Recently, fitting probabilistic models have gained importance in many areas but estimation of such distributional models with very large data sets is a difficult task. In particular, the use of rather complex models can easily lead to memory-related efficiency problems that can make estimation infeasible even on high-performance computers. We therefore propose a novel backfitting algorithm, which is based on the ideas of stochastic gradient descent and can deal virtually with any amount of data on a conventional laptop. The algorithm performs automatic selection of variables and smoothing parameters, and its performance is in most cases superior or at least equivalent to other implementations for structured additive distributional regression, e.g., gradient boosting, while maintaining low computation time. Performance is evaluated using an extensive simulation study and an exceptionally challenging and unique example of lightning count prediction over Austria. A very large dataset with over 9 million observations and 80 covariates is used, so that a prediction model cannot be estimated with standard distributional regression methods but with our new approach.

This paper focuses on how touch interactions on smartphones can provide a continuous user authentication service through behaviour captured by a touchscreen. While efforts are made to advance touch-based behavioural authentication, researchers often focus on gathering data, tuning classifiers, and enhancing performance by evaluating touch interactions in a sequence rather than independently. However, such systems only work by providing data representing distinct behavioural traits. The typical approach separates behaviour into touch directions and creates multiple user profiles. This work presents an omnidirectional approach which outperforms the traditional method independent of the touch direction - depending on optimal behavioural features and a balanced training set. Thus, we evaluate five behavioural feature sets using the conventional approach against our direction-agnostic method while testing several classifiers, including an Extra-Tree and Gradient Boosting Classifier, which is often overlooked. Results show that in comparison with the traditional, an Extra-Trees classifier and the proposed approach are superior when combining strokes. However, the performance depends on the applied feature set. We find that the TouchAlytics feature set outperforms others when using our approach when combining three or more strokes. Finally, we highlight the importance of reporting the mean area under the curve and equal error rate for single-stroke performance and varying the sequence of strokes separately. Keywords: Behavioural Biometric; Continuous Authentication; Touch Biometric; Smartphone Security; Model Selection 1. Introduction In 2007, Apple caused a paradigm shift by releasing its first smartphone with a touch screen. Since then, smartphones have become ubiquitous, with an 81% penetration rate in the US [1].

In AI-assisted decision-making, it is critical for human decision-makers to know when to trust AI and when to trust themselves. However, prior studies calibrated human trust only based on AI confidence indicating AI's correctness likelihood (CL) but ignored humans' CL, hindering optimal team decision-making. To mitigate this gap, we proposed to promote humans' appropriate trust based on the CL of both sides at a task-instance level. We first modeled humans' CL by approximating their decision-making models and computing their potential performance in similar instances. We demonstrated the feasibility and effectiveness of our model via two preliminary studies. Then, we proposed three CL exploitation strategies to calibrate users' trust explicitly/implicitly in the AI-assisted decision-making process. Results from a between-subjects experiment (N=293) showed that our CL exploitation strategies promoted more appropriate human trust in AI, compared with only using AI confidence. We further provided practical implications for more human-compatible AI-assisted decision-making.

Detection of news propagation barriers, being economical, cultural, political, time zonal, or geographical, is still an open research issue. We present an approach to barrier detection in news spreading by utilizing Wikipedia-concepts and metadata associated with each barrier. Solving this problem can not only convey the information about the coverage of an event but it can also show whether an event has been able to cross a specific barrier or not. Experimental results on IPoNews dataset (dataset for information spreading over the news) reveals that simple classification models are able to detect barriers with high accuracy. We believe that our approach can serve to provide useful insights which pave the way for the future development of a system for predicting information spreading barriers over the news.

Jazmia Henry July 18, 2022 Summary Discussions about reducing the bias present in algorithms have been on the rise since the mid 2010s. AI ethicists, DEI practitioners, Sociologists, Data Scientists and Social Justice Advocates have decried the lack of understanding of the harms that algorithms pose to people who belong to historically marginalized groups. These cries have become increasingly accepted in industry since 2020, but little is understood of how algorithm and Machine Learning (ML) model builders should go about mitigating bias in models that are intended for deployment. This chapter is written with the Data Scientist or MLOps professional in mind but can be used as a resource for policy makers, reformists, AI Ethicists, sociologists, and others interested in finding methods that help reduce bias in algorithms. I will take a deployment centered approach with the assumption that the professionals reading this work have already read the amazing work on the implications of algorithms on historically marginalized groups by Gebru, Buolamwini, Benjamin and Shane to name a few. If you have not read those works, I refer you to the "Important Reading for Ethical Model Building " list at the end of this paper as it will help give you a framework on how to think about Machine Learning models more holistically taking into account their effect on marginalized people. In the Introduction to this chapter, I root the significance of their work in real world examples of what happens when models are deployed without transparent data collected for the training process and are deployed without the practitioners paying special attention to what happens to models that adapt to exploit gaps between their training environment and the real world. The rest of this chapter builds on the work of the aforementioned researchers and discusses the reality of models performing post production and details ways ML practitioners can identify bias using tools during the MLOps lifecycle to mitigate bias that may be introduced to models in the real world. Introduction "Whether AI will help us reach our aspirations or reinforce the unjust inequalities is ultimately up to us." - Joy Buolowini, 'Facing the Coded Gaze' AI: More than Human Whether you're driving your car using a GPS system, call on Alexa or Siri to turn on your favorite tune, go on social media to perform a well-earned scroll down memory lane, or go to Google search to find a gift to buy for a friend, you have encountered a Machine Learning model.

The usage of deep neural networks in safety-critical systems is limited by our ability to guarantee their correct behavior. Runtime monitors are components aiming to identify unsafe predictions and discard them before they can lead to catastrophic consequences. Several recent works on runtime monitoring have focused on out-of-distribution (OOD) detection, i.e., identifying inputs that are different from the training data. In this work, we argue that OOD detection is not a well-suited framework to design efficient runtime monitors and that it is more relevant to evaluate monitors based on their ability to discard incorrect predictions. We call this setting out-ofmodel-scope detection and discuss the conceptual differences with OOD. We also conduct extensive experiments on popular datasets from the literature to show that studying monitors in the OOD setting can be misleading: 1. very good OOD results can give a false impression of safety, 2. comparison under the OOD setting does not allow identifying the best monitor to detect errors. Finally, we also show that removing erroneous training data samples helps to train better monitors.

Machine learning applications, especially in the fields of me\-di\-cine and social sciences, are slowly being subjected to increasing scrutiny. Similarly to sample size planning performed in clinical and social studies, lawmakers and funding agencies may expect statistical uncertainty estimations in machine learning applications that impact society. In this paper, we present an easy-to-use python package and web application for estimating prediction confidence intervals. The package offers eight different procedures to determine and justify the sample size and confidence of predictions from holdout, bootstrap, cross-validation, and progressive validation experiments. Since the package builds directly on established data analysis libraries, it seamlessly integrates into preprocessing and exploratory data analysis steps. Code related to this paper is available at: https://github.com/dabrze/confidence-planner.

An important aspect of many botnets is their capability to generate pseudorandom domain names using Domain Generation Algorithms (DGAs). A cyber criminal can register such domains to establish periodically changing rendezvous points with the bots. DGAs make use of seeds to generate sets of domains. Seeds can easily be changed in order to generate entirely new groups of domains while using the same underlying algorithm. While this requires very little manual effort for an adversary, security specialists typically have to manually reverse engineer new malware strains to reconstruct the seeds. Only when the seed and DGA are known, past and future domains can be generated, efficiently attributed, blocked, sinkholed or used for a take-down. Common counters in the literature consist of databases or Machine Learning (ML) based detectors to keep track of past and future domains of known DGAs and to identify DGA-generated domain names, respectively. However, database based approaches can not detect domains generated by new DGAs, and ML approaches can not generate future domain names. In this paper, we introduce SESAME, a system that combines the two above-mentioned approaches and contains a module for automatic Seed Reconstruction, which is, to our knowledge, the first of its kind. It is used to automatically classify domain names, rate their novelty, and determine the seeds of the underlying DGAs. SESAME consists of multiple DGA-specific Seed Reconstructors and is designed to work purely based on domain names, as they are easily obtainable from observing the network traffic. We evaluated our approach on 20.8 gigabytes of DNS-lookups. Thereby, we identified 17 DGAs, of which 4 were entirely new to us.

Blockchain technology has been successfully exploited for deploying new economic applications. However, it has started arousing the interest of malicious users who deliver scams to deceive honest users and to gain economic advantages. Among the various scams, Ponzi schemes are one of the most common. Here, we present an automatic technique for detecting smart Ponzi contracts on Ethereum. We release a reusable data set with 4422 unique real-world smart contracts. Then, we introduce a new set of features that allow us to improve the classification. Finally, we identify a small and effective set of features that ensures a good classification quality.

We show, to our knowledge, the first theoretical treatments of two common questions in cross-validation based hyperparameter selection: (1) After selecting the best hyperparameter using a held-out set, we train the final model using {\em all} of the training data -- since this may or may not improve future generalization error, should one do this? (2) During optimization such as via SGD (stochastic gradient descent), we must set the optimization tolerance $\rho$ -- since it trades off predictive accuracy with computation cost, how should one set it? Toward these problems, we introduce the {\em hold-in risk} (the error due to not using the whole training data), and the {\em model class mis-specification risk} (the error due to having chosen the wrong model class) in a theoretical view which is simple, general, and suggests heuristics that can be used when faced with a dataset instance. In proof-of-concept studies in synthetic data where theoretical quantities can be controlled, we show that these heuristics can, respectively, (1) always perform at least as well as always performing retraining or never performing retraining, (2) either improve performance or reduce computational overhead by $2\times$ with no loss in predictive performance.