Techniques for augmenting the visualisation of dynamic raster surfaces

Despite their aesthetic appeal and condensed nature, dynamic raster surface representations such as a temporal series of a landform and an attribute series of a socio-economic attribute of an area, are often criticised for the lack of an effective information delivery and interactivity.In this work, we readdress some of the earlier raised reasons for these limitations -information-laden quality of surface datasets, lack of spatial and temporal continuity in the original data, and a limited scope for a real-time interactivity. We demonstrate with examples that the use of four techniques namely the re-expression of the surfaces as a framework of morphometric features, spatial generalisation, morphing, graphic lag and brushing can augment the visualisation of dynamic raster surfaces in temporal and attribute series

A Reasoning Framework for Dependability in Software Architectures

The degree to which a software system possesses specified levels of software quality attributes, such as performance and modifiability, often have more influence on the success and failure of those systems than the functional requirements. One method of improving the level of a software quality that a product possesses is to reason about the structure of the software architecture in terms of how well the structure supports the quality. This is accomplished by reasoning through software quality attribute scenarios while designing the software architecture of the system. As society relies more heavily on software systems, the dependability of those systems becomes critical. In this study, a framework for reasoning about the dependability of a software system is presented. Dependability is a multi-faceted software quality attribute that encompasses reliability, availability, confidentiality, integrity, maintainability and safety. This makes dependability more complex to reason about than other quality attributes. The goal of this reasoning framework is to help software architects build dependable software systems by using quantitative and qualitative techniques to reason about dependability in software architectures

Facial Image Verification and Quality Assessment System -FaceIVQA

Although several techniques have been proposed for predicting biometric system performance using quality values, many of the research works were based on no-reference assessment technique using a single quality attribute measured directly from the data. These techniques have proved to be inappropriate for facial verification scenarios and inefficient because no single quality attribute can sufficient measure the quality of a facial image. In this research work, a facial image verification and quality assessment framework (FaceIVQA) was developed. Different algorithms and methods were implemented in FaceIVQA to extract the faceness, pose, illumination, contrast and similarity quality attributes using an objective full-reference image quality assessment approach. Structured image verification experiments were conducted on the surveillance camera (SCface) database to collect individual quality scores and algorithm matching scores from FaceIVQA using three recognition algorithms namely principal component analysis (PCA), linear discriminant analysis (LDA) and a commercial recognition SDK. FaceIVQA produced accurate and consistent facial image assessment data. The Result shows that it accurately assigns quality scores to probe image samples. The resulting quality score can be assigned to images captured for enrolment or recognition and can be used as an input to quality-driven biometric fusion systems.DOI:http://dx.doi.org/10.11591/ijece.v3i6.503

STATED INTENTIONS VS. ACTUAL BEHAVIOR: CHOICE-BASED CONJOINT (CBC) IN DOD SOURCE SELECTIONS

Current source selection procedures have exposed the Department of Defense (DoD) to increased protest risk. This, in part, is due to contradictions in the U.S. government’s stated order of importance for acquisition evaluation criteria (pre-award) versus its actual choice behavior during source selection, as Butler found in 2014. The objectives of this MBA project included the following: 1) Determine the degree of disconnect between stated preferences during pre-award acquisition phase and actual choice behavior in source selections. 2) Develop an understanding of quality attributes for logistics-based services. 3) Provide a Choice-Based Conjoint (CBC) framework that could be utilized to enhance source selection criteria development. Methodology included interviews and spend analysis techniques to identify quality attributes of logistics-based acquisitions. Then, after the attributes were identified, they were employed to develop a CBC model that calculated the attribute utilities and relative importance for each attribute. Using these important scores, the disconnect between stated preferences and choice behavior was found. None of the subjects in this investigative study could accurately order attribute importance in stated form to match their actual choices in simulated source selections. This report offers a framework and methods to mitigate the weaknesses found in developing evaluation attribute importance from stated preferences and reduce the risks of protests.Outstanding ThesisFirst Lieutenant, United States Air ForceApproved for public release. Distribution is unlimited

Green BPM as a business-oriented discipline : a systematic mapping study and research agenda

Green Business Process Management (BPM) focuses on the ecological impact of business processes. This article provides a systematic mapping study of Green BPM literature to evaluate five attributes of the Green BPM research area: (1) scope, (2) disciplines, (3) accountability, (4) researchers and (5) quality control. The results allow developing a research agenda to enhance Green BPM as an approach for environmentally sustainable organizations. We rely on a dichotomy of knowledge production to present research directives relevant for both academics and practitioners in order to help close a rigor-relevance gap. The involvement of both communities is crucial for Green BPM to advance as an applied, business-oriented discipline

A Testability Analysis Framework for Non-Functional Properties

This paper presents background, the basic steps and an example for a testability analysis framework for non-functional properties

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective