A Music Recommendation System based on Emotion, Age, and Ethnicity is developed in this study, using FER-2013 and ``Age, Gender, and Ethnicity (Face Data) CSV'' datasets. The CNN architecture, which is extensively used for this kind of purpose has been applied to the training of the models. After adding several appropriate layers to the training end of the project, in total, 3 separate models are trained in the Deep Learning side of the project: Emotion, Ethnicity, and Age. After the training step of these models, they are used as classifiers on the web application side. The snapshot of the user taken through the interface is sent to the models to predict their mood, age, and ethnic origin. According to these classifiers, various kinds of playlists pulled from Spotify API are proposed to the user in order to establish a functional and user-friendly atmosphere for the music selection. Afterward, the user can choose the playlist they want and listen to it by following the given link.

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.

狗主人通常能够识别出揭示其狗的主观状态的行为线索，例如疼痛。但是自动识别疼痛状态非常具有挑战性。本文提出了一种基于视频的新型，两流深的神经网络方法，以解决此问题。我们提取和预处理身体关键点，并在视频中计算关键点和RGB表示的功能。我们提出了一种处理自我十分和缺少关键点的方法。我们还提出了一个由兽医专业人员收集的独特基于视频的狗行为数据集，并注释以进行疼痛，并通过建议的方法报告良好的分类结果。这项研究是基于机器学习的狗疼痛状态估计的第一批作品之一。

已经提出了分裂学习（SL）以分散的方式训练深度学习模型。对于具有垂直数据分配的分散医疗保健应用，SL可以有益，因为它允许具有互补功能或图像的机构为一组共享的患者共同开发更强大且可推广的模型。在这项工作中，我们提出了“ split-u-net”，并成功地将SL应用于协作生物医学图像分割。但是，SL需要交换中间激活图和梯度，以允许跨不同特征空间的训练模型，这可能会泄漏数据并提高隐私问题。因此，我们还量化了用于生物医学图像分割的常见SL情况下的数据泄漏量，并通过应用适当的防御策略提供了抵消此类泄漏的方法。

了解人类行为是一项重要的任务，并且在许多领域（例如针对性的广告，健康分析，安全和娱乐等）都有应用。为此，设计活动识别系统（AR）很重要。但是，由于每个人都可以具有不同的行为，因此理解和分析共同模式成为一项艰巨的任务。由于现代世界中的每个人都很容易获得智能手机，因此使用它们来跟踪人类活动变得可能是可能的。在本文中，我们通过构建Android移动应用程序的Android智能手机的加速度计，磁力计和陀螺仪传感器提取了不同的人类活动。使用不同的社交媒体应用程序，例如Facebook，Instagram，WhatsApp和Twitter，我们提取了原始传感器值以及$ 29 $主题的属性及其属性（类标签），例如年龄，性别，左/右/右/双手的应用使用情况。我们从原始信号中提取功能，并使用它们使用不同的机器学习（ML）算法进行分类。使用统计分析，我们显示了不同特征对类标签预测的重要性。最后，我们在数据上使用训练有素的ML模型来从UCI存储库中众所周知的活动识别数据中提取未知功能，该数据突出了使用ML模型的隐私漏洞的潜力。这种安全分析可以帮助研究人员将来采取适当的步骤来保护人类受试者的隐私。

口语医学对话系统越来越引起人们的兴趣，以增强获得医疗服务的机会并提高患者护理的质量和可追溯性。在本文中，我们专注于通过口语对话在智能手机上获得的医疗药物处方。这样的系统将促进护理的可追溯性，并可以释放临床医生的时间。但是，由于大多数相关语料库都是文本形式和英语，因此缺乏语音语料库来开发此类系统。为了促进口头医学对话系统的研究和开发，据我们所知，我们介绍了第一个名为PXSLU的口语医学药物处方语料库。它包含通过与55名参与者专家的实验获得的法国药物处方的4小时和注释对话，并在处方中进行了非专家。我们还提出了一些实验，这些实验证明了该语料库对医学对话系统的评估和开发的兴趣。

联合学习（FL）是一种分布式机器学习技术，可以在避免明确的数据共享的同时进行协作模型培训。 FL算法的固有保护属性使其对医疗领域特别有吸引力。但是，如果有异质的客户数据分布，则标准FL方法是不稳定的，需要密集的超参数调整以实现最佳性能。常规的超参数优化算法在现实世界中的FL应用中是不切实际的，因为它们涉及大量的培训试验，而计算预算有限，这些试验通常是不起作用的。在这项工作中，我们提出了一种有效的增强学习（RL）的联合次数超参数优化算法，称为自动FEDRL，其中在线RL代理可以根据当前的培训进度动态调整每个客户的超参数。进行了广泛的实验以研究不同的搜索策略和RL代理。该方法的有效性在CIFAR-10数据集的异质数据分配以及两个现实世界中的医学图像分割数据集上进行了验证，用于胸部CT中的COVID-19变病变分段，腹部CT中的胰腺细分。

It is common practice in deep learning to represent a measurement of the world on a discrete grid, e.g. a 2D grid of pixels. However, the underlying signal represented by these measurements is often continuous, e.g. the scene depicted in an image. A powerful continuous alternative is then to represent these measurements using an implicit neural representation, a neural function trained to output the appropriate measurement value for any input spatial location. In this paper, we take this idea to its next level: what would it take to perform deep learning on these functions instead, treating them as data? In this context we refer to the data as functa, and propose a framework for deep learning on functa. This view presents a number of challenges around efficient conversion from data to functa, compact representation of functa, and effectively solving downstream tasks on functa. We outline a recipe to overcome these challenges and apply it to a wide range of data modalities including images, 3D shapes, neural radiance fields (NeRF) and data on manifolds. We demonstrate that this approach has various compelling properties across data modalities, in particular on the canonical tasks of generative modeling, data imputation, novel view synthesis and classification. Code: https://github.com/deepmind/functa

头视点标签的成本是改善细粒度头姿势估计算法的主要障碍。缺乏大量标签的一种解决方案正在使用自我监督的学习（SSL）。 SSL可以从未标记的数据中提取良好的功能，用于下游任务。因此，本文试图显示头部姿势估计的SSL方法之间的差异。通常，使用SSL的两个主要方法：（1）使用它以预先培训权重，（2）在一个训练期间除了监督学习（SL）之外的SSL作为辅助任务。在本文中，我们通过设计混合多任务学习（HMTL）架构并使用两个SSL预先文本任务，旋转和令人困惑来评估两种方法。结果表明，两种方法的组合在其中使用旋转进行预训练和使用令人难以用于辅助头的令人费示。与基线相比，误差率降低了23.1％，这与电流的SOTA方法相当。最后，我们比较了初始权重对HMTL和SL的影响。随后，通过HMTL，使用各种初始权重减少错误：随机，想象成和SSL。