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Found 25 papers, 9 papers with code
Cross Domain Early Crop Mapping using CropGAN and CNN Classifier
Instead, it learns a mapping function to transform the spectral features of the target domain to the source domain (with labels) while preserving their local structure.
Know Your Model (KYM): Increasing Trust in AI and Machine Learning
The widespread utilization of AI systems has drawn attention to the potential impacts of such systems on society.
Frontrunner Jones and the Raiders of the Dark Forest: An Empirical Study of Frontrunning on the Ethereum Blockchain
Ethereum prospered the inception of a plethora of smart contract applications, ranging from gambling games to decentralized finance.
Cryptography and Security
Oversampling Tabular Data with Deep Generative Models: Is it worth the effort?
In practice, machine learning experts are often confronted with imbalanced data.
Minority Class Oversampling for Tabular Data with Deep Generative Models
In practice, machine learning experts are often confronted with imbalanced data.
A Data Science Approach for Honeypot Detection in Ethereum
To this end, we add transaction aggregated features, such as the number of transactions and the corresponding mean value and other contract features, for example compilation information and source code length.
SynGAN: Towards Generating Synthetic Network Attacks using GANs
SynGAN generates malicious packet flow mutations using real attack traffic, which can improve NIDS attack detection rates.
MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning
We propose MQLV, Modified Q-Learner for the Vasicek model, a new reinforcement learning approach that determines the optimal policy of money management based on the aggregated financial transactions of the clients.
Visualization of AE's Training on Credit Card Transactions with Persistent Homology
PHom-WAE minimizes the Wasserstein distance between the true distribution and the reconstructed distribution and uses persistent homology, the study of the topological features of a space at different spatial resolutions, to compare the nature of the latent manifold and the reconstructed distribution.
User-Device Authentication in Mobile Banking using APHEN for Paratuck2 Tensor Decomposition
Nonetheless, the retail banks are looking to leverage the user-device authentication on the mobile banking applications to enhance the personal financial advertisement.
PHom-GeM: Persistent Homology for Generative Models
PHom-GeM minimizes an objective function between the true and the reconstructed distributions and uses persistent homology, the study of the topological features of a space at different spatial resolutions, to compare the nature of the true and the generated distributions.
Predicting Sparse Clients' Actions with CPOPT-Net in the Banking Environment
The digital revolution of the banking system with evolving European regulations have pushed the major banking actors to innovate by a newly use of their clients' digital information.
Improving Missing Data Imputation with Deep Generative Models
Datasets with missing values are very common on industry applications, and they can have a negative impact on machine learning models.
The Art of The Scam: Demystifying Honeypots in Ethereum Smart Contracts
We identify 690 honeypot smart contracts as well as 240 victims in the wild, with an accumulated profit of more than $90, 000 for the honeypot creators.
Cryptography and Security
Generating Multi-Categorical Samples with Generative Adversarial Networks
We propose a method to train generative adversarial networks on mutivariate feature vectors representing multiple categorical values.
Impact of Biases in Big Data
In this work, we provide a review of different sorts of biases in (big) data sets in machine learning.
On the Reduction of Biases in Big Data Sets for the Detection of Irregular Power Usage
Our models have the potential to generate significant economic value in a real world application, as they are being deployed in a commercial software for the detection of irregular power usage.
Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations
Second, in order to allow human experts to feed their knowledge in the decision loop, we propose a method for visualizing prediction results at various granularity levels in a spatial hologram.
Human in the Loop: Interactive Passive Automata Learning via Evidence-Driven State-Merging Algorithms
We present an interactive version of an evidence-driven state-merging (EDSM) algorithm for learning variants of finite state automata.
The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study
It reveals new and up-to-date insights into what the 10 most prolific topics in machine learning research are.
Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?
We apply it to a commercial data set from Brazil that consists of 3. 6M customers and 820K inspection results.
Interpreting Finite Automata for Sequential Data
Automaton models are often seen as interpretable models.
Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets
Using the neighborhood features instead of only analyzing the time series has resulted in appreciable results for Big Data sets for varying NTL proportions of 1%-90%.
The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science.
Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets
We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets.
