ACM Computing Surveys (CSUR)最新文献_第2页

Image and Video Coding Techniques for Ultra-low Latency 超低延迟的图像和视频编码技术

ACM Computing Surveys (CSUR) Pub Date : 2022-02-07 DOI: 10.1145/3512342

Jakub Žádník, M. Mäkitalo, Jarno Vanne, P. Jääskeläinen

引用次数: 10

Security and Privacy in Unified Communication 统一通信中的安全和隐私

ACM Computing Surveys (CSUR) Pub Date : 2022-02-03 DOI: 10.1145/3498335

T. Reisinger, Isabel Wagner, E. Boiten

引用次数: 6

Constructive or Optimized: An Overview of Strategies to Design Networks for Time-Critical Applications 建设性或优化:时间关键应用网络设计策略概述

ACM Computing Surveys (CSUR) Pub Date : 2022-02-03 DOI: 10.1145/3501294

Voica Gavriluţ, Aleksander Pruski, M. Berger

引用次数: 11

Electrical-Level Attacks on CPUs, FPGAs, and GPUs: Survey and Implications in the Heterogeneous Era 对cpu、fpga和gpu的电级攻击:异构时代的调查和启示

ACM Computing Surveys (CSUR) Pub Date : 2022-02-03 DOI: 10.1145/3498337

Dina G. Mahmoud, Vincent Lenders, Mirjana Stojilović

引用次数: 8

Understanding Online Privacy—A Systematic Review of Privacy Visualizations and Privacy by Design Guidelines 理解在线隐私——隐私可视化和隐私设计指南的系统回顾

ACM Computing Surveys (CSUR) Pub Date : 2022-02-03 DOI: 10.1145/3502288

Susanne Barth, D. Ionita, P. Hartel

引用次数: 18

Survey on Digital Video Stabilization: Concepts, Methods, and Challenges 数字视频防抖研究:概念、方法和挑战

ACM Computing Surveys (CSUR) Pub Date : 2022-02-03 DOI: 10.1145/3494525

M. R. Souza, H. Maia, H. Pedrini

引用次数: 15

Spatial Data Quality in the Internet of Things: Management, Exploitation, and Prospects 物联网空间数据质量:管理、开发与展望

ACM Computing Surveys (CSUR) Pub Date : 2022-02-03 DOI: 10.1145/3498338

Huan Li, Hua Lu, Christian S. Jensen, Bo Tang, M. A. Cheema

{"title":"Spatial Data Quality in the Internet of Things: Management, Exploitation, and Prospects","authors":"Huan Li, Hua Lu, Christian S. Jensen, Bo Tang, M. A. Cheema","doi":"10.1145/3498338","DOIUrl":"https://doi.org/10.1145/3498338","url":null,"abstract":"With the continued deployment of the Internet of Things (IoT), increasing volumes of devices are being deployed that emit massive spatially referenced data. Due in part to the dynamic, decentralized, and heterogeneous architecture of the IoT, the varying and often low quality of spatial IoT data (SID) presents challenges to applications built on top of this data. This survey aims to provide unique insight to practitioners who intend to develop IoT-enabled applications and to researchers who wish to conduct research that relates to data quality in the IoT setting. The survey offers an inventory analysis of major data quality dimensions in SID and covers significant data characteristics and associated quality considerations. The survey summarizes data quality related technologies from both task and technique perspectives. Organizing the technologies from the task perspective, it covers recent progress in SID quality management, encompassing location refinement, uncertainty elimination, outlier removal, fault correction, data integration, and data reduction; and it covers low-quality SID exploitation, encompassing querying, analysis, and decision-making techniques. Finally, the survey covers emerging trends and open issues concerning the quality of SID.","PeriodicalId":7000,"journal":{"name":"ACM Computing Surveys (CSUR)","volume":"33 1","pages":"1 - 41"},"PeriodicalIF":0.0,"publicationDate":"2022-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78925617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

LoRa Networking Techniques for Large-scale and Long-term IoT: A Down-to-top Survey 大规模和长期物联网的LoRa网络技术:自上而下的调查

ACM Computing Surveys (CSUR) Pub Date : 2022-02-03 DOI: 10.1145/3494673

Chenning Li, Zhichao Cao

{"title":"LoRa Networking Techniques for Large-scale and Long-term IoT: A Down-to-top Survey","authors":"Chenning Li, Zhichao Cao","doi":"10.1145/3494673","DOIUrl":"https://doi.org/10.1145/3494673","url":null,"abstract":"Low-Power Wide-Area Networks (LPWANs) are an emerging Internet-of-Things (IoT) paradigm, which caters to large-scale and long-term sensory data collection demand. Among the commercialized LPWAN technologies, LoRa (Long Range) attracts much interest from academia and industry due to its open-source physical (PHY) layer and standardized networking stack. In the flourishing LoRa community, many observations and countermeasures have been proposed to understand and improve the performance of LoRa networking in practice. From the perspective of the LoRa networking stack; however, we lack a whole picture to comprehensively understand what has been done or not and reveal what the future trends are. This survey proposes a taxonomy of a two-dimensional (i.e., networking layers, performance metrics) to categorize and compare the cutting-edge LoRa networking techniques. One dimension is the layered structure of the LoRa networking stack. From down to the top, we have the PHY layer, Link layer, Media-access Control (MAC) layer, and Application (App) layer. In each layer, we focus on the three most representative layer-specific research issues for fine-grained categorizing. The other dimension is LoRa networking performance metrics, including range, throughput, energy, and security. We compare different techniques in terms of these metrics and further overview the open issues and challenges, followed by our observed future trends. According to our proposed taxonomy, we aim at clarifying several ways to achieve a more effective LoRa networking stack and find more LoRa applicable scenarios, leading to a brand-new step toward a large-scale and long-term IoT.","PeriodicalId":7000,"journal":{"name":"ACM Computing Surveys (CSUR)","volume":"101 1","pages":"1 - 36"},"PeriodicalIF":0.0,"publicationDate":"2022-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90530661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 37

A Review on Fairness in Machine Learning 机器学习公平性研究综述

ACM Computing Surveys (CSUR) Pub Date : 2022-02-03 DOI: 10.1145/3494672

Dana Pessach, E. Shmueli

{"title":"A Review on Fairness in Machine Learning","authors":"Dana Pessach, E. Shmueli","doi":"10.1145/3494672","DOIUrl":"https://doi.org/10.1145/3494672","url":null,"abstract":"An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans, and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop ML algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision making may be inherently prone to unfairness, even when there is no intention for it. This article presents an overview of the main concepts of identifying, measuring, and improving algorithmic fairness when using ML algorithms, focusing primarily on classification tasks. The article begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness. Fairness-enhancing mechanisms are then reviewed and divided into pre-process, in-process, and post-process mechanisms. A comprehensive comparison of the mechanisms is then conducted, toward a better understanding of which mechanisms should be used in different scenarios. The article ends by reviewing several emerging research sub-fields of algorithmic fairness, beyond classification.","PeriodicalId":7000,"journal":{"name":"ACM Computing Surveys (CSUR)","volume":"122 1","pages":"1 - 44"},"PeriodicalIF":0.0,"publicationDate":"2022-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88079188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 163

Survey of Approaches for Postprocessing of Static Analysis Alarms 静态分析报警后处理方法综述

ACM Computing Surveys (CSUR) Pub Date : 2022-02-03 DOI: 10.1145/3494521

Tukaram Muske, Alexander Serebrenik

{"title":"Survey of Approaches for Postprocessing of Static Analysis Alarms","authors":"Tukaram Muske, Alexander Serebrenik","doi":"10.1145/3494521","DOIUrl":"https://doi.org/10.1145/3494521","url":null,"abstract":"Static analysis tools have showcased their importance and usefulness in automated detection of defects. However, the tools are known to generate a large number of alarms which are warning messages to the user. The large number of alarms and cost incurred by their manual inspection have been identified as two major reasons for underuse of the tools in practice. To address these concerns plentitude of studies propose postprocessing of alarms: processing the alarms after they are generated. These studies differ greatly in their approaches to postprocess alarms. A comprehensive overview of the postprocessing approaches is, however, missing. In this article, we review 130 primary studies that propose postprocessing of alarms. The studies are collected by combining keywords-based database search and snowballing. We categorize approaches proposed by the collected studies into six main categories: clustering, ranking, pruning, automated elimination of false positives, combination of static and dynamic analyses, and simplification of manual inspection. We provide overview of the categories and sub-categories identified for them, their merits and shortcomings, and different techniques used to implement the approaches. Furthermore, we provide (1) guidelines for selection of the postprocessing techniques by the users/designers of static analysis tools; and (2) directions that can be explored by the researchers.","PeriodicalId":7000,"journal":{"name":"ACM Computing Surveys (CSUR)","volume":"18 1","pages":"1 - 39"},"PeriodicalIF":0.0,"publicationDate":"2022-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83632725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5