### Table 1. Analysis of several ElGamal variants.

2001

"... In PAGE 11: ... Analysis of several ElGamal variants. ElGamal variant Type Attack Zheng and Seberry I, II, III [28] Decrypt-then-validate Yes Tsiounis and Yung [27] Validate-then-decrypt No Cramer and Shoup [8] Validate-then-decrypt No Fujisaki and Okamoto [12] Decrypt-then-validate Yes Fujisaki and Okamoto [13] Decrypt-then-validate Yes Pointcheval [20] Decrypt-then-validate Yes Baek, Lee, and Kim [1] Decrypt-then-validate Yes Schnorr and Jakobsson [23] Validate-then-decrypt No Okamoto and Pointcheval [19] Decrypt-then-validate Yes Table1 summarizes the cryptographic characteristics of the previously described schemes. According to the table, the \decrypt-then-validate quot;-type schemes are all susceptible to our extended attacks.... ..."

Cited by 1

### Table 1. Analysis of several ElGamal variants.

2001

"... In PAGE 11: ... Analysis of several ElGamal variants. ElGamal variant Type Attack Zheng and Seberry I, II, III [28] Decrypt-then-validate Yes Tsiounis and Yung [27] Validate-then-decrypt No Cramer and Shoup [8] Validate-then-decrypt No Fujisaki and Okamoto [12] Decrypt-then-validate Yes Fujisaki and Okamoto [13] Decrypt-then-validate Yes Pointcheval [20] Decrypt-then-validate Yes Baek, Lee, and Kim [1] Decrypt-then-validate Yes Schnorr and Jakobsson [23] Validate-then-decrypt No Okamoto and Pointcheval [19] Decrypt-then-validate Yes Table1 summarizes the cryptographic characteristics of the previously described schemes. According to the table, the decrypt-then-validate -type schemes are all susceptible to our extended attacks.... ..."

Cited by 1

### Table 1. Analysis of several ElGamal variants.

"... In PAGE 11: ... Analysis of several ElGamal variants. ElGamal variant Type Attack Zheng and Seberry I, II, III [28] Decrypt-then-validate Yes Tsiounis and Yung [27] Validate-then-decrypt No Cramer and Shoup [8] Validate-then-decrypt No Fujisaki and Okamoto [12] Decrypt-then-validate Yes Fujisaki and Okamoto [13] Decrypt-then-validate Yes Pointcheval [20] Decrypt-then-validate Yes Baek, Lee, and Kim [1] Decrypt-then-validate Yes Schnorr and Jakobsson [23] Validate-then-decrypt No Okamoto and Pointcheval [19] Decrypt-then-validate Yes Table1 summarizes the cryptographic characteristics of the previously described schemes. According to the table, the decrypt-then-validate -type schemes are all susceptible to our extended attacks.... ..."

### Table 5. Analysis of several RSA and ElGamal variants.

### Table 1: Number of Analysis Runs for Several De- signs of Experiments13

"... In PAGE 5: ... The response surface is limited to the second degree due to the high number of terms that arise from increasing the order for a large num- ber of inputs (see Equation 1). Table1 shows a summary of di erent design of experiments for- mulations and the required number of runs for a problem consisting of seven variables, where n is the number of factors in the equation. It is possible though to do a transformation on the variables to obtain higher order equations without running more cases.... ..."

### Table 2: Analysis of the results for the minimization of several test problems.

"... In PAGE 5: ... For each problem 100 runs were performed using the SPSO and the average performance is exhibited in terms of the mean value and standard deviation of the number of function evaluations, and the percentage of SPSO success. Results for all of the aforementioned problems can be seen in Table2 , while the size of the population used for each problem as well as the initial hypercube into which the initial population was randomly taken, are presented in Table 3.... In PAGE 6: ... There are about 760 local minima for this function and 18 global minima with function value f = ;176:542. As can be seen from Table2 , in 85 out of 100 cases PSO found the global minimum without any help, while in 15 cases it got stuckinalocalminimum and function \Stretching quot; has been successfully applied. Thus the success rate of PSO increased by 15%.... In PAGE 6: ... The Corana function in 4 dimensions is de ned by the equation: f(x)= 4 X j=1 8 gt; lt; gt; : 0:15 z j ; 0:05 sgn(z j ) 2 d j ;; if jx j ; z j j lt; 0:05;; d j x 2 j ;; otherwise;; where x j 2 [;1000;; 1000], d j =1;; 1000;; 10;; 100 and z j = j x j 0:2 +0:49999 k sgn(x j ) 0:2: and is a very di cult minimization problem for many methods. As can be seen from Table2 , plain PSO has only 74% success rate, but using SPSO the success rate increases to 100%. Similar results can be seen for the Freudenstein{Roth and Goldstein{Price functions, where the PSO success rate is increased by 40% and 5% respectively.... In PAGE 7: ...nterval [;1;; 1]. It is well known from the neural networks literature that successful training in this case, i.e. reaching a global minimizer, strongly depends on the initial weight values and that the above{mentioned function presentsamultitude of local minima [1]. It is obvious from the results reported in Table2 that the function \Stretching quot; technique helped to increase signi cantly the success percentage of the PSO, i.e.... ..."

### Table 14. Analysis of profile definition languages of several systems and applications

### Table 1. Multiattribute Utility Analysis for Several Outcomes for Strategies 1 and 2 for a Particular Sub-Group

"... In PAGE 15: ... could also be used, depending on the situation (Morgan and Henrion, 1990). To illustrate the MUA process, Table1 shows an example multiattribute utility analysis (MUA) for a few outcomes for the two strategies from Figure 4, MeHg Potential mitigation focus (Strategy 1) and mine site Hg load reduction focus (Strategy 2), as evaluated by a hypothetical decision maker sub-group. Other sub-groups could be expected to have different results.... In PAGE 22: ...ecision tree. There are many possibilities (see, e.g., Morgan and Henrion, 1990 and Clemen, 1996) and a lot can be learned about the decision problem using relatively simple methods. Optimal Decisions and Sensitivity Analysis Using Influence Diagrams With a Multiattribute Utility Value Model Figure 8 shows an influence diagram model with a multiattribute utility function used to determine optimal decisions, using utility values similar to those in Table1 for some hypothetical sub-group. For this simple example, a strategy with a high reduction for the mine site Hg load, a medium reduction for creek Hg load, and a medium reduction for mercury methylation potential is optimal in terms of maximizing expected utility.... ..."

### Table 4. CBP-based buffer merging and lifetime analysis applied to several practical SDF systems.

2004

"... In PAGE 13: ... We observe that a significant proportion of the actors ex- amined in Table 3 admit a CBP efficiency of 100%, while the CBP efficiencies of other actors can be sig- nificantly lower and heavily parameter-dependent. To illustrate the practical impact of CBP-based analysis, we provide in Table4 the overall buffer memory re- quirements from our hybrid SDF compiler that com- bines CBP-based buffer merging and lifetime analy- sis techniques for several practical systems specified... ..."

Cited by 2

### Table 1: Rand Index value reached by applying several mi- croarray data analysis tools.

"... In PAGE 6: ... The major unsupervised data analysis methods involved include: K-means, self-organizing maps (SOM), clustering algorithms based on the graph-partitioning paradigm (CLUTO) and the dimensionality reduction method PCA (principal component analysis). Table1 provides pattern matching result obtained by directly ap- plying the above algorithms to high gene-dimension data without an iterative process. All these algorithms were applied to the ma- Figure 3: Rand Index values for the Iterative Pattern-Discovery approach and comparison with other methods.... ..."