The present study puts forward a novel biographical knowledge graph (KG) on Prof. S. R. Ranganathan, one of the pioneering figures in the Library and Information Science (LIS) domain. It has been found that most of the relevant facts about Ranganathan exist in a variety of resources (e.g., books, essays, journal articles, websites, blogs, etc.), offering information in a fragmented and piecemeal way. With this dedicated KG (henceforth known as RKG), we hope to furnish a 360-degree view of his life and achievements. To the best of our knowledge, such a dedicated representation is unparalleled in its scope and coverage: using state-of-the-art technology for anyone to openly access, use/re-use, and contribute. Inspired by Ranganathan's theories and ideas, the KG was developed using a "facet-based methodology" at two levels: in the identification of the vital biographical aspects and the development of the ontological model. Finally, with this study, we call for a community-driven effort to enhance the KG and pay homage to the Father of Library Science on the hundredth anniversary of his revitalizing the LIS domain through his enduring participation.

Knowledge Representation (KR) and facet-analytical Knowledge Organization (KO) have been the two most prominent methodologies of data and knowledge modelling in the Artificial Intelligence community and the Information Science community, respectively. KR boasts of a robust and scalable ecosystem of technologies to support knowledge modelling while, often, underemphasizing the quality of its models (and model-based data). KO, on the other hand, is less technology-driven but has developed a robust framework of guiding principles (canons) for ensuring modelling (and model-based data) quality. This paper elucidates both the KR and facet-analytical KO methodologies in detail and provides a functional mapping between them. Out of the mapping, the paper proposes an integrated KO-enriched KR methodology with all the standard components of a KR methodology plus the guiding canons of modelling quality provided by KO. The practical benefits of the methodological integration has been exemplified through a prominent case study of KR-based image annotation exercise.

Microsoft is building AI into Windows 11 proper, making it the first major consumer PC platform to embrace large language models. Here's how it could help you.

Tau Drones, which helped conduct the audit, now is taking part in a commercialization program sponsored by the National Science Foundation. Tau Drones, an energy analytics company, was accepted to and has started the National Science Foundation's National I-Corps Program. Tau Drones is a local company in Grand Forks, N.D., owned by Matt Dunlevy. The company evaluates commercial and residential buildings for energy efficiency. "We are trying to automate the energy auditing process as well as make sense of why energy is lost from certain buildings," said Karthik Balaji, Tau Drones' product development lead.

Ontological knowledge modelling of epics, though being an established research arena backed by concrete multilingual and multicultural works, still suffer from two key shortcomings. Firstly, all epic ontological models developed till date have been designed following ad-hoc methodologies, most often, combining existing general purpose ontology development methodologies. Secondly, none of the ad-hoc methodologies consider the potential reuse of existing epic ontological models for enrichment, if available. The paper presents, as a unified solution to the above shortcomings, the design and development of GENOME - the first dedicated methodology for iterative ontological modelling of epics, potentially extensible to works in different research arenas of digital humanities in general. GENOME is grounded in transdisciplinary foundations of canonical norms for epics, knowledge modelling best practices, application satisfiability norms and cognitive generative questions. It is also the first methodology (in epic modelling but also in general) to be flexible enough to integrate, in practice, the options of knowledge modelling via reuse or from scratch. The feasibility of GENOME is validated via a first brief implementation of ontological modelling of the Indian epic - Mahabharata by reusing an existing ontology. The preliminary results are promising, with the GENOME-produced model being both ontologically thorough and performance-wise competent

We base our work on the teleosemantic modelling of concepts as abilities implementing the distinct functions of recognition and classification. Accordingly, we model two types of concepts - substance concepts suited for object recognition exploiting visual properties, and classification concepts suited for classification of substance concepts exploiting linguistically grounded properties. The goal in this paper is to demonstrate that object recognition can be construed as classification via visual properties, as distinct from work in mainstream computer vision. Towards that, we present an object recognition process based on Ranganathan's four-phased faceted knowledge organization process, grounded in the teleosemantic distinctions of substance concept and classification concept. We also briefly introduce the ongoing project MultiMedia UKC, whose aim is to build an object recognition resource following our proposed process.

Human body motions can be captured as a high-dimensional continuous signal using motion sensor technologies. The resulting data can be surprisingly rich in information, even when captured from persons with limited mobility. In this work, we explore the use of limited upper-body motions, captured via motion sensors, as inputs to control a 7 degree-of-freedom assistive robotic arm. It is possible that even dense sensor signals lack the salient information and independence necessary for reliable high-dimensional robot control. As the human learns over time in the context of this limitation, intelligence on the robot can be leveraged to better identify key learning challenges, provide useful feedback, and support individuals until the challenges are managed. In this short paper, we examine two uninjured participants' data from an ongoing study, to extract preliminary results and share insights. We observe opportunities for robot intelligence to step in, including the identification of inconsistencies in time spent across all control dimensions, asymmetries in individual control dimensions, and user progress in learning. Machine reasoning about these situations may facilitate novel interface learning in the future.

We assume that substances in the world are represented by two types of concepts, namely substance concepts and classification concepts, the former instrumental to (visual) perception, the latter to (language based) classification. Based on this distinction, we introduce a general methodology for building lexico-semantic hierarchies of substance concepts, where nodes are annotated with the media, e.g., videos or photos, from which substance concepts are extracted, and are associated with the corresponding classification concepts. The methodology is based on Ranganathan's original faceted approach, contextualized to the problem of classifying substance concepts. The key novelty is that the hierarchy is built exploiting the visual properties of substance concepts, while the linguistically defined properties of classification concepts are only used to describe substance concepts. The validity of the approach is exemplified by providing some highlights of an ongoing project whose goal is to build a large scale multimedia multilingual concept hierarchy.

A team lead by researchers in the Pritzker School of Molecular Engineering (PME) at the University of Chicago reports that it has developed an artificial intelligence-led process that uses big data to design new proteins that could have implications across the healthcare, agriculture, and energy sectors. By developing machine-learning models that can review protein information culled from genome databases, the scientists say they found relatively simple design rules for building artificial proteins. When the team constructed these artificial proteins in the lab, they discovered that they performed chemistries so well that they rivaled those found in nature. "We have all wondered how a simple process like evolution can lead to such a high-performance material as a protein," said Rama Ranganathan, PhD, Joseph Regenstein Professor in the Department of Biochemistry and Molecular Biology, Pritzker Molecular Engineering, and the College. "We found that genome data contains enormous amounts of information about the basic rules of protein structure and function, and now we've been able to bottle nature's rules to create proteins ourselves."

A team of researchers from the Pritzker School of Molecular Engineering (PME) at the University of Chicago has recently succeeded in the creation of an AI system that can create entirely new, artificial proteins by analyzing stores of big data. Proteins are macromolecules essential for the construction of tissues in living things, and critical to the life of cells in general. Proteins are used by cells as chemical catalysts to make various chemical reactions occur and to carry out complex tasks. If scientists can figure out how to reliably engineer artificial proteins, it could open the door to new ways of carbon capturing, new methods of harvesting energy, and new disease treatments. Artificial proteins have the power to dramatically alter the world we live in.