Advanced mapping of environmental data: Geostatistics, Machine Learning and Bayesian Maximum Entropy

This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.

Information systems for water resources management improvement in developing countries

Water resources management, as also water service provision projects in developing countries have difficulties to take adequate decisions due to scarce reliable information, and a lack of proper information managing. Some appropriate tools need to be developed in order to improve decision making to improve water management and access of the poorest, through the design of Decision Support Systems (DSS). On the one side, a DSS for developing co-operation projects on water access improvement has been developed. Such a tool has specific context constrains (structure of the system, software requirements) and needs (Logical Framework Approach monitoring, organizational-learning, accountability and evaluation) that shall be considered for its design. Key aspects for its successful implementation have appeared to be a participatory design of the system and support of the managerial positions at the inception phase. A case study in Tanzania was conducted, together with the Spanish NGO ONGAWA – Ingeniería para el Desarrollo. On the other side, DSS are required also to improve decision making on water management resources in order to achieve a sustainable development that not only improves the living conditions of the population in developing countries, but that also does not hinder opportunities of the poorest on those context. A DSS made to fulfil these requirements shall be using information from water resources modelling, as also on the environment and the social context. Through the research, a case study has been conducted in the Central Rift Valley of Ethiopia, an endhorreic basin 160 km south of Addis Ababa. There, water has been modelled using ArcSWAT, a physically based model which can assess the impact of land management practices on large complex watersheds with varying soils, land use and management conditions over long periods of time. Moreover, governance on water and environment as also the socioeconomic context have been studied.

Open Learning Network : the evidence of OER impact

Much of the initial work on Open Educational Resources (OER) has inevitably concentrated on how to produce the resources themselves and to establish the idea in the community. It is now eight years since the term OER was first used and more than ten years since the concept of open content was described and a greater focus is now emerging on the way in which OER can influence policy and change the way in which educational systems help people learn. The Open University UK and Carnegie Mellon University are working in partnership on the OLnet (Open Learning Network), funded by The William and Flora Hewlett Foundation with the aims to search out the evidence for use and reuse of OER and to establish a network for information sharing about research in the field. This means both gathering evidence and developing approaches for how to research and understand ways to learn in a more open world, particularly linked to OER, but also looking at other influences.

A Classification of Tools for Learning Logic

A table showing a comparison and classification of tools (intelligent tutoring systems) for e-learning of Logic at a college level.

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task

Towards Direct Policy Search Reinforcement Learning for Robot Control

This paper proposes a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, when using RL, has been to apply value function based algorithms, the system here detailed is characterized by the use of direct policy search methods. Rather than approximating a value function, these methodologies approximate a policy using an independent function approximator with its own parameters, trying to maximize the future expected reward. The policy based algorithm presented in this paper is used for learning the internal state/action mapping of a behavior. In this preliminary work, we demonstrate its feasibility with simulated experiments using the underwater robot GARBI in a target reaching task

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 increasinglypopular 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 navigationaladaptability 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

Electronic voting system for computer supported collaborative learning

The EVS4CSCL project starts in the context of a Computer Supported Collaborative Learning environment (CSCL). Previous UOC projects created a CSCL generic platform (CLPL) to facilitate the development of CSCL applications. A discussion forum (DF) was the first application developed over the framework. This discussion forum was different from other products on the marketplace because of its focus on the learning process. The DF carried out the specification and elaboration phases from the discussion learning process but there was a lack in the consensus phase. The consensus phase in a learning environment is not something to be achieved but tested. Common tests are done by Electronic Voting System (EVS) tools, but consensus test is not an assessment test. We are not evaluating our students by their answers but by their discussion activity. Our educational EVS would be used as a discussion catalyst proposing a discussion about the results after an initial query or it would be used after a discussion period in order to manifest how the discussion changed the students mind (consensus). It should be also used by the teacher as a quick way to know where the student needs some reinforcement. That is important in a distance-learning environment where there is no direct contact between the teacher and the student and it is difficult to detect the learning lacks. In an educational environment, assessment it is a must and the EVS will provide direct assessment by peer usefulness evaluation, teacher marks on every query created and indirect assessment from statistics regarding the user activity.

An empirical study of machine learning techniques applied to image watermarking

A study of how the machine learning technique, known as gentleboost, could improve different digital watermarking methods such as LSB, DWT, DCT2 and Histogram shifting.

Repeating with the right hemisphere: reduced interactions between phonological and lexical-semantic systems in crossed aphasia?

Knowledge on the patterns of repetition amongst individuals who develop language deficits in association with right hemisphere lesions (crossed aphasia) is very limited. Available data indicate that repetition in some crossed aphasics experiencing phonological processing deficits is not heavily influenced by lexical-semantic variables (lexicality, imageability, and frequency) as is regularly reported in phonologically-impaired cases with left hemisphere damage. Moreover, in view of the fact that crossed aphasia is rare, information on the role of right cortical areas and white matter tracts underpinning language repetition deficits is scarce. In this study, repetition performance was assessed in two patients with crossed conduction aphasia and striatal/capsular vascular lesions encompassing the right arcuate fasciculus (AF) and inferior frontal-occipital fasciculus (IFOF), the temporal stem and the white matter underneath the supramarginal gyrus. Both patients showed lexicality effects repeating better words than non-words, but manipulation of other lexical-semantic variables exerted less influence on repetition performance. Imageability and frequency effects, production of meaning-based paraphrases during sentence repetition, or better performance on repeating novel sentences than overlearned clichés were hardly ever observed in these two patients. In one patient, diffusion tensor imaging disclosed damage to the right long direct segment of the AF and IFOF with relative sparing of the anterior indirect and posterior segments of the AF, together with fully developed left perisylvian white matter pathways. These findings suggest that striatal/capsular lesions extending into the right AF and IFOF in some individuals with right hemisphere language dominance are associated with atypical repetition patterns which might reflect reduced interactions between phonological and lexical-semantic processes.

Embedding Emotional Context in Recommender Systems

Emotions are crucial for user's decision making in recommendation processes. We first introduce ambient recommender systems, which arise from the analysis of new trends on the exploitation of the emotional context in the next generation of recommender systems. We then explain some results of these new trends in real-world applications through the smart prediction assistant (SPA) platform in an intelligent learning guide with more than three million users. While most approaches to recommending have focused on algorithm performance. SPA makes recommendations to users on the basis of emotional information acquired in an incremental way. This article provides a cross-disciplinary perspective to achieve this goal in such recommender systems through a SPA platform. The methodology applied in SPA is the result of a bunch of technology transfer projects for large real-world rccommender systems

Curriculum sequencing for an e-learning system based on learning styles

This work shows the use of adaptation techniques involved in an e-learning system that considers students' learning styles and students' knowledge states. The mentioned e-learning system is built on a multiagent framework designed to examine opportunities to improve the teaching and to motivate the students to learn what they want in a user-friendly and assisted environment

Computational Representation of Collaborative Learning Flow Patterns Using IMS Learning Design

The identification and integration of reusable and customizable CSCL (Computer Supported Collaborative Learning) may benefit from the capture of best practices in collaborative learning structuring. The authors have proposed CLFPs (Collaborative Learning Flow Patterns) as a way of collecting these best practices. To facilitate the process of CLFPs by software systems, the paper proposes to specify these patterns using IMS Learning Design (IMS-LD). Thus, teachers without technical knowledge can particularize and integrate CSCL tools. Nevertheless, the support of IMS-LD for describing collaborative learning activities has some deficiencies: the collaborative tools that can be defined in these activities are limited. Thus, this paper proposes and discusses an extension to IMS-LD that enables to specify several characteristics of the use of tools that mediate collaboration. In order to obtain a Unit of Learning based on a CLFP, a three stage process is also proposed. A CLFP-based Unit of Learning example is used to illustrate the process and the need of the proposed extension.

CLFP intrinsic constraints-based group management of blended learning situations

When applying a Collaborative Learning Flow Pattern (CLFP) to structure sequences of activities in real contexts, one of the tasks is to organize groups of students according to the constraints imposed by the pattern. Sometimes,unexpected events occurring at runtime force this pre-defined distribution to be changed. In such situations, an adjustment of the group structures to be adapted to the new context is needed. If the collaborative pattern is complex, this group redefinitionmight be difficult and time consuming to be carried out in real time. In this context, technology can help on notifying the teacher which incompatibilitiesbetween the actual context and the constraints imposed by the pattern. This chapter presents a flexible solution for supporting teachers in the group organization profiting from the intrinsic constraints defined by a CLFPs codified in IMS Learning Design. A prototype of a web-based tool for the TAPPS and Jigsaw CLFPs and the preliminary results of a controlled user study are alsopresented as a first step towards flexible technological systems to support grouping tasks in this context.