“Network infrastructure is directly tied to the
ability to gain competitive advantage in the
marketplace, today and in the future.”Data communications technologies are evolving and expanding at an unparalleled rate. The growth in demand for Internet access and intranet services continues to fuel rapid technical adaptation by both implementers and developers. Unfortunately,...
This book is for anyone who is using InfoMaker® to work with data. Although the book does not assume you have knowledge about any particular topic, having some familiarity with relational databases and SQL is helpful. Consult books on these topics as needed.Server, server or end system  (English: server computer, end system) is a networked computer, a static IP, with high processing power and on which it is installed set the software to cater to other computers (clients) to request access to the services provided...
Model validation is the problem of deciding whether observed data are consistent
with a nominal model . Change detection based on model validation aims
at applying a consistency test in one of the following ways:
Consider a batch of data over a sliding window, collected in a measurement
vector Y and input vector U. As in Chapter 6, the idea of a consistency
test is to apply a linear transformation to a batch of data, AiY + BiU + ci.
The matrices Ai, Bi and vector G are chosen so that the norm of the linear
transformation is small when there is no change/fault according to hypothesis
Hi, and large when fault Hi has appeared.
This chapter provides background information and problem descriptions
of the applications treated in this book . Most of the applications include
real data and many of them are used as case studies examined throughout the
book with different algorithms
This chapter surveys off-line formulations of single and multiple change point
estimation . Although the problem formulation yields algorithms that process
data batch.wise, many important algorithms have natural on-line implementations
and recursive approximations . This chapter is basically a projection of
the more general results in Chapter 7 to the case of signal estimation . There
are, however. some dedicated algorithms for estimating one change point offline
that apply to the current case of a scalar signal model . In the literature
of mathematical statistics.
In most digital data transmission systems the dispersive linear channel encountered exhibits amplitude and phase distortion. As a result, the received signal is contaminatedby Intersymbo1 Interference (ISI). In a system, which transmits a sequenceof pulse-shaped information symbols, the time domain full response signalling pulses are smeared hostile dispersive by the channel, resulting in intersymbol interference. At the receiver, the linearly distorted signal has to be equalized in order torecover the information....
High data rate communications are limited not only by noise, --butespecially with increas(ISI) ing symbolrates -often more significantly by the Inter Symbol Interference due to the memory of the dispersive wireless communications channel . Explicitly, this channel memory is caused by the dispersive channel impulse response (CIR) due to the differentlength propagation paths between the transmitting and the receiving antennae. This dispersion effect could theoretically be measured by transmitting an infinitely short impulse and “receiving” the CIR itself....
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Data granularity: The data within a P2P system can be accessible at many degrees of granularity. At the atomic
granularity level, data consists of a collection of indivisible objects, e.g., complete MP3 ﬁles. For data placement at
this level, we have to either place an entire object at a peer, or not at all; this is the semantics currently supported by
today’s P2P systems. At the hierarchical granularity level, sets of objects can be grouped into larger objects, thus
We consider a semi-supervised setting for domain adaptation where only unlabeled data is available for the target domain. One way to tackle this problem is to train a generative model with latent variables on the mixture of data from the source and target domains. Such a model would cluster features in both domains and ensure that at least some of the latent variables are predictive of the label on the source domain.
We consider the problem of correcting errors made by English as a Second Language (ESL) writers and address two issues that are essential to making progress in ESL error correction - algorithm selection and model adaptation to the ﬁrst language of the ESL learner. A variety of learning algorithms have been applied to correct ESL mistakes, but often comparisons were made between incomparable data sets. We conduct an extensive, fair comparison of four popular learning methods for the task, reversing conclusions from earlier evaluations. ...
We consider a very simple, yet effective, approach to cross language adaptation of dependency parsers. We ﬁrst remove lexical items from the treebanks and map part-of-speech tags into a common tagset. We then train a language model on tag sequences in otherwise unlabeled target data and rank labeled source data by perplexity per word of tag sequences from less similar to most similar to the target. We then train our target language parser on the most similar data points in the source labeled data. ...
Mining bilingual data (including bilingual sentences and terms1) from the Web can benefit many NLP applications, such as machine translation and cross language information retrieval. In this paper, based on the observation that bilingual data in many web pages appear collectively following similar patterns, an adaptive pattern-based bilingual data mining method is proposed.
We investigate a recently proposed Bayesian adaptation method for building style-adapted maximum entropy language models for speech recognition, given a large corpus of written language data and a small corpus of speech transcripts. Experiments show that the method consistently outperforms linear interpolation which is typically used in such cases.
We present a pointwise approach to Japanese morphological analysis (MA) that ignores structure information during learning and tagging. Despite the lack of structure, it is able to outperform the current state-of-the-art structured approach for Japanese MA, and achieves accuracy similar to that of structured predictors using the same feature set. We also ﬁnd that the method is both robust to outof-domain data, and can be easily adapted through the use of a combination of partial annotation and active learning. ...
We describe an approach to domain adaptation that is appropriate exactly in the case when one has enough “target” data to do slightly better than just using only “source” data. Our approach is incredibly simple, easy to implement as a preprocessing step (10 lines of Perl!) and outperforms stateof-the-art approaches on a range of datasets. Moreover, it is trivially extended to a multidomain adaptation problem, where one has data from a variety of different domains.
Domain adaptation is an important problem in natural language processing (NLP) due to the lack of labeled data in novel domains. In this paper, we study the domain adaptation problem from the instance weighting perspective. We formally analyze and characterize the domain adaptation problem from a distributional view, and show that there are two distinct needs for adaptation, corresponding to the different distributions of instances and classiﬁcation functions in the source and the target domains. ...
Creating large amounts of annotated data to train statistical PCFG parsers is expensive, and the performance of such parsers declines when training and test data are taken from different domains. In this paper we use selftraining in order to improve the quality of a parser and to adapt it to a different domain, using only small amounts of manually annotated seed data. We report signiﬁcant improvement both when the seed and test data are in the same domain and in the outof-domain adaptation scenario. ...