function. %so the performance of learning is fast and precise. However, an RC model has several hyper-parameters, therefore EAs are helpful tools to figure out optimal RC architectures. We provide an overview of the results on the area, discuss novel advances, and we present our vision regarding the new trends and still open questions.

Effectively exploring the environment is a key challenge in reinforcement learning (RL). We address this challenge by defining a novel intrinsic reward based on a foundation model, such as contrastive language image pretraining (CLIP), which can encode a wealth of domain-independent semantic visual-language knowledge about the world. Specifically, our intrinsic reward is defined based on pre-trained CLIP embeddings without any fine-tuning or learning on the target RL task. We demonstrate that CLIP-based intrinsic rewards can drive exploration towards semantically meaningful states and outperform state-of-the-art methods in challenging sparse-reward procedurally-generated environments.

文献中有许多不同的方法来解释机器学习结果。但是，这些方法的方法有所不同，通常没有提供相同的解释。在本文中，我们考虑了两种最新方法：集成梯度（Sundararajan，Taly和Yan，2017年）和基线Shapley（Sundararajan和Najmi，2020年）。原始作者已经研究了两种方法的公理属性，并提供了一些比较。我们的工作为表格数据提供了一些有关其比较行为的其他见解。我们讨论两者提供相同解释及其不同的常见情况。我们还使用仿真研究来检查具有Relu激活函数的神经网络拟合模型时的差异。

跨数据集的语义细分的域适应性，由相同类别组成，已经获得了一些最近的成功。但是，更一般的情况是源和目标数据集对应于非重叠标签空间时。例如，分割数据集中的类别根据环境或应用程序的类型发生了很大变化，但共享许多有价值的语义关系。基于特征对齐或差异最小化的现有方法不会考虑此类类别的转移。在这项工作中，我们提出了群集到适应（C2A），这是一种基于计算有效的聚类方法，用于跨分割数据集的域适应性，这些方法完全不同但可能相关类别。我们表明，在变换的特征空间中强制执行的这种聚类目标可以自动选择跨源和目标域的类别，这些类别可以对齐以改善目标性能，同时防止对无关类别的负转移。我们通过实验对室外的挑战性问题进行了实验，以少量拍摄和零拍设置来证明室内适应性的挑战性问题，在所有情况下，性能对现有方法和基准的绩效持续改善。

用于评估表结构识别算法的现有指标在捕获文本和空细胞对齐方面存在缺点。在本文中，我们以先前的工作为基础，并提出了一个新的度量标准的IOU相似性（TEDS（iou）），用于表结构识别，该识别使用边界框而不是文本，同时对上述缺点也是强大的。我们通过各种示例证明了对以前的度量标准的有效性。

实用的现实世界数据集具有丰富的类别，为无监督的领域适应带来了新的挑战，例如小型阶层歧视性，仅依靠域不变性的现有方法不能很好地处理。在这项工作中，我们提出了MEMSAC，该MEMSAC利用了跨源和目标域的样本级别相似性​​，以实现判别性转移，以​​及扩展到大量类别的体系结构。为此，我们首先引入一种内存增强方法，以在标记的源和未标记的目标域实例之间有效提取成对的相似性关系，该实例适用于处理任意数量的类。接下来，我们建议和理论上证明对比损失的新型变体，以促进阶层内跨域样本之间的局部一致性，同时在类别之间执行分离，从而保留从源到目标的歧视性转移。我们验证了MEMSAC的优势，比以前的最先进的最先进的转移任务有了显着改进。我们还提供了深入的分析和对MEMSAC有效性的见解。

尽管现在使用自我监督方法构建的计算机视觉模型现在很普遍，但仍然存在一些重要问题。自我监督的模型是否学习高度冗余的频道功能？如果一个自我监督的网络可以动态选择重要的渠道并摆脱不必要的渠道怎么办？目前，与计算机视觉中的有监督的对手相比，通过自我训练预先训练的Convnet在下游任务上获得了可比的性能。但是，有一些自我监督模型的缺点，包括大量参数，计算昂贵的培训策略以及对下游任务更快推断的明确需求。在这项工作中，我们的目标是通过研究如何将用于监督学习的标准渠道选择方法应用于经过自学训练的网络。我们验证我们在一系列目标预算上验证我们的发现$ t_ {d} $，用于跨不同数据集的图像分类任务的频道计算，特别是CIFAR-10，CIFAR-100和IMAGENET-100，获得了与原始网络的可比性性能when selecting all channels but at a significant reduction in computation reported in terms of FLOPs.