ACM Computing Surveys最新文献

Prediction of Certification in MOOCs: A Systematic Literature Review mooc认证预测：系统文献综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-20 DOI: 10.1145/3743671

Ahmed Alamri, Mohammad Alshehri, Laila Alrajhi, Alexandra Cristea

{"title":"Prediction of Certification in MOOCs: A Systematic Literature Review","authors":"Ahmed Alamri, Mohammad Alshehri, Laila Alrajhi, Alexandra Cristea","doi":"10.1145/3743671","DOIUrl":"https://doi.org/10.1145/3743671","url":null,"abstract":"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 models contributed to addressing the very low certification level. We adopted the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) for transparently conducting the present review and reporting the results from the works reviewed. Additionally, this SLR highlights several trends and limitations within the present predictive models, including some methodological concerns: the extent to which the present models are generalisable, the excessive filtration of the experimental population, the incompatibility of some experiments with real-time scenarios (nonrealistic modelling), and the shallow reporting of model performances. We have also discussed the replicability of the present models and ongoing efforts towards building a state-of-the-art predictive model. Finally, we highlight future research opportunities in the field of MOOC certification prediction that either deal with the limitations of the present models or address unanswered questions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"24 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144328699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computational Approaches to the Detection of Lesser-Known Rhetorical Figures: A Systematic Survey and Research Challenges 不为人知修辞格检测的计算方法：系统调查和研究挑战

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-19 DOI: 10.1145/3744554

Ramona Kühn, Jelena Mitrović, Michael Granitzer

引用次数: 0

Causality in Bandits: A Survey 《强盗》中的因果关系：一项调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-19 DOI: 10.1145/3744917

Chandrasekar Subramanian, Balaraman Ravindran

引用次数: 0

Static Code Analysis for IoT Security: A Systematic Literature Review 物联网安全静态代码分析：系统文献综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-19 DOI: 10.1145/3745019

Diego Gomes, Eduardo Felix, Fernando Aires, Marco Vieira

{"title":"Static Code Analysis for IoT Security: A Systematic Literature Review","authors":"Diego Gomes, Eduardo Felix, Fernando Aires, Marco Vieira","doi":"10.1145/3745019","DOIUrl":"https://doi.org/10.1145/3745019","url":null,"abstract":"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 examine and identify security vulnerabilities, including insecure functions, buffer overflows, and code injection. However, recent research has shown several challenges associated with this approach, such as limited understanding of vulnerabilities, inadequate threat detection, and insufficient semantic analysis of IoT device source code. Consequently, several IoT security research studies integrate static analysis with other methods, such as dynamic analysis, machine learning, and natural language processing, to enhance vulnerability analysis and detection. To provide a comprehensive understanding of the current state of static analysis in IoT security, this systematic literature review explores existing vulnerabilities, techniques, and methods while highlighting the challenges that hinder the extraction of meaningful insights from such analyses.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"36 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144319556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing 5G Security and Privacy with AI: A Survey 用人工智能推进5G安全和隐私：一项调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-16 DOI: 10.1145/3744555

Haoxin He, Shufan Fei, Zheng Yan

{"title":"Advancing 5G Security and Privacy with AI: A Survey","authors":"Haoxin He, Shufan Fei, Zheng Yan","doi":"10.1145/3744555","DOIUrl":"https://doi.org/10.1145/3744555","url":null,"abstract":"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. Artificial intelligence (AI), with its advanced data analysis and pattern recognition capabilities, is a promising solution to enhance security and privacy in 5G networks. While existing surveys discuss AI applications for 5G, there is a lack of a comprehensive survey on the performance of various AI-based solutions for 5G security and privacy. This paper aims to fill this gap by providing an in-depth review of the latest advancements in AI for 5G security and privacy. We begin with an overview of the security and privacy challenges in 5G networks, including potential vulnerabilities, attack vectors, and privacy issues. We then propose a set of evaluation criteria for assessing various AI-based solutions. Following this, we present a taxonomy of AI-based security and privacy solutions and review the latest advancements. Finally, we identify open issues and propose future directions for utilizing AI to enhance 5G security and privacy.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"45 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144304564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning in Stance Detection: A Survey 深度学习在姿态检测中的应用综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-13 DOI: 10.1145/3744641

Parush Gera, Tempestt Neal

引用次数: 0

Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey 知识增强图机器学习用于药物发现：综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-12 DOI: 10.1145/3744237

Zhiqiang Zhong, Anastasia Barkova, Davide Mottin

{"title":"Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey","authors":"Zhiqiang Zhong, Anastasia Barkova, Davide Mottin","doi":"10.1145/3744237","DOIUrl":"https://doi.org/10.1145/3744237","url":null,"abstract":"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 leverage biomedical knowledge databases, e.g. , Knowledge Graphs (KGs), to explore unknown knowledge. Nevertheless, KG construction is resource-intensive and often neglects crucial structural information in biomedical data. In response, recent studies have proposed integrating external biomedical knowledge into the GML pipeline to realise more precise and interpretable drug discovery with scarce training data. Nevertheless, a systematic definition for this burgeoning research direction is yet to be established. This survey formally summarises Knowledge-augmented Graph Machine Learning (KaGML) for drug discovery and organises collected KaGML works into four categories following a novel-defined taxonomy. We also present a comprehensive overview of long-standing drug discovery principles and provide the foundational concepts and cutting-edge techniques for graph-structured data and knowledge databases. To facilitate research in this promptly emerging field, we share collected practical resources that are valuable for intelligent drug discovery and provide an in-depth discussion of the potential avenues for future advancements.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"26 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144278228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey on the Implementations, Attacks, and Countermeasures of the NIST Lightweight Cryptography Standard: ASCON NIST轻量级密码标准ASCON的实现、攻击与对策综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-12 DOI: 10.1145/3744640

Jasmin Kaur, Alvaro Cintas Canto, Mehran Mozaffari Kermani, Reza Azarderakhsh

{"title":"A Survey on the Implementations, Attacks, and Countermeasures of the NIST Lightweight Cryptography Standard: ASCON","authors":"Jasmin Kaur, Alvaro Cintas Canto, Mehran Mozaffari Kermani, Reza Azarderakhsh","doi":"10.1145/3744640","DOIUrl":"https://doi.org/10.1145/3744640","url":null,"abstract":"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 usage models. National Institute of Standards and Technology (NIST) initiated a standardization process for lightweight cryptography and after a relatively-long multi-year effort, eventually, in Feb. 2023, the competition ended with ASCON as the winner. ASCON can be viewed as the dual of the widely-deployed AES-GCM block-cipher construction, which while still state-of-the-art for general-purpose platforms, is resource-intensive for constrained devices, thus it is useful in deeply-embedded architectures to provide security through confidentiality and integrity/authentication. ASCON’s lightweight design utilizes a 320-bit permutation which is bit-sliced into five 64-bit register words, providing 128-bit level security. This work summarizes the different implementations of ASCON on field-programmable gate array (FPGA) and ASIC hardware platforms on the basis of area, power, throughput, energy, and efficiency overheads. The presented work also reviews various differential and side-channel analysis attacks (SCAs) performed across variants of ASCON cipher suite in terms of algebraic, cube/cube-like, forgery, fault injection, and power analysis attacks as well as the countermeasures for these attacks. We also provide our insights and visions throughout this survey to provide new future directions in different domains. This survey is the first one in its kind and a step forward towards scrutinizing the advantages and future directions of the NIST lightweight cryptography standard introduced in 2023.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"18 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144278244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing the Effectiveness of ChatGPT in Secure Code Development: A Systematic Literature Review 评估ChatGPT在安全代码开发中的有效性：系统的文献综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-12 DOI: 10.1145/3744553

Rezika Bouzid, Raphaël Khoury

{"title":"Assessing the Effectiveness of ChatGPT in Secure Code Development: A Systematic Literature Review","authors":"Rezika Bouzid, Raphaël Khoury","doi":"10.1145/3744553","DOIUrl":"https://doi.org/10.1145/3744553","url":null,"abstract":"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. However, this technological advancement also brings to the forefront significant apprehensions concerning the security of code produced by LLMs. In this paper, we survey recent research that examines the use of ChatGPT to generate secure code, detect vulnerabilities in code, or perform other tasks related to secure code development. Beyond categorizing and synthesizing these studies, we identify important insights into ChatGPT’s potential impact on secure programming. Key findings indicate that while ChatGPT shows great promise as an aid in writing secure code, challenges remain. Its effectiveness varies across security tasks, depending on the context of experimentation (e.g., programming language, CWE, code length, etc.) and the benchmark used for comparison—whether against other LLMs, traditional analysis tools, or its own versions. The overall trend indicates that GPT-4 consistently surpasses its predecessor in most tasks.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"25 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144278242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of Question Answering Systems: Complexity of Judging a Natural Language 问答系统的评价：判断自然语言的复杂性

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-12 DOI: 10.1145/3744663

Amer Farea, Zhen Yang, Kien Duong, Nadeesha Perera, Frank Emmert-Streib

{"title":"Evaluation of Question Answering Systems: Complexity of Judging a Natural Language","authors":"Amer Farea, Zhen Yang, Kien Duong, Nadeesha Perera, Frank Emmert-Streib","doi":"10.1145/3744663","DOIUrl":"https://doi.org/10.1145/3744663","url":null,"abstract":"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 is intricate, as many of the available evaluation scores are not task-agnostic. Furthermore, translating human judgment into measurable metrics continues to be an open issue. These complexities add challenges to their assessment. This survey provides a systematic overview of evaluation scores and introduces a taxonomy with two main branches: Human-Centric Evaluation Scores (HCES) and Automatic Evaluation Scores (AES). Since many of these scores were originally designed for specific tasks but have been applied more generally, we also cover the basics of QA frameworks and core paradigms to provide a deeper understanding of their capabilities and limitations. Lastly, we discuss benchmark datasets that are critical for conducting systematic evaluations across various QA tasks.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"10 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144278227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0