Adaptive intelligent agent approach to guide the web navigation on the PLAN-G distance learning platform

Hypermedia systems based on the Web for open distance education are becoming increasingly popular as tools for user-driven access learning information. Adaptive hypermedia is a new direction in research within the area of user-adaptive systems, to increase its functionality by making it personalized [Eklu 961. This paper sketches a general agents architecture to include navigational adaptability and user-friendly processes which would guide and accompany the student during hislher learning on the PLAN-G hypermedia system (New Generation Telematics Platform to Support Open and Distance Learning), with the aid of computer networks and specifically WWW technology [Marz 98-1] [Marz 98-2]. The PLAN-G actual prototype is successfully used with some informatics courses (the current version has no agents yet). The propased multi-agent system, contains two different types of adaptive autonomous software agents: Personal Digital Agents {Interface), to interacl directly with the student when necessary; and Information Agents (Intermediaries), to filtrate and discover information to learn and to adapt navigation space to a specific student

Adaptive business intelligence for an open negotiation environment

Engineering of negotiation model allows to develop effective heuristic for business intelligence. Digital ecosystems demand open negotiation models. To define in advance effective heuristics is not compliant with the requirement of openness. The new challenge is to develop business intelligence in advance exploiting an adaptive approach. The idea is to learn business strategy once new negotiation model rise in the e-market arena. In this paper we present how recommendation technology may be deployed in an open negotiation environment where the interaction protocol models are not known in advance. The solution we propose is delivered as part of the ONE Platform, open source software that implements a fully distributed open environment for business negotiation

An analysis of trust in anonymity networks in the presence of adaptive attackers

Introduces extended Crowds protocols. Optional reading

Genomic architecture of adaptive color pattern divergence and convergence in Heliconius butterflies

Identifying the genetic changes driving adaptive variation in natural populations is key to understanding the origins of biodiversity. The mosaic of mimetic wing patterns in Heliconius butterflies makes an excellent system for exploring adaptive variation using next-generation sequencing. In this study, we use a combination of techniques to annotate the genomic interval modulating red color pattern variation, identify a narrow region responsible for adaptive divergence and convergence in Heliconius wing color patterns, and explore the evolutionary history of these adaptive alleles. We use whole genome resequencing from four hybrid zones between divergent color pattern races of Heliconius erato and two hybrid zones of the co-mimic Heliconius melpomene to examine genetic variation across 2.2 Mb of a partial reference sequence. In the intergenic region near optix, the gene previously shown to be responsible for the complex red pattern variation in Heliconius, population genetic analyses identify a shared 65-kb region of divergence that includes several sites perfectly associated with phenotype within each species. This region likely contains multiple cis-regulatory elements that control discrete expression domains of optix. The parallel signatures of genetic differentiation in H. erato and H. melpomene support a shared genetic architecture between the two distantly related co-mimics; however, phylogenetic analysis suggests mimetic patterns in each species evolved independently. Using a combination of next-generation sequencing analyses, we have refined our understanding of the genetic architecture of wing pattern variation in Heliconius and gained important insights into the evolution of novel adaptive phenotypes in natural populations.

Conceptuals Mentefactos as a Didactic-Pedagogical Strategy of the Basic Concepts in the Sampling Theory Applyed to the Health Research

In populational sampling it is vitally important to clarify and discern: first, the design or sampling method used to solve the research problem; second, the sampling size, taking into account different components (precision, reliability, variance); third, random selection and fourth, the precision estimate (sampling errors), so as to determine if it is possible to infer the obtained estimates from the target population. The existing difficulty to use concepts from the sampling theory is to understand them with absolute clarity and, to achieve it, the help from didactic-pedagogical strategies arranged as conceptual “mentefactos” (simple hierarchic diagrams organized from propositions) may prove useful. This paper presents the conceptual definition, through conceptual “mentefactos”, of the most important populational probabilistic sampling concepts, in order to obtain representative samples from populations in health research.

Adaptive computer assisted assessment of free-text students answers : an approach to automatically generate students' conceptual models.

Las formas de evaluación basadas en el uso de tests no pueden identificar muchos errores conceptuales de los estudiantes. Esta investigación tiene como objetivo facilitar un nuevo procedimiento capaz de generar los modelos conceptuales de los estudiantes de forma automática a partir de respuestas en texto libre.. Este trabajo se organiza en tres apartados. En primer lugar, se procede a la revisión del estado de la cuestión. A continuación se describen el procedimiento para generar automáticamente los modelos conceptuales de los estudiantes y los sistemas que implementan dicho procedimiento. Por último se ofrecen: una explicación de los experimentos realizados y sus resultados, las conclusiones obtenidas y las líneas de trabajo futuro. Además se proporciona información para aplicar el procedimiento en otro idioma y/o área de conocimiento.. Se propone un procedimiento para generar automáticamente modelos conceptuales de cada estudiante y de una clase a partir de las respuestas facilitadas al sistema de evaluación automático y adaptativo. Estos sistemas son la evolución de los actuales de evaluación de respuestas en texto libre, que evalúan respuestas en texto libre automáticamente y de forma adaptada al modelo de cada estudiante..

Medical image registration based on random line sampling

One of the key aspects in 3D-image registration is the computation of the joint intensity histogram. We propose a new approach to compute this histogram using uniformly distributed random lines to sample stochastically the overlapping volume between two 3D-images. The intensity values are captured from the lines at evenly spaced positions, taking an initial random offset different for each line. This method provides us with an accurate, robust and fast mutual information-based registration. The interpolation effects are drastically reduced, due to the stochastic nature of the line generation, and the alignment process is also accelerated. The results obtained show a better performance of the introduced method than the classic computation of the joint histogram

View-dependent information theory quality measures for pixel sampling and scene discretization in flatland

In this paper, we present view-dependent information theory quality measures for pixel sampling and scene discretization in flatland. The measures are based on a definition for the mutual information of a line, and have a purely geometrical basis. Several algorithms exploiting them are presented and compare well with an existing one based on depth differences

Point sampling with uniformly distributed lines

In this paper we address the problem of extracting representative point samples from polygonal models. The goal of such a sampling algorithm is to find points that are evenly distributed. We propose star-discrepancy as a measure for sampling quality and propose new sampling methods based on global line distributions. We investigate several line generation algorithms including an efficient hardware-based sampling method. Our method contributes to the area of point-based graphics by extracting points that are more evenly distributed than by sampling with current algorithms

Land Cover Sampling Biases Associated with Roadside Bird Surveys

Roadside surveys such as the Breeding Bird Survey (BBS) are widely used to assess the relative abundance of bird populations. The accuracy of roadside surveys depends on the extent to which surveys from roads represent the entire region under study. We quantified roadside land cover sampling bias in Tennessee, USA, by comparing land cover proportions near roads to proportions of the surrounding region. Roadside surveys gave a biased estimate of patterns across the region because some land cover types were over- or underrepresented near roads. These biases changed over time, introducing varying levels of distortion into the data. We constructed simulated population trends for five bird species of management interest based on these measured roadside sampling biases and on field data on bird abundance. These simulations indicated that roadside surveys may give overly negative assessments of the population trends of early successional birds and of synanthropic birds, but not of late-successional birds. Because roadside surveys are the primary source of avian population trend information in North America, we conclude that these surveys should be corrected for roadside land cover sampling bias. In addition, current recommendations about the need to create more early successional habitat for birds may need reassessment in the light of the undersampling of this habitat by roads.