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检索条件"机构=Saudi Authority for Data and Artificial Intelligence"

共 24 条记录，以下是1-10 订阅

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Evaluating LLMs for Arabic Code Summarization: Challenges and Insights from GPT-4 8

Evaluating LLMs for Arabic Code Summarization: Challenges an...

引用

8th International Conference on data Science and Machine Learning Applications, CDMA 2025

作者： Aljohani, Ahmed Alharbi, Raed Alkhaldi, Asma Aljedaani, Wajdi University of North Texas United States Saudi Electronic University Saudi Arabia Saudi Data and Artificial Intelligence Authority Saudi Arabia

ISBN: (纸本)9798331539696

GPT-4 -the backbone of ChatGPT-has demon-strated remarkable performance in both natural language and source code tasks. Recently, Large Language Models (LLMs) like GPT-4 have significantly advanced software engineering tasks such as code summarization. These advancements boost developer productivity and help address often neglected tasks like code documentation. While code summarization and commenting are essential for maintaining code quality and facilitating communication among developers, writing comments manually is time-consuming. Although several studies have proposed and evaluated deep learning-based approaches and LLMs to automate comment generation, these efforts primarily focus on the English language, leaving a gap for other languages, particularly Arabic. In this study, we evaluate the ability of GPT-4 to generate accurate Arabic comments. We support our evaluation with both manual and automatic analysis to measure the correctness and nature of the generated comments. Our findings reveal that while GPT-4 generally produces correct Arabic summaries, they often do not align with the developer's intent as reflected in the BERT-Similarity, ROUGE and BLEU scores. We also show that GPT-4's comments are more verbose due to the morphological richness of the Arabic language and a systematic approach that tends to describe each code component in detail. Finally, the readability of these comments is moderate, with scores ranging from 30.29 to 100. © 2025 IEEE.

关键词： Codes (symbols)

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Automating Accessibility Compliance: Leveraging Machine Learning to Analyze Developer Challenges with WCAG Guidelines 8

Automating Accessibility Compliance: Leveraging Machine Lear...

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8th International Conference on data Science and Machine Learning Applications, CDMA 2025

作者： Alghamdi, Asmaa Mansour Aljedaani, Wajdi Ludi, Stephanie Javed, Yasir University of North Texas United States Jazan University Saudi Arabia Saudi Authority for Data and Artificial Intelligence Saudi Arabia Prince Sultan University Saudi Arabia

ISBN: (纸本)9798331539696

The prevailing strategy for ensuring web application accessibility relies heavily on adherence to established guidelines, particularly the Web Content Accessibility Guidelines (WCAG). However, the process of interpreting and implementing these guidelines presents significant challenges for developers, which are often compounded by the time-consuming nature of manual analysis required to ensure compliance. Despite the critical importance of accessibility, there remains limited exploration of these challenges within the development process. In this paper, we aim to address these challenges by leveraging machine learning techniques to automatically classify and analyze accessibility-related issues discussed by developers on Stack Overflow, with a focus on understanding their connection to WCAG 2.2 guidelines. To achieve this, we analyzed 5,644 developers' posts related to web accessibility collected from Stack Overflow between 2008 and 2022. These posts were categorized according to the principles of WCAG 2.2 using advanced machine learning models, including Random Forest, Support Vector Machine (SVM), Gradient Boosting, AdaBoost, and Multi-Layer Perceptron (MLP). The results highlight a strong emphasis among developers on addressing complex, non-linear accessibility issues. Our analysis demonstrated that Random Forest and Gradient Boosting models were most effective, achieving accuracy rates of 0.987 and 0.986, respectively. These findings underscore the challenges developers face in aligning with accessibility standards, particularly in areas such as time-based media customization and screen reader configuration, and the potential for machine learning to support more efficient and accurate compliance with accessibility guidelines. © 2025 IEEE.

关键词： Adversarial machine learning

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Evaluating LLMs for Arabic Code Summarization: Challenges and Insights from GPT-4

Evaluating LLMs for Arabic Code Summarization: Challenges an...

引用

Conference on data Science and Machine Learning Applications (CDMA)

作者： Ahmed Aljohani Raed Alharbi Asma Alkhaldi Wajdi Aljedaani University of North Texas Saudi Electronic University Saudi Data and Artificial Intelligence Authority

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

关键词： Productivity Codes Systematics Source coding Natural languages Manuals Documentation Machine learning Multiaccess communication Software engineering

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Automating Accessibility Compliance: Leveraging Machine Learning to Analyze Developer Challenges with WCAG Guidelines

Automating Accessibility Compliance: Leveraging Machine Lear...

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Conference on data Science and Machine Learning Applications (CDMA)

作者： Asmaa Mansour Alghamdi Wajdi Aljedaani Stephanie Ludi Yasir Javed University of North Texas Jazan University Saudi Authority for Data and Artificial Intelligence Prince Sultan University

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

关键词： Support vector machines Accuracy Manuals Boosting Multiaccess communication Random forests Faces Standards Guidelines Testing

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Investigating User Perceptions of Epilepsy-Related Seizure Triggers in Mobile Apps: An Analysis of User Reviews 25

Investigating User Perceptions of Epilepsy-Related Seizure T...

引用

Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Marcelo Medeiros Eler Wajdi Aljedaani School of Arts Sciences and Humanities University of São Paulo São Paulo São Paulo Brazil Saudi Data and Artificial Intelligence Authority Riyadh Saudi Arabia and Computer science and engineering University of North Texas Denton Texas USA

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LocalBins: Improving Depth Estimation by Learning Local Distributions 17th

LocalBins: Improving Depth Estimation by Learning Local Dist...

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17th European Conference on Computer Vision (ECCV)

作者： Bhat, Shariq Farooq Alhashim, Ibraheem Wonka, Peter KAUST Thuwal Saudi Arabia Saudi Data & Artificial Intelligence Authority SD Natl Ctr Artificial Intelligence NCAI Riyadh Saudi Arabia

ISBN: (纸本)9783031197680;9783031197697

We propose a novel architecture for depth estimation from a single image. The architecture itself is based on the popular encoder-decoder architecture that is frequently used as a starting point for all dense regression tasks. We build on AdaBins which estimates a global distribution of depth values for the input image and evolve the architecture in two ways. First, instead of predicting global depth distributions, we predict depth distributions of local neighborhoods at every pixel. Second, instead of predicting depth distributions only towards the end of the decoder, we involve all layers of the decoder. We call this new architecture LocalBins. Our results demonstrate a clear improvement over the state-of-the-art in all metrics on the NYU-Depth V2 dataset. Code and pretrained models will be made publicly available (https://***/sharigfarooq123/LocalBins).

关键词： Single image depth estimation Encoder-decoder architecture Deep learning Dense regression Histogram prediction

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A new approach to detect next generation of malware based on machine learning

A new approach to detect next generation of malware based on...

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2021 International Conference on Innovation and intelligence for Informatics, Computing, and Technologies, 3ICT 2021

作者： Ben Abdel Ouahab, Ikram Elaachak, Lotfi Alluhaidan, Yasser A. Bouhorma, Mohammed Faculty of Sciences and Techniques Tangier Morocco Saudi Data and Artificial Intelligence Authority Saudi Arabia

ISBN: (纸本)9781665440325

In these days, malware attacks target different kinds of devices as IoT, mobiles, servers even the cloud. It causes several hardware damages and financial losses especially for big companies. Malware attacks represent a serious issue to cybersecurity specialists. In this paper, we propose a new approach to detect unknown malware families based on machine learning classification and visualization technique. A malware binary is converted to grayscale image, then for each image a GIST descriptor is used as input to the machine learning model. For the malware classification part we use 3 machine learning algorithms. These classifiers are so efficient where the highest precision reach 98%. Once we train, test and evaluate models we move to simulate 2 new malware families. We do not expect a good prediction since the model did not know the family;however our goal is to analyze the behavior of our classifiers in the case of new family. Finally, we propose an approach using a filter to know either the classification is normal or it's a zero-day malware. © 2021 IEEE.

关键词： Malware

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HIERARCHICAL VARIATIONAL MEMORY FOR FEW-SHOT LEARNING ACROSS DOMAINS 10

HIERARCHICAL VARIATIONAL MEMORY FOR FEW-SHOT LEARNING ACROSS...

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10th International Conference on Learning Representations, ICLR 2022

作者： Du, Yingjun Zhen, Xiantong Shao, Ling Snoek, Cees G.M. AIM Lab University of Amsterdam Netherlands Inception Institute of Artificial Intelligence National Center for Artificial Intelligence Saudi Data and Artificial Intelligence Authority

Neural memory enables fast adaptation to new tasks with just a few training samples. Existing memory models store features only from the single last layer, which does not generalize well in presence of a domain shift between training and test distributions. Rather than relying on a flat memory, we propose a hierarchical alternative that stores features at different semantic levels. We introduce a hierarchical prototype model, where each level of the prototype fetches corresponding information from the hierarchical memory. The model is endowed with the ability to flexibly rely on features at different semantic levels if the domain shift circumstances so demand. We meta-learn the model by a newly derived hierarchical variational inference framework, where hierarchical memory and prototypes are jointly optimized. To explore and exploit the importance of different semantic levels, we further propose to learn the weights associated with the prototype at each level in a data-driven way, which enables the model to adaptively choose the most generalizable features. We conduct thorough ablation studies to demonstrate the effectiveness of each component in our model. The new state-of-the-art performance on cross-domain and competitive performance on traditional few-shot classification further substantiates the benefit of hierarchical variational memory. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

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Real-time Facemask Detector using Deep Learning and Raspberry Pi 2

Real-time Facemask Detector using Deep Learning and Raspberr...

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2nd International Conference on Digital Age and Technological Advances for Sustainable Development, ICdata 2021

作者： Ouahab, Ikram Ben Abdel Elaachak, Lotfi Bouhorma, Mohammed Alluhaidan, Yasser A. Faculty of Sciences and Techniques Tangier Morocco Saudi Data and Artificial Intelligence Authority Saudi Arabia

ISBN: (纸本)9781665429016

Medical staffs wear face masks to prevent the spread of the disease. Nowadays, with the coronavirus pandemic everyone must wear a facemask for the same reason. When a person near to you coughs, talks, sneezes he could release germs into the air that may infect you or anyone nearby. Wearing a facemask is a part of an infection control strategy to avoid and eliminate cross-contamination. Even so, people are getting tired of wearing facemasks or they are not conscious enough of the seriousness of the actual covid19. In this paper, we propose a facemask detector based on IoT embedded devices and deep learning algorithm. Our main goal is to warn people in real-time if they are not wearing a facemask or they are not wearing it correctly. The proposed solution generates loud vocal alerts after detection disrespect of facemask wear in real-time for a fast reaction. To have the most efficient detector in real-time we tested the facemask detection model using various versions of the Raspberry Pi and NCS2. As a result, the facemask detector works perfectly on powerful devices, however its performance decrease in realtime using less powerful devices such as an old version of the Raspberry Pi. © 2021 IEEE.

关键词： Computer vision

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LEARNING TO GENERALIZE ACROSS DOMAINS ON SINGLE TEST SAMPLES 10

LEARNING TO GENERALIZE ACROSS DOMAINS ON SINGLE TEST SAMPLES

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10th International Conference on Learning Representations, ICLR 2022

作者： Xiao, Zehao Zhen, Xiantong Shao, Ling Snoek, Cees G.M. AIM Lab University of Amsterdam Netherlands Inception Institute of Artificial Intelligence National Center for Artificial Intelligence Saudi Data and Artificial Intelligence Authority

We strive to learn a model from a set of source domains that generalizes well to unseen target domains. The main challenge in such a domain generalization scenario is the unavailability of any target domain data during training, resulting in the learned model not being explicitly adapted to the unseen target domains. We propose learning to generalize across domains on single test samples. We leverage a meta-learning paradigm to learn our model to acquire the ability of adaptation with single samples at training time so as to further adapt itself to each single test sample at test time. We formulate the adaptation to the single test sample as a variational Bayesian inference problem, which incorporates the test sample as a conditional into the generation of model parameters. The adaptation to each test sample requires only one feed-forward computation at test time without any fine-tuning or self-supervised training on additional data from the unseen domains. Extensive ablation studies demonstrate that our model learns the ability to adapt models to each single sample by mimicking domain shifts during training. Further, our model achieves at least comparable - and often better - performance than state-of-the-art methods on multiple benchmarks for domain generalization. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Benchmarking

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