攻击者每天都在越来越多地使用新的攻击，但其中许多攻击并未被入侵检测系统检测到，因为大多数ID忽略了原始数据包信息，并且仅关心从PCAP文件中提取的一些基本统计信息。使用网络程序从数据包中提取固定的统计功能是不错的，但可能不足以检测到当今的挑战。我们认为现在是时候利用大数据和深度学习来从数据包中提取自动动态功能。现在是时候受到计算机视觉和自然语言处理的深度学习预训练模型的启发了，因此安全深度学习解决方案将在大型数据集上使用其预先培训的模型，以在未来的研究中使用。在本文中，我们提出了一种基于字符级嵌入的数据包的新方法，灵感来自文本数据上的FastText成功。我们称这种方法fastpacket。结果是在CIC-IDS-2017数据集的子集上测量的，但我们希望大数据预训练的模型有希望的结果。我们建议在Mawi Big Dataset上构建预先训练的FastPacket，并将其提供给社区，类似于FastText。为了能够胜过当前使用的NID，开始了可以更好地检测复杂攻击的数据包级NID的新时代。

基于异常的入侵检测系统（IDS）一直是一个热门研究主题，因为它具有检测新威胁的能力，而不仅仅是记忆的签名威胁基于签名的ID的威胁。尤其是在增加了增加黑客工具数量并增加攻击影响的高级技术之后。任何基于异常的模型的问题是其高阳性率。高阳性速率是为什么在实践中通常不使用异常ID的原因。因为基于异常的模型将看不见的模式分类为一种正常但不包括在培训数据集中的威胁。这种类型的问题称为模型无法概括的过度拟合。通过拥有包括所有可能正常情况的大型培训数据集来优化基于异常的模型可能是一个最佳解决方案，但不能在实践中应用。尽管我们可以增加培训样本的数量以包括更多正常情况，但我们仍然需要一个具有更多概括能力的模型。在本研究论文中，我们建议应用深层模型，而不是传统模型，因为它具有更大的概括能力。因此，我们将通过使用大数据和深层模型获得较少的假阳性。我们通过降低假阳性速率在优化基于异常ID的ID中进行了机器学习和深度学习算法进行比较。我们在NSL-KDD基准测试中进行了一个实验，并将我们的结果与IDS优化中传统学习中使用最佳的分类器之一进行了比较。该实验显示，通过使用深度学习而不是传统学习，假阳性降低了10％。

随着信息技术在所有生命领域中的日益增长的使用，黑客攻击变得比以往任何时候都变得更加有效。同样，随着技术的发展，攻击数字每隔几个月就会成倍增长，并变得更加复杂，因此传统ID效率低下。本文提出了一种解决方案，不仅检测具有更高检测率的新威胁和比已经使用的ID更低的假阳性，而且还可以检测集体和上下文安全攻击。我们通过使用网络聊天机器人（一个深度的复发神经网络：apache Spark框架上的长期短期内存（LSTM））来实现这些结果异常。我们建议合并语言处理，上下文分析，分布式深度学习，大数据，流量分析的异常检测的概念。我们提出了一个模型，该模型描述了网络在其上下文中从数百万数据包中的序列中抽象正常行为，并将它们实时分析以检测点，集体和上下文异常。实验是在MAWI数据集上进行的，它显示出比签名ID的检测率更好，而且比传统异常ID更好。该实验显示较低的假阳性，较高的检测率和更好的点异常检测。至于有上下文和集体异常检测的证明，我们讨论了我们的主张和假设背后的原因。但是，由于硬件限制，该实验是在数据集的随机小子集上进行的，因此我们分享了实验和未来的愿景思想，因为我们希望将来的其他感兴趣的研究人员将来能够充分证明，这些研究人员拥有比我们的硬件基础架构更好的研究人员。

自然语言推论（NLI）是自然语言处理中的热门话题研究，句子之间的矛盾检测是NLI的特殊情况。这被认为是一项困难的NLP任务，当在许多NLP应用程序中添加为组件时，其影响很大，例如问答系统，文本摘要。阿拉伯语是由于其丰富的词汇，语义歧义而检测矛盾的最具挑战性的低资源语言之一。我们创建了一个超过12K句子的数据集并命名为Arnli，这将是公开可用的。此外，我们采用了一种新的模型，该模型受到斯坦福大学矛盾检测的启发，提出了有关英语的解决方案。我们提出了一种方法，以使用矛盾向量与语言模型向量作为机器学习模型的输入来检测阿拉伯语对句子之间的矛盾。我们分析了不同传统的机器学习分类器的结果，并比较了他们在创建的数据集（Arnli）和Pheme，病态的英语数据集的自动翻译上进行了比较。使用随机森林分类器，精度为99％，60％和75％的Pheme，Sick和Arnli的最佳结果。

Recent work has shown the benefits of synthetic data for use in computer vision, with applications ranging from autonomous driving to face landmark detection and reconstruction. There are a number of benefits of using synthetic data from privacy preservation and bias elimination to quality and feasibility of annotation. Generating human-centered synthetic data is a particular challenge in terms of realism and domain-gap, though recent work has shown that effective machine learning models can be trained using synthetic face data alone. We show that this can be extended to include the full body by building on the pipeline of Wood et al. to generate synthetic images of humans in their entirety, with ground-truth annotations for computer vision applications. In this report we describe how we construct a parametric model of the face and body, including articulated hands; our rendering pipeline to generate realistic images of humans based on this body model; an approach for training DNNs to regress a dense set of landmarks covering the entire body; and a method for fitting our body model to dense landmarks predicted from multiple views.

To generate high quality rendering images for real time applications, it is often to trace only a few samples-per-pixel (spp) at a lower resolution and then supersample to the high resolution. Based on the observation that the rendered pixels at a low resolution are typically highly aliased, we present a novel method for neural supersampling based on ray tracing 1/4-spp samples at the high resolution. Our key insight is that the ray-traced samples at the target resolution are accurate and reliable, which makes the supersampling an interpolation problem. We present a mask-reinforced neural network to reconstruct and interpolate high-quality image sequences. First, a novel temporal accumulation network is introduced to compute the correlation between current and previous features to significantly improve their temporal stability. Then a reconstruct network based on a multi-scale U-Net with skip connections is adopted for reconstruction and generation of the desired high-resolution image. Experimental results and comparisons have shown that our proposed method can generate higher quality results of supersampling, without increasing the total number of ray-tracing samples, over current state-of-the-art methods.

In this paper we explore the task of modeling (semi) structured object sequences; in particular we focus our attention on the problem of developing a structure-aware input representation for such sequences. In such sequences, we assume that each structured object is represented by a set of key-value pairs which encode the attributes of the structured object. Given a universe of keys, a sequence of structured objects can then be viewed as an evolution of the values for each key, over time. We encode and construct a sequential representation using the values for a particular key (Temporal Value Modeling - TVM) and then self-attend over the set of key-conditioned value sequences to a create a representation of the structured object sequence (Key Aggregation - KA). We pre-train and fine-tune the two components independently and present an innovative training schedule that interleaves the training of both modules with shared attention heads. We find that this iterative two part-training results in better performance than a unified network with hierarchical encoding as well as over, other methods that use a {\em record-view} representation of the sequence \cite{de2021transformers4rec} or a simple {\em flattened} representation of the sequence. We conduct experiments using real-world data to demonstrate the advantage of interleaving TVM-KA on multiple tasks and detailed ablation studies motivating our modeling choices. We find that our approach performs better than flattening sequence objects and also allows us to operate on significantly larger sequences than existing methods.

In this chapter, we review and discuss the transformation of AI technology in HCI/UX work and assess how AI technology will change how we do the work. We first discuss how AI can be used to enhance the result of user research and design evaluation. We then discuss how AI technology can be used to enhance HCI/UX design. Finally, we discuss how AI-enabled capabilities can improve UX when users interact with computing systems, applications, and services.

Deep neural networks (DNNs) are vulnerable to a class of attacks called "backdoor attacks", which create an association between a backdoor trigger and a target label the attacker is interested in exploiting. A backdoored DNN performs well on clean test images, yet persistently predicts an attacker-defined label for any sample in the presence of the backdoor trigger. Although backdoor attacks have been extensively studied in the image domain, there are very few works that explore such attacks in the video domain, and they tend to conclude that image backdoor attacks are less effective in the video domain. In this work, we revisit the traditional backdoor threat model and incorporate additional video-related aspects to that model. We show that poisoned-label image backdoor attacks could be extended temporally in two ways, statically and dynamically, leading to highly effective attacks in the video domain. In addition, we explore natural video backdoors to highlight the seriousness of this vulnerability in the video domain. And, for the first time, we study multi-modal (audiovisual) backdoor attacks against video action recognition models, where we show that attacking a single modality is enough for achieving a high attack success rate.

Modern deep neural networks have achieved superhuman performance in tasks from image classification to game play. Surprisingly, these various complex systems with massive amounts of parameters exhibit the same remarkable structural properties in their last-layer features and classifiers across canonical datasets. This phenomenon is known as "Neural Collapse," and it was discovered empirically by Papyan et al. \cite{Papyan20}. Recent papers have theoretically shown the global solutions to the training network problem under a simplified "unconstrained feature model" exhibiting this phenomenon. We take a step further and prove the Neural Collapse occurrence for deep linear network for the popular mean squared error (MSE) and cross entropy (CE) loss. Furthermore, we extend our research to imbalanced data for MSE loss and present the first geometric analysis for Neural Collapse under this setting.