Computer information and decision management

As a mechanism for the exchange, transmission, and reporting of accounting and financial data, eXtensible Business Reporting Language (XBRL) makes great strides toward standardisation. However, given the global nature of contemporary financial markets, XBRL reporting must transcend or accommodate differences in reporting standards. Presently, the two standards for XBRL reporting, the US Generally Accepted Accounting Principles (US GAAP) or International Financial Reporting Standards (IFRS), make reconciling meanings between these standards a laborious and error-prone affair. As the audience for this reporting often spans countries and cultures, many have a stake in digesting XBRL-formatted financial reports. This paper addresses the question of how cross-cultural and transnational consumers of XBRL statements can derive meaning from these reports. We present an ontological approach toward solving this problem which includes a financial reporting ontology and associated architecture for comparing concepts across XBRL taxonomies. The architecture is explained, evaluation criteria offered, and future research approaches toward realising artefacts using this architecture are proffered. . His research interests are in software systems development, small team development, mobile applications development and information systems security. Many of his other interests are student-focused where he advises student organisations and coaches students in software competitions.

The increasing model of e-commerce infrastructure opens the door for secure transaction of information over the Net, keeping some records private as users' choice within a few seconds. A client, while frequently retrieving his records, seldom wishes to hide the identity of the records to the ...

Co-adapted learning involves complex, dynamically unfolding interactions between human and artificial pedagogical agents (PAs) during learning with intelligent systems. In general, these interactions lead to effective learning when (1) learners correctly monitor and regulate their cognitive and metacognitive processes in response to internal (e.g., accurate metacognitive judgments followed by the selection of effective learning strategies) and external (e.g., response to agents' prompting and feedback) conditions, and (2) pedagogical agents can adequately and correctly detect, track, model, and foster learners' self-regulatory processes. In this study, we tested the effectiveness of PAs' prompting and feedback on learners' self-regulated learning about the human circulatory system with MetaTutor, an adaptive, multi-agent learning environment. Sixty-nine (N=69) undergraduates learned about the topic with MetaTutor, during a 2-hour session under one of three conditions: prompt and feedback (PF), prompt-only (PO), and no prompt (NP) condition. The PF condition received timely prompts from several pedagogical agents to deploy various SRL processes and received immediate directive feedback concerning the deployment of the processes. The PO condition received the same timely prompts, without feedback. Finally, the NP condition learned without assistance from the agents. Results indicate that those in the PF condition had significantly higher learning efficiency scores than those in both the PO and control conditions. In addition, log-file data provided evidence of the effectiveness of the PA's timely scaffolding and feedback in facilitating learners' (in the PF condition) metacognitive monitoring and regulation during learning.

Abstract. In assistive technology design, it is indispensible to consider the sensory, physical and cognitive level of target users. Cognitive load is an important indicator of cognitive feedback during interaction and became the critical research issue in designing assistive ...

Task-evoked pupillary responses reveal the relationship between working memory capacity and its effect on cognitive states. Understanding the effects of cognitive load requires robust analysis of pupillary responses. In this paper, we introduced a Hilbert transform analytic phase based method to compute temporal patterns from pupillary responses. Analysis reveals that sharp change in incorrect task response may be attributed to the cognitive overload. It was also observed that a sharp change and continuation of the ramp in Hilbert unwrapped phase relates to the cognitive dissonance.

Co-adapted learning involves complex, dynamically unfolding interactions between human and artificial pedagogical agents (PAs) during learning with intelligent systems. In general, these interactions lead to effective learning when (1) learners correctly monitor and regulate their cognitive and metacognitive processes in response to internal (e.g., accurate metacognitive judgments followed by the selection of effective learning strategies) and external (e.g., response to agents' prompting and feedback) conditions, and (2) pedagogical agents can adequately and correctly detect, track, model, and foster learners' self-regulatory processes. In this study, we tested the effectiveness of PAs' prompting and feedback on learners' self-regulated learning about the human circulatory system with MetaTutor, an adaptive, multi-agent learning environment. Sixty-nine (N=69) undergraduates learned about the topic with MetaTutor, during a 2-hour session under one of three conditions: prompt and feedback (PF), prompt-only (PO), and no prompt (NP) condition. The PF condition received timely prompts from several pedagogical agents to deploy various SRL processes and received immediate directive feedback concerning the deployment of the processes. The PO condition received the same timely prompts, without feedback. Finally, the NP condition learned without assistance from the agents. Results indicate that those in the PF condition had significantly higher learning efficiency scores than those in both the PO and control conditions. In addition, log-file data provided evidence of the effectiveness of the PA's timely scaffolding and feedback in facilitating learners' (in the PF condition) metacognitive monitoring and regulation during learning.