Abstract

Robustness towards adversarial attacks is a vital property for classifiers in several applications such as autonomous driving, medical diagnosis, etc. Also, in such scenarios, where the cost of misclassification is very high, knowing when to abstain from prediction becomes crucial. A natural question is which surrogates can be used to ensure learning in scenarios where the input points are adversarially perturbed and the classifier can abstain from prediction? This paper aims to characterize and design surrogates calibrated in enquote{Adversarial Robust Reject Option} setting. First, we propose an adversarial robust reject option loss $ell_{d}^{gamma}$ and analyze it for the hypothesis set of linear classifiers ($mathcal{H}_{textrm{lin}}$). Next, we provide a complete characterization result for any surrogate to be $(ell_{d}^{gamma},mathcal{H}_{textrm{lin}})$- calibrated. To demonstrate the difficulty in designing surrogates to $ell_{d}^{gamma}$, we show negative calibration results for convex surrogates and quasi-concave conditional risk cases (these gave positive calibration in adversarial setting without reject option). We also empirically argue that Shifted Double Ramp Loss (DRL) and Shifted Double Sigmoid Loss (DSL) satisfy the calibration conditions. Finally, we demonstrate the robustness of shifted DRL and shifted DSL against adversarial perturbations on a synthetically generated dataset.

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Purpose : This study aims to develop an artificial intelligence (AI)-based system for accurately grading arrested retinal development using optical coherence tomography (OCT) across various manufacturers. We employ deep learning techniques, specifically Unsupervised Domain Adaptation (UDA), to create a device-agnostic classification model distinguishing normal and abnormal retinal development. Methods : Foveal scans from three OCT manufacturers (TM-OCT1, HH-OCT1, TM-OCT2, TM-OCT3) were collected and annotated from datasets exceeding 20,000 OCT scans. The dataset was divided into training (80%) and testing (20%) sets. We utilised Convolutional Neural Networks (CNN) with ResNet50 backbone, assessing each pair as the source (for supervised training) and target (for unsupervised training). The diagnostic accuracy of the AI models was compared to seven clinician graders with varying experience (1 to 10 years), evaluating sensitivity, specificity, and overall accuracy. Results : The cross-domain binary classification demonstrated exceptional diagnostic accuracy ranging from 87.75% to 96.06%. Sensitivity and specificity metrics further validated the robustness of our AI system (sensitivity: 88.68% to 98.38%, specificity: 78.00% to 98.02%). Notably, the model trained on TM-OCT1 and HH-OCT1 achieved 96.06% accuracy, 98.38% sensitivity, and 89.11% specificity on the HH-OCT1 test set, with an Area Under the Curve (AUC) of 99.4 (95% CI). These outcomes highlight the system's ability to distinguish normal and abnormal retinal development across diverse OCT devices. Conclusions : Our study demonstrates, for the first time, the feasibility of employing a device-agnostic AI system in paediatric OCT interpretation without additional labelled data. The AI system's diagnostic performance is comparable to a clinician with over 10 years of experience, showcasing its potential to reduce inter-examiner variability and enhance clinical care pathways. With integration of paediatric OCT into routine clinical assessment, our AI system serves as a robust foundation for the development of a real-time, frontline diagnostic tool for retinal developmental disorders.

Abstract

Flapping-wing robots (FWR) have domain-specific applications, where the lack of a fast-rotating propeller makes them safer when operating in complex environments with human proximity. However, most existing research in flapping-wing robots focuses on improving range/endurance or increasing payload capacity. This paper proposes a modular powertrain-based flapping-wing robot as a versatile solution to a mission-specific priority switch between payload or range for the same FWR. As the flapping frequency and stroke amplitude directly influence the flight characteristics of the FWR, we exploit this relation when designing our swappable powertrain with different motorgearbox combinations and 4-bar crank lengths to obtain the desired frequency and amplitude. We calculate initial estimates for default configuration and simulate it using pterasoftware. We then fabricate two powertrain modules - a default configuration with a higher flapping frequency for payload purposes and an extended-range configuration with a tandem propeller for higher flight velocity for longer range and endurance. To verify the results, we compare the flight test data of both power train configurations using the same FWR platform.

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We propose an approach to train a fair cost-based abstain option classifier. Existing literature on fairness in classification with abstention is limited, covering only coverage-based abstention models. In coverage-based abstention models, the target coverage is decided before-hand and is kept the same for all the groups. In contrast, cost-based approaches introduce a cost for abstention, which can cause uneven abstention rates between different groups, leading to an unfair system. We extend the independence and separation fairness criteria to consider abstention. We provide a model-agnostic in-processing algorithm to incorporate these constraints in the models. We demonstrate the algorithm’s efficacy by experimenting with two different cost-based abstain option classifiers. Additionally, we explore mixing constraints from independence and separation criteria into one model, which is impossible in a binary classification task.

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The recent rise of CubeSat has revolutionized global space explorations, as it offers cost-effective solutions for low-orbit space applications (including climate monitoring, weather measurements, communications, and earth observation). A salient feature of CubeSat is that applications currently on-boarded can either be updated or entirely replaced by new applications via software updates, which allows reusing in-orbit hardware, reduces space debris, and saves cost as well as time. Securing software updates employing traditional methods (e.g., encryption) remains impractical mainly due to the low-resource capabilities of CubeSat. Therefore, the security of software updates for CubeSats remains a critical issue. In this paper, we propose CubeSat Update Mechanism~(CSUM), a lightweight scheme to provide integrity, authentication, and data freshness guarantees for software update broadcasts to CubeSats using a hash chain. We empirically evaluate our proof of concept implementation to demonstrate the feasibility and effectiveness of our approach. CSUM can validate 50,000 consecutive updates successfully in less than a second. We also perform a comparative analysis of different cryptographic primitives. Our empirical evaluations show that the hash-based approach is at least 61× faster than the conventional mechanisms, even in resource-constrained environments.

Abstract

Automating end-to-end Exploratory Data Analysis (AutoEDA) is a challenging open problem, often tackled through Reinforcement Learning (RL) by learning to predict a sequence of analysis operations (FILTER, GROUP, etc). Defining rewards for each operation is a challenging task and existing methods rely on various emph{interestingness measures} to craft reward functions to capture the importance of each operation. In this work, we argue that not all of the essential features of what makes an operation important can be accurately captured mathematically using rewards. We propose an AutoEDA model trained through imitation learning from expert EDA sessions, bypassing the need for manually defined interestingness measures. Our method, based on generative adversarial imitation learning (GAIL), generalizes well across datasets, even with limited expert data. We also introduce a novel approach for generating synthetic EDA demonstrations for training. Our method outperforms the existing state-of-the-art end-to-end EDA approach on benchmarks by upto 3x, showing strong performance and generalization, while naturally capturing diverse interestingness measures in generated EDA sessions.

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Electrically conductive fibers are attracting attention because of the newly emerging areas of interactive textiles and smart fabrics. In this paper, we present the fabrication and characterization of a flexible conductive cotton fabric (CCF) of size 20 cm×20 cm coated with polypyrrole synthesized using in-situ chemical oxidative polymerization. This is the largest area reported in the literature for a polypyrrole-coated conducting fabric. The characteristics of the conducting fabric were investigated at 25 points by sandwiching it between the electrodes in the form of a 5×5 array. To test its reliability, the CCF was washed with distilled water 10 times, and its response was measured after every wash. We observed that there was a change in the resistance after the first four washes, however, the response stabilized thereafter. The average deviation in resistance until first four washes was found to be 27.45%, whereas for washes 6 to 10, it was 5.11%.

Abstract

Skip to content IOP Science home Accessibility Help Search Journals Books Publishing Support Login Engineering Research Express Inclusive Publishing Trusted Science, find out more. Purpose-led Publishing, find out more. ACCEPTED MANUSCRIPT A label-free sensing of creatinine using radio frequency-driven lab-on-chip (loc) system Andleeb Zahra1, Swarnim Sinha2, Alimpan Modak3, Imran Siddiqui4, Azeemuddin Syed5, Prabhakar Bhimalapuram6, Tapan K. Sau7, Pawan Kumar8 and Zia Abbas9 Accepted Manuscript online 2 August 2024 • © 2024 IOP Publishing Ltd What is an Accepted Manuscript? DOI 10.1088/2631-8695/ad6ad5 DownloadAccepted Manuscript PDF Download PDF Article metrics 6 Total downloads Submit Submit to this Journal Permissions Get permission to re-use this article Share this article Article and author information Abstract This paper presents a promising avenue of Radio Frequency (RF) biosensors for sensitive and real-time monitoring of creatinine detection. Knowing creatinine levels in the human body is related to its possible association with renal, muscular, and thyroid dysfunction. The detection was performed using an Inter-Digitated Capacitor (IDC) made of copper (Cu) metal over an FR4 substrate. To demonstrate our methodology, we have chosen Phosphate Buffer (PB) as our solvent for making the creatinine solutions of different concentrations. Moreover, Assayed Chemistry Control (ACC), a reference control consisting of human serum-based solutions has been mixed with the different concentrations of creatinine in a ratio of 1:9 to spike the creatinine value in the ACC solution. The sensor has been designed using a High-Frequency Structure Simulator (HFSS) tool with an operating frequency of 2.53 GHz. Then the design is fabricated over the FR4 printed circuit board (PCB) and tested using a Vector Network Analyzer (VNA). However, the sensitive area of the IDC is introduced to grade 4 Whatman filter paper for the Sample Under Test (SUT) handling unit. The main advantage of using Whatman filter paper is that the uniform spreading of liquid reduces experimental error, and less volume is required for testing the sample. The principal idea implemented in the biosensor design is to track the shift in the operating frequency in the presence of different concentrations of creatinine mix in ACC solution with Phosphate Buffer (PB) solution as a reference.

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From an study of food delivery workers, we examine the precarity and consolidation of working conditions driven by the platform economy in the context of Urban India. The study considers the apparent threats in ‘gigging’, informalisation of labour and the obvious need to reconceptualise worker well-being, risks, stress and the eventuality of workplace and worker alignment in food delivery labour.