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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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DEEP LEARNING-BASED CHANNEL ESTIMATION FOR RIS-ASSISTED UAV-ENABLED WIRELESS COMMUNICATIONS SYSTEM

DEEP LEARNING-BASED CHANNEL ESTIMATION FOR RIS-ASSISTED UAV-...

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2023 International conference on Innovation, Communication and Engineering, ICICE 2023

作者： Tesfaw, Belayneh A. Lin, Hsin-Piao Tarekegn, Getaneh B. Juang, Rong-Terng Jeng, Shiann-Shiun Department of International Graduate Program in Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Institute of Aerospace and System Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Electrical and Computer Engineering National Yang-Ming Chiao Tung University Hsinchu30010 Taiwan Department of Electrical Engineering National Dong Hwa University Hualien Taiwan

ISBN: (纸本)9781839539954

Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environments, the movement of UAVs can be blocked by trees and high-rise buildings that obstruct directional paths. This problem can be effectively addressed by employing reconfigurable intelligent surfaces (RIS). To achieve this goal, accurate channel estimation in RIS-assisted UAV-enabled wireless communications is crucial. This paper proposes an accurate channel estimation model using long short-term memory (LSTM) for a multi-user RIS-assisted UAV-enabled wireless communications system. Simulation results showed that LSTM can effectively improve the channel estimation performance of RIS-assisted UAV-enabled wireless communications. © The Institution of Engineering & Technology 2023.

关键词： Channel estimation

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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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performance Analysis of Knowledge Tracing Models with Mixed Precision: A Comparative Study on Server and Raspberry Pi Environments 39

Performance Analysis of Knowledge Tracing Models with Mixed ...

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39th International conference on Information Networking, ICOIN 2025

作者： Park, Junhyeong Zou, Chengxing Kim, Inseo Park, Myung Gyu Kim, Jinsung Chung-Ang University School of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798331506940

With the recent advancements in artificial intelligence (AI) and the improved performance of mobile devices, Knowledge Tracing (KT) models have gained significant attention in the education area, where they play a crucial role in tracking students' learning progress and providing personalized learning experiences. This study aims to compare the performance of KT models on a Raspberry Pi and a GPU server while examining the feasibility of model light-weighting using the Mixed Precision technique in low-power environments. Specifically, we performed fine-tuning and inference of pretrained models on both low-power devices and high-performance servers. The experimental results showed minimal performance differences in terms of AUC between the two environments, while accuracy (ACC) varied across models. Additionally, Attention-based models experienced significantly longer inference times when AMP was applied on low-power devices, indicating that complex computations require more resources on such devices. Based on these findings, applying AMP to the DKT+ model was identified as the most suitable option for low-power environments. © 2025 IEEE.

关键词： Students

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AutoCaps-Zero: Searching for Hardware-Efficient Squash Function in Capsule Networks

AutoCaps-Zero: Searching for Hardware-Efficient Squash Funct...

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2024 IEEE International conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2024

作者： Kang, Jieui Kwon, Sooyoung Kim, Hyojin Sim, Jaehyeong Ewha Womans University Dept of Artificial Intelligence Convergence Seoul Korea Republic of Ewha Womans University Dept of Computer Science & Engineering Seoul Korea Republic of

ISBN: (纸本)9798350349832

Capsule networks (CapsNets) offer distinct advantages over conventional convolutional neural networks (CNNs) by introducing the concept of a capsule. Specifically, this innovation achieves both rotational invariance and spatial awareness, making CapsNets a powerful tool in the field of machine learning. However, this breakthrough comes with an increased level of computational complexity. In our comprehensive experimental analysis of CapsNets, we meticulously inspected its various components and identified the squash function as the main computational bottleneck. To address this challenge, In this paper, we adapts the principles of neural architecture search (NAS) and introduces AutoCaps-Zero, a framework that automatically searches the hardware-efficient squash function to reduce model inference time. Meanwhile, CapsNet models incorporating the searched squash function have exhibited excellent performance across datasets of various sizes, while retaining robust features that make them resistant to adversarial attacks. Besides, these models maintain high performance even on challenging datasets like multiMNIST. Particularly, our experimental results demonstrate that the squash function searched by AutoCaps-Zero reduces the execution time of the squash function itself by approximately 68 %. Consequently, deploying the searched squash function on our benchmark models can reduce the end-To-end graphic processing unit (GPU) inference time by up to 34%. Overall, with the searched function, the CapsNet code will be released. © 2024 IEEE.

关键词： Graphics processing unit

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Fine-Grained Scene Graph Generation with Overlap Region and Geometrical Center

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computer GRAPHICS FORUM 2022年第7期41卷 359-370页

作者： Zhao, Y. Q. Jin, Z. Zhao, H. Y. Zhang, F. Tao, Z. W. Dou, C. F. Xu, X. H. Liu, D. H. Peking Univ Sch Comp Sci Beijing Peoples R China Acad Mil Sci Beijing Peoples R China Minist Educ Key Lab High Confidence Software Technol PKU Beijing Peoples R China

Scene graph generation refers to the task of identifying the objects and specifically the relationships between the objects from an image. Existing scene graph generation methods generally use the bounding boxes region features of objects to identify the relationships between objects. However, we feel that the overlap region features of two objects may play an important role in fine-grained relationship identification. In fact, some fine-grained relationships can only be obtained from the overlap region features of two objects. Therefore, we propose the Multi-Branch Feature Combination (MFC) module and Overlap Region Transformer (ORT) module to comprehensively obtain the visual features contained in the overlap regions of two objects. Concretely, the MFC module uses deconvolution and multi-branch dilation convolution to obtain high-pixels and multi-receptive field features in the overlap regions. The ORT module uses the vision transformer to obtain the self-attention of the overlap regions. The joint use of these two modules achieves the mutual complementation of local connectivity properties of convolution and the global connectivity properties of attention. We also design a Geometrical Center Augmented (GCA) module to obtain the relative position information of the geometric centers between two objects, to prevent the problem that only relying on the scale of the overlap region cannot accurately capture the relationship between two objects. Experiments show that our model ORGC (Overlap Region and Geometrical Center), the combination of the MFC module, the ORT module, and the GCA module, can enhance the performance of fine-grained relation identification. On the Visual Genome dataset, our model outperforms the current state-of-the-art model by 4.4% on the R@50 evaluation metric, reaching a state-of-the-art result of 33.88.

关键词： CCS Concepts • Computing methodologies → Artificial Intelligence Neural Networks computer Vision

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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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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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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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