Pub Date : 2020-12-11DOI: 10.1109/ICCC51575.2020.9344961
Yukai Chen, T. Pu, Jiling Zheng, Jin Li, Xin Zhang, Yunkun Li
Quantum-noise randomized cipher (QNRC) is an anti-interception communication technology, which can realize high-speed encryption for data stream in true randomness. In this paper, the characteristics of the phase shift keying (PSK) and quadrature phase shift keying(QPSK) QNRC systems are verified by the simulation system with digital coherent demodulation. The decryption process of the QNRC coherent system is carried out in the optical domain for the first time. Specifically, at the quadrature coupler, the modulated local light is coupled with the received encrypted optical signal. The multi-symbol phase estimation(MSPE) module is employed to eliminate the influence of the phase deviation on the signal decision. Finally, error-free secure transmission is realized at 2.5Gsymbol/s over 500km optically amplified links. We reveal the new fact that the mesoscopic power of QPSK-QNRC system is higher than that of PSK-QNRC system.
{"title":"A Novel Realization of PSK and QPSK Quantum-Noise Randomized Cipher Systems with Optical Domain Decryption","authors":"Yukai Chen, T. Pu, Jiling Zheng, Jin Li, Xin Zhang, Yunkun Li","doi":"10.1109/ICCC51575.2020.9344961","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9344961","url":null,"abstract":"Quantum-noise randomized cipher (QNRC) is an anti-interception communication technology, which can realize high-speed encryption for data stream in true randomness. In this paper, the characteristics of the phase shift keying (PSK) and quadrature phase shift keying(QPSK) QNRC systems are verified by the simulation system with digital coherent demodulation. The decryption process of the QNRC coherent system is carried out in the optical domain for the first time. Specifically, at the quadrature coupler, the modulated local light is coupled with the received encrypted optical signal. The multi-symbol phase estimation(MSPE) module is employed to eliminate the influence of the phase deviation on the signal decision. Finally, error-free secure transmission is realized at 2.5Gsymbol/s over 500km optically amplified links. We reveal the new fact that the mesoscopic power of QPSK-QNRC system is higher than that of PSK-QNRC system.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131025908","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}
Pub Date : 2020-12-11DOI: 10.1109/ICCC51575.2020.9345268
Yanxing Li, Yuan Lei, Meixia Wang
In the last decade, information technology has gained a rapid development in the education industry. Particularly on the university campuses, information infrastructure is becoming more common and classrooms are becoming more modern. The increase of information infrastructure, however, while creating a comfortable teaching environment for students, it also brings more security risks. Thus, the safety of the classroom has also attracted more attention. Most classroom fire monitoring systems currently used are part of the traditional wired fire monitoring system. It is susceptible to a variety of objective factors, such as aging circuits, and some problems such as failure of the fire detection system. To address these challenges, this paper presents a CC2530 based classroom wireless fire monitoring system, and designs a fire monitoring system suitable for classrooms. The hardware part monitors the temperature and smoke concentration in the classroom through sensors to determine whether a fire occurs. The software part uses the ZigBee protocol stack Z-Stuck to send the monitored data to the monitoring center, enabling staff to monitor the classroom environment in real time. Experimental results show that the combined software and hardware system are well adapted to a modern classroom with many electrical devices and dense personnel. At the same time, the system possesses the characteristic of real-time data collection, high positioning accuracy and large transmission rate, which can meet the standards and requirements in practical applications.
{"title":"Design of Classroom Wireless Fire Monitoring and Alarm System Based on CC2530","authors":"Yanxing Li, Yuan Lei, Meixia Wang","doi":"10.1109/ICCC51575.2020.9345268","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9345268","url":null,"abstract":"In the last decade, information technology has gained a rapid development in the education industry. Particularly on the university campuses, information infrastructure is becoming more common and classrooms are becoming more modern. The increase of information infrastructure, however, while creating a comfortable teaching environment for students, it also brings more security risks. Thus, the safety of the classroom has also attracted more attention. Most classroom fire monitoring systems currently used are part of the traditional wired fire monitoring system. It is susceptible to a variety of objective factors, such as aging circuits, and some problems such as failure of the fire detection system. To address these challenges, this paper presents a CC2530 based classroom wireless fire monitoring system, and designs a fire monitoring system suitable for classrooms. The hardware part monitors the temperature and smoke concentration in the classroom through sensors to determine whether a fire occurs. The software part uses the ZigBee protocol stack Z-Stuck to send the monitored data to the monitoring center, enabling staff to monitor the classroom environment in real time. Experimental results show that the combined software and hardware system are well adapted to a modern classroom with many electrical devices and dense personnel. At the same time, the system possesses the characteristic of real-time data collection, high positioning accuracy and large transmission rate, which can meet the standards and requirements in practical applications.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126948995","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}
Pub Date : 2020-12-11DOI: 10.1109/ICCC51575.2020.9345192
Feng Zheng, Bowen Pang
DOCSIS 3.1, the new international standard for high-speed cable television (CATV), uses Low Density Parity Check (LDPC) code for forward error correction (FEC). It is widely known that accurate soft information is critical to the performance of LDPC decoding, but existing bit log-likelihood ratio (LLR) expression only works well under high code rate. In this paper, an improved bit LLR expression is proposed to compensate the difference between the existing LLR expression under low code rate for Gray coded M-ary quadrature amplitude modulation (QAM). In addition to high accuracy, the improved LLR is simple and efficient to manipulate. Besides LDPC, the improved LLR is also applicable to other codes that require soft information. Simulation results show that the improved LLR expression achieves a significant improvement in performance for both LDPC and Turbo decoding.
{"title":"A Novel Soft-Output Demapper for DOCSIS 3.1","authors":"Feng Zheng, Bowen Pang","doi":"10.1109/ICCC51575.2020.9345192","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9345192","url":null,"abstract":"DOCSIS 3.1, the new international standard for high-speed cable television (CATV), uses Low Density Parity Check (LDPC) code for forward error correction (FEC). It is widely known that accurate soft information is critical to the performance of LDPC decoding, but existing bit log-likelihood ratio (LLR) expression only works well under high code rate. In this paper, an improved bit LLR expression is proposed to compensate the difference between the existing LLR expression under low code rate for Gray coded M-ary quadrature amplitude modulation (QAM). In addition to high accuracy, the improved LLR is simple and efficient to manipulate. Besides LDPC, the improved LLR is also applicable to other codes that require soft information. Simulation results show that the improved LLR expression achieves a significant improvement in performance for both LDPC and Turbo decoding.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132105004","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}
The sensitivity evaluation of image quality indicator (IQI) is an important factor to judge the qualification of radiographs. In order to solve the problems of subjective and low efficiency of manual evaluation method, as well as the difficulty in IQI features extraction and accurate evaluation of sensitivity, a method for determining the sensitivity of radiographs based on coupling analysis of two-dimensional image and one-dimensional gray data is proposed. Firstly, the IQI location technology based on gradient region growth is proposed to realize IQI location in complex background in digital image. Secondly, the IQI image detail enhancement and noise reduction are realized through angle self-correction and enhancement processing. Thirdly, the two-dimensional image data is transformed into one-dimensional gray change data by integral projection method, and a method of IQI feature recognition and sensitivity evaluation based on gray data extreme value change analysis is proposed. The proposed method is evaluated using the actual industrial radiographs. The evaluation results demonstrate the effectiveness and superiority of the proposed method in automatic sensitivity evaluation of radiographs under complex background and low contrast.
{"title":"Sensitivity Evaluation of Radiographs Based on Multi-dimensional Data Coupling Analysis","authors":"Hongquan Jiang, Deyan Yang, Qihang Hu, Zelin Zhi, Jianmin Gao, Xiaoqiao Wang, Xiaoming Zhang, Huaxiang Pu, Hua Li","doi":"10.1109/ICCC51575.2020.9344895","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9344895","url":null,"abstract":"The sensitivity evaluation of image quality indicator (IQI) is an important factor to judge the qualification of radiographs. In order to solve the problems of subjective and low efficiency of manual evaluation method, as well as the difficulty in IQI features extraction and accurate evaluation of sensitivity, a method for determining the sensitivity of radiographs based on coupling analysis of two-dimensional image and one-dimensional gray data is proposed. Firstly, the IQI location technology based on gradient region growth is proposed to realize IQI location in complex background in digital image. Secondly, the IQI image detail enhancement and noise reduction are realized through angle self-correction and enhancement processing. Thirdly, the two-dimensional image data is transformed into one-dimensional gray change data by integral projection method, and a method of IQI feature recognition and sensitivity evaluation based on gray data extreme value change analysis is proposed. The proposed method is evaluated using the actual industrial radiographs. The evaluation results demonstrate the effectiveness and superiority of the proposed method in automatic sensitivity evaluation of radiographs under complex background and low contrast.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130941393","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}
Pub Date : 2020-12-11DOI: 10.1109/ICCC51575.2020.9344934
Zhibin Feng, Yijie Luo, Xueqiang Chen, Wen Li
In this paper, we investigate the discrete power control problem in anti-jamming relay communication networks. Based on the hierarchical competitive relationships between transmitters (user and relay) and jammer, a three-layer Stackelberg game is formulated, in which user acts as leader, relay acts as vice-leader and jammer acts as follower. From the perspective of hierarchical-game theoretic, we formulate the power optimization problem as a multi-armed bandit (MAB) problem, where user, relay and jammer act as players and each optional power strategy is considered as an arm to select. Based on MAB theory, we give the regret function to express the loss of payoff of the whole communication process. To minimize the regrets of user and relay, we propose a UCB1-based discrete power control online learning algorithm. Simulation results give the power selection rate and logarithmic incremental regrets in the proposed anti-jamming scenario. The user's and relay's utilities are also compared under different algorithms.
{"title":"A MAB-Based Discrete Power Control Approach in Anti-jamming Relay Communication via Three-layer Stackelberg Game","authors":"Zhibin Feng, Yijie Luo, Xueqiang Chen, Wen Li","doi":"10.1109/ICCC51575.2020.9344934","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9344934","url":null,"abstract":"In this paper, we investigate the discrete power control problem in anti-jamming relay communication networks. Based on the hierarchical competitive relationships between transmitters (user and relay) and jammer, a three-layer Stackelberg game is formulated, in which user acts as leader, relay acts as vice-leader and jammer acts as follower. From the perspective of hierarchical-game theoretic, we formulate the power optimization problem as a multi-armed bandit (MAB) problem, where user, relay and jammer act as players and each optional power strategy is considered as an arm to select. Based on MAB theory, we give the regret function to express the loss of payoff of the whole communication process. To minimize the regrets of user and relay, we propose a UCB1-based discrete power control online learning algorithm. Simulation results give the power selection rate and logarithmic incremental regrets in the proposed anti-jamming scenario. The user's and relay's utilities are also compared under different algorithms.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131248286","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}
Pub Date : 2020-12-11DOI: 10.1109/ICCC51575.2020.9345143
Jingzhi Tan, H Zhao
With the increasing number and application of unmanned aerial vehicle (UAV) in urban areas, positioning of UAV has become one of the key technologies for maintaining city security and managing airspace resources. Time of arrival (TOA) based location technology is widely used for its high precision, but its performance may suffer from strong multipath and NLOS propagation in urban scenario. However, the NLOS components may also be useful for positioning if the propagation path can be analyzed in a map model. In this paper, a multipath fingerprint dataset for an urban area is built by ray tracing simulation. Based on this dataset, we propose a two-stage localization method on machine learning framework. Firstly, in the stage of coarse positioning, the Random Forest (RF) algorithm is applied to determine which region the UAV is located in. Then, in the fine positioning stage, a neural network is trained to predict the specific location within the region. The simulation results in a $600 times 600 m^{2}$ region show that 90% of the positioning error of this method is less than 16m.
{"title":"UAV Localization with Multipath Fingerprints and Machine Learning in Urban NLOS Scenario","authors":"Jingzhi Tan, H Zhao","doi":"10.1109/ICCC51575.2020.9345143","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9345143","url":null,"abstract":"With the increasing number and application of unmanned aerial vehicle (UAV) in urban areas, positioning of UAV has become one of the key technologies for maintaining city security and managing airspace resources. Time of arrival (TOA) based location technology is widely used for its high precision, but its performance may suffer from strong multipath and NLOS propagation in urban scenario. However, the NLOS components may also be useful for positioning if the propagation path can be analyzed in a map model. In this paper, a multipath fingerprint dataset for an urban area is built by ray tracing simulation. Based on this dataset, we propose a two-stage localization method on machine learning framework. Firstly, in the stage of coarse positioning, the Random Forest (RF) algorithm is applied to determine which region the UAV is located in. Then, in the fine positioning stage, a neural network is trained to predict the specific location within the region. The simulation results in a $600 times 600 m^{2}$ region show that 90% of the positioning error of this method is less than 16m.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131332373","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}
Pub Date : 2020-12-11DOI: 10.1109/ICCC51575.2020.9345256
Liwu Zhang, Liangliang Gong, Hankun Qian
With rapid growth of the number of IoT device, there are more and more challenges in the secure manage for numbers of vulnerable IoT devices in practical network environment. One effective solution to this challenge is to develop a smart system which can identify the type of a device quickly and precisely. To aim this purpose, an advanced device identification method is presented in this paper. First, features during periodic flow inference and protocol inference are extracted to form the device fingerprints, and then a machine learning based classifier is used to identify the device type by using the importance of features. Experiment results show that not only the known types within a SOHO network such as smart speakers, cameras and sweeping robots can be identified successfully with an accuracy of 95%, but also new types can be classified without labeled data.
{"title":"An Effiective IoT Device Identification Using Machine Learning Algorithm","authors":"Liwu Zhang, Liangliang Gong, Hankun Qian","doi":"10.1109/ICCC51575.2020.9345256","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9345256","url":null,"abstract":"With rapid growth of the number of IoT device, there are more and more challenges in the secure manage for numbers of vulnerable IoT devices in practical network environment. One effective solution to this challenge is to develop a smart system which can identify the type of a device quickly and precisely. To aim this purpose, an advanced device identification method is presented in this paper. First, features during periodic flow inference and protocol inference are extracted to form the device fingerprints, and then a machine learning based classifier is used to identify the device type by using the importance of features. Experiment results show that not only the known types within a SOHO network such as smart speakers, cameras and sweeping robots can be identified successfully with an accuracy of 95%, but also new types can be classified without labeled data.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131654384","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}
Pub Date : 2020-12-11DOI: 10.1109/ICCC51575.2020.9344893
Ming Lu, Z. Nie, Yatong Feng
The complexity of many IT services and facilities has been continuously increasing, and the complexity of related monitoring systems and the difficulty of managing it are also growing rapidly. The integration and analysis of the time-series data acquired from monitoring systems are based on intelligent operation and maintenance. Due to the complex deployment of IT service and its infrastructure, as well as the large scale and high frequency of monitored indicators, the monitoring service and the integration of monitored data are further complicated. In the meantime, monitoring data is featured by low-value density, large volume, high requirements for real-time performance and reliability, complex process of transforming the time series data, which brings great challenges for the existing data integration systems. This paper proposes a distributed monitoring data integration system. The system achieves the efficient and reliable integration of time series monitoring data through a lightweight distributed architecture. Different methods of distributed scheduling are adopted by the system to achieve the elastic scaling of integrated computing power and adjust the load capacities of upstream and downstream time-series databases. The effectiveness of the designed system is verified in a data integration scenario from Prometheus/VictoriaMetrics to InfluxDB.
{"title":"A Transnational Multi-cloud Distributed Monitoring Data Integration System","authors":"Ming Lu, Z. Nie, Yatong Feng","doi":"10.1109/ICCC51575.2020.9344893","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9344893","url":null,"abstract":"The complexity of many IT services and facilities has been continuously increasing, and the complexity of related monitoring systems and the difficulty of managing it are also growing rapidly. The integration and analysis of the time-series data acquired from monitoring systems are based on intelligent operation and maintenance. Due to the complex deployment of IT service and its infrastructure, as well as the large scale and high frequency of monitored indicators, the monitoring service and the integration of monitored data are further complicated. In the meantime, monitoring data is featured by low-value density, large volume, high requirements for real-time performance and reliability, complex process of transforming the time series data, which brings great challenges for the existing data integration systems. This paper proposes a distributed monitoring data integration system. The system achieves the efficient and reliable integration of time series monitoring data through a lightweight distributed architecture. Different methods of distributed scheduling are adopted by the system to achieve the elastic scaling of integrated computing power and adjust the load capacities of upstream and downstream time-series databases. The effectiveness of the designed system is verified in a data integration scenario from Prometheus/VictoriaMetrics to InfluxDB.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131655823","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}
Pub Date : 2020-12-11DOI: 10.1109/ICCC51575.2020.9345099
Yuetian Zhou, Xiaohan Xu, Na Lu, Weiliang Xie
Dynamic spectrum sharing (DSS) technology is a dynamic and flexible allocation of spectrum resources for different communication technologies in the same frequency band. This technology is of great significance in the rapid deployment of 5G network, the smooth evolution from 4G to 5G, and the wide coverage of 5G network by using low frequency band. Although DSS can improve the utilization rate of spectrum resources by flexibly allocating spectrum resources in most cases, the introduction of DSS also brings additional spectrum overhead. At present, there is no standard calculation method to evaluate the additional overhead of different technical schemes in the industry. In this paper, we will introduce the current popular DSS technical solutions and propose a unified overhead calculation method, summarize the additional overhead of the mainstream implementation schemes, and analyze the efficiency of various DSS technical schemes by comparing the overhead loss before and after the introduction of DSS technology. In addition, a simple DSS network environment is set up in the laboratory to verify the performance of DSS technical scheme and overhead calculation method.
{"title":"Research on Technical Scheme and Overhead Calculation of Dynamic Spectrum Sharing","authors":"Yuetian Zhou, Xiaohan Xu, Na Lu, Weiliang Xie","doi":"10.1109/ICCC51575.2020.9345099","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9345099","url":null,"abstract":"Dynamic spectrum sharing (DSS) technology is a dynamic and flexible allocation of spectrum resources for different communication technologies in the same frequency band. This technology is of great significance in the rapid deployment of 5G network, the smooth evolution from 4G to 5G, and the wide coverage of 5G network by using low frequency band. Although DSS can improve the utilization rate of spectrum resources by flexibly allocating spectrum resources in most cases, the introduction of DSS also brings additional spectrum overhead. At present, there is no standard calculation method to evaluate the additional overhead of different technical schemes in the industry. In this paper, we will introduce the current popular DSS technical solutions and propose a unified overhead calculation method, summarize the additional overhead of the mainstream implementation schemes, and analyze the efficiency of various DSS technical schemes by comparing the overhead loss before and after the introduction of DSS technology. In addition, a simple DSS network environment is set up in the laboratory to verify the performance of DSS technical scheme and overhead calculation method.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125329459","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}
Pub Date : 2020-12-11DOI: 10.1109/ICCC51575.2020.9344966
Qi Wang, Li Lv, Bihui Yu, Si‐nian Li
There are more and more researches on joint relation extraction, however, the current popular joint extraction method has more or less limitations, either the training time is too long or the effect is not very good. In this paper, we propose an end-to-end relation extraction model, without using handcraft features, and propose a novel graph convolutional neural network based on entity attention mechanism which can perform better feature extraction on tree nodes. In addition, for preserving relevant information on the dependency tree to the greatest extent, we use a path-centric pruning strategy to remove irrelevant content, it makes the model more robust. Our model consists of five parts: Bert layer for vector representation, BiGRU layer, CRF layer for sequence labeling, GCN layer and Predict layer. To evaluate our method, we conduct experiments on the public dataset NYT and ACE05. Our model achieve the state of the art on the task of entity and relation extraction.
{"title":"End-to-end Relation Extraction Using Graph Convolutional Network with a Novel Entity Attention","authors":"Qi Wang, Li Lv, Bihui Yu, Si‐nian Li","doi":"10.1109/ICCC51575.2020.9344966","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9344966","url":null,"abstract":"There are more and more researches on joint relation extraction, however, the current popular joint extraction method has more or less limitations, either the training time is too long or the effect is not very good. In this paper, we propose an end-to-end relation extraction model, without using handcraft features, and propose a novel graph convolutional neural network based on entity attention mechanism which can perform better feature extraction on tree nodes. In addition, for preserving relevant information on the dependency tree to the greatest extent, we use a path-centric pruning strategy to remove irrelevant content, it makes the model more robust. Our model consists of five parts: Bert layer for vector representation, BiGRU layer, CRF layer for sequence labeling, GCN layer and Predict layer. To evaluate our method, we conduct experiments on the public dataset NYT and ACE05. Our model achieve the state of the art on the task of entity and relation extraction.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123152154","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}
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