提出了联合学习（FL），以促进分布式环境中模型的培训。它支持（本地）数据隐私的保护，并使用本地资源进行模型培训。到目前为止，大多数研究一直致力于“核心问题”，例如机器学习算法对FL，数据隐私保护或处理客户之间不均匀数据分布的影响。此贡献锚定在实际的用例中，在这种情况下，FL将实际部署在生态系统的互联网中。因此，在文献中发现了一些流行的考虑之外，还需要考虑一些不同的问题。此外，引入了一种构建灵活和适应性的FL解决方案的体系结构。

大量注释数据的可用性是深度学习成功的支柱之一。尽管已经提供了许多大数据集进行研究，但在现实生活中通常并非如此（例如，由于GDPR或与知识产权保护有关的疑虑，公司无法共享数据）。联合学习（FL）是解决此问题的潜在解决方案，因为它可以对散布在多个节点的数据进行培训，而无需共享本地数据本身。但是，即使无法正确处理，即使是FL方法也会对数据隐私构成威胁。因此，我们提出了使用图像统计数据来改善FL方案的结果的增强方法STATMIX。使用两个神经网络体系结构，在CIFAR-10和CIFAR-100上经验测试了STATMIX。在所有FL实验中，与基线训练相比，STATMIX的应用都提高了平均准确性（不使用Statmix）。在非FL设置中也可以观察到一些改进。

联合学习的重要问题之一是如何处理不平衡的数据。该贡献引入了一种新型技术，旨在使用I-FGSM方法创建的对抗输入来处理标签偏斜的非IID数据。对抗输入指导培训过程，并允许加权联合的平均值，以更重要的是具有“选定”本地标签分布的客户。报告并分析了从图像分类任务，用于MNIST和CIFAR-10数据集的实验结果。

实践中的本体论仍然非常具有挑战性，尤其是在涉及多个本体论的情况下。此外，尽管最近进步，系统本体论质量保证的实现仍然是一个困难的问题。在这项工作中，从实际用例的角度研究了30个生物医学本体论和计算机科学本体论的质量。对交叉主体论的参考进行了特殊审查，这对于结合本体论至关重要。提出了检测潜在问题的多种方法，包括自然语言处理和网络分析。此外，提出了一些改善本体论及其质量保证过程的建议。有人认为，尽管前进的自动工具用于本体质量保证对于本体论的改善至关重要，但它们并不能完全解决该问题。本体论重用是连续验证和改善本体质量以及指导其未来发展的最终方法。具体而言，可以通过实用和多样化的本体论点方案找到多个问题和修复。

庞大的科学出版物呈现出越来越大的挑战，找到与给定的研究问题相关的那些，并在其基础上做出明智的决定。如果不使用自动化工具，这变得非常困难。在这里，一个可能的改进区域是根据其主题自动分类出版物摘要。这项工作介绍了一种新颖的知识基础的出色出版物分类器。该方法侧重于实现可扩展性和对其他域的容易适应性。在非常苛刻的食品安全领域，分类速度和准确度被证明是令人满意的。需要进一步发展和评估该方法，因为所提出的方法显示出很大的潜力。

Audio DeepFakes are artificially generated utterances created using deep learning methods with the main aim to fool the listeners, most of such audio is highly convincing. Their quality is sufficient to pose a serious threat in terms of security and privacy, such as the reliability of news or defamation. To prevent the threats, multiple neural networks-based methods to detect generated speech have been proposed. In this work, we cover the topic of adversarial attacks, which decrease the performance of detectors by adding superficial (difficult to spot by a human) changes to input data. Our contribution contains evaluating the robustness of 3 detection architectures against adversarial attacks in two scenarios (white-box and using transferability mechanism) and enhancing it later by the use of adversarial training performed by our novel adaptive training method.

This paper proposes the use of an event camera as a component of a vision system that enables counting of fast-moving objects - in this case, falling corn grains. These type of cameras transmit information about the change in brightness of individual pixels and are characterised by low latency, no motion blur, correct operation in different lighting conditions, as well as very low power consumption. The proposed counting algorithm processes events in real time. The operation of the solution was demonstrated on a stand consisting of a chute with a vibrating feeder, which allowed the number of grains falling to be adjusted. The objective of the control system with a PID controller was to maintain a constant average number of falling objects. The proposed solution was subjected to a series of tests to determine the correctness of the developed method operation. On their basis, the validity of using an event camera to count small, fast-moving objects and the associated wide range of potential industrial applications can be confirmed.

Realistic synthetic image data rendered from 3D models can be used to augment image sets and train image classification semantic segmentation models. In this work, we explore how high quality physically-based rendering and domain randomization can efficiently create a large synthetic dataset based on production 3D CAD models of a real vehicle. We use this dataset to quantify the effectiveness of synthetic augmentation using U-net and Double-U-net models. We found that, for this domain, synthetic images were an effective technique for augmenting limited sets of real training data. We observed that models trained on purely synthetic images had a very low mean prediction IoU on real validation images. We also observed that adding even very small amounts of real images to a synthetic dataset greatly improved accuracy, and that models trained on datasets augmented with synthetic images were more accurate than those trained on real images alone. Finally, we found that in use cases that benefit from incremental training or model specialization, pretraining a base model on synthetic images provided a sizeable reduction in the training cost of transfer learning, allowing up to 90\% of the model training to be front-loaded.

We leverage probabilistic models of neural representations to investigate how residual networks fit classes. To this end, we estimate class-conditional density models for representations learned by deep ResNets. We then use these models to characterize distributions of representations across learned classes. Surprisingly, we find that classes in the investigated models are not fitted in an uniform way. On the contrary: we uncover two groups of classes that are fitted with markedly different distributions of representations. These distinct modes of class-fitting are evident only in the deeper layers of the investigated models, indicating that they are not related to low-level image features. We show that the uncovered structure in neural representations correlate with memorization of training examples and adversarial robustness. Finally, we compare class-conditional distributions of neural representations between memorized and typical examples. This allows us to uncover where in the network structure class labels arise for memorized and standard inputs.

Background and Purpose: Colorectal cancer is a common fatal malignancy, the fourth most common cancer in men, and the third most common cancer in women worldwide. Timely detection of cancer in its early stages is essential for treating the disease. Currently, there is a lack of datasets for histopathological image segmentation of rectal cancer, which often hampers the assessment accuracy when computer technology is used to aid in diagnosis. Methods: This present study provided a new publicly available Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg). To demonstrate the validity and extensiveness of EBHI-Seg, the experimental results for EBHI-Seg are evaluated using classical machine learning methods and deep learning methods. Results: The experimental results showed that deep learning methods had a better image segmentation performance when utilizing EBHI-Seg. The maximum accuracy of the Dice evaluation metric for the classical machine learning method is 0.948, while the Dice evaluation metric for the deep learning method is 0.965. Conclusion: This publicly available dataset contained 5,170 images of six types of tumor differentiation stages and the corresponding ground truth images. The dataset can provide researchers with new segmentation algorithms for medical diagnosis of colorectal cancer, which can be used in the clinical setting to help doctors and patients.