Differentially private federated learning (DP-FL) is a promising technique for collaborative model training while ensuring provable privacy for clients. However, optimizing the tradeoff between privacy and accuracy remains a critical challenge. To our best knowledge, we propose the first DP-FL framework (namely UDP-FL), which universally harmonizes any randomization mechanism (e.g., an optimal one) with the Gaussian Moments Accountant (viz. DP-SGD) to significantly boost accuracy and convergence. Specifically, UDP-FL demonstrates enhanced model performance by mitigating the reliance on Gaussian noise. The key mediator variable in this transformation is the R\'enyi Differential Privacy notion, which is carefully used to harmonize privacy budgets. We also propose an innovative method to theoretically analyze the convergence for DP-FL (including our UDP-FL ) based on mode connectivity analysis. Moreover, we evaluate our UDP-FL through extensive experiments benchmarked against state-of-the-art (SOTA) methods, demonstrating superior performance on both privacy guarantees and model performance. Notably, UDP-FL exhibits substantial resilience against different inference attacks, indicating a significant advance in safeguarding sensitive data in federated learning environments.

Graph contrastive learning (GCL) is an effective paradigm for node representation learning in graphs. The key components hidden behind GCL are data augmentation and positive-negative pair selection. Typical data augmentations in GCL, such as uniform deletion of edges, are generally blind and resort to local perturbation, which is prone to producing under-diversity views. Additionally, there is a risk of making the augmented data traverse to other classes. Moreover, most methods always treat all other samples as negatives. Such a negative pairing naturally results in sampling bias and likewise may make the learned representation suffer from semantic drift. Therefore, to increase the diversity of the contrastive view, we propose two simple and effective global topological augmentations to compensate current GCL. One is to mine the semantic correlation between nodes in the feature space. The other is to utilize the algebraic properties of the adjacency matrix to characterize the topology by eigen-decomposition. With the help of both, we can retain important edges to build a better view. To reduce the risk of semantic drift, a prototype-based negative pair selection is further designed which can filter false negative samples. Extensive experiments on various tasks demonstrate the advantages of the model compared to the state-of-the-art methods.

Machine learning models have the potential to identify cardiovascular diseases (CVDs) early and accurately in primary healthcare settings, which is crucial for delivering timely treatment and management. Although population-based CVD risk models have been used traditionally, these models often do not consider variations in lifestyles, socioeconomic conditions, or genetic predispositions. Therefore, we aimed to develop machine learning models for CVD detection using primary healthcare data, compare the performance of different models, and identify the best models. We used data from the UK Biobank study, which included over 500,000 middle-aged participants from different primary healthcare centers in the UK. Data collected at baseline (2006--2010) and during imaging visits after 2014 were used in this study. Baseline characteristics, including sex, age, and the Townsend Deprivation Index, were included. Participants were classified as having CVD if they reported at least one of the following conditions: heart attack, angina, stroke, or high blood pressure. Cardiac imaging data such as electrocardiogram and echocardiography data, including left ventricular size and function, cardiac output, and stroke volume, were also used. We used 9 machine learning models (LSVM, RBFSVM, GP, DT, RF, NN, AdaBoost, NB, and QDA), which are explainable and easily interpretable. We reported the accuracy, precision, recall, and F-1 scores; confusion matrices; and area under the curve (AUC) curves.

Researchers in the political and social sciences often rely on classification models to analyze trends in information consumption by examining browsing histories of millions of webpages. Automated scalable methods are necessary due to the impracticality of manual labeling. In this paper, we model the detection of topic-related content as a binary classification task and compare the accuracy of fine-tuned pre-trained encoder models against in-context learning strategies. Using only a few hundred annotated data points per topic, we detect content related to three German policies in a database of scraped webpages. We compare multilingual and monolingual models, as well as zero and few-shot approaches, and investigate the impact of negative sampling strategies and the combination of URL & content-based features. Our results show that a small sample of annotated data is sufficient to train an effective classifier. Fine-tuning encoder-based models yields better results than in-context learning. Classifiers using both URL & content-based features perform best, while using URLs alone provides adequate results when content is unavailable.

Long-running machine learning models face the issue of concept drift (CD), whereby the data distribution changes over time, compromising prediction performance. Updating the model requires detecting drift by monitoring the data and/or the model for unexpected changes. We show that, however, spurious correlations (SCs) can spoil the statistics tracked by detection algorithms. Motivated by this, we introduce ebc-exstream, a novel detector that leverages model explanations to identify potential SCs and human feedback to correct for them. It leverages an entropy-based heuristic to reduce the amount of necessary feedback, cutting annotation costs. Our preliminary experiments on artificially confounded data highlight the promise of ebc-exstream for reducing the impact of SCs on detection.

--The increasing popularity of online services has made Internet Traffic Classification a critical field of study. However, the rapid development of internet protocols and encryption limits usable data availability. This paper addresses the challenges of classifying encrypted internet traffic, focusing on the scarcity of open-source datasets and limitations of existing ones. We propose two Data Augmentation (DA) techniques to synthetically generate data based on real samples: Average augmentation and MTU augmentation. Both augmentations are aimed to improve the performance of the classifier, each from a different perspective: The Average augmentation aims to increase dataset size by generating new synthetic samples, while the MTU augmentation enhances classifier robustness to varying Maximum Transmission Units (MTUs). Our experiments, conducted on two well-known academic datasets and a commercial dataset, demonstrate the effectiveness of these approaches in improving model performance and mitigating constraints associated with limited and homogeneous datasets. Our findings underscore the potential of data augmentation in addressing the challenges of modern internet traffic classification. Specifically, we show that our augmentation techniques significantly enhance encrypted traffic classification models. This improvement can positively impact user Quality of Experience (QoE) by more accurately classifying traffic as video streaming (e.g., Y ouT ube) or chat (e.g., Google Chat). Additionally, it can enhance Quality of Service (QoS) for file downloading activities (e.g., Google Docs).

Principal component analysis (PCA) is undoubtedly one of the most important unsupervised statistical methods available. The basic idea is to project the observations in a given dataset onto a new vector space with orthonormal basis where each basis vector is a linear combination of the original variables constructed to capture the highest variability for the first basis vector, the second highest variability for the second basis vector and so on. The new variables are called Principal Components (PC), the coordinates of the PCs in the original variable space are called loadings and the coordinates of the observations with respect to the PCs are called scores. Often, only the first few PCs that catch a majority of the variance and thus of the available information are analyzed. As such, PCA finds widespread application across numerous areas, such as dimensionality reduction, visualization, clustering, feature engineering and many more. For standard PCA we get loadings that are often a combination of all variables involved. Especially nowadays with datasets consisting of many variables, sensible, efficient and correct interpretation of scores and loadings can get difficult. Moreover, by implicitly (or also explicitly) focusing the interpretation on large (absolute) loading entries and ignoring small ones, misleading interpretation results can be produced as discussed in Cadima and Jolliffe (1995). Therefore, induced sparsity in the loading entries is necessary to ensure correct interpretation of PCA results.

Autism Spectrum Disorder (ASD) is a complex neuro-developmental challenge, presenting a spectrum of difficulties in social interaction, communication, and the expression of repetitive behaviors in different situations. This increasing prevalence underscores the importance of ASD as a major public health concern and the need for comprehensive research initiatives to advance our understanding of the disorder and its early detection methods. This study introduces a novel hierarchical feature fusion method aimed at enhancing the early detection of ASD in children through the analysis of code-switched speech (English and Hindi). Employing advanced audio processing techniques, the research integrates acoustic, paralinguistic, and linguistic information using Transformer Encoders. This innovative fusion strategy is designed to improve classification robustness and accuracy, crucial for early and precise ASD identification. The methodology involves collecting a code-switched speech corpus, CoSAm, from children diagnosed with ASD and a matched control group. The dataset comprises 61 voice recordings from 30 children diagnosed with ASD and 31 from neurotypical children, aged between 3 and 13 years, resulting in a total of 159.75 minutes of voice recordings. The feature analysis focuses on MFCCs and extensive statistical attributes to capture speech pattern variability and complexity. The best model performance is achieved using a hierarchical fusion technique with an accuracy of 98.75% using a combination of acoustic and linguistic features first, followed by paralinguistic features in a hierarchical manner.

In recent years, the clandestine nature of darknet activities has presented an escalating challenge to cybersecurity efforts, necessitating sophisticated methods for the detection and classification of network traffic associated with these covert operations. The system addresses the significant challenge of class imbalance within Darknet traffic datasets, where malicious traffic constitutes a minority, hindering effective discrimination between normal and malicious behavior. By leveraging boosting algorithms like AdaBoost and Gradient Boosting coupled with decision trees, this study proposes a robust solution for network traffic classification. Boosting algorithms ensemble learning corrects errors iteratively and assigns higher weights to minority class instances, complemented by the hierarchical structure of decision trees. The additional Feature Selection which is a preprocessing method by utilizing Information Gain metrics, Fisher's Score, and Chi-Square test selection for features is employed. Rigorous experimentation with diverse Darknet traffic datasets validates the efficacy of the proposed multistage classifier, evaluated through various performance metrics such as accuracy, precision, recall, and F1-score, offering a comprehensive solution for accurate detection and classification of Darknet activities.

This work presents the solution of the Signal Sleuths team for the 2024 HASCA WEAR challenge. The challenge focuses on detecting 18 workout activities (and the null class) using accelerometer data from 4 wearables - one worn on each limb. Data analysis revealed inconsistencies in wearable orientation within and across participants, leading to exploring novel multi-wearable data augmentation techniques. We investigate three models using a fixed feature set: (i) "raw": using all data as is, (ii) "left-right swapping": augmenting data by swapping left and right limb pairs, and (iii) "upper-lower limb paring": stacking data by using upper-lower limb pair combinations (2 wearables). Our experiments utilize traditional machine learning with multi-window feature extraction and temporal smoothing. Using 3-fold cross-validation, the raw model achieves a macro F1-score of 90.01%, whereas left-right swapping and upper-lower limb paring improve the scores to 91.30% and 91.87% respectively.