Existing automated techniques for software documentation typically attempt to reason between two main sources of information: code and natural language. However, this reasoning process is often complicated by the lexical gap between more abstract natural language and more structured programming languages. One potential bridge for this gap is the Graphical User Interface (GUI), as GUIs inherently encode salient information about underlying program functionality into rich, pixel-based data representations. This paper offers one of the first comprehensive empirical investigations into the connection between GUIs and functional, natural language descriptions of software. First, we collect, analyze, and open source a large dataset of functional GUI descriptions consisting of 45,998 descriptions for 10,204 screenshots from popular Android applications. The descriptions were obtained from human labelers and underwent several quality control mechanisms. To gain insight into the representational potential of GUIs, we investigate the ability of four Neural Image Captioning models to predict natural language descriptions of varying granularity when provided a screenshot as input. We evaluate these models quantitatively, using common machine translation metrics, and qualitatively through a large-scale user study. Finally, we offer learned lessons and a discussion of the potential shown by multimodal models to enhance future techniques for automated software documentation.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

Our work is at the crossroads of two categories of technologies. On the one hand, omnichannel digit services, to address the needs of users in the most seamless way. On the other hand, low code approaches, to build simply even complex software applications. In this twofold context, we propose DSUL (Digital Service Universal Language). It allows to build omnichannel services with minimal work from their designers. We describe precisely how DSUL operates, and its innovation in regard to the state of the art. We also consider the various methods to evaluate this framework.

蒙版的视觉建模（MVM）最近已被证明对视觉预训练有效。虽然在视频输入（例如，蒙版框架建模）上进行了类似的重建目标，在视频语言（VIDL）预训练中探索了类似的重建目标，但先前研究中的预提取的视频功能在预训练期间无法通过MVM进行完善，因此无法通过MVM进行完善为下游性能不满意。在这项工作中，我们系统地检查了MVM在VIDL学习的背景下的潜力。具体而言，我们的研究基于完全端到端的视频变压器（Violet），该视频变压器（Violet）减轻了固定视频表示与MVM培训之间的断开连接。总共探索了MVM的八个不同的重建目标，从低级像素值和定向梯度到高级深度图，光流，离散的视觉令牌和潜在的视觉特征。我们进行全面的实验，并就导致有效MVM培训的因素提供见解。从经验上讲，我们展示了通过MVM目标预先训练的紫罗兰色，可以在13个VIDL基准测试中取得显着改进，从视频问题回答，视频字幕到文本到视频检索等等。

质量不足的质量生产可能会对工具，生产下降和低质量产品造成极为昂贵的损害。非常需要自动，快速和廉价的策略来估算质量控制，降低风险和故障预测的重要材料特性。在这项工作中，我们分析了高吞吐量的基于钢的产品。目前，使用手动破坏性测试检查材料质量，该测试缓慢，浪费，仅覆盖一小部分材料。为了获得完整的测试覆盖范围，我们的工业合作者开发了一种非接触式，无创的电磁传感器，以实时测量所有材料。我们的贡献是三个方面：1）我们在受控的实验中表明，传感器可以通过故意改变特性区分钢。 2）对48个钢管进行了全面测量，并对样品进行了其他破坏性测试，以作为地面真理。拟合线性模型可从非侵入性测量中预测通常通过破坏性测试获得的两种关键材料特性（屈服强度和拉伸强度）。在剩余的交叉验证中评估性能。 3）所得模型用于分析用非侵入性传感器测量的〜108 km的处理材料的实际生产数据上的材料特性和与记录的产品故障的关系。该模型实现了出色的性能（F3得分为0.95），预测材料的拉伸强度规格不足。模型预测和记录的产品故障的组合表明，如果大量的估计收益应力值不超出规格，则产品故障的风险很高。我们的分析证明了实时质量控制，风险监控和故障检测的有希望的方向。

深度神经网络的面部识别模型已显示出容易受到对抗例子的影响。但是，过去的许多攻击都要求对手使用梯度下降来解决输入依赖性优化问题，这使该攻击实时不切实际。这些对抗性示例也与攻击模型紧密耦合，并且在转移到不同模型方面并不那么成功。在这项工作中，我们提出了Reface，这是对基于对抗性转换网络（ATN）的面部识别模型的实时，高度转移的攻击。 ATNS模型对抗性示例生成是馈送前向神经网络。我们发现，纯U-NET ATN的白盒攻击成功率大大低于基于梯度的攻击，例如大型面部识别数据集中的PGD。因此，我们为ATN提出了一个新的架构，该架构缩小了这一差距，同时维持PGD的10000倍加速。此外，我们发现在给定的扰动幅度下，与PGD相比，我们的ATN对抗扰动在转移到新的面部识别模型方面更有效。 Reface攻击可以在转移攻击环境中成功欺骗商业面部识别服务，并将面部识别精度从AWS SearchFaces API和Azure Face验证准确性从91％降低到50.1％，从而将面部识别精度从82％降低到16.4％。

由于筛选乳房X线照片的假阴性评估，通常在晚期检测到与其他癌症更差的间隔和大型侵入性乳腺癌。错过的筛选时间检测通常由其周围乳腺组织模糊的肿瘤引起的，这是一种称为掩蔽的现象。为了研究和基准爆发癌症的乳房Xmmpare掩蔽，在这项工作中，我们引入CSAW-M，最大的公共乳房数据集，从10,000多个人收集并用潜在的掩蔽注释。与以前的方法对比测量乳房图像密度作为代理的方法，我们的数据集直接提供了五个专家屏蔽潜在评估的注释。我们还培训了CSAW-M的深入学习模型来估计掩蔽水平，并显示估计的掩蔽更加预测筛查患有间隔和大型侵入性癌症的参与者 - 而不是明确培训这些任务 - 而不是其乳房密度同行。

雅典娜2.0是一家亚历克萨奖的社会奖，这是最后两个Alexa奖奖挑战的决赛。雅典娜成功的一个原因是其新的对话管理战略，它允许它动态构建组件模块的对话和响应，导致每个互动的新型对话。在这里，我们在20/21竞争期间描述了Athena的Alexa奖的系统设计和性能。雅典娜的活跃演示以及视频录音将挑起对话AI的艺术状态的讨论。

多元时间序列（MTS）分类在过去十年中获得了重要性，随着多个域中的时间数数据集数量的增加。目前的最先进的MTS分类器是一种重量级的深度学习方法，其仅在大型数据集上优于第二个最佳MTS分类器。此外，这种深入学习方法不能提供忠诚的解释，因为它依赖于后的HOC模型 - 无止性解释性方法，这可能会阻止其在许多应用中的应用。在本文中，我们展示了XCM，可解释的卷积神经网络用于MTS分类。 XCM是一种新的紧凑型卷积神经网络，其直接从输入数据中提取相对于观察变量的信息。因此，XCM架构在大小的数据集中实现了良好的泛化能力，同时通过精确地识别所观察到的变量和时间戳，允许完全利用忠实的后HOC模型特定的解释方法（梯度加权类激活映射）对预测很重要的数据。首先表明XCM在大型公共UEA数据集中优于最先进的MTS分类器。然后，我们说明了XCM如何在合成数据集上调和性能和解释性，并显示XCM对预测的输入数据的区域的区域更精确地识别，与当前的深度学习MTS分类器相比也提供忠诚的解释性。最后，我们介绍了XCM如何优于现实世界应用中最准确的最先进的算法，同时通过提供忠诚和更具信息性的解释来提高可解释性。