Automating the product derivation process of multi-agent systems product lines

Agent-oriented software engineering and software product lines are two promising software engineering techniques. Recent research work has been exploring their integration, namely multi-agent systems product lines (MAS-PLs), to promote reuse and variability management in the context of complex software systems. However, current product derivation approaches do not provide specific mechanisms to deal with MAS-PLs. This is essential because they typically encompass several concerns (e.g., trust, coordination, transaction, state persistence) that are constructed on the basis of heterogeneous technologies (e.g., object-oriented frameworks and platforms). In this paper, we propose the use of multi-level models to support the configuration knowledge specification and automatic product derivation of MAS-PLs. Our approach provides an agent-specific architecture model that uses abstractions and instantiation rules that are relevant to this application domain. In order to evaluate the feasibility and effectiveness of the proposed approach, we have implemented it as an extension of an existing product derivation tool, called GenArch. The approach has also been evaluated through the automatic instantiation of two MAS-PLs, demonstrating its potential and benefits to product derivation and configuration knowledge specification.

The influence of emotional contagion on products evaluation

Emotional Contagion is the mechanism that includes mimicking and the automatic synchronization of facial expressions, vocalizations, postures, and movements with another person and, consequently, convergence of emotions between the sender and receiver. Researches of this mechanism conducted usually in the fields of Psychology and Marketing tends to investigate face-to-face interactions. However, the question remains to what extent, if any, emotional contagion may occur with facial expressions in photos, since many purchase situations are brought on by catalogues or websites. This thesis has the goal to verify this gap and, in addition, verify whether emotional contagion is more common in females than in males as stated in previous studies. Emotions have been studied because it is intuitively apparent that emotions affect the dynamics of the interaction between a salesperson and customers (Verbeke, 1997); in other words, emotions may significantly affect consumer behavior. Therefore, this thesis also verified whether the facial expressions that transmit emotions could be associated to product evaluations. To investigate these questions, an experiment was done with 171 participants, which were exposed to either smiling (positive emotion) or neutral advertising. The differences between the individual advertisements were limited to the facial expressions of figures in the advertisements (either smiling or neutral/without smiling). One specialist and two students analyzed videotaped records of the participants’ responses, and found that participants who saw the positive stimulus mimicked the picture (smiling back) confirming the Emotional Contagion in Photos (the first hypothesis). The second hypothesis was to analyze if there is difference based in gender. The results demonstrated that there is not a significant difference between genders; female and male equally suffer Emotional Contagion. The third hypothesis was related to whether the positive emotions vs. neutral emotions acquired from the positive facial expression in the photo are associated to a positive evaluation of the product also displayed in the photo. Evidences show that the ad with a positive expression could change more positively the attitude, the sympathy, the reliability, and the intention of purpose of the participant compared to those who were exposed to the neutral condition. Therefore, the analysis concludes that the facial expressions displayed in photos produce emotional contagion and may interfere on the evaluation product. A discussion of the theoretical and practical implications and limitations for these findings are presented.

Interactive music visualization: implementation, realization and evaluation

This thesis describes all process of the development of music visualization, starting with the implementation, followed by realization and then evaluation. The main goal is to have to knowledge of how the audience live performance experience can be enhanced through music visualization. With music visualization is possible to give a better understanding about the music feelings constructing an intensive atmosphere in the live music performance, which enhances the connection between the live music and the audience through visuals. These visuals have to be related to the live music, furthermore has to quickly respond to live music changes and introduce novelty into the visuals. The mapping between music and visuals is the focus of this project, in order to improve the relationship between the live performance and the spectators. The implementation of music visualization is based on the translation of music into graphic visualizations, therefore at the beginning the project was based on the existent works. Later on, it was decided to introduce new ways of conveying music into visuals. Several attempts were made in order to discover the most efficient mapping between music and visualization so people can fully connect with the performance. Throughout this project, those attempts resulted in several music visualizations created for four live music performances, afterwards it was produced an online survey to evaluate those live performances with music visualization. In the end, all conclusions are presented based on the results of the online survey, and also is explained which music elements should be depicted in the visuals, plus how those visuals should respond to the selected music elements.

DEMO models based automatic workflow process generation

Nowadays, more than half of the computer development projects fail to meet the final users' expectations. One of the main causes is insufficient knowledge about the organization of the enterprise to be supported by the respective information system. The DEMO methodology (Design and Engineering Methodology for Organizations) has been proved as a well-defined method to specify, through models and diagrams, the essence of any organization at a high level of abstraction. However, this methodology is platform implementation independent, lacking the possibility of saving and propagating possible changes from the organization models to the implemented software, in a runtime environment. The Universal Enterprise Adaptive Object Model (UEAOM) is a conceptual schema being used as a basis for a wiki system, to allow the modeling of any organization, independent of its implementation, as well as the previously mentioned change propagation in a runtime environment. Based on DEMO and UEAOM, this project aims to develop efficient and standardized methods, to enable an automatic conversion of DEMO Ontological Models, based on UEAOM specification into BPMN (Business Process Model and Notation) models of processes, using clear semantics, without ambiguities, in order to facilitate the creation of processes, almost ready for being executed on workflow systems that support BPMN.

Aquatic weed automatic classification using machine learning techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Extraction of Building Roof Contours From LiDAR Data Using a Markov-Random-Field-Based Approach

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Image processing algorithm for the identification of Martian dust devil tracks in MOC and HiRISE images

This paper presents a method for automatic identification of dust devils tracks in MOC NA and HiRISE images of Mars. The method is based on Mathematical Morphology and is able to successfully process those images despite their difference in spatial resolution or size of the scene. A dataset of 200 images from the surface of Mars representative of the diversity of those track features was considered for developing, testing and evaluating our method, confronting the outputs with reference images made manually. Analysis showed a mean accuracy of about 92%. We also give some examples on how to use the results to get information about dust devils, namelly mean width, main direction of movement and coverage per scene. (c) 2012 Elsevier Ltd. All rights reserved.

Application of wavelets to the evaluation of phantom images for mammography quality control

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estimation of crowd density using image processing

Human beings perceive images through their properties, like colour, shape, size, and texture. Texture is a fertile source of information about the physical environment. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. This paper describes a new technique for automatic estimation of crowd density, which is a part of the problem of automatic crowd monitoring, using texture information based on grey-level transition probabilities on digitised images. Crowd density feature vectors are extracted from such images and used by a self organising neural network which is responsible for the crowd density estimation. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented.

Fuzzy logic to predict thermal damages of ground parts

One of the critical problems in implementing an intelligent grinding process is the automatic detection of workpiece surface burn. This work uses fuzzy logic as a tool to classify and predict burn levels in the grinding process. Based on acoustic emission signals, cutting power, and the mean-value deviance (MVD), linguistic rules were established for the various burn situations (slight, intermediate, severe) by applying fuzzy logic using the Matlab Toolbox. Three practical fuzzy system models were developed. The first model with two inputs resulted only in a simple analysis process. The second and third models have an additional MVD statistic input, associating information and precision. These two models differ from each other in terms of the rule base developed. The three developed models presented valid responses, proving effective, accurate, reliable and easy to use for the determination of ground workpiece burn. In this analysis, fuzzy logic translates the operator's human experience associated with powerful computational methods.

FTMOntology: An ontology to fill the semantic gap between music, mood, personality, and human physiology

This paper presents a domain ontology, the FeelingTheMusic Ontology - FTMOntology. FTMOntology is designed to represent the complex domain of music and how it relates to other domains like mood, personality and physiology. This includes representing the main concepts and relations of music domain with each of the above-mentioned domains. The concepts and relations between music, mood, personality and physiology. The main contribution of this work is to model and relate these different domains in a consistent ontology. © 2011 Springer-Verlag.

A study on automatic methods based on mathematical morphology for Martian dust devil tracks detection

This paper presents three methods for automatic detection of dust devils tracks in images of Mars. The methods are mainly based on Mathematical Morphology and results of their performance are analyzed and compared. A dataset of 21 images from the surface of Mars representative of the diversity of those track features were considered for developing, testing and evaluating our methods, confronting their outputs with ground truth images made manually. Methods 1 and 3, based on closing top-hat and path closing top-hat, respectively, showed similar mean accuracies around 90% but the time of processing was much greater for method 1 than for method 3. Method 2, based on radial closing, was the fastest but showed worse mean accuracy. Thus, this was the tiebreak factor. © 2011 Springer-Verlag.

Specialist tool for monitoring the measurement degradation process of induction active energy meters

This paper presents a methodology and a specialist tool for failure probability analysis of induction type watt-hour meters, considering the main variables related to their measurement degradation processes. The database of the metering park of a distribution company, named Elektro Electricity and Services Co., was used for determining the most relevant variables and to feed the data in the software. The modeling developed to calculate the watt-hour meters probability of failure was implemented in a tool through a user friendly platform, written in Delphi language. Among the main features of this tool are: analysis of probability of failure by risk range; geographical localization of the meters in the metering park, and automatic sampling of induction type watt-hour meters, based on a risk classification expert system, in order to obtain information to aid the management of these meters. The main goals of the specialist tool are following and managing the measurement degradation, maintenance and replacement processes for induction watt-hour meters. © 2011 IEEE.

Automatic code generation for microcontrollers from place-transition Petri net models

This paper describes a program for the automatic generation of code for Intel's 8051 microcontroller. The code is generated from a place-transition Petri net specification. Our goal is to minimize programming time. The code generated by our program has been observed to exactly match the net model. It has also been observed that no change is needed to be made to the generated code for its compilation to the target architecture. © 2011 IFAC.

Identification of foliar diseases in cotton crop

The pathogens manifestation in plantations are the largest cause of damage in several cultivars, which may cause increase of prices and loss of crop quality. This paper presents a method for automatic classification of cotton diseases through feature extraction of leaf symptoms from digital images. Wavelet transform energy has been used for feature extraction while Support Vector Machine has been used for classification. Five situations have been diagnosed, namely: Healthy crop, Ramularia disease, Bacterial Blight, Ascochyta Blight, and unspecified disease. © 2012 Taylor & Francis Group.