Egocentric interaction as a tool for designing ambient ecologies—The case of the easy ADL ecology

The visions of ambient intelligence demand novel interaction paradigms that enable designers and system developers to frame and manage the dynamic and complex interaction between humans and environments populated with physical (real) and virtual (digital) objects of interest. So far, many proposed approaches have adhered to a device-centric stance when including virtual objects into the ambient ecology; a stance inherited from existing interaction paradigms for mobile and stationary interactive devices. In this article, we introduce egocentric interaction as an alternative approach, taking the human agent’s body and mind as the center of reference. We show how this interaction paradigm has influenced both the conception and implementation of the easy ADL ecology, comprising of smart objects, a personal activity-centric middleware attempting to simplify interaction given available resources, ambient intelligence applications aimed at everyday activity support, and a human agent literally in the middle of it all.     

The base model of role-based access control and the “cost-risk” criterion of data processing

This paper analyzes the base model of role-based access control, specifically, the risks in developing an information-secure computational system. In developing the plan of the ??cost-risk?? analysis, the area of application should be identified on the basis of documents and on detailed knowledge of the given model of information security and software means. The requirements for role-based security models are defined.     

A new approach for manufacturing forecast problems with insufficient data: the case of TFT–LCDs

Manufacturing forecast problems have been widely discussed in recent years, where more accurate predictions could reduce the overall manufacturing costs. This study uses the case of ensuring the heights of thin film transistor–liquid crystal display photo-spacers. It is a small sample size prediction problem, because the data available for analysis is limited on the manufacturing lines. A new approach is developed to deal with this problem, which involves three steps. The first step is using K-means clustering to separate data into K clusters, while the second step is to compute the possibility through the fuzzy membership function in each cluster for attribute extension. The last step is to put the data with new generate attributes into a backpropagation neural network (BPNN) machine learning algorithm. Two performance evaluation methods, cross-validation and data specification testing, are selected to compare the proposed method with three popular prediction models: linear regression, support vector machine for regression (SVR), and BPNN. The results show that the proposed method outperforms the others with regard to the total errors, mean square error, and standard deviation.     

Model organism proteomics as a tool for the study of host–microbiome interactions

All organisms live in constant contact with the microbial world. In recent years it has become evident that these microbial communities are not only responsible for the development of certain diseases, but are also an indispensable factor for homeostasis. The inherent complexity of meta-organisms hampers a straightforward elucidation of the molecular processes regulating the interactions of the host and its microbiome, as well as the influence of exogenic factors, for example, nutrition. Modern approaches such as meta-proteomics are now capable of deciphering the major processes in microbial communities, but the complete analysis of their interactions with their host is still in its infancy. In order to get easier access, the study of nonmammalian model organisms bears great potential. These organisms provide advantages such as reduced complexity, ease of cultivation in great numbers, and amenity to a range of genetic and biochemical manipulations. We highlight the potentials provided by model organism proteomics for the study of host–microbiome interactions and outline major challenges and demands for technological improvements that will be necessary for the understanding of the manifold interactions within meta-organisms.     

A closed-form expression for the quantile function of the Gompertz–Makeham distribution

In this note, we give a closed-form expression in terms of the Lambert W function for the quantile function of the Gompertz–Makeham distribution. This probability distribution has frequently been used to describe human mortality and to establish actuarial tables. The analytical expression provided for the quantile function is helpful to generate random samples drawn from the Gompertz–Makeham distribution by means of the inverse transform method.     

A fully discrete scheme for the pressure–stress formulation of the time-domain fluid–structure interaction problem

We propose an implicit Newmark method for the time integration of the pressure–stress formulation of a fluid–structure interaction problem. The space Galerkin discretization is based on the Arnold–Falk–Winther mixed finite element method with weak symmetry in the solid and the usual Lagrange finite element method in the acoustic medium. We prove that the resulting fully discrete scheme is well-posed and uniformly stable with respect to the discretization parameters and Poisson ratio, and we provide asymptotic error estimates. Finally, we present numerical tests to confirm the asymptotic error estimates predicted by the theory.     

The Discrete Event Concept as a Paradigm for the “Perception-Based Diagnosis” of Sachem

Sachem is an extensive large-scale real time knowledge-based system designed to monitor and diagnose blast furnaces. This paper aims at illustrating the way the concept of discrete event allowed the definition of a perception-based diagnosis approach as a recursive and holographic abstraction process of a discrete event. The example of the diagnosis of a thermal load phenomenon on a blast furnace is used in order to illustrate the way Sachem apply the perception-based diagnosis approach. Some considerations about the blast furnace and the development of Sachem are also presented in the paper to recall the complexity and the issue of the design of powerful perception systems.     

EPSILON: a versatile microcomputer program for the spectrophotometric data analysis of metal–ligand equilibria

We have developed the microcomputer program EPSILON which allows the computation of equilibrium constants and extinction coefficients of several interacting species from spectrophotometric single- and multi-wavelength pH-titration data. Novel features of EPSILON include: easy treatment of the data through a windowed environment, minimization of errors with respect to the read variable (absorbance), simultaneous plotting of both the spectrophotometric titration curve and the species distribution as a function of pH during refinement and estimation of standard deviations for the refined parameters. Several examples of applications are provided and discussed in order to illustrate the use of EPSILON. The results are compared with literature values and with those obtained by BEST from potentiometric data.     

Could the doctrine of moral rights be used as a basis for understanding the notion of control within data protection law?

This article considers the notion of individual control of personal data as envisaged by the European data protection framework and makes the argument that it is a poorly-understood and under-developed concept, but that our understanding of it may be improved by way of analyses and comparisons with the doctrine of moral rights, an important constituent element of intellectual property law. The article starts by examining the concept of personal data itself, and why an enhanced level of individual control over personal data is thought to be a desirable regulatory objective. Following this, the article examines the scholarly literature pertaining to individual control of personal data, as well as a range of relevant EU policy documents. Having done so, the article argues that the notion of control is muddled and confused from both theoretical and practical perspectives. Following this, the article considers the doctrine of moral rights, and through an exploration of its theoretical and practical elements highlights why it may be of assistance in terms of enhancing our understanding of individual control in the data protection context.     

The impact of assimilation of MODIS data for the prediction of a tropical low‐pressure system over India using a mesoscale model

A low‐pressure system formed over the Bay of Bengal, India on 15 October 2003 and crossed the east coast of India during the late hours of 17 October 2003. The system, which provided copious rainfall over the Bay of Bengal and stations on the east coast, is investigated in this study using the fifth Generation Mesoscale Model (MM5). Three sets of numerical experiments are designed in this study. While the first set utilizes National Center for Environmental Prediction—Aviation (NCEP‐AVN) analysis (for the initial conditions and lateral boundary conditions) only in the MM5 simulation, the second set utilizes the vertical profiles of temperature and humidity obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) (as well as a few radiosonde station data) to provide an improved analysis. The third set used the vertical profiles of temperature and humidity from MODIS alone to provide an improved analysis. The results of the three sets of simulation are compared with one another as well as with the analysis and observations. It is found that the predicted sea level pressure of the MM5 simulation which utilized the improved analysis: reproduces the large‐scale structure of the low‐pressure system as manifested in the NCEP‐AVN analysis; provides a stronger and deeper low‐pressure system as seen from the sea level pressure field; and shows a larger northward extent of the associated precipitation pattern as compared with the simulation with just the analysis. The results of the third experiment (impact of vertical profiles of temperature and humidity using MODIS alone) compare well with the results of the second experiment except that in the former, the associated cyclonic circulation in the lower troposphere appears weaker. The results of this study, although restricted to a single case study, demonstrate that inclusion of MODIS derived vertical temperature and humidity profiles together with radiosonde data caused a favourable impact on the simulated structure of the low‐pressure system.     

The development of a comprehensive computer program for the study of solid–solid atomic interfaces

This paper describes the construction of a comprehensive computer program to model the structure of the silicon–silicon dioxide (Si–SiO₂) boundary layer of semiconductor devices by combining experimental data from an X-ray storage ring with graphical simulations on a multiprocessor distributed net (or a multiprocessor supercomputer). The term ‘comprehensive’ refers to the program's planned ability to incorporate all stages necessary to translate 2D X-ray spectra to the final 3D atomic image with little or no human intervention. If successful, our efforts will provide materials scientists, engineers, and manufacturers of integrated circuits with an additional tool for understanding interfacial chemistry and thereby suggest new ways to improve the chemical and electronic behavior of their devices.     

An analysis of the distribution of nitrogen dioxide in the south-eastern Mediterranean for the period 1985–1989

The abundance of ozone in the stratosphere depends upon a photochemical cycle in which nitrogen dioxide ( N0₂) plays a predominant role. Although the zonal mean cross-sections for N0₂ averaged for 34 months have been calculated–on the basis of L1MS data–lack of detailed information at local or regional scale could result in difficulties in terms of modelling the distribution of ozone in the stratosphere. In this paper we use SAGE II measurements to calculate the altitude profiles of N0₂ mixing ratios for the geographical region 15 to 30° E and 32·5 to 42·5° N for the period 1985 to 1989.Analysis of the findings shows that N0₂ experiences higher values in summer months, that the N0₂ maximum mixing ratios decrease from 1985 to 1989, that maximum mixing ratios are found at an altitude ofapproximately 33 km, with variations depending on season and year and that N0₂ mixing ratios for the period 1985–1989 are higher compared to the relevant mixing ratios for 1979–1981.     

Airborne thermal data for evaluating the spatial distribution of actual evapotranspiration over a watershed in oceanic climatic conditions—application of semi-empirical models

Considerable work has been done on estimating daily fluxes at the regional scale from thermal data acquired in the early afternoon using semi-empirical models. But these studies have dealt only with clear days and homogeneous surfaces. We evaluate the use of some of these models under variable climatic conditions. A campaign of micrometeorological measurements performed from May to October 1993 on a small agricultural catchment in Brittany (NW France) allowed us to monitor the water budget and fluxes of three characteristical surfaces (meadow, wheat and stubble field). Thermal images covering the whole catchment were acquired on 7 August 1993 using an infrared camera aboard a small aircraft. They were corrected for atmospheric effects with the LOWTRAN 7 model. After geometrical corrections to superimpose these images on a digitilized land use map, a mapping of surface fluxes at the catchment scale is proposed and discussed.     

Using the conflict in Dempster–Shafer evidence theory as a rejection criterion in classifier output combination for 3D human action recognition

In this paper, we propose a comprehensive solution to 3D human action recognition including feature extraction, classification, and multiple classifier combination. We effectively present two feature extraction methods, four different types of well-known classifiers, and four multiple classifier combination strategies including a specially designed belief based method. In order to enhance the recognition accuracy, we propose a new rejection criterion based on the conflict from the information sources: the classifier outputs. We test our method on the MSRAction 3D dataset. Discarding examples using the conflict based criterion shows superior results than other combination approaches. Moreover this criterion allows choosing a tradeoff between the performance and rejection rate.     

Behavioural data in the design of ergonomic computer terminals and workstations—a case study

Abstract

Manufacturers of office automation equipment, in their design of ergonomic computer terminals and office furniture, are encountering needed design data which is sometimes weak, absent, erroneous or inapplicable to the office-worker situation under consideration. In some instances, office automation manufacturers simply follow standards. In others, manufacturers make assumptions as to the best design, and in others, design decisions were based on testing results and conclusions.This paper reviews the ergonomic tools and features incorporated into the design of one office automation manufacturer's terminals and workstations. The short fall of applicable design data relevant to comfort versus extremes of motion in biomechanics, to keyboard home row height and slope angle to viewing distance, as well as to reverse video and surface colour are presented, along with the design resolution of these parameters. Further, certain aspects of posture recently published are discussed.     

The EM algorithm in a distributed computing environment for modelling environmental space–time data

Statistical models for spatio-temporal data are increasingly used in environmetrics, climate change, epidemiology, remote sensing and dynamical risk mapping. Due to the complexity of the relationships among the involved variables and dimensionality of the parameter set to be estimated, techniques for model definition and estimation which can be worked out stepwise are welcome. In this context, hierarchical models are a suitable solution since they make it possible to define the joint dynamics and the full likelihood starting from simpler conditional submodels. Moreover, for a large class of hierarchical models, the maximum likelihood estimation procedure can be simplified using the Expectation–Maximization (EM) algorithm.In this paper, we define the EM algorithm for a rather general three-stage spatio-temporal hierarchical model, which includes also spatio-temporal covariates. In particular, we show that most of the parameters are updated using closed forms and this guarantees stability of the algorithm unlike the classical optimization techniques of the Newton–Raphson type for maximizing the full likelihood function. Moreover, we illustrate how the EM algorithm can be combined with a spatio-temporal parametric bootstrap for evaluating the parameter accuracy through standard errors and non-Gaussian confidence intervals.To do this a new software library in form of a standard R package has been developed. Moreover, realistic simulations on a distributed computing environment allow us to discuss the algorithm properties and performance also in terms of convergence iterations and computing times.     

A combinational incremental ensemble of classifiers as a technique for predicting students’ performance in distance education

The ability to predict a student’s performance could be useful in a great number of different ways associated with university-level distance learning. Students’ marks in a few written assignments can constitute the training set for a supervised machine learning algorithm. Along with the explosive increase of data and information, incremental learning ability has become more and more important for machine learning approaches. The online algorithms try to forget irrelevant information instead of synthesizing all available information (as opposed to classic batch learning algorithms). Nowadays, combining classifiers is proposed as a new direction for the improvement of the classification accuracy. However, most ensemble algorithms operate in batch mode. Therefore a better proposal is an online ensemble of classifiers that combines an incremental version of Naive Bayes, the 1-NN and the WINNOW algorithms using the voting methodology. Among other significant conclusions it was found that the proposed algorithm is the most appropriate to be used for the construction of a software support tool.