Multilingual variation in the context of Linked Data

In this paper we present a revisited classification of term variation in the light of the Linked Data initiative. Linked Data refers to a set of best practices for publishing and connecting structured data on the Web with the idea of transforming it into a global graph. One of the crucial steps of this initiative is the linking step, in which datasets in one or more languages need to be linked or connected with one another. We claim that the linking process would be facilitated if datasets are enriched with lexical and terminological information. Being that the final aim, we propose a classification of lexical, terminological and semantic variants that will become part of a model of linguistic descriptions that is currently being proposed within the framework of the W3C Ontology- Lexica Community Group to enrich ontologies and Linked Data vocabularies. Examples of modeling solutions of the different types of variants are also provided.

Increasing the Intelligence of Virtual Sales Assistants through Knowledge Modeling Techniques

Shopping agents are web-based applications that help consumers to find appropriate products in the context of e-commerce. In this paper we argue about the utility of advanced model-based techniques that recently have been proposed in the fields of Artificial Intelligence and Knowledge Engineering, in order to increase the level of support provided by this type of applications. We illustrate this approach with a virtual sales assistant that dynamically configures a product according to the needs and preferences of customers.

Modeling nonthermal emission from stellar bow shocks (Research Note)

Context. Runaway O- and early B-type stars passing through the interstellar medium at supersonic velocities and characterized by strong stellar winds may produce bow shocks that can serve as particle acceleration sites. Previous theoretical models predict the production of high-energy photons by nonthermal radiative processes, but their efficiency is still debated. Aims. We aim to test and explain the possibility of emission from the bow shocks formed by runaway stars traveling through the interstellar medium by using previous theoretical models. Methods. We applied our model to AE Aurigae, the first reported star with an X-ray detected bow shock, to BD+43 3654, in which the observations failed in detecting high-energy emission, and to the transition phase of a supergiant star in the late stages of its life. Results. From our analysis, we confirm that the X-ray emission from the bow shock produced by AE Aurigae can be explained by inverse Compton processes involving the infrared photons of the heated dust. We also predict low high-energy flux emission from the bow shock produced by BD+43 3654, and the possibility of high-energy emission from the bow shock formed by a supergiant star during the transition phase from blue to red supergiant. Conclusions. Bow shocks formed by different types of runaway stars are revealed as a new possible source of high-energy photons in our neighborhood.

Modeling nonthermal emission from stellar bow shocks (Research Note)

Context. Runaway O- and early B-type stars passing through the interstellar medium at supersonic velocities and characterized by strong stellar winds may produce bow shocks that can serve as particle acceleration sites. Previous theoretical models predict the production of high-energy photons by nonthermal radiative processes, but their efficiency is still debated. Aims. We aim to test and explain the possibility of emission from the bow shocks formed by runaway stars traveling through the interstellar medium by using previous theoretical models. Methods. We applied our model to AE Aurigae, the first reported star with an X-ray detected bow shock, to BD+43 3654, in which the observations failed in detecting high-energy emission, and to the transition phase of a supergiant star in the late stages of its life. Results. From our analysis, we confirm that the X-ray emission from the bow shock produced by AE Aurigae can be explained by inverse Compton processes involving the infrared photons of the heated dust. We also predict low high-energy flux emission from the bow shock produced by BD+43 3654, and the possibility of high-energy emission from the bow shock formed by a supergiant star during the transition phase from blue to red supergiant. Conclusions. Bow shocks formed by different types of runaway stars are revealed as a new possible source of high-energy photons in our neighborhood.

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.

Graph-based Semi-Supervised Learning in Acoustic Modeling for Automatic Speech Recognition

Thesis (Ph.D.)--University of Washington, 2016-06

Modeling and exploiting behavior patterns in dynamic environments

This paper presents a new approach to improving the effectiveness of autonomous systems that deal with dynamic environments. The basis of the approach is to find repeating patterns of behavior in the dynamic elements of the system, and then to use predictions of the repeating elements to better plan goal directed behavior. It is a layered approach involving classifying, modeling, predicting and exploiting. Classifying involves using observations to place the moving elements into previously defined classes. Modeling involves recording features of the behavior on a coarse grained grid. Exploitation is achieved by integrating predictions from the model into the behavior selection module to improve the utility of the robot's actions. This is in contrast to typical approaches that use the model to select between different strategies or plays. Three methods of adaptation to the dynamic features of the environment are explored. The effectiveness of each method is determined using statistical tests over a number of repeated experiments. The work is presented in the context of predicting opponent behavior in the highly dynamic and multi-agent robot soccer domain (RoboCup)

Modelling context information with ORM

Context-aware applications rely on implicit forms of input, such as sensor-derived data, in order to reduce the need for explicit input from users. They are especially relevant for mobile and pervasive computing environments, in which user attention is at a premium. To support the development of context-aware applications, techniques for modelling context information are required. These must address a unique combination of requirements, including the ability to model information supplied by both sensors and people, to represent imperfect information, and to capture context histories. As the field of context-aware computing is relatively new, mature solutions for context modelling do not exist, and researchers rely on information modelling solutions developed for other purposes. In our research, we have been using a variant of Object-Role Modeling (ORM) to model context. In this paper, we reflect on our experiences and outline some research challenges in this area.

Towards a Modeling Environment for Earth Systems Simulations

The developments of models in Earth Sciences, e.g. for earthquake prediction and for the simulation of mantel convection, are fare from being finalized. Therefore there is a need for a modelling environment that allows scientist to implement and test new models in an easy but flexible way. After been verified, the models should be easy to apply within its scope, typically by setting input parameters through a GUI or web services. It should be possible to link certain parameters to external data sources, such as databases and other simulation codes. Moreover, as typically large-scale meshes have to be used to achieve appropriate resolutions, the computational efficiency of the underlying numerical methods is important. Conceptional this leads to a software system with three major layers: the application layer, the mathematical layer, and the numerical algorithm layer. The latter is implemented as a C/C++ library to solve a basic, computational intensive linear problem, such as a linear partial differential equation. The mathematical layer allows the model developer to define his model and to implement high level solution algorithms (e.g. Newton-Raphson scheme, Crank-Nicholson scheme) or choose these algorithms form an algorithm library. The kernels of the model are generic, typically linear, solvers provided through the numerical algorithm layer. Finally, to provide an easy-to-use application environment, a web interface is (semi-automatically) built to edit the XML input file for the modelling code. In the talk, we will discuss the advantages and disadvantages of this concept in more details. We will also present the modelling environment escript which is a prototype implementation toward such a software system in Python (see www.python.org). Key components of escript are the Data class and the PDE class. Objects of the Data class allow generating, holding, accessing, and manipulating data, in such a way that the actual, in the particular context best, representation is transparent to the user. They are also the key to establish connections with external data sources. PDE class objects are describing (linear) partial differential equation objects to be solved by a numerical library. The current implementation of escript has been linked to the finite element code Finley to solve general linear partial differential equations. We will give a few simple examples which will illustrate the usage escript. Moreover, we show the usage of escript together with Finley for the modelling of interacting fault systems and for the simulation of mantel convection.

Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.

Outcomes of service encounter quality in a business-to-business context

Service encounter quality is an area of growing interest to researchers and managers alike, yet little is known about the effects of face-to-face service encounter quality within a business-to-business setting. In this paper, a psychometrically sound measure of such service encounter quality is proposed, and consequences of this construct are empirically assessed. Both a literature review and a dyadic in-depth interview approach were used to develop a conceptual framework and a pool of items to capture service encounter quality. A mail survey of customers was undertaken, and a response rate of 36% was obtained. Data analysis was conducted via confirmatory factor analysis and structural equation modeling. Findings reveal a four-factor structure of service encounter quality, encompassing professionalism, civility, friendliness and competence dimensions. Service encounter quality was found to be directly related to customer satisfaction and service quality perceptions, and indirectly to loyalty. The importance of these findings for practitioners and for future research on service encounter quality is discussed.

Reasoning by Structural Analogy Taking into Account the Context for Intelligent Decision Support Systems

Development of methods and tools for modeling human reasoning (common sense reasoning) by analogy in intelligent decision support systems is considered. Special attention is drawn to modeling reasoning by structural analogy taking the context into account. The possibility of estimating the obtained analogies taking into account the context is studied. This work was supported by RFBR.

Business Modeling of the Application Architecture of the Bulgarian Folklore Artery

In an attempt to answer the need of wider accessibility and popularization of the treasury of Bulgarian folklore, a team from the Institute of Mathematics and Informatics at the Bulgarian Academy of Sciences has planned to develop the Bulgarian folklore artery within the national project ―Knowledge Technologies for Creation of Digital Presentation and Significant Repositories of Folklore Heritage‖. This paper presents the process of business modeling of the application architecture of the Bulgarian folklore artery, which aids requirements analysis, application design and its software implementation. The folklore domain process model is made in the context of the target social applications—e-learning, virtual expositions of folklore artifacts, research, news, cultural/ethno-tourism, etc. The basic processes are analyzed and modeled and some inferences are made for the use cases and requirements specification of the Bulgarian folklore artery application. As a conclusion the application architecture of the Bulgarian folklore artery is presented.

Modeling of shape memory alloys with plasticity

Shape memory alloys are a special class of metals that can undergo large deformation yet still be able to recover their original shape through the mechanism of phase transformations. However, when they experience plastic slip, their ability to recover their original shape is reduced. This is due to the presence of dislocations generated by plastic flow that interfere with shape recovery through the shape memory effect and the superelastic effect. A one-dimensional model that captures the coupling between shape memory effect, the superelastic effect and plastic deformation is introduced. The shape memory alloy is assumed to have only 3 phases: austenite, positive variant martensite and negative variant martensite. If the SMA flows plastically, each phase will exhibit a dislocation field that permanently prevents a portion of it from being transformed back to other phases. Hence, less of the phase is available for subsequent phase transformations. A constitutive model was developed to depict this phenomena and simulate the effect of plasticity on both the shape memory effect and the superelastic effect in shape memory alloys. In addition, experimental tests were conducted to characterize the phenomenon in shape memory wire and superelastic wire. ^ The constitutive model was then implemented in within a finite element context as UMAT (User MATerial Subroutine) for the commercial finite element package ABAQUS. The model is phenomenological in nature and is based on the construction of stress-temperature phase diagram. ^ The model has been shown to be capable of capturing the qualitative and quantitative aspects of the coupling between plasticity and the shape memory effect and plasticity and the super elastic effect within acceptable limits. As a verification case a simple truss structure was built and tested and then simulated using the FEA constitutive model. The results where found to be close the experimental data. ^

The examination of the relationships among secondary principals' leadership behaviors, school climate, and student achievement in an urban context

School principals' leadership is key to successful school reform, as is increased student achievement. This nonexperimental ex post facto study tested relationships among secondary school principals' leadership behaviors, school climate, and student achievement. Of 165 secondary school principals from the three largest school districts in South Florida, 58 completed three online survey instruments: the Leadership Practices Inventory, School Climate Inventory-Revised, and researcher-designed Demographic Questionnaire. Student achievement was measured by students' scores on the reading and mathematics Florida Comprehensive Assessment Tests. Three null hypotheses tested relationships among (a) five principals' leadership behaviors and seven domains of school climate; (b) principals' leadership behaviors and student achievement; and (c) principals' leadership behaviors, school climate, and student achievement. Multiple linear regressions were used to determine the degree to which the independent variables predicted the dependent variables for the first two hypotheses. ANOVAs tested possible group differences between the demographic and research variables as controls for the third hypothesis. Partial correlational analyses tested the strength and direction of relationships among leadership behaviors, climate, and achievement. Results revealed partial support of the hypotheses. None of the leadership variables significantly predicted school climate. No significant relationships were found among the five leadership behaviors and student achievement. Demographic group differences in school climate and student achievement were marginally significant. The leadership behaviors of Inspiring a Shared Vision and Enabling Others to Act were positively linked to reading achievement. Partial correlations were found (r .27 to −.35) among school climate variables of Order, Involvement, and Expectation and achievement variables. The Modeling the Way leadership variable was negatively associated with reading achievement. After controlling for gender, years at current school, and years in the district, partial positive correlations were found among leadership, school climate, and student achievement. Inspiring a Shared Vision, Enabling Others to Act, Encouraging the Heart, and Challenging the Process leadership variables were partially correlated to Order, Leadership (Instructional), and Expectation climate variables. Study results should provide policymakers and educators with a leadership profile for school leaders challenging the status quo who can create schools for enhanced student learning and relevance to the needs of students, families, and society.