合并个人喜好对于高级机器翻译任务至关重要。尽管机器翻译最近进步，但正确反映个人风格仍然是一项艰巨的任务。在本文中，我们引入了一个个性化的自动后编辑框架来应对这一挑战，该挑战有效地产生了考虑不同个人行为的句子。为了构建此框架，我们首先收集后编辑数据，该数据表示来自Live Machine Translation系统的用户偏好。具体而言，现实世界的用户输入源句子进行翻译，并根据用户的首选样式编辑机器翻译的输出。然后，我们提出了一个模型，该模型结合了APE框架上的歧视器模块和特定于用户的参数。实验结果表明，该方法的表现优于四个不同指标（即BLEU，TER，YISI-1和人类评估）的其他基线模型。

预测交通状况非常具有挑战性，因为每条道路在空间和时间上都高度依赖。最近，为了捕获这种空间和时间依赖性，已经引入了专门设计的架构，例如图形卷积网络和时间卷积网络。尽管流量预测取得了显着进展，但我们发现基于深度学习的流量预测模型仍然在某些模式中失败，主要是在事件情况下（例如，快速速度下降）。尽管通常认为这些故障是由于不可预测的噪声造成的，但我们发现可以通过考虑以前的失败来纠正这些故障。具体而言，我们观察到这些失败中的自相关错误，这表明仍然存在一些可预测的信息。在这项研究中，为了捕获错误的相关性，我们引入了Rescal，Rescal是流量预测的剩余估计模块，作为广泛适用的附加模块，用于现有的流量预测模型。我们的恢复通过使用以前的错误和图形信号来估算未来错误，从而实时校准现有模型的预测。对METR-LA和PEMS-BAY进行的广泛实验表明，我们的恢复可以正确捕获错误的相关性，并在事件情况下纠正各种流量预测模型的故障。

通过深度学习（DL）优于不同任务的常规方法，已经努力利用DL在各个领域中使用。交通域中的研究人员和开发人员还为预测任务（例如交通速度估算和到达时间）设计和改进了DL模型。但是，由于DL模型的黑盒属性和流量数据的复杂性（即时空依赖性），在分析DL模型方面存在许多挑战。我们与域专家合作，我们设计了一个视觉分析系统Attnanalyzer，该系统使用户能够探索DL模型如何通过允许有效的时空依赖性分析来进行预测。该系统结合了动态时间扭曲（DTW）和Granger因果关系测试，用于计算时空依赖性分析，同时提供映射，表格，线图和像素视图，以帮助用户执行依赖性和模型行为分析。为了进行评估，我们提出了三个案例研究，表明Attnanalyzer如何有效地探索模型行为并改善两个不同的道路网络中的模型性能。我们还提供域专家反馈。

The automated segmentation and tracking of macrophages during their migration are challenging tasks due to their dynamically changing shapes and motions. This paper proposes a new algorithm to achieve automatic cell tracking in time-lapse microscopy macrophage data. First, we design a segmentation method employing space-time filtering, local Otsu's thresholding, and the SUBSURF (subjective surface segmentation) method. Next, the partial trajectories for cells overlapping in the temporal direction are extracted in the segmented images. Finally, the extracted trajectories are linked by considering their direction of movement. The segmented images and the obtained trajectories from the proposed method are compared with those of the semi-automatic segmentation and manual tracking. The proposed tracking achieved 97.4% of accuracy for macrophage data under challenging situations, feeble fluorescent intensity, irregular shapes, and motion of macrophages. We expect that the automatically extracted trajectories of macrophages can provide pieces of evidence of how macrophages migrate depending on their polarization modes in the situation, such as during wound healing.

Data-centric AI has shed light on the significance of data within the machine learning (ML) pipeline. Acknowledging its importance, various research and policies are suggested by academia, industry, and government departments. Although the capability of utilizing existing data is essential, the capability to build a dataset has become more important than ever. In consideration of this trend, we propose a "Data Management Operation and Recipes" that will guide the industry regardless of the task or domain. In other words, this paper presents the concept of DMOps derived from real-world experience. By offering a baseline for building data, we want to help the industry streamline its data operation optimally.

According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based autonomous nonlinear drone mobility control algorithm in cyber-physical loitering munition applications. On the battlefield, the design of DRL-based autonomous control algorithm is not straightforward because real-world data gathering is generally not available. Therefore, the approach in this paper is that cyber-physical virtual environment is constructed with Unity environment. Based on the virtual cyber-physical battlefield scenarios, a DRL-based automated nonlinear drone mobility control algorithm can be designed, evaluated, and visualized. Moreover, many obstacles exist which is harmful for linear trajectory control in real-world battlefield scenarios. Thus, our proposed autonomous nonlinear drone mobility control algorithm utilizes situation-aware components those are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Therefore, this approach is obviously beneficial for avoiding obstacles in obstacle-deployed battlefields. Our visualization-based performance evaluation shows that the proposed algorithm is superior from the other linear mobility control algorithms.

This paper proposes a new regularization algorithm referred to as macro-block dropout. The overfitting issue has been a difficult problem in training large neural network models. The dropout technique has proven to be simple yet very effective for regularization by preventing complex co-adaptations during training. In our work, we define a macro-block that contains a large number of units from the input to a Recurrent Neural Network (RNN). Rather than applying dropout to each unit, we apply random dropout to each macro-block. This algorithm has the effect of applying different drop out rates for each layer even if we keep a constant average dropout rate, which has better regularization effects. In our experiments using Recurrent Neural Network-Transducer (RNN-T), this algorithm shows relatively 4.30 % and 6.13 % Word Error Rates (WERs) improvement over the conventional dropout on LibriSpeech test-clean and test-other. With an Attention-based Encoder-Decoder (AED) model, this algorithm shows relatively 4.36 % and 5.85 % WERs improvement over the conventional dropout on the same test sets.

Affect understanding capability is essential for social robots to autonomously interact with a group of users in an intuitive and reciprocal way. However, the challenge of multi-person affect understanding comes from not only the accurate perception of each user's affective state (e.g., engagement) but also the recognition of the affect interplay between the members (e.g., joint engagement) that presents as complex, but subtle, nonverbal exchanges between them. Here we present a novel hybrid framework for identifying a parent-child dyad's joint engagement by combining a deep learning framework with various video augmentation techniques. Using a dataset of parent-child dyads reading storybooks together with a social robot at home, we first train RGB frame- and skeleton-based joint engagement recognition models with four video augmentation techniques (General Aug, DeepFake, CutOut, and Mixed) applied datasets to improve joint engagement classification performance. Second, we demonstrate experimental results on the use of trained models in the robot-parent-child interaction context. Third, we introduce a behavior-based metric for evaluating the learned representation of the models to investigate the model interpretability when recognizing joint engagement. This work serves as the first step toward fully unlocking the potential of end-to-end video understanding models pre-trained on large public datasets and augmented with data augmentation and visualization techniques for affect recognition in the multi-person human-robot interaction in the wild.

Training agents via off-policy deep reinforcement learning (RL) requires a large memory, named replay memory, that stores past experiences used for learning. These experiences are sampled, uniformly or non-uniformly, to create the batches used for training. When calculating the loss function, off-policy algorithms assume that all samples are of the same importance. In this paper, we hypothesize that training can be enhanced by assigning different importance for each experience based on their temporal-difference (TD) error directly in the training objective. We propose a novel method that introduces a weighting factor for each experience when calculating the loss function at the learning stage. In addition to improving convergence speed when used with uniform sampling, the method can be combined with prioritization methods for non-uniform sampling. Combining the proposed method with prioritization methods improves sampling efficiency while increasing the performance of TD-based off-policy RL algorithms. The effectiveness of the proposed method is demonstrated by experiments in six environments of the OpenAI Gym suite. The experimental results demonstrate that the proposed method achieves a 33%~76% reduction of convergence speed in three environments and an 11% increase in returns and a 3%~10% increase in success rate for other three environments.

Neural fields, also known as coordinate-based or implicit neural representations, have shown a remarkable capability of representing, generating, and manipulating various forms of signals. For video representations, however, mapping pixel-wise coordinates to RGB colors has shown relatively low compression performance and slow convergence and inference speed. Frame-wise video representation, which maps a temporal coordinate to its entire frame, has recently emerged as an alternative method to represent videos, improving compression rates and encoding speed. While promising, it has still failed to reach the performance of state-of-the-art video compression algorithms. In this work, we propose FFNeRV, a novel method for incorporating flow information into frame-wise representations to exploit the temporal redundancy across the frames in videos inspired by the standard video codecs. Furthermore, we introduce a fully convolutional architecture, enabled by one-dimensional temporal grids, improving the continuity of spatial features. Experimental results show that FFNeRV yields the best performance for video compression and frame interpolation among the methods using frame-wise representations or neural fields. To reduce the model size even further, we devise a more compact convolutional architecture using the group and pointwise convolutions. With model compression techniques, including quantization-aware training and entropy coding, FFNeRV outperforms widely-used standard video codecs (H.264 and HEVC) and performs on par with state-of-the-art video compression algorithms.