Desing and Validation of a Light Inference System to Support Embedded Context Reasoning

Embedded context management in resource-constrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system (LIS) aiming at simplifying embedded inference processes offering a set of functionalities to avoid redundancy in context management operations. The system is part of a service-oriented mobile software framework, conceived to facilitate the creation of context-aware applications—it decouples sensor data acquisition and context processing from the application logic. LIS, composed of several modules, encapsulates existing lightweight tools for ontology data management and rule-based reasoning, and it is ready to run on Java-enabled handheld devices. Data management and reasoning processes are designed to handle a general ontology that enables communication among framework components. Both the applications running on top of the framework and the framework components themselves can configure the rule and query sets in order to retrieve the information they need from LIS. In order to test LIS features in a real application scenario, an ‘Activity Monitor’ has been designed and implemented: a personal health-persuasive application that provides feedback on the user’s lifestyle, combining data from physical and virtual sensors. In this case of use, LIS is used to timely evaluate the user’s activity level, to decide on the convenience of triggering notifications and to determine the best interface or channel to deliver these context-aware alerts.d

A generator of efficient abstract machine implementations and its application to emulator minimization

The implementation of abstract machines involves complex decisions regarding, e.g., data representation, opcodes, or instruction specialization levéis, all of which affect the final performance of the emulator and the size of the bytecode programs in ways that are often difficult to foresee. Besides, studying alternatives by implementing abstract machine variants is a time-consuming and error-prone task because of the level of complexity and optimization of competitive implementations, which makes them generally difficult to understand, maintain, and modify. This also makes it hard to genérate specific implementations for particular purposes. To ameliorate those problems, we propose a systematic approach to the automatic generation of implementations of abstract machines. Different parts of their definition (e.g., the instruction set or the infernal data and bytecode representation) are kept sepárate and automatically assembled in the generation process. Alternative versions of the abstract machine are therefore easier to produce, and variants of their implementation can be created mechanically, with specific characteristics for a particular application if necessary. We illustrate the practicality of the approach by reporting on an implementation of a generator of production-quality WAMs which are specialized for executing a particular fixed (set of) program(s). The experimental results show that the approach is effective in reducing emulator size.

Design and Validation of a Light Inference System to Support Embedded Context Reasoning

Embedded context management in resource-constrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system (LIS) aiming at simplifying embedded inference processes offering a set of functionalities to avoid redundancy in context management operations. The system is part of a service-oriented mobile software framework, conceived to facilitate the creation of context-aware applications?it decouples sensor data acquisition and context processing from the application logic. LIS, composed of several modules, encapsulates existing lightweight tools for ontology data management and rule-based reasoning, and it is ready to run on Java-enabled handheld devices. Data management and reasoning processes are designed to handle a general ontology that enables communication among framework components. Both the applications running on top of the framework and the framework components themselves can configure the rule and query sets in order to retrieve the information they need from LIS. In order to test LIS features in a real application scenario, an ?Activity Monitor? has been designed and implemented: a personal health-persuasive application that provides feedback on the user?s lifestyle, combining data from physical and virtual sensors. In this case of use, LIS is used to timely evaluate the user?s activity level, to decide on the convenience of triggering notifications and to determine the best interface or channel to deliver these context-aware alerts.

Territorial cohesion impacts of high-speed rail under different zoning systems

Large-scale transport infrastructure projects such as high-speed rail (HSR) produce significant effects on the spatial distribution of accessibility. These effects, commonly known as territorial cohesion effects, are receiving increasing attention in the research literature. However, there is little empirical research into the sensitivity of these cohesion results to methodological issues such as the definition of the limits of the study area or the zoning system. In a previous paper (Ortega et al., 2012), we investigated the influence of scale issues, comparing the cohesion results obtained at four different planning levels. This paper makes an additional contribution to our research with the investigation of the influence of zoning issues. We analyze the extent to which changes in the size of the units of analysis influence the measurement of spatial inequalities. The methodology is tested by application to the Galician (north-western) HSR corridor, with a length of nearly 670 km, included in the Spanish PEIT (Strategic Transport and Infrastructure Plan) 2005-2020. We calculated the accessibility indicators for the Galician HSR corridor and assessed their corresponding territorial distribution. We used five alternative zoning systems depending on the method of data representation used (vector or raster), and the level of detail (cartographic accuracy or cell size). Our results suggest that the choice between a vector-based and raster-based system has important implications. The vector system produces a higher mean accessibility value and a more polarized accessibility distribution than raster systems. The increased pixel size of raster-based systems tends to give rise to higher mean accessibility values and a more balanced accessibility distribution. Our findings strongly encourage spatial analysts to acknowledge that the results of their analyses may vary widely according to the definition of the units of analysis.

Reflexión y refracción de la luz en medios anisótropos

En este proyecto se pretende estudiar el comportamiento de la luz al atravesar medios de diversos materiales, tanto isótropos como anisótropos uniáxicos. Para ello se requiere realizar un estudio previo de las condiciones de contorno aplicables a las ecuaciones de Maxwell en la interfase de dos medios que pueden ser isótropos o anisótropos. En el caso de dos materiales isótropos, la solución del problema son los conocidos coeficientes de Fresnel de reflexión y transmisión. En este trabajo se pretende generalizar el estudio al caso del paso de la luz desde un medio isótropo a otro anisótropo uniáxico (con su eje óptico en orientación arbitraria) y viceversa y al caso de dos materiales anisótropos uniáxicos con ejes ópticos en orientaciones arbitrarias. Es de especial interés el caso de un mismo material uniáxico en el que las dos partes tienen el eje óptico con distinta orientación. Una vez planteadas las condiciones de contorno específicas en cada caso, se obtendrá un conjunto de ecuaciones algebraicas cuya resolución permitirá obtener los coeficientes de reflexión y transmisión buscados. Para plantear el sistema de ecuaciones adecuado, será necesario tener una descripción de las características ópticas de los materiales empleados, la orientación de los ejes ópticos en cada caso, y los posibles ángulos de incidencia. Se realizará un tratamiento matricial de modo que el paquete MatLab permite su inversión de manera inmediata. Se desarrollará una interfaz sencilla, realizada con MatLab, que permita al usuario introducir sin dificultad los datos correspondientes a los materiales de los medios incidente y transmitido, la orientación en espacial del o de los ejes ópticos, de la longitud de onda de trabajo y del ángulo de incidencia del haz de luz, con los que la aplicación realizará los cálculos. Los coeficientes de reflexión y refracción obtenidos serán representados gráficamente en función del ángulo de incidencia. Así mismo se representarán los ángulos transmitidos y reflejados en función del de incidencia. Todo ello de esta forma, que resulte sencilla la interpretación de los datos por parte del usuario. ABSTRACT. The reason for this project is to study the behavior of light when light crosses different media of different materials, isotropic materials and uniaxial anisotropic materials. For this, a previous study is necessary where the boundary conditions apply to Maxwell equations at the interface between two media which can be isotropic and anisotropic. If both materials are isotropic, the Fresnel ccoefficients of reflection and refraction are used to solve the problem. The aim of this work is to generalize a study when light crosses from an isotropic media to a uniaxial anisotropic media, where its axis have arbitrary directions, and vicecersa. The system consisting of two materials with axis in arbitrary directions are also being studied. Once the specific boundary conditions are known in each case, a set of algebraic equations are obtained whose solution allows obtaining the reflection coefficients and refraction coefficients. It is necessary to have a description of the optical characteristics of the materials used; of the directions axis in each case and the possible angle of incidence. A matrix is proposed for later treatment in Matlab that allows the immediate inversion. A simple interface will de developed, manufactured with Matlab, that allows the user to enter data easily corresponding to the incident media and transmission media of the different materials, the special axis directions, the wavelength and the angle of incidence of the light beam. This data is used by the application to perform the necessary calculations to solve the problem. When reflection coefficients and refraction coefficients are obtained, the application draws the graphics in function of the angle of incidence. Also transmitted and reflected angles depending on the incidence are represented. This is to perform a data representation which is a simple interpretation of the user data.

Onyx: A Linked Data approach to emotion representation

Extracting opinions and emotions from text is becoming increasingly important, especially since the advent of micro-blogging and social networking. Opinion mining is particularly popular and now gathers many public services, datasets and lexical resources. Unfortunately, there are few available lexical and semantic resources for emotion recognition that could foster the development of new emotion aware services and applications. The diversity of theories of emotion and the absence of a common vocabulary are two of the main barriers to the development of such resources. This situation motivated the creation of Onyx, a semantic vocabulary of emotions with a focus on lexical resources and emotion analysis services. It follows a linguistic Linked Data approach, it is aligned with the Provenance Ontology, and it has been integrated with the Lexicon Model for Ontologies (lemon), a popular RDF model for representing lexical entries. This approach also means a new and interesting way to work with different theories of emotion. As part of this work, Onyx has been aligned with EmotionML and WordNet-Affect.

MedVir: an interactive representation system of multidimensional medical data applied to Traumatic Brain Injury's rehabilitation prediction

Clinicians could model the brain injury of a patient through his brain activity. However, how this model is defined and how it changes when the patient is recovering are questions yet unanswered. In this paper, the use of MedVir framework is proposed with the aim of answering these questions. Based on complex data mining techniques, this provides not only the differentiation between TBI patients and control subjects (with a 72% of accuracy using 0.632 Bootstrap validation), but also the ability to detect whether a patient may recover or not, and all of that in a quick and easy way through a visualization technique which allows interaction.

Towards data-aware resource analysis for service orchestrations

Compile-time program analysis techniques can be applied to Web service orchestrations to prove or check various properties. In particular, service orchestrations can be subjected to resource analysis, in which safe approximations of upper and lower resource usage bounds are deduced. A uniform analysis can be simultaneously performed for different generalized resources that can be directiy correlated with cost- and performance-related quality attributes, such as invocations of partners, network traffic, number of activities, iterations, and data accesses. The resulting safe upper and lower bounds do not depend on probabilistic assumptions, and are expressed as functions of size or length of data components from an initiating message, using a finegrained structured data model that corresponds to the XML-style of information structuring. The analysis is performed by transforming a BPEL-like representation of an orchestration into an equivalent program in another programming language for which the appropriate analysis tools already exist.

Optimizing Functional Network Representation of Multivariate Time Series

By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.

Ontology-based access to sensor data streams

Sensor networks are increasingly becoming one of the main sources of Big Data on the Web. However, the observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse these data for other purposes than those for which they were originally set up. In this thesis we address these challenges, considering how we can transform streaming raw data to rich ontology-based information that is accessible through continuous queries for streaming data. Our main contribution is an ontology-based approach for providing data access and query capabilities to streaming data sources, allowing users to express their needs at a conceptual level, independent of implementation and language-specific details. We introduce novel query rewriting and data translation techniques that rely on mapping definitions relating streaming data models to ontological concepts. Specific contributions include: • The syntax and semantics of the SPARQLStream query language for ontologybased data access, and a query rewriting approach for transforming SPARQLStream queries into streaming algebra expressions. • The design of an ontology-based streaming data access engine that can internally reuse an existing data stream engine, complex event processor or sensor middleware, using R2RML mappings for defining relationships between streaming data models and ontology concepts. Concerning the sensor metadata of such streaming data sources, we have investigated how we can use raw measurements to characterize streaming data, producing enriched data descriptions in terms of ontological models. Our specific contributions are: • A representation of sensor data time series that captures gradient information that is useful to characterize types of sensor data. • A method for classifying sensor data time series and determining the type of data, using data mining techniques, and a method for extracting semantic sensor metadata features from the time series.

Knowledge Discovery in Spectral Data by Means of Complex Networks

In the last decade, complex networks have widely been applied to the study of many natural and man-made systems, and to the extraction of meaningful information from the interaction structures created by genes and proteins. Nevertheless, less attention has been devoted to metabonomics, due to the lack of a natural network representation of spectral data. Here we define a technique for reconstructing networks from spectral data sets, where nodes represent spectral bins, and pairs of them are connected when their intensities follow a pattern associated with a disease. The structural analysis of the resulting network can then be used to feed standard data-mining algorithms, for instance for the classification of new (unlabeled) subjects. Furthermore, we show how the structure of the network is resilient to the presence of external additive noise, and how it can be used to extract relevant knowledge about the development of the disease.

Feature selection in the reconstruction of complex network representations of spectral data

Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitude

The Phase Space as a New Representation of the Dynamical Behaviour of Temperature and Enthalpy in a Reefer monitored with a Multidistributed Sensors Network

The study of temperature gradients in cold stores and containers is a critical issue in the food industry for the quality assurance of products during transport, as well as forminimizing losses. The objective of this work is to develop a new methodology of data analysis based on phase space graphs of temperature and enthalpy, collected by means of multidistributed, low cost and autonomous wireless sensors and loggers. A transoceanic refrigerated transport of lemons in a reefer container ship from Montevideo (Uruguay) to Cartagena (Spain) was monitored with a network of 39 semi-passive TurboTag RFID loggers and 13 i-button loggers. Transport included intermodal transit from transoceanic to short shipping vessels and a truck trip. Data analysis is carried out using qualitative phase diagrams computed on the basis of Takens?Ruelle reconstruction of attractors. Fruit stress is quantified in terms of the phase diagram area which characterizes the cyclic behaviour of temperature. Areas within the enthalpy phase diagram computed for the short sea shipping transport were 5 times higher than those computed for the long sea shipping, with coefficients of variation above 100% for both periods. This new methodology for data analysis highlights the significant heterogeneity of thermohygrometric conditions at different locations in the container.

Knowledge representation tool for cognitiveprocesses modeling

In the last decades, neuropsychological theories tend to consider cognitive functions as a result of the whole brainwork and not as individual local areas of its cortex. Studies based on neuroimaging techniques have increased in the last years, promoting an exponential growth of the body of knowledge about relations between cognitive functions and brain structures [1]. However, so fast evolution make complicated to integrate them in verifiable theories and, even more, translated in to cognitive rehabilitation. The aim of this research work is to develop a cognitive process-modeling tool. The purpose of this system is, in the first term, to represent multidimensional data, from structural and functional connectivity, neuroimaging, data from lesion studies and derived data from clinical intervention [2][3]. This will allow to identify consolidated knowledge, hypothesis, experimental designs, new data from ongoing studies and emerging results from clinical interventions. In the second term, we pursuit to use Artificial Intelligence to assist in decision making allowing to advance towards evidence based and personalized treatments in cognitive rehabilitation. This work presents the knowledge base design of the knowledge representation tool. It is compound of two different taxonomies (structure and function) and a set of tags linking both taxonomies at different levels of structural and functional organization. The remainder of the abstract is organized as follows: Section 2 presents the web application used for gathering necessary information for generating the knowledge base, Section 3 describes knowledge base structure and finally Section 4 expounds reached conclusions.

Onyx: describing emotions on the web of data

There are several different standardised and widespread formats to represent emotions. However, there is no standard semantic model yet. This paper presents a new ontology, called Onyx, that aims to become such a standard while adding concepts from the latest Semantic Web models. In particular, the ontology focuses on the representation of Emotion Analysis results. But the model is abstract and inherits from previous standards and formats. It can thus be used as a reference representation of emotions in any future application or ontology. To prove this, we have translated resources from EmotionML representation to Onyx. We also present several ways in which developers could benefit from using this ontology instead of an ad-hoc presentation. Our ultimate goal is to foster the use of semantic technologies for emotion Analysis while following the Linked Data ideals.