Finding and localizing the conceptual changes in two scenes in terms of the presence or removal of objects in two images belonging to the same scene at different times in special care applications is of great significance. This is mainly due to the fact that addition or removal of important objects for some environments can be harmful. As a result, there is a need to design a program that locates these differences using machine vision. The most important challenge of this problem is the change in lighting conditions and the presence of shadows in the scene. Therefore, the proposed methods must be resistant to these challenges. In this article, a method based on deep convolutional neural networks using transfer learning is introduced, which is trained with an intelligent data synthesis process. The results of this method are tested and presented on the dataset provided for this purpose. It is shown that the presented method is more efficient than other methods and can be used in a variety of real industrial environments.

Convolutional Neural Networks (CNN) have shown promising results for displacement estimation in UltraSound Elastography (USE). Many modifications have been proposed to improve the displacement estimation of CNNs for USE in the axial direction. However, the lateral strain, which is essential in several downstream tasks such as the inverse problem of elasticity imaging, remains a challenge. The lateral strain estimation is complicated since the motion and the sampling frequency in this direction are substantially lower than the axial one, and a lack of carrier signal in this direction. In computer vision applications, the axial and the lateral motions are independent. In contrast, the tissue motion pattern in USE is governed by laws of physics which link the axial and lateral displacements. In this paper, inspired by Hooke's law, we first propose Physically Inspired ConsTraint for Unsupervised Regularized Elastography (PICTURE), where we impose a constraint on the Effective Poisson's ratio (EPR) to improve the lateral strain estimation. In the next step, we propose self-supervised PICTURE (sPICTURE) to further enhance the strain image estimation. Extensive experiments on simulation, experimental phantom and in vivo data demonstrate that the proposed methods estimate accurate axial and lateral strain maps.

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.

Named entity recognition models (NER), are widely used for identifying named entities (e.g., individuals, locations, and other information) in text documents. Machine learning based NER models are increasingly being applied in privacy-sensitive applications that need automatic and scalable identification of sensitive information to redact text for data sharing. In this paper, we study the setting when NER models are available as a black-box service for identifying sensitive information in user documents and show that these models are vulnerable to membership inference on their training datasets. With updated pre-trained NER models from spaCy, we demonstrate two distinct membership attacks on these models. Our first attack capitalizes on unintended memorization in the NER's underlying neural network, a phenomenon NNs are known to be vulnerable to. Our second attack leverages a timing side-channel to target NER models that maintain vocabularies constructed from the training data. We show that different functional paths of words within the training dataset in contrast to words not previously seen have measurable differences in execution time. Revealing membership status of training samples has clear privacy implications, e.g., in text redaction, sensitive words or phrases to be found and removed, are at risk of being detected in the training dataset. Our experimental evaluation includes the redaction of both password and health data, presenting both security risks and privacy/regulatory issues. This is exacerbated by results that show memorization with only a single phrase. We achieved 70% AUC in our first attack on a text redaction use-case. We also show overwhelming success in the timing attack with 99.23% AUC. Finally we discuss potential mitigation approaches to realize the safe use of NER models in light of the privacy and security implications of membership inference attacks.

面部视频中心率的估计在医疗和健身行业中有许多应用。此外，它在游戏领域也变得有用。已经提出了几种方法，可以从面部视频中无缝获得心率，但是这些方法在处理运动和照明工件方面存在问题。在这项工作中，我们使用用户的光谱反射率提出了一个可靠的人力资源估计框架，这使运动和照明干扰变得强大。我们采用基于学习的深度框架，例如更快的RCNNS来执行面部检测，而不是先前方法使用的中提琴琼斯算法。我们在Mahnob HCI数据集上评估了我们的方法，发现所提出的方法能够超越先前的方法。从面部视频中估计心率在医疗和健身行业中有许多应用。此外，它在游戏领域也变得有用。已经提出了几种方法，可以从面部视频中无缝获得心率，但是这些方法在处理运动和照明工件方面存在问题。在这项工作中，我们使用用户的光谱反射率提出了一个可靠的人力资源估计框架，这使运动和照明干扰变得强大。我们采用基于学习的深度框架，例如更快的RCNNS来执行面部检测，而不是先前方法使用的中提琴算法。我们在MAHNOB HCI数据集上评估了我们的方法，发现所提出的方法能够超过以前的方法。

基于AI的计算机视觉的进步已导致合成图像产生和人工图像篡改的显着增长，对破坏人识别的不道德剥削产生了严重的影响，并可能使AI预测降低。面部生物识别技术使用不同的电子ID文档的可靠性。电子passports上的脸部照片可以欺骗自动化的边界控制系统和人类警卫。这篇论文扩展了我们先前的工作，以使用持续的同源性（pH）来检测变形攻击的质地标志。可解释（手工艺品d）篡改错误率较低且适合在受限设备上实施的检测器。

如今，有了大数据和数据湖泊，我们面临着大量数据，这些数据很难手动管理。在这种情况下，对个人数据的保护需要自动分析数据发现。存储在知识库中已经分析的属性名称可以优化此自动发现。要拥有更好的知识库，我们不应存储任何名称没有意义的属性。在本文中，要检查属性的名称是否具有含义，我们提出了一个解决方案来计算此名称和字典中的单词之间的距离。我们对距离的研究诸如N-gram，Jaro-Winkler和Levenshtein的功能，显示了在知识库中设定属性的接受阈值的限制。为了克服这些局限性，我们的解决方案旨在通过基于最长序列使用指数函数来增强得分计算。此外，还提出了词典中的双扫描，以处理具有复合名称的属性。

人工智能领域的成就用于计算和制造智能机器的进步，以促进人类和改善用户体验。情绪对人来说是基本的，影响了对应，学习和方向等思维和普通练习。语音情感识别是在这方面感兴趣的领域，在这项工作中，我们提出了一种新型的MEL频谱学习方法，其中我们的模型使用数据点从普遍的Crema-d数据集中从给定的WAV表格音符中学习情感。我们的模型使用对数MEL光谱图作为特征，其中MELS = 64。与解决情感语音识别问题的其他方法相比，训练时间较少。

定量超声（QUS）提供了有关组织特性的重要信息。可以通过将包络数据分为小重叠贴片并计算不同的斑点统计信息，例如中Nakagami的参数和knody k-Distribution（HK-Distribution）来形成QUS参数图像。计算出的QUS参数图像可能是错误的，因为补丁中只有几个独立的样本可用。另一个挑战是，假定斑块内的包膜样品来自相同的分布，这一假设通常会违反，因为该组织通常不是同质的。在本文中，我们提出了一种基于卷积神经网络（CNN）的方法，以估算QUS参数图像而无需修补。我们构建一个从HK分布中采样的大数据集，具有随机形状和QUS参数值的区域。然后，我们使用众所周知的网络以多任务学习方式估算QUS参数。我们的结果证实，所提出的方法能够减少错误并改善QUS参数图像中的边界定义。

位移估计是几乎所有超声弹性图（使用）技术的关键步骤。与一般的光流问题相比，这两个主要功能使这项任务与众不同：超声射频（RF）数据的高频性质和位移字段上物理的管理定律。最近，已经对光流网络的体系结构进行了修改，以便能够使用RF数据。同样，通过考虑以第一和第二个衍生式正规化器的形式考虑位移连续性的先验知识，已采用半监督和无监督的技术来使用。尽管尝试了这些尝试，但尚未考虑组织压缩模式，并且假定轴向和横向方向的位移是独立的。然而，组织运动模式受使用的物理定律的控制，使轴向和横向位移高度相关。在本文中，我们提出了对无监督的正则弹性图（图）的身体启发的约束，在此我们对泊松比的约束以改善侧向位移估计值。有关幻影和体内数据的实验表明，图片大大提高了横向位移估计的质量。