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Quantum Key Distribution Networks - Key Management: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-19 Emir Dervisevic, Amina Tankovic, Ehsan Fazel, Ramana Kompella, Peppino Fazio, Miroslav Voznak, Miralem Mehic
Secure communication makes the widespread use of telecommunication networks and services possible. With the constant progress of computing and mathematics, new cryptographic methods are being diligently developed. Quantum Key Distribution (QKD) is a promising technology that provides an Information-Theoretically Secure (ITS) solution to the secret-key agreement problem between two remote parties. QKD
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Unravelling Digital Forgeries: A Systematic Survey on Image Manipulation Detection and Localization ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-19 Vijaya Kumar Kadha, Sambit Bakshi, Santos Kumar Das
In recent years, deep learning has made significant strides, especially in computer vision applications and, more specifically, in information forensics. On the other hand, data-driven approaches have shown much promise in identifying manipulations in images and videos. However, most forensic tools ignore deep learning in favour of more traditional methodologies. This article thoroughly analyses the
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A Systematic Review of XR-Enabled Remote Human-Robot Interaction Systems ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-19 Xian Wang, Luyao Shen, Lik-Hang Lee
The rising interest in creating versatile robots to handle multiple tasks in various environments, with humans interacting through immersive interfaces. This survey provides a comprehensive review of extended reality (XR) applications in remote human-robot interaction (HRI). We developed a systematic search strategy based on the PRISMA methodology, focusing on peer-reviewed publications that demonstrate
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A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-19 Haopeng Zhang, Philip S. Yu, Jiawei Zhang
Text summarization research has undergone several significant transformations with the advent of deep neural networks, pre-trained language models (PLMs), and recent large language models (LLMs). This survey thus provides a comprehensive review of the research progress and evolution in text summarization through the lens of these paradigm shifts. It is organized into two main parts: (1) a detailed
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War on JITs: Software-Based Attacks and Hybrid Defenses for JIT Compilers - A Comprehensive Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-19 Quentin Ducasse, Pascal Cotret, Loïc Lagadec
Programming Language Virtual Machines (VMs) are composed of several components that together execute and manage languages efficiently. They are deployed in virtually all computing systems through modern web browsers. However, vulnerabilities in any VM component pose a significant threat to security and privacy. In this paper, we present a survey of software attacks on Just-In-Time (JIT) compilers,
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A Survey on the State of the Art of Causally Consistent Cloud Systems ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-19 Diana Freitas, Paul deGrandis, Tiago Boldt Sousa
In geo-replicated distributed systems, data is redundantly stored across nodes at different geographical sites, increasing fault tolerance and ensuring low access latency by placing data closer to the end user. With data being concurrently updated across sites, replicas should converge to a consistent view of the data, which leads toward adopting fine-tuned consistency models, namely causal consistency
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State-of-the-Art and Challenges of Engineering ML- Enabled Software Systems in the Deep Learning Era ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-19 Gebremariam Assres, Guru Bhandari, Andrii Shalaginov, Tor-Morten Gronli, Gheorghita Ghinea
Emerging from the software crisis of the 1960s, conventional software systems have vastly improved through Software Engineering (SE) practices. Simultaneously, Artificial Intelligence (AI) endeavors to augment or replace human decision-making. In the contemporary landscape, Machine Learning (ML), a subset of AI, leverages extensive data from diverse sources, fostering the development of ML-enabled
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Security and Privacy Challenges of AIGC in Metaverse: A Comprehensive Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-18 Shoulong Zhang, Haomin Li, Kaiwen Sun, Hejia Chen, Yan Wang, Shuai Li
The Metaverse is a hybrid environment that integrates both physical and virtual realms. The Metaverse has been accessible due to many facilitating technologies. One of the essential technologies that contribute to the Metaverse is AIGC. It is crucial in creating artificial assets and presenting natural interactions efficiently and effectively. Nevertheless, AIGC models encounter external and internal
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A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-18 Pierre-Daniel Arsenault, Shengrui Wang, Jean-Marc Patenaude
Artificial Intelligence (AI) models have reached a very significant level of accuracy. While their superior performance offers considerable benefits, their inherent complexity often decreases human trust, which slows their application in high-risk decision-making domains, such as finance. The field of eXplainable AI (XAI) seeks to bridge this gap, aiming to make AI models more understandable. This
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A Survey of Autonomous Driving from a Deep Learning Perspective ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-18 Jingyuan Zhao, Yuyan Wu, Rui Deng, Susu Xu, Jinpeng Gao, Andrew Burke
Autonomous driving represents a significant advancement in the transportation industry, enhancing vehicle intelligence, optimizing traffic management, and improving user experiences. Central to these innovations is deep learning, which enables systems to handle complex data and make informed decisions. Our survey explores critical applications of deep learning in autonomous driving, such as perception
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Anonymization Techniques for Behavioral Biometric Data: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-18 Simon Hanisch, Patricia Arias-Cabarcos, Javier Parra-Arnau, Thorsten Strufe
Our behavior —the way we talk, walk, act, or think— is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions and health conditions. With more and more behavior tracking techniques (e.g. fitness trackers, mixed reality) entering our everyday lives more of our behavior is captured and processed. Hence, techniques to protect individuals’ privacy against
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Privacy Preserving Prompt Engineering: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-18 Kennedy Edemacu, Xintao Wu
Pre-trained language models (PLMs) have demonstrated significant proficiency in solving a wide range of general natural language processing (NLP) tasks. Researchers have observed a direct correlation between the performance of these models and their sizes. As a result, the sizes of these models have notably expanded in recent years, persuading researchers to adopt the term large language models (LLMs)
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Smart Road Traffic Monitoring: Unveiling the Synergy of IoT and AI for Enhanced Urban Mobility ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-18 Komal Saini, Sandeep Sharma
Emerging technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) have transformed intelligent transportation systems, providing novel solutions to the increasing complexity of managing traffic on roads as cities grow and traffic density rises, particularly in developing countries. Smart road traffic management systems seek to alleviate traffic-related issues, benefiting citizens
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A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-18 Cristina Silvano, Daniele Ielmini, Fabrizio Ferrandi, Leandro Fiorin, Serena Curzel, Luca Benini, Francesco Conti, Angelo Garofalo, Cristian Zambelli, Enrico Calore, Sebastiano Schifano, Maurizio Palesi, Giuseppe Ascia, Davide Patti, Nicola Petra, Davide De Caro, Luciano Lavagno, Teodoro Urso, Valeria Cardellini, Gian Cardarilli, Robert Birke, Stefania Perri
Recent trends in deep learning (DL) have made hardware accelerators essential for various high-performance computing (HPC) applications, including image classification, computer vision, and speech recognition. This survey summarizes and classifies the most recent developments in DL accelerators, focusing on their role in meeting the performance demands of HPC applications. We explore cutting-edge approaches
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A Survey on Services Placement Algorithms in Integrated Cloud-Fog / Edge Computing ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-18 Imane Taleb, Jean-Loup Guillaume, Benjamin Duthil
The evolution of computing paradigms, such as Fog and Edge, has led to the emergence of new applications that require a placement of services close to the end users. Optimal placement of such applications over network nodes is therefore an important issue in integrated Cloud-Fog / Edge computing environments. The service placement problem is challenging due to the complexity of such distributed systems
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Towards Hybrid Architectures for Big Data Analytics: Insights from Spark-MPI Integration IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2025-04-18 Mengbing Zhou, Qiuyan Li, Mingyuan Cai, Chengzhong Xu, Yang Wang
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Integrating Deep Spiking Q-network into Hypergame-theoretic Deceptive Defense for Mitigating Malware Propagation in Edge Intelligence-enabled IoT Systems IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2025-04-18 Yizhou Shen, Carlton Shepherd, Chuadhry Mujeeb Ahmed, Shigen Shen, Shui Yu
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LEAGAN: A Decentralized Version-Control Framework for Upgradeable Smart Contracts IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2025-04-18 Gulshan Kumar, Rahul Saha, Mauro Conti, William J Buchanan
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Battery Swapping Tour Optimization Problem in Dockless Electric Bike Sharing Service Systems With Distance-Aware User Incentives IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2025-04-18 Chun-An Yang, Shih-Chieh Chen, Jian-Jhih Kuo, Yi-Hsuan Peng, Yu-Wen Chen, Ming-Jer Tsai
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Seamless Graph Task Scheduling over Dynamic Vehicular Clouds: A Hybrid Methodology for Integrating Pilot and Instantaneous Decisions IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2025-04-18 Bingshuo Guo, Minghui Liwang, Xiaoyu Xia, Li Li, Zhenzhen Jiao, Seyyedali Hosseinalipour, Xianbin Wang
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Enhancing LLM QoS through Cloud-Edge Collaboration: A Diffusion-based Multi-Agent Reinforcement Learning Approach IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2025-04-18 Zhi Yao, Zhiqing Tang, Wenmian Yang, Weijia Jia
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A Novel Cross-Chain Hierarchical Federated Learning Framework for Enhancing Service Security and Communication Efficiency IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2025-04-18 Li Duan, He Huang, Chao Li, Wei Ni, Bo Cheng
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Puncturable Signature and Applications in Privacy-Aware Data Reporting for VDTNs IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2025-04-18 Chenhao Wang, Yang Ming, Hang Liu, Songnian Zhang, Rongxing Lu
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Dual-View Deep Learning Approach for Predictive Business Process Monitoring IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2025-04-18 Binbin Chen, Shuangyao Zhao, Qiang Zhang, Chunhua Tang, Leilei Lin
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HGDRec:Next POI Recommendation Based on Hypergraph Neural Network and Diffusion Model IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2025-04-18 Yinchen Pan, Jun Zeng, Ziwei Wang, Haoran Tang, Junhao Wen, Min Gao
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Artificial Impostors: An Efficient and Scalable Scheme for Location Privacy Preservation IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2025-04-18 Hao Tang, Kunfeng Chen, Zhiyang Xie, Cheng Wang
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Implicit Supervision-Assisted Graph Collaborative Filtering for Third-Party Library Recommendation IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2025-04-18 Lianrong Chen, Mingdong Tang, Naidan Mei, Fenfang Xie, Guo Zhong, Qiang He
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From Foundations to GPT in Text Classification: A Comprehensive Survey on Current Approaches and Future Trends Found. Trends Inf. Ret. (IF 8.3) Pub Date : 2025-4-16 Marco Siino, Ilenia Tinnirello, Marco La Cascia
Text classification stands as a cornerstone within the realm of Natural Language Processing (NLP), particularly when viewed through computer science and engineering. The past decade has seen deep learning revolutionize text classification, propelling advancements in text retrieval, categorization, information extraction, and summarization. The scholarly literature includes datasets, models, and evaluation
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A Survey on the Recent Advancements in Human-Centered Dialog Systems ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-16 Roland Oruche, Sai Keerthana Goruganthu, Rithika Akula, Xiyao Cheng, Ashraful Md Goni, Bruce W. Shibo, Kerk Kee, Marcos Zampieri, Prasad Calyam
Dialog systems (e.g., chatbots) have been widely studied, yet related research that leverages artificial intelligence (AI) and natural language processing (NLP) is constantly evolving. These systems have typically been developed to interact with humans in the form of speech, visual, or text conversation. As humans continue to adopt dialog systems for various objectives, there is a need to involve humans
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Networking Systems for Video Anomaly Detection: A Tutorial and Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-16 Jing Liu, Yang Liu, Jieyu Lin, Jielin Li, Liang Cao, Peng Sun, Bo Hu, Liang Song, Azzedine Boukerche, Victor C.M. Leung
The increasing utilization of surveillance cameras in smart cities, coupled with the surge of online video applications, has heightened concerns regarding public security and privacy protection, which propelled automated Video Anomaly Detection (VAD) into a fundamental research task within the Artificial Intelligence (AI) community. With the advancements in deep learning and edge computing, VAD has
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Domain Generalization in Computational Pathology: Survey and Guidelines ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-16 Mostafa Jahanifar, Manahil Raza, Kesi Xu, Trinh Thi Le Vuong, Robert Jewsbury, Adam Shephard, Neda Zamanitajeddin, Jin Tae Kwak, Shan E Ahmed Raza, Fayyaz Minhas, Nasir Rajpoot
Deep learning models have exhibited exceptional effectiveness in Computational Pathology (CPath) for various tasks on multi-gigapixel histology images. Nevertheless, the presence of out-of-distribution data (stemming from different sources such as disparate imaging devices) can cause domain shift (DS). DS decreases the generalization of trained models to unseen datasets with slightly different data
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Transforming Disaster Risk Reduction With AI and Big Data: Legal and Interdisciplinary Perspectives WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-04-15 Kwok P. Chun, Thanti Octavianti, Nilay Dogulu, Hristos Tyralis, Georgia Papacharalampous, Ryan Rowberry, Pingyu Fan, Mark Everard, Maria Francesch‐Huidobro, Wellington Migliari, David M. Hannah, John Travis Marshall, Rafael Tolosana Calasanz, Chad Staddon, Ida Ansharyani, Bastien Dieppois, Todd R. Lewis, Juli Ponce, Silvia Ibrean, Tiago Miguel Ferreira, Chinkie Peliño‐Golle, Ye Mu, Manuel Davila Delgado
Managing complex disaster risks requires interdisciplinary efforts. Breaking down silos between law, social sciences, and natural sciences is critical for all processes of disaster risk reduction. It is essential to explore how AI enhances understanding of legal frameworks and environmental management, while also examining how legal and environmental factors may limit AI's role in society. From a co‐production
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Sandarśana: A Survey on Sanskrit Computational Linguistics and Digital Infrastructure for Sanskrit ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-14 Anagha Pradeep, Radhika Mamidi
Computational Linguistics is an interdisciplinary field of computer science and linguistics that focuses on designing computational models and algorithms for processing, analyzing, and generating human language. Over recent years, this field has made substantial progress. While its primary emphasis tends to center around widely spoken languages, there is equal importance in investigating languages
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Sustaining focus when It's hardest: Emotional design strengthens sustained learning, especially in contexts with attractive alternatives Comput. Educ. (IF 8.9) Pub Date : 2025-04-12 Tino Endres, Charlotte Vössing, K. Ann Renninger, Alexander Eitel, Alexander Renkl
Learners increasingly use digital devices such as laptops or tablets for studying. While these devices offer advantages, they also pose challenges. They present attractive alternative opportunities for learners, such as communication or entertainment opportunities that may distract from learning. Such alternatives increase demand for self-control, particularly over time. We investigated the potential
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A Survey of Reasoning with Foundation Models: Concepts, Methodologies, and Outlook ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-11 Jiankai Sun, Chuanyang Zheng, Enze Xie, Zhengying Liu, Ruihang Chu, Jianing Qiu, Jiaqi Xu, Mingyu Ding, Hongyang Li, Mengzhe Geng, Yue Wu, Wenhai Wang, Junsong Chen, Zhangyue Yin, Xiaozhe Ren, Jie Fu, Junxian He, Yuan Wu, Qi Liu, Xihui Liu, Yu Li, Hao Dong, Yu Cheng, Ming Zhang, Pheng Ann Heng, Jifeng Dai, Ping Luo, Jingdong Wang, Ji-Rong Wen, Xipeng Qiu, Yike Guo, Hui Xiong, Qun Liu, Zhenguo Li
Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation. It serves as a fundamental methodology in the field of Artificial General Intelligence (AGI). With the ongoing development of foundation models, there is a growing interest in exploring their abilities in reasoning tasks. In
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Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-11 Yuan Jin, Antonio Pepe, Jianning Li, Christina Gsaxner, Yuxuan Chen, Behrus Puladi, Fen-hua Zhao, Kelsey Pomykala, Jens Kleesiek, Alejandro Frangi, Jan Egger
The aortic vessel tree, composed of the aorta and its branches, is crucial for blood supply to the body. Aortic diseases, such as aneurysms and dissections, can lead to life-threatening ruptures, often requiring open surgery. Therefore, patients commonly undergo treatment under constant monitoring, which requires regular inspections of the vessels through medical imaging techniques. Overlapping and
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Increasing knowledge about plasma and plasma donation through a serious game: Evidence from a mixed-method study Comput. Educ. (IF 8.9) Pub Date : 2025-04-11 Alexandra Ciausescu, Eva-Maria Merz, Rene Bekkers, Arjen de Wit
Many European countries are facing plasma shortages, with lack of awareness and knowledge about plasma donation being a potential explanation for low donor numbers. One approach to increasing knowledge and awareness about plasma is through informal educational methods, such as serious games. We developed a serious game focused on plasma and plasma donation for children and adolescents (8–17 years)
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Socio-technical phenomena involving blockchain use: Literature review, conceptual framework, and research agenda J. Strategic Inf. Syst. (IF 8.7) Pub Date : 2025-04-11 Shaoxin Wang, Daniel Schlagwein, Mike Seymour
This paper reviews the emerging literature on blockchain, structuring key insights in a conceptual framework and setting out a research agenda from a socio-technical and information systems (IS) perspective. The review covers 234 blockchain-focused papers across IS, management, computer science and related fields. It categorises blockchain use into three main types and reframes blockchain use within
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Algorithmic Profiling and Facial Recognition in EU Border Control: Examining ETIAS Decision‐Making, Privacy and Law WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-04-11 Abhishek Thommandru, Varda Mone, Fayzulloyev Shokhijakhon, Giyosbek Mirzayev
The growing use of algorithmic and biometric technologies in border control is part of a larger trend in global security governance that has significant legal and ethical implications for their effect on individual rights and procedural justice. As central features in the EU's shifting security regime, ETIAS and facial recognition technologies deploy algorithmic profiling and biometric risk assessment
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Mapping the Landscape of Personalization: A Comprehensive Review of Prediction and Trends in Recommendation Systems WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-04-11 Tamanna Sachdeva, Lalit Mohan Goyal, Mamta Mittal
Recommendation systems (RSs) have become indispensable features in nearly all web applications. Sifting through data and alleviating information overload, these systems offer more streamlined and personalized recommendations. E‐commerce giants such as Amazon, Netflix, and YouTube are offering recommendations to users based on their interests, past experiences, demographic information, etc. hence, increasing
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An Adaptive and Interpretable Congestion Control Service Based on Multi-Objective Reinforcement Learning IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2025-04-11 Jiacheng Liu, Xu Li, Feilong Tang, Peng Li, Long Chen, Jiadi Yu, Yanmin Zhu, Pheng-Ann Heng, Laurence T. Yang
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An Efficient Replication-Based Aggregation Verification and Correctness Assurance Scheme for Federated Learning IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2025-04-11 Shihong Wu, Yuchuan Luo, Shaojing Fu, Yingwen Chen, Ming Xu
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Geometric Constraints in Deep Learning Frameworks: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-10 Vibhas K Vats, David Crandall
Stereophotogrammetry [62] is an established technique for scene understanding. Its origins go back to at least the 1800s when people first started to investigate using photographs to measure the physical properties of the world. Since then, thousands of approaches have been explored. The classic geometric technique of Shape from Stereo is built on using geometry to define constraints on scene and camera
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Recent Advances in Vision Transformer Robustness Against Adversarial Attacks in Traffic Sign Detection and Recognition: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-10 Oluwajuwon Fawole, Danda Rawat
The emergence of Vision Transformers (ViTs) has marked a significant advancement in machine learning, particularly in applications requiring robust visual recognition capabilities, such as traffic sign detection for autonomous driving systems. But, deploying these models in adversarial environments where robustness is critical remains a challenge. This survey provides a comprehensive review of the
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Privacy-Enhanced Federated Expanded Graph Learning for Secure QoS Prediction IEEE Trans. Serv. Comput. (IF 5.5) Pub Date : 2025-04-10 Guobing Zou, Zhi Yan, Shengxiang Hu, Yanglan Gan, Bofeng Zhang, Yixin Chen
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A Generic Taxonomy for Steganography Methods ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-09 Steffen Wendzel, Luca Caviglione, Wojciech Mazurczyk, Aleksandra Mileva, Jana Dittmann, Christian Krätzer, Kevin Lamshöft, Claus Vielhauer, Laura Hartmann, Jörg Keller, Tom Neubert, Sebastian Zillien
A unified understanding of terms is essential for every scientific discipline: steganography is no exception. Being divided into several domains (e.g., network and text steganography), it is crucial to provide a unified terminology as well as a taxonomy that is not limited to few applications or areas. A prime attempt towards a unified understanding of terms was conducted in 2015 with the introduction
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Proof Scores: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-09 Adrián Riesco, Kazuhiro Ogata, Masaki Nakamura, Daniel Gaina, Duong Dinh Tran, Kokichi Futatsugi
Proof scores can be regarded as outlines of the formal verification of system properties. They have been historically used by the OBJ family of specification languages. The main advantage of proof scores is that they follow the same syntax as the specification language they are used in, so specifiers can easily adopt them and use as many features as the particular language provides. In this way, proof
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A Brief Review on Benchmarking for Large Language Models Evaluation in Healthcare WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-04-09 Leona Cilar Budler, Hongyu Chen, Aokun Chen, Maxim Topaz, Wilson Tam, Jiang Bian, Gregor Stiglic
This paper reviews benchmarking methods for evaluating large language models (LLMs) in healthcare settings. It highlights the importance of rigorous benchmarking to ensure LLMs' safety, accuracy, and effectiveness in clinical applications. The review also discusses the challenges of developing standardized benchmarks and metrics tailored to healthcare‐specific tasks such as medical text generation
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Efficient Compressing and Tuning Methods for Large Language Models: A Systematic Literature Review ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-08 Gun Il Kim, Sunga Hwang, Beakcheol Jang
Efficient compression and tuning techniques have become indispensable in addressing the increasing computational and memory demands of large language models (LLMs). While these models have demonstrated exceptional performance across a wide range of natural language processing tasks, their growing size and resource requirements pose significant challenges to accessibility and sustainability. This survey
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Carbon-Efficient Software Design and Development: A Systematic Literature Review ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-08 Ornela Danushi, Stefano Forti, Jacopo Soldani
The ICT sector, responsible for 2% of global carbon emissions, is under scrutiny calling for methodologies and tools to design and develop software in an environmentally sustainable-by-design manner. However, the software engineering solutions for designing and developing carbon-efficient software are currently scattered over multiple different pieces of literature, which makes it difficult to consult
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Ante-Hoc Methods for Interpretable Deep Models: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-08 Antonio Di Marino, Vincenzo Bevilacqua, Angelo Ciaramella, Ivanoe De Falco, Giovanna Sannino
The increasing use of black-box networks in high-risk contexts has led researchers to propose explainable methods to make these networks transparent. Most methods that allow us to understand the behavior of Deep Neural Networks (DNNs) are post-hoc approaches, implying that the explainability is questionable, as these methods do not clarify the internal behavior of a model. Thus, this demonstrates the
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Hybrids of Reinforcement Learning and Evolutionary Computation in Finance: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-08 Sandarbh Yadav, Vadlamani Ravi, Shivaram Kalyanakrishnan
Many sequential decision-making problems in finance like trading, portfolio optimisation, etc. have been modelled using reinforcement learning (RL) and evolutionary computation (EC). Recent studies on problems from various domains have shown that EC can be used to improve the performance of RL and vice versa. Over the years, researchers have proposed different ways of hybridising RL and EC for trading
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Visual Question Answering: A Survey of Methods, Datasets, Evaluation, and Challenges ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-08 Byeong Su Kim, Jieun Kim, Deokwoo Lee, Beakcheol Jang
Visual question answering (VQA) is a dynamic field of research that aims to generate textual answers from given visual and question information. It is a multimodal field that has garnered significant interest from the computer vision and natural language processing communities. Furthermore, recent advances in these fields have yielded numerous achievements in VQA research. In VQA research, achieving
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Similarity of Neural Network Models: A Survey of Functional and Representational Measures ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-08 Max Klabunde, Tobias Schumacher, Markus Strohmaier, Florian Lemmerich
Measuring similarity of neural networks to understand and improve their behavior has become an issue of great importance and research interest. In this survey, we provide a comprehensive overview of two complementary perspectives of measuring neural network similarity: (i) representational similarity, which considers how activations of intermediate layers differ, and (ii) functional similarity, which
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AI-Generated Content (AIGC) for Various Data Modalities: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-08 Lin Geng Foo, Hossein Rahmani, Jun Liu
AI-generated content (AIGC) methods aim to produce text, images, videos, 3D assets, and other media using AI algorithms. Due to its wide range of applications and the potential of recent works, AIGC developments – especially in Machine Learning (ML) and Deep Learning (DL) – have been attracting significant attention, and this survey focuses on comprehensively reviewing such advancements in ML/DL. AIGC
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Widening the Digital Divide: The mediating role of Intelligent Tutoring Systems in the relationship between rurality, socioeducational advantage, and mathematics learning outcomes Comput. Educ. (IF 8.9) Pub Date : 2025-04-08 Brody Hannan, Rebecca Eynon
This study examines how the effects of school socioeducational advantage and rurality upon mathematics learning outcomes, are impacted by students’ usage of the ITS platform AdaptiveMath. Activity log data from the AdaptiveMath platform was merged with school sociodemographic data from the public MySchool database. The final analytic sample comprised of 66,451 Australian high school students across
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Where are the processes in IS research on digital transformation? A critical literature review and future research directions J. Strategic Inf. Syst. (IF 8.7) Pub Date : 2025-04-08 Martin Wiener, Susanne Strahringer, Julia Kotlarsky
Digital transformation (DT) has emerged as a central and strategically relevant research phenomenon in information systems (IS) and related disciplines. As a result, a significant body of research on the DT phenomenon has accumulated in recent years. However, while existing research highlights the processual nature of the phenomenon, our theoretical understanding of DT processes remains fragmented
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A Comprehensive Review on Data‐Driven Methods of Lithium‐Ion Batteries State‐of‐Health Forecasting WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-04-08 Thien Pham, Hung Bui, Mao Nguyen, Quang Pham, Vinh Vu, Triet Le, Tho Quan
Lithium‐ion batteries are widely used in moving devices due to their many advantages compared to other battery types. The prevalence of Lithium‐ion batteries is evident, playing its clear role in the operation of small devices as well as large systems such as electric vehicles, flying devices, mobile devices, and more. Monitoring lithium‐ion battery health is crucial for assessing, minimizing degradation
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A Systematic Survey of Graph Convolutional Networks for Artificial Intelligence Applications WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-04-08 Amutha Sadasivan, Kavipriya Gananathan, Joe Dhanith Pal Nesamony Rose Mary, Surendiran Balasubramanian
Graph Convolutional Networks (GCNs) have become an essential tool for handling graph‐structured data, enhancing the functionality of conventional convolutional neural networks (CNNs) in non‐Euclidean contexts. GCNs are particularly proficient in tasks such as node classification, link prediction, and graph clustering by collecting information from neighboring nodes. These models are utilized in a range
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Diffusion-Based Visual Art Creation: A Survey and New Perspectives ACM Comput. Surv. (IF 23.8) Pub Date : 2025-04-05 Bingyuan Wang, Qifeng Chen, Zeyu Wang
The integration of generative AI in visual art has revolutionized not only how visual content is created but also how AI interacts with and reflects the underlying domain knowledge. This survey explores the emerging realm of diffusion-based visual art creation, examining its development from both artistic and technical perspectives. We structure the survey into three phases, data feature and framework