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Welcome Message from the ieee Cluster 2024 Posters Chair

Welcome Message from the IEEE Cluster 2024 Posters Chair

引用

ieee International conference on Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS)

ISBN: (数字)9798350383454

ISBN: (纸本)9798350383461

We are pleased to present the Posters program for this year's ieee Cluster 2024 conference at Kobe, Hyogo, Japan, to be held from September 24 to 27, 2024. ieee Cluster 2024 invited submissions from academia, laboratory, and industry professionals to present their latest research findings and work-in-progress in all aspects of cluster, cloud, and grid technologies in the form of posters in four major areas of 1) Application, Algorithms, and Libraries, 2) Architecture, Network/Communications, and Management, 3) Programming and System Software, and 4) Data, Storage, and visualization.

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Heart Health Insights: Analysing Data and Visualising Predictive Factors for Cardiovascular Disease

Heart Health Insights: Analysing Data and Visualising Predic...

引用

Engineering Informatics, ieee

作者： Udayini Vedantham Nithish Chouti Karan Raghuram Naik C K Sunil Department of Computer Science and Engineering Indian Institute of Information Technology Dharwad Karnataka India

ISBN: (数字)9798331505776

ISBN: (纸本)9798331505783

Cardiovascular Diseases (CVDs) are a leading cause of mortality worldwide, posing a significant public health challenge. This study aims to contribute to the existing research on CVD prediction by exploring the application of data analysis and visualization techniques. The researchers employed a range of machine learning algorithms, such as decision trees, support vector machines, random forest, and logistic regression, to analyze a comprehensive dataset of $\mathbf{1, 1 9 0}$ observations with 11 independent variables and the presence or absence of heart disease. The study focused on identifying the most significant factors contributing to the risk of heart disease using Principal Component Analysis (PCA) and evaluating the accuracy of different machine learning models in predicting the likelihood of CVDs. The findings offer important insights into how data analytics and visualization can be applied to detect and prevent cardiovascular diseases early on.

关键词： Heart Support vector machines Data analysis Machine learning algorithms Accuracy Data visualization Cardiovascular diseases Public healthcare Random forests Principal component analysis

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Where is Your App Frustrating Users? 22

Where is Your App Frustrating Users?

引用

ACM/ieee 44th International conference on Software Engineering (ICSE)

作者： Wang, Yawen Wang, Junjie Zhang, Hongyu Ming, Xuran Shi, Lin Wang, Qing Chinese Acad Sci Inst Software Lab Internet Software Technol Beijing Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Chinese Acad Sci Inst Software Sci & Technol Integrated Infomat Syst Lab Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Newcastle Callaghan NSW Australia

ISBN: (纸本)9781450392211

User reviews of mobile apps provide a communication channel for developers to perceive user satisfaction. Many app features that users have problems with are usually expressed by key phrases such as "upload pictures", which could be buried in the review texts. The lack of fine-grained view about problematic features could obscure the developers' understanding of where the app is frustrating users, and postpone the improvement of the apps. Existing pattern-based approaches to extract target phrases suffer from low accuracy due to insufficient semantic understanding of the reviews, thus can only summarize the high-level topics/aspects of the reviews. This paper proposes a semantic-aware, fine-grained app review analysis approach (SIRA) to extract, cluster, and visualize the problematic features of apps. The main component of SIRA is a novel BERT+Attr-CRF model for fine-grained problematic feature extraction, which combines textual descriptions and review attributes to better model the semantics of reviews and boost the performance of the traditional BERT-CRF model. SIRA also clusters the extracted phrases based on their semantic relations and presents a visualization of the summaries. Our evaluation on 3,426 reviews from six apps confirms the effectiveness of SIRA in problematic feature extraction and clustering. We further conduct an empirical study with SIRA on 318,534 reviews of 18 popular apps to explore its potential application and examine its usefulness in real-world practice.

关键词： App Review information Extraction Deep Learning

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SUBJECTIVE QUALITY EVALUATION OF POINT CLOUDS WITH 3D STEREOSCOPIC visualization 29

SUBJECTIVE QUALITY EVALUATION OF POINT CLOUDS WITH 3D STEREO...

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ieee International conference on Image Processing (ICIP)

作者： Prazeres, Joao Pereira, Manuela Pinheiro, Antonio M. G. Inst Telecomunicacoes Covilha Portugal

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

In this paper, a subjective evaluation of static point clouds encoded with several codecs is described. Unlike other studies, a stereoscopic 3D display was used to visualize the 3D representation. A set of six point clouds were encoded using a set of state of the art point cloud coding solutions, notably the two MPEG codecs V-PCC and G-PCC, a deep learning solution RS-DLPCC that was the response to a call for evidence on point cloud coding of JPEG Pleno, and the popular DRACO codec. The results of this subjective quality evaluation using a 3D representation visualized in a stereoscopic display were compared with a previous subjective study that used the same content visualized in a 2D display. For that, the results of both tests were compared with the Pearson correlation, Spearman rank order correlation, the root mean square error and the outlier ratio. Moreover, the two subjective evaluation results were statistically analysed to seek for any statistical difference. The two subjective evaluations reveal a very high level of similarity.

关键词： Point cloud quality Subjective quality evaluation Point cloud coding

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Highly-Efficient Binary Neural Networks for Visual Place Recognition

Highly-Efficient Binary Neural Networks for Visual Place Rec...

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ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Ferrarini, Bruno Milford, Michael McDonald-Maier, Klaus D. Ehsan, Shoaib Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England QUT Ctr Robot Sch Elect Engn & Robot Brisbane Qld 4000 Australia

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

VPR is a fundamental task for autonomous navigation as it enables a robot to localize itself in the workspace when a known location is detected. Although accuracy is an essential requirement for a VPR technique, computational and energy efficiency are not less important for real-world applications. CNN-based techniques archive state-of-the-art VPR performance but are computationally intensive and energy demanding. Binary neural networks (BNN) have been recently proposed to address VPR efficiently. Although a typical BNN is an order of magnitude more efficient than a CNN, its processing time and energy usage can be further improved. In a typical BNN, the first convolution is not completely binarized for the sake of accuracy. Consequently, the first layer is the slowest network stage, requiring a large share of the entire computational effort. This paper presents a class of BNNs for VPR that combines depthwise separable factorization and binarization to replace the first convolutional layer to improve computational and energy efficiency. Our best model achieves higher VPR performance while spending considerably less time and energy to process an image than a BNN using a non-binary convolution as a first stage.

关键词： visualization Convolution Computational modeling Neural networks Object detection Network architecture Energy efficiency

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Visualizing Program Behavior: A Study of Enhanced Program Diagrams Using LLM

Visualizing Program Behavior: A Study of Enhanced Program Di...

引用

Frontiers in Education (FIE) conference

作者： Ying Li Runze Yang ShiJie Gui Peng Shi Xuefei Huang Da Yang Xiaozhou Zhang Yiming Gai School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang University Hangzhou China

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This paper aims to address the difficulties faced by novice programmers in grasping code structure and execution flow, improving programming thinking, and pinpointing code errors with accuracy. It proposes providing students with program behavior diagrams based on large language models (LLMs) and visualization techniques to achieve personalized guidance. Specifically, these program behavior diagrams include programming thinking visualization diagrams and code vulnerability visualization diagrams. A programming thinking visualization diagram employs static code analysis to gather code structure information, combined with the structured chain-of-thought method to collectively optimize the LLM. This enables the LLM to explain each interpretable part of the code from top to bottom, detailing the programming concepts, and displaying them on a modularized code structure diagram. The code vulnerability visualization diagram primarily utilizes the fine-tuned LLM, optimizing it based on program analysis and clustering analysis methods to accurately identify vulnerabilities in student code and display them on a code flow diagram. Its feature is to visually display to students the error location, error information, and the impact of errors on program flow, rather than providing the programming answers. Lastly, through experiments and statistical analysis of actual teaching data, this paper serves a demonstration that the enhanced models used in the visualization diagram generation process have a noticeable effect on mainstream LLMs, and that visualization diagrams hold significant value for students at different stages of learning.

关键词： Location awareness Analytical models Codes Accuracy Statistical analysis Large language models Education Data visualization Optimization methods Programming profession

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An Object Detection Method Enhanced by Sparse Point Cloud for Low Illumination in Autonomous Driving 25

An Object Detection Method Enhanced by Sparse Point Cloud fo...

引用

ieee 25th International conference on Intelligent Transportation Systems (ITSC)

作者： Li, Shuguang Liu, Bei Zhao, Yang Zheng, Ke Cheng, Hong Univ Elect Sci & Technol China Sch Automat Engn Chengdu Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Object Detection is an essential task in autonomous driving. The existing object detection methods are susceptible to the variable lighting, which leads to the decrease of detection accuracy. To address the above issue, we introduce LRPN, a robust detection network with multi-modal feature fusion based on prior knowledge of lidar points. LRPN aims to improve the accuracy of object detection in light changing scenes by using fusion method of sparse point cloud and image. We propose two fusion modules: 1) Sparse lidar feature are fused with image in the feature extractor network. 2) Guided anchors are created based on visual projection using lidar points. Our method is applied to Faster RCNN and achieved higher precision under different illumination conditions. Compared with the original Faster RCNN network, the mAP of the method proposed in this paper is raised by 2.2% under good illumination and 3.7% under low illumination. The results show that the introduction of sparse point cloud can significantly improve the detection effect, especially in low illumination scenarios.

关键词： Point cloud compression visualization Laser radar Lighting Object detection Feature extraction Robustness

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Cascaded Deep Search: Achieving High Object Localization Precision for Roughly Positioned Autonomous Mobile Manipulator

Cascaded Deep Search: Achieving High Object Localization Pre...

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ieee/ASME International conference on Advanced Intelligent Mechatronics (AIM)

作者： Choophutthakan, Kerk Lin, Hong-Zhi Li, Chih-Hung G. Natl Taipei Univ Technol Grad Inst Mech Engn Taipei 10608 Taiwan Natl Taipei Univ Technol Grad Inst Mfg Technol Taipei 10608 Taiwan

ISBN: (数字)9781665413084

ISBN: (纸本)9781665413084

Autonomous Mobile Manipulators (AMMs) transport between locations and execute manipulation tasks such as object handling. As the manipulator resides on the mobile platform, the performance of manipulation is often limited by the position accuracy of the mobile platform. The robot can often be poorly positioned for dynamically balanced robots or those without rigid tracks. As a result, there exists a high uncertainty on the relative position between the target and AMM's parking position. This article contemplated the above scenario and proposed a cascaded visual object localization architecture to achieve vast-area search and progressive localization precision. We demonstrated a three-stage cascaded design utilizing the Convolutional Neural Network for illumination-robust localization in the final stages. Our field tests showed that given approximately 60 x 20 cm(2) position variations of the target object, the proposed system located the target object and performed pick-and-place with a success rate of 95%. The method was demonstrated on a two-wheeled mobile robot;however, it can also be applied to other mobile platforms that lack positioning accuracy due to rugged terrains or other reasons. The entire localization module is light and operates on a Raspberry Pi 4.

关键词： Location awareness visualization Uncertainty Target tracking Lighting Search problems Visual servoing

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Survey on Query Optimization of GPU database 23

Survey on Query Optimization of GPU database

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23rd ieee International conference on High Performance Computing and Communications, 7th ieee International conference on Data Science and Systems, 19th ieee International conference on Smart City and 7th ieee International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yang, Jiahui Gai, Rongli Duan, Liming School of Information Engineering Dalian University Dalian University Dalian China Doctor of Dalian Commodity Exchange Dalian Commodity Exchange Dalian China

ISBN: (纸本)9781665494571

In the era of artificial intelligence, CPU-driven database is not applicable in terms of data access speed and processing speed. With ultra-high-speed computing power and ultra-large data processing bandwidth capabilities, GPU is favored by people. The database branch with GPU computing as the core (GDBMS) is booming. Because of its high characteristics of concurrency (simple) operation, high throughput operation, computationally intensive (ALU unit), the ability to interact with artificial intelligence, time-space data analysis, and data visualization, it has completely changed the traditional database format. The query operation is a common operation of the database system, and the optimization of the query is always a core task. This article first systematically introduces the history of database development, and then introduces several query optimization methods that currently exist. Finally, according to the existing query optimization methods of the database, the development trend of GPU database query optimization is prospected. © 2021 ieee.

关键词： Concurrent computing Training Machine learning algorithms Databases Query processing Graphics processing units Training data

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Exploring Geometric Consistency for Monocular 3D Object Detection

Exploring Geometric Consistency for Monocular 3D Object Dete...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lian, Qing Ye, Botao Xu, Ruijia Yao, Weilong Zhang, Tong Hong Kong Univ Sci & Technol Hong Kong Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Autowise AI Hayward CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper investigates the geometric consistency for monocular 3D object detection, which suffers from the ill-posed depth estimation. We first conduct a thorough analysis to reveal how existing methods fail to consistently localize objects when different geometric shifts occur. In particular, we design a series of geometric manipulations to diagnose existing detectors and then illustrate their vulnerability to consistently associate the depth with object apparent sizes and positions. To alleviate this issue, we propose four geometry-aware data augmentation approaches to enhance the geometric consistency of the detectors. We first modify some commonly used data augmentation methods for 2D images so that they can maintain geometric consistency in 3D spaces. We demonstrate such modifications are important. In addition, we propose a 3D-specific image perturbation method that employs the camera movement. During the augmentation process, the camera system with the corresponding image is manipulated, while the geometric visual cues for depth recovery are preserved. We show that by using the geometric consistency constraints, the proposed augmentation techniques lead to improvements on the KITTI and nuScenes monocular 3D detection benchmarks with state-of-the-art results. In addition, we demonstrate that the augmentation methods are well suited for semi-supervised training and cross-dataset generalization.

关键词： Training visualization Computer vision Three-dimensional displays Perturbation methods Estimation Object detection

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