2022 International Communication Engineering and Cloud Computing Conference (CECCC)最新文献

Hybrid Path Selection and Overall Optimization for Traffic Engineering 交通工程混合路径选择与整体优化

2022 International Communication Engineering and Cloud Computing Conference (CECCC) Pub Date : 2022-10-28 DOI: 10.1109/CECCC56460.2022.10069947

Xiaoqing Xu, Hong Tang, Juan Wu, Han Zeng, Liuyihui Qian, Xiaojun Liu

{"title":"Hybrid Path Selection and Overall Optimization for Traffic Engineering","authors":"Xiaoqing Xu, Hong Tang, Juan Wu, Han Zeng, Liuyihui Qian, Xiaojun Liu","doi":"10.1109/CECCC56460.2022.10069947","DOIUrl":"https://doi.org/10.1109/CECCC56460.2022.10069947","url":null,"abstract":"Due to the innovation and development of network technologies, many services and applications have emerged with diversified requirements. Therefore, network operators need to dynamically select paths with different Service Level Agreements (SLAs) to satisfy the services and applications. Traditional path selection algorithms mainly deal with single routing criterion, which is insufficient in this context. In addition, network operators need to optimize the traffic distribution of all flows with limited network resources. In this paper, we propose a method of hybrid path selection for individual flows and overall optimization for all the flows. In our method, we use different algorithms to find paths for two types of traffic, SLA-required traffic and background traffic. The paths for SLA-required traffic are selected based on multi criteria such as delay, cost, and availability, and that for background traffic are based on K shortest paths (KSP) regarding one criterion. Moreover, we consider optimization of the overall objective and determine the bandwidth allocation on the selected paths by linear programming and a greedy algorithm. Specifically, we consider a typical traffic engineering scenario: minimizing the maximal link utilization. The results show that our method is better than Equal Cost Multi-Path with KSP in terms of the amount of satisfactory SLA-required flows and minimization of maximal link utilization.","PeriodicalId":155272,"journal":{"name":"2022 International Communication Engineering and Cloud Computing Conference (CECCC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125176388","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

Improved Routing Algorithm for Wireless Sensor Networks Based on LEACH 基于LEACH的无线传感器网络改进路由算法

2022 International Communication Engineering and Cloud Computing Conference (CECCC) Pub Date : 2022-10-28 DOI: 10.1109/CECCC56460.2022.10069668

Junqi Mao, Minming Gu, Yu Huo

引用次数: 1

Budget in the Cloud: Analyzing Cost and Recommending Virtual Machine Workload 云中的预算:分析成本和推荐虚拟机工作负载

2022 International Communication Engineering and Cloud Computing Conference (CECCC) Pub Date : 2022-10-28 DOI: 10.1109/CECCC56460.2022.10069750

Brian Zhang, Valencia Zhang, Michael Hum

{"title":"Budget in the Cloud: Analyzing Cost and Recommending Virtual Machine Workload","authors":"Brian Zhang, Valencia Zhang, Michael Hum","doi":"10.1109/CECCC56460.2022.10069750","DOIUrl":"https://doi.org/10.1109/CECCC56460.2022.10069750","url":null,"abstract":"Due to the increasing popularity of cloud computing, the cost companies pay to use the cloud and its services are growing significantly. Higher costs of cloud computing technology contribute to the volatility of the cloud and the financial instability of companies that depend on the cloud. Companies have their own unique cloud budget, and staying within that budget can become problematic in light of increasing costs. To save cost, many cloud users look to their cloud provider rather than looking at their own cloud purchases. A lack of studies on cloud users’ virtual machine (VM) workload and how cloud users spend money necessitates analysis of the spending of cloud customers. In this research paper, we analyzed a real-world data set from Microsoft Azure collected in 2019 that includes approximately 2.7 million VM traces. We developed a linear regression based pricing model to calculate the cost and used this model to analyze Microsoft Azure’s VM workload by comparing the cores, memory, lifetime, average utilization, and cost of each trace. By analyzing Microsoft’s data, we observed that users are not fully utilizing the cloud resources they have paid for. With this idea in mind, we then quantified the waste and developed an algorithm to determine which VMs are the most ineffective. We applied our algorithm to Microsoft Azure’s data set, and our results show that our algorithm discovered over one million wasteful VMs and helped 6,600 users save $3.3 million dollars. Even though cloud computing prices are increasing, cloud customers can save significantly by understanding VM workload better and selecting better-fitting VMs.","PeriodicalId":155272,"journal":{"name":"2022 International Communication Engineering and Cloud Computing Conference (CECCC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114570788","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

Developing a Leukemia Diagnostic System Based on Hybrid and Ensemble Deep Learning Architectures 基于混合和集成深度学习架构的白血病诊断系统开发

2022 International Communication Engineering and Cloud Computing Conference (CECCC) Pub Date : 2022-10-28 DOI: 10.1109/CECCC56460.2022.10069094

Skyler Kim

{"title":"Developing a Leukemia Diagnostic System Based on Hybrid and Ensemble Deep Learning Architectures","authors":"Skyler Kim","doi":"10.1109/CECCC56460.2022.10069094","DOIUrl":"https://doi.org/10.1109/CECCC56460.2022.10069094","url":null,"abstract":"This research designed and implemented a leukemia diagnostic system targeted for a real clinical environment based on deep learning approaches. The dataset consists of 15,135 total images. This research develops four independent models (VGG19 1 epoch, VGG19 30 epochs, ResNet50 1 epoch, and ResNet50 30 epochs), two Hybrid models (trained at 1 epoch and 30 epochs), and four Ensemble models (two Ensemble models of VGG19 and ResNet50 and two Ensemble models with an additional Hybrid model). All models are pre-trained on ImageNet. By using transfer learning, the models were fine-tuned (further trained) on the leukemia domain at a much greater speed as the existing layers will have benefitted from the pre-training done on ImageNet. This research indicates that Hybrid models can help improve predictive capabilities by leveraging different feature patterns extracted from running images through two different architectures. Meanwhile, Ensemble models will take the prediction votes from multiple final model outputs to further incorporate different model capabilities and also help generalize. Among all independent models, the best model is ResNet50 30 epochs, which achieved an accuracy of 84%. Among the Hybrid models, the best model is Hybrid 30 epochs, which achieved an accuracy of 84%. Ensemble 4 (Hybrid 30 epochs, VGG19 1 epoch, and ResNet50 1 epoch) achieved an accuracy of 86%, which is 2% better than the second-best model, Ensemble 2. The diagnostic system developed in this research can be used in other medical diagnostic applications.","PeriodicalId":155272,"journal":{"name":"2022 International Communication Engineering and Cloud Computing Conference (CECCC)","volume":"22 6S 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122814498","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: 2

Effectiveness of Transfer Learning, Convolutional Neural Network and Standard Machine Learning in Computer Vision Assisted Bee Health Assessment 迁移学习、卷积神经网络和标准机器学习在计算机视觉辅助蜜蜂健康评估中的有效性

2022 International Communication Engineering and Cloud Computing Conference (CECCC) Pub Date : 2022-10-28 DOI: 10.1109/CECCC56460.2022.10069892

Andrew Liang

{"title":"Effectiveness of Transfer Learning, Convolutional Neural Network and Standard Machine Learning in Computer Vision Assisted Bee Health Assessment","authors":"Andrew Liang","doi":"10.1109/CECCC56460.2022.10069892","DOIUrl":"https://doi.org/10.1109/CECCC56460.2022.10069892","url":null,"abstract":"Honeybees are vital to society, as they pollinate over 80% of plants. Unfortunately, honeybee colonies have been losing at an average rate of 39.7% per year. Beehive monitoring depends on human visual examinations, which is time consuming and disruptive to colonies. It is critical to apply an effective and efficient technique to monitor bee health and save bee colonies. The paper provides a systematic study of applying transfer learning, classic convolutional neural network (CNN) and standard machine learning models on image-based bee health classification. Five models (SVM, CNN, VGG19, InceptionV3, MobileNet) have been evaluated on a real-world dataset with more than 5000 bee images and six health sub-classes. The accuracy rates from all five models were above 90% in the test dataset. In particular, VGG19 and CNN achieved 98.65% and 96.91% accuracy, respectively. These accuracy rates were higher than the best accuracy rate of 95% in other previous research. The promising model results demonstrate the potential of applying AI techniques to build an intelligent beehive inspection system.","PeriodicalId":155272,"journal":{"name":"2022 International Communication Engineering and Cloud Computing Conference (CECCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131113794","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

Deep Learning for Meditation’s Impact on Brain-Computer Interface Performance 深度学习冥想对脑机接口性能的影响

2022 International Communication Engineering and Cloud Computing Conference (CECCC) Pub Date : 2022-10-28 DOI: 10.1109/CECCC56460.2022.10069731

B. Liu

{"title":"Deep Learning for Meditation’s Impact on Brain-Computer Interface Performance","authors":"B. Liu","doi":"10.1109/CECCC56460.2022.10069731","DOIUrl":"https://doi.org/10.1109/CECCC56460.2022.10069731","url":null,"abstract":"Recent studies uncovered the mindfulness meditation’s impact on the Brain-Computer Interface (BCI) performance. The traditional predictive method for BCI control requires domain expertise in electroencephalogram (EEG) and complicated and time-consuming processing of EEG data. In this paper, for the first time, deep learning models feed-forward neural network (FFNN) and convolutional neural network (CNN) were developed to classify BCI controls for meditators, using a meditation group and a control group. Both models, when applied to raw data with minimal noise filtering, demonstrated slightly better accuracy rates than the traditional predictive methods. The optimal pre-preprocessing method to obtain fixed-length BCI feedback control data was invented. A novel BCI experiment design was created to fix the length of the BCI feedback control period to better utilize the trial time and EEG data. This research also provides the foundation for further application of deep learning models to meditation’s impact on BCI in more complicated investigations that the traditional methods are incapable of handling due to the large dimensions of both temporal and spatial data.","PeriodicalId":155272,"journal":{"name":"2022 International Communication Engineering and Cloud Computing Conference (CECCC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127428066","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

Inter-satellite Pseudorange Difference Indoor Positioning Using Simulator Pseudolites 利用模拟器伪卫星进行卫星间伪距差分室内定位

2022 International Communication Engineering and Cloud Computing Conference (CECCC) Pub Date : 2022-10-28 DOI: 10.1109/CECCC56460.2022.10069801

Feng Yan, Mao-zhong Song

引用次数: 0

A Review of Natural Language Processing Technology for Chinese Language and Literature 汉语言文学自然语言处理技术综述

2022 International Communication Engineering and Cloud Computing Conference (CECCC) Pub Date : 2022-10-28 DOI: 10.1109/CECCC56460.2022.10069077

Lingbin Zeng, Jing-Wen Su, Cheng Yang, Yue Qian

引用次数: 0

A High Speed Frequency Hopping Capture Synchronization Method 一种高速跳频捕获同步方法

2022 International Communication Engineering and Cloud Computing Conference (CECCC) Pub Date : 2022-10-28 DOI: 10.1109/CECCC56460.2022.10068903

Yuchen Liu, Yubing Han

引用次数: 0

APPly: A Design of an Online Tertiary Level Scholarship Application Management System 高等教育奖学金在线申请管理系统的设计

2022 International Communication Engineering and Cloud Computing Conference (CECCC) Pub Date : 2022-10-28 DOI: 10.1109/CECCC56460.2022.10069218

E. Blancaflor, Patricia Anne I. Caseńas, Lester Jacob H. Rocamora, Jose Immanuel Y. Rosete, W. Rey

{"title":"APPly: A Design of an Online Tertiary Level Scholarship Application Management System","authors":"E. Blancaflor, Patricia Anne I. Caseńas, Lester Jacob H. Rocamora, Jose Immanuel Y. Rosete, W. Rey","doi":"10.1109/CECCC56460.2022.10069218","DOIUrl":"https://doi.org/10.1109/CECCC56460.2022.10069218","url":null,"abstract":"Scholarship applications are continually escalating, 58% of families availed scholarships as it serves as a financial aid for college. The increasing number of scholarship applications caused difficulty in processing the applications without a time delay due to a large volume of requests received by the scholarship institutions. The present study investigated the effectiveness, efficacy, and efficiency of using scholarship management systems to manage scholarships at a Philippine University. Considering the rising applications for the Need-Based Academic Scholarship (NBAS), it became the primary focus of the study. The study utilizes the descriptive quantitative method wherein the data is statistically treated using descriptive statistics. The respondents consisted of 15 applicants and three scholarship administrators. The results showed that the web-based management system, APPly, received an overall PSSUQ score of 1.35 for the applicant-user and 1.46 for the administrator-user. The users rated the system’s usefulness at 1.27 and 1.33, respectively. The application was met positively by both use cases and was satisfied with its ease and the efficiency it offers for their tasks. Therefore, it is concluded that APPly is a functional scholarship management system as it satisfies the parameters set by the researchers. The study can be further improved by adding an SMS notification system, connecting the application to a centralized file and fund management system to simplify the scholarship transaction processes, and integrating it to be available for mobile use and into the university’s existing system.","PeriodicalId":155272,"journal":{"name":"2022 International Communication Engineering and Cloud Computing Conference (CECCC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128422317","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