arXiv subjects

Maryam Hashemi

Publications and source records attributed to Maryam Hashemi.

5 recordsLinked to original sources

arXiv2024

FarExStance: Explainable Stance Detection for Farsi

We introduce FarExStance, a new dataset for explainable stance detection in Farsi. Each instance in this dataset contains a claim, the stance of an article or social media post towards that claim, and an extractive explanation which provides evidence for the stance label. We compare the performance of a fine-tuned multilingual RoBERTa model to several large language models in zero-shot, few-shot, and parameter-efficient fine-tuned settings on our new dataset. On stance detection, the most accurate models are the fine-tuned RoBERTa model, the LLM Aya-23-8B which has been fine-tuned using parameter-efficient fine-tuning, and few-shot Claude-3.5-Sonnet. Regarding the quality of the explanations, our automatic evaluation metrics indicate that few-shot GPT-4o generates the most coherent explanations, while our human evaluation reveals that the best Overall Explanation Score (OES) belongs to few-shot Claude-3.5-Sonnet. The fine-tuned Aya-32-8B model produced explanations most closely aligned with the reference explanations.

cs.CL↗

arXiv2023

LXMERT Model Compression for Visual Question Answering

Large-scale pretrained models such as LXMERT are becoming popular for learning cross-modal representations on text-image pairs for vision-language tasks. According to the lottery ticket hypothesis, NLP and computer vision models contain smaller subnetworks capable of being trained in isolation to full performance. In this paper, we combine these observations to evaluate whether such trainable subnetworks exist in LXMERT when fine-tuned on the VQA task. In addition, we perform a model size cost-benefit analysis by investigating how much pruning can be done without significant loss in accuracy. Our experiment results demonstrate that LXMERT can be effectively pruned by 40%-60% in size with 3% loss in accuracy.

cs.CV↗

arXiv2023

Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposal

The increasing complexity of AI systems has led to the growth of the field of Explainable Artificial Intelligence (XAI), which aims to provide explanations and justifications for the outputs of AI algorithms. While there is considerable demand for XAI, there remains a scarcity of studies aimed at comprehensively understanding the practical distinctions among different methods and effectively aligning each method with users individual needs, and ideally, offer a mapping function which can map each user with its specific needs to a method of explainability. This study endeavors to bridge this gap by conducting a thorough review of extant research in XAI, with a specific focus on Explainable Machine Learning (XML), and a keen eye on user needs. Our main objective is to offer a classification of XAI methods within the realm of XML, categorizing current works into three distinct domains: philosophy, theory, and practice, and providing a critical review for each category. Moreover, our study seeks to facilitate the connection between XAI users and the most suitable methods for them and tailor explanations to meet their specific needs by proposing a mapping function that take to account users and their desired properties and suggest an XAI method to them. This entails an examination of prevalent XAI approaches and an evaluation of their properties. The primary outcome of this study is the formulation of a clear and concise strategy for selecting the optimal XAI method to achieve a given goal, all while delivering personalized explanations tailored to individual users.

cs.AI↗

arXiv2021

Driver Safety Development Real Time Driver Drowsiness Detection System Based on Convolutional Neural Network

This paper focuses on the challenge of driver safety on the road and presents a novel system for driver drowsiness detection. In this system, to detect the falling sleep state of the driver as the sign of drowsiness, Convolutional Neural Networks (CNN) are used with regarding the two goals of real-time application, including high accuracy and fastness. Three networks introduced as a potential network for eye status classifcation in which one of them is a Fully Designed Neural Network (FD-NN) and others use Transfer Learning in VGG16 and VGG19 with extra designed layers (TL-VGG). Lack of an available and accurate eye dataset strongly feels in the area of eye closure detection. Therefore, a new comprehensive dataset proposed. The experimental results show the high accuracy and low computational complexity of the eye closure estimation and the ability of the proposed framework on drowsiness detection.

eess.IV↗

arXiv2021

BO Ari Light Curve Analysis using Ground-Based and TESS Data

We present new BVR band photometric light curves of BO Aries obtained in 2020 and combined them with the Transiting Exoplanet Survey Satellite (TESS) light curves. We obtained times of minima based on Gaussian and Cauchy distributions and then applied the Monte Carlo Markov Chain (MCMC) method to measure the amount of uncertainty from our CCD photometry and TESS data. A new ephemeris of the binary system was computed employing 204 times of minimum. The light curves were analyzed using the Wilson-Devinney binary code combined with the Monte Carlo (MC) simulation. For this light curve solution, we considered a dark spot on the primary component. We conclude that this binary is an A-type system with a mass ratio of q=0.2074+-0.0001, an orbital inclination of i=82.18+-0.02 deg, and a fillout factor of f=75.7+-0.8%. Our results for the a(Rsun) and q parameters are consistent with the results of the Xu-Dong Zhang and Sheng-Bang Qian (2020) model. The absolute parameters of the two components were calculated and the distance estimate of the binary system was found to be 142+-9 pc.

astro-ph.SR↗