Modelling and solving grid resource allocation problem with network resources for workflow applications

A problem of allocating resources of a grid to workflow applications is considered. The problem consists, generally, in allocating distributed grid resources to tasks of a workflow in such a way that the resource demands of each task are satisfied. Grid resources are divided into computational resources and network resources. Computational tasks and transmission tasks of a workflow are distinguished. We present a model of the problem, and an algorithm for finding feasible resource allocations. A numerical example is included, showing the importance of the resource allocation phase on a grid. Some conclusions and directions for future research are given.     

Investigating acceptance toward mobile learning to assist individual knowledge management: Based on activity theory approach

Mobile devices could facilitate human interaction and access to knowledge resources anytime and anywhere. With respect to wide application possibilities of mobile learning, investigating learners’ acceptance towards it is an essential issue. Based on activity theory approach, this research explores positive factors for the acceptance of m-learning systems. In the research, we developed an m-learning system for learners’ knowledge management and invited 152 participants who knew how to use the m-learning system then report on their experience. The results show that enhancing learners’ satisfaction, encouraging learners’ autonomy, empowering system functions, and enriching interaction and communication activities have a significant positive influence on the acceptance of m-learning systems.     

Adaptive multi-scale segmentation of surface data using unsupervised learning of seed positions

This paper presents a method for multi-scale segmentation of surface data using scale-adaptive region growing. The proposed segmentation algorithm is initiated by an unsupervised learning of optimal seed positions through the surface attribute clustering with a two-criterion score function. The seeds are selected as consecutive local maxima of the clustering map, which is computed by an aggregation of the local isotropic contrast and local variance maps. The proposed method avoids typical segmentation errors caused by an inappropriate choice of seed points and thresholds used in the region-growing algorithm. The scale-adaptive threshold estimate is based on the image local statistics in the neighborhoods of seed points. The performance of this method was evaluated on LiDAR surface images.     

TSK fuzzy modeling for tool wear condition in turning processes: An experimental study

This paper presents an experimental study for turning process in machining by using Takagi-Sugeno-Kang (TSK) fuzzy modeling to accomplish the integration of multi-sensor information and tool wear information. It generates fuzzy rules directly from the input-output data acquired from sensors, and provides high accuracy and high reliability of the tool wear prediction over a wide range of cutting conditions. The experimental results show its effectiveness and satisfactory comparisons relative to other artificial intelligence methods.     

A Fast Algorithm for Adaptive Prefix Coding

In this paper we present a new algorithm for adaptive prefix coding. Our algorithm encodes a text S of m symbols in O(m) time, i.e., in O(1) amortized time per symbol. The length of the encoded string is bounded above by (H+1)m+O(nlog ² m) bits where n is the alphabet size and H is the entropy. This is the first algorithm that adaptively encodes a text in O(m) time and achieves an almost optimal bound on the encoding length in the worst case. Besides that, our algorithm does not depend on an explicit code tree traversal. A preliminary version of this paper appeared in the Proceedings of the 2006 IEEE International Symposium on Information Theory (ISIT 2006). M. Karpinski’s work partially supported by a DFG grant, Max-Planck Research Prize, and IST grant 14036 (RAND-APX). Y. Nekrich’s work partially supported by IST grant 14036 (RAND-APX).     

Integrating working and enrichment approach learning: a document

Integrating working and learning is seen as a desirable alternative to traditional training regimes. An integrated approach to working and learning is more gradual, contextual, problem oriented and cost effective. Knowledge technology aims to catalyze workplace learning, but requires the right organisational culture and brings additional costs regarding the articulation, representation and transmission of knowledge. Our approach reduces these costs by making articulation a natural part of collaborative working, designing for both informal and formal knowledge, and facilitating the transition of socially situated knowledge through enriched documents. To be successful, our approach has certain prequesites concerning organisational culture, and the nature of shared documents, organisational knowledge and work activities.     

Building ensemble classifiers using belief functions and OWA operators

A pervasive task in many forms of human activity is classification. Recent interest in the classification process has focused on ensemble classifier systems. These types of systems are based on a paradigm of combining the outputs of a number of individual classifiers. In this paper we propose a new approach for obtaining the final output of ensemble classifiers. The method presented here uses the Dempster–Shafer concept of belief functions to represent the confidence in the outputs of the individual classifiers. The combing of the outputs of the individual classifiers is based on an aggregation process which can be seen as a fusion of the Dempster rule of combination with a generalized form of OWA operator. The use of the OWA operator provides an added degree of flexibility in expressing the way the aggregation of the individual classifiers is performed.     

First-order syntactic characterizations of minimal entailment,domain-minimal entailment,and Herbrand entailment

This paper investigates a consistent versioncwa _s of Reiter's closed-world assumptioncwa. It provides a syntactic characterization of minimal entailment ? _min : for every ∨-sentence ? and for every ∨-theory Σ, $$\varphi \varepsilon cwa_S \left( \sum \right)iff\sum \vdash _{min} \varphi .$$ A version of this characterization remains valid if not all relations are subject to minimization. These two characterizations do not use the domain-closure axiom nor the unique-names assumptionuna, although they may be easily modified to ones that incorporateuna. A similar result for Herbrand entailment ? _Her , by means of generalized domain-closure axiomdca _s , is provided: for every ∨-sentence ? and every ∨-theory Σ, $$\varphi \varepsilon dca_S \left( \sum \right)iff\sum \vdash _{Her} \varphi .$$ Finally, a syntactic characterization of domain-minimal entailment ? _dom in terms of a versionmda _s of minimal-domain assumption is shown: for every ∨-sentence ? and for every ∨-theory Σ, $$\varphi \varepsilon mda_S \left( \sum \right)iff\sum \vdash _{dom} \varphi .$$ The proving power of these entailments is then evaluated. In particular, it is shown that (1) neither ? _min nor its versions are strong enough to derive positive sentences from Σ unless they are first-order provable from Σ however, a double application of ? _min has enough power to derive such positive sentences; (2) ? _Her has the strength of infinitary rule of inference but cannot derive existential nor quantifier-free sentences from Σ unless they are first-order provable from Σ (3) ? _Her and ? _dom can derive from Σ certain positive facts about = which are otherwise unprovable from Σ and (4) ? _dom cannot derive from Σ sentences without positive occurrences of = unless they are first-order provable from Σ. Moreover, the paper relatescwa _s to Reiter'scwa and to Minker's generalized closed-world assumptionGCWA and its extension.