Making on-line logistics training sustainable through e-learning

The purpose of this study is to investigate the possibility of using an online logistics certification learning environment as a training tool to equip future logisticians with required logistics skills. This study incorporates an online logistics certification website that was constructed for college students to familiarize themselves with the certification. In addition, this study also performed comparison tests on students before and after their interaction with the web-based learning environment system to ascertain the system’s effectiveness. Our findings suggest that such a system might motivate students to familiarize themselves with logistics-related certification information and can enhance students’ professional capabilities. In addition, the web-based learning environment might possibly motivate students to join logistics related industries in the future.     

A new semi-empirical sea spectrum for estimating the scattering coefficient

A large data base comprising SEASAT A satellite scatterometer measurements and quasi-concurrent high quality sea wind data, as represented by Britt and Schroeder (1984) in the form of a multivariate regression, has been used to tune the overall magnitude of the assumed sea spectrum by inverting the two scale scattering theory. Scattering coefficient values computed for the derived spectrum have been compared with those computed for the spectrum suggested by earlier authors, and an improvement is found in the surface wind-scattering coefficient relationship as verified by comparison with the independent AAFE RADSCAT data set.     

Separately balanced realization of two-dimensional separable-denominator transfer functions†

In this paper a method is developed for the computation of a separately balanced state-space model for a given two-dimensional transfer function with separable denominator. The procedure relies on the eigenvalue/eigenvector decomposition (or singular-value decomposition) of two real symmetric matrices determined directly from the given transfer function.     

Evaluation of haptic guidance virtual fixtures and 3D visualization methods in telemanipulation—a user study

Intelligent Service Robotics - This work presents a user-study evaluation of various visual and haptic feedback modes on a real telemanipulation platform. Of particular interest is the potential...     

An SDP Primal-Dual Algorithm for Approximating the Lovász-Theta Function

The Lovász ?-function (Lovász in IEEE Trans. Inf. Theory, 25:1–7, 1979) of a graph G=(V,E) can be defined as the maximum of the sum of the entries of a positive semidefinite matrix X, whose trace Tr(X) equals 1, and X _ij =0 whenever {i,j}∈E. This function appears as a subroutine for many algorithms for graph problems such as maximum independent set and maximum clique. We apply Arora and Kale’s primal-dual method for SDP to design an algorithm to approximate the ?-function within an additive error of δ>0, which runs in time $O(\frac{\vartheta ^{2} n^{2}}{\delta^{2}} \log n \cdot M_{e})$ , where ?=?(G) and M _e =O(n ³) is the time for a matrix exponentiation operation. It follows that for perfect graphs G, our primal-dual method computes ?(G) exactly in time O(? ² n ⁵logn). Moreover, our techniques generalize to the weighted Lovász ?-function, and both the maximum independent set weight and the maximum clique weight for vertex weighted perfect graphs can be approximated within a factor of (1+?) in time O(? ^?2 n ⁵logn).     

Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data

Siberia's boreal forests represent an economically and ecologically precious resource, a significant part of which is not monitored on a regular basis. Synthetic aperture radars (SARs), with their sensitivity to forest biomass, offer mapping capabilities that could provide valuable up-to-date information, for example about fire damage or logging activity. The European Commission SIBERIA project had the aim of mapping an area of approximately 1 million km² in Siberia using SAR data from two satellite sources: the tandem mission of the European Remote Sensing Satellites ERS-1/2 and the Japanese Earth Resource Satellite JERS-1. Mosaics of ERS tandem interferometric coherence and JERS backscattering coefficient show the wealth of information contained in these data but they also show large differences in radar response between neighbouring images. To create one homogeneous forest map, adaptive methods which are able to account for brightness changes due to environmental effects were required. In this paper an adaptive empirical model to determine growing stock volume classes using the ERS tandem coherence and the JERS backscatter data is described. For growing stock volume classes up to 80 m³/ha, accuracies of over 80% are achieved for over a hundred ERS frames at a spatial resolution of 50 m.     

Wireless LAN Security: Things You Should Know about WLAN Security

Given the well-publicised security holes in baseline wireless LAN (WLAN) standards, just how should security-conscious network managers proceed with safeguarding their wireless environments?     

Legal information retrieval for understanding statutory terms

In this work we study, design, and evaluate computational methods to support interpretation of statutory terms. We propose a novel task of discovering sentences for argumentation about the meaning of statutory terms. The task models the analysis of past treatment of statutory terms, an exercise lawyers routinely perform using a combination of manual and computational approaches. We treat the discovery of sentences as a special case of ad hoc document retrieval. The specifics include retrieval of short texts (sentences), specialized document types (legal case texts), and, above all, the unique definition of document relevance provided in detailed annotation guidelines. To support our experiments we assembled a data set comprising 42 queries (26,959 sentences) which we plan to release to the public in the near future in order to support further research. Most importantly, we investigate the feasibility of developing a system that responds to a query with a list of sentences that mention the term in a way that is useful for understanding and elaborating its meaning. This is accomplished by a systematic assessment of different features that model the sentences’ usefulness for interpretation. We combine features into a compound measure that accounts for multiple aspects. The definition of the task, the assembly of the data set, and the detailed task analysis provide a solid foundation for employing a learning-to-rank approach.

     

Bidding agents for online auctions with hidden bids

There is much active research into the design of automated bidding agents, particularly for environments that involve multiple decoupled auctions. These settings are complex partly because an agent’s strategy depends on information about other bidders’interests. When bidders’ valuation distributions are not known ex ante, machine learning techniques can be used to approximate them from historical data. It is a characteristic feature of auctions, however, that information about some bidders’valuations is systematically concealed. This occurs in the sense that some bidders may fail to bid at all because the asking price exceeds their valuations, and also in the sense that a high bidder may not be compelled to reveal her valuation. Ignoring these “hidden bids” can introduce bias into the estimation of valuation distributions. To overcome this problem, we propose an EM-based algorithm. We validate the algorithm experimentally using agents that react to their environments both decision-theoretically and game-theoretically, using both synthetic and real-world (eBay) datasets. We show that our approach estimates bidders’ valuation distributions and the distribution over the true number of bidders significantly more accurately than more straightforward density estimation techniques. Editors: Amy Greenwald and Michael Littman An earlier version of this work was presented at the Workshop on Game-Theoretic and Decision-Theoretic Agents (GTDT) 2005, Edinburgh, Scotland.     

Biometric recognition using 3D ear shape

Previous works have shown that the ear is a promising candidate for biometric identification. However, in prior work, the preprocessing of ear images has had manual steps and algorithms have not necessarily handled problems caused by hair and earrings. We present a complete system for ear biometrics, including automated segmentation of the ear in a profile view image and 3D shape matching for recognition. We evaluated this system with the largest experimental study to date in ear biometrics, achieving a rank-one recognition rate of 97.8 percent for an identification scenario and an equal error rate of 1.2 percent for a verification scenario on a database of 415 subjects and 1,386 total probes.