Computer Science Review最新文献_第10页

New paradigm of distributed artificial intelligence for LLM implementation and its key technologies 面向LLM实现的分布式人工智能新范式及其关键技术

IF 12.7 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Science Review

Pub Date : 2025-09-21 DOI: 10.1016/j.cosrev.2025.100817

Yijin Wu , Zirun Li , Bingrui Guo , Shanshan He , Bijing Liu , Xiaojie Liu , Shan He , Donghui Guo

With the Internet’s development and information technology advancement, current network applications and services, such as e-commerce, industrial automation, and vehicular automation, have experienced substantial expansion. Foundation models, represented by large language models (LLMs), have emerged in response to growing demands. Their broad range of applications has brought significant advancements to various industries. While such developments have improved people’s economic lives and social activities, the challenges posed by the rapid growth of data volume and network traffic cannot be overlooked. Intelligent systems aimed at enhancing knowledge computation and learning capabilities are gradually gaining attention. Nevertheless, efficient and flexible intelligent systems are still in their early stages, leaving ample space for further optimization. This study provides an overview of Distributed Artificial Intelligence (DAI) with its related paradigm, briefly introduces the evolution of LLMs, and proposes a novel optimization framework named PCD Tri-Tuning for DAI workflows: leveraging caching-related technologies to enhance perceptual capabilities, adopting load-balancing techniques for computational optimization, and developing reasoning methodologies and cooperation techniques to improve decision-making. Subsequently, the study examines the pivotal role of the proposed optimization framework in practical domains such as e-commerce, smart manufacturing, and vehicular automation while also discussing the challenges and outlining strategies for further development.

随着互联网的发展和信息技术的进步，当前的网络应用和服务，如电子商务、工业自动化、车辆自动化等都得到了大幅度的扩展。以大型语言模型（llm）为代表的基础模型已经出现，以响应日益增长的需求。其广泛的应用范围为各个行业带来了显著的进步。这些发展在改善人们经济生活和社会活动的同时，数据量和网络流量的快速增长所带来的挑战也不容忽视。以提高知识计算和学习能力为目标的智能系统逐渐受到关注。然而，高效灵活的智能系统仍处于起步阶段，还有很大的优化空间。本研究概述了分布式人工智能（DAI）及其相关范式，简要介绍了llm的发展，并提出了一种名为PCD三调优的新型DAI工作流优化框架：利用缓存相关技术增强感知能力，采用负载平衡技术进行计算优化，开发推理方法和合作技术以改进决策。随后，该研究考察了所提出的优化框架在电子商务、智能制造和车辆自动化等实际领域的关键作用，同时也讨论了挑战并概述了进一步发展的战略。

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引用次数: 0

Research hotspots and trends of human-computer collaboration: A bibliometric analysis 人机协作研究热点与趋势：文献计量学分析

IF 12.7 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Science Review

Pub Date : 2025-09-21 DOI: 10.1016/j.cosrev.2025.100830

Chang Guo , Anglu Li

Human-computer collaboration represents a crucial model for future human-machine relationships. There is an urgent need to summarize its current state, delineate its research trajectory, and explore future trends. This paper employs a bibliometric approach, using 1713 human-computer collaboration-related publications from the Web of Science core database as initial data. With the scientific bibliometrics and software tools like VOSviewer and CiteSpace, this study creates a scientific knowledge map, which includes publication year distribution, countries, research institutions, authors, keyword clustering, and citation network analysis, facilitating a comprehensive understanding of the field. This research aims to provide a comprehensive overview of international human-computer collaboration research from 1990 to November 2024, identifying current research hotspots and theoretical foundations and exploring new trends in line with current research focuses. This research trend will drive the field of human-computer collaboration to continue to evolve towards intelligence, user-orientation, efficiency and provides a wealth of research opportunities for closer human-computer collaboration.

人机协作是未来人机关系的重要模式。迫切需要总结其现状，勾画其研究轨迹，探索其未来发展趋势。本文采用文献计量学方法，以Web of Science核心数据库中1713篇与人机协作相关的出版物为初始数据。本研究利用科学文献计量学和VOSviewer、CiteSpace等软件工具，构建了包含出版年份分布、国家、研究机构、作者、关键词聚类、引文网络分析等内容的科学知识图谱，促进了对该领域的全面了解。本研究旨在全面梳理1990年至2024年11月国际人机协作研究概况，识别当前的研究热点和理论基础，并根据当前的研究重点探索新的发展趋势。这一研究趋势将推动人机协作领域不断向智能化、用户导向、高效化方向发展，并为更紧密的人机协作提供丰富的研究机会。

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引用次数: 0

Twenty years of Nešetřil’s classification programme of Ramsey classes 20年的Nešetřil拉姆齐类分类计划

IF 12.7 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Science Review

Pub Date : 2025-09-19 DOI: 10.1016/j.cosrev.2025.100814

Jan Hubička , Matěj Konečný

In the 1970s, structural Ramsey theory emerged as a new branch of combinatorics. This development came with the isolation of the concepts of the

A

-Ramsey property and Ramsey class. Following the influential Nešetřil–Rödl theorem, several Ramsey classes have been identified. In the 1980s, Nešetřil, inspired by a seminar of Lachlan, discovered a crucial connection between Ramsey classes and Fraïssé classes, and, in his 1989 paper, connected the classification programme of homogeneous structures to structural Ramsey theory. In 2005, Kechris, Pestov, and Todorčević revitalized the field by connecting Ramsey classes to topological dynamics. This breakthrough motivated Nešetřil to propose a program for classifying Ramsey classes. We review the progress made on this program in the past two decades, list open problems, and discuss recent extensions to new areas, namely the extension property for partial automorphisms (EPPA), and big Ramsey structures.

20世纪70年代，结构拉姆齐理论作为组合学的一个新分支出现。这种发展伴随着A-Ramsey属性和Ramsey类概念的分离而来。根据有影响力的Nešetřil-Rödl定理，已经确定了几个拉姆齐类。20世纪80年代，Nešetřil受拉克兰研讨会的启发，发现了拉姆齐类与Fraïssé类之间的关键联系，并在其1989年的论文中，将同质结构的分类程序与结构拉姆齐理论联系起来。2005年，Kechris， Pestov和todor eviki通过将Ramsey类与拓扑动力学联系起来，使该领域重新焕发活力。这一突破促使Nešetřil提出了一个分类拉姆齐类的程序。我们回顾了过去二十年来在这方面取得的进展，列出了开放的问题，并讨论了最近的扩展到新的领域，即部分自同构（EPPA）的可拓性质，以及大Ramsey结构。

引用次数: 0

Understanding data spaces: A Systematic Mapping Study of foundations, technical building blocks, and sectoral adoption 理解数据空间：基础、技术构建块和部门采用的系统映射研究

IF 12.7 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Science Review

Pub Date : 2025-09-17 DOI: 10.1016/j.cosrev.2025.100819

Anhelina Kovach , Leticia Montalvillo , Jorge Lanza , Pablo Sotres , Aitor Urbieta

Data spaces are emerging as a key paradigm for enabling sovereign, secure, and interoperable data sharing across sectors. Beyond data governance, they represent a transformation in communication architectures—where communication is no longer merely about establishing connections, but about who is allowed to share what, under which conditions, and for what purpose. Despite growing attention, the research landscape remains fragmented and under-synthesized. This paper presents a Systematic Mapping Study (SMS) of 149 peer-reviewed publications, analyzing the conceptual foundations, technical building blocks, and sectoral adoption of data spaces. Following established SMS methodologies, we classify the literature across key technical themes defined by the Data Spaces Support Centre (DSSC) and assess methodological maturity, technical novelty, and application domains. Our findings show that 46.3% of studies address data value creation enablers, 30.8% focus on data interoperability, and 22.9% explore data sovereignty. The study provides a structured synthesis of current research and offers guidance for advancing federated, trust-aware communication infrastructures.

数据空间正在成为跨部门实现主权、安全和可互操作的数据共享的关键范例。除了数据治理之外，它们还代表了通信体系结构中的一种转变——通信不再仅仅是建立连接，而是允许谁在什么条件下、出于什么目的共享什么。尽管受到越来越多的关注，但研究领域仍然是碎片化和不综合的。本文提出了一项针对149份同行评议出版物的系统地图研究（SMS），分析了数据空间的概念基础、技术构建模块和部门采用情况。根据已建立的SMS方法，我们对数据空间支持中心（DSSC）定义的关键技术主题的文献进行分类，并评估方法成熟度、技术新颖性和应用领域。我们的研究结果表明，46.3%的研究涉及数据价值创造推动者，30.8%的研究关注数据互操作性，22.9%的研究探讨数据主权。该研究提供了当前研究的结构化综合，并为推进联邦、信任感知通信基础设施提供了指导。

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引用次数: 0

Explainable AI for the diagnosis of neurodegenerative diseases: Unveiling methods, opportunities, and challenges 用于神经退行性疾病诊断的可解释人工智能：揭示方法、机遇和挑战

IF 12.7 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Science Review

Pub Date : 2025-09-16 DOI: 10.1016/j.cosrev.2025.100821

Alden Jenish S , Karthik R , Suganthi K

Artificial Intelligence (AI) has exhibited significant potential in diagnosis and operational efficiency across medical domains. Nevertheless, the opacity of the AI-driven diagnostic models creates a major roadblock to clinical deployment. Explainable Artificial Intelligence (XAI) techniques have emerged to improve physician trust and transparency in AI-based predictions by addressing interpretability and explainability. This review aims to explore and analyze recent advancements in XAI techniques applied to the diagnosis of Neurodegenerative Diseases (NDs). Based on their approaches toward interpretability, the included studies were categorized into model-agnostic and model-specific techniques. These interpretability techniques provide deeper insights into the factors influencing clinical diagnoses. The review examines various interpretative methods that enhance the transparency of AI-driven models, ensuring alignment with clinical decision-making. This summary reflects all major findings and critical analysis of the responses to the research questions posed. The next stage of analysis describes how XAI enhances model reliability and eases the clinical decision-making process. This review presents a cross-disease comparative evaluation of XAI techniques applied to major NDs such as Alzheimer’s Disease (AD), Parkinson’s Disease (PD), and Multiple Sclerosis (MS), offering a unified perspective on interpretability across modalities and disorders. This study explores existing approaches, highlights their strengths and limitations, and discusses future research directions in this domain.

人工智能（AI）在医疗领域的诊断和操作效率方面显示出巨大的潜力。然而，人工智能驱动的诊断模型的不透明性为临床部署造成了主要障碍。可解释的人工智能（XAI）技术已经出现，通过解决可解释性和可解释性，提高医生对基于人工智能的预测的信任和透明度。本文旨在探讨和分析XAI技术在神经退行性疾病（NDs）诊断中的最新进展。根据其可解释性的方法，纳入的研究分为模型不可知和模型特定技术。这些可解释性技术为影响临床诊断的因素提供了更深入的见解。该综述考察了各种提高人工智能驱动模型透明度的解释方法，确保与临床决策保持一致。这个总结反映了所有的主要发现和对所提出的研究问题的反应的批判性分析。下一阶段的分析描述了XAI如何提高模型可靠性并简化临床决策过程。本文综述了XAI技术在阿尔茨海默病（AD）、帕金森氏病（PD）和多发性硬化症（MS）等主要NDs中的跨疾病比较评价，提供了跨模式和疾病可解释性的统一观点。本研究对现有的研究方法进行了探讨，突出了它们的优势和局限性，并讨论了该领域未来的研究方向。

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引用次数: 0

Sequential recommender systems: A methodological taxonomy and research frontiers 顺序推荐系统：方法分类与研究前沿

IF 12.7 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Science Review

Pub Date : 2025-09-15 DOI: 10.1016/j.cosrev.2025.100818

Yanbo Zhou , Gang-Feng Ma , Xilin Wen , Xu-Hua Yang , Yi-Cheng Zhang

In the era of information overload, sequential recommender systems have emerged as pivotal tools for modeling user preferences through dynamic behavioral pattern mining. These systems transcend conventional recommendation paradigms by explicitly modeling temporal dependencies in user–item interactions, preference evolution, and contextual dynamics. This study presents a methodologically structured taxonomy of sequential recommender systems through four analytical dimensions: (1) Sequential Modeling, which includes methods ranging from statistical techniques to deep learning architectures to understand user behavior patterns; (2) Temporal Dynamics Modeling, which involves time-aware collaborative filtering and deep temporal modeling; (3) Network-Enhanced Modeling, which leverages graph neural networks, heterogeneous graphs, dynamic graphs, and hypergraphs to explore structural dependencies; and (4) Robust Representation Learning, which encompasses contrastive mechanisms and techniques driven by large language models (LLMs). These algorithms focus on different aspects of sequential recommendation, including but not limited to capturing dynamic interests, modeling long- and short-term preferences, and addressing issues such as data sparsity, noise, and bias, which affect the performance and user experience of recommender systems in practical applications. Furthermore, we summarize and discuss promising future research directions to provide theoretical and methodological insights. The constructed taxonomy not only organizes existing methodological innovations, but also reveals fundamental limitations in current evaluation protocols, providing a roadmap for advancing both theoretical foundations and practical applications in this domain.

在信息过载的时代，顺序推荐系统已经成为通过动态行为模式挖掘来建模用户偏好的关键工具。这些系统通过显式地建模用户-物品交互、偏好演变和上下文动态中的时间依赖性，超越了传统的推荐范例。本研究通过四个分析维度提出了顺序推荐系统的方法结构化分类法：(1)顺序建模，包括从统计技术到深度学习架构的方法，以理解用户行为模式；(2)时间动态建模，包括时间感知协同滤波和深度时间建模；(3)网络增强建模，利用图神经网络、异构图、动态图和超图来探索结构依赖关系；(4)稳健表示学习，包括由大型语言模型（llm）驱动的对比机制和技术。这些算法关注顺序推荐的不同方面，包括但不限于捕获动态兴趣、建模长期和短期偏好，以及解决数据稀疏性、噪声和偏差等问题，这些问题会影响推荐系统在实际应用中的性能和用户体验。在此基础上，对未来的研究方向进行了总结和讨论，以提供理论和方法上的见解。构建的分类法不仅组织了现有的方法创新，而且揭示了当前评估协议的基本局限性，为推进该领域的理论基础和实际应用提供了路线图。

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引用次数: 0

Gender Diversity Interventions in Software Engineering: A Comprehensive Review of Existing Practices 软件工程中的性别多样性干预：对现有实践的全面回顾

IF 12.7 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Science Review

Pub Date : 2025-09-12 DOI: 10.1016/j.cosrev.2025.100812

Claudia Maria Cutrupi , Letizia Jaccheri , Alexander Serebrenik

Context:

Over the years, the software engineering (SE) research community has examined the role of gender within the field, leading to numerous studies on the challenges faced by women and minorities. Despite this, there is a significant research gap on the effectiveness of various interventions designed to address these persistent challenges.

Objective:

This review analyzed the current state of interventions that address gender disparities in the SE context. The goal of the review is to understand the various interventions implemented in educational and industrial settings and to identify their impact on women and other targeted minorities.

Methods:

We conducted a systematic literature review, analyzing 33 studies that reported on interventions addressing gender disparities in SE.

Results:

We identified the most common interventions implemented over the years, the challenges they addressed, the organizations and target users involved, the specific actions proposed, the underlying theories, the research methodologies used, and the impact of these interventions.

Conclusion:

The findings revealed a lack of interventions that aim at retaining women in the SE field, with few programs investing in employees’ satisfaction with the work environment. The findings also show a lack of outreach programs to create meaningful connections with companies and provide support in finding job opportunities. Moreover, only one intervention incorporates long-term activities, and very few interventions build on a theoretical background.

背景：多年来，软件工程（SE）研究界研究了性别在该领域的作用，导致了许多关于女性和少数民族面临的挑战的研究。尽管如此，在旨在解决这些持续挑战的各种干预措施的有效性方面，仍存在重大的研究差距。目的：本综述分析了目前在SE背景下解决性别差异的干预措施的现状。审查的目的是了解在教育和工业环境中实施的各种干预措施，并查明其对妇女和其他目标少数群体的影响。方法：我们进行了系统的文献综述，分析了33项关于干预措施解决SE性别差异的研究。结果：我们确定了多年来实施的最常见的干预措施，它们解决的挑战，涉及的组织和目标用户，提出的具体行动，基础理论，使用的研究方法以及这些干预措施的影响。结论：调查结果表明，缺乏旨在留住SE领域女性的干预措施，很少有项目投资于员工对工作环境的满意度。调查结果还显示，在与公司建立有意义的联系以及为寻找工作机会提供支持方面，缺乏拓展项目。此外，只有一种干预措施包含长期活动，很少有干预措施建立在理论背景之上。

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引用次数: 0

AI based advances in diagnosis of chronic obstructive pulmonary disease: A systematic review 基于人工智能的慢性阻塞性肺疾病诊断进展综述

IF 12.7 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Science Review

Pub Date : 2025-09-11 DOI: 10.1016/j.cosrev.2025.100820

Dhanashree Vipul Yevle , Palvinder Singh Mann , Dinesh Kumar

Chronic Obstructive Pulmonary Disease (COPD) is one of the major global health problems, and early detection plays a great role in improving outcomes for patients. Traditional methods of diagnosis are generally based on subjective interpretation, thus delaying diagnosis in many cases. Artificial Intelligence presents a disruptive opportunity, making the detection and classification of COPD possible with a variety of data types. This paper reviews the use of AI-based approaches in COPD diagnosis by using three primary types of datasets: text data, such as clinical notes and electronic health records; audio data, including lung sounds, cough signals, and so on; and image data from chest X-rays and CT scans. Discussing the use of deep learning techniques, specifically CNNs, in analyzing images, we identify how these networks can successfully classify COPD cases along with the level of severity. The potential of AI models in COPD diagnostics is very promising, though there are areas of challenges like data standardization, model generalizability, and interpretability. This review emphasizes the AI potential for COPD diagnostics revolution and outlines future research directions: integration of multi-modal data and advancements in model transparency to support clinical adoption.

慢性阻塞性肺疾病（COPD）是全球主要的健康问题之一，早期发现对改善患者的预后起着重要作用。传统的诊断方法通常基于主观解释，因此在许多情况下延误了诊断。人工智能提供了一个颠覆性的机会，使各种数据类型的COPD检测和分类成为可能。本文通过使用三种主要类型的数据集回顾了基于人工智能的方法在COPD诊断中的应用：文本数据，如临床记录和电子健康记录；音频数据，包括肺音、咳嗽信号等；以及胸部x光和CT扫描的图像数据。讨论深度学习技术的使用，特别是cnn，在分析图像时，我们确定了这些网络如何成功地分类COPD病例以及严重程度。人工智能模型在COPD诊断中的潜力是非常有希望的，尽管存在数据标准化、模型通用性和可解释性等方面的挑战。这篇综述强调了人工智能在COPD诊断革命中的潜力，并概述了未来的研究方向：整合多模式数据和提高模型透明度以支持临床采用。

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引用次数: 0

Quantum artificial intelligence: A survey 量子人工智能：综述

IF 12.7 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Science Review

Pub Date : 2025-09-10 DOI: 10.1016/j.cosrev.2025.100807

Giovanni Acampora, Angela Chiatto, Roberto Schiattarella, Autilia Vitiello

Quantum computing and artificial intelligence are two highly topical fields of research that can benefit from each other’s discoveries by opening a completely new scenario in computation, that of quantum artificial intelligence. Indeed, on the one hand, artificial intelligence algorithms can be made computationally more efficient due to the potential speedup enabled by quantum phenomena; on the other hand, the complex development of quantum computing technologies and methodologies can be properly supported by the use of classical artificial intelligence approaches. The “entanglement” of these two disciplines is opening up completely new directions in computer science research, and this survey aims to provide a systematic and taxonomic overview of the work that has already been done and that which will begin in the near future.

量子计算和人工智能是两个备受关注的研究领域，它们可以通过打开一个全新的计算场景，即量子人工智能，从彼此的发现中受益。事实上，一方面，由于量子现象带来的潜在加速，人工智能算法可以在计算上更有效率；另一方面，量子计算技术和方法的复杂发展可以通过使用经典的人工智能方法得到适当的支持。这两个学科的“纠缠”为计算机科学研究开辟了全新的方向，本调查旨在对已经完成的工作和在不久的将来将开始的工作提供系统和分类的概述。

引用次数: 0

Homomorphic cryptography: Challenges and perspectives 同态密码学：挑战与展望

IF 12.7 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Science Review

Pub Date : 2025-09-07 DOI: 10.1016/j.cosrev.2025.100815

Khalil Hariss , Jean Paul A. Yaacoub , Hassan N. Noura

The study proposes practical recommendations and future directions to improve HE’s relevance in the real world, especially in healthcare, digital forensics, Machine Learning (ML), and smart infrastructure. The primary product of this study is a cohesive framework that unifies mathematical foundations with real-world applications to direct the implementation and development of Homomorphic Cryptography (HC) in privacy-preserving computing. Homomorphic Encryption (HE), which is an innovative cryptographic technique, protects confidentiality, integrity, and authenticity in untrusted computing environments such as cloud infrastructure and Internet of Things (IoT) ecosystems by allowing computations on encrypted data without the need for decryption. The present study provides an in-depth investigation of HC, identifying symmetric and asymmetric methods and assigning them to the appropriate security services. The mathematical complexities of well-known HE algorithms like BGV, BFV, DGHV, and CKKS are further explained, and experimental performance evaluations are provided using the Python-SEAL and Microsoft SEAL libraries. The paper examines contemporary attacks and defences while highlighting significant drawbacks, such as computational cost and security flaws.

该研究提出了切实可行的建议和未来发展方向，以提高高等教育在现实世界中的相关性，特别是在医疗保健、数字取证、机器学习（ML）和智能基础设施方面。本研究的主要成果是一个内聚框架，它将数学基础与现实世界的应用相结合，以指导同态密码学（HC）在隐私保护计算中的实现和发展。同态加密（HE）是一种创新的加密技术，它允许在不需要解密的情况下对加密数据进行计算，从而在云基础设施和物联网（IoT）生态系统等不可信的计算环境中保护机密性、完整性和真实性。本研究对HC进行了深入的研究，确定了对称和非对称方法，并将其分配给适当的安全服务。进一步解释了知名HE算法（如BGV、BFV、DGHV和CKKS）的数学复杂性，并使用Python-SEAL和Microsoft SEAL库提供了实验性能评估。本文研究了当代的攻击和防御，同时强调了显著的缺点，如计算成本和安全漏洞。

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引用次数: 0