An ENO-Based Method for Second-Order Equations and Application to the Control of Dike Levels

This work aims to model the optimal control of dike heights. The control problem leads to so-called Hamilton-Jacobi-Bellman (HJB) variational inequalities, where the dike-increase and reinforcement times act as input quantities to the control problem. The HJB equations are solved numerically with an Essentially Non-Oscillatory (ENO) method. The ENO methodology is originally intended for hyperbolic conservation laws and is extended to deal with diffusion-type problems in this work. The method is applied to the dike optimisation of an island, for both deterministic and stochastic models for the economic growth.     

On behavioural pseudometrics and closure ordinals

A behavioural pseudometric is often defined as the least fixed point of a monotone function F on a complete lattice of 1-bounded pseudometrics. According to Tarski?s fixed point theorem, this least fixed point can be obtained by (possibly transfinite) iteration of F, starting from the least element ⊥ of the lattice. The smallest ordinal α such that $F^{\alpha }(\perp )=F^{\alpha +1}(\perp )$ is known as the closure ordinal of F. We prove that if F is also continuous with respect to the sup-norm, then its closure ordinal is ω. We also show that our result gives rise to simpler and modular proofs that the closure ordinal is ω.     

Intelligent product search with soft-boundary preference relaxation

This paper proposes a novel method for preference relaxation in online product search, which enables consumers to make quality choices without suffering from the commonly experienced information overload. In online shopping scenarios that involve multi-attribute choice tasks, it can be difficult for consumers to process the vast amounts of information available and to make satisfactory buying decisions. In such situations consumers are likely to eliminate potentially good choices early on, using hard-constraint filtering tools. Our approach uses edge sets to identify the alternatives on the soft boundary and the principle of alternative domination to suppress the alternatives on this boundary that are irrelevant. We demonstrate how our approach outperforms existing methods for product search in a set of simulations using two sets of 2650 car advertisements and 1813 digital cameras gathered from a popular online store.     

Machine learning for vessel trajectories using compression,alignments and domain knowledge

In this paper we present a machine learning framework to analyze moving object trajectories from maritime vessels. Within this framework we perform the tasks of clustering, classification and outlier detection with vessel trajectory data. First, we apply a piecewise linear segmentation method to the trajectories to compress them. We adapt an existing technique to better retain stop and move information and show the better performance of our method with experimental results. Second, we use a similarity based approach to perform the clustering, classification and outlier detection tasks using kernel methods. We present experiments that investigate different alignment kernels and the effect of piecewise linear segmentation in the three different tasks. The experimental results show that compression does not negatively impact task performance and greatly reduces computation time for the alignment kernels. Finally, the alignment kernels allow for easy integration of geographical domain knowledge. In experiments we show that this added domain knowledge enhances performance in the clustering and classification tasks.     

Flicker sensitivity measured with intermittent stimuli: II. Comparison between temporal gradient detection and the "De Lange" curves.

The threshold gradient for perception of a linear change in luminance with time has been measured as a function of the stimulus duration. These data are compared with the "De Lange" curves representing the flicker sensitivity measured with intermittent presentation of the stimulus as a function of the stimulus duration. For this purpose it is necessary to take into account the fact that a linear luminance gradient, being a relatively low-frequency signal, is affected less by the low-pass filter supposedly situated in the visual system than by a sinusoidal signal (flicker) of the same duration.     

Software release management for component‐based software

Software release management is the process through which software is made available to and obtained by its users. Until now, this process has been relatively straightforward. However, the emergence of component‐based software is complicating software release management. Increasingly, software is constructed via the assembly of pre‐existing, independently produced, and independently released components. Both developers and users of such software are affected by these complications. Developers need to accurately document the complex and changing dependencies among the components constituting the software. Users must be involved in locating, retrieving, and assembling components in order to appropriately bring the software into their particular environment. In this paper, we introduce the problem of release management for component‐based software and discuss SRM, a prototype software release management tool we have developed that supports both developers and users in the software release management process. Copyright © 2002 John Wiley & Sons, Ltd.     

Logic and the Dynamics of Information

We discuss how issues of information and computation interact with logic today, and what might be a natural extended agenda of investigation.     

ISO 9000 versus CMM: Standardization and certification of IS development

A growing number of software developers use standards as a basis for their quality systems. Some of them go one step further and have their quality systems certified. In this paper, two well-known quality standards, ISO 9000 and the CMM, are compared. It is concluded that both standards are useful but there is a growing need for more specific standards.     

Providing adaptive QoS to layered video over wireless local area networks through real-time retry limit adaptation

Robust streaming of video over 802.11 wireless LANs (WLANs) poses many challenges, including coping with packets losses caused by network buffer overflow or link erasures. In this paper, we propose a novel error protection method that can provide adaptive quality-of-service (QoS) to layered coded video by utilizing priority queueing at the network layer and retry-limit adaptation at the link layer. The design of our method is motivated by the observation that the retry limit settings of the MAC layer can be optimized in such a way that the overall packet losses that are caused by either link erasure or buffer overflow are minimized. We developed a real-time retry limit adaptation algorithm to trace the optimal retry limit for both the single-queue (or single-layer) and multiqueue (or multilayer) cases. The video layers are unequally protected over the wireless link by the MAC with different retry limits. In our proposed transmission framework, these retry limits are dynamically adapted depending on the wireless channel conditions and traffic characteristics. Furthermore, the proposed priority queueing discipline is enhanced with packet filtering and purging functionalities that can significantly save bandwidth by discarding obsolete or un-decodable packets from the buffer. Simulations show that the proposed cross-layer protection mechanism can significantly improve the received video quality.