人工智能学术速递[9.8]

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cs.AI人工智能，共计39篇

【1】 Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression 标题：超越保留的准确性：评估BERT压缩的忠诚度和稳健性 链接：https://arxiv.org/abs/2109.03228

作者：Canwen Xu,Wangchunshu Zhou,Tao Ge,Ke Xu,Julian McAuley,Furu Wei 机构： University of California, San Diego , Stanford University , Microsoft Research Asia , Beihang University 备注：Accepted to EMNLP 2021 (main conference) 摘要：最近关于预训练语言模型（例如，BERT）压缩的研究通常使用保留精度作为评估指标。在本文中，我们提出了两个新的度量标准，标签忠诚和概率忠诚，用于衡量压缩模型（即学生）与原始模型（即教师）的模仿程度。我们还探讨了在对抗性攻击下压缩对鲁棒性的影响。我们将量化、剪枝、知识提炼和渐进式模块替换为忠诚和健壮性。通过结合多种压缩技术，我们提供了一种实用的策略，以实现更好的准确性、忠诚度和鲁棒性。 摘要：Recent studies on compression of pretrained language models (e.g., BERT) usually use preserved accuracy as the metric for evaluation. In this paper, we propose two new metrics, label loyalty and probability loyalty that measure how closely a compressed model (i.e., student) mimics the original model (i.e., teacher). We also explore the effect of compression with regard to robustness under adversarial attacks. We benchmark quantization, pruning, knowledge distillation and progressive module replacing with loyalty and robustness. By combining multiple compression techniques, we provide a practical strategy to achieve better accuracy, loyalty and robustness.

【2】 Robust Predictable Control 标题：鲁棒可预测控制 链接：https://arxiv.org/abs/2109.03214

作者：Benjamin Eysenbach,Ruslan Salakhutdinov,Sergey Levine 机构：Carnegie Mellon University, Google Brain, UC Berkeley 备注：Project site with videos and code: this https URL 摘要：当今强化学习（RL）算法面临的许多挑战，如鲁棒性、泛化、迁移和计算效率，都与压缩密切相关。先前的工作令人信服地论证了为什么最小化信息在有监督的学习环境中是有用的，但标准的RL算法缺乏明确的压缩机制。RL设置是唯一的，因为（1）其顺序性质允许代理使用过去的信息来避免查看未来的观察结果，（2）代理可以优化其行为，以选择决策需要少量比特的状态。我们利用这些特性提出了一种学习简单策略的方法（RPC）。该方法将信息瓶颈、基于模型的RL和位反向编码的思想结合到一个简单且理论上合理的算法中。我们的方法联合优化了潜在空间模型和策略，使其具有自一致性，从而使策略避免了模型不准确的状态。我们证明，我们的方法比以前的方法实现了更严格的压缩，实现了比标准信息瓶颈高5倍的回报。我们还证明了我们的方法学习的策略更加健壮，并且能够更好地推广到新任务。 摘要：Many of the challenges facing today's reinforcement learning (RL) algorithms, such as robustness, generalization, transfer, and computational efficiency are closely related to compression. Prior work has convincingly argued why minimizing information is useful in the supervised learning setting, but standard RL algorithms lack an explicit mechanism for compression. The RL setting is unique because (1) its sequential nature allows an agent to use past information to avoid looking at future observations and (2) the agent can optimize its behavior to prefer states where decision making requires few bits. We take advantage of these properties to propose a method (RPC) for learning simple policies. This method brings together ideas from information bottlenecks, model-based RL, and bits-back coding into a simple and theoretically-justified algorithm. Our method jointly optimizes a latent-space model and policy to be self-consistent, such that the policy avoids states where the model is inaccurate. We demonstrate that our method achieves much tighter compression than prior methods, achieving up to 5x higher reward than a standard information bottleneck. We also demonstrate that our method learns policies that are more robust and generalize better to new tasks.

【3】 On the impact of MDP design for Reinforcement Learning agents in Resource Management 标题：强化学习Agent的MDP设计在资源管理中的作用 链接：https://arxiv.org/abs/2109.03202

作者：Renato Luiz de Freitas Cunha,Luiz Chaimowicz 机构：Programa de P´os Gradua¸c˜ao em Ciˆencia da Computa¸c˜ao, Universidade Federal de Minas Gerais (PPGCC-UFMG), Belo Horizonte, MG, Brazil 备注：15 pages, 6 figures. Accepted for publication at BRACIS 2021 摘要：强化学习应用于资源管理的最新进展提出了MDP，但没有深入分析设计决策对代理性能的影响。在本文中，我们比较和对比了四种不同的MDP变体，通过实证分析讨论了它们的计算需求和对代理性能的影响。我们的结论是，在我们的实验中，当使用多层感知器作为近似函数时，紧凑的状态表示允许在环境之间传输代理，并且传输的代理在80%的测试场景中具有良好的性能并优于专门代理，即使没有再训练。 摘要：The recent progress in Reinforcement Learning applications to Resource Management presents MDPs without a deeper analysis of the impacts of design decisions on agent performance. In this paper, we compare and contrast four different MDP variations, discussing their computational requirements and impacts on agent performance by means of an empirical analysis. We conclude by showing that, in our experiments, when using Multi-Layer Perceptrons as approximation function, a compact state representation allows transfer of agents between environments, and that transferred agents have good performance and outperform specialized agents in 80\% of the tested scenarios, even without retraining.

【4】 ExCode-Mixed: Explainable Approaches towards Sentiment Analysis on Code-Mixed Data using BERT models 标题：Excode-Mixed：使用ERT模型对混合代码数据进行情感分析的可解释方法 链接：https://arxiv.org/abs/2109.03200

作者：Aman Priyanshu,Aleti Vardhan,Sudarshan Sivakumar,Supriti Vijay,Nipuna Chhabra 机构：Manipal Institute of Technology 备注：3 pages, 1 figure 摘要：在印度等国家，社交媒体网站的使用越来越多，导致了大量代码混合数据。对这些数据的情绪分析可以为人们的观点和观点提供完整的见解。开发强大的解释性技术，解释模型为什么做出预测变得至关重要。在本文中，我们提出了一种适当的方法，将可解释的方法集成到代码混合情感分析中。 摘要：The increasing use of social media sites in countries like India has given rise to large volumes of code-mixed data. Sentiment analysis of this data can provide integral insights into people's perspectives and opinions. Developing robust explainability techniques which explain why models make their predictions becomes essential. In this paper, we propose an adequate methodology to integrate explainable approaches into code-mixed sentiment analysis.

【5】 Aspartix-V21 标题：ASPARX-V21 链接：https://arxiv.org/abs/2109.03166

作者：Wolfgang Dvořák,Matthias König,Johannes P. Wallner,Stefan Woltran 机构：Institute of Logic and Computation, TU Wien, Vienna, Austria, Institute of Software Technology, Graz University of Technology, Graz, Austria 备注：Part of ICCMA 2021 proceedings 摘要：在本说明中，我们介绍了Aspatix-V，其2021版，参与2021年国际辩论计算模型竞赛（ICCMA）。Aspatix-V能够解决ICCMA'21中的所有经典（静态）推理任务，并通过将最新的ASP语言结构（例如，条件文字）、ASP中的域启发法、，和多镜头方法。在这种情况下，Aspatix-V偏离了Aspatix传统的单一方法（即，通过单个ASP编码进行一次性解决），以进一步提高性能。 摘要：In this solver description we present ASPARTIX-V, in its 2021 edition, which participates in the International Competition on Computational Models of Argumentation (ICCMA) 2021. ASPARTIX-V is capable of solving all classical (static) reasoning tasks part of ICCMA'21 and extends the ASPARTIX system suite by incorporation of recent ASP language constructs (e.g. conditional literals), domain heuristics within ASP, and multi-shot methods. In this light ASPARTIX-V deviates from the traditional focus of ASPARTIX on monolithic approaches (i.e., one-shot solving via a single ASP encoding) to further enhance performance.

【6】 The pyglaf argumentation reasoner (ICCMA2021) 标题：“派格洛夫论证推理机”(ICCMA2021) 链接：https://arxiv.org/abs/2109.03162

作者：Mario Alviano 备注：Part of ICCMA 2021 proceedings 摘要：pyglaf推理机利用限制来解决抽象论证框架的计算问题。事实上，这些问题中的许多都是通过线性编码简化为限定的，还有一些是通过调用oracle进行限定的序列来解决的。在pyglaf中，Python用于构建编码和控制外部边界解算器的执行，外部边界解算器扩展了SAT解算器，并利用不可满足的核心分析和增量计算实现了算法。 摘要：The pyglaf reasoner takes advantage of circumscription to solve computational problems of abstract argumentation frameworks. In fact, many of these problems are reduced to circumscription by means of linear encodings, and a few others are solved by means of a sequence of calls to an oracle for circumscription. Within pyglaf, Python is used to build the encodings and to control the execution of the external circumscription solver, which extends the SAT solver glucose and implements algorithms taking advantage of unsatisfiable core analysis and incremental computation.

【7】 PAUSE: Positive and Annealed Unlabeled Sentence Embedding 标题：暂停：积极的、退火式的无标记句子嵌入 链接：https://arxiv.org/abs/2109.03155

作者：Lele Cao,Emil Larsson,Vilhelm von Ehrenheim,Dhiana Deva Cavalcanti Rocha,Anna Martin,Sonja Horn 机构：Motherbrain, EQT Group, Stockholm, Sweden, Modulai, Stockholm, Sweden 备注：Accepted by EMNLP 2021 main conference as long paper (12 pages and 2 figures). For source code, see this https URL 摘要：句子嵌入是指将原始文本转换为数字向量表示的一组有效且通用的技术，可用于广泛的自然语言处理（NLP）应用。这些技术大多是有监督或无监督的。与无监督方法相比，有监督方法对优化目标的假设较少，通常获得更好的结果。然而，训练需要大量的标记句子对，这在许多工业场景中是不可用的。为此，我们提出了一种通用的端到端方法——暂停（积极和退火的未标记句子嵌入），能够从部分标记的数据集中学习高质量的句子嵌入。我们的实验表明，在各种基准测试任务中，停顿仅使用了一小部分标记句子对，就达到了，有时甚至超过了，最先进的结果。当应用于实际的工业用例时，如果标签样本很少，PAUSE鼓励我们扩展数据集，而不需要大量的手动注释工作。 摘要：Sentence embedding refers to a set of effective and versatile techniques for converting raw text into numerical vector representations that can be used in a wide range of natural language processing (NLP) applications. The majority of these techniques are either supervised or unsupervised. Compared to the unsupervised methods, the supervised ones make less assumptions about optimization objectives and usually achieve better results. However, the training requires a large amount of labeled sentence pairs, which is not available in many industrial scenarios. To that end, we propose a generic and end-to-end approach -- PAUSE (Positive and Annealed Unlabeled Sentence Embedding), capable of learning high-quality sentence embeddings from a partially labeled dataset. We experimentally show that PAUSE achieves, and sometimes surpasses, state-of-the-art results using only a small fraction of labeled sentence pairs on various benchmark tasks. When applied to a real industrial use case where labeled samples are scarce, PAUSE encourages us to extend our dataset without the liability of extensive manual annotation work.

【8】 Recommendation Fairness: From Static to Dynamic 标题：推荐公平：从静电到动态 链接：https://arxiv.org/abs/2109.03150

作者：Dell Zhang,Jun Wang 机构：ByteDance AI Lab, London, UK, University College London 备注：A position paper for the FAccTRec-2021 workshop 摘要：由于需要捕捉用户不断变化的兴趣并优化其长期体验，越来越多的推荐系统开始将推荐建模为马尔可夫决策过程，并采用强化学习来解决该问题。对推荐系统公平性的研究是否应该遵循从静态评估和一次性干预到动态监控和不间断控制的相同趋势？在本文中，我们首先描述了推荐系统的最新发展，然后讨论了如何将公平性融入到推荐的强化学习技术中。此外，我们认为，为了进一步促进推荐公平性，我们可以考虑在随机博弈的一般框架中考虑多智能体（博弈论）优化、多目标（帕累托）优化和基于仿真的优化。 摘要：Driven by the need to capture users' evolving interests and optimize their long-term experiences, more and more recommender systems have started to model recommendation as a Markov decision process and employ reinforcement learning to address the problem. Shouldn't research on the fairness of recommender systems follow the same trend from static evaluation and one-shot intervention to dynamic monitoring and non-stop control? In this paper, we portray the recent developments in recommender systems first and then discuss how fairness could be baked into the reinforcement learning techniques for recommendation. Moreover, we argue that in order to make further progress in recommendation fairness, we may want to consider multi-agent (game-theoretic) optimization, multi-objective (Pareto) optimization, and simulation-based optimization, in the general framework of stochastic games.

【9】 GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition 标题：Ganser：一种基于EEG情感识别的自监督数据增强框架 链接：https://arxiv.org/abs/2109.03124

作者：Ahi Zhang,Sheng-hua Zhong,Yan Liu 机构：Department of Computing, The Hong Kong Polytechnic University Hong Kong, Hong Kong, China, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China 摘要：基于脑电图（EEG）的情感计算中存在的数据稀缺问题使得使用机器学习算法尤其是深度学习模型难以建立高精度、高稳定性的有效模型。数据增强最近在深度学习模型方面取得了相当大的性能改进：提高了准确性、稳定性，并减少了过度拟合。在本文中，我们提出了一种新的数据扩充框架，即基于生成对抗网络的自监督数据扩充（GANSER）。作为第一个将对抗训练与自监督学习相结合的脑电情感识别框架，该框架能够生成高质量、高多样性的模拟脑电样本。特别是，我们利用对抗性训练来学习脑电发生器，并强制生成的脑电信号近似真实样本的分布，从而确保增强样本的质量。变换函数用于屏蔽部分脑电信号，并迫使发生器根据剩余部分合成潜在的脑电信号，以产生各种各样的样本。引入变换过程中的掩蔽可能性作为先验知识，指导对模拟脑电信号提取可分辨特征，并将分类器推广到增广样本空间。最后，大量的实验表明，我们提出的方法可以帮助情感识别提高性能，并取得最先进的结果。 摘要：The data scarcity problem in Electroencephalography (EEG) based affective computing results into difficulty in building an effective model with high accuracy and stability using machine learning algorithms especially deep learning models. Data augmentation has recently achieved considerable performance improvement for deep learning models: increased accuracy, stability, and reduced over-fitting. In this paper, we propose a novel data augmentation framework, namely Generative Adversarial Network-based Self-supervised Data Augmentation (GANSER). As the first to combine adversarial training with self-supervised learning for EEG-based emotion recognition, the proposed framework can generate high-quality and high-diversity simulated EEG samples. In particular, we utilize adversarial training to learn an EEG generator and force the generated EEG signals to approximate the distribution of real samples, ensuring the quality of augmented samples. A transformation function is employed to mask parts of EEG signals and force the generator to synthesize potential EEG signals based on the remaining parts, to produce a wide variety of samples. The masking possibility during transformation is introduced as prior knowledge to guide to extract distinguishable features for simulated EEG signals and generalize the classifier to the augmented sample space. Finally, extensive experiments demonstrate our proposed method can help emotion recognition for performance gain and achieve state-of-the-art results.

【10】 Fudge: A light-weight solver for abstract argumentation based on SAT reductions 标题：基于SAT约简的轻量级抽象论证求解器FUGY 链接：https://arxiv.org/abs/2109.03106

作者：Matthias Thimm,Federico Cerutti,Mauro Vallati 机构：Institute for Web Science and Technologies, University of Koblenz-Landau, Germany, Department of Information Engineering, University of Brescia, Italy, School of Computing and Engineering, University of Huddersfield, United Kingdom 备注：Part of ICCMA 2021 proceedings 摘要：我们提出了福吉，一个抽象的论证解决方案，紧密结合了可满足性解决技术来解决一系列的抽象论证问题。虽然福吉使用的大多数编码源于标准翻译方法，但福吉使用了完全新颖的编码来解决wrt的怀疑推理问题。首选语义和问题wrt。理想语义学。 摘要：We present Fudge, an abstract argumentation solver that tightly integrates satisfiability solving technology to solve a series of abstract argumentation problems. While most of the encodings used by Fudge derive from standard translation approaches, Fudge makes use of completely novel encodings to solve the skeptical reasoning problem wrt. preferred semantics and problems wrt. ideal semantics.

【11】 OdoNet: Untethered Speed Aiding for Vehicle Navigation Without Hardware Wheeled Odometer 标题：OdoNet：无硬件轮式里程表的车辆导航无绳速度辅助 链接：https://arxiv.org/abs/2109.03091

作者：Hailiang Tang,Xiaoji Niu,Tisheng Zhang,You Li,Jingnan Liu 机构：Wuhan , China., Center, Wuhan University, Wuhan , China, and also with the, Collaborative Innovation Center of Geospatial Technology, Wuhan University, Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 备注：13 pages, 15 figures 摘要：里程表已被证明能显著提高全球导航卫星系统/惯性导航系统（GNSS/INS）组合车辆导航在受到GNSS挑战的环境中的精度。然而，里程表在许多应用中无法使用，尤其是对于售后设备。为了在不使用硬件轮式里程表的情况下应用前进速度辅助，我们提出了一种无约束的基于一维卷积神经网络（CNN）的伪里程表模型ODNET，该模型可以作为轮式里程表的替代方案。已经进行了专门的实验，以验证Ordonet的可行性和鲁棒性。结果表明，IMU个性、车辆载荷和道路条件对齿形网的鲁棒性和精度影响不大，而IMU偏差和安装角度可能会显著损坏齿形网。因此，增加了数据清理程序，以有效缓解IMU偏置和安装角度的影响。与仅使用非完整约束（NHC）的过程相比，采用伪里程表后，定位误差降低了68%左右，而硬件轮式里程表的定位误差降低了74%左右。综上所述，提出的ODNET可以作为一种无约束的车辆导航伪里程表，可以有效地提高GNSS环境下定位的精度和可靠性。 摘要：Odometer has been proven to significantly improve the accuracy of the Global Navigation Satellite System / Inertial Navigation System (GNSS/INS) integrated vehicle navigation in GNSS-challenged environments. However, the odometer is inaccessible in many applications, especially for aftermarket devices. To apply forward speed aiding without hardware wheeled odometer, we propose OdoNet, an untethered one-dimensional Convolution Neural Network (CNN)-based pseudo-odometer model learning from a single Inertial Measurement Unit (IMU), which can act as an alternative to the wheeled odometer. Dedicated experiments have been conducted to verify the feasibility and robustness of the OdoNet. The results indicate that the IMU individuality, the vehicle loads, and the road conditions have little impact on the robustness and precision of the OdoNet, while the IMU biases and the mounting angles may notably ruin the OdoNet. Thus, a data-cleaning procedure is added to effectively mitigate the impacts of the IMU biases and the mounting angles. Compared to the process using only non-holonomic constraint (NHC), after employing the pseudo-odometer, the positioning error is reduced by around 68%, while the percentage is around 74% for the hardware wheeled odometer. In conclusion, the proposed OdoNet can be employed as an untethered pseudo-odometer for vehicle navigation, which can efficiently improve the accuracy and reliability of the positioning in GNSS-denied environments.

【12】 Distributed Allocation and Scheduling of Tasks with Cross-Schedule Dependencies for Heterogeneous Multi-Robot Teams 标题：具有交叉调度依赖的异构多机器人团队任务分布式分配与调度 链接：https://arxiv.org/abs/2109.03089

作者：Barbara Arbanas Ferreira,Tamara Petrović,Matko Orsag,J. Ramiro Martínez-de-Dios,Stjepan Bogdan 机构：University of Zagreb, Unska , Zagreb, Croatia, Universidad de Sevilla, GRVC Robotics Lab Sevilla, Camino de los Descubrimientos sn, Sevilla, Spain 摘要：为了在日常生活中安全高效地使用多机器人系统，必须开发一种鲁棒快速的方法来协调它们的动作。在本文中，我们提出了一种分布式任务分配和调度算法，用于不同机器人的任务与时间和优先级约束紧密耦合的任务。该方法基于将问题表示为车辆路径问题的一个变体，并使用基于进化计算的分布式元启发式算法（CBM pop）找到解决方案。这种方法允许快速且接近最优的分配，因此可用于任务更改时的在线重新规划。仿真结果表明，与现有的分布式方法相比，该方法具有更好的计算速度和可扩展性。给出了规划程序在由多机器人系统维护的温室实际用例中的应用。 摘要：To enable safe and efficient use of multi-robot systems in everyday life, a robust and fast method for coordinating their actions must be developed. In this paper, we present a distributed task allocation and scheduling algorithm for missions where the tasks of different robots are tightly coupled with temporal and precedence constraints. The approach is based on representing the problem as a variant of the vehicle routing problem, and the solution is found using a distributed metaheuristic algorithm based on evolutionary computation (CBM-pop). Such an approach allows a fast and near-optimal allocation and can therefore be used for online replanning in case of task changes. Simulation results show that the approach has better computational speed and scalability without loss of optimality compared to the state-of-the-art distributed methods. An application of the planning procedure to a practical use case of a greenhouse maintained by a multi-robot system is given.

【13】 Learning grounded word meaning representations on similarity graphs 标题：基于相似图的基础词义表征学习 链接：https://arxiv.org/abs/2109.03084

作者：Mariella Dimiccoli,Herwig Wendt,Pau Batlle 机构：Institut de Robòtica i Informàtica Industrial, (CSIC-UPC), Barcelona, Spain, CNRS, IRIT, Univ. of Toulouse, France, California Institute of Technology, Pasadena, California 备注：Accepted to EMNLP 2021 (long paper) 摘要：本文介绍了一种新的方法来学习视觉扎根的意义表示的话作为低维节点嵌入在一个潜在的图形层次。层次结构的较低层次通过专用但可通信的图形对特定于模态的单词表示进行建模，而较高层次将这些表示放在一个图形上，以便从两种模态共同学习表示。每个图的拓扑结构建立了词与词之间的相似关系模型，并与图嵌入联合进行估计。该模型的基本假设是，在低维空间中，具有相似意义的单词对应于底层相似图中的社区。我们将该模型命名为分层多模态相似图嵌入（HM-SGE）。实验结果验证了HM-SGE模拟人类相似性判断和概念分类的能力，优于现有技术。 摘要：This paper introduces a novel approach to learn visually grounded meaning representations of words as low-dimensional node embeddings on an underlying graph hierarchy. The lower level of the hierarchy models modality-specific word representations through dedicated but communicating graphs, while the higher level puts these representations together on a single graph to learn a representation jointly from both modalities. The topology of each graph models similarity relations among words, and is estimated jointly with the graph embedding. The assumption underlying this model is that words sharing similar meaning correspond to communities in an underlying similarity graph in a low-dimensional space. We named this model Hierarchical Multi-Modal Similarity Graph Embedding (HM-SGE). Experimental results validate the ability of HM-SGE to simulate human similarity judgements and concept categorization, outperforming the state of the art.

【14】 Sequential Diagnosis Prediction with Transformer and Ontological Representation 标题：基于Transformer和本体表示的序贯诊断预测 链接：https://arxiv.org/abs/2109.03069

作者：Xueping Peng,Guodong Long,Tao Shen,Sen Wang,Jing Jiang 机构：∗ Australian Artificial Intelligence Institute, FEIT, University of Technology Sydney, Australia, † School of Information Technology and Electrical Engineering, The University of Queensland, Australia 备注：10 pages, 5 figures, Accepted by IEEE ICDM 2021. arXiv admin note: text overlap with arXiv:2107.09288 摘要：电子健康记录（EHR）上的顺序诊断预测已被证明对医学领域的预测分析至关重要。EHR数据是患者与医疗系统互动的连续记录，具有许多固有的暂时性、不规则性和数据不足的特征。最近的一些工作通过利用EHR数据中的序列信息来训练医疗保健预测模型，但是它们容易受到不规则的、暂时的EHR数据的影响，这些数据具有入院/出院状态，并且数据不足。为了缓解这种情况，我们提出了一种称为SETOR的端到端鲁棒Transformer模型，该模型利用神经常微分方程处理患者就诊之间的不规则时间间隔和每次就诊的住院时间，通过整合医学本体来缓解数据不足的限制，并通过使用多层转换块来捕获患者就诊之间的依赖关系。在两个真实的医疗数据集上进行的实验表明，我们的顺序诊断预测模型SETOR不仅比以前的最先进的方法获得了更好的预测结果，无论训练数据是否充足，而且还得到了更多可解释的医疗代码嵌入。实验代码可在GitHub存储库中获得(https://github.com/Xueping/SETOR). 摘要：Sequential diagnosis prediction on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in the medical domain. EHR data, sequential records of a patient's interactions with healthcare systems, has numerous inherent characteristics of temporality, irregularity and data insufficiency. Some recent works train healthcare predictive models by making use of sequential information in EHR data, but they are vulnerable to irregular, temporal EHR data with the states of admission/discharge from hospital, and insufficient data. To mitigate this, we propose an end-to-end robust transformer-based model called SETOR, which exploits neural ordinary differential equation to handle both irregular intervals between a patient's visits with admitted timestamps and length of stay in each visit, to alleviate the limitation of insufficient data by integrating medical ontology, and to capture the dependencies between the patient's visits by employing multi-layer transformer blocks. Experiments conducted on two real-world healthcare datasets show that, our sequential diagnoses prediction model SETOR not only achieves better predictive results than previous state-of-the-art approaches, irrespective of sufficient or insufficient training data, but also derives more interpretable embeddings of medical codes. The experimental codes are available at the GitHub repository (https://github.com/Xueping/SETOR).

【15】 Reconfigurable co-processor architecture with limited numerical precision to accelerate deep convolutional neural networks 标题：加速深卷积神经网络的有限数值精度可重构协处理器结构 链接：https://arxiv.org/abs/2109.03040

作者：Sasindu Wijeratne,Sandaruwan Jayaweera,Mahesh Dananjaya,Ajith Pasqual 机构：Dept. of Electronic and Telecommunication Engineering, University of Moratuwa, Sri Lanka 摘要：卷积神经网络（CNN）广泛应用于深度学习应用，例如视觉系统、机器人等。然而，现有的软件解决方案并不高效。因此，已经提出了许多硬件加速器来优化实现的性能、功率和资源利用率。在现有的解决方案中，基于现场可编程门阵列（FPGA）的体系结构提供了更好的成本-能源-性能权衡，以及可扩展性和最小化开发时间。在本文中，我们提出了一种与模型无关的可重构协同处理体系结构来加速CNN。我们的体系结构由并行乘法和累加（MAC）单元和缓存技术以及互连网络组成，以利用最大的数据并行性。与现有解决方案相比，我们为算术表示和运算引入了有限精度32位Q格式定点量化。因此，我们的体系结构实现了资源利用率的显著降低，并且具有竞争性的准确性。此外，我们还开发了一种汇编类型的微指令来访问协同处理结构，以管理分层并行性，从而重用有限的资源。最后，我们在Xilinx Virtex 7 FPGA上测试了高达9x9内核大小的体系结构，实现了高达226.2 GOp/S的吞吐量（3x3内核大小）。 摘要：Convolutional Neural Networks (CNNs) are widely used in deep learning applications, e.g. visual systems, robotics etc. However, existing software solutions are not efficient. Therefore, many hardware accelerators have been proposed optimizing performance, power and resource utilization of the implementation. Amongst existing solutions, Field Programmable Gate Array (FPGA) based architecture provides better cost-energy-performance trade-offs as well as scalability and minimizing development time. In this paper, we present a model-independent reconfigurable co-processing architecture to accelerate CNNs. Our architecture consists of parallel Multiply and Accumulate (MAC) units with caching techniques and interconnection networks to exploit maximum data parallelism. In contrast to existing solutions, we introduce limited precision 32 bit Q-format fixed point quantization for arithmetic representations and operations. As a result, our architecture achieved significant reduction in resource utilization with competitive accuracy. Furthermore, we developed an assembly-type microinstructions to access the co-processing fabric to manage layer-wise parallelism, thereby making re-use of limited resources. Finally, we have tested our architecture up to 9x9 kernel size on Xilinx Virtex 7 FPGA, achieving a throughput of up to 226.2 GOp/S for 3x3 kernel size.

【16】 POSSCORE: A Simple Yet Effective Evaluation of Conversational Search with Part of Speech Labelling 标题：POSSCORE：一种简单有效的词性标注会话搜索评测 链接：https://arxiv.org/abs/2109.03039

作者：Zeyang Liu,Ke Zhou,Jiaxin Mao,Max L. Wilson 机构：Nottingham, UK, University of Nottingham & Nokia Bell Labs, Renmin University of China, Beijing, China 备注：11 pages 摘要：对话式搜索系统，如谷歌助手和微软Cortana，提供了一种新的搜索模式，允许用户通过自然语言对话与搜索系统进行交流。由于搜索结果以自然语言句子的形式呈现，因此评估此类系统非常具有挑战性。鉴于可能的答复数量不限，为所有可能的答复收集相关性评估是不可行的。本文提出了一种简单而有效的会话搜索自动评价方法POSSCORE。所提出的基于嵌入的度量方法考虑了词性对响应的影响。据我们所知，我们的工作是第一次系统地证明了在会话搜索评估中加入句法信息（如词性标签）的重要性。实验结果表明，我们的指标可以与人类偏好相关联，与最先进的基线指标相比，取得了显著的改进。 摘要：Conversational search systems, such as Google Assistant and Microsoft Cortana, provide a new search paradigm where users are allowed, via natural language dialogues, to communicate with search systems. Evaluating such systems is very challenging since search results are presented in the format of natural language sentences. Given the unlimited number of possible responses, collecting relevance assessments for all the possible responses is infeasible. In this paper, we propose POSSCORE, a simple yet effective automatic evaluation method for conversational search. The proposed embedding-based metric takes the influence of part of speech (POS) of the terms in the response into account. To the best knowledge, our work is the first to systematically demonstrate the importance of incorporating syntactic information, such as POS labels, for conversational search evaluation. Experimental results demonstrate that our metrics can correlate with human preference, achieving significant improvements over state-of-the-art baseline metrics.

【17】 Generate & Rank: A Multi-task Framework for Math Word Problems 标题：生成与排序：数学应用题的多任务框架 链接：https://arxiv.org/abs/2109.03034

作者：Jianhao Shen,Yichun Yin,Lin Li,Lifeng Shang,Xin Jiang,Ming Zhang,Qun Liu 机构：Department of Computer Science, School of EECS, Peking University, Huawei Noah’s Ark Lab, Huawei HiSilicon 备注：Findings of EMNLP2021 摘要：数学单词问题（MWP）是自然语言处理中一项具有挑战性和关键性的任务。最近的许多研究将MWP形式化为生成任务，并采用序列到序列模型将问题描述转换为数学表达式。然而，数学表达式容易出现小错误，而生成目标没有明确处理此类错误。为了解决这个局限性，我们设计了一个新的MWP排序任务，并提出了Generate&Rank，一个基于生成预训练语言模型的多任务框架。通过与生成和排序的联合训练，该模型从自己的错误中学习，能够区分正确和错误的表达式。同时，我们执行专为MWP设计的基于树的干扰和在线更新，以提高ranker。我们在基准测试上证明了我们提出的方法的有效性，结果表明，我们的方法在所有数据集中都始终优于基线。特别是在经典的Math23k中，我们的方法比最先进的方法高7%（78.4%$\rightarrow$85.4%）。 摘要：Math word problem (MWP) is a challenging and critical task in natural language processing. Many recent studies formalize MWP as a generation task and have adopted sequence-to-sequence models to transform problem descriptions to mathematical expressions. However, mathematical expressions are prone to minor mistakes while the generation objective does not explicitly handle such mistakes. To address this limitation, we devise a new ranking task for MWP and propose Generate & Rank, a multi-task framework based on a generative pre-trained language model. By joint training with generation and ranking, the model learns from its own mistakes and is able to distinguish between correct and incorrect expressions. Meanwhile, we perform tree-based disturbance specially designed for MWP and an online update to boost the ranker. We demonstrate the effectiveness of our proposed method on the benchmark and the results show that our method consistently outperforms baselines in all datasets. Particularly, in the classical Math23k, our method is 7% (78.4% $\rightarrow$ 85.4%) higher than the state-of-the-art.

【18】 Sequential Attention Module for Natural Language Processing 标题：自然语言处理中的顺序注意模块 链接：https://arxiv.org/abs/2109.03009

作者：Mengyuan Zhou,Jian Ma,Haiqin Yang,Lianxin Jiang,Yang Mo 机构：Ping An Life Insurance, Ltd., Shenzhen City, Guangdong Province, China 备注：10 pages, 4 figures, 5 tables 摘要：最近，大型预训练神经语言模型通过微调在许多下游自然语言处理（NLP）应用中取得了显著的性能。在本文中，我们的目标是如何进一步改进语言模型上的标记表示。因此，我们提出了一个简单而有效的即插即用模块，即顺序注意模块（SAM），该模块基于从预先训练的语言模型中学习到的令牌嵌入。我们提出的SAM由两个主要的注意模块组成：特征注意模块（FAM）和令牌注意模块（TAM）。更具体地说，FAM可以有效地识别每个维度上特征的重要性，并通过点积对下游NLP应用程序的原始令牌嵌入提高效果。同时，TAM可以在令牌级别进一步重新加权特征。此外，我们在FAM上提出了一种自适应滤波器，以防止噪声影响和增加信息吸收。最后，我们进行了大量实验，以证明我们提出的SAM的优势和特性。我们首先展示SAM如何在SemEval'21 Task 7的两个子任务的冠军解决方案中发挥主要作用。之后，我们将SAM应用于情绪分析和三个流行的NLP任务，并证明SAM始终优于最先进的基线。 摘要：Recently, large pre-trained neural language models have attained remarkable performance on many downstream natural language processing (NLP) applications via fine-tuning. In this paper, we target at how to further improve the token representations on the language models. We, therefore, propose a simple yet effective plug-and-play module, Sequential Attention Module (SAM), on the token embeddings learned from a pre-trained language model. Our proposed SAM consists of two main attention modules deployed sequentially: Feature-wise Attention Module (FAM) and Token-wise Attention Module (TAM). More specifically, FAM can effectively identify the importance of features at each dimension and promote the effect via dot-product on the original token embeddings for downstream NLP applications. Meanwhile, TAM can further re-weight the features at the token-wise level. Moreover, we propose an adaptive filter on FAM to prevent noise impact and increase information absorption. Finally, we conduct extensive experiments to demonstrate the advantages and properties of our proposed SAM. We first show how SAM plays a primary role in the champion solution of two subtasks of SemEval'21 Task 7. After that, we apply SAM on sentiment analysis and three popular NLP tasks and demonstrate that SAM consistently outperforms the state-of-the-art baselines.

【19】 Empathetic Dialogue Generation with Pre-trained RoBERTa-GPT2 and External Knowledge 标题：利用预先训练的Roberta-GPT2和外部知识生成移情对话 链接：https://arxiv.org/abs/2109.03004

作者：Ye Liu,Wolfgang Maier,Wolfgang Minker,Stefan Ultes 机构：comWolfgang MinkerUlm University 备注：accepted at International Workshop on Spoken Dialog System Technology (IWSDS) 2021 摘要：对话代理面临的一个挑战是识别对话伙伴的感受并做出相应的反应。在这项工作中，RoBERTa-GPT2被提议用于移情对话生成，其中预训练的自动编码RoBERTa被用作编码器，预训练的自动回归GPT-2被用作解码器。通过将预先训练好的RoBERTa和GPT-2相结合，我们的模型实现了最先进的情感准确性。为了实现RoBERTa-GPT2模型的移情能力，我们提出了一种常识性知识和情感概念抽取器，该抽取器为GPT-2解码器抽取对话上下文中的常识性和情感性概念。实验结果表明，移情对话的生成得益于预先训练的编译码器结构和外部知识。 摘要：One challenge for dialogue agents is to recognize feelings of the conversation partner and respond accordingly. In this work, RoBERTa-GPT2 is proposed for empathetic dialogue generation, where the pre-trained auto-encoding RoBERTa is utilised as encoder and the pre-trained auto-regressive GPT-2 as decoder. With the combination of the pre-trained RoBERTa and GPT-2, our model realizes a new state-of-the-art emotion accuracy. To enable the empathetic ability of RoBERTa-GPT2 model, we propose a commonsense knowledge and emotional concepts extractor, in which the commonsensible and emotional concepts of dialogue context are extracted for the GPT-2 decoder. The experiment results demonstrate that the empathetic dialogue generation benefits from both pre-trained encoder-decoder architecture and external knowledge.

【20】 Smart Automotive Technology Adherence to the Law: (De)Constructing Road Rules for Autonomous System Development, Verification and Safety 标题：智能汽车技术守法：(去)构建自主系统开发、验证和安全的道路规则 链接：https://arxiv.org/abs/2109.02956

作者：Scott McLachlan,Martin Neil,Kudakwashe Dube,Ronny Bogani,Norman Fenton,Burkhard Schaffer 摘要：驾驶是一项直观的任务，需要技能、持续的警觉和对意外事件的警惕。驾驶任务还需要长时间专注于整个任务，以及与其他道路使用者（包括野生动物）的复杂谈判技巧。在接近交叉口、超车、让路、合并、转弯以及遵守大量道路规则时，这些要求尤为重要。现代机动车辆现在包括一系列智能辅助和自动驾驶系统，能够完成部分、大部分或有限情况下的所有驾驶任务。英国交通部对自动车道保持系统安全使用咨询的回应建议对这些系统进行测试，以确保其符合相关交通规则。构建这些智能汽车系统需要具有高度技术软件工程技能的软件开发人员，现在还需要律师对交通法规的深入了解。这些技能是确保系统能够在遵守法律的同时安全执行其任务所必需的。本文提出了一种解构复杂的交通法法律术语并表达其要求和流程的方法。该方法（de）用法律术语构建道路规则，并用结构化的英语逻辑（表示为布尔逻辑以实现自动化）和法律地图以实现可视化。我们展示了一个使用这些工具构建和验证贝叶斯网络模型的示例。我们坚信这些工具是程序员和公众可以接近的，能够用于开发人工智能以支持机动车辆智能系统，并用于验证以确保这些系统在决策时考虑到法律。 摘要：Driving is an intuitive task that requires skills, constant alertness and vigilance for unexpected events. The driving task also requires long concentration spans focusing on the entire task for prolonged periods, and sophisticated negotiation skills with other road users, including wild animals. These requirements are particularly important when approaching intersections, overtaking, giving way, merging, turning and while adhering to the vast body of road rules. Modern motor vehicles now include an array of smart assistive and autonomous driving systems capable of subsuming some, most, or in limited cases, all of the driving task. The UK Department of Transport's response to the Safe Use of Automated Lane Keeping System consultation proposes that these systems are tested for compliance with relevant traffic rules. Building these smart automotive systems requires software developers with highly technical software engineering skills, and now a lawyer's in-depth knowledge of traffic legislation as well. These skills are required to ensure the systems are able to safely perform their tasks while being observant of the law. This paper presents an approach for deconstructing the complicated legalese of traffic law and representing its requirements and flow. The approach (de)constructs road rules in legal terminology and specifies them in structured English logic that is expressed as Boolean logic for automation and Lawmaps for visualisation. We demonstrate an example using these tools leading to the construction and validation of a Bayesian Network model. We strongly believe these tools to be approachable by programmers and the general public, and capable of use in developing Artificial Intelligence to underpin motor vehicle smart systems, and in validation to ensure these systems are considerate of the law when making decisions.

【21】 Dutch Comfort: The limits of AI governance through municipal registers 标题：荷兰人的安慰：通过市政登记进行人工智能治理的局限性 链接：https://arxiv.org/abs/2109.02944

作者：Corinne Cath,Fieke Jansen 机构： Oxford Internet Institute, University of Oxford, St Giles, Oxford, OX,JS, United, The Alan Turing Institute, headquartered at the British Library, Euston Road, London 摘要：在这篇评论中，我们回应了卢西亚诺·弗洛里迪教授最近发表的一封题为“人工智能作为公共服务：向阿姆斯特丹和赫尔辛基学习”的社论信。在这里，Floridi考虑了这些市政AI登记册的积极影响，这些登记册收集了阿姆斯特丹市和赫尔辛基市使用的有限数量的算法系统。对于人工智能注册作为自动化系统的治理模型，有许多假设，我们试图提出质疑。从最近通过解构和非政治化人工智能来规范人工智能的尝试开始，这是一个令人担忧的政治项目，鼓励我们称之为“道德剧场”，因为在数字福利国家的背景下使用这些系统已被证明是危险的。我们同意Floridi的观点，即可以从这些登记册中了解到人工智能系统在市政城市管理中的作用。然而，我们从与数字州广泛的人种学接触中吸取的教训显然不那么乐观。 摘要：In this commentary, we respond to a recent editorial letter by Professor Luciano Floridi entitled 'AI as a public service: Learning from Amsterdam and Helsinki'. Here, Floridi considers the positive impact of these municipal AI registers, which collect a limited number of algorithmic systems used by the city of Amsterdam and Helsinki. There are a number of assumptions about AI registers as a governance model for automated systems that we seek to question. Starting with recent attempts to normalize AI by decontextualizing and depoliticizing it, which is a fraught political project that encourages what we call 'ethics theater' given the proven dangers of using these systems in the context of the digital welfare state. We agree with Floridi that much can be learned from these registers about the role of AI systems in municipal city management. Yet, the lessons we draw, on the basis of our extensive ethnographic engagement with digital well-fare states are distinctly less optimistic.

【22】 Naturalness Evaluation of Natural Language Generation in Task-oriented Dialogues using BERT 标题：基于ERT的任务型对话中自然语言生成的自然度评估 链接：https://arxiv.org/abs/2109.02938

作者：Ye Liu,Wolfgang Maier,Wolfgang Minker,Stefan Ultes 机构：Mercedes-Benz AG, Sindelfingen, Germany, Ulm University, Ulm, Germany 备注：accepted to RANLP 2021 摘要：本文提出了一种自动评估对话系统自然语言生成自然性的方法。虽然这项任务以前是通过昂贵和耗时的人力完成的，但我们提出了自动评估生成语言自然度的新任务。通过微调BERT模型，我们提出的自然度评估方法显示了稳健的结果，并优于基线：支持向量机、双向LSTM和BLEURT。此外，通过从质量和信息性语言知识迁移学习，提高了自然度模型的训练速度和评估性能。 摘要：This paper presents an automatic method to evaluate the naturalness of natural language generation in dialogue systems. While this task was previously rendered through expensive and time-consuming human labor, we present this novel task of automatic naturalness evaluation of generated language. By fine-tuning the BERT model, our proposed naturalness evaluation method shows robust results and outperforms the baselines: support vector machines, bi-directional LSTMs, and BLEURT. In addition, the training speed and evaluation performance of naturalness model are improved by transfer learning from quality and informativeness linguistic knowledge.

【23】 Fishr: Invariant Gradient Variances for Out-of-distribution Generalization 标题：FISIR：离散型泛化的不变梯度方差 链接：https://arxiv.org/abs/2109.02934

作者：Alexandre Rame,Corentin Dancette,Matthieu Cord 机构：Sorbonne Universit´e, CNRS, LIP, Paris, France, Valeo.ai 备注：31 pages, 12 tables, 6 figures 摘要：学习在数据分布发生变化时能够很好地概括的健壮模型对于实际应用至关重要。为此，人们对同时从多个训练领域学习的兴趣日益高涨，同时在这些领域中强制执行不同类型的不变性。然而，所有现有的方法都未能在公平评估协议下显示出系统性的好处。在本文中，我们提出了一种新的学习方案来在损失函数的梯度空间中实现域不变性：具体地说，我们引入了一个正则化项来匹配跨训练域的梯度的域级方差。关键的是，我们的策略名为Fishr，它与Fisher信息和损失的Hessian密切相关。我们表明，在学习过程中，强制域级梯度协方差相似最终会使域级损失景观围绕最终权重局部对齐。大量的实验证明了Fishr在非分布泛化中的有效性。特别是，Fishr改进了领域基准的最新技术，其性能明显优于经验风险最小化。该代码发布于https://github.com/alexrame/fishr. 摘要：Learning robust models that generalize well under changes in the data distribution is critical for real-world applications. To this end, there has been a growing surge of interest to learn simultaneously from multiple training domains - while enforcing different types of invariance across those domains. Yet, all existing approaches fail to show systematic benefits under fair evaluation protocols. In this paper, we propose a new learning scheme to enforce domain invariance in the space of the gradients of the loss function: specifically, we introduce a regularization term that matches the domain-level variances of gradients across training domains. Critically, our strategy, named Fishr, exhibits close relations with the Fisher Information and the Hessian of the loss. We show that forcing domain-level gradient covariances to be similar during the learning procedure eventually aligns the domain-level loss landscapes locally around the final weights. Extensive experiments demonstrate the effectiveness of Fishr for out-of-distribution generalization. In particular, Fishr improves the state of the art on the DomainBed benchmark and performs significantly better than Empirical Risk Minimization. The code is released at https://github.com/alexrame/fishr.

【24】 IndicBART: A Pre-trained Model for Natural Language Generation of Indic Languages 标题：IndicBART：一种印地语自然语言生成的预训练模型 链接：https://arxiv.org/abs/2109.02903

作者：Raj Dabre,Himani Shrotriya,Anoop Kunchukuttan,Ratish Puduppully,Mitesh M. Khapra,Pratyush Kumar 机构：National Institute of Information and Communications Technology, IIT Madras, Microsoft, University Of Edinburgh 备注：Preliminary work on Natural Language Generation for Indic languages. We work on pre-training Indic specific sequence to sequence models and evaluate them for Machine Translation and Summarization 摘要：在本文中，我们介绍了IndicBART，一个多语言、序列到序列的预训练模型，重点关注11种印度语和英语。与现有的预训练模型不同，indibart利用Indic脚本之间的拼写相似性来改进相似Indic语言之间的迁移学习。我们评估了两个NLG任务：神经机器翻译（NMT）和极端概括。我们对12种语言对的NMT和使用多语言微调的7种语言的极端摘要进行的实验表明，IndicBART与mBART50具有竞争性或优于mBART50，尽管包含的参数明显较少。我们的分析侧重于确定脚本统一（对Devanagari）、语料库大小以及多种语言对最终性能的影响。IndicBART模型在麻省理工学院许可下可在https://indicnlp.ai4bharat.org/indic-bart . 摘要：In this paper we present IndicBART, a multilingual, sequence-to-sequence pre-trained model focusing on 11 Indic languages and English. Different from existing pre-trained models, IndicBART utilizes the orthographic similarity between Indic scripts to improve transfer learning between similar Indic languages. We evaluate IndicBART on two NLG tasks: Neural Machine Translation (NMT) and extreme summarization. Our experiments on NMT for 12 language pairs and extreme summarization for 7 languages using multilingual fine-tuning show that IndicBART is competitive with or better than mBART50 despite containing significantly fewer parameters. Our analyses focus on identifying the impact of script unification (to Devanagari), corpora size as well as multilingualism on the final performance. The IndicBART model is available under the MIT license at https://indicnlp.ai4bharat.org/indic-bart .

【25】 Blockchains through ontologies: the case study of the Ethereum ERC721 standard in \ONT{} (Extended Version) 链接：https://arxiv.org/abs/2109.02899

作者：Giampaolo Bella,Domenico Cantone,Cristiano Longo,Marianna Nicolosi-Asmundo,Daniele Francesco Santamaria 机构： University of Catania, Department of Mathematics and Computer Science, Viale Andrea Doria, - , - Catania, ITALY 备注：Extended version of Blockchains through ontologies: the case study of the Ethereum ERC721 standard in \ONT{}, Proceedings of IDC 2021 摘要：由于行业和人们对分散应用程序（DAPP）的兴趣，特别是通过区块链上的数字证书（称为代币）交易资产的行业和人们，区块链正在获得动力。因此，对区块链上进行的任何活动提供清晰明确的描述变得至关重要，我们感到迫切需要实现这一描述，至少对于交易而言是如此。本文报告了如何利用代理、系统和服务集成本体（“\ONT{}”）作为存储在区块链上作为软件代理的智能合约语义表示的一般方法。特别注意不可替代代币（NFT），通过ERC721标准对其进行管理是一个案例研究。 摘要：Blockchains are gaining momentum due to the interest of industries and people in \emph{decentralized applications} (Dapps), particularly in those for trading assets through digital certificates secured on blockchain, called tokens. As a consequence, providing a clear unambiguous description of any activities carried out on blockchains has become crucial, and we feel the urgency to achieve that description at least for trading. This paper reports on how to leverage the \emph{Ontology for Agents, Systems, and Integration of Services} ("\ONT{}") as a general means for the semantic representation of smart contracts stored on blockchain as software agents. Special attention is paid to non-fungible tokens (NFTs), whose management through the ERC721 standard is presented as a case study.

【26】 Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach 标题：资源约束下的规定性过程监控：一种因果推理方法 链接：https://arxiv.org/abs/2109.02894

作者：Mahmoud Shoush,Marlon Dumas 机构：University of Tartu, Tartu, Estonia 摘要：规定性流程监控是一系列通过在运行时触发干预来优化业务流程性能的技术。现有的规定性过程监控技术假设可能触发的干预数量是无限的。然而，在实践中，具体的干预措施消耗有限的资源。例如，在贷款发放过程中，干预可能包括准备替代贷款，以增加申请人获得贷款的机会。这种干预需要信贷官员花费一定的时间，因此，不可能在所有情况下触发这种干预。本文提出了一种规定性的过程监控技术，在固定资源约束下触发干预措施以优化成本函数。所提出的技术依赖于预测建模来识别可能导致负面结果的案例，并结合因果推理来估计干预对案例结果的影响。然后利用这些产出将资源分配给干预措施，以使成本函数最大化。初步的经验评估表明，与纯粹的预测（非因果）基线相比，拟议的方法产生了更高的净收益。 摘要：Prescriptive process monitoring is a family of techniques to optimize the performance of a business process by triggering interventions at runtime. Existing prescriptive process monitoring techniques assume that the number of interventions that may be triggered is unbounded. In practice, though, specific interventions consume resources with finite capacity. For example, in a loan origination process, an intervention may consist of preparing an alternative loan offer to increase the applicant's chances of taking a loan. This intervention requires a certain amount of time from a credit officer, and thus, it is not possible to trigger this intervention in all cases. This paper proposes a prescriptive process monitoring technique that triggers interventions to optimize a cost function under fixed resource constraints. The proposed technique relies on predictive modeling to identify cases that are likely to lead to a negative outcome, in combination with causal inference to estimate the effect of an intervention on the outcome of the case. These outputs are then used to allocate resources to interventions to maximize a cost function. A preliminary empirical evaluation suggests that the proposed approach produces a higher net gain than a purely predictive (non-causal) baseline.

【27】 HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learning 标题：HMSG：基于元路径子图学习的异构图神经网络 链接：https://arxiv.org/abs/2109.02868

作者：Xinjun Cai,Jiaxing Shang,Fei Hao,Dajiang Liu,Linjiang Zheng 机构： College of Computer Science, Chongqing University, Chongqing , China, Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing , China 备注：12 pages, 3 figures, 6 tables 摘要：许多真实世界的数据可以表示为具有不同类型节点和连接的异构图。异构图神经网络模型旨在将节点或子图嵌入到低维向量空间中，用于各种下游任务，如节点分类、链路预测等。尽管最近提出了几种模型，但它们要么仅聚合来自相同类型邻居的信息，或者只是不加区别地以同样的方式对待同质和异质邻居。基于这些观察结果，我们提出了一种新的异构图神经网络模型HMSG，该模型能够全面地从同质和异构邻居中获取结构、语义和属性信息。具体来说，我们首先将异构图分解为多个基于元路径的同构子图和异构子图，每个子图关联特定的语义和结构信息。然后将消息聚合方法独立地应用于每个子图，以便以更具针对性和效率的方式学习信息。通过特定于类型的属性转换，还可以在不同类型的节点之间传输节点属性。最后，我们将子图中的信息融合在一起，得到完整的表示。在节点分类、节点聚类和链路预测任务的多个数据集上进行的大量实验表明，HMSG在所有评估指标上的性能都优于最先进的基线。 摘要：Many real-world data can be represented as heterogeneous graphs with different types of nodes and connections. Heterogeneous graph neural network model aims to embed nodes or subgraphs into low-dimensional vector space for various downstream tasks such as node classification, link prediction, etc. Although several models were proposed recently, they either only aggregate information from the same type of neighbors, or just indiscriminately treat homogeneous and heterogeneous neighbors in the same way. Based on these observations, we propose a new heterogeneous graph neural network model named HMSG to comprehensively capture structural, semantic and attribute information from both homogeneous and heterogeneous neighbors. Specifically, we first decompose the heterogeneous graph into multiple metapath-based homogeneous and heterogeneous subgraphs, and each subgraph associates specific semantic and structural information. Then message aggregation methods are applied to each subgraph independently, so that information can be learned in a more targeted and efficient manner. Through a type-specific attribute transformation, node attributes can also be transferred among different types of nodes. Finally, we fuse information from subgraphs together to get the complete representation. Extensive experiments on several datasets for node classification, node clustering and link prediction tasks show that HMSG achieves the best performance in all evaluation metrics than state-of-the-art baselines.

【28】 Readying Medical Students for Medical AI: The Need to Embed AI Ethics Education 标题：医学生为医学人工智能做好准备：嵌入人工智能伦理教育的必要性 链接：https://arxiv.org/abs/2109.02866

作者：Thomas P Quinn,Simon Coghlan 机构：Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia, Centre for AI and Digital Ethics, School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia 摘要：医学生在职业生涯早期几乎不可避免地会遇到强大的医疗AI系统。然而，现代医学教育并没有充分地使学生具备安全有效地使用这些工具所需的医学人工智能的基本临床能力。教育改革迫在眉睫，但实施起来并不容易，这主要是因为已经塞满了医学课程。在本文中，我们提出了一个教育改革框架作为一个有效的解决方案，我们称之为嵌入式人工智能道德教育框架。与其他要求教育改革以适应范围更为激进的人工智能教学的呼吁不同，我们的框架是适度和渐进的。它利用现有的生物伦理学或医学伦理学课程来开发和提供与医疗AI相关的伦理问题的内容，特别是影响医疗保健核心风险效益分析的技术滥用、废弃和滥用的危害。通过这样做，该框架提供了一个简单的工具，可以超越医学AI道德教育的“什么？”和“为什么”，回答“如何”的问题，为大学、课程主管和/或教授提供一个广泛的路线图，让他们的学生掌握医学AI的必要临床技能。 摘要：Medical students will almost inevitably encounter powerful medical AI systems early in their careers. Yet, contemporary medical education does not adequately equip students with the basic clinical proficiency in medical AI needed to use these tools safely and effectively. Education reform is urgently needed, but not easily implemented, largely due to an already jam-packed medical curricula. In this article, we propose an education reform framework as an effective and efficient solution, which we call the Embedded AI Ethics Education Framework. Unlike other calls for education reform to accommodate AI teaching that are more radical in scope, our framework is modest and incremental. It leverages existing bioethics or medical ethics curricula to develop and deliver content on the ethical issues associated with medical AI, especially the harms of technology misuse, disuse, and abuse that affect the risk-benefit analyses at the heart of healthcare. In doing so, the framework provides a simple tool for going beyond the "What?" and the "Why?" of medical AI ethics education, to answer the "How?", giving universities, course directors, and/or professors a broad road-map for equipping their students with the necessary clinical proficiency in medical AI.

【29】 GCsT: Graph Convolutional Skeleton Transformer for Action Recognition 标题：GCsT：面向动作识别的图卷积骨架变换器 链接：https://arxiv.org/abs/2109.02860

作者：Ruwen Bai,Min Li,Bo Meng,Fengfa Li,Junxing Ren,Miao Jiang,Degang Sun 机构：Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China, School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China, Beijing Institute of Technology, Beijing, China 备注：8 pages, 5 figures 摘要：图卷积网络（GCN）在基于骨架的动作识别中具有良好的性能。然而，在大多数基于GCN的方法中，时空图卷积受到图拓扑的严格限制，而只捕获短期的时间上下文，因此缺乏特征提取的灵活性。在这项工作中，我们提出了一种新的架构，名为图卷积骨架变换器（GCsT），它通过引入变换器来解决GCNs中的限制。我们的GCsT利用了Transformer的所有优势（即动态注意和全局上下文），同时保留了GCNs的优势（即层次结构和局部拓扑结构）。在GCsT中，时空GCN强制捕获局部依赖，而Transformer动态提取全局时空关系。此外，建议的GCsT通过添加骨架序列中的附加信息显示出更强的表达能力。合并Transformer允许几乎毫不费力地将信息引入模型中。我们通过大量实验验证了所提出的GCsT，在NTU RGB+D、NTU RGB+D 120和西北加州大学洛杉矶分校数据集上实现了最先进的性能。 摘要：Graph convolutional networks (GCNs) achieve promising performance for skeleton-based action recognition. However, in most GCN-based methods, the spatial-temporal graph convolution is strictly restricted by the graph topology while only captures the short-term temporal context, thus lacking the flexibility of feature extraction. In this work, we present a novel architecture, named Graph Convolutional skeleton Transformer (GCsT), which addresses limitations in GCNs by introducing Transformer. Our GCsT employs all the benefits of Transformer (i.e. dynamical attention and global context) while keeps the advantages of GCNs (i.e. hierarchy and local topology structure). In GCsT, the spatial-temporal GCN forces the capture of local dependencies while Transformer dynamically extracts global spatial-temporal relationships. Furthermore, the proposed GCsT shows stronger expressive capability by adding additional information present in skeleton sequences. Incorporating the Transformer allows that information to be introduced into the model almost effortlessly. We validate the proposed GCsT by conducting extensive experiments, which achieves the state-of-the-art performance on NTU RGB+D, NTU RGB+D 120 and Northwestern-UCLA datasets.

【30】 A new neighborhood structure for job shop scheduling problems 标题：求解作业车间调度问题的一种新邻域结构 链接：https://arxiv.org/abs/2109.02843

作者：Jin Xie,Xinyu Li,Liang Gao,Lin Gui 机构：State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan , China 摘要：作业车间调度问题是一个广泛研究的NP完全组合优化问题。邻域结构在解决JSP问题中起着关键作用。目前，有三种最先进的邻里结构，即N5、N6和N7。改善一些著名基准的上界与这些邻域结构的作用密不可分。然而，这些现有的邻域结构只考虑关键块内的关键操作的移动。根据我们的实验，通过将关键操作移到其关键块之外，也可以提高调度方案的最大完工时间。根据上述发现，本文提出了一种新的N8邻域结构，考虑了关键操作在关键块内的移动和关键操作在关键块外的移动。此外，设计了邻域裁剪方法，避免了无效运动，减少了计算时间。禁忌搜索（TS）是一种结合邻域结构的常用算法框架。本文使用该框架在四个著名的基准上比较了N8邻域结构与N5、N6和N7邻域结构。实验结果表明，N8邻域结构比其他最先进的邻域结构更能有效地解决JSP问题。 摘要：Job shop scheduling problem (JSP) is a widely studied NP-complete combinatorial optimization problem. Neighborhood structures play a critical role in solving JSP. At present, there are three state-of-the-art neighborhood structures, i.e., N5, N6, and N7. Improving the upper bounds of some famous benchmarks is inseparable from the role of these neighborhood structures. However, these existing neighborhood structures only consider the movement of critical operations within a critical block. According to our experiments, it is also possible to improve the makespan of a scheduling scheme by moving a critical operation outside its critical block. According to the above finding, this paper proposes a new N8 neighborhood structure considering the movement of critical operations within a critical block and the movement of critical operations outside the critical block. Besides, a neighborhood clipping method is designed to avoid invalid movement, reducing the computational time. Tabu search (TS) is a commonly used algorithm framework combined with neighborhood structures. This paper uses this framework to compare the N8 neighborhood structure with N5, N6, and N7 neighborhood structures on four famous benchmarks. The experimental results verify that the N8 neighborhood structure is more effective and efficient in solving JSP than the other state-of-the-art neighborhood structures.

【31】 Robot Sound Interpretation: Learning Visual-Audio Representations for Voice-Controlled Robots 标题：机器人声音解释：声控机器人的视听表示学习 链接：https://arxiv.org/abs/2109.02823

作者：Peixin Chang,Shuijing Liu,Katherine Driggs-Campbell 机构： Driggs-Campbell are with the Department ofElectrical and Computer Engineering at the University of Illinois at Urbana-Champaign 摘要：受感觉运动理论的启发，我们提出了一种新的语音控制机器人管道。以前的工作依赖于声音和图像的明确标签以及外在的奖励功能。这样的方法不仅与人类感觉运动发育几乎没有相似之处，而且还需要手调节奖励和大量的人力劳动。为了解决这些问题，我们学习了一种表示法，它将图像和声音命令与最少的监督相关联。利用这种表示，我们生成了一个内在的奖励函数，用强化学习来学习机器人任务。我们在三个机器人平台上演示了我们的方法，一个TurtleBot3、一个Kuka IIWA手臂和一个Kinova Gen3机器人，它们可以听到命令词，识别相关目标物体，并执行精确控制以接近目标。我们表明，我们的方法在各种声音类型和机器人任务中的经验表现优于以前的工作。我们成功地将在模拟器中学习到的策略部署到真实世界的Kinova Gen3。 摘要：Inspired by sensorimotor theory, we propose a novel pipeline for voice-controlled robots. Previous work relies on explicit labels of sounds and images as well as extrinsic reward functions. Not only do such approaches have little resemblance to human sensorimotor development, but also require hand-tuning rewards and extensive human labor. To address these problems, we learn a representation that associates images and sound commands with minimal supervision. Using this representation, we generate an intrinsic reward function to learn robotic tasks with reinforcement learning. We demonstrate our approach on three robot platforms, a TurtleBot3, a Kuka-IIWA arm, and a Kinova Gen3 robot, which hear a command word, identify the associated target object, and perform precise control to approach the target. We show that our method outperforms previous work across various sound types and robotic tasks empirically. We successfully deploy the policy learned in simulator to a real-world Kinova Gen3.

【32】 Few-shot Learning via Dependency Maximization and Instance Discriminant Analysis 标题：基于依赖最大化和实例判别分析的少概率学习 链接：https://arxiv.org/abs/2109.02820

作者：Zejiang Hou,Sun-Yuan Kung 机构：Princeton University 摘要：我们研究了少数镜头学习（FSL）问题，模型学习识别新对象，每个类别的标记训练数据非常少。以往的FSL方法大多采用元学习范式，通过学习大量训练任务来积累归纳偏差，从而解决一个新的看不见的少数镜头任务。相比之下，我们提出了一种简单的方法来利用伴随Few-Shot任务的未标记数据来提高Few-Shot性能。首先，我们提出了一种基于互协方差算子Hilbert-Schmidt范数的相关性最大化方法，该方法最大化了未标记数据的嵌入特征与其标签预测之间的统计相关性，以及支持集上的监督损失。然后，我们使用得到的模型来推断那些未标记数据的伪标签。此外，我们还提出了一种立场判别分析来评估每个伪标记示例的可信度，并将最忠实的示例选择到一个扩展支持集中，以便像第一步一样重新训练模型。我们迭代上述过程，直到未标记数据的伪标签变得稳定。根据标准的转换和半监督FSL设置，我们的实验表明，所提出的方法在四个广泛使用的基准上，包括mini ImageNet、tiered ImageNet、CUB和CIFARF，优于以前的最先进方法。 摘要：We study the few-shot learning (FSL) problem, where a model learns to recognize new objects with extremely few labeled training data per category. Most of previous FSL approaches resort to the meta-learning paradigm, where the model accumulates inductive bias through learning many training tasks so as to solve a new unseen few-shot task. In contrast, we propose a simple approach to exploit unlabeled data accompanying the few-shot task for improving few-shot performance. Firstly, we propose a Dependency Maximization method based on the Hilbert-Schmidt norm of the cross-covariance operator, which maximizes the statistical dependency between the embedded feature of those unlabeled data and their label predictions, together with the supervised loss over the support set. We then use the obtained model to infer the pseudo-labels for those unlabeled data. Furthermore, we propose anInstance Discriminant Analysis to evaluate the credibility of each pseudo-labeled example and select the most faithful ones into an augmented support set to retrain the model as in the first step. We iterate the above process until the pseudo-labels for the unlabeled data becomes stable. Following the standard transductive and semi-supervised FSL setting, our experiments show that the proposed method out-performs previous state-of-the-art methods on four widely used benchmarks, including mini-ImageNet, tiered-ImageNet, CUB, and CIFARFS.

【33】 A Scalable AI Approach for Clinical Trial Cohort Optimization 标题：一种可扩展的人工智能临床试验队列优化方法 链接：https://arxiv.org/abs/2109.02808

作者：Xiong Liu,Cheng Shi,Uday Deore,Yingbo Wang,Myah Tran,Iya Khalil,Murthy Devarakonda 机构： AI Innovation Center, Novartis, Cambridge, MA, USA, RWE Data Science, Novartis Pharma, East Hanover, NJ, USA, Global Drug Development, Novartis, East Hanover, NJ, USA, Global Drug Development, Novartis, Basel, Switzerland 备注：PharML 2021 (Machine Learning for Pharma and Healthcare Applications) at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021) 摘要：FDA一直在推广可通过扩大资格标准提高临床试验人群多样性的登记做法。然而，如何扩大资格仍然是一项重大挑战。我们提出了一种队列优化（AICO）的人工智能方法，通过基于转换器的自然语言处理合格标准，并使用真实数据评估标准。该方法可以从大量相关试验中提取通用资格标准变量，并测量试验设计对真实患者的普遍性。它克服了现有手动方法的可扩展性限制，并能够快速模拟感兴趣疾病的合格标准设计。乳腺癌试验设计的案例研究证明了该方法在提高试验可推广性方面的实用性。 摘要：FDA has been promoting enrollment practices that could enhance the diversity of clinical trial populations, through broadening eligibility criteria. However, how to broaden eligibility remains a significant challenge. We propose an AI approach to Cohort Optimization (AICO) through transformer-based natural language processing of the eligibility criteria and evaluation of the criteria using real-world data. The method can extract common eligibility criteria variables from a large set of relevant trials and measure the generalizability of trial designs to real-world patients. It overcomes the scalability limits of existing manual methods and enables rapid simulation of eligibility criteria design for a disease of interest. A case study on breast cancer trial design demonstrates the utility of the method in improving trial generalizability.

【34】 Symbolic Computation in Software Science: My Personal View 标题：软件科学中的符号计算：我个人的看法 链接：https://arxiv.org/abs/2109.02806

作者：Bruno Buchberger 机构：Research Institute for Symbolic Computation (RISC), Johannes Kepler University, Linz Schloss Hagenberg, Austria 备注：None 摘要：在本文中，我对软件科学中符号计算的范围和相关性提出了自己的观点。为此，我将讨论符号计算、软件科学、自动编程、数学知识管理、人工智能、算法智能、数值计算和机器学习之间的相互作用和区别。在讨论这些概念的过程中，我允许自己也参考我的论文（1982、1985、2001、2003、2013），其中我在一些领域的早期阶段表达了我对这些领域的观点。 摘要：In this note, I develop my personal view on the scope and relevance of symbolic computation in software science. For this, I discuss the interaction and differences between symbolic computation, software science, automatic programming, mathematical knowledge management, artificial intelligence, algorithmic intelligence, numerical computation, and machine learning. In the discussion of these notions, I allow myself to refer also to papers (1982, 1985, 2001, 2003, 2013) of mine in which I expressed my views on these areas at early stages of some of these fields.

【35】 Puzzle Solving without Search or Human Knowledge: An Unnatural Language Approach 标题：在没有搜索或人类知识的情况下解谜：一种非自然语言的方法 链接：https://arxiv.org/abs/2109.02797

作者：David Noever,Ryerson Burdick 机构：PeopleTec, Inc., University of Maryland, College Park, Huntsville, AL, College Park, MD 摘要：生成式预训练转换器（GPT-2）在学习文本存档游戏符号方面的应用为探索稀疏奖励游戏提供了一个模型环境。事实证明，transformer架构适合于对描述迷宫、魔方和数独解算器的已解决文本档案进行训练。该方法得益于微调transformer架构，以可视化从人类启发式或领域专业知识的任何指导之外衍生的合理策略。游戏的大搜索空间（$>10^{19}$）提供了一个益智环境，在这个环境中，解决方案几乎没有中间奖励，只有解决挑战的最后一步。 摘要：The application of Generative Pre-trained Transformer (GPT-2) to learn text-archived game notation provides a model environment for exploring sparse reward gameplay. The transformer architecture proves amenable to training on solved text archives describing mazes, Rubik's Cube, and Sudoku solvers. The method benefits from fine-tuning the transformer architecture to visualize plausible strategies derived outside any guidance from human heuristics or domain expertise. The large search space ($>10^{19}$) for the games provides a puzzle environment in which the solution has few intermediate rewards and a final move that solves the challenge.

【36】 An Empirical Study on Few-shot Knowledge Probing for Pretrained Language Models 标题：基于预训练语言模型的少发知识挖掘的实证研究 链接：https://arxiv.org/abs/2109.02772

作者：Tianxing He,Kyunghyun Cho,James Glass 机构：MIT, New York University 摘要：基于提示的1-hop关系知识探测已被用于测量预训练语言模型中存储了多少世界知识。现有工作使用大量数据来调整提示以获得更好的性能。在这项工作中，我们比较了在少数镜头知识探测设置下的各种方法，其中只有少量（例如，10或20）示例三元组可用。此外，我们还创建了一个名为TREx-2p的新数据集，其中包含两跳关系。我们报告说，很少有镜头示例可以强烈提高1跳和2跳关系的探测性能。特别是，我们发现一种简单而有效的微调模型偏差向量的方法优于现有的快速工程方法。我们的数据集和代码位于\url{https://github.com/cloudygoose/fewshot_lama}. 摘要：Prompt-based knowledge probing for 1-hop relations has been used to measure how much world knowledge is stored in pretrained language models. Existing work uses considerable amounts of data to tune the prompts for better performance. In this work, we compare a variety of approaches under a few-shot knowledge probing setting, where only a small number (e.g., 10 or 20) of example triples are available. In addition, we create a new dataset named TREx-2p, which contains 2-hop relations. We report that few-shot examples can strongly boost the probing performance for both 1-hop and 2-hop relations. In particular, we find that a simple-yet-effective approach of finetuning the bias vectors in the model outperforms existing prompt-engineering methods. Our dataset and code are available at \url{https://github.com/cloudygoose/fewshot_lama}.

【37】 Graph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms 标题：基于图注意力层进化语义分割的道路坑洞检测基准与算法 链接：https://arxiv.org/abs/2109.02711

作者：Rui Fan,Hengli Wang,Yuan Wang,Ming Liu,Ioannis Pitas 机构： the Hong Kong University of Science and Technology 备注：accepted as a regular paper to IEEE Transactions on Image Processing 摘要：现有的道路坑洼检测方法可分为基于计算机视觉的方法和基于机器学习的方法。前一种方法通常采用二维图像分析/理解或三维点云建模和分割算法从视觉传感器数据中检测道路凹坑。后一种方法通常以端到端的方式使用卷积神经网络（CNN）处理道路坑洞检测。然而，道路坑洼不一定无处不在，为CNN训练准备一个大型注释良好的数据集是一项挑战。在这方面，基于计算机视觉的方法是过去十年的主流研究趋势，而基于机器学习的方法只是讨论而已。最近，我们发布了第一个基于立体视觉的道路凹坑检测数据集和一种新的视差变换算法，从而可以高度区分受损和未受损的道路区域。然而，目前还没有使用视差图像或变换视差图像训练的最先进（SoTA）CNN的基准。因此，在本文中，我们首先讨论了用于语义分割的SoTA CNN，并通过大量实验评估了它们在道路坑洞检测中的性能。此外，受图神经网络（GNN）的启发，我们提出了一种新的CNN层，称为图注意层（GAL），它可以很容易地部署在任何现有的CNN中，以优化用于语义分割的图像特征表示。我们的实验将性能最好的实现GAL-DeepLabv3+与九个SoTA CNN在三种模式的训练数据上进行了比较：RGB图像、视差图像和变换的视差图像。实验结果表明，我们提出的GAL-DeepLabv3+在所有训练数据模式下都达到了最佳的整体坑洞检测精度。 摘要：Existing road pothole detection approaches can be classified as computer vision-based or machine learning-based. The former approaches typically employ 2-D image analysis/understanding or 3-D point cloud modeling and segmentation algorithms to detect road potholes from vision sensor data. The latter approaches generally address road pothole detection using convolutional neural networks (CNNs) in an end-to-end manner. However, road potholes are not necessarily ubiquitous and it is challenging to prepare a large well-annotated dataset for CNN training. In this regard, while computer vision-based methods were the mainstream research trend in the past decade, machine learning-based methods were merely discussed. Recently, we published the first stereo vision-based road pothole detection dataset and a novel disparity transformation algorithm, whereby the damaged and undamaged road areas can be highly distinguished. However, there are no benchmarks currently available for state-of-the-art (SoTA) CNNs trained using either disparity images or transformed disparity images. Therefore, in this paper, we first discuss the SoTA CNNs designed for semantic segmentation and evaluate their performance for road pothole detection with extensive experiments. Additionally, inspired by graph neural network (GNN), we propose a novel CNN layer, referred to as graph attention layer (GAL), which can be easily deployed in any existing CNN to optimize image feature representations for semantic segmentation. Our experiments compare GAL-DeepLabv3+, our best-performing implementation, with nine SoTA CNNs on three modalities of training data: RGB images, disparity images, and transformed disparity images. The experimental results suggest that our proposed GAL-DeepLabv3+ achieves the best overall pothole detection accuracy on all training data modalities.

【38】 Backpropagation and fuzzy algorithm Modelling to Resolve Blood Supply Chain Issues in the Covid-19 Pandemic 标题：反向传播和模糊算法建模解决冠状病毒大流行中的血液供应链问题 链接：https://arxiv.org/abs/2109.02645

作者：Aan Erlansari,Rusdi Effendi,Funny Farady C,Andang Wijanarko,Boko Susilo,Reza Hardiansyah 机构：University of Bengkulu 摘要：血液短缺及其不确定的需求已成为全世界所有国家的一个重大问题。因此，本研究旨在为印度尼西亚本古鲁新冠病毒-19大流行期间的血液分布问题提供解决方案。反向传播算法用于提高发现可用和潜在供体的可能性。此外，还测量了献血的距离、年龄和长度，以便在需要时找到合适的献血者。反向传播使用三个输入层对符合条件的供体进行分类，即年龄、体重和偏差。此外，系统通过其查询功能，通过Fuzzy Tahani自动统计变量，同时访问庞大的数据库。 摘要：Bloodstock shortages and its uncertain demand has become a major problem for all countries worldwide. Therefore, this study aims to provide solution to the issues of blood distribution during the Covid-19 Pandemic at Bengkulu, Indonesia. The Backpropagation algorithm was used to improve the possibility of discovering available and potential donors. Furthermore, the distances, age, and length of donation were measured to obtain the right person to donate blood when it needed. The Backpropagation uses three input layers to classify eligible donors, namely age, body, weight, and bias. In addition, the system through its query automatically counts the variables via the Fuzzy Tahani and simultaneously access the vast database.

【39】 IEEE BigData 2021 Cup: Soft Sensing at Scale 标题：IEEE BigData 2021Cup：尺度软测量 链接：https://arxiv.org/abs/2109.03181

作者：Sergei Petrov,Chao Zhang,Jaswanth Yella,Yu Huang,Xiaoye Qian,Sthitie Bom 机构：Seagate Technology, MN, US, Stanford University, CA, US, University of Chicago, IL, US, University of Cincinnati, OH, US, Florida Atlantic University, FL, US, Case Western Reserve University, OH, US 备注：4 pages, 4 figures, for IEEE Big Data Cup challenge 2021 摘要：IEEE BigData 2021 Cup:Soft Sensing at Scale是由Seagate Technology与IEEE BigData 2021大会联合举办的数据挖掘竞赛。这一挑战的范围是解决使用机器学习技术对软测量数据进行分类的任务。在本文中，我们将详细介绍挑战，并描述提供给参与者的数据集。我们定义了感兴趣的指标、基线模型，并描述了我们发现有意义的方法，这可能是进一步分析的良好起点。我们讨论通过我们的方法获得的结果，并对参与者可能遇到的潜在挑战提出见解。欢迎学生、研究人员和任何对解决重大工业问题感兴趣的人参加挑战！ 摘要：IEEE BigData 2021 Cup: Soft Sensing at Scale is a data mining competition organized by Seagate Technology, in association with the IEEE BigData 2021 conference. The scope of this challenge is to tackle the task of classifying soft sensing data with machine learning techniques. In this paper we go into the details of the challenge and describe the data set provided to participants. We define the metrics of interest, baseline models, and describe approaches we found meaningful which may be a good starting point for further analysis. We discuss the results obtained with our approaches and give insights on what potential challenges participants may run into. Students, researchers, and anyone interested in working on a major industrial problem are welcome to participate in the challenge!

机器翻译，仅供参考