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41 Papers

MUFF: A Low-cost Motorized Positioner Developed by Additive Manufacturing to Obtain Multi-focus Images

Topic: Applied Industrial Optics

Authors: Gustavo Lelis da Silva ¹ , Jorge Stolfi ¹ and Helena Leitão ²

Affiliations: ¹ State University of Campinas (UNICAMP), Campinas, SP, Brazil , ² Federal Fluminense University (UFF) Niteroi, RJ, Brazil

Digital Object Identifier: 10.5220/0010913500003121

Proceedings of PHOTOPTICS 2022, ISBN: 978-989-758-554-8

Quick Abstract

Multifocal microscopy is an established technique to combine several reflected-light microscopy images of an object, each with a limited depth of focus, into a single image that shows the whole object in focus. Photometric stereo is an independent technique to recover the third dimension of an opaque object, given several images taken from the same viewpoint with different illumination conditions. We describe a low-cost motorized positioning device for the automatic acquisition of multiple reflected-light low-power microscope images of an object for combined multifocal and photometric stereo capture of millimeter-scale objects. The device automatically varies the microscope-to-object distance and the light direction, under control of an Arduino microcontroller. The device consists of a supporting frame, a microscope holder with motorized vertical displacement, an illumination stage with multiple light sources, and control electronics. The mechanical and structural parts are largely 3D-printed. The electronics is based on the Arduino microcontroller. The device is driven through an USB cable by a Python program that runs on a PC.

Automatic Construction of Benchmarks for RDF Keyword Search Systems Evaluation

Topics: Performance Evaluation and Benchmarking; Non-Relational Databases

Authors: Angelo Neves ¹ , Luiz Leme ² , Yenier Izquierdo ¹ and Marco Casanova ¹

Affiliations: ¹ Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil , ² Universidade Federal Fluminense, Niterói, RJ, Brazil

Digital Object Identifier: 10.5220/0010519401260137

Proceedings of ICEIS 2021, ISBN: 978-989-758-509-8

Quick Abstract

Keyword search systems provide users with a friendly alternative to access Resource Description Framework (RDF) datasets. The evaluation of such systems requires adequate benchmarks, consisting of RDF datasets and keyword queries, with their correct answers. However, the sets of correct answers such benchmarks provide for each query are often incomplete, mostly because they are manually built with experts’ help. The central contribution of this paper is an offline method that helps build RDF keyword search benchmarks automatically, leading to more complete sets of correct answers, called solution generators. The paper focuses on computing sets of generators and describes heuristics that circumvent the combinatorial nature of the problem. The paper then describes five benchmarks, constructed with the proposed method and based on three real datasets, DBpedia, IMDb, and Mondial, and two synthetic datasets, LUBM and BSBM. Finally, the paper compares the constructed benchmarks with keyword search benchmarks published in the literature.

An Approach for Acquiring Knowledge in Complex Domains Involving Different Data Sources and Uncertinty in Label Information: A Case Study on Cementation Quality Evaluation

Topics: Coupling and Integrating Heterogeneous Data Sources; Group Decision Support Systems; Applications of Expert Systems

Authors: Flavia Bernardini ¹ , Rodrigo Monteiro ¹ , Inhauma Ferraz ² , Jose Viterbo ¹ and Adriel Araujo ¹

Affiliations: ¹ Institute of Computing, Fluminense Federal University, Niteroi, RJ, Brazil, ADDLabs — Active Documentation Design Laboratory, Fluminense Federal University, Niteroi, RJ, Brazil , ² ADDLabs — Active Documentation Design Laboratory, Fluminense Federal University, Niteroi, RJ, Brazil

Digital Object Identifier: 10.5220/0009418905630570

Proceedings of ICEIS 2020, ISBN: 978-989-758-423-7

Quick Abstract

Oil and Gas area presents many problems in which the experts need to analyze different data sources and they must be very specialized in the domain to correctly analyze the case. So, approaches that uses artificial intelligence techniques to help the experts to help them turning explicit their expert knowledge and analysing the cases is very important. Analysing cementation quality in oil wells is one of these cases. Primary cementation operation of an oil well is creating a hydraulic seal in the annular space formed between the coating pipe and the open well wall, preventing the flow between different geological zones bearing water or hydrocarbons. To evaluate the quality of this seal at determined depths, acoustic tools are used, aiming to collect sonic and ultrasonic signals. Verifying the quality of the available data for cementation quality evaluation is a task that consumes time and effort of the domain experts, mainly due to data dispersion in different data sources and missing labels in data. This work presents an approach for helping acquiring knowledge from domains where these problems are presented using machine learning. Interactive labeling and multiple data sources for acquiring knowledge from experts can help to construct better systems in complex scenarios, such as cementation quality. We obtained promising results in our case study scenario.

SLang: A Domain-specific Language for Survey Questionnaires

Topics: Tools, Techniques and Methodologies for System Development; Modeling Formalisms, Languages, and Notations (e.g. UML, ER variants)

Authors: Luciane Araújo ¹ , Marco Casanova ² , Luiz Leme ³ and Antonio Furtado ²

Affiliations: ¹ Brazilian Institute of Geography and Statistics, Rio de Janeiro, Brazil , ² Dept. Informatics, PUC-Rio, Rua Marquês de São Vicente, 225, Rio de Janeiro, Brazil , ³ Institute of Computing, Federal Fluminense University, Niterói, Brazil

Digital Object Identifier: 10.5220/0009407401330144

Proceedings of ICEIS 2020, ISBN: 978-989-758-423-7

Quick Abstract

The use of surveys permeates the economy, ranging from customer satisfaction measurement to tracking global economic trends. At the core of the survey process lies the codification of questionnaires, which vary from simple lists of questions to complex forms that include validations, computation of derived data, use of triggers to guarantee consistency, and dynamic creation of objects of interest. Questionnaire specification is part of what is called survey metadata and is a key factor for the quality of the data collected and of the survey itself. In this context, the paper first introduces a comprehensive complex questionnaire model. Then, based on the model, it proposes a prototype domain-specific language (DSL) for modeling complex questionnaires, called SLang. The paper also describes a prototype implementation of SLang and an evaluation of the usefulness of the language in practical scenarios.

Towards Adaptive Deep Reinforcement Game Balancing

Topics: Machine Learning; Agent Models and Architectures; Cooperation and Coordination; Deep Learning

Authors: Ashey Noblega , Aline Paes and Esteban Clua

Affiliation: Department of Computer Science, Institute of Computing, Niterói, RJ and Brazil

Digital Object Identifier: 10.5220/0007395406930700

Proceedings of ICAART 2019, ISBN: 978-989-758-350-6

Quick Abstract

The experience of a player regarding the difficulty of a video game is one of the main reasons for he/she decide to keep playing the game or abandon it. Effectively, player retention is one of the primary concerns related to the game development process. However, the experience of a player with a game is unique, making impractical to anticipate how they will face the gameplay. This work leverages the recent advances in Reinforcement Learning (RL) and Deep Learning (DL) to create intelligent agents that are able to adapt to the abilities of distinct players. We focus on balancing the difficulty of the game based on the information that the agent observes from the 3D environment as well as the current state of the game. In order to design an agent that learns how to act while still maintaining the balancing, we propose a reward function based on a balancing constant. We require that the agent remains inside a range around this constant during the training. Our experimental results show that by using such a reward function and combining information from different types of players it is possible to have adaptable agents that fit the player.

Related Ontology Subjects/Areas/Topics (11)

Agent Models and Architectures	Cooperation and Coordination	Machine Learning
Agents	Evolutionary Computing	Soft Computing
Artificial Intelligence	Knowledge Discovery and Information Retrieval	Symbolic Systems
Computational Intelligence	Knowledge-Based Systems

Deep Light Source Estimation for Mixed Reality

Topics: Augmented, Mixed and Virtual Environments; Lighting and Appearance

Authors: Bruno Augusto Dorta Marques ¹ , Rafael Rego Drumond ² , Cristina Nader Vasconcelos ¹ and Esteban Clua ¹

Affiliations: ¹ Universidade Federal Fluminense, Brazil , ² Universität Hildesheim, Germany

Digital Object Identifier: 10.5220/0006724303030311

Proceedings of GRAPP 2018, ISBN: 978-989-758-287-5

Quick Abstract

Mixed reality is the union of virtual and real elements in a single scene. In this composition, of real and virtual elements, perceptual discrepancies in the illumination of objects may occur. We call these discrepancies the illumination mismatch problem. Recovering the lighting information from a real scene is a difficult task. Usually, such task requires prior knowledge of the scene, such as the scene geometry and special measuring equipment. We present a deep learning based technique that estimates point light source position from a single color image. The estimated light source position is used to create a composite image containing both the real and virtual environments. The proposed technique allows the final composite image to have consistent illumination between the real and virtual worlds, effectively reducing the effects of the illumination mismatch in Mixed Reality applications.

Related Ontology Subjects/Areas/Topics (5)

Augmented, Mixed and Virtual Environments		Interactive Environments		Rendering
Computer Vision, Visualization and Computer Graphics		Lighting and Appearance

PEEK - An LSTM Recurrent Network for Motion Classification from Sparse Data

Topics: Advanced User Interfaces; Animation from Motion Capture

Authors: Rafael Rego Drumond ¹ , Bruno A. Dorta Marques ² , Cristina Nader Vasconcelos ² and Esteban Clua ²

Affiliations: ¹ University of Hildesheim, Germany , ² Universidade Federal Fluminense, Brazil

Digital Object Identifier: 10.5220/0006585202150222

Proceedings of GRAPP 2018, ISBN: 978-989-758-287-5

Quick Abstract

Games and other applications are exploring many different modes of interaction in order to create intuitive interfaces, such as touch screens, motion controllers, recognition of gesture or body movements among many others. In that direction, human motion is being captured by different sensors, such as accelerometers, gyroscopes, heat sensors and cameras. However, there is still room for investigation the analysis of motion data captured from low-cost sensors. This article explores the extent to which a full body motion classification can be achieved by observing only sparse data captured by two separate inherent wereable measurement unit (IMU) sensors. For that, we developed a novel Recurrent Neural Network topology based on Long Short-Term Memory cells (LSTMs) that are able to classify motions sequences of different sizes. Using cross-validation tests, our model achieves an overall accuracy of 96% which is quite significant considering that the raw data used was obtained using only 2 simple and accessible IMU sensors capturing arms movements. We also built and made public a motion database constructed by capturing sparse data from 11 actors performing five different actions. For comparison with existent methods, other deep learning approaches for sequence evaluation (more specifically, based on convolutional neural networks), were adapted to our problem and evaluated.

Related Ontology Subjects/Areas/Topics (5)

Advanced User Interfaces		Animation from Motion Capture		Interactive Environments
Animation and Simulation		Computer Vision, Visualization and Computer Graphics

Exchanging Solutions for Information Systems Development using a Model Pattern Perspective - Diagram Templates in the Context of the Mdarte Collaborative Evolution Process

Topics: Software Factories and Software Product Lines; Generative Programming

Authors: Rodrigo Salvador Monteiro ¹ , Geraldo Zimbrão ² and Jano Moreira de Souza ²

Affiliations: ¹ Universidade Federal Fluminense, Brazil , ² COPPE / Universidade Federal do Rio de Janeiro, Brazil

Digital Object Identifier: 10.5220/0005690306400647

Proceedings of MODELSWARD 2016, ISBN: 978-989-758-168-7

Quick Abstract

Information Systems Development faces many recurrent issues that must be addressed in every project. A lot of common requirements and features repeatedly appear on different projects challenging the development team. Exchanging solutions and the expertise gained over the evaluation of such solutions among projects can prevent the development teams from reinventing the wheel. The MDArte framework has been used to develop information systems through the Model Driven Architecture approach, automating the generation from models reaching around 80% of the application code. Most important is that the MDArte framework turned out to be a common platform between the adherent projects used to share new solutions and features. This paper focus on recurrent issues present at the modeling phase. We applied the concept of Model Patterns in order to provide diagram templates. A set of diagram templates were designed, implemented and evaluated by a volunteer project. The feedback was extremely positive and the proposed approach proves to be very promising. This level of collaboration between different projects on one hand accelerates the development and on the other hand prevents from neglecting important issues on information systems development.

Related Ontology Subjects/Areas/Topics (5)

Applications and Software Development		Model-Driven Software Development		Software Factories and Software Product Lines
Generative Programming		Software Engineering

Identifying Indirect Normative Conflicts using the WordNet Database

Topic: Multi-Agent Systems

Authors: Jéssica Soares dos Santos ¹ and Viviane Torres da Silva ²

Affiliations: ¹ Universidade Federal Fluminense, Brazil , ² IBM Research (on leave from Universidade Federal Fluminense), Brazil

Digital Object Identifier: 10.5220/0005904701860193

Proceedings of ICEIS 2016, ISBN: 978-989-758-187-8

Quick Abstract

A challenging issue in Multi-agent systems governed by multiple norms is to deal with normative conflicts, which are situations where the fulfilment of a norm violates another one. There are several approaches in the literature to detect conflicting norms. Some kinds of conflicts, here called direct conflicts, can be detected through a simple comparison between the elements of the norms in order to check if they apply to the same elements. For instance, if an obligation and a prohibition are applied to the same entity and govern the same behavior in periods of time that intersects, these norms are in conflict. However, there are conflicts, here called indirect conflicts, that can only be detected if the relationships among the elements of the norms are taken into account. The majority of approaches that are able to detect indirect conflict considers the relationships of the application domain that have been previously defined by the designer. Different from those approaches, this paper focuses on the detection of indirect conflicts by taking into account domain-independent relationships that have not been declared by the designer of the multi-agent system. Our proposal searches for domain-independent relationships among the elements of the norms in the WordNet database.

Related Ontology Subjects/Areas/Topics (10)

Agents	Enterprise Information Systems	Multi-Agent Systems
Artificial Intelligence	Knowledge Engineering and Ontology Development	Software Engineering
Artificial Intelligence and Decision Support Systems	Knowledge-Based Systems	Symbolic Systems
Distributed and Mobile Software Systems

Knowing the past to Plan for the Future - An In-depth Analysis of the First 10 Editions of the WEBIST Conference

Topics: Knowledge Management; Communities of Interest; Data Mining; Meta-Knowledge Discovery and Representation

Authors: Giseli Rabello Lopes ¹ , Bernardo Pereira Nunes ² , Luiz André P. Paes Leme ³ , Terhi Nurmikko-Fuller ⁴ and Marco A. Casanova ²

Affiliations: ¹ Federal University of Rio de Janeiro, Brazil , ² Pontifical Catholic University of Rio de Janeiro, Brazil , ³ Fluminense Federal University, Brazil , ⁴ Oxford University, United Kingdom

Digital Object Identifier: 10.5220/0005447704310442

Proceedings of WEBIST 2015, ISBN: 978-989-758-106-9

Quick Abstract

Over the last ten years, members of the WEBIST community have dedicated their time and efforts to face a number of research challenges, making significant advances in Information Systems and pointing to new directions for innovation and learning. After ten successful WEBIST conferences and several scientific publications, an extensive analysis of the WEBIST conferences has been carried out (involving authors, publications, conference impact, topics coverage, community analysis and other aspects) to possibly assist us to further advance Information Systems. Thus, in this paper, we present an in-depth analysis of the last ten WEBIST conferences based on social network analysis, bibliometrics and statistical measures and describe a Web-based application built on top of triplified datasets to interactively explore the findings and possibly assist the Information Systems community to reveal new directions.

Related Ontology Subjects/Areas/Topics (16)

Artificial Intelligence	Enterprise Information Systems	Sensor Networks
Biomedical Engineering	Health Information Systems	Signal Processing
Communities of Interest	Information Systems Analysis and Specification	Society, e-Business and e-Government
Data Engineering	Knowledge Management	Soft Computing
Data Mining	Ontologies and the Semantic Web	Web Information Systems and Technologies
Databases and Information Systems Integration

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