室内环境中的热舒适感会对乘员的健康，福祉和表现产生巨大影响。鉴于对能源效率和实现智能建筑的关注，机器学习（ML）越来越多地用于数据驱动的热舒适度（TC）预测。通常，提出了用于空调或HVAC通风建筑物的基于ML的解决方案，这些模型主要是为成年人设计的。另一方面，在大多数国家 /地区，自然通风（NV）的建筑物是常态。它们也是节能和长期可持续性目标的理想选择。但是，NV建筑物的室内环境缺乏热调节，并且在空间环境中差异很大。这些因素使TC预测极具挑战性。因此，确定建筑环境对TC模型性能的影响很重要。此外，需要研究跨不同NV室内空间的TC预测模型的概括能力。这项工作解决了这些问题。数据是通过在5个自然通风的学校建筑中进行的为期一个月的实地实验，涉及512名小学生。空间变异性对学生舒适度的影响通过预测准确性的变化（高达71％）来证明。还通过特征重要性的变化来证明建筑环境对TC预测的影响。此外，对儿童（我们的数据集）和成人（ASHRAE-II数据库）进行了模型性能的空间变异性比较分析。最后，评估了NV教室中热舒适模型的概括能力，并强调了主要挑战。

未经许可的LTE-WiFi共存网络正在经历一致的致密化，以满足上升的移动数据需求。随着共存网络复杂性的增加，研究网络功能关系（NFR）非常重要，并利用它们来优化密集的共存网络性能。这项工作通过监督从现实世界实验中收集的网络数据的监督学习来研究未经许可的LTE-WiFi（LTE-U和LTE-LAA）网络中的NFR。在实验中考虑不同的802.11标准和不同的通道带宽，并且精确概述了学习模型选择策略。此后，通过学习模型参数如R-SQ，残差，异常值，预测器的选择等进行不同LTE-WiFi网络配置的比较分析。此外，提出了一种基于网络特征关系的优化（NEFRO）框架。通过利用从网络数据中学到的特征关系方程，NEFRO改善了传统的优化制剂。它被证明是通过两个优化目标，VIZ的时间关键密集共存网络非常适合。，网络容量和信号强度。 NEFRO针对四个关于网络优化的工作验证。 NEFRO成功地将优化收敛时间降低多达24％，同时平均保持高达97.16％的精度。

Color is a critical design factor for web pages, affecting important factors such as viewer emotions and the overall trust and satisfaction of a website. Effective coloring requires design knowledge and expertise, but if this process could be automated through data-driven modeling, efficient exploration and alternative workflows would be possible. However, this direction remains underexplored due to the lack of a formalization of the web page colorization problem, datasets, and evaluation protocols. In this work, we propose a new dataset consisting of e-commerce mobile web pages in a tractable format, which are created by simplifying the pages and extracting canonical color styles with a common web browser. The web page colorization problem is then formalized as a task of estimating plausible color styles for a given web page content with a given hierarchical structure of the elements. We present several Transformer-based methods that are adapted to this task by prepending structural message passing to capture hierarchical relationships between elements. Experimental results, including a quantitative evaluation designed for this task, demonstrate the advantages of our methods over statistical and image colorization methods. The code is available at https://github.com/CyberAgentAILab/webcolor.

Human pose estimation, particularly in athletes, can help improve their performance. However, this estimation is difficult using existing methods, such as human annotation, if the subjects wear loose-fitting clothes such as ski/snowboard wears. This study developed a method for obtaining the ground truth data on two-dimensional (2D) poses of a human wearing loose-fitting clothes. This method uses fast-flushing light-emitting diodes (LEDs). The subjects were required to wear loose-fitting clothes and place the LED on the target joints. The LEDs were observed directly using a camera by selecting thin filmy loose-fitting clothes. The proposed method captures the scene at 240 fps by using a high-frame-rate camera and renders two 30 fps image sequences by extracting LED-on and -off frames. The temporal differences between the two video sequences can be ignored, considering the speed of human motion. The LED-on video was used to manually annotate the joints and thus obtain the ground truth data. Additionally, the LED-off video, equivalent to a standard video at 30 fps, confirmed the accuracy of existing machine learning-based methods and manual annotations. Experiments demonstrated that the proposed method can obtain ground truth data for standard RGB videos. Further, it was revealed that neither manual annotation nor the state-of-the-art pose estimator obtains the correct position of target joints.

Robotic hands with soft surfaces can perform stable grasping, but the high friction of the soft surfaces makes it difficult to release objects, or to perform operations that require sliding. To solve this issue, we previously developed a contact area variable surface (CAVS), whose friction changed according to the load. However, only our fundamental results were previously presented, with detailed analyses not provided. In this study, we first investigated the CAVS friction anisotropy, and demonstrated that the longitudinal direction exhibited a larger ratio of friction change. Next, we proposed a sensible CAVS, capable of providing a variable-friction mechanism, and tested its sensing and control systems in operations requiring switching between sliding and stable-grasping modes. Friction sensing was performed using an embedded camera, and we developed a gripper using the sensible CAVS, considering the CAVS friction anisotropy. In CAVS, the low-friction mode corresponds to a small grasping force, while the high-friction mode corresponds to a greater grasping force. Therefore, by controlling only the friction mode, the gripper mode can be set to either the sliding or stable-grasping mode. Based on this feature, a methodology for controlling the contact mode was constructed. We demonstrated a manipulation involving sliding and stable grasping, and thus verified the efficacy of the developed sensible CAVS.

Our team, Hibikino-Musashi@Home (the shortened name is HMA), was founded in 2010. It is based in the Kitakyushu Science and Research Park, Japan. We have participated in the RoboCup@Home Japan open competition open platform league every year since 2010. Moreover, we participated in the RoboCup 2017 Nagoya as open platform league and domestic standard platform league teams. Currently, the Hibikino-Musashi@Home team has 20 members from seven different laboratories based in the Kyushu Institute of Technology. In this paper, we introduce the activities of our team and the technologies.

提出了一种表示每个数据集的消化信息的方法，以创新思想的帮助以及试图使用或组合数据集创建有价值的产品，服务和业务模型的数据用户的通信。与通过共享属性（即变量）连接数据集的方法相比，此方法通过在现实世界中应活跃的情况下通过事件，情况或操作连接数据集。该方法反映了每个元数据对特征概念的适应性的考虑，这是预期从数据中获得的信息或知识的摘要；因此，数据的用户获得了适合真实企业和现实生活需求的实践知识，以及将AI技术应用于数据的基础。

捍卫深层神经网络免受对抗性示例是AI安全的关键挑战。为了有效地提高鲁棒性，最近的方法集中在对抗训练中的决策边界附近的重要数据点上。但是，这些方法容易受到自动攻击的影响，这是无参数攻击的合奏，可用于可靠评估。在本文中，我们通过实验研究了其脆弱性的原因，发现现有方法会减少真实标签和其他标签的逻辑之间的利润，同时保持其梯度规范非微小值。减少的边缘和非微小梯度规范会导致其脆弱性，因为最大的logit可以轻松地被扰动翻转。我们的实验还表明，logit边缘的直方图具有两个峰，即小和大的logit边缘。从观察结果来看，我们提出了切换单重损失（SOVR），当数据具有较小的logit rumgins时，它会使用单重损失，从而增加边缘。我们发现，SOVR比现有方法增加了logit的利润率，同时使梯度规范保持较小，并且在针对自动攻击的鲁棒性方面超越了它们。

我们提出了一种使用条件生成对抗网络（CGANS）在机器人关节空间和潜在空间之间转换的新方法，以进行无碰撞路径计划，该方法仅捕获以障碍物图来捕获关节空间的无碰撞区域。操纵机器人臂时，很方便地生成多个合理的轨迹进行进一步选择。此外，出于安全原因，有必要生成轨迹，以避免与机器人本身或周围环境发生碰撞。在提出的方法中，可以通过将开始和目标状态与此生成的潜在空间中的任意线段连接起来和目标状态来产生各种轨迹。我们的方法提供了此无碰撞潜在空间，此后，任何使用任何优化条件的计划者都可以使用任何计划器来生成最合适的路径。我们通过模拟和实际的UR5E 6-DOF机器人臂成功验证了这种方法。我们确认可以根据优化条件的选择生成不同的轨迹。

提供有关学习者论证的反馈对于发展批判性思维技能至关重要，但是，它需要大量的时间和精力。为了减轻教师的过载，我们旨在自动化提供反馈的过程，尤其是给出诊断评论，以指出论点固有的弱点。建议给出特定的诊断评论，以便学习者可以识别诊断而不会误解。但是，如何制定提供特定的诊断评论的任务并不明显。我们将任务的表述作为模板选择和插槽填充，以使自动评估变得更加容易，并且模型的行为更加可行。该公式的关键是创建足以实用的模板集的可能性。在本文中，我们定义了三个标准，即模板集应满足：表达性，信息性和唯一性，并验证创建一个满足这些标准作为第一个试验的模板集的可行性。我们将通过一项注释研究证明，将文本中给出的诊断评论转换为模板格式是可行的。注释研究中使用的语料库公开可用。