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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

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Proceedings - 2023 IEEE 47th Annual computers, Software, and Applications conference, COMPSAC 2023

Proceedings - 2023 IEEE 47th Annual Computers, Software, and...

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47th IEEE Annual computers, Software, and Applications conference, COMPSAC 2023

ISBN: (纸本)9798350326970

The proceedings contain 283 papers. The topics discussed include: when graphs meet game theory: a scalable approach for robotic car racing;robotic gel dispensing based on visual servoing for fiber threading;the anatomy of software changes and bugs in autonomous operating system;robust 6D pose estimation for texture-varying objects in autonomous system;towards environment-dependent model switching for performance and accuracy conscious object detection in day- and nighttime urban traffic environments;adaptive expected reactive algorithm for heterogeneous patrolling systems based on target uncertainty;program balancing in compilation for buffered hybrid dataflow processors;memory-virtualizing and -devirtualizing VM migration with private virtual memory;multilevel modeling for the analysis and prediction of school dropout: a systematic review;orientative teaching with open educational resources: the role of teachers in students’ transition from high school to university;personalizing student graduation paths using expressed student interests;hybrid/online teaching: a survey and key issues;and context-aware, composable anomaly detection in large-scale mobile networks.

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Enhancing Multi-exposure high Dynamic Range Imaging with Overlapped Codebook for Improved Representation Learning 27th

Enhancing Multi-exposure High Dynamic Range Imaging with Ov...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Lee, Keuntek Park, Jaehyun Cho, Nam Ik Department of ECE INMC Seoul National University Seoul Korea Republic of IPAI INMC Seoul National University Seoul Korea Republic of

ISBN: (纸本)9783031781247

high dynamic range (HDR) imaging technique aims to create realistic HDR images from low dynamic range (LDR) inputs. Specifically, Multi-exposure HDR imaging uses multiple LDR frames taken from the same scene to improve reconstruction performance. However, there are often discrepancies in motion among the frames, and different exposure settings for each capture can lead to saturated regions. In this work, we first propose an Overlapped codebook (OLC) scheme, which can improve the capability of the VQGAN framework for learning implicit HDR representations by modeling the common exposure bracket process in the shared codebook structure. Further, we develop a new HDR network that utilizes HDR representations obtained from a pre-trained VQ network and OLC. This allows us to compensate for saturated regions and enhance overall visual quality. We have tested our approach extensively on various datasets and have demonstrated that it outperforms previous methods both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Exposure fusion HDR imaging Vector quantization

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Hyperspectral Image Classification Using Custom Spectral Convolutional Neural Networks (CSCNNs) 14

Hyperspectral Image Classification Using Custom Spectral Con...

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14th International conference on Information Science and Technology, ICIST 2024

作者： Rouamba, Stephanie Zhang, Nian Mahmoud, Wagdy Thompson, Lara Denis, Max Deksissa, Tolessa University of the District of Columbia Department of Electrical and Computer Engineering WashingtonDC United States University of the District of Columbia Department of Mechanical Engineering Biomedical Engineering Program WashingtonDC United States University of the District of Columbia Department of Mechanical Engineering WashingtonDC United States Water Resources Research Institute University of the District of Columbia WashingtonDC United States

ISBN: (纸本)9798350353334

Custom spectral convolutional neural networks (CSCNNs) combine the strengths of convolutional neural networks with specialized spectral processing, resulting in improved classification accuracy by effectively capturing subtle variations in hyperspectral data. This paper proposes a CSCNNs based approach to classify hyperspectral images. The proposed method leverages the high-dimensional spectral data inherent in hyperspectral images, employing convolutional layers specifically designed to capture spectral-spatial features. By reducing dimensionality through principal component analysis and creating image patches, the model is trained to recognize complex patterns across different spectral bands. In addition, a comprehensive analysis of CSCNN performance is carried out, focusing on its architecture, key features, and benefits in computational efficiency and spectral representation. Experimental results on datasets such as Salinas-A, Pavia University (Pavia-U), and Indian Pines demonstrate that the CSCNN model surpasses traditional methods, achieving higher classification accuracy and more robust performance metrics like overall accuracy (OA), average accuracy (AA), and Kappa coefficient. The proposed model achieved an overall accuracy (OA) of 98.2%, 96.7%, and 92.5% for the Salinas-A, Pavia-U, and Indian Pines datasets, respectively. The model's average accuracy (AA) was 97.5%, 95.4%, and 89.8% for the same datasets. Furthermore, the model attained Kappa coefficients of 97.7%, 95.2%, and 89.1% for Salinas-A, Pavia-U, and Indian Pines, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Lightweight Network Based Real-time Anomaly Detection Method for Caregiving at Home 25

Lightweight Network Based Real-time Anomaly Detection Method...

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25th IEEE International conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Wang, Bin Wu, Xingjiao Gong, Miaomiao Zhao, Jin Sun, Yuling School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (纸本)9781665405270

Using data-driven technologies to support the healthcare of the elderly has been largely celebrated as an effective means. This paper focuses on the issue of using video-based sensing technologies to remotely monitor the activities and conditions of the elderly. Although it is a widely explored field, the high cost and high infrastructural requirements of most existing technologies usually challenge their effectiveness and efficiency in practical caregiving context. To address these challenges, we propose a lightweight network based real-time anomaly detection system, which consists of video-based ADL sensing and pre-processing, AI streaming aggregating and cluster computing. We examine our method by implementing and deploying it into a real-world care facility for the elderly in Shanghai China. The results show that our method has good performance in expansibility, reliability, bandwidth availability, accuracy and privacy protection. © 2022 IEEE.

关键词： Fall detection

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An Explainable Machine Learning-Based Phishing Website Detection using Gradient Boosting

An Explainable Machine Learning-Based Phishing Website Detec...

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2024 International conference on Information Technology Research and Innovation, ICITRI 2024

作者： Prayogo, Rizal Dwi Alfisyahrin, Alvin Rizqi Gambetta, Windy Karimah, Siti Amatullah Nambo, Hidetaka Kanazawa University Division of Electrical Engineering and Computer Science Graduate School of Natural Science and Technology Kanazawa Japan Telkom University School of Computing Bandung Indonesia Kanazawa University Faculty of Transdisciplinary Sciences Institute of Philosophy in Interdisciplinary Sciences Kanazawa Japan Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350376210

As the need for achieving optimal performance in prediction models, several recent and complex models have been developed. However, many of these models operate as black boxes, providing little insight into their predictive results. In recent years, three gradient-boosting methods based on Decision Trees have been recommended, i.e., XGBoost, CatBoost, and LightGBM. These methods have proven to deliver competitive performance with fast training times. Nevertheless, in critical domains like security, there is a growing need for increased transparency among stakeholders. One pressing concern in the security field is the proliferation of phishing websites. To address this issue, we propose an explainable machine learning-based approach using gradient-boosting methods with hyperparameter optimization on three phishing website datasets. Our best methods surpass the state-of-the-art phishing website detection methods, achieving accuracy rates of 97.45%, 99.16%, and 97.85% for the UCI (2015), Mendeley (2018), and Mendeley (2020) datasets, respectively. Subsequently, we implement posthoc explainability using SHAP and LIME for the selected dataset. The experimental results indicate that three features, i.e., length_url, directory_length, and time_domain_activation, are consistently identified as the most influential features in the dataset. Moreover, our proposed approach demonstrates promising results for detecting phishing websites with both high accuracy and explainability. © 2024 IEEE.

关键词： Phishing

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Adaboost-based Graph Convolutional Networks with Channel Attention for Intrusion Detection in Internet-of-Things 21

Adaboost-based Graph Convolutional Networks with Channel Att...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International conference on Big Data and Cloud Computing, 16th IEEE International conference on Social Computing and Networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Long, Jing Luo, Cuiting Chen, Ruxin Liang, Wei Qin, Zheng Hunan Normal University College of Information Science and Engineering Changsha410081 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

ISBN: (纸本)9798350329223

With the increasing proliferation of Internet of Things (IoT) and industrial big data technologies, while IoT scenarios offer intelligent services, cyberspace faces various types of threats and attacks. Despite Graph Convolutional Networks (GCN) having been demonstrated as a potent tool for learning graph data representations, many current IoT intrusion detection methods tend to overlook the adoption of graph-related techniques, given the non-graph-structured nature of most IoT traffic data. This leads to the model design overlooking the potential correlations among variables, thus limiting the accuracy of intrusion detection. In this study, we introduce an integrated intrusion detection model that fuses graph learning with a dual attention mechanism(A-GCA). Initially, our research adopts a combination of GCN and Convolutional Neural Networks (CNN) to deeply capture intricate interactions between data nodes. To further enhance the model's feature-learning capability, we incorporate a channel attention mechanism for emphasizing features with pivotal information. Additionally, a multi-head attention mechanism is adopted to amalgamate a wealth of contextual information, ensuring more vivid feature representations. Regarding the training strategy, we employ a variant of Adaboost which uniquely steers weak classifiers from focusing on high-precision fitting of raw data towards precise binary classification tasks, aiming to augment the model's overall performance in intrusion detection. Evaluations and performance analyses using real-world datasets UNSW-NB15 and CICIDS2017 indicate that the proposed method achieves improvements of 2.5% and 3.7% over the best baseline model in the accuracy and F1-score, respectively, relative to the baseline, surpassing other approaches. © 2023 IEEE.

关键词： Adaptive boosting

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performance Portability Evaluation of Fluid-Structure Interaction Simulations on Heterogeneous Platforms

Performance Portability Evaluation of Fluid-Structure Intera...

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ISC high performance 2025 Research Paper Proceedings (40th International conference)

作者： Aristotle Martin Ayman Yousef Geng Liu William Ladd Antigoni Georgiadou Jorik Stoop Amanda Randles Biomedical Engineering Duke University Durham USA Leadership Computing Facility Argonne National Laboratory Lemont USA Oak Ridge National Laboratory National Center for Computational Sciences Oak Ridge USA

ISBN: (数字)9783982633619

The rapid proliferation of heterogeneous programming languages and multi-vendor hardware has underscored the critical need to evaluate the performance portability of scientific applications. In this work, we present the systematic porting and optimization of a massively parallel fluid-structure interaction code across multiple heterogeneous programming frameworks for deployment on leadership-class supercomputers from major vendors. Our analysis focuses on at-scale performance for simulations involving hundreds of millions of deformable cells, executed on a combination of CPUs and GPUs spanning thousands of nodes on exascale machines. We benchmark the performance of each implementation, highlighting the trade-offs inherent in adopting diverse programming models. Key insights regarding the portability of CUDA on multi-vendor platforms, the superior multi-core CPU performance from SYCL, and architectural considerations on performance optimization are distilled from our experience, offering guidance to other users of high performance computing based on our findings.

关键词： Codes Biological system modeling Computational modeling high performance computing Graphics processing units computer architecture programming Hardware Supercomputers Optimization

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MALT: Fine-Grained Microservice Profiling for Request Latency Anomaly Localization 25

MALT: Fine-Grained Microservice Profiling for Request Latenc...

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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： Zhang, Dingjie Wu, Qinghua Zhao, Yuankang Li, Zhenyu He, Peng Zhang, Yongsu Duan, Xiongchun Zhang, Yu Miao, Gen Tong, Jiancong Xie, Gaogang Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Purple Mountain Laboratories Nanjing China ByteDance Beijing China Computer Network Information Center Chinese Academy Sciences Beijing China

ISBN: (纸本)9798350330014

Recent years have witnessed the shift of cloud datacenter architecture from monolithic to microservice. In microservice, request latency which fundamentally determines application performance, has become a crucial concern for cloud operators. Due to various issues with hosts, networks, and remote services, microservices can encounter anomalies when processing requests, causing sudden latency spikes and potential Service- Level Agreement (SLA) violations. Existing performance monitoring tools lack the ability of collecting performance data containing request-level semantics under low overhead. They also do not address the diverse threading models of microservices, posing challenges in locating Request Latency Anomalies (RLAs). This paper proposes MALT, a performance profiling tool that collects fine-grained, request-level data and locates RLAs in microservices. MALT hooks the critical functions when microser-vices process requests and extracts request-level semantic data from function parameters or packet payloads. To characterize the complex and diverse microservice threading behaviors, MALT maps the collected data to each request according to request-level semantics and models the request execution process of microservices as a Directed Acyclic Graph (DAG), from which the path with longest latency is extracted to locate RLAs. We apply MALT to an open-source microservice in an experimental network and a microservice deployed in a production network. Results show that MALT achieves an average precision of 93% in locating four types of RLAs. These results further guide the optimization of request latency in production microservice, where the average request latency is reduced by up to 41.8 % by the heuristic methods. © 2023 IEEE.

关键词： Semantics

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Interpretable deep learning quantifies the impact of genomic variations on CD8+ T cell infiltration

Interpretable deep learning quantifies the impact of genomic...

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2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： He, Yi Xu, Liwen Wang, Xiang Peng, Shaoliang Hunan University College of Computer Science and Electronic Engineering Changsha China Central South University 2nd Xiangya Hospital Department of Thoracic Surgery Changsha China

ISBN: (纸本)9798350386226

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, but challenges remain in identifying molecular determinants of clinical response. To address this, we developed a Transformer-based model, ImmRegVar, to predict CD8+ T cell infiltration using somatic genomic variations from 7972 solid tumor samples. ImmRegVar outperforms traditional regression methods in predictive accuracy and model performance. We introduce an innovative explanatory factor that combines Transformer attention weights with feature ablation techniques, allowing precise quantification of each gene mutation's contribution. This factor offers an interpretable metric for understanding how specific mutations affect CD8+ T cell infiltration, shedding light on immune regulatory mechanisms. Validation through random permutation tests confirms its robustness and stability in high-dimensional sparse data. Our analysis also aligns with known immune regulatory genes, supporting the biological credibility of ImmRegVar. This research advances the understanding of key factors in immunotherapy and opens new pathways for exploring immune regulation and clinical applications. ImmRegVar and the explanatory factor are available at //***/Liwen-Liberty/ImmRegVar. © 2024 IEEE.

关键词： Ablation

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ACPNet: Enhancing Small-Scale Dieases Detection in Panoramic X-rays

ACPNet: Enhancing Small-Scale Dieases Detection in Panoramic...

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2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Gao, Nan Zhu, Junchao Chen, Peng Tang, Jijun Jiang, Feng Liang, Ronghua Zhejiang University of Technology College of Computer Science and Technology Hangzhou China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China Zhejiang Cancer Hospital Department of Radiotherapy Hangzhou China

ISBN: (纸本)9798350386226

Deep learning-based disease detection can automatically identify dental diseases in panoramic X-rays and improve the accuracy and efficiency of doctors' diagnoses. However, due to the complex data distribution of panoramic oral X-rays, significant scale differences among lesions, and the presence of many small-scale diseases, automated disease detection in panoramic oral X-rays faces considerable challenges. To alleviate the aforementioned issues, we propose ACPNet, which introduces a novel two-stage approach for detecting small-scale dental diseases in panoramic X-rays using the Contextual Attention Alignment Network (CAAN) and the Point-to-Patch Module (PTPM). To the best of our knowledge, we are the first to explore the detection of small-scale dental diseases in panoramic X-rays under limited sample conditions. Specifically, CAAN integrates deformable convolution with the global attention mechanism of transformer attention, enabling the model to more accurately extract small target foreground features in the complex background of panoramic X-rays. PTPM employs key point detection and cascade dynamic patches to adjust the bounding boxes of lesions, ensuring that small-scale diseases have sufficient high-quality proposals, thereby enhancing detector performance. Additionally, we collected a dataset containing 1157 instances of dental diseases to validate the effectiveness of our algorithm. Extensive experiments demonstrate that ACPNet achieves state-of-the-art performance, highlighting its superiority over baseline and other detection methods. © 2024 IEEE.

关键词： Deep learning

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