人工智能学术速递[1.10]
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cs.AI人工智能，共计15篇
【1】 Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution
标题：基于知识图增强多视图卷积的医学文本再入院风险预测
链接：https://arxiv.org/abs/2201.02510
机构：University of Oregon, Eugene, OR, USA, Baidu Research
备注：SIGIR 2021
摘要：计划外重症监护病房（ICU）再入院率是评估医院护理质量的重要指标。有效准确地预测ICU再入院风险不仅有助于防止患者不当出院和潜在危险，还可以降低相关的医疗成本。在本文中，我们提出了一种新的方法，使用电子健康记录（EHR）的医学文本进行预测，这为以前严重依赖患者数字和时间序列特征的研究提供了另一种视角。更具体地说，我们从EHR中提取患者的出院总结，并用外部知识图增强的多视图图表示。然后使用图卷积网络进行表示学习。实验结果证明了我们的方法的有效性，为这项任务提供了最先进的性能。
摘要：Unplanned intensive care unit (ICU) readmission rate is an important metric
for evaluating the quality of hospital care. Efficient and accurate prediction
of ICU readmission risk can not only help prevent patients from inappropriate
discharge and potential dangers, but also reduce associated costs of
healthcare. In this paper, we propose a new method that uses medical text of
Electronic Health Records (EHRs) for prediction, which provides an alternative
perspective to previous studies that heavily depend on numerical and
time-series features of patients. More specifically, we extract discharge
summaries of patients from their EHRs, and represent them with multiview graphs
enhanced by an external knowledge graph. Graph convolutional networks are then
used for representation learning. Experimental results prove the effectiveness
of our method, yielding state-of-the-art performance for this task.
【2】 Repairing Adversarial Texts through Perturbation
标题：通过扰动修复敌意文本
链接：https://arxiv.org/abs/2201.02504
机构：sg•Sudipta Chattopadhyay is with Singapore University of Technology andDesign
摘要：众所周知，神经网络会受到对抗性扰动的攻击，即通过扰动恶意构建的输入，以诱导错误的预测。此外，此类攻击不可能消除，即在应用对抗性训练等缓解方法后，仍然可能出现对抗性干扰。已经开发了多种方法来检测和拒绝此类敌对输入，主要是在图像领域。然而，拒绝可疑输入可能并不总是可行或理想的。首先，由于检测算法产生的假警报，可能会拒绝正常输入。其次，拒绝服务攻击可以通过向此类系统提供敌对输入来实施。为了解决这一差距，在这项工作中，我们提出了一种在运行时自动修复敌对文本的方法。给定一个怀疑是敌对的文本，我们以积极的方式应用多种敌对扰动方法来识别修复，即神经网络正确分类的稍微变异但语义等价的文本。我们的方法已经在为自然语言处理任务训练的多个模型上进行了实验，结果表明我们的方法是有效的，即它成功地修复了大约80\%的对抗性文本。此外，根据应用的扰动方法，对抗性文本可以在平均1秒的时间内修复。
摘要：It is known that neural networks are subject to attacks through adversarial
perturbations, i.e., inputs which are maliciously crafted through perturbations
to induce wrong predictions. Furthermore, such attacks are impossible to
eliminate, i.e., the adversarial perturbation is still possible after applying
mitigation methods such as adversarial training. Multiple approaches have been
developed to detect and reject such adversarial inputs, mostly in the image
domain. Rejecting suspicious inputs however may not be always feasible or
ideal. First, normal inputs may be rejected due to false alarms generated by
the detection algorithm. Second, denial-of-service attacks may be conducted by
feeding such systems with adversarial inputs. To address the gap, in this work,
we propose an approach to automatically repair adversarial texts at runtime.
Given a text which is suspected to be adversarial, we novelly apply multiple
adversarial perturbation methods in a positive way to identify a repair, i.e.,
a slightly mutated but semantically equivalent text that the neural network
correctly classifies. Our approach has been experimented with multiple models
trained for natural language processing tasks and the results show that our
approach is effective, i.e., it successfully repairs about 80\% of the
adversarial texts. Furthermore, depending on the applied perturbation method,
an adversarial text could be repaired in as short as one second on average.
【3】 Sign Language Video Retrieval with Free-Form Textual Queries
标题：基于自由格式文本查询的手语视频检索
链接：https://arxiv.org/abs/2201.02495
机构：Xavier Gir´o-i-Nieto, G¨ul Varol, Universitat Politecnica de Catalunya, Spain, Barcelona Supercomputing Center, Spain, Machine Intelligence Laboratory, University of Cambridge, UK, Institut de Robotica i Informatica Industrial, CSIC-UPC, Spain
摘要：能够高效搜索手语视频集的系统已经被认为是手语技术的一个有用的应用。然而，在文献中，在单个关键词之外搜索视频的问题受到了有限的关注。为了解决这一差距，在这项工作中，我们介绍了使用自由形式文本查询进行手语检索的任务：给定一个书面查询（例如，一个句子）和大量手语视频集合，目标是在集合中找到与书面查询最匹配的手语视频。我们建议通过在最近引入的美国手语（ASL）大规模How2Sign数据集上学习跨模态嵌入来解决这一问题。我们发现，系统性能的一个关键瓶颈是符号视频嵌入的质量，这是由于缺乏标记的训练数据造成的。因此，我们提出SPOT-ALIGN，这是一个交叉迭代的符号定位和特征对齐框架，以扩大可用训练数据的范围和规模。我们通过改进符号识别和视频检索任务，验证了SPOT-ALIGN学习鲁棒符号视频嵌入的有效性。
摘要：Systems that can efficiently search collections of sign language videos have
been highlighted as a useful application of sign language technology. However,
the problem of searching videos beyond individual keywords has received limited
attention in the literature. To address this gap, in this work we introduce the
task of sign language retrieval with free-form textual queries: given a written
query (e.g., a sentence) and a large collection of sign language videos, the
objective is to find the signing video in the collection that best matches the
written query. We propose to tackle this task by learning cross-modal
embeddings on the recently introduced large-scale How2Sign dataset of American
Sign Language (ASL). We identify that a key bottleneck in the performance of
the system is the quality of the sign video embedding which suffers from a
scarcity of labeled training data. We, therefore, propose SPOT-ALIGN, a
framework for interleaving iterative rounds of sign spotting and feature
alignment to expand the scope and scale of available training data. We validate
the effectiveness of SPOT-ALIGN for learning a robust sign video embedding
through improvements in both sign recognition and the proposed video retrieval
task.
【4】 Automated Dissipation Control for Turbulence Simulation with Shell Models
标题：壳模型湍流模拟中的自动耗散控制
链接：https://arxiv.org/abs/2201.02485
机构：M¨ullere,∗, Machine Learning Group, TU-Berlin, Germany, Department of Artificial Intelligence, Korea University, Seoul, Korea, Max Planck Institute for Informatics, Saarbr¨ucken, Germany
摘要：机器学习（ML）技术的应用，特别是神经网络，在处理图像和语言方面取得了巨大的成功。这是因为我们通常缺乏理解视觉和音频输入的正式模型，所以在这里，神经网络可以展现它们的能力，因为它们可以仅从数据建模。在物理学领域，我们通常有一些模型，这些模型能够在形式化的层次上很好地描述自然过程。尽管如此，近年来，ML在这些领域也被证明是有用的，无论是通过加速数值模拟还是通过提高精度。经典物理学中一个重要且至今尚未解决的问题是理解湍流流体运动。在这项工作中，我们使用Gledzer-Ohkitani-Yamada（GOY）壳模型构建了湍流的强简化表示。利用该系统，我们打算研究ML支持和物理约束的小尺度湍流建模的潜力。代替标准的监督学习，我们提出了一种旨在重建湍流统计特性的方法，如自相似惯性距离标度，在这种方法中，我们可以获得令人鼓舞的实验结果。此外，我们还讨论了将机器学习与微分方程相结合时的陷阱。
摘要：The application of machine learning (ML) techniques, especially neural
networks, has seen tremendous success at processing images and language. This
is because we often lack formal models to understand visual and audio input, so
here neural networks can unfold their abilities as they can model solely from
data. In the field of physics we typically have models that describe natural
processes reasonably well on a formal level. Nonetheless, in recent years, ML
has also proven useful in these realms, be it by speeding up numerical
simulations or by improving accuracy. One important and so far unsolved problem
in classical physics is understanding turbulent fluid motion. In this work we
construct a strongly simplified representation of turbulence by using the
Gledzer-Ohkitani-Yamada (GOY) shell model. With this system we intend to
investigate the potential of ML-supported and physics-constrained small-scale
turbulence modelling. Instead of standard supervised learning we propose an
approach that aims to reconstruct statistical properties of turbulence such as
the self-similar inertial-range scaling, where we could achieve encouraging
experimental results. Furthermore we discuss pitfalls when combining machine
learning with differential equations.
【5】 Spatial-Temporal Sequential Hypergraph Network for Crime Prediction
标题：时空序列超图网络在犯罪预测中的应用
链接：https://arxiv.org/abs/2201.02435
机构：South China University of Technology, China, University of Hong Kong, Hong Kong, Communication and Computer Network Laboratory of Guangdong, China, Peng Cheng Laboratory, China, JD Finance America Corporation, USA
备注：IJCAI 2021 Research Paper
摘要：犯罪预测对于公共安全和资源优化至关重要，但由于以下两个方面的原因，犯罪预测极具挑战性：i）犯罪模式在时间和空间上的动态变化，犯罪事件在空间和时间域上分布不均；ii）不同类型犯罪（如盗窃、抢劫、袭击、破坏）之间的时间演化依赖性，揭示了犯罪的细粒度语义。为了应对这些挑战，我们提出了时空顺序超图网络（ST-SHN）来集体编码复杂的犯罪时空模式以及潜在的基于类别的犯罪语义关系。具体来说，为了处理长距离和全局环境下的时空动态，我们设计了一个集成超图学习范式的图结构消息传递体系结构。为了在动态环境中捕获不同类别的犯罪异构关系，我们引入了一种多通道路由机制来学习不同犯罪类型的时间演化结构依赖关系。我们在两个真实数据集上进行了大量实验，结果表明，与各种最先进的基线相比，我们提出的ST-SHN框架可以显著提高预测性能。源代码可从以下网址获得：https://github.com/akaxlh/ST-SHN.
摘要：Crime prediction is crucial for public safety and resource optimization, yet
is very challenging due to two aspects: i) the dynamics of criminal patterns
across time and space, crime events are distributed unevenly on both spatial
and temporal domains; ii) time-evolving dependencies between different types of
crimes (e.g., Theft, Robbery, Assault, Damage) which reveal fine-grained
semantics of crimes. To tackle these challenges, we propose Spatial-Temporal
Sequential Hypergraph Network (ST-SHN) to collectively encode complex crime
spatial-temporal patterns as well as the underlying category-wise crime
semantic relationships. In specific, to handle spatial-temporal dynamics under
the long-range and global context, we design a graph-structured message passing
architecture with the integration of the hypergraph learning paradigm. To
capture category-wise crime heterogeneous relations in a dynamic environment,
we introduce a multi-channel routing mechanism to learn the time-evolving
structural dependency across crime types. We conduct extensive experiments on
two real-world datasets, showing that our proposed ST-SHN framework can
significantly improve the prediction performance as compared to various
state-of-the-art baselines. The source code is available at:
https://github.com/akaxlh/ST-SHN.
【6】 Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement
标题：基于拍卖的带声誉和贡献度的横向联合学习支付后激励机制设计
链接：https://arxiv.org/abs/2201.02410
摘要：联邦学习训练跨分布式数据的设备模型，同时保护隐私和获得类似于集中式ML的模型。大量的数据和计算能力的工人是联邦学习的基础。然而，不可避免的成本阻止了自私自利的员工免费服务。此外，由于数据隔离，任务发布者缺乏有效的方法来选择、评估和支付具有高质量数据的可靠员工。因此，我们设计了一个基于拍卖的水平联合学习激励机制，该机制具有声誉和贡献度量。通过设计一种合理的衡量贡献的方法，我们建立了员工的声誉，这种声誉容易下降，难以提高。通过反向拍卖，工人为任务出价，任务发布者结合声誉和出价选择工人。在预算限制下，获奖员工的薪酬基于绩效。我们证明了我们的机制满足诚实员工的个人理性、预算可行性、真实性和计算效率。
摘要：Federated learning trains models across devices with distributed data, while
protecting the privacy and obtaining a model similar to that of centralized ML.
A large number of workers with data and computing power are the foundation of
federal learning. However, the inevitable costs prevent self-interested workers
from serving for free. Moreover, due to data isolation, task publishers lack
effective methods to select, evaluate and pay reliable workers with
high-quality data. Therefore, we design an auction-based incentive mechanism
for horizontal federated learning with reputation and contribution measurement.
By designing a reasonable method of measuring contribution, we establish the
reputation of workers, which is easy to decline and difficult to improve.
Through reverse auctions, workers bid for tasks, and the task publisher selects
workers combining reputation and bid price. With the budget constraint, winning
workers are paid based on performance. We proved that our mechanism satisfies
the individual rationality of the honest worker, budget feasibility,
truthfulness, and computational efficiency.
【7】 Offline Reinforcement Learning for Road Traffic Control
标题：用于道路交通控制的离线强化学习
链接：https://arxiv.org/abs/2201.02381
机构：Qatar Computing Research Institute, Doha, Qatar
备注：8 pages
摘要：交通信号控制是城市交通中的一个重要问题，具有巨大的经济和环境影响潜力。虽然人们对交通控制中的强化学习（RL）越来越感兴趣，但迄今为止的工作主要集中在通过互动进行学习，而互动在实践中成本高昂。相反，交通方面的真实经验数据是可用的，可以以最低的成本加以利用。脱机或批处理RL的最新进展正好支持了这一点。特别是基于模型的离线RL方法，已经被证明比其他方法更好地推广到经验数据。我们建立了一个基于模型的学习框架a-DAC，该框架从具有悲观代价的数据集推断出一个马尔可夫决策过程（MDP）来处理数据不确定性。成本通过MDP中的奖励自适应成形进行建模，与之前的相关工作相比，MDP提供了更好的数据规则化。在复杂的信号环形交叉口上，使用大小不同的多个数据集和批量收集策略对A-DAC进行评估。评估结果表明，可以使用简单的批收集策略以数据高效的方式构建高性能控制策略。
摘要：Traffic signal control is an important problem in urban mobility with a
significant potential of economic and environmental impact. While there is a
growing interest in Reinforcement Learning (RL) for traffic control, the work
so far has focussed on learning through interactions which, in practice, is
costly. Instead, real experience data on traffic is available and could be
exploited at minimal costs. Recent progress in offline or batch RL has enabled
just that. Model-based offline RL methods, in particular, have been shown to
generalize to the experience data much better than others. We build a
model-based learning framework, A-DAC, which infers a Markov Decision Process
(MDP) from dataset with pessimistic costs built in to deal with data
uncertainties. The costs are modeled through an adaptive shaping of rewards in
the MDP which provides better regularization of data compared to the prior
related work. A-DAC is evaluated on a complex signalized roundabout using
multiple datasets varying in size and in batch collection policy. The
evaluation results show that it is possible to build high performance control
policies in a data efficient manner using simplistic batch collection policies.
【8】 Mirror Learning: A Unifying Framework of Policy Optimisation
标题：镜像学习：政策优化的统一框架
链接：https://arxiv.org/abs/2201.02373
机构： Thegeneralisation capabilities of neural networks (Goodfellow 1Department of Statistics, University of Oxford 2Departmentof Engineering Science
摘要：一般策略改进（GPI）和信赖域学习（TRL）是当代强化学习（RL）的主要框架，是解决马尔可夫决策过程（MDP）的核心模型。不幸的是，在数学形式上，它们对修改很敏感，因此，实现它们的实际实例化不会自动继承它们的改进保证。因此，可用的严格MDP解算器的范围很窄。事实上，许多最先进的（SOTA）算法，如TRPO和PPO，都没有被证明是收敛的。在本文中，我们提出了\textsl{mirror learning}——RL问题的一般解决方案。我们揭示了GPI和TRL在这个更大的算法空间中只是一个小点，它具有单调改进特性并收敛到最优策略。我们表明，几乎所有用于RL的SOTA算法都是镜像学习的实例，因此表明它们的经验性能是其理论特性的结果，而不是近似类比的结果。令人兴奋的是，我们证明了镜像学习为具有收敛保证的策略学习方法开辟了一个全新的空间。
摘要：General policy improvement (GPI) and trust-region learning (TRL) are the
predominant frameworks within contemporary reinforcement learning (RL), which
serve as the core models for solving Markov decision processes (MDPs).
Unfortunately, in their mathematical form, they are sensitive to modifications,
and thus, the practical instantiations that implement them do not automatically
inherit their improvement guarantees. As a result, the spectrum of available
rigorous MDP-solvers is narrow. Indeed, many state-of-the-art (SOTA)
algorithms, such as TRPO and PPO, are not proven to converge. In this paper, we
propose \textsl{mirror learning} -- a general solution to the RL problem. We
reveal GPI and TRL to be but small points within this far greater space of
algorithms which boasts the monotonic improvement property and converges to the
optimal policy. We show that virtually all SOTA algorithms for RL are instances
of mirror learning, and thus suggest that their empirical performance is a
consequence of their theoretical properties, rather than of approximate
analogies. Excitingly, we show that mirror learning opens up a whole new space
of policy learning methods with convergence guarantees.
【9】 A Transfer Learning Pipeline for Educational Resource Discovery with Application in Leading Paragraph Generation
标题：教育资源发现的迁移学习流水线及其在前导段落生成中的应用
链接：https://arxiv.org/abs/2201.02312
机构：Yale University, USA,University of Waterloo, Canada,Tata Consultancy Services Limited, India
摘要：有效的人类学习取决于广泛选择的教育材料，这些材料与学习者当前对主题的理解相一致。虽然互联网已经彻底改变了人类的学习或教育，但仍然存在巨大的资源获取障碍。也就是说，过多的在线信息会使导航和发现高质量的学习材料变得具有挑战性。在本文中，我们提出了教育资源发现（ERD）管道，该管道可以自动化新领域的web资源发现。管道包括三个主要步骤：数据收集、特征提取和资源分类。我们从一个已知的源域开始，通过迁移学习在两个不可见的目标域上进行资源发现。我们首先从一组种子文档中收集频繁的查询，并在web上搜索以获取候选资源，例如讲座幻灯片和介绍性博客文章。然后，我们引入一种新的预训练信息检索深度神经网络模型，即查询文档屏蔽语言建模（QD-MLM），来提取这些候选资源的深度特征。我们使用一个基于树的分类器来判断候选对象是否是一个积极的学习资源。当在两个相似但新颖的目标域上进行评估时，管道的F1得分分别为0.94和0.82。最后，我们将演示此管道如何有利于应用程序：调查的前导段落生成。据我们所知，这是第一次考虑调查生成的各种web资源的研究。我们还从NLP、计算机视觉（CV）和统计（STATS）发布了39728个手动标记的web资源和659个查询的语料库。
摘要：Effective human learning depends on a wide selection of educational materials
that align with the learner's current understanding of the topic. While the
Internet has revolutionized human learning or education, a substantial resource
accessibility barrier still exists. Namely, the excess of online information
can make it challenging to navigate and discover high-quality learning
materials. In this paper, we propose the educational resource discovery (ERD)
pipeline that automates web resource discovery for novel domains. The pipeline
consists of three main steps: data collection, feature extraction, and resource
classification. We start with a known source domain and conduct resource
discovery on two unseen target domains via transfer learning. We first collect
frequent queries from a set of seed documents and search on the web to obtain
candidate resources, such as lecture slides and introductory blog posts. Then
we introduce a novel pretrained information retrieval deep neural network
model, query-document masked language modeling (QD-MLM), to extract deep
features of these candidate resources. We apply a tree-based classifier to
decide whether the candidate is a positive learning resource. The pipeline
achieves F1 scores of 0.94 and 0.82 when evaluated on two similar but novel
target domains. Finally, we demonstrate how this pipeline can benefit an
application: leading paragraph generation for surveys. This is the first study
that considers various web resources for survey generation, to the best of our
knowledge. We also release a corpus of 39,728 manually labeled web resources
and 659 queries from NLP, Computer Vision (CV), and Statistics (STATS).
【10】 Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling
标题：基于交叉交互协同关系建模的多行为增强推荐
链接：https://arxiv.org/abs/2201.02307
机构：South China University of Technology, JD Finance America Corporation
备注：Published on ICDE 2021
摘要：以前的许多研究都试图利用深度神经网络技术来增强协同过滤，从而获得更好的推荐性能。然而，现有的基于深度学习的推荐系统大多是针对单一类型的用户-项目交互行为设计的，难以提取用户与项目之间的异构关系。在实际的推荐场景中，存在着浏览、购买等多种类型的用户行为。由于忽略了用户对不同项目的多行为模式，现有的推荐方法不足以从用户多行为数据中捕获异构的协作信号。受图神经网络用于结构化数据建模的优势启发，本文提出了一个图神经多行为增强推荐（GNMR）框架，该框架在基于图的消息传递体系结构下显式地建模不同类型的用户项交互之间的依赖关系。GNMR设计了一个关系聚合网络来模拟交互异构性，并在用户项交互图上递归地执行相邻节点之间的嵌入传播。在现实世界的推荐数据集上的实验表明，我们的GNMR始终优于最先进的方法。源代码可在https://github.com/akaxlh/GNMR.
摘要：Many previous studies aim to augment collaborative filtering with deep neural
network techniques, so as to achieve better recommendation performance.
However, most existing deep learning-based recommender systems are designed for
modeling singular type of user-item interaction behavior, which can hardly
distill the heterogeneous relations between user and item. In practical
recommendation scenarios, there exist multityped user behaviors, such as browse
and purchase. Due to the overlook of user's multi-behavioral patterns over
different items, existing recommendation methods are insufficient to capture
heterogeneous collaborative signals from user multi-behavior data. Inspired by
the strength of graph neural networks for structured data modeling, this work
proposes a Graph Neural Multi-Behavior Enhanced Recommendation (GNMR) framework
which explicitly models the dependencies between different types of user-item
interactions under a graph-based message passing architecture. GNMR devises a
relation aggregation network to model interaction heterogeneity, and
recursively performs embedding propagation between neighboring nodes over the
user-item interaction graph. Experiments on real-world recommendation datasets
show that our GNMR consistently outperforms state-of-the-art methods. The
source code is available at https://github.com/akaxlh/GNMR.
【11】 Learning Multi-Tasks with Inconsistent Labels by using Auxiliary Big Task
标题：利用辅助大任务学习标签不一致的多任务
链接：https://arxiv.org/abs/2201.02305
机构：College of Computer Science and Technology, Nanjing University of Aeronautics and, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of, Aeronautics and Astronautics, Nanjing, Jiangsu, China
摘要：多任务学习是通过在任务之间传递和利用公共知识来提高模型的性能。现有的MTL工作主要关注多个任务（MTs）之间的标签集通常相同的场景，因此它们可以用于跨任务学习。几乎很少有作品探讨这样的场景：每个任务只有少量的训练样本，而它们的标签集只是部分重叠，甚至没有重叠。由于这些任务之间的相关性信息较少，因此学习此类MTs更具挑战性。为此，我们提出了一个学习这些任务的框架，通过联合利用学习到的辅助大任务中的丰富信息，该辅助大任务有足够多的类来覆盖所有这些任务，以及这些部分重叠任务之间共享的信息。在我们使用学习到的辅助任务的相同神经网络结构来学习单个任务的实现中，关键思想是利用可用的标签信息自适应地修剪辅助网络的隐层神经元，为每个任务构建相应的网络，同时伴随着跨单个任务的联合学习。我们的实验结果表明，它的有效性与国家的最先进的方法相比。
摘要：Multi-task learning is to improve the performance of the model by
transferring and exploiting common knowledge among tasks. Existing MTL works
mainly focus on the scenario where label sets among multiple tasks (MTs) are
usually the same, thus they can be utilized for learning across the tasks.
While almost rare works explore the scenario where each task only has a small
amount of training samples, and their label sets are just partially overlapped
or even not. Learning such MTs is more challenging because of less correlation
information available among these tasks. For this, we propose a framework to
learn these tasks by jointly leveraging both abundant information from a learnt
auxiliary big task with sufficiently many classes to cover those of all these
tasks and the information shared among those partially-overlapped tasks. In our
implementation of using the same neural network architecture of the learnt
auxiliary task to learn individual tasks, the key idea is to utilize available
label information to adaptively prune the hidden layer neurons of the auxiliary
network to construct corresponding network for each task, while accompanying a
joint learning across individual tasks. Our experimental results demonstrate
its effectiveness in comparison with the state-of-the-art approaches.
【12】 Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey
标题：基于模糊认知图的时间序列预测研究综述
链接：https://arxiv.org/abs/2201.02297
机构：Machine Intelligence and Data Science (MINDS) Laboratory, Federal University of Minas Gerais, Belo Horizonte, Brazil, Federal Institute of Education Science and Technology of Northern Minas Gerais, Janu´aria Campus, Brazil
摘要：在用于时间序列预测的各种软计算方法中，模糊认知图（FCM）作为一种建模和分析复杂系统动力学的工具已经显示出显著的效果。FCM与递归神经网络有相似之处，可以归类为一种神经模糊方法。换句话说，FCMs是模糊逻辑、神经网络和专家系统的混合体，是模拟和研究复杂系统动态行为的有力工具。最有趣的特征是知识的可解释性、动态特性和学习能力。本文的目的主要是概述文献中提出的最相关和最新的基于FCM的时间序列预测模型。此外，本文还将介绍FCM模型的基本原理和学习方法。此外，本调查还为未来的研究提供了一些思路，以增强FCM的能力，从而应对现实世界实验中的一些挑战，如处理非平稳数据和可伸缩性问题。此外，为FCMs配备快速学习算法是该领域的主要关注点之一。
摘要：Among various soft computing approaches for time series forecasting, Fuzzy
Cognitive Maps (FCM) have shown remarkable results as a tool to model and
analyze the dynamics of complex systems. FCM have similarities to recurrent
neural networks and can be classified as a neuro-fuzzy method. In other words,
FCMs are a mixture of fuzzy logic, neural network, and expert system aspects,
which act as a powerful tool for simulating and studying the dynamic behavior
of complex systems. The most interesting features are knowledge
interpretability, dynamic characteristics and learning capability. The goal of
this survey paper is mainly to present an overview on the most relevant and
recent FCM-based time series forecasting models proposed in the literature. In
addition, this article considers an introduction on the fundamentals of FCM
model and learning methodologies. Also, this survey provides some ideas for
future research to enhance the capabilities of FCM in order to cover some
challenges in the real-world experiments such as handling non-stationary data
and scalability issues. Moreover, equipping FCMs with fast learning algorithms
is one of the major concerns in this area.
【13】 Applying Word Embeddings to Measure Valence in Information Operations Targeting Journalists in Brazil
标题：在巴西以记者为目标的信息操作中应用词嵌入来衡量价位
链接：https://arxiv.org/abs/2201.02257
机构：Department of Engineering Management and Systems Engineering, The George Washington University, Washington, DC, USA
摘要：信息业务的目标之一是改变针对特定参与者的总体信息环境。例如，“推特运动”试图破坏特定公众人物的信誉，导致其他人不信任他们，并威胁这些人物保持沉默。为了实现这些目标，信息运营部门经常使用“巨魔”——恶意的网络参与者，他们对这些人物进行口头攻击。特别是在巴西，巴西现任总统的盟友被指控操纵“仇恨内阁”——这是一项针对指控这位政治家及其政权其他成员腐败的记者的跟踪行动。检测有害言论的领先方法，如谷歌的透视API，试图识别含有有害内容的特定信息。虽然这种方法有助于识别要降级、标记或删除的内容，但众所周知，它很脆弱，可能会错过在话语中引入更微妙偏见的尝试。在这里，我们的目标是制定一项措施，用于评估有针对性的信息行动如何寻求改变特定行为者的总体价值或评价。初步结果表明，已知的活动以女性记者为目标，而非男性记者，而且这些活动可能会在整个推特话语中留下可察觉的痕迹。
摘要：Among the goals of information operations are to change the overall
information environment vis-\'a-vis specific actors. For example, "trolling
campaigns" seek to undermine the credibility of specific public figures,
leading others to distrust them and intimidating these figures into silence. To
accomplish these aims, information operations frequently make use of "trolls"
-- malicious online actors who target verbal abuse at these figures. In Brazil,
in particular, allies of Brazil's current president have been accused of
operating a "hate cabinet" -- a trolling operation that targets journalists who
have alleged corruption by this politician and other members of his regime.
Leading approaches to detecting harmful speech, such as Google's Perspective
API, seek to identify specific messages with harmful content. While this
approach is helpful in identifying content to downrank, flag, or remove, it is
known to be brittle, and may miss attempts to introduce more subtle biases into
the discourse. Here, we aim to develop a measure that might be used to assess
how targeted information operations seek to change the overall valence, or
appraisal, of specific actors. Preliminary results suggest known campaigns
target female journalists more so than male journalists, and that these
campaigns may leave detectable traces in overall Twitter discourse.
【14】 The E-Intelligence System
标题：电子情报系统
链接：https://arxiv.org/abs/2201.02590
摘要：电子情报（ELINT），通常被称为电子情报，是通过电子传感器获得的情报。除了个人通信之外，通常还可以获得电子情报。目标通常是确定目标的能力，如雷达位置。可以使用主动或被动传感器来收集数据。对提供的信号进行分析，并与收集的数据进行对比，以识别信号类型。如果检测到信号类型，则可以存储信息；如果没有找到匹配项，则可以将其归类为新的。ELINT收集和分类数据。在军事环境中（以及其他采用这种用法的环境，如商业环境），情报可以帮助组织做出决策，使其在竞争中获得战略优势。“英特尔”一词经常被缩短。信号情报（SIGINT）的两个主要子领域是ELINT和通信情报（COMINT）。美国国防部指定了术语，情报机构使用全球审查的数据类别。
摘要：Electronic Intelligence (ELINT), often known as E-Intelligence, is
intelligence obtained through electronic sensors. Other than personal
communications, ELINT intelligence is usually obtained. The goal is usually to
determine a target's capabilities, such as radar placement. Active or passive
sensors can be employed to collect data. A provided signal is analyzed and
contrasted to collected data for recognized signal types. The information may
be stored if the signal type is detected; it can be classed as new if no match
is found. ELINT collects and categorizes data. In a military setting (and
others that have adopted the usage, such as a business), intelligence helps an
organization make decisions that can provide them a strategic advantage over
the competition. The term "intel" is frequently shortened. The two main
subfields of signals intelligence (SIGINT) are ELINT and Communications
Intelligence (COMINT). The US Department of Defense specifies the
terminologies, and intelligence communities use the categories of data reviewed
worldwide.
【15】 Effect of Prior-based Losses on Segmentation Performance: A Benchmark
标题：基于先前损失对分割性能的影响：一个基准
链接：https://arxiv.org/abs/2201.02428
机构：Abdallahd, e, Normandie Univ, INSA Rouen, UNIROUEN, UNIHAVRE, LITIS, Rouen, France, Computer Science Department, IT University of Copenhagen, Denmark, Medical Image Analysis group, Eindhoven University of Technology, Eindhoven, The Netherlands
备注：To be submitted to SPIE: Journal of Medical Imaging
摘要：今天，深卷积神经网络（CNN）已经在各种成像模式和任务上展示了最先进的医学图像分割性能。尽管早期取得了成功，分割网络仍可能产生解剖学上异常的分割，在对象边界附近有孔洞或不准确。为了加强解剖学上的合理性，最近的研究集中于将先验知识（如物体形状或边界）纳入损失函数中作为约束条件。先前的整合可以是低级的，指从基本真实分割中提取的重新制定的表示，或者高级的，表示外部医疗信息，例如器官的形状或大小。在过去几年中，基于先前的损失在研究领域表现出越来越高的兴趣，因为它们允许专家知识的集成，同时仍然是架构不可知的。然而，鉴于不同医学成像挑战和任务导致的基于先前的损失的多样性，很难确定哪种损失最适合哪个数据集。在本文中，我们建立了一个基准最近基于先验的损失医学图像分割。主要目的是提供直觉，在给定特定任务或数据集的情况下选择哪些损失。为此，选择了四种低水平和高水平的基于先验的损失。所考虑的损失在来自各种医学图像分割挑战的8个不同数据集上得到验证，包括十项全能、岛屿和WMH挑战。结果表明，尽管无论数据集特征如何，低水平的基于先验的损失都可以保证比骰子损失基线的性能有所提高，但根据数据特征，高水平的基于先验的损失可以提高解剖学上的合理性。
摘要：Today, deep convolutional neural networks (CNNs) have demonstrated
state-of-the-art performance for medical image segmentation, on various imaging
modalities and tasks. Despite early success, segmentation networks may still
generate anatomically aberrant segmentations, with holes or inaccuracies near
the object boundaries. To enforce anatomical plausibility, recent research
studies have focused on incorporating prior knowledge such as object shape or
boundary, as constraints in the loss function. Prior integrated could be
low-level referring to reformulated representations extracted from the
ground-truth segmentations, or high-level representing external medical
information such as the organ's shape or size. Over the past few years,
prior-based losses exhibited a rising interest in the research field since they
allow integration of expert knowledge while still being architecture-agnostic.
However, given the diversity of prior-based losses on different medical imaging
challenges and tasks, it has become hard to identify what loss works best for
which dataset. In this paper, we establish a benchmark of recent prior-based
losses for medical image segmentation. The main objective is to provide
intuition onto which losses to choose given a particular task or dataset. To
this end, four low-level and high-level prior-based losses are selected. The
considered losses are validated on 8 different datasets from a variety of
medical image segmentation challenges including the Decathlon, the ISLES and
the WMH challenge. Results show that whereas low-level prior-based losses can
guarantee an increase in performance over the Dice loss baseline regardless of
the dataset characteristics, high-level prior-based losses can increase
anatomical plausibility as per data characteristics.
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