Abstract

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

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

The focus of recent research has shifted from merely improving the metrics based performance of Deep Neural Networks (DNNs) to DNNs which are more interpretable to humans. The field of eXplainable Artificial Intelligence (XAI) has observed various techniques, including saliency-based and concept-based approaches. These approaches explain the model's decisions in simple human understandable terms called Concepts. Concepts are known to be the thinking ground of humans}. Explanations in terms of concepts enable detecting spurious correlations, inherent biases, or clever-hans. With the advent of concept-based explanations, a range of concept representation methods and automatic concept discovery algorithms have been introduced. Some recent works also use concepts for model improvement in terms of interpretability and generalization. We provide a systematic review and taxonomy of various concept representations and their discovery algorithms in DNNs, specifically in vision. We also provide details on concept-based model improvement literature marking the first comprehensive survey of these methods.

Abstract

To implement a blockchain, we need a blockchain protocol for all the nodes to follow. To design a blockchain protocol, we need a block publisher selection mechanism and a chain selection rule. In Proof-of-Stake (PoS) based blockchain protocols, block publisher selection mechanism selects the node to publish the next block based on the relative stake held by the node. However, PoS protocols may face vulnerability to fully adaptive corruptions. In literature, researchers address this issue at the cost of performance. In this paper, we propose a novel PoS-based blockchain protocol, QuickSync, to achieve security against fully adaptive corruptions without compromising on performance. We propose a metric called block power, a value defined for each block, derived from the output of the verifiable random function based on the digital signature of the block publisher. With this metric, we compute chain power, the sum of block powers of all the blocks comprising the chain, for all the valid chains. These metrics are a function of the block publisher's stake to enable the PoS aspect of the protocol. The chain selection rule selects the chain with the highest chain power as the one to extend. This chain selection rule hence determines the selected block publisher of the previous block. When we use metrics to define the chain selection rule, it may lead to vulnerabilities against Sybil attacks. QuickSync uses a Sybil attack resistant function implemented using histogram matching. We prove that QuickSync satisfies common prefix, chain growth, and chain quality properties and hence it is secure. We also show that it is resilient to different types of adversarial …

Abstract

One of the widely used peak reduction methods in smart grids is demand response, where one analyzes the shift in customers’ (agents’) usage patterns in response to the signal from the dis- tribution company. Often, these signals are in the form of incentives offered to agents. This work studies the effect of incentives on the prob- abilities of accepting such offers in a real-world smart grid simulator, PowerTAC. We first show that there exists a function that depicts the prob- ability of an agent reducing its load as a func- tion of the discounts offered to them. We call it reduction probability (RP). RP function is fur- ther parametrized by the rate of reduction (RR), which can differ for each agent. We provide an optimal algorithm, MJS–EXPRESPONSE, that outputs the discounts to each agent by maximiz- ing the expected reduction under a budget con- straint. When RRs are unknown, we propose a Multi-Armed Bandit (MAB) based online algo- rithm, namely MJSUCB–EXPRESPONSE, to learn RRs. Experimentally we show that it exhibits sub- linear regret. Finally, we showcase the efficacy of the proposed algorithm in mitigating demand peaks in a real-world smart grid system using the Power- TAC simulator as a test bed.

Abstract

The introduction of emojis (or emoticons) in social media platforms has given the users an increased potential for expression. We propose a novel method called Classification of Emojis using Siamese Network Architecture (CESNA) to learn emoji-based representations of resource-poor languages by jointly training them with resource-rich languages using a siamese network. CESNA model consists of twin Bi-directional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM RNN) with shared parameters joined by a contrastive loss function based on a similarity metric. The model learns the representations of resource-poor and resource-rich language in a common emoji space by using a similarity metric based on the emojis present in sentences from both languages. The model, hence, projects sentences with similar emojis closer to each other and the sentences with different emojis farther from one another. Experiments on large-scale Twitter datasets of resource-rich languages-English and Spanish and resource-poor languages-Hindi and Telugu reveal that CESNA outperforms the state-of-the-art emoji prediction approaches based on distributional semantics, semantic rules, lexicon lists and deep neural network representations without shared parameters.

Abstract

Social media platforms such as Twitter and Facebook are becoming popular in multilingual societies. This trend induces portmanteau of South Asian languages with English. The blend of multiple languages as code-mixed data has recently become popular in research communities for various NLP tasks. Code-mixed data consist of anomalies such as grammatical errors and spelling variations. In this paper, we leverage the contextual property of words where the different spelling variation of words share similar context in a large noisy social media text. We capture different variations of words belonging to same context in an unsupervised manner using distributed representations of words. Our experiments reveal that preprocessing of the code-mixed dataset based on our approach improves the performance in state-of-the-art part-of-speech tagging (POS-tagging) and sentiment analysis tasks.

Abstract

Code-mixed data is an important challenge of natural language processing because its characteristics completely vary from the traditional structures of standard languages. In this paper, we propose a novel approach called Sentiment Analysis of Code-Mixed Text (SACMT) to classify sentences into their corresponding sentiment-positive, negative or neutral, using contrastive learning. We utilize the shared parameters of siamese networks to map the sentences of code-mixed and standard languages to a common sentiment space. Also, we introduce a basic clustering based preprocessing method to capture variations of code-mixed transliterated words. Our experiments reveal that SACMT outperforms the state-of-the-art approaches in sentiment analysis for code-mixed text by 7.6% in accuracy and 10.1% in F-score.

Abstract

Text articles with false claims, especially news, have recently become aggravating for the Internet users. These articles are in wide circulation and readers face difficulty discerning fact from fiction. Previous work on credibility assessment has focused on factual analysis and linguistic features. The task's main challenge is the distinction between the features of true and false articles. In this paper, we propose a novel approach called Credibility Outcome (CREDO) which aims at scoring the credibility of an article in an open domain setting. CREDO consists of different modules for capturing various features responsible for the credibility of an article. These features includes credibility of the article's source and author, semantic similarity between the article and related credible articles retrieved from a knowledge base, and sentiments conveyed by the article. A neural network architecture learns the contribution of each of these modules to the overall credibility of an article. Experiments on Snopes dataset reveals that CREDO outperforms the state-of-the-art approaches based on linguistic features.

Abstract

This paper presents the analysis and results of a data-driven approach for fault detection and isolation in case of complete failure of a single motor on a hexacopter. The proposed approach consists of a two-stage architecture using the Rotation Forest algorithm, which can detect faults without any false alarms and achieve a true positive classification rate of 92.6% and a false positive classification rate of 0.06%. The classification results are compared to other methods such as Logistic Regression, Gaussian Naive Bayes, AdaBoost, and Random Forest. Over 120 datasets containing approximately 21,000 data points are generated in simulation - divided into two sets for training and validation of the model. Outdoor flight tests are performed to validate the classifier algorithm further. We can detect and classify the fault within 60ms of its occurrence. A dataset is published in the open-source domain and can be used for training similar models. The work presented in this paper is data-driven (or model free) since the classifier has no knowledge of the parameters of the UAV and is derived only based on the functional relationship between input and output variables.