In 2024, the Linux kernel became its own Common Vulnerabilities and Exposures (CVE) Numbering Authority (CNA), formalizing how kernel vulnerabilities are identified and tracked. We analyze the anatomy and dynamics of kernel CVEs using metadata, associated commits, and patch latency to understand what drives patching. Results show that severity and Common Vulnerability Scoring System (CVSS) metrics have a negligible association with patch latency, whereas kernel recency is a reasonable predictor in survival models. Kernel developers fix newer kernels sooner, while older ones retain unresolved CVEs. Commits introducing vulnerabilities are typically broader and more complex than their fixes, though often only approximate reconstructions of development history. The Linux kernel remains a unique open-source project—its CVE process is no exception.

Process mining provides valuable insights by discovering process models from execution logs.However, its effectiveness depends heavily on high-quality, well-structured logs.Many real-world systems produce low-level, semi-structured logs lacking clear process identifiers, causing misalignment with their intended process models.This paper introduces a method for structuring raw event logs by segmenting event streams and mapping them to known processes.Using process traces from experienced users, we develop a model that infers process assignments in unstructured logs.Our approach is motivated by a modular enterprise system without predefined workflows, where dynamic processes generate low-level logs requiring interpretation.We validate our method on a semi-synthetic business dataset and a fully synthetic dataset from PLG2.Our results demonstrate that trace segmentation improves process discovery, aligns logs with meaningful structures, and significantly enhances process mining in unstructured environments.This work was supported by the Regional Operational Programme of the Kuyavian-Pomeranian Voivodeship for 2014-2020 under the grant titled "Budowa zaplecza badawczo-rozwojowego w MGA Sp. z o.o." variability of clients, the heterogeneity of business processes, and the system's flexibility, incorporating process identifiers into the logs is not feasible from a business perspective.Therefore, our methodology relies exclusively on semi-structured data.By implementing this approach, we provide a solution that enhances process mining capabilities in environments where structured event logs are unavailable.This research contributes to process mining by introducing a method for structuring semi-structured event logs, enabling more effective business process analysis, anomaly detection, and performance monitoring.a) Replication Package: To facilitate reproducibility and further research, we provide a complete replication package containing code, data, and experimental scripts.It is publicly available at:The remainder of this paper is organized as follows.Section II discusses related work.Sections III and IV reviews necessary preliminaries on event logs, process mining, and similarity measures.Section V states the problem, and Section VI describes our methodology for structuring semi-structured event logs.In Section VII, we discuss our experimental design, and Section VIII presents the results.In Section IX, we assess threats to validity.Finally, we conclude and propose future directions in Section X. A. Running Example (Motivation)Consider a customer-support system where each event is logged as [time, user, activity, . ..].A typical log snippet might look like:

The modern software development landscape heavily relies on transitive dependencies. They enable seamless integration of third-party libraries. However, they also introduce security challenges. Transitive vulnerabilities that arise from indirect dependencies expose projects to risks associated with Common Vulnerabilities and Exposures (CVEs). It happens even when direct dependencies remain secure. This paper examines the lifecycle of transitive vulnerabilities in the Maven ecosystem. We employ survival analysis to measure the time projects remain exposed after a CVE is introduced. Using a large dataset of Maven projects, we identify factors that influence the resolution of these vulnerabilities. Our findings offer practical advice on improving dependency management.

Spectral fingerprints (SFs) are unique power spectra signatures of human brain regions of interest (ROIs, Keitel & Gross, 2016). SFs allow for accurate ROI identification and can serve as biomarkers of differences exhibited by non-neurotypical groups. At present, there are no open-source, versatile tools to calculate spectral fingerprints. We have filled this gap by creating a modular, highly-configurable MATLAB Toolbox for Frequency-based Fingerprinting (ToFFi). It can transform magnetoencephalographic and electroencephalographic signals into unique spectral representations using ROIs provided by anatomical (AAL, Desikan-Killiany), functional (Schaefer), or other custom volumetric brain parcellations. Toolbox design supports reproducibility and parallel computations.

In this work, we compare the performance of three selected techniques in open set acoustic scenes classification (ASC). We test thresholding of the softmax output of a deep network classifier, which is the most popular technique nowadays employed in ASC. Further we compare the results with the Openmax classifier which is derived from the computer vision field. As the third model, we use the Adapted Class-Conditioned Autoencoder (Adapted C2AE) which is our variation of another computer vision related technique called C2AE. Adapted C2AE encompasses a more fair comparison of the given experiments and simplifies the original inference procedure, making it more applicable in the real-life scenarios. We also analyse two training scenarios: without additional knowledge of unknown classes and another where a limited subset of examples from the unknown classes is available. We find that the C2AE based method outperforms the thresholding and Openmax, obtaining $85.5\%$ Area Under the Receiver Operating Characteristic curve (AUROC) and $66\%$ of open set accuracy on data used in Detection and Classification of Acoustic Scenes and Events Challenge 2019 Task 1C.

This paper describes a method for spatial tracking of a strapdown device that can be used for design of humancomputer interfaces.Inertial Measurement Unit (IMU) is used to obtain 6-dof position exploiting the so-called ZUPT technique by the means of the Kalman Filter.Additional corrections of position are done using magnetometer readings in the presence of static magnetic field induced by permanent magnet that overshadow geomagnetic field.This correction allows us to overcome drifting errors of integration of IMU readings.We have also presented comparisons of different models for magnetic field reconstruction that is crucial for this system.

This paper summarizes our approach and experimental evaluation of infrastructure-based Indoor Positioning System (IPS) designed to be used by First Responders.We are using 868 MHz single channel, power-efficient radio markers and RSSI (Receiver Signal Strength Indicator) fingerprinting.Artificial Neural Network translates vectors of RSSI constructed using mobile units into position.Special preprocessing needs to be applied to on-line signal to construct a vector for classification.

In this paper, we summarize AAIA'15 data mining competition: Tagging Firefighter Activities at a Fire Scene, which was held between March 9 and July 6, 2015.We describe the scope and background of the competition.We also reveal details regarding the data set used in the competition, which was collected and tagged specifically for the purpose of this data challenge.We explain the data acquisition process which involved using a body sensor network system consisting of several inertial measurement units and a physiological data sensor.Finally, we briefly discuss submitted results with respect to their possible real-life application in our decision support system.