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检索条件"机构=Scientific Computing in Computer Science"

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***: A Deep Learning System for Digital Smile Design 11

***: A Deep Learning System for Digital Smile Design

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11th IEEE International Conference on Intelligent computing and Information Systems, ICICIS 2023

作者： Khaled Ali, Ganna Allah Abdelbadei, Khloud Aboelhasan Essam, Mohammad Gamal, Riham Essam Mohamed, Taghreed Samir Al-Berry, Maryam Nabil Nabil, Sarah Hatem Said, Amr Mohamed Ain Shams University Bioinformatics Department Computer and Information Science Cairo Egypt Ain Shams University Scientific Computing Department Computer and Information Science Cairo Egypt

ISBN: (纸本)9798350322101

Digital Smile Design (DSD) is an advanced technique that enables dentists to create customized treatment plans for each patient by creating a digital representation of their desired smile. This approach promotes collaboration between dentists and patients to meet individual needs and goals. Special software like DSD allows dentists to plan treatments more precisely and achieve the patient's desired outcome. *** is an application inspired by DSD, which utilizes machine learning, deep learning, and image processing to simulate a desired *** helps patients participate in the smile creation process and allows for more predictable outcomes, improving patient acceptance of suggested treatment plans. *** also helps dentists to simplify the connection with their patients, educate them about the benefits of treatment, and trying to reduce patient reluctance. The first approach is based on image segmentation using a segmentation model. Unet++, MAnet, DeepLabv3+, FPN, while the second approach is based on ControlNet, ControlNet is a type of neural network architecture that controls diffusion models by introducing additional constraints. This innovation represents a major advance in the field of AI imaging as it allows excellent control over stable diffusion ControlNet is growing in popularity for several reasons. First, it produces high-quality output with better control compared to other methods like depth-to-image. Second, it is highly efficient and requires significantly less training time than traditional approaches. Finally, ControlNet prevents overfitting. Overall, *** is a revolutionary tool that can improve the quality of dental treatments and patient satisfaction. © 2023 IEEE.

关键词： Image segmentation

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On the Scalability of computing Genomic Diversity Using SparkLeBLAST: A Feasibility Study

On the Scalability of Computing Genomic Diversity Using Spar...

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2024 IEEE High Performance Extreme computing Conference, HPEC 2024

作者： Prabhu, Ritvik Moussad, Bernard Youssef, Karim Vatai, Emil Feng, Wu-Chun Virginia Tech Department of Computer Science United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States Riken Center for Computational Science Japan

ISBN: (纸本)9798350387131

Studying the genomic diversity of viruses can help us understand how viruses evolve and how that evolution can impact human health. Rather than use a laborious and tedious wet-lab approach to conduct a genomic diversity study, we take a computational approach, using the ubiquitous NCBI BLAST and our parallel and distributed SparkLeBLAST, across 53 patients (40,000,000 query sequences) on Fugaku, the world's fastest homogeneous supercomputer with 158,976 nodes, where each code contains a 48-core A64FX processor and 32 GB RAM. To project how long BLAST and SparkLeBLAST would take to complete a genomic diversity study of COVID-19, we first perform a feasibility study on a subset of 50 query sequences from a single COVID-19 patient to identify bottlenecks in sequence alignment processing. We then create a model using Amdahl's law to project the run times of NCBI BLAST and SparkLeBLAST on supercomputing systems like Fugaku. Based on the data from this 50-sequence feasibility study, our model predicts that NCBI BLAST, when running on all the cores of the Fugaku supercomputer, would take approximately 26.7 years to complete the full-scale study. In contrast, SparkLeBLAST, using both our query and database segmentation, would reduce the execution time to 0.026 years (i.e., 22.9 hours) - resulting in more than a 10,000× speedup over using the ubiquitous NCBI BLAST. © 2024 IEEE.

关键词： COVID-19

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Text-to-Image Synthesis using Generative Adversarial Networks 11

Text-to-Image Synthesis using Generative Adversarial Network...

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11th IEEE International Conference on Intelligent computing and Information Systems, ICICIS 2023

作者： Ibrahim, Mostafa Samy Nabil El-Berry, Mariam Fahmy Tolba, Mohamed Fadel Bahgat, Sayed Faculty of Computer and Information Sciences-Ain Shams University Bachelor of Computer and Information Science Egypt Faculty of Computer and Information Sciences-Ain Shams University Maryam Lecturer in Scientific Computing Department Egypt Faculty of Computer and Information Sciences-Ain Shams University Scientific Computing Department Egypt

ISBN: (纸本)9798350322101

Text-to-image synthesis is considered a unique problem in image synthesis, which is the task of generating image from input text. Previously, the purpose of text-to-image synthesiswas to match words and images using retrieval methods basedon a phrase or collection of keywords. With the evolution ofdeep learning, particularly the usage of generative models, image synthesis has made potentially significant advancements. One of the most significant generative models are Generative Adversarial Networks (GANs), and they have been widely successfully utilized in various domains. In this study proposes our model, examines and summarizes recent work on GAN-based text-to-image synthesis, as well as providing an overview of the evolution of both classic and cutting-edge models. The usual structure of each category is described in depth. The survey presents a thorough review of the development of GAN-based text to image synthesis. Finally, Dynamic Memory GAN with Contrastive Learning (DMGAN+CL) with prompt engineering, which optimizes and acquires consistent textual representations for captions using the contrastive learning technique. We tested our technique on the CUB-200 dataset, and the findings demonstrate that our framework achieved comparable results with other typical GANs architectures in text-to-image synthesis. © 2023 IEEE.

关键词： Generative adversarial networks

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Short Time Series Forecasting Method Based on Genetic Programming and Kalman Filter 1st

Short Time Series Forecasting Method Based on Genetic Progr...

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1st International Conference on Complex Computational Ecosystems, CCE 2023

作者： Kone, Lalla Aicha Leonteva, Anna Ouskova Diallo, Mamadou Tourad Haouba, Ahmedou Collet, Pierre ICUBE Laboratory Strasbourg University Strasbourg France Reasearch Unit of Scientific Computing Computer Science and Data Science University of Nouakchott Nouakchott Mauritania

ISBN: (纸本)9783031443541

Accurate forecasting of the baccalaureate admission statistics is a crucial step towards an improvement of the educational system in Mauritania and its responsiveness to the economical needs. Since an available historical information is collected only over last ten years, an accurate forecasting technique for short time series is required. Addressing this issue, the presented paper proposes a tool based on the genetic programming and Kalman filter. This tool allows to make accurate short term prediction for short time series and easily set up experiments. A tool validation on different data sets is presented, where the provided tool provides more robust forecasting results comparatively with the state-of-the-art techniques. It is expected that this tool can help to make a qualitative jump in the improvement of Mauritanian education system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Kalman filters

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Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov models

Modeling stochastic eye tracking data: A comparison of quant...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Bhandari, Shailendra Lencastre, Pedro Lind, Pedro Department of Computer Science OsloMet - Oslo Metropolitan University OsloN-0130 Norway Simula Research Laboratory Numerical Analysis and Scientific Computing Oslo0164 Norway

ISBN: (纸本)9798400704956

We explore the use of quantum generative adversarial networks QGANs for modeling eye movement velocity data. We assess whether the advanced computational capabilities of QGANs can enhance the modeling of complex stochastic distribution beyond the traditional mathematical models, particularly the Markov model. The findings indicate that while QGANs demonstrate potential in approximating complex distributions, the Markov model consistently outperforms in accurately replicating the real data distribution. This comparison underlines the challenges and avenues for refinement in time series data generation using quantum computing techniques. It emphasizes the need for further optimization of quantum models to better align with real-world data characteristics. © 2024 is held by the owner/author(s).

关键词： Generative adversarial networks

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A Graph Data Model-based Micro-Provenance Approach for Multi-level Provenance Exploration in End-to-End Climate Workflows

A Graph Data Model-based Micro-Provenance Approach for Multi...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Fiore, Sandro Rampazzo, Mattia Elia, Donatello Sacco, Ludovica Antonio, Fabrizio Nassisi, Paola University of Trento Department of Information Engineering and Computer Science Trento Italy Cmcc Foundation Advanced Scientific Computing Division Lecce Italy

ISBN: (纸本)9798350324457

Open science is a vital part in the current and future research agenda worldwide. In order to meet Open science goals, it is of paramount importance to fully support the research process, which includes also properly addressing provenance and reproducibility of scientific experiments. Indeed, provenance and reproducibility are two key requirements for analytics workflows in Open science contexts. Handling provenance at different levels of granularity and during the entire experiment lifecycle becomes key to properly and flexibly managing lineage information related to large-scale experiments as well as enabling reproducibility scenarios. To this end, this work introduces the micro-provenance concept, and it provides an in-depth description of its design, implementation and exploitation in the context of a multi-model climate analytics workflow. © 2023 IEEE.

关键词： climate domain graph data model micro-provenance Open science reproducibility

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AI-Safe Transportation: Real-Time Incident Detection and Alerting System in Smart Cities 11

AI-Safe Transportation: Real-Time Incident Detection and Ale...

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11th IEEE International Conference on Intelligent computing and Information Systems, ICICIS 2023

作者： Al-Agroudy, Ziad Mohamed, Abdelrahman Ashraf, Ziad Al-Sayed, Salma Gad, Walaa Hassan, Ziad Reda, Radwa Ain Shams University Faculty of Computer and Information Science Information Systems Department Cairo Egypt Ain Shams University Faculty of Computer and Information Science Scientific Computing Department Cairo Egypt

ISBN: (纸本)9798350322101

Recently, with the increasing complexity of urban environments, the need for advanced technologies to ensure safe transportation in smart cities has become paramount. It has become necessary also to propose a model based on the latest technology, which can detect various road and environmental accidents in real time using live feed from street cameras. In this work, the proposed model can identify incidents such as car accidents, fires, floods, and other malfunctions in the road environment, and automatically generates two reports - one for the government to seek assistance and another for drivers to avoid the affected area and mitigate traffic congestion. The developed model utilizes state-of-the-art Deep learning techniques. It is implemented by convolutional neural networks (CNNs), for efficient processing and analysis of live video feeds. The model is trained on a comprehensive dataset of images, encompassing different types of road and environmental incidents. The trained model is subsequently integrated into a real-time monitoring system that continuously receives and analyzes live feeds from multiple cameras installed in the streets. The main features of the developed system include real-time detection and classification of road and environmental incidents, automatic generation of reports for government and users, and proactive traffic management to reduce traffic congestion in affected areas. The system is designed to work seamlessly with existing smart city infrastructure and can be easily integrated into transportation management systems. The experimental results proved that the deep learning YOLO model with a dataset licensed by MIT achieved an average accuracy of 91% in detecting various types of road and environmental incidents in real-time. © 2023 IEEE.

关键词： Roads and streets

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Multitask learning for Arabic Dialects Identification and Machine Translation

Multitask learning for Arabic Dialects Identification and Ma...

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International Conference on Artificial Intelligence and Its Practical Applications in the Age of Digital Transformation, I2COMSAPP 2024

作者： Dhleima, Mohamed Tourad, Mohamedou Cheikh Telmoud, Cheikh Abdelkader Ahmed Abdelmounaim, Abdali Nanne, Mohamedade Farouk Scientific Computing Computer Science and Data Science Research Unit University of Nouakchott Nouakchott Mauritania Research Team Control and Informatics for Intelligent Systems and Green Energy FST cadi Ayyad Marrakech Morocco

ISBN: (纸本)9783031714283

Multitask learning (MTL) is a popular approach in natural language processing (NLP) to improve the performance of related functions. This paper presents a comprehensive approach to address dialect identification and translation challenges in Arabic NLP. The proposed model utilizes state-of-the-art transformer architectures for both tasks, ensuring robust performance in capturing contextual dependencies and linguistic nuances inherent in Arabic dialects. The primary objectives are to accurately identify Arabic dialects and facilitate their translation to Modern Standard Arabic (MSA) using a shared representation learning approach. The model architecture consists of a shared embedding layer followed by a transformer encoder for each task, employing multi-head self-attention mechanisms for hierarchical contextual information extraction. The model’s performance is evaluated using the F1-score for dialect classification, and the BLEU-score for translation performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Testing GPU Numerics: Finding Numerical Differences Between NVIDIA and AMD GPUs

Testing GPU Numerics: Finding Numerical Differences Between ...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Zahid, Anwar Hossain Laguna, Ignacio Le, Wei Iowa State University Department of Computer Science AmesIA United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing LivermoreCA United States

ISBN: (纸本)9798350355543

As scientific codes are ported between GPU platforms, continuous testing is required to ensure numerical robustness and identify potential numerical differences between platforms. Compiler-induced numerical differences can occur when a program is compiled and run on different GPUs and compilers, and the numerical outcomes are different for the same input. We present a study of compiler-induced numerical differences between NVIDIA and AMD GPUs, two widely used GPUs in HPC clusters. Our approach uses a random program generator (Varity) to generate thousands of short numerical tests in CUDA and HIP, and their inputs;then, we use differential testing to check if the program produced a numerical inconsistency when run on NVIDIA and AMD GPUs, using the same compiler optimization level. We also use the AMD's HIPIFY tool to convert CUDA tests into HIP tests and test if there are numerical inconsistencies induced by HIPIFY. In our study, we generated more than 600,000 tests and found subtle numerical differences occurring between the two classes of GPUs. We found that some of the differences come from (1) math library calls, (2) differences in floating-point precision (FP64 versus FP32), and (3) converting code to HIP with HIPIFY. © 2024 IEEE.

关键词： CUDA Differential testing floating-point HIP random program generation

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Fast Symmetric Eigenvalue Decomposition via WY Representation on Tensor Core 23

Fast Symmetric Eigenvalue Decomposition via WY Representatio...

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28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023

作者： Zhang, Shaoshuai Shah, Ruchi Ootomo, Hiroyuki Yokota, Rio Wu, Panruo Department of Computer Science University of Houston TX United States Global Scientific Information and Computing Center Tokyo Institute of Technology Tokyo Japan

ISBN: (纸本)9798400700156

Symmetric eigenvalue decomposition (EVD) is a fundamental analytic and numerical tool used in many scientific areas. The state-of-the-art algorithm in terms of performance is typically the two-stage tridiagonalization method. The first stage in the two-stage tridiagonalization is called successive band reduction (SBR), which reduces a symmetric matrix to a band form, and its computational cost usually dominates. When Tensor Core (specialized matrix computational accelerator) is used to accelerate the expensive EVD, the conventional ZY-representation-based method results in suboptimal performance due to unfavorable shapes of the matrix computations. In this paper, we propose a new method that uses WY representation instead of ZY representation (see Section 3.2 for details), which can provide a better combination of locality and parallelism so as to perform better on Tensor Cores. Experimentally, the proposed method can bring up to 3.7x speedup in SBR and 2.3x in the entire EVD compared to state-of-the-art implementations. © 2023 ACM.

关键词： Singular value decomposition

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