The concepts of information and media literacy have been central components of digital literacy since the digitization of information began. However, the increasing influence of artificial intelligence on how individuals locate, evaluate, and create content has significant implications for what it means to be information and media literate. This paper begins by exploring the role artificial intelligence plays at the various stages of information retrieval and creation processes. Following this, the paper reviews existing digital literacy frameworks to ascertain their definitions of information and media literacy and the potential impact of artificial intelligence on them. We find that digital literacy frameworks have been slow to react to artificial intelligence and its repercussions, and we recommend a number of strategies for the future. These strategies center around a more agile, responsive, and participatory approach to digital literacy framework development and maintenance.

Semantic interoperability establishes intercommunications and enables data sharing across disparate systems. In this study, we propose an ostensive information architecture for healthcare information systems to decrease ambiguity caused by using signs in different contexts for different purposes. The ostensive information architecture adopts a consensus-based approach initiated from the perspective of information systems re-design and can be applied to other domains where information exchange is required between heterogeneous systems. Driven by the issues in FHIR (Fast Health Interoperability Resources) implementation, an ostensive approach that supplements the current lexical approach in semantic exchange is proposed. A Semantic Engine with an FHIR knowledge graph as the core is constructed using Neo4j to provide semantic interpretation and examples. The MIMIC III (Medical Information Mart for Intensive Care) datasets and diabetes datasets have been employed to demonstrate the effectiveness of the proposed information architecture. We further discuss the benefits of the separation of semantic interpretation and data storage from the perspective of information system design, and the semantic reasoning towards patient-centric care underpinned by the Semantic Engine.

• This paper presents a new method for generating 50K diverse synthetic time series. • We present a discussion on time series characteristics and metrics with a view to understanding time series diversity. • We developed a robust framework for validating diversity in synthetic time series generation.

The global food and agricultural industry has a total market value of USD 8 trillion in 2016, and decision makers in the Agri sector require appropriate tools and up-to-date information to make predictions across a range of products and areas. Traditionally, these requirements are met with information processed into a data warehouse and data marts constructed for analyses. Increasingly however, data are coming from outside the enterprise and often in unprocessed forms. As these sources are outside the control of companies, they are prone to change and new sources may appear. In these cases, the process of accommodating these sources can be costly and very time consuming. To automate this process, what is required is a sufficiently robust extract–transform–load process; external sources are mapped to some form of ontology, and an integration process to merge the specific data sources. In this paper, we present an approach to automating the integration of data sources in an Agri environment, where new sources are examined before an attempt to merge them with existing data marts. Our validation uses a case study of real world Agri data to demonstrate the robustness of our approach and the efficiency of materializing data marts.

The purpose of this study was to generate movement category velocity thresholds for elite Gaelic football (GF) and hurling referees using a two-stage unsupervised clustering technique. Activity data from 41 GF and 38 hurling referees was collected using global positioning system technology during 338 and 221 competitive games, respectively. The elbow method was used in stage one to identify the number of movement categories in the datasets. In stage two, the respective velocity thresholds for each category were identified using spectral clustering. The efficacy of these thresholds was examined using a regression analysis performed between the median of each of the velocity thresholds and the raw velocity data. Five velocity thresholds were identified for both GF and hurling referees (mean ± standard deviation: GF referees; 0.70±0.09, 1.66±0.19, 3.28±0.41, 4.87±0.61, 6.49±0.50 m·s⁻¹; hurling referees; 0.69±0.11, 1.60±0.25, 3.09±0.52, 4.63±0.58, 6.35±0.43 m·s⁻¹). With the exception of the lowest velocity threshold, all other thresholds were significantly higher for GF referees. The newly generated velocity thresholds were more strongly associated with the raw velocity data than traditional generic categories. The provision of unique velocity thresholds will allow applied practitioners to better quantify the activity profile of elite GF and hurling referees during training and competition.