References of "Camino, Ramiro Daniel 50024963"
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See detailA Data Science Approach for Honeypot Detection in Ethereum
Camino, Ramiro Daniel UL; Ferreira Torres, Christof UL; Baden, Mathis UL et al

in 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (2020, August 17)

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See detailWorking with Deep Generative Models and Tabular Data Imputation
Camino, Ramiro Daniel UL; Hammerschmidt, Christian UL; State, Radu UL

Scientific Conference (2020, July 17)

Datasets with missing values are very common in industry applications. Missing data typically have a negative impact on machine learning models. With the rise of generative models in deep learning, recent ... [more ▼]

Datasets with missing values are very common in industry applications. Missing data typically have a negative impact on machine learning models. With the rise of generative models in deep learning, recent studies proposed solutions to the problem of imputing missing values based various deep generative models. Previous experiments with Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) showed promising results in this domain. Initially, these results focused on imputation in image data, e.g. filling missing patches in images. Recent proposals addressed missing values in tabular data. For these data, the case for deep generative models seems to be less clear. In the process of providing a fair comparison of proposed methods, we uncover several issues when assessing the status quo: the use of under-specified and ambiguous dataset names, the large range of parameters and hyper-parameters to tune for each method, and the use of different metrics and evaluation methods. [less ▲]

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See detailGenerating Multi-Categorical Samples with Generative Adversarial Networks
Camino, Ramiro Daniel UL; Hammerschmidt, Christian UL; State, Radu UL

Scientific Conference (2018, July)

We propose a method to train generative adversarial networks on mutivariate feature vectors representing multiple categorical values. In contrast to the continuous domain, where GAN-based methods have ... [more ▼]

We propose a method to train generative adversarial networks on mutivariate feature vectors representing multiple categorical values. In contrast to the continuous domain, where GAN-based methods have delivered considerable results, GANs struggle to perform equally well on discrete data. We propose and compare several architectures based on multiple (Gumbel) softmax output layers taking into account the structure of the data. We evaluate the performance of our architecture on datasets with different sparsity, number of features, ranges of categorical values, and dependencies among the features. Our proposed architecture and method outperforms existing models. [less ▲]

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See detailGAN Applications with Discrete Data
Camino, Ramiro Daniel UL

Poster (2018, June 27)

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See detailOn non-parametric models for detecting outages in the mobile network
Falk, Eric UL; Camino, Ramiro Daniel UL; State, Radu UL et al

in Integrated Network and Service Management 2017 (2017, May)

The wireless/cellular communications network is composed of a complex set of interconnected computation units that form the mobile core network. The mobile core network is engineered to be fault tolerant ... [more ▼]

The wireless/cellular communications network is composed of a complex set of interconnected computation units that form the mobile core network. The mobile core network is engineered to be fault tolerant and redundant; small errors that manifest themselves in the network are usually resolved automatically. However, some errors remain latent, and if discovered early enough can provide warnings to the network operator about a pending service outage. For mobile network operators, it is of high interest to detect these minor anomalies near real-time. In this work we use performance data from a 4G-LTE network carrier to train two parameter-free models. A first model relies on isolation forests, and the second is histogram based. The trained models represent the data characteristics for normal periods; new data is matched against the trained models to classify the new time period as being normal or abnormal. We show that the proposed methods can gauge the mobile network state with more subtlety than standard success/failure thresholds used in real-world networks today. [less ▲]

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See detailFinding Suspicious Activities in Financial Transactions and Distributed Ledgers
Camino, Ramiro Daniel UL; State, Radu UL; Montero, Leandro UL et al

in Proceedings of the 17th IEEE International Conference on Data Mining Workshops (ICDMW 2017) (2017)

Banks and financial institutions around the world must comply with several policies for the prevention of money laundering and in order to combat the financing of terrorism. Nowadays, there is a raise in ... [more ▼]

Banks and financial institutions around the world must comply with several policies for the prevention of money laundering and in order to combat the financing of terrorism. Nowadays, there is a raise in the popularity of novel financial technologies such as digital currencies, social trading platforms and distributed ledger payments, but there is a lack of approaches to enforce the aforementioned regulations accordingly. Software tools are developed to detect suspicious transactions usually based on knowledge from experts in the domain, but as new criminal tactics emerge, detection mechanisms must be updated. Suspicious activity examples are scarce or nonexistent, hindering the use of supervised machine learning methods. In this paper, we describe a methodology for analyzing financial information without the use of ground truth. A user suspicion ranking is generated in order to facilitate human expert validation using an ensemble of anomaly detection algorithms. We apply our procedure over two case studies: one related to bank fund movements from a private company and the other concerning Ripple network transactions. We illustrate how both examples share interesting similarities and that the resulting user ranking leads to suspicious findings, showing that anomaly detection is a must in both traditional and modern payment systems. [less ▲]

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