Multitasking: multiple, domain-specific cognitive functions in a virtual environment

Multitasking among three or more different tasks is a ubiquitous requirement of everyday cognition, yet rarely is it addressed in research on healthy adults who have had no specific training in multitasking skills. Participants completed a set of diverse subtasks within a simulated shopping mall and office environment, the Edinburgh Virtual Errands Test (EVET). The aim was to investigate how different cognitive functions, such as planning, retrospective and prospective memory, and visuospatial and verbal working memory, contribute to everyday multitasking. Subtasks were chosen to be diverse, and predictions were derived from a statistical model of everyday multitasking impairments associated with frontal-lobe lesions (Burgess, Veitch, de Lacy Costello, & Shallice, 2000b). Multiple regression indicated significant independent contributions from measures of retrospective memory, visuospatial working memory, and online planning, but not from independent measures of prospective memory or verbal working memory. Structural equation modelling showed that the best fit to the data arose from three underlying constructs, with Memory and Planning having a weak link, but with both having a strong directional pathway to an Intent construct that reflected implementation of intentions. Participants who followed their preprepared plan achieved higher scores than those who altered their plan during multitask performance. This was true regardless of whether the plan was efficient or poor. These results substantially develop and extend the Burgess et al. (2000b) model to healthy adults and yield new insight into the poorly understood area of everyday multitasking. The findings also point to the utility of using virtual environments for investigating this form of complex human cognition.

Total and domain-specific sitting time among employees in desk-based work settings in Australia

Objective: To describe the total and domain-specific daily sitting time among a sample of Australian office-based employees. Methods: In April 2010, paper-based surveys were provided to desk-based employees (n=801) in Victoria, Australia. Total daily and domain-specific (work, leisure-time and transport-related) sitting time (minutes/day) were assessed by validated questionnaires. Differences in sitting time were examined across socio-demographic (age, sex, occupational status) and lifestyle characteristics (physical activity levels, body mass index [BMI]) using multiple linear regression analyses. Results: The median (95% confidence interval [CI]) of total daily sitting time was 540 (531-557) minutes/day. Insufficiently active adults (median=578 minutes/day, [95%CI: 564-602]), younger adults aged 18-29 years (median=561 minutes/day, [95%CI: 540-577]) reported the highest total daily sitting times. Occupational sitting time accounted for almost 60% of total daily sitting time. In multivariate analyses, total daily sitting time was negatively associated with age (unstandardised regression coefficient [B]=-1.58, p<0.001) and overall physical activity (minutes/week) (B=-0.03, p<0.001) and positively associated with BMI (B=1.53, p=0.038). Conclusions: Desk-based employees reported that more than half of their total daily sitting time was accrued in the work setting. Implications: Given the high contribution of occupational sitting to total daily sitting time among desk-based employees, interventions should focus on the work setting.

HorusCML: context-aware domain-specific visual languages designer

The objective behind building domain-specific visual languages (DSVLs) is to provide users with the most appropriate concepts and notations that best fit with their domain and experience. However, the existing DSVL designers do not support integrating environment and user context information when modeling, editing or viewing DSVL models at different locations, permissions, devices, etc. In this paper, we introduce HorusCML, a context-aware DSVL designer, which supports DSVL experts in integrating necessary context details within their DSVLs. The resultant DSVLs can reflect different facets, layouts, and behaviours according to context it is used in. We show a case study on developing a context-aware data flow diagram DSVL tool using HorusCML.

SecDSVL: a domain-specific visual language to support enterprise security modelling

Enterprise security management requires capturing different security and IT systems' details, analyzing and enforcing these security details, and improving employed security to meet new risks. Adopting structured models greatly helps in simplifying and organizing security specification and enforcement processes. However, existing security models are generally limited to specific security details and do not deliver a comprehensive security model. They also often do not have user-friendly notations, being complicated extensions of existing modeling languages (such as UML). In this paper, we introduce a comprehensive Security Domain Specific Visual Language (SecDSVL), which enables capturing of key security details to support enterprise systems security management process. We discuss our SecDSVL, tool support and the model-based enterprise security management approach it supports, give a usage example, and present evaluation experiments of SecDSVL.

Generating domain-specific visual language tools from abstract visual specifications

Domain-specific visual languages support high-level modeling for a wide range of application domains. However, building tools to support such languages is very challenging. We describe a set of key conceptual requirements for such tools and our approach to addressing these requirements, a set of visual language-based metatools. These support definition of metamodels, visual notations, views, modeling behaviors, design critics, and model transformations and provide a platform to realize target visual modeling tools. Extensions support collaborative work, human-centric tool interaction, and multiplatform deployment. We illustrate application of the metatoolset on tools developed with our approach. We describe tool developer and cognitive evaluations of our platform and our exemplar tools, and summarize key future research directions.

A domain-specific approach for assessing physical activity efficacy in adolescents: from scale conception to predictive validity

Objective: To develop and test the utility of a domain-specific physical activity efficacy scale in adolescents for predicting physical activity behaviour. Design: Two independent studies were conducted. Study 1 examined the psychometric properties of a newly constructed Domain-Specific Physical Activity Efficacy Questionnaire (DSPAEQ) and study 2 tested the utility of the scale for predicting leisure- and school-time physical activity. Methods: In study 1, descriptive physical activity data were used to generate scale items. The scales factor structure and internal consistency were tested in a sample of 272 adolescents. A subsequent sample of Canadian (N = 104) and New Zealand (N = 29) adolescents, was recruited in study 2 to explore the scale's predictive validity using a subjective measure of leisure- and school-time physical activity. Results: A principle axis factor analysis in study 1 revealed a 26-item, five-factor coherent and interpretable solution; representative of leisure and recreation, household, ambulatory, transportation, and school physical activity efficacy constructs, respectively. The five-factor solution explained 81% of the response variance. In study 2 the domain-specific efficacy model explained 16% and 1% of leisure- and school-time physical activity response variance, respectively, with leisure time physical activity efficacy identified as a unique and significant contributor of leisure-time physical activity. Conclusion: Study 1 provides evidence for the tenability of a five factor DSPEAQ, while study 2 shows that the DSPEAQ has utility in predicting domain-specific physical activity. This latter finding underscores the importance of scale correspondence between the behavioural elements (leisure-time physical activity) and cognitive assessment of those elements (leisure-time physical activity efficacy).

A domain-specific visual modeling language for testing environment emulation

Software integration testing plays an increasingly important role as the software industry has experienced a major change from isolated applications to highly distributed computing environments. Conducting integration testing is a challenging task because it is often very difficult to replicate a real enterprise environment. Emulating testing environment is one of the key solutions to this problem. However, existing specification-based emulation techniques require manual coding of their message processing engines, therefore incurring high development cost. In this paper, we present a suite of domain-specific visual modelinglanguages to describe emulated testing environments at a highabstraction level. Our solution allows domain experts to model atesting environment from abstract interface layers. These layermodels are then transformed to runtime environment for application testing. Our user study shows that our visual languages are easy to use, yet with sufficient expressive power to model complex testing applications.

Role of knowledge and ability in student nurses` clinical decision-making

Little is known about the acquisition of decision-making skills in nursing students as a function of experience and academic ability. Knowing how experience and academic skills interact may help inform clinical education programs and formulate ways of assessing students' progress. The aims of the present study were to develop a problem-solving task capable of measuring clinical decision-making skills in novice nurses at different levels of domain-specific knowledge; and to establish the relative impact on decision-making of domain-specific knowledge and general ability as determinants of the acquisition of decision-making skills. Three types of clinical problems of increasing complexity were developed. Sixty second-year and third-year student nurses with high and low academic scores were studied in terms of their ability to generate hypotheses for a hypothetical case, recognize disconfirming information and the need to access additional information, and diagnostic accuracy. The results showed that general academic ability and knowledge function partly independently in the acquisition of expertise in nursing. Academic ability affects decision-making in low complexity tasks, but as case complexity increases, domain-specific knowledge and experience determines decision-making skills. There are important differences in the way novices with different levels of knowledge and ability make clinical decisions and these can be studied by systematically increasing the complexity of the decision task. These results have implications for the way in which clinical education is structured and evaluated.

Predicting consumer innovative behavior using alternative theories and likelihood measures: a longitudinal study

This paper reports on a longitudinal study of consumers, where two dominant theories that purport to predict innovative behavior are applied and compared directly, using a methodology suggested as ideal by past researchers. Predictions made prior to launch were then evaluated against multiple measures of purchase likelihood, and against actual adoption behavior up to 12 months after launch. The results of this study suggest that perceptions of the innovations characteristics (PIC) predicted the selfreported likelihood of adoption better than the Domain Specific Innovativeness (DSI) scale, a personality-based measure. Prediction of actual adoption was largely inaccurate and both theories massively over predicted adoption levels, however the DSI scale was slightly more accurate. The conclusions here are that no one theory could make adequate predictions of behavior, that purchase likelihood measures are a poor substitute for measuring actual behavior but that purchase probability scales should be used more often in adoption research.

Grounded theory as foundations for methods in applied ontology

Research into domain specific ontologies is difficult to treat empirically. This is because it is difficult to ground domain ontology while simultaneously being true to its guiding philosophy or theory. Further, ontology generation is often introspective and reflective or relies on experts for ontology generation. Even those relying on expert generation lack rigour and tend to be more ad-hoc. We ask how Grounded Theory can be used to generate domain specific ontologies where appropriate high level theory and suitable textual data sources are available. We are undertaking generation of a domain ontology for the discipline of information systems by applying the Grounded Theory method. Specifically we are using Roman Ingarden’s theory of scientific works to seed a coding family and adapting the method to ask relevant questions when analysing rich textual data. We have found that a guiding ontological theory, such as Ingarden’s, can be used to seed a coding family giving rise to a viable method for generating ontologies for research. This is significant because Grounded Theory may be one of the key methods for generating ontologies where substantial uniform quality text is available to the ontologist. We also present our partial analysis of information systems research.

Adaptive communal detection in search of adversarial identity crime

This paper is on adaptive real-time searching of credit application data streams for identity crime with many search parameters. Specifically, we concentrated on handling our domain-specific adversarial activity problem with the adaptive Communal Analysis Suspicion Scoring (CASS) algorithm. CASS's main novel theoretical contribution is in the formulation of State-of- Alert (SoA) which sets the condition of reduced, same, or heightened watchfulness; and Parameter-of-Change (PoC) which improves detection ability with pre-defined parameter values for each SoA. With pre-configured SoA policy and PoC strategy, CASS determines when, what, and how much to adapt its search parameters to ongoing adversarial activity. The above approach is validated with three sets of experiments, where each experiment is conducted on several million real credit applications and measured with three appropriate performance metrics. Significant improvements are achieved over previous work, with the discovery of some practical insights of adaptivity into our domain.

Ontology learning from text: a soft computing paradigm

Text-based information accounts for more than 80% of today’s Web content. They consist of Web pages written in different natural languages. As the semantic Web aims at turning the current Web into a machine-understandable knowledge repository, availability of multilingual ontology thus becomes an issue at the core of a multilingual semantic Web. However, multilingual ontology is too complex and resource intensive to be constructed manually. In this paper, we propose a three-layer model built on top of a soft computing framework to automatically acquire a multilingual ontology from domain specific parallel texts. The objective is to enable semantic smart information access regardless of language over the Semantic Web.

Ontological engineering for conceptual modeling

In acknowledging the importance of ontologies in conceptual modeling, database integration and business process modeling, this paper introduces a set of principles for building ontologies. Starting from Guarino's meta-properties of ontological terms, the paper describes the denotational semantics of the meta-properties and derives from them some engineering rules and checks for constructing domain specific conceptual models, based on the overarching requirement to assign meanings to concepts using tags and labels. Parallel research by the authors into the use of contextual references and roles to restrict such meanings will be published elsewhere.