Decoding Digital Curation for Cultural Heritage: A Survey and Extended Proposal

Digital curation in cultural heritage organizations is becoming more and more established as a practice as empirical research into tools, techniques, skills, standards and best practices in particular contexts emerge. Curators typically tackle technical obsolescence, ignorance of good practices, uncertainty over appropriate infrastructure as well as lack of resources. Digital curation activities are complex processes that involve diverse skills and techniques, software tools and systems, and a range of professional and paraprofessional practices. The lack of acknowledged models opens the way to the usage of algorithmic approaches as an alternative/support/aid to human curation, especially as personal filters and as generators of public experiences realized through machine learning and virtual reality tools. However, the relationship of curation with materials and their digital counterparts lacks an encompassing effective framework. Here, we claim that an encompassing framework of digital curation is strongly needed: this would provide and open to the development of software environments in support. It addresses all the phases that concern the digital curation activities, as well as broader technological factors impacting digital curation practice. Therefore, this research, on the one hand, formulates a definition of digital curation and then discusses the involved activities and the roles and responsibilities of the digital curators; on the other hand, it unpacks the notions of semantic encoding and argues for the importance of the data lifecycle and of metadata to digital curation. Finally, it addresses researcher behaviours and the ways in which multidisciplinarity, collective practice, and collaboration shape the sharing and reuse of data in the context of the knowledge transfer. In developing or following an appropriate digital curation workflow, designers should consider issues such as scope (what services will be offered and to which audiences), best user experience practices or community outreach or both, and model’s ability to represent work practices. The focus of the research will be on digital curation tools that are interactive, pervasive, multimodal, physical and social for further research on examining how we can better map human perceptions and experiences of the built environment. Although this is a mainly survey-type investigation, this research will also draw on other disciplines, including design, cultural heritage, science, and technology. It aims to surface similarities and differences in digital curation activities, as well as broader technological factors impacting digital curation practice.This project is also related to a project called "Be- archaeo" which is funded by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie, Grant Agreement N°. 82382

Achieving Equity and Excellence: A Multilevel Modeling of the Relationships among School Climate, Students’ Motivation, and Achievement with TIMSS 2019

This study examined the effects of students’ motivational factors and school climate factors on eighth-grade math and science achievement in the U.S. using data from the 2019 Trends in International Mathematics and Science Study (TIMSS). Two-level, cross-sectional hierarchical linear models were developed to analyze the data. The results showed that there was a significant variation in math and science achievement across schools. Only students’ confidence in math and school SES had statistically significant effects on students’ math achievement. School location and school SES had statistically significant effects on science achievement

Transdisciplinary approach to archaeological investigations in a Semantic Web perspective

In recent years, the transdisciplinarity of archaeological studies has greatly increased because of the mature interactions between archaeologists and scientists from different disciplines (called "archaeometers"). A number of diverse scientific disciplines collaborate to get an objective account of the archaeological records. A large amount of digital data support the whole process, and there is a great value in keeping the coherence of information and knowledge, as contributed by each intervening discipline. During the years, a number of representation models have been developed to account for the recording of the archaeological process in data bases. Lately, some semantic models, compliant with the CRMarchaeo reference model, have been developed to account for linking the institutional forms with the formal knowledge concerning the archaeological excavations and the related findings. On the contrary, the archaeometric processes have not been addressed yet in the Semantic Web community and only an upper reference model, called CRMsci, accounts for the representation of the scientific investigations in general. This paper presents a modular computational ontology for the interlinked representation of all the facts related to the archaeological and archaeometric analyses and interpretations, also connected to the recording catalogues. The computational ontology is compliant with CIDOC-CRM reference models CRMarchaeo and CRMsci and introduces a number of novel classes and properties to merge the two worlds in a joint representation. The ontology is in use in "Beyond Archaeology", a methodological project for the establishing of a transdisciplinary approach to archaeology and archaeometry, interlinked through a semantic model of processes and objects

Effects of peripheral neuropathy on exercise capacity and quality of life in patients with chronic obstructive pulmonary diseases

Introduction: Chronic obstructive pulmonary diseases (COPD) have some systemic effects including systemic inflammation, nutritional abnormalities, skeletal muscle dysfunction, and cardiovascular, skeletal and neurological disorders. Some studies have reported the presence of peripheral neuropathy (PNP) at an incidence of 28-94% in patients with COPD. Our study aimed to identify whether PNP affects exercise performance and quality of life in COPD patients. Material and methods: Thirty mild-very severe patients with COPD (male/female = 29/1, mean age = 64 +/- 10 years) and 14 normal subjects (male/female = 11/5, mean age = 61 +/- 8 years) were included in the present study. All subjects underwent pulmonary function testing (PFT), cardiopulmonary exercise testing, electroneuromyography and short form 36 (SF-36). Results: Peak oxygen uptake (PeakVO(2)) was lower in COPD patients (115 +/- 0.53 l/min) than healthy subjects (2.02 +/- 0.46 l/min) (p = 0.0001). There was no PUP in healthy subjects while 16 (53%) of the COPD patients had PNP. Forced expiratory volume in 1 s (FEV1) and PeakVO(2) were significantly different between patients with PNP and those without (p = 0.009, p = 0.03 respectively). Quality of life of patients with PNP was lower than that of patients without PNP (p < 0.05). Conclusions: The present study demonstrates the exercise limitation in COPD patients with PUP Thus, presence of PNP has a poor effect on exercise capacity and quality of life in patients with COPD. Furthermore, treatment modalities for PNP can be recommended to these patients in order to improve exercise capacity and quality of life.Wo

Applications of AI in Non-Stationary Markets

Artificial intelligence, AI, has received increasing attention from the finance industry over recent years. There have been many successful applications of AI in finance, including but not limited to derivative pricing, asset management, credit risk, algorithmic trading, and simulation of time series with stylized facts. This thesis introduces various applications of AI in two major fields, namely (a) quantitative/computational finance and (b) asset management. In each chapter, we address non-stationarity of markets under consideration and focus on building methodologies that would work under the non-stationary behavior of those market. In computational finance, fast and accurate algorithms are of great importance, especially when analytical solutions are unavailable. Although traditional methods are reliable and easily explainable, they are computationally expensive. Transform methods like Fast-Fourier transform provide faster option pricing, yet they cannot be applied to path-dependent products. In Chapter 2, we build a pricing engine based on supervised deep neural networks. We show that neural networks can replicate major stochastic processes with or without stochastic volatility in both pure diffusion and pure jump frameworks. We validate our models across different ranges of model parameters. Supervised neural networks accelerate the derivative pricing significantly compared to traditional methods. Applications of AI in asset management are triggered by different dynamics, but they are fully data-driven and thus rely on the availability of data. Chapter 3 proposes a novel prediction framework for cash flow forecasting of illiquid products/assets. Our single-step neural network model provides the investors and managers of funds with a tool to manage the liquidity of their cash flows for financial planning. Our framework is also sensitive to adverse market conditions that could help prepare for upcoming crises such as Covid. In Chapter 4, we propose novel methodologies for mergers and acquisitions (M&A) to predict the deal announcement based on rumors and takeover success. M&A data is highly imbalanced in nature, and the cost of misclassifying a cancelled rumor/cancelled deal as announced deal/takeover success is higher than the other. Hence, we utilize sequential model-based optimization with tree-parzen estimators to maximize the recall score by tuning hyperparameters of neural networks. We improve the recall by 10% without sacrificing accuracy, and our results show that the proposed methodology is robust against changing market environments. In the last chapter, we build a two-step neural network model for sector rotation strategies using macroeconomic variables. The portfolio built based on our proposed model not only beats the benchmark portfolio but can also predict longer horizons

Establishing Trust and Transparency in the Context of Contemporary and Digital History: Implementing Digital Curation Strategies for Digital Research Infrastructure

editorial reviewedThis poster delves into the intricate interplay between global expectations for research in contemporary history, the evolving landscape of digital curation, the imperative for efficient research data management, the creation of a well-structured and effective plan for digital infrastructure by emphasizing the significance of developing a comprehensive framework for the analysis and interpretation of contemporary history.9. Industry, innovation and infrastructur