Image processing for the extraction of nutritional information from food labels

Current techniques for tracking nutritional data require undesirable amounts of either time or man-power. People must choose between tediously recording and updating dietary information or depending on unreliable crowd-sourced or costly maintained databases. Our project looks to overcome these pitfalls by providing a programming interface for image analysis that will read and report the information present on a nutrition label directly. Our solution involves a C++ library that combines image pre-processing, optical character recognition, and post-processing techniques to pull the relevant information from an image of a nutrition label. We apply an understanding of a nutrition label\u27s content and data organization to approach the accuracy of traditional data-entry methods. Our system currently provides around 80% accuracy for most label images, and we will continue to work to improve our accuracy

Reconciliation through Description: Using Metadata to Realize the Vision of the National Research Centre for Truth and Reconciliation

PostprintThis articlewill discuss the history and context surrounding the document collection and statement gathering mandates of the Truth and Reconciliation Commission of Canada and the challenges the newly established National Research Centre for Truth and Reconciliation will face in applying the Commission’s metadata set in the realization of its vision. By working respectfully with Indigenous people through the implementation of Indigenous knowledge best practices and the application of contrasting traditional/nontraditional, archival/user-generated, and institutional/Indigenous descriptive elements, the Centre will attempt to create a “living archive” and facilitate Indigenous participation, collaboration, and ultimately, the process of reconciliation.https://www-tandfonline-com.uwinnipeg.idm.oclc.org/doi/full/10.1080/01639374.2015.100871

The Knowledge Graph Construction in the Educational Domain: Take an Australian School Science Course as an Example

The evolution of the Internet technology and artificial intelligence has changed the ways we gain knowledge, which has expanded to every aspect of our lives. In recent years, Knowledge Graphs technology as one of the artificial intelligence techniques has been widely used in the educational domain. However, there are few studies dedicating the construction of knowledge graphs for K-10 education in Australia, and most of the existing studies only focus on at the theory level, and little research shows practical pipeline steps to complete the complex flow of constructing the educational knowledge graph. Apart from that, most studies focused on concept entities and their relations but ignored the features of concept entities and the relations between learning knowledge points and required learning outcomes. To overcome these shortages and provide the data foundation for the development of downstream research and applications in this educational domain, the construction processes of building a knowledge graph for Australian K-10 education were analyzed at the theory level and implemented in a practical way in this research. We took the Year 9 science course as a typical data source example fed to the proposed method called K10EDU-RCF-KG to construct this educational knowledge graph and to enrich the features of entities in the knowledge graph. In the construction pipeline, a variety of techniques were employed to complete the building process. Firstly, the POI and OCR techniques were applied to convert Word and PDF format files into text, followed by developing an educational resources management platform where the machine-readable text could be stored in a relational database management system. Secondly, we designed an architecture framework as the guidance of the construction pipeline. According to this architecture, the educational ontology was initially designed, and a backend microservice was developed to process the entity extraction and relation extraction by NLP-NER and probabilistic association rule mining algorithms, respectively. We also adopted the NLP-POS technique to find out the neighbor adjectives related to entitles to enrich features of these concept entitles. In addition, a subject dictionary was introduced during the refinement process of the knowledge graph, which reduced the data noise rate of the knowledge graph entities. Furthermore, the connections between learning outcome entities and topic knowledge point entities were directly connected, which provides a clear and efficient way to identify what corresponding learning objectives are related to the learning unit. Finally, a set of REST APIs for querying this educational knowledge graph were developed

Using Knowledge Analytics to Search and Characterize Mass Properties Aerospace Data

There is growing capability in the field of Big Data and Data Analytics which Mass Properties Engineers might like to take advantage of. This paper utilizes an implementation of the IBM Knowledge Analytics and Watson search capabilities to explore a corpus of material developed primarily with the interests of Mass Properties Engineers and vehicle concept developers at its forefront. The full collection of SAWE (Society of Allied Weight Engineers, Inc.) Technical Papers from 1939 through 2015 is a major portion of the knowledge content. Additional aerospace vehicle design information includes metadata from AIAA (American Institute for Aeronautics and Astronautics), and INCOSE (International Council on Systems Engineering) as well as author-provided personal search material. This data is processed with respect to certain expected content, data taxonomies and key words to become the core data in NASA Langley Research Centers Vehicle Analysis Analytics, IBM Watson Content. Processed data becomes the corpus of information which is interrogated to provide examples of finding data for mass regression analysis, technology impacts on MPE (Mass Properties Engineering), mass properties control, standards, and other aspects of interest