scholarly journals Multimodality Data Analysis in Information Security ETCC: Encrypted Two-Label Classification Using CNN

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yan Li ◽  
Yifei Lu
Keyword(s):  
Data Analysis ◽  
Information Security ◽  
Experimental Results ◽  
Two Stage ◽  
Second Stage ◽  
Public Dataset ◽  
Encrypted Traffic ◽  
Application Classification ◽  
Refined Classification

Due to the increasing variety of encryption protocols and services in the network, the characteristics of the application are very different under different protocols. However, there are very few existing studies on encrypted application classification considering the type of encryption protocols. In order to achieve the refined classification of encrypted applications, this paper proposes an Encrypted Two-Label Classification using CNN (ETCC) method, which can identify both the protocols and the applications. ETCC is a two-stage two-label classification method. The first stage classifies the protocol used for encrypted traffic. The second stage uses the corresponding classifier to classify applications according to the protocol used by the traffic. Experimental results show that the ETCC achieves 97.65% accuracy on a public dataset (CICDarknet2020).

A novel two-stage method of plant seedlings classification based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Tianhong Dai ◽  
Shijie Cong ◽  
Jianping Huang ◽  
Yanwen Zhang ◽  
Xinwang Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Technology ◽  
Two Stage ◽  
Second Stage ◽  
Stage Classification ◽  
Different Types ◽  
Two Stages ◽  
Plant Seedlings

In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%.

scholarly journals The Tactics of Verbal Countermanipulation in Russian Political Video Blogs

2019 ◽  
Vol 8 (3) ◽  
pp. 625-639
Author(s):  
Marina Petrova
Keyword(s):  
Information Security ◽  
Social Information ◽  
Two Stage ◽  
Second Stage ◽  
Cooperative Strategies ◽  
Pragmatic Analysis ◽  
Manipulative Techniques

The author of the article states that in spite of the fact that manipulation has been broadly studied in various areas of science, such as psychology, sociology, politology, linguistics, etc., countermanipulation (the response action to manipulation) has been analysed mainly by psychologists. This phenomenon has not been studied in linguistics yet. The author of the article gives a linguotypological description of the verbal countermanipulation tactics as exemplified in political video blogs reviewing the events with the participation of A. Navalny. A special two-stage algorithm which included a communicative pragmatic analysis was used for revealing countermanipulative intention in political video blogs. The first stage included attentive viewing of the videos and their analysis to determine manipulation upon certain criteria (a hidden aim, use of non-cooperative strategies and tactics and special verbal manipulative techniques). The second stage consisted of analysing the response utterances to define countermanipulative intention (neutralising manipulation), identification of countermanipulative tactics, their definition and classification. As a result of the conducted research, the author of the article has distinguished two groups of countermanipulative tactics: overt opposition tactics and covert opposition tactics. The group of overt opposition tactics includes the tactic of manipulative intention revealing, the tactic of making a question about manipulator’s intentions, the tactic of manipulation technique revealing, the tactic of counterargumentation and the tactic of refusing to change one’s behaviour towards manipulator’s intentions. The covert opposition tactics are the tactic of clarification questions and the tactic of repeating the manipulator’s words. The author points out the importance of further study of verbal countermanipulation from the perspective of solving the problems of the personal and social information security.

scholarly journals Efficacy-specific Herbal Group Detection from Traditional Chinese Medicine Prescriptions via Hierarchical Attentive Neural Network Model

2020 ◽  
Author(s):  
Li Chen ◽  
Xinglong Liu ◽  
Siyuan Zhang ◽  
Hong Yi ◽  
Yongmei Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Network Model ◽  
Neural Network Model ◽  
Experimental Results ◽  
Frequent Patterns ◽  
Two Stage ◽  
Second Stage ◽  
Group Detection

Abstract Background: Mining massive prescriptions in Traditional Chinese Medicine (TCM) accumulated in the lengthy period of several thousand years to discover essential herbal groups for distinct efficacies is of significance for TCM modernization, thus starting to draw attentions recently. However, most existing methods for the task treat herbs with different surface forms orthogonally and determine efficacy-specific herbal groups based on the raw frequencies an herbal group occur in a collection of prescriptions. Such methods entirely overlook the fact that prescriptions in TCM are formed empirically by different people at different historical stages, and thus full of herbs with different surface forms expressing the same material, or even noisy and redundant herbs.Methods: We propose a two-stage approach for efficacy-specific herbal group detection from prescriptions in TCM. For the first stage we devise a hierarchical attentive neural network model to capture essential herbs in a prescription for its efficacy, where herbs are encoded with dense real-valued vectors learned automatically to identify their differences on the semantical level. For the second stage, frequent patterns are mined to discover essential herbal groups for an efficacy from distilled prescriptions obtained in the first stage.Results: We verify the effectiveness of our proposed approach from two aspects, the first one is the ability of the hierarchical attentive neural network model to distill a prescription, and the second one is the accuracy in discovering efficacy-specific herbal groups.Conclusion: The experimental results demonstrate that the hierarchical attentive neural network model is capable to capture herbs in a prescription essential to its efficacy, and the distilled prescriptions significantly could improve the performance of efficacy-specific herbal group detection.

A Novel Initial Alignment Scheme for Low-Cost INS Aided by GPS for Land Vehicle Applications

2010 ◽  
Vol 63 (4) ◽  
pp. 663-680 ◽  
Author(s):  
Songlai Han ◽  
Jinling Wang
Keyword(s):  
Kalman Filtering ◽  
Inertial Sensors ◽  
Low Cost ◽  
Experimental Results ◽  
Initial Alignment ◽  
Two Stage ◽  
Second Stage ◽  
Attitude Error ◽  
Error Dynamic ◽  
Error Dynamics

This paper proposes a novel mechanism for the initial alignment of low-cost INS aided by GPS. For low-cost INS, the initial alignment is still a challenging issue because of the high noises from low-cost inertial sensors. In this paper, a two-stage Kalman Filtering mechanism is proposed for the initial alignment of low-cost INS. The first stage is designed for the coarse alignment. To solve the problems encountered by the general coarse alignment approach, an INS error dynamic accounting for unknown initial heading error is developed, and the corresponding observation equation, taking into account the unknown heading error, is also developed. The second stage is designed for the fine alignment, where the classical INS error dynamics based on small attitude error is used. Experimental results indicate that the proposed alignment approach can complete the initial alignment more quickly and more accurately compared with the conventional approach.

Research on Contrastive Viewpoint Summarization for Opinionated Texts

2013 ◽  
Vol 14 (03) ◽  
pp. 1360003 ◽  
Author(s):  
XU LIANG ◽  
YOULI QU ◽  
GUIXIANG MA
Keyword(s):  
Experimental Results ◽  
Two Stage ◽  
Second Stage

This paper presents a two-stage approach for multi-topic contrastive viewpoint summarization on opinionated texts. In the first stage, we model the opinionated texts with TAM and get the topic and aspect attribute of each sentence. In the second stage, we successively use the basic LexRank, Comparative LexRank, Topic-sensitive tf*idf LexRank, Topic-sensitive tf*idf & Comparative LexRank, and Biased & Comparative LexRank to evaluate the centrality of each sentence, based on which the summary is finally generated. Experimental results show that the best summary comes from the proposed Topic-sensitive tf*idf LexRank and Topic-sensitive tf*idf & Comparative LexRank.

scholarly journals Efficacy-specific Herbal Group Detection from Traditional Chinese Medicine Prescriptions via Hierarchical Attentive Neural Network Model

2020 ◽  
Author(s):  
li Chen ◽  
Xinglong Liu ◽  
Siyuan Zhang ◽  
Hong Yi ◽  
Yongmei Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Network Model ◽  
Neural Network Model ◽  
Experimental Results ◽  
Frequent Patterns ◽  
Two Stage ◽  
Second Stage ◽  
Group Detection

Abstract Background: Mining massive prescriptions in Traditional Chinese Medicine (TCM) accumulated in the lengthy period of several thousand years to discover essential herbal groups for distinct efficacies is of significance for TCM modernization, thus starting to draw attentions recently. However, most existing methods for the task treat herbs with different surface forms orthogonally and determine efficacy-specific herbal groups based on the raw frequencies an herbal group occur in a collection of prescriptions. Such methods entirely overlook the fact that prescriptions in TCM are formed empirically by different people at different historical stages, and thus full of herbs with different surface forms expressing the same material, or even noisy and redundant herbs. Methods: We propose a two-stage approach for efficacy-specific herbal group detection from prescriptions in TCM. For the first stage we devise a hierarchical attentive neural network model to capture essential herbs in a prescription for its efficacy, where herbs are encoded with dense real-valued vectors learned automatically to identify their differences on the semantical level. For the second stage, frequent patterns are mined to discover essential herbal groups for an efficacy from distilled prescriptions obtained in the first stage. Results: We verify the effectiveness of our proposed approach from two aspects, the first one is the ability of the hierarchical attentive neural network model to distill a prescription, and the second one is the accuracy in discovering efficacy-specific herbal groups. Conclusion: The experimental results demonstrate that the hierarchical attentive neural network model is capable to capture herbs in a prescription essential to its efficacy, and the distilled prescriptions significantly could improve the performance of efficacy-specific herbal group detection.

scholarly journals CBD: A Deep-Learning-Based Scheme for Encrypted Traffic Classification with a General Pre-Training Method

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8231
Author(s):  
Xinyi Hu ◽  
Chunxiang Gu ◽  
Yihang Chen ◽  
Fushan Wei
Keyword(s):  
Traffic Classification ◽  
Traffic Data ◽  
Training Method ◽  
Proposed Model ◽  
Public Dataset ◽  
Basic Characteristics ◽  
Encrypted Traffic ◽  
Closed Environment ◽  
Better Than

With the rapid increase in encrypted traffic in the network environment and the increasing proportion of encrypted traffic, the study of encrypted traffic classification has become increasingly important as a part of traffic analysis. At present, in a closed environment, the classification of encrypted traffic has been fully studied, but these classification models are often only for labeled data and difficult to apply in real environments. To solve these problems, we propose a transferable model called CBD with generalization abilities for encrypted traffic classification in real environments. The overall structure of CBD can be generally described as a of one-dimension CNN and the encoder of Transformer. The model can be pre-trained with unlabeled data to understand the basic characteristics of encrypted traffic data, and be transferred to other datasets to complete the classification of encrypted traffic from the packet level and the flow level. The performance of the proposed model was evaluated on a public dataset. The results showed that the performance of the CBD model was better than the baseline methods, and the pre-training method can improve the classification ability of the model.

scholarly journals A Two-Stage Industrial Defect Detection Framework Based on Improved-YOLOv5 and Optimized-Inception-ResnetV2 Models

2022 ◽  
Vol 12 (2) ◽  
pp. 834
Author(s):  
Zhuang Li ◽  
Xincheng Tian ◽  
Xin Liu ◽  
Yan Liu ◽  
Xiaorui Shi
Keyword(s):  
Defect Detection ◽  
Network Architecture ◽  
Feature Fusion ◽  
Visual Object ◽  
Two Stage ◽  
The Public ◽  
Second Stage ◽  
Steel Equipment ◽  
Public Dataset ◽  
Classification Tasks

Aiming to address the currently low accuracy of domestic industrial defect detection, this paper proposes a Two-Stage Industrial Defect Detection Framework based on Improved-YOLOv5 and Optimized-Inception-ResnetV2, which completes positioning and classification tasks through two specific models. In order to make the first-stage recognition more effective at locating insignificant small defects with high similarity on the steel surface, we improve YOLOv5 from the backbone network, the feature scales of the feature fusion layer, and the multiscale detection layer. In order to enable second-stage recognition to better extract defect features and achieve accurate classification, we embed the convolutional block attention module (CBAM) attention mechanism module into the Inception-ResnetV2 model, then optimize the network architecture and loss function of the accurate model. Based on the Pascal Visual Object Classes 2007 (VOC2007) dataset, the public dataset NEU-DET, and the optimized dataset Enriched-NEU-DET, we conducted multiple sets of comparative experiments on the Improved-YOLOv5 and Inception-ResnetV2. The testing results show that the improvement is obvious. In order to verify the superiority and adaptability of the two-stage framework, we first test based on the Enriched-NEU-DET dataset, and further use AUBO-i5 robot, Intel RealSense D435 camera, and other industrial steel equipment to build actual industrial scenes. In experiments, a two-stage framework achieves the best performance of 83.3% mean average precision (mAP), evaluated on the Enriched-NEU-DET dataset, and 91.0% on our built industrial defect environment.

Modeling and Dynamic Evaluation of a Two-Stage Two-Spool Servovalve Used for Pressure Control

1991 ◽  
Vol 113 (4) ◽  
pp. 709-713 ◽  
Author(s):  
S. T. Tsai ◽  
A. Akers ◽  
S. J. Lin
Keyword(s):  
Mathematical Model ◽  
Control Valve ◽  
Pressure Control ◽  
Experimental Results ◽  
Two Stage ◽  
Dynamic Evaluation ◽  
Second Stage ◽  
Experimental Values ◽  
Pressure Control Valve ◽  
Good Agreement

Experimental results for a unique design of a two-spool pressure control valve were reported by Anderson (1984). The first stage is a dynamically stable flapper-nozzle valve for which a mathematical model is already available (Lin and Akers, 1989a). For the second stage, however, which consists of two parallel spools in a common body, no such model existed. The purpose of this paper was therefore to construct such a model and to compare results calculated from it to experimental values. Moderately good agreement with experimental values was obtained.

scholarly journals TREATMENT OF TUMORS PANCREATODUODENAL ZONE, COMPLICATED BY OBSTRUCTIVE JAUNDICE USING BILIARY DECOMPRESSION

2020 ◽  
Vol 28 (1) ◽  
pp. 29-33
Author(s):  
N. Veligotsky ◽  
S. Arutyunov ◽  
S. Balaka ◽  
A. Chebotarev
Keyword(s):  
Obstructive Jaundice ◽  
Pancreatic Fistula ◽  
Pancreatic Tumors ◽  
Plastic Stents ◽  
Endoscopic Stenting ◽  
Two Stage ◽  
Biliary Decompression ◽  
Second Stage ◽  
High Bilirubin

The aim of the research. To develop an algorithm for the two-stage treatment of patients with tumors pancreatoduodenal zone complicated by obstructive jaundice using biliary decompression techniques at the first stage and conducting pancreatoduodenal resection at the second stage of treatment. Materials and methods. Preliminary biliary decompression was conducted in 51 patients with prolonged obstructive jaundice and high bilirubin numbers (above 250 μmol/l). The following minimally invasive options were used for biliary decompression: percutaneous transhepatic cholangio-drainage in 21 (41.2 %), endoscopic stenting in 18 (35.3 %), various cholecystostomy (percutaneous transhepatic, contact, video laparoscopic) in 12 (25, 8 %) patients. A two-stage method has been developed for the treatment of pancreatic tumors complicated by obstructive jaundice. Results. Percutaneous transhepatic cholangio-drainage was performed under ultrasound-X-ray navigation — in 11 (52.4 %), under angiographic control — in 10 (47.6 %) patients. Endoscopic stenting was performed in 18 (35.3 %) patients; plastic stents were used. Three options were used for pancreatojejunoanastomos: invagination ductopancreatojejunal — in 31 (60.8 %), invagination pancreatojejunal — in 16 (31.4 %), pancreatojejunal with bandage plasty of the crescent ligament of the liver — in 4 (7.8 %) patients. Diagnosis of pancreatic fistula was carried out according to the classification of ISGPF (2016). Biochemical leak was observed in 3 (5.9 %), pancreatic fistula (type B) in 2 (3.9 %) patients. Post-operative gastrostasis was detected in 3 (5.9 %) patients. Conclusions. Percutaneous transhepatic cholangio-drainage and endoscopic stenting are the most effective methods of biliary decompression. The use of biliary decompression in patients with pancreatic tumors complicated by the development of obstructive jaundice allows patients to prepare for the execution of PDR with reduced perioperative risk.

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