ACM Computing Surveys最新文献_第9页

Blockchain-Empowered Trustworthy Data Sharing: Fundamentals, Applications, and Challenges

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-15 DOI: 10.1145/3718082

Thanh Linh Nguyen, Lam Nguyen, Thong Hoang, Dilum Bandara, Qin Wang, Qinghua Lu, Xiwei Xu, Liming Zhu, Shiping Chen

{"title":"Blockchain-Empowered Trustworthy Data Sharing: Fundamentals, Applications, and Challenges","authors":"Thanh Linh Nguyen, Lam Nguyen, Thong Hoang, Dilum Bandara, Qin Wang, Qinghua Lu, Xiwei Xu, Liming Zhu, Shiping Chen","doi":"10.1145/3718082","DOIUrl":"https://doi.org/10.1145/3718082","url":null,"abstract":"The rise of data-sharing platforms, driven by public demand for open data and legislative mandates, has raised several pertinent issues. These encompass uncertainties over data accuracy, provenance and lineage, privacy concerns, consent management, and the lack of equitable incentives for data providers. The advanced nature of blockchain makes it well-suited to address these concerns. Yet, the limitations of blockchains, particularly their restricted performance, scalability, and high cost, make them less adept at managing the four “V” of big data - volume, variety, velocity, and veracity. As the body of work proposing blockchain-based data-sharing solutions grows, so does the confusion in selecting between these platforms, particularly in terms of sharing mechanisms, services, quality of services, and applications. In this paper, we aim to fill this knowledge gap through an in-depth survey of blockchain-based data-sharing architectures and applications. We first identify the key challenges of existing data-sharing techniques and lay out the foundations of blockchains. Our focus then shifts to the intersection of blockchain and data sharing, wherein we aim to clarify the existing landscape and propose a reference architecture for blockchain-based data sharing. Subsequently, we explore various industrial applications of blockchain-based data sharing, spanning healthcare, smart grids, transportation, and decarbonization. For each application, we draw from real-world deployments to present key lessons learned in the implementation of blockchain-based data sharing. Lastly, we shed light on current research challenges and open avenues for further study in this space. This paper aims to serve as a comprehensive resource for researchers/practitioners looking to navigate the complex terrain of blockchain-based data-sharing solutions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"28 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Facial Expression Analysis in Parkinson's Disease Using Machine Learning: A Review

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-14 DOI: 10.1145/3716818

Guilherme Camargo, Quoc Ngo, Leandro Passos, Danilo Jodas, Joao Papa, Dinesh Kumar

引用次数: 0

Towards Robust Cyber Attack Taxonomies: A Survey with Requirements, Structures, and Assessment

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-13 DOI: 10.1145/3717606

Paulo Roberto da Paz Ferraz Santos, Paulo Angelo Alves Resende, João José Costa Gondim, André Costa Drummond

{"title":"Towards Robust Cyber Attack Taxonomies: A Survey with Requirements, Structures, and Assessment","authors":"Paulo Roberto da Paz Ferraz Santos, Paulo Angelo Alves Resende, João José Costa Gondim, André Costa Drummond","doi":"10.1145/3717606","DOIUrl":"https://doi.org/10.1145/3717606","url":null,"abstract":"Cyber attacks have become a growing threat in today’s interconnected society, and taxonomies play a crucial role in understanding and preventing these attacks. However, the lack of comprehensive assessment methods for evaluating attack taxonomies represents a significant gap in the literature, hindering their development and applicability. This paper aims to address this gap by conducting a survey of 20 attack taxonomies published between 2011 and 2022 and evaluating them with a novel set of qualitative and quantitative assessment criteria, grounded in fundamental taxonomy requirements and key structural attributes. In pursuit of clear and objective assessment criteria, the authors investigated the main taxonomy properties in the literature, identifying dependencies and relationships. This investigation extracted the fundamental requirements for a relevant and widely accepted attack taxonomy in the cybersecurity community. Noteworthy structural aspects, such as organization, scheme, labeling, and approach, are also addressed, considering their impact on taxonomy effectiveness and applicability constraints. Finally, the paper poses some open questions and challenges, along with suggestions for future research directions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"67 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Lifelong Learning of Large Language Models: A Survey

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-13 DOI: 10.1145/3716629

Junhao Zheng, Shengjie Qiu, Chengming Shi, Qianli Ma

{"title":"Towards Lifelong Learning of Large Language Models: A Survey","authors":"Junhao Zheng, Shengjie Qiu, Chengming Shi, Qianli Ma","doi":"10.1145/3716629","DOIUrl":"https://doi.org/10.1145/3716629","url":null,"abstract":"As the applications of large language models (LLMs) expand across diverse fields, their ability to adapt to ongoing changes in data, tasks, and user preferences becomes crucial. Traditional training methods with static datasets are inadequate for coping with the dynamic nature of real-world information. Lifelong learning, or continual learning, addresses this by enabling LLMs to learn continuously and adapt over their operational lifetime, integrating new knowledge while retaining previously learned information and preventing catastrophic forgetting. Our survey explores the landscape of lifelong learning, categorizing strategies into two groups based on how new knowledge is integrated: Internal Knowledge, where LLMs absorb new knowledge into their parameters through full or partial training, and External Knowledge, which incorporates new knowledge as external resources like Wikipedia or APIs without updating model parameters. The key contributions of our survey include: (1) Introducing a novel taxonomy to categorize the extensive literature of lifelong learning into 12 scenarios; (2) Identifying common techniques across all lifelong learning scenarios and classifying existing literature into various technique groups; (3) Highlighting emerging techniques such as model expansion and data selection, which were less explored in the pre-LLM era. Resources are available at https://github.com/qianlima-lab/awesome-lifelong-learning-methods-for-llm.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"63 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adversarial Patterns: Building Robust Android Malware Classifiers

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-13 DOI: 10.1145/3717607

Dipkamal Bhusal, Nidhi Rastogi

{"title":"Adversarial Patterns: Building Robust Android Malware Classifiers","authors":"Dipkamal Bhusal, Nidhi Rastogi","doi":"10.1145/3717607","DOIUrl":"https://doi.org/10.1145/3717607","url":null,"abstract":"Machine learning models are increasingly being adopted across various fields, such as medicine, business, autonomous vehicles, and cybersecurity, to analyze vast amounts of data, detect patterns, and make predictions or recommendations. In the field of cybersecurity, these models have made significant improvements in malware detection. However, despite their ability to understand complex patterns from unstructured data, these models are susceptible to adversarial attacks that perform slight modifications in malware samples, leading to misclassification from malignant to benign. Numerous defense approaches have been proposed to either detect such adversarial attacks or improve model robustness. These approaches have resulted in a multitude of attack and defense techniques and the emergence of a field known as ‘adversarial machine learning.’ In this survey paper, we provide a comprehensive review of adversarial machine learning in the context of Android malware classifiers. Android is the most widely used operating system globally and is an easy target for malicious agents. The paper first presents an extensive background on Android malware classifiers, followed by an examination of the latest advancements in adversarial attacks and defenses. Finally, the paper provides guidelines for designing robust malware classifiers and outlines research directions for the future.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"27 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transfer Learning in Sensor-Based Human Activity Recognition: A Survey

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-13 DOI: 10.1145/3717608

Sourish Gunesh Dhekane, Thomas Ploetz

{"title":"Transfer Learning in Sensor-Based Human Activity Recognition: A Survey","authors":"Sourish Gunesh Dhekane, Thomas Ploetz","doi":"10.1145/3717608","DOIUrl":"https://doi.org/10.1145/3717608","url":null,"abstract":"Sensor-based human activity recognition (HAR) has been an active research area for many years, resulting in practical applications in smart environments, assisted living, fitness, healthcare, etc. Recently, deep learning based end-to-end training has pushed the state-of-the-art performance in domains such as computer vision and natural language, where large amounts of annotated data are available. However, large quantities of annotated data are typically not available for sensor-based HAR. Moreover, the real-world settings on which HAR is performed differ in terms of sensor modalities, classification tasks, and target users. To address this problem, transfer learning has been explored extensively. In this survey, we focus on these transfer learning methods in the application domains of smart home and wearables-based HAR. In particular, we provide a problem-solution perspective by categorizing and presenting the works in terms of their contributions and the challenges they address. We present an overview of the state-of-the-art for both application domains. Based on our analysis of 246 papers, we highlight the gaps in the literature and provide a roadmap for addressing these. This survey provides a reference to the HAR community, by summarizing the existing works and providing a promising research agenda.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"10 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Embodied Intelligence: A Synergy of Morphology, Action, Perception and Learning

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-12 DOI: 10.1145/3717059

Huaping Liu, Di Guo, Angelo Cangelosi

引用次数: 0

Compact Data Structures for Network Telemetry

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-12 DOI: 10.1145/3716819

Shir Landau-Feibish, Zaoxing Liu, Jennifer Rexford

引用次数: 0

Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-12 DOI: 10.1145/3716845

Vasileios Leon, Muhammad Abdullah Hanif, Giorgos Armeniakos, Xun Jiao, Muhammad Shafique, Kiamal Pekmestzi, Dimitrios Soudris

{"title":"Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques","authors":"Vasileios Leon, Muhammad Abdullah Hanif, Giorgos Armeniakos, Xun Jiao, Muhammad Shafique, Kiamal Pekmestzi, Dimitrios Soudris","doi":"10.1145/3716845","DOIUrl":"https://doi.org/10.1145/3716845","url":null,"abstract":"The rapid growth of demanding applications in domains applying multimedia processing and machine learning has marked a new era for edge and cloud computing. These applications involve massive data and compute-intensive tasks, and thus, typical computing paradigms in embedded systems and data centers are stressed to meet the worldwide demand for high performance. Concurrently, over the last 15 years, the semiconductor industry has established power efficiency as a first-class design concern. As a result, the community of computing systems is forced to find alternative design approaches to facilitate high-performance and power-efficient computing. Among the examined solutions, Approximate Computing has attracted an ever-increasing interest, which has resulted in novel approximation techniques for all the layers of the traditional computing stack. More specifically, during the last decade, a plethora of approximation techniques in software (programs, frameworks, compilers, runtimes, languages), hardware (circuits, accelerators), and architectures (processors, memories) have been proposed in the literature. The current article is Part I of a comprehensive survey on Approximate Computing. It reviews its motivation, terminology and principles, as well it classifies the state-of-the-art software & hardware approximation techniques, presents their technical details, and reports a comparative quantitative analysis.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"61 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143401618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-11 DOI: 10.1145/3716846

Neeloy Chakraborty, Melkior Ornik, Katherine Driggs-Campbell

{"title":"Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art","authors":"Neeloy Chakraborty, Melkior Ornik, Katherine Driggs-Campbell","doi":"10.1145/3716846","DOIUrl":"https://doi.org/10.1145/3716846","url":null,"abstract":"Autonomous systems are soon to be ubiquitous, spanning manufacturing, agriculture, healthcare, entertainment, and other industries. Most of these systems are developed with modular sub-components for decision-making, planning, and control that may be hand-engineered or learning-based. While these approaches perform well under the situations they were specifically designed for, they can perform especially poorly in out-of-distribution scenarios that will undoubtedly arise at test-time. The rise of foundation models trained on multiple tasks with impressively large datasets has led researchers to believe that these models may provide “common sense” reasoning that existing planners are missing, bridging the gap between algorithm development and deployment. While researchers have shown promising results in deploying foundation models to decision-making tasks, these models are known to hallucinate and generate decisions that may sound reasonable, but are in fact poor. We argue there is a need to step back and simultaneously design systems that can quantify the certainty of a model’s decision, and detect when it may be hallucinating. In this work, we discuss the current use cases of foundation models for decision-making tasks, provide a general definition for hallucinations with examples, discuss existing approaches to hallucination detection and mitigation with a focus on decision problems, present guidelines, and explore areas for further research in this exciting field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"10 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143393479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0