Scientific hypothesis generation requires tracking how knowledge evolves, not just what is currently known. We introduce Continuous Knowledge Metabolism (CKM), a framework that processes scientific literature through sliding time windows and incrementally updates a structured knowledge base as new findings arrive. We present CKM-Lite, an efficient variant that achieves strong predictive coverage through incremental accumulation, outperforming batch processing on hit rate (+2.8%, p=0.006), hypothesis yield (+3.6, p<0.001), and best-match alignment (+0.43, p<0.001) while reducing token cost by 92%. To understand what drives these differences, we develop CKM-Full, an instrumented variant that categorizes each new finding as novel, confirming, or contradicting, detects knowledge change signals, and conditions hypothesis generation on the full evolution trajectory. Analyzing 892 hypotheses generated by CKM-Full across 50 research topics, alongside parallel runs of the other variants, we report four empirical observations: (1) incremental processing outperforms batch baseline across predictive and efficiency metrics; (2) change-aware instrumentation is associated with higher LLM-judged novelty (Cohen's d=3.46) but lower predictive coverage, revealing a quality-coverage trade-off; (3) a field's trajectory stability is associated with hypothesis success (r=-0.28, p=0.051), suggesting boundary conditions for literature-based prediction; (4) knowledge convergence signals are associated with nearly 5x higher hit rate than contradiction signals, pointing to differential predictability across change types. These findings suggest that the character of generated hypotheses is shaped not only by how much literature is processed, but also by how it is processed. They further indicate that evaluation frameworks must account for the quality-coverage trade-off rather than optimize for a single metric.
O objetivo deste artigo é propor uma leitura do conto “O Sagrado Pão dos Filhos” (2016), de Conceição Evaristo, destacando de que maneira, por meio da personagem Andina Magnólia, as experiências de negritude, reafirmadas por meio da escrevivência, traduzem traços de um tempo da ancestralidade e a potência da língua. A hipótese levantada em nossa análise é a de que no corpo e por meio do corpo (corpo da escrita e corpo feminino negro) colocado em performance há o efeito de retorno de temporalidades e possibilidades de transfiguração de sentidos, rearranjo da memória e da história. Para fundamentar nossa leitura, nos valemos de estudiosos da performance dos corpos negros no tempo e no espaço, tais como Lélia Gonzales, Leda Maria Martins e Edimilson de Almeida Pereira. Concluímos que o conto de Evaristo se constrói por meio de uma linguagem que reinaugura a narrativa fundacional do mito da multiplicação cristã.
French literature - Italian literature - Spanish literature - Portuguese literature
Associer le nom de Babeuf à la Bible peut sembler paradoxal, voire déroutant, tant le tribun du peuple s’est illustré par son rejet du catholicisme dès les premiers temps de la Révolution. Renonçant à son prénom de baptême, François-Noël, il adopte successivement ceux de Camille, puis de Gracchus, empruntés à l’Antiquité romaine, afin d’incarner plus pleinement l’idéal égalitaire qui le conduira à l’échafaud en 1797. Pourtant, les références au livre sacré du christianisme abondent dans ses écrits, et la figure du Christ y occupe une place centrale, tantôt comme modèle d’apôtre, tantôt comme image du martyr. Le babouvisme, préfiguration du communisme, puise ainsi dans la Bible une matière symbolique et rhétorique qu’il détourne par le pastiche ou la parodie. La mission de la Conjuration des Égaux s’inscrit dans cette logique : elle vise à promulguer un nouveau code moral et politique, un « décalogue de la sainte humanité, du sans-culottisme, de l’imprescriptible équité », selon les mots de Babeuf dans son Manifeste des plébéiens (1795), véritable évangile séculier destiné aux classes opprimées.
Literature (General), French literature - Italian literature - Spanish literature - Portuguese literature
Victor Morel, Leonardo Iwaya, Simone Fischer-Hübner
In recent years, several personalized assistants based on AI have been researched and developed to help users make privacy-related decisions. These AI-driven Personalized Privacy Assistants (AI-driven PPAs) can provide significant benefits for users, who might otherwise struggle with making decisions about their personal data in online environments that often overload them with different privacy decision requests. So far, no studies have systematically investigated the emerging topic of AI-driven PPAs, classifying their underlying technologies, architecture and features, including decision types or the accuracy of their decisions. To fill this gap, we present a Systematic Literature Review (SLR) to map the existing solutions found in the scientific literature, which allows reasoning about existing approaches and open challenges for this research field. We screened several hundred unique research papers over the recent years (2013-2025), constructing a classification from 41 included papers. As a result, this SLR reviews several aspects of existing research on AI-driven PPAs in terms of types of publications, contributions, methodological quality, and other quantitative insights. Furthermore, we provide a comprehensive classification for AI-driven PPAs, delving into their architectural choices, system contexts, types of AI used, data sources, types of decisions, and control over decisions, among other facets. Based on our SLR, we further underline the research gaps and challenges and formulate recommendations for the design and development of AI-driven PPAs as well as avenues for future research.
Large language models (LLMs) are rapidly transforming various domains, including biomedicine and healthcare, and demonstrate remarkable potential from scientific research to new drug discovery. Graph-based retrieval-augmented generation (RAG) systems, as a useful application of LLMs, can improve contextual reasoning through structured entity and relationship identification from long-context knowledge, e.g. biomedical literature. Even though many advantages over naive RAGs, most of graph-based RAGs are computationally intensive, which limits their application to large-scale dataset. To address this issue, we introduce fastbmRAG, an fast graph-based RAG optimized for biomedical literature. Utilizing well organized structure of biomedical papers, fastbmRAG divides the construction of knowledge graph into two stages, first drafting graphs using abstracts; and second, refining them using main texts guided by vector-based entity linking, which minimizes redundancy and computational load. Our evaluations demonstrate that fastbmRAG is over 10x faster than existing graph-RAG tools and achieve superior coverage and accuracy to input knowledge. FastbmRAG provides a fast solution for quickly understanding, summarizing, and answering questions about biomedical literature on a large scale. FastbmRAG is public available in https://github.com/menggf/fastbmRAG.
Context: Technical debt (TD) refers to the additional costs incurred due to compromises in software quality, providing short-term advantages during development but potentially compromising long-term quality. Accurate TD forecasting and prediction are vital for informed software maintenance and proactive management. However, this research area lacks comprehensive documentation on the available forecasting techniques. Objective: This study aims to explore existing knowledge in software engineering to gain insights into approaches proposed in research and industry for forecasting TD evolution. Methods: To achieve this objective, we conducted a Systematic Literature Review encompassing 646 distinct papers published until 2023. Following established methodology in software engineering, we identified and included 14 primary studies for analysis. Result: Our analysis unveiled various approaches for TD evolution forecasting. Notably, random forest and temporal convolutional networks demonstrated superior performance compared to other methods based on the result from the primary studies. However, these approaches only address two of the fifteen identified TD types, specifically Code debt and Architecture debt, while disregarding the remaining types. Conclusion: Our findings indicate that research on TD evolution forecasting is still in its early stages, leaving numerous challenges unaddressed. Therefore, we propose several research directions that require further investigation to bridge the existing gaps. Keywords: Systematic literature review, Technical debt, Technical debt prediction, Technical debt forecasting, Technical debt metrics
Sarah Ghidalia, Ouassila Labbani Narsis, Aurélie Bertaux
et al.
Motivated by the desire to explore the process of combining inductive and deductive reasoning, we conducted a systematic literature review of articles that investigate the integration of machine learning and ontologies. The objective was to identify diverse techniques that incorporate both inductive reasoning (performed by machine learning) and deductive reasoning (performed by ontologies) into artificial intelligence systems. Our review, which included the analysis of 128 studies, allowed us to identify three main categories of hybridization between machine learning and ontologies: learning-enhanced ontologies, semantic data mining, and learning and reasoning systems. We provide a comprehensive examination of all these categories, emphasizing the various machine learning algorithms utilized in the studies. Furthermore, we compared our classification with similar recent work in the field of hybrid AI and neuro-symbolic approaches.
Background: Accurate effort estimation is crucial for planning in Agile iterative development. Agile estimation generally relies on consensus-based methods like planning poker, which require less time and information than other formal methods (e.g., COSMIC) but are prone to inaccuracies. Understanding the common reasons for inaccurate estimations and how proposed approaches can assist practitioners is essential. However, prior systematic literature reviews (SLR) only focus on the estimation practices (e.g., [26, 127]) and the effort estimation approaches (e.g., [6]). Aim: We aim to identify themes of reasons for inaccurate estimations and classify approaches to improve effort estimation. Method: We conducted an SLR and identified the key themes and a taxonomy. Results: The reasons for inaccurate estimation are related to information quality, team, estimation practice, project management, and business influences. The effort estimation approaches were the most investigated in the literature, while only a few aim to support the effort estimation process. Yet, few automated approaches are at risk of data leakage and indirect validation scenarios. Recommendations: Practitioners should enhance the quality of information for effort estimation, potentially by adopting an automated approach. Future research should aim to improve the information quality, while avoiding data leakage and indirect validation scenarios.
Agnieszka Olechnicka, Adam Ploszaj, Ewa Zegler-Poleska
This study reviews the literature on virtual academic conferences, which have gained significant attention due to the COVID-19 pandemic. We conducted a scoping review, analyzing 147 documents available up to October 5th, 2021. We categorized this literature, identified main themes, examined theoretical approaches, evaluated empirical findings, and synthesized the advantages and disadvantages of virtual academic conferences. We find that the existing literature on virtual academic conferences is mainly descriptive and lacks a solid theoretical framework for studying the phenomenon. Despite the rapid growth of the literature documenting and discussing virtual conferencing induced by the pandemic, the understanding of the phenomenon is limited. We provide recommendations for future research on academic virtual conferences: their impact on research productivity, quality, and collaboration; relations to social, economic, and geopolitical inequalities in science; and their environmental aspects. We stress the need for further research encompassing the development of a theoretical framework that will guide empirical studies.
Este estudio parte del concepto “eurotaoísmo” (Sloterdijk) para aproximarse a la producción poética de Jesús Aguado y José Watanabe, donde se percibe una clara influencia de literaturas orientales. El artículo investiga cómo ambos autores expresan en sus textos el malestar de la postmodernidad y la nostalgia por un origen perdido que se sitúa en la cultura oriental de su elección, esto es, el deseo de regresar a ese origen y la imposibilidad de realizarlo.
Language and Literature, French literature - Italian literature - Spanish literature - Portuguese literature
In the human species, what primarily defines an individual is his or her personality and history, i.e. the awareness of the individuation process that governs the history of the self. We will begin by asking how self-consciousness and individual identity are constructed, and how time, memory and identity are correlated in the history of the self. Then, we’ll look at the history of the self when it goes through an identity crisis. We’ll take the example of the individual identity crisis associated with the experience of the extermination camps, and we’ll see that another articulation of time, memory and identity emerges to constitute, through poetic experience, the safeguard of a collective memory. Our hypothesis is that poetry is a total social fact, unifying the group and assigning it a rhythm and an identity through the collective memory that builds the history of the group through the cult of heroines and heroes. To test this hypothesis, we will highlight poetry’s original function of constructing the “moral person” or collective self of the group in the extreme experiences of the Nazi extermination camps, through the texts of Robert Antelme and Charlotte Delbo. When human beings are confronted with an extreme experience such as that of having to survive in a Nazi extermination camp, in order to hold on, they have to reconnect with poetry's primitive function of expressing a personality or a collective memory.
French literature - Italian literature - Spanish literature - Portuguese literature
Ahmad Haji Mohammadkhani, Nitin Sai Bommi, Mariem Daboussi
et al.
Context: In recent years, leveraging machine learning (ML) techniques has become one of the main solutions to tackle many software engineering (SE) tasks, in research studies (ML4SE). This has been achieved by utilizing state-of-the-art models that tend to be more complex and black-box, which is led to less explainable solutions that reduce trust and uptake of ML4SE solutions by professionals in the industry. Objective: One potential remedy is to offer explainable AI (XAI) methods to provide the missing explainability. In this paper, we aim to explore to what extent XAI has been studied in the SE community (XAI4SE) and provide a comprehensive view of the current state-of-the-art as well as challenge and roadmap for future work. Method: We conduct a systematic literature review on 24 (out of 869 primary studies that were selected by keyword search) most relevant published studies in XAI4SE. We have three research questions that were answered by meta-analysis of the collected data per paper. Results: Our study reveals that among the identified studies, software maintenance (\%68) and particularly defect prediction has the highest share on the SE stages and tasks being studied. Additionally, we found that XAI methods were mainly applied to classic ML models rather than more complex models. We also noticed a clear lack of standard evaluation metrics for XAI methods in the literature which has caused confusion among researchers and a lack of benchmarks for comparisons. Conclusions: XAI has been identified as a helpful tool by most studies, which we cover in the systematic review. However, XAI4SE is a relatively new domain with a lot of untouched potentials, including the SE tasks to help with, the ML4SE methods to explain, and the types of explanations to offer. This study encourages the researchers to work on the identified challenges and roadmap reported in the paper.
Como es sabido, la Inquisición mandó eliminar dos párrafos del discurso 11 del tomo VIII del Teatro crítico universal (1739) de B. J. Feijoo por contener «doctrina peligrosa». Feijoo intentó en vano reclamar esa supresión, para lo que escribió dos Explicaciones que fueron finalmente desestimadas. Hasta el momento, solo se había publicado una de esas dos explicaciones. En este trabajo, damos a conocer la segunda Explicación, que editamos a partir del cotejo de los manuscritos que hemos ido localizando. Hacemos preceder el texto crítico de un sucinto estudio sobre la pieza.
French literature - Italian literature - Spanish literature - Portuguese literature
Flavia Serra, Veronika Peralta, Adriana Marotta
et al.
The importance of context in data quality (DQ) was shown many years ago and nowadays is widely accepted. Early approaches and surveys defined DQ as \textit{fitness for use} and showed the influence of context on DQ. This paper presents a Systematic Literature Review (SLR) for investigating how context is taken into account in recent proposals for DQ management. We specifically present the planning and execution of the SLR, the analysis criteria and our results reflecting the relationship between context and DQ in the state of the art and, particularly, how that context is defined and used for DQ management.
Morteza Ansarinia, Paul Schrater, Pedro Cardoso-Leite
Traditionally, theory and practice of Cognitive Control are linked via literature reviews by human domain experts. This approach, however, is inadequate to track the ever-growing literature. It may also be biased, and yield redundancies and confusion. Here we present an alternative approach. We performed automated text analyses on a large body of scientific texts to create a joint representation of tasks and constructs. More specifically, 385,705 scientific abstracts were first mapped into an embedding space using a transformers-based language model. Document embeddings were then used to identify a task-construct graph embedding that grounds constructs on tasks and supports nuanced meaning of the constructs by taking advantage of constrained random walks in the graph. This joint task-construct graph embedding, can be queried to generate task batteries targeting specific constructs, may reveal knowledge gaps in the literature, and inspire new tasks and novel hypotheses.
Much like most of cognition research, music cognition is an interdisciplinary field, which attempts to apply methods of cognitive science (neurological, computational and experimental) to understand the perception and process of composition of music. In this paper, we first motivate why music is relevant to cognitive scientists and give an overview of the approaches to computational modelling of music cognition. We then review literature on the various models of music perception, including non-computational models, computational non-cognitive models and computational cognitive models. Lastly, we review literature on modelling the creative behaviour and on computer systems capable of composing music. Since a lot of technical terms from music theory have been used, we have appended a list of relevant terms and their definitions at the end.
Research addressing the greening of internet user behaviours at hedonic and utilitarian levels is scarce. To identify dimensions, scales and strong relationships arising from motivation, we reviewed a sample of research articles related to the personal green IT context. We used Self-determination theory as the theoretical framework to categorize factors into different motivation dimensions. A qualitative literature review analyses five pair-wise associations between motivation constructs of the theory and green IT use. This work builds on the prior research related to environmental motivation by summarizing the measures applied to the evaluation of personal green IT behaviours and by examining the relationships broadly defined in the Self-determination theory, distinguishing between hedonic and utilitarian green IT use.
Saeed Nosratabadi, Roya Khayer Zahed, Vadim Vitalievich Ponkratov
et al.
Background/Purpose: The use of artificial intelligence (AI) models for data-driven decision-making in different stages of employee lifecycle (EL) management is increasing. However, there is no comprehensive study that addresses contributions of AI in EL management. Therefore, the main goal of this study was to address this theoretical gap and determine the contribution of AI models to EL. Methods: This study applied the PRISMA method, a systematic literature review model, to ensure that the maximum number of publications related to the subject can be accessed. The output of the PRISMA model led to the identification of 23 related articles, and the findings of this study were presented based on the analysis of these articles. Results: The findings revealed that AL algorithms were used in all stages of EL management (i.e., recruitment, on-boarding, employability and benefits, retention, and off-boarding). It was also disclosed that Random Forest, Support Vector Machines, Adaptive Boosting, Decision Tree, and Artificial Neural Network algorithms outperform other algorithms and were the most used in the literature. Conclusion: Although the use of AI models in solving EL problems is increasing, research on this topic is still in its infancy stage, and more research on this topic is necessary.
En este trabajo me interesa interrogar dos relatos disímiles, realizados por mujeres que proyectan lugares de enunciación antitéticos: uno, el regreso de una letrada desde una metrópoli europea a un enclave colonial americano, el de Mercedes de Santa Cruz y Montalvo y, otro, el que realiza una viajera (no-escritora), Francisca Espínola, entre dos periferias geoculturales desde la provincia de Buenos Aires a la comuna francesa de Sète. De ambos recorridos se derivan diferencias sustantivas en la escritura de los textos Viaje a La Habana y Memoria de un viaje a Francia de una argentina de la provincia de Buenos Aires. La narración de este último –entre Buenos Aires, Sète y Marsella– reviste particular interés para mi artículo ya que se trata de un texto casi desconocido y de iniciación literaria (acaso ocasional) que expone diversos dispositivos y tretas de construcción del lugar de «la mujer que escribe» y que busca su diferenciación. En mi hipótesis, estos textos exhiben un modo peculiar de escritura donde la distancia territorial opera como un catalizador de los vínculos entre nación, letra y género.
Literature (General), French literature - Italian literature - Spanish literature - Portuguese literature