TR-2015001: A Survey and Critique of Facial Expression Synthesis in Sign Language Animation

Sign language animations can lead to better accessibility of information and services for people who are deaf and have low literacy skills in spoken/written languages. Due to the distinct word-order, syntax, and lexicon of the sign language from the spoken/written language, many deaf people find it difficult to comprehend the text on a computer screen or captions on a television. Animated characters performing sign language in a comprehensible way could make this information accessible. Facial expressions and other non-manual components play an important role in the naturalness and understandability of these animations. Their coordination to the manual signs is crucial for the interpretation of the signed message. Software to advance the support of facial expressions in generation of sign language animation could make this technology more acceptable for deaf people. In this survey, we discuss the challenges in facial expression synthesis and we compare and critique the state of the art projects on generating facial expressions in sign language animations. Beginning with an overview of facial expressions linguistics, sign language animation technologies, and some background on animating facial expressions, a discussion of the search strategy and criteria used to select the five projects that are the primary focus of this survey follows. This survey continues on to introduce the work from the five projects under consideration. Their contributions are compared in terms of support for specific sign language, categories of facial expressions investigated, focus range in the animation generation, use of annotated corpora, input data or hypothesis for their approach, and other factors. Strengths and drawbacks of individual projects are identified in the perspectives above. This survey concludes with our current research focus in this area and future prospects.

A Harmonized Vocabulary For Water Quality

Interoperability of water quality data depends on the use of common models, schemas and vocabularies. However, terms are usually collected during different activities and projects in isolation of one another, resulting in vocabularies that have the same scope being represented with different terms, using different formats and formalisms, and published in various access methods. Significantly, most water quality vocabularies conflate multiple concepts in a single term, e.g. quantity kind, units of measure, substance or taxon, medium and procedure. This bundles information associated with separate elements from the OGC Observations and Measurements (O&M) model into a single slot. We have developed a water quality vocabulary, formalized using RDF, and published as Linked Data. The terms were extracted from existing water quality vocabularies. The observable property model is inspired by O&M but aligned with existing ontologies. The core is an OWL ontology that extends the QUDT ontology for Unit and QuantityKind definitions. We add classes to generalize the QuantityKind model, and properties for explicit description of the conflated concepts. The key elements are defined to be sub-classes or sub-properties of SKOS elements, which enables a SKOS view to be published through standard vocabulary APIs, alongside the full view. QUDT terms are re-used where possible, supplemented with additional Unit and QuantityKind entries required for water quality. Along with items from separate vocabularies developed for objects, media, and procedures, these are linked into definitions in the actual observable property vocabulary. Definitions of objects related to chemical substances are linked to items from the Chemical Entities of Biological Interest (ChEBI) ontology. Mappings to other vocabularies, such as DBPedia, are in separately maintained files. By formalizing the model for observable properties, and clearly labelling the separate concerns, water quality observations from different sources may be more easily merged and also transformed to O&M for cross-domain applications.