Semantic parameterization

Abstract: Requirements engineers need to have a comprehensive requirements modeling framework for modeling legal requirements, particularly for privacy-related regulations, which are required for IT systems. The nature of law demands a special approach for dealing with the complexity of regulations. In this paper, we integrate different approaches for modeling legal requirements into one unified framework. We use semantic parameterization technique and first-order logic (FOL) approach for extracting legal requirements from legal documents. We then use Goal-oriented Requirements Language (GRL) to illustrate and evaluate the models. The aim of this paper is to improve and extend the existing Legal-GRL framework using semantic parameterization process and FOL. We use social media as the example to illustrate our approach.

Abstract: Statistical human body models, like SCAPE, capture static 3D human body shapes and poses and are applied to many Computer Vision problems. Defined in a statistical context, their parameters do not explicitly capture semantics of the human body shapes such as height, weight, limb length, etc. Having a set of semantic parameters would allow users and automated algorithms to sample the space of possible body shape variations in a more intuitive way. Therefore, in this paper we propose a method for re-parameterization of statistical human body models such that shapes are controlled by a small set of intuitive semantic parameters. These parameters are learned directly from the available statistical human body model. In order to apply any arbitrary animation to our human body shape model we perform retargeting. From any set of 3D scans, a semantic parametrized model can be generated and animated with the presented methods using any animation data. We quantitatively show that our semantic parameterization is more reliable than standard semantic parameterizations, and show a number of animations retargeted to our semantic body shape model.

Abstract: Visualizing natural language description is a difficult and complex task. When dealing with the process of generating images from natural language descriptions, we firstly should consider the real world and find out what key visual information can be extracted from the sentences which represents the most fundament concepts in both virtual and real environments. In this paper, we present the result of a prototype system called 3DSV (3D Story Visualiser) that generates a virtual scene by using simplified story-based descriptions. In particular, we describe the methodology used to parameterize the visual and describable words into XML formatted data structure. Then we discuss how to interpret the parameterized data and create an interactive real-time 3D virtual environment.

Abstract: Software engineers must systematically account for the broad scope of environmental behavior, including nonfunctional requirements, intended to coordinate the actions of stakeholders and software systems The Inquiry Cycle Model (ICM) provides engineers with a strategy to acquire and refine these requirements by having domain experts answer six questions: who, what, where, when, how, and why Goal-based requirements engineering has led to the formalization of requirements to answer the ICM questions about when, how, and why goals are achieved, maintained, or avoided In this article, we present a systematic process called Semantic Parameterization for expressing natural language domain descriptions of goals as specifications in description logic The formalization of goals in description logic allows engineers to automate inquiries using who, what, and where questions, completing the formalization of the ICM questions The contributions of this approach include new theory to conceptually compare and disambiguate goal specifications that enables querying goals and organizing goals into specialization hierarchies The artifacts in the process include a dictionary that aligns the domain lexicon with unique concepts, distinguishing between synonyms and polysemes, and several natural language patterns that aid engineers in mapping common domain descriptions to formal specifications Semantic Parameterization has been empirically validated in three case studies on policy and regulatory descriptions that govern information systems in the finance and health-care domains