1,305,365 research outputs found
KM Maturity Factors Affecting High Performance in Universities
This paper aims to measure Knowledge Management Maturity (KMM) in the universities to determine the impact of knowledge
management on high performance. This study was applied on Al-Quds Open University in Gaza strip, Palestine. Asian
productivity organization model was applied to measure KMM. Second dimension which assess high performance was
developed by the authors. The controlled sample was (306). Several statistical tools were used for data analysis and hypotheses
testing, including reliability Correlation using Cronbach’s alpha, “ANOVA”, Simple Linear Regression and Step Wise
Regression.The overall findings of the current study suggest that KMM is suitable for measuring high performance. KMM
assessment shows that maturity level is in level three. Findings also support the main hypothesis and it is sub- hypotheses. The
most important factors effecting high performance are: Processes, KM leadership, People, KM Outcomes and Learning and
Innovation. Furthermore the current study is unique by the virtue of its nature, scope and way of implied investigation, as it is
the first comparative study in the universities of Palestine explores the status of KMM using the Asian productivity Model
Numerical Bayesian state assignment for a three-level quantum system. I. Absolute-frequency data; constant and Gaussian-like priors
This paper offers examples of concrete numerical applications of Bayesian
quantum-state-assignment methods to a three-level quantum system. The
statistical operator assigned on the evidence of various measurement data and
kinds of prior knowledge is computed partly analytically, partly through
numerical integration (in eight dimensions) on a computer. The measurement data
consist in absolute frequencies of the outcomes of N identical von Neumann
projective measurements performed on N identically prepared three-level
systems. Various small values of N as well as the large-N limit are considered.
Two kinds of prior knowledge are used: one represented by a plausibility
distribution constant in respect of the convex structure of the set of
statistical operators; the other represented by a Gaussian-like distribution
centred on a pure statistical operator, and thus reflecting a situation in
which one has useful prior knowledge about the likely preparation of the
system.
In a companion paper the case of measurement data consisting in average
values, and an additional prior studied by Slater, are considered.Comment: 23 pages, 14 figures. V2: Added an important note concerning
cylindrical algebraic decomposition and thanks to P B Slater, corrected some
typos, added reference
Automated post-fault diagnosis of power system disturbances
In order to automate the analysis of SCADA and digital fault recorder (DFR) data for a transmission network operator in the UK, the authors have developed an industrial strength multi-agent system entitled protection engineering diagnostic agents (PEDA). The PEDA system integrates a number of legacy intelligent systems for analyzing power system data as autonomous intelligent agents. The integration achieved through multi-agent systems technology enhances the diagnostic support offered to engineers by focusing the analysis on the most pertinent DFR data based on the results of the analysis of SCADA. Since November 2004 the PEDA system has been operating online at a UK utility. In this paper the authors focus on the underlying intelligent system techniques, i.e. rule-based expert systems, model-based reasoning and state-of-the-art multi-agent system technology, that PEDA employs and the lessons learnt through its deployment and online use
Knowledge Base Population using Semantic Label Propagation
A crucial aspect of a knowledge base population system that extracts new
facts from text corpora, is the generation of training data for its relation
extractors. In this paper, we present a method that maximizes the effectiveness
of newly trained relation extractors at a minimal annotation cost. Manual
labeling can be significantly reduced by Distant Supervision, which is a method
to construct training data automatically by aligning a large text corpus with
an existing knowledge base of known facts. For example, all sentences
mentioning both 'Barack Obama' and 'US' may serve as positive training
instances for the relation born_in(subject,object). However, distant
supervision typically results in a highly noisy training set: many training
sentences do not really express the intended relation. We propose to combine
distant supervision with minimal manual supervision in a technique called
feature labeling, to eliminate noise from the large and noisy initial training
set, resulting in a significant increase of precision. We further improve on
this approach by introducing the Semantic Label Propagation method, which uses
the similarity between low-dimensional representations of candidate training
instances, to extend the training set in order to increase recall while
maintaining high precision. Our proposed strategy for generating training data
is studied and evaluated on an established test collection designed for
knowledge base population tasks. The experimental results show that the
Semantic Label Propagation strategy leads to substantial performance gains when
compared to existing approaches, while requiring an almost negligible manual
annotation effort.Comment: Submitted to Knowledge Based Systems, special issue on Knowledge
Bases for Natural Language Processin
Semantic HMC for Big Data Analysis
Analyzing Big Data can help corporations to im-prove their efficiency. In
this work we present a new vision to derive Value from Big Data using a
Semantic Hierarchical Multi-label Classification called Semantic HMC based in a
non-supervised Ontology learning process. We also proposea Semantic HMC
process, using scalable Machine-Learning techniques and Rule-based reasoning
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