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The proposed approach achieves promising results on several standard pedestrian detection benchmarks.
Moreover, OSLOM has performed well in recent community detection benchmarks [56] and it outperformed other algorithms we tested.
In tests against known error detection benchmarks, RTC found 98% of all memory related bugs and had zero false positives.
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Figure 9 shows similar patterns for the face detection benchmark.
The original tcpdump files were preprocessed for utilization of intrusion detection benchmark.
We achieve state-of-the-art performance on the common face detection benchmark datasets.
Intrusion detection benchmark dataset, NSL-KDD, consisting of 41 features is used to evaluate the efficiency of our proposed method in Waikato environment for knowledge analysis (Weka) [19].
We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier.
A DM-inspired methodology is applied to a fault detection benchmark provided by DAMADICS to compare its relative performance to others algorithms.
The proposed approach has been specialized and tested in three variants, each aimed at detecting one of the three categories of mandatory, prohibitory and danger traffic signs, according to the experimental setup of the recent German Traffic Sign Detection Benchmark competition.
On a 65 nm ASIC implementation VThreads achieves up to x7.2 performance increase on synthetic benchmarks, x5 on a parallel Mandelbrot implementation, 66% better on a threaded JPEG implementation, 79% better on an edge-detection benchmark and ∼13% improvement on DES compared to the Leon3MP CMP.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com