Interleaved Sequence RNNs for Fraud Detection

Payment card fraud causes multibillion dollar losses for banks and merchants worldwide, often fueling complex criminal activities. To address this, many real-time fraud detection systems use tree-based models, demanding complex feature engineering systems to efficiently enrich transactions with historical data while complying with millisecond-level latencies. In this work, we do not require those expensive features by using recurrent neural networks and treating payments as an interleaved sequence, where the history of each card is an unbounded, irregular sub-sequence. We present a complete RNN framework to detect fraud in real-time, proposing an efficient ML pipeline from preprocessing to deployment. We show that these feature-free, multi-sequence RNNs outperform state-of-the-art models saving millions of dollars in fraud detection and using fewer computational resources.Comment: 9 pages, 4 figures, to appear in SIGKDD'20 Industry Trac

Improved Deep Forest Mode for Detection of Fraudulent Online Transaction

As the rapid development of online transactions, transaction frauds have also emerged seriously. The fraud strategies are characterized by specialization, industrialization, concealment and scenes. Anti-fraud technologies face many challenges under the trend of new situations. In this paper, aiming at sample imbalance and strong concealment of online transactions, we enhance the original deep forest framework to propose a deep forest-based online transaction fraud detection model. Based on the BaggingBalance method we propose, we establish a global sample imbalance processing mechanism to deal with the problem of sample imbalance. In addition, the autoencoder model is introduced into the detection model to enhance the representation learning ability. Via the three-month real online transactions data of a China's bank, the experimental results show that, evaluating by the metric of precision and recall rate, the proposed model has a beyond 10 % improvement compared to the random forest model, and a beyond 5 % improvement compared to the original deep forest model

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time series analytics is therefore crucial to unlocking the wealth of information implicit in available data. With the recent advancements in graph neural networks (GNNs), there has been a surge in GNN-based approaches for time series analysis. Approaches can explicitly model inter-temporal and inter-variable relationships, which traditional and other deep neural network-based methods struggle to do. In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: Forecasting, classification, anomaly detection, and imputation. Our aim is to guide designers and practitioners to understand, build applications, and advance research of GNN4TS. At first, we provide a comprehensive task-oriented taxonomy of GNN4TS. Then, we present and discuss representative research works and, finally, discuss mainstream applications of GNN4TS. A comprehensive discussion of potential future research directions completes the survey. This survey, for the first time, brings together a vast array of knowledge on GNN-based time series research, highlighting both the foundations, practical applications, and opportunities of graph neural networks for time series analysis.Comment: 27 pages, 6 figures, 5 table

Exploring variability in medical imaging

Although recent successes of deep learning and novel machine learning techniques improved the perfor- mance of classification and (anomaly) detection in computer vision problems, the application of these methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this is the amount of variability that is encountered and encapsulated in human anatomy and subsequently reflected in medical images. This fundamental factor impacts most stages in modern medical imaging processing pipelines. Variability of human anatomy makes it virtually impossible to build large datasets for each disease with labels and annotation for fully supervised machine learning. An efficient way to cope with this is to try and learn only from normal samples. Such data is much easier to collect. A case study of such an automatic anomaly detection system based on normative learning is presented in this work. We present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative models, which are trained only utilising normal/healthy subjects. However, despite the significant improvement in automatic abnormality detection systems, clinical routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis and localise abnormalities. Integrating human expert knowledge into the medical imaging processing pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per- spective of building an automated medical imaging system, it is still an open issue, to what extent this kind of variability and the resulting uncertainty are introduced during the training of a model and how it affects the final performance of the task. Consequently, it is very important to explore the effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as on the model’s performance in a specific machine learning task. A thorough investigation of this issue is presented in this work by leveraging automated estimates for machine learning model uncertainty, inter-observer variability and segmentation task performance in lung CT scan images. Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging was attempted. This state-of-the-art survey includes both conventional pattern recognition methods and deep learning based methods. It is one of the first literature surveys attempted in the specific research area.Open Acces

Modelo preditivo de risco de irregularidades em compras públicas no Estado de Goiás

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2020.O exercício eficaz do controle externo, dada as limitações de recursos públicos, deve acontecer em um cenário em que o esforço de fiscalização se concentra nos casos de maior probabilidade de risco de se encontrar irregularidades. No tocante às compras governamentais, na ausência de uma métrica objetiva de seleção de licitações para fins de auditoria, este trabalho apresenta uma proposta de modelo preditivo para estabelecer um indicador de risco de irregularidades em compras públicas em dois momentos da licitação: publicação do edital e disputa. A partir desse indicador foi construído um ranking de licitações com indícios de irregularidades que pode ser utilizado como ferramenta para tomada de decisão nas auditorias. Para isso, foram utilizadas licitações realizadas nos Estado de Goiás nos anos de 2014 à 2019, coletados em bases de dados que o TCE- GO tem acesso. Inicialmente foi realizado um estudo da fundamentação legal das irregularidades em compras públicas com vistas a identificar quais atributos que podem influenciar no risco do certame e suas respectivas bases de dados. A partir dos dados levantados, foram aplicados quatro métodos de seleção de atributos para identificar as variáveis mais importantes e em que medida contribuem para explicar o risco da licitação. O risco foi calculado utilizando técnicas de treinamento supervisionado com quatro classificadores. Como resultado, foi constatado que modelos especialistas por modalidade de licitação têm melhor desempenho do que modelos genéricos treinados com todo o conjunto de dados. Para a fase de publicação do edital, licitações da modalidade pregão, dispensa e inexigibilidade apresentaram resultados de AUROC acima de 70%, no entanto, a modalidade concorrência não apresentou resultados aceitáveis. Para a etapa de disputa, todas as modalidades tiveram AUROC acima de 70%.The effective exercise of external control must be optimized so that the inspection effort is concentrated in cases of high risk of irregularities. With regard to government procurement, in the absence of an objective metric for selecting public procurement for audit purposes, this paper presents a proposal for a predictive model to establish an indicator of the risk of irregularities in public procurement in two phases: “Publication” phase and “Dispute” phase. A ranking of public purchases classified by the probability of irregularities was built. This ranking can be used as a tool for decision making in audits. Public purchases made by the State of Goiás between 2014 and 2019, which were collected in databases to which the TCE-GO has access. Initially, a study was carried out on the legal requirements of irregularities in public purchases, in order to identify which attributes may influence the risk of irregularity and which are the most important databases Four methods of attribute selection were applied to identify the variables Most important and to identify how much these variables contribute to explain the risk in public purchases. The risk was calculated using supervised training techniques with four classifiers. As a result, it was found that the specialized models by bidding modality performed better than the generic models trained with the entire data set. For the “Publication” phase, the “Pregão”, “Dispensa” and “Inexigibilidade” modalities presented AUROC results above 70%, but the “Concorrência” modality did not present acceptable results. For the ‘ “Dispute” phase, all modalities had AUROC above 70%

Multimodal Approach for Big Data Analytics and Applications

The thesis presents multimodal conceptual frameworks and their applications in improving the robustness and the performance of big data analytics through cross-modal interaction or integration. A joint interpretation of several knowledge renderings such as stream, batch, linguistics, visuals and metadata creates a unified view that can provide a more accurate and holistic approach to data analytics compared to a single standalone knowledge base. Novel approaches in the thesis involve integrating multimodal framework with state-of-the-art computational models for big data, cloud computing, natural language processing, image processing, video processing, and contextual metadata. The integration of these disparate fields has the potential to improve computational tools and techniques dramatically. Thus, the contributions place multimodality at the forefront of big data analytics; the research aims at mapping and under- standing multimodal correspondence between different modalities. The primary contribution of the thesis is the Multimodal Analytics Framework (MAF), a collaborative ensemble framework for stream and batch processing along with cues from multiple input modalities like language, visuals and metadata to combine benefits from both low-latency and high-throughput. The framework is a five-step process: Data ingestion. As a first step towards Big Data analytics, a high velocity, fault-tolerant streaming data acquisition pipeline is proposed through a distributed big data setup, followed by mining and searching patterns in it while data is still in transit. The data ingestion methods are demonstrated using Hadoop ecosystem tools like Kafka and Flume as sample implementations. Decision making on the ingested data to use the best-fit tools and methods. In Big Data Analytics, the primary challenges often remain in processing heterogeneous data pools with a one-method-fits all approach. The research introduces a decision-making system to select the best-fit solutions for the incoming data stream. This is the second step towards building a data processing pipeline presented in the thesis. The decision-making system introduces a Fuzzy Graph-based method to provide real-time and offline decision-making. Lifelong incremental machine learning. In the third step, the thesis describes a Lifelong Learning model at the processing layer of the analytical pipeline, following the data acquisition and decision making at step two for downstream processing. Lifelong learning iteratively increments the training model using a proposed Multi-agent Lambda Architecture (MALA), a collaborative ensemble architecture between the stream and batch data. As part of the proposed MAF, MALA is one of the primary contributions of the research.The work introduces a general-purpose and comprehensive approach in hybrid learning of batch and stream processing to achieve lifelong learning objectives. Improving machine learning results through ensemble learning. As an extension of the Lifelong Learning model, the thesis proposes a boosting based Ensemble method as the fourth step of the framework, improving lifelong learning results by reducing the learning error in each iteration of a streaming window. The strategy is to incrementally boost the learning accuracy on each iterating mini-batch, enabling the model to accumulate knowledge faster. The base learners adapt more quickly in smaller intervals of a sliding window, improving the machine learning accuracy rate by countering the concept drift. Cross-modal integration between text, image, video and metadata for more comprehensive data coverage than a text-only dataset. The final contribution of this thesis is a new multimodal method where three different modalities: text, visuals (image and video) and metadata, are intertwined along with real-time and batch data for more comprehensive input data coverage than text-only data. The model is validated through a detailed case study on the contemporary and relevant topic of the COVID-19 pandemic. While the remainder of the thesis deals with text-only input, the COVID-19 dataset analyzes both textual and visual information in integration. Post completion of this research work, as an extension to the current framework, multimodal machine learning is investigated as a future research direction

Cyber-Human Systems, Space Technologies, and Threats

CYBER-HUMAN SYSTEMS, SPACE TECHNOLOGIES, AND THREATS is our eighth textbook in a series covering the world of UASs / CUAS/ UUVs / SPACE. Other textbooks in our series are Space Systems Emerging Technologies and Operations; Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD); Disruptive Technologies with applications in Airline, Marine, Defense Industries; Unmanned Vehicle Systems & Operations On Air, Sea, Land; Counter Unmanned Aircraft Systems Technologies and Operations; Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets, 2nd edition; and Unmanned Aircraft Systems (UAS) in the Cyber Domain Protecting USA’s Advanced Air Assets, 1st edition. Our previous seven titles have received considerable global recognition in the field. (Nichols & Carter, 2022) (Nichols, et al., 2021) (Nichols R. K., et al., 2020) (Nichols R. , et al., 2020) (Nichols R. , et al., 2019) (Nichols R. K., 2018) (Nichols R. K., et al., 2022)https://newprairiepress.org/ebooks/1052/thumbnail.jp

Fortress of the Soul

French Huguenots made enormous contributions to the life and culture of colonial New York during the seventeenth and eighteenth centuries. Huguenot craftsmen were the city's most successful artisans, turning out unrivaled works of furniture which were distinguished by unique designs and arcane details. More than just decorative flourishes, however, the visual language employed by Huguenot artisans reflected a distinct belief system shaped during the religious wars of sixteenth-century France.In Fortress of the Soul, historian Neil Kamil traces the Huguenots' journey to New York from the Aunis-Saintonge region of southwestern France. There, in the sixteenth century, artisans had created a subterranean culture of clandestine workshops and meeting places inspired by the teachings of Bernard Palissy, a potter, alchemist, and philosopher who rejected the communal, militaristic ideology of the Huguenot majority which was centered in the walled city of La Rochelle. Palissy and his followers instead embraced a more fluid, portable, and discrete religious identity that encouraged members to practice their beliefs in secret while living safely—even prospering—as artisans in hostile communities. And when these artisans first fled France for England and Holland, then left Europe for America, they carried with them both their skills and their doctrine of artisanal security.Drawing on significant archival research and fresh interpretations of Huguenot material culture, Kamil offers an exhaustive and sophisticated study of the complex worldview of the Huguenot community. From the function of sacred violence and alchemy in the visual language of Huguenot artisans, to the impact among Protestants everywhere of the destruction of La Rochelle in 1628, to the ways in which New York's Huguenots interacted with each other and with other communities of religious dissenters and refugees, Fortress of the Soul brilliantly places American colonial history and material life firmly within the larger context of the early modern Atlantic world