GoRIM: a model-driven method for enhancing regulatory intelligence

Regulators are under constant pressure to demonstrate if and how the regulations they administer, which impose many requirements on various systems and processes, achieve intended societal outcomes. Traditionally, regulators have relied on impact assessments, risk analysis, and cost–benefit analysis to assess compliance with regulations. These methods, however, are effort and time intensive and focus on the efficiency of regulatory processes rather than on the effectiveness of the regulatory initiatives meant to improve compliance to regulations and the latter’s impact on intended societal outcomes. Goal-oriented modelling and data analytics approaches provide the basis for the development of more sophisticated methods and tools to better address the needs of regulators. This paper introduces the goal-oriented regulatory intelligence method (GoRIM), which enables effective management of regulations through modelling and data analytics. Through continuous monitoring, assessing, and reporting on efficiency and effectiveness aspects, GoRIM is meant to facilitate the analysis of feedback loops between regulations, regulatory initiatives, and societal outcomes. To demonstrate the applicability and perceived usefulness of GoRIM in addressing the first feedback loop between regulations and initiatives, we evaluated it through three case studies involving regulators from different contexts, with positive results. GoRIM extends the concept of regulatory intelligence beyond the analysis of compliance. It also provides practical guidelines and tools to regulators for making, in a timely way, evidence-based decisions related to the addition, modification, or repeal of regulations and related regulatory initiatives. In addition, GoRIM helps better identify software and information needs for enabling such decisions.

     

Encouraging collaboration and building Community in Online Asynchronous Professional Development: designing for social capital

International Journal of Computer-Supported Collaborative Learning - This research investigates a design and development approach to improving science teachers’ access to effective...     

Automated formal synthesis of provably safe digital controllers for continuous plants

Acta Informatica - We present a sound and automated approach to synthesizing safe, digital controllers for physical plants represented as time-invariant models. Models are linear differential...     

Expanding the nature and scope of requirements for service systems through Service-Dominant Logic: the case of a telemonitoring service

Requirements Engineering - A renewed understanding of service as a process of resource application and integration, rather than as digital or physical products, shifts the focus of service...     

An ICA-based method for stress classification from voice samples

Emotion detection is a hot topic nowadays for its potential application to intelligent systems in different fields such as neuromarketing, dialogue systems, friendly robotics, vending platforms and amiable banking. Nevertheless, the lack of a benchmarking standard makes it difficult to compare results produced by different methodologies, which could help the research community improve existing approaches and design new ones. Besides, there is the added problem of accurate dataset production. Most of the emotional speech databases and associated documentation are either privative or not publicly available. Therefore, in this work, two stress-elicited databases containing speech from male and female speakers were recruited, and four classification methods are compared in order to detect and classify speech under stress. Results from each method are presented to show their quality performance, besides the final scores attained, in what is a novel approach to the field of study.

     

The neighborhood distribution effect in visual word recognition: Words with single and twin neighbors.

Lexical-decision tasks were used to test the role of neighborhood distribution in visual word recognition. Predictions based on the interactive activation model were generated by running simulations. The data were compared for words with 2 higher frequency neighbors that differed in their neighborhood distribution. The neighbors were "single" when they did not share a neighborhood relationship (e.g., neighbors of flanc: franc–blanc) or "twin" when they shared a neighborhood relationship (e.g., neighbors of firme: ferme–forme). Results show a facilitatory neighborhood distribution effect on words in Experiments 1 (easy pseudowords) and 3 (difficult pseudowords and easy pseudowords) and on pseudowords in Experiment 2. These data can be accounted for in terms of lexical inhibition in the interactive activation framework. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A comparison of rCBF patterns during letter and semantic fluency.

To evaluate the functional neuroanatomies underlying letter and category fluency, 18 normal controls were studied with oxygen-15 water regional cerebral blood flow positron emission tomography. Three counterbalanced conditions each consisted of 6 trials (45 s each): letter fluency (generating words when cued with a particular letter), semantic fluency (generating words when cued with a particular category), and a control condition (generating days of the week and months of the year). Relative to the control, participants activated similar brain regions during both fluency tasks, including the anterior cingulate, left prefrontal regions, thalamus, and cerebellum; reductions were found in parietal and temporal regions. In a direct comparison of the 2 fluency tasks, inferior frontal cortex and temporoparietal cortex (hypothesized to participate in a phonologic loop for accessing word pronunciation) were activated more during letter than semantic fluency, whereas left temporal cortex (associated with access to semantic storage) was activated more during semantic than letter fluency. This study identifies subtle differences in the neural networks underlying letter and semantic fluency that may underlie the dissociation of these abilities in patients. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A new device for performing reference point indentation without a reference probe

Here we describe a novel, hand-held reference point indentation (RPI), instrument that is designed for clinical measurements of bone material properties in living patients. This instrument differs from previous RPI instruments in that it requires neither a reference probe nor removal of the periosteum that covers the bone, thus significantly simplifying its use in patient testing. After describing the instrument, we discuss five guidelines for optimal and reproducible results. These are: (1) the angle between the normal to the surface and the axis of the instrument should be less than 10°, (2) the compression of the main spring to trigger the device must be performed slowly (>1 s), (3) the probe tip should be sharper than 10 μm; however, a normalized parameter with a calibration phantom can correct for dull tips up to a 100 μm radius, (4) the ambient room temperature should be between 4?°C and 37 °C, and (5) the effective mass of the bone or material under test must exceed 1 kg, or if under 1 kg, the specimen should be securely anchored in a fixation device with sufficient mass (which is not a requirement of previous RPI instruments). Our experience is that a person can be trained with these guidelines in about 5 min and thereafter obtain accurate and reproducible results. The portability, ease of use, and minimal training make this instrument suitable to measure bone material properties in a clinical setting.