The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed.
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Workloads in modern cloud data centers are becoming increasingly complex. The number of workloads running in cloud data centers has been growing exponentially for the last few years, and cloud service providers (CSP) have been supporting on-demand services in real-time. Realizing the growing complexity of cloud environment and cloud workloads, hardware vendors such as Intel and AMD are increasingly introducing cloud-specific workload acceleration features in their CPU platforms. These features are typically targeted towards popular and commonly-used cloud workloads. Nonetheless, uncommon, customer-specific workloads (unknown workloads), if their characteristics are different from common workloads (known workloads), may not realize the potential of the underlying platform. To address this problem of realizing the full potential of the underlying platform, we develop a machine learning based technique to characterize, profile and predict workloads running in the cloud environment. Experimental evaluation of our technique demonstrates good prediction performance. We also develop techniques to analyze the performance of the model in a standalone manner.
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Cartoons are an important part of our entertainment culture. Though drawing a cartoon is not for everyone, creating it using an arrangement of basic geometric primitives that approximates that character is a fairly frequent technique in art. The key motivation behind this technique is that human bodies - as well as cartoon figures - can be split down into various basic geometric primitives. Numerous tutorials are available that demonstrate how to draw figures using an appropriate arrangement of fundamental shapes, thus assisting us in creating cartoon characters. This technique is very beneficial for children in terms of teaching them how to draw cartoons. In this paper, we develop a tool - shape2toon - that aims to automate this approach by utilizing a generative adversarial network which combines geometric primitives (i.e. circles) and generate a cartoon figure (i.e. Mickey Mouse) depending on the given approximation. For this purpose, we created a dataset of geometrically represented cartoon characters. We apply an image-to-image translation technique on our dataset and report the results in this paper. The experimental results show that our system can generate cartoon characters from input layout of geometric shapes. In addition, we demonstrate a web-based tool as a practical implication of our work.
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洪水是大自然最灾难性的灾难之一,对人类生活,农业,基础设施和社会经济系统造成了不可逆转和巨大的破坏。已经进行了几项有关洪水灾难管理和洪水预测系统的研究。实时对洪水的发作和进展的准确预测是具有挑战性的。为了估计大面积的水位和速度,有必要将数据与计算要求的洪水传播模型相结合。本文旨在减少这种自然灾害的极端风险,并通过使用不同的机器学习模型为洪水提供预测来促进政策建议。这项研究将使用二进制逻辑回归,K-Nearest邻居(KNN),支持向量分类器(SVC)和决策树分类器来提供准确的预测。通过结果,将进行比较分析,以了解哪种模型具有更好的准确性。
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人们的个人卫生习惯在每日生活方式中照顾身体和健康的状况。保持良好的卫生习惯不仅减少了患疾病的机会,而且还可以降低社区中传播疾病的风险。鉴于目前的大流行,每天的习惯,例如洗手或定期淋浴,在人们中至关重要,尤其是对于单独生活在家里或辅助生活设施中的老年人。本文提出了一个新颖的非侵入性框架,用于使用我们采用机器学习技术的振动传感器监测人卫生。该方法基于地球通传感器,数字化器和实用外壳中具有成本效益的计算机板的组合。监测日常卫生常规可能有助于医疗保健专业人员积极主动,而不是反应性,以识别和控制社区内潜在暴发的传播。实验结果表明,将支持向量机(SVM)用于二元分类,在不同卫生习惯的分类中表现出约95%的有希望的准确性。此外,基于树的分类器(随机福雷斯特和决策树)通过实现最高精度(100%)优于其他模型,这意味着可以使用振动和非侵入性传感器对卫生事件进行分类,以监测卫生活动。
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深度学习模型通过从训练的数据集学习来提供图像处理的令人难以置信的结果。菠菜是一种含有维生素和营养素的叶蔬菜。在我们的研究中,已经使用了一种可以自动识别菠菜的深度学习方法,并且该方法具有总共五种菠菜的数据集,其中包含3785个图像。四种卷积神经网络(CNN)模型用于对我们的菠菜进行分类。这些模型为图像分类提供更准确的结果。在应用这些模型之前,存在一些预处理图像数据。为了预处理数据,需要发生一些方法。那些是RGB转换,过滤,调整大小和重新划分和分类。应用这些方法后,图像数据被预处理并准备好在分类器算法中使用。这些分类器的准确性在98.68%至99.79%之间。在这些模型中,VGG16实现了99.79%的最高精度。
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肺癌是最致命的癌症之一,部分诊断和治疗取决于肿瘤的准确描绘。目前是最常见的方法的人以人为本的分割,须遵守观察者间变异性,并且考虑到专家只能提供注释的事实,也是耗时的。最近展示了有前途的结果,自动和半自动肿瘤分割方法。然而,随着不同的研究人员使用各种数据集和性能指标验证了其算法,可靠地评估这些方法仍然是一个开放的挑战。通过2018年IEEE视频和图像处理(VIP)杯竞赛创建的计算机断层摄影扫描(LOTUS)基准测试的肺起源肿瘤分割的目标是提供唯一的数据集和预定义的指标,因此不同的研究人员可以开发和以统一的方式评估他们的方法。 2018年VIP杯始于42个国家的全球参与,以获得竞争数据。在注册阶段,有129名成员组成了来自10个国家的28个团队,其中9个团队将其达到最后阶段,6队成功完成了所有必要的任务。简而言之,竞争期间提出的所有算法都是基于深度学习模型与假阳性降低技术相结合。三种决赛选手开发的方法表明,有希望的肿瘤细分导致导致越来越大的努力应降低假阳性率。本次竞争稿件概述了VIP-Cup挑战,以及所提出的算法和结果。
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心血管疾病是世界各地最常见的死亡原因。为了检测和治疗心脏相关的疾病,需要连续血压(BP)监测以及许多其他参数。为此目的开发了几种侵入性和非侵入性方法。用于持续监测BP的医院中使用的大多数现有方法是侵入性的。相反,基于袖带的BP监测方法,可以预测收缩压(SBP)和舒张压(DBP),不能用于连续监测。几项研究试图从非侵​​入性可收集信号(例如光学肌谱(PPG)和心电图(ECG))预测BP,其可用于连续监测。在这项研究中,我们探讨了自动化器在PPG和ECG信号中预测BP的适用性。在12,000岁的MIMIC-II数据集中进行了调查,发现了一个非常浅的一维AutoEncoder可以提取相关功能,以预测与最先进的SBP和DBP在非常大的数据集上的性能。从模拟-II数据集的一部分的独立测试分别为SBP和DBP提供了2.333和0.713的MAE。在40个主题的外部数据集上,模型在MIMIC-II数据集上培训,分别为SBP和DBP提供2.728和1.166的MAE。对于这种情况来说,结果达到了英国高血压协会(BHS)A级并超越了目前文学的研究。
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在从训练的数据集中学习后,AI Chatbot提供了令人印象深刻的响应。在这十年中,大多数研究工作都表现出深层神经模型优于任何其他模型。 RNN模型定期用于确定序列相关的问题,如问题和IT答案。这种方法熟悉每个人都是SEQ2SEQ学习。在SEQ2SEQ模型机制中,它具有编码器和解码器。编码器嵌入任何输入序列,以及解码器嵌入输出序列。为了加强SEQ2SEQ模型性能,请将注意力添加到编码器和解码器中。之后,变压器模型已经将其自身作为高性能模型引入,具有多种关注机制,用于解决与序列相关的困境。该模型与基于RNN的模型相比减少了训练时间,并且还实现了序列转换的最先进的性能。在这项研究中,我们基于孟加拉普通知识问题答案(QA)数据集,应用了孟加拉一般知识聊天聊天的变压器模型。它在应用的QA数据上得分为85.0 BLEU。要检查变压器模型性能的比较,我们将注意到SEQ2SEQ模型,请注意我们的数据集得分23.5 BLEU。
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Deep neural networks (DNNs) are vulnerable to a class of attacks called "backdoor attacks", which create an association between a backdoor trigger and a target label the attacker is interested in exploiting. A backdoored DNN performs well on clean test images, yet persistently predicts an attacker-defined label for any sample in the presence of the backdoor trigger. Although backdoor attacks have been extensively studied in the image domain, there are very few works that explore such attacks in the video domain, and they tend to conclude that image backdoor attacks are less effective in the video domain. In this work, we revisit the traditional backdoor threat model and incorporate additional video-related aspects to that model. We show that poisoned-label image backdoor attacks could be extended temporally in two ways, statically and dynamically, leading to highly effective attacks in the video domain. In addition, we explore natural video backdoors to highlight the seriousness of this vulnerability in the video domain. And, for the first time, we study multi-modal (audiovisual) backdoor attacks against video action recognition models, where we show that attacking a single modality is enough for achieving a high attack success rate.
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