Detecting fraudulent activities in financial and e-commerce transaction networks is crucial. One effective method for this is Densest Subgraph Discovery (DSD). However, deploying DSD methods in production systems faces substantial scalability challenges due to the predominantly sequential nature of existing methods, which impedes their ability to handle large-scale transaction networks and results in significant detection delays. To address these challenges, we introduce Dupin, a novel parallel processing framework designed for efficient DSD processing in billion-scale graphs. Dupin is powered by a processing engine that exploits the unique properties of the peeling process, with theoretical guarantees on detection quality and efficiency. Dupin provides user-friendly APIs for flexible customization of DSD objectives and ensures robust adaptability to diverse fraud detection scenarios. Empirical evaluations indicate that Dupin consistently outperforms several existing DSD methods, achieving performance improvements of up to two orders of magnitude compared to traditional approaches. On billion-scale graphs, Dupin demonstrates the potential to enhance the prevention of fraudulent transactions by approximately 49.5 basis points and reduces density error from 30.3% to below 5.0%, as supported by our experimental results.

Graph analytics have been effective in the data science pipeline of fraud detections. In the ever-evolving landscape of e-commerce platforms like Grab or transaction networks such as cryptos, we have witnessed the phenomenon of 'burst subgraphs,' characterized by rapid increases in subgraph density within short timeframes---as a common pattern for fraud detections on dynamic graphs. However, existing graph processing frameworks struggle to efficiently manage these due to their inability to handle sudden surges in data. In this paper, we propose RUSH ( R eal-time b U rst S ubgrap H detection framework), a pioneering framework tailored for real-time fraud detection within dynamic graphs. By focusing on both the density and the rate of change of subgraphs, RUSH identifies crucial indicators of fraud. Utilizing a sophisticated incremental update mechanism, RUSH processes burst subgraphs on large-scale graphs with high efficiency. Furthermore, RUSH is designed with user-friendly APIs that simplify the customization and integration of specific fraud detection metrics. In the deployment within Grab's operations, detecting burst subgraphs can be achieved with approximately ten lines of code. Through extensive evaluations on real-world datasets, we show RUSH's effectiveness in fraud detection and its robust scalability across various data sizes. In case studies, we illustrate how RUSH can detect fraud communities within various Grab business scenarios, such as customer-merchant collusion and promotion abuse, and identify wash trading in crypto networks.

In this demonstration, we introduce Spade, a sophisticated real-time fraud detection framework adept at navigating the complex transaction graph. Unlike conventional methods that are limited by performance and lack incremental update capabilities, Spade leverages advanced incremental updates in dense subgraph peeling algorithms to enhance efficiency, usability, and reduce latency, achieving a significantly better fraud prevention ratio. The demo showcases an interactive GUI prototype, allowing users to customize and explore dense subgraphs with various metrics and algorithms. This interactive demonstration also effectively highlights Spade's robust capacity to unearth fraudulent transactions within varied settings, including Grab's services and cryptocurrency transactions.

This paper looks into the voltage stability and network scalability of self-contained converter-based direct current (DC) microgrids (MG) under an innovative control approach, namely a nonlinear adaptive droop-based controller with overcurrent protection, devised for hybrid electric aircraft (HEA) applications. Apart from guaranteeing tight voltage regulation and accurate adaptive distribution of load power across parallel batteries proportional with their current state of charge (SoC), the controller features an inherent overcurrent protection. Notably, the applied nonlinear adaptive droop-based controller introduces a virtual voltage and a constant virtual resistance, placed in series with the inductance and parasitic resistance of each DC/DC bidirectional boost converter. Moreover, the voltage stability for the n-dimensional system is subsequently investigated, providing valuable insights into the voltage dynamic behaviour, followed by a network scalability study based on the system's passivity properties. Finally, numerical simulations replicating various in-flight scenarios align with and validate our theoretical developments in the pursuit of minimising emissions, environmental impact, and operational costs.

Trust in autonomy is essential for effective human-robot collaboration and user adoption of autonomous systems such as robot assistants. This article introduces a computational model that integrates trust into robot decision making. Specifically, we learn from data a partially observable Markov decision process (POMDP) with human trust as a latent variable. The trust-POMDP model provides a principled approach for the robot to (i) infer the trust of a human teammate through interaction, (ii) reason about the effect of its own actions on human trust, and (iii) choose actions that maximize team performance over the long term. We validated the model through human subject experiments on a table clearing task in simulation (201 participants) and with a real robot (20 participants). In our studies, the robot builds human trust by manipulating low-risk objects first. Interestingly, the robot sometimes fails intentionally to modulate human trust and achieve the best team performance. These results show that the trust-POMDP calibrates trust to improve human-robot team performance over the long term. Further, they highlight that maximizing trust alone does not always lead to the best performance.

This work contributes both experimental findings and novel computational human-robot trust models for multi-task settings. We describe Bayesian non-parametric and neural models, and compare their performance on data collected from real-world human-subjects study. Our study spans two distinct task domains: household tasks performed by a Fetch robot, and a virtual reality driving simulation of an autonomous vehicle performing a variety of maneuvers. We find that human trust changes and transfers across tasks in a structured manner based on perceived task characteristics. Our results suggest that task-dependent functional trust models capture human trust in robot capabilities more accurately, and trust transfer across tasks can be inferred to a good degree. We believe these models are key for enabling trust-based robot decision-making for natural human-robot interaction.

Trust is essential for human-robot collaboration and user adoption of autonomous systems, such as robot assistants. This paper introduces a computational model which integrates trust into robot decision-making. Specifically, we learn from data a partially observable Markov decision process (POMDP) with human trust as a latent variable. The trust-POMDP model provides a principled approach for the robot to (i) infer the trust of a human teammate through interaction, (ii) reason about the effect of its own actions on human behaviors, and (iii) choose actions that maximize team performance over the long term. We validated the model through human subject experiments on a table-clearing task in simulation (201 participants) and with a real robot (20 participants). The results show that the trust-POMDP improves human-robot team performance in this task. They further suggest that maximizing trust in itself may not improve team performance.

The partially observable Markov decision process (POMDP) provides a principled general model for planning under uncertainty. However, solving a general POMDP is computationally intractable in the worst case. This paper introduces POMDP-lite, a subclass of POMDPs in which the hidden state variables are constant or only change deterministically. We show that a POMDP-lite is equivalent to a set of fully observable Markov decision processes indexed by a hidden parameter and is useful for modeling a variety of interesting robotic tasks. We develop a simple model-based Bayesian reinforcement learning algorithm to solve POMDP-lite models. The algorithm performs well on large-scale POMDP-lite models with up to 1020 states and outperforms the state-of-the-art general-purpose POMDP algorithms. We further show that the algorithm is near-Bayesian-optimal under suitable conditions.