Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing, but no previous work has explored the possibility of whether abstractive dialogue summarization can also be used as a means to boost an NLP system's performance on other important dialogue comprehension tasks. In this paper, we propose a novel type of dialogue summarization task - STRUctured DiaLoguE Summarization - that can help pre-trained language models to better understand dialogues and improve their performance on important dialogue comprehension tasks. We further collect human annotations of STRUDEL summaries over 400 dialogues and introduce a new STRUDEL dialogue comprehension modeling framework that integrates STRUDEL into a graph-neural-network-based dialogue reasoning module over transformer encoder language models to improve their dialogue comprehension abilities. In our empirical experiments on two important downstream dialogue comprehension tasks - dialogue question answering and dialogue response prediction - we show that our STRUDEL dialogue comprehension model can significantly improve the dialogue comprehension performance of transformer encoder language models.

As an important data selection schema, active learning emerges as the essential component when iterating an Artificial Intelligence (AI) model. It becomes even more critical given the dominance of deep neural network based models, which are composed of a large number of parameters and data hungry, in application. Despite its indispensable role for developing AI models, research on active learning is not as intensive as other research directions. In this paper, we present a review of active learning through deep active learning approaches from the following perspectives: 1) technical advancements in active learning, 2) applications of active learning in computer vision, 3) industrial systems leveraging or with potential to leverage active learning for data iteration, 4) current limitations and future research directions. We expect this paper to clarify the significance of active learning in a modern AI model manufacturing process and to bring additional research attention to active learning. By addressing data automation challenges and coping with automated machine learning systems, active learning will facilitate democratization of AI technologies by boosting model production at scale.

部分标签学习（PLL）是一项奇特的弱监督学习任务，其中训练样本通常与一组候选标签而不是单个地面真理相关联。尽管在该域中提出了各种标签歧义方法，但他们通常假设在许多现实世界应用中可能不存在类平衡的方案。从经验上讲，我们在面对长尾分布和部分标记的组合挑战时观察到了先前方法的退化性能。在这项工作中，我们首先确定先前工作失败的主要原因。随后，我们提出了一种新型的基于最佳运输的框架太阳能，它允许完善被歧义的标签，以匹配边缘级别的先验分布。太阳能还结合了一种新的系统机制，用于估计PLL设置下的长尾类先验分布。通过广泛的实验，与先前的最先进的PLL方法相比，太阳能在标准化基准方面表现出基本优势。代码和数据可在以下网址获得：https：//github.com/hbzju/solar。

半监督异常检测（AD）是一种数据挖掘任务，旨在从部分标记的数据集中学习功能，以帮助检测异常值。在本文中，我们将现有的半监督AD方法分为两类：无监督和基于监督的基于监督的，并指出其中大多数人对标记数据的利用不足和未经标记的数据的探索不足。为了解决这些问题，我们提出了深度的异常检测和搜索（DADS），该检测（DADS）应用了增强学习（RL）以平衡剥削和探索。在培训过程中，代理商通过层次结构的数据集搜索可能的异常情况，并使用搜索异常来增强性能，从本质上讲，这本质上从合奏学习的想法中汲取了教训。在实验上，我们将DAD与利用标记已知异常的标记为检测其他已知异常和未知异常的几种最新方法进行了比较。结果表明，爸爸可以从未标记的数据中有效，精确地搜索异常，并向它们学习，从而实现良好的性能。

在现实世界中，尽管对该领域的兴趣激增，但在稀疏回报协同环境下进行的加强学习仍然具有挑战性。先前的尝试表明，内在的奖励可以减轻稀疏引起的问题。在本文中，我们提出了一种新颖的固有奖励，该奖励受人类学习的启发，因为人类通过将当前的观察结果与历史知识进行比较来评估好奇心。具体而言，我们训练一个自我监督的预测模型，并保存一组模型参数的快照，而不会产生加法培训成本。然后，我们采用核规范来评估不同快照的预测之间的时间不一致，这可以进一步部署为内在的奖励。此外，提出了一种变异的加权机制，以自适应方式将权重分配给不同的快照。我们证明了所提出的方法在各种基准环境中的功效。结果表明，与其他基于奖励的方法相比，我们的方法可以提供压倒性的最先进性能，而不会产生额外的培训成本并保持更高的噪声耐受性。我们的代码将公开发布以提高可重复性。

外部奖励的稀疏性对加强学习（RL）构成了严重的挑战。当前，对好奇心已经做出了许多努力，这些努力可以为有效探索提供代表性的内在奖励。但是，挑战尚未得到解决。在本文中，我们提出了一种名为Dymecu的RL的好奇心，它代表了基于动态记忆的好奇心。受到人类好奇心和信息理论的启发，Dymecu由动态记忆和双重在线学习者组成。好奇心引起的话，如果记忆的信息无法处理当前状态，并且双重学习者之间的信息差距可以作为对代理的内在奖励进行表述，然后可以将这些状态信息巩固到动态内存中。与以前的好奇方法相比，dymecu可以更好地模仿人类的好奇心与动态记忆，并且可以根据双重学习者的引导范式动态地生长内存模块。在包括DeepMind Control Suite和Atari Suite在内的多个基准测试中，进行了大规模的经验实验，结果表明，Dymecu在有或没有外部奖励的情况下优于基于好奇心的方法。我们将发布代码以增强可重复性。

可解释性已成为某些高风险领域（例如医疗保健，银行和安全性）中人工智能的重要主题。对于常用的表格数据，传统方法仅使用数值和分类数据训练了端到端的机器学习模型，并且不利用人类可理解的知识，例如数据描述。然而，从表格数据中挖掘人类水平的知识并将其用于预测仍然是一个挑战。因此，我们提出了一个基于概念和论证的模型（CAM），其中包括以下两个组成部分：一种新颖的概念挖掘方法，可从特征和基础数据的描述中获得人类可理解的概念及其关系，以及基于定量论证的方法进行知识表示和推理。因此，CAM提供了基于人类水平知识的决策，而推理过程本质上是可解释的。最后，为了可视化有目的的可解释模型，我们提供了一个对话解释，该解释包含CAM内主导的推理路径。开源基准数据集和现实词业务数据集的实验结果表明，CAM是透明且可解释的，CAM内部的知识与人类的理解是一致的； （2）与其他最先进模型相比，我们的可解释方法可以达到竞争结果。

用于流量操作和控制的现有数据收集方法通常依赖于基于基础架构的环路探测器或探测器车辆轨迹。连接和自动化的车辆（CAVS）不仅可以报告有关自己的数据，而且可以提供所有检测到的周围车辆的状态。从多个CAVS以及基础设施传感器（例如Lidar）的感知数据集成，即使在非常低的渗透率下也可以提供更丰富的信息。本文旨在开发合作数据收集系统，该系统集成了来自基础架构和CAVS的LiDar Point Cloud数据，以为各种运输应用创建合作感知环境。最新的3D检测模型用于在合并点云中检测车辆。我们在与Carla和Sumo的共模拟平台中测试了具有最大压力自适应信号控制模型的提出的合作感知环境。结果表明，CAV和基础设施传感器的渗透率非常低，足以实现可比性的性能，而连接车辆（CV）的渗透率为30％或更高。我们还显示了不同CAV渗透率下的等效CV渗透率（E-CVPR），以证明合作感知环境的数据收集效率。

As one of the most important psychic stress reactions, micro-expressions (MEs), are spontaneous and transient facial expressions that can reveal the genuine emotions of human beings. Thus, recognizing MEs (MER) automatically is becoming increasingly crucial in the field of affective computing, and provides essential technical support in lie detection, psychological analysis and other areas. However, the lack of abundant ME data seriously restricts the development of cutting-edge data-driven MER models. Despite the recent efforts of several spontaneous ME datasets to alleviate this problem, it is still a tiny amount of work. To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7,526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years. Afterwards, we adopt four classical spatiotemporal feature learning models on DFME to perform MER experiments to objectively verify the validity of DFME dataset. In addition, we explore different solutions to the class imbalance and key-frame sequence sampling problems in dynamic MER respectively on DFME, so as to provide a valuable reference for future research. The comprehensive experimental results show that our DFME dataset can facilitate the research of automatic MER, and provide a new benchmark for MER. DFME will be published via https://mea-lab-421.github.io.

Increasing research interests focus on sequential recommender systems, aiming to model dynamic sequence representation precisely. However, the most commonly used loss function in state-of-the-art sequential recommendation models has essential limitations. To name a few, Bayesian Personalized Ranking (BPR) loss suffers the vanishing gradient problem from numerous negative sampling and predictionbiases; Binary Cross-Entropy (BCE) loss subjects to negative sampling numbers, thereby it is likely to ignore valuable negative examples and reduce the training efficiency; Cross-Entropy (CE) loss only focuses on the last timestamp of the training sequence, which causes low utilization of sequence information and results in inferior user sequence representation. To avoid these limitations, in this paper, we propose to calculate Cumulative Cross-Entropy (CCE) loss over the sequence. CCE is simple and direct, which enjoys the virtues of painless deployment, no negative sampling, and effective and efficient training. We conduct extensive experiments on five benchmark datasets to demonstrate the effectiveness and efficiency of CCE. The results show that employing CCE loss on three state-of-the-art models GRU4Rec, SASRec, and S3-Rec can reach 125.63%, 69.90%, and 33.24% average improvement of full ranking NDCG@5, respectively. Using CCE, the performance curve of the models on the test data increases rapidly with the wall clock time, and is superior to that of other loss functions in almost the whole process of model training.