A collaborative filtering similarity measure based on singularities.

Recommender systems play an important role in reducing the negative impact of informa- tion overload on those websites where users have the possibility of voting for their prefer- ences on items. The most normal technique for dealing with the recommendation mechanism is to use collaborative filtering, in which it is essential to discover the most similar users to whom you desire to make recommendations. The hypothesis of this paper is that the results obtained by applying traditional similarities measures can be improved by taking contextual information, drawn from the entire body of users, and using it to cal- culate the singularity which exists, for each item, in the votes cast by each pair of users that you wish to compare. As such, the greater the measure of singularity result between the votes cast by two given users, the greater the impact this will have on the similarity. The results, tested on the Movielens, Netflix and FilmAffinity databases, corroborate the excellent behaviour of the singularity measure proposed.

A collaborative filtering approach to mitigate the new user cold start problem.

The new user cold start issue represents a serious problem in recommender systems as it can lead to the loss of new users who decide to stop using the system due to the lack of accuracy in the recommenda- tions received in that first stage in which they have not yet cast a significant number of votes with which to feed the recommender system?s collaborative filtering core. For this reason it is particularly important to design new similarity metrics which provide greater precision in the results offered to users who have cast few votes. This paper presents a new similarity measure perfected using optimization based on neu- ral learning, which exceeds the best results obtained with current metrics. The metric has been tested on the Netflix and Movielens databases, obtaining important improvements in the measures of accuracy, precision and recall when applied to new user cold start situations. The paper includes the mathematical formalization describing how to obtain the main quality measures of a recommender system using leave- one-out cross validation.

A Balanced Memory-Based Collaborative Filtering Similarity Measure.

Collaborative filtering recommender systems contribute to alleviating the problem of information overload that exists on the Internet as a result of the mass use of Web 2.0 applications. The use of an adequate similarity measure becomes a determining factor in the quality of the prediction and recommendation results of the recommender system, as well as in its performance. In this paper, we present a memory-based collaborative filtering similarity measure that provides extremely high-quality and balanced results; these results are complemented with a low processing time (high performance), similar to the one required to execute traditional similarity metrics. The experiments have been carried out on the MovieLens and Netflix databases, using a representative set of information retrieval quality measures.

Idea relationship analysis in open innovation crowdsourcing systems

Idea Management Systems are an implementation of open innovation notion in the Web environment with the use of crowdsourcing techniques. In this area, one of the popular methods for coping with large amounts of data is duplicate de- tection. With our research, we answer a question if there is room to introduce more relationship types and in what degree would this change affect the amount of idea metadata and its diversity. Furthermore, based on hierarchical dependencies between idea relationships and relationship transitivity we propose a number of methods for dataset summarization. To evaluate our hypotheses we annotate idea datasets with new relationships using the contemporary methods of Idea Management Systems to detect idea similarity. Having datasets with relationship annotations at our disposal, we determine if idea features not related to idea topic (e.g. innovation size) have any relation to how annotators perceive types of idea similarity or dissimilarity.

NGCPV: A new generation of concentrator photovoltaic cells, modules and systems

This work introduces the lines of research that the NGCPV project is pursuing and some of the first results obtained. Sponsored by the European Commission under the 7th Framework Program and NEDO (Japan) within the first collaborative call launched by both Bodies in the field of energy, NGCPV project aims at approaching the cost of the photovoltaic kWh to competitive prices in the framework of high concentration photovoltaics (CPV) by exploring the development and assessment of concentrator photovoltaic solar cells and modules, novel materials and new solar cell structures as well as methods and procedures to standardize measurement technology for concentrator photovoltaic cells and modules. More specific objectives we are facing are: (1) to manufacture a cell prototype with an efficiency of at least 45% and to undertake an experimental activity, (2) to manufacture a 35% module prototype and elaborate the roadmap towards the achievement of 40%, (3) to develop reliable characterization techniques for III-V materials and quantum structures, (4) to achieve and agreement within 5% in the characterization of CPV cells and modules in a round robin scheme, and (5) to evaluate the potential of new materials, devices technologies and quantum nanostructures to improve the efficiency of solar cells for CPV.

NGCPV: a new generation of concentrator photovoltaic cells, modules and systems

Starting on June 2011, NGCPV is the first project funded jointly between the European Commission (EC) and the New Energy and Industrial Technology Development Organization (NEDO) of Japan to research on new generation concentration photovoltaics (CPV). The Project, through a collaborative research between seven European and nine Japanese leading research centers in the field of CPV, aims at lowering the cost of the CPVproduced photovoltaic kWh down to 5 ?cents. The main objective of the project is to improve the present concentrator cell, module and system efficiency, as well as developing advanced characterization tools for CPV components and systems. As particular targets, the project aims at achieving a cell efficiency of at least 45% and a CPV module with an efficiency greater than 35%. This paper describes the R&D activities that are being carried out within the NGCPV project and summarizes some of the most relevant results that have already been attained, for instance: the manufacturing of a 44.4% world record efficiency triple junction solar cell (by Sharp Corp.) and the installation of a 50 kWp experimental CPV plant in Spain, which will be used to obtain accurate forecasts of the energy produced at system level.

Outline of Europe-Japan collaborative research on concentrator photovoltaics

The Europe-Japan Collaborative Research Project on Concentrator Photovoltaics (CPV) has been initiated under support by the EC (European Commission) and NEDO (New Energy and Industrial Technology Development Organization) since June 2011. This is project (NGCPV Project; a New Generation of Concentrator PhotoVoltaic cells, modules and systems) is aiming to accelerate the move to very high efficiency and lower cost CPV technologies and to enhance widespread deployment of CPV systems. 7 organizations such as UPM, FhG-ISE Imperial College, BSQ, CEA-INES, ENEA, and PSE in Europe and 9 organizations such as TTI, Univ. Tokyo, AIST, Sharp Co. Daido Steel Co., Kobe Univ., Miyazaki Univ., Asahi Kasei Co., and Takano Co. participate in this project. The targets of this project are 1) to develop world-record efficiency CPV cells of more than 45%, 2) to develop world-record efficiency CPV modules of 35%, 3) to establish standard measurements of CPV cells and modules, 4) to install 50kW CPV system in Spain, to carry out field test of CPV system and to manage power generation of CPV systems, and 5) to develop high-efficiency and low-cost new materials and structure cells such as III-V-N, III-V-on-Si tandem, quantum dots and wells. This paper presents outline of this project and most recent results such as world record efficiency (37.9% under 1-sun) cell and high-efficiency (43.5% under 240-306 suns) concentrator cell with inverted epitaxial grown InGaP/GaAs/InGaAs 3-junction solar cells.

Hardening digital systems with distributed functionality: robust networks

Collaborative hardening and hardware redundancy are nowadays the most interesting solutions in terms of fault tolerance achieved and low extra cost imposed to the project budget. Thanks to the powerful and cheap digital devices that are available in the market, extra processing capabilities can be used for redundant tasks, not only in early data processing (sensed data) but also in routing and interfacing1

Incorporating Group Recommendations to Recommender Systems: Alternatives and Performance

La importancia de los sistemas de recomendación ha experimentado un crecimiento exponencial como consecuencia del auge de las redes sociales. En esta tesis doctoral presentaré una amplia visión sobre el estado del arte de los sistemas de recomendación. Incialmente, estos estaba basados en fitrado demográfico, basado en contendio o colaborativo. En la actualidad, estos sistemas incorporan alguna información social al proceso de recomendación. En el futuro utilizarán información implicita, local y personal proveniente del Internet de las cosas. Los sistemas de recomendación basados en filtrado colaborativo se pueden modificar con el fin de realizar recomendaciones a grupos de usuarios. Existen trabajos previos que han incluido estas modificaciones en diferentes etapas del algoritmo de filtrado colaborativo: búsqueda de los vecinos, predicción de las votaciones y elección de las recomendaciones. En esta tesis doctoral proporcionaré un nuevo método que realizar el proceso de unficación (pasar de varios usuarios a un grupo) en el primer paso del algoritmo de filtrado colaborativo: cálculo de la métrica de similaridad. Proporcionaré una formalización completa del método propuesto. Explicaré cómo obtener el conjunto de k vecinos del grupo de usuarios y mostraré cómo obtener recomendaciones usando dichos vecinos. Asimismo, incluiré un ejemplo detallando cada paso del método propuesto en un sistema de recomendación compuesto por 8 usuarios y 10 items. Las principales características del método propuesto son: (a) es más rápido (más eficiente) que las alternativas proporcionadas por otros autores, y (b) es al menos tan exacto y preciso como otras soluciones estudiadas. Para contrastar esta hipótesis realizaré varios experimentos que miden la precisión, la exactitud y el rendimiento del método. Los resultados obtenidos se compararán con los resultados de otras alternativas utilizadas en la recomendación de grupos. Los experimentos se realizarán con las bases de datos de MovieLens y Netflix. ABSTRACT The importance of recommender systems has grown exponentially with the advent of social networks. In this PhD thesis I will provide a wide vision about the state of the art of recommender systems. They were initially based on demographic, contentbased and collaborative filtering. Currently, these systems incorporate some social information to the recommendation process. In the future, they will use implicit, local and personal information from the Internet of Things. As we will see here, recommender systems based on collaborative filtering can be used to perform recommendations to group of users. Previous works have made this modification in different stages of the collaborative filtering algorithm: establishing the neighborhood, prediction phase and determination of recommended items. In this PhD thesis I will provide a new method that carry out the unification process (many users to one group) in the first stage of the collaborative filtering algorithm: similarity metric computation. I will provide a full formalization of the proposed method. I will explain how to obtain the k nearest neighbors of the group of users and I will show how to get recommendations using those users. I will also include a running example of a recommender system with 8 users and 10 items detailing all the steps of the method I will present. The main highlights of the proposed method are: (a) it will be faster (more efficient) that the alternatives provided by other authors, and (b) it will be at least as precise and accurate as other studied solutions. To check this hypothesis I will conduct several experiments measuring the accuracy, the precision and the performance of my method. I will compare these results with the results generated by other methods of group recommendation. The experiments will be carried out using MovieLens and Netflix datasets.

Collaborative Evolution Strategies on Evolvable Hardware Networked Elements

Dynamic and Partial Reconfiguration allows systems to change some parts of their hardware at run time. This feature favours the inclusion of evolutionary strategies to provide optimised solutions to the same problem so that they can be mixed and compared in a way that only the best ones prevail. At the same time, distributed intelligence permits systems to work in a collaborative way to jointly improve their global capabilities. This work presents a combination of both approaches where hardware evolution is performed both at local and network level in order to improve an image filter application in terms of performance, robustness and providing the capacity of avoiding local minimums, which is the main drawback of some evolutionary approaches.

Inter-organizational information systems adoption for service innovation in building sector

The building sector has experienced a significant decline in recent years in Spain and Europe as a result of the financial crisis that began in 2007. This drop accompanies a low penetration of information and communication technologies in inter-organizational oriented business processes. The market decrease is causing a slowdown in the building sector, where only flexible small and medium enterprises (SMEs) survive thanks to specialization and innovation in services, which allow them to face new market demands. Inter-organizational information systems (IOISs) support innovation in services, and are thus a strategic tool for SMEs to obtain competitive advantage. Because of the inherent complexity of IOIS adoption, this research extends Kurnia and Johnston's (2000) theoretical model of IOIS adoption with an empirical model of IOIS characterization. The resultant model identifies the factors influencing IOIS adoption in SMEs in the building sector, to promote further service innovation for competitive and collaborative advantages. An empirical longitudinal study over six consecutive years using data from Spanish SMEs in the building sector validates the model, using the partial least squares technique and analyzing temporal stability. The main findings of this research are the four ways an IOIS might contribute to service innovation in the building sector. Namely: a) improving client interfaces and the link between service providers and end users; b) defining a specific market where SMEs can develop new service concepts; c) enhancing the service delivery system in traditional customer?supplier relationships; and d) introducing information and communication technologies and tools to improve information management.

Data normalization and integration in robotic systems using web services technologies

The robotics is one of the most active areas. We also need to join a large number of disciplines to create robots. With these premises, one problem is the management of information from multiple heterogeneous sources. Each component, hardware or software, produces data with different nature: temporal frequencies, processing needs, size, type, etc. Nowadays, technologies and software engineering paradigms such as service-oriented architectures are applied to solve this problem in other areas. This paper proposes the use of these technologies to implement a robotic control system based on services. This type of system will allow integration and collaborative work of different elements that make up a robotic system.

Errors Associated to Keratoconus Grading using Systems based on Corneal Power

Purpose: To analyze and define the possible errors that may be introduced in keratoconus classification when the keratometric corneal power is used in such classification. Materials and methods: Retrospective study including a total of 44 keratoconus eyes. A comprehensive ophthalmologic examination was performed in all cases, which included a corneal analysis with the Pentacam system (Oculus). Classical keratometric corneal power (Pk), Gaussian corneal power (Pc Gauss), True Net Power (TNP) (Gaussian power neglecting the corneal thickness effect), and an adjusted keratometric corneal power (Pkadj) (keratometric power considering a variable keratometric index) were calculated. All cases included in the study were classified according to five different classification systems: Alió-Shabayek, Amsler-Krumeich, Rabinowitz-McDonnell, collaborative longitudinal evaluation of keratoconus (CLEK), and McMahon. Results: When Pk and Pkadj were compared, differences in the type of grading of keratoconus cases was found in 13.6% of eyes when the Alió-Shabayek or the Amsler-Krumeich systems were used. Likewise, grading differences were observed in 22.7% of eyes with the Rabinowitz-McDonnell and McMahon classification systems and in 31.8% of eyes with the CLEK classification system. All reclassified cases using Pkadj were done in a less severe stage, indicating that the use of Pk may lead to the classification of a cornea as keratoconus, being normal. In general, the results obtained using Pkadj, Pc Gauss or the TNP were equivalent. Differences between Pkadj and Pc Gauss were within ± 0.7D. Conclusion: The use of classical keratometric corneal power may lead to incorrect grading of the severity of keratoconus, with a trend to a more severe grading.

Advances in camera trap data management tools: Towards collaborative development and integration with GIS

Camera traps have become a widely used technique for conducting biological inventories, generating a large number of database records of great interest. The main aim of this paper is to describe a new free and open source software (FOSS), developed to facilitate the management of camera-trapped data which originated from a protected Mediterranean area (SE Spain). In the last decade, some other useful alternatives have been proposed, but ours focuses especially on a collaborative undertaking and on the importance of spatial information underpinning common camera trap studies. This FOSS application, namely, “Camera Trap Manager” (CTM), has been designed to expedite the processing of pictures on the .NET platform. CTM has a very intuitive user interface, automatic extraction of some image metadata (date, time, moon phase, location, temperature, atmospheric pressure, among others), analytical (Geographical Information Systems, statistics, charts, among others), and reporting capabilities (ESRI Shapefiles, Microsoft Excel Spreadsheets, PDF reports, among others). Using this application, we have achieved a very simple management, fast analysis, and a significant reduction of costs. While we were able to classify an average of 55 pictures per hour manually, CTM has made it possible to process over 1000 photographs per hour, consequently retrieving a greater amount of data.

Integrating science and management through collaborative learning and better information management

Achieving more sustainable land and water use depends on high-quality information and its improved use. In other words, better linkages are needed between science and management. Since many stakeholders with different relationships to the natural resources are inevitably involved, we suggest that collaborative learning environments and improved information management are prerequisites for integrating science and management. Case studies that deal with resource management issues are presented that illustrate the creation of collaborative learning environments through systems analyses with communities, and an integration of scientific and experiential knowledge of components of the system. This new knowledge needs to be captured and made accessible through innovative information management systems designed collaboratively with users, in forms which fit the users' 'mental models' of how their systems work. A model for linking science and resource management more effectively is suggested. This model entails systems thinking in a collaborative learning environment, and processes to help convergence of views and value systems, and make scientists and different kinds of managers aware of their interdependence. Adaptive management provides a mechanism for applying and refining scientists' and managers' knowledge. Copyright (C) 2003 John Wiley Sons, Ltd.