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Four Decades of Symbolic Knowledge Extraction from Sub-Symbolic Predictors. A Survey ACM Comput. Surv. (IF 28.0) Pub Date : 2025-07-15 Federico Sabbatini
Issues deriving from the opaque behaviour of prediction-effective, yet non-interpretable, machine learning predictors are being studied and analysed since many decades. One of the main research branches consists of adopting anyway the unintelligible models, thanks to their predictive performance, but queueing to the learning workflow a dedicated technique aimed at post-hoc extracting human-interpretable
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Survey and Analysis for the Challenges in Computer Science to the Automation of Grading Systems ACM Comput. Surv. (IF 28.0) Pub Date : 2025-07-14 Joan Lu, Bhavya Krishna Balasubramanian, Mike Joy, Qiang Xu
Assessment is essential to educational system. Automatic grading reduces the time and effort taken by tutors to assess the answers written by the students. To understand recent computational methods used for automatic grading, a review has been conducted. 4084 papers were initially identified using a keyword search. After filtering, the number was reduced to 57. It was found that statistical models
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A Survey on the Shared Address Space with Privatized Static Variables (SAS-PSV) Execution Model for Many-Core Era ACM Comput. Surv. (IF 28.0) Pub Date : 2025-07-04 Atsushi Hori, Kaiming Ouyang, Min Si, Pavan Balaji, Julien Jaeger, Marc Pérache, Sam` White, Evan Ramos, Laxmikant Kale, Kevin Pedretti, Ron Brightwell, Balazs Gerofi, Yutaka Ishikawa
Parallel applications often use MPI processes and OpenMP threads. Those parallel execution models, multi-process and multi-thread, were invented to increase efficiency on uniprocessor systems. In the multi-process approach, each process’s isolated address space may make communication expensive; in the multi-thread design, shared variables may cause access conflicts and stall executions. Processes or
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Few-Shot Relation Extraction Based on Prompt Learning: A Taxonomy, Survey, Challenges and Future Directions ACM Comput. Surv. (IF 28.0) Pub Date : 2025-07-04 Tingting Hang, Shuting Liu, Jun Feng, Hamza Djigal, Jun Huang
Relation extraction (RE) is critical in information extraction (IE) and knowledge graph construction. RE aims to identify the semantic relations between entities from natural language texts. Traditional RE models often rely on many manually annotated training samples, which are limited when data is scarce. Therefore, exploring how to perform relation extraction under few-shot conditions has become
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Digital Twins in Security Operations: State of the Art and Future Perspectives ACM Comput. Surv. (IF 28.0) Pub Date : 2025-07-01 Philip Empl, David Koch, Marietheres Dietz, Günther Pernul
In an era of rapid technological advancements, digital twins are gaining attention in industry and research. These virtual representations of real-world entities, enabled by the Internet of Things (IoT), offer advanced simulation and analysis capabilities. Their application spans various sectors, from smart manufacturing to healthcare, highlighting their versatility. However, the rise of digital technologies
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Privacy Preserving Identity Federation: A Literature Study ACM Comput. Surv. (IF 28.0) Pub Date : 2025-07-01 Anne Bumiller, Elisavet Kozyri, Håvard Dagenborg
Within an Identity federation (IF) system, users gain access to multiple Service Providers (SPs) by submitting credentials issued by one or more Identity Providers (IdPs). Such Identity Federations (IFs) raise several privacy concerns: IdPs might track user activity, by recording the accessed services, and SPs might mismanage sensitive user attributes that comprise the submitted credentials. An extensive
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Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions ACM Comput. Surv. (IF 28.0) Pub Date : 2025-07-01 Cheng-Te Li, Yu-Che Tsai, Chih-Yao Chen, Jay Chiehen Liao
This survey dives into Tabular Data Learning (TDL) using Graph Neural Networks (GNNs), a domain where deep learning-based approaches have increasingly shown superior performance in both classification and regression tasks compared to traditional methods. We highlight a critical gap in deep neural TDL: the underrepresentation of latent correlations among data instances and feature values. GNNs, with
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A Survey of AIOps in the Era of Large Language Models ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-27 Lingzhe Zhang, Tong Jia, Mengxi Jia, Yifan Wu, Aiwei Liu, Yong Yang, Zhonghai Wu, Xuming Hu, Philip Yu, Ying Li
As large language models (LLMs) grow increasingly sophisticated and pervasive, their application to various Artificial Intelligence for IT Operations (AIOps) tasks has garnered significant attention. However, a comprehensive understanding of the impact, potential, and limitations of LLMs in AIOps remains in its infancy. To address this gap, we conducted a detailed survey of LLM4AIOps, focusing on how
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A Comprehensive Survey on Self-Supervised Learning for Recommendation ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-27 Xubin Ren, Wei Wei, Lianghao Xia, Chao Huang
Recommender systems play a crucial role in tackling the challenge of information overload by delivering personalized recommendations based on individual user preferences. Deep learning techniques, such as RNNs, GNNs, and Transformer architectures, have significantly propelled the advancement of recommender systems by enhancing their comprehension of user behaviors and preferences. However, supervised
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Few-Shot Learning for Medical Image Segmentation: A Review and Comparative Study ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-27 Theekshana Dissanayake, Yasmeen George, Dwarikanath Mahapatra, Shridha Sridharan, Clinton Fookes, Zongyuan Ge
Medical image segmentation plays a crucial role in assisting clinicians with diagnosing critical medical conditions. In deep learning, few-shot learning methods aim to replicate human learning by leveraging fewer examples for determining a prediction for a novel class. Researchers in the medical imaging community have also explored novel methods for few-shot medical image segmentation, leveraging meta-learning
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Understanding World or Predicting Future? A Comprehensive Survey of World Models ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-27 Jingtao Ding, Yunke Zhang, Yu Shang, Yuheng Zhang, Zefang Zong, Jie Feng, Yuan Yuan, Hongyuan Su, Nian Li, Nicholas Sukiennik, Fengli Xu, Yong Li
The concept of world models has garnered significant attention due to advancements in multimodal large language models such as GPT-4 and video generation models such as Sora, which are central to the pursuit of artificial general intelligence. This survey offers a comprehensive review of the literature on world models. Generally, world models are regarded as tools for either understanding the present
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Prediction of Certification in MOOCs: A Systematic Literature Review ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-20 Ahmed Alamri, Mohammad Alshehri, Laila Alrajhi, Alexandra Cristea
Massive Open Online Courses (MOOCs) have been proliferating, offering free or low-cost content for learners. Nevertheless, the certification rate of both free and paid courses has been low (between 4.5% - 13% and 1% - 3%, respectively). Thus, this study aims to survey MOOCs certification predictive models, synthesise results for a comprehensive and deep understanding of this field and explore how these
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Causality in Bandits: A Survey ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-19 Chandrasekar Subramanian, Balaraman Ravindran
The literature on bandits has developed largely independently of advances in causal inference. Work in the last few years has started investigating the close connections between these two areas and that has led to fruitful ideas that have produced advances in bandit algorithms. We present the first survey focusing specifically on the intersection of these two areas. We first provide a taxonomy for
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Static Code Analysis for IoT Security: A Systematic Literature Review ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-19 Diego Gomes, Eduardo Felix, Fernando Aires, Marco Vieira
The growth of the Internet of Things (IoT) has provided significant advances in several areas of the industry, but security concerns have also increased due to this expansion. Many IoT devices are the target of cyber attacks due to various firmware, source code, and software vulnerabilities. In this context, static code analysis, leveraging various techniques, has emerged as an effective approach to
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Computational Approaches to the Detection of Lesser-Known Rhetorical Figures: A Systematic Survey and Research Challenges ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-19 Ramona Kühn, Jelena Mitrović, Michael Granitzer
Rhetorical figures play a major role in everyday communication, making text and speech more interesting, memorable, or persuasive through their association between form and meaning. Computational detection of rhetorical figures plays an important part in thorough understanding of complex communication patterns. In this survey, we provide a comprehensive overview of computational approaches to lesser-known
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Advancing 5G Security and Privacy with AI: A Survey ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-16 Haoxin He, Shufan Fei, Zheng Yan
With the global deployment of the fifth-generation (5G) mobile technology, a new era characterized by ultra-high data speeds, ultra-low latency, and massive connectivity has emerged. However, these advancements also introduce new security and privacy challenges. The integration of new technologies in 5G has fundamentally altered the network structure, rendering traditional security methods inadequate
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Deep Learning in Stance Detection: A Survey ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-13 Parush Gera, Tempestt Neal
The analysis of an author’s perspective on a given topic within text presents a challenging problem in natural language processing. Stance detection, or the identification of an author’s inclination either in favor, against, or neutral towards some target entity, is an important classification task in this context. Significant progress has been made in stance detection, especially facilitated by deep
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Evaluation of Question Answering Systems: Complexity of Judging a Natural Language ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-12 Amer Farea, Zhen Yang, Kien Duong, Nadeesha Perera, Frank Emmert-Streib
Question answering (QA) systems are a leading and rapidly advancing field of natural language processing (NLP) research. One of their key advantages is that they enable more natural interactions between humans and machines, such as in virtual assistants or search engines. Over the past few decades, many QA systems have been developed to handle diverse QA tasks. However, the evaluation of these systems
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Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-12 Zhiqiang Zhong, Anastasia Barkova, Davide Mottin
Artificial Intelligence has become integral to intelligent drug discovery, with Graph Machine Learning (GML) emerging as a powerful structure-based method for modelling graph-structured biomedical data and investigating their properties. However, GML faces challenges such as limited interpretability and heavy dependency on abundant high-quality training data. On the other hand, knowledge-based methods
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Assessing the Effectiveness of ChatGPT in Secure Code Development: A Systematic Literature Review ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-12 Rezika Bouzid, Raphaël Khoury
ChatGPT, a Large Language Model (LLM) maintained by OpenAI, has demonstrated a remarkable ability to seemingly comprehend and contextually generate text. Among its myriad applications, its capability to autonomously generate and analyze computer code stands out as particularly promising. This functionality has piqued substantial interest due to its potential to streamline the software development process
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Review-Aware Recommender Systems (RARSs): Recent Advances, Experimental Comparative Analysis, Discussions, and New Directions ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-12 Guilherme Bittencourt, Naan Vasconcelos, Yan Andrade, Nicollas Silva, Washington Cunha, Diego Roberto Colombo Dias, Marcos André Gonçalves, Leonardo Rocha
Recommender systems (RSs) have emerged as an effective strategy for dealing with information overload. Their importance is undeniable as they are widely adopted in various web applications, presenting the potential to solve various problems associated with the abundance of choices. In recent years, the literature has witnessed the proposal of numerous recommendation techniques, constantly seeking to
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A Survey on the Implementations, Attacks, and Countermeasures of the NIST Lightweight Cryptography Standard: ASCON ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-12 Jasmin Kaur, Alvaro Cintas Canto, Mehran Mozaffari Kermani, Reza Azarderakhsh
This survey is the first work on the current standard for lightweight cryptography, standardized in 2023. Lightweight cryptography plays a vital role in securing resource-constrained embedded systems such as deeply-embedded systems (implantable and wearable medical devices, smart fabrics, smart homes, and the like), radio frequency identification (RFID) tags, sensor networks, and privacy-constrained
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Resource Provisioning in Fog Computing - A Survey ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-11 Divya Vetriveeran, Leena Sri R
The internet world has created an era where any device can interconnect with each other. Gathering intelligence from streaming data is challenging and can create wonders and valuable innovations for humanity. The shortcomings of connectivity due to the remote location of the cloud induce latency and performance issues in real-time. Thus, a traditional cloud may not be suitable for all applications
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Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutions ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-10 Christophe El Zeinaty, Wassim Hamidouche, Glenn Herrou, Daniel Menard
Object detection (OD) has become vital for numerous computer vision applications, but deploying it on resource-constrained internet of things (IoT) devices presents a significant challenge. These devices, often powered by energy-efficient microcontrollers, struggle to handle the computational load of deep learning-based OD models. This issue is compounded by the rapid proliferation of IOT devices,
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Smart Water-IoT: Harnessing IoT and AI for Efficient Water Management ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-10 Vlastimil Slany, Eva Krcalova, Jiri Balej, Martin Zach, Tereza Kucova, Michal Prauzek, Radek Martinek
The treatment, monitoring and distribution of drinking water is an integral component of critical national infrastructure and therefore places continually increasing demands on Water Distribution Networks (WDNs). This domain and its sub-sectors face several major problems, namely climate change and drought-induced rises in water consumption from surface and underground reservoirs, in addition to the
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A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-07 Ola Shorinwa, Zhiting Mei, Justin Lidard, Allen Z. Ren, Anirudha Majumdar
The remarkable performance of large language models (LLMs) in content generation, coding, and common-sense reasoning has spurred widespread integration into many facets of society. However, integration of LLMs raises valid questions on their reliability and trustworthiness, given their propensity to generate hallucinations: plausible, factually-incorrect responses, which are expressed with striking
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A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-06 Janik-Vasily Benzin, Stefanie Rinderle-Ma
Event prediction is the ability of anticipating future events, i.e., future real-world occurrences, and aims to support the user in deciding on actions that change future events towards a desired state. An event prediction method learns the relation between features of past events and future events. It is applied to newly observed events to predict corresponding future events that are evaluated with
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A Survey on Kolmogorov-Arnold Network ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-06 Shriyank Somvanshi, Syed Aaqib Javed, Md Monzurul Islam, Diwas Pandit, Subasish Das
This review study explores the theoretical foundations, evolution, applications, and future potential of Kolmogorov-Arnold Networks (KAN), a neural network model inspired by the Kolmogorov-Arnold representation theorem. KANs set themselves apart from traditional neural networks by employing learnable, spline-parameterized functions rather than fixed activation functions, allowing for flexible and interpretable
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Adversarial Attacks of Vision Tasks in the Past 10 Years: A Survey ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-06 Chiyu Zhang, Lu Zhou, Xiaogang Xu, Jiafei Wu, Zhe Liu
With the advent of Large Vision-Language Models (LVLMs), new attack vectors, such as cognitive bias, prompt injection, and jailbreaking, have emerged. Understanding these attacks promotes system robustness improvement and neural networks demystification. However, existing surveys often target attack taxonomy and lack in-depth analysis like 1) unified insights into adversariality, transferability, and
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Towards Message Brokers for Generative AI: Survey, Challenges, and Opportunities ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-05 Alaa Saleh, Roberto Morabito, Schahram Dustdar, Sasu Tarkoma, Susanna Pirttikangas, Lauri Lovén
In today’s digital world, GenAI is becoming increasingly prevalent by enabling unparalleled content generation capabilities for a wide range of advanced applications. This surge in adoption has sparked a significant increase in demand for data-centric GenAI models spanning the distributed edge-cloud continuum, placing increasing demands on communication infrastructures, highlighting the necessity for
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RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-05 Shreyas Chaudhari, Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, Ameet Deshpande, Bruno Castro da Silva
A significant challenge in training large language models (LLMs) as effective assistants is aligning them with human preferences. Reinforcement learning from human feedback (RLHF) has emerged as a promising solution. However, our understanding of RLHF is often limited to initial design choices. This paper analyzes RLHF through reinforcement learning principles, focusing on the reward model. It examines
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A Survey of Program Analysis for Distributed Software Systems ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-03 Haipeng Cai
Distributed software systems are pervasive today and they are increasingly developed/deployed to meet the growing needs for scalable computing. Given their critical roles in modern information infrastructures, assuring the quality of distributed software is crucial. As a fundamental methodology for software quality assurance in general, program analysis underlies a range of techniques and tools for
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A Survey on Employing Large Language Models for Text-to-SQL Tasks ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-03 Liang Shi, Zhengju Tang, Nan Zhang, Xiaotong Zhang, Zhi Yang
With the development of the Large Language Models (LLMs), a large range of LLM-based Text-to-SQL(Text2SQL) methods have emerged. This survey provides a comprehensive review of LLM-based Text2SQL studies. We first enumerate classic benchmarks and evaluation metrics. For the two mainstream methods, prompt engineering and finetuning, we introduce a comprehensive taxonomy and offer practical insights into
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Investigating EEG Microstate Analysis in Cognitive Software Engineering Tasks: A Systematic Mapping Study and Taxonomy ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-03 Willian Bolzan, Kleinner Farias
Performing software engineering (SE) tasks requires the activation of software developers’ brain neural networks. Electroencephalography (EEG) microstate analysis emerges as a promising neurophysiological method to investigate the spatiotemporal dynamics of brain networks at high temporal resolution. An EEG microstate represents a unique topography of electric potentials over the multichannel EEG records
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Graph Deep Learning for Time Series Forecasting ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-03 Andrea Cini, Ivan Marisca, Daniele Zambon, Cesare Alippi
Graph deep learning methods have become popular tools to process collections of correlated time series. Unlike traditional multivariate forecasting methods, graph-based predictors leverage pairwise relationships by conditioning forecasts on graphs spanning the time series collection. The conditioning takes the form of architectural inductive biases on the forecasting architecture, resulting in a family
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Benchmarking Relaxed Differential Privacy in Private Learning: A Comparative Survey ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-03 Zhaolong Zheng, Lin Yao, Haibo Hu, Guowei Wu
Differential privacy (DP), a rigorously quantifiable privacy preservation technique, has found widespread application within the domain of machine learning. As DP techniques are implemented in machine learning algorithms, a significant and intricate trade-off between privacy and utility emerges, garnering extensive attention from researchers. In the pursuit of striking a delicate equilibrium between
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Survey on Factuality in Large Language Models ACM Comput. Surv. (IF 28.0) Pub Date : 2025-06-02 Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, Qipeng Guo, Xiangkun Hu, Xiangru Tang, Tianhang Zhang, Cheng Jiayang, Yunzhi Yao, Xuming Hu, Zehan Qi, Wenyang Gao, Yidong Wang, Linyi Yang, Jindong Wang, Xing Xie, Zheng Zhang, Yue Zhang
This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital. We define the “factuality issue” as the probability of LLMs to produce content inconsistent with established facts. We first delve into the implications of these inaccuracies. Subsequently, we analyze the
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An End-to-End Pipeline Perspective on Video Streaming in Best-Effort Networks: A Survey and Tutorial ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-31 Leonardo Peroni, Sergey Gorinsky
Remaining a dominant force in Internet traffic, video streaming captivates end users, service providers, and researchers. This paper takes a pragmatic approach to reviewing recent advances in the field by focusing on the prevalent streaming paradigm that involves delivering long-form two-dimensional videos over the best-effort Internet with client-side adaptive bitrate (ABR) algorithms and assistance
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Myoelectric Prosthetic Hands: A Review of Muscle Synergy, Machine Learning and Edge Computing ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-31 Hamdy Farag, Mohamed Medhat Gaber, Mohammed Awad, Nancy Emad
Over the past decade, the integration of electromyography (EMG) techniques with machine learning has significantly advanced prosthetic device control. Researchers have developed sophisticated deep learning classifiers for gesture recognition and created EMG controllers capable of simultaneous proportional control across multiple degrees of freedom. However, the increasing complexity of these machine
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Review-based Recommender Systems: A Survey of Approaches, Challenges and Future Perspectives ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-30 Emrul Hasan, Mizanur Rahman, Chen Ding, Jimmy Huang, Shaina Raza
Recommender systems play a pivotal role in helping users navigate a vast selection of products and services. On online platforms, users have the opportunity to share feedback in various modes, such as numerical ratings, textual reviews, and likes/dislikes. Traditional recommendation systems rely on users’ explicit ratings or implicit interactions (e.g., likes, clicks, shares, and saves) to learn user
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Comprehensive Review of Path Planning Techniques for Unmanned Aerial Vehicles (UAVs) ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-29 Pawan Kumar, Kunwar Pal, Mahesh Govil
Unmanned Aerial Vehicles (UAVs) have gained significant attention in recent years for their potential applications in surveillance, monitoring, search and rescue, and mapping. However, efficient and optimal path planning remains a key challenge for UAV navigation. This survey paper reviews various UAV path planning algorithms, encompassing Sampling-Based techniques, Potential Field methods, Bio-Inspired
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Supervised Learning from Data Streams: An Overview and Update ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-27 Jesse Read, Indre Zliobaite
The literature on machine learning in the context of data streams is vast and growing. This indicates not only an ongoing interest, but also an ongoing need for a synthesis of new developments in this area. Here we reformulate the definitions of supervised data-stream learning, alongside consideration of contemporary concept drift and temporal dependence. Equipped with this, carry out a fresh discussion
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Sentiment Diffusion in Online Social Networks: A Survey from the Computational Perspective ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-27 Han Xu, Minghua Xu, Xianjun Deng, Bang Wang
With the development of mobile technologies, users can easily access Online social networks (OSNs), consequently, massive contents including personal experiences, observations, or opinions are generated online. These contents are being shared and exchanged in OSNs, which have a significant influence on the minds of people toward politics, societies, economics, etc. In this case, sentiment diffusion
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A Taxonomy and Systematic Review of Gaze Interactions for 2D Displays: Promising Techniques and Opportunities ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-26 Asma Shakil, Christof Lutteroth, Gerald Weber
Gaze input offers strong potential for creating intuitive and engaging user interfaces, but remains constrained by inherent limitations in accuracy and precision. Although extensive research has explored gaze-based interaction over the past three decades, a systematic framework that fully captures the diversity of gaze interaction techniques is still lacking. To address this gap, we present a novel
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A Systematic Review of Multimodal Signal Fusion for Acute Pain Assessment Systems ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-26 Muhammad Umar Khan, Girija Chetty, Roland Goecke, Raul Fernandez-Rojas
Pain assessment poses unique challenges due to its subjective and multifaceted nature, often requiring the integration of various sensor modalities. This review aims to provide a comprehensive overview of recent research focused specifically on acute pain assessment, with specific attention to: (a) identifying combinations of sensor modalities utilised for pain assessment, (b) exploring methods for
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Towards the Deployment of Realistic Autonomous Cyber Network Defence: A Systematic Review ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-24 Sanyam Vyas, Vasilios Mavroudis, Pete Burnap
In the ongoing network cybersecurity arms race, the defenders face a significant disadvantage as they must detect and counteract every attack. Conversely, the attacker only needs to succeed once to achieve their goal. To balance the odds, Autonomous Cyber Network Defence (ACND) employs autonomous agents for proactive and intelligent cyber-attack response. This article surveys the state of the art of
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A Survey of Subgraph Optimization for Expert Team Formation ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-24 Mahdis Saeedi, Hawre Hosseini, Christine Wong, Hossein Fani
Expert Team Formation is the search for gathering a team of experts who are expected to collaboratively work towards accomplishing a given project, a problem that has historically been solved in a variety of ways, including manually in a time-consuming and bias-filled manner, and algorithmically within disciplines like social sciences and management. In the present effort, while providing a taxonomy
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A Functionally-Grounded Benchmark Framework for XAI Methods: Insights and Foundations from a Systematic Literature Review ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-24 Dulce Canha, Sylvain Kubler, Kary Främling, Guy Fagherazzi
Artificial Intelligence (AI) is transforming industries, offering new opportunities to manage and enhance innovation. However, these advancements bring significant challenges for scientists and businesses, with one of the most critical being the ‘trustworthiness” of AI systems. A key requirement of trustworthiness is transparency , closely linked to explicability . Consequently, the exponential growth
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Facial Expression Analysis and Its Potentials in IoT Systems: A Contemporary Survey ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-24 Zixuan Shangguan, Yanjie Dong, Song Guo, Victor Leung, Jamal Deen, Xiping Hu
Facial expressions convey human emotions and can be categorized into macro-expressions (MaEs) and micro-expressions (MiEs) based on duration and intensity. While MaEs are voluntary and easily recognized, MiEs are involuntary, rapid, and can reveal concealed emotions. The integration of facial expression analysis with Internet-of-Thing (IoT) systems has significant potential across diverse scenarios
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A Comprehensive Review of Causal Inference in Banking, Finance, and Insurance ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-23 Satyam Kumar, Yelleti Vivek, Vadlamani Ravi, Indranil Bose
This is a comprehensive survey of the applications of causal inference in the Banking, Financial Services and Insurance (BFSI) domain based on 45 papers published from 1992 to 2023. It categorizes papers into (i) Banking and risk management (ii) Finance (covering investment, asset and portfolio management; behavioral finance and time series), (iii) Financial markets and (iv) Insurance. Exploring methods
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The Ubiquitous Skiplist: A Survey of What Cannot be Skipped About the Skiplist and its Applications in Data Systems ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-22 Lu Xing, Venkata Sai Pavan Kumar Vadrevu, Walid G. Aref
Skiplists have become prevalent in systems. The main advantages of skiplists are their simplicity and ease of implementation, and the ability to support operations in the same asymptotic complexities as their tree-based counterparts. In this survey, we explore skiplists and their many variants. We highlight many scenarios about how skiplists are useful, and how they fit well in these usage scenarios
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Attack Vectors for Face Recognition Systems: A Comprehensive Review ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-22 Roberto Leyva, Epiphaniou Gregory, Carsten Maple
Face Recognition Systems (FRS) are critical and essential components for user authentication via biometrics. To name a few, baking, e-Commerce, and border control are entities propelling their progress. These are of immense importance due to their economic and social relevance. FRS widespread usage leads to security vulnerabilities that need to be identified and mitigated. This paper provides a comprehensive
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Maintainability and Scalability in Machine Learning: Challenges and Solutions ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-22 Karthik Shivashankar, Ghadi Al Hajj, Antonio Martini
Rapid advancements in Machine Learning (ML) introduce unique maintainability and scalability challenges. Our research addresses the evolving challenges and identifies ML maintainability and scalability solutions by conducting a thorough literature review of over 17,000 papers, ultimately refining our focus to 124 relevant sources that meet our stringent selection criteria. We present a catalogue of
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Intelligent Root Cause Localization in MicroService Systems: A Survey and New Perspectives ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-22 Nan Fu, Guang Cheng, Yue Teng, Guangye Dai, Shui Yu, Zihan Chen
Root cause localization is the process of monitoring system behavior and analyzing fault patterns from behavioral data. It is applicable in software development, network operations, and cloud computing. However, with the advent of microservice architectures and cloud-native technologies, root cause localization becomes an arduous task. Frequent updates in systems result in large-scale data and complex
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A Survey and Experimental Study of Real-Time Scheduling Methods for 802.1Qbv TSN Networks ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-21 Chuanyu Xue, Tianyu Zhang, Yuanbin Zhou, Mark Nixon, Andrew Loveless, Song Han
Time-sensitive networking (TSN) has been recognized as one of the key enabling technologies for Industry 4.0 and has been deployed in many mission- and safety-critical applications e.g., industry automation, automotive and aerospace systems. Given the stringent real-time requirements of these applications, the Time-Aware Shaper (TAS) draws special attention among TSN’s many traffic shapers due to its
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Conformal Prediction: A Data Perspective ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-21 Xiaofan Zhou, Baiting Chen, Yu Gui, Lu Cheng
Conformal prediction (CP), a distribution-free uncertainty quantification (UQ) framework, reliably provides valid predictive inference for black-box models. CP constructs prediction sets or intervals that contain the true output with a specified probability. However, modern data science’s diverse modalities, along with increasing data and model complexity, challenge traditional CP methods. These developments
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Self-supervised Learning for Electroencephalogram: A Systematic Survey ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-20 Weining Weng, Yang Gu, Shuai Guo, Yuan Ma, Zhaohua Yang, Yuchen Liu, Yiqiang Chen
Electroencephalography (EEG) is a non-invasive technique to record bioelectrical signals. Integrating supervised deep learning techniques with EEG signals has recently facilitated automatic analysis across diverse EEG-based tasks. However, the label issues of EEG signals have constrained the development of EEG-based deep models. Obtaining EEG annotations is difficult and requires domain experts to
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Lost in Translation? Found in Evaluation: A Comprehensive Survey on Sentence-Level Translation Evaluation ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-16 Ananya Mukherjee, Manish Shrivastava
Machine Translation (MT) revolutionizes cross-lingual communication but is prone to errors, necessitating thorough evaluation for enhancement. Translation quality can be assessed by humans and automatic evaluation metrics. Human evaluation, though valuable, is costly and subject to limitations in scalability and consistency. While automated metrics supplement manual evaluations, this field still has
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Fundamental Capabilities and Applications of Large Language Models: A Survey ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-15 Jiawei Li, Yang Gao, Yizhe Yang, Yu Bai, Xiaofeng Zhou, Yinghao Li, Huashan Sun, Yuhang Liu, Xingpeng Si, Yuhao Ye, Yixiao Wu, Yiguan Lin, Bin Xu, Bowen Ren, Chong Feng, Heyan Huang
Large Language Models (LLMs) have demonstrated remarkable effectiveness across various domain-specific applications. However, which fundamental capabilities most contribute to their success in different domains remains unclear. This uncertainty complicates LLM evaluation, as existing benchmark-based assessments often fail to capture their real-world performance, where the required capabilities may
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Detecting Misuse of Security APIs: A Systematic Review ACM Comput. Surv. (IF 28.0) Pub Date : 2025-05-15 Seyedehzahra Mosavi, Chadni Islam, Muhammad Ali Babar, Sharif Abuadbba, Kristen Moore
Security Application Programming Interfaces (APIs) are crucial for ensuring software security. However, their misuse introduces vulnerabilities, potentially leading to severe data breaches and substantial financial loss. Complex API design, inadequate documentation, and insufficient security training often lead to unintentional misuse by developers. The software security community has devised and evaluated