arXiv subjects

Muhammad Rahman

Publications and source records attributed to Muhammad Rahman.

5 recordsLinked to original sources

arXiv2023

Artificial Intelligence in Career Counseling: A Test Case with ResumAI

The rise of artificial intelligence (AI) has led to various means of integration of AI aimed to provide efficiency in tasks, one of which is career counseling. A key part of getting a job is having a solid resume that passes through the first round of programs and recruiters. It is difficult to find good resources or schedule an appointment with a career counselor to help with editing a resume for a specific role. With the rise of ChatGPT, Bard, and several other AI chat programs it is possible to provide specific, automated feedback on various concerns to suggest places for improvement within the context of career counseling. This paper begins with a quick literature review on the ethical considerations and limitations of AI in career counseling. The authors also have created their own website service, called ResumAI, to test and review the functionality of an AI career counselor. The findings of this study will contribute to the understanding of chat AI ResumAI reviewer programs and sites. The implications of the findings for the field of career counseling, AI development, and ethical practice will be discussed.

cs.AI↗

arXiv2023

Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection

Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real world problems. QML has the potential to address cybersecurity related challenges. Considering the novelty and complex architecture of QML, resources are not yet explicitly available that can pave cybersecurity learners to instill efficient knowledge of this emerging technology. In this research, we design and develop QML-based ten learning modules covering various cybersecurity topics by adopting student centering case-study based learning approach. We apply one subtopic of QML on a cybersecurity topic comprised of pre-lab, lab, and post-lab activities towards providing learners with hands-on QML experiences in solving real-world security problems. In order to engage and motivate students in a learning environment that encourages all students to learn, pre-lab offers a brief introduction to both the QML subtopic and cybersecurity problem. In this paper, we utilize quantum support vector machine (QSVM) for malware classification and protection where we use open source Pennylane QML framework on the drebin215 dataset. We demonstrate our QSVM model and achieve an accuracy of 95% in malware classification and protection. We will develop all the modules and introduce them to the cybersecurity community in the coming days.

cs.CR↗

arXiv2020

Twitter Corpus of the #BlackLivesMatter Movement And Counter Protests: 2013 to 2021

Black Lives Matter (BLM) is a decentralized social movement protesting violence against Black individuals and communities, with a focus on police brutality. The movement gained significant attention following the killings of Ahmaud Arbery, Breonna Taylor, and George Floyd in 2020. The #BlackLivesMatter social media hashtag has come to represent the grassroots movement, with similar hashtags counter protesting the BLM movement, such as #AllLivesMatter, and #BlueLivesMatter. We introduce a data set of 63.9 million tweets from 13.0 million users from over 100 countries which contain one of the following keywords: BlackLivesMatter, AllLivesMatter, and BlueLivesMatter. This data set contains all currently available tweets from the beginning of the BLM movement in 2013 to 2021. We summarize the data set and show temporal trends in use of both the BlackLivesMatter keyword and keywords associated with counter movements. Additionally, for each keyword, we create and release a set of Latent Dirichlet Allocation (LDA) topics (i.e., automatically clustered groups of semantically co-occuring words) to aid researchers in identifying linguistic patterns across the three keywords.

cs.SI↗

arXiv2009

"Graphene-Like" Exfoliation of Atomically-Thin Bismuth Telluride Films

We report on graphene-like exfoliation of the large-area crystalline films and ribbons of bismuth telluride with the thicknesses of a few atoms. It is demonstrated that bismuth telluride, the most important material for thermoelectric industry, can be mechanically separated into its building blocks -[Te-Bi-Te-Bi-Te]- atomic five-folds with the thickness of ~1 nm and even further - to subunits with smaller thicknesses. The atomically-thin crystals can be structured into suspended crystalline ribbons providing quantum confinement in two dimensions. The quasi two-dimensional (2-D) crystals of bismuth telluride revealed high electrical conductivity. The proposed atomic-layer engineering of bismuth telluride opens up a principally new route for drastic enhancement of the thermoelectric figure of merit.

cond-mat.mtrl-sci↗

arXiv2009

Ultraviolet Raman Spectroscopy of Single and Multi-layer Graphene

We investigated Raman spectra of single-layer and multi-layer graphene under ultraviolet laser excitation at the wavelength of 325 nm. It was found that while the G peak of graphene remains pronounced in UV Raman spectra, the 2D band intensity undergoes severe quenching. The evolution of the ratio of the intensities of the G and 2D peaks, I(G)/I(2D), as the number of graphene layers n changes from n=1 to n=5, is different in UV Raman spectra from that in conventional visible Raman spectra excited at the 488 nm and 633 nm wavelengths. The 2D band under UV excitation shifts to larger wave numbers and is found near 2825 1/cm. The observed UV Raman features of graphene were explained by invoking the resonant scattering model. The obtained results contribute to the Raman nanometrology of graphene by providing an additional metric for determining the number of graphene layers and assessing its quality.

cond-mat.mtrl-sci↗