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cs.AI人工智能，共计27篇
【1】 Impossibility Results in AI: A Survey 标题：人工智能中的不可能结果：综述 链接：https://arxiv.org/abs/2109.00484
作者：Mario Brcic,Roman V. Yampolskiy 机构：University of Louisville 备注：19 pages, 2 figures, 93 references 摘要：一个不可能性定理证明了一个特定的问题或一组问题不能像权利要求中描述的那样解决。这些定理限制了人工智能，特别是超智能智能智能的可能性。因此，这些结果可作为AI安全、AI政策和治理研究人员的指南、提醒和警告。这些方法可以在约束满足的框架下，以形式化理论的形式解决一些长期存在的问题，而无需承诺一个选项。在本文中，我们将适用于人工智能领域的不可能性定理分为五类：演绎性、不可区分性、归纳性、权衡性和难处理性。我们发现某些定理过于具体，或者有限制应用的隐含假设。此外，我们还增加了一个关于可解释性不公平的新结果（定理），这是归纳范畴中第一个与可解释性相关的结果。我们得出结论，演绎不可能性否定了100%的安全保证。最后，我们给出了一些在解释性、可控性、价值取向、伦理和群体决策方面具有潜力的观点。它们可以通过进一步的调查来深化。 摘要：An impossibility theorem demonstrates that a particular problem or set of problems cannot be solved as described in the claim. Such theorems put limits on what is possible to do concerning artificial intelligence, especially the super-intelligent one. As such, these results serve as guidelines, reminders, and warnings to AI safety, AI policy, and governance researchers. These might enable solutions to some long-standing questions in the form of formalizing theories in the framework of constraint satisfaction without committing to one option. In this paper, we have categorized impossibility theorems applicable to the domain of AI into five categories: deduction, indistinguishability, induction, tradeoffs, and intractability. We found that certain theorems are too specific or have implicit assumptions that limit application. Also, we added a new result (theorem) about the unfairness of explainability, the first explainability-related result in the induction category. We concluded that deductive impossibilities deny 100%-guarantees for security. In the end, we give some ideas that hold potential in explainability, controllability, value alignment, ethics, and group decision-making. They can be deepened by further investigation.
【2】 From Movement Kinematics to Object Properties: Online Recognition of Human Carefulness 标题：从运动运动学到物体属性：人类细心的在线识别 链接：https://arxiv.org/abs/2109.00460
作者：Linda Lastrico,Alessandro Carfì,Francesco Rea,Alessandra Sciutti,Fulvio Mastrogiovanni 机构：Robotics, Brain and Cognitive Science Department (RBCS), Italian Institute of Technology, Genoa, Italy, Cognitive Architecture for Collaborative Technologies Unit (CONTACT), Department of Informatics, Bioengineering, Robotics, and Systems Engineering 备注：Accepted for full paper publication in the Proceedings of the Thirteenth International Conference on Social Robotics (ICSR2021) 10 pages, 7 figures 摘要：当操纵物体时，人类会根据他们所处理的物体的特征来调整他们的运动。因此，细心的观察者可以预见被操纵对象的隐藏特性，例如其重量、温度，甚至操纵时是否需要特别小心。这项研究是赋予仿人机器人这最后一项能力的一步。具体来说，我们研究机器人如何仅从视觉在线推断人类伙伴在移动物体时是否小心。我们证明，即使使用低分辨率的摄像机，仿人机器人也能以高精度（高达81.3%）执行此推断。只有在没有障碍物的短动作中，谨慎性识别是不够的。通过观察同伴的动作，迅速识别出动作的谨慎性，这将允许机器人调整其在物体上的动作，以表现出与人类同伴相同的谨慎程度。 摘要：When manipulating objects, humans finely adapt their motions to the characteristics of what they are handling. Thus, an attentive observer can foresee hidden properties of the manipulated object, such as its weight, temperature, and even whether it requires special care in manipulation. This study is a step towards endowing a humanoid robot with this last capability. Specifically, we study how a robot can infer online, from vision alone, whether or not the human partner is careful when moving an object. We demonstrated that a humanoid robot could perform this inference with high accuracy (up to 81.3%) even with a low-resolution camera. Only for short movements without obstacles, carefulness recognition was insufficient. The prompt recognition of movement carefulness from observing the partner's action will allow robots to adapt their actions on the object to show the same degree of care as their human partners.
【3】 Planning from video game descriptions 标题：根据视频游戏描述进行规划 链接：https://arxiv.org/abs/2109.00449
作者：Ignacio Vellido,Carlos Núñez-Molina,Vladislav Nikolov,Juan Fdez-Olivares 机构：Dpto. Ciencias de la Computacin e I.A, University of Granada, Spain 备注：To be submitted to the Knowledge Engineering Review (KER) journal 摘要：该项目提出了一种从视频游戏动力学描述中自动生成动作模型的方法，并将其与计划代理集成，以执行和监控计划。规划者使用这些行为模型来获得许多不同视频游戏中代理的审慎行为，并结合反应性模块，解决确定性和非确定性水平。实验结果验证了该方法的有效性，并证明了知识工程师在定义此类复杂领域时所付出的努力可以大大减少。此外，还制定了国际规划界感兴趣的领域基准，以评估国际规划竞赛中的规划者。 摘要：This project proposes a methodology for the automatic generation of action models from video game dynamics descriptions, as well as its integration with a planning agent for the execution and monitoring of the plans. Planners use these action models to get the deliberative behaviour for an agent in many different video games and, combined with a reactive module, solve deterministic and no-deterministic levels. Experimental results validate the methodology and prove that the effort put by a knowledge engineer can be greatly reduced in the definition of such complex domains. Furthermore, benchmarks of the domains has been produced that can be of interest to the international planning community to evaluate planners in international planning competitions.
【4】 Proceedings of KDD 2020 Workshop on Data-driven Humanitarian Mapping: Harnessing Human-Machine Intelligence for High-Stake Public Policy and Resilience Planning 标题：KDD 2020数据驱动人道主义绘图研讨会论文集：利用人机智能制定高风险的公共政策和复原力规划 链接：https://arxiv.org/abs/2109.00435
作者：Snehalkumar,S. Gaikwad,Shankar Iyer,Dalton Lunga,Yu-Ru Lin 备注：The proceedings of the 1st Data-driven Humanitarian Mapping workshop at the 26th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. August 24th, 2020 摘要：人道主义挑战，2019冠状病毒疾病、食物不安全、气候变化、种族和性别暴力、环境危机、COVID-19冠状病毒大流行、人权侵犯和被迫转移，不成比例地影响了世界范围内的弱势群体。根据联合国人道协调厅的数据，20211年将有2.35亿人需要人道主义援助。尽管这些危险越来越大，但数据科学研究仍显不足，无法科学地为改善高危人群生计的公平公共政策决策提供信息。为了应对这些挑战，存在着分散的数据科学工作，但它们仍然与实践隔离，并且容易受到算法方面的损害，涉及缺乏隐私、公平性、可解释性、问责制、透明度和道德。数据驱动方法中的偏见有可能放大影响数百万人生计的高风险决策中的不平等。因此，作为人道主义行动和全球发展核心的决策者、实践者和边缘化社区仍然无法获得数据驱动创新的公开好处。为了填补这一空白，我们提出了数据驱动的人道主义测绘研究计划，该计划的重点是开发新的数据科学方法，利用人机智能制定高风险的公共政策和恢复力规划。 摘要：Humanitarian challenges, including natural disasters, food insecurity, climate change, racial and gender violence, environmental crises, the COVID-19 coronavirus pandemic, human rights violations, and forced displacements, disproportionately impact vulnerable communities worldwide. According to UN OCHA, 235 million people will require humanitarian assistance in 20211 . Despite these growing perils, there remains a notable paucity of data science research to scientifically inform equitable public policy decisions for improving the livelihood of at-risk populations. Scattered data science efforts exist to address these challenges, but they remain isolated from practice and prone to algorithmic harms concerning lack of privacy, fairness, interpretability, accountability, transparency, and ethics. Biases in data-driven methods carry the risk of amplifying inequalities in high-stakes policy decisions that impact the livelihood of millions of people. Consequently, proclaimed benefits of data-driven innovations remain inaccessible to policymakers, practitioners, and marginalized communities at the core of humanitarian actions and global development. To help fill this gap, we propose the Data-driven Humanitarian Mapping Research Program, which focuses on developing novel data science methodologies that harness human-machine intelligence for high-stakes public policy and resilience planning.
【5】 M^2-MedDialog: A Dataset and Benchmarks for Multi-domain Multi-service Medical Dialogues 标题：M^2-MedDialog：多领域多服务医疗对话的数据集和基准 链接：https://arxiv.org/abs/2109.00430
作者：Guojun Yan,Jiahuan Pei,Pengjie Ren,Zhumin Chen,Zhaochun Ren,Huasheng Liang 机构：Shandong University, University of Amsterdam, WeChat, Tencent 摘要：医疗对话系统（MDS）旨在为医生和患者提供一系列专业医疗服务，即诊断、咨询和治疗。然而，一站式MDS仍然没有被开发，因为：（1）没有数据集有如此大规模的对话同时包含多个医疗服务和细粒度医疗标签（即意图、时隙、值）(2） 没有一个模型能够在统一的框架中解决基于多个服务对话的MDS。在这项工作中，我们首先构建了一个多域多服务医疗对话（M^2-MEDIALOG）数据集，其中包含1557个医生和患者之间的对话，涵盖276种疾病、2468个医疗实体和3个医疗服务专业。据我们所知，它是唯一一个包含多种医疗服务和细粒度医疗标签的医疗对话数据集。然后，我们将一站式MDS描述为序列到序列的生成问题。我们将MDS分别与因果语言建模和条件因果语言建模相结合。具体而言，我们采用了几种预训练模型（即BERT-WWM、BERT-MED、GPT2和MT5）及其变体，以在M^2-MEDIALOG数据集上获得基准。我们还提出了伪标记和自然扰动方法来扩展数据集并增强最先进的预训练模型。我们通过在M2 MedDialog上的大量实验，展示了迄今为止基准测试所取得的结果。我们发布了数据集、代码以及评估脚本，以促进这一重要研究方向的未来研究。 摘要：Medical dialogue systems (MDSs) aim to assist doctors and patients with a range of professional medical services, i.e., diagnosis, consultation, and treatment. However, one-stop MDS is still unexplored because: (1) no dataset has so large-scale dialogues contains both multiple medical services and fine-grained medical labels (i.e., intents, slots, values); (2) no model has addressed a MDS based on multiple-service conversations in a unified framework. In this work, we first build a Multiple-domain Multiple-service medical dialogue (M^2-MedDialog)dataset, which contains 1,557 conversations between doctors and patients, covering 276 types of diseases, 2,468 medical entities, and 3 specialties of medical services. To the best of our knowledge, it is the only medical dialogue dataset that includes both multiple medical services and fine-grained medical labels. Then, we formulate a one-stop MDS as a sequence-to-sequence generation problem. We unify a MDS with causal language modeling and conditional causal language modeling, respectively. Specifically, we employ several pretrained models (i.e., BERT-WWM, BERT-MED, GPT2, and MT5) and their variants to get benchmarks on M^2-MedDialog dataset. We also propose pseudo labeling and natural perturbation methods to expand M2-MedDialog dataset and enhance the state-of-the-art pretrained models. We demonstrate the results achieved by the benchmarks so far through extensive experiments on M2-MedDialog. We release the dataset, the code, as well as the evaluation scripts to facilitate future research in this important research direction.
【6】 Masked Adversarial Generation for Neural Machine Translation 标题：神经机器翻译中的掩蔽敌意生成 链接：https://arxiv.org/abs/2109.00417
作者：Badr Youbi Idrissi,Stéphane Clinchant 机构：Centrale Supelec, Naver Labs Europe 备注：5 pages 摘要：攻击神经机器翻译模型是一个固有的组合任务在离散序列，解决近似启发式。大多数方法使用梯度对每个样本单独攻击模型。我们是否可以学习产生有意义的对抗性攻击，而不是机械地应用梯度？与现有方法相比，我们通过训练基于语言模型的对抗生成器来学习攻击模型。我们提出了蒙面对抗生成（MAG）模型，该模型学习在整个训练过程中扰动翻译模型。实验表明，该方法提高了机器翻译模型的鲁棒性，同时比其他同类方法更快。 摘要：Attacking Neural Machine Translation models is an inherently combinatorial task on discrete sequences, solved with approximate heuristics. Most methods use the gradient to attack the model on each sample independently. Instead of mechanically applying the gradient, could we learn to produce meaningful adversarial attacks ? In contrast to existing approaches, we learn to attack a model by training an adversarial generator based on a language model. We propose the Masked Adversarial Generation (MAG) model, that learns to perturb the translation model throughout the training process. The experiments show that it improves the robustness of machine translation models, while being faster than competing methods.
【7】 Balancing Performance and Human Autonomy with Implicit Guidance Agent 标题：用隐式引导智能体平衡性能和人的自主性 链接：https://arxiv.org/abs/2109.00414
作者：Ryo Nakahashi,Seiji Yamada 机构：Department of Informatics, School of Multidisciplinary Sciences, The Graduate University for Advanced Studies(SOKENDAI), Chiyoda, Tokyo, Japan, Digital Contentand MediaSciences Research Division, National Institute of Informatics, Chiyoda, Tokyo,Japan 摘要：人工智能团队是人工智能协作中的一个典型问题，在这个问题中，人类和自治代理协作完成一项任务。为了有效的合作，人们希望有一个有效的计划，但在现实情况下，由于认知限制，他们可能难以计算出最佳计划。在这种情况下，来自具有许多计算资源的代理的指导可能是有用的。然而，如果一个代理明确地指导人类的行为，那么人类可能会觉得他们已经失去了自主性，并被代理控制。因此，我们研究了通过代理人行为提供的隐性指导。在这种类型的指导下，代理的行为方式使人能够轻松地为协作任务找到有效的计划，然后人可以改进计划。因为人类自愿改进他们的计划，所以他或她保持自主。通过将贝叶斯心理理论与现有的协作计划算法相结合，我们建立了一个具有内隐指导的协作agent模型，并通过行为实验证明，内隐指导能够有效地使人类在改进计划和保持自主性之间保持平衡。 摘要：The human-agent team, which is a problem in which humans and autonomous agents collaborate to achieve one task, is typical in human-AI collaboration. For effective collaboration, humans want to have an effective plan, but in realistic situations, they might have difficulty calculating the best plan due to cognitive limitations. In this case, guidance from an agent that has many computational resources may be useful. However, if an agent guides the human behavior explicitly, the human may feel that they have lost autonomy and are being controlled by the agent. We therefore investigated implicit guidance offered by means of an agent's behavior. With this type of guidance, the agent acts in a way that makes it easy for the human to find an effective plan for a collaborative task, and the human can then improve the plan. Since the human improves their plan voluntarily, he or she maintains autonomy. We modeled a collaborative agent with implicit guidance by integrating the Bayesian Theory of Mind into existing collaborative-planning algorithms and demonstrated through a behavioral experiment that implicit guidance is effective for enabling humans to maintain a balance between improving their plans and retaining autonomy.
【8】 Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis 标题：多模态情感分析的分层互信息最大化改进多模态融合 链接：https://arxiv.org/abs/2109.00412
作者：Wei Han,Hui Chen,Soujanya Poria 机构：† Singapore University of Technology and Design, Singapore 备注：Accepted as a long paper at EMNLP 2021 摘要：在多模态情感分析（MSA）中，模型的性能在很大程度上取决于合成嵌入的质量。这些嵌入是从称为多模态融合的上游过程中生成的，该过程旨在提取和组合输入的单峰原始数据，以产生更丰富的多模态表示。以前的工作要么反向传播任务损失，要么操纵特征空间的几何特性以产生有利的融合结果，这忽略了从输入到融合结果的关键任务相关信息的保留。在这项工作中，我们提出了一个称为多模态InfoMax（MMIM）的框架，该框架分层最大化了单峰输入对中的互信息（MI）以及多模态融合结果和单峰输入之间的互信息（MI），以便通过多模态融合来维护任务相关信息。该框架与主任务（MSA）联合训练，以提高下游MSA任务的性能。为了解决MI边界的棘手问题，我们进一步制定了一套计算简单的参数和非参数方法来近似其真值。在两个广泛使用的数据集上的实验结果证明了我们方法的有效性。这项工作的实施可在https://github.com/declare-lab/Multimodal-Infomax. 摘要：In multimodal sentiment analysis (MSA), the performance of a model highly depends on the quality of synthesized embeddings. These embeddings are generated from the upstream process called multimodal fusion, which aims to extract and combine the input unimodal raw data to produce a richer multimodal representation. Previous work either back-propagates the task loss or manipulates the geometric property of feature spaces to produce favorable fusion results, which neglects the preservation of critical task-related information that flows from input to the fusion results. In this work, we propose a framework named MultiModal InfoMax (MMIM), which hierarchically maximizes the Mutual Information (MI) in unimodal input pairs (inter-modality) and between multimodal fusion result and unimodal input in order to maintain task-related information through multimodal fusion. The framework is jointly trained with the main task (MSA) to improve the performance of the downstream MSA task. To address the intractable issue of MI bounds, we further formulate a set of computationally simple parametric and non-parametric methods to approximate their truth value. Experimental results on the two widely used datasets demonstrate the efficacy of our approach. The implementation of this work is publicly available at https://github.com/declare-lab/Multimodal-Infomax.
【9】 Boolean proportions 标题：布尔比例 链接：https://arxiv.org/abs/2109.00388
作者：Christian Antić 机构：comInstitute of Discrete Mathematics and GeometryVienna University of TechnologyWiedner Hauptstraße 8- 10 摘要：类比推理是人类智力和创造力的核心，可应用于常识推理、学习、语言习得和讲故事等多种任务。本文研究了形式为“$a$is to$b$which$c$is to$d$”的布尔数之间的类比比例，称之为布尔比例。从技术上讲，我们在由真值true和false以及布尔函数组成的布尔域中实例化了一个类似比例的抽象代数框架——作者最近介绍了这个框架。事实证明，我们的布尔比例概念具有吸引人的数学特性，并且它与一般情况下布尔比例的一个突出模型相吻合。从更广泛的意义上讲，本文是类比推理和学习系统理论的进一步发展，有可能应用于常识推理、计算学习和创造力等基本人工智能问题。 摘要：Analogy-making is at the core of human intelligence and creativity with applications to such diverse tasks as commonsense reasoning, learning, language acquisition, and story telling. This paper studies analogical proportions between booleans of the form `$a$ is to $b$ what $c$ is to $d$' called boolean proportions. Technically, we instantiate an abstract algebraic framework of analogical proportions -- recently introduced by the author -- in the boolean domain consisting of the truth values true and false together with boolean functions. It turns out that our notion of boolean proportions has appealing mathematical properties and that it coincides with a prominent model of boolean proportions in the general case. In a broader sense, this paper is a further step towards a theory of analogical reasoning and learning systems with potential applications to fundamental AI-problems like commonsense reasoning and computational learning and creativity.
【10】 Building a Legal Dialogue System: Development Process, Challenges and Opportunities 标题：法律对话制度建设：发展历程、挑战与机遇 链接：https://arxiv.org/abs/2109.00381
作者：Mudita Sharma,Tony Russell-Rose,Lina Barakat,Akitaka Matsuo 机构：University of Essex 备注：15 pages, 4 figures 摘要：本文介绍了为法律领域设计特定于领域的会话代理所面临的挑战的关键原则和解决方案。它包括范围、平台、体系结构和输入数据准备等问题。它提供了回答用户查询和记录用户信息（包括联系人详细信息和案例相关信息）的功能。它利用基于Amazon Web Services（AWS）LEX和AWS Lambda的深度学习技术。由于缺乏公开可用的数据，我们确定了两种方法，包括众包实验和归档查询，以开发大量语言资源。这包括训练数据集、会话代理的预定响应集、回归测试用例集和进一步的会话测试集。我们提出了一种层次化的bot结构，它有助于在回归测试集上实现多级委托和报告模型准确性。此外，我们还将重点介绍添加到bot中的功能，以改善会话流和总体用户体验。 摘要：This paper presents key principles and solutions to the challenges faced in designing a domain-specific conversational agent for the legal domain. It includes issues of scope, platform, architecture and preparation of input data. It provides functionality in answering user queries and recording user information including contact details and case-related information. It utilises deep learning technology built upon Amazon Web Services (AWS) LEX in combination with AWS Lambda. Due to lack of publicly available data, we identified two methods including crowdsourcing experiments and archived enquiries to develop a number of linguistic resources. This includes a training dataset, set of predetermined responses for the conversational agent, a set of regression test cases and a further conversation test set. We propose a hierarchical bot structure that facilitates multi-level delegation and report model accuracy on the regression test set. Additionally, we highlight features that are added to the bot to improve the conversation flow and overall user experience.
【11】 Intrinsic Argument Strength in Structured Argumentation: a Principled Approach 标题：结构化论证中的内在论据强度：一种原则性的方法 链接：https://arxiv.org/abs/2109.00318
作者：Jeroen Paul Spaans 机构：a Principled ApproachJeroen Paul Spaans[0000−000 1−70 27−8 10 2]Utrecht University 摘要：抽象论证为我们提供了渐进语义和Dung语义等方法，用于在其他参数潜在攻击后评估参数。其中一些方法可以将参数的内在强度作为输入，用来调节参数之间攻击的效果。这些方法来源于抽象论证，只关注论点之间的关系，而不关注论点本身的结构。在结构化论证中，通过从前提开始链接推理规则来构造论证的方式被考虑在内。在这篇文章中，我们研究了根据构成论点的前提和推理规则的强度，赋予论点内在强度的方法。我们首先定义一组原则，这些原则是强度分配方法可能满足的属性。然后，我们提出了两种这样的方法，并分析了它们满足哪些原则。最后，我们提出了一个用于创建新强度分配方法的通用系统，并讨论了该系统关于所提出原则的特性。 摘要：Abstract argumentation provides us with methods such as gradual and Dung semantics with which to evaluate arguments after potential attacks by other arguments. Some of these methods can take intrinsic strengths of arguments as input, with which to modulate the effects of attacks between arguments. Coming from abstract argumentation, these methods look only at the relations between arguments and not at the structure of the arguments themselves. In structured argumentation the way an argument is constructed, by chaining inference rules starting from premises, is taken into consideration. In this paper we study methods for assigning an argument its intrinsic strength, based on the strengths of the premises and inference rules used to form said argument. We first define a set of principles, which are properties that strength assigning methods might satisfy. We then propose two such methods and analyse which principles they satisfy. Finally, we present a generalised system for creating novel strength assigning methods and speak to the properties of this system regarding the proposed principles.
【12】 Complex Event Forecasting with Prediction Suffix Trees: Extended Technical Report 标题：基于预测后缀树的复杂事件预测：扩展技术报告 链接：https://arxiv.org/abs/2109.00287
作者：Elias Alevizos,Alexander Artikis,Georgios Paliouras 机构：Department of Informatics, National and Kapodistrian University of Athens, Greece, Institute of Informatics & Telecommunications, National Center for Scientific Research, “Demokritos”, Greece, Department of Maritime Studies, University of Piraeus, Greece 摘要：复杂事件识别（CER）系统由于能够“即时”检测实时事件流上的模式，在过去二十年中变得非常流行。然而，在CER引擎实际检测到模式发生之前，缺乏预测模式何时发生的方法。我们提出了一个正式的框架，试图解决复杂事件预测（CEF）的问题。我们的框架结合了两种形式：a）用于编码复杂事件模式的符号自动机；b）预测后缀树，可提供自动机行为的简洁概率描述。我们将我们提出的方法与最先进的方法进行了比较，并展示了它在准确性和效率方面的优势。特别是，预测后缀树作为可变阶马尔可夫模型，能够通过只记住那些信息量足够大的过去序列来捕获流中的长期依赖关系。我们的实验结果表明，在准确性方面，能够捕获这种长期依赖性的好处。这是通过将模型的阶数增加到全阶马尔可夫模型可能的阶数之外来实现的，全阶马尔可夫模型需要对给定阶数的所有可能的过去序列执行穷举枚举。我们还广泛讨论了如何对CEF解决方案的预测质量进行最佳评估。 摘要：Complex Event Recognition (CER) systems have become popular in the past two decades due to their ability to "instantly" detect patterns on real-time streams of events. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of Complex Event Forecasting (CEF). Our framework combines two formalisms: a) symbolic automata which are used to encode complex event patterns; and b) prediction suffix trees which can provide a succinct probabilistic description of an automaton's behavior. We compare our proposed approach against state-of-the-art methods and show its advantage in terms of accuracy and efficiency. In particular, prediction suffix trees, being variable-order Markov models, have the ability to capture long-term dependencies in a stream by remembering only those past sequences that are informative enough. Our experimental results demonstrate the benefits, in terms of accuracy, of being able to capture such long-term dependencies. This is achieved by increasing the order of our model beyond what is possible with full-order Markov models that need to perform an exhaustive enumeration of all possible past sequences of a given order. We also discuss extensively how CEF solutions should be best evaluated on the quality of their forecasts.
【13】 Extracting all Aspect-polarity Pairs Jointly in a Text with Relation Extraction Approach 标题：用关系抽取方法联合提取文本中的所有方面-极性对 链接：https://arxiv.org/abs/2109.00256
作者：Lingmei Bu,Li Chen,Yongmei Lu,Zhonghua Yu 机构：Department of Computer Science, Sichuan University, China 摘要：从文本中提取方面极性对是细粒度情感分析的一项重要任务。虽然现有的方法已经取得了许多进展，但它们在捕获文本中的方面极性对之间的关系方面受到限制，从而降低了提取性能。此外，现有的最先进的方法，即基于标记的序列标记和基于广度的分类，都有其自身的缺陷，如前者中单独标记标记导致的极性不一致，后者中混合了方面相关和极性相关标记的异构分类。为了弥补上述缺陷，在关系提取的最新进展的启发下，我们建议使用关系提取技术直接从文本生成方面极性对，将方面对视为一元关系，其中方面是实体，相应的极性是关系。基于这一观点，我们提出了一个位置和方向感知的Sequence2序列模型，用于方向极性对的联合提取。该模型的特点是不仅能够通过序列解码捕获文本中方面极性对之间的关系，而且能够通过位置和方面感知注意捕获方面与其极性之间的相关性。在三个基准数据集上进行的实验表明，我们的模型优于现有的最新方法，对它们进行了显著改进。 摘要：Extracting aspect-polarity pairs from texts is an important task of fine-grained sentiment analysis. While the existing approaches to this task have gained many progresses, they are limited at capturing relationships among aspect-polarity pairs in a text, thus degrading the extraction performance. Moreover, the existing state-of-the-art approaches, namely token-based se-quence tagging and span-based classification, have their own defects such as polarity inconsistency resulted from separately tagging tokens in the former and the heterogeneous categorization in the latter where aspect-related and polarity-related labels are mixed. In order to remedy the above defects, in-spiring from the recent advancements in relation extraction, we propose to generate aspect-polarity pairs directly from a text with relation extraction technology, regarding aspect-pairs as unary relations where aspects are enti-ties and the corresponding polarities are relations. Based on the perspective, we present a position- and aspect-aware sequence2sequence model for joint extraction of aspect-polarity pairs. The model is characterized with its ability to capture not only relationships among aspect-polarity pairs in a text through the sequence decoding, but also correlations between an aspect and its polarity through the position- and aspect-aware attentions. The experi-ments performed on three benchmark datasets demonstrate that our model outperforms the existing state-of-the-art approaches, making significant im-provement over them.
【14】 Multi-Sample based Contrastive Loss for Top-k Recommendation 标题：基于多样本的Top-k推荐对比损失 链接：https://arxiv.org/abs/2109.00217
作者：Hao Tang,Guoshuai Zhao,Yuxia Wu,Xueming Qian 机构：Zhao (corresponding author) is with the School of Software En-gineering, Xi’an Jiaotong University 备注：12 pages,7 figures,6 tables 摘要：top-k推荐是推荐系统中的一项基本任务，一般通过比较正负对来学习。对比损失（CL）是对比学习的关键，近年来受到了越来越多的关注，我们发现它非常适合top-k推荐。然而，CL将阳性和阴性样本的重要性视为相同，这是一个问题。一方面，CL面临着一个正样本和多个负样本的不平衡问题。另一方面，在稀疏的数据集中，正项非常少，因此应该强调它们的重要性。此外，另一个重要问题是，建议中仍然没有充分利用稀疏的积极项目。因此，我们提出了一种新的数据扩充方法，即同时使用多个正项（或样本）和CL损失函数。因此，我们提出了一种基于多样本的对比损失（MSCL）函数，通过平衡正负样本的重要性和数据扩充来解决这两个问题。基于图卷积网络（GCN）方法，实验结果证明了MSCL的最新性能。所提出的MSCL方法简单，可应用于多种方法。我们将在接受后在GitHub上发布我们的代码。 摘要：The top-k recommendation is a fundamental task in recommendation systems which is generally learned by comparing positive and negative pairs. The Contrastive Loss (CL) is the key in contrastive learning that has received more attention recently and we find it is well suited for top-k recommendations. However, it is a problem that CL treats the importance of the positive and negative samples as the same. On the one hand, CL faces the imbalance problem of one positive sample and many negative samples. On the other hand, positive items are so few in sparser datasets that their importance should be emphasized. Moreover, the other important issue is that the sparse positive items are still not sufficiently utilized in recommendations. So we propose a new data augmentation method by using multiple positive items (or samples) simultaneously with the CL loss function. Therefore, we propose a Multi-Sample based Contrastive Loss (MSCL) function which solves the two problems by balancing the importance of positive and negative samples and data augmentation. And based on the graph convolution network (GCN) method, experimental results demonstrate the state-of-the-art performance of MSCL. The proposed MSCL is simple and can be applied in many methods. We will release our code on GitHub upon the acceptance.
【15】 Federated Learning: Issues in Medical Application 标题：联合学习：医学应用中的问题 链接：https://arxiv.org/abs/2109.00202
作者：Joo Hun Yoo,Hyejun Jeong,Jaehyeok Lee,Tai-Myoung Chung 机构：College of Computing and Informatics, Sungkyunkwan University, Suwon, Korea (Republic of) 备注：20 pages, 3 figures, 1 table, submitted to FDSE2021 摘要：自2017年谷歌引入联邦学习（federated learning）以来，它一直在积极研究，尤其是在医学领域。联邦学习使人工智能学习成为可能，而无需移动本地数据。事实上，在人工智能中，不从本地客户收集数据的机器学习的想法非常有吸引力，因为数据保留在本地站点。然而，联合学习技术由于其自身的特点，如不相同的分布、客户参与管理和易受攻击的环境，仍然存在各种未决问题。在本演示中，将简要概述当前使联合学习在现实世界中发挥完美作用的问题。它们与数据/系统异构性、客户机管理、可追溯性和安全性相关。此外，我们还介绍了我们目前开发的模块化联邦学习框架，以试验各种技术和协议，找到上述问题的解决方案。该框架将在开发完成后向公众开放。 摘要：Since the federated learning, which makes AI learning possible without moving local data around, was introduced by google in 2017 it has been actively studied particularly in the field of medicine. In fact, the idea of machine learning in AI without collecting data from local clients is very attractive because data remain in local sites. However, federated learning techniques still have various open issues due to its own characteristics such as non identical distribution, client participation management, and vulnerable environments. In this presentation, the current issues to make federated learning flawlessly useful in the real world will be briefly overviewed. They are related to data/system heterogeneity, client management, traceability, and security. Also, we introduce the modularized federated learning framework, we currently develop, to experiment various techniques and protocols to find solutions for aforementioned issues. The framework will be open to public after development completes.
【16】 An Empirical Study on the Joint Impact of Feature Selection and Data Resampling on Imbalance Classification 标题：特征选择和数据重采样对不平衡分类联合影响的实证研究 链接：https://arxiv.org/abs/2109.00201
作者：Chongsheng Zhang,Paolo Soda,Jingjun Bi,Gaojuan Fan,George Almpanidis,Salvador Garcia 机构：School of Computer and Information Engineering, Henan University, China, Department of Engineering, University Campus Bio-Medico of Rome, Italy, Department of Computer Science and Artificial Intelligence, University of Granada, Spain 备注：25 pages, 12 figures 摘要：现实世界的数据集通常呈现不同程度的不平衡（即长尾或倾斜）分布。虽然大多数（又称头部或频繁）类有足够的样本，但少数（又称尾部或罕见）类的样本数量可能不足。一方面，数据重采样是解决类不平衡的常用方法。另一方面，降维是一种传统的机器学习技术，用于在数据集上建立更强的分类模型，它减少了特征空间。然而，在高性能不平衡分类中，特征选择和数据重采样之间可能的协同作用以前很少被研究。为了解决这一问题，本文对特征选择和重采样对两类不平衡分类的联合影响进行了全面的实证研究。具体来说，我们研究了两条相反的管道在不平衡分类中的性能，即在数据重采样之前或之后应用特征选择。我们在52个公开数据集上进行了大量实验（共9225个实验），使用了9种特征选择方法、6种用于类不平衡学习的重采样方法和3种著名的分类算法。实验结果表明，这两条管道之间不存在恒定的赢家，因此，应考虑这两条管道来推导性能最佳的不平衡分类模型。我们还发现，不平衡分类模型的性能取决于所采用的分类器、多数样本数与少数样本数之比（IR）以及样本数与特征数之比（SFR）。总的来说，本研究为研究者和实践者提供了新的参考价值。 摘要：Real-world datasets often present different degrees of imbalanced (i.e., long-tailed or skewed) distributions. While the majority (a.k.a., head or frequent) classes have sufficient samples, the minority (a.k.a., tail or rare) classes can be under-represented by a rather limited number of samples. On one hand, data resampling is a common approach to tackling class imbalance. On the other hand, dimension reduction, which reduces the feature space, is a conventional machine learning technique for building stronger classification models on a dataset. However, the possible synergy between feature selection and data resampling for high-performance imbalance classification has rarely been investigated before. To address this issue, this paper carries out a comprehensive empirical study on the joint influence of feature selection and resampling on two-class imbalance classification. Specifically, we study the performance of two opposite pipelines for imbalance classification, i.e., applying feature selection before or after data resampling. We conduct a large amount of experiments (a total of 9225 experiments) on 52 publicly available datasets, using 9 feature selection methods, 6 resampling approaches for class imbalance learning, and 3 well-known classification algorithms. Experimental results show that there is no constant winner between the two pipelines, thus both of them should be considered to derive the best performing model for imbalance classification. We also find that the performance of an imbalance classification model depends on the classifier adopted, the ratio between the number of majority and minority samples (IR), as well as on the ratio between the number of samples and features (SFR). Overall, this study should provide new reference value for researchers and practitioners in imbalance learning.
【17】 Deep mathcal{L}^1 Stochastic Optimal Control Policies for Planetary Soft-landing链接：https://arxiv.org/abs/2109.00183
作者：Marcus A. Pereira,Camilo A. Duarte,Ioannis Exarchos,Evangelos A. Theodorou 机构：The Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA , School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA , Microsoft 摘要：本文基于非线性随机最优控制（SOC）原理和费曼-卡克理论，提出了一种新的基于深度学习的动力下降制导（PDG）问题的解决方案。我们的算法通过将PDG问题构造为$mathcal{L}^1$SOC问题来解决，以获得最小的燃油消耗。此外，它可以处理实际有用的控制约束、非线性动力学，并将状态约束强制为软约束。这是通过建立最近关于深层向前向后随机微分方程（FBSDE）和基于随机搜索的可微非凸优化神经网络层的工作来实现的。与以前的方法相比，我们的算法不需要约束的凸化或动力学的线性化，并且经验证明对随机干扰和航天器的初始位置具有鲁棒性。离线训练后，一旦航天器位于着陆区预先指定的半径范围内，并且处于预先指定的高度，即尖端位于着陆区的倒锥底部，我们的控制器就可以激活。我们的经验证明，我们的控制器可以成功和安全地降落在这个圆锥底部初始化的所有轨迹，同时最大限度地减少燃料消耗。 摘要：In this paper, we introduce a novel deep learning based solution to the Powered-Descent Guidance (PDG) problem, grounded in principles of nonlinear Stochastic Optimal Control (SOC) and Feynman-Kac theory. Our algorithm solves the PDG problem by framing it as an $mathcal{L}^1$ SOC problem for minimum fuel consumption. Additionally, it can handle practically useful control constraints, nonlinear dynamics and enforces state constraints as soft-constraints. This is achieved by building off of recent work on deep Forward-Backward Stochastic Differential Equations (FBSDEs) and differentiable non-convex optimization neural-network layers based on stochastic search. In contrast to previous approaches, our algorithm does not require convexification of the constraints or linearization of the dynamics and is empirically shown to be robust to stochastic disturbances and the initial position of the spacecraft. After training offline, our controller can be activated once the spacecraft is within a pre-specified radius of the landing zone and at a pre-specified altitude i.e., the base of an inverted cone with the tip at the landing zone. We demonstrate empirically that our controller can successfully and safely land all trajectories initialized at the base of this cone while minimizing fuel consumption.
【18】 CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations 标题：CTAL：用于音频和语言表示的预训练跨模式转换器 链接：https://arxiv.org/abs/2109.00181
作者：Hang Li,Yu Kang,Tianqiao Liu,Wenbiao Ding,Zitao Liu 机构：TAL Education Group, Beijing, China 备注：The 2021 Conference on Empirical Methods in Natural Language Processing 摘要：现有的音频语言任务特定预测方法侧重于构建复杂的后期融合机制。然而，这些模型面临着过度拟合的挑战，标签有限，模型泛化能力低。在本文中，我们提出了一种用于音频和语言的跨模态转换器，即CTAL，其目的是通过对大量音频和语言对的两个代理任务：掩蔽语言建模和掩蔽跨模态声学建模来学习音频和语言之间的模态内和模态间连接。在对多个下游音频和语言任务的预训练模型进行微调后，我们观察到各种任务的显著改进，如情绪分类、情绪分析和说话人验证。在此基础上，我们进一步提出了一种专门设计的融合机制，可用于微调阶段，使我们预先训练的模型获得更好的性能。最后，我们展示了详细的消融研究，以证明我们新的跨模态融合组件和音频语言预训练方法对有希望的结果有显著贡献。 摘要：Existing audio-language task-specific predictive approaches focus on building complicated late-fusion mechanisms. However, these models are facing challenges of overfitting with limited labels and low model generalization abilities. In this paper, we present a Cross-modal Transformer for Audio-and-Language, i.e., CTAL, which aims to learn the intra-modality and inter-modality connections between audio and language through two proxy tasks on a large amount of audio-and-language pairs: masked language modeling and masked cross-modal acoustic modeling. After fine-tuning our pre-trained model on multiple downstream audio-and-language tasks, we observe significant improvements across various tasks, such as, emotion classification, sentiment analysis, and speaker verification. On this basis, we further propose a specially-designed fusion mechanism that can be used in fine-tuning phase, which allows our pre-trained model to achieve better performance. Lastly, we demonstrate detailed ablation studies to prove that both our novel cross-modality fusion component and audio-language pre-training methods significantly contribute to the promising results.
【19】 Problem Learning: Towards the Free Will of Machines 标题：问题学习：走向机器的自由意志 链接：https://arxiv.org/abs/2109.00177
作者：Yongfeng Zhang 机构：Department of Computer Science, Rutgers University, New Brunswick, NJ 备注：17 pages, 1 figure 摘要：机器智能管道通常由六个部分组成：问题、表示、模型、损失、优化器和度量。研究人员一直在努力使管道的许多组件实现自动化。然而，管道的一个关键组成部分——问题定义——在自动化方面仍然没有得到充分的探索。通常，它需要领域专家的广泛努力来识别、定义和阐述某一领域的重要问题。然而，自动发现某个领域的研究或应用问题是有益的，因为它有助于识别隐藏在数据中的有效和潜在的重要问题，而这些问题是领域专家所不知道的，可以扩大我们在某个领域可以做的任务范围，甚至可以激发全新的发现。本文描述了问题学习，旨在学习从数据或机器与环境的交互中发现和定义有效的道德问题。我们将问题学习形式化为在问题空间中识别有效的道德问题，并介绍几种可能的问题学习方法。从广义上讲，问题学习是一种实现智能机器自由意志的方法。目前，机器仍然局限于解决人类定义的问题，没有能力或灵活性自由探索人类甚至未知的各种可能问题。尽管许多机器学习技术已经被开发并集成到智能系统中，但它们仍然关注于机器解决人类定义的问题的方法而不是目的。然而，提出好的问题有时甚至比解决问题更重要，因为一个好的问题有助于激发新的想法和获得更深的理解。本文还讨论了负责任人工智能背景下问题学习的伦理含义。 摘要：A machine intelligence pipeline usually consists of six components: problem, representation, model, loss, optimizer and metric. Researchers have worked hard trying to automate many components of the pipeline. However, one key component of the pipeline--problem definition--is still left mostly unexplored in terms of automation. Usually, it requires extensive efforts from domain experts to identify, define and formulate important problems in an area. However, automatically discovering research or application problems for an area is beneficial since it helps to identify valid and potentially important problems hidden in data that are unknown to domain experts, expand the scope of tasks that we can do in an area, and even inspire completely new findings. This paper describes Problem Learning, which aims at learning to discover and define valid and ethical problems from data or from the machine's interaction with the environment. We formalize problem learning as the identification of valid and ethical problems in a problem space and introduce several possible approaches to problem learning. In a broader sense, problem learning is an approach towards the free will of intelligent machines. Currently, machines are still limited to solving the problems defined by humans, without the ability or flexibility to freely explore various possible problems that are even unknown to humans. Though many machine learning techniques have been developed and integrated into intelligent systems, they still focus on the means rather than the purpose in that machines are still solving human defined problems. However, proposing good problems is sometimes even more important than solving problems, because a good problem can help to inspire new ideas and gain deeper understandings. The paper also discusses the ethical implications of problem learning under the background of Responsible AI.
【20】 A Survey of Exploration Methods in Reinforcement Learning 标题：强化学习中的探索方法综述 链接：https://arxiv.org/abs/2109.00157
作者：Susan Amin,Maziar Gomrokchi,Harsh Satija,Herke van Hoof,Doina Precup 机构：Department of Computer Science, McGill University, Mila- Québec Artificial Intelligence Institute, Montréal, Québec, Canada, Informatics Institute, University of Amsterdam, Amsterdam, the Netherlands, Editor: xxx 摘要：探索是强化学习算法的一个重要组成部分，其中代理需要学习如何预测和控制未知且通常是随机的环境。强化学习代理主要依靠探索来获取学习过程中的信息数据，因为缺乏足够的信息可能会阻碍有效的学习。在这篇文章中，我们对（顺序）强化学习中的现代探索方法进行了综述，并对探索方法进行了分类。 摘要：Exploration is an essential component of reinforcement learning algorithms, where agents need to learn how to predict and control unknown and often stochastic environments. Reinforcement learning agents depend crucially on exploration to obtain informative data for the learning process as the lack of enough information could hinder effective learning. In this article, we provide a survey of modern exploration methods in (Sequential) reinforcement learning, as well as a taxonomy of exploration methods.
【21】 Cognitive science as a source of forward and inverse models of human decisions for robotics and control 标题：认知科学是人类机器人和控制决策正反向模型的来源 链接：https://arxiv.org/abs/2109.00127
作者：Mark K. Ho,Thomas L. Griffiths 机构：Princeton University, Department of Computer Science, Princeton, NJ, USA, Princeton University, Department of Psychology, Princeton, NJ, USA 备注：Invited submission for Annual Review of Control, Robotics, and Autonomous Systems 摘要：那些设计与人类互动的自主系统的人将不可避免地面临人类如何思考和决策的问题。幸运的是，计算认知科学提供了对人类决策的洞察，使用的工具将是那些熟悉优化和控制背景的人所熟悉的（例如，概率论、统计机器学习和强化学习）。在这里，我们回顾了其中的一些工作，重点关注认知科学如何提供人类决策的正向模型和人类思考他人决策的反向模型。我们重点介绍了相关的最新发展，包括综合黑盒和理论驱动建模的方法、将启发式和偏见重新定义为有界最优性形式的说明，以及用决策理论术语描述人类思维和沟通理论的模型。通过这样做，我们的目的是让读者对认知科学和控制研究交叉点的一系列框架、方法和可操作的见解一瞥。 摘要：Those designing autonomous systems that interact with humans will invariably face questions about how humans think and make decisions. Fortunately, computational cognitive science offers insight into human decision-making using tools that will be familiar to those with backgrounds in optimization and control (e.g., probability theory, statistical machine learning, and reinforcement learning). Here, we review some of this work, focusing on how cognitive science can provide forward models of human decision-making and inverse models of how humans think about others' decision-making. We highlight relevant recent developments, including approaches that synthesize blackbox and theory-driven modeling, accounts that recast heuristics and biases as forms of bounded optimality, and models that characterize human theory of mind and communication in decision-theoretic terms. In doing so, we aim to provide readers with a glimpse of the range of frameworks, methodologies, and actionable insights that lie at the intersection of cognitive science and control research.
【22】 MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics 标题：MiniF2F：正式奥林匹克级别数学的跨系统基准 链接：https://arxiv.org/abs/2109.00110
作者：Kunhao Zheng,Jesse Michael Han,Stanislas Polu 机构：´Ecole Polytechnique, OpenAI, University of Pittsburgh 摘要：我们提出miniF2F，一个正式的奥运会级别数学问题陈述的数据集，旨在为神经定理证明提供一个统一的跨系统基准。miniF2F基准目前以Metamath、Lean和Isabelle为目标，由488个问题陈述组成，这些问题陈述来自AIME、AMC和国际数学奥林匹克（IMO），以及高中和本科数学课程的材料。我们报告了基于GPT-3的神经定理证明器GPT-f的基线结果，并对其性能进行了分析。我们打算让miniF2F成为一项由社区推动的工作，并希望我们的基准将有助于推动神经定理证明方面的进步。 摘要：We present miniF2F, a dataset of formal Olympiad-level mathematics problems statements intended to provide a unified cross-system benchmark for neural theorem proving. The miniF2F benchmark currently targets Metamath, Lean, and Isabelle and consists of 488 problem statements drawn from the AIME, AMC, and the International Mathematical Olympiad (IMO), as well as material from high-school and undergraduate mathematics courses. We report baseline results using GPT-f, a neural theorem prover based on GPT-3 and provide an analysis of its performance. We intend for miniF2F to be a community-driven effort and hope that our benchmark will help spur advances in neural theorem proving.
【23】 Automatic non-invasive Cough Detection based on Accelerometer and Audio Signals 标题：基于加速度计和音频信号的无创咳嗽自动检测 链接：https://arxiv.org/abs/2109.00103
作者：Madhurananda Pahar,Igor Miranda,Andreas Diacon,Thomas Niesler 机构：Department of Electrical and Electronic Engineering, Stellenbosch University, South Africa 备注：arXiv admin note: text overlap with arXiv:2102.04997 摘要：我们提出了一种基于加速度计和音频信号的自动无创检测咳嗽事件的方法。加速度信号由牢牢固定在患者床上的智能手机通过其集成的加速计捕获。同一部智能手机使用外部麦克风同时捕获音频信号。我们已经编译了一个手动注释的数据集，其中包含同时捕获的加速度和音频信号，用于结核病诊所14名成年男性患者的大约6000例咳嗽和68000例非咳嗽事件。LR、SVM和MLP作为基线分类器进行评估，并使用留一交叉验证方案与深层结构（如CNN、LSTM和Resnet50）进行比较。我们发现，所研究的分类器可以使用加速度或音频信号来区分咳嗽和其他活动，包括打喷嚏、清嗓子和在床上移动，具有很高的准确性。然而，在所有情况下，深度神经网络的性能明显优于浅层分类器，而Resnet50的性能最好，加速度和音频信号的AUC分别超过0.98和0.99。虽然基于音频的分类始终比基于加速的分类提供更好的性能，但我们观察到，对于最好的系统来说，差异非常小。由于加速信号需要更少的处理能力，由于录音的需要被回避，因此隐私被固有地保护，并且由于录音设备被连接到床上，并且没有佩戴，基于加速度计的高精度无创咳嗽检测器可能是长期咳嗽监测中更方便、更容易接受的方法。 摘要：We present an automatic non-invasive way of detecting cough events based on both accelerometer and audio signals. The acceleration signals are captured by a smartphone firmly attached to the patient's bed, using its integrated accelerometer. The audio signals are captured simultaneously by the same smartphone using an external microphone. We have compiled a manually-annotated dataset containing such simultaneously-captured acceleration and audio signals for approximately 6000 cough and 68000 non-cough events from 14 adult male patients in a tuberculosis clinic. LR, SVM and MLP are evaluated as baseline classifiers and compared with deep architectures such as CNN, LSTM, and Resnet50 using a leave-one-out cross-validation scheme. We find that the studied classifiers can use either acceleration or audio signals to distinguish between coughing and other activities including sneezing, throat-clearing, and movement on the bed with high accuracy. However, in all cases, the deep neural networks outperform the shallow classifiers by a clear margin and the Resnet50 offers the best performance by achieving an AUC exceeding 0.98 and 0.99 for acceleration and audio signals respectively. While audio-based classification consistently offers a better performance than acceleration-based classification, we observe that the difference is very small for the best systems. Since the acceleration signal requires less processing power, and since the need to record audio is sidestepped and thus privacy is inherently secured, and since the recording device is attached to the bed and not worn, an accelerometer-based highly accurate non-invasive cough detector may represent a more convenient and readily accepted method in long-term cough monitoring.
【24】 Proceedings of KDD 2021 Workshop on Data-driven Humanitarian Mapping: Harnessing Human-Machine Intelligence for High-Stake Public Policy and Resilience Planning 标题：KDD 2021数据驱动的人道主义绘图研讨会论文集：利用人机智能制定高风险的公共政策和复原力规划 链接：https://arxiv.org/abs/2109.00100
作者：Snehalkumar,S. Gaikwad,Shankar Iyer,Dalton Lunga,Elizabeth Bondi 备注：The proceedings of the 2nd Data-driven Humanitarian Mapping workshop at the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. August 15th, 2021 摘要：人道主义挑战，2019冠状病毒疾病、食物不安全、气候变化、种族和性别暴力、环境危机、COVID-19冠状病毒大流行、人权侵犯和被迫转移，不成比例地影响了世界范围内的弱势群体。根据联合国人道协调厅的数据，20211年将有2.35亿人需要人道主义援助。尽管这些危险越来越大，但数据科学研究仍显不足，无法科学地为改善高危人群生计的公平公共政策决策提供信息。为了应对这些挑战，存在着分散的数据科学工作，但它们仍然与实践隔离，并且容易受到算法方面的损害，涉及缺乏隐私、公平性、可解释性、问责制、透明度和道德。数据驱动方法中的偏见有可能放大影响数百万人生计的高风险决策中的不平等。因此，作为人道主义行动和全球发展核心的决策者、实践者和边缘化社区仍然无法获得数据驱动创新的公开好处。为了填补这一空白，我们提出了数据驱动的人道主义测绘研究计划，该计划的重点是开发新的数据科学方法，利用人机智能制定高风险的公共政策和恢复力规划。 摘要：Humanitarian challenges, including natural disasters, food insecurity, climate change, racial and gender violence, environmental crises, the COVID-19 coronavirus pandemic, human rights violations, and forced displacements, disproportionately impact vulnerable communities worldwide. According to UN OCHA, 235 million people will require humanitarian assistance in 20211 . Despite these growing perils, there remains a notable paucity of data science research to scientifically inform equitable public policy decisions for improving the livelihood of at-risk populations. Scattered data science efforts exist to address these challenges, but they remain isolated from practice and prone to algorithmic harms concerning lack of privacy, fairness, interpretability, accountability, transparency, and ethics. Biases in data-driven methods carry the risk of amplifying inequalities in high-stakes policy decisions that impact the livelihood of millions of people. Consequently, proclaimed benefits of data-driven innovations remain inaccessible to policymakers, practitioners, and marginalized communities at the core of humanitarian actions and global development. To help fill this gap, we propose the Data-driven Humanitarian Mapping Research Program, which focuses on developing novel data science methodologies that harness human-machine intelligence for high-stakes public policy and resilience planning.
【25】 Informing Autonomous Deception Systems with Cyber Expert Performance Data 标题：用网络专家性能数据通知自主欺骗系统 链接：https://arxiv.org/abs/2109.00066
作者：Maxine Major,Brian Souza,Joseph DiVita,Kimberly Ferguson-Walter 机构：Naval Information Warfare Center Pacific, Laboratory for Advanced Cybersecurity Research 备注：Presented at 1st International Workshop on Adaptive Cyber Defense, 2021 (arXiv:2108.08476) 摘要：人工智能（AI）算法在实践中的性能取决于提供给算法的数据、模型和反馈（标签或奖励）的真实性和正确性。本文讨论了通过探索使用反向强化学习（IRL）来深入了解攻击者行为、这些行为的效用以及网络欺骗可能阻止的最终决策点，从而提高用于自主网络防御的AI的真实性和生态有效性的方法。例如，Tularosa研究提供了攻击者常用的现实技术和工具的实验数据，从中可以利用核心数据向量来通知自主网络防御系统。 摘要：The performance of artificial intelligence (AI) algorithms in practice depends on the realism and correctness of the data, models, and feedback (labels or rewards) provided to the algorithm. This paper discusses methods for improving the realism and ecological validity of AI used for autonomous cyber defense by exploring the potential to use Inverse Reinforcement Learning (IRL) to gain insight into attacker actions, utilities of those actions, and ultimately decision points which cyber deception could thwart. The Tularosa study, as one example, provides experimental data of real-world techniques and tools commonly used by attackers, from which core data vectors can be leveraged to inform an autonomous cyber defense system.
【26】 Deep DNA Storage: Scalable and Robust DNA Storage via Coding Theory and Deep Learning 标题：深度DNA存储：基于编码理论和深度学习的可扩展、健壮的DNA存储 链接：https://arxiv.org/abs/2109.00031
作者：Daniella Bar-Lev,Itai Orr,Omer Sabary,Tuvi Etzion,Eitan Yaakobi 机构：Department of Computer Science, Technion – Israel institute of Technology, Haifa, Israel, Bar Ilan, University, Ramat-Gan, Israel, Wisense Technologies Ltd., Tel Aviv, Israel, These authors contributed equally 摘要：1959年，理查德·费曼（Richard Feynman）首次提出了DNA存储的概念，他在“底部有足够的空间”的演讲中分享了他对纳米技术的看法。后来，在20世纪末，由于人类基因组计划，人们对基于DNA分子的存储解决方案的兴趣增加，这反过来又导致测序和组装方法的重大进步。与成熟的磁存储和光存储解决方案相比，DNA存储具有重大优势。与磁性解决方案相反，DNA存储不需要电源来保持数据完整性，并且在密度和耐久性方面都优于其他存储解决方案。考虑到DNA合成和测序成本下降的趋势，现在已经认识到，在未来10-15年内，DNA存储可能会成为一种极具竞争力的存档技术，并且可能在以后成为主要的存档技术。尽管如此，目前基于DNA的存储系统的实现非常有限，并且没有完全优化以解决合成和测序过程特有的错误模式。在这项工作中，我们提出了一个健壮、高效和可扩展的解决方案来实现基于DNA的存储系统。我们的方法部署了深度神经网络（DNN），该网络基于合成和测序过程产生的不完美拷贝簇重构字母序列。使用定制的纠错码（ECC）来对抗在此过程中出现的错误模式。由于我们的重建方法适用于不完全聚类，因此我们的方法通过允许使用快速且可伸缩的伪聚类来克服噪声DNA拷贝聚类过程的时间瓶颈。我们的架构结合了卷积和Transformer模块，并使用真实数据统计数据建模的合成数据进行训练。 摘要：The concept of DNA storage was first suggested in 1959 by Richard Feynman who shared his vision regarding nanotechnology in the talk "There is plenty of room at the bottom". Later, towards the end of the 20-th century, the interest in storage solutions based on DNA molecules was increased as a result of the human genome project which in turn led to a significant progress in sequencing and assembly methods. DNA storage enjoys major advantages over the well-established magnetic and optical storage solutions. As opposed to magnetic solutions, DNA storage does not require electrical supply to maintain data integrity and is superior to other storage solutions in both density and durability. Given the trends in cost decreases of DNA synthesis and sequencing, it is now acknowledged that within the next 10-15 years DNA storage may become a highly competitive archiving technology and probably later the main such technology. With that said, the current implementations of DNA based storage systems are very limited and are not fully optimized to address the unique pattern of errors which characterize the synthesis and sequencing processes. In this work, we propose a robust, efficient and scalable solution to implement DNA-based storage systems. Our method deploys Deep Neural Networks (DNN) which reconstruct a sequence of letters based on imperfect cluster of copies generated by the synthesis and sequencing processes. A tailor-made Error-Correcting Code (ECC) is utilized to combat patterns of errors which occur during this process. Since our reconstruction method is adapted to imperfect clusters, our method overcomes the time bottleneck of the noisy DNA copies clustering process by allowing the use of a rapid and scalable pseudo-clustering instead. Our architecture combines between convolutions and transformers blocks and is trained using synthetic data modelled after real data statistics.
【27】 Working Memory Connections for LSTM 标题：LSTM的工作内存连接 链接：https://arxiv.org/abs/2109.00020
作者：Federico Landi,Lorenzo Baraldi,Marcella Cornia,Rita Cucchiara 机构：Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, Modena, Italy 备注：Accepted for publication in Neural Networks 摘要：长短时记忆递归神经网络（LSTM）在学习长期依赖关系时，利用门控机制来缓解梯度的爆炸和消失。因此，LSTM和其他门控RNN被广泛采用，成为许多序列建模任务的事实标准。尽管LSTM内的存储单元包含基本信息，但不允许直接影响选通机制。在这项工作中，我们通过包含来自内部单元状态的信息来提高门电位。所提议的修改名为“工作记忆连接”，包括在网络门中添加一个可学习的单元内容非线性投影。这种修改可以适用于经典的LSTM门，而无需对底层任务进行任何假设，在处理较长序列时尤其有效。以前在这方面的研究工作可以追溯到21世纪初，与香草LSTM相比，无法带来一致的改进。作为本文的一部分，我们确定了一个与以前的连接相关的关键问题，该问题严重限制了它们的有效性，从而阻止了来自内部单元状态的知识的成功集成。我们通过广泛的实验评估表明，工作记忆连接不断提高LSTM在各种任务上的性能。数值结果表明，单元状态包含有用的信息，值得包含在栅极结构中。 摘要：Recurrent Neural Networks with Long Short-Term Memory (LSTM) make use of gating mechanisms to mitigate exploding and vanishing gradients when learning long-term dependencies. For this reason, LSTMs and other gated RNNs are widely adopted, being the standard de facto for many sequence modeling tasks. Although the memory cell inside the LSTM contains essential information, it is not allowed to influence the gating mechanism directly. In this work, we improve the gate potential by including information coming from the internal cell state. The proposed modification, named Working Memory Connection, consists in adding a learnable nonlinear projection of the cell content into the network gates. This modification can fit into the classical LSTM gates without any assumption on the underlying task, being particularly effective when dealing with longer sequences. Previous research effort in this direction, which goes back to the early 2000s, could not bring a consistent improvement over vanilla LSTM. As part of this paper, we identify a key issue tied to previous connections that heavily limits their effectiveness, hence preventing a successful integration of the knowledge coming from the internal cell state. We show through extensive experimental evaluation that Working Memory Connections constantly improve the performance of LSTMs on a variety of tasks. Numerical results suggest that the cell state contains useful information that is worth including in the gate structure.
机器翻译，仅供参考
