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Mix-Zones as an Effective Privacy Enhancing Technique in Mobile and Vehicular Ad-hoc Networks ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-22 Nirupama Ravi, C. M. Krishna, Israel Koren
Intelligent Transportation Systems (ITS) promise significant increases in throughput and reductions in trip delay. ITS makes extensive use of Connected and Autonomous Vehicles (CAV) frequently broadcasting location, speed, and intention information. However, with such extensive communication comes the risk to privacy. Preserving privacy while still exchanging vehicle state information has been recognized
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Qualitative Approaches to Voice UX ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-20 Katie Seaborn, Jacqueline Urakami, Peter Pennefather, Norihisa P. Miyake
Voice is a natural mode of expression offered by modern computer-based systems. Qualitative perspectives on voice-based user experiences (voice UX) offer rich descriptions of complex interactions that numbers alone cannot fully represent. We conducted a systematic review of the literature on qualitative approaches to voice UX, capturing the nature of this body of work in a systematic map and offering
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Applying Generative Machine Learning to Intrusion Detection: A Systematic Mapping Study and Review ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-20 James Halvorsen, Clemente Izurieta, Haipeng Cai, Assefaw H. Gebremedhin
Intrusion Detection Systems (IDSs) are an essential element of modern cyber defense, alerting users to when and where cyber-attacks occur. Machine learning can enable IDSs to further distinguish between benign and malicious behaviors, but it comes with several challenges, including lack of quality training data and high false positive rates. Generative Machine Learning Models (GMLMs) can help overcome
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A Survey on Resilience in Information Sharing on Networks: Taxonomy and Applied Techniques ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-20 Agnaldo de Souza Batista, Aldri L. dos Santos
Information sharing is vital in any communication network environment to enable network operating services take decisions based on the information collected by several deployed computing devices. The various networks that compose cyberspace, as Internet-of-Things (IoT) ecosystems, have significantly increased the need to constantly share information, which is often subject to disturbances. In this
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Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-18 Jiajun Wu, Fan Dong, Henry Leung, Zhuangdi Zhu, Jiayu Zhou, Steve Drew
The ultra-low latency requirements of 5G/6G applications and privacy constraints call for distributed machine learning systems to be deployed at the edge. With its simple yet effective approach, federated learning (FL) is a natural solution for massive user-owned devices in edge computing with distributed and private training data. FL methods based on FedAvg typically follow a naive star topology,
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A challenge-based survey of e-recruitment recommendation systems ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-18 Yoosof Mashayekhi, Nan Li, Bo Kang, Jefrey Lijffijt, Tijl De Bie
E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of job seekers for positions and on job seekers’ and recruiters’ preferences. Therefore, e-recruitment recommendation systems may greatly impact people’s careers. Moreover, by affecting the hiring processes of the companies, e-recruitment recommendation
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Deceived by Immersion: A Systematic Analysis of Deceptive Design in Extended Reality ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-17 Hilda Hadan, Lydia Choong, Leah Zhang-Kennedy, Lennart E. Nacke
The well-established deceptive design literature has focused on conventional user interfaces. With the rise of extended reality (XR), understanding deceptive design’s unique manifestations in this immersive domain is crucial. However, existing research lacks a full, cross-disciplinary analysis that analyzes how XR technologies enable new forms of deceptive design. Our study reviews the literature on
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Integration of Sensing, Communication and Computing for Metaverse: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-17 Xiaojie Wang, Qi Guo, Zhaolong Ning, Lei Guo, Guoyin Wang, Xinbo Gao, Yan Zhang
The metaverse is an Artificial Intelligence (AI)-generated virtual world, in which people can game, work, learn and socialize. The realization of metaverse not only requires a large amount of computing resources to realize the rendering of the virtual world, but also requires communication resources to realize real-time transmission of massive data to ensure a good user experience. The metaverse is
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Ethereum Transaction Replay Platform Based on State-wise Account Input Data IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-04-17 Yuan Huang, Rong Wang, Xiangping Chen, Changlin Yang, Zibin Zheng
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A Monitoring-Free Bitcoin Payment Channel Scheme With Support for Real-Time Settlement IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-04-17 Yankai Xie, Ruian Li, Yan Huang, Chi Zhang, Lingbo Wei, Yani Sun
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Selling by contributing: the monetization strategy of individual content providers in the light of human brand Internet Res. (IF 5.9) Pub Date : 2024-04-16 Sha Zhou, Yaqin Su, Muhammad Aamir Shahzad, Zhengchi Liu
Purpose The integration of social media and e-commerce has resulted in a rising phenomenon among individual content providers (ICPs), who used to offer free content, to provide consumers with paid content, such as online courses, Q&As or consultations. Despite the prevalence of ICPs’ content monetization, empirical research has rarely studied its underlying mechanism. This paper examines how the characteristics
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Systems Interoperability Types: A Tertiary Study ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-15 Rita S. P. Maciel, Pedro H. Valle, Kécia S. Santos, Elisa Y. Nakagawa
Interoperability has been a focus of attention over at least four decades, with the emergence of several interoperability types (or levels), diverse models, frameworks, and solutions, also as a result of a continuous effort from different domains. The current heterogeneity in technologies such as blockchain, IoT and new application domains such as Industry 4.0 brings not only new interaction possibilities
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Blockchained Federated Learning for Internet of Things: A Comprehensive Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-15 Yanna Jiang, Baihe Ma, Xu Wang, Guangsheng Yu, Ping Yu, Zhe Wang, Wei Ni, Ren Ping Liu
The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comprehensively reviews Blockchained Federated Learning (BlockFL) that joins the benefits of both Blockchain and Federated Learning to provide a secure and efficient solution for the demand. We compare the existing BlockFL models
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Maintenance Operations on Cloud, Edge, and IoT Environments: Taxonomy, Survey, and Research Challenges ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-13 Paulo S. Souza, Tiago C. Ferreto, Rodrigo N. Calheiros
The emergence of the Internet of Things (IoT) introduced new classes of applications whose latency and bandwidth requirements could not be satisfied by the traditional Cloud Computing model. Consequently, the IT community promoted the cooperation of two paradigms, Cloud Computing and Edge Computing, combining large-scale computing power and real-time processing capabilities. A significant management
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A Systematic Survey of Deep Learning-based Single-Image Super-Resolution ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-13 Juncheng Li, Zehua Pei, Wenjie Li, Guangwei Gao, Longguang Wang, Yingqian Wang, Tieyong Zeng
Single-image super-resolution (SISR) is an important task in image processing, which aims to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and has achieved promising results with the help of deep learning (DL). In this survey, we give an overview of DL-based SISR methods and group them according to their design targets. Specifically, we first introduce the problem definition
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From Detection to Application: Recent Advances in Understanding Scientific Tables and Figures ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-12 Jiani Huang, Haihua Chen, Fengchang Yu, Wei Lu
Tables and figures are usually used to present information in a structured and visual way in scientific documents. Understanding the tables and figures in scientific documents is significant for a series of downstream tasks, such as academic search, scientific knowledge graphs, and so on. Existing studies mainly focus on detecting figures and tables from scientific documents, interpreting their semantics
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A Systematic Literature Review of Novelty Detection in Data Streams: Challenges and Opportunities ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-12 Jean-Gabriel Gaudreault, Paula Branco
Novelty detection in data streams is the task of detecting concepts that were not known prior, in streams of data. Many machine learning algorithms have been proposed to detect these novelties, as well as integrate them. This study provides a systematic literature review of the state of novelty detection in data streams, including its advancement in recent years, its main challenges and solutions,
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Visual Tuning ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-12 Bruce X.B. Yu, Jianlong Chang, Haixin Wang, Lingbo Liu, Shijie Wang, Zhiyu Wang, Junfan Lin, Lingxi Xie, Haojie Li, Zhouchen Lin, Qi Tian, Chang Wen Chen
Fine-tuning visual models has been widely shown promising performance on many downstream visual tasks. With the surprising development of pre-trained visual foundation models, visual tuning jumped out of the standard modus operandi that fine-tunes the whole pre-trained model or just the fully connected layer. Instead, recent advances can achieve superior performance than full-tuning the whole pre-trained
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A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless Functions IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-04-12 Siddharth Agarwal, Maria A. Rodriguez, Rajkumar Buyya
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Multi-Attribute Auction-Based Grouped Federated Learning IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-04-12 Renhao Lu, Hongwei Yang, Yan Wang, Hui He, Qiong Li, Xiaoxiong Zhong, Weizhe Zhang
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Artificial Intelligence for Web 3.0: A Comprehensive Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-11 Meng Shen, Zhehui Tan, Dusit Niyato, Yuzhi Liu, Jiawen Kang, Zehui Xiong, Liehuang Zhu, Wei Wang, Xuemin (Sherman) Shen
Web 3.0 is the next generation of the Internet built on decentralized technologies such as blockchain and cryptography. It is born to solve the problems faced by the previous generation of the Internet such as imbalanced distribution of interests, monopoly of platform resources, and leakage of personal privacy. In this survey, We discuss the latest development status of Web 3.0 and the application
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Exploring Blockchain Technology through a Modular Lens: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-11 Minghui Xu, Yihao Guo, Chunchi Liu, Qin Hu, Dongxiao Yu, Zehui Xiong, Dusit Niyato, Xiuzhen Cheng
Blockchain has attracted significant attention in recent years due to its potential to revolutionize various industries by providing trustlessness. To comprehensively examine blockchain systems, this article presents both a macro-level overview on the most popular blockchain systems, and a micro-level analysis on a general blockchain framework and its crucial components. The macro-level exploration
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Interactive Question Answering Systems: Literature Review ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-11 Giovanni Maria Biancofiore, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Fedelucio Narducci
Question-answering systems are recognized as popular and frequently effective means of information seeking on the web. In such systems, information seekers can receive a concise response to their queries by presenting their questions in natural language. Interactive question answering is a recently proposed and increasingly popular solution that resides at the intersection of question answering and
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A Survey on the Applications of Semi-Supervised Learning to Cyber-Security ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-11 Paul K. Mvula, Paula Branco, Guy-Vincent Jourdan, Herna L. Viktor
Machine Learning’s widespread application owes to its ability to develop accurate and scalable models. In cyber-security, where labeled data is scarce, Semi-Supervised Learning (SSL) emerges as a potential solution. SSL excels at tasks challenging traditional supervised and unsupervised algorithms by leveraging limited labelled data alongside abundant unlabeled data. This paper presents a comprehensive
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RCME: A Reputation Incentive Committee Consensus-Based for Matchmaking Encryption in IoT Healthcare IEEE Trans. Serv. Comput. (IF 8.1) Pub Date : 2024-04-11 Ningbin Yang, Chunming Tang, Zehui Xiong, Debiao He
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Security, Privacy, and Decentralized Trust Management in VANETs: A Review of Current Research and Future Directions ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-10 Mishri Saleh AlMarshoud, Ali H. Al-Bayatti, Mehmet Sabir Kiraz
Vehicular Ad Hoc Networks (VANETs) are powerful platforms for vehicular data services and applications. The increasing number of vehicles has made the vehicular network diverse, dynamic, and large-scale, making it difficult to meet the 5G network’s demanding requirements. Decentralized systems are interesting and provide attractive services because they are publicly available (transparency), have an
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A Survey of Multi-modal Knowledge Graphs: Technologies and Trends ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-10 Wanying Liang, Pasquale De Meo, Yong Tang, Jia Zhu
In recent years, Knowledge Graphs (KGs) have played a crucial role in the development of advanced knowledge-intensive applications, such as recommender systems and semantic search. However, the human sensory system is inherently multi-modal, as objects around us are often represented by a combination of multiple signals, such as visual and textual. Consequently, Multi-modal Knowledge Graphs (MMKGs)
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Deep Learning for Table Detection and Structure Recognition: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-10 Mahmoud Kasem, Abdelrahman Abdallah, Alexander Berendeyev, Ebrahem Elkady, Mohamed Mahmoud, Mahmoud Abdalla, Mohamed Hamada, Sebastiano Vascon, Daniyar Nurseitov, Islam Taj-Eddin
Tables are everywhere, from scientific journals, papers, websites, and newspapers all the way to items we buy at the supermarket. Detecting them is thus of utmost importance to automatically understanding the content of a document. The performance of table detection has substantially increased thanks to the rapid development of deep learning networks. The goals of this survey are to provide a profound
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Adapting Neural Networks at Runtime: Current Trends in At-Runtime Optimizations for Deep Learning ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-10 Max Sponner, Bernd Waschneck, Akash Kumar
Adaptive optimization methods for deep learning adjust the inference task to the current circumstances at runtime to improve the resource footprint while maintaining the model’s performance. These methods are essential for the widespread adoption of deep learning, as they offer a way to reduce the resource footprint of the inference task while also having access to additional information about the
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UAV-Assisted IoT Applications, QoS Requirements and Challenges with Future Research Directions ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-10 Muhammad Adil, Houbing Song, Mian Jan, Muhammad Khan, Xiangjian He, Ahmed Farouk, Zhanpeng Jin
ABSTRACT: Unmanned Aerial Vehicle (UAV)-assisted Internet of Things application communication is an emerging concept that effectuate the foreknowledge of innovative technologies. With the accelerated advancements in IoT applications, the importance of this technology became more impactful and persistent. Moreover, this technology have demonstrated useful contributions across various domains, ranging
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Distributed Graph Neural Network Training: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-10 Yingxia Shao, Hongzheng Li, Xizhi Gu, Hongbo Yin, Yawen Li, Xupeng Miao, Wentao Zhang, Bin Cui, Lei Chen
Graph neural networks (GNNs) are a type of deep learning models that are trained on graphs and have been successfully applied in various domains. Despite the effectiveness of GNNs, it is still challenging for GNNs to efficiently scale to large graphs. As a remedy, distributed computing becomes a promising solution of training large-scale GNNs, since it is able to provide abundant computing resources
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Social Network Analysis: A Survey on Process, Tools, and Application ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-10 Shashank Sheshar Singh, Samya Muhuri, Shivansh Mishra, Divya Srivastava, Harish Kumar Shakya, Neeraj Kumar
Due to the explosive rise of online social networks, social network analysis (SNA) has emerged as a significant academic field in recent years. Understanding and examining social relationships in networks through network analysis opens up numerous research avenues in sociology, literature, media, biology, computer science, sports, and more. Therefore, certain studies review and discuss some research
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A Systematic Literature Review on Maintenance of Software Containers ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-10 Ruchika Malhotra, Anjali Bansal, Marouane Kessentini
Nowadays, cloud computing is gaining tremendous attention to deliver information via the internet. Virtualization plays a major role in cloud computing as it deploys multiple virtual machines on the same physical machine and thus results in improving resource utilization. Hypervisor-based virtualization and containerization are two commonly used approaches in operating system virtualization. In this
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Survey on Haptic Feedback through Sensory Illusions in Interactive Systems ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-10 Marco Kurzweg, Yannick Weiss, Marc O. Ernst, Albrecht Schmidt, Katrin Wolf
A growing body of work in human-computer interaction (HCI), particularly work on haptic feedback and haptic displays, relies on sensory illusions, which is a phenomenon investigated in perception research. However, an overview of which illusions are prevalent in HCI for generating haptic feedback in computing systems and which remain underrepresented, as well as the rationales and possible undiscovered
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A Survey on Robotic Prosthetics: Neuroprosthetics, Soft Actuators, and Control Strategies ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-10 K. J. Jyothish, Subhankar Mishra
The field of robotics is a quickly evolving feat of technology that accepts contributions from various genres of science. Neuroscience, Physiology, Chemistry, Material science, Computer science, and the wide umbrella of mechatronics have all simultaneously contributed to many innovations in the prosthetic applications of robotics. This review begins with a discussion of the scope of the term robotic
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A Comprehensive Review and New Taxonomy on Superpixel Segmentation ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-10 Isabela Borlido Barcelos, Felipe De Castro Belém, Leonardo De Melo João, Zenilton K. G. Do Patrocínio, Alexandre Xavier Falcão, Silvio Jamil Ferzoli Guimarães
Superpixel segmentation consists of partitioning images into regions composed of similar and connected pixels. Its methods have been widely used in many computer vision applications, since it allows for reducing the workload, removing redundant information, and preserving regions with meaningful features. Due to the rapid progress in this area, the literature fails to catch up on more recent works
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Mobile Near-infrared Sensing—A Systematic Review on Devices, Data, Modeling, and Applications ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-10 Weiwei Jiang, Jorge Goncalves, Vassilis Kostakos
Mobile near-infrared sensing is becoming an increasingly important method in many research and industrial areas. To help consolidate progress in this area, we use the PRISMA guidelines to conduct a systematic review of mobile near-infrared sensing, including (1) existing prototypes and commercial products, (2) data collection techniques, (3) machine learning methods, and (4) relevant application areas
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Students’ active cognitive engagement with instructional videos predicts STEM learning Comput. Educ. (IF 12.0) Pub Date : 2024-04-10 Shelbi L. Kuhlmann, Robert Plumley, Zoe Evans, Matthew L. Bernacki, Jeffrey A. Greene, Kelly A. Hogan, Michael Berro, Kathleen Gates, Abigail Panter
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Neuromorphic Perception and Navigation for Mobile Robots: A Review ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 A. Novo, F. Lobon, H.G. De Marina, S. Romero, F. Barranco
With the fast and unstoppable evolution of robotics and artificial intelligence, effective autonomous navigation in real-world scenarios has become one of the most pressing challenges in the literature. However, demanding requirements, such as real-time operation, energy and computational efficiency, robustness, and reliability, make most current solutions unsuitable for real-world challenges. Thus
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Foundations & Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency
Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design computer agents with intelligent capabilities such as understanding, reasoning, and learning through integrating multiple communicative modalities, including linguistic, acoustic, visual, tactile, and physiological messages. With the recent interest in video understanding, embodied autonomous agents, text-to-image
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Exploiting Blockchain to Make AI Trustworthy: A Software Development Lifecycle View ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Peiyun Zhang, Song Ding, Qinglin Zhao
Artificial intelligence (AI) is a very powerful technology and can be a potential disrupter and essential enabler. As AI expands into almost every aspect of our lives, people raise serious concerns about AI misbehaving and misuse. To address this concern, international organizations have put forward ethics guidelines for constructing trustworthy AI (TAI), including privacy, transparency, fairness,
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A Survey of Algorithmic Methods for Competency Self-Assessments in Human-Autonomy Teaming ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Nicholas Conlon, Nisar R. Ahmed, Daniel Szafir
Humans working with autonomous artificially intelligent systems may not be experts in the inner workings of their machine teammates, but need to understand when to employ, trust, and rely on the system. A critical challenge is to develop machine agents with the capacity to understand their own capabilities and limitations, and the ability to communicate this information to human partners. Self-assessment
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Non-imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Xiaodan Xing, Huanjun Wu, Lichao Wang, Iain Stenson, May Yong, Javier Del Ser, Simon Walsh, Guang Yang
Data quality is a key factor in the development of trustworthy AI in healthcare. A large volume of curated datasets with controlled confounding factors can improve the accuracy, robustness, and privacy of downstream AI algorithms. However, access to high-quality datasets is limited by the technical difficulties of data acquisition, and large-scale sharing of healthcare data is hindered by strict ethical
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Fairness in Machine Learning: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Simon Caton, Christian Haas
When Machine Learning technologies are used in contexts that affect citizens, companies as well as researchers need to be confident that there will not be any unexpected social implications, such as bias towards gender, ethnicity, and/or people with disabilities. There is significant literature on approaches to mitigate bias and promote fairness, yet the area is complex and hard to penetrate for newcomers
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Trusting My Predictions: On the Value of Instance-Level Analysis ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Ana C. Lorena, Pedro Y. A. Paiva, Ricardo B. C. Prudêncio
Machine Learning solutions have spread along many domains, including critical applications. The development of such models usually relies on a dataset containing labeled data. This dataset is then split into training and test sets and the accuracy of the models in replicating the test labels is assessed. This process is often iterated in a cross-validation procedure for obtaining average performance
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Explainable Reinforcement Learning: A Survey and Comparative Review ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Stephanie Milani, Nicholay Topin, Manuela Veloso, Fei Fang
Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine learning that has attracted considerable attention in recent years. The goal of XRL is to elucidate the decision-making process of reinforcement learning (RL) agents in sequential decision-making settings. Equipped with this information, practitioners can better understand important questions about RL agents (especially
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Byzantine Machine Learning: A Primer ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Rachid Guerraoui, Nirupam Gupta, Rafael Pinot
The problem of Byzantine resilience in distributed machine learning, a.k.a. Byzantine machine learning, consists of designing distributed algorithms that can train an accurate model despite the presence of Byzantine nodes—that is, nodes with corrupt data or machines that can misbehave arbitrarily. By now, many solutions to this important problem have been proposed, most of which build upon the classical
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Secure and Trustworthy Artificial Intelligence-extended Reality (AI-XR) for Metaverses ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Adnan Qayyum, Muhammad Atif Butt, Hassan Ali, Muhammad Usman, Osama Halabi, Ala Al-Fuqaha, Qammer H. Abbasi, Muhammad Ali Imran, Junaid Qadir
Metaverse is expected to emerge as a new paradigm for the next-generation Internet, providing fully immersive and personalized experiences to socialize, work, and play in self-sustaining and hyper-spatio-temporal virtual world(s). The advancements in different technologies such as augmented reality, virtual reality, extended reality (XR), artificial intelligence (AI), and 5G/6G communication will be
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A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Mario Alfonso Prado-Romero, Bardh Prenkaj, Giovanni Stilo, Fosca Giannotti
Graph Neural Networks (GNNs) perform well in community detection and molecule classification. Counterfactual Explanations (CE) provide counter-examples to overcome the transparency limitations of black-box models. Due to the growing attention in graph learning, we focus on the concepts of CE for GNNs. We analysed the SoA to provide a taxonomy, a uniform notation, and the benchmarking datasets and evaluation
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A Survey of Dataset Refinement for Problems in Computer Vision Datasets ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Zhijing Wan, Zhixiang Wang, Cheukting Chung, Zheng Wang
Large-scale datasets have played a crucial role in the advancement of computer vision. However, they often suffer from problems such as class imbalance, noisy labels, dataset bias, or high resource costs, which can inhibit model performance and reduce trustworthiness. With the advocacy of data-centric research, various data-centric solutions have been proposed to solve the dataset problems mentioned
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Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Qinghua Lu, Liming Zhu, Xiwei Xu, Jon Whittle, Didar Zowghi, Aurelie Jacquet
Responsible Artificial Intelligence (RAI) is widely considered as one of the greatest scientific challenges of our time and is key to increase the adoption of Artificial Intelligence (AI). Recently, a number of AI ethics principles frameworks have been published. However, without further guidance on best practices, practitioners are left with nothing much beyond truisms. In addition, significant efforts
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It Is All about Data: A Survey on the Effects of Data on Adversarial Robustness ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Peiyu Xiong, Michael Tegegn, Jaskeerat Singh Sarin, Shubhraneel Pal, Julia Rubin
Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to confuse the model into making a mistake. Such examples pose a serious threat to the applicability of machine learning-based systems, especially in life- and safety-critical domains. To address this problem, the area of adversarial robustness investigates mechanisms behind adversarial attacks and
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The Path to Defence: A Roadmap to Characterising Data Poisoning Attacks on Victim Models ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Tarek Chaalan, Shaoning Pang, Joarder Kamruzzaman, Iqbal Gondal, Xuyun Zhang
Data Poisoning Attacks (DPA) represent a sophisticated technique aimed at distorting the training data of machine learning models, thereby manipulating their behavior. This process is not only technically intricate but also frequently dependent on the characteristics of the victim (target) model. To protect the victim model, the vast number of DPAs and their variants make defenders rely on trial and
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Artificial Intelligence for Safety-Critical Systems in Industrial and Transportation Domains: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Jon Perez-Cerrolaza, Jaume Abella, Markus Borg, Carlo Donzella, Jesús Cerquides, Francisco J. Cazorla, Cristofer Englund, Markus Tauber, George Nikolakopoulos, Jose Luis Flores
Artificial Intelligence (AI) can enable the development of next-generation autonomous safety-critical systems in which Machine Learning (ML) algorithms learn optimized and safe solutions. AI can also support and assist human safety engineers in developing safety-critical systems. However, reconciling both cutting-edge and state-of-the-art AI technology with safety engineering processes and safety standards
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A Systematic Review of Cross-Lingual Sentiment Analysis: Tasks, Strategies, and Prospects ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Chuanjun Zhao, Meiling Wu, Xinyi Yang, Wenyue Zhang, Shaoxia Zhang, Suge Wang, Deyu Li
Traditional methods for sentiment analysis, when applied in a monolingual context, often yield less than optimal results in multilingual settings. This underscores the need for a more thorough exploration of cross-lingual sentiment analysis (CLSA) methodologies to improve analytical effectiveness. CLSA, confronted with obstacles such as linguistic disparities and a lack of resources, seeks to evaluate
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Timing Side-channel Attacks and Countermeasures in CPU Microarchitectures ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Jiliang Zhang, Congcong Chen, Jinhua Cui, Keqin Li
Microarchitectural vulnerabilities, such as Meltdown and Spectre, exploit subtle microarchitecture state to steal the user’s secret data and even compromise the operating systems. In recent years, considerable discussion lies in understanding the attack-defense mechanisms and exploitability of such vulnerabilities. Unfortunately, there have been few investigations into a systematic elaboration of threat
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Blockchain Data Storage Optimisations: A Comprehensive Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Jun Wook Heo, Gowri Sankar Ramachandran, Ali Dorri, Raja Jurdak
Blockchain offers immutability, transparency, and security in a decentralised way for many applications, including finance, supply chain, and the Internet of Things (IoT). Due to its popularity and widespread adoption, it has started to process an enormous number of transactions, placing an ever-growing demand for storage. As the technology gains more popularity, the storage requirements of blockchain
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A Survey on Reversible Data Hiding for Uncompressed Images ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Cheng Zhang, Bo Ou, Fei Peng, Yao Zhao, Keqin Li
Reversible data hiding (RDH) has developed various theories and algorithms since the early 1990s. The existing works involve a large amount of specialized knowledge, making it difficult for researchers, especially primary learners, to have a good grounding in the basic ideas. In this survey, we will review the mainstream RDH algorithms in uncompressed images and analyze their unique features to provide
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Efficient High-Resolution Deep Learning: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis
Cameras in modern devices such as smartphones, satellites and medical equipment are capable of capturing very high resolution images and videos. Such high-resolution data often need to be processed by deep learning models for cancer detection, automated road navigation, weather prediction, surveillance, optimizing agricultural processes and many other applications. Using high-resolution images and
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A Systematic Mapping Study on Social Network Privacy: Threats and Solutions ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-09 Andrey Rodrigues, Maria Lúcia Villela, Eduardo Feitosa
Online Social Networks (OSNs) are becoming pervasive in today’s world. Millions of people worldwide are involved in different forms of online networking. However, this ease of use of OSNs comes with a cost in terms of privacy. Users of OSNs become victims of identity theft, cyberstalking, and information leakage, which are real threats to privacy. These threats can obtain a user’s personal information