--We propose an automated approach to bug assignment to developers in large open-source software projects. This way, we assist human bug triagers who are in charge of finding the best developer with the right level of expertise in a particular area to be assigned to a newly reported issue. Our approach is based on the history of software development as documented in the issue tracking systems. Our approach works based on the bug reports' features, such as the corresponding products and components, as well as their priority and severity levels. We sort developers based on their experience with specific combinations of new reports. The evaluation is performed using T op-k accuracy, and the results are compared with the reported results in prior work, namely T opicMiner MTM, BUGZIE, Bug triaging via deep Reinforcement Learning BT -RL, and LDA-SVM. The evaluation data come from various Eclipse and Mozilla projects, such as JDT, Firefox, and Thunderbird. Large open-source projects offer an issue tracking system or open bug repository, where developers and users can report the software defects they find or any new feature requests they may have. These reports are called bug reports or issues . In some cases, developers can volunteer to work on the reported issues they find interesting or relevant to their field of expertise. Additionally, they sometimes report issues and assign them to themselves. However, in many cases, particularly in large open-source projects, a group of developers, called bug triagers, decide who should process and fix a newly reported issue.

The delivery of mental healthcare through psychotherapy stands to benefit immensely from developments within Natural Language Processing (NLP), in particular through the automatic identification of patient specific qualities, such as attachment style. Currently, the assessment of attachment style is performed manually using the Patient Attachment Coding System (PACS; Talia et al., 2017), which is complex, resource-consuming and requires extensive training. To enable wide and scalable adoption of attachment informed treatment and research, we propose the first exploratory analysis into automatically assessing patient attachment style from psychotherapy transcripts using NLP classification models. We further analyze the results and discuss the implications of using automated tools for this purpose -- e.g., confusing `preoccupied' patients with `avoidant' likely has a more negative impact on therapy outcomes with respect to other mislabeling. Our work opens an avenue of research enabling more personalized psychotherapy and more targeted research into the mechanisms of psychotherapy through advancements in NLP.

This study performs a comprehensive evaluation of quantitative measurements as extracted from automated deep-learning-based segmentation methods, beyond traditional Dice Similarity Coefficient assessments, focusing on six quantitative metrics, namely SUVmax, SUVmean, total lesion activity (TLA), tumor volume (TMTV), lesion count, and lesion spread. We analyzed 380 prostate-specific membrane antigen (PSMA) targeted [18F]DCFPyL PET/CT scans of patients with biochemical recurrence of prostate cancer, training deep neural networks, U-Net, Attention U-Net and SegResNet with four loss functions: Dice Loss, Dice Cross Entropy, Dice Focal Loss, and our proposed L1 weighted Dice Focal Loss (L1DFL). Evaluations indicated that Attention U-Net paired with L1DFL achieved the strongest correlation with the ground truth (concordance correlation = 0.90-0.99 for SUVmax and TLA), whereas models employing the Dice Loss and the other two compound losses, particularly with SegResNet, underperformed. Equivalence testing (TOST, alpha = 0.05, Delta = 20%) confirmed high performance for SUV metrics, lesion count and TLA, with L1DFL yielding the best performance. By contrast, tumor volume and lesion spread exhibited greater variability. Bland-Altman, Coverage Probability, and Total Deviation Index analyses further highlighted that our proposed L1DFL minimizes variability in quantification of the ground truth clinical measures. The code is publicly available at: https://github.com/ObedDzik/pca\_segment.git.

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, highlighting the critical need for efficient and accurate diagnostic tools. Electrocardiograms (ECGs) are indispensable in diagnosing various heart conditions; however, their manual interpretation is time-consuming and error-prone. In this paper, we propose xLSTM-ECG, a novel approach that leverages an extended Long Short-Term Memory (xLSTM) network for multi-label classification of ECG signals, using the PTB-XL dataset. To the best of our knowledge, this work represents the first design and application of xLSTM modules specifically adapted for multi-label ECG classification. Our method employs a Short-Time Fourier Transform (STFT) to convert time-series ECG waveforms into the frequency domain, thereby enhancing feature extraction. The xLSTM architecture is specifically tailored to address the complexities of 12-lead ECG recordings by capturing both local and global signal features. Comprehensive experiments on the PTB-XL dataset reveal that our model achieves strong multi-label classification performance, while additional tests on the Georgia 12-Lead dataset underscore its robustness and efficiency. This approach significantly improves ECG classification accuracy, thereby advancing clinical diagnostics and patient care. The code will be publicly available upon acceptance.

Class imbalance occurs when data used for training classification models has a different number of observations or samples within each category or class. Models built on such data can be biased towards the majority class and have poor predictive performance and generalisation for the minority class. We propose a Bayesian weighted-likelihood (power-likelihood) approach to deal with class imbalance: each observation's likelihood is raised to a weight inversely proportional to its class proportion, with weights normalized to sum to the number of samples. This embeds cost-sensitive learning directly into Bayesian updating and is applicable to binary, multinomial and ordered logistic prediction models. Example models are implemented in Stan, PyMC, and Turing.jl, and all code and reproducible scripts are archived on Github: https://github.com/stanlazic/weighted_likelihoods. This approach is simple to implement and extends naturally to arbitrary error-cost matrices.

The intrinsic ill-posedness of this problem can be addressed within the framework of regularization theory (Kaipio & Somersalo 2006), i.e., as the problem of minimizing a non-linear functional made of the sum of two terms: a fitting term in which the empirical risk is assessed by means of a loss function, and a penalty term that allows generalization while controlling the complexity of the solution. Finally, a real positive regularization parameter that balances the trade-off between the two terms has to be chosen by means of some regularization algorithm (Engl et al. 1996). When described in a Hilbert space setting, a representer theorem (Sch olkopf et al. 2001; De Vito et al. 2004) provides an analytical solution of the minimum problem that is given by the action of a feature-dependent kernel operator onto a vector whose components can be analytically determined by means of classical Tikhonov regularization (Tikhonov 1963). From an operational perspective, a feature-based supervised machine learning process works as follows. Given an archive of annotated descriptors of the physical phenomenon, named features, 1. A standardization procedure generates a corresponding archive of annotated standardized features that are re-scaled and made dimensionless.

Fairness-aware learning aims to mitigate discrimination against specific protected social groups (e.g., those categorized by gender, ethnicity, age) while minimizing predictive performance loss. Despite efforts to improve fairness in machine learning, prior studies have shown that many models remain unfair when measured against various fairness metrics. In this paper, we examine whether the way training and testing data are sampled affects the reliability of reported fairness metrics. Since training and test sets are often randomly sampled from the same population, bias present in the training data may still exist in the test data, potentially skewing fairness assessments. To address this, we propose FairMatch, a post-processing method that applies propensity score matching to evaluate and mitigate bias. FairMatch identifies control and treatment pairs with similar propensity scores in the test set and adjusts decision thresholds for different subgroups accordingly. For samples that cannot be matched, we perform probabilistic calibration using fairness-aware loss functions. Experimental results demonstrate that our approach can (a) precisely locate subsets of the test data where the model is unbiased, and (b) significantly reduce bias on the remaining data. Overall, propensity score matching offers a principled way to improve both fairness evaluation and mitigation, without sacrificing predictive performance.

Since data annotation is costly, benchmark datasets often incorporate labels from established image datasets. In this work, we assess the impact of label errors in MSCOCO on the frequently used object hallucination benchmark POPE. We re-annotate the benchmark images and identify an imbalance in annotation errors across different subsets. Evaluating multiple models on the revised labels, which we denote as RePOPE, we observe notable shifts in model rankings, highlighting the impact of label quality. Code and data are available at https://github.com/YanNeu/RePOPE .

Exosomes are small vesicles crucial for cell signaling and disease biomarkers. Due to their complexity, an "omics" approach is preferable to individual biomarkers. While Raman spectroscopy is effective for exosome analysis, it requires high sample concentrations and has limited sensitivity to lipids and proteins. Surface-enhanced Raman spectroscopy helps overcome these challenges. In this study, we leverage Neo4j graph databases to organize 3,045 Raman spectra of exosomes, enhancing data generalization. To further refine spectral analysis, we introduce a novel spectral filtering process that integrates the PageRank Filter with optimal Dimensionality Reduction. This method improves feature selection, resulting in superior classification performance. Specifically, the Extra Trees model, using our spectral processing approach, achieves 0.76 and 0.857 accuracy in classifying hyperglycemic, hypoglycemic, and normal exosome samples based on Raman spectra and surface, respectively, with group 10-fold cross-validation. Our results show that graph-based spectral filtering combined with optimal dimensionality reduction significantly improves classification accuracy by reducing noise while preserving key biomarker signals. This novel framework enhances Raman-based exosome analysis, expanding its potential for biomedical applications, disease diagnostics, and biomarker discovery.

Area under the receiving operator characteristic curve (AUC) is commonly reported alongside binary prediction models. However, there are concerns that AUC might be a misleading measure of prediction performance in the rare event setting. This setting is common since many events of clinical importance are rare events. We conducted a simulation study to determine when or whether AUC is unstable in the rare event setting. Specifically, we aimed to determine whether the bias and variance of AUC are driven by the number of events or the event rate. We also investigated the behavior of other commonly used measures of prediction performance, including positive predictive value, accuracy, sensitivity, and specificity. Our results indicate that poor AUC behavior -- as measured by empirical bias, variability of cross-validated AUC estimates, and empirical coverage of confidence intervals -- is driven by the minimum class size, not event rate. Performance of sensitivity is driven by the number of events, while that of specificity is driven by the number of non-events. Other measures, including positive predictive value and accuracy, depend on the event rate even in large samples. AUC is reliable in the rare event setting provided that the total number of events is moderately large.