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Equality of access has been an issue for computer-based testing since its earliest days and has become more important with the advent of internetbased testing Yet, one has to wonder whether it was ever really a problem The assumption has been that those who can afford their own access will be at an advantage, but it is usually only low stakes testing which is readily available on the internet As soon as the stakes are higher, controls need to be put in place that constrain access The highest stakes testing nearly always requires supervision in test administration centres, so offering no access advantage to those with their own equipment The situation described by Melnick and Clauser for the USMLE tests seems fairly typical, with tests being offered at dedicated, secure testing sites either continuously or during discrete testing windows This requirement for control tends to be even more apparent for computer-based simulations, which, as well as supervised administration, also often need special equipment Access can affect participation in less direct ways, however There are many examples of tests available on the internet, even if they are of variable quality Many test publishers provide practice tests on their web sites Therefore, individuals with ready access to the internet will have many more opportunities to practice a variety of test types On the other hand, there are not many, if any, practice simulations available on the internet, so there is no direct advantage there Perhaps the greatest advantage will come in the reduction of anxiety, which Melnick and Clauser note seems to become apparent whenever new computer-based testing programmes are introduced These arguments do not extend to all aspects of selection and recruitment For example, many companies and recruitment agencies now require applicants to apply using electronic forms or to email CVs via the internet If this is the main or only way of attracting applicants, it is likely that it may be a biased sample of candidates ho make it through to subsequent selection stages Indirect discrimination is a more serious issue Experienced users of computers are likely to be at an advantage, particularly with simulations that can make signi cant demands on skills such as screen navigation, cursor movement, use of graphical interfaces and the like More importantly, there are known to be signi cant differences between groups in their ability to handle and process material on screen For example, it is well known that females are more likely than males to suffer from pseudomotion sickness, sometimes called cyber-sickness, particularly where motion is simulated using visual cues that are not supported by full-motion simulation (Kass, Ahlers, & Dugger, 1998; Kennedy and Fowlkes, 1992) Likewise, people who are not good at the mental manipulation of representations of three dimensional objects in two dimensions can have problems coping with screen-based simulations Often their ability to estimate distances and directions is markedly affected When such problems are recognised, the proposed solutions are often ad hoc and only partially successful One train operating company in the United Kingdom that identi ed the pseudo-motion sickness problem found that some.

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of a given attack, are useful for detecting events of the attack Such data variables are identi ed for each of the eleven attacks in s 8 11 A feature is a measure of a property which exists in a sample of data bservations for one or multiple data variables Only univariate mathematical/statistical features features of a data sample from one data variable are investigated in Part III These univariate mathematical/ statistical features include the statistical mean in 8, the probability distribution in 9, the autocorrelation in 10, and the wavelet-based signal strength in 11 covering the Haar wavelet, the Daubechies wavelet, the Derivative of Gaussian wavelet, the Paul wavelet and the Morlet wavelet These wavelets are used to extract the time-frequency signal changes associated with the data patterns of step change, steady change, random change, spike change and sine-cosine wave with noise s 8 11 provide mathematical/statistical methods of extracting the mean, probability distribution, autocorrelation, and wavelet features from attack and normal use data Among the four features, the distribution feature gives a more comprehensive picture of a data sample than the mean feature Both the wavelet feature and the autocorrelation feature reveal relations of data observations over time The autocorrelation feature focuses on the general autocorrelation aspect of time series data, whereas the wavelet feature focuses on special forms of time-frequency data patterns Both various wavelet forms and various probability distributions are linked to certain data patterns The distribution feature describes the general pattern of the data, whereas the wavelet feature reveals time locations and frequencies of special data patterns Hence, the wavelet feature reveals more special data features than the distribution feature and the autocorrelation feature Note that there are other types of univariate features (eg, features extracting other trends or patterns of data) as well as multivariate features (ie, features of data from multiple data variables) which are not investigated in Part III but may be useful in revealing data characteristics of various attacks If one or more events of a given attack cause a signi cant change in a speci c feature of a data variable, this change is considered a data characteristic of the attack s 8 11 describe statistical tests to identify a signi cant change in a given data feature, and reveal data characteristics of eleven attacks in the mean, probability distribution, autocorrelation, and wavelet features If a speci c data characteristic appears during a given attack but not during other attacks or normal use activities, this data characteristic is considered a unique data characteristic of that attack and can be used to uniquely detect and identify this attack Note that an event may manifest through more than one data characteristic (eg, more than one data variable or more than one feature of a data variable) The identi ed attack characteristics in the mean, distribution, autocorrelation and wavelet features are used to uncover the similarity and difference of the attacks The data characteristics of attack and normal use activities discovered in Part III are essential to building attack detection models for detection accuracy and earliness Attack detection models are covered in Parts IV VI.

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Recently the OWL-DL and SWRL (Semantic Web Rule Language3) reasoning engine KAON24 has been added to the KAON landscape of tools. he extension of the KAON tool suit is part of the SEKT project. Protege (Noy et al., 2000) is a well-established ontology editor with a large user community. It was the rst editor with an extensible plug-in structure and it relies on the frame paradigm for modeling. Numerous plug-ins from external developers exist. It also supports current standards like RDF(S) and OWL. Recently support for axioms was added ge through the PAL tab (Prote axiom language, cf. Hou et al., 2002). WebODE (Arpirez et al., 2001) is an ontology engineering workbench that provides various services for ontology engineering. It is accompanied by a sophisticated methodology of ontology engineering, viz. METHONTOLOGY. WebODE is purely web-based and is built on top of an application server. For inferencing services it relies on Prolog. It provides basic translators to current standards such as RDF(S) and OWL. OntoEdit (Sure et al., 2002, 2003) supports explicitly the OTK Methodology. The open plug-in framework enables the integration of a number of extensions to the basic ontology management services OntoEdit provides. In particular OntoEdit offers advanced support for collaboration and integration of the inferencing capabilities. A reimplementation of OntoEdit based on the Eclipse5 framework has recently been made available under the new name OntoStudio. Using Barcode encoder for VS .NET Control to generate, create Code 39 Full SCII image in .NET framework applications.Related: Make EAN-8 .NET , Creating UPC-E .NET , ISBN Creating .NET

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