可信的研究环境（TRE）S是安全和安全的环境，其中研究人员可以访问敏感数据。随着电子健康记录（EHR），医学成像和基因组数据等医疗数据的增长和多样性，通常在使用人工智能（AI）和机器学习子场（ML）的使用增加医疗领域。这产生了披露从TRES的新类型输出的希望，例如培训的机器学习模型。虽然特定的指导方针和政策存在于TRES中的统计披露控制，但它们并不令人满意地涵盖这些新类型的输出请求。在本文中，我们定义了在TRES内医疗保健机器学习的应用程序和披露的一些挑战。我们描述了各种漏洞，引入AI带来了TRES。我们还提供了与培训ML模型的披露相关的不同类型和风险水平的介绍。我们终于描述了开发和调整政策和工具的新研究机会，以安全地披露从TRES的机器学习输出。

近年来，苏格兰的参与式预算（PB）已从少数社区主导的过程中成长为当地和国家政府支持的运动。这是苏格兰政府与苏格兰地方当局（COSLA）之间的协议介绍，至少1％的地方当局预算将受到PB。这个正在进行的研究论文探讨了从苏格兰的32名地方当局“缩放”或“主流”出现的挑战。主要目标是评估当地的管理局使用数字平台领事，这适用自然语言处理（NLP）来解决这些挑战。该项目采用采访，对PB流程的观察以及数字平台数据的分析来采用定性纵向设计。采用主题分析来捕捉出现的主要问题和主题。然后纵向分析探讨这些随着时间的推移方式。 32个直播学习网站的潜力提供了一个独特的机会，探索离散的政治和社会背景，这些环境变化，允许更深层次的潜水到可能存在的挑战和问题，更广泛的横断面研究会错过。初始结果表明，可以使用NLP技术来解决缩放的问题和挑战，在先前的受控用案例的评估中，已显示提高公民参与的有效性。

Driven by the global decarbonization effort, the rapid integration of renewable energy into the conventional electricity grid presents new challenges and opportunities for the battery energy storage system (BESS) participating in the energy market. Energy arbitrage can be a significant source of revenue for the BESS due to the increasing price volatility in the spot market caused by the mismatch between renewable generation and electricity demand. In addition, the Frequency Control Ancillary Services (FCAS) markets established to stabilize the grid can offer higher returns for the BESS due to their capability to respond within milliseconds. Therefore, it is crucial for the BESS to carefully decide how much capacity to assign to each market to maximize the total profit under uncertain market conditions. This paper formulates the bidding problem of the BESS as a Markov Decision Process, which enables the BESS to participate in both the spot market and the FCAS market to maximize profit. Then, Proximal Policy Optimization, a model-free deep reinforcement learning algorithm, is employed to learn the optimal bidding strategy from the dynamic environment of the energy market under a continuous bidding scale. The proposed model is trained and validated using real-world historical data of the Australian National Electricity Market. The results demonstrate that our developed joint bidding strategy in both markets is significantly profitable compared to individual markets.

Unmanned air vehicles (UAVs) popularity is on the rise as it enables the services like traffic monitoring, emergency communications, deliveries, and surveillance. However, the unauthorized usage of UAVs (a.k.a drone) may violate security and privacy protocols for security-sensitive national and international institutions. The presented challenges require fast, efficient, and precise detection of UAVs irrespective of harsh weather conditions, the presence of different objects, and their size to enable SafeSpace. Recently, there has been significant progress in using the latest deep learning models, but those models have shortcomings in terms of computational complexity, precision, and non-scalability. To overcome these limitations, we propose a precise and efficient multiscale and multifeature UAV detection network for SafeSpace, i.e., \textit{MultiFeatureNet} (\textit{MFNet}), an improved version of the popular object detection algorithm YOLOv5s. In \textit{MFNet}, we perform multiple changes in the backbone and neck of the YOLOv5s network to focus on the various small and ignored features required for accurate and fast UAV detection. To further improve the accuracy and focus on the specific situation and multiscale UAVs, we classify the \textit{MFNet} into small (S), medium (M), and large (L): these are the combinations of various size filters in the convolution and the bottleneckCSP layers, reside in the backbone and neck of the architecture. This classification helps to overcome the computational cost by training the model on a specific feature map rather than all the features. The dataset and code are available as an open source: github.com/ZeeshanKaleem/MultiFeatureNet.

由国家科学基金会（NSF）资助的DILPORT项目http://dialport.org/涵盖了一组工具和服务，旨在满足对话研究社区的需求。在六年的时间里，已经创建了几种产品，包括Dialport Portal和DialCrowd。本文描述了这些贡献，这些贡献将在Sigdial中进行演示，包括实施，先前的研究，相应的发现以及工具将继续可为社区免费提供的位置。

急诊部门（EDS）是葡萄牙国家卫生服务局的基本要素，可作为具有多样化和非常严重医疗问题的用户的切入点。由于ED的固有特征；预测使用服务的患者数量特别具有挑战性。富裕和医疗专业人员人数之间的不匹配可能会导致提供的服务质量下降，并造成对整个医院产生影响的问题，并从其他部门征用医疗保健工作者以及推迟手术。 。 ED人满为患的部分是由非紧急患者驱动的，尽管没有医疗紧急情况，但诉诸于紧急服务，几乎占每日患者总数的一半。本文描述了一种新颖的深度学习体系结构，即时间融合变压器，该结构使用日历和时间序列协变量来预测预测间隔和4周期间的点预测。我们得出的结论是，可以预测葡萄牙健康区域（HRA）（HRA）的平均绝对百分比误差（MAPE）和均方根误差（RMSE）为84.4102人/天的平均绝对百分比误差（MAPE）。本文显示了支持使用静态和时间序列协变量的多元方法的经验证据，同时超越了文献中常见的其他模型。

预计将在2026年促使新兴的无人机航空公司（UAV）服务市场达到584亿美元，促使常规将常规无人机运营促进到国家空域中的重大努力，以至于它们不会损害现有的安全水平。通过感觉和避免潜在的中空碰撞威胁，将提高无人机的商业用途，但是在缺乏可用的数据集时，该领域的研究是缺乏可用的数据集，因为它们昂贵且技术上是为了捕获。在本文中，我们为基于视觉的飞机检测提供了一个数据集。 DataSet由15个图像序列组成，其中包含55,521张固定翼飞机的图像，接近固定式接地的摄像头。还提供了地面真理标签和绩效基准。为了我们的知识，这是第一个在碰撞课程上学习中型固定翼飞机的第一个公共数据集。完整的数据集和地面真理标签在https://qcr.github.io/dataset/aircraft -collision-.c资料/航空公司

自2020年初以来，COVID-19的大流行对日常生活的许多方面产生了相当大的影响。在全球范围内已经采取了一系列不同的措施，以降低新感染的速度并管理国家卫生服务的压力。主要策略是通过优先考虑远程工作和教育来减少聚会和传播的潜力。当不可避免的聚会时，增强的手卫生和面膜的使用减少了病原体的扩散。这些特殊的措施提出了可靠的生物识别识别的挑战，例如用于面部，语音和手工生物识别技术。同时，新的挑战创造了新的机会和研究方向，例如对无约束的虹膜或眼周识别，基于无触摸的指纹和基于静脉的身份验证以及生物特征特征进行疾病检测的重新兴趣。本文概述了为解决这些挑战和新兴机会而进行的研究。

Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how Federated Learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to * Disclaimer: The opinions expressed herein are those of the authors and do not necessarily represent those of the institutions they are affiliated with, e.g. the U.S. Department of Health and Human Services or the National Institutes of Health. This is a pre-print version of https://www.nature.com/articles/s41746-020-00323-1 be addressed.

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.