PurposeThis study investigates the level of artificial intelligence (AI) awareness among library leaders, practitioners and scientists of Indonesian academic libraries to elucidate the benefits of AI implementation and its necessary infrastructure and challenges.Design/methodology/approachThe study adopted a purposive sampling technique to select the 38 participants and thematic analysis to analyze the data, identifying eight themes: understanding of AI, AI adoption, benefits of AI, competencies needed to support AI, facilities to support AI, factors supporting AI adoption, AI-inhibiting factors and expectations of AI.FindingsDifferent viewpoints provided full awareness among library stakeholders and sufficient information to begin AI initiatives in Indonesian libraries as leaders, practitioners and scientists had a favorable, open and encouraging outlook on AI.Research limitations/implicationsThe study does not investigate variations in perspectives between the participants, but it examines their understanding of AI and elaborates the results into the concept of an intelligent library. Moreover, this study only uses samples from academic libraries.Practical implicationsLibraries can take these results into consideration before implementing AI, especially in technology and facilities, librarian competency with regard to AI and leadership roles in AI projects.Social implicationsLibrary boards and library associations can use this research as a source to create guidelines about AI implementation in academic libraries.Originality/valueThe study addresses the gap in the research on university libraries' readiness and awareness to implement AI, especially in developing countries.

https://repository.unair.ac.id/120004/1/Leaders%2C%20practitioners%20and%20scientists%27%20awareness%20of%20artificial%20intelligence%20in%20libraries_%20a%20pilot%20study.pdf

Leaders, practitioners and scientists' awareness of artificial intelligence in libraries: a pilot study

This paper presents our modeling and architecture approaches for building a highly accurate low-latency language identification system to support multilingual spoken queries for voice assistants. A common approach to solve multilingual speech recognition is to run multiple monolingual ASR systems in parallel and rely on a language identification (LID) component that detects the input language. Conventionally, LID relies on acoustic only information to detect input language. We propose an approach that learns and combines acoustic level representations with embeddings estimated on ASR hypotheses resulting in up to 50% relative reduction of identification error rate, compared to a model that uses acoustic only features. Furthermore, to reduce the processing cost and latency, we exploit a streaming architecture to identify the spoken language early when the system reaches a predetermined confidence level, alleviating the need to run multiple ASR systems until the end of input query. The combined acoustic and text LID, coupled with our proposed streaming runtime architecture, results in an average of 1500ms early identification for more than 50% of utterances, with almost no degradation in accuracy. We also show improved results by adopting a semi-supervised learning (SSL) technique using the newly proposed model architecture as a teacher model.

Streaming Language Identification using Combination of Acoustic Representations and ASR Hypotheses

Artificial Intelligence (AI) in Pakistani university library services

This study aims to investigate the implementation of artificial intelligence (AI) in libraries from 2011 to 2020. This study uses PRISMA guidelines to perform a systematic literature review (SLR). The articles were obtained mainly from the SCOPUS database, with Google Scholar as the supporting database. AI can easily be adopted in libraries, especially for technical services such as classification and cataloguing, library management such as staffing and decision-making, library services such as referencing and information service, and for information literacy. Successful AI adoption is, however, still debatable, because there are many requirements that need to be met, so that it can be inclusively adopted in libraries. There is a lack of research on the application of AI in libraries, especially in the context of its actual implementation. The results of this study offer insights on the implementation of AI in library support services.

Is adopting artificial intelligence in libraries urgency or a buzzword? A systematic literature review

This paper provides a follow-up audit of security checkpoints (or simply checkpoints) for mass transportation hubs such as airports and seaports aiming at the post-pandemic R&D adjustments. The goal of our study is to determine biometric-enabled resources of checkpoints for a counter-epidemic response. To achieve the follow-up audit goals, we embedded the checkpoint into the Emergency Management Cycle (EMC) – the core of any doctrine that challenges disaster. This embedding helps to identify the technology-societal gaps between contemporary and post-pandemic checkpoints. Our study advocates a conceptual exploration of the problem using EMC profiling and formulates new tasks for checkpoints based on the COVID-19 pandemic lessons learned. In order to increase practical value, we chose a case study of face biometrics for an experimental post-pandemic follow-up audit.

https://doi.org/10.1109/access.2023.3238311

Post-Pandemic Follow-Up Audit of Security Checkpoints

This article describes experiments in the use of machine learning techniques at the British Library to assign language codes to catalog records, in order to provide information about the language o...

Victoria Morris

Papers

Automated Language Identification of Bibliographic Resources