2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC)最新文献_第9页

Structured Compressed Sensing of Double Selective Channel for High-Frequency OFDM Systems 高频OFDM系统双选择信道的结构化压缩感知

2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC) Pub Date : 2020-07-01 DOI: 10.1109/ICEIEC49280.2020.9152331

Wang Kai, Jingzhi Liu, L. Haibo, Fengbin Zhang

引用次数: 0

Convolutional Neural Network based Matchmaking for Service Oriented System Construction 基于卷积神经网络的面向服务系统构建匹配

2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC) Pub Date : 2020-07-01 DOI: 10.1109/ICEIEC49280.2020.9152343

Junju Liu

引用次数: 1

Optimized Unequal APSK Constellation Mapping and Demapping for Non-equiprobable Transmission Systems 非等概率传输系统的优化不等APSK星座映射与解映射

2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC) Pub Date : 2020-07-01 DOI: 10.1109/ICEIEC49280.2020.9152321

Yuanfang Xie, Can Zhang, Kenan Zhang, Lei Hou

引用次数: 0

Research on Teachers’ Behavior in the Class Recognition on Based on Text Classification Technology 基于文本分类技术的教师课堂识别行为研究

2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC) Pub Date : 2020-07-01 DOI: 10.1109/ICEIEC49280.2020.9152304

Qing Han, L. Luo, Zhong Sun

引用次数: 0

[ICEIEC 2020 Front Matter] [ICEIEC 2020前沿事项]

2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC) Pub Date : 2020-07-01 DOI: 10.1109/iceiec49280.2020.9152314

引用次数: 0

Experimental Evaluation of the Defense Capability of ARM-based Systems against Buffer Overflow Attacks in Wireless Networks 基于arm的无线网络系统防御缓冲区溢出攻击能力的实验评估

2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC) Pub Date : 2020-07-01 DOI: 10.1109/ICEIEC49280.2020.9152302

Sun Zhou, Jun Chen

{"title":"Experimental Evaluation of the Defense Capability of ARM-based Systems against Buffer Overflow Attacks in Wireless Networks","authors":"Sun Zhou, Jun Chen","doi":"10.1109/ICEIEC49280.2020.9152302","DOIUrl":"https://doi.org/10.1109/ICEIEC49280.2020.9152302","url":null,"abstract":"Buffer overflow attack is one of the mainstream attacks towards the ARM architecture. It may lead to consequences such as program failure or system privileges loss. The mainstream operating systems deploy multiple defense mechanisms to mitigate such attacks. However, so far there are few reports on evaluation of the defense capability of ARM-based operating systems from buffer overflow attacks. In this paper, firstly, we implemented the Runtime Intrusion Prevention Evaluator on ARM-based operating systems, which we called RIPE-ARM. In that evaluator, 850 kinds of effective buffer overflow attacks are integrated for test. Secondly, by using the QEMU virtual machine, an ARM-based system, Raspberry Pi, was set up for the experiment; and then, the RIPE-ARM was used to test and evaluate the defense capability of Raspberry Pi. We identified the kinds of attacks that each defense or defense combination can successfully prevent, respectively. Among all the defense methods, the Canary + DEP combination turns out to be optimal that is able to make 840 out of the total 850 kinds of attacks fail. Furthermore, for comparison, the defense capability of Ubuntu 16.04 LTS system based on X86 architecture was also tested. The results show that the optimal defense method of that system can prevent only 790 attack kinds.","PeriodicalId":352285,"journal":{"name":"2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133701315","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: 4

A Text Generation and Prediction System: Pre-training on New Corpora Using BERT and GPT-2 文本生成与预测系统:基于BERT和GPT-2的新语料库预训练

2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC) Pub Date : 2020-07-01 DOI: 10.1109/ICEIEC49280.2020.9152352

Yuanbin Qu, Peihan Liu, Wei Song, Lizhen Liu, Miaomiao Cheng

引用次数: 25

Improving CIMDE with An Mixed Selection Method 用混合选择方法改进CIMDE

2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC) Pub Date : 2020-07-01 DOI: 10.1109/ICEIEC49280.2020.9152316

Chen Dan, Xia Da-hai, Xiong Cai-quan, Gu Wei

引用次数: 0

Research on Application and Deployment of UAV in Emergency Response 无人机在应急响应中的应用与部署研究

2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC) Pub Date : 2020-07-01 DOI: 10.1109/ICEIEC49280.2020.9152338

Wenbo Jin, Jixing Yang, Yudong Fang, Wenchuan Feng

引用次数: 16

Using a Pre-Trained Language Model for Medical Named Entity Extraction in Chinese Clinic Text 基于预训练语言模型的中文临床文本医学命名实体提取

2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC) Pub Date : 2020-07-01 DOI: 10.1109/ICEIEC49280.2020.9152257

Mengyuan Zhang, Jin Wang, Xuejie Zhang

{"title":"Using a Pre-Trained Language Model for Medical Named Entity Extraction in Chinese Clinic Text","authors":"Mengyuan Zhang, Jin Wang, Xuejie Zhang","doi":"10.1109/ICEIEC49280.2020.9152257","DOIUrl":"https://doi.org/10.1109/ICEIEC49280.2020.9152257","url":null,"abstract":"The implementation of name entity recognition (NER) in Chinese clinic text is challenging. These methods have several limitations, such as the complexity of the medical text structure, the vast difference in entity length, and identical entities with different entity categories in different contexts. To address these problems, we propose a combination model of both pre-trained bi-directional long short-term memory (Bi- LSTM) and the conditional random field (CRF) model. Due to the specification of medical texts, we do not employ Chinese word segmentation tools. A character-level feature is introduced as an input feature, which is subsequently mapped into char embeddings by using an embedding layer of the bi-directional encoder representation from transformers (BERT) model. A BiLSTM layer and a CRF are utilized to encode the char embeddings and output the final label. The experiments are conducted with CNMER2019 to evaluate the performance and compared with several previous models. The results show that the proposed model outperformed other models and achieved better performance with NER in Chinese clinic text.","PeriodicalId":352285,"journal":{"name":"2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126893576","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: 5