Hybrid Approach Combining Machine Learning and a Rule-Based Expert System for Text Categorization

This paper discusses a novel hybrid approach for text categorization that combines a machine learning algorithm, which provides a base model trained with a labeled corpus, with a rule-based expert system, which is used to improve the results provided by the previous classifier, by filtering false positives and dealing with false negatives. The main advantage is that the system can be easily fine-tuned by adding specific rules for those noisy or conflicting categories that have not been successfully trained. We also describe an implementation based on k-Nearest Neighbor and a simple rule language to express lists of positive, negative and relevant (multiword) terms appearing in the input text. The system is evaluated in several scenarios, including the popular Reuters-21578 news corpus for comparison to other approaches, and categorization using IPTC metadata, EUROVOC thesaurus and others. Results show that this approach achieves a precision that is comparable to top ranked methods, with the added value that it does not require a demanding human expert workload to train

Combining statistical and semantic approaches to the translation of ontologies and taxonomies

Ontologies and taxonomies are widely used to organize concepts providing the basis for activities such as indexing, and as background knowledge for NLP tasks. As such, translation of these resources would prove useful to adapt these systems to new languages. However, we show that the nature of these resources is significantly different from the "free-text" paradigm used to train most statistical machine translation systems. In particular, we see significant differences in the linguistic nature of these resources and such resources have rich additional semantics. We demonstrate that as a result of these linguistic differences, standard SMT methods, in particular evaluation metrics, can produce poor performance. We then look to the task of leveraging these semantics for translation, which we approach in three ways: by adapting the translation system to the domain of the resource; by examining if semantics can help to predict the syntactic structure used in translation; and by evaluating if we can use existing translated taxonomies to disambiguate translations. We present some early results from these experiments, which shed light on the degree of success we may have with each approach

Improving abstract interpretations by combining domains

This article considers static analysis based on abstract interpretation of logic programs over combined domains. It is known that analyses over combined domains provide more information potentially than obtained by the independent analyses. However, the construction of a combined analysis often requires redefining the basic operations for the combined domain. A practical approach to maintain precision in combined analyses of logic programs which reuses the individual analyses and does not redefine the basic operations is illustrated. The advantages of the approach are that proofs of correctness for the new domains are not required and implementations can be reused. The approach is demonstrated by showing that a combined sharing analysis — constructed from "old" proposals — compares well with other "new" proposals suggested in recent literature both from the point of view of efficiency and accuracy.

Combining series elastic actuation and magneto-rheological damping for the control of agile locomotion

All-terrain robot locomotion is an active topic of research. Search and rescue maneuvers and exploratory missions could benefit from robots with the abilities of real animals. However, technological barriers exist to ultimately achieving the actuation system, which is able to meet the exigent requirements of these robots. This paper describes the locomotioncontrol of a leg prototype, designed and developed to make a quadruped walk dynamically while exhibiting compliant interaction with the environment. The actuation system of the leg is based on the hybrid use of series elasticity and magneto-rheological dampers, which provide variable compliance for natural-looking motion and improved interaction with the ground. The locomotioncontrol architecture has been proposed to exploit natural leg dynamics in order to improve energy efficiency. Results show that the controller achieves a significant reduction in energy consumption during the leg swing phase thanks to the exploitation of inherent leg dynamics. Added to this, experiments with the real leg prototype show that the combined use of series elasticity and magneto-rheologicaldamping at the knee provide a 20 % reduction in the energy wasted in braking the knee during its extension in the leg stance phase.

Combining static analysis and profiling for estimating execution times

Effective static analyses have been proposed which infer bounds on the number of resolutions. These have the advantage of being independent from the platform on which the programs are executed and have been shown to be useful in a number of applications, such as granularity control in parallel execution. On the other hand, in distributed computation scenarios where platforms with different capabilities come into play, it is necessary to express costs in metrics that include the characteristics of the platform. In particular, it is specially interesting to be able to infer upper and lower bounds on actual execution times. With this objective in mind, we propose an approach which combines compile-time analysis for cost bounds with a one-time profiling of a given platform in order to determine the valúes of certain parameters for that platform. These parameters calibrate a cost model which, from then on, is able to compute statically time bound functions for procedures and to predict with a significant degree of accuracy the execution times of such procedures in that concrete platform. The approach has been implemented and integrated in the CiaoPP system.

Translational research combining orthologous genes and human diseases with the OGOLOD dataset

OGOLOD is a Linked Open Data dataset derived from different biomedical resources by an automated pipeline, using a tailored ontology as a scaffold. The key contribution of OGOLOD is that it links, in new RDF triples, genetic human diseases and orthologous genes, paving the way for a more efficient translational biomedical research exploiting the Linked Open Data cloud.

Combining static analysis and profiling for estimating execution times in logic programs

Effective static analyses have been proposed which allow inferring functions which bound the number of resolutions or reductions. These have the advantage of being independent from the platform on which the programs are executed and such bounds have been shown useful in a number of applications, such as granularity control in parallel execution. On the other hand, in certain distributed computation scenarios where different platforms come into play, with each platform having different capabilities, it is more interesting to express costs in metrics that include the characteristics of the platform. In particular, it is specially interesting to be able to infer upper and lower bounds on actual execution time. With this objective in mind, we propose a method which allows inferring upper and lower bounds on the execution times of procedures of a program in a given execution platform. The approach combines compile-time cost bounds analysis with a one-time profiling of the platform in order to determine the values of certain constants for that platform. These constants calibrate a cost model which from then on is able to compute statically time bound functions for procedures and to predict with a significant degree of accuracy the execution times of such procedures in the given platform. The approach has been implemented and integrated in the CiaoPP system.

Examination of the quality of spinach leaves using hyperspectral imaging

The present research is focused on the application of hyperspectral images for the supervision of quality deterioration in ready to use leafy spinach during storage (Spinacia oleracea). Two sets of samples of packed leafy spinach were considered: (a) a first set of samples was stored at 20 °C (E-20) in order to accelerate the degradation process, and these samples were measured the day of reception in the laboratory and after 2 days of storage; (b) a second set of samples was kept at 10 °C (E-10), and the measurements were taken throughout storage, beginning the day of reception and repeating the acquisition of Images 3, 6 and 9 days later. Twenty leaves per test were analyzed. Hyperspectral images were acquired with a push-broom CCD camera equipped with a spectrograph VNIR (400–1000 nm). Calibration set of spectra was extracted from E-20 samples, containing three classes of degradation: class A (optimal quality), class B and class C (maximum deterioration). Reference average spectra were defined for each class. Three models, computed on the calibration set, with a decreasing degree of complexity were compared, according to their ability for segregating leaves at different quality stages (fresh, with incipient and non-visible symptoms of degradation, and degraded): spectral angle mapper distance (SAM), partial least squares discriminant analysis models (PLS-DA), and a non linear index (Leafy Vegetable Evolution, LEVE) combining five wavelengths were included among the previously selected by CovSel procedure. In sets E-10 and E-20, artificial images of the membership degree according to the distance of each pixel to the reference classes, were computed assigning each pixel to the closest reference class. The three methods were able to show the degradation of the leaves with storage time.

Combining data mining and ontology engineering to enrich ontologies and linked data

In this position paper, we claim that the need for time consuming data preparation and result interpretation tasks in knowledge discovery, as well as for costly expert consultation and consensus building activities required for ontology building can be reduced through exploiting the interplay of data mining and ontology engineering. The aim is to obtain in a semi-automatic way new knowledge from distributed data sources that can be used for inference and reasoning, as well as to guide the extraction of further knowledge from these data sources. The proposed approach is based on the creation of a novel knowledge discovery method relying on the combination, through an iterative ?feedbackloop?, of (a) data mining techniques to make emerge implicit models from data and (b) pattern-based ontology engineering to capture these models in reusable, conceptual and inferable artefacts.

Combining a Similar Coefficient Identification Algorithm with the Boundary Element Method

The boundary element method (BEM) has been applied successfully to many engineering problems during the last decades. Compared with domain type methods like the finite element method (FEM) or the finite difference method (FDM) the BEM can handle problems where the medium extends to infinity much easier than domain type methods as there is no need to develop special boundary conditions (quiet or absorbing boundaries) or infinite elements at the boundaries introduced to limit the domain studied. The determination of the dynamic stiffness of arbitrarily shaped footings is just one of these fields where the BEM has been the method of choice, especially in the 1980s. With the continuous development of computer technology and the available hardware equipment the size of the problems under study grew and, as the flop count for solving the resulting linear system of equations grows with the third power of the number of equations, there was a need for the development of iterative methods with better performance. In [1] the GMRES algorithm was presented which is now widely used for implementations of the collocation BEM. While the FEM results in sparsely populated coefficient matrices, the BEM leads, in general, to fully or densely populated ones, depending on the number of subregions, posing a serious memory problem even for todays computers. If the geometry of the problem permits the surface of the domain to be meshed with equally shaped elements a lot of the resulting coefficients will be calculated and stored repeatedly. The present paper shows how these unnecessary operations can be avoided reducing the calculation time as well as the storage requirement. To this end a similar coefficient identification algorithm (SCIA), has been developed and implemented in a program written in Fortran 90. The vertical dynamic stiffness of a single pile in layered soil has been chosen to test the performance of the implementation. The results obtained with the 3-d model may be compared with those obtained with an axisymmetric formulation which are considered to be the reference values as the mesh quality is much better. The entire 3D model comprises more than 35000 dofs being a soil region with 21168 dofs the biggest single region. Note that the memory necessary to store all coefficients of this single region is about 6.8 GB, an amount which is usually not available with personal computers. In the problem under study the interface zone between the two adjacent soil regions as well as the surface of the top layer may be meshed with equally sized elements. In this case the application of the SCIA leads to an important reduction in memory requirements. The maximum memory used during the calculation has been reduced to 1.2 GB. The application of the SCIA thus permits problems to be solved on personal computers which otherwise would require much more powerful hardware.

Combining Wireless Sensor Networks and Semantic Middleware for an Internet of Things-Based Sportsman/Woman Monitoring Application.

Wireless Sensor Networks (WSNs) are spearheading the efforts taken to build and deploy systems aiming to accomplish the ultimate objectives of the Internet of Things. Due to the sensors WSNs nodes are provided with, and to their ubiquity and pervasive capabilities, these networks become extremely suitable for many applications that so-called conventional cabled or wireless networks are unable to handle. One of these still underdeveloped applications is monitoring physical parameters on a person. This is an especially interesting application regarding their age or activity, for any detected hazardous parameter can be notified not only to the monitored person as a warning, but also to any third party that may be helpful under critical circumstances, such as relatives or healthcare centers. We propose a system built to monitor a sportsman/woman during a workout session or performing a sport-related indoor activity. Sensors have been deployed by means of several nodes acting as the nodes of a WSN, along with a semantic middleware development used for hardware complexity abstraction purposes. The data extracted from the environment, combined with the information obtained from the user, will compose the basis of the services that can be obtained.

Evaluation and implementation of a hybrid cross-platform application combining mobile web-technologies and native device functions

Este Proyecto de Fin de Carrera presenta un prototipo de aplicación móvil híbrida multi-plataforma para Android y iOS. Las aplicaciones móviles híbridas son una combinación de aplicaciones web móviles y aplicaciones móviles nativas. Se desarrollan parcialmente con tecnologías web y pueden acceder a la capa nativa y sensores del teléfono. Para el usuario se presentan como aplicaciones nativas, ya que se pueden descargar de las tiendas de aplicaciones y son instaladas en el dispositivo. El prototipo consiste en la migración del módulo de noticias financieras de las aplicaciones actuales para móviles de una compañía bancaria reimplementándolo como aplicación híbrida utilizando uno de los entornos de desarrollo disponibles en el mercado para este propósito. El desarrollo de aplicaciones híbridas puede ahorrar tiempo y dinero cuando se pretende alcanzar más de una plataforma móvil. El objetivo es la evaluación de las ventajas e inconvenientes que ofrece el desarrollo de aplicaciones híbridas en términos de reducción de costes, tiempo de desarrollo y resultado final de la aplicación. El proyecto consta de varias fases. Durante la primera fase se realiza un estudio sobre las aplicaciones híbridas que podemos encontrar hoy en día en el mercado utilizando los ejemplos de linkedIn, Facebook y Financial times. Se hace hincapié en las tecnologías utilizadas, uso de la red móvil y problemas encontrados. Posteriormente se realiza una comparación de distintos entornos de desarrollo multi-plataforma para aplicaciones híbridas en términos de la estrategia utilizada, plataformas soportadas, lenguajes de programación, acceso a capacidades nativas de los dispositivos y licencias de uso. Esta primera fase da como resultado la elección del entorno de desarrollo más adecuado a las exigencias del proyecto, que es PhoneGap, y continua con un análisis más detallado de dicho entorno en cuanto a su arquitectura, características y componentes. La siguiente fase comienza con un estudio de las aplicaciones actuales de la compañía para extraer el código fuente necesario y adaptarlo a la arquitectura que tendrá la aplicación. Para la realización del prototipo se hace uso de la característica que ofrece PhoneGap para acceder a la capa nativa del dispositivo, esto es, el uso de plugins. Se diseña y desarrolla un plugin que permite acceder a la capa nativa para cada plataforma. Una vez desarrollado el prototipo para la plataforma Android, se migra y adapta para la plataforma iOS. Por último se hace una evaluación de los prototipos en cuanto a su facilidad y tiempo de desarrollo, rendimiento, funcionalidad y apariencia de la interfaz de usuario. ABSTRACT. This bachelor's thesis presents a prototype of a hybrid cross-platform mobile application for Android and iOS. Hybrid mobile applications are a combination of mobile web and mobile native applications. They are built partially with web technologies and they can also access native features and sensors of the device. For a user, they look like native applications as they are downloaded from the application stores and installed on the device. This prototype consists of the migration of the financial news module of current mobile applications from a financial bank reimplementing them as a hybrid application using one of the frameworks available in the market for that purpose. Development of applications on a hybrid way can help reducing costs and effort when targeting more than one platform. The target of the project is the evaluation of the advantages and disadvantages that hybrid development can offer in terms of reducing costs and efforts and the final result of the application. The project starts with an analysis of successfully released hybrid applications using the examples of linkedIn, Facebook and Financial Times, emphasizing the different used technologies, the transmitted network data and the encountered problems during the development. This analysis is followed by a comparison of most popular hybrid crossplatform development frameworks in terms of the different approaches, supported platforms, programming languages, access to native features and license. This first stage has the outcome of finding the development framework that best fits to the requirements of the project, that is PhoneGap, and continues with a deeper analysis of its architecture, features and components. Next stage analyzes current company's applications to extract the needed source code and adapt it to the architecture of the prototype. For the realization of the application, the feature that PhoneGap offers to access the native layer of the device is used. This feature is called plugin. A custom plugin is designed and developed to access the native layer of each targeted platform. Once the prototype is finished for Android, it is migrated and adapted to the iOS platform. As a final conclusion the prototypes are evaluated in terms of ease and time of development, performance, functionality and look and feel.

A quality assessment system for fresh fruits in hypermarkets and fruit trading centres.

When the fresh fruit reaches the final markets from the suppliers, its quality is not always as good as it should, either because it has been mishandled during transportation or because it lacks an adequate quality control at the producer level, before being shipped. This is why it is necessary for the final markets to establish their own quality assessment system if they want to ensure to their customers the quality they want to sell. In this work, a system to control fruit quality at the last level of the distribution channel has been designed. The system combines rapid control techniques with laboratory equipment and statistical sampling protocols, to obtain a dynamic, objective process, which can substitute advantageously the quality control inspections carried out visually by human experts at the reception platform of most hypermarkets. Portable measuring equipment have been chosen (firmness tester, temperature and humidity sensors...) as well as easy-to-use laboratory equipment (texturometer, colorimeter, refractometer..,) combining them to control the most important fruit quality parameters (firmness, colour, sugars, acids). A complete computer network has been designed to control all the processes and store the collected data in real time, and to perform the computations. The sampling methods have been also defined to guarantee the confidence of the results. Some of the advantages of a quality assessment system as the proposed one are: the minimisation of human subjectivity, the ability to use modern measuring techniques, and the possibility of using it also as a supplier's quality control system. It can be also a way to clarify the quality limits of fruits among members of the commercial channel, as well as the first step in the standardisation of quality control procedures.

Non-Destructive Identification of Woolly Peaches combining Mechanical Impact Response and NIR Spectroscopy.

Woolliness (mealiness in other fruits) is a negative attribute of peach sensory texture that is a physiological disorder associated with inadequate cold storage. It is characterised by lack of crispness and juiciness without variation in the tissue water content (Harker and Hallet, 1992). Many attempts have been made to develop destructive instrumental procedures to detect mealiness and woolliness. Non-destructive procedures attempted include using nuclear magnetic resonance (Sonego et al., 1995). However, this technique has economical limitations and is not practical at present. Non-destructive impact tests and NIR are non-destructive techniques which have been used to assess internal characteristics of fruits (Chen and Sun, 1991). The objective of this study was to develop a novel non-destructive procedure to identify woolly peaches by combining impact and NIR approaches.

Combining Text and Graphics for Interactive Exploration of Behavior Datasets

Modern sensor technologies and simulators applied to large and complex dynamic systems (such as road traffic networks, sets of river channels, etc.) produce large amounts of behavior data that are difficult for users to interpret and analyze. Software tools that generate presentations combining text and graphics can help users understand this data. In this paper we describe the results of our research on automatic multimedia presentation generation (including text, graphics, maps, images, etc.) for interactive exploration of behavior datasets. We designed a novel user interface that combines automatically generated text and graphical resources. We describe the general knowledge-based design of our presentation generation tool. We also present applications that we developed to validate the method, and a comparison with related work.