Zobrazeno 1 - 10
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pro vyhledávání: '"Mohamed IN"'
Autor:
Saeed, Muhammed, Mohamed, Elgizouli, Mohamed, Mukhtar, Raza, Shaina, Shehata, Shady, Abdul-Mageed, Muhammad
Large language models (LLMs) are widely used but raise ethical concerns due to embedded social biases. This study examines LLM biases against Arabs versus Westerners across eight domains, including women's rights, terrorism, and anti-Semitism and ass
Externí odkaz:
http://arxiv.org/abs/2410.24049
Distracted driving is a critical safety issue that leads to numerous fatalities and injuries worldwide. This study addresses the urgent need for efficient and real-time machine learning models to detect distracted driving behaviors. Leveraging the Pr
Externí odkaz:
http://arxiv.org/abs/2410.15602
Thanks to the high potential for profit, trading has become increasingly attractive to investors as the cryptocurrency and stock markets rapidly expand. However, because financial markets are intricate and dynamic, accurately predicting prices remain
Externí odkaz:
http://arxiv.org/abs/2410.06935
Autor:
Talafha, Bashar, Kadaoui, Karima, Magdy, Samar Mohamed, Habiboullah, Mariem, Chafei, Chafei Mohamed, El-Shangiti, Ahmed Oumar, Zayed, Hiba, tourad, Mohamedou cheikh, Alhamouri, Rahaf, Assi, Rwaa, Alraeesi, Aisha, Mohamed, Hour, Alwajih, Fakhraddin, Mohamed, Abdelrahman, Mekki, Abdellah El, Nagoudi, El Moatez Billah, Saadia, Benelhadj Djelloul Mama, Alsayadi, Hamzah A., Al-Dhabyani, Walid, Shatnawi, Sara, Ech-Chammakhy, Yasir, Makouar, Amal, Berrachedi, Yousra, Jarrar, Mustafa, Shehata, Shady, Berrada, Ismail, Abdul-Mageed, Muhammad
In spite of the recent progress in speech processing, the majority of world languages and dialects remain uncovered. This situation only furthers an already wide technological divide, thereby hindering technological and socioeconomic inclusion. This
Externí odkaz:
http://arxiv.org/abs/2410.04527
Autor:
Elnoor, Mohamed, Weerakoon, Kasun, Seneviratne, Gershom, Xian, Ruiqi, Guan, Tianrui, Jaffar, Mohamed Khalid M, Rajagopal, Vignesh, Manocha, Dinesh
We present a novel autonomous robot navigation algorithm for outdoor environments that is capable of handling diverse terrain traversability conditions. Our approach, VLM-GroNav, uses vision-language models (VLMs) and integrates them with physical gr
Externí odkaz:
http://arxiv.org/abs/2409.20445
Autor:
Seneviratne, Gershom, Weerakoon, Kasun, Elnoor, Mohamed, Rajgopal, Vignesh, Varatharajan, Harshavarthan, Jaffar, Mohamed Khalid M, Pusey, Jason, Manocha, Dinesh
We present CROSS-GAiT, a novel algorithm for quadruped robots that uses Cross Attention to fuse terrain representations derived from visual and time-series inputs, including linear accelerations, angular velocities, and joint efforts. These fused rep
Externí odkaz:
http://arxiv.org/abs/2409.17262
Autor:
Weerakoon, Kasun, Elnoor, Mohamed, Seneviratne, Gershom, Rajagopal, Vignesh, Arul, Senthil Hariharan, Liang, Jing, Jaffar, Mohamed Khalid M, Manocha, Dinesh
We present BehAV, a novel approach for autonomous robot navigation in outdoor scenes guided by human instructions and leveraging Vision Language Models (VLMs). Our method interprets human commands using a Large Language Model (LLM) and categorizes th
Externí odkaz:
http://arxiv.org/abs/2409.16484
Publikováno v:
Electronic Journal of Structural Engineering, Vol 23, Iss 2 (2023)
The adoption of steel in the construction industry will consistently grow due to rapid urbanisation and the demand of more structures and infrastructures. The main reasons of steel adaptation in construction industry are due to steel attributes that
Externí odkaz:
https://doaj.org/article/187d48c3bfac43238bd91a52b7eab74a
Publikováno v:
Reviews on Advanced Materials Science, Vol 60, Iss 1, Pp 801-817 (2021)
Considerable attention has been given to graphene as a reinforcement material for metal matrix composite (MMC) because of its great potential for use in the automotive and aerospace industry. In general, the difficulty in achieving optimally improved
Externí odkaz:
https://doaj.org/article/d0dc267306214508bbf777a579cac75a
Poisoning attacks are a primary threat to machine learning models, aiming to compromise their performance and reliability by manipulating training datasets. This paper introduces a novel attack - Outlier-Oriented Poisoning (OOP) attack, which manipul
Externí odkaz:
http://arxiv.org/abs/2411.00519