Computer Science Review最新文献

{"title":"LLM-driven paradigm for agent-based modeling and simulation: A review and organizing framework","authors":"Jiaqi Wang, Xiong Li, Gang Xiao","doi":"10.1016/j.cosrev.2026.101054","DOIUrl":"https://doi.org/10.1016/j.cosrev.2026.101054","url":null,"abstract":"Agent-based modeling and simulation (ABMS) serves as a pivotal bottom up paradigm for revealing macro level emergent behaviors through micro level interactions. Classic ABMS remains foundational to the field, while they typically necessitate manual development and labor intensive construction of agent logics. Based on a synthesis of recent advancements, this article synthesizes the literature through a lifecycle-oriented organizing framework that supports the ABMS lifecycle, including (1) a generative workflow leveraging prior knowledge for conceptual abstraction; (2) a simulation modeling framework comprising an agent decision-making architecture integrating multimodal perception, retrieval augmented generation enhanced memory, and chain of thought reasoning alongside communication design and LLM-based code generation; and (3) a simulation data analysis framework that synthesizes micro level quantitative analysis with macro level qualitative analysis. This study aims to provide a methodological perspective for the integration of LLMs into ABMS.","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"44 1","pages":""},"PeriodicalIF":12.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884337","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}

{"title":"Preface: Special issue in honor of Josep Díaz","authors":"Carme Àlvarez, Maria Serna","doi":"10.1016/j.cosrev.2026.101040","DOIUrl":"https://doi.org/10.1016/j.cosrev.2026.101040","url":null,"abstract":"","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"29 1","pages":""},"PeriodicalIF":12.9,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148692812","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}

{"title":"A multi-dimensional study of IoT DDoS in smart environments: SDN integration, taxonomy, security gaps, and emerging defenses","authors":"Shivani Rathore,&nbsp;Abhinav Bhandari,&nbsp;Raman Maini","doi":"10.1016/j.cosrev.2026.100903","DOIUrl":"10.1016/j.cosrev.2026.100903","url":null,"abstract":"<div><div>The proliferation of Internet of Things (IoT) devices in Software-Defined Networking (SDN)-enabled smart environments has significantly expanded the threat landscape, especially for Distributed Denial of Service (DDoS) attacks. This issue has gained scientific attention due to its complex socio-technical nature. This article examines the architecture of emerging IoT technologies, provides visual illustrations of vulnerabilities in smart environments, and discusses the increase in DDoS attacks related to IoT botnets. Also, we provide a market study of the current IoT and SDN deployments and DDoS landscape that reveals critical gaps between theoretical frameworks and practical adoption. This industry-driven perspective strengthens the motivation for conducting a structured review that aligns emerging academic research with evolving market statistics and deployment challenges. Additionally, we introduce a new IoT DDoS attack taxonomy designed to address protocol-level vulnerabilities in commonly used IoT communication protocols, including MQTT, CoAP, and SSDP. Our taxonomy is comprehensive and innovative, representing the first framework dedicated solely to analyzing IoT-driven DDoS attacks. It offers researchers a better understanding of this problem. By developing this framework, our goal is to help practitioners and researchers identify vulnerabilities, develop mitigation strategies, and strengthen the resilience of IoT environments against DDoS attacks.</div><div>Furthermore, we critically examine state-of-the-art DDoS mitigation techniques within SDN-enabled smart infrastructure. We conduct an incisive evaluation of contemporary DDoS mitigation strategies presented by many researchers, scrutinizing their operational potency, deployment feasibility, and resilience under dynamic, heterogeneous smart surroundings. Our review reveals that while a significant body of research claims to address IoT-based DDoS attacks, the majority of defenses remain centered on conventional network protocols, overlooking the distinct characteristics and vulnerabilities of IoT-specific protocols such as MQTT, CoAP, and SSDP. Building on this study, we identify critical research gaps and highlight novel possibilities for enhancing the resilience of next-generation smart networks against evolving DDoS adversaries.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"60 ","pages":"Article 100903"},"PeriodicalIF":12.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033304","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}

{"title":"Revisiting workflow scheduling with the power of edge computing: Taxonomy, review, and open challenges","authors":"Shenghai Li ,&nbsp;Wentai Wu ,&nbsp;Haotong Zhang ,&nbsp;Yongheng Liu ,&nbsp;Weiwei Lin ,&nbsp;Keqin Li","doi":"10.1016/j.cosrev.2025.100887","DOIUrl":"10.1016/j.cosrev.2025.100887","url":null,"abstract":"<div><div>Edge computing has emerged as a pivotal paradigm for overcoming the limitations of traditional cloud computing, especially in latency-sensitive applications such as autonomous driving and video streaming. As mobile applications grow in complexity, they often consist of interdependent tasks that can be modeled as workflows. Scheduling these workflows over heterogeneous resources at the network edge presents unique challenges due to the diverse characteristics of workflows and the complex nature of edge environments. Despite recent advances, a comprehensive overview of the fundamentals and state-of-the-art approaches in this field remains lacking. This survey systematically reviews workflow scheduling in edge computing by first addressing its motivation, typical application scenarios, and core challenges. The survey then introduces basic models and performance metrics, followed by a taxonomy of existing scheduling strategies categorized by research issues, optimization objectives, and techniques. Finally, we discuss open challenges and propose future research directions, providing a guide for the development of efficient edge workflow scheduling strategies.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"60 ","pages":"Article 100887"},"PeriodicalIF":12.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938394","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}

{"title":"Parameterized complexity of graph isomorphism testing","authors":"Daniel Neuen","doi":"10.1016/j.cosrev.2026.100918","DOIUrl":"10.1016/j.cosrev.2026.100918","url":null,"abstract":"<div><div>We survey recent developments in the parameterized complexity of the graph isomorphism problem. Our focus lies on the fixed-parameter tractability of isomorphism testing for various graph parameters. Additionally, following Babai’s quasipolynomial time isomorphism test, there has been a series of results obtaining isomorphism algorithms running in time <span><math><msup><mi>n</mi><mrow><mi>polylog</mi><mo>⁡</mo><mo>(</mo><mi>k</mi><mo>)</mo></mrow></msup></math></span> for different graph parameters <span><math><mi>k</mi></math></span>.</div><div>We highlight the main technical ideas underlying these algorithms and state various open questions for future research.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"60 ","pages":"Article 100918"},"PeriodicalIF":12.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134545","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}

{"title":"A comprehensive review on quantum steganography techniques for data hiding","authors":"Ahana Patra ,&nbsp;Ram Chandra Barik ,&nbsp;Suvamoy Changder","doi":"10.1016/j.cosrev.2026.100911","DOIUrl":"10.1016/j.cosrev.2026.100911","url":null,"abstract":"<div><div>Quantum steganography is an emerging field that blends the principles of quantum mechanics with the traditional art of steganography. It is an art of transmitting important information secretly without letting others know about the communication. Steganography is the practice of hiding information within other information. The exciting feature of quantum steganography is that it provides the capability to hide both classical and quantum qubits or quantum states. Thus, it can achieve better capacity, time complexity, and imperceptibility in comparison to the classical system. This survey paper describes various methodologies that have been applied in different steganography domains, such as text steganography, image steganography, audio steganography, and video steganography, and discusses the strengths and weaknesses of each methodology separately. The aim of this paper is to provide an overview of the theoretical foundation, recent advances, and open challenges in the fields of both classical and quantum steganography. It also helps to explore new research directions in quantum steganography across various domains, including quantum image steganography, quantum text steganography, quantum audio steganography, and quantum video steganography, depending on the requirements of imperceptibility, payload capacity, and robustness against attacks.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"60 ","pages":"Article 100911"},"PeriodicalIF":12.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072497","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}

{"title":"A comprehensive review on expansion and statistical evaluation of Coati optimization algorithm","authors":"Vimal Kumar Pathak","doi":"10.1016/j.cosrev.2026.100922","DOIUrl":"10.1016/j.cosrev.2026.100922","url":null,"abstract":"<div><div>The Coati optimization algorithm (COA) is a recently introduced metaheuristic algorithm inspired from the hunting and escaping activities of Coati in their habitat. Since its development, COA has achieved new heights owing its efficacy in solving diverse optimization problems, flexibility and simple procedure. Alike other metaheuristic optimization algorithms, COA based on intelligent metaphor have seen rapid advancements and improvements, however with similar intrinsic weaknesses. The probable reason being inadequate investigations on COA procedure, limitations, statistical evaluation prior to its enhancement. The importance of the current review stems from its in-depth exploration of COA advancements, foundation principles and practical scenarios. This comprehensive review features 91 COA articles for analysing its adaptability and increasing interest in solving non-trivial optimization problems. It precisely explores the COA expansion, researching historical developments and modifications that enhances the algorithm capability in adapting to diverse landscapes of optimization problems. This review investigates the COA articles in terms of year wise publications, including enhancements, chaotic, opposition based learning variants, hybridizations, binary version, multi-objective variants, and different applications. It was shown that Elsevier and Springer have published highest number of COA papers having 54 and 51 counts, respectively. The published articles on classical COA confirm that it is mostly utilized for solving complex optimization problems in the field of electrical engineering (35%), I-o-T (22%) and medical diagnostics (17%) applications. Moreover, it is established that the COA performance has been improved by mostly hybridizing with other metaheuristic algorithms (29%), followed by introducing chaotic mapping (24%) and OBL scheme (15%), respectively. In addition, the present review significantly assesses the COA’s convergence behaviour and statistical performance in comparison to some well-known recent metaheuristic algorithms. Finally, concluding remarks with future research directions were recommended for scholars and scientists including improvements and critical suggestion for minimizing its limitations.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"60 ","pages":"Article 100922"},"PeriodicalIF":12.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072499","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}

{"title":"Artificial intelligence-based approaches for preprocessing, segmentation, and classification in dental radiographs for furcation detection: A comparative analysis","authors":"Priyanka ,&nbsp;Mamta Juneja ,&nbsp;Naveen Aggarwal ,&nbsp;Manoj Kumar Jaiswal ,&nbsp;Priyanka Rana","doi":"10.1016/j.cosrev.2025.100890","DOIUrl":"10.1016/j.cosrev.2025.100890","url":null,"abstract":"<div><div>Early detection of dental problems, such as Furcation Radiolucency (FR), plays a vital role in ensuring effective treatment and maintaining oral health, particularly in pediatric dentistry. FR, often associated with deep dental decay, can be identified across various dental radiographic modalities, typically appearing as a dark, radiolucent area between the tooth roots. However, accurate interpretation of extraoral and intraoral dental radiographs can be challenging due to the subtle nature of early lesions and variability in image quality. This review explores the transformative potential of Artificial Intelligence (AI)- driven Computer-Aided Diagnosis (CAD) systems, which enhance the detection and analysis of FR, offering clear advantages over traditional methods. AI technologies, particularly Machine Learning (ML) and Deep Learning (DL), enhance critical stages of dental radiographic analysis, including image preprocessing, segmentation, and classification. These advancements enable early identification of subtle radiographic changes, reducing the need for invasive treatments and fostering more proactive treatment planning. The paper provides a comprehensive review of both traditional diagnostic techniques and recent AI-driven innovations, highlighting their impact on improving dental image quality, segmentation precision and classification accuracy. Focusing on powerful AI models such as U-Net, Mask Region-based Convolutional Neural Network (R-CNN), and Vision Transformers (ViTs), along with lightweight deep Convolutional Networks (ConvNets) like MobileNetV2 or EfficientNetV2, the review highlights the potential of these systems to identify dental problems more effectively and facilitate efficient clinical decision-making. Additionally, the paper addresses ongoing challenges, including the need for large-scale validation and multi-modal data integration, and offers actionable insights for researchers and dental practitioners to further leverage AI in pediatric dental care. This review bridges the gap between traditional diagnostic practices and AI-enhanced methods, underscoring the future potential of AI to revolutionize dental diagnostics and treatment planning.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"60 ","pages":"Article 100890"},"PeriodicalIF":12.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938396","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}

{"title":"Traversing the landscape of aspect-based sentiment analysis: Delving deeper into techniques, trends, and future directions","authors":"Gyananjaya Tripathy,&nbsp;Aakanksha Sharaff","doi":"10.1016/j.cosrev.2025.100885","DOIUrl":"10.1016/j.cosrev.2025.100885","url":null,"abstract":"<div><div>Advanced sentiment analysis algorithms are necessary to analyze the growing volume of online reviews. Aspect-based sentiment analysis (ABSA) not only discerns overall sentiment but also elucidates user opinions on specific elements. ABSA’s capacity to analyze intricate thoughts and emotions expressed in textual data has garnered significant attention in recent years. Researchers have conducted many investigations on ABSA, although numerous aspects remain unexplored. This study encompasses several inadequacies and challenges, with a thorough examination of developments in ABSA approaches, methodologies, and applications. This study specifically examines methods of ABSA, encompassing fundamental knowledge-based tactics, machine learning and deep learning techniques, hybrid approaches, and advanced large language models. It offers a summary of recent technological advancements, evaluation criteria, and the accessibility of benchmark datasets. Additionally, it addresses the typical obstacles and constraints while providing insights into future trends, offering a perspective on the evolution from traditional rule-based to advanced sentiment analysis methodologies. This work provides researchers the opportunity to utilize this thorough analysis of ABSA as a basis for their work, enabling them to recognize essential features to make informed judgments and direct future research on this swiftly evolving subject.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"60 ","pages":"Article 100885"},"PeriodicalIF":12.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823146","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}

{"title":"Strategic offloading in autonomous vehicles: A systematic survey of current schemes, challenges, and future prospects","authors":"Amir Masoud Rahmani ,&nbsp;Amir Haider ,&nbsp;Farhad Soleimanian Gharehchopogh ,&nbsp;Komeil Moghaddasi ,&nbsp;Aso Darwesh ,&nbsp;Mehdi Hosseinzadeh","doi":"10.1016/j.cosrev.2026.100898","DOIUrl":"10.1016/j.cosrev.2026.100898","url":null,"abstract":"<div><div>Autonomous vehicles, most notably self-driving cars, are seen by many as a generational shift in transportation, offering the potential to reduce crash rates and relieve congestion. In order to operate, autonomous vehicles combine data from cameras, radar, and LiDAR and need to act on this data in real-time creating a heavy and bursty computational demand. Processing these heavy and bursty demands within the autonomy system's power and thermal envelope will require considerable effort in determining which tasks to perform when and in what order. In some cases, offloading workloads to edge or cloud servers creates an opportunity to offload compute, reduce end-to-end latency, and enhance overall responsiveness if workloads are offloaded under strict latency constraints. In this work, we survey state-of-the-art offloading methods, identify significant challenges such as latency management, network reliability, and security, and outline future improvements within the area of vehicular systems. From an analysis of the state of the literature, we also objectively evaluate when and how offloading can enhance multi-faceted computational demands of autonomous driving stacks, with the overall goal of support safer and more capable vehicular systems.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"60 ","pages":"Article 100898"},"PeriodicalIF":12.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146000913","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}