Experimental Results. To compare the actual performance, we implemented the four protocols and compared their costs in this section. We simulated the total computation delay from the time when the membership event happens to the time when group key agreement finishes. Average delay has been measured, since all members do not finish group key agreement simultaneously.
Experimental Results. This section describes the results of evaluating the agreement among the outputs of multiple LVCSR models as an estimate of confi- dence for each hypothesized word. Xxxxxx-SPOJUS Average Precision 98.0 Average Precision 96.8 Average Precision 96.1 Xxxxxx-Xxxxxx SPOJUS-SPOJUS with/without short pause states (Xxxxxx) units in HMMs (Xxxxxx) with/without short pause states (SPOJUS) frame shift lengths (SPOJUS) sampling frequencies (SPOJUS) feature parameters (SPOJUS) duration control / self loop (SPOJUS) 99 98 98 97 Precision (%) Precision (%) 97 96 95 95 94 94 93 0 20 40 60 80 100 Order sorted by Precision Order sorted by Precision
Experimental Results. As in the previous chapters, we tested our approach on the criminal record database (cf. Appendix B), that contains approximately one million offenders and as many criminal careers. Since a large number of the offenders in the list are one-time offenders, these individuals are left out of most of the calculation, greatly reducing the computation time. Furthermore, this class is also left out of the search for common subcareers, since only careers of length 1 are present in this class (except for a small percentage (<0.01%) that was erroneously classified as one-time offender). As a first test a simple search for common subcareers was performed that yielded the following results: Table 6.4: Amount of common subcareers discovered per threshold 50% 40% 30% 20% 10% 5% Subcareers Found 37 98 243 1,017 5,869 20,017 It appears that 30% is a reasonable threshold, yielding a manageable amount of ca- reers. Figure 6.5 shows clearly how the amount of discovered common subcareers rises in the graph even with an exponential vertical axis. The longest common subcareer we discovered when using a threshold of 30% was of length 7: 100000 10000 Amount 1000 100 10 1 50% 40% 30% 20% 10% 5% threshold Figure 6.5: The relation between threshold and number of discovered subcareers Minor Theft → Minor Theft → Minor Theft → Minor Theft → Major Theft → Major Theft → Major Theft Figures 6.6 and 6.7 show how the discovered common subcareers are distributed over length and how the amount changes with length. Figure 6.6: The distribution over length It might be interesting to know how many of the discovered subcareers occur in the different classes. Figure 6.8 shows the division over these classes. It turns out that, when added, they together support 628 common subcareers, being approximately 2.5 times the total amount. This means that an average discovered subcareer appears in 2.6 different classes. The standard deviation in this matter is 1.1. 140 120 100 Amount 80 60 40 20
Experimental Results. The detection, analysis, progression and prediction of criminal careers is an important part of automated law enforcement analysis [10, 11]. Our approach of temporal extrapo- lation was tested on the criminal record database (cf. Appendix B), containing approxi- mately one million offenders and their respective crimes As a first step we clustered 1000 criminals on their criminal careers, i.e., all the crimes they committed throughout their careers. In this test-case r will be set to 30. We employed a ten-fold cross validation technique within this group using all of the different extrapola- tion methods in this static visualization and compared them with each other and standard extrapolation on each of the attributes (methods 7 and 8). For each item in the test set we only consider the first 4 time periods. The accuracy is described as the percentage of individuals that was correctly classified (cf. Chapter 5, compared to the total amount of individuals under observation. The results are presented in Table 7.1, where time factor represents how many times longer the method took to complete its calculation than the fastest method under consideration. Although the calculation time needed for visual extrapolation is much less than that of regular methods, the accuracy is very comparable. For this database the best result is still a regular second degree extrapolation but its accuracy is just marginally higher than that of the spline extrapolation with a straight line, where its computation complexity is much higher. The simpler x,y system with third degree extrapolation has got a very low runtime complexity but still manages to reach an accuracy that is only 1.5 percentage points lower than the best performing method.
Experimental Results. 17 The Decay Chain Nd140 - Pr140 - Ce140 ... 17 Americium 241 ... 24 Lead 210 ... 35 Plutonium 239 ... 43 Uranium 237 ... 45 Protactinium 232 ... 49 Protactinium 233 ... 54 DISCUSSIONS AND CONCLUSIONS ... 66 ACKNOWLEDGMENTS ... 68 REFERENCES ... 69 LIST OF TABLES Table Page I. Extrapolated values of the Κ and L x-ray energies of the transuranium elements ... 15
Experimental Results. After the completion of each blast, a thorough inspection of each column was performed. Crack patterns were observed, and the damage to each column and its respective strengthening system was assessed. Crack sensor measurements were taken both before and after each blast, along with dynamic measurements on Column 1. It should be noted that on the two columns that were strengthened, cracks cannot be observed visually. For these columns, the coaxial cable crack sensors were used to locate cracks after each blast.
Experimental Results. First experiments showed a very high concentration of the larger particles measured by APS at the beginning of the generation, which were above the APS upper concentration limit (>> 103 particles/cm3). Thus, the APS was implemented a dilution line by a factor of 25, as drawn in Figure 4, to decrease aiborne particle concentration. Figure 5 present the evolution of the normalized number concentration of airborne particles measured by SMPS (a) and by APS (b) with a bypass flow rate of 8 L/min with the different agitation speeds (1500 and 2500 rpm). The results for SMPS measurement presented were obtained with only the diffusion corrections. Normalized Number Concentration (C / Ct=0) 6.5 6 5.5 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 a) Agitation speed = 1500 rpm Agitation speed = 2500 rpm 5 15 25 Time of generation-measurement (minute) Normalized Number Concentration (C / Ct=0) 7 6.5 6 5.5 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 b) Agitation speed =1500 rpm Agitation speed = 2500 rpm 5 15 25 Time of generation-measurement (minute) Figure 5. Evolution of the normalized number concentration (C / C t=0) of airborne particles measured by SMPS
Experimental Results. In all experiments we present here we used a single channel of the 3D-DRAM cube connected to the flexible bandwidth and burst length adaption interface of the controller. We considered in our analysis only a single channel to put emphasis on the interface and the power savings per channel. Thus the results can be scaled when multiple channels or slices [17] are used. A single channel allows us also a fair and valid comparison to LPDDR/LPDDR2 devices. However, we had to scale down the applied bandwidth to 750 MB/s for LPDDRx32-333 or LPDDR2x32-667 devices as they support only peak bandwidths up to 1.33 or 2.66 GB/s respectively.
Experimental Results. In the proposed approach a new key management system is used for secure communication. A new member can join and also existing member can get deleted from the group. The keys are updated automatically by using the group member. The keys are distributed before starting the transmissions. Group is created to join the node, sender will encrypt the message and the session key is placed in the header. The receiver will decrypt the key and also the encrypted message. In the receiver side the sender node, transmission times are displayed.
Experimental Results. RQ 1. For the first research question, we need to show that we can construct interactive verifiers from automatic verifiers, and that they can be useful in terms of effectiveness. By “interactive verifier”, we mean a verifier that can verify more programs correct if we feed it with invariants, for example, by annotating the input program with ACSL annotations. Using our building blocks from Sect. 3, an interactive verifier can be composed as illustrated in Fig. 4e (that is, configurations of the form ACSL2Witness|Witness2Assert|Verifier). For a meaningful evaluation we need a large number of annotated programs, which we would be able to get if we converted the witnesses from SV-COMP using Witness2ACSL in advance. But since the first component ACSL2Witness in Fig. 4e essentially does the inverse operation, we can generalize and directly consider witnesses as input, as illustrated in Fig. 4b (that is, configurations of the form Witness2Assert|Verifier). Now we look at the results in Table 1: The first row reports that cooperation improves the verifier 2ls in 179 cases, that is, there are 179 witnesses that contain information that helps 2ls to prove a program that it could not prove without the information. In other words, for 179 witnesses, we ran Witness2Assert to transform the original program to one in which the invariants from the witness were written as assertions, and 2ls was then able to verify the program. Since there are often several witnesses for the same program, 2ls verified in total 111 unique programs that it was not able to verify without the annotated invariants as assertion.
