2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)最新文献

Energy Management Systems and Smart Phones: A Systematic Literature Survey 能源管理系统和智能手机:一个系统的文献调查

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583184

Youssef Mansour, Hamdan Hammad, O. A. Waraga, M. A. Talib

{"title":"Energy Management Systems and Smart Phones: A Systematic Literature Survey","authors":"Youssef Mansour, Hamdan Hammad, O. A. Waraga, M. A. Talib","doi":"10.1109/CCCI52664.2021.9583184","DOIUrl":"https://doi.org/10.1109/CCCI52664.2021.9583184","url":null,"abstract":"Smartphones are a crucial necessity in people’s lives nowadays. Many smartphone hardware components (i.e., CPU, Wi-Fi, etc…) are in rapid development, improving their performance. However, battery technology has not been able to keep up with this pace of development where battery life becomes shorter and shorter with every new update. Therefore, smartphone energy management has become very important and has been studied by many researchers. In this research paper, we conducted a Systematic Literature Review (SLR) to review the research contribution made in the field of smartphone Energy Management Systems (EMS). We analyzed 72 relevant papers and grouped them into two categories: 1) energy management techniques that reduce or limit energy consumption, and 2) smartphone energy assessments that focus on analyzing energy consumption by smartphone components. Then, we found that mobile application was the most studied smartphone module followed by network and operating system. Around 29 studies built their solution on Android which shows the importance of open source. Finally, the study highlights the research gap in power management for closed source systems and drivers as well as specific hardware modules.","PeriodicalId":136382,"journal":{"name":"2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115455825","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

Code-based Computation Offloading in Vehicular Fog Networks 基于代码的车辆雾网络计算卸载

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583208

Fangzhe Chen, Zhibin Gao, Zhang Liu, Lianfeng Huang, Yuliang Tang

引用次数: 0

Optimization Method of Pneumonia Image Classification Model Based on Deep Transfer Learning 基于深度迁移学习的肺炎图像分类模型优化方法

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583196

Shanyin Peng, Ning Wang

{"title":"Optimization Method of Pneumonia Image Classification Model Based on Deep Transfer Learning","authors":"Shanyin Peng, Ning Wang","doi":"10.1109/CCCI52664.2021.9583196","DOIUrl":"https://doi.org/10.1109/CCCI52664.2021.9583196","url":null,"abstract":"Pneumonia is one of the most common infectious diseases in clinic. X-ray chest is an important basis for early diagnosis of pneumonia. With the development of computer vision technology, using convolutional neural network to train pneumonia image classification model has been gradually applied to the process of medical clinical diagnosis. However, there are many problems in the process of using convolutional neural network to train pneumonia image classification model, such as too long model training time, over fitting and low accuracy due to too small training dataset. To solve these problems, this paper proposes an optimization method of pneumonia image classification model based on transfer learning and feature fusion, which is called Transfer Fusion. The Transfer Fusion optimization method will transplant the trained source model parameters to the target model, and add a specific feature fusion classification layer, so as to significantly shorten the training time of the new model, improve the accuracy and prevent over fitting. In this paper, Transfer Fusion optimization method is applied to three common convolutional neural network models: Google InceptionNetV3, MobileNetV2 and ResNet50. Through a large number of experiments, the performance of the three models has been significantly improved and improved.","PeriodicalId":136382,"journal":{"name":"2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114380547","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

An Effective Fair Off-Line Electronic Cash Protocol using Extended Chaotic Maps with Anonymity Revoking Trustee 具有匿名撤销受托人的扩展混沌映射的有效公平离线电子现金协议

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583217

C. Meshram, M. Obaidat, K. Hsiao, A. Imoize, A. Meshram

引用次数: 5

System Level simulation for 5G Ultra-Reliable Low-Latency Communication 5G超可靠低延迟通信的系统级仿真

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583197

Lianfeng Huang, Tao Chen, Zhibin Gao, Manman Luo, Zhang Liu

引用次数: 4

Cross-modal Retrieval of Archives based on Principal Affinity Representation 基于主关联表示的档案跨模态检索

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583202

Xiaoqing Yang, Yuelong Zhu, Jun Feng, Jiamin Lu

引用次数: 0

Gibbs Free-energy Prediction Method for Iron-base Alloy Materials Based on Deep Learning* 基于深度学习的铁基合金材料Gibbs自由能预测方法*

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583189

Yabin Xu, Shengjie Sun, Zhuang Wu

{"title":"Gibbs Free-energy Prediction Method for Iron-base Alloy Materials Based on Deep Learning*","authors":"Yabin Xu, Shengjie Sun, Zhuang Wu","doi":"10.1109/CCCI52664.2021.9583189","DOIUrl":"https://doi.org/10.1109/CCCI52664.2021.9583189","url":null,"abstract":"In order to speed up the development of new iron-base alloy materials and reduce the consumption of time and resources caused by a large number of experiments, a prediction method for Gibbs free energy of iron-base alloy materials was proposed based on the theory of material genetic engineering. Firstly, the collected data were preprocessed by splicing, filling, normalization and one-hot coding to adapt to the training of the model. Then, based on the DeepFM model, a fusion model based on Factorization Machine (FM), bitwise self-attention mechanism and Bi-directional Long Short-term Memory Network (Bi-LSTM) was proposed to predict the Gibbs free energy of iron-base alloy materials. It can not only extract the low-order and high-order features of the data effectively, but also the weight coefficients of each data feature can be reasonably optimized and the correlation between the data can be fully considered. The comparative experimental results show that the Gibbs free energy prediction method based on deep learning has a good prediction effect. It provides a new method to predict the Gibbs free energy of iron-base alloys.","PeriodicalId":136382,"journal":{"name":"2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132340931","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

Legal and Regulation Retrieval System Based on Hierarchical Retrieval 基于分层检索的法律法规检索系统

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583204

Yue Chen, Yu Guo, Yuanyan Xie, Zhenqiang Mi

引用次数: 0

tCLD-Net: A Transfer Learning Internet Encrypted Traffic Classification Scheme Based on Convolution Neural Network and Long Short-Term Memory Network tCLD-Net:一个基于卷积神经网络和长短期记忆网络的迁移学习互联网加密流量分类方案

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583214

Xinyi Hu, Chunxiang Gu, Yihang Chen, Fushan Wei

{"title":"tCLD-Net: A Transfer Learning Internet Encrypted Traffic Classification Scheme Based on Convolution Neural Network and Long Short-Term Memory Network","authors":"Xinyi Hu, Chunxiang Gu, Yihang Chen, Fushan Wei","doi":"10.1109/CCCI52664.2021.9583214","DOIUrl":"https://doi.org/10.1109/CCCI52664.2021.9583214","url":null,"abstract":"The Internet is about to enter the era of full encryption. Traditional traffic classification methods only work well in non-encrypted environments. How to identify the specific types of network encrypted traffic in an encrypted environment without decryption is one of the foundations for maintaining cyberspace security. Traffic classification based on machine learning relies heavily on the prior knowledge of experts to construct feature sets. Although traffic classification based on deep learning can reduce human intervention, it requires a large amount of labeled data for parameter determination. This paper proposes a tCLD-Net model that combines transfer learning and deep learning. It can be trained on a small amount of labeled data to distinguish network encrypted traffic with a high accuracy. It pre-trains a CLD-Net model in the source domain data set, and fixes the parameters of the convolutional neural network module in it, and trains and tests it in the target domain data set. In order to verify the effectiveness of the tCLD-Net model, we use the ISCX public data set to conduct experiments. The results show that our proposed model can complete 100 epoches training in 208 seconds when the training set only occupies 20% of the target domain. And achieve a classification accuracy rate about 86%. This is 4% higher than the model without pre-training, and the training time is only one third of the model without pre-training.","PeriodicalId":136382,"journal":{"name":"2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132395791","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

Detection of Coastal Green Macroalgae based on SLIC, CNN and SVM 基于SLIC、CNN和SVM的海岸绿藻检测

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583195

Jinghu Li, Lili Wang, Q. Xing

{"title":"Detection of Coastal Green Macroalgae based on SLIC, CNN and SVM","authors":"Jinghu Li, Lili Wang, Q. Xing","doi":"10.1109/CCCI52664.2021.9583195","DOIUrl":"https://doi.org/10.1109/CCCI52664.2021.9583195","url":null,"abstract":"Video surveillance is an important method to obtain the dynamic changes of green macroalgae along the coast. The paper proposes a coastal green macroalgae extraction method based on the SLIC superpixel segmentation, CNN and SVM to realize the automated recognition of green macroalgae from lots of high-resolution RGB video data collected by unmanned aerial vehicle (UAV) and handheld devices. Firstly, SLIC algorithm is used to generate the multi-scale patches on the original high-resolution image. Then, three classification CNN is used to divide the multi-scale patches into three types: green macroalgae, background and mixing. Finally, SVM algorithm is used to extract the green macroalgae to improve the accuracy at the pixel level in the mixed patches. In order to evaluate the performance of the proposed method, experiments are conducted on our coastal green macroalgae image dataset. Compared with the method of RGB vegetation indices (such as ExR, RGBVI, NGBDI), the overall accuracy (OA), F1 score, and Kappa of the green macroalgae extraction with the method proposed in this paper are up to 95.23%, 0.9612, 0.9436, respectively. The results show that our method is significantly better than that of RGB vegetation indices since it effectively reduces the influence of sea waves and light on the recognition results. The automated extraction method for coastal green macroalgae proposed in this paper can provide a reference for the automatic monitoring of coastal green macroalgae with high precision.","PeriodicalId":136382,"journal":{"name":"2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126208547","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