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27th International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data, DOLAP 2025

作者： Morejón, Adriana Berenguer, Alberto de Espona, Lucía Tomás, David Mazón, Jose-Norberto University Institute for Computing Research Department of Software and Computing Systems University of Alicante Spain

In the realm of data sharing, effective data discovery is critical for fostering collaboration and innovation across organizations. Data spaces merge the interoperability and secure sharing features of data ecosystems with the transactional and economic aspects of data markets, enabling seamless data exchange among data providers and consumers while preserving data sovereignty. Central to this paradigm is the data catalog, an entity that handles metadata and provides it for data discovery services. However, traditional data catalogs rely heavily on metadata related to high-level representation of data, as well as the overall features of datasets (e.g., dataset name, license or keywords, following standards such as DCAT). This limitation hampers data discovery, as these metadata alone may not effectively describe datasets to fully convey the relevance of datasets. To address this challenge, this paper proposes content-based catalogs to enhance data discovery within data spaces. Our approach for content-based catalogs incorporates three key assets for data consumers to discover relevant datasets and evaluate their utility before accessing them: (i) high-quality structural metadata for datasets;(ii) representative data samples from the dataset;and (iii) a discovery service for searching relevant datasets. Our novel content-based catalog reduces the risk of unintended exposure while still showcasing the value of data, thus preserving the interests of both data providers and consumers within data spaces. © 2025 Copyright for this paper by its authors.

关键词： Data assimilation

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Adapting CRISP-DM to model enteric fermentation emission: farm level application

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International Journal of Applied Decision Sciences 2025年第3期18卷 330-359页

作者： Guerrón, Philippe Belmont Hallo, María Luján-Mora, Sergio Faculty of Engineering Systems Escuela Politecnica Nacional Ladron de Guevara E11-253 Edif 20 Quito Ecuador Department of Software and Computing Systems University of Alicante Alicante03690 Spain

Enteric fermentation contributes substantially to greenhouse gas emissions (GGEs) in agriculture, but may be reversible in the short-term. To date, numerous attempts have been made to model the environmental impact of agriculture, but have failed to integrate multiple dimensions of production. The objective of this study is to adapt the cross industry standard process for data mining (CRISP-DM) at farm level, using the concept of life cycle assessment (LCA) and implemented a modified version of the global livestock environmental assessment model (GLEAM). Using local data collected over 20 years and secondary data, our results show that for dairy cattle, the methane emissions factor from cattle is lower among marginal farms 86 Kg CH4 head−1 year−1 compared to semi-intensive and intensive farms across time and geographical regions (107.4 and 113.5 respectively) and demonstrate that this type of application is relevant for developing countries and smallholder agriculture, where production data is often unavailable. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Farms

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A Systematic Literature Review of Cyber Security Monitoring in Maritime

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IEEE Access 2025年 13卷 85307-85329页

作者： Vaarandi, Risto Tsiopoulos, Leonidas Visky, Gabor Rehman, Muaan Ur Bahsi, Hayretdin Tallinn University of Technology Centre for Digital Forensics and Cyber Security Department of Software Science Tallinn19086 Estonia Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ86011 United States

In recent years, many cyber incidents have occurred in the maritime sector, targeting the information technology (IT) and operational technology (OT) infrastructure. Although several literature review papers have been published in the maritime field, none of the previous studies has focused on cyber security monitoring, which aims at timely detection of cyber attacks with automated methods. The current article addresses this research gap and surveys the methods, algorithms, tools and architectures used for cyber security monitoring in the maritime sector. For the survey, a systematic literature review of cyber security monitoring studies is conducted. The first contribution of this article is the bibliometric analysis of related literature and the identification of the main research themes in previous works. For that purpose, our article presents a taxonomy for existing studies which highlights the main properties of maritime cyber security monitoring research. The second contribution of this article is an in-depth analysis of previous works and the identification of research gaps and limitations in existing literature. Based on our findings, we outline future research directions for cyber security monitoring in the maritime field. © 2013 IEEE.

关键词： Network intrusion

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Augmenting Websites with Voice Commands: An Approach Focused on Accessibility

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Journal of Web Engineering 2025年第2期24卷 163-198页

作者： González-Mora, César Garrigós, Irene Casteleyn, Sven Firmenich, Sergio Department of Software and Computing Systems University of Alicante Alicante Spain GEOTEC INIT University of Jaime I Castellón de la Plana Spain Universidad Loyola Andalucía Spain

Even now, users with disabilities encounter serious barriers when accessing the Web. In particular, blind and visually impaired users encounter difficulties browsing and reading the contents of a website. Screen readers provide some assistance, yet, as they are unable to interpret the Web structure, they summarise information and read specific labelled fragments. Therefore, the overall comprehension of the text remains challenging. In this sense, in order to improve the accessibility of websites on the fly, we propose a Web augmentation framework for accessibility (WAFRA). Our framework uses Web augmentation techniques that extend the website with voice interaction and new actions: label text fragments, read aloud these fragments, facilitate navigation, increase font size and show videos. In order to perform this accessibility improvement, we automatically provide annotations from DBPedia regarding important information for end users. Moreover, we also provide the option that intermediary users add new annotations for labelling or including more specific information, which can be shared with other users by crowdsourcing. The evaluation of the framework shows its usefulness to ease website access for users with visual disabilities compared to using screen readers. © 2025 River Publishers.

关键词： Information websites voice interaction web accessibility web augmentation

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Digital Maturity Assessment Model (DMAM): assimilation of Design Science Research (DSR) and Capability Maturity Model Integration (CMMI)

Digital Transformation and Society

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Digital Transformation and Society 2025年第2期4卷 128-152页

作者： Omol, Edwin Juma Mburu, Lucy Waruguru Abuonji, Paul Anyango Department of Software Development and Information Systems (SDIS) School of Technology KCA University Nairobi Kenya Chair of Department - Networks and Applied Computing (NAC) School of Technology KCA University Nairobi Kenya KCA University Nairobi Kenya

Purpose: This study introduces the Digital Maturity Assessment Model (DMAM), a model tailored to assess the digital maturity of SMEs, tracing its development from addressing business challenges to establishing a comparative analysis framework grounded in Resource Dependence Theory (RDT). Design/methodology/approach: DMAM is based on positivist philosophy and objectivist epistemology, supported by Design Science Research (DSR) and Capability Maturity Model Integration (CMMI). The methodology involves iterative development, from problem identification to creating a practical solution for assessing SMEs' digital maturity and guiding digitalization efforts. Findings: DMAM offers a clear and specific methodology, distinguishing itself by addressing the unique needs of SMEs, particularly resource-dependent ones. The model’s development fills critical gaps in existing literature and provides a practical artifact for SMEs' digitalization. Originality/value: DMAM is original in its focus on the specific needs of resource-dependent SMEs, offering actionable recommendations and addressing shortcomings in existing models. It serves as a foundational framework for SMEs' digital transformation, making a significant contribution to the digital maturity assessment literature. © 2024, Edwin Juma Omol, Lucy Waruguru Mburu and Paul Anyango Abuonji.

关键词： Capability Maturity Model Integration (CMMI) Design Science Research (DSR) Digital Maturity Assessment Model (DMAM) Goal Question Metric (GQM) Proof-of-Concept (POC) & Minimum Viable Product (MVP) Resource Dependence Theory (RDT)

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Holistic Cyber Threat Modeling for Machine Learning-Based systems: A Case Study in Healthcare

Holistic Cyber Threat Modeling for Machine Learning-Based Sy...

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18th International Joint Conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Jaal, Janno Bahsi, Hayretdin Department of Software Science Tallinn University of Technology Tallinn Estonia Cybernetica AS Tallinn Estonia School of Informatics Computing and Cyber Systems Northern Arizona University United States

Considering the immense pace in machine learning (ML) technology and related products, it may be difficult to imagine a software system, including healthcare systems, without any subsystem containing an ML model in the near future. However, ensuring the resiliency of these ML-based systems against cyber attacks is vital for more seamless and widespread technology usage. The secure-by-design principle, considering security from the early stages of development, is a cornerstone to achieving sufficient security at a reasonable cost. The realization of this principle starts with conducting threat modeling to understand the relevant security posture and identify cyber security requirements before system design. Although threat modeling of software systems is widely known, it is unclear how to apply it to software systems with machine learning models. Although adversarial machine learning is a widely studied research topic, it has yet to be thoroughly researched how adversarial and conventional cybersecurity attacks can be holistically considered to identify applicable cyber threats at the early stage of a software development life cycle. This paper adapts STRIDE, a widely-known threat modeling method, for the holistic cyber threat analysis of an ML-based healthcare system. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Adversarial machine learning

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in Engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Summarization Metrics for Spanish and Basque: Do Automatic Scores and LLM-Judges Correlate with Humans?

arXiv

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arXiv 2025年

作者： Barnes, Jeremy Perez, Naiara Bonet-Jover, Alba Altuna, Begoña HiTZ Center Spain Spain Department of Software and Computing Systems University of Alicante Spain Spain

Studies on evaluation metrics and LLM-as-a-Judge models for automatic text summarization have largely been focused on English, limiting our understanding of their effectiveness in other languages. Through our new dataset BASSE (BAsque and Spanish Summarization Evaluation), we address this situation by collecting human judgments on 2,040 abstractive summaries in Basque and Spanish, generated either manually or by five LLMs with four different prompts. For each summary, annotators evaluated five criteria on a 5-point Likert scale: coherence, consistency, fluency, relevance, and 5W1H. We use these data to reevaluate traditional automatic metrics used for evaluating summaries, as well as several LLM-as-a-Judge models that show strong performance on this task in English. Our results show that currently proprietary judge LLMs have the highest correlation with human judgments, followed by criteria-specific automatic metrics, while open-sourced judge LLMs perform poorly. We release BASSE1 and our code publicly, along with the first large-scale Basque summarization dataset containing 22,525 news articles with their subheads. © 2025, CC BY-NC-SA.

关键词： Large datasets

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From Medical Consultation to Diagnosis Generation Using Deep Learning 19th

From Medical Consultation to Diagnosis Generation Using Deep...

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19th International Conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Ortiz-Perez, David Merino-Balaguer, Alejandro Rodriguez-Juan, Javier Garcia-Rodriguez, Jose Tomás, David J. Nalepa, Grzegorz Department of Computer Science and Technology University of Alicante Carr. de San Vicente del Raspeig Spain Department of Software and Computing Systems University of Alicante Carr. de San Vicente del Raspeig Spain Jagiellonian University Kraków Poland

ISBN: (纸本)9783031750090

Sanitary professionals often experience burnout due to long working hours and the stress associated with handling sensitive situations. Consequently, in recent years, significant efforts have been made to automate various tasks within their roles using deep learning methods. This study proposes an automated system for generating summaries of medical consultations, aiming to alleviate doctors’ workloads. The architecture takes audio recordings of medical consultations as input, which are then transcribed and diarized for further processing. Subsequently, the entire transcription is summarized using state-of-the-art transformer-based models. For this study, a dataset comprising fourteen consultations, along with two summaries provided by different sanitary professionals, was utilized. In order to address the limitations of the available data, a data augmentation technique was implemented which makes use of the capabilities of large language models. The proposed models achieved a result of 0.52 in the ROGUE-1 metric, demonstrating the effectiveness of the proposed methodology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Junior software Developers’ Perspectives on Adopting LLMs for software Engineering: a Systematic Literature Review

arXiv

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arXiv 2025年

作者： Ferino, Samuel Hoda, Rashina Grundy, John Treude, Christoph Department of Software Systems and Cybersecurity Faculty of IT Monash University Melbourne Australia School of Computing and Information Systems Singapore Management University Singapore

Context: Many studies exploring the adoption of Large Language Model-based tools for software development by junior developers have emerged in recent years. These studies have sought to understand developers’ perspectives about using those tools, a fundamental pillar for successfully adopting LLM-based tools in software Engineering. Objective: The aim of this paper is to provide an overview of junior software developers’ perspectives and use of LLM-based tools for software engineering (LLM4SE). Method: We conducted a systematic literature review (SLR) following guidelines by Kitchenham et al. on 56 primary studies, applying the definition for junior software developers as software developers with equal or less than five years of experience, including Computer Science/software Engineering students. Results: We found that the majority of the studies focused on comprehending the different aspects of integrating AI tools in SE. Only 8.9% of the studies provide a clear definition for junior software developers, and there is no uniformity. Searching for relevant information is the most common task using LLM tools. ChatGPT was the most common LLM tool present in the studies (and experiments). A majority of the studies (83.9%) report both positive and negative perceptions about the impact of adopting LLM tools. We also found and categorized advantages, challenges, and recommendations regarding LLM adoption. Conclusion: Our results indicate that developers are using LLMs not just for code generation, but also to improve their development skills. Critically, they are not just experiencing the benefits of adopting LLM tools, but they are also aware of at least a few LLM limitations, such as the generation of wrong suggestions, potential data leaking, and AI hallucination. Our findings offer implications for software engineering researchers, educators, and developers. © 2025, CC BY-NC-ND.

关键词： Computer aided software engineering

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