Fake videos represent an important misinformation threat. While existing forensic networks have demonstrated strong performance on image forgeries, recent results reported on the Adobe VideoSham dataset show that these networks fail to identify fake content in videos. In this paper, we propose a new network that is able to detect and localize a wide variety of video forgeries and manipulations. To overcome challenges that existing networks face when analyzing videos, our network utilizes both forensic embeddings to capture traces left by manipulation, context embeddings to exploit forensic traces' conditional dependencies upon local scene content, and spatial attention provided by a deep, transformer-based attention mechanism. We create several new video forgery datasets and use these, along with publicly available data, to experimentally evaluate our network's performance. These results show that our proposed network is able to identify a diverse set of video forgeries, including those not encountered during training. Furthermore, our results reinforce recent findings that image forensic networks largely fail to identify fake content in videos.

Test log-likelihood is commonly used to compare different models of the same data and different approximate inference algorithms for fitting the same probabilistic model. We present simple examples demonstrating how comparisons based on test log-likelihood can contradict comparisons according to other objectives. Specifically, our examples show that (i) conclusions about forecast accuracy based on test log-likelihood comparisons may not agree with conclusions based on other distributional quantities like means; and (ii) that approximate Bayesian inference algorithms that attain higher test log-likelihoods need not also yield more accurate posterior approximations.

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.

Recognizing handwriting images is challenging due to the vast variation in writing style across many people and distinct linguistic aspects of writing languages. In Vietnamese, besides the modern Latin characters, there are accent and letter marks together with characters that draw confusion to state-of-the-art handwriting recognition methods. Moreover, as a low-resource language, there are not many datasets for researching handwriting recognition in Vietnamese, which makes handwriting recognition in this language have a barrier for researchers to approach. Recent works evaluated offline handwriting recognition methods in Vietnamese using images from an online handwriting dataset constructed by connecting pen stroke coordinates without further processing. This approach obviously can not measure the ability of recognition methods effectively, as it is trivial and may be lack of features that are essential in offline handwriting images. Therefore, in this paper, we propose the Transferring method to construct a handwriting image dataset that associates crucial natural attributes required for offline handwriting images. Using our method, we provide a first high-quality synthetic dataset which is complex and natural for efficiently evaluating handwriting recognition methods. In addition, we conduct experiments with various state-of-the-art methods to figure out the challenge to reach the solution for handwriting recognition in Vietnamese.

Image captioning is currently a challenging task that requires the ability to both understand visual information and use human language to describe this visual information in the image. In this paper, we propose an efficient way to improve the image understanding ability of transformer-based method by extending Object Relation Transformer architecture with Attention on Attention mechanism. Experiments on the VieCap4H dataset show that our proposed method significantly outperforms its original structure on both the public test and private test of the Image Captioning shared task held by VLSP.

成功的人工智能系统通常需要大量标记的数据来从文档图像中提取信息。在本文中，我们研究了改善人工智能系统在理解文档图像中的性能的问题，尤其是在培训数据受到限制的情况下。我们通过使用加强学习提出一种新颖的填充方法来解决问题。我们的方法将信息提取模型视为策略网络，并使用策略梯度培训来更新模型，以最大程度地提高补充传统跨凝结损失的综合奖励功能。我们使用标签和专家反馈在四个数据集上进行的实验表明，我们的填充机制始终提高最先进的信息提取器的性能，尤其是在小型培训数据制度中。

尽管最近关于了解深神经网络（DNN）的研究，但关于DNN如何产生其预测的问题仍然存在许多问题。特别是，给定对不同输入样本的类似预测，基本机制是否会产生这些预测？在这项工作中，我们提出了Neucept，这是一种局部发现关键神经元的方法，该神经元在模型的预测中起着重要作用，并确定模型的机制在产生这些预测中。我们首先提出一个关键的神经元识别问题，以最大程度地提高相互信息目标的序列，并提供一个理论框架，以有效地解决关键神经元，同时控制精度。Neucept接下来以无监督的方式学习了不同模型的机制。我们的实验结果表明，Neucept鉴定的神经元不仅对模型的预测具有强大的影响，而且还具有有关模型机制的有意义的信息。

现有的最新3D点云实例分割方法依赖于基于分组的方法，该方法指向获得对象实例。尽管产生准确的分割结果方面有所改善，但这些方法缺乏可扩展性，通常需要将大量输入分为多个部分。为了处理数百万点的场景，现有的最快方法软组\ cite {vu2022222222222222222222222222222222222222ggroup}需要数十秒钟，这是满意的。我们的发现是，$ k $ neart的邻居（$ k $ -nn）是分组的先决条件，是计算瓶颈。这种瓶颈严重使现场的推理时间恶化了很多。本文提出了软组++来解决此计算瓶颈，并进一步优化了整个网络的推理速度。 SoftGroup ++建立在软组上，这在三个重要方面有所不同：（1）执行OCTREE $ K $ -NN而不是Vanilla $ k $ -nn，以将时间复杂性从$ \ Mathcal {o}（n^2）缩短到$ \ Mathcal {o}（n \ log n）$，（2）执行金字塔缩放，适应性下降样本骨干输出以减少$ k $ -nn和分组的搜索空间，并且（3）执行后期的Devoxelization，延迟了Voxels的转换指向模型的结束，以使中间组件以低计算成本运行。在各种室内和室外数据集上进行了广泛的实验，证明了拟议的软组++的功效。值得注意的是，SoftGroup ++在一个前方的情况下通过单个前方进行了大量的场景，而无需将输入分为多个部分，从而丰富了上下文信息。特别是，SoftGroup ++达到2.4点AP $ _ {50} $改进，而$ 6 \ $ 6 \ times $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $。代码和训练有素的模型将公开可用。

我们提出了对使用Rademacher和Vapnik-Chervonenkis边界学习有条件的价值（VAR）和预期短缺的两步方法的非反应收敛分析。我们的VAR方法扩展到了一次学习的问题，该问题对应于不同的分数水平。这导致基于神经网络分位数和最小二乘回归的有效学习方案。引入了一个后验蒙特卡洛（非巢）程序，以估计地面真相和ES的距离，而无需访问后者。使用高斯玩具模型中的数值实验和财务案例研究中的目标是学习动态初始边缘的情况。

如今，随着发现的OSS漏洞的数量，开源软件（OSS）漏洞管理流程随着时间的流逝而增加。监视漏洞固定提交是防止脆弱性开发的标准过程的一部分。但是，由于可能有大量的审查，手动检测漏洞固定的犯罪是耗时的。最近，已经提出了许多技术来自动检测使用机器学习的漏洞固定提交。这些解决方案要么：（1）不使用深度学习，或（2）仅对有限的信息来源使用深度学习。本文提出了藤条，该工具利用了更丰富的信息来源，包括提交消息，代码更改和针对漏洞固定的提交分类的报告。我们的实验结果表明，在F1得分方面，沃尔维尔剂的表现优于最先进的基线。 Vulcurator工具可在https://github.com/ntgiang71096/vfdetector和https://zenodo.org/record/7034132#.yw3mn-xbzdi上公开获得。