Dynamics of innovation in European regions

There is interest in both academic literature and regional governments about the innovativeness of regions and the drivers of that competitiveness, especially if considering the impact on economic development and social progress. Innovation is the base for the global competitiveness. Innovative capacity enables regions to increase their productivity and attract investments, thereby sustaining continuous progress in the quality and standard of living. This study aims to measure regions’ innovativeness in different European regions and to evaluate the nature of the innovation process and the relationship existing between its innovativeness’ and its region of origin. It proceeds from the assumption that the competitiveness of a region is reflected in its innovation capacity or innovation dynamic. The literature review regarding regions’ innovativeness produces some insights regarding to the effect of contextual elements on regions performance. Thus, the objective is to compare the European regions to verify the existence of subjacent clusters and find out the characteristics that distinguish the different group of regions. The innovative capacity is considered in terms of innovative output and several factors are analysed to identify and differentiate the dynamics of innovations of the regions. The results point to the existence of five groups of regions, and the factors identified are related to innovation process, namely forms of innovation, factors and objectives of innovation and with aspects related to the innovation framework such as tertiary education and life-long learning, business and public R&D expenses, and level of collaboration for innovating.

Dynamics of innovation in european regions

There is interest in both academic literature and regional governments about the innovativeness of regions and the drivers of that competitiveness, especially if considering the impact on economic development and social progress. Innovation is the base for the global competitiveness. Innovative capacity enables regions to increase their productivity and attract investments, thereby sustaining continuous progress in the quality and standard of living. The literature review regarding regions’ innovativeness produces some insights regarding to the effect of contextual elements on regions performance and reveals some new perspectives of this issue. This study aims to measure regions’ innovativeness in different European regions and to evaluate the nature of the innovation process and the relationship existing between its innovativeness’ and its region of origin. It proceeds from the assumption that the competitiveness of a region is reflected in its innovation capacity or innovation dynamic. Thus, it compares the European regions verifying the existence of subjacent clusters and finding out the characteristics that distinguish the different group of regions. The innovative capacity is considered in terms of innovative output and several factors are analysed to identify and differentiate the dynamics of innovations of the regions. The results point to the existence of five groups of regions, and the factors identified are related to innovation process, namely forms of innovation, factors and objectives of innovation and with aspects related to the innovation framework such as tertiary education and life-long learning, business and public R&D expenses, and level of collaboration for innovating

Adversarial training for tabular data with attack propagation

Adversarial attacks are a major concern in security-centered applications, where malicious actors continuously try to mislead Machine Learning (ML) models into wrongly classifying fraudulent activity as legitimate, whereas system maintainers try to stop them. Adversarially training ML models that are robust against such attacks can prevent business losses and reduce the work load of system maintainers. In such applications data is often tabular and the space available for attackers to manipulate undergoes complex feature engineering transformations, to provide useful signals for model training, to a space attackers cannot access. Thus, we propose a new form of adversarial training where attacks are propagated between the two spaces in the training loop. We then test this method empirically on a real world dataset in the domain of credit card fraud detection. We show that our method can prevent about 30% performance drops under moderate attacks and is essential under very aggressive attacks, with a trade-off loss in performance under no attacks smaller than 7%

Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity

Lorenz, J., Silva, M. I., Aparício, D., Ascensão, J. T., & Bizarro, P. (2020). Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity. In ICAIF 2020 - 1st ACM International Conference on AI in Finance (pp. 1-8). [3422549] (ICAIF 2020 - 1st ACM International Conference on AI in Finance). Association for Computing Machinery, Inc. https://doi.org/10.1145/3383455.3422549Every year, criminals launder billions of dollars acquired from serious felonies (e.g., terrorism, drug smuggling, or human trafficking), harming countless people and economies. Cryptocurrencies, in particular, have developed as a haven for money laundering activity. Machine Learning can be used to detect these illicit patterns. However, labels are so scarce that traditional supervised algorithms are inapplicable. Here, we address money laundering detection assuming minimal access to labels. First, we show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset. Then, we show that our proposed active learning solution is capable of matching the performance of a fully supervised baseline by using just 5% of the labels. This solution mimics a typical real-life situation in which a limited number of labels can be acquired through manual annotation by experts.publishersversionpublishe

Lightweight Automated Feature Monitoring for Data Streams

Monitoring the behavior of automated real-time stream processing systems has become one of the most relevant problems in real world applications. Such systems have grown in complexity relying heavily on high dimensional input data, and data hungry Machine Learning (ML) algorithms. We propose a flexible system, Feature Monitoring (FM), that detects data drifts in such data sets, with a small and constant memory footprint and a small computational cost in streaming applications. The method is based on a multi-variate statistical test and is data driven by design (full reference distributions are estimated from the data). It monitors all features that are used by the system, while providing an interpretable features ranking whenever an alarm occurs (to aid in root cause analysis). The computational and memory lightness of the system results from the use of Exponential Moving Histograms. In our experimental study, we analyze the system's behavior with its parameters and, more importantly, show examples where it detects problems that are not directly related to a single feature. This illustrates how FM eliminates the need to add custom signals to detect specific types of problems and that monitoring the available space of features is often enough.Comment: 10 pages, 5 figures. AutoML, KDD22, August 14-17, 2022, Washington, DC, U

Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs

Money laundering is a global problem that concerns legitimizing proceeds from serious felonies (1.7-4 trillion euros annually) such as drug dealing, human trafficking, or corruption. The anti-money laundering systems deployed by financial institutions typically comprise rules aligned with regulatory frameworks. Human investigators review the alerts and report suspicious cases. Such systems suffer from high false-positive rates, undermining their effectiveness and resulting in high operational costs. We propose a machine learning triage model, which complements the rule-based system and learns to predict the risk of an alert accurately. Our model uses both entity-centric engineered features and attributes characterizing inter-entity relations in the form of graph-based features. We leverage time windows to construct the dynamic graph, optimizing for time and space efficiency. We validate our model on a real-world banking dataset and show how the triage model can reduce the number of false positives by 80% while detecting over 90% of true positives. In this way, our model can significantly improve anti-money laundering operations.Comment: 8 pages, 5 figure

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

Evaluating The Determinants Of National Innovative Capacity Among European Countries

This paper reflects upon the factors that influence the national innovative capacity that is based on the European Innovation Scoreboard database. The aim is to reflect on, and evaluate, the factors influencing national innovative capacity. A cluster analysis was conducted to verify how different countries are positioned in terms of innovation outputs and determine which factors distinguish their level of innovative capacity. The results point to the existence of four groups of countries. On the other hand, the factors identified are related to the dimensions of institutional efficiency, namely the efficiency of institutions, types of regulation, effective rule of law and level of corruption, societies’ cultural values associated with the level of hierarchy or "power distance" and "uncertainty avoidance." Aspects are related to the innovation framework, such as doctorates in science and engineering, business Research & Development expenses, and the level of collaboration for innovation