人工智能学术速递[7.9]

cs.AI人工智能，共计39篇

【1】 RMA: Rapid Motor Adaptation for Legged Robots 标题：RMA：腿部机器人的快速运动适应

作者：Ashish Kumar,Zipeng Fu,Deepak Pathak,Jitendra Malik 机构：Carnegie Mellon University, UC Berkeley, Facebook 备注：RSS 2021. Webpage at this https URL 链接：https://arxiv.org/abs/2107.04034 摘要：腿型机器人在现实世界中的成功部署需要它们实时适应未知场景，如地形变化、有效载荷变化、磨损等。针对四足机器人的实时在线自适应问题，提出了一种快速运动自适应算法。RMA由两部分组成：基本策略和自适应模块。这些部件的组合使机器人能够在几秒钟内适应新的环境。RMA完全在仿真中训练，不需要参考轨迹或预定义的脚轨迹生成器等领域知识，并且在A1机器人上部署，无需任何微调。我们使用生物能学启发的奖励，在各种地形发生器上训练RMA，并将其部署在各种困难地形上，包括岩石、光滑、可变形表面，以及有草、长植被、混凝土、卵石、楼梯、沙子的环境中，RMA在各种真实世界和模拟实验中展示了最先进的性能。视频结果https://ashish-kmr.github.io/rma-legged-robots/ 摘要：Successful real-world deployment of legged robots would require them to adapt in real-time to unseen scenarios like changing terrains, changing payloads, wear and tear. This paper presents Rapid Motor Adaptation (RMA) algorithm to solve this problem of real-time online adaptation in quadruped robots. RMA consists of two components: a base policy and an adaptation module. The combination of these components enables the robot to adapt to novel situations in fractions of a second. RMA is trained completely in simulation without using any domain knowledge like reference trajectories or predefined foot trajectory generators and is deployed on the A1 robot without any fine-tuning. We train RMA on a varied terrain generator using bioenergetics-inspired rewards and deploy it on a variety of difficult terrains including rocky, slippery, deformable surfaces in environments with grass, long vegetation, concrete, pebbles, stairs, sand, etc. RMA shows state-of-the-art performance across diverse real-world as well as simulation experiments. Video results at https://ashish-kmr.github.io/rma-legged-robots/

【2】 CVEH: A Dynamic Framework To Profile Vehicle Movements To Mitigate Hit And Run Cases Using Crowdsourcing 标题：CVEH：一种动态框架，用于描述车辆运动，以减少使用众包的肇事逃逸案件

作者：Attiq ur Rehman,Asad Waqar Malik,Anis ur Rahman,Sohail Iqbal,Ghalib Ahmed Tahir 链接：https://arxiv.org/abs/2107.04026 摘要：在美国、德国和英国等发达国家，安全部队使用高度复杂的设备、快速车辆、无人机和直升机来抓捕罪犯的车辆。然而，在资源有限的发展中国家，由于管理费用和其他制约因素，这种办法无法得到利用。在本文中，我们提出了一个称为CVEH的框架，使发展中国家能够通过众包技术来分析犯罪车辆的移动情况，并作为执法机构的预警系统。它还促使公民在改善安全条件方面发挥作用。建议的CVEH框架允许车辆到基础设施（V2I）通信，以监控罪犯车辆的移动，并与指挥控制中心（CC）共享其信息。CC中心规划了这条路，并与附近的执法机构进行了接触。CVEH是在android智能手机上开发和评估的。为这项研究进行的模拟显示了我们的框架的有效性。 摘要：In developed countries like the USA, Germany, and the UK, the security forces used highly sophisticated equipment, fast vehicles, drones, and helicopters to catch offenders' vehicles. Whereas, in developing countries with limited resources such schemes cannot be utilized due to management cost and other constraints. In this paper, we proposed a framework called CVEH that enables developing countries to profile the offender vehicle movements through crowdsourcing technique and act as an early warning system to the law forcing agencies. It also engages citizens to play their role in improving security conditions. The proposed CVEH framework allows Vehicle-to-Infrastructure (V2I) communication to monitor the movement of the offender's vehicle and shared its information with the Command and Control (CC) centre. The CC centre projects the path and engages nearly located law enforcement agencies. CVEH is developed and evaluated on android smartphones. Simulations conducted for this study exhibit the effectiveness of our framework.

【3】 Multi-Modality Task Cascade for 3D Object Detection 标题：基于多模态任务级联的三维目标检测

作者：Jinhyung Park,Xinshuo Weng,Yunze Man,Kris Kitani 机构：Carnegie Mellon University 链接：https://arxiv.org/abs/2107.04013 摘要：点云和RGB图像自然是3D视觉理解的互补模式——前者提供了稀疏但精确的对象上点的位置，而后者包含了密集的颜色和纹理信息。尽管这种潜在的密切传感器融合，许多方法训练两个模型在隔离和使用简单的特征拼接来表示三维传感器数据。这种分离的训练方案会导致潜在的次优性能，并阻止3D任务被用来帮助2D任务，而2D任务本身通常是有用的。为了提供一种更为综合的方法，我们提出了一种新的多模态任务级联网络（MTC-RCNN），该网络利用3D盒子方案来改进2D分割预测，然后使用3D盒子进一步细化3D盒子。我们发现在两个阶段的3D模块之间加入2D网络可以显著提高2D和3D任务的性能。此外，为了防止3D模块过度依赖于过度拟合的2D预测，我们提出了一种双头2D分割训练和推理方案，允许第二个3D模块学习解释不完美的2D分割预测。在具有挑战性的SUN RGB-D数据集上评估我们的模型，我们大大改进了单模态和融合网络的最新结果（$\textbf{+3.8}$mAP@0.5). 代码将被释放$\href{https://github.com/Divadi/MTC_RCNN}{\text{这里。}}$ 摘要：Point clouds and RGB images are naturally complementary modalities for 3D visual understanding - the former provides sparse but accurate locations of points on objects, while the latter contains dense color and texture information. Despite this potential for close sensor fusion, many methods train two models in isolation and use simple feature concatenation to represent 3D sensor data. This separated training scheme results in potentially sub-optimal performance and prevents 3D tasks from being used to benefit 2D tasks that are often useful on their own. To provide a more integrated approach, we propose a novel Multi-Modality Task Cascade network (MTC-RCNN) that leverages 3D box proposals to improve 2D segmentation predictions, which are then used to further refine the 3D boxes. We show that including a 2D network between two stages of 3D modules significantly improves both 2D and 3D task performance. Moreover, to prevent the 3D module from over-relying on the overfitted 2D predictions, we propose a dual-head 2D segmentation training and inference scheme, allowing the 2nd 3D module to learn to interpret imperfect 2D segmentation predictions. Evaluating our model on the challenging SUN RGB-D dataset, we improve upon state-of-the-art results of both single modality and fusion networks by a large margin ($\textbf{+3.8}$ mAP@0.5). Code will be released $\href{https://github.com/Divadi/MTC_RCNN}{\text{here.}}$

【4】 Knowledge Transfer by Discriminative Pre-training for Academic Performance Prediction 标题：用于学业成绩预测的识别性预训练知识转移

作者：Byungsoo Kim,Hangyeol Yu,Dongmin Shin,Youngduck Choi 机构：Riiid! AI Research 备注：EDM 2021 链接：https://arxiv.org/abs/2107.04009 摘要：随着智能教学系统（ITS）越来越受到人们的重视，人们越来越重视对学生学习成绩的精确评估。然而，由于学业成绩的标签（如考试成绩）是从ITS外部收集的，获取标签的成本很高，导致了标签的稀缺性问题，这给采用机器学习方法进行学业成绩预测带来了挑战。为此，受近年来自然语言处理领域预训练方法研究进展的启发，我们提出了DPA，这是一个基于区分性预训练任务的迁移学习框架。DPA预先训练两个模型，一个生成器和一个鉴别器，并对鉴别器的学习成绩预测进行微调。在DPA的预训练阶段，向生成器提供一个交互序列，其中一些令牌被屏蔽，生成器被训练来重构原始序列。然后，鉴别器获取一个交互序列，其中被屏蔽的令牌被生成器的输出所替换，并且被训练来预测序列中所有令牌的原始性。与以往最先进的生成性预训练方法相比，DPA具有更高的样本效率，收敛速度快，学习成绩预测误差小。我们在一个多平台的真实数据集上进行了大量的实验研究，结果表明，DPA方法的平均绝对误差降低了4.05%，并且对增加的标签稀缺性具有更强的鲁棒性。 摘要：The needs for precisely estimating a student's academic performance have been emphasized with an increasing amount of attention paid to Intelligent Tutoring System (ITS). However, since labels for academic performance, such as test scores, are collected from outside of ITS, obtaining the labels is costly, leading to label-scarcity problem which brings challenge in taking machine learning approaches for academic performance prediction. To this end, inspired by the recent advancement of pre-training method in natural language processing community, we propose DPA, a transfer learning framework with Discriminative Pre-training tasks for Academic performance prediction. DPA pre-trains two models, a generator and a discriminator, and fine-tunes the discriminator on academic performance prediction. In DPA's pre-training phase, a sequence of interactions where some tokens are masked is provided to the generator which is trained to reconstruct the original sequence. Then, the discriminator takes an interaction sequence where the masked tokens are replaced by the generator's outputs, and is trained to predict the originalities of all tokens in the sequence. Compared to the previous state-of-the-art generative pre-training method, DPA is more sample efficient, leading to fast convergence to lower academic performance prediction error. We conduct extensive experimental studies on a real-world dataset obtained from a multi-platform ITS application and show that DPA outperforms the previous state-of-the-art generative pre-training method with a reduction of 4.05% in mean absolute error and more robust to increased label-scarcity.

【5】 Inspiration through Observation: Demonstrating the Influence of Automatically Generated Text on Creative Writing 标题：从观察中获得灵感：论自动生成文本对创作的影响

作者：Melissa Roemmele 机构：Language Weaver (RWS Group), Los Angeles, CA, USA 备注：Accepted at ICCC 2021 链接：https://arxiv.org/abs/2107.04007 摘要：让机器产生被认为是有创意的文本是一个长期追求的目标。越来越多的研究将这一目标导向增强人类作者的创造性写作能力。在本文中，我们通过分析观察自动生成文本的例子对写作的影响来追求这一目标。特别是，我们研究了一个被称为句子填充的任务，它涉及到将一个单词列表转换成一个完整的句子。我们强调“可储存性”是句子的一个可取特征，其中“可储存性”句子是指那些暗示读者对某个故事感兴趣的句子。人类和一个自动化系统（基于神经语言模型）都执行了这个句子填充任务。在一种情况下，人们自己写句子；在另一个环境中，人们在写自己的句子时观察模型产生的句子。然后，在随后的评估中，读者将可储存性偏好分配给生成的句子。我们发现，当作者观察生成的例子时，人类创作的句子被判断为更易储存，并且随着作者从例子中获得更多的语义内容，可储存性增加。这一结果为人机合作写作提供了一种“观察启发”的范式，通过这种范式，文本生成模型可以在不直接复制文本输出的情况下增强人类的写作能力。 摘要：Getting machines to generate text perceived as creative is a long-pursued goal. A growing body of research directs this goal towards augmenting the creative writing abilities of human authors. In this paper, we pursue this objective by analyzing how observing examples of automatically generated text influences writing. In particular, we examine a task referred to as sentence infilling, which involves transforming a list of words into a complete sentence. We emphasize "storiability" as a desirable feature of the resulting sentences, where "storiable" sentences are those that suggest a story a reader would be curious to hear about. Both humans and an automated system (based on a neural language model) performed this sentence infilling task. In one setting, people wrote sentences on their own; in a different setting, people observed the sentences produced by the model while writing their own sentences. Readers then assigned storiability preferences to the resulting sentences in a subsequent evaluation. We find that human-authored sentences were judged as more storiable when authors observed the generated examples, and that storiability increased as authors derived more semantic content from the examples. This result gives evidence of an "inspiration through observation" paradigm for human-computer collaborative writing, through which human writing can be enhanced by text generation models without directly copying their output.

【6】 Active Safety Envelopes using Light Curtains with Probabilistic Guarantees 标题：使用具有概率保证的轻型窗帘的主动安全封套

作者：Siddharth Ancha,Gaurav Pathak,Srinivasa G. Narasimhan,David Held 机构：Carnegie Mellon University, Pittsburgh PA , USA 备注：18 pages, Published at Robotics: Science and Systems (RSS) 2021 链接：https://arxiv.org/abs/2107.04000 摘要：为了安全地在未知环境中航行，机器人必须准确地感知动态障碍物。我们不使用激光雷达传感器直接测量景深，而是探索使用更便宜、分辨率更高的传感器：可编程光幕。光幕是一种可控的深度传感器，只能沿用户选择的表面进行感应。我们使用光幕来估计场景的安全范围：一个将机器人与所有障碍物隔开的假想表面。我们表明，生成感测随机位置（来自特定分布）的光幕可以快速发现未知对象场景的安全包络。重要的是，我们提供了理论上的安全保证的概率检测障碍物使用随机窗帘。我们结合随机窗帘与机器学习为基础的模型，预测和跟踪运动的安全包络有效。我们的方法在提供概率安全保证的同时，准确估计了安全包络线，可以用来验证机器人感知系统检测和避开动态障碍物的有效性。我们在模拟城市驾驶环境和真实行人环境中使用光幕装置对我们的方法进行了评估，结果表明我们可以有效地估计安全包络线。项目网站：https://siddancha.github.io/projects/active-safety-envelopes-with-guarantees 摘要：To safely navigate unknown environments, robots must accurately perceive dynamic obstacles. Instead of directly measuring the scene depth with a LiDAR sensor, we explore the use of a much cheaper and higher resolution sensor: programmable light curtains. Light curtains are controllable depth sensors that sense only along a surface that a user selects. We use light curtains to estimate the safety envelope of a scene: a hypothetical surface that separates the robot from all obstacles. We show that generating light curtains that sense random locations (from a particular distribution) can quickly discover the safety envelope for scenes with unknown objects. Importantly, we produce theoretical safety guarantees on the probability of detecting an obstacle using random curtains. We combine random curtains with a machine learning based model that forecasts and tracks the motion of the safety envelope efficiently. Our method accurately estimates safety envelopes while providing probabilistic safety guarantees that can be used to certify the efficacy of a robot perception system to detect and avoid dynamic obstacles. We evaluate our approach in a simulated urban driving environment and a real-world environment with moving pedestrians using a light curtain device and show that we can estimate safety envelopes efficiently and effectively. Project website: https://siddancha.github.io/projects/active-safety-envelopes-with-guarantees

【7】 Offline Meta-Reinforcement Learning with Online Self-Supervision 标题：在线自我监控的离线元强化学习

作者：Vitchyr H. Pong,Ashvin Nair,Laura Smith,Catherine Huang,Sergey Levine 机构：UC Berkeley 备注：10 pages, 6 figures 链接：https://arxiv.org/abs/2107.03974 摘要：元强化学习（Meta-reinforcement learning，RL）可以用来训练快速适应新任务的策略，其数据量比标准RL少几个数量级，但这种快速适应的代价往往是在元训练期间大大增加奖励监督的数量。离线meta-RL消除了持续提供奖励监督的需要，因为当离线数据集生成时，奖励只能提供一次。除了离线RL的挑战外，元RL中还存在一个独特的分布变化：代理学习探索策略，可以收集学习新任务所需的经验，还学习适应策略，当呈现数据集中的轨迹时，这些策略效果很好，但适应策略并不适应所学探索策略所收集的数据分布。与在线环境不同，适应策略和探索策略不能有效地相互适应，导致表现不佳。本文提出了一种混合离线meta-RL算法，该算法利用带奖励的离线数据对自适应策略进行元训练，然后在没有任何地面真实奖励标签的情况下收集额外的无监督在线数据，以解决分布转移问题。该方法利用离线数据来学习奖励函数的分布，然后对这些函数进行抽样，对额外的在线数据进行奖励标签的自我监督。通过消除为在线体验提供奖励标签的需要，我们的方法可以更实际地用于奖励监督将手动提供的设置中。我们将我们的方法与先前的离线meta-RL在模拟机器人运动和操作任务上的工作进行了比较，发现使用额外的数据和自生成的奖励可以显著提高agent的泛化能力。 摘要：Meta-reinforcement learning (RL) can be used to train policies that quickly adapt to new tasks with orders of magnitude less data than standard RL, but this fast adaptation often comes at the cost of greatly increasing the amount of reward supervision during meta-training time. Offline meta-RL removes the need to continuously provide reward supervision because rewards must only be provided once when the offline dataset is generated. In addition to the challenges of offline RL, a unique distribution shift is present in meta RL: agents learn exploration strategies that can gather the experience needed to learn a new task, and also learn adaptation strategies that work well when presented with the trajectories in the dataset, but the adaptation strategies are not adapted to the data distribution that the learned exploration strategies collect. Unlike the online setting, the adaptation and exploration strategies cannot effectively adapt to each other, resulting in poor performance. In this paper, we propose a hybrid offline meta-RL algorithm, which uses offline data with rewards to meta-train an adaptive policy, and then collects additional unsupervised online data, without any ground truth reward labels, to bridge this distribution shift problem. Our method uses the offline data to learn the distribution of reward functions, which is then sampled to self-supervise reward labels for the additional online data. By removing the need to provide reward labels for the online experience, our approach can be more practical to use in settings where reward supervision would otherwise be provided manually. We compare our method to prior work on offline meta-RL on simulated robot locomotion and manipulation tasks and find that using additional data and self-generated rewards significantly improves an agent's ability to generalize.

【8】 Computational Benefits of Intermediate Rewards for Hierarchical Planning 标题：分层规划的中级奖励的计算效益

作者：Yuexiang Zhai,Christina Baek,Zhengyuan Zhou,Jiantao Jiao,Yi Ma 机构：Department of Electrical Engineering and Computer Sciences, UC Berkeley, Department of Statistics, UC Berkeley, Stern School of Business, NYU 链接：https://arxiv.org/abs/2107.03961 摘要：许多层次强化学习（RL）的应用经验证明，在奖励设计中加入先验知识可以提高收敛速度和实际性能。我们试图从规划的角度量化分层RL的计算效益，假设中间状态和中间报酬在实践中经常（但往往是隐含的）采用。我们的方法揭示了分层规划中计算复杂性和追求最短路径之间的折衷：使用中间奖励显著降低了寻找成功策略的计算复杂性，但不能保证找到最短路径，而使用稀疏终端奖励则会以更高的计算成本找到最短路径。我们还通过在微网格环境下使用Q-学习和其他流行的深度RL算法进行的大量实验验证了我们的理论结果。 摘要：Many hierarchical reinforcement learning (RL) applications have empirically verified that incorporating prior knowledge in reward design improves convergence speed and practical performance. We attempt to quantify the computational benefits of hierarchical RL from a planning perspective under assumptions about the intermediate state and intermediate rewards frequently (but often implicitly) adopted in practice. Our approach reveals a trade-off between computational complexity and the pursuit of the shortest path in hierarchical planning: using intermediate rewards significantly reduces the computational complexity in finding a successful policy but does not guarantee to find the shortest path, whereas using sparse terminal rewards finds the shortest path at a significantly higher computational cost. We also corroborate our theoretical results with extensive experiments on the MiniGrid environments using Q-learning and other popular deep RL algorithms.

【9】 Privacy Concerns in Chatbot Interactions: When to Trust and When to Worry 标题：聊天机器人交互中的隐私问题：何时信任，何时担忧

作者：Rahime Belen Saglam,Jason R. C. Nurse,Duncan Hodges 机构： University of Kent, UK, Cranfield University, Defence Academy of the United Kingdom, UK 备注：None 链接：https://arxiv.org/abs/2107.03959 摘要：聊天机器人通过其会话能力的提高，已经开始请求和处理越来越多的敏感个人信息。敏感信息的准确披露对于向医疗和金融部门的用户提供建议和支持至关重要。在这项研究中，我们探讨了用户对聊天机器人提供商使用敏感数据相关因素的关注。我们调查了491名英国公民的代表性样本。我们的研究结果表明，用户关注的焦点集中在删除个人信息和关注他们的数据的不当使用。我们还发现，在与会话代理交谈后，个体担心失去对其数据的控制。我们没有发现使用者的性别或教育程度会对聊天机器人产生影响，但确实发现使用者的年龄会对聊天机器人产生影响，45岁以上的人比45岁以下的人更关心聊天机器人。我们还考虑了在聊天机器人中产生信任的因素。我们的受访者主要关注聊天机器人的技术要素，其中响应质量等因素被认为是最关键的因素。我们再次发现使用者的性别或教育程度没有影响；然而，当我们考虑一些社会因素（例如化身或感知到的“友好性”）时，我们发现45岁以下的人认为这些因素比45岁以上的人更重要。本文最后在设计支持广泛用户的包容性数字系统的背景下对这些结果进行了讨论。 摘要：Through advances in their conversational abilities, chatbots have started to request and process an increasing variety of sensitive personal information. The accurate disclosure of sensitive information is essential where it is used to provide advice and support to users in the healthcare and finance sectors. In this study, we explore users' concerns regarding factors associated with the use of sensitive data by chatbot providers. We surveyed a representative sample of 491 British citizens. Our results show that the user concerns focus on deleting personal information and concerns about their data's inappropriate use. We also identified that individuals were concerned about losing control over their data after a conversation with conversational agents. We found no effect from a user's gender or education but did find an effect from the user's age, with those over 45 being more concerned than those under 45. We also considered the factors that engender trust in a chatbot. Our respondents' primary focus was on the chatbot's technical elements, with factors such as the response quality being identified as the most critical factor. We again found no effect from the user's gender or education level; however, when we considered some social factors (e.g. avatars or perceived 'friendliness'), we found those under 45 years old rated these as more important than those over 45. The paper concludes with a discussion of these results within the context of designing inclusive, digital systems that support a wide range of users.

【10】 Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects 标题：人工智能时代的智能医疗：最新进展、挑战和未来前景

作者：Mahmoud Nasr,MD. Milon Islam,Shady Shehata,Fakhri Karray,Yuri Quintana 机构：Centre for Pattern Analysis and Machine Intelligence, Department of Electrical and Computer Engineering, University of Waterloo, ON, Canada, N,L ,G 链接：https://arxiv.org/abs/2107.03924 摘要：慢性病患者（包括老年人和残疾人）数量的显著增加表明迫切需要一种创新的医疗系统模式。进化后的模式将更加个性化，减少对传统实体医疗机构（如医院、养老院和长期医疗中心）的依赖。智能医疗系统是近年来人们越来越感兴趣的话题，随着现代技术的发展，特别是人工智能（AI）和机器学习（ML）的发展，对智能医疗系统的要求也越来越高。本文旨在讨论当前最先进的智能医疗系统，重点关注可穿戴和智能手机设备等主要领域，用于健康监测、用于疾病诊断的机器学习以及辅助框架，包括为环境辅助生活环境开发的社交机器人。此外，本文还展示了软件集成架构，这些架构对于创建智能医疗系统非常重要，可以无缝集成数据分析和其他人工智能工具的好处。所解释的已开发系统侧重于几个方面：每个已开发框架的贡献、详细的工作程序、作为结果的绩效以及比较的优点和局限性。目前的研究挑战和潜在的未来发展方向是强调现有系统的缺点和可能的方法来引入新的框架，分别。本综述旨在全面了解智能医疗系统的最新发展，使专家能够为该领域做出贡献。 摘要：The significant increase in the number of individuals with chronic ailments (including the elderly and disabled) has dictated an urgent need for an innovative model for healthcare systems. The evolved model will be more personalized and less reliant on traditional brick-and-mortar healthcare institutions such as hospitals, nursing homes, and long-term healthcare centers. The smart healthcare system is a topic of recently growing interest and has become increasingly required due to major developments in modern technologies, especially in artificial intelligence (AI) and machine learning (ML). This paper is aimed to discuss the current state-of-the-art smart healthcare systems highlighting major areas like wearable and smartphone devices for health monitoring, machine learning for disease diagnosis, and the assistive frameworks, including social robots developed for the ambient assisted living environment. Additionally, the paper demonstrates software integration architectures that are very significant to create smart healthcare systems, integrating seamlessly the benefit of data analytics and other tools of AI. The explained developed systems focus on several facets: the contribution of each developed framework, the detailed working procedure, the performance as outcomes, and the comparative merits and limitations. The current research challenges with potential future directions are addressed to highlight the drawbacks of existing systems and the possible methods to introduce novel frameworks, respectively. This review aims at providing comprehensive insights into the recent developments of smart healthcare systems to equip experts to contribute to the field.

【11】 CANDLE: Decomposing Conditional and Conjunctive Queries for Task-Oriented Dialogue Systems 标题：CANDLE：面向任务对话系统的条件和合取查询分解

作者：Aadesh Gupta,Kaustubh D. Dhole,Rahul Tarway,Swetha Prabhakar,Ashish Shrivastava 机构：Amelia Science, IPsoft R&D 链接：https://arxiv.org/abs/2107.03884 摘要：特定领域的对话系统通常依靠句子级分类器来确定用户意图，而句子级分类器主要关注单个动作句。这样的分类器不能有效地处理由表示多个动作的条件子句和顺序子句组成的复杂查询。我们试图将这些查询分解成更小的单动作子查询，以便意图分类器在对话管道中理解。我们发布了CANDLE（Conditional&AND type Expressions），这是一个由3124条语句组成的数据集，这些语句被手动标记为条件和顺序标签，并通过训练两个基线标记器来演示这种分解。 摘要：Domain-specific dialogue systems generally determine user intents by relying on sentence-level classifiers which mainly focus on single action sentences. Such classifiers are not designed to effectively handle complex queries composed of conditional and sequential clauses that represent multiple actions. We attempt to decompose such queries into smaller single-action sub-queries that are reasonable for intent classifiers to understand in a dialogue pipeline. We release CANDLE (Conditional & AND type Expressions), a dataset consisting of 3124 utterances manually tagged with conditional and sequential labels and demonstrates this decomposition by training two baseline taggers.

【12】 Bootstrapping Generalization of Process Models Discovered From Event Data 标题：从事件数据中发现的流程模型的自举概括

作者：Artem Polyvyanyy,Alistair Moffat,Luciano García-Bañuelos 机构：The University of Melbourne, Luciano Garc´ıa-Ba˜nuelos, Tecnol´ogico de Monterrey 备注：8 pages 链接：https://arxiv.org/abs/2107.03876 摘要：流程挖掘研究从IT系统的事件日志中记录的流程执行中获取价值的方法，流程发现的任务是为某个未知系统发出的事件日志推断流程模型。发现的过程模型的一个质量标准是泛化。泛化旨在量化发现的模型对系统未来执行的描述程度，这可能是流程挖掘中最难理解的质量标准。缺乏理解主要是泛化的结果，泛化试图度量系统的整个未来行为的属性，而唯一可用的行为样本是事件日志本身提供的。在本文中，我们从计算统计学中得到启发，并利用bootstrap方法来估计基于样本的种群的性质。具体地说，我们定义了一个基于事件日志的模型泛化估计量，然后使用bootstrapping来度量模型相对于系统的泛化程度及其统计显著性。实验证明了该方法在工业环境下的可行性。 摘要：Process mining studies ways to derive value from process executions recorded in event logs of IT-systems, with process discovery the task of inferring a process model for an event log emitted by some unknown system. One quality criterion for discovered process models is generalization. Generalization seeks to quantify how well the discovered model describes future executions of the system, and is perhaps the least understood quality criterion in process mining. The lack of understanding is primarily a consequence of generalization seeking to measure properties over the entire future behavior of the system, when the only available sample of behavior is that provided by the event log itself. In this paper, we draw inspiration from computational statistics, and employ a bootstrap approach to estimate properties of a population based on a sample. Specifically, we define an estimator of the model's generalization based on the event log it was discovered from, and then use bootstrapping to measure the generalization of the model with respect to the system, and its statistical significance. Experiments demonstrate the feasibility of the approach in industrial settings.

【13】 Explainable AI (XAI) for PHM of Industrial Asset: A State-of-The-Art, PRISMA-Compliant Systematic Review 标题：工业资产PHM的可解释性人工智能(XAI)：符合PRISMA的最新系统评价

作者：Ahmad Kamal BIN MOHD NOR,Srinivasa Rao PEDAPATI,Masdi MUHAMMAD 机构：Mechanical Department, Universiti Teknologi Petronas, Malaysia. 链接：https://arxiv.org/abs/2107.03869 摘要：综述了XAI在工业资产预测与健康管理中的应用现状。本文试图概述PHM中XAI的发展趋势，回答准确性与可解释性的问题，探讨人在PHM-XAI中的作用范围、可解释性评价和不确定性管理。2015年至2021年与PHM XAI相关的英文研究文章选自IEEE Xplore、ScienceDirect、SpringerLink、ACM数字图书馆和Scopus数据库，使用PRISMA指南。从35篇文章中提取数据，并用MS.Excel进行检查。综合了几个发现。首先，在这门学科还很年轻的时候，分析表明XAI在PHM领域的接受度越来越高。其次，XAI是一把双刃剑，在这里它被当作执行PHM任务的工具和解释的手段，特别是在诊断和异常检测方面。因此，PHM中需要XAI。第三，回顾表明PHM-XAI论文总体上产生了好的或优秀的结果，表明PHM性能不受XAI的影响。第四，人的角色、可解释性度量和不确定性管理是PHM界需要进一步关注的领域。迫切需要足够的可解释性指标来满足PHM的需要。最后，大多数被接受的文章中的案例研究都是基于真实的，这表明可用的AI和XAI方法能够解决复杂的现实世界挑战，增加了业界采用AI模型的信心。这项工作是由大学TekOrni Petri基金会资助的。 摘要：A state-of-the-art systematic review on XAI applied to Prognostic and Health Management (PHM) of industrial asset is presented. The work attempts to provide an overview of the general trend of XAI in PHM, answers the question of accuracy versus explainability, investigates the extent of human role, explainability evaluation and uncertainty management in PHM XAI. Research articles linked to PHM XAI, in English language, from 2015 to 2021 are selected from IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library and Scopus databases using PRISMA guidelines. Data was extracted from 35 selected articles and examined using MS. Excel. Several findings were synthesized. Firstly, while the discipline is still young, the analysis indicates the growing acceptance of XAI in PHM domain. Secondly, XAI functions as a double edge sword, where it is assimilated as a tool to execute PHM tasks as well as a mean of explanation, in particular in diagnostic and anomaly detection. There is thus a need for XAI in PHM. Thirdly, the review shows that PHM XAI papers produce either good or excellent results in general, suggesting that PHM performance is unaffected by XAI. Fourthly, human role, explainability metrics and uncertainty management are areas requiring further attention by the PHM community. Adequate explainability metrics to cater for PHM need are urgently needed. Finally, most case study featured on the accepted articles are based on real, indicating that available AI and XAI approaches are equipped to solve complex real-world challenges, increasing the confidence of AI model adoption in the industry. This work is funded by the Universiti Teknologi Petronas Foundation.

【14】 Imitation by Predicting Observations 标题：预测性观测模拟

作者：Andrew Jaegle,Yury Sulsky,Arun Ahuja,Jake Bruce,Rob Fergus,Greg Wayne 备注：ICML 2021 链接：https://arxiv.org/abs/2107.03851 摘要：模仿学习使代理能够重用和适应他人来之不易的专业知识，为学习行为中的几个关键挑战提供了解决方案。虽然在现实世界中很容易观察到行为，但潜在的行为可能无法访问。我们提出了一种新的仅从观测值进行模拟的方法，该方法在具有挑战性的连续控制任务上取得了与专家相当的性能，同时在存在与任务无关的观测值时也表现出鲁棒性。我们的方法，我们称之为形式（为“未来观察奖励模型”）是从一个反向RL目标派生出来的，并使用通过专家观察的生成性建模学习的专家行为模型进行模拟，而不需要地面真实行动。我们证明了FORM在deepmindcontrolsuitebenchmark上的性能与强基线IRL方法（GAIL）相当，而在任务无关特征的存在下，它的性能优于GAIL方法。 摘要：Imitation learning enables agents to reuse and adapt the hard-won expertise of others, offering a solution to several key challenges in learning behavior. Although it is easy to observe behavior in the real-world, the underlying actions may not be accessible. We present a new method for imitation solely from observations that achieves comparable performance to experts on challenging continuous control tasks while also exhibiting robustness in the presence of observations unrelated to the task. Our method, which we call FORM (for "Future Observation Reward Model") is derived from an inverse RL objective and imitates using a model of expert behavior learned by generative modelling of the expert's observations, without needing ground truth actions. We show that FORM performs comparably to a strong baseline IRL method (GAIL) on the DeepMind Control Suite benchmark, while outperforming GAIL in the presence of task-irrelevant features.

【15】 A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models 标题：孟加拉自然语言处理任务与Transformer模型实用性述评

作者：Firoj Alam,Arid Hasan,Tanvir Alam,Akib Khan,Janntatul Tajrin,Naira Khan,Shammur Absar Chowdhury 机构：TANVIRUL ALAM, BJIT Limited, Bangladesh, JANNATUL TAJRIN, Cognitive Insight Limited, Bangladesh, Bangla – ranked as the ,?ℎ most widely spoken language across the world, with , million native speakers – 备注：Under Review, Bangla language processing, text classification, sequence tagging, datasets, benchmarks, transformer models 链接：https://arxiv.org/abs/2107.03844 摘要：孟加拉语是世界上使用最广泛的第六大语言(https://www.ethnologue.com/guides/ethnologue200)在自然语言处理（NLP）社区中，仍被视为低资源语言。经过三十年的研究，孟加拉邦民族解放党（BNLP）仍然落后，主要原因是资源匮乏和随之而来的挑战。BNLP的不同领域的工作比较少；然而，报告先前工作和最近进展的全面调查尚待完成。在这项研究中，我们首先提供了一个审查孟加拉语NLP的任务，资源和工具提供给研究界；我们使用当前最先进的算法（即基于Transformer的模型）对从不同平台收集的9个NLP任务的数据集进行基准测试。通过比较不同大小的单语和多语模型，我们为所研究的自然语言处理任务提供了比较结果。我们使用单独和合并的数据集报告我们的结果，并为将来的研究提供数据分割。我们共复习了108篇论文，进行了175组实验。我们的结果表明，使用基于Transformer的模型有很好的性能，同时强调了计算成本的权衡。我们希望，这样一个全面的调查将激励社会上建立和进一步推进孟加拉语民族解放党的研究。 摘要：Bangla -- ranked as the 6th most widely spoken language across the world (https://www.ethnologue.com/guides/ethnologue200), with 230 million native speakers -- is still considered as a low-resource language in the natural language processing (NLP) community. With three decades of research, Bangla NLP (BNLP) is still lagging behind mainly due to the scarcity of resources and the challenges that come with it. There is sparse work in different areas of BNLP; however, a thorough survey reporting previous work and recent advances is yet to be done. In this study, we first provide a review of Bangla NLP tasks, resources, and tools available to the research community; we benchmark datasets collected from various platforms for nine NLP tasks using current state-of-the-art algorithms (i.e., transformer-based models). We provide comparative results for the studied NLP tasks by comparing monolingual vs. multilingual models of varying sizes. We report our results using both individual and consolidated datasets and provide data splits for future research. We reviewed a total of 108 papers and conducted 175 sets of experiments. Our results show promising performance using transformer-based models while highlighting the trade-off with computational costs. We hope that such a comprehensive survey will motivate the community to build on and further advance the research on Bangla NLP.

【16】 Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation 标题：基于会话的个性化推荐的异构全局图神经网络

作者：Yitong Pang,Lingfei Wu,Qi Shen,Yiming Zhang,Zhihua Wei,Fangli Xu,Ethan Chang,Bo Long 机构：Tongji University, Shanghai, China, JD Silicon Valley Research Center, United States, Squirrel AI Learning, Middlesex School, JD.COM 备注：11 pages, 2 figures 链接：https://arxiv.org/abs/2107.03813 摘要：在基于会话的推荐中，预测短期交互会话的下一次交互是一项具有挑战性的任务。现有的研究大多依赖于项目转换模式，在对用户偏好进行建模时忽略了用户历史会话的影响，这往往导致非个性化推荐。另外，现有的基于会话的个性化推荐方法仅基于当前用户的会话获取用户偏好，而忽略了其他用户历史会话中有用的项目转换模式。为了解决这些问题，我们提出了一种新的异构全局图神经网络（HG-GNN），它以一种微妙的方式利用所有会话中的项目转换，以便从当前和历史会话中更好地推断用户偏好。为了有效地利用用户在所有会话中的项转换，我们提出了一种新的异构全局图，其中包含会话的项转换、用户项交互和全局共现项。此外，为了全面地从会话中获取用户偏好，我们建议通过两个图增强偏好编码器从全局图中学习两个层次的用户表示。具体来说，我们在异构全局图上设计了一个新的异构图神经网络（HGNN）来学习具有丰富语义的长期用户偏好和项目表示。基于HGNN，我们提出了当前偏好编码器和历史偏好编码器，分别从当前会话和历史会话中获取不同级别的用户偏好。为了实现个性化推荐，我们将用户当前偏好和历史兴趣的表示结合起来，生成最终的用户偏好表示。在三个真实数据集上的大量实验结果表明，我们的模型优于其他最先进的方法。 摘要：Predicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation. Almost all existing works rely on item transition patterns, and neglect the impact of user historical sessions while modeling user preference, which often leads to non-personalized recommendation. Additionally, existing personalized session-based recommenders capture user preference only based on the sessions of the current user, but ignore the useful item-transition patterns from other user's historical sessions. To address these issues, we propose a novel Heterogeneous Global Graph Neural Networks (HG-GNN) to exploit the item transitions over all sessions in a subtle manner for better inferring user preference from the current and historical sessions. To effectively exploit the item transitions over all sessions from users, we propose a novel heterogeneous global graph that contains item transitions of sessions, user-item interactions and global co-occurrence items. Moreover, to capture user preference from sessions comprehensively, we propose to learn two levels of user representations from the global graph via two graph augmented preference encoders. Specifically, we design a novel heterogeneous graph neural network (HGNN) on the heterogeneous global graph to learn the long-term user preference and item representations with rich semantics. Based on the HGNN, we propose the Current Preference Encoder and the Historical Preference Encoder to capture the different levels of user preference from the current and historical sessions, respectively. To achieve personalized recommendation, we integrate the representations of the user current preference and historical interests to generate the final user preference representation. Extensive experimental results on three real-world datasets show that our model outperforms other state-of-the-art methods.

【17】 Quadruplet Deep Metric Learning Model for Imbalanced Time-series Fault Diagnosis 标题：非平衡时间序列故障诊断的四元组深度度量学习模型

作者：Xingtai Gui,Jiyang Zhang 机构：University of Electronic Science and Technology of China 链接：https://arxiv.org/abs/2107.03786 摘要：基于数据驱动和深度学习的智能诊断方法是近年来研究的热点。然而，在实际应用场景中，时序故障的不平衡性是一个亟待解决的问题。从贝叶斯概率的角度分析了如何通过调整类间距离和类内分布来提高非平衡分类的性能，提出了一种基于深度度量学习的时间序列故障诊断模型。作为深度度量学习的核心，在借鉴传统深度度量学习的基础上，提出了一种考虑不平衡类的四重数据对设计方法。基于这类数据对，本文提出了一种考虑类间距离和类内数据分布的四重损失函数，并对不平衡样本对进行了重点研究。四线阵损耗和softmax损耗函数的合理组合可以减小不平衡的影响。在两个开放数据集上进行了实验，验证了模型的有效性和鲁棒性。实验结果表明，该方法能有效地提高非平衡分类的性能。 摘要：Intelligent diagnosis method based on data-driven and deep learning is an attractive and meaningful field in recent years. However, in practical application scenarios, the imbalance of time-series fault is an urgent problem to be solved. From the perspective of Bayesian probability, this paper analyzes how to improve the performance of imbalanced classification by adjusting the distance between classes and the distribution within a class and proposes a time-series fault diagnosis model based on deep metric learning. As a core of deep metric learning, a novel quadruplet data pair design considering imbalance class is proposed with reference to traditional deep metric learning. Based on such data pair, this paper proposes a quadruplet loss function which takes into account the inter-class distance and the intra-class data distribution, and pays special attention to imbalanced sample pairs. The reasonable combination of quadruplet loss and softmax loss function can reduce the impact of imbalance. Experiments on two open datasets are carried out to verify the effectiveness and robustness of the model. Experimental results show that the proposed method can effectively improve the performance of imbalanced classification.

【18】 Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space 标题：基于隐式分层学习的双曲空间离散时间网络嵌入

作者：Menglin Yang,Min Zhou,Marcus Kalander,Zengfeng Huang,Irwin King 机构：The Chinese University of Hong Kong, Noah’s Ark Lab, Huawei Technologies, Fudan University 备注：KDD2021 链接：https://arxiv.org/abs/2107.03767 摘要：时态网络上的表征学习近年来受到了广泛的关注。目前的研究主要集中在欧几里德空间的结构依赖和时间演化规律的建模上，然而，欧几里德空间低估了现实世界中许多时间网络固有的复杂性和层次性，导致了次优嵌入。为了研究复杂时态网络的这些性质，我们提出了一种双曲时态图网络（HTGN），它充分利用了双曲几何的指数能力和层次意识。更特别的是，HTGN将时间图映射到双曲空间，并结合双曲图神经网络和双曲门回归神经网络，以捕获演化行为，同时隐式地保留层次信息。此外，在双曲空间中，我们提出了两个重要的模块，使得HTGN能够成功地对时间网络进行建模：（1）双曲时间上下文自我注意（HTA）模块关注历史状态；（2）双曲时间一致性（HTC）模块确保稳定性和泛化性。在多个真实数据集上的实验结果证明了HTGN在时态图嵌入方面的优越性，因为在各种时态链接预测任务中，HTGN的性能始终优于同类方法。具体来说，HTGN在链路预测和新链路预测中分别实现了高达9.98%和11.4%的AUC改进。此外，烧蚀研究进一步验证了双曲线几何的表征能力以及所提出的HTA和HTC模块的有效性。 摘要：Representation learning over temporal networks has drawn considerable attention in recent years. Efforts are mainly focused on modeling structural dependencies and temporal evolving regularities in Euclidean space which, however, underestimates the inherent complex and hierarchical properties in many real-world temporal networks, leading to sub-optimal embeddings. To explore these properties of a complex temporal network, we propose a hyperbolic temporal graph network (HTGN) that fully takes advantage of the exponential capacity and hierarchical awareness of hyperbolic geometry. More specially, HTGN maps the temporal graph into hyperbolic space, and incorporates hyperbolic graph neural network and hyperbolic gated recurrent neural network, to capture the evolving behaviors and implicitly preserve hierarchical information simultaneously. Furthermore, in the hyperbolic space, we propose two important modules that enable HTGN to successfully model temporal networks: (1) hyperbolic temporal contextual self-attention (HTA) module to attend to historical states and (2) hyperbolic temporal consistency (HTC) module to ensure stability and generalization. Experimental results on multiple real-world datasets demonstrate the superiority of HTGN for temporal graph embedding, as it consistently outperforms competing methods by significant margins in various temporal link prediction tasks. Specifically, HTGN achieves AUC improvement up to 9.98% for link prediction and 11.4% for new link prediction. Moreover, the ablation study further validates the representational ability of hyperbolic geometry and the effectiveness of the proposed HTA and HTC modules.

【19】 Exploiting the relationship between visual and textual features in social networks for image classification with zero-shot deep learning 标题：利用社会网络中视觉和文本特征之间的关系进行Zero-Shot深度学习的图像分类

作者：Luis Lucas,David Tomas,Jose Garcia-Rodriguez 机构：Institute of Informatics Research, University of Alicante, Alicante, Spain 链接：https://arxiv.org/abs/2107.03751 摘要：与无监督机器学习相关的一个主要问题是从大型数据集中处理和提取有用信息的成本。在这项工作中，我们提出了一个分类器集成的基础上转移学习能力的剪辑神经网络结构在多模态环境（图像和文本）从社会媒体。为此，我们使用InstaNY100K数据集，提出了一种基于抽样技术的验证方法。我们的实验是基于位置数据集标签的图像分类任务，首先只考虑视觉部分，然后添加相关文本作为支持。结果表明，CLIP等训练好的神经网络可以在较小的微调下成功地应用于图像分类，并且根据目标考虑图像的相关文本有助于提高分类精度。结果表明，这似乎是一个很有前途的研究方向。 摘要：One of the main issues related to unsupervised machine learning is the cost of processing and extracting useful information from large datasets. In this work, we propose a classifier ensemble based on the transferable learning capabilities of the CLIP neural network architecture in multimodal environments (image and text) from social media. For this purpose, we used the InstaNY100K dataset and proposed a validation approach based on sampling techniques. Our experiments, based on image classification tasks according to the labels of the Places dataset, are performed by first considering only the visual part, and then adding the associated texts as support. The results obtained demonstrated that trained neural networks such as CLIP can be successfully applied to image classification with little fine-tuning, and considering the associated texts to the images can help to improve the accuracy depending on the goal. The results demonstrated what seems to be a promising research direction.

【20】 Probabilistic Time Series Forecasting with Implicit Quantile Networks 标题：基于隐式分位数网络的概率时间序列预测

作者：Adèle Gouttes,Kashif Rasul,Mateusz Koren,Johannes Stephan,Tofigh Naghibi 备注：Accepted at the ICML 2021 Time Series Workshop 链接：https://arxiv.org/abs/2107.03743 摘要：本文提出了一种概率时间序列预测的通用方法。我们将自回归回归回归神经网络与隐式分位数网络相结合来建立时间动力学模型，以学习时间序列目标上的一大类分布。与其他基于真实数据和模拟数据的概率神经网络预测模型相比，该方法在预测精度和时间分布估计方面都有优势。 摘要：Here, we propose a general method for probabilistic time series forecasting. We combine an autoregressive recurrent neural network to model temporal dynamics with Implicit Quantile Networks to learn a large class of distributions over a time-series target. When compared to other probabilistic neural forecasting models on real- and simulated data, our approach is favorable in terms of point-wise prediction accuracy as well as on estimating the underlying temporal distribution.

【21】 Demystifying the Draft EU Artificial Intelligence Act 标题：揭开欧盟人工智能法案草案的神秘面纱

作者：Michael Veale,Frederik Zuiderveen Borgesius 机构：University College London, United Kingdom, Interdisciplinary Hub for Security, Privacy and Data Governance, Radboud University, The Netherlands, Pre-print 备注：16 pages, 1 table 链接：https://arxiv.org/abs/2107.03721 摘要：2021年4月，欧盟委员会提出了一项关于人工智能的法规，即《人工智能法》。我们提出了该法案的概述和分析其影响，借鉴了从当代人工智能实践的研究到过去40年欧盟产品安全制度结构的学术成果。《人工智能法》的各个方面，比如针对人工智能不同风险等级的不同规则，都是有意义的。但我们也发现，人工智能法案草案的一些条款具有令人惊讶的法律含义，而其他条款可能在很大程度上无法实现其既定目标。一些主要方面，包括强制执行制度和最大程度的协调对人工智能政策空间的影响，引起了广泛关注。这些问题应作为立法进程中的优先事项加以解决。 摘要：In April 2021, the European Commission proposed a Regulation on Artificial Intelligence, known as the AI Act. We present an overview of the Act and analyse its implications, drawing on scholarship ranging from the study of contemporary AI practices to the structure of EU product safety regimes over the last four decades. Aspects of the AI Act, such as different rules for different risk-levels of AI, make sense. But we also find that some provisions of the draft AI Act have surprising legal implications, whilst others may be largely ineffective at achieving their stated goals. Several overarching aspects, including the enforcement regime and the effect of maximum harmonisation on the space for AI policy more generally, engender significant concern. These issues should be addressed as a priority in the legislative process.

【22】 Bag of Tricks for Neural Architecture Search 标题：用于神经结构搜索的一袋小把戏

作者：Thomas Elsken,Benedikt Staffler,Arber Zela,Jan Hendrik Metzen,Frank Hutter 机构：Bosch Center for Artificial Intelligence,University of Freiburg 链接：https://arxiv.org/abs/2107.03719 摘要：虽然神经结构搜索方法在前几年取得了成功，并在各种问题上取得了新的最新进展，但它们也因不稳定、对其超参数高度敏感以及通常表现不优于随机搜索而受到批评。为了阐明这个问题，我们讨论了一些有助于提高稳定性、效率和整体性能的实际考虑因素。 摘要：While neural architecture search methods have been successful in previous years and led to new state-of-the-art performance on various problems, they have also been criticized for being unstable, being highly sensitive with respect to their hyperparameters, and often not performing better than random search. To shed some light on this issue, we discuss some practical considerations that help improve the stability, efficiency and overall performance.

【23】 Complete Scanning Application Using OpenCv 标题：使用OpenCV完成扫描应用程序

作者：Ayushe Gangal,Peeyush Kumar,Sunita Kumari 机构：Dept. of CSE, GB Pant Govt. Engineering College, New Delhi-, India 备注：10 pages, 14 figures 链接：https://arxiv.org/abs/2107.03700 摘要：在下面的论文中，我们结合了NumPy库和OpenCv库提供的各种基本功能，OpenCv库是一个开源的计算机视觉应用程序，如彩色图像到灰度的转换，计算阈值，利用pythonversion3.7对用户输入的图像进行透视变换。其他功能还包括裁剪、旋转和保存。所有这些功能和特性，当一步一步地实现时，就会产生一个完整的扫描应用程序。应用步骤包括：轮廓提取、透视变换和图像加亮、自适应阈值和滤波器去噪、旋转特征和透视变换等特殊裁剪算法。所描述的技术在各种样本上实现。 摘要：In the following paper, we have combined the various basic functionalities provided by the NumPy library and OpenCv library, which is an open source for Computer Vision applications, like conversion of colored images to grayscale, calculating threshold, finding contours and using those contour points to take perspective transform of the image inputted by the user, using Python version 3.7. Additional features include cropping, rotating and saving as well. All these functions and features, when implemented step by step, results in a complete scanning application. The applied procedure involves the following steps: Finding contours, applying Perspective transform and brightening the image, Adaptive Thresholding and applying filters for noise cancellation, and Rotation features and perspective transform for a special cropping algorithm. The described technique is implemented on various samples.

【24】 Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning 标题：基于分层强化学习的管道自主检测

作者：Nicolò Botteghi,Luuk Grefte,Mannes Poel,Beril Sirmacek,Christoph Brune,Edwin Dertien,Stefano Stramigioli 机构： University of Twente, nl 2Beril Sirmacek with the Department of Computer Science, J¨onk¨opingUniversity 链接：https://arxiv.org/abs/2107.03685 摘要：检测和维护是工业管道设备的两个重要方面。虽然机器人技术在管道检测机器人的机械设计方面取得了巨大的进步，但由于执行机构数量多，操作复杂，机器人的自主控制仍然是一个巨大的挑战。为了解决这个问题，我们研究了在复杂拓扑结构的管道网络中使用深度强化学习来实现管道机器人的自主导航。此外，我们引入了一种基于分层强化学习的分层策略分解方法来学习鲁棒的高级导航技能。我们证明了该策略中引入的层次结构是解决管道导航任务的基础，也是实现优于人类水平控制的导航性能的必要条件。 摘要：Inspection and maintenance are two crucial aspects of industrial pipeline plants. While robotics has made tremendous progress in the mechanic design of in-pipe inspection robots, the autonomous control of such robots is still a big open challenge due to the high number of actuators and the complex manoeuvres required. To address this problem, we investigate the usage of Deep Reinforcement Learning for achieving autonomous navigation of in-pipe robots in pipeline networks with complex topologies. Moreover, we introduce a hierarchical policy decomposition based on Hierarchical Reinforcement Learning to learn robust high-level navigation skills. We show that the hierarchical structure introduced in the policy is fundamental for solving the navigation task through pipes and necessary for achieving navigation performances superior to human-level control.

【25】 A Dataset and Method for Hallux Valgus Angle Estimation Based on Deep Learing 标题：一种基于深度学习的Hallux Valgus角估计数据集和方法

作者：Ningyuan Xu,Jiayan Zhuang,Yaojun Wu,Jiangjian Xiao 机构：University of Chinese Academy of Sciences, No., Yuquan Road, Shijingshan District, Beijing, China, Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, No., Zhongguan West Road, Zhenhai District, Ningbo City, Zhejiang 备注：7pages, 12 figures 链接：https://arxiv.org/abs/2107.03640 摘要：角度测量是必要的，使一个合理的治疗拇趾外翻（HV），一种常见的前足畸形。然而，它仍然依赖于人工标记和测量，这是费时的，有时是不可靠的。自动化这个过程是一个值得关注的问题。然而，由于缺乏数据集，基于关键点的姿态估计方法不适合该领域，为此，我们制作了一个数据集，开发了一种基于深度学习和线性回归的姿态估计算法。它显示出对地面真相的极大拟合能力。 摘要：Angular measurements is essential to make a resonable treatment for Hallux valgus (HV), a common forefoot deformity. However, it still depends on manual labeling and measurement, which is time-consuming and sometimes unreliable. Automating this process is a thing of concern. However, it lack of dataset and the keypoints based method which made a great success in pose estimation is not suitable for this field.To solve the problems, we made a dataset and developed an algorithm based on deep learning and linear regression. It shows great fitting ability to the ground truth.

【26】 Validation and Inference of Agent Based Models 标题：基于Agent的模型验证与推理

作者：D. Townsend 机构：Validation and Inference of AgentBased ModelsA dissertationsubmitted in partial fulfilmentof the requirements for the DegreeofBachelor of Computing and Mathematical Sciences with HonoursatThe University of WaikatobyDale Townsend 20 2 1arXiv 链接：https://arxiv.org/abs/2107.03619 摘要：基于Agent的建模（ABM）是一种模拟自主Agent行为和交互作用的计算框架。由于基于Agent的模型通常代表复杂系统，因此获取模型参数的似然函数几乎总是很困难的。为了理解模型的输出，有必要在无似然的上下文中进行推理。近似贝叶斯计算是一种适合这种推理的方法。它可以应用于基于Agent的模型，既可以验证仿真结果，又可以推断出一组参数来描述模型。最近在ABC中的研究已经产生了越来越有效的算法来计算近似似然。这些都是调查和比较使用行人模型在汉密尔顿CBD。 摘要：Agent Based Modelling (ABM) is a computational framework for simulating the behaviours and interactions of autonomous agents. As Agent Based Models are usually representative of complex systems, obtaining a likelihood function of the model parameters is nearly always intractable. There is a necessity to conduct inference in a likelihood free context in order to understand the model output. Approximate Bayesian Computation is a suitable approach for this inference. It can be applied to an Agent Based Model to both validate the simulation and infer a set of parameters to describe the model. Recent research in ABC has yielded increasingly efficient algorithms for calculating the approximate likelihood. These are investigated and compared using a pedestrian model in the Hamilton CBD.

【27】 CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks 标题：主张：未知社会网络中影响力最大化的课程学习策略

作者：Dexun Li,Meghna Lowalekar,Pradeep Varakantham 机构：School of Computing and Information Systems, Singapore Management University, Singapore 链接：https://arxiv.org/abs/2107.03603 摘要：影响最大化是指在网络中找到一小部分能够最大化信息扩散的节点。最近，它还被应用于艾滋病毒预防、药物滥用预防、小额信贷采用等领域，其目标是在现实世界的物理社会网络中确定一组能够向大量人群传播信息的同级领导人。与在线社交网络不同，现实世界中的网络并不完全为人所知，收集有关网络的信息成本高昂，因为这需要调查多个人。本文主要研究影响最大化的网络发现问题。这方面的现有工作提出了一个强化学习框架。由于现实环境中的环境交互代价很高，因此增强学习算法必须尽可能减少环境交互，即保持样本有效性。本文提出了影响最大化的索赔课程学习策略，以提高RL方法的样本效率。我们在真实数据集上进行了实验，结果表明我们的方法比目前最好的方法有更好的性能。 摘要：Influence maximization is the problem of finding a small subset of nodes in a network that can maximize the diffusion of information. Recently, it has also found application in HIV prevention, substance abuse prevention, micro-finance adoption, etc., where the goal is to identify the set of peer leaders in a real-world physical social network who can disseminate information to a large group of people. Unlike online social networks, real-world networks are not completely known, and collecting information about the network is costly as it involves surveying multiple people. In this paper, we focus on this problem of network discovery for influence maximization. The existing work in this direction proposes a reinforcement learning framework. As the environment interactions in real-world settings are costly, so it is important for the reinforcement learning algorithms to have minimum possible environment interactions, i.e, to be sample efficient. In this work, we propose CLAIM - Curriculum LeArning Policy for Influence Maximization to improve the sample efficiency of RL methods. We conduct experiments on real-world datasets and show that our approach can outperform the current best approach.

【28】 Adaptive Stress Testing for Adversarial Learning in a Financial Environment 标题：金融环境下对抗性学习的自适应压力测试

作者：Khalid El-Awady 链接：https://arxiv.org/abs/2107.03577 摘要：我们演示了如何使用自适应压力测试来检测和解决金融环境中的潜在漏洞。我们开发了一个简化的信用卡欺诈检测模型，该模型利用基于历史支付交易数据和业务规则的线性回归分类器。然后，我们应用被称为自适应压力测试（adaptivestress Testing）的强化学习模型来训练一个可以被认为是潜在欺诈者的代理，以找到最有可能导致系统失败的路径——成功地欺诈系统。我们展示了这种最可能的故障路径与分类器限制之间的联系，并讨论了如何进一步增强欺诈检测系统的业务规则以减轻这些故障模式。 摘要：We demonstrate the use of Adaptive Stress Testing to detect and address potential vulnerabilities in a financial environment. We develop a simplified model for credit card fraud detection that utilizes a linear regression classifier based on historical payment transaction data coupled with business rules. We then apply the reinforcement learning model known as Adaptive Stress Testing to train an agent, that can be thought of as a potential fraudster, to find the most likely path to system failure -- successfully defrauding the system. We show the connection between this most likely failure path and the limits of the classifier and discuss how the fraud detection system's business rules can be further augmented to mitigate these failure modes.

【29】 Deep Structural Point Process for Learning Temporal Interaction Networks 标题：学习时态交互网络的深层结构点过程

作者：Jiangxia Cao,Xixun Lin,Xin Cong,Shu Guo,Hengzhu Tang,Tingwen Liu,Bin Wang 机构： Institute of Information Engineering, Chinese Academy of Sciences, School of Cyber Security, University of Chinese Academy of Sciences, National Computer Network Emergency Response Technical TeamCoordination, Center of China, Xiaomi AI Lab, Xiaomi Inc. 备注：Accepted by ECML/PKDD 2021, 16 pages, 2 figures 链接：https://arxiv.org/abs/2107.03573 摘要：本文研究了时间交互网络的学习问题。时间交互网络由用户和项目之间的一系列按时间顺序排列的交互组成。以前的方法通过使用递归神经网络的不同变体来解决这个问题来模拟顺序交互，这不能考虑时间交互网络的结构信息，并且不可避免地导致次优结果。为此，我们提出了一种新的深结构点过程，称为DSPP，用于学习时间交互网络。DSPP同时将拓扑结构和长程依赖结构结合到强度函数中，增强了模型的表达能力。具体地说，利用拓扑结构作为强先验，我们首先设计了一个拓扑融合编码器来获得节点嵌入。然后开发了一个专注的移位编码器来学习连续时间内用户和项目之间的长程依赖结构。所提出的两个模块使得我们的模型能够捕捉到时态交互网络中用户项的相关性和动态影响。在三个真实数据集上对项目预测和时间预测任务的DSPP进行了评估。大量的实验表明，我们的模型实现了一致的和显著的改进，超过国家的最先进的基线。 摘要：This work investigates the problem of learning temporal interaction networks. A temporal interaction network consists of a series of chronological interactions between users and items. Previous methods tackle this problem by using different variants of recurrent neural networks to model sequential interactions, which fail to consider the structural information of temporal interaction networks and inevitably lead to sub-optimal results. To this end, we propose a novel Deep Structural Point Process termed as DSPP for learning temporal interaction networks. DSPP simultaneously incorporates the topological structure and long-range dependency structure into our intensity function to enhance model expressiveness. To be specific, by using the topological structure as a strong prior, we first design a topological fusion encoder to obtain node embeddings. An attentive shift encoder is then developed to learn the long-range dependency structure between users and items in continuous time. The proposed two modules enable our model to capture the user-item correlation and dynamic influence in temporal interaction networks. DSPP is evaluated on three real-world datasets for both tasks of item prediction and time prediction. Extensive experiments demonstrate that our model achieves consistent and significant improvements over state-of-the-art baselines.

【30】 Unsupervised Proxy Selection for Session-based Recommender Systems 标题：基于会话的推荐系统中的无监督代理选择

作者：Junsu Cho,SeongKu Kang,Dongmin Hyun,Hwanjo Yu 机构：Pohang University of Science and Technology, Pohang, South Korea 备注：Accepted to SIGIR 2021 链接：https://arxiv.org/abs/2107.03564 摘要：基于会话的推荐系统（Session-based Recommender system，SRSs）已经被积极地开发出来，用于推荐匿名短项序列的下一项（即Session）。与序列感知推荐系统不同的是，SRSs中没有依赖于用户的信息，因此很难直接从数据中获得用户的一般兴趣。因此，现有的srs侧重于如何有效地对会话中的短期兴趣信息进行建模，但这些模型不足以捕获用户的一般兴趣。为此，我们提出了一个新的框架来克服SRSs的局限性，即ProxySR，它通过对会话代理的建模来模拟SRSs中缺失的信息（即用户的一般兴趣）。ProxySR以无监督的方式为输入会话选择一个代理，并将其与会话的编码短期兴趣相结合。当代理与短期兴趣共同学习并由多个会话选择时，代理学习扮演用户一般兴趣的角色，ProxySR学习如何为输入会话选择合适的代理。此外，我们还提出了srs的另一种实际情况，即少数用户登录并在会话中留下他们的标识符，并针对这种情况修改了ProxySR。我们在真实数据集上的实验表明，ProxySR的性能明显优于最先进的竞争对手，并且代理成功地模拟了用户的一般兴趣，没有任何依赖于用户的信息。 摘要：Session-based Recommender Systems (SRSs) have been actively developed to recommend the next item of an anonymous short item sequence (i.e., session). Unlike sequence-aware recommender systems where the whole interaction sequence of each user can be used to model both the short-term interest and the general interest of the user, the absence of user-dependent information in SRSs makes it difficult to directly derive the user's general interest from data. Therefore, existing SRSs have focused on how to effectively model the information about short-term interest within the sessions, but they are insufficient to capture the general interest of users. To this end, we propose a novel framework to overcome the limitation of SRSs, named ProxySR, which imitates the missing information in SRSs (i.e., general interest of users) by modeling proxies of sessions. ProxySR selects a proxy for the input session in an unsupervised manner, and combines it with the encoded short-term interest of the session. As a proxy is jointly learned with the short-term interest and selected by multiple sessions, a proxy learns to play the role of the general interest of a user and ProxySR learns how to select a suitable proxy for an input session. Moreover, we propose another real-world situation of SRSs where a few users are logged-in and leave their identifiers in sessions, and a revision of ProxySR for the situation. Our experiments on real-world datasets show that ProxySR considerably outperforms the state-of-the-art competitors, and the proxies successfully imitate the general interest of the users without any user-dependent information.

【31】 Federated Learning with Downlink Device Selection 标题：具有下行链路设备选择功能的联合学习

作者：Mohammad Mohammadi Amiri,Sanjeev R. Kulkarni,H. Vincent Poor 机构：Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ , USA 备注：accepted in IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2021 链接：https://arxiv.org/abs/2107.03510 摘要：我们研究联合边缘学习，其中一个全局模型是协同训练使用隐私敏感的数据在无线网络的边缘。参数服务器（PS）跟踪全局模型并与无线边缘设备共享，以便使用其私有本地数据进行训练。然后，设备将其本地模型更新（用于更新全局模型）传输到PS。该算法涉及通过PS到设备和设备到PS链路的传输，直到全局模型收敛或缺少任何参与设备为止。在这项研究中，我们考虑基于下行链路信道的设备选择，其中PS与设备共享全局模型。在执行数字下行链路传输时，我们设计了一个部分设备参与框架，其中在每个迭代中选择一个子集进行训练。因此，与全设备参与情况相比，参与设备可以更好地估计全局模型，全设备参与情况是由于广播信道的共享性质以及相对于较小的数据集更新全局模型的代价。在每次迭代中，PS基于设备上可用的最后全局模型估计向不同的参与设备广播不同的量化全局模型更新。通过实验结果，我们研究了使用偏态分布的MNIST数据集进行图像分类的最佳参与设备数。 摘要：We study federated edge learning, where a global model is trained collaboratively using privacy-sensitive data at the edge of a wireless network. A parameter server (PS) keeps track of the global model and shares it with the wireless edge devices for training using their private local data. The devices then transmit their local model updates, which are used to update the global model, to the PS. The algorithm, which involves transmission over PS-to-device and device-to-PS links, continues until the convergence of the global model or lack of any participating devices. In this study, we consider device selection based on downlink channels over which the PS shares the global model with the devices. Performing digital downlink transmission, we design a partial device participation framework where a subset of the devices is selected for training at each iteration. Therefore, the participating devices can have a better estimate of the global model compared to the full device participation case which is due to the shared nature of the broadcast channel with the price of updating the global model with respect to a smaller set of data. At each iteration, the PS broadcasts different quantized global model updates to different participating devices based on the last global model estimates available at the devices. We investigate the best number of participating devices through experimental results for image classification using the MNIST dataset with biased distribution.

【32】 CHASE: Robust Visual Tracking via Cell-Level Differentiable Neural Architecture Search 标题：Chase：基于细胞级可区分神经结构搜索的鲁棒视觉跟踪

作者：Seyed Mojtaba Marvasti-Zadeh,Javad Khaghani,Li Cheng,Hossein Ghanei-Yakhdan,Shohreh Kasaei 机构： Vision and Learning Lab, University of Alberta, Edmonton, Canada, Digital Image & Video Processing Lab, Yazd University, Yazd, Iran, Image Processing Lab, Sharif University of Technology, Tehran, Iran 备注：The first two authors contributed equally to this work 链接：https://arxiv.org/abs/2107.03463 摘要：如今，一个强大的视觉目标跟踪器依赖于其精心设计的模块，这些模块通常由手动设计的网络体系结构组成，以提供高质量的跟踪结果。不足为奇，手工设计过程成为一个特别具有挑战性的障碍，因为它需要足够的经验，巨大的努力，直觉，也许还有一些好运。同时，神经网络结构搜索在图像分割等实际应用中有着广泛的应用前景，是解决可行网络结构自动搜索问题的一种很有前途的方法。在这项工作中，我们提出了一种新的单元级可微结构搜索机制来自动化跟踪模块的网络设计，目的是在离线训练期间使主干特征适应跟踪网络的目标。该方法简单、高效，无需堆叠一系列模组来建构网路。我们的方法很容易被合并到现有的跟踪器中，并使用不同的基于可微结构搜索的方法和跟踪目标进行了实证验证。广泛的实验评估表明，我们的方法优于五个常用的基准测试。同时，我们在TrackingNet数据集上的二阶（一阶）DARTS方法的自动搜索过程需要41（18）小时。 摘要：A strong visual object tracker nowadays relies on its well-crafted modules, which typically consist of manually-designed network architectures to deliver high-quality tracking results. Not surprisingly, the manual design process becomes a particularly challenging barrier, as it demands sufficient prior experience, enormous effort, intuition and perhaps some good luck. Meanwhile, neural architecture search has gaining grounds in practical applications such as image segmentation, as a promising method in tackling the issue of automated search of feasible network structures. In this work, we propose a novel cell-level differentiable architecture search mechanism to automate the network design of the tracking module, aiming to adapt backbone features to the objective of a tracking network during offline training. The proposed approach is simple, efficient, and with no need to stack a series of modules to construct a network. Our approach is easy to be incorporated into existing trackers, which is empirically validated using different differentiable architecture search-based methods and tracking objectives. Extensive experimental evaluations demonstrate the superior performance of our approach over five commonly-used benchmarks. Meanwhile, our automated searching process takes 41 (18) hours for the second (first) order DARTS method on the TrackingNet dataset.

【33】 Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling 标题：预测E2E会话人工智能中的安全问题：框架和工具

作者：Emily Dinan,Gavin Abercrombie,A. Stevie Bergman,Shannon Spruit,Dirk Hovy,Y-Lan Boureau,Verena Rieser 机构：Facebook AI Research, Heriot-Watt University, Responsible AI, Facebook, Independent Ethics Advisor at Populytics, Netherlands, Bocconi University 链接：https://arxiv.org/abs/2107.03451 摘要：在过去的几年里，端到端的神经会话代理在与人类进行聊天的能力上有了很大的提高。然而，这些模型通常是在互联网上的大型数据集上训练的，因此，可能会从这些数据中学习不受欢迎的行为，例如有毒或有害的语言。因此，研究人员必须努力解决如何以及何时发布这些模型的问题。在本文中，我们调查了端到端会话人工智能的安全问题，并讨论了最近和相关的工作。我们强调了价值观之间的紧张关系、潜在的积极影响和潜在的危害，并根据价值敏感设计的原则，为是否以及如何发布这些模型提供了一个决策框架。此外，我们还提供了一套工具，使研究人员能够就训练和发布端到端会话人工智能模型做出更明智的决策。 摘要：Over the last several years, end-to-end neural conversational agents have vastly improved in their ability to carry a chit-chat conversation with humans. However, these models are often trained on large datasets from the internet, and as a result, may learn undesirable behaviors from this data, such as toxic or otherwise harmful language. Researchers must thus wrestle with the issue of how and when to release these models. In this paper, we survey the problem landscape for safety for end-to-end conversational AI and discuss recent and related work. We highlight tensions between values, potential positive impact and potential harms, and provide a framework for making decisions about whether and how to release these models, following the tenets of value-sensitive design. We additionally provide a suite of tools to enable researchers to make better-informed decisions about training and releasing end-to-end conversational AI models.

【34】 Deep Learning for Two-Sided Matching 标题：深度学习在双边匹配中的应用

作者：Sai Srivatsa Ravindranath,Zhe Feng,Shira Li,Jonathan Ma,Scott D. Kominers,David C. Parkes 机构：John A. Paulson School of Engineering and Applied Sciences, Harvard University, Harvard College, Harvard Business School 链接：https://arxiv.org/abs/2107.03427 摘要：我们开始使用多层神经网络来模拟双边匹配，并探索策略验证性和稳定性之间的设计空间。众所周知，这两个属性不能同时实现，但在这个设计空间的有效前沿是不理解的。我们的经验表明，通过延迟接受（仅对市场的一方稳定和策略证明）和随机序列专政（策略证明但不稳定）的凸组合，有可能在稳定性和策略证明之间达成一个很好的折衷，远远好于通过延迟接受（仅对市场的一方稳定和策略证明）和随机序列专政（策略证明但不稳定）实现的折衷。 摘要：We initiate the use of a multi-layer neural network to model two-sided matching and to explore the design space between strategy-proofness and stability. It is well known that both properties cannot be achieved simultaneously but the efficient frontier in this design space is not understood. We show empirically that it is possible to achieve a good compromise between stability and strategy-proofness-substantially better than that achievable through a convex combination of deferred acceptance (stable and strategy-proof for only one side of the market) and randomized serial dictatorship (strategy-proof but not stable).

【35】 A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems 标题：一种基于图的推荐系统中消除多面曝光偏差的方法

作者：Masoud Mansoury,Himan Abdollahpouri,Mykola Pechenizkiy,Bamshad Mobasher,Robin Burke 机构： Eindhoven University of Technology, Northwestern UniversityMYKOLA PECHENIZKIY, DePaul University, University of Colorado Boulder 备注：arXiv admin note: substantial text overlap with arXiv:2005.01148 链接：https://arxiv.org/abs/2107.03415 摘要：在推荐系统中，公平性是一个关键的系统级目标，也是最近广泛研究的主题。公平的一种具体形式是供应商曝光公平，其目标是确保在向用户提供的建议中公平涵盖所有供应商的项目。在多利益相关者推荐方案中，这一点尤其重要，在这种情况下，不仅要优化最终用户的效用，还要优化其他利益相关者（如物品销售商或生产商）的效用，因为他们希望公平地代表他们的物品。这种类型的供应商公平性有时是通过尝试增加总的多样性来实现的，以减轻流行性偏差，并提高建议中长尾项目的覆盖率。在本文中，我们介绍了FairMatch，一种通用的基于图的算法，作为推荐生成后的后处理方法，以提高商品和供应商的曝光公平性。该算法迭代地将可见性较低的高质量项目或低曝光度供应商的项目添加到用户的最终推荐列表中。在两个数据集上进行的一组综合实验以及与最新基线的比较表明，FairMatch在显著提高曝光公平性和总体多样性的同时，保持了建议的可接受的相关性水平。 摘要：Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. A specific form of fairness is supplier exposure fairness where the objective is to ensure equitable coverage of items across all suppliers in recommendations provided to users. This is especially important in multistakeholder recommendation scenarios where it may be important to optimize utilities not just for the end-user, but also for other stakeholders such as item sellers or producers who desire a fair representation of their items. This type of supplier fairness is sometimes accomplished by attempting to increasing aggregate diversity in order to mitigate popularity bias and to improve the coverage of long-tail items in recommendations. In this paper, we introduce FairMatch, a general graph-based algorithm that works as a post processing approach after recommendation generation to improve exposure fairness for items and suppliers. The algorithm iteratively adds high quality items that have low visibility or items from suppliers with low exposure to the users' final recommendation lists. A comprehensive set of experiments on two datasets and comparison with state-of-the-art baselines show that FairMatch, while significantly improves exposure fairness and aggregate diversity, maintains an acceptable level of relevance of the recommendations.

【36】 Rating and aspect-based opinion graph embeddings for explainable recommendations 标题：用于可解释推荐的评级和基于方面的意见图嵌入

作者：Iván Cantador,Andrés Carvallo,Fernando Diez 机构：Universidad Autónoma de Madrid, Madrid, Spain, Pontificia Universidad Católica de, Santiago, Chile 备注：arXiv admin note: substantial text overlap with arXiv:2107.03226 链接：https://arxiv.org/abs/2107.03385 摘要：随着神经网络嵌入技术的成功，人们对利用知识图进行各种机器学习和信息检索产生了新的兴趣。特别地，最近基于图嵌入的推荐方法已经显示出最先进的性能。通常，这些方法对潜在的评级模式和内容特征进行编码。不同于以往的工作，在本文中，我们建议利用嵌入提取的图表，结合信息评级和方面的意见表达在文本审查。然后，我们在6个域上采用并评估了最先进的图嵌入技术，这些技术比Amazon和Yelp评论生成的图具有更好的性能。此外，我们的方法的优点是提供解释，包括用户对推荐项目基于方面的意见。 摘要：The success of neural network embeddings has entailed a renewed interest in using knowledge graphs for a wide variety of machine learning and information retrieval tasks. In particular, recent recommendation methods based on graph embeddings have shown state-of-the-art performance. In general, these methods encode latent rating patterns and content features. Differently from previous work, in this paper, we propose to exploit embeddings extracted from graphs that combine information from ratings and aspect-based opinions expressed in textual reviews. We then adapt and evaluate state-of-the-art graph embedding techniques over graphs generated from Amazon and Yelp reviews on six domains, outperforming baseline recommenders. Additionally, our method has the advantage of providing explanations that involve the coverage of aspect-based opinions given by users about recommended items.

【37】 Quantum belief function 标题：量子信任函数

作者：Qianli Zhou,Guojing Tian,Yong Deng 机构：Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu, China, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China, School of Eduction Shannxi Normal University, Xi’an, China 链接：https://arxiv.org/abs/2107.03930 摘要：Dempster-Shafer证据理论中的信念函数比传统的贝叶斯分布能表达更多的信息。它广泛应用于近似推理、决策和信息融合等领域。然而，它的幂指数爆炸特性导致经典计算机在处理大量元素时计算复杂度极高。为了解决这个问题，我们将基本信念分配（BBA）编码成量子态，使每个量子比特对应一个控制元素。这种量子表达式不仅效率高，而且非常有利于测量两个bba之间的相似性，而且我们提出的测量量子算法与相应的经典算法相比，在理论上具有指数加速度。此外，我们在Qiskit平台上模拟了量子版的BBA算法，实验证明了算法的合理性。我们相信我们的结果对利用量子计算的特性更方便地处理信念函数有一定的启示。 摘要：The belief function in Dempster Shafer evidence theory can express more information than the traditional Bayesian distribution. It is widely used in approximate reasoning, decision-making and information fusion. However, its power exponential explosion characteristics leads to the extremely high computational complexity when handling large amounts of elements in classic computers. In order to solve the problem, we encode the basic belief assignment (BBA) into quantum states, which makes each qubit correspond to control an element. Besides the high efficiency, this quantum expression is very conducive to measure the similarity between two BBAs, and the measuring quantum algorithm we come up with has exponential acceleration theoretically compared to the corresponding classical algorithm. In addition, we simulate our quantum version of BBA on Qiskit platform, which ensures the rationality of our algorithm experimentally. We believe our results will shed some light on utilizing the characteristic of quantum computation to handle belief function more conveniently.

【38】 Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease 标题：多中心影像诊断的联合学习：一项心血管疾病的研究

作者：Akis Linardos,Kaisar Kushibar,Sean Walsh,Polyxeni Gkontra,Karim Lekadir 机构：University of Barcelona, Department of Mathematics and Computer Science, Barcelona, Spain, Radiomics, Liege, Belgium 备注：Code used in this study can be found in: this https URL 链接：https://arxiv.org/abs/2107.03901 摘要：深度学习模型可以实现准确和高效的疾病诊断，但迄今为止一直受到医学界数据匮乏的阻碍。自动化诊断研究一直受到动力不足的单中心数据集的限制，尽管一些结果显示出了希望，但由于没有考虑机构间的数据异质性，它们对其他机构的普遍性仍然值得怀疑。通过允许模型以一种分布式的方式进行训练，从而保护患者的隐私，联邦学习通过支持勤奋的多中心研究，有望缓解这些问题。我们提出了第一个关于心血管磁共振（CMR）模式的联合学习研究，并使用来自M\&M和ACDC数据集子集的四个中心，集中于肥厚型心肌病（HCM）的诊断。我们采用一个预先训练动作识别的3D-CNN网络，探索了两种将形状先验信息整合到模型中的方法，以及四种不同的数据增强设置，系统地分析了它们对不同协作学习选择的影响。我们发现，尽管数据量很小（来自四个中心的180名受试者），隐私保护的联合学习仍然取得了与传统集中式学习相竞争的好结果。我们进一步发现，联邦训练模型表现出更强的鲁棒性和更敏感的领域转移的影响。 摘要：Deep learning models can enable accurate and efficient disease diagnosis, but have thus far been hampered by the data scarcity present in the medical world. Automated diagnosis studies have been constrained by underpowered single-center datasets, and although some results have shown promise, their generalizability to other institutions remains questionable as the data heterogeneity between institutions is not taken into account. By allowing models to be trained in a distributed manner that preserves patients' privacy, federated learning promises to alleviate these issues, by enabling diligent multi-center studies. We present the first federated learning study on the modality of cardiovascular magnetic resonance (CMR) and use four centers derived from subsets of the M\&M and ACDC datasets, focusing on the diagnosis of hypertrophic cardiomyopathy (HCM). We adapt a 3D-CNN network pretrained on action recognition and explore two different ways of incorporating shape prior information to the model, and four different data augmentation set-ups, systematically analyzing their impact on the different collaborative learning choices. We show that despite the small size of data (180 subjects derived from four centers), the privacy preserving federated learning achieves promising results that are competitive with traditional centralized learning. We further find that federatively trained models exhibit increased robustness and are more sensitive to domain shift effects.

【39】 Degrees of riskiness, falsifiability, and truthlikeness. A neo-Popperian account applicable to probabilistic theories 标题：风险、可证伪性和真实性的程度。一种适用于概率论的新波普尔学说

作者：Leander Vignero,Sylvia Wenmackers 机构：KU Leuven, Institute of Philosophy, Centre for Logic and Philosophy of Science, Kardinaal Mercierplein , – bus , Leuven, Belgium., (Forthcoming in Synthese.) 备注：41 pages; 3 figures; accepted for publication in Synthese 链接：https://arxiv.org/abs/2107.03772 摘要：在本文中，我们重新审视了波普尔的三个概念：科学假设或理论的风险性、可证伪性和真实性。首先，我们明确了风险概念的基本维度。其次，我们检验了可证伪程度是否可以定义以及如何定义，以及它们如何与风险概念的各个维度以及实验背景相关。第三，我们考虑风险的关系（预期程度）的真实性。自始至终，我们特别关注概率理论，并为概率理论的逼真性提供了一个尝试性的、定量的解释。 摘要：In this paper, we take a fresh look at three Popperian concepts: riskiness, falsifiability, and truthlikeness (or verisimilitude) of scientific hypotheses or theories. First, we make explicit the dimensions that underlie the notion of riskiness. Secondly, we examine if and how degrees of falsifiability can be defined, and how they are related to various dimensions of the concept of riskiness as well as the experimental context. Thirdly, we consider the relation of riskiness to (expected degrees of) truthlikeness. Throughout, we pay special attention to probabilistic theories and we offer a tentative, quantitative account of verisimilitude for probabilistic theories.