Hasil untuk "French literature - Italian literature - Spanish literature - Portuguese literature"

Sina Jani, Arman Heidari, Amirmohammad Anvari et al.

The rapid acceleration of scientific publishing has created substantial challenges for researchers attempting to discover, contextualize, and interpret relevant literature. Traditional keyword-based search systems provide limited semantic understanding, while existing AI-driven tools typically focus on isolated tasks such as retrieval, clustering, or bibliometric visualization. This paper presents an integrated system for scientific literature exploration that combines large-scale data acquisition, hybrid retrieval, semantic topic modeling, and heterogeneous knowledge graph construction. The system builds a comprehensive corpus by merging full-text data from arXiv with structured metadata from OpenAlex. A hybrid retrieval architecture fuses BM25 lexical search with embedding-based semantic search using Reciprocal Rank Fusion. Topic modeling is performed on retrieved results using BERTopic or non-negative matrix factorization depending on computational resources. A knowledge graph unifies papers, authors, institutions, countries, and extracted topics into an interpretable structure. The system provides a multi-layered exploration environment that reveals not only relevant publications but also the conceptual and relational landscape surrounding a query. Evaluation across multiple queries demonstrates improvements in retrieval relevance, topic coherence, and interpretability. The proposed framework contributes an extensible foundation for AI-assisted scientific discovery.

Ali Nouri, Beatriz Cabrero-Daniel, Fredrik Törner et al.

Developing autonomous driving (AD) systems is challenging due to the complexity of the systems and the need to assure their safe and reliable operation. The widely adopted approach of DevOps seems promising to support the continuous technological progress in AI and the demand for fast reaction to incidents, which necessitate continuous development, deployment, and monitoring. We present a systematic literature review meant to identify, analyse, and synthesise a broad range of existing literature related to usage of DevOps in autonomous driving development. Our results provide a structured overview of challenges and solutions, arising from applying DevOps to safety-related AI-enabled functions. Our results indicate that there are still several open topics to be addressed to enable safe DevOps for the development of safe AD.

Ali Shiraee Kasmaee, Mohammad Khodadad, Mehdi Astaraki et al.

Retrieval-Augmented Generation (RAG) systems in chemistry heavily depend on accurate and relevant retrieval of chemical literature. However, general-purpose text embedding models frequently fail to adequately represent complex chemical terminologies, resulting in suboptimal retrieval quality. Specialized embedding models tailored to chemical literature retrieval have not yet been developed, leaving a substantial performance gap. To address this challenge, we introduce ChEmbed, a domain-adapted family of text embedding models fine-tuned on a dataset comprising chemistry-specific text from the PubChem, Semantic Scholar, and ChemRxiv corpora. To create effective training data, we employ large language models to synthetically generate queries, resulting in approximately 1.7 million high-quality query-passage pairs. Additionally, we augment the tokenizer by adding 900 chemically specialized tokens to previously unused slots, which significantly reduces the fragmentation of chemical entities, such as IUPAC names. ChEmbed also maintains a 8192-token context length, enabling the efficient retrieval of longer passages compared to many other open-source embedding models, which typically have a context length of 512 or 2048 tokens. Evaluated on our newly introduced ChemRxiv Retrieval benchmark, ChEmbed outperforms state-of-the-art general embedding models, raising nDCG@10 from 0.82 to 0.91 (+9 pp). ChEmbed represents a practical, lightweight, and reproducible embedding solution that effectively improves retrieval for chemical literature search.

Mingyu Huang, Shasha Zhou, Ke Li

We are living in an era of "big literature", where scientific literature is expanding exponentially. While this growth presents new opportunities, it complicates mapping global scientific research landscapes, as manual review methods become infeasible. Recent advancements in machine learning, complex networks, and natural language processing have enabled numerous data-driven discovery methods. Building upon these tools, we introduce an end-to-end workflow for analyzing large-scale literature landscapes, LitLA. This workflow first integrates diverse publication metadata into a bibliographic knowledge graph (KG) representing the research landscape. It then offers tools for exploratory analysis of various landscape aspects. We demonstrate the effectiveness of LitLA via a case study on follow-up works of multi-objective evolutionary algorithm based on decomposition (MOEA/D). In doing so, we constructed the MOEA/D research landscape as a KG comprising over 5,400 papers, 10,000 authors, 1,600 institutions, and 78,000 keywords. With this landscape, we start with descriptive statistics and investigate prominent topics pertaining to MOEA/D and interrogate their spatial-temporal and bilateral relationships. We then map the collaboration and citation networks to reveal the community's growth over time. We further experiment whether learning on latent patterns of this landscape can hint on future research directions.

Ahmet Yasin Aytar, Kemal Kilic, Kamer Kaya

In the rapidly evolving field of data science, efficiently navigating the expansive body of academic literature is crucial for informed decision-making and innovation. This paper presents an enhanced Retrieval-Augmented Generation (RAG) application, an artificial intelligence (AI)-based system designed to assist data scientists in accessing precise and contextually relevant academic resources. The AI-powered application integrates advanced techniques, including the GeneRation Of BIbliographic Data (GROBID) technique for extracting bibliographic information, fine-tuned embedding models, semantic chunking, and an abstract-first retrieval method, to significantly improve the relevance and accuracy of the retrieved information. This implementation of AI specifically addresses the challenge of academic literature navigation. A comprehensive evaluation using the Retrieval-Augmented Generation Assessment System (RAGAS) framework demonstrates substantial improvements in key metrics, particularly Context Relevance, underscoring the system's effectiveness in reducing information overload and enhancing decision-making processes. Our findings highlight the potential of this enhanced Retrieval-Augmented Generation system to transform academic exploration within data science, ultimately advancing the workflow of research and innovation in the field.

Caleb Peppiatt

Generative Artificial Intelligence constitutes a new wave of automation. There is broad agreement among economists that humanity is potentially entering into a period of profound change. However, significant uncertainties and disagreements exist concerning a variety of overlapping topics: the share of jobs in which human labour is displaced and/or reinstated through automation; the effects on income inequality; the effects on job satisfaction; and, finally, what policy changes ought to be pursued to reduce potential negative impacts. This literature review seeks to clarify this landscape by mapping out key disagreements between positions, and to identify the critical elements upon which such disagreements rest. By surveying the current literature, the effects of AI on the future of work will be clarified.

Andrew Gray

The use of ChatGPT and similar Large Language Model (LLM) tools in scholarly communication and academic publishing has been widely discussed since they became easily accessible to a general audience in late 2022. This study uses keywords known to be disproportionately present in LLM-generated text to provide an overall estimate for the prevalence of LLM-assisted writing in the scholarly literature. For the publishing year 2023, it is found that several of those keywords show a distinctive and disproportionate increase in their prevalence, individually and in combination. It is estimated that at least 60,000 papers (slightly over 1% of all articles) were LLM-assisted, though this number could be extended and refined by analysis of other characteristics of the papers or by identification of further indicative keywords.

Alejandro Buitrago López, Javier Pastor-Galindo, José A. Ruipérez-Valiente

The prevalence of misinformation and disinformation poses a significant challenge in today's digital landscape. That is why several methods and tools are proposed to analyze and understand these phenomena from a scientific perspective. To assess how the mis/disinformation is being conceptualized and evaluated in the literature, this paper surveys the existing frameworks, models and simulations of mis/disinformation dynamics by performing a systematic literature review up to 2023. After applying the PRISMA methodology, 57 research papers are inspected to determine (1) the terminology and definitions of mis/disinformation, (2) the methods used to represent mis/disinformation, (3) the primary purpose beyond modeling and simulating mis/disinformation, (4) the context where the mis/disinformation is studied, and (5) the validation of the proposed methods for understanding mis/disinformation. The main findings reveal a consistent essence definition of misinformation and disinformation across studies, with intent as the key distinguishing factor. Research predominantly uses social frameworks, epidemiological models, and belief updating simulations. These studies aim to estimate the effectiveness of mis/disinformation, primarily in health and politics. The preferred validation strategy is to compare methods with real-world data and statistics. Finally, this paper identifies current trends and open challenges in the mis/disinformation research field, providing recommendations for future work agenda.

Zheyuan He, Zihao Li, Sen Yang et al.

Large Language Models (LLMs) have emerged as powerful tools across various domains within cyber security. Notably, recent studies are increasingly exploring LLMs applied to the context of blockchain security (BS). However, there remains a gap in a comprehensive understanding regarding the full scope of applications, impacts, and potential constraints of LLMs on blockchain security. To fill this gap, we undertake a literature review focusing on the studies that apply LLMs in blockchain security (LLM4BS). Our study aims to comprehensively analyze and understand existing research, and elucidate how LLMs contribute to enhancing the security of blockchain systems. Through a thorough examination of existing literature, we delve into the integration of LLMs into various aspects of blockchain security. We explore the mechanisms through which LLMs can bolster blockchain security, including their applications in smart contract auditing, transaction anomaly detection, vulnerability repair, program analysis of smart contracts, and serving as participants in the cryptocurrency community. Furthermore, we assess the challenges and limitations associated with leveraging LLMs for enhancing blockchain security, considering factors such as scalability, privacy concerns, and ethical concerns. Our thorough review sheds light on the opportunities and potential risks of tasks on LLM4BS, providing valuable insights for researchers, practitioners, and policymakers alike.

G. Abbondanza

Irregular migration is one of the most significant phenomena of the 20th and 21st centuries, a life-changing process for countless migrants seeking better lives elsewhere, a thorny and complicated reality for transit and destination countries, and a transnational issue for the international community. As a result, it has become a polarising element of political debates in many destination countries (see Mudde, 2019), yet many aspects of its related policies remain understudied. Matilde Rosina’s monograph, titled The Criminalisation of Irregular Migration in Europe: Globalisation, Deterrence, and Vicious Cycles, aims to fill this gap by exploring the goals and effects of irregular migration criminalisation. Is the latter effective in stemming irregular arrivals, deterring new irregular flows, and fostering repatriations? The author convincingly argues that ‘no’ is the answer. To address these important questions, she analyses two highly-relevant case studies – Italy and France – as developed nations experiencing high numbers of irregular arrivals and adopting a criminalisation approach. The volume is structured into seven large chapters which are here discussed. Chapter 1 introduces the topic by presenting both qualitative and quantitative accounts that provide useful context, and then offers an overview of the three main theories employed in this research (realism, neoliberalism, and transnationalism), as well as the triangulated methodologies (policy evaluation, interviews, questionnaires, and datasets). The author specifies that her book is centred on IPE and criminology as disciplines of interest, and the case study justification is logically-sound. Chapter 2 is a solid theory and literature review chapter. The tripartite theoretical framework may have benefitted from other disciplinary works (see Echeverría, 2020) and international relations literature that gave birth to two of its component, but it is otherwise very well presented. The following discussion on the policy gaps hypothesis is analytically-strong and conceptually-interesting. On a related note, a short digression on Australia would have further nuanced this section (and the following chapter), since it represents a noticeable exception to the cited literature. Chapter 3 delves into the book’s core concepts. While it does not draw on the original international security literature, the strategy of deterrence in the context of irregular migration criminalisation is presented accurately and effectively. The relevant framework comprising legal costs, perceptions, and social costs is equally good, as are the negative implications that are discussed afterwards. Chapter 4 is a long, well-written, and empirically-rich chapter focusing on the Italian case study. Although some relevant publications are not included (e.g. Ceccorulli and Labanca, 2014; Abbondanza, 2017), it is also well-sourced. It begins with a solid account of Rome’s irregular migration policies, followed by a useful outline of its administrative and criminal procedures concerning irregular migrants. It then provides a quantitative context through two previouslyunpublished datasets, an important feature of this book for which the author is to be

Ana Martínez Pereira

Un emblema es un producto gráfico, retórico y simbólico, y aunque su nacimiento se produjo en las páginas de un libro el mecanismo que da lugar a su invención se puede practicar en otros «soportes» y en otros géneros, entre ellos el dramático. El emblema llega al texto teatral no solo a partir de uno ya creado sino sobre todo mediante un proceso de intertextualidad en el que intervienen libros de emblemas y fuentes que también lo son para la emblemática: fábulas, mitología, tratados naturalistas, pinturas, etc. En este trabajo analizamos la presencia de la cultura emblemática en las primeras comedias pastoriles de Lope de Vega.

Christian Leibel, Lutz Bornmann

The purpose of this paper is to provide a review of the literature on the original disruption index (DI1) and its variants in scientometrics. The DI1 has received much media attention and prompted a public debate about science policy implications, since a study published in Nature found that papers in all disciplines and patents are becoming less disruptive over time. This review explains in the first part the DI1 and its variants in detail by examining their technicaland theoretical properties. The remaining parts of the review are devoted to studies that examine the validity and the limitations of the indices. Particular focus is placed on (1) possible biases that affect disruption indices (2) the convergent and predictive validity of disruption scores, and (3) the comparative performance of the DI1 and its variants. The review shows that, while the literature on convergent validity is not entirely conclusive, it is clear that some modified index variants, in particular DI5, show higher degrees of convergent validity than DI1. The literature draws attention to the fact that (some) disruption indices suffer from inconsistency, time-sensitive biases, and several data-induced biases. The limitations of disruption indices are highlighted and best practice guidelines are provided. The review encourages users of the index to inform about the variety of DI1 variants and to apply the most appropriate variant. More research on the validity of disruption scores as well as a more precise understanding of disruption as a theoretical construct is needed before the indices can be used in the research evaluation practice.

Jaike van Twiller, Agnieszka Sivertsen, Dario Pacino et al.

Container shipping drives the global economy and is an eco-friendly mode of transportation. A key objective is to maximize the utilization of vessels, which is challenging due to the NP-hardness of stowage planning. This article surveys the literature on the Container Stowage Planning Problem (CSPP). We introduce a classification scheme to analyze single-port and multi-port CSPPs, as well as the hierarchical decomposition of CSPPs into the master and slot planning problem. Our survey shows that the area has a relatively small number of publications and that it is hard to evaluate the industrial applicability of many of the proposed solution methods due to the oversimplification of problem formulations. To address this issue, we propose a research agenda with directions for future work, including establishing a representative problem definition and providing new benchmark instances where needed.

Irene S. Gabashvili

The conversational artificial-intelligence (AI) technology ChatGPT has become one of the most widely used natural language processing tools. With thousands of published papers demonstrating its applications across various industries and fields, ChatGPT has sparked significant interest in the research community. Reviews of primary data have also begun to emerge. An overview of the available evidence from multiple reviews and studies could provide further insights, minimize redundancy, and identify areas where further research is needed. Objective: To evaluate the existing reviews and literature related to ChatGPT's applications and its potential impact on different fields by conducting a systematic review of reviews and bibliometric analysis of primary literature. Methods: PubMed, EuropePMC, Dimensions AI, medRxiv, bioRxiv, arXiv, and Google Scholar were searched for ChatGPT-related publications from 2022 to 4/30/2023. Studies including secondary data related to the application of ChatGPT were considered. Reporting and risk of bias assesment was performed using PRISMA guidelines. Results: A total of 305 unique records with potential relevance to the review were identified from a pool of over 2,000 original articles. After multi-step screening process, 11 reviews were selected, consisting of 9 reviews specifically focused on ChatGPT and 2 reviews on broader AI topics that also included discussions on ChatGPT. We also conducted bibliometric analysis of primary data. Conclusions: While AI has the potential to revolutionize various industries, further interdisciplinary research, customized integrations, and ethical innovation are necessary to address existing concerns and ensure its responsible use. Protocol Registration: PROSPERO registration no. CRD42023417336, DOI 10.17605/OSF.IO/87U6Q.

Tianna Barber-Cross, Alessandro Filazzola, Charlotte Brown et al.

Grazing by wild and domesticated grazers occurs within many terrestrial ecosystems worldwide, with positive and negative impacts on biodiversity. Management of grazed lands in support of biological conservation could benefit from a compiled dataset of animal biodiversity within and adjacent to grazed sites. In this database, we have assembled data from the peer-reviewed literature that included all forms of grazing, co-occurring species, and site information. We reviewed 3,489 published articles and found 245 studies in 41 countries that surveyed animal biodiversity co-occurring with grazers. We extracted 16,105 observations of animal surveys for over 1,200 species in all terrestrial ecosystems and on all continents except Antarctica. We then compiled 28 different grazing variables that focus on management systems, assemblages of grazer species, ecosystem characteristics, and survey type. Our database provides the most comprehensive summary of animal biodiversity patterns that co-occur with wild and domesticated grazers. This database could be used in future conservation initiatives and grazing management to enhance the prolonged maintenance of ecosystems and ecosystem services. Measurement(s) Kingdom • Phylum • Class • Order • Taxon • Family • Genus • Species • Year of observation • Survey time period • Grazing estimate • Grazing species • Grazer domestication • Last grazing event • Latitude • Longitude • Elevation • Country • Number of sites • Survey technique • Survey type • Presence of wild grazers • Presence of domesticated grazers • Plant community • Ecosystem class • Fence presence • Tilled status • Herbivores present • Fertilization • Fire history • Land ownership • Year of study initiation • Year of study finished Technology Type(s) Data extraction Sample Characteristic - Organism Vertebrate • Invertebrate • Plants • Fungi Sample Characteristic - Environment Grassland ecosystem • Forest ecosystem • Shrubland ecosystem • Wetland ecosystem • Desert ecosystem • Tundra ecosystem • Coastal ecosystem Sample Characteristic - Location Argentina • Australia • Austria • Canada • China • Kingdom of Denmark • England • Ethiopia • Finland • French Republic • Germany • Hong Kong • Hungary • India • Iran • Ireland • Israel • Italy • Kazakhstan • Kenya • Lesotho • Mongolia • Kingdom of the Netherlands • New Zealand • Kingdom of Norway • Poland • Portuguese Republic • Romania • Scotland • Senegal • Republic of South Africa • Kingdom of Spain • Sweden • Switzerland • Tanzania • Tunisia • Uganda • United Kingdom • United States of America

Josef Opatrný

Book review: Claudia Varella – Manuel Barcía, Wage-Earning Slaves. Coartación in Nineteenth-Century Cuba. Gainesville, Florida: University of Florida Press 2020, 217 p. ISBN 9781683401650.

Sara Lafia, Lizhou Fan, Libby Hemphill

Discovering authoritative links between publications and the datasets that they use can be a labor-intensive process. We introduce a natural language processing pipeline that retrieves and reviews publications for informal references to research datasets, which complements the work of data librarians. We first describe the components of the pipeline and then apply it to expand an authoritative bibliography linking thousands of social science studies to the data-related publications in which they are used. The pipeline increases recall for literature to review for inclusion in data-related collections of publications and makes it possible to detect informal data references at scale. We contribute (1) a novel Named Entity Recognition (NER) model that reliably detects informal data references and (2) a dataset connecting items from social science literature with datasets they reference. Together, these contributions enable future work on data reference, data citation networks, and data reuse.

Yanwu Yang, Panyu Zhai

Predicting the probability that a user will click on a specific advertisement has been a prevalent issue in online advertising, attracting much research attention in the past decades. As a hot research frontier driven by industrial needs, recent years have witnessed more and more novel learning models employed to improve advertising CTR prediction. Although extant research provides necessary details on algorithmic design for addressing a variety of specific problems in advertising CTR prediction, the methodological evolution and connections between modeling frameworks are precluded. However, to the best of our knowledge, there are few comprehensive surveys on this topic. We make a systematic literature review on state-of-the-art and latest CTR prediction research, with a special focus on modeling frameworks. Specifically, we give a classification of state-of-the-art CTR prediction models in the extant literature, within which basic modeling frameworks and their extensions, advantages and disadvantages, and performance assessment for CTR prediction are presented. Moreover, we summarize CTR prediction models with respect to the complexity and the order of feature interactions, and performance comparisons on various datasets. Furthermore, we identify current research trends, main challenges and potential future directions worthy of further explorations. This review is expected to provide fundamental knowledge and efficient entry points for IS and marketing scholars who want to engage in this area.

E. Borzova, S. Salugina, V. Rameev et al.

Schnitzler syndrome is a rare syndrome associated with recurrent whealing and monoclonal gammopathy. Since its first description in 1972 by the French dermatologist Prof. Liliane Schnitzler, around 500 cases have been described in Europe. The diagnostic delay in Schnitzler syndrome of 5 years was previously reported [Lipsker et al, 2001]. Despite a growing clinical experience [de Koning et al, 2014], Schnitzler syndrome presents a diagnostic challenge in clinical practice. The variation in the diagnostic delay in Europe is a subject of an ongoing systematic review by our multidisciplinary team.In this work, we would like to analyze a diagnostic delay in the patients with Schnitzler syndrome followed up at tertiary clinical and research setting.We present the data from the patients with Schnitzler syndrome that have been under our care by a multidisciplinary team over a period from 2015 to 2021. All patients were analyzed for the age of disease onset and a diagnostic delay for Schnitzler syndrome.There are currently 14 patients with Schnitzler syndrome under our care. Of these, there were eight men and six women. The median age at the disease onset was 50.5 years, with the range from 25 to 79 years. The median diagnostic delay in our patient series was 3 years, ranging from 1 to 22 years. Noteworthy, in three patients the diagnostic delay for Schnitzler syndrome was over 5 years [6, 12 and 22 years]. Nine patients were treated with IL-1 inhibitors (iIL-1) (canakinumab and anakinra).Our analysis suggest that the diagnostic delay remains considerable even 50 years after the initial description of this syndrome. Our data on the diagnostic delay in Schnitzler syndrome is in keeping with the clinical experience in most countries with over 5 published cases, including France, Germany, Spain, Portugal and Italy. An early recognition of Schnitzler syndrome is crucial for prompt treatment targeting IL-1. The rarity of this syndrome and a wide range of initial signs and symptoms in these patients delay the correct diagnosis and a start of the biological therapy in these patients. An increased awareness of Schnitzler syndrome may reduce the diagnostic delay in these patients. A multidisciplinary approach is essential for an early diagnosis of Schnitzler syndrome.[1]Lipsker D et al. The Schnitzler Syndrome. Four new cases and review of the literature. Medicine. 2001; 80:37-44.[2]de Koning HD. Schniztler’s syndrome: lessons from 281 cases. Clin Transl Allergy. 2014; 4:41.None declared

Terence Wong, Markus Wagner, Christoph Treude

Context: Championed by IBM's vision of autonomic computing paper in 2003, the autonomic computing research field has seen increased research activity over the last 20 years. Several conferences and workshops have been established and have contributed to the autonomic computing knowledge base in search of a new kind of system -- a self-adaptive system (SAS). These systems are characterized by being context-aware and can act on that awareness. The actions carried out could be on the system or on the context (or environment). The underlying goal of a SAS is the sustained achievement of its goals despite changes in its environment. Objective: Despite a number of literature reviews on specific aspects of SASs ranging from their requirements to quality attributes, we lack a systematic understanding of the current state of the art. Method: This paper contributes a systematic literature review into self-adaptive systems using the dblp computer science bibliography as a database. We filtered the records systematically in successive steps to arrive at 293 relevant papers. Each paper was critically analyzed and categorized into an attribute matrix. This matrix consisted of five categories, with each category having multiple attributes. The attributes of each paper, along with the summary of its contents formed the basis of the literature review that spanned 30 years. Results: We characterize the maturation process of the research area from theoretical papers over practical implementations to more holistic and generic approaches, frameworks, and exemplars, applied to areas such as networking, web services, and robotics, with much of the recent work focusing on IoT and IaaS. Conclusion: While there is an ebb and flow of application domains, domains like bio-inspired approaches, security, and cyber physical systems showed promise to grow heading into the 2020s.