Context: Blockchain and AI are increasingly explored to enhance trustworthiness in software engineering (SE), particularly in supporting software evolution tasks. Method: We conducted a systematic literature review (SLR) using a predefined protocol with clear eligibility criteria to ensure transparency, reproducibility, and minimized bias, synthesizing research on blockchain-enabled trust in AI-driven SE tools and processes. Results: Most studies focus on integrating AI in SE, with only 31% explicitly addressing trustworthiness. Our review highlights six recent studies exploring blockchain-based approaches to reinforce reliability, transparency, and accountability in AI-assisted SE tasks. Conclusion: Blockchain enhances trust by ensuring data immutability, model transparency, and lifecycle accountability, including federated learning with blockchain consensus and private data verification. However, inconsistent definitions of trust and limited real-world testing remain major challenges. Future work must develop measurable, reproducible trust frameworks to enable reliable, secure, and compliant AI-driven SE ecosystems, including applications involving large language models.
Mahtab Jamali, Paul Davidsson, Reza Khoshkangini
et al.
Context is an important factor in computer vision as it offers valuable information to clarify and analyze visual data. Utilizing the contextual information inherent in an image or a video can improve the precision and effectiveness of object detectors. For example, where recognizing an isolated object might be challenging, context information can improve comprehension of the scene. This study explores the impact of various context-based approaches to object detection. Initially, we investigate the role of context in object detection and survey it from several perspectives. We then review and discuss the most recent context-based object detection approaches and compare them. Finally, we conclude by addressing research questions and identifying gaps for further studies. More than 265 publications are included in this survey, covering different aspects of context in different categories of object detection, including general object detection, video object detection, small object detection, camouflaged object detection, zero-shot, one-shot, and few-shot object detection. This literature review presents a comprehensive overview of the latest advancements in context-based object detection, providing valuable contributions such as a thorough understanding of contextual information and effective methods for integrating various context types into object detection, thus benefiting researchers.
Background: Clinical natural language processing (NLP) refers to the use of computational methods for extracting, processing, and analyzing unstructured clinical text data, and holds a huge potential to transform healthcare in various clinical tasks. Objective: The study aims to perform a systematic review to comprehensively assess and analyze the state-of-the-art NLP methods for the mainland Scandinavian clinical text. Method: A literature search was conducted in various online databases including PubMed, ScienceDirect, Google Scholar, ACM digital library, and IEEE Xplore between December 2022 and February 2024. Further, relevant references to the included articles were also used to solidify our search. The final pool includes articles that conducted clinical NLP in the mainland Scandinavian languages and were published in English between 2010 and 2024. Results: Out of the 113 articles, 18% (n=21) focus on Norwegian clinical text, 64% (n=72) on Swedish, 10% (n=11) on Danish, and 8% (n=9) focus on more than one language. Generally, the review identified positive developments across the region despite some observable gaps and disparities between the languages. There are substantial disparities in the level of adoption of transformer-based models. In essential tasks such as de-identification, there is significantly less research activity focusing on Norwegian and Danish compared to Swedish text. Further, the review identified a low level of sharing resources such as data, experimentation code, pre-trained models, and rate of adaptation and transfer learning in the region. Conclusion: The review presented a comprehensive assessment of the state-of-the-art Clinical NLP for electronic health records (EHR) text in mainland Scandinavian languages and, highlighted the potential barriers and challenges that hinder the rapid advancement of the field in the region.
This Data Descriptor introduces the dataset Enevaeldens Nyheder Online (News during Absolutism Online). The Enevaeldens Nyheder Online (ENO) dataset provides a reconstruction of the contents of major newspapers in Denmark and Norway during the period of Absolutism (1660-1849). The dataset contains approx. 474 million words, created using neural networks designed to process digitised microfilm versions of Danish newspapers as well as a smaller selection of Norwegian publications that were all hitherto illegible for computers. The contributions details this process and its results, including a way to derive standalone texts from the editions, and the accompanying BERT-model trained on a beta-version of the dataset.
Eduardo Vyhmeister, Bastien Pietropaoli, Alejando Martinez Molina
et al.
Data valuation and data monetisation are complex subjects but essential to most organisations today. Unfortunately, they still lack standard procedures and frameworks for organisations to follow. In this survey, we introduce the reader to the concepts by providing the definitions and the background required to better understand data, monetisation strategies, and finally metrics and KPIs used in these strategies. We have conducted a systematic literature review on metrics and KPIs used in data valuation and monetisation, in every aspect of an organisation's business, and by a variety of stakeholders. We provide an expansive list of such metrics and KPIs with 162 references. We then categorise all the metrics and KPIs found into a large taxonomy, following the Balanced Scorecard (BSC) approach with further subclustering to cover every aspect of an organisation's business. This taxonomy will help every level of data management understand the complex landscape of the domain. We also discuss the difficulty in creating a standard framework for data valuation and data monetisation and the major challenges the domain is currently facing.
This study dives into the interactive functions of the palm-up across four language ecologies drawing on comparable corpus data from American Sign Language (ASL)-American English and French Belgian Sign Language (LSFB)-Belgian French. While researchers have examined palm-up in many different spoken and signed language contexts, they have primarily focused on the canonical forms and its epistemic variants. Work that directly compares palm-up across modalities and language ecologies remains scarce. This study addresses such gaps by documenting all instances of the palm approaching supination in four language ecologies to analyze its interactive functions cross-linguistically and cross-modally. Capitalizing on an existing typology of interactive gestures, palm-up annotations were conducted using ELAN on a total sample of 48 participants interacting face-to-face in dyads. Findings highlight the multifunctional nature of palm-up in terms of conversational dynamics with cross-modal differences in the specific interactive use of palm-up between spoken and signed language contexts. These findings underscore the versatility of the palm-up and reinforce its role in conversational dynamics as not merely supplementary but integral to human interaction.
In recent years there has been a growing body of literature on nature based art therapies. Within this relatively new specialism there are a number of emerging models of environmental art therapy practiced by art therapists in the UK. Alongside this interest in the natural world, there is a growing awareness that for therapeutic practices to remain relevant in today’s world they need to recognise that we exist as a part of, and not apart from, nature and that humans are having a significant detrimental impact on the natural world as seen in the climate emergency, Bird (2023) and Deco (2021). The British Association of Art Therapist’s (BAAT) focused on this concern in their 2023 annual conference on the theme ‘Art therapy and the climate crisis.’
In writing this paper I seek to explore an area where I think art therapy would benefit from developing deeper reflections on the systemic nature of the ecological crises we face. The relationship between ourselves and the natural world necessarily involves ecological thinking such as Deep ecology, [a concept developed by the Norwegian philosopher Arne Naess (1990)], and the systemic thinking of Gregory Bateson (1972).
Here I explore how, in taking my art therapy practice outdoors, (rather than the traditional art therapy studio) my therapeutic practice comes into immediate contact with the natural world, its dynamic rhythms, and its ecosystems. This makes it possible to develop a practice which is open to engaging in a dialogue between humans (therapist and clients) and the many other life forms, plants, animals, insects, etc. found in a garden.
Walter Hernandez Cruz, Kamil Tylinski, Alastair Moore
et al.
Distributed Ledger Technology (DLT) faces increasing environmental scrutiny, particularly concerning the energy consumption of the Proof of Work (PoW) consensus mechanism and broader Environmental, Social, and Governance (ESG) issues. However, existing systematic literature reviews of DLT rely on limited analyses of citations, abstracts, and keywords, failing to fully capture the field's complexity and ESG concerns. We address these challenges by analyzing the full text of 24,539 publications using Natural Language Processing (NLP) with our manually labeled Named Entity Recognition (NER) dataset of 39,427 entities for DLT. This methodology identified 505 key publications at the DLT/ESG intersection, enabling comprehensive domain analysis. Our combined NLP and temporal graph analysis reveals critical trends in DLT evolution and ESG impacts, including cryptography and peer-to-peer networks research's foundational influence, Bitcoin's persistent impact on research and environmental concerns (a "Lindy effect"), Ethereum's catalytic role on Proof of Stake (PoS) and smart contract adoption, and the industry's progressive shift toward energy-efficient consensus mechanisms. Our contributions include the first DLT-specific NER dataset addressing the scarcity of high-quality labeled NLP data in blockchain research, a methodology integrating NLP and temporal graph analysis for large-scale interdisciplinary literature reviews, and the first NLP-driven literature review focusing on DLT's ESG aspects.
Ainayya Almira, Anisah Rachmawati, Insi Norma Jelita
et al.
The aim of this research is to provide insight to chemistry education teachers and researchers regarding the effectiveness of the guided inquiry learning model and provide direction for further research in this field. The research method used in this article is Systematic Literature Review (SLR), to help compile and evaluate various research related to the guided inquiry learning model. The instrument used in this research is to present the results of a literature review of various articles discussing the application of this model in chemistry learning by exploring the definition, application, strengths, weaknesses and effectiveness of the guided inquiry learning model in chemistry learning. The research results show that the application of this model can be carried out both in the theoretical and practical aspects of chemistry learning. The advantages of the guided inquiry model involve students actively, increase learning independence, and provide students with the opportunity to discuss and find their own answers. Students who study with this model tend to have higher learning achievements. However, there are also disadvantages, such as the time required to implement this model and obstacles in dealing with students who are not yet familiar with this approach.
Lucas Georges Gabriel Charpentier, Sondre Wold, David Samuel
et al.
Retrieval-based language models are increasingly employed in question-answering tasks. These models search in a corpus of documents for relevant information instead of having all factual knowledge stored in its parameters, thereby enhancing efficiency, transparency, and adaptability. We develop the first Norwegian retrieval-based model by adapting the REALM framework and evaluating it on various tasks. After training, we also separate the language model, which we call the reader, from the retriever components, and show that this can be fine-tuned on a range of downstream tasks. Results show that retrieval augmented language modeling improves the reader's performance on extractive question-answering, suggesting that this type of training improves language models' general ability to use context and that this does not happen at the expense of other abilities such as part-of-speech tagging, dependency parsing, named entity recognition, and lemmatization. Code, trained models, and data are made publicly available.
Erkan Karabulut, Salvatore F. Pileggi, Paul Groth
et al.
Digital Twins (DT) facilitate monitoring and reasoning processes in cyber-physical systems. They have progressively gained popularity over the past years because of intense research activity and industrial advancements. Cognitive Twins is a novel concept, recently coined to refer to the involvement of Semantic Web technology in DTs. Recent studies address the relevance of ontologies and knowledge graphs in the context of DTs, in terms of knowledge representation, interoperability and automatic reasoning. However, there is no comprehensive analysis of how semantic technologies, and specifically ontologies, are utilized within DTs. This Systematic Literature Review (SLR) is based on the analysis of 82 research articles, that either propose or benefit from ontologies with respect to DT. The paper uses different analysis perspectives, including a structural analysis based on a reference DT architecture, and an application-specific analysis to specifically address the different domains, such as Manufacturing and Infrastructure. The review also identifies open issues and possible research directions on the usage of ontologies and knowledge graphs in DTs.
Context: Microservices running and being powered by Edge Computing have been gaining much attention in the industry and academia. Since 2014, when Martin Fowler popularized the Microservice term, many studies have been published relating these subjects to explore how the Edge's low-latency feature could be combined with the high throughput of the distributed paradigm from Microservices. Objective: Identifying how Microservices work together with Edge Computing whereas they take advantage when running on Edge. Method: In order to better understand this relationship, we first identified its key concepts, which are: architecture approaches and features, microservice composition (orchestration/choreography), and offloading. Afterward, we conducted a Systematic Literature Review (SLR) as the survey method. Results: We reviewed 111 selected studies and built a taxonomy of Microservices on Edge Computing demonstrating their current architecture approaches and features, composition, and offloading modes. Moreover, we identify the research gaps and trends. Conclusion: This paper is a step forward to help researchers and professionals get a general overview of how Microservices and Edge have been related in the last years. It also discusses gaps and research trends. This SLR will also be a good introduction for new researchers in Edge and Microservices.
Introduction: The Norwegian working life and welfare model has contributed to creating a society with high income for citizens, relatively low social inequality, good public health and high trust.
Main part: Based on extensive research literature we focus on what we regard is the core of the Norwegian working life and welfare model. In addition, we make clear how this model has contributed to characteristics often associated with a good society. A decrease in unionization is, in combination with increased globalization, atypical employment contracts and automation, a factor that may weaken the Norwegian working life and welfare model.
Conclusion: In an international perspective, public health is good in Norway. This is closely related to how the country’s working life and welfare model has been designed. As an important professional group within public health work, it is important that physiotherapists are aware of these relationships.
The present book is a collection of articles which summarises the doctoral papers conducted by Sanda Tomescu Baciu at the Doctoral School of Linguistic and Literary Studies of the Faculty of Letters and the Department of Scandinavian Languages and Literature during the ten years which preceded the book’s release (the period between 2010 and 2020). The expressed intention of this compendium was “to reunite [...] the Norwegian academic community established in Romania, in Cluj-Napoca: both doctoral students and the present team of teaching staff at the department” (7). The compendium shows how much the interest in Norwegian philology has increased in the Romanian humanities’ sphere in a relatively short period of time and, also, what a wide range of subjects it did manage to discuss in the process.
The role and importance of imaginative literature in L1 education is a topic of continuous debate. In Norway, working with imaginative literature in several genres, and from various linguistic origins, is only one of several components in the L1 subject. In this article, we present the results of a survey aiming to investigate what literature can and should do in school, according to teachers. Using a qualitative hermeneutic content analysis, we analyze and categorize the survey results. We are guided by an affirmative approach to teachers’ competences, discussing the extent to which respondents demonstrate subject matter content knowledge.
 Our analysis enables us to isolate eight categories of justification, many of which show significant overlap with central tenets in literary theory, the curriculum, and L1 scholarship. However, we also find indications that critical literacy is undervalued. Furthermore, the Norwegian curriculum arguably motivates an instrumental use of literature as a way of developing general literacy or adding perspectives to topics addressed in other subjects.
 We propose visualizing the justifications teachers express in a model taking into account two dimensions: 1) whether they imply a primary focus on the text, the reader, or the context including the author; and 2) whether their goal is benefitting the student (e.g., in terms of skills) or promoting societal change. This model is intended to provide a flexible typology which literary educators at any level can use in order to critically assess their practice.
David Leslie, Michael Katell, Mhairi Aitken
et al.
The Advancing Data Justice Research and Practice (ADJRP) project aims to widen the lens of current thinking around data justice and to provide actionable resources that will help policymakers, practitioners, and impacted communities gain a broader understanding of what equitable, freedom-promoting, and rights-sustaining data collection, governance, and use should look like in increasingly dynamic and global data innovation ecosystems. In this integrated literature review we hope to lay the conceptual groundwork needed to support this aspiration. The introduction motivates the broadening of data justice that is undertaken by the literature review which follows. First, we address how certain limitations of the current study of data justice drive the need for a re-location of data justice research and practice. We map out the strengths and shortcomings of the contemporary state of the art and then elaborate on the challenges faced by our own effort to broaden the data justice perspective in the decolonial context. The body of the literature review covers seven thematic areas. For each theme, the ADJRP team has systematically collected and analysed key texts in order to tell the critical empirical story of how existing social structures and power dynamics present challenges to data justice and related justice fields. In each case, this critical empirical story is also supplemented by the transformational story of how activists, policymakers, and academics are challenging longstanding structures of inequity to advance social justice in data innovation ecosystems and adjacent areas of technological practice.
It is essential to discuss the role, difficulties, and opportunities concerning people of different gender in the field of software engineering research, education, and industry. Although some literature reviews address software engineering and gender, it is still unclear how research and practices in Asia exist for handling gender aspects in software development and engineering. We conducted a systematic literature review to grasp the comprehensive view of gender research and practices in Asia. We analyzed the 32 identified papers concerning countries and publication years among 463 publications. Researchers and practitioners from various organizations actively work on gender research and practices in some countries, including China, India, and Turkey. We identified topics and classified them into seven categories varying from personal mental health and team building to organization. Future research directions include investigating the synergy between (regional) gender aspects and cultural concerns and considering possible contributions and dependency among different topics to have a solid foundation for accelerating further research and getting actionable practices.
The Journal of Economic Literature codes classification system (JEL) published by the American Economic Association (AEA) is the de facto standard classification system for research literature in economics. The JEL classification system is used to classify articles, dissertations, books, book reviews, and working papers in EconLit, a database maintained by the AEA. Over time, it has evolved and extended to a system with over 850 subclasses. This paper reviews the history and development of the JEL classification system, describes the current version, and provides a selective overview of its uses and applications in research. The JEL codes classification system has been adopted by several publishers, and their instructions are reviewed. There are interesting avenues for future research as the JEL classification system has been surprisingly little used in existing bibliometric and scientometric research as well as in library classification systems.