Reliability and performance of UEGO, a clustering-based global optimizer

Den nya tullagen Union Customs Code (UCC) med målet elektroniskt tullhantering inom unionen kommer att börja tillämpas 1 maj 2016. Tullkodex för unionen innebär elektroniskt tullhantering såväl mellan näringslivet och Tullverket som mellan medlemsstaternas tulladministrationer. Syftet med lagen är att förenkla uppgiftslämning och tullhantering för företag vilket kräver omfattande uppgradering av IT-lösningar för både näringslivet och Tullverket. Kvalitet på de uppgifter som överförs till Tullverkets tull-system (TDS) antas ha större betydelse än tidigare då det genom UCC läggs en större vikt på spårbarhet.Organisationer bör därför optimera kvaliteten på de uppgifter som i samband med deklarationer lämnas till Tullverket. Detta för att undvika eventuella problem i framtiden. Optimal datakvalitet erhålls genom effektiva administrativa processer. Denna uppsats har därför genom en fallstudie studerat faktorer som förhindrar ett effektivt administrativt arbete. Resultatet visar att affärssystemets undermåliga funktionalitet, datakvalitetens opålitlighet, utebliven intern kommunikation samt avsaknad av standardiserade arbetsrutiner påverkar effektiviteten på de administrativa processerna negativt.The new customs regulation, Union Customs Code (UCC), with the goal of electronic customs handling within the European Union will apply 1 May 2016. The Union Customs Code means electronic customs handling both between business and customs Administration and between member states' customs administrations. The law aims to simplify disclosure and customs management for businesses, which requires an extensive upgrade of IT solutions for both business and customs administrations. Quality of the data transferred to Customs' Tariff System (TDS) is assumed will havegreater importance than today as the UCC places a greater emphasis on traceability. Organisations should therefore optimize the quality of the data associated with declarations submitted to Customs. It is to avoid any future problems. Optimal data quality is achieved through efficient administrative processes. This paper has therefore through a case study, studied factors that prevent efficient administrative processes. The results show that the business system's substandard functionality, data quality's unreliability, loss of internal communication and a lack of standard in operating procedures are affecting the efficiency of the administrative processes negatively

Control y mejora de la coordinación entre asignaturas de una titulación universitaria

Entre las múltiples exigencias que impone el EEES, la mejora de la coordinación entre las asignaturas de una titulación es una de las que más preocupan, y se ha convertido en uno de los temas de debate más vivos en la comunidad educativa. Por una parte los docentes nos hemos encontrado impartiendo asignaturas a un alumnado con notorias carencias en algunos contenidos, mientras que ellos, por su parte, no sólo tienen que suplir dichas carencias con esfuerzo adicional, sino que además se encuentran con numerosas duplicidades de contenidos que restan tiempo y calidad a su formación. Una de las causas de estos defectos es la celeridad en la implantación de titulaciones, unido posiblemente a una falta de recursos por parte de los responsables de la elaboración de los planes. Este trabajo presenta un conjunto de aplicaciones web orientadas a la elaboración de un mapa de dependencias entre las asignaturas de una titulación. Basándose en un sistema de encuestas, se ha implementado una base de datos de dependencias y un sistema web que permiten detectar e informar de los defectos de coordinación existentes, proporcionando una herramienta de gran valor para mejorar la coherencia y la calidad de los planes de estudiosUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Parallelization of an algorithm for the automatic detection of deformable objects

This work presents the parallelization of an algorithm for the detection of deformable objects in digital images. The parallelization has been implemented with the message passing paradigm, using a master-slave model. Two versions have been developed, with synchronous and asynchronous communications

Authoring and dynamic generation of adaptive e-courses

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-27834-4_93Proceedings of the 4th International Conference, ICWE 2004, Munich, Germany, July 26-30, 2004.Adaptive hypermedia constitutes a pretty rich resource for developing web-based courses. With the aim of dynamically generating adaptive e-courses, we have developed the TANGOW system which, starting from the course components and their adaptation capabilities (specified independently and out of the adaptation engine), generates different courses for students with different profiles, supporting several adaptation strategies. An integral part of any adaptive hypermedia system is the set of authoring tools to specify the course components and their adaptation capabilities. Without adequate tool support, authors may feel that it is “not worth the effort” to add adaptation to their courses. However, the development of this type of tools is not an easy task. The main goal of our authoring and visualization tools is to provide a simple interface to create such courses. This demo would demonstrate i) the dynamic generation of tailored e-courses that include individual and collaborative activities and ii) the use of authoring tools for the creation of such courses

DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition

Bi-Level Optimization to Enhance Intensity Modulated Radiation Therapy Planning

Intensity Modulated Radiation Therapy is an effective cancer treatment. Models based on the Generalized Equivalent Uniform Dose (gEUD) provide radiation plans with excellent planning target volume coverage and low radiation for organs at risk. However, manual adjustment of the parameters involved in gEUD is required to ensure that the plans meet patient-specific physical restrictions. This paper proposes a radiotherapy planning methodology based on bi-level optimization. We evaluated the proposed scheme in a real patient and compared the resulting irradiation plans with those prepared by clinical planners in hospital devices. The results in terms of efficiency and effectiveness are promising

A multi-objective approach to estimate parameters of compartmental epidemiological models. Application to Ebola Virus Disease epidemics.

In this work, we propose a novel methodology to adjust parameters of compartmental epidemiological models. It is based on solving a multi-objective optimization problem that consists in fitting some of the model outputs to real observations. First, according to the available data of the considered epidemic, we define a multi-objective optimization problem where the model parameters are the optimization variables. Then, this problem is solved by considering a particular optimization algorithm called ParWASF-GA (ParallelWeighting Achievement Scalarizing Function Genetic Algorithm). Finally, the decision maker chooses, within the set of possible solutions, the values of parameters that better suit his/her preferences. In order to illustrate the benefit of using our approach, it is applied to estimate the parameters of a deterministic epidemiological model, called Be-CoDiS (Between-Countries Disease Spread), used to forecast the possible spread of human diseases within and between countries. We consider data from different Ebola outbreaks from 2014 up to 2019. In all cases, the proposed methodology helps to obtain reasonable predictions of the epidemic magnitudes with the considered model