La société nord-camerounaise est régie par un système marqué des contraintes et exigences. L’ignorance des différentes règles de vie inspirées du Pulaaku constitue une déviance. Mais, au-delà de toutes ces considérations, certains musiciens sont passés outre ces réalités pour se tourner vers des chants érotiques. Ce début d’émancipation a été vécu comme un fait marginal et sans importance à cause de la classe sociale peu enviable de ses initiateurs. Toutefois, le fait majeur de ces musiques fut la production des chants évoquant directement l’amour physique. Ce qui a attiré l’attention de plus d’un dans ce revirement vers une mélodie atypique. Des réactions sont venues de toutes les couches sociales pour donner une appréciation sur cette nouvelle donne dans la musique du septentrion. Ce travail démontre la rupture d’avec les habitudes anciennes. En mettant en lumière la façon dont les populations ont accueilli ces musiques de type nouveau, cette étude apporte un éclairage sur le renouveau comportemental des populations. En ressortant les conséquences sur les habitudes de différents acteurs sociaux, cette étude permet également de faire part de l’impact de la musique locale sur les mœurs des populations.
Le patrimoine et le tourisme sont des facteurs avérés de développement. Or, s’ils ne sont pas bien exploités, ils ne peuvent en aucun cas contribuer à l’amélioration des conditions d’existence des populations d’une région donnée. L’Est-Cameroun est le fief de ressources naturelles et culturelles d’une étonnante diversité pouvant impulser une forte activité touristique. Cependant, même si elle reçoit chaque année des touristes nationaux et étrangers, la région ne peut se targuer de faire profiter sa population des effets directs, indirects et induits qu’engendrerait une activité touristique convenablement menée. Le présent article expose dans une perspective historique (1980-2010) les raisons pour lesquelles le tourisme et le développement de l’Est-Cameroun piétinent et ce, malgré ses nombreuses ressources patrimoniales. La collecte des données (écrites, orales, iconographiques, électroniques) sur le terrain de recherche associée à l’observation permet de constater que le tourisme fut très vite relégué au second plan. L’exploitation minière et forestière intensive, au lieu de faciliter l’émergence de la région, l’a plutôt hypothéquée.
Florian Atzenhofer-Baumgartner, Georg Vogeler, Dominik Kowald
This paper presents the first multistakeholder approach for translating diverse stakeholder values into an evaluation metric setup for Recommender Systems (RecSys) in digital archives. While commercial platforms mainly rely on engagement metrics, cultural heritage domains require frameworks that balance competing priorities among archivists, platform owners, researchers, and other stakeholders. To address this challenge, we conducted high-profile focus groups (5 groups x 5 persons) with upstream, provider, system, consumer, and downstream stakeholders, identifying value priorities across critical dimensions: visibility/representation, expertise adaptation, and transparency/trust. Our analysis shows that stakeholder concerns naturally align with four sequential research funnel stages: discovery, interaction, integration, and impact. The resulting evaluation setup addresses domain-specific challenges including collection representation imbalances, non-linear research patterns, and tensions between specialized expertise and broader accessibility. We propose directions for tailored metrics in each stage of this research journey, such as research path quality for discovery, contextual appropriateness for interaction, metadata-weighted relevance for integration, and cross-stakeholder value alignment for impact assessment. Our contributions extend beyond digital archives to the broader RecSys community, offering transferable evaluation approaches for domains where value emerges through sustained engagement rather than immediate consumption.
Knowledge-based conversational question answering (KBCQA) confronts persistent challenges in resolving coreference, modeling contextual dependencies, and executing complex logical reasoning. Existing approaches, whether end-to-end semantic parsing or stepwise agent-based reasoning, often suffer from structural inaccuracies and prohibitive computational costs, particularly when processing intricate queries over large knowledge graphs. To address these limitations, we introduce SEAL, a novel two-stage semantic parsing framework grounded in self-evolving agentic learning. In the first stage, a large language model (LLM) extracts a minimal S-expression core that captures the essential semantics of the input query. This core is then refined by an agentic calibration module, which corrects syntactic inconsistencies and aligns entities and relations precisely with the underlying knowledge graph. The second stage employs template-based completion, guided by question-type prediction and placeholder instantiation, to construct a fully executable S-expression. This decomposition not only simplifies logical form generation but also significantly enhances structural fidelity and linking efficiency. Crucially, SEAL incorporates a self-evolving mechanism that integrates local and global memory with a reflection module, enabling continuous adaptation from dialog history and execution feedback without explicit retraining. Extensive experiments on the SPICE benchmark demonstrate that SEAL achieves state-of-the-art performance, especially in multi-hop reasoning, comparison, and aggregation tasks. The results validate notable gains in both structural accuracy and computational efficiency, underscoring the framework's capacity for robust and scalable conversational reasoning.
The Faint Object Camera (FOC) on board the Hubble Space Telescope (HST) acquired high-resolution spatially resolved polarimetric images of nearby active galactic nuclei (AGNs) in the near-ultraviolet (near-UV) band. Eight of the 25 individual targets in the polarized archives had no published analysis until the beginning of this series of papers. We describe the last 5 targets here. In this paper, we finalize the publication of near-UV imaging polarimetry of AGNs in the HST/FOC archives. We render available spatially resolved polarization maps of the [OIII] emission lines for Mrk 3 and Mrk 78, as well as near-UV continuum polarization maps for Mrk 3, NGC 3862, Cygnus A, and 3C 109. We used the generalized reduction pipeline presented in the first paper in this series to homogeneously analyze the five remaining polarized observations of AGNs in the FOC archives. The polarization pattern in Mrk 3 and Mrk 78 in the narrow-line regions is consistent with scattering from an obscured nucleus. For NGC 3862, we confirm marginal UV polarization parallel with the inner radio jet that is related to synchrotron emission. In Cygnus A, we report spatially resolved centro-symmetric polarization patterns in the two opposite outflows, which highlights the scattering origin of the polarized light. Finally, 3C 109 shows high nuclear polarization that is consistent with AGN-dominated emission and parallel with the radio axis, but differs from the polarization from dichroic absorption invoked by previous authors. The imaging polarimetry we obtained for the narrow-line region and the extended scattering medium surrounding the obscured AGNs is aligned with the predictions of the unified AGN model and demonstrates the power of spatially resolved polarimetric observation to decipher the complex morphologies at work in AGNs.
Astronomical archives contain vast quantities of unexplored data that potentially harbour rare and scientifically valuable cosmic phenomena. We leverage new semi-supervised methods to extract such objects from the Hubble Legacy Archive. We have systematically searched approximately 100 million image cutouts from the entire Hubble Legacy Archive using the recently developed AnomalyMatch method, which combines semi-supervised and active learning techniques for the efficient detection of astrophysical anomalies. This comprehensive search rapidly uncovered a multitude of astrophysical anomalies presented here that significantly expand the inventory of known rare objects. Among our discoveries are 138 new candidate gravitational lenses, 18 jellyfish galaxies, and 417 mergers or interacting galaxies. The efficiency and accuracy of our iterative detection strategy allows us to trawl the complete archive within just 2-3 days, highlighting its potential for large-scale astronomical surveys. We present a detailed overview of these newly identified objects, discuss their astrophysical significance, and demonstrate the considerable potential of AnomalyMatch to efficiently explore extensive astronomical datasets, including, e.g., upcoming Euclid data releases.
Jinghua Groppe, Andreas Marquet, Annabel Walz
et al.
Enforcing archival standards requires specialized expertise, and manually creating metadata descriptions for archival materials is a tedious and error-prone task. This work aims at exploring the potential of agentic AI and large language models (LLMs) in addressing the challenges of implementing a standardized archival description process. To this end, we introduce an agentic AI-driven system for automated generation of high-quality metadata descriptions of archival materials. We develop a federated optimization approach that unites the intelligence of multiple LLMs to construct optimal archival metadata. We also suggest methods to overcome the challenges associated with using LLMs for consistent metadata generation. To evaluate the feasibility and effectiveness of our techniques, we conducted extensive experiments using a real-world dataset of archival materials, which covers a variety of document types and formats. The evaluation results demonstrate the feasibility of our techniques and highlight the superior performance of the federated optimization approach compared to single-model solutions in metadata quality and reliability.
Autonomous driving technologies face significant safety challenges while operating under rare, diverse, and visually degraded weather scenarios. These challenges become more critical in cooperative settings, where vehicles and infrastructure jointly perceive and reason across complex environments. To address these issues, we propose SEAL, a vision-language model-based framework with adaptive multimodal learning for robust cooperative autonomous driving under long-tail scenarios. SEAL introduces three core innovations: (i) a prompt-driven long-tail scenario generation and evaluation pipeline that leverages foundation models to synthesize realistic long-tail conditions such as snow and fog across vehicle- and infrastructure-side views, enriching training diversity efficiently; (ii) a gated multi-scenario adaptive attention module that modulates the visual stream using scenario priors to recalibrate ambiguous or corrupted features; and (iii) a multi-task scenario-aware contrastive learning objective that improves multimodal alignment and promotes cross-scenario feature separability. Extensive experiments demonstrate that SEAL significantly outperforms existing baselines in reasoning, safety, and planning accuracy under complex, challenging driving conditions, advancing the safety, robustness, and scalability of autonomous driving.
Florian Hantke, Peter Snyder, Hamed Haddadi
et al.
Recently, reproducibility has become a cornerstone in the security and privacy research community, including artifact evaluations and even a new symposium topic. However, Web measurements lack tools that can be reused across many measurement tasks without modification, while being robust to circumvention, and accurate across the wide range of behaviors in the Web. As a result, most measurement studies use custom tools and varied archival formats, each of unknown correctness and significant limitations, systematically affecting the research's accuracy and reproducibility. To address these limitations, we present WebREC, a Web measurement tool that is, compared against the current state-of-the-art, accurate (i.e., correctly measures and attributes events not possible with existing tools), general (i.e., reusable without modification for a broad range of measurement tasks), and comprehensive (i.e., handling events from all relevant browser behaviors). We also present .web, an archival format for the accurate and reproducible measurement of a wide range of website behaviors. We empirically evaluate WebREC's accuracy by replicating well-known Web measurement studies and showing that WebREC's results more accurately match our baseline. We then assess if WebREC and .web succeed as general-purpose tools, which could be used to accomplish many Web measurement tasks without modification. We find that this is so: 70% of papers discussed in a 2024 web crawling SoK paper could be conducted using WebREC as is, and a larger number (48%) could be leveraged against .web archives without requiring any new crawling.
Ricardo Serraglio Polucha, Paulo Nascimento Neto, Mario Procopiuck
A historiografia brasileira sobre informalidade urbana tem avançado para novos lócus de análise, superando a tradicional predominância das favelas cariocas. Nesse cenário, tem-se por objetivo investigar a gênese do pensamento e da ação estatal sobre favelas em Curitiba entre 1946 e 1965, destacando a influência do circuito pan-americano de difusão de ideias urbanas.
Palavras-chave: favela; informalidade urbana; política habitacional; Curitiba.
Diplomatics. Archives. Seals, Bibliography. Library science. Information resources
Thaís de Araujo da Costa, Daniele Barros de Souza , Luis Fernando da Silva Fernandes
et al.
Sustentando-nos no arcabouço da análise de discurso materialista e da história das ideias linguísticas, consideramos o arquivo como produto de gestos de interpretação determinados sócio-historicamente. Calcados na proposta de Costa (2023) em relação à constituição de arquivos em rede, relatamos o processo de montagem do Arquivo Said Ali, desenvolvido no âmbito do projeto Arquivo de Saberes Linguísticos (ILE-Uerj), e apresentamos uma prévia do catálogo em construção.
Palavras-chaves: arquivos em rede; Arquivo Said Ali; análise de discurso materialista; história das ideias linguísticas.
Diplomatics. Archives. Seals, Bibliography. Library science. Information resources
This study aims to improve the photometric calibration of astronomical photo plates. The Sonneberg Observatory's sky patrol was selected, comprising about 300,000 plates, and the digitization workflow is implemented using PyPlate. The challenge is to remove zero point offsets resulting from differences in color sensitivity in the photo plates' emulsion response. By utilizing the Gaia DR3 dataset and the GaiaXPy tool, we are able to obtain a consistent astrometric and photometric calibration of the Sonneberg plates and those of other archives such as APPLAUSE.
Gökberk Özsoy, Luis Salamanca, Matthew Connelly
et al.
In the current paper, we present the KG-FRUS dataset, comprised of more than 300,000 US government diplomatic documents encoded in a Knowledge Graph (KG). We leverage the data of the Foreign Relations of the United States (FRUS) (available as XML files) to extract information about the documents and the individuals and countries mentioned within them. We use the extracted entities, and associated metadata, to create a graph-based dataset. Further, we supplement the created KG with additional entities and relations from Wikidata. The relations in the KG capture the synergies and dynamics required to study and understand the complex fields of diplomacy, foreign relations, and politics. This goes well beyond a simple collection of documents which neglects the relations between entities in the text. We showcase a range of possibilities of the current dataset by illustrating different approaches to probe the KG. In the paper, we exemplify how to use a query language to answer simple research questions and how to use graph algorithms such as Node2Vec and PageRank, that benefit from the complete graph structure. More importantly, the chosen structure provides total flexibility for continuously expanding and enriching the graph. Our solution is general, so the proposed pipeline for building the KG can encode other original corpora of time-dependent and complex phenomena. Overall, we present a mechanism to create KG databases providing a more versatile representation of time-dependent related text data and a particular application to the all-important FRUS database.
O artigo compara as representações de d. João VI e d. Pedro I em retratos de Estado, e suas expectativas de regência, por meio da análise das vestimentas e dos acessórios com que são ilustrados, entre 1808 e 1831. Argumenta-se que os estilos de indumentária traduzem parcialmente seus entendimentos e planos sobre a política nacional e o papel de monarca ou imperador.
Palavras-chave: indumentária; regência; Independência do Brasil; retratos de Estado.
Diplomatics. Archives. Seals, Bibliography. Library science. Information resources
In this paper, we introduce a novel large-scale video dataset dubbed MM-SEAL for multi-person multi-grained spatio-temporal action localization among human daily life. We are the first to propose a new benchmark for multi-person spatio-temporal complex activity localization, where complex semantic and long duration bring new challenges to localization tasks. We observe that limited atomic actions can be combined into many complex activities. MM-SEAL provides both atomic action and complex activity annotations, producing 111.7k atomic actions spanning 172 action categories and 17.7k complex activities spanning 200 activity categories. We explore the relationship between atomic actions and complex activities, finding that atomic action features can improve the complex activity localization performance. Also, we propose a new network which generates temporal proposals and labels simultaneously, termed Faster-TAD. Finally, our evaluations show that visual features pretrained on MM-SEAL can improve the performance on other action localization benchmarks. We will release the dataset and the project code upon publication of the paper.
The Novelty Search (NS) algorithm was proposed more than a decade ago. However, the mechanisms behind its empirical success are still not well formalized/understood. This short note focuses on the effects of the archive on exploration. Experimental evidence from a few application domains suggests that archive-based NS performs in general better than when Novelty is solely computed with respect to the population. An argument that is often encountered in the literature is that the archive prevents exploration from backtracking or cycling, i.e. from revisiting previously encountered areas in the behavior space. We argue that this is not a complete or accurate explanation as backtracking - beside often being desirable - can actually be enabled by the archive. Through low-dimensional/analytical examples, we show that a key effect of the archive is that it counterbalances the exploration biases that result, among other factors, from the use of inadequate behavior metrics and the non-linearities of the behavior mapping. Our observations seem to hint that attributing a more active role to the archive in sampling can be beneficial.
Historians and archivists often find and analyze the occurrences of query words in newspaper archives, to help answer fundamental questions about society. But much work in text analytics focuses on helping people investigate other textual units, such as events, clusters, ranked documents, entity relationships, or thematic hierarchies. Informed by a study into the needs of historians and archivists, we thus propose ClioQuery, a text analytics system uniquely organized around the analysis of query words in context. ClioQuery applies text simplification techniques from natural language processing to help historians quickly and comprehensively gather and analyze all occurrences of a query word across an archive. It also pairs these new NLP methods with more traditional features like linked views and in-text highlighting to help engender trust in summarization techniques. We evaluate ClioQuery with two separate user studies, in which historians explain how ClioQuery's novel text simplification features can help facilitate historical research. We also evaluate with a separate quantitative comparison study, which shows that ClioQuery helps crowdworkers find and remember historical information. Such results suggest possible new directions for text analytics in other query-oriented settings.
Devendra Singh Chaplot, Murtaza Dalal, Saurabh Gupta
et al.
In this paper, we explore how we can build upon the data and models of Internet images and use them to adapt to robot vision without requiring any extra labels. We present a framework called Self-supervised Embodied Active Learning (SEAL). It utilizes perception models trained on internet images to learn an active exploration policy. The observations gathered by this exploration policy are labelled using 3D consistency and used to improve the perception model. We build and utilize 3D semantic maps to learn both action and perception in a completely self-supervised manner. The semantic map is used to compute an intrinsic motivation reward for training the exploration policy and for labelling the agent observations using spatio-temporal 3D consistency and label propagation. We demonstrate that the SEAL framework can be used to close the action-perception loop: it improves object detection and instance segmentation performance of a pretrained perception model by just moving around in training environments and the improved perception model can be used to improve Object Goal Navigation.