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have to be created, but this will also have to result in changing the IP address of the node This can cause problems in some applications In addition, this approach does not lend itself easily to those cases where node addresses are not numbers but some meaningful strings In [31] the authors propose to make use of geographical nearness of the parties involved to solve the key management problem partially The authors assume that the various nodes execute the DH protocol (see 2) As discussed there, a problem with the DH protocol is the absence of authentication during the initial key agreement As a result, an active adversary can launch a man-in-the-middle attack on a system based on the DH protocol The authors propose to address this aspect of the DH protocol using physical presence The basic idea is simple The authors propose that users set up keying material or more precisely execute the initial key agreement aspect of the DH protocol when they are in the same location, since users are assumed to recognize each other The authors devise mechanisms to ensure the integrity of the parameters (derived from the random secret that each user selects) of the DH key agreement during this initial key agreement The shared key resulting from a successful key agreement can then be used even when the users are not in close proximity to protect communications between the two users Note that in this case a man-in-the-middle attack on the parameters of the DH protocol is assumed to be infeasible on account of the small geographical separation between the entities The authors proposed three mechanisms intended to ensure the integrity of the parameters of the DH key agreement A basic assumption for all three mechanisms is that the users are in very close proximity The rst mechanism proposes that both the users check the validity of the exchanged DH public parameters by comparing them directly For example, this comparison can be done by representing the DH public parameters as a string and then by looking at the screen of the party or reading aloud the values to be compared The string can also be formed by hashing the public key parameters to ensure extra security Thus, this solution belongs to the family of solutions requiring users to compare strings of words [32 35] also propose a similar method for key veri cation in which key hashes are converted to readable words or to an appropriate graphical representation The second mechanism requires the users to estimate the distance between them This could be done using a mechanism such as distance bounding (see 7) The measured distance appears on both displays, which is veri ed by the users Finally, the users also visually check to see if there are other users/devices in the same area If this is not the case, then the exchanged DH public parameters are accepted The third mechanism makes use of integrity codes to exchange the DH public parameters These codes ensure that it is not possible to block emitted signals without being detected As a result any modi cations to the DH public parameters are detected An advantage of this approach is that it can be used in situations where the users do not share any authentication information in advance A drawback is that the system depends on the users veri ng the keys or the keying material; devices in the same area might be visually undetectable due to their small size; people might be unreliable (for accidental or malicious reasons) when dealing with meaningless strings Perrig and Song [36] address this using the concept of hash visualization to improve the security of such systems In this case the users compare structured images instead of meaningless strings Of course, comparing complex images might also not be easy We would like.



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because time is on a non-negative scale, its distribution tends to have a short lower tail and a longer upper tail Lognormal distributions for response times have been applied used successfully by Schnipke and Scrams (1997), Thissen (1982), van der Linden, Scrams, and Schnipke (1999), van der Linden and van Krimpen-Stoop (2003) We will use a parameterization for this distribution similar to the one for the two-parameter logistic version of the response model in (21) proposed by van der Linden (in press) Let Tij denote the response time by examinee j 1; ; J on item i 1; ; I The proposed model for the distribution of Tij is i 1 2 f tij ; j ; i ; i p exp i ln tij i j 2 tij 2 2:20





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Subject: Alter Employee Deductions 1 PURPOSE AND SCOPE This procedure is used by the payroll clerk to alter employee deductions in the payroll system 2 RESPONSIBILITIES PR CLERK 3 PROCEDURES 31 PR CLERK Obtain Deduction Information 1 Obtain employee payroll deduction information from the human resources department 2 Verify that all information on the deduction forms are clear and that each one is authorized by the employee Update Payroll Database 1 Go into the payroll software and access the EMPLOYEE menu Go to the DEDUCT screen and enter the deduction code and the amount of the deduction for each documented deduction Be sure to enter a deduction termination code for those that are of limited duration 2 Verify that deductions are correctly allocated to each payroll period, so that the total amount of each deduction is accurate on a monthly or annual basis 3 Print the Updates Report from the option at the bottom of the screen to verify that the correct entries were made File Documentation Return all employee documentation to the human resources department, so that they can file it in employee folders Payroll Clerk

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which is the probability density for the normal distribution, with ln tij substituted for tij on the original scale (eg time measured in seconds) The model has an parameter j which represents the speed at which this examinee j responds to the items in the test A larger value of j implies a larger probability for a shorter time In addition, the model has two parameters, i and i , which parallel the item dif culty and discrimination parameters in (21) Parameter i represents the amount of time item i tends to require from the examinees The larger this parameter, the larger the amount of time each examinee tends to spend on the item It is therefore appropriate to call i the time-intensity parameter for item i Parameter i can be interpreted as a discrimination parameter A larger value for i means less dispersion for the response-time distribution of each examinee on item i, and hence better discrimination by the item between the distributions of two persons with levels of speed slightly above and below i The model in (220) does not have the equivalent of a guessing parameter; it does not need to have one because response times have a natural lower limit at zero The scale for parameters j and i in the model is not yet determined; an increase in the former can always be compensated by an increase in the latter We remove this indeterminacy by imposing the following identi ability constraint:

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