Data-driven learning algorithms are employed in many online applications, in which data become available over time, like network monitoring, stock price prediction, job applications, etc. The underlying data distribution might evolve over time calling for model adaptation as new instances arrive and old instances become obsolete. In such dynamic environments, the so-called data streams, fairness-aware learning cannot be considered as a one-off requirement, but rather it should comprise a continual requirement over the stream. Recent fairness-aware stream classifiers ignore the problem of class imbalance, which manifests in many real-life applications, and mitigate discrimination mainly because they "reject" minority instances at large due to their inability to effectively learn all classes. In this work, we propose \ours, an online fairness-aware approach that maintains a valid and fair classifier over the stream. \ours~is an online boosting approach that changes the training distribution in an online fashion by monitoring stream's class imbalance and tweaks its decision boundary to mitigate discriminatory outcomes over the stream. Experiments on 8 real-world and 1 synthetic datasets from different domains with varying class imbalance demonstrate the superiority of our method over state-of-the-art fairness-aware stream approaches with a range (relative) increase [11.2\%-14.2\%] in balanced accuracy, [22.6\%-31.8\%] in gmean, [42.5\%-49.6\%] in recall, [14.3\%-25.7\%] in kappa and [89.4\%-96.6\%] in statistical parity (fairness).

Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (StaPLR) is a recently introduced method that can be used for classification and automatically selecting the views that are most important for prediction. We show how this method can easily be extended to a setting where the data has a hierarchical multi-view structure. We apply StaPLR to Alzheimer's disease classification where different MRI measures have been calculated from three scan types: structural MRI, diffusion-weighted MRI, and resting-state fMRI. StaPLR can identify which scan types and which MRI measures are most important for classification, and it outperforms elastic net regression in classification performance.

The Reverse Transcription Polymerase Chain Reaction (RTPCR) test is the silver bullet diagnostic test to discern COVID infection. Rapid antigen detection is a screening test to identify COVID positive patients in little as 15 minutes, but has a lower sensitivity than the PCR tests. Besides having multiple standardized test kits, many people are getting infected & either recovering or dying even before the test due to the shortage and cost of kits, lack of indispensable specialists and labs, time-consuming result compared to bulk population especially in developing and underdeveloped countries. Intrigued by the parametric deviations in immunological & hematological profile of a COVID patient, this research work leveraged the concept of COVID-19 detection by proposing a risk-free and highly accurate Stacked Ensemble Machine Learning model to identify a COVID patient from communally available-widespread-cheap routine blood tests which gives a promising accuracy, precision, recall & F1-score of 100%. Analysis from R-curve also shows the preciseness of the risk-free model to be implemented. The proposed method has the potential for large scale ubiquitous low-cost screening application. This can add an extra layer of protection in keeping the number of infected cases to a minimum and control the pandemic by identifying asymptomatic or pre-symptomatic people early.

This will be a part of series of Machine Learning stories and this is the first one where we will cover few interesting but very basic concepts which is kind of must know for every budding data scientists or may be a professional one. A correlation coefficient tells you how strong, or how weak, the relationship is between two sets of data. In Mathematics, a coefficient is usually the number that is used to multiply a variable. So for this expression: 9x, the number 9 is the coefficient. A correlation between two variables or data sets indicates that as one variable changes in value, the other variable tends to change in a specific direction. It is also called the cross-correlation coefficient, Pearson correlation coefficient (PCC), or the Pearson product-moment correlation coefficient (PPMCC). Understanding this relationship is useful because the value of one variable allows us to predict the value of the other variable. For example, height and weight are correlated when it comes to your physique -- as height increases, the weight tends to increase too.

With the rise of social media in recent years, there has been a surge in interest in automatically identifying users based on their informal content. In this context, the research of other aspects intrinsic to users, such as political inclinations, personality, and gender, as well as the categorization of users in demographic categories such as age, ethnicity, origin, and race has gained a lot of interest notably based on Twitter data. The current work focuses on the job of gender categorization in tweets written in Portuguese by extracting gender expression linguistic cues utilizing 25 attributes, which are often employed on text attribution tasks. Predict user gender based on Twitter Profile information. The Data has been extracted from Kaggle.

Background: Cervical cancer seriously affects the health of the female reproductive system. Optical coherence tomography (OCT) emerges as a non-invasive, high-resolution imaging technology for cervical disease detection. However, OCT image annotation is knowledge-intensive and time-consuming, which impedes the training process of deep-learning-based classification models. Objective: This study aims to develop a computer-aided diagnosis (CADx) approach to classifying in-vivo cervical OCT images based on self-supervised learning. Methods: Besides high-level semantic features extracted by a convolutional neural network (CNN), the proposed CADx approach leverages unlabeled cervical OCT images' texture features learned by contrastive texture learning. We conducted ten-fold cross-validation on the OCT image dataset from a multi-center clinical study on 733 patients from China. Results: In a binary classification task for detecting high-risk diseases, including high-grade squamous intraepithelial lesion (HSIL) and cervical cancer, our method achieved an area-under-the-curve (AUC) value of 0.9798 Plus or Minus 0.0157 with a sensitivity of 91.17 Plus or Minus 4.99% and a specificity of 93.96 Plus or Minus 4.72% for OCT image patches; also, it outperformed two out of four medical experts on the test set. Furthermore, our method achieved a 91.53% sensitivity and 97.37% specificity on an external validation dataset containing 287 3D OCT volumes from 118 Chinese patients in a new hospital using a cross-shaped threshold voting strategy. Conclusion: The proposed contrastive-learning-based CADx method outperformed the end-to-end CNN models and provided better interpretability based on texture features, which holds great potential to be used in the clinical protocol of "see-and-treat."

Adversarial patch attack against image classification deep neural networks (DNNs), in which the attacker can inject arbitrary distortions within a bounded region of an image, is able to generate adversarial perturbations that are robust (i.e., remain adversarial in physical world) and universal (i.e., remain adversarial on any input). It is thus important to detect and mitigate such attack to ensure the security of DNNs. This work proposes Jujutsu, a technique to detect and mitigate robust and universal adversarial patch attack. Jujutsu leverages the universal property of the patch attack for detection. It uses explainable AI technique to identify suspicious features that are potentially malicious, and verify their maliciousness by transplanting the suspicious features to new images. An adversarial patch continues to exhibit the malicious behavior on the new images and thus can be detected based on prediction consistency. Jujutsu leverages the localized nature of the patch attack for mitigation, by randomly masking the suspicious features to "remove" adversarial perturbations. However, the network might fail to classify the images as some of the contents are removed (masked). Therefore, Jujutsu uses image inpainting for synthesizing alternative contents from the pixels that are masked, which can reconstruct the "clean" image for correct prediction. We evaluate Jujutsu on five DNNs on two datasets, and show that Jujutsu achieves superior performance and significantly outperforms existing techniques. Jujutsu can further defend against various variants of the basic attack, including 1) physical-world attack; 2) attacks that target diverse classes; 3) attacks that use patches in different shapes and 4) adaptive attacks.

Perceiving obstacles and avoiding collisions is fundamental to the safe operation of a robot system, particularly when the robot must operate in highly dynamic human environments. Proximity detection using on-robot sensors can be used to avoid or mitigate impending collisions. However, existing proximity sensing methods are orientation and placement dependent, resulting in blind spots even with large numbers of sensors. In this paper, we introduce the phenomenon of the Leaky Surface Wave (LSW), a novel sensing modality, and present AuraSense, a proximity detection system using the LSW. AuraSense is the first system to realize no-dead-spot proximity sensing for robot arms. It requires only a single pair of piezoelectric transducers, and can easily be applied to off-the-shelf robots with minimal modifications. We further introduce a set of signal processing techniques and a lightweight neural network to address the unique challenges in using the LSW for proximity sensing. Finally, we demonstrate a prototype system consisting of a single piezoelectric element pair on a robot manipulator, which validates our design. We conducted several micro benchmark experiments and performed more than 2000 on-robot proximity detection trials with various potential robot arm materials, colliding objects, approach patterns, and robot movement patterns. AuraSense achieves 100% and 95.3% true positive proximity detection rates when the arm approaches static and mobile obstacles respectively, with a true negative rate over 99%, showing the real-world viability of this system.

Although the fairness community has recognized the importance of data, researchers in the area primarily rely on UCI Adult when it comes to tabular data. Derived from a 1994 US Census survey, this dataset has appeared in hundreds of research papers where it served as the basis for the development and comparison of many algorithmic fairness interventions. We reconstruct a superset of the UCI Adult data from available US Census sources and reveal idiosyncrasies of the UCI Adult dataset that limit its external validity. Our primary contribution is a suite of new datasets derived from US Census surveys that extend the existing data ecosystem for research on fair machine learning. We create prediction tasks relating to income, employment, health, transportation, and housing. The data span multiple years and all states of the United States, allowing researchers to study temporal shift and geographic variation. We highlight a broad initial sweep of new empirical insights relating to trade-offs between fairness criteria, performance of algorithmic interventions, and the role of distribution shift based on our new datasets. Our findings inform ongoing debates, challenge some existing narratives, and point to future research directions. Our datasets are available at https://github.com/zykls/folktables.

Vision-and-language(V&L) models take image and text as input and learn to capture the associations between them. Prior studies show that pre-trained V&L models can significantly improve the model performance for downstream tasks such as Visual Question Answering (VQA). However, V&L models are less effective when applied in the medical domain (e.g., on X-ray images and clinical notes) due to the domain gap. In this paper, we investigate the challenges of applying pre-trained V&L models in medical applications. In particular, we identify that the visual representation in general V&L models is not suitable for processing medical data. To overcome this limitation, we propose BERTHop, a transformer-based model based on PixelHop++ and VisualBERT, for better capturing the associations between the two modalities. Experiments on the OpenI dataset, a commonly used thoracic disease diagnosis benchmark, show that BERTHop achieves an average Area Under the Curve (AUC) of 98.12% which is 1.62% higher than state-of-the-art (SOTA) while it is trained on a 9 times smaller dataset.