Search results for " Computer Science"
showing 10 items of 3983 documents
Use of driving simulators for advanced driver assistance Systems evaluation in emergency situations
2012
Introducing advanced driver assistance systems in cars has numerous consequences, particularly on drivers' behavior and their interaction with the vehicle. The expected safety benefits of assistance systems can thus be jeopardized. In order to assess the actual safety benefits, driving simulators offer a safe and cheap alternative. This doctoral work relies on experiments carried out on driving simulators with different assistance systems. A first experimental part focuses on longitudinal control assistance: we studied drivers' familiarization with a Forward Collision Warning system in critical situations. A second experimental part focuses on lateral control assistance: we studied drivers'…
Measuring Distraction at the Levels of Tactical and Strategic Control: The Limits of Capacity-Based Measures for Revealing Unsafe Visual Sampling Mod…
2011
The control theory of driving suggests that driver distraction can be analyzed as a breakdown of control at three levels. Common approach for analyzing distraction experimentally is to utilize capacity-based measures to assess distraction at the level of operational control. Three driving simulation experiments with 61 participants were organized to evaluate which kind of measures could be used to analyze drivers' tactical visual sampling models and the related effects of distraction while searching textual information on in-car display. The effects of two different text types were evaluated. The utilized capacity-based measures seemed to be insufficient for revealing participants' tactical…
A Cooperative Coevolution Framework for Parallel Learning to Rank
2015
We propose CCRank, the first parallel framework for learning to rank based on evolutionary algorithms (EA), aiming to significantly improve learning efficiency while maintaining accuracy. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimization for problems with large search space and complex structures. Moreover, CC naturally allows parallelization of sub-solutions to the decomposed sub-problems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. We implement CCRank with three EA-based learning to rank algorithms for demonstration. E…
E-NAUTILUS: A decision support system for complex multiobjective optimization problems based on the NAUTILUS method
2015
Interactive multiobjective optimization methods cannot necessarily be easily used when (industrial) multiobjective optimization problems are involved. There are at least two important factors to be considered with any interactive method: computationally expensive functions and aspects of human behavior. In this paper, we propose a method based on the existing NAUTILUS method and call it the Enhanced NAUTILUS (E-NAUTILUS) method. This method borrows the motivation of NAUTILUS along with the human aspects related to avoiding trading-off and anchoring bias and extends its applicability for computationally expensive multiobjective optimization problems. In the E-NAUTILUS method, a set of Pareto…
A novel heuristic memetic clustering algorithm
2013
In this paper we introduce a novel clustering algorithm based on the Memetic Algorithm meta-heuristic wherein clusters are iteratively evolved using a novel single operator employing a combination of heuristics. Several heuristics are described and employed for the three types of selections used in the operator. The algorithm was exhaustively tested on three benchmark problems and compared to a classical clustering algorithm (k-Medoids) using the same performance metrics. The results show that our clustering algorithm consistently provides better clustering solutions with less computational effort.
Teaching programming by emphasizing self-direction: How did students react to the active role required of them?
2013
Lecturing is known to be a controversial form of teaching. With massed classrooms, in particular, it tends to constrain the active participation of students. One of the remedies applied to programming education is to use technology that can vitalize interaction in the classroom, while another is to base teaching increasingly on programming activities. In this article, we present the first results of an exploratory study, in which we teach programming without lectures, exams, or grades, by heavily emphasizing programming activity, and, in a pedagogical sense, student self-direction. This article investigates how students reacted to the active role required of them and what issues emerged in …
A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion
2015
Collaborative Filtering (CF) is one of the most successful algorithms in recommender systems. However, it suffers from data sparsity and scalability problems. Although many clustering techniques have been incorporated to alleviate these two problems, most of them fail to achieve further significant improvement in recommendation accuracy. First of all, most of them assume each user or item belongs to a single cluster. Since usually users can hold multiple interests and items may belong to multiple categories, it is more reasonable to assume that users and items can join multiple clusters (groups), where each cluster is a subset of like-minded users and items they prefer. Furthermore, most of…
Reduced Order Models for Pricing European and American Options under Stochastic Volatility and Jump-Diffusion Models
2017
Abstract European options can be priced by solving parabolic partial(-integro) differential equations under stochastic volatility and jump-diffusion models like the Heston, Merton, and Bates models. American option prices can be obtained by solving linear complementary problems (LCPs) with the same operators. A finite difference discretization leads to a so-called full order model (FOM). Reduced order models (ROMs) are derived employing proper orthogonal decomposition (POD). The early exercise constraint of American options is enforced by a penalty on subset of grid points. The presented numerical experiments demonstrate that pricing with ROMs can be orders of magnitude faster within a give…
Interactive Multiple Criteria Decision Making based on preference driven Evolutionary Multiobjective Optimization with controllable accuracy
2012
Abstract We present an approach to interactive Multiple Criteria Decision Making based on preference driven Evolutionary Multiobjective Optimization with controllable accuracy. The approach relies on formulae for lower and upper bounds on coordinates of the outcome of an arbitrary efficient variant corresponding to preference information expressed by the Decision Maker. In contrast to earlier works on that subject, here lower and upper bounds can be calculated and their accuracy controlled entirely within evolutionary computation framework. This is made possible by exploration of not only the region of feasible variants – a standard within evolutionary optimization, but also the region of i…
Scalable Hierarchical Clustering: Twister Tries with a Posteriori Trie Elimination
2015
Exact methods for Agglomerative Hierarchical Clustering (AHC) with average linkage do not scale well when the number of items to be clustered is large. The best known algorithms are characterized by quadratic complexity. This is a generally accepted fact and cannot be improved without using specifics of certain metric spaces. Twister tries is an algorithm that produces a dendrogram (i.e., Outcome of a hierarchical clustering) which resembles the one produced by AHC, while only needing linear space and time. However, twister tries are sensitive to rare, but still possible, hash evaluations. These might have a disastrous effect on the final outcome. We propose the use of a metaheuristic algor…