Finding an application-appropriate model for XML data warehouses

Decision support systems help the decision making process with the use of OLAP (On-Line Analytical Processing) and data warehouses. These systems allow the analysis of corporate data. As OLAP and data warehousing evolve, more and more complex data is being used. XML (Extensible Markup Language) is a flexible text format allowing the interchange and the representation of complex data. Finding an appropriate model for an XML data warehouse tends to become complicated as more and more solutions appear. Hence, in this survey paper we present an overview of the different proposals that use XML within data warehousing technology. These proposals range from using XML data sources for regular warehouses to those using full XML warehousing solutions. Some researches merely focus on document storage facilities while others present adaptations of XML technology for OLAP. Even though there are a growing number of researches on the subject, many issues still remain unsolved.     

Conferring human action recognition skills to life-like agents

Most of today's virtual environments are populated with some kind of autonomous life - like agents . Such agents follow a preprogrammed sequence of behaviors that excludes the user as a participating entity in the virtual society . In order to make inhabited virtual reality an attractive place for information exchange and social interaction , we need to equip the autonomous agents with some perception and interpretation skills . In this paper we present one skill: human action recognition . By opposition to human - computer interfaces that focus on speech or hand gestures , we propose a full - body integration of the user . We present a model of human actions along with a real - time recognition system . To cover the bilateral aspect in human - computer interfaces , we also discuss some action response issues . In particular , we describe a motion management library that solves animation continuity and mixing problems . Finally , we illustrate our systemwith two examples and discuss what we have learned .     

Robust distributed speech recognition in noise and packet loss conditions

This paper examines the performance of a Distributed Speech Recognition (DSR) system in the presence of both background noise and packet loss. Recognition performance is examined for feature vectors extracted from speech using a physiologically-based auditory model, as an alternative to the more commonly-used Mel Frequency Cepstral Coefficient (MFCC) front-end. The feature vectors produced by the auditory model are vector quantised and combined in pairs for transmission over a statistically modelled channel that is subject to packet burst loss. In order to improve recognition performance in the presence of noise, the speech is enhanced prior to feature extraction using Wiener filtering. Packet loss mitigation to compensate for missing features is also used to further improve performance. Speech recognition results show the benefit of combining speech enhancement and packet loss mitigation to compensate for channel and environmental degradations.     

Evolving General Term-Weighting Schemes for Information Retrieval: Tests on Larger Collections

Term-weighting schemes are vital to the performance of Information Retrieval models that use term frequency characteristics to determine the relevance of a document. The vector space model is one such model in which the weights assigned to the document terms are of crucial importance to the accuracy of the retrieval system. This paper describes a genetic programming framework used to automatically determine term-weighting schemes that achieve a high average precision. These schemes are tested on standard test collections and are shown to perform as well as, and often better than, the modern BM25 weighting scheme. We present an analysis of the schemes evolved to explain the increase in performance. Furthermore, we show that the global (collection wide) part of the evolved weighting schemes also increases average precision over idf on larger TREC data. These global weighting schemes are shown to adhere to Luhn’s resolving power as middle frequency terms are assigned the highest weight. However, the complete weighting schemes evolved on small collections do not perform as well on large collections. We conclude that in order to evolve improved local (within-document) weighting schemes it is necessary to evolve these on large collections     

Catalytic wet peroxide oxidation of phenol over Fe-exchanged pillared beidellite

This study presents an evaluation of the catalytic performances of a Fe-exchanged Al-pillared synthetic beidellite for the wet hydrogen peroxide oxidation of phenolic aqueous wastes. The catalyst was prepared by a cation doping technique, its properties being determined by DRX, BET and chemical analysis techniques. All the tests were performed on a laboratory scale set-up.Important factors affecting catalyst activity and phenol removal efficiencies were studied, i.e. the effect of pH, temperature, catalyst concentration and the stability of the catalyst. The experimental results indicate that the use of this catalyst allows a total elimination of phenol and a significant removal of chemical oxygen demand, without significant leaching of Fe ions. Thus, considering the lowest Fe concentrations in solution after oxidation, at pH=5, 50 degrees C, and 180 min. COD removal efficiency of 87.9% was obtained. It was also observed that by using this catalyst, it is possible to extend the range of pH values for which Fenton-type oxidations can occur.     

Using viscosity‐time plots of Escherichia coli cells undergoing chemical lysis to measure the impact of physiological changes occurring during batch cell growth

BACKGROUND: Viscosity–time plots for plasmid‐bearing E. coli cells undergoing alkaline lysis are reported in this study. The plots demonstrate generic features that reflect the progress of fermentation and allow an assessment of the genomic DNA denaturation following cellular release into the alkaline solution. This rheological analysis could offer useful insights to the state of fermentation or the selection of operational specifications and predictions of the performance of subsequent downstream operations. RESULTS: Studies showed a distinct change in the rheological profile throughout the batch fermentation, with different viscosity versus time profiles for lag, exponential and stationary microbial growth phases. The DNA denaturation time was found to increase with fermentation time from about 120 s after 3 h of fermentation to about 180 s after 7 h of fermentation. CONCLUSION: The increase of denaturation time was mainly caused by a rise in the genomic content of cells during the exponential growth phase. The viscosity–time profiles were found to provide a good indication of the cellular contents, reflecting the physiological changes occurring during a batch fermentation process. Copyright © 2009 Society of Chemical Industry     

An axiomatic comparison of learned term-weighting schemes in information retrieval: clarifications and extensions

Machine learning approaches to information retrieval are becoming increasingly widespread. In this paper, we present term-weighting functions reported in the literature that were developed by four separate approaches using genetic programming. Recently, a number of axioms (constraints), from which all good term-weighting schemes should be deduced, have been developed and shown to be theoretically and empirically sound. We introduce a new axiom and empirically validate it by modifying the standard BM25 scheme. Furthermore, we analyse the BM25 scheme and the four learned schemes presented to determine if the schemes are consistent with the axioms. We find that one learned term-weighting approach is consistent with more axioms than any of the other schemes. An empirical evaluation of the schemes on various test collections and query lengths shows that the scheme that is consistent with more of the axioms outperforms the other schemes.     

Motion constraint

In this paper, we propose a hybrid postural control approach taking advantage of data-driven and goal-oriented methods while overcoming their limitations. In particular, we take advantage of the latent space characterizing a given motion database. We introduce a motion constraint operating in the latent space to benefit from its much smaller dimension compared to the joint space. This allows its transparent integration into a Prioritized Inverse Kinematics framework. If its priority is high the constraint may restrict the solution to lie within the motion database space. We are more interested in the alternate case of an intermediate priority level that channels the postural control through a spatiotemporal pattern representative of the motion database while achieving a broader range of goals. We illustrate this concept with a sparse database of large range full-body reach motions.     

Combined Direct and Inverse Kinematic Control for Articulated Figure Motion Editing

A new approach for the animation of articulated figures is presented. We propose a system of articulated motion design which offers a full combination of both direct and inverse kinematic control of the joint parameters. Such an approach allows an animator to specify interactively goal-directed changes to existing sampled joint motions, resulting in a more general and expressive class of possible joint motions. The fundamental idea is to consider any desired-joint space motion as a reference model inserted into the secondary task of an inverse kinematic control scheme. This approach profits from the use of half-space Cartesian main tasks in conjunction with a parallel control of the articulated figure called the coach-trainee metaphor. In addition, a transition function is introduced so as to guarantee the continuity of the control. The resulting combined kinematic control scheme leads to a new methodology of joint-motion editing which is demonstrated through the improvement of a functional model of human walking.