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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 applica...
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The Eye of Horus: Spotting and Analyzing Attacks on Ethereum Smart Contracts
In recent years, Ethereum gained tremendously in popularity, growing fro...
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Towards Smart Hybrid Fuzzing for Smart Contracts
Smart contracts are Turing-complete programs that are executed across a ...
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Minority Class Oversampling for Tabular Data with Deep Generative Models
In practice, data scientists are often confronted with imbalanced data. ...
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Blockchain Governance: An Overview and Prediction of Optimal Strategies using Nash Equilibrium
Blockchain governance is a subject of ongoing research and an interdisci...
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A Data Science Approach for Honeypot Detection in Ethereum
Ethereum smart contracts have recently drawn a considerable amount of at...
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SynGAN: Towards Generating Synthetic Network Attacks using GANs
The rapid digital transformation without security considerations has res...
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PHom-GeM: Persistent Homology for Generative Models
Generative neural network models, including Generative Adversarial Netwo...
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User-Device Authentication in Mobile Banking using APHEN for Paratuck2 Tensor Decomposition
The new financial European regulations such as PSD2 are changing the ret...
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Non-Negative PARATUCK2 Tensor Decomposition Combined to LSTM Network For Smart Contracts Profiling
Smart contracts are programs stored and executed on a blockchain. The Et...
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Infer Your Enemies and Know Yourself, Learning in Real-Time Bidding with Partially Observable Opponents
Real-time bidding, as one of the most popular mechanisms for selling onl...
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Improving Missing Data Imputation with Deep Generative Models
Datasets with missing values are very common on industry applications, a...
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Generating Multi-Categorical Samples with Generative Adversarial Networks
We propose a method to train generative adversarial networks on mutivari...
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Impact of Biases in Big Data
The underlying paradigm of big data-driven machine learning reflects the...
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On the Reduction of Biases in Big Data Sets for the Detection of Irregular Power Usage
In machine learning, a bias occurs whenever training sets are not repres...
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Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations
Power grids are critical infrastructure assets that face non-technical l...
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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 (E...
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The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study
Which topics of machine learning are most commonly addressed in research...
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Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?
Non-technical losses (NTL) occur during the distribution of electricity ...
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Interpreting Finite Automata for Sequential Data
Automaton models are often seen as interpretable models. Interpretabilit...
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Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets
Electricity theft is a major problem around the world in both developed ...
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Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets
Non-technical losses (NTL) such as electricity theft cause significant h...
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