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Deep Learning Based Image Aesthetic Quality Assessment- A Review ACM Comput. Surv. (IF 23.8) Pub Date : 2025-02-08 Maedeh Daryanavard Chounchenani, Asadollah Shahbahrami, Reza Hassanpour, Georgi Gaydadjiev
Image Aesthetic Quality Assessment (IAQA) spans applications such as the fashion industry, AI-generated content, product design, and e-commerce. Recent deep learning advancements have been employed to evaluate image aesthetic quality. A few surveys have been conducted on IAQA models; however, details of recent deep learning models and challenges have not been fully mentioned. This paper aims to fill
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AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways ACM Comput. Surv. (IF 23.8) Pub Date : 2025-02-07 Zehang Deng, Yongjian Guo, Changzhou Han, Wanlun Ma, Junwu Xiong, Sheng Wen, Yang Xiang
An Artificial Intelligence (AI) agent is a software entity that autonomously performs tasks or makes decisions based on pre-defined objectives and data inputs. AI agents, capable of perceiving user inputs, reasoning and planning tasks, and executing actions, have seen remarkable advancements in algorithm development and task performance. However, the security challenges they pose remain under-explored
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Deep Learning Library Testing: Definition, Methods and Challenges ACM Comput. Surv. (IF 23.8) Pub Date : 2025-02-06 Xiaoyu Zhang, Weipeng Jiang, Chao Shen, Qi Li, Qian Wang, Chenhao Lin, Xiaohong Guan
Recently, software systems powered by deep learning (DL) techniques have significantly facilitated people’s lives in many aspects. As the backbone of these DL systems, various DL libraries undertake the underlying optimization and computation. However, like traditional software, DL libraries are not immune to bugs. These bugs may be propagated to programs and software developed based on DL libraries
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A Survey on Exploring Real and Virtual Social Network Rumors: State-of-the-Art and Research Challenges ACM Comput. Surv. (IF 23.8) Pub Date : 2025-02-06 Qiang He, Songyangjun Zhang, Yuliang Cai, Wei Yuan, Lianbo Ma, Keping Yu
This survey reviews the phenomenon of rumor propagation in social networks, defining rumors and their manifestations, and highlighting the societal confusion, panic, and harm they cause. It explores the psychological, social, and technical factors contributing to rumors and their impact on reputation, panic, and decision-making. The review covers theoretical frameworks of rumor propagation, analyzing
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Making Sense of Big Data in Intelligent Transportation Systems: Current Trends, Challenges and Future Directions ACM Comput. Surv. (IF 23.8) Pub Date : 2025-02-05 Ahmad Jan Mian, Muhammad Adil, Bouziane Brik, Saad Harous, Sohail Abbas
Intelligent Transportation Systems (ITS) generate massive amounts of Big Data through both sensory and non-sensory platforms. The data support batch processing as well as stream processing, which are essential for reliable operations on the roads and connected vehicles in ITS. Despite the immense potential of Big Data intelligence in ITS, autonomous vehicles are largely confined to testing and trial
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ENDEMIC: End-to-End Network Disruptions - Examining Middleboxes, Issues, and Countermeasures - A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-02-05 Ilies Benhabbour, Marc Dacier
Network middleboxes are important components in modern networking systems, impacting approximately 40% of network paths according to recent studies [1]. This survey paper delves into their endemic presence, enriches the original 2002 RFC with over two decades of findings, and emphasizes the significance of their impact in terms of security and performance. Furthermore, it categorizes network middleboxes
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Artificial Intelligence as a Service (AIaaS) for Cloud, Fog and the Edge: State-of-the-Art Practices ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-31 Naeem Syed, Adnan Anwar, Zubair Baig, Sherali Zeadally
Artificial Intelligence (AI) fosters enormous business opportunities that build and utilize private AI models. Implementing AI models at scale and ensuring cost-effective production of AI-based technologies through entirely in-house capabilities is a challenge. The success of the Infrastructure as a Service (IaaS) and Software as a Service (SaaS) Cloud Computing models can be leveraged to facilitate
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Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-31 Jiayi Kuang, Ying Shen, Jingyou Xie, Haohao Luo, Zhe Xu, Ronghao Li, Yinghui Li, Xianfeng Cheng, Xika Lin, Yu Han
Visual Question Answering (VQA) is a challenge task that combines natural language processing and computer vision techniques and gradually becomes a benchmark test task in multimodal large language models (MLLMs). The goal of our survey is to provide an overview of the development of VQA and a detailed description of the latest models with high timeliness. This survey gives an up-to-date synthesis
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Separation of Duty in Information Security ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-30 Sebastian Groll, Ludwig Fuchs, Günther Pernul
Separation of Duty (SoD) is a fundamental security principle that ensures that critical tasks or functions are divided upon multiple users in order to prevent fraud. The topic of SoD spans over many different areas like Identity and Access Management, Workflows, Petri nets or high-level enterprise management. In this survey paper we conduct a systematic and stand-alone literature review on SoD. We
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Knowledge Distillation on Graphs: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-30 Yijun Tian, Shichao Pei, Xiangliang Zhang, Chuxu Zhang, Nitesh Chawla
Graph Neural Networks (GNNs) have received significant attention for demonstrating their capability to handle graph data. However, they are difficult to be deployed in resource-limited devices because of model sizes and scalability constraints imposed by the multi-hop data dependency. In addition, real-world graphs usually possess complex structural information and features. Therefore, to improve the
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Resource Sharing on the Internet: A Comprehensive Survey on ISP Peering ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-29 Anindo Mahmood, Murat Yuksel
Peering has rapidly emerged as a critical element of the Internet architecture in ensuring low latency, bandwidth efficient and high Quality-of-Service operation for Internet Service Providers (ISPs). In this paper, we delve into the multifaceted domain of peering, providing a comprehensive overview of its fundamental elements. We investigate the various peering models and policies available to ISPs
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Advances in Set Function Learning: A Survey of Techniques and Applications ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-29 Jiahao Xie, Guangmo Tong
Set function learning has emerged as a crucial area in machine learning, addressing the challenge of modeling functions that take sets as inputs. Unlike traditional machine learning that involves fixed-size input vectors where the order of features matters, set function learning demands methods that are invariant to permutations of the input set, presenting a unique and complex problem. This survey
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User-Centred Privacy and Data Protection: An Overview of Current Research Trends and Challenges for the Human-Computer Interaction Field ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-29 Shirlei Aparecida de Chaves, Fabiane Benitti
A user-focused technological approach is essential for privacy and data protection, so a systematic mapping study was conducted to review how researchers approach such matters. Of 8867 papers, 231 were systematically selected and analysed. Through thematic analysis, we identified three main themes: improving privacy policies, raising privacy awareness, and controlling information disclosure. Notably
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Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-28 Dinh-Viet-Toan Le, Louis Bigo, Dorien Herremans, Mikaela Keller
– Music is frequently associated with the notion of language as both domains share several similarities, including the ability for their content to be represented as sequences of symbols. In computer science, the fields of Natural Language Processing (NLP) and Music Information Retrieval (MIR) reflect this analogy through a variety of similar tasks, such as author detection or content generation. This
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Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-27 Vasileios Leon, Muhammad Abdullah Hanif, Giorgos Armeniakos, Xun Jiao, Muhammad Shafique, Kiamal Pekmestzi, Dimitrios Soudris
The challenging deployment of compute-intensive applications from domains such as Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of computing systems to explore new design approaches. Approximate Computing appears as an emerging solution, allowing to tune the quality of results in the design of a system in order to improve the energy efficiency and/or performance
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Point Cloud-Based Deep Learning in Industrial Production: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-27 Yi Liu, Changsheng Zhang, Xingjun Dong, Jiaxu Ning
With the rapid development of 3D acquisition technology, point clouds have received increasing attention. In recent years, point cloud-based deep learning has been applied to various industrial scenarios, promoting industrial intelligence. However, there is still a lack of review on the application of point cloud-based deep learning in industrial production. To bridge this gap and inspire future research
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A Comprehensive Survey of Benchmarks for Improvement of Software's Non-Functional Properties ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-27 Aymeric Blot, Justyna Petke
Despite recent increase in research on improvement of non-functional properties of software, such as energy usage or program size, there is a lack of standard benchmarks for such work. This absence hinders progress in the field, and raises questions about the representativeness of current benchmarks of real-world software. To address these issues and facilitate further research on improvement of non-functional
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Adaptive Strategies in Enhancing Physical Layer Security: A Comprehensive Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-27 Zhuoying Duan, Zikai Chang, Ning Xie, Weize Sun, Dusit (Tao) Niyato
Adaptive schemes in physical layer security, designed to dynamically respond to the evolving conditions of wireless channels, play a crucial role in fortifying the security of wireless communication systems. We offer a thorough analysis of the current state of research on adaptive schemes in physical layer security, introducing a novel taxonomy to categorize and understand these schemes more effectively
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Scientific Large Language Models: A Survey on Biological & Chemical Domains ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-26 Qiang Zhang, Keyan Ding, Tianwen Lv, Xinda Wang, Qingyu Yin, Yiwen Zhang, Jing Yu, Yuhao Wang, Xiaotong Li, Zhuoyi Xiang, Xiang Zhuang, Zeyuan Wang, Ming Qin, Mengyao Zhang, Jinlu Zhang, Jiyu Cui, Renjun Xu, Hongyang Chen, Xiaohui Fan, Huabin Xing, Huajun Chen
Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension, representing a significant stride toward artificial general intelligence. The application of LLMs extends beyond conventional linguistic boundaries, encompassing specialized linguistic systems developed within various scientific disciplines. This growing interest has led to the advent of
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Secured Network Architectures Based on Blockchain Technologies: A Systematic Review ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-25 Song-Kyoo Kim, Hou Cheng Vong
Blockchain applications have emerged in recent decades, among which blockchain secured-networks serve as a prevalent application. This paper provides the potential of networks secured by blockchain technology to enhance various domains and provides a structured view of the current landscape of blockchain applications, capturing the practical applications and potential of blockchain technology. Followed
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A Container Security Survey: Exploits, Attacks, and Defenses ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-25 Omar Jarkas, Ryan Ko, Naipeng Dong, Redowan Mahmud
Containerization significantly boosts cloud computing efficiency by reducing resource consumption, enhancing scalability, and simplifying orchestration. Yet, these same features introduce notable security vulnerabilities due to the shared Linux kernel and reduced isolation compared to traditional virtual machines (VMs). This architecture, while resource-efficient, increases susceptibility to software
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Performance Modeling of Public Permissionless Blockchains: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-24 Molud Esmaili, Ken Christensen
Public permissionless blockchains facilitate peer-to-peer digital transactions, yet face performance challenges, specifically minimizing transaction confirmation time to decrease energy and time consumption per transaction. Performance evaluation and prediction is crucial in achieving this objective, with performance modeling as a key solution despite the complexities involved in assessing these blockchains
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Deep Learning Methods for Abstract Visual Reasoning: A Survey on Raven's Progressive Matrices ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-24 Mikołaj Małkiński, Jacek Mańdziuk
visual reasoning (AVR) domain encompasses problems solving which requires the ability to reason about relations among entities present in a given scene. While humans, generally, solve AVR tasks in a “natural” way, even without prior experience, this type of problems has proven difficult for current machine learning systems. The paper summarises recent progress in applying deep learning methods to solving
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A Survey on Speech Deepfake Detection ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-24 Menglu Li, Yasaman Ahmadiadli, Xiao-Ping Zhang
The availability of smart devices leads to an exponential increase in multimedia content. However, advancements in deep learning have also enabled the creation of highly sophisticated deepfake content, including speech Deepfakes, which pose a serious threat by generating realistic voices and spreading misinformation. To combat this, numerous challenges have been organized to advance speech Deepfake
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A Review on Trustworthiness of Digital Assistants for Personal Healthcare ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-22 Tania Bailoni, Mauro Dragoni
Artificial Intelligence (AI) is widely used within the healthcare domain. One of the branches of digital health concerns the design and development of digital assistant solutions. AI-enabled digital assistants highlighted the need to be trustworthy given their intrusiveness within people’s lives. Such solutions aim to provide intelligent tools to ease the management of care pathways or to enhance the
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Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-21 Lea Demelius, Roman Kern, Andreas Trügler
Differential Privacy has become a widely popular method for data protection in machine learning, especially since it allows formulating strict mathematical privacy guarantees. This survey provides an overview of the state-of-the-art of differentially private centralized deep learning, thorough analyses of recent advances and open problems, as well as a discussion of potential future developments in
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Clustering on Attributed Graphs: From Single-view to Multi-view ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-21 Mengyao Li, Zhibang Yang, Xu Zhou, Yixiang Fang, Kenli Li, Keqin Li
Attributed graphs with both topological information and node information have prevalent applications in the real world, including recommendation systems, biological networks, community analysis, and so on. Recently, with rapid development of information gathering and extraction technology, the sources of data become more extensive and multi-view data attracts growing attention. Consequently, attributed
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Deep Generative Models for Therapeutic Peptide Discovery: A Comprehensive Review ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-21 Leshan Lai, Yuansheng Liu, Bosheng Song, Keqin Li, Xiangxiang Zeng
Deep learning tools, especially deep generative models (DGMs), provide opportunities to accelerate and simplify the design of drugs. As drug candidates, peptides are superior to other biomolecules because they combine potency, selectivity, and low toxicity. This review examines the fundamental aspects of current DGMs for designing therapeutic peptide sequences. First, relevant databases in this field
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Text Classification Using Graph Convolutional Networks: A Comprehensive Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-21 Syed Mustafa Haider Rizvi, Ramsha Imran, Arif Mahmood
Text classification is a quintessential and practical problem in natural language processing with applications in diverse domains such as sentiment analysis, fake news detection, medical diagnosis, and document classification. A sizable body of recent works exists where researchers have studied and tackled text classification from different angles with varying degrees of success. Graph convolution
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Toward the Construction of Affective Brain-Computer Interface: A Systematic Review ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-20 Huayu Chen, Junxiang Li, Huanhuan He, Jing Zhu, Shuting Sun, Xiaowei Li, Bin Hu
Electroencephalogram(EEG)-based affective computing aims to recognize the emotional state, which is the core technology of affective brain-computer interface(aBCI). This concept encompasses aspects of physiological computing, human-computer interaction(HCI), mental health care, and brain-computer interfaces(BCI), presenting significant theoretical and practical value. However, the field reached a bottleneck
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Getting the Data in Shape for Your Process Mining Analysis: An In-Depth Analysis of the Pre-Analysis Stage ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-18 Shameer K. Pradhan, Mieke Jans, Niels Martin
Process mining enables organizations to analyze the data stored in their information systems and derive insights regarding their business processes. However, raw data needs to be converted into a format that can be fed into process mining algorithms. Various pre-analysis activities can be performed on the raw data, such as imperfection removal or granularity level change. Although pre-analysis activities
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A Survey of Multimodal Learning: Methods, Applications, and Future ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-18 Yuan Yuan, Zhaojian Li, Bin Zhao
The multimodal interplay of the five fundamental senses—Sight, Hearing, Smell, Taste, and Touch—provides humans with superior environmental perception and learning skills. Adapted from the human perceptual system, multimodal machine learning tries to incorporate different forms of input, such as image, audio, and text, and determine their fundamental connections through joint modeling. As one of the
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Decoding Fake News and Hate Speech: A Survey of Explainable AI Techniques ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-17 Mikel Ngueajio, Saurav Aryal, Marcellin Atemkeng, Gloria Washington, Danda Rawat
This survey emphasizes the significance of Explainable AI (XAI) techniques in detecting hateful speech and misinformation/Fake news. It explores recent trends in detecting these phenomena, highlighting current research that reveals a synergistic relationship between them. Additionally, it presents recent trends in the use of XAI methods to mitigate the occurrences of hateful land Fake contents in conversations
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Dense Video Captioning: A Survey of Techniques, Datasets and Evaluation Protocols ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-14 Iqra Qasim, Alexander Horsch, Dilip Prasad
Untrimmed videos have interrelated events, dependencies, context, overlapping events, object-object interactions, domain specificity, and other semantics that are worth highlighting while describing a video in natural language. Owing to such a vast diversity, a single sentence can only correctly describe a portion of the video. Dense Video Captioning (DVC) aims to detect and describe different events
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Security and Privacy Challenges of Large Language Models: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-13 Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu
Large language models (LLMs) have demonstrated extraordinary capabilities and contributed to multiple fields, such as generating and summarizing text, language translation, and question-answering. Nowadays, LLMs have become very popular tools in natural language processing (NLP) tasks, with the capability to analyze complicated linguistic patterns and provide relevant responses depending on the context
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Natural Language Processing for Dialects of a Language: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-13 Aditya Joshi, Raj Dabre, Diptesh Kanojia, Zhuang Li, Haolan Zhan, Gholamreza Haffari, Doris Dippold
State-of-the-art natural language processing (NLP) models are trained on massive training corpora, and report a superlative performance on evaluation datasets. This survey delves into an important attribute of these datasets: the dialect of a language. Motivated by the performance degradation of NLP models for dialectal datasets and its implications for the equity of language technologies, we survey
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Generative AI Empowered Network Digital Twins: Architecture, Technologies, and Applications ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-10 Tong Li, Qingyue Long, Haoye Chai, Shiyuan Zhang, Fenyu Jiang, Haoqiang Liu, Wenzhen Huang, Depeng Jin, Yong Li
The rapid advancement of mobile networks highlights the limitations of traditional network planning and optimization methods, particularly in modeling, evaluation, and application. Network Digital Twins, which simulate networks in the digital domain for evaluation, offer a solution to these challenges. This concept is further enhanced by generative AI technology, which promises more efficient and accurate
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Location Privacy Schemes in Vehicular Networks: Taxonomy, Comparative Analysis, Design Challenges, and Future Opportunities ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-10 Ikram Ullah, Munam Ali Shah, Abid Khan, Mohsen Guizani
Vehicular ad-hoc networks (VANETs) have revolutionized the world with smart traffic management, better utilizing the road environment, and providing safety and convenience to the vehicles’ drivers. Despite the useful features of VANETs, there are some privacy issues, which hinder their way toward achieving smarter and safer traffic in the world. Location privacy is one of the critical research challenges
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Towards Trustworthy AI-Empowered Real-Time Bidding for Online Advertisement Auctioning ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-10 Xiaoli Tang, Han Yu
Artificial intelligence-empowred Real-Time Bidding (AIRTB) is regarded as one of the most enabling technologies for online advertising. It has attracted significant research attention from diverse fields such as pattern recognition, game theory and mechanism design. Despite of its remarkable development and deployment, the AIRTB system can sometimes harm the interest of its participants (e.g., depleting
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A Comprehensive Review on Group Re-identification in Surveillance Videos ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-10 KAMAKSHYA NAYAK, Debi Prosad Dogra
Computer vision plays an important role in the automated analysis of human groups. The appearance of human groups has been studied for various reasons, including detection, identification, tracking, and re-identification. Person re-identification has been studied extensively over the last decade. Despite significant efforts by the computer vision research community, person re-identification often suffers
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Trustworthy AI-based Performance Diagnosis Systems for Cloud Applications: A Review ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-09 Ruyue Xin, Jingye Wang, Peng Chen, Zhiming Zhao
Performance diagnosis systems are defined as detecting abnormal performance phenomena and play a crucial role in cloud applications. An effective performance diagnosis system is often developed based on artificial intelligence (AI) approaches, which can be summarized into a general framework from data to models. However, the AI-based framework has potential hazards that could degrade the user experience
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Deep Learning on Network Traffic Prediction: Recent Advances, Analysis, and Future Directions ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-06 Ons Aouedi, Van An Le, Kandaraj Piamrat, Yusheng Ji
From the perspective of telecommunications, next-generation networks or beyond 5G will inevitably face the challenge of a growing number of users and devices. Such growth results in high-traffic generation with limited network resources. Thus, the analysis of the traffic and the precise forecast of user demands is essential for developing an intelligent network. In this line, Machine Learning (ML)
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Data-centric Artificial Intelligence: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-06 Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, Zhimeng Jiang, Shaochen Zhong, Xia Hu
Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for building machine learning models. Recently, the role of data in AI has been significantly magnified, giving rise to the emerging concept of data-centric AI . The attention of researchers and practitioners has gradually shifted
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Can Graph Neural Networks be Adequately Explained? A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-06 Xuyan Li, Jie Wang, Zheng Yan
To address the barrier caused by the black-box nature of Deep Learning (DL) for practical deployment, eXplainable Artificial Intelligence (XAI) has emerged and is developing rapidly. While significant progress has been made in explanation techniques for DL models targeted to images and texts, research on explaining DL models for graph data is still in its infancy. As Graph Neural Networks (GNNs) have
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An In-Depth Analysis of Password Managers and Two-Factor Authentication Tools ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-06 Mohammed Jubur, PrakashPrakash Shrestha, Nitesh Saxena
Passwords remain the primary authentication method in online services, a domain increasingly crucial in our digital age. However, passwords suffer from several well-documented security and usability issues. Addressing these concerns, password managers and two-factor authentication (2FA) have emerged as key solutions. This paper examines these methods with a focus on enhancing password security without
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Regulating Information and Network Security: Review and Challenges ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-06 Tayssir Bouraffa, Kai-Lung Hui
The rapid expansion of internet activities in daily life has elevated cyberattacks to a significant global threat. As a result, protecting the networks and systems of industries, organizations, and individuals against cybercrimes has become an increasingly critical challenge. This monograph provides a comprehensive review and analysis of national, international, and industry regulations on cybercrimes
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Characterization of Android Malwares and their families ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-06 Tejpal Sharma, Dhavleesh Rattan
Nowadays, smartphones have made our lives easier and have become essential gadgets for us. Apart from calling, mobiles are used for various purposes, such as banking, chatting, data storage, connecting to the internet and running apps which make life easier. Therefore, attackers are developing new methods or malware to steal smartphone data. Primarily, the study outlines various types of Android malware
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A Survey on Online Aggression: Content Detection and Behavioural Analysis on Social Media Platforms ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-04 Swapnil Mane, Suman Kundu, Rajesh Sharma
The proliferation of social media has increased cyber-aggressive behavior behind the freedom of speech, posing societal risks from online anonymity to real-world consequences. This article systematically reviews Aggression Content Detection and Behavioral Analysis to address these risks. Content detection is vital for handling explicit aggression, and behavior analysis offers insights into underlying
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A survey of heuristics for profile and wavefront reductions ACM Comput. Surv. (IF 23.8) Pub Date : 2025-01-03 Sanderson Gonzaga de Oliveira
This paper surveys heuristic methods for profile and wavefront reductions. These graph layout problems represent a challenge for optimization methods and heuristics especially. This paper presents the graph layout problems with their formal definition. The study provides an ample perspective of techniques for designing heuristic methods for these graph layout problems but concentrates on the approaches
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A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold ACM Comput. Surv. (IF 23.8) Pub Date : 2024-12-26 Yanhong Fei, Yingjie Liu, Chentao Jia, Zhengyu Li, Xian Wei, Mingsong Chen
Deep Learning (DL) has achieved remarkable success in tackling complex Artificial Intelligence tasks. The standard training of neural networks employs backpropagation to compute gradients and utilizes various optimization algorithms in the Euclidean space \(\mathbb {R}^n \) . However, this optimization process faces challenges, such as the local optimal issues and the problem of gradient vanishing
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Privacy-preserved and Responsible Recommenders: From Conventional Defense to Federated Learning and Blockchain ACM Comput. Surv. (IF 23.8) Pub Date : 2024-12-19 Waqar Ali, Xiangmin Zhou, Jie Shao
Recommender systems (RS) play an integral role in many online platforms. Exponential growth and potential commercial interests are raising significant concerns around privacy, security, fairness, and overall responsibility. The existing literature around responsible recommendation services is diverse and multi-disciplinary. Most literature reviews cover a specific aspect or a single technology for
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ISP Meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing ACM Comput. Surv. (IF 23.8) Pub Date : 2024-12-19 Claudio Filipi Goncalves dos Santos, Rodrigo Reis Arrais, Jhessica Victoria Santos da Silva, Matheus Henrique Marques da Silva, Wladimir Barroso Guedes de Araujo Neto, Leonardo Tadeu Lopes, Guilherme Augusto Bileki, Iago Oliveira Lima, Lucas Borges Rondon, Bruno Melo de Souza, Mayara Costa Regazio, Rodolfo Coelho Dalapicola, Arthur Alves Tasca
The entire Image Signal Processor (ISP) of a camera relies on several processes to transform the data from the Color Filter Array (CFA) sensor, such as demosaicing, denoising, and enhancement. These processes can be executed either by some hardware or via software. In recent years, Deep Learning(DL) has emerged as one solution for some of them or even to replace the entire ISP using a single neural
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Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning ACM Comput. Surv. (IF 23.8) Pub Date : 2024-12-18 Maxwell Standen, Junae Kim, Claudia Szabo
Multi-Agent Reinforcement Learning (MARL) is susceptible to Adversarial Machine Learning (AML) attacks. Execution-time AML attacks against MARL are complex due to effects that propagate across time and between agents. To understand the interaction between AML and MARL, this survey covers attacks and defences for MARL, Multi-Agent Learning (MAL), and Deep Reinforcement Learning (DRL). This survey proposes
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Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art ACM Comput. Surv. (IF 23.8) Pub Date : 2024-12-18 Kaisei Fukaya, Damon Daylamani-Zad, Harry Agius
Procedural content generation (PCG) can be applied to a wide variety of tasks in games, from narratives, levels and sounds, to trees and weapons. A large amount of game content is comprised of graphical assets , such as clouds, buildings or vegetation, that do not require gameplay function considerations. There is also a breadth of literature examining the procedural generation of such elements for
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Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach ACM Comput. Surv. (IF 23.8) Pub Date : 2024-12-18 Firas Bayram, Bestoun S. Ahmed
Artificial intelligence (AI), and especially its sub-field of Machine Learning (ML), are impacting the daily lives of everyone with their ubiquitous applications. In recent years, AI researchers and practitioners have introduced principles and guidelines to build systems that make reliable and trustworthy decisions. From a practical perspective, conventional ML systems process historical data to extract
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A Comprehensive Survey on Physical Layer Authentication Techniques: Categorization and Analysis of Model-Driven and Data-Driven Approaches ACM Comput. Surv. (IF 23.8) Pub Date : 2024-12-16 Zhifan Lai, Zikai Chang, Mingrui Sha, Qihong Zhang, Ning Xie, Changsheng Chen, Dusit (Tao) Niyato
The open and broadcast nature of wireless mediums introduces significant security vulnerabilities, making authentication a critical concern in wireless networks. In recent years, Physical-Layer Authentication (PLA) techniques have garnered considerable research interest due to their advantages over Upper-Layer Authentication (ULA) methods, such as lower complexity, enhanced security, and greater compatibility
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Visual Content Privacy Protection: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2024-12-16 Ruoyu Zhao, Yushu Zhang, Tao Wang, Wenying Wen, Yong Xiang, Xiaochun Cao
Vision is the most important sense for people, and it is also one of the main ways of cognition. As a result, people tend to utilize visual content to capture and share their life experiences, which greatly facilitates the transfer of information. Meanwhile, it also increases the risk of privacy violations, e.g., an image or video can reveal different kinds of privacy-sensitive information. Scholars
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Distributed Machine Learning in Edge Computing: Challenges, Solutions and Future Directions ACM Comput. Surv. (IF 23.8) Pub Date : 2024-12-13 Jingke Tu, Lei Yang, Jiannong Cao
Distributed machine learning on edges is widely used in intelligent transportation, smart home, industrial manufacturing, and underground pipe network monitoring to achieve low latency and real time data processing and prediction. However, the presence of a large number of sensing and edge devices with limited computing, storage, and communication capabilities prevents the deployment of huge machine
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A Survey on Privacy-Preserving Caching at Network Edge: Classification, Solutions, and Challenges ACM Comput. Surv. (IF 23.8) Pub Date : 2024-12-10 Xianzhi Zhang, Yipeng Zhou, Di Wu, Quan Z. Sheng, Shazia Riaz, Miao Hu, Linchang Xiao
Caching content at the edge network is a popular and effective technique widely deployed to alleviate the burden of network backhaul, shorten service delay and improve service quality. However, there has been some controversy over privacy violations in caching content at the edge network. On the one hand, the multi-access open edge network provides an ideal entrance or interface for external attackers
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Recent Advances of Foundation Language Models-based Continual Learning: A Survey ACM Comput. Surv. (IF 23.8) Pub Date : 2024-12-09 Yutao Yang, Jie Zhou, Xuanwen Ding, Tianyu Huai, Shunyu Liu, Qin Chen, Yuan Xie, Liang He
Recently, foundation language models (LMs) have marked significant achievements in the domains of natural language processing (NLP) and computer vision (CV). Unlike traditional neural network models, foundation LMs obtain a great ability for transfer learning by acquiring rich commonsense knowledge through pre-training on extensive unsupervised datasets with a vast number of parameters. Despite these