Pedro de Azambuja Varela, João Luís Marques, Afonso de Matos Silva
This study reconstructs the unfinished architectural project of the São Torcato Sanctuary, designed by Luís Inácio Barros Lima in 1825, using modern digital visualisation techniques. The objective is to create a three-dimensional (3D) model that accurately represents the sanctuary’s interiors, facilitating broader public engagement with this historical structure. Employing a systematic methodology, we analysed historical documentation and employed photogrammetry and parametric modelling through Rhinoceros (Rhino) software to reconstruct key architectural elements. We established a metric identification system based on regional measurement units, enabling a cohesive modelling process despite challenges posed by distorted and incomplete source material. The modelling process was organised into Levels of Development (LOD), allowing for a hierarchical approach from basic geometries to intricate features. We utilised Grasshopper for the efficient generation of various openings and detailed cornices, while photogrammetry facilitated the accurate modelling of existing capitals and the baldachin. A critical component of this reconstruction involved quantifying uncertainty within the model, utilising a false colour scheme to represent varying levels of confidence in the accuracy of different elements based on source availability. The average uncertainty score of the model was determined to be 40%, highlighting the speculative nature of some components due to incomplete documentation. This digital reconstruction contributes significantly to the architectural narrative of the São Torcato Sanctuary and serves as a resource for future research and public education. Despite inherent uncertainties, the model provides valuable insights into an architectural vision that remains unrealised, underscoring the importance of digital methods in the preservation and interpretation of architectural heritage.
Resumo Os portais de periódicos científicos constituem um importante recurso de apoio à produção e à disseminação dessas publicações no país, onde o exercício da atividade de editoria científica - centrado nas instituições de ensino superior públicas - enfrenta enormes desafios. Diversos trabalhos relatam a implantação de portais em universidades brasileiras e revelam que serviços de apoio aos periódicos e políticas de desenvolvimento das coleções são fundamentais para garantir a qualidade das publicações, mas exigem esforço de coordenação nas instituições. O presente estudo visa investigar os portais das instituições de ensino superior públicas brasileiras identificando se possuem políticas publicadas, que serviços oferecem e quem são os responsáveis por eles nas instituições. De natureza descritiva e exploratória, o estudo investiga 289 instituições identificadas nos microdados do censo da educação superior e, como métodos, utiliza pesquisa documental, análise de conteúdo e estatísticas descritivas. Como resultados, foram identificados 139 portais vinculados a 132 instituições (46% da população), dos quais 26% trazem informações sobre serviços oferecidos, sendo os mais comuns os de assessoria e capacitação e de controle, normalização, edição e indexação; 35% possuem políticas de desenvolvimento de coleções; e cerca de 65% identificam as unidades responsáveis por sua gestão. Embora revelem um crescimento significativo no número de portais na comparação com estudos anteriores, especialmente entre as universidades federais, os resultados sugerem haver espaço para o desenho de políticas públicas e institucionais específicas para estimular a institucionalização dos portais, mirando o aperfeiçoamento das políticas de desenvolvimento de coleções e o incremento da oferta de serviços para as publicações.
Museums. Collectors and collecting, Bibliography. Library science. Information resources
The military conflict in Ukraine has, of course, had a tangible impact on the preservation and presentation of its cultural heritage. Some exhibitions have brought it to the attention of the European public. This article focuses on the gallery presentation of old art from contemporary Ukraine in a Central European context. Using the last two years’ exhibitions of Johann Georg Pinsel’s famous sculptures at the Royal Castle of Wawel in Krakow as a model, it explores the possibilities of intimate presentations of iconic exhibits from Ukrainian collections. The pair of small-scale exhibitions in question reveals the potential of these quasi-marginal projects in the dramaturgy and image-building strategy of large museum institutions.
Choosing the right resource can speed up job completion, better utilize the available hardware, and visibly reduce costs, especially when renting computers in the cloud. This was demonstrated in earlier studies on HEPCloud. However, the benchmarking of the resources proved to be a laborious and time-consuming process. This paper presents GlideinBenchmark, a new Web application leveraging the pilot infrastructure of GlideinWMS to benchmark resources, and it shows how to use the data collected and published by GlideinBenchmark to automate the optimal selection of resources. An experiment can select the benchmark or the set of benchmarks that most closely evaluate the performance of its workflows. GlideinBenchmark, with the help of the GlideinWMS Factory, controls the benchmark execution. Finally, a scheduler like HEPCloud's Decision Engine can use the results to optimize resource provisioning.
Davide Cenzato, Zsuzsanna Lipták, Nadia Pisanti
et al.
We survey the different methods used for extending the BWT to collections of strings, following largely [Cenzato and Lipták, CPM 2022, Bioinformatics 2024]. We analyze the specific aspects and combinatorial properties of the resulting BWT variants and give a categorization of publicly available tools for computing the BWT of string collections. We show how the specific method used impacts on the resulting transform, including the number of runs, and on the dynamicity of the transform with respect to adding or removing strings from the collection. We then focus on the number of runs of these BWT variants and present the optimal BWT introduced in [Cenzato et al., DCC 2023], which implements an algorithm originally proposed by [Bentley et al., ESA 2020] to minimize the number of BWT-runs. We also discuss several recent heuristics and study their impact on the compression of biological sequences. We conclude with an overview of the applications and the impact of the BWT of string collections in bioinformatics.
Traditional robotic systems typically decompose intelligence into independent modules for computer vision, natural language processing, and motion control. Vision-Language-Action (VLA) models fundamentally transform this approach by employing a single neural network that can simultaneously process visual observations, understand human instructions, and directly output robot actions -- all within a unified framework. However, these systems are highly dependent on high-quality training datasets that can capture the complex relationships between visual observations, language instructions, and robotic actions. This tutorial reviews three representative systems: the PyBullet simulation framework for flexible customized data generation, the LIBERO benchmark suite for standardized task definition and evaluation, and the RT-X dataset collection for large-scale multi-robot data acquisition. We demonstrated dataset generation approaches in PyBullet simulation and customized data collection within LIBERO, and provide an overview of the characteristics and roles of the RT-X dataset for large-scale multi-robot data acquisition.
Traditional industrial robot programming is often complex and time-consuming, typically requiring weeks or even months of effort from expert programmers. Although Programming by Demonstration (PbD) offers a more accessible alternative, intuitive interfaces for robot control and demonstration collection remain challenging. To address this, we propose an Augmented Reality (AR)-enhanced robot teleoperation system that integrates AR-based control with spatial point cloud rendering, enabling intuitive, contact-free demonstrations. This approach allows operators to control robots remotely without entering the workspace or using conventional tools like the teach pendant. The proposed system is generally applicable and has been demonstrated on ABB robot platforms, specifically validated with the IRB 1200 industrial robot and the GoFa 5 collaborative robot. A user study evaluates the impact of real-time environmental perception, specifically with and without point cloud rendering, on task completion accuracy, efficiency, and user confidence. Results indicate that enhanced perception significantly improves task performance by 28% and enhances user experience, as reflected by a 12% increase in the System Usability Scale (SUS) score. This work contributes to the advancement of intuitive robot teleoperation, AR interface design, environmental perception, and teleoperation safety mechanisms in industrial settings for demonstration collection. The collected demonstrations may serve as valuable training data for machine learning applications.
Data perturbation-based privacy-preserving methods have been widely adopted in various scenarios due to their efficiency and the elimination of the need for a trusted third party. However, these methods primarily focus on individual statistical indicators, neglecting the overall quality of the collected data from a distributional perspective. Consequently, they often fall short of meeting the diverse statistical analysis requirements encountered in practical data analysis. As a promising sensitive data perturbation method, negative survey methods is able to complete the task of collecting sensitive information distribution while protecting personal privacy. Yet, existing negative survey methods are primarily designed for discrete sensitive information and are inadequate for real-valued data distributions. To bridge this gap, this paper proposes a novel real-value negative survey model, termed RVNS, for the first time in the field of real-value sensitive information collection. The RVNS model exempts users from the necessity of discretizing their data and only requires them to sample a set of data from a range that deviates from their actual sensitive details, thereby preserving the privacy of their genuine information. Moreover, to accurately capture the distribution of sensitive information, an optimization problem is formulated, and a novel approach is employed to solve it. Rigorous theoretical analysis demonstrates that the RVNS model conforms to the differential privacy model, ensuring robust privacy preservation. Comprehensive experiments conducted on both synthetic and real-world datasets further validate the efficacy of the proposed method.
This study explores the development of a customized project management methodology tailored for the implementation of online archives exhibitions, offering insights and evaluation derived from a research and development organization. Recognizing the distinctive requirements of such projects, the research investigates the design and application of a specialized project management approach. Through a comprehensive analysis, this study presents key insights into the methodology’s efficacy and evaluates its impact within the unique context of a research and development setting. The findings contribute valuable perspectives to the evolving field of project management, particularly in the realm of digital exhibition development within innovative organizational environments.
Anatole Bach, Antoine Chapuis, Corentin Morin
et al.
We demonstrate efficient in-plane optical fiber collection of single photon emission from quantum dots embedded in photonic crystal cavities. This was achieved via adiabatic coupling between a tapered optical fiber and a tapered on-chip photonic waveguide coupled to the photonic crystal cavity. The collection efficiency of a dot in a photonic crystal cavity was measured to be 5 times greater via the tapered optical fiber compared to collection by a microscope objective lens above the cavity. The single photon source was also characterized by second order photon correlations measurements giving g(2)(0)=0.17 under non-resonant excitation. Numerical calculations demonstrate that the collection efficiency could be further increased by improving the dot-cavity coupling and by increasing the overlap length of the tapered fiber with the on-chip waveguide. An adiabatic coupling of near unity is predicted for an overlap length of 5 microns.
Prasasthy Balasubramanian, Sadaf Nazari, Danial Khosh Kholgh
et al.
The extraction of cyber threat intelligence (CTI) from open sources is a rapidly expanding defensive strategy that enhances the resilience of both Information Technology (IT) and Operational Technology (OT) environments against large-scale cyber-attacks. While previous research has focused on improving individual components of the extraction process, the community lacks open-source platforms for deploying streaming CTI data pipelines in the wild. To address this gap, the study describes the implementation of an efficient and well-performing platform capable of processing compute-intensive data pipelines based on the cloud computing paradigm for real-time detection, collecting, and sharing CTI from different online sources. We developed a prototype platform (TSTEM), a containerized microservice architecture that uses Tweepy, Scrapy, Terraform, ELK, Kafka, and MLOps to autonomously search, extract, and index IOCs in the wild. Moreover, the provisioning, monitoring, and management of the TSTEM platform are achieved through infrastructure as a code (IaC). Custom focus crawlers collect web content, which is then processed by a first-level classifier to identify potential indicators of compromise (IOCs). If deemed relevant, the content advances to a second level of extraction for further examination. Throughout this process, state-of-the-art NLP models are utilized for classification and entity extraction, enhancing the overall IOC extraction methodology. Our experimental results indicate that these models exhibit high accuracy (exceeding 98%) in the classification and extraction tasks, achieving this performance within a time frame of less than a minute. The effectiveness of our system can be attributed to a finely-tuned IOC extraction method that operates at multiple stages, ensuring precise identification of relevant information with low false positives.
With the beginning of the full-scale war in Ukraine, along with the preservation of the integrity of the state and the preservation of people's lives, the issues of preserving cultural heritage and national identification became acute. No matter how strange and difficult it is, the museums and nature reserves that did not fall under the occupation did not stop working. And this year, archaeological research, conferences, and thematic excursions were held. In part, the reenactment community was active. All this was combined with volunteering for the front and helping the victims, in particular colleagues, because a large part of cultural and scientific workers went to the front зdefend Ukraine with volunteers. Some of them, being in the Armed Forces, continue to contribute to the development of the industry in a new format. Those who remained in their places faced new realities, daily challenges (shelling, lack of electricity, water, reduction of personnel) but with a new powerful motivation that moves our people to victory. Museum workers and reenactors decided to share their thoughts.
Autonomous vehicles (AVs) require comprehensive and reliable pedestrian trajectory data to ensure safe operation. However, obtaining data of safety-critical scenarios such as jaywalking and near-collisions, or uncommon agents such as children, disabled pedestrians, and vulnerable road users poses logistical and ethical challenges. This paper evaluates a Virtual Reality (VR) system designed to collect pedestrian trajectory and body pose data in a controlled, low-risk environment. We substantiate the usefulness of such a system through semi-structured interviews with professionals in the AV field, and validate the effectiveness of the system through two empirical studies: a first-person user evaluation involving 62 participants, and a third-person evaluative survey involving 290 respondents. Our findings demonstrate that the VR-based data collection system elicits realistic responses for capturing pedestrian data in safety-critical or uncommon vehicle-pedestrian interaction scenarios.
The rapid entry of machine learning approaches in our daily activities and high-stakes domains demands transparency and scrutiny of their fairness and reliability. To help gauge machine learning models' robustness, research typically focuses on the massive datasets used for their deployment, e.g., creating and maintaining documentation for understanding their origin, process of development, and ethical considerations. However, data collection for AI is still typically a one-off practice, and oftentimes datasets collected for a certain purpose or application are reused for a different problem. Additionally, dataset annotations may not be representative over time, contain ambiguous or erroneous annotations, or be unable to generalize across issues or domains. Recent research has shown these practices might lead to unfair, biased, or inaccurate outcomes. We argue that data collection for AI should be performed in a responsible manner where the quality of the data is thoroughly scrutinized and measured through a systematic set of appropriate metrics. In this paper, we propose a Responsible AI (RAI) methodology designed to guide the data collection with a set of metrics for an iterative in-depth analysis of the factors influencing the quality and reliability} of the generated data. We propose a granular set of measurements to inform on the internal reliability of a dataset and its external stability over time. We validate our approach across nine existing datasets and annotation tasks and four content modalities. This approach impacts the assessment of data robustness used for AI applied in the real world, where diversity of users and content is eminent. Furthermore, it deals with fairness and accountability aspects in data collection by providing systematic and transparent quality analysis for data collections.
The remarkable success of the use of machine learning-based solutions for network security problems has been impeded by the developed ML models' inability to maintain efficacy when used in different network environments exhibiting different network behaviors. This issue is commonly referred to as the generalizability problem of ML models. The community has recognized the critical role that training datasets play in this context and has developed various techniques to improve dataset curation to overcome this problem. Unfortunately, these methods are generally ill-suited or even counterproductive in the network security domain, where they often result in unrealistic or poor-quality datasets. To address this issue, we propose an augmented ML pipeline that leverages explainable ML tools to guide the network data collection in an iterative fashion. To ensure the data's realism and quality, we require that the new datasets should be endogenously collected in this iterative process, thus advocating for a gradual removal of data-related problems to improve model generalizability. To realize this capability, we develop a data-collection platform, netUnicorn, that takes inspiration from the classic "hourglass" model and is implemented as its "thin waist" to simplify data collection for different learning problems from diverse network environments. The proposed system decouples data-collection intents from the deployment mechanisms and disaggregates these high-level intents into smaller reusable, self-contained tasks. We demonstrate how netUnicorn simplifies collecting data for different learning problems from multiple network environments and how the proposed iterative data collection improves a model's generalizability.
Intrusion research frequently collects data on attack techniques currently employed and their potential symptoms. This includes deploying honeypots, logging events from existing devices, employing a red team for a sample attack campaign, or simulating system activity. However, these observational studies do not clearly discern the cause-and-effect relationships between the design of the environment and the data recorded. Neglecting such relationships increases the chance of drawing biased conclusions due to unconsidered factors, such as spurious correlations between features and errors in measurement or classification. In this paper, we present the theory and empirical data on methods that aim to discover such causal relationships efficiently. Our adaptive design (AD) is inspired by the clinical trial community: a variant of a randomized control trial (RCT) to measure how a particular ``treatment'' affects a population. To contrast our method with observational studies and RCT, we run the first controlled and adaptive honeypot deployment study, identifying the causal relationship between an ssh vulnerability and the rate of server exploitation. We demonstrate that our AD method decreases the total time needed to run the deployment by at least 33%, while still confidently stating the impact of our change in the environment. Compared to an analogous honeypot study with a control group, our AD requests 17% fewer honeypots while collecting 19% more attack recordings than an analogous honeypot study with a control group.
Collectors are major actors in the global art market as they often spend large sums of money fostering the business. Concerning sustainable collecting practices—i.e., the balance between what is the best for people and for the environment—collectors’ actions seem contradictory. Firstly, ontologically, to collect is to accumulate artworks; secondly, art—the object of the collectors’ desire—and the global art world are not closely aligned with the climate crisis. The art ecosystem encourages trips to participate in art events worldwide, increasing the carbon footprint impact, and rarely uses recycled materials, causing waste. The economic model of the art market lacks sustainability, raising the question: how can we promote a sustainable collecting attitude? In this exploratory study, we will observe art market players, especially the Iberian Peninsula collectors’ actions, in terms of their contribution to reducing the environmental impact of purchases. Based on data, reports, interviews, and published sources, we will investigate collectors’ awareness of the subject and evaluate their adopted actions. As, to date, no analysis has been carried out on the trends of Iberian collecting in the field of climate sustainability, we have focused our study on finding data from the primary source par excellence: the collectors themselves. The aim is to fuel the need for a paradigm shift, concluding on a slow collecting attitude.
The digital circulation and forms of digital artworks appear to be immaterial. However, our analysis of their materiality discloses new dimensions of affinities between the art market and the financial market. These relations have been recognized in the social sciences in order to understand the transformation of standardized mass markets into markets in which the highest value is attached to the singularity and authenticity of a commodity. Financial markets are undergoing such a transformation. The art market is essentially associated with singularity and authenticity. New digital technologies transform the art market’s working. Despite the hopes and visions of art being liberated from the present curatorship of gallery and museum representatives, curators, critics, collectors, and gallery owners, art’s valuation perpetuated in blockchain infrastructure comes closer to the valuation and appreciation stemming from financial markets. We study three auctions of artworks that took place in Poland and were hailed as the first auctions of NFT tokens associated with art. Thus, we delve into the most common and propagating forms of digitalization based on blockchains that have been associated with art. The focus on materiality enables us to identify new dimensions of this process. We present two understandings of art’s materiality. The first assumes that materiality is a transmitter of meaning. In the second, materiality refers to the interaction with – and usage of – not only physical, but also digital objects. From the first perspective, artworks’ manifestations are anchored in physical objects or singularized data files whose value is assessed by current decision-makers, such as gallery and museum representatives, collectors, curators, art critics, and gallery owners. Physical objects are kept in galleries, museums, and among collectors. Such a vantage point hampers how digital circulation co-creates the valuation of artworks, their originality, and the logics of circulation. From the second perspective, the standards of smart contracts, the means of token collecting, and their pricing are used not only by humans, but are also submitted to data processing.
Two wax busts depicting a man and a woman, with authentic clothing and hair, once part of the Royal Collections and
recently placed on display in the Museo del Prado, were previously ascribed to Giovanni Francesco Pieri, a Tuscan wax modeller
active in Naples. Thanks to documentation hitherto not linked to them, the author proposes to attribute the busts to Filippo Scandellari, the first artist to create realistic wax portrait busts in his birthplace of Bologna. Although these works were intended for
private collecting, from the late eighteenth century onwards in Spain there was also a widespread trend for organising popular exhibitions of wax figures, often by Italian artists and entrepreneurs, of whom this article provides an overview, with a particular focus
on wax portraits of Spanish royalty.