对图像分类器的最新基于模型的攻击压倒性地集中在单对象（即单个主体对象）图像上。与此类设置不同，我们解决了一个更实用的问题，即使用多对象（即多个主导对象）图像生成对抗性扰动，因为它们代表了大多数真实世界场景。我们的目标是设计一种攻击策略，该策略可以通过利用此类图像中固有的本地贴片差异来从此类自然场景中学习（例如，对象上的局部贴片在“人”上的局部贴片与在交通场景中的对象`自行车'之间的差异）。我们的关键想法是：为了误解对抗性的多对象图像，图像中的每个本地贴片都会使受害者分类器感到困惑。基于此，我们提出了一种新颖的生成攻击（称为局部斑块差异或LPD攻击），其中新颖的对比损失函数使用上述多对象场景特征空间的局部差异来优化扰动生成器。通过各种受害者卷积神经网络的各种实验，我们表明我们的方法在不同的白色盒子和黑色盒子设置下进行评估时，我们的方法优于基线生成攻击，具有高度可转移的扰动。

制作对抗性攻击的大多数方法都集中在具有单个主体对象的场景上（例如，来自Imagenet的图像）。另一方面，自然场景包括多个在语义上相关的主要对象。因此，探索设计攻击策略至关重要，这些攻击策略超出了在单对象场景上学习或攻击单对象受害者分类器。由于其固有的属性将扰动向未知模型的强大可传递性强，因此本文介绍了使用生成模型对多对象场景的对抗性攻击的第一种方法。为了代表输入场景中不同对象之间的关系，我们利用开源的预训练的视觉语言模型剪辑（对比语言图像 - 预训练），并动机利用语言中的编码语义来利用编码的语义空间与视觉空间一起。我们称这种攻击方法生成对抗性多对象场景攻击（GAMA）。 GAMA展示了剪辑模型作为攻击者的工具的实用性，以训练可强大的扰动发电机为多对象场景。使用联合图像文本功能来训练发电机，我们表明GAMA可以在各种攻击环境中制作有效的可转移扰动，以欺骗受害者分类器。例如，GAMA触发的错误分类比在黑框设置中的最新生成方法高出约16％，在黑框设置中，分类器体系结构和攻击者的数据分布都与受害者不同。我们的代码将很快公开提供。

尖峰神经网络（SNN）正在受到越来越多的关注，作为开发“生物学上合理”的机器学习模型的一种手段。这些网络模仿人大脑中的突触连接并产生尖峰列车，可以通过二进制值近似，从而排除了浮点算术电路的高计算成本。最近，已经引入了卷积层与SNNS的计算效率相结合的卷积层。在本文中，研究了使用脑电图（EEG）使用卷积尖峰神经网络（CSNN）作为分类器的可行性。脑电图数据是在一个实验中收集的，该实验参与者在旨在模拟城市环境的测试台上操作遥控车辆。参与者通过音频倒计时通知了进入传入的制动事件，以引起预期潜力，然后使用脑电图测量。将CSNN的性能与标准的卷积神经网络（CNN）和三个图形神经网络（GNN）进行了比较。结果表明，CSNN的表现优于其他神经网络。

近年来，图像分类器的BlackBox传输攻击已被广泛研究。相比之下，对对象探测器的转移攻击取得了很小的进展。对象探测器采用图像的整体视图，并检测一个对象（或缺乏）通常取决于场景中的其他对象。这使得这种探测器本质上的上下文感知和对抗的攻击比目标图像分类器更具挑战性。在本文中，我们提出了一种新的方法来为对象检测器生成上下文感知攻击。我们表明，通过使用对象及其相关位置的共同发生和尺寸作为上下文信息，我们可以成功地生成目标的错误分类攻击，该攻击比最先进的Blackbox对象探测器上实现更高的转移成功率。我们在帕斯卡VOC和MS Coco Datasets的各种对象探测器上测试我们的方法，与其他最先进的方法相比，性能提高了高达20美元的百分点。

Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.

Modelling and forecasting real-life human behaviour using online social media is an active endeavour of interest in politics, government, academia, and industry. Since its creation in 2006, Twitter has been proposed as a potential laboratory that could be used to gauge and predict social behaviour. During the last decade, the user base of Twitter has been growing and becoming more representative of the general population. Here we analyse this user base in the context of the 2021 Mexican Legislative Election. To do so, we use a dataset of 15 million election-related tweets in the six months preceding election day. We explore different election models that assign political preference to either the ruling parties or the opposition. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods. These results demonstrate that analysis of public online data can outperform conventional polling methods, and that political analysis and general forecasting would likely benefit from incorporating such data in the immediate future. Moreover, the same Twitter dataset with geographical attributes is positively correlated with results from official census data on population and internet usage in Mexico. These findings suggest that we have reached a period in time when online activity, appropriately curated, can provide an accurate representation of offline behaviour.

Existing federated classification algorithms typically assume the local annotations at every client cover the same set of classes. In this paper, we aim to lift such an assumption and focus on a more general yet practical non-IID setting where every client can work on non-identical and even disjoint sets of classes (i.e., client-exclusive classes), and the clients have a common goal which is to build a global classification model to identify the union of these classes. Such heterogeneity in client class sets poses a new challenge: how to ensure different clients are operating in the same latent space so as to avoid the drift after aggregation? We observe that the classes can be described in natural languages (i.e., class names) and these names are typically safe to share with all parties. Thus, we formulate the classification problem as a matching process between data representations and class representations and break the classification model into a data encoder and a label encoder. We leverage the natural-language class names as the common ground to anchor the class representations in the label encoder. In each iteration, the label encoder updates the class representations and regulates the data representations through matching. We further use the updated class representations at each round to annotate data samples for locally-unaware classes according to similarity and distill knowledge to local models. Extensive experiments on four real-world datasets show that the proposed method can outperform various classical and state-of-the-art federated learning methods designed for learning with non-IID data.

This is paper for the smooth function approximation by neural networks (NN). Mathematical or physical functions can be replaced by NN models through regression. In this study, we get NNs that generate highly accurate and highly smooth function, which only comprised of a few weight parameters, through discussing a few topics about regression. First, we reinterpret inside of NNs for regression; consequently, we propose a new activation function--integrated sigmoid linear unit (ISLU). Then special charateristics of metadata for regression, which is different from other data like image or sound, is discussed for improving the performance of neural networks. Finally, the one of a simple hierarchical NN that generate models substituting mathematical function is presented, and the new batch concept ``meta-batch" which improves the performance of NN several times more is introduced. The new activation function, meta-batch method, features of numerical data, meta-augmentation with metaparameters, and a structure of NN generating a compact multi-layer perceptron(MLP) are essential in this study.

The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. The use of videos with identifiable faces raises privacy concerns, especially when used in a hospital or community-based setting. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in the background and making it difficult to focus on the actions of humans in the foreground. Structural information in the form of skeletons describing the human motion in the videos is privacy-protecting and can overcome some of the problems posed by appearance-based features. In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. We present a novel taxonomy of algorithms based on the various learning approaches. We conclude that skeleton-based approaches for anomaly detection can be a plausible privacy-protecting alternative for video anomaly detection. Lastly, we identify major open research questions and provide guidelines to address them.

The Government of Kerala had increased the frequency of supply of free food kits owing to the pandemic, however, these items were static and not indicative of the personal preferences of the consumers. This paper conducts a comparative analysis of various clustering techniques on a scaled-down version of a real-world dataset obtained through a conjoint analysis-based survey. Clustering carried out by centroid-based methods such as k means is analyzed and the results are plotted along with SVD, and finally, a conclusion is reached as to which among the two is better. Once the clusters have been formulated, commodities are also decided upon for each cluster. Also, clustering is further enhanced by reassignment, based on a specific cluster loss threshold. Thus, the most efficacious clustering technique for designing a food kit tailored to the needs of individuals is finally obtained.