This study investigates possessive pronoun placement in Norwegian-Italian bilingual children, focusing on the potential effects of cross-linguistic influence (CLI) and the impact of heritage language status. Both Norwegian and Italian allow prenominal and postnominal possessives, but their contextual distributions are inverse across the two languages. Twenty-four bilingual children residing in Norway completed an elicitation task in both languages designed to elicit neutral and contrastive contexts. The results show that while the children produced both possessive variants in Norwegian, their Italian responses were overwhelmingly prenominal. This suggests a simplification of the Italian heritage grammar, consistent with patterns observed in other heritage languages. Regarding CLI, a non-significant trend was observed: children who spoke only Italian in the home produced more prenominal possessives in contrastive contexts in Norwegian. This pattern points to a possible influence of Italian on Norwegian, particularly among children exposed only to Italian at home.
Visual analytics using dimensionality reduction (DR) can easily be unreliable for various reasons, e.g., inherent distortions in representing the original data. The literature has thus proposed a wide range of methodologies to make DR-based visual analytics reliable. However, the diversity and extensiveness of the literature can leave novice analysts and researchers uncertain about where to begin and proceed. To address this problem, we propose a guide for reading papers for reliable visual analytics with DR. Relying on the previous classification of the relevant literature, our guide helps both practitioners to (1) assess their current DR expertise and (2) identify papers that will further enhance their understanding. Interview studies with three experts in DR and data visualizations validate the significance, comprehensiveness, and usefulness of our guide.
The scope of this literature review is observations of the products of first-stage evolution for binaries having components with M < 2 $M_\odot$. A taxonomy for these products comprises dwarfs ("blue stragglers"), giants ("yellow stragglers"), subdwarf B stars, and giant-like stars ("sub-subgiants" and "red stragglers"). This literature review is organized according to this taxonomy within three distinct environments: open star clusters, globular star clusters, and the Galactic field. This literature review is the Supplemental Material for Blue Stragglers and Friends: Initial Evolutionary Pathways in Close Low-Mass Binaries (Mathieu & Pols, 2025, ARAA, 63:467-512, doi: www.annualreviews.org/content/journals/10.1146/annurev-astro-071221-054402). It is intended to support and expand on Section 3 of the main text of this Annual Review of Astronomy and Astrophysics, where an integrated perspective on the common and contrasting astrophysical properties of these binary evolution products is provided. Figures used in the main text to highlight key observational results are referenced in this literature review. The closing date of this review is January 17, 2025, with some citations subsequently updated.
De flesta av de tidigaste källorna till samiskt tänkande är sekundära. Informationen finns framför allt i rättegångsprotokoll och i texter av missionärer. Trots de källkritiska problemen ger dessa källor intressanta exempel på två nivåer, både om hur olika noaiddit, rituella specialister och kulturella vägvisare, formulerade sina idéer om människan, världen och de koloniala förhållandena, och från några områden dessutom exempel på tänkande hos andra samer, både kvinnor och män. Artikeln ger en kort översikt över källmaterialet och tar därefter upp två exempel, (1) det vi kan veta om samiskt tänkande om människan, naturen och världen i de lule- och umesamiska områdena på 1670-talet tack vare texterna till en av de få dåtida samer som skrev ned samiska idéer om sådana teman, Ánndarisa Nijlas (Nicolaus Lundius), och (2) de fragment av en ”filosofi nedifrån” som samlades in i Njaarke (Namdalen) i det sydsamiska området under 1720-talet.
Fredrik Backmans Björnstad-trilogi (2016-2021) handlar om den fiktiva svenska småstaden Björnstad och hur staden påverkas av att en ung tjej våldtas av en av ortens lovande unga hockeytalanger. I artikeln undersöks hur djur och djurmetaforer används för att skapa en berättelse om kollektivitet och periferi. Med hjälp av Deleuzes och Guattaris teorier om djurblivande undersöks hur mänsklighet fram- och omförhandlas genom närheten till naturen. Vidare diskuterar artikeln frågor om det metaforiska kontra det materiella och menar att det inte går att dra en tydlig linje, utan visar hur metaforiska och verkliga kroppar läcker in i varandra.
France is one of the pioneer countries in the use of Electric Vehicles (EVs). The French government aims to complete the transition to EVs by 2040. Therefore, modeling related to the adoption of EVs is needed in order to determine the potential policies needed to achieve this goal. This modeling is based on a literature study to identify the factors and the causal relationship between those factors. The systematic literature review (SLR) analysis was performed on 20 journals selected based on PRISMA filtering. From this SLR analysis, five direct factors and four indirect factors were obtained which were then used as the basis for modeling. Based on the model developed, four balancing (B) loops and three reinforcing (R) loops were obtained. From the analysis, it was found that the advertising factor has a goal seeking structure, while the word of mouth, environmentally friendly image, and total cost of ownership factors have an S-shaped structure.
Is it sensical to ascribe psychological predicates to AI systems like chatbots based on large language models (LLMs)? People have intuitively started ascribing emotions or consciousness to social AI ('affective artificial agents'), with consequences that range from love to suicide. The philosophical question of whether such ascriptions are warranted is thus very relevant. This paper advances the argument that LLMs instantiate language users in Ludwig Wittgenstein's sense but that ascribing psychological predicates to these systems remains a functionalist temptation. Social AIs are not full-blown language users, but rather more like Italo Calvino's literature machines. The ideas of LLMs as Wittgensteinian language users and Calvino's literature-producing writing machine are combined. This sheds light on the misguided functionalist temptation inherent in moving from equating the two to the ascription of psychological predicates to social AI. Finally, the framework of mortal computation is used to show that social AIs lack the basic autopoiesis needed for narrative façons de parler and their role in the sensemaking of human (inter)action. Such psychological predicate ascriptions could make sense: the transition 'from quantity to quality' can take place, but its route lies somewhere between life and death, not between affective artifacts and emotion approximation by literature machines.
Filippo Ricca, Alessandro Marchetto, Andrea Stocco
Context: Test Automation (TA) techniques are crucial for quality assurance in software engineering but face limitations such as high test suite maintenance costs and the need for extensive programming skills. Artificial Intelligence (AI) offers new opportunities to address these issues through automation and improved practices. Objectives: Given the prevalent usage of AI in industry, sources of truth are held in grey literature as well as the minds of professionals, stakeholders, developers, and end-users. This study surveys grey literature to explore how AI is adopted in TA, focusing on the problems it solves, its solutions, and the available tools. Additionally, the study gathers expert insights to understand AI's current and future role in TA. Methods: We reviewed over 3,600 grey literature sources over five years, including blogs, white papers, and user manuals, and finally filtered 342 documents to develop taxonomies of TA problems and AI solutions. We also cataloged 100 AI-driven TA tools and interviewed five expert software testers to gain insights into AI's current and future role in TA. Results: The study found that manual test code development and maintenance are the main challenges in TA. In contrast, automated test generation and self-healing test scripts are the most common AI solutions. We identified 100 AI-based TA tools, with Applitools, Testim, Functionize, AccelQ, and Mabl being the most adopted in practice. Conclusion: This paper offers a detailed overview of AI's impact on TA through grey literature analysis and expert interviews. It presents new taxonomies of TA problems and AI solutions, provides a catalog of AI-driven tools, and relates solutions to problems and tools to solutions. Interview insights further revealed the state and future potential of AI in TA. Our findings support practitioners in selecting TA tools and guide future research directions.
Software misconfiguration has consistently been a major reason for software failures. Over the past two decades, much work has been done to detect and diagnose software misconfigurations. However, there is still a gap between real-world misconfigurations and the literature. It is desirable to investigate whether existing taxonomy and tools are applicable for real-world misconfigurations in modern software. In this paper, we conduct an empirical study on 772 real-world misconfiguration issues, based on which we propose a novel classification of the root causes of software misconfigurations, i.e., constraint violation, resource unavailability, component-dependency error, and configuration semantic misinterpretation. Then, we systematically review the literature on misconfiguration troubleshooting to study the trends of research and the practicality of the tools and datasets in this field. We find that the research targets have changed from system and infrastructure software to advanced applications (e.g., cloud service). In the meanwhile, the research on non-crash misconfigurations also has significant growth. Despite the progress, a majority of studies lack reproducibility due to the unavailable tools and evaluation datasets. In total, only ten tools and four datasets are publicly available. We analyze the trends of existing literature on misconfiguration troubleshooting, summarize the challenges that users are faced with, and highlight the suggestions to mitigate and diagnose software misconfigurations. We release the real-world dataset of misconfiguration issues for follow-up research.
The ICT sector, responsible for 2% of global carbon emissions, is under scrutiny calling for methodologies and tools to design and develop software in an environmentally sustainable-by-design manner. However, the software engineering solutions for designing and developing carbon-efficient software are currently scattered over multiple different pieces of literature, which makes it difficult to consult the body of knowledge on the topic. In this article, we precisely conduct a systematic literature review on state-of-the-art proposals for designing and developing carbon-efficient software. We identify and analyse 65 primary studies by classifying them through a taxonomy aimed at answering the 5W1H questions of carbon-efficient software design and development. We first provide a reasoned overview and discussion of the existing guidelines, reference models, measurement solutions and techniques for measuring, reducing, or minimising the carbon footprint of software. Ultimately, we identify open challenges and research gaps, offering insights for future work in this field.
Background: The emergence of generative AI tools, empowered by Large Language Models (LLMs), has shown powerful capabilities in generating content. To date, the assessment of the usefulness of such content, generated by what is known as prompt engineering, has become an interesting research question. Objectives Using the mean of prompt engineering, we assess the similarity and closeness of such contents to real literature produced by scientists. Methods In this exploratory analysis, (1) we prompt-engineer ChatGPT and Google Bard to generate clinical content to be compared with literature counterparts, (2) we assess the similarities of the contents generated by comparing them with counterparts from biomedical literature. Our approach is to use text-mining approaches to compare documents and associated bigrams and to use network analysis to assess the terms' centrality. Results The experiments demonstrated that ChatGPT outperformed Google Bard in cosine document similarity (38% to 34%), Jaccard document similarity (23% to 19%), TF-IDF bigram similarity (47% to 41%), and term network centrality (degree and closeness). We also found new links that emerged in ChatGPT bigram networks that did not exist in literature bigram networks. Conclusions: The obtained similarity results show that ChatGPT outperformed Google Bard in document similarity, bigrams, and degree and closeness centrality. We also observed that ChatGPT offers linkage to terms that are connected in the literature. Such connections could inspire asking interesting questions and generate new hypotheses.
Umme Ayman Koana, Quang Hy Le, Shadikur Rahman
et al.
Effective ownership of software artifacts, particularly code, is crucial for accountability, knowledge sharing, and code quality enhancement. Researchers have proposed models linking ownership of software artifacts with developer performance and code quality. Our study aims to systematically examine various ownership models and provide a structured literature overview. Conducting a systematic literature review, we identified 79 relevant papers published between 2005 and 2022. We developed a taxonomy of ownership artifacts based on type, owners, and degree of ownership, along with compiling modeling variables and analytics types used in each study. Additionally, we assessed the replication status of each study. As a result, we identified nine distinct software artifacts whose ownership has been discussed in the literature, with "Code" being the most frequently analyzed artifact. We found that only three papers (3.79%) provided code and data, whereas nine papers (11.4%) provided only data. Using our systematic literature review results, we replicated experiments on nine priority projects at \texttt{Brightsquid}. The company aimed to compare its code quality against ownership factors in other teams, so we conducted a replication study using their data. Unlike prior studies, we found no strong correlation between minor contributors and bug numbers. Surprisingly, we found no strong link between the total number of developers modifying a file and bug counts, contrasting previous findings. However, we observed a significant correlation between major contributors and bug counts, diverging from earlier research.
Fadzilah Hanum Mohd Mydin, Christopher Mikton, Wan Yuen Choo
et al.
Abstract Background The examination of psychometric properties in instruments measuring abuse of older people (AOP) is a crucial area of study that has, unfortunately, received relatively little attention. Poor psychometric properties in AOP measurement instruments can significantly contribute to inconsistencies in prevalence estimates, casting a shadow of uncertainty over the magnitude of the problem at national, regional, and global levels. Objectives This review rigorously employed the Consensus‐based Standards for the Selection of Health Measurement Instruments (COSMIN) guideline on the quality of outcome measures. It was designed to identify and review the instruments used to measure AOP, assess the instruments' measurement properties, and identify the definitions of AOP and abuse subtypes measured by these instruments, ensuring the reliability and validity of the findings. Search Methods A comprehensive search was conducted up to May 2023 across various online databases, including AgeLine via EBSCOhost, ASSIA via ProQuest, CINAHL via EBSCOhost, EMBASE, LILACS, ProQuest Dissertation & Theses Global, PsycINFO via EBSCOhost, PubMed, SciELO, Scopus, Sociological Abstract via ProQuest, Chinese National Knowledge Infrastructure (CNKI), Google Scholar and WHO Global Index Medicus. Additionally, relevant studies were identified by thoroughly searching the grey literature from resources such as Campbell Collaboration, OpenAIRE, and GRAFT. Selection Criteria All quantitative, qualitative (addressing face and content validity), and mixed‐method empirical studies published in peer‐reviewed journals or grey literature were included in this review. The included studies were primary studies that (1) evaluated one or more psychometric properties, (2) contained information on instrument development, or (3) examined the content validity of the instruments designed to measure AOP in community or institutional settings. The selected studies describe at least one psychometric property: reliability, validity, and responsiveness. Study participants represent the population of interest, including males and females aged 60 or older in community or institutional settings. Data Collection and Analysis Two reviewers evaluated the screening of the selected studies' titles, abstracts, and full texts based on the preset selection criteria. Two reviewers assessed the quality of each study using the COSMIN Risk of Bias checklist and the overall quality of evidence for each psychometric property of the instrument against the updated COSMIN criteria of good measurement properties. Disagreements were resolved through consensus discussion or with assistance from a third reviewer. The overall quality of the measurement instrument was graded using a modified GRADE approach. Data extraction was performed using data extraction forms adapted from the COSMIN Guideline for Systematic Reviews of Outcome Measurement Instruments. The extracted data included information on the characteristics of included instruments (name, adaptation, language used, translation and country of origin), characteristics of the tested population, instrument development, psychometric properties listed in the COSMIN criteria, including details on content validity, structural validity, internal consistency, cross‐cultural validity/measurement invariance, reliability, measurement error, criterion validity, hypotheses testing for construct validity, responsiveness, and interoperability. All data were synthesised and summarised qualitatively, and no meta‐analysis was performed. Main Results We found 15,200 potentially relevant records, of which 382 were screened in full text. A total of 114 studies that met the inclusion criteria were included. Four studies reported on more than one instrument. The primary reasons for excluding studies were their focus on instruments used solely for screening and diagnostic purposes, those conducted in hospital settings, or those without evaluating psychometric properties. Eighty‐seven studies reported on 46 original instruments and 29 studies on 22 modified versions of an original instrument. The majority of the studies were conducted in community settings (97 studies) from the perspective of older adults (90 studies) and were conducted in high‐income countries (69 studies). Ninety‐five studies assessed multiple forms of abuse, ranging from 2 to 13 different subscales; four studies measured overall abuse and neglect among older adults, and 14 studies measured one specific type of abuse. Approximately one‐quarter of the included studies reported on the psychometric properties of the most frequently used measurement instruments: HS‐EAST (assessed in 11 studies), VASS‐12 items (in 9 studies), and CASE (in 9 studies). The instruments with the most evidence available in studies reporting on instrument development and content validity in all domains (relevance, comprehensiveness and comprehensibility) were the DEAQ, OAPAM, *RAAL‐31 items, *ICNH (Norwegian) and OAFEM. For other psychometric properties, instruments with the most evidence available in terms of the number of studies were the HS‐EAST (11 studies across 5 of 9 psychometric properties), CASE (9 studies across 6 of 9 psychometric properties), VASS‐12 items (9 studies across 5 of 9 psychometric properties) and GMS (5 studies across 4 of 9 psychometric properties). Based on the overall rating and quality of evidence, the psychometric properties of the AOP measurement instruments used for prevalence measurement in community and institutional settings were insufficient and of low quality. Authors' Conclusions This review aimed to assess the overall rating and quality of evidence for instruments measuring AOP in the community and institutional settings. Our findings revealed various measurement instruments, with ratings and evidence quality predominantly indicating insufficiency and low quality. In summary, the psychometric properties of AOP measurement instruments have not been comprehensively investigated, and existing instruments lack sufficient evidence to support their validity and reliability.
Pierre-Olivier Côté, Amin Nikanjam, Nafisa Ahmed
et al.
Context: Machine Learning (ML) is integrated into a growing number of systems for various applications. Because the performance of an ML model is highly dependent on the quality of the data it has been trained on, there is a growing interest in approaches to detect and repair data errors (i.e., data cleaning). Researchers are also exploring how ML can be used for data cleaning; hence creating a dual relationship between ML and data cleaning. To the best of our knowledge, there is no study that comprehensively reviews this relationship. Objective: This paper's objectives are twofold. First, it aims to summarize the latest approaches for data cleaning for ML and ML for data cleaning. Second, it provides future work recommendations. Method: We conduct a systematic literature review of the papers published between 2016 and 2022 inclusively. We identify different types of data cleaning activities with and for ML: feature cleaning, label cleaning, entity matching, outlier detection, imputation, and holistic data cleaning. Results: We summarize the content of 101 papers covering various data cleaning activities and provide 24 future work recommendations. Our review highlights many promising data cleaning techniques that can be further extended. Conclusion: We believe that our review of the literature will help the community develop better approaches to clean data.
Nils-Øivind Haagensen (b. 1971) is a Norwegian journalist, poet, writer and the head of Flamme Publishing House. He was born in Ålesund and at present he lives in Oslo, Norway. He made his literary debut in 1998 with the volume of poems Hender og hukommelse (Hands and Memories). In 2001 he published his first novel, Det radioaktige (The Radioactive). In 2004 he was awarded Sultprisen (Hunger Prize) for his literary work, and in 2013 he was nominated for Nordisk råds litteraturpris (Nordic Council Literature Prize) with the volume of poems God morgen og god natt (Good Morning and Good Night, 2012). He was also awarded the Stiftelsen Kjell Holms kulturpris (Stiftelsen Kjell Holm Culture Prize, 2017). In 2019 he published the novel Dette norske livet (This Norwegian Life), and in 2013 he published the novel Liten (Little). Sangria i parken (Sangria in the Park, 2021) is his most recent novel published by Oktober Publishing House. He also published a series of volumes of poems Det uregjerlige (Hard to Control, 2020), Stor (Big, 2018) and Spredning (Spreading, 2014).
Samuel Solgon Santos, Marcelo Brutti Righi, Eduardo de Oliveira Horta
Risk measures satisfying the axiom of comonotonic additivity are extensively studied, arguably because of the plethora of results indicating interesting aspects of such risk measures. Recent research, however, has shown that this axiom is incompatible with central properties in specific contexts. In this paper, we present a literature review of these incompatibilities. In addition, we use the Choquet representation of comonotonic additive risk measures to show they cannot be surplus invariant.
Systematic Literature Reviews aim at investigating current approaches to conclude a research gap or determine a futuristic approach. They represent a significant part of a research activity, from which new concepts stem. However, with the massive availability of publications at a rapid growing rate, especially digitally, it becomes challenging to efficiently screen and assess relevant publications. Another challenge is the continuous assessment of related work over a long period of time and the consequent need for a continuous update, which can be a time-consuming task. Knowledge graphs model entities in a connected manner and enable new insights using different reasoning and analysis methods. The objective of this work is to present an approach to partially automate the conduction of a Systematic Literature Review as well as classify and visualize the results as a knowledge graph. The designed software prototype was used for the conduction of a review on context-awareness in automation systems with considerably accurate results compared to a manual conduction.
Business economics research on digital platforms often overlooks existing knowledge from other fields of research leading to conceptual ambiguity and inconsistent findings. To reduce these restrictions and foster the utilization of the extensive body of literature, we apply a mixed methods design to summarize the key findings of scientific platform research. Our bibliometric analysis identifies 14 platform-related research fields. Conducting a systematic qualitative content analysis, we identify three primary research objectives related to platform ecosystems: (1) general literature defining and unifying research on platforms; (2) exploitation of platform and ecosystem strategies; (3) improvement of platforms and ecosystems. Finally, we discuss the identified insights from a business economics perspective and present promising future research directions that could enhance business economics and management research on digital platforms and platform ecosystems.
Emmanuel Adetiba, Temitope John, Adekunle Akinrinmade
et al.
The field of Artificial Intelligence (AI) has undoubtedly received significant attention in recent years. AI is being adopted to provide solutions to problems in fields such as medicine, engineering, education, government and several other domains. In order to analyze the state of the art of research in the field of AI, we present a systematic literature review focusing on the Evolution of AI programming languages. We followed the systematic literature review method by searching relevant databases like SCOPUS, IEEE Xplore and Google Scholar. EndNote reference manager was used to catalog the relevant extracted papers. Our search returned a total of 6565 documents, whereof 69 studies were retained. Of the 69 retained studies, 15 documents discussed LISP programming language, another 34 discussed PROLOG programming language, the remaining 20 documents were spread between Logic and Object Oriented Programming (LOOP), ARCHLOG, Epistemic Ontology Language with Constraints (EOLC), Python, C++, ADA and JAVA programming languages. This review provides information on the year of implementation, development team, capabilities, limitations and applications of each of the AI programming languages discussed. The information in this review could guide practitioners and researchers in AI to make the right choice of languages to implement their novel AI methods.