Community Question Answering (CQA) sites have spread and multiplied significantly in recent years. Sites like Reddit, Quora, and Stack Exchange are becoming popular amongst people interested in finding answers to diverse questions. One practical way of finding such answers is automatically predicting the best candidate given existing answers and comments. Many studies were conducted on answer prediction in CQA but with limited focus on using the background information of the questionnaires. We address this limitation using a novel method for predicting the best answers using the questioner's background information and other features, such as the textual content or the relationships with other participants. Our answer classification model was trained using the Stack Exchange dataset and validated using the Area Under the Curve (AUC) metric. The experimental results show that the proposed method complements previous methods by pointing out the importance of the relationships between users, particularly throughout the level of involvement in different communities on Stack Exchange. Furthermore, we point out that there is little overlap between user-relation information and the information represented by the shallow text features and the meta-features, such as time differences.

This study demonstrates the feasibility of point cloud-based proactive link quality prediction for millimeter-wave (mmWave) communications. Image-based methods to quantitatively and deterministically predict future received signal strength using machine learning from time series of depth images to mitigate the human body line-of-sight (LOS) path blockage in mmWave communications have been proposed. However, image-based methods have been limited in applicable environments because camera images may contain private information. Thus, this study demonstrates the feasibility of using point clouds obtained from light detection and ranging (LiDAR) for the mmWave link quality prediction. Point clouds represent three-dimensional (3D) spaces as a set of points and are sparser and less likely to contain sensitive information than camera images. Additionally, point clouds provide 3D position and motion information, which is necessary for understanding the radio propagation environment involving pedestrians. This study designs the mmWave link quality prediction method and conducts two experimental evaluations using different types of point clouds obtained from LiDAR and depth cameras, as well as different numerical indicators of link quality, received signal strength and throughput. Based on these experiments, our proposed method can predict future large attenuation of mmWave link quality due to LOS blockage by human bodies, therefore our point cloud-based method can be an alternative to image-based methods.

Classification bandits are multi-armed bandit problems whose task is to classify a given set of arms into either positive or negative class depending on whether the rate of the arms with the expected reward of at least h is not less than w for given thresholds h and w. We study a special classification bandit problem in which arms correspond to points x in d-dimensional real space with expected rewards f(x) which are generated according to a Gaussian process prior. We develop a framework algorithm for the problem using various arm selection policies and propose policies called FCB and FTSV. We show a smaller sample complexity upper bound for FCB than that for the existing algorithm of the level set estimation, in which whether f(x) is at least h or not must be decided for every arm's x. Arm selection policies depending on an estimated rate of arms with rewards of at least h are also proposed and shown to improve empirical sample complexity. According to our experimental results, the rate-estimation versions of FCB and FTSV, together with that of the popular active learning policy that selects the point with the maximum variance, outperform other policies for synthetic functions, and the version of FTSV is also the best performer for our real-world dataset.

Advanced visual localization techniques encompass image retrieval challenges and 6 Degree-of-Freedom (DoF) camera pose estimation, such as hierarchical localization. Thus, they must extract global and local features from input images. Previous methods have achieved this through resource-intensive or accuracy-reducing means, such as combinatorial pipelines or multi-task distillation. In this study, we present a novel method called SuperGF, which effectively unifies local and global features for visual localization, leading to a higher trade-off between localization accuracy and computational efficiency. Specifically, SuperGF is a transformer-based aggregation model that operates directly on image-matching-specific local features and generates global features for retrieval. We conduct experimental evaluations of our method in terms of both accuracy and efficiency, demonstrating its advantages over other methods. We also provide implementations of SuperGF using various types of local features, including dense and sparse learning-based or hand-crafted descriptors.

3D点云可以灵活地表示连续表面，可用于各种应用；但是，缺乏结构信息使点云识别具有挑战性。最近的边缘感知方法主要使用边缘信息作为描述局部结构以促进学习的额外功能。尽管这些方法表明，将边缘纳入网络设计是有益的，但它们通常缺乏解释性，使用户想知道边缘如何有所帮助。为了阐明这一问题，在这项研究中，我们提出了以可解释方式处理边缘的扩散单元（DU），同时提供了不错的改进。我们的方法可以通过三种方式解释。首先，我们从理论上表明，DU学会了执行任务呈纤维边缘的增强和抑制作用。其次，我们通过实验观察并验证边缘增强和抑制行为。第三，我们从经验上证明，这种行为有助于提高绩效。在具有挑战性的基准上进行的广泛实验验证了DU在可解释性和绩效增长方面的优势。具体而言，我们的方法使用S3DIS使用Shapenet零件和场景分割来实现对象零件分割的最新性能。我们的源代码将在https://github.com/martianxiu/diffusionunit上发布。

本文提出了一种用于拆分计算的神经体系结构搜索（NAS）方法。拆分计算是一种新兴的机器学习推理技术，可解决在物联网系统中部署深度学习的隐私和延迟挑战。在拆分计算中，神经网络模型通过网络使用Edge服务器和IoT设备进行了分离和合作处理。因此，神经网络模型的体系结构显着影响通信有效载荷大小，模型准确性和计算负载。在本文中，我们解决了优化神经网络体系结构以进行拆分计算的挑战。为此，我们提出了NASC，该NASC共同探讨了最佳模型架构和一个拆分点，以达到延迟需求（即，计算和通信的总延迟较小，都比某个阈值较小）。 NASC采用单发NAS，不需要重复模型培训进行计算高效的体系结构搜索。我们使用硬件（HW） - 基准数据的NAS基础的绩效评估表明，拟议的NASC可以改善``通信潜伏期和模型准确性''的权衡，即，将延迟降低了约40-60％，从基线降低了约40-60％有轻微的精度降解。

本文收集了提交给核心挑战2022的求解器和ISR实例的所有描述。

本文考虑了$ k $ actions和$ d $ outcomes的部分监测问题，并提供了第一个最佳世界世界算法，其遗憾是在随机制度中的多层次，在随机状态下，在对抗性中近乎看法。政权。更具体地说，我们证明对于非分类本地可观察的游戏，随机制度中的遗憾是由$ o（k^3 m^2 \ log（t）\ log（k _ {\ pi} t） / \ delta _ {\ mathrm {\ min}}）$，在$ o（k^{2/3} m \ sqrt {t \ log（t）\ log k _ {\ log k _ {\ pi}}}）$中，在对抗状态下$ t $是回合的数量，$ m $是每个动作不同观察值的最大数量，$ \ delta _ {\ min} $是最小的最佳差距，$ k _ {\ pi} $是帕累托的最佳数量动作。此外，我们表明，对于非分类全球可观察的游戏，随机制度中的遗憾是由$ o（\ max \ {c _ {c _ {\ Mathcal {g}}}}^2 / k，\，c _ { }}} \} \ log（t）\ log（k _ {\ pi} t） / \ delta _ {\ min}^2）$，在$ o（\ max \ {c _ { }}}^2/k，\，c _ {\ mathcal {g}}} \} \ log（t）\ log（k _ {\ pi} t）））^{1/3} t} t^{2/3}） $，其中$ c _ {\ Mathcal {g}} $是游戏依赖的常数。我们的算法基于以下规范化领导者框架，该框架考虑了部分监视问题的性质，灵感来自在线学习领域中使用反馈图的算法。

随着姿势估计和图形卷积网络的进步，基于骨架的两人互动识别一直在越来越多的关注。尽管准确性逐渐提高，但计算复杂性的提高使其在现实环境中更不切实际。由于常规方法不能完全代表体内关节之间的关系，因此仍然存在准确性改善的空间。在本文中，我们提出了一个轻巧的模型，以准确识别两人的交互。除了结合了中间融合的体系结构外，我们还引入了一种分解卷积技术，以减少模型的重量参数。我们还引入了一个网络流，该网络说明体内关节之间的相对距离变化以提高准确性。使用两个大规模数据集NTU RGB+D 60和120的实验表明，与常规方法相比，我们的方法同时达到了最高准确性和相对较低的计算复杂性。

开放式对象检测（OSOD）最近引起了广泛的关注。它是在正确检测/分类已知对象的同时检测未知对象。我们首先指出，最近的研究中考虑的OSOD方案，该方案考虑了类似于开放式识别（OSR）的无限种类的未知物体，这是一个基本问题。也就是说，我们无法确定要检测到的内容，而对于这种无限的未知对象，这是检测任务所必需的。这个问题导致了对未知对象检测方法的性能的评估困难。然后，我们介绍了OSOD的新颖方案，该方案仅处理与已知对象共享超级类别的未知对象。它具有许多真实的应用程序，例如检测越来越多的细粒对象。这个新环境摆脱了上述问题和评估困难。此外，由于已知和未知对象之间的视觉相似性，它使检测到未知对象更加现实。我们通过实验结果表明，基于标准检测器类别预测的不确定性的简单方法优于先前设置中测试的当前最新OSOD方法。