aGiven the large number of transactions from countries with different levels of financial development in our dataset, we are able to provide a direct test of several theoretical propositions on the effects private benefits of control have on the development of financial markets. 是特定的来自有在...
However, it is impossible to make sure we only identify cardinal numbers in such a large dataset, as doing so would require checking each number in its context of use. The concern that our dataset is ‘contaminated’ by non-cardinal numbers is, however, shared with all previous analyses of...
(ICW-SVM) to treat imbalance credit card transactions data.Bentley et al. (2000) proposed a neural fraud detection system with fraud density map to improve the detection efficiency in context of biased train data due to skewed distribution of data.Pozzolo et al. (2013) proposed to use a ...
2022, IEEE Transactions on Emerging Topics in Computing PERCIVAL: Open-Source Posit RISC-V Core With Quire Capability 2022, IEEE Transactions on Emerging Topics in Computing PositNN: Training deep neural networks with mixed low-precision posit 2021, ICASSP, IEEE International Conference on Acoustics,...
As there exist very few deep nonparametric methods, and some of them reported results only on extremely-small toy datasets [11, 66] (e.g., one of them stated they could not pro- cess even MNIST's train dataset as it was too large for them), we compared Deep...
On the other side, it is shown that interpolation is also an alternative for modeling. This modeling issue enables one to get the desired result without making heavy numerical calculations many times.Geridonmez, Bengisen PekmenTED UnivJournal of Heat and Mass Transfer: Transactions of the ASME...
The dataset has not be split into training and test sets, which we create ourselves, detailed later. Amazon-Google The Amazon-Google dataset consists of 1,363 tuples from Amazon and 3,226 tuples from Google, each a record of some product in their catalog or inventory. Between the two ...
We evaluate the mitigation techniques using the In-Lab dataset instead of the At-Home dataset, because with its higher inference success, the In-Lab dataset can better illustrate the effectiveness of the proposed mitigation techniques. We measure the performance of our framework under the influence ...
The dataset has not be split into training and test sets, which we create ourselves, detailed later. Amazon-Google The Amazon-Google dataset consists of 1,363 tuples from Amazon and 3,226 tuples from Google, each a record of some product in their catalog or inventory. Between the two ...
SHSE has a significant difference with the baselines across all datasets. SHSE not only always achieves the best performance on each dataset but also significantly outperforms each baseline on almost every dataset. On average, SHSE improves the performance over the baselines by 8.7%∼14.4% in...