A novel approach for data fusion and dialog management in user-adapted multimodal dialog systems

Proceedings of: 17th International Conference on Information Fusion (FUSION 2014): Salamanca, Spain 7-10 July 2014.Multimodal dialog systems have demonstrated a high potential for more flexible, usable and natural humancomputer interaction. These improvements are highly dependent on the fusion and dialog management processes, which respectively integrates and interprets multimedia multimodal information and decides the next system response for the current dialog state. In this paper we propose to carry out the multimodal fusion and dialog management processes at the dialog level in a single step. To do this, we describe an approach based on a statistical model that takes user's intention into account, generates a single representation obtained from the different input modalities and their confidence scores, and selects the next system action based on this representation. The paper also describes the practical application of the proposed approach to develop a multimodal dialog system providing travel and tourist information.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485).Publicad

Engineering Adaptive Model-Driven User Interfaces for Enterprise Applications

Enterprise applications such as enterprise resource planning systems have numerous complex user interfaces (UIs). Usability problems plague these UIs because they are offered as a generic off-the-shelf solution to end-users with diverse needs in terms of their required features and layout preferences. Adaptive UIs can help in improving usability by tailoring the features and layout based on the context-of-use. The model-driven UI development approach offers the possibility of applying different types of adaptations on the various UI levels of abstraction. This approach forms the basis for many works researching the development of adaptive UIs. Yet, several gaps were identified in the state-of-the-art adaptive model-driven UI development systems. To fill these gaps, this thesis presents an approach that offers the following novel contributions: - The Cedar Architecture serves as a reference for developing adaptive model-driven enterprise application user interfaces. - Role-Based User Interface Simplification (RBUIS) is a mechanism for improving usability through adaptive behavior, by providing end-users with a minimal feature-set and an optimal layout based on the context-of-use. - Cedar Studio is an integrated development environment, which provides tool support for building adaptive model-driven enterprise application UIs using RBUIS based on the Cedar Architecture. The contributions were evaluated from the technical and human perspectives. Several metrics were established and applied to measure the technical characteristics of the proposed approach after integrating it into an open-source enterprise application. Additional insights about the approach were obtained through the opinions of industry experts and data from real-life projects. Usability studies showed the approach’s ability to significantly improve usability in terms of end-user efficiency, effectiveness and satisfaction

A proposal for the development of adaptive spoken interfaces to access the Web

Spoken dialog systems have been proposed as a solution to facilitate a more natural human–machine interaction. In this paper, we propose a framework to model the user׳s intention during the dialog and adapt the dialog model dynamically to the user needs and preferences, thus developing more efficient, adapted, and usable spoken dialog systems. Our framework employs statistical models based on neural networks that take into account the history of the dialog up to the current dialog state in order to predict the user׳s intention and the next system response. We describe our proposal and detail its application in the Let׳s Go spoken dialog system.Work partially supported by Projects MINECO TEC2012-37832- C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/ TIC-1485

A Neural Network Approach to Intention Modeling forUser-Adapted Conversational Agents

Spoken dialogue systems have been proposed to enable a more natural and intuitive interaction with the environment andhuman-computer interfaces. In this contribution, we present a framework based on neural networks that allows modeling of theuser’s intention during the dialogue and uses this prediction todynamically adapt the dialoguemodel of the system taking intoconsideration the user’s needs and preferences. We have evaluated our proposal to develop a user-adapted spoken dialogue systemthat facilitates tourist information and services and provide a detailed discussion of the positive influence of our proposal in thesuccess of the interaction, the information and services provided, and the quality perceived by the users

Alternatives for testing of context-aware software systems in non-academic settings:results from a <i>Rapid Review</i>

Context: Context-awareness challenges the engineering of contemporary software systems and jeopardizes their testing. The variation of context represents a relevant behavior that deepens the limitations of available software testing practices and technologies. However, such software systems are mainstream. Therefore, researchers in non-academic settings also face challenges when developing and testing contemporary software systems. Objective: To understand how researchers deal with the variation of context when testing context-aware software systems developed in non-academic settings. Method: To undertake a secondary study (Rapid Review) to uncover the necessary evidence from primary sources describing the testing of context-aware software systems outside academia. Results: The current testing initiatives in non-academic settings aim to generate or improve test suites that can deal with the context variation and the sheer volume of test input possibilities. They mostly rely on modeling the systems’ dynamic behavior and increasing computing resources to generate test inputs to achieve this. We found no evidence of test results aiming at managing context variation through the testing lifecycle process. Conclusions: So far, the identified testing initiatives and strategies are not ready for mainstream adoption. They are all domain-specific, and while the ideas and approaches can be reproduced in distinct settings, the technologies are to be re-engineered and tailored to the context-awareness of contemporary software systems in different problem domains. Further and joint investigations in academia and experiences in non-academic set- tings can evolve the body of knowledge regarding the testing of contemporary software systems in the field

Building multi-domain conversational systems from single domain resources

Current Advances In The Development Of Mobile And Smart Devices Have Generated A Growing Demand For Natural Human-Machine Interaction And Favored The Intelligent Assistant Metaphor, In Which A Single Interface Gives Access To A Wide Range Of Functionalities And Services. Conversational Systems Constitute An Important Enabling Technology In This Paradigm. However, They Are Usually Defined To Interact In Semantic-Restricted Domains In Which Users Are Offered A Limited Number Of Options And Functionalities. The Design Of Multi-Domain Systems Implies That A Single Conversational System Is Able To Assist The User In A Variety Of Tasks. In This Paper We Propose An Architecture For The Development Of Multi-Domain Conversational Systems That Allows: (1) Integrating Available Multi And Single Domain Speech Recognition And Understanding Modules, (2) Combining Available System In The Different Domains Implied So That It Is Not Necessary To Generate New Expensive Resources For The Multi-Domain System, (3) Achieving Better Domain Recognition Rates To Select The Appropriate Interaction Management Strategies. We Have Evaluated Our Proposal Combining Three Systems In Different Domains To Show That The Proposed Architecture Can Satisfactory Deal With Multi-Domain Dialogs. (C) 2017 Elsevier B.V. All Rights Reserved.Work partially supported by projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02

A context aware recommender system for tourism with ambient intelligence

Recommender system (RS) holds a significant place in the area of the tourism sector. The major factor of trip planning is selecting relevant Points of Interest (PoI) from tourism domain. The RS system supposed to collect information from user behaviors, personality, preferences and other contextual information. This work is mainly focused on user’s personality, preferences and analyzing user psychological traits. The work is intended to improve the user profile modeling, exposing relationship between user personality and PoI categories and find the solution in constraint satisfaction programming (CSP). It is proposed the architecture according to ambient intelligence perspective to allow the best possible tourist place to the end-user. The key development of this RS is representing the model in CSP and optimizing the problem. We implemented our system in Minizinc solver with domain restrictions represented by user preferences. The CSP allowed user preferences to guide the system toward finding the optimal solutions; RESUMO O sistema de recomendação (RS) detém um lugar significativo na área do sector do turismo. O principal fator do planeamento de viagens é selecionar pontos de interesse relevantes (PoI) do domínio do turismo. O sistema de recomendação (SR) deve recolher informações de comportamentos, personalidade, preferências e outras informações contextuais do utilizador. Este trabalho centra-se principalmente na personalidade, preferências do utilizador e na análise de traços fisiológicos do utilizador. O trabalho tem como objetivo melhorar a modelação do perfil do utilizador, expondo a relação entre a personalidade deste e as categorias dos POI, assim como encontrar uma solução com programação por restrições (CSP). Propõe-se a arquitetura de acordo com a perspetiva do ambiente inteligente para conseguir o melhor lugar turístico possível para o utilizador final. A principal contribuição deste SR é representar o modelo como CSP e tratá-lo como problema de otimização. Implementámos o nosso sistema com o solucionador em Minizinc com restrições de domínio representadas pelas preferências dos utilizadores. O CSP permitiu que as preferências dos utilizadores guiassem o sistema para encontrar as soluções ideais