ACM Computing Surveys (CSUR)最新文献

Experimental Comparisons of Clustering Approaches for Data Representation 数据表示聚类方法的实验比较

ACM Computing Surveys (CSUR) Pub Date : 2022-03-30 DOI: 10.1145/3490384

S. Anand, Suresh Kumar

{"title":"Experimental Comparisons of Clustering Approaches for Data Representation","authors":"S. Anand, Suresh Kumar","doi":"10.1145/3490384","DOIUrl":"https://doi.org/10.1145/3490384","url":null,"abstract":"Clustering approaches are extensively used by many areas such as IR, Data Integration, Document Classification, Web Mining, Query Processing, and many other domains and disciplines. Nowadays, much literature describes clustering algorithms on multivariate data sets. However, there is limited literature that presented them with exhaustive and extensive theoretical analysis as well as experimental comparisons. This experimental survey paper deals with the basic principle, and techniques used, including important characteristics, application areas, run-time performance, internal, external, and stability validity of cluster quality, etc., on five different data sets of eleven clustering algorithms. This paper analyses how these algorithms behave with five different multivariate data sets in data representation. To answer this question, we compared the efficiency of eleven clustering approaches on five different data sets using three validity metrics-internal, external, and stability and found the optimal score to know the feasible solution of each algorithm. In addition, we have also included four popular and modern clustering algorithms with only their theoretical discussion. Our experimental results for only traditional clustering algorithms showed that different algorithms performed different behavior on different data sets in terms of running time (speed), accuracy and, the size of data set. This study emphasized the need for more adaptive algorithms and a deliberate balance between the running time and accuracy with their theoretical as well as implementation aspects.","PeriodicalId":7000,"journal":{"name":"ACM Computing Surveys (CSUR)","volume":"21 1","pages":"1 - 33"},"PeriodicalIF":0.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78630865","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: 15

On the Structure of the Boolean Satisfiability Problem: A Survey 布尔可满足问题的结构:综述

ACM Computing Surveys (CSUR) Pub Date : 2022-03-30 DOI: 10.1145/3491210

Tasniem Alyahya, M. Menai, H. Mathkour

{"title":"On the Structure of the Boolean Satisfiability Problem: A Survey","authors":"Tasniem Alyahya, M. Menai, H. Mathkour","doi":"10.1145/3491210","DOIUrl":"https://doi.org/10.1145/3491210","url":null,"abstract":"The Boolean satisfiability problem (SAT) is a fundamental NP-complete decision problem in automated reasoning and mathematical logic. As evidenced by the results of SAT competitions, the performance of SAT solvers varies substantially between different SAT categories (random, crafted, and industrial). A suggested explanation is that SAT solvers may exploit the underlying structure inherent to SAT instances. There have been attempts to define the structure of SAT in terms of structural measures such as phase transition, backbones, backdoors, small-world, scale-free, treewidth, centrality, community, self-similarity, and entropy. Still, the empirical evidence of structural measures for SAT has been provided for only some SAT categories. Furthermore, the evidence has not been theoretically proven. Also, the impact of structural measures on the behavior of SAT solvers has not been extensively examined. This work provides a comprehensive study on structural measures for SAT that have been presented in the literature. We provide an overview of the works on structural measures for SAT and their relatedness to the performance of SAT solvers. Accordingly, a taxonomy of structural measures for SAT is presented. We also review in detail important applications of structural measures for SAT, focusing mainly on enhancing SAT solvers, generating SAT instances, and classifying SAT instances.","PeriodicalId":7000,"journal":{"name":"ACM Computing Surveys (CSUR)","volume":"432 1","pages":"1 - 34"},"PeriodicalIF":0.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82867837","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: 3

A Brief Overview of Universal Sentence Representation Methods: A Linguistic View 语言视角下的普遍句子表示方法综述

ACM Computing Surveys (CSUR) Pub Date : 2022-03-26 DOI: 10.1145/3482853

Ruiqi Li, Xiang Zhao, M. Moens

引用次数: 10

The Eye in Extended Reality: A Survey on Gaze Interaction and Eye Tracking in Head-worn Extended Reality 扩展现实中的眼:头戴式扩展现实中凝视交互与眼动追踪研究

ACM Computing Surveys (CSUR) Pub Date : 2022-03-25 DOI: 10.1145/3491207

Alexander Plopski, Teresa Hirzle, Nahal Norouzi, Long Qian, G. Bruder, T. Langlotz

引用次数: 32

A Comprehensive Report on Machine Learning-based Early Detection of Alzheimer's Disease using Multi-modal Neuroimaging Data 基于多模态神经成像数据的机器学习早期检测阿尔茨海默病的综合报告

ACM Computing Surveys (CSUR) Pub Date : 2022-03-14 DOI: 10.1145/3492865

Shallu Sharma, P. Mandal

引用次数: 21

Automated Analysis of Blood Smear Images for Leukemia Detection: A Comprehensive Review 用于白血病检测的血液涂片图像的自动分析:一个全面的综述

ACM Computing Surveys (CSUR) Pub Date : 2022-03-01 DOI: 10.1145/3514495

Ajay Mittal, S. Dhalla, Savita Gupta, Aastha Gupta

引用次数: 10

A Survey and Taxonomy of Latency Compensation Techniques for Network Computer Games 网络游戏延迟补偿技术综述与分类

ACM Computing Surveys (CSUR) Pub Date : 2022-02-24 DOI: 10.1145/3519023

Shengmei Liu, Xiaokun Xu, M. Claypool

引用次数: 20

Few-Shot Object Detection: A Survey 少射目标检测:综述

ACM Computing Surveys (CSUR) Pub Date : 2022-02-24 DOI: 10.1145/3519022

Simone Antonelli, D. Avola, L. Cinque, Donato Crisostomi, G. Foresti, Fabio Galasso, Marco Raoul Marini, Alessio Mecca, D. Pannone

{"title":"Few-Shot Object Detection: A Survey","authors":"Simone Antonelli, D. Avola, L. Cinque, Donato Crisostomi, G. Foresti, Fabio Galasso, Marco Raoul Marini, Alessio Mecca, D. Pannone","doi":"10.1145/3519022","DOIUrl":"https://doi.org/10.1145/3519022","url":null,"abstract":"Deep learning approaches have recently raised the bar in many fields, from Natural Language Processing to Computer Vision, by leveraging large amounts of data. However, they could fail when the retrieved information is not enough to fit the vast number of parameters, frequently resulting in overfitting and therefore in poor generalizability. Few-Shot Learning aims at designing models that can effectively operate in a scarce data regime, yielding learning strategies that only need few supervised examples to be trained. These procedures are of both practical and theoretical importance, as they are crucial for many real-life scenarios in which data is either costly or even impossible to retrieve. Moreover, they bridge the distance between current data-hungry models and human-like generalization capability. Computer vision offers various tasks that can be few-shot inherent, such as person re-identification. This survey, which to the best of our knowledge is the first tackling this problem, is focused on Few-Shot Object Detection, which has received far less attention compared to Few-Shot Classification due to the intrinsic challenge level. In this regard, this review presents an extensive description of the approaches that have been tested in the current literature, discussing their pros and cons, and classifying them according to a rigorous taxonomy.","PeriodicalId":7000,"journal":{"name":"ACM Computing Surveys (CSUR)","volume":"232 ","pages":"1 - 37"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91458742","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: 17

Left Ventricle Segmentation in Cardiac MR: A Systematic Mapping of the Past Decade 心脏磁共振左心室分割:过去十年的系统映射

ACM Computing Surveys (CSUR) Pub Date : 2022-02-24 DOI: 10.1145/3517190

Matheus A. O. Ribeiro, Fátima L. S. Nunes

{"title":"Left Ventricle Segmentation in Cardiac MR: A Systematic Mapping of the Past Decade","authors":"Matheus A. O. Ribeiro, Fátima L. S. Nunes","doi":"10.1145/3517190","DOIUrl":"https://doi.org/10.1145/3517190","url":null,"abstract":"Left ventricle segmentation in short-axis cardiac magnetic resonance images is important to diagnose heart disease. However, repetitive manual segmentation of these images requires considerable human effort and can decrease diagnostic accuracy. In recent years, several fully and semi-automatic approaches have been proposed, mainly using image-based, atlas, graph, deformable model, and artificial intelligence methods. This article presents a systematic mapping on left ventricle segmentation, considering 74 studies published in the past decade. The main contributions of this review are definition of the main segmentation challenges in these images; proposal of a new schematization, dividing the segmentation process into stages; categorization and analysis of the segmentation methods, including hybrid combinations; and analysis of the evaluation process, metrics, and databases. The performance of the methods in the most used public database is assessed, and the main limitations, weaknesses, and strengths of each method category are presented. Finally, trends, challenges, and research opportunities are discussed. The analysis indicates that methods from all categories can achieve good performance, and hybrid methods combining deep learning and deformable models obtain the best results. Methods still fail in specific slices, segment wrong regions, and produce anatomically impossible segmentations.","PeriodicalId":7000,"journal":{"name":"ACM Computing Surveys (CSUR)","volume":"42 1","pages":"1 - 38"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89837350","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

Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future Directions 雾计算和万物互联环境中的资源分配和任务调度:分类、回顾和未来方向

ACM Computing Surveys (CSUR) Pub Date : 2022-02-07 DOI: 10.1145/3513002

Bushra Jamil, H. Ijaz, M. Shojafar, K. Munir, R. Buyya

{"title":"Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future Directions","authors":"Bushra Jamil, H. Ijaz, M. Shojafar, K. Munir, R. Buyya","doi":"10.1145/3513002","DOIUrl":"https://doi.org/10.1145/3513002","url":null,"abstract":"The Internet of Everything paradigm is being rapidly adopted in developing applications for different domains like smart agriculture, smart city, big data streaming, and so on. These IoE applications are leveraging cloud computing resources for execution. Fog computing, which emerged as an extension of cloud computing, supports mobility, heterogeneity, geographical distribution, context awareness, and services such as storage, processing, networking, and analytics on nearby fog nodes. The resource-limited, heterogeneous, dynamic, and uncertain fog environment makes task scheduling a great challenge that needs to be investigated. The article is motivated by this consideration and presents a systematic, comprehensive, and detailed comparative study by discussing the merits and demerits of different scheduling algorithms, focused optimization metrics, and evaluation tools in the fog computing and IoE environment. The goal of this survey article is fivefold. First, we review the fog computing and IoE paradigms. Second, we delineate the optimization metric engaged with fog computing and IoE environment. Third, we review, classify, and compare existing scheduling algorithms dealing with fog computing and IoE environment paradigms by leveraging some examples. Fourth, we rationalize the scheduling algorithms and point out the lesson learned from the survey. Fifth, we discuss the open issues and future research directions to improve scheduling in fog computing and the IoE environment.","PeriodicalId":7000,"journal":{"name":"ACM Computing Surveys (CSUR)","volume":"14 1","pages":"1 - 38"},"PeriodicalIF":0.0,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77749045","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: 44