Since decades, the modelling of metadata has been core to the functioning of any academic library. Its importance has only enhanced with the increasing pervasiveness of Generative Artificial Intelligence (AI)-driven information activities and services which constitute a library's outreach. However, with the rising importance of metadata, there arose several outstanding problems with the process of designing a library metadata model impacting its reusability, crosswalk and interoperability with other metadata models. This paper posits that the above problems stem from an underlying thesis that there should only be a few core metadata models which would be necessary and sufficient for any information service using them, irrespective of the heterogeneity of intra-domain or inter-domain settings. To that end, this paper advances a contrary view of the above thesis and substantiates its argument in three key steps. First, it introduces a novel way of thinking about a library metadata model as an ontology-driven composition of five functionally interlinked representation levels from perception to its intensional definition via properties. Second, it introduces the representational manifoldness implicit in each of the five levels which cumulatively contributes to a conceptually entangled library metadata model. Finally, and most importantly, it proposes a Generative AI-driven Human-Large Language Model (LLM) collaboration based metadata modelling approach to disentangle the entanglement inherent in each representation level leading to the generation of a conceptually disentangled metadata model. Throughout the paper, the arguments are exemplified by motivating scenarios and examples from representative libraries handling cancer information.

MLOps or ML Ops is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. The word is a compound of “machine learning” and the continuous…

The potential of model zoos is currently hindered by the lack of a structured, queryable metadata format. Current Machine learning (ML) practitioners and organizations repositories include a wide range of information, in a form are building model zoos of pre-trained of a model card (Mitchell et al., 2019), but such information models, containing metadata describing properties is mostly for human consumption, making it hard for automatic of the ML models and datasets that are useful extension or management. At the same time, the level for reporting, auditing, reproducibility, and interpretability of the detail remains coarse-grained: for instance, Amazon purposes. The metatada is currently SageMaker, AzureML, MLflow (Zaharia et al., 2018) do not not standardised; its expressivity is limited; and require mandatory reporting of the related metadata, except there is no interoperable way to store and query for model name and version. Practitioners have to search on it. Consequently, model search, reuse, comparison, external websites for further metadata information such as and composition are hindered. In this paper, the data instances, and they even have to evaluate the model we advocate for standardized ML model metadata at hand in order to assess its performance.

Millions of stray animals suffer on the streets or are euthanized in shelters every day around the world. In order to better adopt stray animals, scoring the pawpularity (cuteness) of stray animals is very important, but evaluating the pawpularity of animals is a very labor-intensive thing. Consequently, there has been an urgent surge of interest to develop an algorithm that scores pawpularity of animals. However, the dataset in Kaggle not only has images, but also metadata describing images. Most methods basically focus on the most advanced image regression methods in recent years, but there is no good method to deal with the metadata of images. To address the above challenges, the paper proposes an image regression model called PETS-SWINF that considers metadata of the images. Our results based on a dataset of Kaggle competition, "PetFinder.my", show that PETS-SWINF has an advantage over only based images models. Our results shows that the RMSE loss of the proposed model on the test dataset is 17.71876 but 17.76449 without metadata. The advantage of the proposed method is that PETS-SWINF can consider both low-order and high-order features of metadata, and adaptively adjust the weights of the image model and the metadata model. The performance is promising as our leadboard score is ranked 15 out of 3545 teams (Gold medal) currently for 2021 Kaggle competition on the challenge "PetFinder.my".

As Chief Data Officer for the OSTHUS Group, Eric Little co-founded LeapAnalysis, a new approach to AI, data integration and analytics. LeapAnalysis is the first fully federated and virtualised search and analytics engine that runs on semantic metadata. It allows users to combine semantic models (ontologies) with machine learning algorithms to provide customers with unparalleled flexibility in utilizing their data. Nearly all technologies surrounding AI and analytics are purely statistical in nature, using algorithmic approaches that are not incredibly novel, such as decision trees, neural networks, etc. The logical framework that contextualises these things is often missing.