This paper explores the novel topic of data breach journalism and data breach news through the case of databreaches.net, a news outlet dedicated to data breaches and related cyber crime. Motivated by the issues in traditional crime news and crime journalism, the case is explored by the means of text mining. According to the results, the outlet has kept a steady publishing pace, mainly focusing on plain and short reporting but with generally high-quality source material for the news articles. Despite these characteristics, the news articles exhibit fairly strong sentiments, which is partially expected due to the presence of emotionally laden crime and the long history of sensationalism in crime news. The news site has also covered the full scope of data breaches, although many of these are fairly traditional, exposing personal identifiers and financial details of the victims. Also hospitals and the healthcare sector stand out. With these results, the paper advances the study of data breaches by considering these from the perspective of media and journalism.

This study explores the application of supervised and unsupervised machine learning algorithms for predicting the sex of sheep using measurements of the talus bone in archaeozoological research. Leveraging data from well-documented sheep populations, we trained and tested various machine learning algorithms, such as kNN, SVMs, Decision Trees, Neural Networks, k-Means, DBSCAN, and GMM – demonstrating high accuracy in sex classification across multiple datasets from various time periods. We furthermore evaluate a variety of clustering results on unlabeled data and highlight their respective strengths and drawbacks. Our results suggest that machine learning offers a promising direction for enhancing the analysis of ancient and recent animal remains, providing valuable insights into past animal husbandry practices and their implications for understanding human history.

We tasked two supervised keypoint detection networks to automatically identify anatomically relevant landmarks on sheep bones. The results of the models were compared to manual labeling by a domain expert, yielding satisfactory initial results that provide a promising foundation for further research.

Abstract Unsupervised learning methods are well established in the area of anomaly detection and achieve state of the art performances on outlier datasets. Outliers play a significant role, since they bear the potential to distort the predictions of a machine learning algorithm on a given dataset. Especially among PCA-based methods, outliers have an additional destructive potential regarding the result: they may not only distort the orientation and translation of the principal components, they also make it more complicated to detect outliers. To address this problem, we propose the robust outlier detection algorithm CoMadOut, which satisfies two required properties: (1) being robust towards outliers and (2) detecting them. Our CoMadOut outlier detection variants using comedian PCA define, dependent on its variant, an inlier region with a robust noise margin by measures of in-distribution (variant CMO) and optimized scores by measures of out-of-distribution (variants CMO*), e.g. kurtosis-weighting by CMO+k. These measures allow distribution based outlier scoring for each principal component, and thus, an appropriate alignment of the degree of outlierness between normal and abnormal instances. Experiments comparing CoMadOut with traditional, deep and other comparable robust outlier detection methods showed that the performance of the introduced CoMadOut approach is competitive to well established methods related to average precision (AP), area under the precision recall curve (AUPRC) and area under the receiver operating characteristic (AUROC) curve. In summary our approach can be seen as a robust alternative for outlier detection tasks.

Recent successes in AI research concerning traditional games like GO, have led to increased interest in the field of reinforcement learning. Modern board game design, however, has risen in complexity. This paper introduces a novel task for reinforcement learning: “Quacks of Quedlinburg”. A modern board game with risk management, deck building, and the option to choose a specific rule set out of thousands of possible combinations for every game. We provide an environment based on the game and perform initial experiments. In these, we found that Deep Q-Learning agents can significantly outperform simple heuristics.