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A Meta-Study of Software-Change Intentions ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Jacob Krüger, Yi Li, Kirill Lossev, Chenguang Zhu, Marsha Chechik, Thorsten Berger, Julia Rubin
Every software system undergoes changes, for example, to add new features, fix bugs, or refactor code. The importance of understanding software changes has been widely recognized, resulting in various techniques and studies, for instance, on change-impact analysis or classifying developers’ activities. Since changes are triggered by developers’ intentions—something they plan or want to change in the
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Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Navid Mohammadi Foumani, Lynn Miller, Chang Wei Tan, Geoffrey I. Webb, Germain Forestier, Mahsa Salehi
Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer vision and holds great promise in other fields such as time series analysis where the relevant features must often be abstracted from the raw data but are not known a priori. This article surveys the current state of the
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SoK: Security in Real-Time Systems ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Monowar Hasan, Ashish Kashinath, Chien-Ying Chen, Sibin Mohan
Security is an increasing concern for real-time systems (RTS). Over the last decade or so, researchers have demonstrated attacks and defenses aimed at such systems. In this article, we identify, classify and measure the effectiveness of the security research in this domain. We provide a high-level summary [identification] and a taxonomy [classification] of this existing body of work. Furthermore, we
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Fuzzers for Stateful Systems: Survey and Research Directions ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Cristian Daniele, Seyed Behnam Andarzian, Erik Poll
Fuzzing is a very effective testing methodology to find bugs. In a nutshell, a fuzzer sends many slightly malformed messages to the software under test, hoping for crashes or incorrect system behaviour. The methodology is relatively simple, although applications that keep internal states are challenging to fuzz. The research community has responded to this challenge by developing fuzzers tailored to
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A Survey of Cutting-edge Multimodal Sentiment Analysis ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Upendra Singh, Kumar Abhishek, Hiteshwar Kumar Azad
The rapid growth of the internet has reached the fourth generation, i.e., web 4.0, which supports Sentiment Analysis (SA) in many applications such as social media, marketing, risk management, healthcare, businesses, websites, data mining, e-learning, psychology, and many more. Sentiment analysis is a powerful tool for governments, businesses, and researchers to analyse users’ emotions and mental states
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Controllable Data Generation by Deep Learning: A Review ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Shiyu Wang, Yuanqi Du, Xiaojie Guo, Bo Pan, Zhaohui Qin, Liang Zhao
Designing and generating new data under targeted properties has been attracting various critical applications such as molecule design, image editing and speech synthesis. Traditional hand-crafted approaches heavily rely on expertise experience and intensive human efforts, yet still suffer from the insufficiency of scientific knowledge and low throughput to support effective and efficient data generation
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Warm-Starting and Quantum Computing: A Systematic Mapping Study ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Felix Truger, Johanna Barzen, Marvin Bechtold, Martin Beisel, Frank Leymann, Alexander Mandl, Vladimir Yussupov
Due to low numbers of qubits and their error-proneness, Noisy Intermediate-Scale Quantum (NISQ) computers impose constraints on the size of quantum algorithms they can successfully execute. State-of-the-art research introduces various techniques addressing these limitations by utilizing known or inexpensively generated approximations, solutions, or models as a starting point to approach a task instead
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Pre-Trained Language Models for Text Generation: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen
Text Generation aims to produce plausible and readable text in human language from input data. The resurgence of deep learning has greatly advanced this field, in particular, with the help of neural generation models based on pre-trained language models (PLMs). Text generation based on PLMs is viewed as a promising approach in both academia and industry. In this article, we provide a survey on the
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DevOps Metrics and KPIs: A Multivocal Literature Review ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Ricardo Amaro, Rúben Pereira, Miguel Mira da Silva
Context: Information Technology organizations are aiming to implement DevOps capabilities to fulfill market, customer, and internal needs. While many are successful with DevOps implementation, others still have difficulty measuring DevOps success in their organization. As a result, the effectiveness of assessing DevOps remains erratic. This emphasizes the need to withstand management in measuring the
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Local Interpretations for Explainable Natural Language Processing: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Siwen Luo, Hamish Ivison, Soyeon Caren Han, Josiah Poon
As the use of deep learning techniques has grown across various fields over the past decade, complaints about the opaqueness of the black-box models have increased, resulting in an increased focus on transparency in deep learning models. This work investigates various methods to improve the interpretability of deep neural networks for Natural Language Processing (NLP) tasks, including machine translation
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A Deep Dive into Robot Vision - An Integrative Systematic Literature Review Methodologies and Research Endeavor Practices ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Saima Sultana, Muhammad Mansoor Alam, Mazliham Mohd Su’ud, Jawahir Che Mustapha, Mukesh Prasad
Novel technological swarm and industry 4.0 mold the recent Robot vision research into innovative discovery. To enhance technological paradigm Deep Learning offers remarkable pace to move towards diversified advancement. This research considers the most topical, recent, related and state-of-the-art research reviews that revolve around Robot vision, and shapes the research into Systematic Literature
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Intelligent Edge-powered Data Reduction: A Systematic Literature Review ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Laércio Pioli, Douglas D. J. de Macedo, Daniel G. Costa, Mario A. R. Dantas
The development of the Internet of Things (IoT) paradigm and its significant spread as an affordable data source has brought many challenges when pursuing efficient data collection, distribution, and storage. Since such hierarchical logical architecture can be inefficient and costly in many cases, Data Reduction (DR) solutions have arisen to allow data preprocessing before actual transmission. To increase
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Extended Reality (XR) Toward Building Immersive Solutions: The Key to Unlocking Industry 4.0 ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 A’aeshah Alhakamy
When developing XR applications for Industry 4.0, it is important to consider the integration of visual displays, hardware components, and multimodal interaction techniques that are compatible with the entire system. The potential use of multimodal interactions in industrial applications has been recognized as a significant factor in enhancing humans’ ability to perform tasks and make informed decisions
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Intel TDX Demystified: A Top-Down Approach ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-25 Pau-Chen Cheng, Wojciech Ozga, Enriquillo Valdez, Salman Ahmed, Zhongshu Gu, Hani Jamjoom, Hubertus Franke, James Bottomley
Intel Trust Domain Extensions (TDX) is an architectural extension in the 4th Generation Intel Xeon Scalable Processor that supports confidential computing. TDX allows the deployment of virtual machines in the Secure-Arbitration Mode (SEAM) with encrypted CPU state and memory, integrity protection, and remote attestation. TDX aims at enforcing hardware-assisted isolation for virtual machines and minimize
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A study on the cross-platform influence mechanism of physicians’ live streaming behavior on performance Internet Res. (IF 5.9) Pub Date : 2024-04-25 Chen Chen, Hong Wu
Purpose The advent of online live streaming platforms (OLSPs) and online health communities (OHCs) has expedited the integration of traditional medical services with Internet new media technology. Since the practice of physicians conducting live streaming is a relatively new phenomenon, the potential cross-platform effects of such physicians’ live streaming have not received adequate attention. De
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Deep Multimodal Data Fusion ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 Fei Zhao, Chengcui Zhang, Baocheng Geng
Multimodal Artificial Intelligence (Multimodal AI), in general, involves various types of data (e.g., images, texts, or data collected from different sensors), feature engineering (e.g., extraction, combination/fusion), and decision-making (e.g., majority vote). As architectures become more and more sophisticated, multimodal neural networks can integrate feature extraction, feature fusion, and decision-making
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Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 Jasmina Gajcin, Ivana Dusparic
While AI algorithms have shown remarkable success in various fields, their lack of transparency hinders their application to real-life tasks. Although explanations targeted at non-experts are necessary for user trust and human-AI collaboration, the majority of explanation methods for AI are focused on developers and expert users. Counterfactual explanations are local explanations that offer users advice
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Financial Sentiment Analysis: Techniques and Applications ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 Kelvin Du, Frank Xing, Rui Mao, Erik Cambria
Financial Sentiment Analysis (FSA) is an important domain application of sentiment analysis that has gained increasing attention in the past decade. FSA research falls into two main streams. The first stream focuses on defining tasks and developing techniques for FSA, and its main objective is to improve the performances of various FSA tasks by advancing methods and using/curating human-annotated datasets
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Non-invasive Techniques for Muscle Fatigue Monitoring: A Comprehensive Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 Na Li, Rui Zhou, Bharath Krishna, Ashirbad Pradhan, Hyowon Lee, Jiayuan He, Ning Jiang
Muscle fatigue represents a complex physiological and psychological phenomenon that impairs physical performance and increases the risks of injury. It is important to continuously monitor fatigue levels for early detection and management of fatigue. The detection and classification of muscle fatigue also provide important information in human-computer interactions (HMI), sports injuries and performance
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Deep Learning for Iris Recognition: A Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 Kien Nguyen, Hugo Proença, Fernando Alonso-Fernandez
In this survey, we provide a comprehensive review of more than 200 articles, technical reports, and GitHub repositories published over the last 10 years on the recent developments of deep learning techniques for iris recognition, covering broad topics on algorithm designs, open-source tools, open challenges, and emerging research. First, we conduct a comprehensive analysis of deep learning techniques
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Resilient Machine Learning: Advancement, Barriers, and Opportunities in the Nuclear Industry ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 Anita Khadka, Saurav Sthapit, Gregory Epiphaniou, Carsten Maple
The widespread adoption and success of Machine Learning (ML) technologies depend on thorough testing of the resilience and robustness to adversarial attacks. The testing should focus on both the model and the data. It is necessary to build robust and resilient systems to withstand disruptions and remain functional despite the action of adversaries, specifically in the security-sensitive Nuclear Industry
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Towards Hybrid-Optimization Video Coding ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 Shuai Huo, Dong Liu, Haotian Zhang, Li Li, Siwei Ma, Feng Wu, Wen Gao
Video coding that pursues the highest compression efficiency is the art of computing for rate-distortion optimization. The optimization has been approached in different ways, exemplified by two typical frameworks: block-based hybrid video coding and end-to-end learned video coding. The block-based hybrid framework encompasses more and more coding modes that are available at the decoder side; an encoder
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Contactless Diseases Diagnoses Using Wireless Communication Sensing: Methods and Challenges Survey ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 Najah Abed Abu Ali, Mubashir Rehman, Shahid Mumtaz, Muhammad Bilal Khan, Mohammad Hayajneh, Farman Ullah, Raza Ali Shah
Respiratory illness diagnosis and continuous monitoring are becoming popular as sensitive markers of chronic diseases. This interest has motivated the increased development of respiratory illness diagnosis by exploiting wireless communication as a sensing system. Several methods for diagnosing a respiratory illness are based on multiple sensors and techniques. Depending on whether the device embeds
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Optimizing with Attractor: A Tutorial ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 Weiqi Li
This tutorial presents a novel search system—the Attractor-Based Search System (ABSS)—that can solve the Traveling Salesman Problem very efficiently with optimality guarantee. From the perspective of dynamical systems, a heuristic local search algorithm for an NP-complete combinatorial problem is a discrete dynamical system. In a local search system, an attractor drives the search trajectories into
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Tutorial on Matching-based Causal Analysis of Human Behaviors Using Smartphone Sensor Data ACM Comput. Surv. (IF 16.6) Pub Date : 2024-04-24 Gyuwon Jung, Sangjun Park, Eun-Yeol Ma, Heeyoung Kim, Uichin Lee
Smartphones can unobtrusively capture human behavior and contextual data such as user interaction and mobility. Thus far, smartphone sensor data have primarily been used to gain behavioral insights through correlation analysis. This article provides a tutorial on the causal analysis of human behavior using smartphone sensor data by reviewing well-known matching methods. The key steps of the causal
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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