ACM Computing Surveys最新文献_第8页

A Survey on the Recent Advancements in Human-Centered Dialog Systems 以人为本的对话系统最新进展概览

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-16 DOI: 10.1145/3729220

Roland Oruche, Sai Keerthana Goruganthu, Rithika Akula, Xiyao Cheng, Ashraful Md Goni, Bruce W. Shibo, Kerk Kee, Marcos Zampieri, Prasad Calyam

{"title":"A Survey on the Recent Advancements in Human-Centered Dialog Systems","authors":"Roland Oruche, Sai Keerthana Goruganthu, Rithika Akula, Xiyao Cheng, Ashraful Md Goni, Bruce W. Shibo, Kerk Kee, Marcos Zampieri, Prasad Calyam","doi":"10.1145/3729220","DOIUrl":"https://doi.org/10.1145/3729220","url":null,"abstract":"Dialog systems (e.g., chatbots) have been widely studied, yet related research that leverages artificial intelligence (AI) and natural language processing (NLP) is constantly evolving. These systems have typically been developed to interact with humans in the form of speech, visual, or text conversation. As humans continue to adopt dialog systems for various objectives, there is a need to involve humans in every facet of the dialog development life cycle for synergistic augmentation of both the humans and the dialog system actors in real-world settings. We provide a holistic literature survey on the recent advancements in human-centered dialog systems (HCDS). Specifically, we provide background context surrounding the recent advancements in machine learning-based dialog systems and human-centered AI. We then bridge the gap between the two AI sub-fields and organize the research works on HCDS under three major categories (i.e., Human-Chatbot Collaboration, Human-Chatbot Alignment, Human-Centered Chatbot Design & Governance). In addition, we discuss the applicability and accessibility of the HCDS implementations through benchmark datasets, application scenarios, and downstream NLP tasks.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"22 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841382","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

Domain Generalization in Computational Pathology: Survey and Guidelines 计算病理学领域泛化：调查和指南

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-16 DOI: 10.1145/3724391

Mostafa Jahanifar, Manahil Raza, Kesi Xu, Trinh Thi Le Vuong, Robert Jewsbury, Adam Shephard, Neda Zamanitajeddin, Jin Tae Kwak, Shan E Ahmed Raza, Fayyaz Minhas, Nasir Rajpoot

{"title":"Domain Generalization in Computational Pathology: Survey and Guidelines","authors":"Mostafa Jahanifar, Manahil Raza, Kesi Xu, Trinh Thi Le Vuong, Robert Jewsbury, Adam Shephard, Neda Zamanitajeddin, Jin Tae Kwak, Shan E Ahmed Raza, Fayyaz Minhas, Nasir Rajpoot","doi":"10.1145/3724391","DOIUrl":"https://doi.org/10.1145/3724391","url":null,"abstract":"Deep learning models have exhibited exceptional effectiveness in Computational Pathology (CPath) for various tasks on multi-gigapixel histology images. Nevertheless, the presence of out-of-distribution data (stemming from different sources such as disparate imaging devices) can cause domain shift (DS). DS decreases the generalization of trained models to unseen datasets with slightly different data distributions, prompting the need for innovative domain generalization (DG) solutions. Recognizing the potential of DG to significantly influence diagnostic and prognostic models in cancer studies and clinical practice, we present this survey along with guidelines on achieving DG in CPath. We rigorously define various DS types, systematically review and categorize existing DG approaches and resources in CPath, and provide insights into their advantages, limitations, and applicability. We also conduct thorough benchmarking experiments with 28 cutting-edge DG algorithms to address a complex DG example problem. Our findings suggest that careful experiment design and Stain Augmentation technique can be very effective. However, there is no one-size-fits-all solution for DG in CPath. Therefore, we establish guidelines for detecting and managing DS in different scenarios. While most of the concepts and recommendations are given for applications in CPath, they apply to most medical image analysis tasks as well.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"3 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841387","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

Networking Systems for Video Anomaly Detection: A Tutorial and Survey 视频异常检测的网络系统：教程和综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-16 DOI: 10.1145/3729222

Jing Liu, Yang Liu, Jieyu Lin, Jielin Li, Liang Cao, Peng Sun, Bo Hu, Liang Song, Azzedine Boukerche, Victor C.M. Leung

{"title":"Networking Systems for Video Anomaly Detection: A Tutorial and Survey","authors":"Jing Liu, Yang Liu, Jieyu Lin, Jielin Li, Liang Cao, Peng Sun, Bo Hu, Liang Song, Azzedine Boukerche, Victor C.M. Leung","doi":"10.1145/3729222","DOIUrl":"https://doi.org/10.1145/3729222","url":null,"abstract":"The increasing utilization of surveillance cameras in smart cities, coupled with the surge of online video applications, has heightened concerns regarding public security and privacy protection, which propelled automated Video Anomaly Detection (VAD) into a fundamental research task within the Artificial Intelligence (AI) community. With the advancements in deep learning and edge computing, VAD has made significant progress and advances synergized with emerging applications in smart cities and video internet, which has moved beyond the conventional research scope of algorithm engineering to deployable Networking Systems for VAD (NSVAD), a practical hotspot for intersection exploration in the AI, IoVT, and computing fields. In this article, we delineate the foundational assumptions, learning frameworks, and applicable scenarios of various deep learning-driven VAD routes, offering an exhaustive tutorial for novices in NSVAD. In addition, this article elucidates core concepts by reviewing recent advances and typical solutions and aggregating available research resources accessible at https://github.com/fdjingliu/NSVAD. Lastly, this article projects future development trends and discusses how the integration of AI and computing technologies can address existing research challenges and promote open opportunities, serving as an insightful guide for prospective researchers and engineers.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"43 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841383","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

Sandarśana: A Survey on Sanskrit Computational Linguistics and Digital Infrastructure for Sanskrit Sandarśana：梵文计算语言学和梵文数字基础设施调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-14 DOI: 10.1145/3729530

Anagha Pradeep, Radhika Mamidi

引用次数: 0

A Survey of Reasoning with Foundation Models: Concepts, Methodologies, and Outlook 基础模型推理概览：概念、方法和展望

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-11 DOI: 10.1145/3729218

Jiankai Sun, Chuanyang Zheng, Enze Xie, Zhengying Liu, Ruihang Chu, Jianing Qiu, Jiaqi Xu, Mingyu Ding, Hongyang Li, Mengzhe Geng, Yue Wu, Wenhai Wang, Junsong Chen, Zhangyue Yin, Xiaozhe Ren, Jie Fu, Junxian He, Yuan Wu, Qi Liu, Xihui Liu, Yu Li, Hao Dong, Yu Cheng, Ming Zhang, Pheng Ann Heng, Jifeng Dai, Ping Luo, Jingdong Wang, Ji-Rong Wen, Xipeng Qiu, Yike Guo, Hui Xiong, Qun Liu, Zhenguo Li

{"title":"A Survey of Reasoning with Foundation Models: Concepts, Methodologies, and Outlook","authors":"Jiankai Sun, Chuanyang Zheng, Enze Xie, Zhengying Liu, Ruihang Chu, Jianing Qiu, Jiaqi Xu, Mingyu Ding, Hongyang Li, Mengzhe Geng, Yue Wu, Wenhai Wang, Junsong Chen, Zhangyue Yin, Xiaozhe Ren, Jie Fu, Junxian He, Yuan Wu, Qi Liu, Xihui Liu, Yu Li, Hao Dong, Yu Cheng, Ming Zhang, Pheng Ann Heng, Jifeng Dai, Ping Luo, Jingdong Wang, Ji-Rong Wen, Xipeng Qiu, Yike Guo, Hui Xiong, Qun Liu, Zhenguo Li","doi":"10.1145/3729218","DOIUrl":"https://doi.org/10.1145/3729218","url":null,"abstract":"Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation. It serves as a fundamental methodology in the field of Artificial General Intelligence (AGI). With the ongoing development of foundation models, there is a growing interest in exploring their abilities in reasoning tasks. In this paper, we introduce seminal foundation models proposed or adaptable for reasoning, highlighting the latest advancements in various reasoning tasks, methods, and benchmarks. We then delve into the potential future directions behind the emergence of reasoning abilities within foundation models. We also discuss the relevance of multimodal learning, autonomous agents, and super alignment in the context of reasoning. By discussing these future research directions, we hope to inspire researchers in their exploration of this field, stimulate further advancements in reasoning with foundation models, e.g. Large Language Models (LLMs), and contribute to the development of AGI.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"37 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822819","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

Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo 主动脉血管树分割在心血管疾病治疗中的应用现状

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-11 DOI: 10.1145/3728632

Yuan Jin, Antonio Pepe, Jianning Li, Christina Gsaxner, Yuxuan Chen, Behrus Puladi, Fen-hua Zhao, Kelsey Pomykala, Jens Kleesiek, Alejandro Frangi, Jan Egger

引用次数: 0

Geometric Constraints in Deep Learning Frameworks: A Survey 深度学习框架中的几何约束：综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-10 DOI: 10.1145/3729221

Vibhas K Vats, David Crandall

引用次数: 0

Recent Advances in Vision Transformer Robustness Against Adversarial Attacks in Traffic Sign Detection and Recognition: A Survey 交通标志检测与识别中视觉变换器鲁棒性对抗对抗性攻击的最新进展：调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-10 DOI: 10.1145/3729167

Oluwajuwon Fawole, Danda Rawat

引用次数: 0

A Generic Taxonomy for Steganography Methods 隐写方法的一般分类

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-09 DOI: 10.1145/3729165

Steffen Wendzel, Luca Caviglione, Wojciech Mazurczyk, Aleksandra Mileva, Jana Dittmann, Christian Krätzer, Kevin Lamshöft, Claus Vielhauer, Laura Hartmann, Jörg Keller, Tom Neubert, Sebastian Zillien

{"title":"A Generic Taxonomy for Steganography Methods","authors":"Steffen Wendzel, Luca Caviglione, Wojciech Mazurczyk, Aleksandra Mileva, Jana Dittmann, Christian Krätzer, Kevin Lamshöft, Claus Vielhauer, Laura Hartmann, Jörg Keller, Tom Neubert, Sebastian Zillien","doi":"10.1145/3729165","DOIUrl":"https://doi.org/10.1145/3729165","url":null,"abstract":"A unified understanding of terms is essential for every scientific discipline: steganography is no exception. Being divided into several domains (e.g., network and text steganography), it is crucial to provide a unified terminology as well as a taxonomy that is not limited to few applications or areas. A prime attempt towards a unified understanding of terms was conducted in 2015 with the introduction of a pattern-based taxonomy for network steganography. In 2021, the first work towards a pattern-based taxonomy for all domains of steganography was proposed. However, this initial attempt still faced several shortcomings, e.g., remaining inconsistencies and a lack of patterns for several steganography domains. As the consortium who published the previous studies on steganography patterns, we present the first comprehensive pattern-based taxonomy tailored to fit all known domains of steganography, including smaller and emerging areas, such as filesystem, IoT/CPS, and AI/ML steganography. To make our contribution more effective and promote the use of the taxonomy to advance research, we also provide a unified description method joint with a thorough tutorial on its utilization.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"183 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143805846","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

Proof Scores: A Survey 证明分数：一项调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-09 DOI: 10.1145/3729166

Adrián Riesco, Kazuhiro Ogata, Masaki Nakamura, Daniel Gaina, Duong Dinh Tran, Kokichi Futatsugi

引用次数: 0