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Fran Casino, Constantinos Patsakis, Antoni Martinez-Balleste, Frederic Borras, Edgar Batista

arXiv preprint arXiv:1706.04109,

Description: In this article, we explain in detail the internal structures and databases of a smart health application. Moreover, we describe how to generate a statistically sound synthetic dataset using real-world medical data.

Abdulrahman Al-Molegi, Izzat Alsmadi, Antoni Martínez-Ballesté

Pervasive and Mobile Computing 47, 31-53

Description: Location-Based Services (LBSs) provide users especially in smartphones with information and services based on their location and interests. Predicting people’s next location could help in improving the quality of such services and, in turn, boosting people’s confidence on those services. Any successful LBS algorithm or model should target three major goals: Location predic- tion accuracy, high throughput or fast response and efficiency in terms of utilizing smartphone resources. This paper proposes a new approach to dis- cover and predict people’s next location based on their mobility patterns, while being computationally efficient. The approach starts by discovering Regions-of-Interest (RoIs) in people’s historical trajectories (which denote the locations where users have been previously, frequently). A new model based on Markov Chain (MC) is proposed to overcome the drawback of clas- sical MC. Our proposed model considers both space and time contexts where the space represents a specific location that has been visited by a user while the time represents the location visiting time. We show how classical MC model can be extended to include movement times and how time will im- prove prediction accuracy. One Unique finding in our research is related to the value of integrating users’ mobility location/space with time context. In particular, time context is formulated in a way to add extra information to the space context. For better abstraction during building the model, a general transformation function is used to transform the n-order MC into first order. Results show that the proposed approach provides significant improvements in predicting people’s next location compared with the state- of-the-art models when applied on two large real-life datasets, GeoLife and Gowalla.

J Domingo, A Martínez-Ballesté, F Sebé

Boletines de Rediris, 6-9

Description: Se espera que próximamente lleguen nuevas tecnologías de comunicación móvil. Estas tecnologías permitirán servicios de transmisión de vídeo: emisión de noticias, videoconferencia, canales de noticias, etc. Los proveedores de servicio quizá deseen que los clientes paguen por los contenidos emitidos. Este artículo presenta el prototipo de un servicio de distribución de vídeos de pago a través de Internet, que es la base del proyecto “STREAMOBILE” TIC2001-0633-C03. Los contenidos se distribuyen mediante streaming y se usa un sistema de micropagos, con lo cual los vídeos no se pagan por adelantado y en su totalidad, sino que se van pagando conforme se van recibiendo.

Hatem A Rashwan, Miguel Angel García, Antoni Martínez-Ballesté, Domènec Puig

Machine Vision and Applications 27 (2), 251-262

Description: Face de-identification aims at preserving the privacy of people by concealing faces in images and videos. In this paper, we propose a defeating algorithm for face de-identification methods that are based on DCT-block scrambling. These methods protect faces by scrambling the AC and DC coefficients of the DCT blocks corresponding to a face region in the compressed domain. The proposed approach does not make use of the protection key utilized in the de-identification process. It consists of the following stages. First, random unprotected faces are generated based on a random alteration of the sign of AC coefficients with a fixed value of DC coefficients. Then, the best unprotected faces are selected by an eigenfaces model trained with facial images from a repository of potentially protected people. A single facial image is then generated by merging the selected images through median stacking. Finally, the eigenfaces model is utilized again to choose the face from the repository that is closest to the resulting image in order to improve the aspect of the unprotected face. Experimental results using a proprietary database and the public CALTECH, Utrecht and LFW face databases show the effectiveness of the proposed technique.

JO Casanovas-Marsal, E Batista de Frutos, A Martínez Ballesté, M García Martínez

Boletines de Rediris, 6-9

Description: Introducción y objetivos: Diseñar un sistema inteligente y automático para la valorar el dolor en neonatos ingresados en las unidades de cuidados intensivos (UCIN). Método: El sistema IntelNuc® situado en la incubadora capta los parámetros comportamentales del neonato a través de una cámara Kinect®(movimiento) y un micrófono (llanto) situado dentro de la incubadora. El sistema no es invasivo. Un servidor estima el nivel del dolor e interactúa con los profesionales sanitarios vía interface utilizando las escalas introducidas (PIPP y SGB). El prototipo fue testado en la UCIN del hospital con pacientes reales involucrando a profesionales de la informática y de la salud. Resultados y conclusiones: El sistema de computarización para valorar el dolor está en sus inicios. No obstante se ha observado que la evaluación del dolor es factible a través de este método aportando en las curas enfermeras, de forma natural, un proceso de cura del dolor en su primera etapa que es la correcta evaluación. Palabras clave (MeSH/DeSC): escalas valoración dolor, unidades cuidados intensivos neonatales

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