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Demonstrators BP and Knowledge models
No full textIn this document we identified the demostrators related to the Learn PAd project within the two following scenarios: European Project Budget Reporting and "Sportello Unico Attivit? Produttive". This deliverable will report Business Process, Knowledge and KPI for the two different scenarios. The delivered models will successively support the creation of the Learn PAd platform contents structure. They represent the starting point to demostrate applicability, acceptance and effectiveness of the overall Learn PAd platform and related components
Monitoring of business process execution based on performance indicators
No full textNowadays, more and more industrial organizations are using Business Process Model and Notation (BPMN) for process modeling. Key performance Indicators (KPIs) are set on such process models so to get a quantitative assessment of critical success metrics. A timely and reliable monitoring of KPIs is instrumental to Business Process (BP) management, and several frameworks are being proposed for such purpose. Business process monitoring solutions can be embedded into the Business Process Modeling (BPM) execution framework or integrated as additional facilities. This paper presents an integrated framework that allows for modeling, execution and analysis of business process based on a flexible and adaptable monitoring infrastructure. The main advantage of the proposed approach is that it is independent from any specific business process modeling notation and execution engine and allows for the definition and evaluation of user-specific KPI measures
Learn PAd : Collaborative and Model-based Learning in Public Administrations
No full textIn modern society public administrations (PAs) are undergoing a transformation of their perceived role from controllers to proactive service providers. PAs are today under pressure to constantly improve the quality of delivered services, while coping with quickly changing context (changes in law and regulations, societal globalization, fast technology evolution) and decreasing budgets. As a result civil servants delivering such services to citizens are challenged to understand and put in action latest procedures and rules within tight time constraints. The European project Learn PAd copes with this transformation by proposing an e-learning platform that enables process-driven learning and fosters cooperation and knowledge-sharing. The platform supports both an informative learning approach, based on enriched business process (BP) models, and a procedural learning approach, based on simulation and monitoring, while relating them as well to learning objectives and key performance indicators
A Data Oriented Approach to Derive Public Administration Business Processes
No full textThe delivery of services to citizens by Public Administrations requires to set up and coordinate complex Business Processes. Typically homogeneous Public Administrations, such as municipalities, have to provide the same services to all citizens. Nevertheless their concrete implementation, and the supporting Business Process model and data object models, can slightly differ from one Public Administration to the other due to organizational factors. If such variability is not explicitly represented and managed, each office will have to reflect on and analyse the requirements posed by the delivery of the service; then they will have to derive a specific process and data model. On the other hand the explicit modeling of variability can reduce the work to be done and permits to define general specifications from which specific model variants can be derived according to specific needs. In this paper we propose a novel approach, inspired by Feature Modeling techniques, for data object variability modeling that can be used to provide high level blueprints from which detailed Business Processes and data object specifications can be derived. Finally, a complex scenario has been applied to validate the approach with encouraging results
Indoor air quality, ventilation and respiratory health in elderly from EU Nursing Homes
No full textFew data exist on respiratory effects of indoor air quality and comfort parameters in the elderly. In the context of the GERIE study, we investigated for the first time the relationships of these factors to respiratory morbidity among elderly people permanently living in nursing homes in seven European countries. 600 elderly people from 50 nursing homes underwent a medical examination and completed a standardised questionnaire. Air quality and comfort parameters were objectively assessed in situ in the nursing home. Mean concentrations of air pollutants did not exceed the existing standards. Forced expiratory volume in 1 s/forced vital capacity ratio was highly significantly related to elevated levels of particles with a 50% cut-off aerodynamic diameter of <0.1 ?m (PM0.1) (adjusted OR 8.16, 95% CI 2.24-29.3) and nitrogen dioxide (aOR 3.74, 95% CI 1.06-13.1). Excess risks for usual breathlessness and cough were found with elevated PM10 (aOR 1.53 (95% CI 1.15-2.07) and aOR 1.73 (95% CI 1.17-10.3), respectively) and nitrogen dioxide (aOR 1.58 (95% CI 1.15-2.20) and aOR 1.56 (95% CI 1.03-2.41), respectively). Excess risks for wheeze in the past year were found with PM0.1 (aOR 2.82, 95% CI 1.15-7.02) and for chronic obstructive pulmonary disease and exhaled carbon monoxide with formaldehyde(aOR 3.49 (95% CI 1.17-10.3) and aOR 1.25 (95% CI 1.02-1.55), respectively). Breathlessness and cough were associated with higher carbon dioxide. Relative humidity was inversely related to wheeze in the past year and usual cough. Elderly subjects aged ?80 years were at higher risk. Pollutant effects were more pronounced in the case of poor ventilation. Even at low levels, indoor air quality affected respiratory health in elderly people permanently living in nursing homes, with frailty increasing with age. The effects were modulated by ventilation
Divide et Impera: subgoaling reduces the complexity of probabilistic inference and problem solving
No full textIt has long been recognized that humans (and possibly other animals) usually break problems down into smaller and more manageable problems using subgoals. Despite a general consensus that subgoaling helps problem solving, it is still unclear what the mechanisms guiding online subgoal selection are during the solution of novel problems for which predefined solutions are not available. Under which conditions does subgoaling lead to optimal behaviour? When is subgoaling better than solving a problem from start to finish? Which is the best number and sequence of subgoals to solve a given problem? How are these subgoals selected during online inference? Here, we present a computational account of subgoaling in problem solving. Following Occam\u27s razor, we propose that good subgoals are those that permit planning solutions and controlling behaviour using less information resources, thus yielding parsimony in inference and control. We implement this principle using approximate probabilistic inference: subgoals are selected using a sampling method that considers the descriptive complexity of the resulting sub-problems. We validate the proposed method using a standard reinforcement learning benchmark (four-rooms scenario) and show that the proposed method requires less inferential steps and permits selecting more compact control programs compared to an equivalent procedure without subgoaling. Furthermore, we show that the proposed method offers a mechanistic explanation of the neuronal dynamics found in the prefrontal cortex of monkeys that solve planning problems. Our computational framework provides a novel integrative perspective on subgoaling and its adaptive advantages for planning, control and learning, such as for example lowering cognitive effort and working memory load
A Programmer-Interpreter neural network architecture for prefrontal cognitive control
No full textThere is wide consensus that the prefrontal cortex (PFC) is able to exert cognitive control on behavior by biasing processing toward task-relevant information and by modulating response selection. This idea is typically framed in terms of top-down influences within a cortical control hierarchy, where prefrontal-basal ganglia loops gate multiple input-output channels, which in turn can activate or sequence motor primitives expressed in (pre-)motor cortices. Here we advance a new hypothesis, based on the notion of programmability and an interpreter-programmer computational scheme, on how the PFC can flexibly bias the selection of sensorimotor patterns depending on internal goal and task contexts. In this approach, multiple elementary behaviors representing motor primitives are expressed by a single multi-purpose neural network, which is seen as a reusable area of "recycled" neurons (interpreter). The PFC thus acts as a "programmer" that, without modifying the network connectivity, feeds the interpreter networks with specific input parameters encoding the programs (corresponding to network structures) to be interpreted by the (pre-)motor areas. Our architecture is validated in a standard test for executive function: the 1-2-AX task. Our results show that this computational framework provides a robust, scalable and flexible scheme that can be iterated at different hierarchical layers, supporting the realization of multiple goals. We discuss the plausibility of the "programmer-interpreter" scheme to explain the functioning of prefrontal-(pre)motor cortical hierarchie
Making use of capuchins\u27 behavioral propensities to obtain hair samples for DNA analyses
No full textGenotyping wild and captive capuchins has become a priority and hair bulbs have high quality DNA. Here, we describe a method to non-invasively collect fresh-plucked strands of hair that exploits capuchins\u27 manual dexterity and propensity to grasp and extract food. The apparatus consists of a transparent tube baited with food. Its extraction requires the monkey to place its forearm in contact with double-sided tape applied on the inner surface of the tube entrance. The "tube" method, successfully implemented with captive (N=23) and wild (N=21) capuchins, allowed us to obtain hair bulbs from most individuals and usable genomic DNA was extracted even from a single bulb
Quality assessment strategy: applying business process understandability guidelines for learning
No full textModelling using graphical notations permits to improve communications among stakeholders. The usage and composition of the notation elements can greatly impact on the understandability of the dened models. This is an important factor to consider in complex organizations where activities are performed by the collaboration of many stakeholders. Understandability becomes even more important when models are also used to structure learning activities. In this technical we report our experience on the usage of BPMN for documenting and learning business process activities
Ambiguity as a Resource to Disclose Tacit Knowledge
No full textInterviews are the most common and effective means to perform requirements elicitation and support knowledge transfer between a customer and a requirements analyst. Ambiguity in communication is often perceived as a major obstacle for knowledge transfer, which could lead to unclear and incomplete requirements documents. In this paper, we analyse the role of ambiguity in requirements elicitation interviews. To this end, we have performed a set of customer-analyst interviews to observe how ambiguity occurs during requirements elicitation. From this direct experience, we have observed that ambiguity is a multidimensional cognitive phenomenon with a dominant pragmatic facet, and we have defined a phenomenological framework to describe the different types of ambiguity in interviews. We have also discovered that, rather than an obstacle, the occurrence of an ambiguity is often a resource for discovering tacit knowledge. Starting from this observation, we have envisioned the further steps needed in the research to exploit these findings