Hand dexterity assessment based on mouse pointer trajectory measurements in children with learning disabilities

Universal Access in the Information Society - We present an investigation of the systematic differences in upper limb motor skills between children with learning disabilities (LDs) and children...     

Time-series modeling of fishery landings using ARIMA models and Fuzzy Expected Intervals software

Forecasting, using historic time-series data, has become an important tool for fisheries management. ARIMA modeling, Modeling for Optimal Forecasting techniques and Decision Support Systems based on fuzzy mathematics may be used to predict the general trend of a given fish landings time-series with increased reliability and accuracy. The present paper applies these three modeling methods to forecast anchovy fish catches landed in a given port (Thessaloniki, Greece) during 1979–2000 and hake and bonito total fish catches during 1982–2000. The paper attempts to assess the model's accuracy by comparing model results to the actual monthly fish catches of the year 2000. According to the measures of forecasting accuracy established, the best forecasting performance for anchovy was shown by the DSS model (MAPE = 28.06%, RMSE = 76.56, U-statistic = 0.67 and R² = 0.69). The optimal forecasting technique of genetic modeling improved significantly the forecasting values obtained by the selected ARIMA model. Similarly, the DSS model showed a noteworthy forecasting efficiency for the prediction of hake landings, during the year 2000 (MAPE = 2.88%, RMSE = 13.75, U-statistic = 0.19 and R² = 0.98), as compared to the other two modeling techniques. Optimal forecasting produced by combined modeling scored better than application of the simple ARIMA model. Overall, DSS results showed that the Fuzzy Expected Intervals methodology could be used as a very reliable tool for short-term predictions of fishery landings.     

Active learning of user’s preferences estimation towards a personalized 3D navigation of geo-referenced scenes

The current technological evolutions enter 3D geo-informatics into their digital age, enabling new potential applications in the field of virtual tourism, pleasure, entertainment and cultural heritage. It is argued that 3D information provides the natural way of navigation. However, personalization is a key aspect in a navigation system, since a route that incorporates user preferences is ultimately more suitable than the route with the shortest distance or travel time. Usually, user’s preferences are expressed as a set of weights that regulate the degree of importance of the scene metadata on the route selection process. These weights, however, are defined by the users, setting the complexity to the user’s side, which makes personalization an arduous task. In this paper, we propose an alternative approach in which metadata weights are estimated implicitly and transparently to the users, transferring the complexity to the system side. This is achieved by introducing a relevance feedback on-line learning strategy which automatically adjusts metadata weights by exploiting information fed back to the system about the relevance of user’s preferences judgments given in a form of pair-wise comparisons. Practically implementing a relevance feedback algorithm presents the limitation that several pair-wise comparisons (samples) are required to converge to a set of reliable metadata weights. For this reason, we propose in this paper a weight rectification strategy that improves weight estimation by exploiting metadata interrelations defined through an ontology. In the sequel, a genetic optimization algorithm is incorporated to select the most user preferred routes based on a multi-criteria minimization approach. To increase the degree of personalization in 3D navigation, we have also introduced an efficient algorithm for estimating 3D trajectories around objects of interest by merging best selected 2D projected views that contain faces which are mostly preferred by the users. We have conducted simulations and comparisons with other approaches either in the field of on-line learning or route selection using objective metrics in terms of precision and recall values. The results indicate that our system yields on average a 13.76 % improvement of precision as regards the learning strategy and an improvement of 8.75 % regarding route selection. In addition, we conclude that the ontology driven weight rectification strategy can reduce the number of samples (pair-wise comparisons) required of 76 % to achieve the same precision. Qualitative comparisons have been also performed using a use case route scenario in the city of Athens.     

Rapid Concept Learning for Mobile Robots

Concept learning in robotics is an extremely challenging problem: sensory data is often high-dimensional, and noisy due to specularities and other irregularities. In this paper, we investigate two general strategies to speed up learning, based on spatial decomposition of the sensory representation, and simultaneous learning of multiple classes using a shared structure. We study two concept learning scenarios: a hallway navigation problem, where the robot has to induce features such as opening or wall. The second task is recycling, where the robot has to learn to recognize objects, such as a trash can. We use a common underlying function approximator in both studies in the form of a feedforward neural network, with several hundred input units and multiple output units. Despite the high degree of freedom afforded by such an approximator, we show the two strategies provide sufficient bias to achieve rapid learning. We provide detailed experimental studies on an actual mobile robot called PAVLOV to illustrate the effectiveness of this approach.     

Adaptive Smooth Simulcast Protocol (ASSP) for Video Applications: Description and Performance Evaluation

In this paper, we present Adaptive Smooth Simulcast Protocol (ASSP) for simulcast transmission of multimedia data over best-effort networks. ASSP is a new multiple-rate protocol that implements a single rate TCP-friendly protocol as the underlying congestion control mechanism for each simulcast stream. The key attributes of ASSP are: (a) TCP-friendly behavior, (b) adaptive per-stream transmission rates, (c) adaptive scalability to large sets of receivers and (d) smooth transmission rates that are suitable for multimedia applications. We evaluate the performance of ASSP under an integrated simulation environment which combines the measurements of both network and video performance metrics. We also compare ASSP against other proposed solutions and the results demonstrate that the performance of ASSP is significantly better than the tested solutions. Finally, ASSP is a practical solution with very low implementation complexity for video transmission over best-effort networks.     

Data access in distributed simulations of multi-agent systems

Distributed simulation has emerged as an important instrument for studying large-scale complex systems. Such systems inherently consist of a large number of components, which operate in a large shared state space interacting with it in highly dynamic and unpredictable ways. Optimising access to the shared state space is crucial for achieving efficient simulation executions. Data accesses may take two forms: locating data according to a set of attribute value ranges (range query) or locating a particular state variable from the given identifier (ID query and update). This paper proposes two alternative routing approaches, namely the address-based approach, which locates data according to their address information, and the range-based approach, whose operation is based on looking up attribute value range information along the paths to the destinations. The two algorithms are discussed and analysed in the context of PDES-MAS, a framework for the distributed simulation of multi-agent systems, which uses a hierarchical infrastructure to manage the shared state space. The paper introduces a generic meta-simulation framework which is used to perform a quantitative comparative analysis of the proposed algorithms under various circumstances.     

A <Emphasis Type="Italic">k</Emphasis>-norm pruning algorithm for decision tree classifiers based on error rate estimation

Decision trees are well-known and established models for classification and regression. In this paper, we focus on the estimation and the minimization of the misclassification rate of decision tree classifiers. We apply Lidstone’s Law of Succession for the estimation of the class probabilities and error rates. In our work, we take into account not only the expected values of the error rate, which has been the norm in existing research, but also the corresponding reliability (measured by standard deviations) of the error rate. Based on this estimation, we propose an efficient pruning algorithm, called k-norm pruning, that has a clear theoretical interpretation, is easily implemented, and does not require a validation set. Our experiments show that our proposed pruning algorithm produces accurate trees quickly, and compares very favorably with two other well-known pruning algorithms, CCP of CART and EBP of C4.5. Editor: Hendrik Blockeel.     

A novel HMM-based learning framework for improving dynamic wireless push system performance

A new machine learning framework is introduced in this paper, based on the hidden Markov model (HMM), designed to provide scheduling in dynamic wireless push systems. In realistic wireless systems, the clients’ intentions change dynamically; hence a cognitive scheduling scheme is needed to estimate the desirability of the connected clients. The proposed scheduling scheme is enhanced with self-organized HMMs, supporting the network with an estimated expectation of the clients’ intentions, since the system’s environment characteristics alter dynamically and the base station (server side) has no a priori knowledge of such changes. Compared to the original pure scheme, the proposed machine learning framework succeeds in predicting the clients’ information desires and overcomes the limitation of the original static scheme, in terms of mean delay and system efficiency.