A proposal toward the development of accessible e-learning content by human involvement

Most of the existing efforts for supporting the design, preparation, and deployment of accessible e-learning applications propose guidelines that primarily address technical accessibility issues. Little, if any, consideration is given to the real actors involved in the learning experience, such as didactical experts and disabled learners. Moreover, implementing artifacts addressed to the e-learning world requires a wide range of particular skills which are related not only to technical but also to didactical, pedagogical, usability, and accessibility aspects of the produced material. This paper argues that the know-how of a number of stakeholders should be blended into a joint design activity, and that it should be possible to determine the role of each participant in the successive phases of the development lifecycle of e-learning applications. The paper sketches the methodological guidelines of a design framework based on involving the users with disabilities, as well as pedagogical experts, in the development process. The novelty of this proposal mainly stems from being built up around the core of strategies and choices specifically bound to accessibility requirements. Characteristic elements of learner-centered design are then further integrated into processes and methodologies which are typical of participatory and contextual design approaches. Following such guidelines, it will be possible to gain a deeper understanding of the requirements and of the operational context of people needing accessible material, either as learners or educators. The underlying objective is to increase the potential to realize learning systems that better meet different user needs and that provide a more satisfying learning experience. Moreover, when people get involved in the development process, they gain a sense of ownership of the system and are therefore more likely to accept and “promote” it.     

Reliable real-time robot operation employing intelligent forward recovery

Modern computer-controlled robots typically fail at their tasks whenever they encounter an error, no matter how minor. The physical environment of a typical assembly robot is too unstructured to benefit from conventional software approaches to reliability. We present an approach which interfaces the real-time operation of the robot with an intelligent subsystem that performs error analysis and forward recovery if a failure occurs. Our approach involves a special representation of the robot's program that efficiently tracks the robot's operation in real time and is easy to modify to include automatically generated recovery procedures.     

Searching for Inefficiencies in Exchange Rate Dynamics

This paper develops a new pair trading method to detect inefficiencies in exchange rates movements and arbitrage opportunities using a convergence/divergence indicator (CDI) belonging to the oscillatory class. The proposed technique is applied to 11 exchange rates over the period 2010–2015, and trading rules based on CDI signals are obtained. The CDI indicator is shown to outperform others of the oscillatory class and in some cases (for EURAUD and AUDJPY) to generate profits. The suggested approach is of general interest and can be applied to different financial markets and assets.     

Cloud parameter retrievals from Meteosat and their effects on the shortwave radiation at the surface

A method based on Spinning Enhanced Visible and Infrared Imager (SEVIRI) measured reflectance at 0.6 and 3.9 µm is used to retrieve the cloud optical thickness (COT) and cloud effective radius (r_e) over the Iberian Peninsula. A sensitivity analysis of simulated retrievals to the input parameters demonstrates that the cloud top height is an important factor in satellite retrievals of COT and r_e with uncertainties around 10% for small values of COT and r_e; for water clouds these uncertainties can be greater than 10% for small values of r_e. The uncertainties found related with geometries are around 3%. The COT and r_e are assessed using well-known satellite cloud products, showing that the method used characterize the cloud field with more than 80% (82%) of the absolute differences between COT (r_e) mean values of all clouds (water plus ice clouds) centred in the range from ±10 (±10 µm), with absolute bias lower than 2 (2 μm) for COT (r_e) and root mean square error values lower than 10 (8 μm) for COT (r_e). The cloud water path (CWP), derived from satellite retrievals, and the shortwave cloud radiative effect at the surface (CRE_SW) are related for high fractional sky covers (F_sc >0.8), showing that water clouds produce more negative CRE_SW than ice clouds. The COT retrieved was also related to the cloud modification factor, which exhibits reductions and enhancements of the surface SW radiation of the order of 80% and 30%, respectively, for COT values lower than 10. A selected case study shows, using a ground-based sky camera that some situations classified by the satellite with high F_sc values correspond to situations of broken clouds where the enhancements actually occur. For this case study, a closure between the liquid water path (LWP) obtained from the satellite retrievals and the same cloud quantity obtained from ground-based microwave measurements was performed showing a good agreement between both LWP data set values.     

Optimal allocation of change points in simple step-stress experiments under Type-II censoring

In simple step-stress experiments under Type-II censoring with the cumulative exposure model and exponentially distributed lifetimes, maximum likelihood estimates (MLE) of the expected lifetimes may not exist due to the absence of failure times either before or after the stress change point. For this reason, when planning a step-stress experiment, the change point could be chosen so as to minimize the probability of non-existence of the MLE. These non-existence probabilities are examined and compared in the one- as well as the two-sample situations. Moreover, the optimal allocations of the change points are discussed and the effects of the use of non-optimal choices for the change points are assessed.     

Self-stabilizing minimum degree spanning tree within one from the optimal degree

We propose a self-stabilizing algorithm for constructing a Minimum Degree Spanning Tree (MDST) in undirected networks. Starting from an arbitrary state, our algorithm is guaranteed to converge to a legitimate state describing a spanning tree whose maximum node degree is at most Δ^∗+1, where Δ^∗ is the minimum possible maximum degree of a spanning tree of the network.To the best of our knowledge, our algorithm is the first self-stabilizing solution for the construction of a minimum degree spanning tree in undirected graphs. The algorithm uses only local communications (nodes interact only with the neighbors at one hop distance). Moreover, the algorithm is designed to work in any asynchronous message passing network with reliable FIFO channels. Additionally, we use a fine grained atomicity model (i.e., the send/receive atomicity). The time complexity of our solution is O(mn²logn) where m is the number of edges and n is the number of nodes. The memory complexity is O(δlogn) in the send-receive atomicity model (δ is the maximal degree of the network).     

Rough set based approaches to feature selection for Case-Based Reasoning classifiers

This paper investigates feature selection based on rough sets for dimensionality reduction in Case-Based Reasoning classifiers. In order to be useful, Case-Based Reasoning systems should be able to manage imprecise, uncertain and redundant data to retrieve the most relevant information in a potentially overwhelming quantity of data. Rough Set Theory has been shown to be an effective tool for data mining and for uncertainty management. This paper has two central contributions: (1) it develops three strategies for feature selection, and (2) it proposes several measures for estimating attribute relevance based on Rough Set Theory. Although we concentrate on Case-Based Reasoning classifiers, the proposals are general enough to be applicable to a wide range of learning algorithms. We applied these proposals on twenty data sets from the UCI repository and examined the impact of feature selection over classification performance. Our evaluation shows that all three proposals benefit the basic Case-Based Reasoning system. They also present robustness in comparison to well-known feature selection strategies.     

SVM practical industrial application for mechanical faults diagnostic

A large percentage of the total induction motor failures are due to mechanical faults. It is well known that, machine’s vibration is the best indicator of its overall mechanical condition, and an earliest indicator of arising defects. Support vector machines (SVM) is also well known as intelligent classifier with strong generalization ability. In this paper, both, machine‘s vibrations and SVM are used together for a new intelligent mechanical fault diagnostic method. Using only one vibration sensor and only four SVM’s it was achieved improved results over the available approaches for this purpose in the literature. Therefore, this method becomes more attractive for on line monitoring without maintenance specialist intervention. Vibration signals turns out to occur in different directions (axial, horizontal or vertical) depending on the type of the fault. Thus, to diagnose mechanical faults it is necessary to read signals at various positions or use more them one accelerometer. From this work we also determined the best position for signals acquisition, which is very important information for the maintenance task.     

A novel automatic algorithm for the segmentation of the lumen of the carotid artery in ultrasound B-mode images

A novel algorithm is proposed for the segmentation of the lumen and bifurcation boundaries of the carotid artery in B-mode ultrasound images. It uses the image contrast characteristics of the lumen and bifurcation of the carotid artery in relation to other tissues and structures for their identification. The relevant ultrasound data regarding the artery presented in the input image is identified using morphologic operators and processed by an anisotropic diffusion filter for speckle noise removal. The information obtained is then used to define two initial contours, one corresponding to the lumen and the other one regarding the bifurcation boundaries, for the application of the Chan-Vese level set segmentation model. A set of longitudinal ultrasound B-mode grayscale images of the common carotid artery was acquired using a GE Healthcare Vivid-e ultrasound system. The results reveal that the new algorithm is effective and robust, and that its main advantage relies on the automatic identification of the carotid lumen, which overcomes the known limitations of the traditional algorithms.