Computer Science Review最新文献_第2页

Mathematical programming model mining: A systematic field survey 数学规划模型挖掘：系统的实地调查

IF 12.7 1区计算机科学

Computer Science Review Pub Date : 2026-05-01 Epub Date: 2026-01-23 DOI: 10.1016/j.cosrev.2026.100905

Rafał Stachowiak, Tomasz P. Pawlak

{"title":"Mathematical programming model mining: A systematic field survey","authors":"Rafał Stachowiak, Tomasz P. Pawlak","doi":"10.1016/j.cosrev.2026.100905","DOIUrl":"10.1016/j.cosrev.2026.100905","url":null,"abstract":"<div><div>Mathematical Programming (MP) is a well-established framework for formulating optimization problems using variables, constraints, and an objective function. The task of developing an MP model involves addressing subproblems such as discovering an MP model from domain knowledge, conformance checking of a candidate MP model with domain knowledge, and enhancing an invalid MP model based on domain knowledge. Traditionally, experts manually perform these tasks, leading to iterative processes that are both labor-intensive and error-prone. Recent literature highlights an emerging field of algorithms focused on automating MP model development using domain knowledge artifacts, which we jointly term MP model mining and divide into discovery, conformance checking, and enhancement problems. This study organizes and analyzes existing knowledge on MP model mining, aiming to elucidate the state of the art and pinpoint current gaps and challenges. Through a systematic review via an acknowledged literature search engine, we address 29 research questions concerning various dimensions, identify 15 knowledge gaps, and propose a future research agenda.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"60 ","pages":"Article 100905"},"PeriodicalIF":12.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033303","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

Shaping the future of cybersecurity: The convergence of AI, quantum computing, and ethical frameworks for a secure digital era 塑造网络安全的未来：人工智能、量子计算和安全数字时代伦理框架的融合

IF 12.7 1区计算机科学

Computer Science Review Pub Date : 2026-05-01 Epub Date: 2025-12-24 DOI: 10.1016/j.cosrev.2025.100882

Menahil Khawar , Sohail Khalid , Mujeeb Ur Rehman , Aminu Usman , Wajdan Al Malwi , Fatima Asiri

{"title":"Shaping the future of cybersecurity: The convergence of AI, quantum computing, and ethical frameworks for a secure digital era","authors":"Menahil Khawar , Sohail Khalid , Mujeeb Ur Rehman , Aminu Usman , Wajdan Al Malwi , Fatima Asiri","doi":"10.1016/j.cosrev.2025.100882","DOIUrl":"10.1016/j.cosrev.2025.100882","url":null,"abstract":"<div><div>The increasing sophistication and frequency of cyber threats have rendered conventional protection strategies inadequate. Artificial Intelligence (AI) is becoming central to modern cybersecurity, strengthening capabilities in vulnerability assessment, malware detection, phishing prevention, intrusion detection, and deception technologies. Simultaneously, quantum computing introduces both challenges to classical cryptography and opportunities for new forms of quantum-enhanced defenses. This review integrates advances in AI, quantum methods, and ethical governance to provide an integrated perspective on the future of secure digital systems. It evaluates state-of-the-art AI models, including explainable frameworks and quantum-inspired approaches, such as Quantum Convolutional Neural Networks and Quantum Support Vector Machines, along with recent progress in post-quantum cryptography. Ethical concerns, particularly bias, transparency, privacy, and accountability, are examined as essential foundations for trustworthy cybersecurity design in system-on-chip and embedded AI environments. In addition to technical developments, this study considers regulatory frameworks, governance structures, and societal expectations, highlighting the need for responsible and adaptive approaches. A comparative SWOT analysis outlines the strengths, limitations, and areas for cross-domain integration. Finally, a roadmap of future research directions is presented, aligning AI-driven defenses, quantum resilience, and ethical safeguards into flexible and reliable cybersecurity architectures. By linking the technological, ethical, and policy dimensions, this review offers a consolidated foundation to guide the evolution of cybersecurity in a globally connected era.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"60 ","pages":"Article 100882"},"PeriodicalIF":12.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823147","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 SSD wear leveling techniques SSD磨损均衡技术综述

IF 12.7 1区计算机科学

Computer Science Review Pub Date : 2026-05-01 Epub Date: 2026-01-08 DOI: 10.1016/j.cosrev.2025.100891

Fatemeh Serajeh Hassani , Atiyeh Gheibi-Fetrat , Sana Babayan Vanestan , Mitra Gholipoor , Sahand Zoufan , Jeong-A Lee , Hamid Sarbazi-Azad

引用次数: 0

AI-driven blockchain technology in smart healthcare system: Opportunities, challenges and future implications 智能医疗系统中人工智能驱动的区块链技术：机遇、挑战和未来影响

IF 12.7 1区计算机科学

Computer Science Review Pub Date : 2026-05-01 Epub Date: 2026-01-30 DOI: 10.1016/j.cosrev.2026.100909

Yunsheng Zhang , Syed Muhammad Mohsin , Hana Mujlid , Muhammad Sadiq , Syed Muhammad Abrar Akber , Sheraz Aslam , Junwei Liang

{"title":"AI-driven blockchain technology in smart healthcare system: Opportunities, challenges and future implications","authors":"Yunsheng Zhang , Syed Muhammad Mohsin , Hana Mujlid , Muhammad Sadiq , Syed Muhammad Abrar Akber , Sheraz Aslam , Junwei Liang","doi":"10.1016/j.cosrev.2026.100909","DOIUrl":"10.1016/j.cosrev.2026.100909","url":null,"abstract":"<div><div>Blockchain technology in conjunction with artificial intelligence (AI) is transforming smart healthcare systems, by providing enhanced data security, interoperability, and transparency. Integration of AI along with blockchain into smart healthcare systems offers numerous benefits, including supporting decision-making processes, reducing administrative burdens, improving coordination of patient care and automated, trust-based execution of healthcare agreements. This study presents applications of AI-based blockchain technology in the field of smart healthcare and analyzes the state of affairs, highlights the key issues, and identifies perspectives to strengthen the reliability and trustworthiness of future medical systems. The study uses a structured framework to analyze the effectiveness of blockchain in healthcare by contrasting its advantages and disadvantages. Blockchain systems benefit healthcare by improving data security, streamlining data processing, ensuring trust, facilitating telemedicine and remote monitoring, and enabling efficient consent management, automated workflows and medication traceability. In this context, the study introduces a conceptual model namely the trust–automation–interoperability (TAI) synergy framework to guide the design, analysis, and deployment of AI-enabled blockchain solutions for smart healthcare aiming to achieve a sustainable digital health ecosystem by strengthening three fundamental dimensions: trust, automation, and interoperability. However, challenges such as scalability, interoperability, legal ambiguities, security concerns, user experience, acceptance barriers, long-term data storage, connectivity issues, discrepancies between data formats, user identity management, and cost considerations emphasize the importance of strong solutions.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"60 ","pages":"Article 100909"},"PeriodicalIF":12.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076892","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

Interdiction in network maximum flow and related problems: A survey 网络最大流量阻断及相关问题综述

IF 12.7 1区计算机科学

Computer Science Review Pub Date : 2026-05-01 Epub Date: 2025-12-18 DOI: 10.1016/j.cosrev.2025.100867

Giorgio Ausiello , Lorenzo Balzotti , Paolo Giulio Franciosa , Isabella Lari , Andrea Ribichini

{"title":"Interdiction in network maximum flow and related problems: A survey","authors":"Giorgio Ausiello , Lorenzo Balzotti , Paolo Giulio Franciosa , Isabella Lari , Andrea Ribichini","doi":"10.1016/j.cosrev.2025.100867","DOIUrl":"10.1016/j.cosrev.2025.100867","url":null,"abstract":"<div><div>In a network interdiction model, an attacker tries to maximize disruption to some network function (e.g., maximum flow, connectivity) by disabling/damaging certain network resources (e.g., nodes, arcs), and a defender tries to optimally cope with the above attack.</div><div>Network interdiction problems w.r.t. maximum flow were first studied in the 1960s, mainly for their military and logistics applications. While early papers mostly presented non-polynomial time algorithms to identify the most valuable connections in a network, complexity and approximation results soon followed.</div><div>In an increasingly networked society, interdiction has consistently remained a popular research topic to this day, with the initial formulation being supplemented by an impressive number of variants, and some derived problems, each tailored to the necessities of specific applications.</div><div>This survey’s main focus is on providing a structured overview of the many variants of the max-flow interdiction problem that have emerged over the decades. Derived problems, such as robust flow assignments and vitality computation, are also discussed. Pointers to the techniques involved in achieving the most seminal results are presented as well. We conclude with a brief investigation into open directions to be explored in this rewarding research area.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"60 ","pages":"Article 100867"},"PeriodicalIF":12.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785078","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

The paradigm shift: A comprehensive survey on large vision language models for multimodal fake news detection 范式转换：多模态假新闻检测大视觉语言模型的综合研究

IF 12.7 1区计算机科学

Computer Science Review Pub Date : 2026-05-01 Epub Date: 2026-01-14 DOI: 10.1016/j.cosrev.2026.100893

Wei Ai , Yilong Tan , Yuntao Shou , Tao Meng , Haowen Chen , Zhixiong He , Keqin Li

{"title":"The paradigm shift: A comprehensive survey on large vision language models for multimodal fake news detection","authors":"Wei Ai , Yilong Tan , Yuntao Shou , Tao Meng , Haowen Chen , Zhixiong He , Keqin Li","doi":"10.1016/j.cosrev.2026.100893","DOIUrl":"10.1016/j.cosrev.2026.100893","url":null,"abstract":"<div><div>In recent years, the rapid evolution of large vision–language models (LVLMs) has driven a paradigm shift in multimodal fake news detection (MFND), transforming it from traditional feature-engineering approaches to unified, end-to-end multimodal reasoning frameworks. Early methods primarily relied on shallow fusion techniques to capture correlations between text and images, but they struggled with high-level semantic understanding and complex cross-modal interactions. The emergence of LVLMs has fundamentally changed this landscape by enabling joint modeling of vision and language with powerful representation learning, thereby enhancing the ability to detect misinformation that leverages both textual narratives and visual content. Despite these advances, the field lacks a systematic survey that traces this transition and consolidates recent developments. To address this gap, this paper provides a comprehensive review of MFND through the lens of LVLMs. We first present a historical perspective, mapping the evolution from conventional multimodal detection pipelines to foundation model-driven paradigms. Next, we establish a structured taxonomy covering model architectures, datasets, and performance benchmarks. Furthermore, we analyze the remaining technical challenges, including interpretability, temporal reasoning, and domain generalization. Finally, we outline future research directions to guide the next stage of this paradigm shift. To the best of our knowledge, this is the first comprehensive survey to systematically document and analyze the transformative role of LVLMs in combating multimodal fake news. The summary of existing methods mentioned is in our Github: <span><span>https://github.com/Tan-YiLong/Overview-of-Fake-News-Detection</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"60 ","pages":"Article 100893"},"PeriodicalIF":12.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961765","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

Concepts, taxonomic review, and emerging trends in computational intelligence for green cloud systems 绿色云系统中计算智能的概念、分类回顾和新兴趋势

IF 12.7 1区计算机科学

Computer Science Review Pub Date : 2026-05-01 Epub Date: 2026-01-14 DOI: 10.1016/j.cosrev.2026.100894

Deepika Saxena , Ashutosh Kumar Singh

{"title":"Concepts, taxonomic review, and emerging trends in computational intelligence for green cloud systems","authors":"Deepika Saxena , Ashutosh Kumar Singh","doi":"10.1016/j.cosrev.2026.100894","DOIUrl":"10.1016/j.cosrev.2026.100894","url":null,"abstract":"<div><div>Computational Intelligence (CI) techniques, inspired by natural and adaptive processes, have become essential tools for enhancing energy efficiency and sustainability in cloud data centers, forming the foundation of Green Cloud Resource Management (GCRM). This paper presents a comprehensive taxonomic review of key CI methodologies, including reinforcement learning, optimization algorithms, fuzzy logic, game-theoretic models, and predictive modeling, highlighting their application in critical GCRM tasks such as task scheduling, Virtual Machine (VM) placement, and VM migration. Each CI paradigm is systematically examined, detailing fundamental principles, algorithmic design, and sustainability-driven features. A meta-analytical discussion synthesizes state-of-the-art contributions, emphasizing performance metrics, complexity, scalability, and real-world applicability, while providing comparative insights into trade-offs inherent in energy-aware cloud operations. Lessons learned from prior studies are consolidated to offer practical guidance for designing adaptive, self-optimizing, and eco-efficient cloud infrastructures. Finally, the review identifies emerging trends and prioritized future research directions, advocating the integration of hybrid CI approaches, multi-objective optimization, cross-layer intelligence, and real-world deployment considerations to advance next-generation sustainable cloud environments.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"60 ","pages":"Article 100894"},"PeriodicalIF":12.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976555","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

Neuro-symbolic agentic AI: Architectures, integration patterns, applications, open challenges and future research directions 神经符号人工智能：架构、集成模式、应用、开放挑战和未来研究方向

IF 12.7 1区计算机科学

Computer Science Review Pub Date : 2026-05-01 Epub Date: 2026-01-31 DOI: 10.1016/j.cosrev.2026.100902

Safayat Bin Hakim , Muhammad Adil , Alvaro Velasquez , Houbing Herbert Song

{"title":"Neuro-symbolic agentic AI: Architectures, integration patterns, applications, open challenges and future research directions","authors":"Safayat Bin Hakim , Muhammad Adil , Alvaro Velasquez , Houbing Herbert Song","doi":"10.1016/j.cosrev.2026.100902","DOIUrl":"10.1016/j.cosrev.2026.100902","url":null,"abstract":"<div><div>Neuro-symbolic AI synergizes neural networks’ pattern recognition with symbolic reasoning’s logical structure, addressing fundamental limitations each paradigm exhibits independently. This systematic survey analyzes 178 papers (2020–November 2025) using PRISMA methodology, establishing a comprehensive taxonomy across architectural configurations (single-agent, multi-agent) and integration dimensions: knowledge representation (44%), learning and inference (63%), logic and reasoning (35%), explainability and trustworthiness (28%), and meta-cognition (5%). We identify critical research imbalances, meta-cognitive capabilities remain severely underexplored despite demonstrating greater performance impact than sophisticated integration patterns alone. Through architectural analysis spanning sequential, parallel, end-to-end differentiable, and unified representation approaches, we examine prominent systems (Agent Q, GoalAct, AlphaGeometry, Reflexion, MetaGPT) and evaluate their effectiveness across robotics, natural language processing, autonomous vehicles, healthcare, and education domains. Performance comparisons reveal consistent neuro-symbolic superiority: 23% improvement in robotic task completion, 95.4% autonomous navigation success versus 18.6% neural baselines, and order-of-magnitude reductions in sample complexity. We expose persistent challenges—reproducibility barriers, weak generalization, scalability constraints, symbol grounding difficulties—and propose structured solutions through TRAP-inspired meta-cognitive frameworks, standardized evaluation protocols, and hierarchical agentic architectures balancing symbolic decomposition with neural adaptability.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"60 ","pages":"Article 100902"},"PeriodicalIF":12.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095850","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

Methods and trends in detecting AI-generated images: A comprehensive review 人工智能生成图像检测的方法和趋势：综合综述

IF 12.7 1区计算机科学

Computer Science Review Pub Date : 2026-05-01 Epub Date: 2026-01-21 DOI: 10.1016/j.cosrev.2026.100908

Arpan Mahara, Naphtali Rishe

{"title":"Methods and trends in detecting AI-generated images: A comprehensive review","authors":"Arpan Mahara, Naphtali Rishe","doi":"10.1016/j.cosrev.2026.100908","DOIUrl":"10.1016/j.cosrev.2026.100908","url":null,"abstract":"<div><div>The proliferation of generative models, such as Generative Adversarial Networks (GANs), Diffusion Models, and Variational Autoencoders (VAEs), has enabled the synthesis of high-quality multimedia data. However, these advancements have also raised significant concerns regarding adversarial attacks, unethical usage, and societal harm. Recognizing these challenges, researchers have increasingly focused on developing methodologies to detect synthesized data effectively, aiming to mitigate potential risks. Prior reviews have predominantly focused on deepfake detection and often overlook recent advancements in synthetic image forensics, particularly approaches that incorporate multimodal frameworks, reasoning-based detection, and training-free methodologies. To bridge this gap, this survey provides a comprehensive and up-to-date review of state-of-the-art techniques for detecting and classifying synthetic images generated by advanced generative AI models. The review systematically examines core detection paradigms, categorizes them into spatial-domain, frequency-domain, fingerprint-based, patch-based, training-free, and multimodal reasoning-based frameworks, and offers concise descriptions of their underlying principles. We further provide detailed comparative analyses of these methods on publicly available datasets to assess their generalizability, robustness, and interpretability. Finally, the survey highlights open challenges and future directions, emphasizing the potential of hybrid frameworks that combine the efficiency of training-free approaches with the semantic reasoning of multimodal models to advance trustworthy and explainable synthetic image forensics.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"60 ","pages":"Article 100908"},"PeriodicalIF":12.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014540","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

Applications of flow-augmentation 流量增强的应用

IF 12.7 1区计算机科学

Computer Science Review Pub Date : 2026-05-01 Epub Date: 2025-12-19 DOI: 10.1016/j.cosrev.2025.100869

Stefan Kratsch , Marcin Pilipczuk , Roohani Sharma , Magnus Wahlström

引用次数: 0