Abstract

We study bias and discrimination in the context of Bumble, an online dating platform in India. Drawing on research in AI fairness and inclusion studies we analyze algorithmic bias and their propensity to reproduce bias. We conducted an experiment to identify and address the presence of bias in the matching algorithms Bumble pushes to its users in the form of profiles for potential dates in the real world. Dating apps like Bumble utilize algorithms that learn from user data to make recommendations. Even if the algorithm does not have intentions or consciousness, it is a system created and maintained by humans. We attribute moral agency of such systems to be compositely derived from algorithmic mediations, the design and utilization of these platforms. Developers, designers, and operators of dating platforms thus have a moral obligation to mitigate biases in the algorithms to create inclusive platforms that affirm diverse social identities.

Abstract

One crucial aspect of content organization is the creation of article outlines, which summarize the primary topics and subtopics covered in an article in a structured manner. This paper introduces a solution called XOutlineGen, which generates cross-lingual outlines for encyclopedic texts from reference articles. XOutlineGen uses the XWikiRef dataset, which consists of encyclopedic texts generated from reference articles and section titles. The dataset is enhanced with two new languages and three new domains, resulting in ∼92K articles. Our pipeline employs this dataset to train a two-step generation model, which takes the article title and set of references as inputs and produces the article outline.

Abstract

Infrastructure as Code (IaC) is a revolutionary approach which has gained significant prominence in the Industry. IaC manages and provisions IT infrastructure using machine-readable code by enabling automation, consistency across the environments, reproducibility, version control, error reduction and enhancement in scalability. However, IaC orchestration is often a painstaking effort which requires specialised skills as well as a lot of manual effort. Automation of IaC is a necessity in the present conditions of the Industry and in this survey, we study the feasibility of applying Large Language Models (LLM) to address this problem. LLMs are large neural network-based models which have demonstrated significant language processing abilities and shown to be capable of following a range of instructions within a broad scope. Recently, they have also been adapted for code understanding and generation tasks successfully, which makes them a promising choice for the automatic generation of IaC configurations. In this survey, we delve into the details of IaC, usage of IaC in different platforms, their challenges, LLMs in terms of code-generation aspects and the importance of LLMs in IaC along with our own experiments. Finally, we conclude by presenting the challenges in this area and highlighting the scope for future research.

Abstract

Researchers quantify vegetation characteristics using phytosociology studies. The data collected from vegetation surveys, such as tree girth and height measurements, are used to calculate various phytosociological attributes like frequency, density, basal cover, important value index, etc. In general, they are calculated using an Excel spreadsheet and a lengthy mathematical process. There are a few open-source or free software available to compute some of these parameters. However, they are not user friendly for most researchers. Also, these software primarily doesn’t compute basic phytosociological attributes. In view of these constraints and to ease researchers work, we have developed a simple tool, “PAT” Phytosociology Analysis Tool,” that computes the maximum attributes from the field collected vegetation data. The PAT GUI is simple. PAT computes results using standard formulas after users upload a csv file in the required format. Users can download the table's results and figures in PNG format.

Abstract

The task of headline generation within the realm of Natural Language Processing (NLP) holds immense significance, as it strives to distill the true essence of textual content into concise and attention-grabbing summaries. While noteworthy progress has been made in headline generation for widely spoken languages like English, there persist numerous challenges when it comes to generating headlines in lowresource languages, such as the rich and diverse Indian languages. A prominent obstacle that specifically hinders headline generation in Indian languages is the scarcity of high-quality annotated data. To address this crucial gap, we proudly present Mukhyansh, an extensive multilingual dataset, tailored for Indian language headline generation. Comprising an impressive collection of over 3.39 million article-headline pairs, Mukhyansh spans across eight prominent Indian languages, namely Telugu, Tamil, Kannada, Malayalam, Hindi, Bengali, Marathi, and Gujarati. We present a comprehensive evaluation of several state-of-the-art baseline models. Additionally, through an empirical analysis of existing works, we demonstrate that Mukhyansh outperforms all other models, achieving an impressive average ROUGE-L score of 31.43 across all 8 languages

Abstract

The collapse of buildings due to an earthquake is the leading cause of higher life loss and economic loss. it is not possible to prevent earthquake from occurring,but it disastrous effects can be minimized considerably through measure of scientific methods and understanding

Abstract

Multiple business scenarios require an automated generation of descriptive human-readable long text from structured input data, where the source is typically a high-resource language and the target is a low or medium resource language. We define the Cross-Lingual Fact to Long Text Generation (XFLT) as a novel natural language generation (NLG) task that involves generating descriptive and human-readable long text in a target language from structured input data (such as fact triples) in a source language. XFLT is challenging because of (a) hallucinatory nature of the state-of-the-art NLG models, (b) lack of good quality training data, and (c) lack of a suitable cross-lingual NLG metric. Unfortunately previous work focuses on different related problem settings (cross-lingual facts to short text or monolingual graph to text) and has made no efforts to handle hallucinations. In this paper, we contribute a novel dataset, XLALIGN with over 64,000 paragraphs across 12 different languages, and English facts. We propose a novel solution to the XFLT task which addresses these challenges by training multilingual Transformer-based encoder-decoder models with coverage prompts and grounded decoding. Further, it improves on the XFLT quality by defining task-specific reward functions and training on them using reinforcement learning. On XLALIGN, we compare this novel solution with several strong baselines using a new metric, cross-lingual PARENT. We also make our code and data publicly available

Abstract

Crosstalk, in a sensor matrix, is the unwanted signal obtained at a sensor pixel that is not directly related to the stimulus. This paper presents a novel approach towards quantifying the crosstalk characteristics of a sensor matrix. The method involves obtaining the normalized sum of all the neighbouring pixel readings, weighted by their distance from the stimulated pixel. We have used this methodology to characterise the crosstalk for a 5×5 velostat-based flexible pressure sensing matrix that uses a crossbar electrode architecture. The measure thus obtained is a dimensionless value between 0 to 1, 0 indicating no crosstalk and 1 indicating the maximum possible crosstalk. We characterized sensor matrices with three different pitch lengths: 3mm, 4mm, and 5mm. We observe the crosstalk values to lie between 0.032 to 0.17 across different weights and pitches, which indicates a low measure of crosstalk. The 5 mm pitch matrix displayed the least crosstalk, which was expected due to the larger spacing between the pixels. We also characterised the crosstalk for all 25 pixels of a 4 mm pitch matrix and found the mean to be 0.081±0.002 , within the range from 0.0071 to 0.1656.

Abstract

Polymer-based piezoresistive pressure sensors that possess flexibility and stretchability have received increased recognition in wearable sensing systems. In this paper, we present a flexible pressure sensor designed using polypyrrole-coated cotton (PCC) that was synthesized using in-situ chemical oxidative liquid polymerization. The sensor shows high sensitivity at low-pressure ranges and can measure pressures in the range 160 Pa to 16 kPa. The response time of the sensor was found to be 463 ms. It also exhibits excellent repeatability during continuous loading-unloading for over 1000 cycles. The performance of the sensor was evaluated in terms of resistance change as a function of tensile strain, by applying repeated compression and expansion strains. We report the gauge factor of the resistive sensor to be 0.32 for applied compressive strains from 0% to 99.6%. Such a large compressive strain range can be achieved with cotton because of its fibrous nature. It was observed that there is no change in conductivity, at a given strain, on repeated expansion and compression of the polymer for 20 cycles.

Abstract

Regular plantar pressure monitoring is required for diabetic, wheelchair-bound and bed-ridden individuals. It has been shown that peak plantar pressure for diabetics increases significantly and progressively, and requires regular monitoring for early detection and intervention. Sensor matrices of capacitative, resistive, and piezoelectric transducers are most commonly used for measuring areal pressure. Calibration for such medical devices is crucial for reliability and repeatable results. In this work, we elaborate on the calibration challenges of a 32×32 velostat-based sensor matrix, and demonstrate calibration strategies suitable for a manufactured product. We show that 2N parameters can be used to calibrate an N×N sensor matrix instead of 2N2 . We observe the accuracy of calibration of a 32×32 sensor matrix by comparing the total detected weight of 35 participants at a static load with their reported weight, with 4.2% mean error and 3.1% median error. For participants in the 50–85 kg weight range, the difference was always within ±5%. Further, upon drawing contour plots and using the pressure distribution information, we were able to distinguish participants with possible foot ulcers and flat feet.