2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)最新文献_第8页

Defect Detection Method Of LCD Complex Display Screen Combining Feature Matching and Color Correction 结合特征匹配和色彩校正的LCD复杂显示屏缺陷检测方法

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674152

Shuai Lingyu, Chen Huaixin, Wang Zhixi

{"title":"Defect Detection Method Of LCD Complex Display Screen Combining Feature Matching and Color Correction","authors":"Shuai Lingyu, Chen Huaixin, Wang Zhixi","doi":"10.1109/ICCWAMTIP53232.2021.9674152","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674152","url":null,"abstract":"Aiming at the defect detection problem in the complex detection picture of LCD, a defect detection method of complex display picture combining feature matching and color correction is proposed in this paper. Firstly, the image registration method of Speeded Up Robust Features (SURF) and projection transformation is used for high-precision geometric registration between the detected image and the standard image; Secondly, the average brightness of the RGB three channels of the image is calculated respectively, and the image color correction of adaptive histogram matching is proposed. The histogram of the low brightness channel is specified as the histogram of the high brightness channel, and the final registered image pair is obtained. Finally, support vector machine (SVM) is used to classify the residual image to obtain the binary image of defect detection. The experimental results show that the proposed method can detect complex picture display defects under illumination change and geometric distortion, the detection accuracy is 99.43%, and the recall rate is 86.19%; It has engineering application prospect.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117096953","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

Shared Weighted Continuous Wavelet Capsule Network for Electrocardiogram Biometric Identification 共享加权连续小波胶囊网络用于心电图生物特征识别

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674078

H. Monday, J. Li, G. Nneji, E. James, Y. B. Leta, Saifun Nahar, A. Haq

{"title":"Shared Weighted Continuous Wavelet Capsule Network for Electrocardiogram Biometric Identification","authors":"H. Monday, J. Li, G. Nneji, E. James, Y. B. Leta, Saifun Nahar, A. Haq","doi":"10.1109/ICCWAMTIP53232.2021.9674078","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674078","url":null,"abstract":"In recent times, researchers are showing more interest in the subject of biometric identification, which uses biological traits to confirm a user's identity. Traditional authentication techniques are prone to damage, fraud, and negligence. We investigate a unique biometric based on electrocardiogram (ECG) signals generated from the heart as a biometric security attribute for access control verification. In this research, we propose a shared weighted continuous wavelet capsule network for ECG biometric identification, in which a continuous wavelet transform (CWT) is utilized to convert one-dimensional time-domain ECG signals into scalograms of two-dimensional images to obtain good quality training data. Then, a siamese capsule network framework is utilized to predict the right match or mismatch of ECG query samples using the extracted specific attributes from the scalograms. The dataset utilized in this work is collected from the Physionet MIT-BIH Normal Sinus Rhythm database. Experimental result shows that the proposed approach properly predicted ECG query samples with 99.2% accuracy, which makes our model more robust.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126840097","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

Incremental Learning for Radio Frequency Fingerprint Identification 射频指纹识别的增量学习

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674117

Di Liu, Chuan Liu, Maosen Yuan

引用次数: 0

Estimation for Remaining Useful Life Based on a Complex Context Aggregation Model 基于复杂上下文聚合模型的剩余使用寿命估计

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674108

Zhang Yusen, Zhang Bozhou, Sun Ming

引用次数: 1

Construction of TTPS From APT Reports Using Bert 利用Bert从APT报告构建TTPS

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674158

Li Zongxun, Li Yujun, Zhang Haojie, Li Juan

{"title":"Construction of TTPS From APT Reports Using Bert","authors":"Li Zongxun, Li Yujun, Zhang Haojie, Li Juan","doi":"10.1109/ICCWAMTIP53232.2021.9674158","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674158","url":null,"abstract":"With the ongoing usage of networks, the number of Advanced Persistent Threat (APT) attacks has grown in recent years. When compared to real-time APT attack detection, analyzing APT reports enables faster dissemination of cyber threat intelligence (CTI) and identification of APT attacks. Thus, this paper proposes a model for automatically extracting threat actions and generating Tactics, Techniques and Procedures (TTPs) from APT reports. The model analyzes the semantics of APT reports and extracts threat actions automatically based on BERT-BiLSTM-CRF that can accurately capture the semantics of sentences. A sentence containing a threat action is fed into the trained model, and the model marks the threat action contained in the sentence. Then, we leverage existing knowledge to build a cyber threat ontology, obtain complete attack information by mapping threat actions to the ontology, and generate high-level Indicators of Compromise (IOC) and generate TTPs. Threat actions are mapped to this ontology to construct TTPs. In comparison to traditional approaches, our method achieves an average of 96% precision on the test dataset.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124438836","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

A Glove CNN-Bilstm Sentiment Classification 一个手套CNN-Bilstm情感分类

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674171

Peter Atandoh, Z. Feng, D. Adu-Gyamfi, H. Leka, Paul H. Atandoh

引用次数: 1

Intelligent Phishing Url Detection: A Solution Based On Deep Learning Framework 基于深度学习框架的网络钓鱼Url智能检测解决方案

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674162

Muhammad Hassaan Farooq Butt, J. Li, Tehreem Saboor, M. Arslan, Muhammad Adnan Farooq Butt

{"title":"Intelligent Phishing Url Detection: A Solution Based On Deep Learning Framework","authors":"Muhammad Hassaan Farooq Butt, J. Li, Tehreem Saboor, M. Arslan, Muhammad Adnan Farooq Butt","doi":"10.1109/ICCWAMTIP53232.2021.9674162","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674162","url":null,"abstract":"On the Internet, every different day, kinds of attacks are deployed on innocent users. Among all, phishing is the most severe attack in which users lose their credentials or private information and their financial status quickly. The attacker uses their credibility or sensitive information to harm the target or victim. The attacker is clever and uses different strategies to fetch user-sensitive information. The existing techniques fail to overcome these issues to some extent. This work focuses on discovering the essential features that help to differentiate the legitimate and illegitimate URLs. We applied a deep learning technique on the benchmark datasets to identify the pattern of phishing URLs. We used gradient boosted decision trees algorithm to train our model and applied the regular deeply connected neural network layers in various sequences and Adam optimizer. The most found patterns will help the system to detect phishing URLs and avoid phishing. We consider the accuracy, Ff-score, and Root Mean Square Error (RMSE) as our evaluation metrics for model evaluation. The results show that the trained model can achieve an approximately 92% accuracy and 94% f-score.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127191361","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 Based Matrix Factorization For Collaborative Filtering 基于深度学习的矩阵分解协同过滤

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674157

Abebe Tegene, Qiao Liu, S. Muhammed, H. Leka

引用次数: 0

Research and Analysis Of Extraction and Recognition INS Criptions of Bronzebased On Improved K-Means and Convo Lutional Neural Network 基于改进k均值和卷积神经网络的青铜INS文字提取与识别研究与分析

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674164

L. Wei, Guo Xue, Yu Lin, L. Yi

引用次数: 0

6G, LIFI and WIFI Wireless Systems: Challenges, Development and Prospects 6G, LIFI和WIFI无线系统:挑战，发展和前景

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674090

Chang Zeyu

引用次数: 3