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

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Using Escape Rooms to Apply Team Building and Leadership Skills in an Engineering Leadership Development program: A Work in Progress

Using Escape Rooms to Apply Team Building and Leadership Ski...

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2023 ASEE Annual conference and Exposition - The Harbor of Engineering: Education for 130 Years, ASEE 2023

作者： Melvin, Elizabeth Michelle Bowles, Boz Steele, Adrienne Louisiana State University A&M College United States

Performing under pressure is common in engineering. Many engineers work in fields with pressing deadlines, on projects where millions or even billions of dollars, public safety, or the lives of the end user of their designs are at stake. Engineering and computer science-based professions account for just under 6% the 800+ high-stress professions reported by ONET [1]. Helping students to develop strategies for performing on teams effectively in stressful situations is essential for their successful goal achievement, productivity and team performance in their future engineering careers [2], [3]. This EL work in progress design paper will introduce a model for a non-traditional engineering leadership development activity. Specifically, this paper will explore the incorporation of a simulated stressful situation (escape room) paired with the introduction of psychometric/behavioral profiling, curated reflective activities, and placement of the event in a scaffolded series of workshops. Through pre- and post-survey evaluations and evaluation of the post-activity reflection exercise, the change in students' attitudes towards teamwork, self-perception of their role on teams, their perceptions of the importance of clear communication with teammates and their comfort level of effectively working on teams will be examined. The Chevron Leadership Academy at Louisiana State University is a corporate-sponsored, extracurricular leadership program. As part of this program, students attend a series of workshops covering a variety of leadership development topics. One is a game-based, team-building event-participation in an award-winning escape room activity, 13th Gate Escape in Baton Rouge, Louisiana. The goals of this activity are to help the students apply strategy skills to improve teamwork, to learn how to thrive as a team in a high-pressure situations, and to increase their confidence in their own leadership skills. Recognizing the importance of connecting activities to reflect

关键词： Students

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DP-BERT: A pre-trained deep language model for depression prediction using microarray data

DP-BERT: A pre-trained deep language model for depression pr...

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2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Gao, Junyu Zeng, Min Li, Yiming Wang, Fang Zheng, Ruiqing Liu, Jin Guo, Fei Li, Min Central South University School of Computer Science and Engineering Changsha410083 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China

ISBN: (纸本)9798350386226

In recent years, the increasing number of individuals diagnosed with depression and the growing awareness of its impact on modern society have highlighted the significance of accurate depression diagnosis. Microarray data has played a crucial role in uncovering the genetic mechanisms underlying depression. However, existing methods for depression prediction using microarray data often rely on the selection of differentially expressed genes. This approach disregards important information from other genes and is susceptible to batch effects, thereby limiting generalizability and model stability. To address these limitations, we propose DP-BERT, a depression prediction model based on Bidirectional Encoder Representations from Transformers (BERT). DP-BERT follows a pre-training and fine-tuning paradigm, leveraging a large amount of unlabeled microarray data from diverse sequencing platforms for pretraining to extract comprehensive genetic-level representations of psychiatric disorders. Subsequently, supervised fine-tuning is performed for depression prediction. Experimental results demonstrate that the pre-trained model achieves superior performance in depression prediction. The source code can be obtained from https://***/CSUBioGroup/DP-BERT. © 2024 IEEE.

关键词： Prediction models

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In-Band Management Framework and performance Evaluation for Interconnect Network in the TianHe Exascale Prototype System 24

In-Band Management Framework and Performance Evaluation for ...

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24th IEEE International conference on high performance Computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Cao, Jijun Lai, Mingche Liu, Lu Xu, Jiaqing Hu, Xiaoyue College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9798350319934

In-band management is an important management approach for high performance computer (HPC) interconnect network. This paper first introduces the design of in-band management for proprietary interconnect network in TianHe exascale prototype system located in National Supercomputing Center of China in Tianjin (NSCC-tj), including overall management architecture, message model, management interface, etc. Then it presents two basic in-band management tools, i.e., h-NetManager and INM-SVR/CLI. Finally, the performance of basic operations (including register read/write access and Flash read/write access) is measured and analyzed. The results show that the average access latency of basic operations is less than 20 us in the test subnet with 9.77 average hops, and the average access latency of register read and Flash read access operations can be reduced to 2.5 us and 5.2 us by carrying multiple requests in a single management packet with 9 hops. The access latency increases by approximately 0.8 us for each hop. In-band management implemented in the system enables efficient network management, which not only provides rich management functions, but also makes real-time status monitoring of interconnect network for exascale system possible. © 2022 IEEE.

关键词： In-Band Management Interconnect Networks performance Evaluation

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SDSSE: A Self-Driven RPC Load Balancing Method in Datacenter 25

SDSSE: A Self-Driven RPC Load Balancing Method in Datacenter

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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： Chen, Shuhan Wu, Chunming Shen, Congqi College of Computer Science and Technology Zhejiang University Hangzhou China Quancheng Laboratory Jinan China Zhejiang Lab Hangzhou China

ISBN: (纸本)9798350330014

Remote Procedure Calls (RPC) load balancing is crucial to maximize server resource utilization and reduce tail latency in data center. Existing load balancing methods deploy a frontend server acting as a load balancer, responsible for requesting backend server workload and routing RPC requests to backend server. However, existing methods suffer from two issues. Firstly, they introduce additional latency to RPC by requiring the frontend server to request the resource utilization of backend servers. Secondly, as the number of RPC requests grows, the frontend server becomes a bottleneck in the system, limiting the scalability of the load balancing approach. programmable switches have become an effective way to serve high traffic rates involving many concurrent flows. In this paper, we propose a novel RPC load balancing approach that enables the programmable switch to take over the RPC request load balancing function. By maintaining a local estimation of the distribution of the backend server workload, the switch can directly guide the selection of a suitable backend server. We formulate optimizing server selection policy as a Bandits with Knapsack (BwK) problem. Our objective is to strike a balance between maximizing the cumulative utilization of server resources and minimizing cumulative latency. Extensive experiments in diverse scenarios demonstrate that the proposed method can effectively improve server selection policy and boost RPC load balancing performance. © 2023 IEEE.

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A Hybrid Classification Framework of Centrifugal Pumps Using Wavelet Coherence Visuals and Principal Component Analysis 25

A Hybrid Classification Framework of Centrifugal Pumps Using...

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作者： Ullah, Niamat Siddique, Muhammad Farooq Kim, Jong-Myon Electronics and Computer Engineering University of Ulsan Department of Electrical Ulsan Korea Republic of University of Ulsan Electronics and Computer Engineering Pd Technology Co. Department of Electrical Ulsan Korea Republic of

ISBN: (纸本)9798350330014

Centrifugal pump fault detection and isolation in an early stage is essential from an industry and consumer viewpoint for significantly reducing downtime and maintenance activities., resulting in cost savings. This paper presents a comprehensive framework for centrifugal pump fault diagnosis integrating wavelet coherence analysis and hybrid classification. The proposed framework consists of several modules aiming to attain accurate fault diagnosis. Firstly., the National Instruments-based data acquisition module acquires the vibration signals from the centrifugal pump for healthy baseline and fault-defected signals. A low-pass filter amplifies the fault-specific signal. We perform the Wavelet coherence analysis on the amplified healthy baseline and fault-defected amplified signals to assess the correlation and coherence among different frequency components., resulting in a Wavelet Coherence Visual (WCV) image. Gaussian filtering is applied directly to the WCV., reducing noise and enhancing data quality. Principal Component Analysis (PCA) reduces dimensionality., refining the representation of pump health and fault characteristics. In the classification stage., a tailored hybrid approach., combining t-Distributed Stochastic Neighbor Embedding (t-SNE) and K-Nearest Neighbors (KNN)., significantly enhances the precision of centrifugal pump fault diagnosis and is responsible for categorizing fault classes into specific categories such as healthy., mechanical seal scratch., mechanical seal hole., and impeller defect. Achieving an impressive accuracy of 95.23%., KNN ensures accurate categorization based on health-sensitive WCV features., while t-SNE aids in visualizing data clusters., improving fault interpretability and predictive maintenance reliability. This approach significantly contributes to developing intelligent maintenance strategies., ultimately enhancing the reliability and performance of industrial pump systems. © 2023 IEEE.

关键词： Failure analysis

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Advances in Electronic Health Record Analysis for Diabetes Risk Prediction: A Machine Learning-Enhanced Framework 5

Advances in Electronic Health Record Analysis for Diabetes R...

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5th International conference on Artificial Intelligence and computer Engineering, ICAICE 2024

作者： Ye, Bingqing Shen, Zhangyi Ji, Aohan Jiao, Yu Niu, Yunfei Zuo, Kaizhong Hu, Peng Li, Wenjie School of Computer and Information Anhui Normal University Wuhu China

ISBN: (纸本)9798331528911

Background: Diabetes mellitus is a prevalent chronic condition with escalating global incidence, underscoring the urgency for efficient early detection and management strategies to ameliorate healthcare burdens and enhance patient quality of life. Methods: We conducted a systematic data processing pipeline on electronic health examination data, which included de-identification, imputation of missing data, and feature scaling. Feature selection was performed using Recursive Feature Elimination (RFE) complemented by a literature review to identify the most informative predictors. We developed five machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), XGBoost, and LightGBM (LGBM). Model performance was quantitatively assessed using a comprehensive set of metrics: accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The LGBM model, which demonstrated the highest predictive accuracy, was subjected to hyperparameter optimization and cross-validated on an independent public dataset to ensure its generalizability. Results: The initial evaluation of the LGBM model yielded an accuracy of 90.52%, precision of 89.06%, recall of 88.97%, F1-score of 88.67%, and an AUC-ROC of 0.965. After hyperparameter tuning, these metrics were further improved to 91.38% accuracy, 89.87% precision, 90.13% recall, 89.94% F1-score, and an AUC-ROC of 0.969. The model's predictive capabilities were corroborated through successful cross-validation on external datasets, indicating its robustness and reliability. Conclusion: The study presents a sophisticated diabetes prediction model leveraging machine learning algorithms on electronic health examination data. The model's superior predictive performance and validated generalizability suggest its potential as a valuable asset for healthcare providers, aiding in the early identification of diabetes risk, thereby facilitating more informed clinical

关键词： Electronic health record

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Optimizing Battery Consumption for Android-based Automatic Identification System (AIS) Application for Indonesian Fishermen 11

Optimizing Battery Consumption for Android-based Automatic I...

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11th International conference on Electrical Engineering, computer Science and Informatics, EECSI 2024

作者： Arleiny, Arleiny Arfianto, Afif Zuhri Supriyanto, Eko Maulidi, A.K.H. Suwandi, Bondan Kurniawan Bokau, Joe Ronald Program Studi Nautika Politeknik Pelayaran Surabaya Surabaya Indonesia Politeknik Perkapalan Negeri Surabaya Dept. of Marine Electrical Engineering Surabaya Indonesia Politeknik Negeri Semarang Dept. of Electrical Engineering Semarang Indonesia Politeknik Negeri Madura Safety Engineering Technology Sampang Indonesia Indonesia & Research Center for Electronics National Research and Innovation Agency Jakarta Indonesia Prodi Nautika Politeknik Pelayaran Barombong Makassar Indonesia

ISBN: (纸本)9798350355314

Fishing is integral to Indonesia's economy and the livelihood of many citizens. Indonesian fishermen often spend long periods at sea with limited access to power, making energy efficiency crucial for mobile applications, especially those used for navigation and safety. AIS is vital for maritime navigation, providing real-time information on vessel locations, weather, and emergencies. However, high battery consumption can cause AIS apps to fail when needed most. This study aims to optimize battery consumption for a mobile app used by Indonesian fishermen by implementing techniques like batch data transmission, efficient background task management, and optimized location services. The effectiveness of these techniques was evaluated based on key performance indicators such as battery usage, latency, data transmission, and task completion rates. The results show a significant reduction in average battery usage from 3000 mAh to 2000 mAh and improved application performance with latency decreasing from 150 ms to 120 ms. Data transmission reduced by 10% and task completion rates increased from 85% to 95%. Further analysis revealed that Wi-Fi networks are more energy-efficient than cellular networks, with average battery usage of 1800 mAh compared to 2200 mAh. Enhancing the energy efficiency of mobile applications is crucial for users in remote areas with limited power resources. The implemented techniques improved the reliability and efficiency of the AIS application, thereby enhancing the safety and operational effectiveness of Indonesian fishermen at sea. © 2024 IEEE.

关键词： Fisheries

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Multi-Class Object Classification using Deep Learning Models in Automotive Object Detection Scenarios 16

Multi-Class Object Classification using Deep Learning Models...

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16th International conference on Machine Vision (ICMV)

作者： Soumya, A. Cenkeramaddi, Linga Reddy Vishnu, Chalavadi Mohan, Krishna C. Indian Inst Technol Dept Comp Sci & Engn Hyderabad India Univ Agder Dept Informat & Commun Technol N-4879 Grimstad Norway Univ Agder Dept Comp Sci & Engn Grimstad Norway Indian Inst Technol Dept Comp Sci & Engn Hyderabad India

ISBN: (纸本)9781510674639;9781510674622

This paper presents two deep learning models using a multi-perspective convolutional neural network (CNN) for classifying objects in the context of intelligent transportation systems (ITS). The proposed model categorizes objects accurately, enabling them to make well-informed decisions in multi-object (such as Persons, Trucks, Motorbikes, Cars, and Cyclists.) detection in complex scenarios for automotive applications. The custom backbone model is designed based on experimentation with the VGG backbone network based on the VGG backbone network, incorporating a multilayer prediction head and custom feature extraction blocks for classifying multiple objects in complex scenes. The model is to extract abstract features and features at multiple scales with a custom-designed feature extraction backbone with multiple blocks. The proposed models are lightweight and require fewer computational resources for high classification performance. The automotive publicly available dataset with 19800 images and labels has been used. Results show that when we experimented with the VGG backbone CNN model, the classification accuracy of 99.64% is achieved, and on the other hand, the classification accuracy of custom backbone CNN is 99.46%. The performance of the proposed custom model is also compared to those of pre-trained benchmark models. The experimental findings presented in this paper show that the proposed models achieve higher accuracy than the pre-trained models.

关键词： Deep Learning Convolutional Neural Network Image Classification Multi-class Classification Multi-Object Prediction computer Vision

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AdaDiffAD: Adaptively Segmenting Diffusion Models for Time Series Anomaly Detection in Dynamic JointCloud Environment 30

AdaDiffAD: Adaptively Segmenting Diffusion Models for Time S...

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30th IEEE International conference on Parallel and Distributed Systems, ICPADS 2024

作者： Ma, Chao Yi, Lin Zhou, Linjiang Wang, Zepeng Shi, Xiaochuan Zhu, Weiping Wuhan University School of Cyber Science and Engineering Wuhan China Wuhan University School of Computer Science Wuhan China

ISBN: (纸本)9798331515966

Time series anomaly detection is one kind of critical time series analytical tasks, which is widely applied to various real-world applications. Recently, the diffusion models have shown promising performance on time series imputation for anomaly detection. And the high computing requirements of the diffusion models naturally extend their computation paradigm from the centralized computing to the cloud computing, and then further to JointCloud computing which allows the diffusion models to be deployed across multiple clouds. However, the JointCloud computing paradigm faces the challenge of task offloading over multiple nodes across multiple clouds. Unfortunately, most of existing task offloading methods overlook the dynamic nature of network conditions among clouds. To address this issue, we propose a time series anomaly detection approach named AdaDiffAD by adaptively segmenting diffusion models in JointCloud environment with dynamic network conditions. Specifically, we design a task offloading strategy by segmenting the denoising process of the diffusion model onto both the edge clouds and central clouds, and utilizing the edge cloud results directly for anomaly detection when the network condition is not ideal. By conducting comprehensive experiments on seven datasets, the experimental results demonstrate that our proposed AdaDiffAD always achieves lower time consumption while maintaining competitive anomaly detection performance compared to the state-of-the art without adaptive task offloading strategy. © 2024 IEEE.

关键词： Anomaly Detection Diffusion Model JointCloud Computing Model Segmentation Task Offloading Time Series

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Software Quality Assessment Methods and Standards in Weld Defect Detection for Shipbuilding 6

Software Quality Assessment Methods and Standards in Weld De...

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6th International conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Pernando, Yonky Gaol, Ford Lumban Soeparno, Haryono Arifin, Yulyani Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Shipbuilder safety and performance, structural integrity of welded joints in shipbuilding is of utmost importance. Traditional manual inspection methods to detect any potential defect can be time consuming and susceptible to human errors;alternative inspection systems have proven more cost effective and accurate solutions than manual approaches for defect identification. This paper investigates how advanced software quality assessment standards and machine learning techniques such as Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO) object identification framework can enhance weld defect detection. Research undertaken for this thesis addresses three core questions. 1) Which standards provide effective software quality assessments used for weld defect detection systems?;and 2) In what ways will they impact efficiency and reliability. How will implementation of these standards increase accuracy in manufacturing processes? Integrating high-quality annotated datasets, optimizing joint preparation and parameter settings, and using cloud solutions for computing needs can enable industries to achieve more reliable, efficient, and scalable defect detection. This proactive approach not only enhances immediate product quality, but also contributes to long-term durability and performance, decreasing maintenance costs and increasing overall reliability for steel plates used for marine applications. Furthermore, evidence has revealed that adopting software quality assessment standards in weld defect detection significantly enhances structural integrity as well as operational efficiency of shipbuilding operations. © 2024 IEEE.

关键词： Convolutional neural networks

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