Computer Science Review最新文献

The Beauty and the Beast: A survey on process algebras and cybersecurity 美女与野兽：过程代数与网络安全的调查

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2025-04-23 DOI: 10.1016/j.cosrev.2025.100758

Gabriele Costa, Silvia De Francisci, Rocco De Nicola

{"title":"The Beauty and the Beast: A survey on process algebras and cybersecurity","authors":"Gabriele Costa, Silvia De Francisci, Rocco De Nicola","doi":"10.1016/j.cosrev.2025.100758","DOIUrl":"10.1016/j.cosrev.2025.100758","url":null,"abstract":"<div><div>Process algebras (PAs) provide the mathematical foundation for several verification techniques and have profoundly influenced many areas of computer science. One of the main reasons for their success is their compact yet expressive and flexible syntax, which allows for the modeling of the relevant aspects of computation while abstracting away the irrelevant ones. Cybersecurity is no exception, and most authors acknowledge the importance of PAs in this field. However, estimating the impact of PAs is not trivial.</div><div>In this survey, we consider lines of research that employ PAs to address security problems. Our systematization of knowledge aims to assess and measure the impact of PAs. To achieve this goal, we start by briefly reviewing the evolution of PAs. Then, we analyze the literature by mapping each contribution to three cybersecurity sub-fields: <em>secure development</em>, <em>attack modeling</em>, and <em>vulnerability assessment</em>. Our methodology follows the chronological development of process algebras and identifies the emerging features specifically introduced for dealing with security problems. Although our analysis confirms that PAs have been greatly influential in general, it provides a fine-grained understanding of how PAs have shaped research in cybersecurity. Interestingly, our work highlights that some application areas remain underexplored, thus providing the research community with valuable insights on future directions.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100758"},"PeriodicalIF":13.3,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864756","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 aspect sentiment triplet extraction methods and challenges 面向情感三元提取方法及挑战综述

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2025-04-23 DOI: 10.1016/j.cosrev.2025.100761

Wang Zou , Xia Sun , Xiaodi Zhao , Jun Feng , Yunfei Long , Yaqiong Xing

{"title":"A survey on aspect sentiment triplet extraction methods and challenges","authors":"Wang Zou , Xia Sun , Xiaodi Zhao , Jun Feng , Yunfei Long , Yaqiong Xing","doi":"10.1016/j.cosrev.2025.100761","DOIUrl":"10.1016/j.cosrev.2025.100761","url":null,"abstract":"<div><div>Aspect-based sentiment analysis (ABSA) has gradually become an important technique for mining online reviews and is widely popular across various domains, such as producer–consumer, pharmaceutical reviews, political campaigns, and celebrity popularity. Aspect sentiment triplet extraction (ASTE) is a core technique within the ABSA, as it automatically extracts aspect terms, opinion terms, and sentiment polarity triplets from textual data. Since the ASTE task is a relatively recent research direction, there is still a lack of comprehensive summaries and syntheses of the research in this task. To address this issue, this paper provides a comprehensive introduction to various methods, performance evaluations, challenges, and future research directions for the ASTE task. Specifically, we categorize the current ASTE approaches into six types: Pipeline, End-to-end, Generative, MRC-based, Table-filling, and Span-based methods. Subsequently, we provide a detailed introduction to the characteristics of each method, along with their strengths and weaknesses. Additionally, we organize the performance of these methods on two benchmark datasets, ASTE-Data-v1 and ASTE-Data-v2. Finally, we discuss the challenges faced in current work and potential future directions.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100761"},"PeriodicalIF":13.3,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859282","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

Bridging the gap: A survey of document retrieval techniques for high-resource and low-resource languages 弥合差距：高资源语言和低资源语言的文档检索技术调查

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2025-04-16 DOI: 10.1016/j.cosrev.2025.100756

Samreen Kazi , Shakeel Khoja , Ali Daud

引用次数: 0

A comprehensive survey with critical analysis for deepfake speech detection 对深度假语音检测的全面调查和批判性分析

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2025-04-16 DOI: 10.1016/j.cosrev.2025.100757

Lam Pham , Phat Lam , Dat Tran , Hieu Tang , Tin Nguyen , Alexander Schindler , Florian Skopik , Alexander Polonsky , Hai Canh Vu

{"title":"A comprehensive survey with critical analysis for deepfake speech detection","authors":"Lam Pham , Phat Lam , Dat Tran , Hieu Tang , Tin Nguyen , Alexander Schindler , Florian Skopik , Alexander Polonsky , Hai Canh Vu","doi":"10.1016/j.cosrev.2025.100757","DOIUrl":"10.1016/j.cosrev.2025.100757","url":null,"abstract":"<div><div>Thanks to advancements in deep learning, speech generation systems now power a variety of real-world applications, such as text-to-speech for individuals with speech disorders, voice chatbots in call centers, cross-linguistic speech translation, etc. While these systems can autonomously generate human-like speech and replicate specific voices, they also pose risks when misused for malicious purposes. This motivates the research community to develop models for detecting synthesized speech (e.g., fake speech) generated by deep-learning-based models, referred to as the Deepfake Speech Detection task. As the Deepfake Speech Detection task has emerged in recent years, there are not many survey papers proposed for this task. Additionally, existing surveys for the Deepfake Speech Detection task tend to summarize techniques used to construct a Deepfake Speech Detection system rather than providing a thorough analysis. This gap motivated us to conduct a comprehensive survey, providing a critical analysis of the challenges and developments in Deepfake Speech Detection (This work is a part of our projects of STARLIGHT, EUCINF, and DEFAME FAKEs). Our survey is innovatively structured, offering an in-depth analysis of current challenge competitions, public datasets, and the deep-learning techniques that provide enhanced solutions to address existing challenges in the field. From our analysis, we propose hypotheses on leveraging and combining specific deep learning techniques to improve the effectiveness of Deepfake Speech Detection systems. Beyond conducting a survey, we perform extensive experiments to validate these hypotheses and propose a highly competitive model for the task of Deepfake Speech Detection. Given the analysis and the experimental results, we finally indicate potential and promising research directions for the Deepfake Speech Detection task.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100757"},"PeriodicalIF":13.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838005","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

Advancements in image encryption: A comprehensive review of design principles and performance metrics 图像加密的进展：对设计原则和性能指标的全面回顾

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2025-04-11 DOI: 10.1016/j.cosrev.2025.100759

Biswarup Yogi , Ajoy Kumar Khan

{"title":"Advancements in image encryption: A comprehensive review of design principles and performance metrics","authors":"Biswarup Yogi , Ajoy Kumar Khan","doi":"10.1016/j.cosrev.2025.100759","DOIUrl":"10.1016/j.cosrev.2025.100759","url":null,"abstract":"<div><div>With the rise of digital image sharing in fields like healthcare, defence, and multimedia, strong image encryption is needed to protect sensitive information. This study provides a detailed overview of the analysis of image encryption algorithms, focusing on their design principles and performance metrics. This study covers different encryption methods, from traditional symmetric and asymmetric to modern chaos-based and quantum encryption. The design principles include substitution-permutation, diffusion, and key generation strategies. They are carefully evaluated to understand their actual input towards achieving high security. This study discusses key performance metrics such as encryption speed, sensitivity to key changes, statistical and differential attacks, computational complexity, and efficiency required to analyze the feasibility of various algorithms in practical applications. Explores issues such as the balance between security and resource limits, scalability, and adaptability to new threats. The analysis highlights the strengths and weaknesses of existing techniques, providing useful insights for developing next-generation encryption methods for specific applications. The combination of theoretical concepts along with performance evaluations presented in this work thus provides a significant and valuable reference source for research and practice aimed at designing effective yet secure image encryption algorithms in this rapidly growing world of technology.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100759"},"PeriodicalIF":13.3,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815567","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 quantum-safe blockchain security infrastructure 量子安全区块链安全基础设施研究

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2025-04-10 DOI: 10.1016/j.cosrev.2025.100752

Arya Wicaksana

{"title":"A survey on quantum-safe blockchain security infrastructure","authors":"Arya Wicaksana","doi":"10.1016/j.cosrev.2025.100752","DOIUrl":"10.1016/j.cosrev.2025.100752","url":null,"abstract":"<div><div>Security infrastructure is vital in blockchain for its decentralized and distributed characteristics. Blockchain security infrastructure comprises several components: cryptographic algorithms, consensus protocols, key and identity management, network architecture, and smart contract deployment and execution. These components are vulnerable to the advancement of quantum computing and the realization of more powerful quantum computers. Classical security countermeasures used across the entire blockchain security infrastructure are exposed to quantum computing attacks. The exploitation is catastrophic to the sustainability of blockchain research and applications. This paper outlines the blockchain security infrastructure and quantum-safe solutions. The promising quantum-resistant cryptographic algorithms released by the National Institute of Standards and Technology (NIST) are evaluated for their relevance and use in blockchain security infrastructure. This paper also discusses the practical implementation and adoption of quantum-safe solutions for blockchain security infrastructure, including recent developments on the quantum-safe blockchain. The holy grail in adopting quantum-safe solutions for blockchain security infrastructure solutions is without sacrificing scalability and decentralization.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100752"},"PeriodicalIF":13.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807674","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

Empowering large language models to edge intelligence: A survey of edge efficient LLMs and techniques 赋予大型语言模型以边缘智能：边缘高效llm和技术的调查

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2025-04-09 DOI: 10.1016/j.cosrev.2025.100755

Rui Wang, Zhiyong Gao, Liuyang Zhang, Shuaibing Yue, Ziyi Gao

引用次数: 0

The ML-based sensor data deception targeting cyber–physical systems: A review 针对网络物理系统的基于ml的传感器数据欺骗：综述

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2025-04-03 DOI: 10.1016/j.cosrev.2025.100753

Nektaria Kaloudi , Jingyue Li

引用次数: 0

Empowering multimodal analysis with visualization: A survey 用可视化增强多模态分析：一项调查

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2025-04-02 DOI: 10.1016/j.cosrev.2025.100748

Jiachen Wang , Zikun Deng , Dazhen Deng , Xingbo Wang , Rui Sheng , Yi Cai , Huamin Qu

引用次数: 0

Artificial intelligence in COVID-19 research: A comprehensive survey of innovations, challenges, and future directions 2019冠状病毒病研究中的人工智能：创新、挑战和未来方向的综合调查

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2025-04-02 DOI: 10.1016/j.cosrev.2025.100751

Richard Annan, Letu Qingge

{"title":"Artificial intelligence in COVID-19 research: A comprehensive survey of innovations, challenges, and future directions","authors":"Richard Annan, Letu Qingge","doi":"10.1016/j.cosrev.2025.100751","DOIUrl":"10.1016/j.cosrev.2025.100751","url":null,"abstract":"<div><div>The COVID-19 pandemic has accelerated the use of AI and ML in healthcare, improving diagnosis, treatment, and resource allocation. This survey examines the AI applications in disease detection, differential diagnosis, and post-COVID complication analysis. Our findings show that 53% of the reviewed studies focus on COVID-19 detection, while only 14% address post-COVID complications. This reveals a gap in long-term patient monitoring. Convolutional Neural Networks (CNNs) are the most frequently used models, appearing in 23% of reviewed studies as standalone architectures and even more often in hybrid models. Meanwhile, transformers and multimodal models remain underutilized. Each appears in only 4% of the studies, limiting the integration of diverse data sources, such as imaging, audio, and lab results. Federated learning, a privacy-preserving AI approach, appears in 9% of studies. However, it is still less common than centralized models. This restricts secure and collaborative AI development. Despite these progress, challenges such as data bias, limited model generalization, and ethical concerns persist. Advanced methods including transformer models and knowledge distillation offer potential solutions for improving computational efficiency. To strengthen AI-driven healthcare, this survey highlights three key needs: (1) broader adoption of multimodal AI, (2) development of computationally efficient and interpretable AI models, and (3) increased use of federated learning to support privacy-preserving AI training. By synthesizing insights from various studies, this paper provides a comprehensive evaluation of AI innovations in COVID-19 research and outlines key directions for future advancements in ethical and scalable AI-driven healthcare.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100751"},"PeriodicalIF":13.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760347","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