Abstract Chest X-rays are an inexpensive and widely available imaging modality for diagnosing or monitoring a variety of medical conditions. Given their abundance, healthcare providers could greatly benefit from automated systems capable of screening healthy patients and supporting the diagnosis of pathological cases. Deep learning has become central to such decision-support systems, offering accurate and efficient image classification that can improve clinical workflows and reduce radiologist workload. However, despite the rapid evolution of general-purpose neural architectures, particularly attention-based models, their application to medical imaging remains constrained by limited incorporation of medical domain knowledge. Most existing attention mechanisms optimize only task-specific losses, disregarding crucial anatomical and lesion-location priors, which can hinder generalization and interpretability. In this work, we introduce a fully automated, attention-guided classification framework that integrates medical priors through an on-the-fly segmentation of the lungs, followed by a spatially aware attention loss that directs the network’s focus toward clinically relevant regions. The method requires minimal physician input—only a single annotated X-ray indicating potential lesion areas at initialization and generalizes effectively across patients without relying on absolute bounding-box coordinates. Gradient-based activation mapping is further employed to ensure alignment between attention and lesion-specific regions. Our approach is architecture-agnostic and integrates seamlessly into end-to-end pipelines. Experiments on two medical image datasets demonstrate that the proposed segmentation-enhanced attention loss improves both classification accuracy and representation interpretability compared to the standard cross-entropy loss. The code is available at: https://github.com/rcorizzo/cxr-segmentation-attention/ .

Online hate speech proliferation has created a difficult problem for social media platforms. A particular challenge relates to the use of coded language by groups interested in both creating a sense of belonging for its users and evading detection. Coded language evolves quickly and its use varies over time. This paper proposes a methodology for detecting emerging coded hate-laden terminology. The methodology is tested in the context of online antisemitic discourse. The approach considers posts scraped from social media platforms, often used by extremist users. The posts are scraped using seed expressions related to previously known discourse of hatred towards Jews. The method begins by identifying the expressions most representative of each post and calculating their frequency in the whole corpus. It filters out grammatically incoherent expressions as well as previously encountered ones so as to focus on emergent well-formed terminology. This is followed by an assessment of semantic similarity to known antisemitic terminology using a fine-tuned large language model, and subsequent filtering out of the expressions that are too distant from known expressions of hatred. Emergent antisemitic expressions containing terms clearly relating to Jewish topics are then removed to return only coded expressions of hatred.

A common challenge in classification and data mining is the class imbalance problem, where one class in a dataset has significantly more samples than another. The presence of this problem typically worsens the performance of classifiers trained on these datasets by giving them a strong preference for better-represented classes above others. Resampling is the process of adding (oversampling) or taking away (undersampling) samples from a dataset, typically with the purpose of introducing balance to the dataset before a classifier is trained. With recent advances in generative large language models (LLMs), certain authors have proposed the use of these models to generate new samples as a form of oversampling for imbalanced text data. This method has shown initial success, but has not been systematically compared to more traditional methods of resampling across many domains. We find that this use of generative LLMs generally outperforms other methods on imbalanced multiclass classification, but not on binary classification.

Prolonged air leak is a complication arising from a collapsed lung which can lead to serious illness such as pneumonia and empyema, and patient suffering from indwelling chest tubes. Drainage of air and liquid from chest drains can be monitored and recorded using novel digital chest drainage devices. The collected data can be analyzed by predictive models, which can provide decision support in chest tube management. Despite the promising adoption of predictive models in this context, existing approaches are still in their infancy and are mostly based on autoregressive and conventional machine learning models. In this paper, we present a LSTM-based model architecture for air leak forecasting that is able to deal with non-linear dependencies among different features and contiguous time points. We devise a post-processing procedure that leverages predictions to suggest whether the patient could have their chest tube safely removed in the upcoming hours, and evaluate the results according to a medical protocol. Experimental results show that our model is able to outperform currently adopted models, in terms of both forecasting and classification performance, suggesting the feasibility of our approach for chest tube management.

Anomaly detection tools present the potential to enhance defense policies and protection against different types of threats, supporting public safety and national security. Lifelong anomaly detection showcases new and challenging scenarios in which models are challenged to automatically adapt to changing conditions without forgetting past knowledge. However, the presence of anomalies in incoming data may significantly impact the robustness of models in such scenarios. Although active learning strategies could be an asset to increase model longevity and robustness, they have never been explored in this context. In this paper, we propose an active lifelong anomaly detection framework for class-incremental scenarios that supports any memory-based experience replay method, any query strategy, and any anomaly detection model. While experience replay allows models to consolidate past knowledge and simultaneously adapt to new knowledge, an active learning module reduces the number of anomalies memorized in the replay buffer. We propose two strategies that automatically identify and remove additional data points that are likely to be anomalies based on the oracle’s feedback. Our experiments on popular host-based and network-based intrusion detection datasets show that our framework can improve the anomaly detection performance of models under low labeling budget constraints.

Recently, we have witnessed that deep learning-based approaches have been widely applied. Particularly, some applications involve data that are high dimensional, sparse or imbalanced, which are different from those applications with dense data processing, such as image classification and speech recognition, where deep learning-based approaches have been extensively studied. One of the main applications is the user-centric platform that consists of great deal of users, items and user generated tabular data which are quite high-dimensional. The characteristics of such data pose unique challenges to the adoption of deep learning in these applications, including modeling, training, and online serving, etc. More and more communities from both academia and industry have initiated the endeavors to solve these challenges. This workshop will provide a venue for both the research and engineering communities to discuss and formulate the challenges, utilize opportunities, and propose new ideas in the practice and theory of deep learning on high-dimensional, sparse and imbalanced data.

Recent advances in medical imaging and deep learning have enabled the efficient analysis of large databases of images. Notable examples include the analysis of computed tomography (CT), magnetic resonance imaging (MRI), and X-ray. While the automatic classification of images has proven successful, adopting such a paradigm in the medical healthcare setting is unfeasible. Indeed, the physician in charge of the detailed medical assessment and diagnosis of patients cannot trust a deep learning model’s decisions without further explanations or insights about their classification outcome. In this study, rather than relying on classification, we propose a new method that leverages deep neural networks to extract a representation of images and further analyze them through clustering, dimensionality reduction for visualization, and class activation mapping. Thus, the system does not make decisions on behalf of physicians. Instead, it helps them make a diagnosis. Experimental results on lung images affected by Pneumonia and Covid-19 lesions show the potential of our method as a tool for decision support in a medical setting. It allows the physician to identify groups of similar images and highlight regions of the input that the model deemed important for its predictions.

Detecting relevant changes in dynamic time series data in a timely manner is crucially important for many data analysis tasks in real-world settings. Change point detection methods have the ability to discover changes in an unsupervised fashion, which represents a desirable property in the analysis of unbounded and unlabeled data streams. However, one limitation of most of the existing approaches is represented by their limited ability to handle multivariate and high-dimensional data, which is frequently observed in modern applications such as traffic flow prediction, human activity recognition, and smart grids monitoring. In this paper, we attempt to fill this gap by proposing WATCH, a novel Wasserstein distance-based change point detection approach that models an initial distribution and monitors its behavior while processing new data points, providing accurate and robust detection of change points in dynamic high-dimensional data. An extensive experimental evaluation involving a large number of benchmark datasets shows that WATCH is capable of accurately identifying change points and outperforming state-of-the-art methods.

Structural concept complexity, class overlap, and data scarcity are some of the most important factors influencing the performance of classifiers under class imbalance conditions. When these effects were uncovered in the early 2000s, understandably, the classifiers on which they were demonstrated belonged to the classical rather than Deep Learning categories of approaches. As Deep Learning is gaining ground over classical machine learning and is beginning to be used in critical applied settings, it is important to assess systematically how well they respond to the kind of challenges their classical counterparts have struggled with in the past two decades. The purpose of this paper is to study the behavior of deep learning systems in settings that have previously been deemed challenging to classical machine learning systems to find out whether the depth of the systems is an asset in such settings. The results in both artificial and real-world image datasets show that these settings remain mostly challenging for Deep Learning systems. Deeper architectures help with structural concept complexity but not with data scarcity and class overlap.

After lung surgery, a chest tube and a pump are used to manage air leaks and fluid drainage from the chest. The decision to remove or maintain the chest tube is based on drainage data collected from a digital pump that continuously monitors the patient. We construct a classifier to support this clinical decision-making process by identifying patients who may suffer adverse, extended air leaks early on. Intuitively, this problem can be modelled as a time-series fitted to monitoring data. However, we present a solution using a simple classifier constructed from data collected in a specific time frame (36- 48 hours) after surgery. We hypothesize that after surgery, patients struggle to attain a stable (favourable or adverse) status which prevails after a period of discrepancies and inconsistencies in the data. A solutions, we propose, is to identify this time frame when the majority of patients achieve their states of stability. Advantages of this approach include better classification performance with a lower burden of data collection during patient treatment. The paper presents the chest tube management as a classification task performed in a sliding window over time during patient monitoring. Our results show that reliable predictions can be achieved in the time window we identify, and that our classifier reduces unsafe chest tube removals at the expense of potentially maintaining a few that can be removed, i.e., we ensure that chest tubes that need to be maintained are not removed with potentially maintaining a few unnecessarily.

As the distribution of a data stream evolves over time, a learner must adapt to its distributional shifts in order to make accurate predictions. In the context of anomaly detection, it is crucial for the learner to distinguish between natural changes in distribution and true anomalies in the data stream. This is the problem we focus on in this study which considers the situation where only normal data are available for initial training, but subsequent data can be either normal or anomalous. In that context, it is necessary to train a one-class learning anomaly detection system on the normal data and let the system output a score representing the degree of normalcy or outlierness that each data point in the subsequent data stream exhibits. The system then uses a threshold to discriminate between normal and anomalous instances. In the case of data stream, the data distribution may shift overtime, and a fixed threshold could develop a high false alarm rate or a low outlier detection rate in case of concept drift. To this end, we designed an adaptive sliding window approach which updates the threshold when necessary based on the scores distribution. Experimental results show that our method improves the performance of base anomaly detectors by dynamically updating the threshold of the scores when needed rather than using a fixed threshold or an adaptive threshold with fixed window sizes.

The class imbalance problem is a pervasive issue in many real-world domains. Oversampling methods that inflate the rare class by generating synthetic data are amongst the most popular techniques for resolving class imbalance. However, they concentrate on the characteristics of the minority class and use them to guide the oversampling process. By completely overlooking the majority class, they lose a global view on the classification problem and, while alleviating the class imbalance, may negatively impact learnability by generating borderline or overlapping instances. This becomes even more critical when facing extreme class imbalance, where the minority class is strongly underrepresented and on its own does not contain enough information to conduct the oversampling process. We propose a novel method for synthetic oversampling that uses the rich information inherent in the majority class to synthesize minority class data. This is done by generating synthetic data that is at the same Mahalanbois distance from the majority class as the known minority instances. We evaluate over 26 benchmark datasets, and show that our method offers a distinct performance improvement over the existing state-of-the-art in oversampling techniques.

Modern automobiles have been proven vulnerable to hacking by security researchers. By exploiting vulnerabilities in the car's external interfaces, such as wifi, bluetooth, and physical connections, they can access a car's controller area network (CAN) bus. On the CAN bus, commands can be sent to control the car, for example cutting the brakes or stopping the engine. While securing the car's interfaces to the outside world is an important part of mitigating this threat, the last line of defence is detecting malicious behaviour on the CAN bus. We propose an anomaly detector based on a Long Short-Term Memory neural network to detect CAN bus attacks. The detector works by learning to predict the next data word originating from each sender on the bus. Highly surprising bits in the actual next word are flagged as anomalies. We evaluate the detector by synthesizing anomalies with modified CAN bus data. The synthesized anomalies are designed to mimic attacks reported in the literature. We show that the detector can detect anomalies we synthesized with low false alarm rates. Additionally, the granularity of the bit predictions can provide forensic investigators clues as to the nature of flagged anomalies.

Multi-class learning is an important task in Data Science. One of the ways to achieve good performance on this task is to use Error Correcting Output Codes (ECOC), which is a powerful ensemble learning method that transforms a multi-class problem into a series of binary classifiers which it uses indirectly to learn the original multi-class problem. A crucial component of ECOC is the design of the coding matrix, which determines which binary problems should be combined to achieve multi-class classification. There are two general ways of designing the coding matrix. One is rooted in information theory while the other is data driven. In this work, we investigate the data-driven approach which was previously shown to bear greater promise and propose a better search through the coding-matrix space, keeping in mind the tradeoff between efficiency and effectiveness, as well as considerations about class-imbalance issues in the underlying binary problems. After consolidating our hy! potheses with a study on artificial domains, we propose the Unsupervised Deep Search Algorithm (UDS) coupled with re-sampling, to address both concerns. Our results on real world domains show that our method outperforms traditional multi-class learning methods.

Learning with class imbalance is always a challenging task in many real world applications such as the Internet, surveillance, security, and finance. Like many other successful machine learning algorithms, the success of the support vector machine (SVM) is limited when it is applied to the problem of learning from imbalanced datasets. SVM with different error costs has been widely used to deal with the class imbalanced problem. In this paper, we are trying to apply an instance-weighted variant of the SVM with both 1-norm and 2-norm format to deal with the class imbalance problem. We develop an asymmetric boosting method on the weights of the tradeoff parameters to optimize the instance-weighted SVM. The experimental results on the benchmark datasets show that the proposed algorithm is effective on the class imbalanced problem.

Multi-instance (MI) learning is different than standard propositional classification, as it uses a set of bags containing many instances as input. While the instances in each bag are not labeled, the bags themselves are, as positive or negative. In this paper, we present a novel multi-view, two-level classification framework to address the generalized multi-instance problems. We first apply supervised and unsupervised learning methods to transform a MI dataset into a multi-view, single meta-instance dataset. Then we develop a multi-view learning approach that can integrate the information acquired by individual view learners on the meta-instance dataset from the previous step, and construct a final model. Our empirical studies show that the proposed method performs well compared to other popular MI learning methods.

We have entered the big data age. Knowledge extraction from massive data is becoming more and more rewarding and urgent. MapReduce has provided a feasible framework for programming machine learning algorithms in Map and Reduce functions. The relatively simple programming interface has helped to solve machine learning algorithms' scalability problems. However, this framework suffers from an obvious weakness: it does not support iterations. This makes those algorithms requiring iterations difficult to fully explore the efficiency of MapReduce. In this paper, we propose to apply Meta-learning programmed with MapReduce to avoid parallelizing machine learning algorithms while also improving their scalability to big datasets. The experiments conducted on Hadoop fully distributed mode on Amazon EC2 demonstrate that our algorithm PML reduces the training computational complexity significantly when the number of computing nodes increases while gaining smaller error rates than those on one single node. The comparison of PML with the contemporary parallelized AdaBoost algorithm: AdaBoost.PL shows that PML has lower error rates.

Recently, progressive border sampling (PBS) was proposed for sample selection in supervised learning by progressively learning an augmented full border from small labeled datasets. However, this quadratic learning algorithm is inapplicable to large datasets. In this paper, we incorporate the PBS to a state of the art technique called coupling Markov chain Monte Carlo (CMCMC) in an attempt to scale the original algorithm up on large labeled datasets. The CMCMC can produce an exact sample while a naive strategy for Markov chain Monte Carlo cannot guarantee the convergence to a stationary distribution. The resulting CMCMC-PBS algorithm is thus proposed for border sampling on large datasets. CMCMC-PBS exhibits several remarkable characteristics: linear time complexity, learner-independence, and a consistent convergence to an optimal sample from the original training sets by learning from their subsamples. Our experimental results on the 33 either small or large labeled datasets from the UCIKDD repository and a nuclear security application show that our new approach outperforms many previous sampling techniques for sample selection.

Anti-virus systems traditionally use signatures to detect malicious executables, but signatures are over-fitted features that are of little use in machine learning. Other more heuristic methods seek to utilize more general features, with some degree of success. In this paper, we present a data mining approach that conducts an exhaustive feature search on a set of computer viruses and strives to obviate over-fitting. We also evaluate the predictive power of a classifier by taking into account dependence relationships that exist between viruses, and we show that our classifier yields high detection rates and can be expected to perform as well in real-world conditions.