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Lexical Semantic Change through Large Language Models: a Survey 通过大型语言模型实现词汇语义变化:一项调查
IF 16.6 1区 计算机科学 Q1 Mathematics Pub Date : 2024-06-10 DOI: 10.1145/3672393
Francesco Periti, Stefano Montanelli

Lexical Semantic Change (LSC) is the task of identifying, interpreting, and assessing the possible change over time in the meanings of a target word. Traditionally, LSC has been addressed by linguists and social scientists through manual and time-consuming analyses, which have thus been limited in terms of the volume, genres, and time-frame that can be considered. In recent years, computational approaches based on Natural Language Processing have gained increasing attention to automate LSC as much as possible. Significant advancements have been made by relying on Large Language Models (LLMs), which can handle the multiple usages of the words and better capture the related semantic change. In this article, we survey the approaches based on LLMs for LSC and we propose a classification framework characterized by three dimensions: meaning representation, time-awareness, and learning modality. The framework is exploited to i) review the measures for change assessment, ii) compare the approaches on performance, and iii) discuss the current issues in terms of scalability, interpretability, and robustness. Open challenges and future research directions about the use of LLMs for LSC are finally outlined.

词义变化(LSC)是指识别、解释和评估目标词的词义随时间推移可能发生的变化。传统上,语言学家和社会科学家都是通过耗时的人工分析来处理 LSC 问题的,因此在可考虑的数量、流派和时间范围方面都受到了限制。近年来,基于自然语言处理的计算方法受到越来越多的关注,以尽可能实现 LSC 自动化。大型语言模型(LLM)可以处理词语的多种用法,并能更好地捕捉相关语义变化,因此在这方面取得了重大进展。在本文中,我们对基于 LLM 的 LSC 方法进行了调查,并提出了一个分类框架,其特点包括三个方面:意义表示、时间感知和学习模式。利用该框架,我们可以:i) 回顾变化评估的措施;ii) 比较各种方法的性能;iii) 讨论当前在可扩展性、可解释性和鲁棒性方面存在的问题。最后概述了将 LLMs 用于 LSC 所面临的挑战和未来的研究方向。
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引用次数: 0
Databases in Edge and Fog Environments : A Survey 边缘和雾环境中的数据库 :调查
IF 16.6 1区 计算机科学 Q1 Mathematics Pub Date : 2024-06-04 DOI: 10.1145/3666001
Luís Manuel Meruje Ferreira, Fabio Coelho, José Pereira

While a significant number of databases are deployed in cloud environments, pushing part or all data storage and querying planes closer to their sources (i.e., to the edge) can provide advantages in latency, connectivity, privacy, energy and scalability. This article dissects the advantages provided by databases in edge and fog environments, by surveying application domains and discussing the key drivers for pushing database systems to the edge. At the same time, it also identifies the main challenges faced by developers in this new environment, and analysis the mechanisms employed to deal with them. By providing an overview of the current state of edge and fog databases, this survey provides valuable insights into future research directions.

虽然大量数据库部署在云环境中,但将部分或全部数据存储和查询平面推向更靠近数据源的地方(即边缘),可以在延迟、连接性、隐私、能源和可扩展性方面提供优势。本文通过调查应用领域和讨论将数据库系统推向边缘的关键驱动因素,剖析了数据库在边缘和雾环境中提供的优势。同时,文章还指出了开发人员在这种新环境中面临的主要挑战,并分析了应对这些挑战的机制。通过概述边缘和雾数据库的现状,本调查报告为未来的研究方向提供了宝贵的见解。
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引用次数: 0
Machine Learning with Confidential Computing: A Systematization of Knowledge 利用保密计算进行机器学习:知识系统化
IF 16.6 1区 计算机科学 Q1 Mathematics Pub Date : 2024-06-03 DOI: 10.1145/3670007
Fan Mo, Zahra Tarkhani, Hamed Haddadi

Privacy and security challenges in Machine Learning (ML) have become increasingly severe, along with ML’s pervasive development and the recent demonstration of large attack surfaces. As a mature system-oriented approach, Confidential Computing has been utilized in both academia and industry to mitigate privacy and security issues in various ML scenarios. In this paper, the conjunction between ML and Confidential Computing is investigated. We systematize the prior work on Confidential Computing-assisted ML techniques that provide iconfidentiality guarantees and iiintegrity assurances, and discuss their advanced features and drawbacks. Key challenges are further identified, and we provide dedicated analyses of the limitations in existing Trusted Execution Environment (TEE) systems for ML use cases. Finally, prospective works are discussed, including grounded privacy definitions for closed-loop protection, partitioned executions of efficient ML, dedicated TEE-assisted designs for ML, TEE-aware ML, and ML full pipeline guarantees. By providing these potential solutions in our systematization of knowledge, we aim to build the bridge to help achieve a much stronger TEE-enabled ML for privacy guarantees without introducing computation and system costs.

随着机器学习(ML)的普遍发展和最近展示的巨大攻击面,机器学习(ML)中的隐私和安全挑战变得日益严峻。作为一种成熟的面向系统的方法,保密计算已被学术界和工业界用于缓解各种 ML 场景中的隐私和安全问题。本文研究了 ML 与保密计算之间的结合。我们系统梳理了保密计算辅助 ML 技术(提供 i) 保密性保证和 ii) 完整性保证)的前期工作,并讨论了它们的先进功能和缺点。我们进一步确定了关键挑战,并专门分析了用于 ML 用例的现有可信执行环境 (TEE) 系统的局限性。最后,我们讨论了前瞻性工作,包括闭环保护的基础隐私定义、高效 ML 的分区执行、ML 的专用 TEE 辅助设计、TEE 感知 ML 和 ML 全流水线保证。通过在我们的知识系统化中提供这些潜在的解决方案,我们旨在搭建一座桥梁,帮助实现更强大的 TEE 支持的 ML,从而在不引入计算和系统成本的情况下实现隐私保证。
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引用次数: 0
“Are you feeling sick?” A systematic literature review of cybersickness in virtual reality "你感觉不舒服吗?虚拟现实中的网络病症系统文献综述
IF 16.6 1区 计算机科学 Q1 Mathematics Pub Date : 2024-06-03 DOI: 10.1145/3670008
Nilotpal Biswas, Anamitra Mukherjee, Samit Bhattacharya

Cybersickness (CS), also known as visually induced motion sickness (VIMS) is a condition that can affect individuals when they interact with virtual reality (VR) technology. This condition is characterized by symptoms such as nausea, dizziness, headaches, eye fatigue, etc., and can be caused by a variety of factors. Finding a feasible solution to reduce the impact of CS is extremely important as it will greatly enhance the overall user experience and make VR more appealing to a wider range of people. We have carefully compiled a list of 223 highly pertinent studies to review the current state of research on the most essential aspects of CS. We have provided a novel taxonomy that encapsulates various aspects of CS measurement techniques found in the literature. We have proposed a set of CS mitigation guidelines for both developers and users. We have also discussed various CS-inducing factors and provided a taxonomy that tries to capture the same. Overall, our work provides a comprehensive overview of the current state of research in CS with a particular emphasis on different measurement techniques and CS mitigation strategies, identifies research gaps in the literature, and provides recommendations for future research in the field.

晕动症(CS),又称视觉诱发晕动病(VIMS),是一种在与虚拟现实(VR)技术交互时可能影响个人的病症。这种症状的特点是恶心、头晕、头痛、眼睛疲劳等,可由多种因素引起。找到一种可行的解决方案来减少 CS 的影响极为重要,因为这将大大提升用户的整体体验,使 VR 对更多人更具吸引力。我们精心编制了一份包含 223 项高度相关研究的清单,以回顾有关 CS 最基本方面的研究现状。我们提供了一个新颖的分类法,囊括了文献中发现的 CS 测量技术的各个方面。我们为开发人员和用户提出了一套 CS 缓解指南。我们还讨论了各种诱发 CS 的因素,并提供了一个试图捕捉这些因素的分类法。总之,我们的工作全面概述了 CS 的研究现状,特别强调了不同的测量技术和 CS 缓解策略,确定了文献中的研究空白,并为该领域的未来研究提供了建议。
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引用次数: 0
Advancements in Federated Learning: Models, Methods, and Privacy 联合学习的进步:模式、方法和隐私
IF 16.6 1区 计算机科学 Q1 Mathematics Pub Date : 2024-06-01 DOI: 10.1145/3664650
Huiming Chen, Huandong Wang, Qingyue Long, Depeng Jin, Yong Li

Federated learning (FL) is a promising technique for resolving the rising privacy and security concerns. Its main ingredient is to cooperatively learn the model among the distributed clients without uploading any sensitive data. In this paper, we conducted a thorough review of the related works, following the development context and deeply mining the key technologies behind FL from the perspectives of theory and application. Specifically, we first classify the existing works in FL architecture based on the network topology of FL systems with detailed analysis and summarization. Next, we abstract the current application problems, summarize the general techniques and frame the application problems into the general paradigm of FL base models. Moreover, we provide our proposed solutions for model training via FL. We have summarized and analyzed the existing FedOpt algorithms, and deeply revealed the algorithmic development principles of many first-order algorithms in depth, proposing a more generalized algorithm design framework. With the instantiation of these frameworks, FedOpt algorithms can be simply developed. As privacy and security is the fundamental requirement in FL, we provide the existing attack scenarios and the defense methods. To the best of our knowledge, we are among the first tier to review the theoretical methodology and propose our strategies since there are very few works surveying the theoretical approaches. Our survey targets motivating the development of high-performance, privacy-preserving, and secure methods to integrate FL into real-world applications.

联合学习(FL)是解决日益增长的隐私和安全问题的一种有前途的技术。其主要内容是在不上传任何敏感数据的情况下,在分布式客户端之间合作学习模型。在本文中,我们对相关工作进行了全面回顾,遵循发展脉络,从理论和应用的角度深入挖掘了 FL 背后的关键技术。具体来说,我们首先根据 FL 系统的网络拓扑结构对 FL 架构的现有工作进行了分类,并进行了详细的分析和总结。接着,我们抽象出当前的应用问题,总结出通用技术,并将应用问题框定到 FL 基础模型的一般范式中。此外,我们还提出了通过 FL 进行模型训练的解决方案。我们总结分析了现有的 FedOpt 算法,深入揭示了许多一阶算法的算法开发原理,提出了更具普适性的算法设计框架。通过这些框架的实例化,可以简单地开发出 FedOpt 算法。由于隐私和安全是 FL 的基本要求,我们提供了现有的攻击场景和防御方法。据我们所知,我们是第一批回顾理论方法并提出我们的策略的人,因为很少有著作调查理论方法。我们的调查旨在激励开发高性能、保护隐私和安全的方法,以便将 FL 集成到现实世界的应用中。
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引用次数: 0
Research Progress of EEG-Based Emotion Recognition: A Survey 基于脑电图的情绪识别研究进展:调查
IF 16.6 1区 计算机科学 Q1 Mathematics Pub Date : 2024-05-28 DOI: 10.1145/3666002
Yiming Wang, Bin Zhang, Lamei Di

Emotion recognition based on electroencephalography (EEG) signals has emerged as a prominent research field, facilitating objective evaluation of diseases like depression and motion detection for heathy people. Starting from the basic concepts of temporal-frequency-spatial features in EEG and the methods for cross-domain feature fusion. This survey then extends the overfitting challenge of EEG single-modal to the problem of heterogeneous modality modeling in multi-modal conditions. It explores issues such as feature selection, sample scarcity, cross-subject emotional transfer, physiological knowledge discovery, multi-modal fusion methods and modality missing. These findings provide clues for researchers to further investigate emotion recognition based on EEG signals.

基于脑电图(EEG)信号的情绪识别已成为一个突出的研究领域,有助于对抑郁症等疾病进行客观评估和对健康人进行运动检测。本研究从脑电信号的时间-频率-空间特征的基本概念和跨域特征融合方法入手。然后,本研究将脑电图单模态过拟合挑战扩展到多模态条件下的异构模态建模问题。它探讨了特征选择、样本稀缺性、跨主体情感转移、生理知识发现、多模态融合方法和模态缺失等问题。这些发现为研究人员进一步研究基于脑电信号的情感识别提供了线索。
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引用次数: 0
A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities 人工智能的鲁棒性:从以人为本的角度看技术挑战与机遇
IF 16.6 1区 计算机科学 Q1 Mathematics Pub Date : 2024-05-27 DOI: 10.1145/3665926
Andrea Tocchetti, Lorenzo Corti, Agathe Balayn, Mireia Yurrita, Philip Lippmann, Marco Brambilla, Jie Yang

Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness remains elusive and constitutes a key issue that impedes large-scale adoption. Besides, robustness is interpreted differently across domains and contexts of AI. In this work, we systematically survey recent progress to provide a reconciled terminology of concepts around AI robustness. We introduce three taxonomies to organize and describe the literature both from a fundamental and applied point of view: 1) methods and approaches that address robustness in different phases of the machine learning pipeline; 2) methods improving robustness in specific model architectures, tasks, and systems; and in addition, 3) methodologies and insights around evaluating the robustness of AI systems, particularly the trade-offs with other trustworthiness properties. Finally, we identify and discuss research gaps and opportunities and give an outlook on the field. We highlight the central role of humans in evaluating and enhancing AI robustness, considering the necessary knowledge they can provide, and discuss the need for better understanding practices and developing supportive tools in the future.

尽管人工智能(AI)系统的性能令人印象深刻,但其鲁棒性仍然难以捉摸,成为阻碍大规模应用的关键问题。此外,在不同的人工智能领域和环境中,对鲁棒性的解释也不尽相同。在这项工作中,我们系统地调查了最近的进展,为人工智能的鲁棒性提供了一个协调的概念术语。我们引入了三个分类法,从基础和应用的角度来组织和描述文献:1)在机器学习管道的不同阶段解决鲁棒性问题的方法和途径;2)在特定模型架构、任务和系统中提高鲁棒性的方法;此外,3)评估人工智能系统鲁棒性的方法和见解,特别是与其他可信性属性之间的权衡。最后,我们确定并讨论了研究差距和机遇,并对该领域进行了展望。我们强调了人类在评估和增强人工智能鲁棒性方面的核心作用,考虑了人类可以提供的必要知识,并讨论了未来更好地理解实践和开发辅助工具的必要性。
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引用次数: 0
Human Image Generation: A Comprehensive Survey 人类图像生成:全面调查
IF 16.6 1区 计算机科学 Q1 Mathematics Pub Date : 2024-05-22 DOI: 10.1145/3665869
Zhen Jia, Zhang Zhang, Liang Wang, Tieniu Tan

Image and video synthesis has become a blooming topic in computer vision and machine learning communities along with the developments of deep generative models, due to its great academic and application value. Many researchers have been devoted to synthesizing high-fidelity human images as one of the most commonly seen object categories in daily lives, where a large number of studies are performed based on various models, task settings and applications. Thus, it is necessary to give a comprehensive overview on these variant methods on human image generation. In this paper, we divide human image generation techniques into three paradigms, i.e., data-driven methods, knowledge-guided methods and hybrid methods. For each paradigm, the most representative models and the corresponding variants are presented, where the advantages and characteristics of different methods are summarized in terms of model architectures. Besides, the main public human image datasets and evaluation metrics in the literature are summarized. Furthermore, due to the wide application potentials, the typical downstream usages of synthesized human images are covered. Finally, the challenges and potential opportunities of human image generation are discussed to shed light on future research.

随着深度生成模型的发展,图像和视频合成因其巨大的学术和应用价值,已成为计算机视觉和机器学习领域一个蓬勃发展的课题。作为日常生活中最常见的对象类别之一,许多研究人员都致力于合成高保真人体图像,并基于各种模型、任务设置和应用进行了大量研究。因此,有必要对这些不同的人体图像生成方法进行全面概述。本文将人类图像生成技术分为三种范式,即数据驱动法、知识引导法和混合法。针对每种范式,我们都介绍了最具代表性的模型和相应的变体,并从模型架构的角度总结了不同方法的优势和特点。此外,还总结了文献中主要的公共人类图像数据集和评估指标。此外,由于合成人体图像具有广泛的应用潜力,还介绍了合成人体图像的典型下游用途。最后,讨论了人类图像生成所面临的挑战和潜在机遇,为未来研究提供启示。
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引用次数: 0
A Survey on Malware Detection with Graph Representation Learning 利用图表示学习进行恶意软件检测的调查
IF 16.6 1区 计算机科学 Q1 Mathematics Pub Date : 2024-05-21 DOI: 10.1145/3664649
Tristan Bilot, Nour El Madhoun, Khaldoun Al Agha, Anis Zouaoui

Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization to unknown attacks and can be easily circumvented using obfuscation techniques. In recent years, Machine Learning (ML) and notably Deep Learning (DL) achieved impressive results in malware detection by learning useful representations from data and have become a solution preferred over traditional methods. Recently, the application of Graph Representation Learning (GRL) techniques on graph-structured data has demonstrated impressive capabilities in malware detection. This success benefits notably from the robust structure of graphs, which are challenging for attackers to alter, and their intrinsic explainability capabilities. In this survey, we provide an in-depth literature review to summarize and unify existing works under the common approaches and architectures. We notably demonstrate that Graph Neural Networks (GNNs) reach competitive results in learning robust embeddings from malware represented as expressive graph structures such as Function Call Graphs (FCGs) and Control Flow Graphs (CFGs). This study also discusses the robustness of GRL-based methods to adversarial attacks, contrasts their effectiveness with other ML/DL approaches, and outlines future research for practical deployment.

由于恶意软件的数量和复杂性不断增加,恶意软件检测已成为一个主要问题。传统的恶意软件检测方法基于签名和启发式方法,但遗憾的是,这些方法对未知攻击的泛化能力较差,而且很容易被混淆技术所规避。近年来,机器学习(ML),尤其是深度学习(DL)通过从数据中学习有用的表征,在恶意软件检测方面取得了令人瞩目的成果,并已成为一种优于传统方法的解决方案。最近,图表示学习(GRL)技术在图结构数据上的应用已在恶意软件检测中展现出令人印象深刻的能力。这种成功主要得益于图的强大结构(攻击者很难改变这种结构)及其内在的可解释性。在本调查报告中,我们对文献进行了深入回顾,总结并统一了通用方法和架构下的现有工作。值得注意的是,我们证明了图神经网络(GNN)在从以函数调用图(FCG)和控制流图(CFG)等表现性图结构表示的恶意软件中学习稳健嵌入方面取得了有竞争力的结果。本研究还讨论了基于 GRL 的方法对对抗性攻击的鲁棒性,对比了它们与其他 ML/DL 方法的有效性,并概述了未来的实际部署研究。
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引用次数: 0
Causality for Trustworthy Artificial Intelligence: Status, Challenges and Perspectives 可信人工智能的因果关系:现状、挑战和前景
IF 16.6 1区 计算机科学 Q1 Mathematics Pub Date : 2024-05-20 DOI: 10.1145/3665494
A. Rawal, Adrienne Raglin, Danda B. Rawat, Brian M. Sadler, J. McCoy
Causal inference is the idea of cause-and-effect; this fundamental area of sciences can be applied to problem space associated with Newton’s laws or the devastating COVID-19 pandemic. The cause explains the “why” whereas the effect describes the “what”. The domain itself encompasses a plethora of disciplines from statistics and computer science to economics and philosophy. Recent advancements in machine learning (ML) and artificial intelligence (AI) systems, have nourished a renewed interest in identifying and estimating the cause-and-effect relationship from the substantial amount of available observational data. This has resulted in various new studies aimed at providing novel methods for identifying and estimating causal inference. We include a detailed taxonomy of causal inference frameworks, methods, and evaluation. An overview of causality for security is also provided. Open challenges are detailed, and approaches for evaluating the robustness of causal inference methods are described. This paper aims to provide a comprehensive survey on such studies of causality. We provide an in-depth review of causality frameworks, and describe the different methods.
因果推理是因果关系的概念;这一基本科学领域可应用于与牛顿定律或具有破坏性的 COVID-19 大流行病相关的问题空间。因解释了 "为什么",而果则描述了 "是什么"。这一领域本身涵盖了从统计学、计算机科学到经济学和哲学等众多学科。近来,机器学习(ML)和人工智能(AI)系统的进步再次激发了人们对从大量可用观测数据中识别和估算因果关系的兴趣。这导致了各种新的研究,旨在为因果推理的识别和估算提供新的方法。我们对因果推理框架、方法和评估进行了详细分类。我们还提供了安全因果关系概述。本文详细介绍了尚未解决的挑战,并描述了评估因果推理方法稳健性的方法。本文旨在对此类因果关系研究进行全面调查。我们深入回顾了因果关系框架,并介绍了不同的方法。
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引用次数: 0
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ACM Computing Surveys
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