This report presents the winning solution for Task 2 of Colliding with Adversaries: A Challenge on Robust Learning in High Energy Physics Discovery at ECML-PKDD 2025. The goal of the challenge was to design and train a robust ANN-based model capable of achieving high accuracy in a binary classification task on both clean and adversarial data generated with the Random Distribution Shuffle Attack (RDSA). Our solution consists of two components: a data generation phase and a robust model training phase. In the first phase, we produced 15 million artificial training samples using a custom methodology derived from Random Distribution Shuffle Attack (RDSA). In the second phase, we introduced a robust architecture comprising (i)a Feature Embedding Block with shared weights among features of the same type and (ii)a Dense Fusion Tail responsible for the final prediction. Training this architecture on our adversarial dataset achieved a mixed accuracy score of 80\%, exceeding the second-place solution by two percentage points.

This report presents the winning solution for Task 1 of Colliding with Adversaries: A Challenge on Robust Learning in High Energy Physics Discovery at ECML-PKDD 2025. The task required designing an adversarial attack against a provided classification model that maximizes misclassification while minimizing perturbations. Our approach employs a multi-round gradient-based strategy that leverages the differentiable structure of the model, augmented with random initialization and sample-mixing techniques to enhance effectiveness. The resulting attack achieved the best results in perturbation size and fooling success rate, securing first place in the competition.

For many years the traditional formula for service on the internet was, give us your data, and we will give you service for free. However as data is becoming more valuable, will this formula stay, or be replaced? Should we get paid for giving away data? Should our data be stored on more secure blockchains? As more people become aware of how valuable their information is to companies, and how easily it can be used and sold, people are beginning to wonder just how easily they should give over their information.

In the not so distant future, robots may very well be a major component of modern human life. Whether we look at the driverless revolution to the use of drones, we can expect to see these machines to inhabit the space around us fairly soon. Multi-robot situations will become an increasingly common occurrence, and so engineers will need to ensure they can exist in the same space without hindering each other. In the scenario of drones, the machine needs to be capable of making instant decisions to adjust its trajectory to prevent an accident, whilst also making sure it doesn't lose the target destination. These sorts of changes need to be made in seconds whilst also recognising that the situation may change as a result of a movement from the other drone.

For many years the traditional formula for service on the internet was, give us your data, and we will give you service for free. However as data is becoming more valuable, will this formula stay, or be replaced? Should we get paid for giving away data? Should our data be stored on more secure blockchains? As more people become aware of how valuable their information is to companies, and how easily it can be used and sold, people are beginning to wonder just how easily they should give over their information.

A mall in Silicon Valley this week suspended its security robots after a collision involving one of them resulted in a toddler getting a bruised leg--a reversal from children harassing robots at the mall. The accident could reignite fears about evil robots that many robot makers have tried to overcome with cute designs. The robot company, Knightscope Inc., designed its 300-pound machine to protect against malicious humans. Children usually present the greatest risk to robots' ability to get their jobs done, previous incidents showed. Knightscope was the subject of a Wall Street Journal page one article last month.