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PSP AND LES INVESTIGATION ON THE IMPACT OF TRENCH DEPTH ON FLAT PLATE FILM COOLING THROUGH SHAPED HOLES

PSP AND LES INVESTIGATION ON THE IMPACT OF TRENCH DEPTH ON F...

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ASME Turbomachinery Technical conference and Exposition (Turbo Expo) on Collaborate, Innovate and Empower - Propulsion and Power for a Sustainable Future

作者： Abdeh, H. Barigozzi, G. Zamiri, A. Chung, J. T. Univ Bergamo Dept Engn & Appl Sci Dalmine BG Italy Korea Univ Sch Mech Engn Seoul South Korea

ISBN: (纸本)9780791887004

To increase film cooling effectiveness levels downstream of film cooling holes, trenches manufactured in the thermal barrier coating can be adopted. The performance of this solution depends on the trench geometrical characteristics, namely its depth and width. A LES based numerical study has been performed at Korea University to investigate the effects of trench configuration on the thermal protection, resulting in a 22% increase in cooling performance compared to the reference case without a trench. The present paper reports the results of an experimental investigation carried out at Bergamo University on a Plexiglass flat plate model with a set of three fan-shaped holes incorporated into an existing wind tunnel, replicating the numerical setup. PSP technique was used to measure the adiabatic film cooling effectiveness. Besides the standard shaped hole case, the best and worst trench configurations coming from a DoE approach have been examined at various coolant to mainstream blowing ratios M in the range between 0.5 and 3. Cases at M = 1.5 were used to cross check the prediction capability of the LES numerical simulation for the selected trenched cases. PSP and LES result to be in good agreement, also with literature data. The high depth/low width trench was shown to give improved performance for M larger than 1.

关键词： Film cooling Coolants Cooling Flat plates Experimental design Wind tunnels Thermal barrier coatings computer simulation

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A Novel Method with Transformers for Fine-grained Encrypted Traffic Classification 25

A Novel Method with Transformers for Fine-grained Encrypted ...

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25th IEEE International conferences on high performance Computing and Communications, 9th International conference on Data Science and Systems, 21st IEEE International conference on Smart City and 9th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Kong, He Li, Tong Ge, Jingguo Li, Hui Yang, Liqun Lu, Zhuang Xu, Zhibin Xie, Chuanxi Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9798350330014

With the rise of cyber threats and data breaches, encrypted traffic has become increasingly important in ensuring online privacy and security. However, it also brings significant security challenges. Existing studies have shown that encrypted traffic from applications or web services can be identified using various methods. Nevertheless, there is limited generalization ability across multiple scenarios and difficulties in distinguishing similar traffic traces between web pages of the same application. To overcome the limitations of existing methods, we propose ANT-ET, a novel learning method tailored for fine-grained webpage analysis. ANT-ET extracts the contextual semantics from raw traffic and represents it as a semantic vector. These vector representations are subsequently utilized for classification through a classification layer and discriminator. The experimental results show that ANT-ET can effectively understand the semantics of encrypted traffic across various scenarios, achieving F1-score greater than 95% in four public encrypted traffic tasks, outperforming existing methods. In addition, we collected a real-world traffic dataset to verify the effectiveness of ANT-ET for fine-grained webpage detection. © 2023 IEEE.

关键词： Websites

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Autoscaling MPI Cluster using OpenMPI and OpenStack cloud services 27

Autoscaling MPI Cluster using OpenMPI and OpenStack cloud se...

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27th International conference on System Theory, Control and Computing, ICSTCC 2023

作者： Pavăl, Silviu-Dumitru Amarandei, Cristian-Mihai Şerban, Elena Mironeanu, Cătălin Computer Engineering "Gheorghe Asachi" Technical University Iasi Romania

ISBN: (纸本)9798350337983

In this paper, we present a novel architecture for an auto-scaling MPI cluster that leverages the capabilities of OpenMPI and OpenStack frameworks. Unlike traditional MPI clusters, which are typically static and expand to the maximum available resources, our proposed architecture aims to dynamically allocate resources in a cloud environment. This strategy is essential to mitigate high costs associated with static provisioning and ensure that the parallel tasks running on the cluster can fully benefit from the computational power of the cloud when needed. By integrating OpenMPI, a high-performance MPI implementation, with the flexible resource management capabilities of OpenStack, we enable the dynamic allocation and de-allocation of compute resources based on the cluster's workload demands. This auto-scaling mechanism allows the cluster to scale up or down in response to changing computational requirements, optimizing resource utilization and reducing costs. © 2023 IEEE.

关键词： Cluster computing

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Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud 31

Building a Family of Data Augmentation Models for Low-cost L...

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31st International conference on Computational Linguistics, COLING 2025

作者： Yue, Yuanhao Wang, Chengyu Huang, Jun Wang, Peng School of Computer Science Fudan University Shanghai China Alibaba Cloud Computing Hangzhou China

ISBN: (纸本)9798891761971

Specializing LLMs in various domain-specific tasks has emerged as a critical step towards achieving high performance. However, the construction and annotation of datasets in specific domains are always very costly. Apart from using superior and expensive closed-source LLM APIs to construct datasets, some open-source models have become strong enough to handle dataset construction in many scenarios. Thus, we present a family of data augmentation models designed to significantly improve the efficiency for model fine-tuning. These models, trained based on sufficiently small LLMs, support key functionalities with low inference costs: instruction expansion, instruction refinement, and instruction-response pair expansion. To fulfill this goal, we first construct an automatic data collection system with seed datasets generated from both public repositories and our in-house datasets. This system leverages powerful LLMs to expand, refine and re-write the instructions and responses, incorporating quality assessment techniques. Following this, we introduce the training process of our models, which effectively distills task-solving and text synthesis abilities from teacher LLMs. Finally, we demonstrate how we integrate these functionalities into a machine learning platform to support low-cost LLM fine-tuning from both dataset preparation and training perspectives for users. Experiments and an application study prove the effectiveness of our approach. ©2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing the Discriminative Ability for Multi-Label Classification by Handling Data Imbalance

Enhancing the Discriminative Ability for Multi-Label Classif...

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2023 IEEE International conference on Systems, Man, and Cybernetics, SMC 2023

作者： Lim, Jin-Ha Oh, Myeong-Seok Lee, Seong-Whan Korea University Department of Artificial Intelligence Seoul02841 Korea Republic of Korea University Department of Computer and Radio Communications Engineering Seoul02841 Korea Republic of

ISBN: (纸本)9798350337020

In computer vision, long-tailed multi-label visual recognition is a challenging problem due to the imbalance between classes and the recognition of rare classes. Previous methods for resolving data imbalance mostly originate from single-label classification, which can be obstructed by the label co-occurrence issue, and they attempt to minimize or compensate for it. In this paper, we propose a novel tail class priority sampling method for long-tailed multi-label classification that untangles both issues. Our method samples tail class more often and earlier in order to make the model learn tail classes before it has bias toward common classes. Due to label co-occurrence, other classes will be spontaneously learned in same iteration, ensuring a balanced representation of the head and medium classes. To further enhance the recognition performance, we modify to a bilateral structure that samples both original and proposed sampling distribution to better represent the tail classes. We evaluate our proposed method on two widely used datasets in long-tailed version, COCO-LT and VOC-LT, and compare it with previous methods. The experimental results show that our method achieves a new state-of-the-art performance for tail classes on both datasets. Our method is applicable in various real-world scenarios, making rare class recognition achievable, and can be easily incorporated into conventional recognition frameworks. © 2023 IEEE.

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Unsupervised Pre-Training Using Masked Autoencoders for ECG Analysis

Unsupervised Pre-Training Using Masked Autoencoders for ECG ...

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2023 IEEE Biomedical Circuits and Systems conference, BioCAS 2023

作者： Wang, Guoxin Wang, Qingyuan Iyer, Ganesh Neelakanta Nag, Avishek John, Deepu University College Dublin School of Electrical and Electronic Engineering Dublin 4 Ireland National University of Singapore Department of Computer Science Singapore

ISBN: (纸本)9798350300260

Unsupervised learning methods have become increasingly important in deep learning due to their demonstrated large utilization of datasets and higher accuracy in computer vision and natural language processing tasks. There is a growing trend to extend unsupervised learning methods to other domains, which helps to utilize a large amount of unlabelled data. This paper proposes an unsupervised pre-training technique based on masked autoencoder (MAE) for electrocardiogram (ECG) signals. In addition, we propose a task-specific fine-tuning to form a complete framework for ECG analysis. The framework is high-level, universal, and not individually adapted to specific model architectures or tasks. Experiments are conducted using various model architectures and large-scale datasets, resulting in an accuracy of 94.39% on the MITDB dataset for ECG arrhythmia classification task. The result shows a better performance for the classification of previously unseen data for the proposed approach compared to fully supervised methods. © 2023 IEEE.

关键词： Big Data Electrocardiogram Masked Autoencoder Unsupervised Learning

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Grading Diabetic Retinopathy Using Transfer Learning-Based Convolutional Neural Networks 22nd

Grading Diabetic Retinopathy Using Transfer Learning-Based C...

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22nd International conference on computer Information Systems and Industrial Management (CISIM)

作者： Escorcia-Gutierrez, Jose Cuello, Jose Gamarra, Margarita Romero-Aroca, Pere Caicedo, Eduardo Valls, Aida Puig, Domenec CUC Dept Computat Sci & Elect Barranquilla 080001 Colombia Univ Autonoma Caribe Elect & Telecommun Engn Program Barranquilla 080001 Colombia Univ Norte Dept Syst Engn Barranquilla 080001 Colombia Univ Hosp St Joan Ophthalmol Serv Inst Invest Sanitaria Pere Virgili IISPV Reus 43204 Spain Univ Valle Sch Elect & Elect Engn Cali 760032 Colombia Univ Rovira & Virgili Dept Engn Informat & Matemat Escola Tecn Super Engn Tarragona 43007 Spain

ISBN: (纸本)9783031428227;9783031428234

Diabetic Retinopathy (DR) is a disease that affect the retina, consequence of a diabetes complication. An accurate an on time diagnosis could delay severe damage in the eye or vision loss. Currently, the diagnosis is supported by a retinography and visual evaluation by a trained clinician. However, retinography evaluation is a difficult task due to differences in contrast, brightness and the presence of artifacts. In this work we propose a convolutional neural network (CNN) model to detect and grading DR to support the diagnosis from a fundus image. Moreover, we used the transfer learning technique to reuse the first layers from deep neural networks previously trained. We carried out experiments using different convolutional architectures and their performance for DR grading was evaluated on the APTOS database. The VGG-16 was the architecture with higher results, overcoming the other networks and other related works. The best experimentation we obtained reached an accuracy value of 83.52% for DR grading tasks. Experimental results show that the CNN based transfer learning achieve high performance taking the knowledge learning from pretrained network and saving computational time in the training process, which turns out suitable for small dataset as DR medical images.

关键词： convolutional neural network Deep learning Diabetic retinopathy Image recognition Retinal imaging Transfer learning

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The performance and Impact of LED Floodlights in an Outdoor Electrical Substation During Misty Weather Conditions

The Performance and Impact of LED Floodlights in an Outdoor ...

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2023 IEEE Sustainable Smart Lighting World conference and Expo, LS18 2023

作者： Khan, Mohamed Fayaz Reddy, Shivek Swanson, Andrew Graham Discipline of Electrical Electronic and Computer Engineering University of KwaZulu Natal Durban South Africa

ISBN: (纸本)9798350346992

The reluctance of operational staff in an electrical utility to support the move from traditional high-pressure sodium (HPS) floodlights to light-emitting diode (LED) floodlights in outdoor substation environments has been reported to be based on their belief that LED floodlights are more costly (due to their high initial installation costs), technically not equivalent and that they perform poorly under misty conditions. The benefits of transitioning to LED floodlights has been proven to be both technically viable and financially more feasible, from a total lifecycle cost perspective, in a concurrent study conducted by the authors, and reported on in another paper. The objective of this study was to therefore conduct a series of tests, both outdoor and in a more controlled indoor laboratory setting, to test the postulation that higher temperature LED floodlights (6500 K) do not perform as well as equivalent warmer floodlights (3000 K) during misty weather conditions. The results of these experiments showed that there was an insignificant difference in visibility when using either of these lights under these conditions. © 2023 IEEE.

关键词： Energy efficiency

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Meta-optimized Structural and Semantic Contrastive Learning for Graph Collaborative Filtering 40

Meta-optimized Structural and Semantic Contrastive Learning ...

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40th IEEE International conference on Data Engineering, ICDE 2024

作者： Hao, Yongjing Zhao, Pengpeng Qu, Jianfeng Zhao, Lei Liu, Guanfeng Zhuang, Fuzhen Sheng, Victor S. Zhou, Xiaofang Soochow University Suzhou China Macquarie University Sydney Australia Beihang University Beijing China Institute of Artificial Intelligence SKLSDE School of Computer Science Beihang University Beijing China Texas Tech University Lubbock United States The Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9798350317152

Graph Collaborative Filtering (GCF) is designed to leverage high-order connectivity in user-item graphs, thereby significantly enhancing recommendation performance. Recent advancements have seen the integration of contrastive learning into GCF as a strategy to mitigate the challenges of data sparsity. This approach involves creating contrastive views through augmentations, followed by the generation of self-supervised signals. These signals are produced by maximizing the mutual information between the contrastive views. While this method has proven effective, we argue that current CL-based GCF models are still limited to current augmentation techniques. Existing data augmentation or noise perturbation may destroy the structural and semantic features of the original data and node attribute information is not considered. To tackle the above limitations, we propose a Meta-optimized Structure and Semantic Contrastive Learning for Graph Collaborative Filtering, named Meta-SSCL, which utilizes graph structure information and semantic information contrastive learning for recommendation. Specifically, we first model the structural and node semantic information representations with LightGCN and vanilla attention mechanism, respectively. Then consider the structural and semantic information as two contrastive views for recommendation. Next, the meta-optimized two-step training strategy generates adaptive contrastive views. Finally, we fuse structural and semantic representations for recommendation. Extensive experiments on real-world datasets demonstrate that Meta-SSCL consistently outperforms state-of-the-art sequential recommendation methods. The code is available1. © 2024 IEEE.

关键词： Collaborative filtering

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An Once-for-All Budgeted Pruning Framework for ConvNets Considering Input Resolution

An Once-for-All Budgeted Pruning Framework for ConvNets Cons...

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IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Sun, Wenyu Cao, Jian Xu, Pengtao Liu, Xiangcheng Zhang, Yuan Wang, Yuan Peking Univ Sch Software & Microelect Beijing Peoples R China Peking Univ Sch Integrated Circuits Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose an efficient once-for-all budgeted pruning framework (OFARPruning) to find many compact network architectures close to winner tickets in the early training stage considering the effect of input resolution during the pruning process. In architecture searching stage, we measure the similarity of the pruning mask to get high-quality network architecture with low energy and time consumption. After searching stage, our proposed method randomly sample the compact architectures with different pruning rates and input resolution to achieve joint optimization. Ultimately, we can obtain a cohort of compact networks adaptive to various resolution to meet dynamic FLOPs constraints on different edge devices with only once training. The experiments based on image classification and object detection show that OFARPruning has a higher accuracy than the once-for-all compression methods such as US-Net and MutualNet (1-2% better with less FLOPs), and achieve competitive performance as the conventional pruning methods (72.6% vs. 70.5% on MobileNetv2 under 170 MFLOPs) with much higher efficiency.

关键词： Training Image resolution Adaptive systems Image edge detection Energy resolution Object detection computer architecture

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