Towards automatic evaluation of the Quality-in-Use in context-aware software systems

Context-aware systems adapt their services to the user's intentions and environment to improve the user experience. However, how to evaluate the quality of these systems in terms of user perception and context recognition is still an open problem. Our goal in this work is to evaluate the Quality-in-Use (QinU) for context-aware software systems according to the ISO/IEC 25010 standard and in an automated manner. This evaluation is oriented to be model-based, with domain specification and log data as input, while quality metrics and representations of users' behavior as output. In this process, we use probabilistic models to discover user patterns, heuristic metrics as QinU estimation, clustering techniques to obtain user profiles according to their QinU, and feature selection to identify relevant factors of context. We propose a framework for assessing the QinU in context-aware software systems called Framework for Assessing Quality-in-use of Software (FAQuiS). FAQuiS includes a set of models to represent all dimensions of context, a methodology to apply the quality analysis to any system, and a set of tools and metrics to support and automate the process. We seek to test the impact and ease of integration in the industry for this framework. A case study in a company allows us to validate the applicability in a real environment. We analyze the mechanisms that support the QinU evaluation in context-aware systems, the feasibility of the QinU quantification, and the suitability of the integration in companies. Compared to previous works, our proposal offers a novel data-driven approach with general-purpose and industrial viability. FAQuiS can be used as a solution to assess the QinU based on the ISO 25010 standard and the models of user behaviors in different contexts. This solution analyzes the context changes in the user interaction, can quantify the quality loss in these contexts, and does not require big efforts to be integrated into a software development process.The authors want to acknowledge the financial support from the operative program FEDER (Fondo Europeo de Desarrollo Regional) and SODERCAN (Sociedad para el Desarrollo de Cantabria) within the program "I+C=+C 2019 - APOYO A PROYECTOS DE I+D EN EL ÁMBITO DE LAS TIC"

User identification from mobility traces

Geolocation is a powerful source of information through which user patterns can be extracted. User regions-of-interest, along with these patterns, can be used to recognize and imitate user behavior. In this work we develop a methodology for preprocessing location data in order to discover the most relevant places the user visits, and we propose a Probabilistic Finite Automaton structure as mobility model. We analyse both location prediction and user identification tasks. Our model is assessed with two evaluation metrics regarding its predictive accuracy and user identification accuracy, and compared against other models.The authors gratefully acknowledge the financial support from FEDER (Fondo Europeo de Desarrollo Regional) and SODERCAN (Sociedad para el Desarrollo Regional de Cantabria) for the project TI16-IN-007 within the program "I+C=+C 2016—PROYECTOS DE I+D EN EL ÁMBITO DE LAS TIC, LÍNEA SMART", and from Ministerio de Ciencia e Innovación (MICINN), Spain for the project PAC::LFO (MTM2014-55262-P)

A new linear genetic programming approach based on straight line programs: Some theoretical and experimental aspects

Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described, new recombination operators for GP related to slp's are introduced and a study of the Vapnik-Chervonenkis dimension of families of slp's is done. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representation

Una metodología centrada en el usuario para el desarrollo de sistemas inteligentes basados en modelos de aprendizaje profundo

Esta propuesta doctoral está orientada al análisis de los sistemas inteligentes que utilizan modelos de aprendizaje profundo como núcleo de pensamiento. Dichos sistemas, por su complejidad, son difíciles de entrenar y de mejorar continuamente, además de que suelen ser una caja negra, por lo que sus resultados son poco explicables. En la tesis se está estudiando cómo orientar estos sistemas inteligentes a los usuarios, de tal forma que puedan aprovechar mejor los modelos desarrollados para sus problemas concretos, adaptando el sistema a voluntad. Además, también se mejorará el entendimiento que tienen de los mismos mediante técnicas de explicabilidad de modelos Deep Learning, lo cual repercutirá de forma positiva en la usabilidad general del sistema. Se pretende con esto obtener una metodología para el desarrollo de este tipo de sistemas, la cual pueda ser utilizada en aplicaciones reales y trabajos futuros.Esta tesis está financiada por la Universidad de Cantabria, el Gobierno de Cantabria y el Banco Santander a través de la beca de doctorado industrial DI27, concedida a Santos Bringas en la convocatoria del Programa de Doctorados Industriales 2020

A Framework for Identifying Sequences of Interactions That Cause Usability Problems in Collaborative Systems

Collaborative systems support shared spaces, where groups of users exchange interactions. In order to ensure the usability of these systems, an intuitive interactions´ organization and that each user has awareness information to know the activity of others are necessary. Usability laboratories allow evaluators to verify these requirements. However, laboratory usability evaluations can be problematic for reproducing mobile and ubiquitous contexts, as they restrict the place and time in which the user interacts with the system. This paper presents a framework for building software support that it collects human?machine interactions in mobile and ubiquitous contexts and outputs an assessment of the system´s usability. This framework is constructed through learning that is based on neural networks, identifying sequences of interactions related to usability problems when users carry out collaborative activities. The paper includes a case study that puts the framework into action during the development process of a smartphone application that supports collaborative sport betting.This research and the APC was funded by the University of Cantabria and the Government of Cantabria through the industrial doctorate grant DI27, given to Santos Bringas. Alicia Nieto-Reyes was supported by a Spanish Ministerio de Ciencia, Innovación y Universidades grant MTM2017-86061-C2-2-P

Discovering user's trends and routines from location based social networks

ABSTRACT: Location data is a powerful source of information to discover user's trends and routines. A suitable identification of the user context can be exploited to provide automatically services adapted to the user preferences. In this paper, we define a Dynamic Bayesian Network model and propose a method that processes location annotated data in order to train the model. Finally, our model enables us to predict future location contexts from the user patterns. A case study evaluates the proposal using real-world data of a location-based social network.This research was funded by Fondo Europeo de Desarrollo Regional (FEDER) and Sociedad para el Desarrollo Regional de Cantabria (SODERCAN) grant number TI16-IN-007 (within the program “I+C=+C 2016- PROYECTOS DE I+D EN EL ÁMBITO DE LAS TIC, LÍNEA SMART”), and by Ministerio de Ciencia e Innovación (MICINN), Spain grant number MTM2014-55262-P (project PAC::LFO)

A Convolutional Neural Network-Based Method for Human Movement Patterns Classification in Alzheimer?s Disease

Alzheimer’s disease (AD) constitutes a neurodegenerative pathology that presents mobility disorders as one of its earliest symptoms. Current smartphones integrate accelerometers that can be used to collect mobility data of Alzheimer’s patients. This paper describes a method that processes these accelerometer data and a convolutional neural network (CNN) that classifies the stage of the disease according to the mobility patterns of the patient. The method is applied in a case study with 35 Alzheimer’s patients, in which a classification success rate of 91% was obtaine

The role of keeping "semantic blocks" invariant: effects in linear genetic programming performance

This paper is focused on two different approaches (previously proposed by the authors) that perform better than Genetic Programming in typical symbolic regression problems: straight-line program genetic programming (SLP-GP) and evolution with attribute grammars (AGE). Both approaches have different characteristics. One of themost important is that SLP-GP keeps semantic blocks invariant (the crossover operator always exchanges complete subexpressions). In this paper we compare both methods and study the possible effect on their performance of keeping these blocks invariant.This work was partially supported by the R&D program of the Community of Madrid (S2009/TIC-1650, project “e-Madrid”) as well as by the Spanish Ministry of Science and Innovation (TIN2007-67466-C02-02). The authors thank Dr. Manuel Alfonseca for his help to prepare this document

Automatic apraxia detection using deep convolutional neural networks and similaritymethods

Dementia represents one of the great problems to be solved in medicine for a society that is becoming increasingly long-lived. One of the main causes of dementia is Alzheimer's disease, which accounts for 80% of cases. There is currently no cure for this disease, although there are treatments to try to alleviate its effects, which is why detecting Alzheimer's disease in its early stages is crucial to slow down its evolution and thus help sufferers. One of the symptoms of the disease that manifests in its early stages is apraxia, difficulties in carrying out voluntary movements. In the clinical setting, apraxia is typically assessed by asking the patient to imitate hand gestures that are performed by the examiner. To automate this test, this paper proposes a system that, based on a video of the patient making the gesture, evaluates its execution. This evaluation is done in two steps, first extracting the skeleton of the hands and then using a similarity function to obtain an objective score of the execution of the gesture. The results obtained in an experiment with several patients performing different gestures are shown, showing the effectiveness of the proposed method. The system is intended to serve as a diagnostic tool, enabling medical experts to detect possible mobility impairments in patients that may have signs of Alzheimer's disease.For Alicia Nieto-Reyes this research was funded by Grant No. 21.VP67.64662 of the "Proyectos Puente 2022" from the Spanish "Consejería de Universidades, Igualdad, Cultura y Deporte del Gobierno de Cantabria". Santos Bringas was supported by University of Cantabria, trial doctorate Grant (DI27), awarded in the 2020 Industrial doctorate program. For Rafael Duque this research was funded by Grant No. 21.VP50.64662 of the "Proyectos Puente 2021" from the "Consejería de Universidades, Igualdad, Cultura y Deporte del Gobierno de Cantabria". Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

Activity in the field of Human-Computer Interaction of a work team integrated in the MCFLAI research group

Se presenta la actividad en el ámbito de la Interacción Persona-Ordenador de un equipo de trabajo integrado en el grupo de investigación MCFLAI (Mathematics & Computation: Foundations, Learning, Artificial Intelligence) de la Universidad de CantabriaThe activity in the field of Human-Computer Interaction of a work team integrated in the research group MCFLAI (Mathematics & Computation: Foundations, Learning, Artificial Intelligence) of the University of Cantabria is presented