An agent based approach to distributed task modeling for co-operative task support

This paper describes the design and implementation of an agent based network for the support of collaborative switching tasks within the control room environment of the National Grid Company plc. This work includes aspects from several research disciplines, including operational analysis, human computer interaction, finite state modelling techniques, intelligent agents and computer supported co-operative work. Aspects of these procedures have been used in the analysis of collaborative tasks to produce distributed local models for all involved users. These models have been used as the basis for the production of local finite state automata. These automata have then been embedded within an agent network together with behavioural information extracted from the task and user analysis phase. The resulting support system is capable of task and communication management within the transmission despatch environment.

Activity modeling in practice: a case-based guide to human-centered software engineering

The purpose of this study was to identify whether activity modeling framework supports problem analysis and provides a traceable and tangible connection from the problem identification up to solution modeling. Methodology validation relied on a real problem from a Portuguese teaching syndicate (ASPE), regarding courses development and management. The study was carried out with a perspective to elaborate a complete tutorial of how to apply activity modeling framework to a real world problem. Within each step of activity modeling, we provided a summary elucidation of the relevant elements required to perform it, pointed out some improvements and applied it to ASPE’s real problem. It was found that activity modeling potentiates well structured problem analysis as well as provides a guiding thread between problem and solution modeling. It was concluded that activity-based task modeling is key to shorten the gap between problem and solution. The results revealed that the solution obtained using activity modeling framework solved the core concerns of our customer and allowed them to enhance the quality of their courses development and management. The principal conclusion was that activity modeling is a properly defined methodology that supports software engineers in problem analysis, keeping a traceable guide among problem and solution.

Computational modeling of optical trapping of vaterite microspheres

The ability to grow microscopic spherical birefringent crystals of vaterite, a calcium carbonate mineral, has allowed the development of an optical microrheometer based on optical tweezers. However, since these crystals are birefringent, and worse, are expected to have non-uniform birefringence, computational modeling of the microrheometer is a highly challenging task. Modeling the microrheometer - and optical tweezers in general - typically requires large numbers of repeated calculations for the same trapped particle. This places strong demands on the efficiency of computational methods used. While our usual method of choice for computational modelling of optical tweezers - the T-matrix method - meets this requirement of efficiency, it is restricted to homogeneous isotropic particles. General methods that can model complex structures such as the vaterite particles, such as finite-difference time-domain (FDTD) or finite-difference frequency-domain (FDFD) methods, are inefficient. Therefore, we have developed a hybrid FDFD/T-matrix method that combines the generality of volume-discretisation methods such as FDFD with the efficiency of the T-matrix method. We have used this hybrid method to calculate optical forces and torques on model vaterite spheres in optical traps. We present and compare the results of computational modelling and experimental measurements.

A Methodology for GUI Layer Redefinition Through Virtualization and Computer Vision

Nowadays despite improvements in usability and intuitiveness users have to adapt to the proposed systems to satisfy their needs. For instance, they must learn how to achieve tasks, how to interact with the system, and fulfill system's specifications. This paper proposes an approach to improve this situation enabling graphical user interface redefinition through virtualization and computer vision with the aim of increasing the system's usability. To achieve this goal the approach is based on enriched task models, virtualization and picture-driven computing.

CloudSimDisk: Energy-Aware Storage Simulation in CloudSim

Cloud Computing paradigm is continually evolving, and with it, the size and the complexity of its infrastructure. Assessing the performance of a Cloud environment is an essential but strenuous task. Modeling and simulation tools have proved their usefulness and powerfulness to deal with this issue. This master thesis work contributes to the development of the widely used cloud simulator CloudSim and proposes CloudSimDisk, a module for modeling and simulation of energy-aware storage in CloudSim. As a starting point, a review of Cloud simulators has been conducted and hard disk drive technology has been studied in detail. Furthermore, CloudSim has been identified as the most popular and sophisticated discrete event Cloud simulator. Thus, CloudSimDisk module has been developed as an extension of CloudSim v3.0.3. The source code has been published for the research community. The simulation results proved to be in accordance with the analytic models, and the scalability of the module has been presented for further development.

Building autonomous sensitive artificial listeners (Extended abstract)

This paper describes a substantial effort to build a real-time interactive multimodal dialogue system with a focus on emotional and non-verbal interaction capabilities. The work is motivated by the aim to provide technology with competences in perceiving and producing the emotional and non-verbal behaviours required to sustain a conversational dialogue. We present the Sensitive Artificial Listener (SAL) scenario as a setting which seems particularly suited for the study of emotional and non-verbal behaviour, since it requires only very limited verbal understanding on the part of the machine. This scenario allows us to concentrate on non-verbal capabilities without having to address at the same time the challenges of spoken language understanding, task modeling etc. We first summarise three prototype versions of the SAL scenario, in which the behaviour of the Sensitive Artificial Listener characters was determined by a human operator. These prototypes served the purpose of verifying the effectiveness of the SAL scenario and allowed us to collect data required for building system components for analysing and synthesising the respective behaviours. We then describe the fully autonomous integrated real-time system we created, which combines incremental analysis of user behaviour, dialogue management, and synthesis of speaker and listener behaviour of a SAL character displayed as a virtual agent. We discuss principles that should underlie the evaluation of SAL-type systems. Since the system is designed for modularity and reuse, and since it is publicly available, the SAL system has potential as a joint research tool in the affective computing research community.

Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling.

How a stimulus or a task alters the spontaneous dynamics of the brain remains a fundamental open question in neuroscience. One of the most robust hallmarks of task/stimulus-driven brain dynamics is the decrease of variability with respect to the spontaneous level, an effect seen across multiple experimental conditions and in brain signals observed at different spatiotemporal scales. Recently, it was observed that the trial-to-trial variability and temporal variance of functional magnetic resonance imaging (fMRI) signals decrease in the task-driven activity. Here we examined the dynamics of a large-scale model of the human cortex to provide a mechanistic understanding of these observations. The model allows computing the statistics of synaptic activity in the spontaneous condition and in putative tasks determined by external inputs to a given subset of brain regions. We demonstrated that external inputs decrease the variance, increase the covariances, and decrease the autocovariance of synaptic activity as a consequence of single node and large-scale network dynamics. Altogether, these changes in network statistics imply a reduction of entropy, meaning that the spontaneous synaptic activity outlines a larger multidimensional activity space than does the task-driven activity. We tested this model's prediction on fMRI signals from healthy humans acquired during rest and task conditions and found a significant decrease of entropy in the stimulus-driven activity. Altogether, our study proposes a mechanism for increasing the information capacity of brain networks by enlarging the volume of possible activity configurations at rest and reliably settling into a confined stimulus-driven state to allow better transmission of stimulus-related information.

Analytical modeling of driver response in crash avoidance maneuvering. Volume IV: user's guide for linear analysis, nonlinear simulation, part task simulation. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

On the Use of Regression and Verbal Protocol Analysis in Modeling Analysts’ Behavior in an Unstructured Task Environment: A Methodological Note

This paper discusses areas for future research opportunities by addressing accounting issues faced by management accountants practicing in hospitality organizations. Specifically, the article focuses on the use of the uniform system of accounts by operating properties, the usefulness of allocating support costs to operated departments, extending our understanding of operating costs and performance measurement systems and the certification of practicing accountants.

Race modeling: An effective cognitive strategy for the 100 m sprinter?

The purpose of this study was to investigate the effects of a specific cognitive race plan on 100 m sprint performance, Twelve elite sprinters (11 male and 1 female) performed 100 m time trials under normal (control) conditions and then under experimental conditions (use of race cues). In the experimental condition, participants were asked to think about specific thought content in each of three segments of the 100 m. A multiple baseline design was employed. A mean improvement of 0.26 s was found. Eleven of the 12 participants showed improvement using the specific cognitive race plan (p < .005). Participants also produced more consistent sprint performances when using the cues (p < .01). Subjective evaluations made by the participants unanimously supported the use of the race plan for optimizing sprint performance. Environmental conditions, effort, and practice effects were considered as possible influences on the results.

Using a cartographic modeling language to manipulate spectral satellite imagery

Land related information about the Earth's surface is commonIJ found in two forms: (1) map infornlation and (2) satellite image da ta. Satellite imagery provides a good visual picture of what is on the ground but complex image processing is required to interpret features in an image scene. Increasingly, methods are being sought to integrate the knowledge embodied in mop information into the interpretation task, or, alternatively, to bypass interpretation and perform biophysical modeling directly on derived data sources. A cartographic modeling language, as a generic map analysis package, is suggested as a means to integrate geographical knowledge and imagery in a process-oriented view of the Earth. Specialized cartographic models may be developed by users, which incorporate mapping information in performing land classification. In addition, a cartographic modeling language may be enhanced with operators suited to processing remotely sensed imagery. We demonstrate the usefulness of a cartographic modeling language for pre-processing satellite imagery, and define two nerv cartographic operators that evaluate image neighborhoods as post-processing operations to interpret thematic map values. The language and operators are demonstrated with an example image classification task.

Research commentary: Information systems and conceptual modeling - A research agenda

Within the information systems field, the task of conceptual modeling involves building a representation of selected phenomena in some domain. High-quality conceptual-modeling work is important because it facilitates early detection and correction of system development errors. It also plays an increasingly important role in activities like business process reengineering and documentation of best-practice data and process models in enterprise resource planning systems. Yet little research has been undertaken on many aspects of conceptual modeling. In this paper, we propose a framework to motivate research that addresses the following fundamental question: How can we model the world to better facilitate our developing, implementing, using, and maintaining more valuable information systems? The framework comprises four elements: conceptual-modeling grammars, conceptual-modeling methods, conceptual-modeling scripts, and conceptual-modeling contexts. We provide examples of the types of research that have already been undertaken on each element and illustrate research opportunities that exist.

Neural-network approach to modeling liquid crystals in complex confinement

Finding the structure of a confined liquid crystal is a difficult task since both the density and order parameter profiles are nonuniform. Starting from a microscopic model and density-functional theory, one has to either (i) solve a nonlinear, integral Euler-Lagrange equation, or (ii) perform a direct multidimensional free energy minimization. The traditional implementations of both approaches are computationally expensive and plagued with convergence problems. Here, as an alternative, we introduce an unsupervised variant of the multilayer perceptron (MLP) artificial neural network for minimizing the free energy of a fluid of hard nonspherical particles confined between planar substrates of variable penetrability. We then test our algorithm by comparing its results for the structure (density-orientation profiles) and equilibrium free energy with those obtained by standard iterative solution of the Euler-Lagrange equations and with Monte Carlo simulation results. Very good agreement is found and the MLP method proves competitively fast, flexible, and refinable. Furthermore, it can be readily generalized to the richer experimental patterned-substrate geometries that are now experimentally realizable but very problematic to conventional theoretical treatments.

Assessment of data-driven modeling strategies for water delivery canals

The aim of this paper is to develop models for experimental open-channel water delivery systems and assess the use of three data-driven modeling tools toward that end. Water delivery canals are nonlinear dynamical systems and thus should be modeled to meet given operational requirements while capturing all relevant dynamics, including transport delays. Typically, the derivation of first principle models for open-channel systems is based on the use of Saint-Venant equations for shallow water, which is a time-consuming task and demands for specific expertise. The present paper proposes and assesses the use of three data-driven modeling tools: artificial neural networks, composite local linear models and fuzzy systems. The canal from Hydraulics and Canal Control Nucleus (A parts per thousand vora University, Portugal) will be used as a benchmark: The models are identified using data collected from the experimental facility, and then their performances are assessed based on suitable validation criterion. The performance of all models is compared among each other and against the experimental data to show the effectiveness of such tools to capture all significant dynamics within the canal system and, therefore, provide accurate nonlinear models that can be used for simulation or control. The models are available upon request to the authors.

Neural-network approach to modeling liquid crystals in complex confinement

Finding the structure of a confined liquid crystal is a difficult task since both the density and order parameter profiles are nonuniform. Starting from a microscopic model and density-functional theory, one has to either (i) solve a nonlinear, integral Euler-Lagrange equation, or (ii) perform a direct multidimensional free energy minimization. The traditional implementations of both approaches are computationally expensive and plagued with convergence problems. Here, as an alternative, we introduce an unsupervised variant of the multilayer perceptron (MLP) artificial neural network for minimizing the free energy of a fluid of hard nonspherical particles confined between planar substrates of variable penetrability. We then test our algorithm by comparing its results for the structure (density-orientation profiles) and equilibrium free energy with those obtained by standard iterative solution of the Euler-Lagrange equations and with Monte Carlo simulation results. Very good agreement is found and the MLP method proves competitively fast, flexible, and refinable. Furthermore, it can be readily generalized to the richer experimental patterned-substrate geometries that are now experimentally realizable but very problematic to conventional theoretical treatments.