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检索条件"丛书名=Communications in Computer and Information Science"

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A Study of Performance Portability in Plasma Physics Simulations 11th

A Study of Performance Portability in Plasma Physics Simulat...

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11th Latin American Conference on High Performance Computing

作者： Ruzicka, Josef Asch, Christian Meneses, Esteban Rampp, Markus Laure, Erwin Natl High Technol Ctr Natl Adv Comp Lab San Jose CA USA Costa Rica Inst Technol Sch Comp Cartago Costa Rica Max Planck Gesell Max Planck Comp & Data Facil Garching Germany

ISBN: (纸本)9783031800832;9783031800849

The high-performance computing (HPC) community has recently seen a substantial diversification of hardware platforms and their associated programming models. From traditional multicore processors to highly specialized accelerators, vendors and tool developers back up the relentless progress of those architectures. In the context of scientific programming, it is fundamental to consider performance portability frameworks, i.e., software tools that allow programmers to write code once and run it on different computer architectures without sacrificing performance. We report here on the benefits and challenges of performance portability using a field-line tracing simulation and a particle-incell code, two relevant applications in computational plasma physics with applications to magnetically-confined nuclear-fusion energy research. For these applications we report performance results obtained on four HPC platforms with server-class CPUs from Intel (Xeon) and AMD (EPYC), and high-end GPUs from Nvidia and AMD, including the latest Nvidia H100 GPU and the novel AMD Instinct MI300A APU. Our results show that both Kokkos and OpenMP are powerful tools to achieve performance portability and decent "out-of-the-box" performance, even for the very latest hardware platforms. For our applications, Kokkos provided performance portability to the broadest range of hardware architectures from different vendors.

关键词： Parallel Programming Performance Portability Plasma Physics

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3D Artistic Images Generation from 2D Images Using Improved Wasserstein Generative Adversarial Networks 8th

3D Artistic Images Generation from 2D Images Using Improved ...

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2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： Esan, Dorcas Oladayo Owolawi, Pius Adewale Tu, Chunling Tshwane Univ Technol Dept Comp Syst Engn Pretoria South Africa

ISBN: (纸本)9783031856273;9783031856280

Excellent artistic work frequently showcases the individual inventiveness of the creator, and many artists place a high value on producing images of superior quality. A remarkable success has been achieved by GANs in producing photorealistic 2-D images. Because traditional GANs generate 3D images directly from 2D, their images frequently lack the perfect quality and consistency of multiple views from a single image. With varied degrees of success, numerous attempts have been made to address these issues. With the use of a 3D Variational Autoencoder (VAE) that integrated Improved Wasserstein Generative Adversarial Networks (3D VAE-IWGAN), this research aims to produce high-quality and consistent multi-view three-dimensional (3D) artistic images. The experiments were conducted using the COCO Africa Mask dataset, and the suggested method produced high-quality images that were more closely aligned with the aesthetic qualities of authentic works of art than other complex methods. In comparison to other methods, the proposed method has a high Inception Score(IS) score of 26.37, a low frechet inception distance (FID) score of 12.09, a peak signal-to-noise ratio (PSNR) of 31.35, a mean squared error (MSE) of 65.32, a chamfer distance (CD) of 0.243, Learned Perceptual Image Patch Similarity (LPIPS) score of 0.134 and a structural similarity index measure (SSIM) of 0.772. These results are better than those obtained with other methods on the COCO Africa Mask dataset. The suggested approach can help artists showcase, develop, and improve the originality of their work, which will increase their financial gains.

关键词： Generative Adversarial Networks 3-D Image Variational Autoencoder artistic Image Improved Wasserstein GAN

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Evaluation of Parental Control Tools Functionalities: The Chilean Context

Evaluation of Parental Control Tools Functionalities: The Ch...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Rojas, Nicolas Boettcher, Nicolas Palacios Jativa, Pablo Gutierrez Gaitan, Miguel Univ Diego Portales Santiago Chile Pontificia Univ Catolica Chile Santiago Chile CISTER Res Ctr Porto Portugal

ISBN: (纸本)9783031834318;9783031834325

Choosing a reliable parental control tool is essential to supervising minors accessing information and using electronic devices. For this, it is necessary to determine which tools are the most suitable and if their functionalities are effective. To our knowledge, despite the availability of existing tools that evaluate applications and different actors, e.g., parent control applications recommended by Internet Service Providers (ISPs), none of them properly characterize the various functionalities provided by the application while validating their efficacy. In this paper, we propose a new comprehensive metric to effectively identify and evaluate the functionalities provided by tools in the field of parental control. The metric is applied to some of the most downloaded apps from the Play Store, and the results are compared with the applications recommended by ISPs in Chile, which cover more than 93% of the market. The results from our analysis show that none of the parent control applications recommended by Chilean ISPs managed to provide full confidence in the functionalities implemented since at least one of their functions did not pass the test applied. The end goal for the test metric proposed is to allow future developers to assess their applications beforehand while offering a better match with the customer's expected service.

关键词： Parental control Android PDNS Application evaluation

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Reinforcement Learning for Optimizing Routing in the Production Supply of Matrix Production Systems 5th

Reinforcement Learning for Optimizing Routing in the Product...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Ried, Florian Niederdraenk, Simon Fottner, Johannes Tech Univ Munich Chair Mat Handling Mat Flow Logist Boltzmannstr 15 D-85748 Garching Germany

ISBN: (纸本)9783031807596;9783031807602

Matrix production systems offer the flexibility to meet an increasingly individualized and volatile customer demand. However, production supply processes within these systems have rarely been investigated in detail despite playing an integral role in their performance. Contributing to closing this research gap, this work utilizes reinforcement learning for routing in the production supply of matrix production systems. In particular, it focuses on dispatching orders to the vehicles and scheduling the orders within a route. Various constraints are considered to simulate a realistic setting, including order time windows, vehicle battery limitations, and a vehicle capacity allowing to transport multiple items at once. A reinforcement learning framework is conceptualized and implemented, assigning orders to vehicles based on various route construction heuristics. Its observation space contains abstract information about current orders of the matrix production supply environment and specific data on the vehicles for the reinforcement learning agent to select both a vehicle and a heuristic. The action and observation spaces are complemented by a multi-criteria reward function, prompting the agent to learn not to violate any constraints of the environment while simultaneously choosing actions that lead to the most cost-effective routes after route optimization. The reinforcement learning route constructor approach is trained and deployed on a discrete-event simulation of a matrix production system, which is connected to the reinforcement learning framework via a socket interface. The approach has proven to be successful by outperforming two non-reinforcement learning heuristics for route construction.

关键词： Reinforcement learning Dynamic pickup-and-delivery problem Matrix production systems Routing

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Multi-granularity Semantic Representation and Rule-Based Labeling for Relation Classification 4th

Multi-granularity Semantic Representation and Rule-Based Lab...

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4th International Conference on Artificial Intelligence Logic and Applications

作者： Zhou, Zhenyu Zhang, Qinghua Du, Hongfei Xie, Qin Feng, Shuo Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Big Data Intelligent Comp Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China

ISBN: (纸本)9789819603534;9789819603541

Relation classification aims to determine the relationship between a given entity pair. Many existing methods based on Pre-trained Language Models (PLMs) exhibit weaknesses in the generalization of text embedding. Meanwhile, to cope with imbalanced datasets, researchers have proposed to apply contrastive learning to improve the classification ability of the models, but most of them lack effective data enhancement methods, thus the advantage of contrastive learning in dealing with imbalanced data is underexploited. Focusing on these two problems, the MSRL (Multi-granularity Semantic Representation and Rule-Based Labeling) model is proposed. Firstly, by incorporating semantic representations from different levels of granularity within PLMs, the MSRL achieves entity embedding with better transfer learning and improved generalisation performance. Secondly, MSRL designed a data enhancement method based on the prompt template: reconstructing the labels according to logic rules aims to make them more similar to the input instances in terms of text structure and ensure that the embedding of both is closer in the embedding space, thus fully leveraging contrastive learning. Finally, experiments on public datasets show that the proposed MSRL outperforms existing mainstream baseline models.

关键词： Relation Classification Multi-granularity Contrastive Learning Text Restructuring

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A Web Tool for Representing Algorithmic Flowcharts and Its Support for Pseudocode Learning 1

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Tereso, Marco Pratas, Antonio Beirante, Luis Martinho, Domingos Bento, Fernando Vardasca, Ricardo ISLA Santarem Santarem Portugal Univ Evora VISTA Lab ALGORITMI Evora Portugal ISCTE IUL ISTAR Lisbon Portugal Univ Porto LAETA INEGI Porto Portugal

ISBN: (数字)9783031832079

ISBN: (纸本)9783031832062;9783031832079

Developing computer programming and problem-solving skills are fundamental skills for the future of those who obtain training in the science, Technology, Engineering and Math (STEM) areas. Two simple and basic ways to develop these skills are through flowcharts and pseudocode. The aim of this research is to identify the advantages of using these two methodologies for teaching computer programming and problem solving and how each can help the other, as well as carrying out a survey of the tools currently available to support teaching and finally build a simple, intuitive and easy-to-use web tool that allows you to easily convert between flowcharts and pseudocode. The web tool developed meets the requirements of being simplicity, intuitively and easy-to-use and allows conversion between flowcharts and pseudocode, becoming an important aid to students who can now use it to study anytime and anywhere.

关键词： computer programming Flowcharts Pseudocode Problem Solving

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Balancing Accuracy and Safety in AI: A Novel Adversarial Training Approach 4th

Balancing Accuracy and Safety in AI: A Novel Adversarial Tra...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Atkuri, Veera Venkata Naga Krishna Chaitanya Purple Talk Inc E commerce Technol Lead Dallas TX 75201 USA

ISBN: (纸本)9783031837920;9783031837937

In this paper, This study propose a novel approach to improve the safety of classification models by incorporating adversarial training into the model training process. This study demonstrate that adversarial training enhances the safety of models without significantly affecting their accuracy. The proposed approach methodology involves generating adversarial examples from the original dataset and creating a mixed training dataset that includes both original and adversarial examples. By evaluating the model's performance on both types of datasets, This study can achieve a balance between accuracy and safety. The proposed approach offers a more resource-efficient alternative to traditional model tuning processes, which often require multiple iterations to meet performance standards. The proposed approach results indicate that including at least 20% adversarial examples in the training data effectively improves safety while allowing adjustments based on the relative importance of accuracy and safety. The new method simplifies the process of achieving safety requirements and reduces the resources needed for training and evaluation. Future research will focus on validating this approach with diverse safety-related datasets, various model structures, and different adversarial attack methods, as well as exploring its applicability to other non-functional requirements in AI.

关键词： Adversarial machine learning

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FGA-Net: Feature-Gated Attention for Glioma Brain Tumor Segmentation in Volumetric MRI Images 4th

FGA-Net: Feature-Gated Attention for Glioma Brain Tumor Segm...

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4th International Conference on Artificial Intelligence and Knowledge Processing

作者： Rasool, Novsheena Bhat, Javaid Iqbal Wani, Niyaz Ahmad Miglani, Arzoo Islamic Univ Sci & Technol Kashmir 192122 Jammu & Kashmir India Thapar Inst Engn & Technol Patiala 147004 Punjab India Chitkara Univ Rajpura 140401 Punjab India

ISBN: (纸本)9783031734762;9783031734779

Gliomas are the most common cancerous brain tumors, constituting the primary cause of death associated with brain tumors. Their widespread occurrence and the complexities involved in their treatment highlight the urgent need for continued research and innovation to improve diagnosis, treatment, and patient survival. Manual segmentation of gliomas in MRI (magnetic resonance imaging) scans is a labourous job and subject to variability, necessitating more efficient and accurate automated methods. This paper presents a pioneering approach by integrating a dynamic feature-gated attention mechanism into a 3D U-Net architecture for the semantic segmentation of gliomas in volumetric MRI scans. Our model combines the robust U-Net architecture with adaptive, selective focusing capabilities of feature-gated attention, effectively capturing both local and global dependencies within the volumetric data. The dynamic feature-gated attention mechanism updates attention weights during training, allowing the model to adjust feature importance based on the input data. Integration of this mechanism enhances feature discernment and segmentation accuracy. The experimentation evaluation conducted on the BraTS 2019 dataset, including measures like sensitivity, specificity, precision, IoU score, accuracy, and Dice Similarity Coefficient (DSC), demonstrates promising results. Our method achieves dice scores of 0.90 for the whole tumor (WT), 0.84 for the core tumor (CT), and 0.84 for the enhancing tumor (ET), along with an impressive accuracy rate of 98%.

关键词： Feature-gated Attention 3D-UNet MRI Segmentation Glioma Deep Learning

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Tactile Volcano Maps as Resources for an Inclusive science Education

Tactile Volcano Maps as Resources for an Inclusive Science E...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Ale, Jhon Univ Chile Capitan Ignacio Carrera Pinto 1045 Santiago Chile

ISBN: (纸本)9783031834318;9783031834325

To promote inclusive, accessible, and quality education aligned with sustainable development goals, this study proposed the design and evaluation of four educational tactile maps of volcanoes for visually impaired individuals, using 3D-printed models. The research was based on an educational design method that included conceptualization, prototype development, and validation by experts in differential education. Tactile maps were created representing different types of Chilean volcanoes: shield, caldera, stratovolcanoes, and lava domes. Experts positively evaluated these maps, highlighting their ability to help visually impaired students visualize shapes and spatial features of the volcanoes, compare reliefs and sizes, and understand their evolution. The study achieved its goal, demonstrating that 3D-printed tactile maps are useful for inclusive education in Earth sciences. However, challenges in design and accessibility persist, as well as the need to integrate these resources into real educational settings. Future research should focus on their implementation in the school curriculum and on training teachers in their use, ensuring their pedagogical value and contribution to inclusive and accessible education.

关键词： Tactile Maps Inclusive Education 3D Printed Model Earth and Universe sciences Education for Sustainability

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A Predictive Maintenance Platform for a Conveyor Motor Sensor System Using Recurrent Neural Networks 5th

A Predictive Maintenance Platform for a Conveyor Motor Senso...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Kiangala, Kahiomba Sonia Wang, Zenghui Univ South Africa UNISA Dept Elect & Smart Syst Engn ZA-1709 Johannesburg Gauteng South Africa

ISBN: (纸本)9789819770007;9789819770014

Since the effusion of Industry 4.0 (I40) and smart manufacturing, predictive maintenance (PdM) has become critical to prevent severe system breakdowns and costly production downtime in various industries. Several state-of-the-art Artificial Intelligence (AI) approaches, such as machine learning models (ML), empower the PdM design concept to produce more accurate outcomes. In this study, we propose the development of a PdM platform that effectively detects future possible warnings and failures in the vibration speed sensor of a conveyor motor. We build the platform backend using recurrent neural networks (RNN) regression ML models to predict vibration speed values and their trends. We train our RNN models using univariate time-series historical vibration sensor data recorded over the years. The experimental results demonstrate the robustness of our platform built on RNN models compared to other traditional regression machine learning models such as Extreme Gradient Boosting (XGboost). We compare the effectiveness of the PdM platform with two RNN models: a basic RNN model and a long short-term memory (LSTM) model. Unlike other regression PdM systems focusing on predicting devices Remaining Useful Life (RUL), our PdM platform forecasts and generates new vibration sensor data based on the desired future time range. It also pinpoints set warning and failure values and indicates when these faults are more likely to occur. Factories can utilize this feature for analytics purposes.

关键词： Conveyor Motor Sensor Predictive Maintenance (PdM) Recurrent Neural Networks (RNN) Regression Models time-series data

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