推荐系统，也称为推荐系统，是一种信息过滤系统，其尝试预测用户的额定值或偏好。本文根据类型，Pearson相关系数，基于KNN的基于KNN的基于KNN的滤波，使用TFIDF和SVD，基于TFIDF和SVD的协作滤波，基于TFIDF和SVD，基于TFIDF和SVD，基于TFIDF和SVD，基于SVD，基于TFIDF和SVD，基于SVD的协作的推荐系统技术来设计和实现完整的电影推荐系统原型。除此之外，我们还提供了一种新颖的想法，适用机器学习技术，基于流派构建电影的集群，然后观察定义了截线的惯性数量。已经描述了本工作中讨论的方法的约束，以及一个策略如何克服另一个策略的缺点。在集团镜头网站上的数据集电影镜片上完成了整个工作，其中包含100836个额定值和3683个TAG应用程序，跨越9742部电影。这些数据是由610年3月29日的610名用户在2018年3月29日和2018年9月24日创建。

对于长期来说，研究人员一直在开发可靠而准确的股票价格预测预测模型。根据文献，如果预测模型是正确的设计和精炼，他们可以煞费苦心地和忠实地估计未来的库存价值。本文展示了一组时间序列，计量经济性和各种基于学习的股票价格预测模型。在此处使用来自2004年1月至2019年12月至2019年12月的Infosys，Icici和Sun Pharma的数据用于培训和测试模型，以了解哪种模型在哪个部门中表现最佳。一个时间序列模型（Holt-Winters指数平滑），一个计量计量模型（Arima），两台机器学习模型（随机林和火星），以及两种深度学习的模型（简单的RNN和LSTM）已被列入本文。火星已被证明是最好的执行机器学习模式，而LSTM已被证明是表现最好的深层学习模式。但总体而言，对于所有三个部门 - 它（在Infosys数据上），银行业务（在ICICI数据）和健康（在Sun Pharma数据上），Mars已被证明是销售预测中最佳表现模式。

Search and Rescue (SAR) missions in remote environments often employ autonomous multi-robot systems that learn, plan, and execute a combination of local single-robot control actions, group primitives, and global mission-oriented coordination and collaboration. Often, SAR coordination strategies are manually designed by human experts who can remotely control the multi-robot system and enable semi-autonomous operations. However, in remote environments where connectivity is limited and human intervention is often not possible, decentralized collaboration strategies are needed for fully-autonomous operations. Nevertheless, decentralized coordination may be ineffective in adversarial environments due to sensor noise, actuation faults, or manipulation of inter-agent communication data. In this paper, we propose an algorithmic approach based on adversarial multi-agent reinforcement learning (MARL) that allows robots to efficiently coordinate their strategies in the presence of adversarial inter-agent communications. In our setup, the objective of the multi-robot team is to discover targets strategically in an obstacle-strewn geographical area by minimizing the average time needed to find the targets. It is assumed that the robots have no prior knowledge of the target locations, and they can interact with only a subset of neighboring robots at any time. Based on the centralized training with decentralized execution (CTDE) paradigm in MARL, we utilize a hierarchical meta-learning framework to learn dynamic team-coordination modalities and discover emergent team behavior under complex cooperative-competitive scenarios. The effectiveness of our approach is demonstrated on a collection of prototype grid-world environments with different specifications of benign and adversarial agents, target locations, and agent rewards.

Accurate and robust extrinsic calibration is necessary for deploying autonomous systems which need multiple sensors for perception. In this paper, we present a robust system for real-time extrinsic calibration of multiple lidars in vehicle base frame without the need for any fiducial markers or features. We base our approach on matching absolute GNSS and estimated lidar poses in real-time. Comparing rotation components allows us to improve the robustness of the solution than traditional least-square approach comparing translation components only. Additionally, instead of comparing all corresponding poses, we select poses comprising maximum mutual information based on our novel observability criteria. This allows us to identify a subset of the poses helpful for real-time calibration. We also provide stopping criteria for ensuring calibration completion. To validate our approach extensive tests were carried out on data collected using Scania test vehicles (7 sequences for a total of ~ 6.5 Km). The results presented in this paper show that our approach is able to accurately determine the extrinsic calibration for various combinations of sensor setups.

We study the problem of training and certifying adversarially robust quantized neural networks (QNNs). Quantization is a technique for making neural networks more efficient by running them using low-bit integer arithmetic and is therefore commonly adopted in industry. Recent work has shown that floating-point neural networks that have been verified to be robust can become vulnerable to adversarial attacks after quantization, and certification of the quantized representation is necessary to guarantee robustness. In this work, we present quantization-aware interval bound propagation (QA-IBP), a novel method for training robust QNNs. Inspired by advances in robust learning of non-quantized networks, our training algorithm computes the gradient of an abstract representation of the actual network. Unlike existing approaches, our method can handle the discrete semantics of QNNs. Based on QA-IBP, we also develop a complete verification procedure for verifying the adversarial robustness of QNNs, which is guaranteed to terminate and produce a correct answer. Compared to existing approaches, the key advantage of our verification procedure is that it runs entirely on GPU or other accelerator devices. We demonstrate experimentally that our approach significantly outperforms existing methods and establish the new state-of-the-art for training and certifying the robustness of QNNs.

While the NLP community is generally aware of resource disparities among languages, we lack research that quantifies the extent and types of such disparity. Prior surveys estimating the availability of resources based on the number of datasets can be misleading as dataset quality varies: many datasets are automatically induced or translated from English data. To provide a more comprehensive picture of language resources, we examine the characteristics of 156 publicly available NLP datasets. We manually annotate how they are created, including input text and label sources and tools used to build them, and what they study, tasks they address and motivations for their creation. After quantifying the qualitative NLP resource gap across languages, we discuss how to improve data collection in low-resource languages. We survey language-proficient NLP researchers and crowd workers per language, finding that their estimated availability correlates with dataset availability. Through crowdsourcing experiments, we identify strategies for collecting high-quality multilingual data on the Mechanical Turk platform. We conclude by making macro and micro-level suggestions to the NLP community and individual researchers for future multilingual data development.

This paper surveys some recent developments in measures of association related to a new coefficient of correlation introduced by the author. A straightforward extension of this coefficient to standard Borel spaces (which includes all Polish spaces), overlooked in the literature so far, is proposed at the end of the survey.

Federated learning (FL) on deep neural networks facilitates new applications at the edge, especially for wearable and Internet-of-Thing devices. Such devices capture a large and diverse amount of data, but they have memory, compute, power, and connectivity constraints which hinder their participation in FL. We propose Centaur, a multitier FL framework, enabling ultra-constrained devices to efficiently participate in FL on large neural nets. Centaur combines two major ideas: (i) a data selection scheme to choose a portion of samples that accelerates the learning, and (ii) a partition-based training algorithm that integrates both constrained and powerful devices owned by the same user. Evaluations, on four benchmark neural nets and three datasets, show that Centaur gains ~10% higher accuracy than local training on constrained devices with ~58% energy saving on average. Our experimental results also demonstrate the superior efficiency of Centaur when dealing with imbalanced data, client participation heterogeneity, and various network connection probabilities.

We study the problem of learning controllers for discrete-time non-linear stochastic dynamical systems with formal reach-avoid guarantees. This work presents the first method for providing formal reach-avoid guarantees, which combine and generalize stability and safety guarantees, with a tolerable probability threshold $p\in[0,1]$ over the infinite time horizon. Our method leverages advances in machine learning literature and it represents formal certificates as neural networks. In particular, we learn a certificate in the form of a reach-avoid supermartingale (RASM), a novel notion that we introduce in this work. Our RASMs provide reachability and avoidance guarantees by imposing constraints on what can be viewed as a stochastic extension of level sets of Lyapunov functions for deterministic systems. Our approach solves several important problems -- it can be used to learn a control policy from scratch, to verify a reach-avoid specification for a fixed control policy, or to fine-tune a pre-trained policy if it does not satisfy the reach-avoid specification. We validate our approach on $3$ stochastic non-linear reinforcement learning tasks.

大多数腿部机器人都是由串行安装链路和执行器的腿部结构构建的，并通过复杂的控制器和传感器反馈来控制。相比之下，动物发展了多段腿，关节之间的机械耦合以及多段的脚。它们在所有地形上运行敏捷，可以说是通过更简单的运动控制。在这里，我们专注于开发抗原在自然地形上也滑落和下沉的脚步机制。我们提出了安装在具有多接头机械肌腱耦合的鸟类灵感机器人腿上的多段脚的首先结果。我们的单段和两段机械自适应的脚显示在开始滑动之前，在多个软和硬质基材上显示了可行的水平力。我们还观察到，与球形和圆柱 - 脚相比，分割的脚减少了软底物上的下沉。我们报告了多段脚如何提供非常适合双皮亚机器人的可行压力点的范围范围，还适用于斜坡和自然地形上的四倍机器人。我们的结果还提供了对诸如级别鸟类等动物的分段脚的功能理解。