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Islamic Azad University Najafabad Branch, Iran, Islamic Republic of

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51 Papers

Articulating Socially Aware Design Artifacts and User Stories in the Conception of the OpenDesign Platform

Topics: HCI on Enterprise Information Systems; Human Factors; Requirements Analysis And Management; Interface Design; Semiotics in Information Systems

Authors: Júlio Reis ¹ , Andressa Santos ¹ , Emanuel Duarte ¹ , Fabrício Gonçalves ¹ , Breno Nicolau de França ¹ , Rodrigo Bonacin ² and M. Baranauskas ¹

Affiliations: ¹ Institute of Computing, University of Campinas, Campinas, Brazil , ² Center for Information Technology Renato Archer, Campinas, São Paulo, Brazil

Digital Object Identifier: 10.5220/0009418205230532

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

Quick Abstract

Gathering and understanding requirements and features in a system play a central role in its acceptance and proper use. Unconventional systems involving uncertainties about their requirements demand methods that adequately support the capture of stakeholder’s needs, desires and objectives. The OpenDesign project aims at supporting the design of computational solutions to wide-ranging problems under a holistic and socially-aware perspective through an open and collaborative platform. Designing this platform with a proper understanding of the desired features is for sure a hard task. In this paper, we investigate and characterize an ideation process to be used in situations involving uncertainties about requirements, as experienced in the OpenDesign Project. This process involves the collaborative construction of user stories, articulated with Socially Aware Design artifacts, created through participatory practices, as part of the platform design and prospection of potential uses of it. Based on the results, we organize the core concepts permeating the OpenDesign proposal, expressed in a map synthesizing its features.

OpenDesign: Analyzing Deliberation and Rationale in an Exploratory Case Study

Topics: Tools, Techniques and Methodologies for System Development; Interface Design; Collaborative and Social Interaction; Semiotics in Information Systems

Authors: Fabrício Gonçalves , Alysson Prado and Maria Baranauskas

Affiliation: Institute of Computing, University of Campinas, Campinas, Brazil

Digital Object Identifier: 10.5220/0009385305110522

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

Quick Abstract

The open phenomenon coming from the free-software movement has gained several fields, including services, digital and physical products. Nevertheless, some authors point out the limited availability of supporting methods and online tools to face the challenges of distributed collaboration of volunteers. They claim that online collaborative platforms are still needed for supporting co-creation. In this paper, we investigate the deliberation and design rationale in an (open) design process using the OpenDesign Platform. A case study conducted with 22 participants of a Conference illustrates the use of the platform to cope with a proposed design challenge. Results illustrated with graphical representations based on concepts of the Actor Network Theory provide a visual representation of the network constituted by both the participants and the artifacts (boundary objects) they produce and interpret. Further studies are pointed out suggesting new possibilities of features and platform enhancements.

Enhancing Problem Clarification Artifacts with Online Deliberation

Topics: Requirements Engineering; Software and Systems Modeling; Software Engineering Tools

Authors: Fabrício Matheus Gonçalves , Felipe Rodrigues Jensen , Julio Cesar dos Reis and Maria Cecília Calani Baranauskas

Affiliation: Institute of Computing, University of Campinas (UNICAMP), Campinas and Brazil

Digital Object Identifier: 10.5220/0006869103220329

Proceedings of ICSOFT 2018, ISBN: 978-989-758-320-9

Quick Abstract

Information system design demands understanding requirements from diversified stakeholders. As an initial step, the problem clarification is essential to obtain a shared view of the involved problems and solutions. Several techniques have been proposed and practiced by the systems engineering community for problem clarification. While existing literature has brought problem clarification artifacts via a online computational system, stakeholders still lack means of meaning negotiation practices that usually happen in face-to-face meetings. This paper proposes a deliberation model integrated to the online use of problem clarification artifacts. The deliberation provides a collaborative process for building common ground for reflection. The proposed model illustrates the possibilities of deliberation in statements created in three artefacts of the Organizational Semiotics: Stakeholder Identification, Evaluation Frame and Semiotic Framework.

Related Ontology Subjects/Areas/Topics (5)

Artificial Intelligence		Knowledge-Based Systems		Symbolic Systems
Knowledge Management and Information Sharing		Requirements Engineering

Nonlinear Adaptive Estimation by Kernel Least Mean Square with Surprise Criterion and Parallel Hyperslab Projection along Affine Subspaces Algorithm

Topics: Machine Learning in Control Applications; Adaptive Signal Processing and Control; Nonlinear Signals and Systems; System Modeling; Engineering Applications on Intelligent Control Systems and Optimization

Authors: Angie Forero and Celso P. Bottura

Affiliation: School of Electrical and Computer Engineering, University of Campinas and Brazil

Digital Object Identifier: 10.5220/0006867803620368

Proceedings of ICINCO 2018, ISBN: 978-989-758-321-6

Quick Abstract

In this paper the algorithm KSCP (KLMS with Surprise Criterion and Parallel Hyperslab Projection Along Affine Subspaces) for adaptive estimation of nonlinear systems is proposed. It is based on the combination of: - the reproducing kernel to deal with the high complexity of nonlinear systems; -the parallel hyperslab projection along affine subspace learning algorithm, to deal with adaptive nonlinear estimation problem; - the kernel least mean square with surprise criterion that uses concepts of likelihood and bayesian inference to predict the posterior distribution of data, guaranteeing an appropriate selection of data to the dictionary at low computational cost, to deal with the exponential growth of the dictionary, as new data arrives. The proposed algorithm offers high accuracy estimation and high velocity of computation, characteristics that are very important in estimation and tracking online applications.

Related Ontology Subjects/Areas/Topics (7)

Adaptive Signal Processing and Control	Machine Learning in Control Applications	Signal Processing, Sensors, Systems Modeling and Control
Informatics in Control, Automation and Robotics	Nonlinear Signals and Systems	System Modeling
Intelligent Control Systems and Optimization

Expressive Icons for the Communication of Intentions

Topics: Human Factors; Emotional and Affective Computing

Authors: Julio Cesar dos Reis ¹ , Cristiane Jensen ² , Rodrigo Bonacin ³ , Heiko Hornung ¹ and M. Cecilia C. Baranauskas ¹

Affiliations: ¹ University of Campinas, Brazil , ² Faculty of Campo Limpo Paulista, Brazil , ³ Faculty of Campo Limpo Paulista and Center for Information Technology Renato Archer, Brazil

Digital Object Identifier: 10.5220/0005834003880399

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

Quick Abstract

The mutual understanding of intentions is essential to human communication. A web-mediated communication lacks elements that are natural in face-to-face conversation. This fact requires treating intentions more explicitly in computer systems. Literature hardly explores design methods and interactive mechanisms to support users in this task. In this article, we argue that icons representing emotions play a central role as means for aiding users to express intentions. This research proposes a method to determine and refine icons aiming to represent and communicate the users’ intentions via computer systems. The work explores a theoretical framework based on Speech Act Theory and Semiotics to analyze different classes of intention. The method is experimented in a case study with 40 users and the obtained results suggest its feasibility in the process of filtering, selecting and enhancing icons to communicate intentions.

Related Ontology Subjects/Areas/Topics (5)

Emotional and Affective Computing		Human Factors		Physiological Computing Systems
Enterprise Information Systems		Human-Computer Interaction

A Semiotic-informed Approach to Interface Guidelines for Mobile Applications - A Case Study on Phenology Data Acquisition

Topics: Guidelines, Principles, Patterns and Standards; Interface Design

Authors: Flavio Nicastro ¹ , Roberto Pereira ¹ , Bruna Alberton ² , Leonor Patrícia C. Morellato ² , Cecilia Baranauskas ¹ and Ricardo da S. Torres ¹

Affiliations: ¹ University of Campinas (UNICAMP), Brazil , ² Phenology Lab and Dept. of Botany, Brazil

Digital Object Identifier: 10.5220/0005379600340043

Proceedings of ICEIS 2015, ISBN: 978-989-758-098-7

Quick Abstract

Portable devices have been experimented for data acquisition in different domains, e.g., logistics and census data acquisition. Nevertheless, their large-scale adoption depends on the development of effective applications with a careful interaction design. In this paper, we revisit existing interface design strategies and propose a guideline composed of semiotic-informed rules and questions for mobile user interface design. We demonstrate the use of the guideline in the evaluation of mobile application interfaces proposed for phenological data acquisition in the field.

Related Ontology Subjects/Areas/Topics (4)

Enterprise Information Systems		Human-Computer Interaction		Interface Design
Guidelines, Principles, Patterns and Standards

A Wearable Face Recognition System Built into a Smartwatch and the Visually Impaired User

Topics: Interaction Techniques and Devices; Accessibility and Usability

Authors: Laurindo de Sousa Britto Neto ¹ , Vanessa Regina Margareth Lima Maike ² , Fernando Luiz Koch ³ , Maria Cecília Calani Baranauskas ² , Anderson de Rezende Rocha ² and Siome Klein Goldenstein ²

Affiliations: ¹ Federal University of Piauí (UFPI) and University of Campinas (UNICAMP), Brazil , ² University of Campinas (UNICAMP), Brazil , ³ Samsung Research Institute, Brazil

Digital Object Identifier: 10.5220/0005370200050012

Proceedings of ICEIS 2015, ISBN: 978-989-758-098-7

Quick Abstract

Practitioners usually expect that real-time computer vision systems such as face recognition systems will require hardware components with high processing power. In this paper, we present a concept to show that it is technically possible to develop a simple real-time face recognition system in a wearable device with low processing power – in this case an assistive device for the visually impaired. Our platform of choice here is the first generation Samsung Galaxy Gear smartwatch. Running solely in the watch, without pairing to a phone or tablet, the system detects a face in the image captured by the camera, and then performs face recognition (on a limited dictionary), emitting an audio feedback that either identifies the recognized person or indicates that s/he is unknown. For the face recognition approach we use a variation of the K-NN algorithm which accomplished the task with high accuracy rates. This paper presents the proposed system and preliminary results on its evaluation.

Related Ontology Subjects/Areas/Topics (4)

Accessibility and Usability		Human-Computer Interaction		Interaction Techniques and Devices
Enterprise Information Systems

Off-line State-dependent Parameter Models Identification using Simple Fixed Interval Smoothing

Topics: Optimization Algorithms; Nonlinear Signals and Systems; System Identification; System Modeling; Systems Modeling and Simulation; Systems Modeling and Simulation

Authors: Elvis Omar Jara Alegria , Hugo Tanzarella Teixeira and Celso Pascoli Bottura

Affiliation: State University of Campinas - UNICAMP, Brazil

Digital Object Identifier: 10.5220/0005573903360341

Proceedings of ICINCO 2015, ISBN: 978-989-758-122-9

Quick Abstract

This paper shows a detailed study about the Young’s algorithm for parameter estimation on ARX-SDP models and proposes some improvements. To reduce the high entropy of the unknown parameters, data reordering according to a state ascendant ordering is used on that algorithm. After the Young’s temporal reordering process, the old data do not necessarily continue so. We propose to reconsider the forgetting factor, internally used in the exponential window past, as a fixed and small value. This proposal improves the estimation results, especially in the low data density regions, and improves the algorithm velocity as experimentally shown. Other interesting improvement of our proposal is characterized by the flexibility to the changes on the state-parameter dependency. This is important in a future On-Line version. Interesting features of the SDP estimation algorithm for the case of ARX-SDP models with unitary regressors and the case with correlated state-parameter are also studied. Finally a example shows our results using the INCA toolbox we developed for our proposal.

Related Ontology Subjects/Areas/Topics (9)

Industrial Engineering	Nonlinear Signals and Systems	System Identification
Informatics in Control, Automation and Robotics	Optimization Algorithms	System Modeling
Intelligent Control Systems and Optimization	Signal Processing, Sensors, Systems Modeling and Control	Systems Modeling and Simulation

State-parameter Dependency Estimation of Stochastic Time Series using Data Transformation and Parameterization by Support Vector Regression

Topics: Machine Learning in Control Applications; Nonlinear Signals and Systems; Modeling, Simulation and Architecture; System Modeling; Systems Modeling and Simulation; Systems Modeling and Simulation

Authors: Elvis Omar Jara Alegria , Hugo Tanzarella Teixeira and Celso Pascoli Bottura

Affiliation: State University of Campinas - UNICAMP, Brazil

Digital Object Identifier: 10.5220/0005574103420347

Proceedings of ICINCO 2015, ISBN: 978-989-758-122-9

Quick Abstract

This position paper is about the identification of the dependency among parameters and states in regression models of stochastic time series. Conventional recursive algorithms for parameter estimation do not provide good results in models with state-dependent parameters (SDP) because these may have highly non-linear behavior. To detect this dependence using conventional algorithms, we are studying some data transformations that we implement in this paper. Non-parametric relationships among parameters and states are obtained and parameterized using support vector regression. This way we look for a final non-linear structure to solve the SDP identification problem.

Related Ontology Subjects/Areas/Topics (10)

Industrial Engineering	Modeling, Simulation and Architectures	Signal Processing, Sensors, Systems Modeling and Control
Informatics in Control, Automation and Robotics	Nonlinear Signals and Systems	System Modeling
Intelligent Control Systems and Optimization	Robotics and Automation	Systems Modeling and Simulation
Machine Learning in Control Applications

Temporal-Difference Learning - An Online Support Vector Regression Approach

Topic: Machine Learning in Control Applications

Authors: Hugo Tanzarella Teixeira and Celso Pascoli Bottura

Affiliation: State University of Campinas - UNICAMP, Brazil

Digital Object Identifier: 10.5220/0005572103180323

Proceedings of ICINCO 2015, ISBN: 978-989-758-122-9

Quick Abstract

This paper proposes a new algorithm for Temporal-Difference (TD) learning using online support vector regression. It benefits from the good generalization properties support vector regression (SVR) has, and also can do incremental learning and automatically track variation of environment with time-varying characteristics. Using the online SVR we can obtain good estimation of value function in TD learning in linear and nonlinear prediction problems. Experimental results demonstrate the effectiveness of the proposed method by comparison with others methods.

Related Ontology Subjects/Areas/Topics (3)

Informatics in Control, Automation and Robotics

Intelligent Control Systems and Optimization

Machine Learning in Control Applications

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