The study aims the development of a wearable device to combat the onslaught of covid-19. Likewise, to enhance the regular face shield available in the market. Furthermore, to raise awareness of the health and safety protocols initiated by the government and its affiliates in the enforcement of social distancing with the integration of computer vision algorithms. The wearable device was composed of various hardware and software components such as a transparent polycarbonate face shield, microprocessor, sensors, camera, thin-film transistor on-screen display, jumper wires, power bank, and python programming language. The algorithm incorporated in the study was object detection under computer vision machine learning. The front camera with OpenCV technology determines the distance of a person in front of the user. Utilizing TensorFlow, the target object identifies and detects the image or live feed to get its bounding boxes. The focal length lens requires the determination of the distance from the camera to the target object. To get the focal length, multiply the pixel width by the known distance and divide it by the known width (Rosebrock, 2020). The deployment of unit testing ensures that the parameters are valid in terms of design and specifications.

这项研究的重点是在分析二维肺X射线图像中的特定人工智能子场的应用，以辅助医学诊断普通肺炎。卷积神经网络算法是在基于Python编码的基于烧瓶的Web应用程序中实现的，该应用程序可以分析X射线图像以检测普通肺炎。由于卷积神经网络算法依靠机器学习来识别和检测模式，因此实施了一种称为转移学习的技术来训练神经网络，以识别和检测数据集中的模式。开源肺X射线图像被用作训练数据，以创建一个知识库，该知识库是Web应用程序的核心元素，实验设计采用了5次验证性测试来验证Web应用程序。 5次验证性测试的结果显示，每次试验的诊断精度百分比，一般诊断精度百分比和一般诊断错误百分比的计算，而混淆矩阵进一步显示了标签和Web应用程序相应诊断结果之间的关系。每个测试图像。开发的Web应用程序可以由医生可以在A.I.辅助诊断普通肺炎的诊断中以及计算机科学和生物信息学领域的研究人员中使用。

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

鉴于诸如相关风险和道德问题等潜在影响，人工智能（AI）等先进技术的规定变得越来越重要。由于能够首先提供这种技术，安全预防措施和社会后果所承诺的巨大福利可以忽略或换档以换取加快发展，因此在开发人员之间发挥赛车叙事。从一个游戏理论模型开始，描述了一个在一个混合的球员世界的理想化技术比赛中，我们调查了种族参与者之间的不同互动结构如何改变集体选择和对监管行为的要求。我们的研究结果表明，当参与者在连接和同伴影响方面描绘了强大的多样性时（例如，当缔约方之间的无垢网络形状相互作用）时，均匀设置中存在的冲突显着降低，从而减少了对监管的需求行动。此外，我们的结果表明，技术治理和监管可能从公司和国家之间的专利异质性和不平等中获利，以便能够对少数参与者进行细致的干预措施，这能够影响整个人口一种道德和可持续利用先进技术。

Traditionally, data analysis and theory have been viewed as separate disciplines, each feeding into fundamentally different types of models. Modern deep learning technology is beginning to unify these two disciplines and will produce a new class of predictively powerful space weather models that combine the physical insights gained by data and theory. We call on NASA to invest in the research and infrastructure necessary for the heliophysics' community to take advantage of these advances.

Predictive monitoring is a subfield of process mining that aims to predict how a running case will unfold in the future. One of its main challenges is forecasting the sequence of activities that will occur from a given point in time -- suffix prediction -- . Most approaches to the suffix prediction problem learn to predict the suffix by learning how to predict the next activity only, not learning from the whole suffix during the training phase. This paper proposes a novel architecture based on an encoder-decoder model with an attention mechanism that decouples the representation learning of the prefixes from the inference phase, predicting only the activities of the suffix. During the inference phase, this architecture is extended with a heuristic search algorithm that improves the selection of the activity for each index of the suffix. Our approach has been tested using 12 public event logs against 6 different state-of-the-art proposals, showing that it significantly outperforms these proposals.

Despite being responsible for state-of-the-art results in several computer vision and natural language processing tasks, neural networks have faced harsh criticism due to some of their current shortcomings. One of them is that neural networks are correlation machines prone to model biases within the data instead of focusing on actual useful causal relationships. This problem is particularly serious in application domains affected by aspects such as race, gender, and age. To prevent models from incurring on unfair decision-making, the AI community has concentrated efforts in correcting algorithmic biases, giving rise to the research area now widely known as fairness in AI. In this survey paper, we provide an in-depth overview of the main debiasing methods for fairness-aware neural networks in the context of vision and language research. We propose a novel taxonomy to better organize the literature on debiasing methods for fairness, and we discuss the current challenges, trends, and important future work directions for the interested researcher and practitioner.

在整个计算科学中，越来越需要利用原始计算马力的持续改进，通过对蛮力的尺度锻炼的尺度增加，以增加网状元素数量的增加。例如，如果不考虑分子水平的相互作用，就不可能对纳米多孔介质的转运进行定量预测，即从紧密的页岩地层提取至关重要的碳氢化合物。同样，惯性限制融合模拟依赖于数值扩散来模拟分子效应，例如非本地转运和混合，而无需真正考虑分子相互作用。考虑到这两个不同的应用程序，我们开发了一种新颖的功能，该功能使用主动学习方法来优化局部细尺度模拟的使用来告知粗尺度流体动力学。我们的方法解决了三个挑战：预测连续性粗尺度轨迹，以推测执行新的精细分子动力学计算，动态地更新细度计算中的粗尺度，并量化神经网络模型中的不确定性。

太阳能动力学天文台（SDO）是NASA多光谱十年的长达任务，每天都在日常产生来自Sun的观测数据的trabytes，以证明机器学习方法的潜力并铺路未来深空任务计划的方式。特别是，在最近的几项研究中提出了使用图像到图像翻译实际上产生极端超紫罗兰通道的想法，这是一种增强任务较少通道的提高任务的方法，并且由于低下链接而减轻了挑战。深空的速率。本文通过关注四个通道和基于编码器的建筑的排列来研究这种深度学习方法的潜力和局限性，并特别注意太阳表面的形态特征和亮度如何影响神经网络预测。在这项工作中，我们想回答以下问题：可以将通过图像到图像翻译产生的太阳电晕的合成图像用于太阳的科学研究吗？分析强调，神经网络在计数率（像素强度）上产生高质量的图像，通常可以在1％误差范围内跨通道跨通道重现协方差。但是，模型性能在极高的能量事件（如耀斑）的对应关系中大大减少，我们认为原因与此类事件的稀有性有关，这对模型训练构成了挑战。

这项工作介绍了一种在开放世界游戏中为非演奏世界运动而创作非玩家角色（NPC）的社会建筑模型的实施，该游戏受到基于代理建模的学术研究的启发。就丰富的对话和响应行为而言，可信的NPC创作是繁重的。我们简要介绍了为此任务使用社会代理体系结构的特征和优势，并描述了社会代理体系结构CIF-CK作为Mod Social NPC的实现