2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)最新文献_第9页

A Generic Software Architecture for PoE Power Sourcing Equipment PoE供电设备通用软件体系结构

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00217

Andreas Mäkilä, A. Friebe, Leif Enblom, P. Strandberg, T. Seceleanu

引用次数: 1

Algebraic Semantics for C++11 Memory Model c++ 11内存模型的代数语义

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00240

Lili Xiao, Huibiao Zhu, Mengda He, S. Qin

引用次数: 1

Color-Path: Hair Arrangement Reproduction Support System by Displaying Target Motion in AR 颜色路径:在AR中显示目标运动的毛发排列再现支持系统

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00107

Natsumi Matsui, Ayumi Ohnishi, T. Terada, M. Tsukamoto

引用次数: 0

A Real-Time Background Replacement Method Based on Estimated Depth for AR Applications 基于估计深度的AR应用实时背景替换方法

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00190

Yoshihiro Tsuboki, Tomoya Kawakami, Satoru Matsumoto, T. Yoshihisa, Y. Teranishi

引用次数: 0

Towards Automation for MLOps: An Exploratory Study of Bot Usage in Deep Learning Libraries 迈向MLOps的自动化:深度学习库中Bot使用的探索性研究

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00171

A. Rahman, Farzana Ahamed Bhuiyan, M. M. Hassan, H. Shahriar, Fan Wu

引用次数: 1

TUFA: A TOSCA extension for the specification of accelerator-aware applications in the Cloud Continuum TUFA: TOSCA扩展，用于云连续体中加速器感知应用程序的规范

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00185

Adrian F. Spataru, Gabriel Iuhasz, S. Panica

引用次数: 1

A Framework for Considering Uncertainty in Software Systems 一个考虑软件系统不确定性的框架

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00241

Chawanangwa Lupafya, D. Balasubramaniam

引用次数: 1

NKMH: A Neural Efficient Recommendation Based on Neighborhood Key Information Aggregation of Modified Hawkes NKMH:一种基于邻域关键信息聚合的改进Hawkes神经高效推荐

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00035

Xin Xu, Nan Wang, Huijie Jin, Yang Liu, Kun Li

引用次数: 0

A Comparison of Deep Learning and Traditional Machine Learning Approaches in Detecting Cognitive Impairment Using MRI Scans 深度学习和传统机器学习方法在MRI扫描中检测认知障碍的比较

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00154

Wei Liu, Jiarui Zhang, Yijun Zhao

{"title":"A Comparison of Deep Learning and Traditional Machine Learning Approaches in Detecting Cognitive Impairment Using MRI Scans","authors":"Wei Liu, Jiarui Zhang, Yijun Zhao","doi":"10.1109/COMPSAC54236.2022.00154","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00154","url":null,"abstract":"Deep learning has attracted a great amount of interest in recent years and has become a rapidly emerging field in artificial intelligence. In medical image analysis, deep learning methods have produced promising results comparable to and, in some cases, superior to human experts. Nevertheless, researchers have also noted the limitations and challenges of the deep learning approaches, especially in model selection and interpretability. This paper compares the efficacy of deep learning and traditional machine learning techniques in detecting cognitive impairment (CI) associated with Alzheimer's disease (AD) using brain MRI scans. We base our study on 894 brain MRI scans provided by the open access OASIS platform. In particular, we explore two deep learning approaches: 1) a 3D convolutional neural network (3D-CNN) and 2) a hybrid model with a CNN plus LSTM (CNN-LSTM) architecture. We further examine the performance of five traditional machine learning algorithms based on features extracted from the MRI images using the FreeSurfer software. Our experimental results demonstrate that the deep learning models achieve higher Precision and Recall, while the traditional machine learning methods deliver more stability and better performance in Specificity and overall accuracy. Our findings could serve as a case study to highlight the challenges in adopting deep learning-based approaches.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117215111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Self-adaptive Indicator Selection Approach for Solving Credit Risk Assessment 解决信用风险评估的自适应指标选择方法

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00249

Yongfeng Gu, Yue Ning, Hao Ding, Kecai Gu, Daohong Jian, Zhou Xu, Hua Wu, Jun Zhou

{"title":"A Self-adaptive Indicator Selection Approach for Solving Credit Risk Assessment","authors":"Yongfeng Gu, Yue Ning, Hao Ding, Kecai Gu, Daohong Jian, Zhou Xu, Hua Wu, Jun Zhou","doi":"10.1109/COMPSAC54236.2022.00249","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00249","url":null,"abstract":"Credit risk assessment, which aims at identifying high-risk users, plays a critical role in financial institutions. A common method is to use the greedy strategy to generate an interpretable rule set to classify all the users into high-risk or non-risk users. During each iteration, the greedy strategy utilizes a pre-defined indicator function to evaluate which rule is the best and then adds it to the rule set. However, in reality, the indicator function is designed manually and requires much domain knowledge and expert experience. Worse still, we need to design a suitable indicator for every situation, which is tedious and time-consuming work. This motivates us to propose a self-adaptive indicator that can be adapted to different situations without too much human intervention. In this paper, we see the indicator as a weighted sum of several sub-indicators. By tuning the weights, the indicator can be adapted to different situations automatically. That is, we transform this indicator selection problem into a weights tuning problem. To find the best weight of self-adaptive indicators, machine learning methods and black-box optimization are utilized. The experimental results demonstrated that our self-adaptive indicator can select a better rule set to identify more high-risk users compared to the human-defined indicator.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131438804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0