Pub Date : 2020-05-01DOI: 10.1109/ICCCS49078.2020.9118492
Xi Qian, Jiu-fen Liu, Chunxiang Gu, Yonghui Zheng
The Learning with Errors (LWE) Problem has received much attention since its introduction and been widely used in cryptography. However the error sampled from Gaussian distribution affects efficiency of application based on LWE, the LWE variants with errors taken from uniform distribution came into being in 2013, which has an asymptotic complexity analysis.An improved BKW algorithm has been proposed to the LWE problem with binary uniform errors, and a study of the complexity of the algorithm is given in the paper. As a result, new bounds are provided for the concrete hardness of cryptography based on the variant, and parameter choices for quiet a few important algorithm.
{"title":"An Improved BKW Algorithm For LWE With Binary Uniform Errors","authors":"Xi Qian, Jiu-fen Liu, Chunxiang Gu, Yonghui Zheng","doi":"10.1109/ICCCS49078.2020.9118492","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118492","url":null,"abstract":"The Learning with Errors (LWE) Problem has received much attention since its introduction and been widely used in cryptography. However the error sampled from Gaussian distribution affects efficiency of application based on LWE, the LWE variants with errors taken from uniform distribution came into being in 2013, which has an asymptotic complexity analysis.An improved BKW algorithm has been proposed to the LWE problem with binary uniform errors, and a study of the complexity of the algorithm is given in the paper. As a result, new bounds are provided for the concrete hardness of cryptography based on the variant, and parameter choices for quiet a few important algorithm.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123753206","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 : 2020-05-01DOI: 10.1109/ICCCS49078.2020.9118600
Jawwad Latif, P. Mehryar, Lei Hou, Ali Zulfiqur
Wireless sensor technology has revolutionised healthcare practices to deal with the increasing number of chronically ill patients. Real-time and continuous monitoring of health parameters can help in early diagnosis and timely treatment. Sensor nodes having limited resources in health monitoring systems are equipped with number of sensors which generates huge amount of data. An increase in data results in an increase in power consumption and memory requirement. An efficient data compression algorithm can be applied to reduce the power consumption and memory requirement. Minimalist, Adaptive and Streaming (MAS) algorithm proposed in literature can reduce significant power consumption during data transmission. In current work, MAS algorithm is further optimised to propose O-MAS-R algorithm by introducing R-bit to take advantage of consecutive repetition of data samples. MAS and O-MAS-R algorithms are applied on Electrocardiography (ECG), Electromyography (EMG) and accelerometer (Acc) datasets to compare the performance in terms of compression ratio (CR). O-MAS-R has shown 7.21 % average increase in CR of ECG datasets, 8.25% increase in EMG datasets and 45.24% increase in Acc datasets as compare to MAS algorithm.
无线传感器技术已经彻底改变了医疗保健实践，以应对越来越多的慢性病患者。实时和持续监测健康参数有助于早期诊断和及时治疗。在健康监测系统中，资源有限的传感器节点配备了大量的传感器，产生了大量的数据。数据量的增加导致功耗和内存需求的增加。采用有效的数据压缩算法可以降低功耗和内存需求。文献中提出的MAS (Minimalist, Adaptive and Streaming)算法可以显著降低数据传输过程中的功耗。在目前的工作中，MAS算法进一步优化，通过引入r位，利用数据样本的连续重复，提出O-MAS-R算法。MAS和O-MAS-R算法应用于心电图(ECG)、肌电图(EMG)和加速度计(Acc)数据集，比较压缩比(CR)方面的性能。与MAS算法相比，O-MAS-R在心电数据集、肌电数据集和Acc数据集上的CR平均提高了7.21%、8.25%和45.24%。
{"title":"An Efficient Data Compression Algorithm For Real-Time Monitoring Applications In Healthcare","authors":"Jawwad Latif, P. Mehryar, Lei Hou, Ali Zulfiqur","doi":"10.1109/ICCCS49078.2020.9118600","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118600","url":null,"abstract":"Wireless sensor technology has revolutionised healthcare practices to deal with the increasing number of chronically ill patients. Real-time and continuous monitoring of health parameters can help in early diagnosis and timely treatment. Sensor nodes having limited resources in health monitoring systems are equipped with number of sensors which generates huge amount of data. An increase in data results in an increase in power consumption and memory requirement. An efficient data compression algorithm can be applied to reduce the power consumption and memory requirement. Minimalist, Adaptive and Streaming (MAS) algorithm proposed in literature can reduce significant power consumption during data transmission. In current work, MAS algorithm is further optimised to propose O-MAS-R algorithm by introducing R-bit to take advantage of consecutive repetition of data samples. MAS and O-MAS-R algorithms are applied on Electrocardiography (ECG), Electromyography (EMG) and accelerometer (Acc) datasets to compare the performance in terms of compression ratio (CR). O-MAS-R has shown 7.21 % average increase in CR of ECG datasets, 8.25% increase in EMG datasets and 45.24% increase in Acc datasets as compare to MAS algorithm.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124189254","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 : 2020-05-01DOI: 10.1109/ICCCS49078.2020.9118473
Quanyv Wang, Panpan Fu, Shouzhen Zhang
Towards the 5th generation wireless systems (5G) and beyond, polar codes have become a hot research topic in the field of communications. In our work, concatenated polar codes with different interleaving and decoding schemes are designed to improve the error performance of finite-length polar codes. In this paper, concatenated polar codes with outer BCH codes are first constructed, besides, concatenated LDPC codes and concatenated Turbo codes are also designed for comparison. At the same time, random interleaving (RI) scheme and blind interleaving (BI) scheme are proposed to construct the concatenated codes. From the simulation results of this paper, we can see that the bit error rate (BER) performance of concatenated codes using BI scheme is better than that of concatenated codes using RI scheme. Furthermore, the BER performance of concatenated polar codes outperforms that of concatenated LDPC codes, but not as good as that of concatenated Turbo codes. To improve the BER performance of concatenated polar codes, we adopt the CRC Aided Successive Cancellation List (CA-SCL) decoding scheme instead of the Successive Cancellation (SC) decoding scheme for inner polar decoding. The results of our study indicate that the BER performance of concatenated polar codes with CA-SCL outperforms that of concatenated polar codes with SC. In addition to this, with the same CRC code length, increasing the list size can improve the decoding performance of CA-SCL. However, there is also a bad side: the decoding complexity of CA-SCL increases rapidly as the list size increases. On the other hand, with the same list size, increasing the CRC code length adversely deteriorates the decoding performance of CA-SCL.
{"title":"A Comparison of Concatenated Polar Codes with Different Interleaving and Decoding Schemes","authors":"Quanyv Wang, Panpan Fu, Shouzhen Zhang","doi":"10.1109/ICCCS49078.2020.9118473","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118473","url":null,"abstract":"Towards the 5th generation wireless systems (5G) and beyond, polar codes have become a hot research topic in the field of communications. In our work, concatenated polar codes with different interleaving and decoding schemes are designed to improve the error performance of finite-length polar codes. In this paper, concatenated polar codes with outer BCH codes are first constructed, besides, concatenated LDPC codes and concatenated Turbo codes are also designed for comparison. At the same time, random interleaving (RI) scheme and blind interleaving (BI) scheme are proposed to construct the concatenated codes. From the simulation results of this paper, we can see that the bit error rate (BER) performance of concatenated codes using BI scheme is better than that of concatenated codes using RI scheme. Furthermore, the BER performance of concatenated polar codes outperforms that of concatenated LDPC codes, but not as good as that of concatenated Turbo codes. To improve the BER performance of concatenated polar codes, we adopt the CRC Aided Successive Cancellation List (CA-SCL) decoding scheme instead of the Successive Cancellation (SC) decoding scheme for inner polar decoding. The results of our study indicate that the BER performance of concatenated polar codes with CA-SCL outperforms that of concatenated polar codes with SC. In addition to this, with the same CRC code length, increasing the list size can improve the decoding performance of CA-SCL. However, there is also a bad side: the decoding complexity of CA-SCL increases rapidly as the list size increases. On the other hand, with the same list size, increasing the CRC code length adversely deteriorates the decoding performance of CA-SCL.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125517188","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 : 2020-05-01DOI: 10.1109/ICCCS49078.2020.9118483
Yang Liu, O. Uchida, K. Utsu
During large-scale disasters, a large number of posts about damage reports and rescue requests are shared on social media. The utilization of the posts has become important for disaster response. Sina Weibo is the most famous microblogging service in China. In this study, we propose a web system to facilitate sharing disaster-related information on Sina Weibo. Using the system, users can easily share the information on Sina Weibo. Moreover, the posted information is listed on a Message board on the system to support rescue and assist activities by local governments and rescue experts.
{"title":"A Proposal on Disaster Information and Rescue Request Sharing Application Using Sina Weibo","authors":"Yang Liu, O. Uchida, K. Utsu","doi":"10.1109/ICCCS49078.2020.9118483","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118483","url":null,"abstract":"During large-scale disasters, a large number of posts about damage reports and rescue requests are shared on social media. The utilization of the posts has become important for disaster response. Sina Weibo is the most famous microblogging service in China. In this study, we propose a web system to facilitate sharing disaster-related information on Sina Weibo. Using the system, users can easily share the information on Sina Weibo. Moreover, the posted information is listed on a Message board on the system to support rescue and assist activities by local governments and rescue experts.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126000708","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 : 2020-05-01DOI: 10.1109/ICCCS49078.2020.9118456
Hua Zhang, Jiancun Zuo
In both 4G Long Term Evolution (LTE) and 5G New Radio (NR) systems, uplink power control (UPC) is a key feature of radio resource management to balance the inter-cell interference and guarantee the network performance, especially for physical uplink shared channel (PUSCH). In this paper, a methodology for optimizing UPC parameters is presented via network utility maximization (NUM). The network utility is defined as the sum of user utilities and is proven to be a concave function towards the two most important UPC parameters, i.e., nominal power level and channel path-loss compensation factor. Each cell optimizes its own UPC parameters semi-autonomously with only exchanging a few key variables via inter-cell coordination. Under the assumption of best effort traffic, results via system-level simulation show that network throughput performance with the proposed UPC mechanism is significantly improved when compared with typical fixed UPC parameter settings.
在4G LTE (Long Term Evolution, LTE)和5G NR (New Radio, NR)系统中，上行功率控制(UPC)是无线资源管理的关键特征，以平衡小区间干扰和保证网络性能，特别是对于物理上行共享信道(PUSCH)。本文提出了一种基于网络效用最大化的UPC参数优化方法。网络效用被定义为用户效用的总和，并被证明是两个最重要的UPC参数的凹函数，即标称功率电平和信道路径损耗补偿因子。每个细胞半自主地优化自己的UPC参数，仅通过细胞间协调交换几个关键变量。在最佳努力流量假设下，系统级仿真结果表明，与典型的固定UPC参数设置相比，采用该UPC机制的网络吞吐量性能有显著提高。
{"title":"Optimization of Uplink Power Control Parameters in Wireless Cellular Networks","authors":"Hua Zhang, Jiancun Zuo","doi":"10.1109/ICCCS49078.2020.9118456","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118456","url":null,"abstract":"In both 4G Long Term Evolution (LTE) and 5G New Radio (NR) systems, uplink power control (UPC) is a key feature of radio resource management to balance the inter-cell interference and guarantee the network performance, especially for physical uplink shared channel (PUSCH). In this paper, a methodology for optimizing UPC parameters is presented via network utility maximization (NUM). The network utility is defined as the sum of user utilities and is proven to be a concave function towards the two most important UPC parameters, i.e., nominal power level and channel path-loss compensation factor. Each cell optimizes its own UPC parameters semi-autonomously with only exchanging a few key variables via inter-cell coordination. Under the assumption of best effort traffic, results via system-level simulation show that network throughput performance with the proposed UPC mechanism is significantly improved when compared with typical fixed UPC parameter settings.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126794779","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}
Arousal labeling is one of the important methods in the diagnosis and treatment of sleep-related diseases, and are usually analyzed manually by doctors based on polysomnography (PSG) signals. In order to solve the problem of time-consuming and labor-intensive manual arousal analysis in sleep physiological signals, we propose an automatic arousal detection method using multi-model deep neural networks. Combining methods such as one-to-many formulation, LSTM, and network structure improvements, the performance of deep neural network models on clinical data set has been significantly improved, and multiple indicators have been improved (precision 86.7%, recall 86.0% and F1 86.3%). At the same time, the model parameters have been greatly streamlined, making them more efficient, laying a foundation for the application of automatic arousal detection methods on wearable sleep monitoring device signal analysis.
{"title":"Automatic Arousal Detection Using Multi-model Deep Neural Network","authors":"Ziqian Jia, Xingjun Wang, Xiaoqing Zhang, Mingkai Xu","doi":"10.1109/ICCCS49078.2020.9118530","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118530","url":null,"abstract":"Arousal labeling is one of the important methods in the diagnosis and treatment of sleep-related diseases, and are usually analyzed manually by doctors based on polysomnography (PSG) signals. In order to solve the problem of time-consuming and labor-intensive manual arousal analysis in sleep physiological signals, we propose an automatic arousal detection method using multi-model deep neural networks. Combining methods such as one-to-many formulation, LSTM, and network structure improvements, the performance of deep neural network models on clinical data set has been significantly improved, and multiple indicators have been improved (precision 86.7%, recall 86.0% and F1 86.3%). At the same time, the model parameters have been greatly streamlined, making them more efficient, laying a foundation for the application of automatic arousal detection methods on wearable sleep monitoring device signal analysis.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"1144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113995026","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 : 2020-05-01DOI: 10.1109/ICCCS49078.2020.9118491
Wei Cheng, Qiyue Yin, Junge Zhang
This paper focuses on the task of opponent strategies recognition in Real-time Strategy (RTS) Game, which aims to predict opponent strategies by modeling the observable environmental information. It is a very challenging task due to two folds. (1) In RTS game, the information is imperfect due to the fog of war; and (2) the action and environment spaces of RTS game are too vast and complex to be modeled. This task is also significative since opponent strategies recognition is a crucial component of creating high-level AI system that can defeat high-level human players in RTS game. Most previous approaches focus on predicting tech tree, building order and strategies through game logs, where perfect information is utilized. Accordingly, these prediction methods cannot be applied to real AI systems confronting the fog of war. Furthermore, conventional approaches use machine learning techniques such as Hidden Markov Model (HMM) and Bayesian network, which is difficult to deal with higher-dimensional state spaces. Besides, the hand-crafted features are commonly used instead of high-dimensional feature of the complex environment, which leads to loss of information of the environment. To address these problems, we propose a deep feature fusion neural network to handle the above imperfect and complex information of the environment for opponent strategies recognition in RTS game. We test our method on the canonical RTS game, i.e., Starcraft II, and promising performance has been obtained.
{"title":"Opponent Strategy Recognition In Real Time Strategy Game Using Deep Feature Fusion Neural Network","authors":"Wei Cheng, Qiyue Yin, Junge Zhang","doi":"10.1109/ICCCS49078.2020.9118491","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118491","url":null,"abstract":"This paper focuses on the task of opponent strategies recognition in Real-time Strategy (RTS) Game, which aims to predict opponent strategies by modeling the observable environmental information. It is a very challenging task due to two folds. (1) In RTS game, the information is imperfect due to the fog of war; and (2) the action and environment spaces of RTS game are too vast and complex to be modeled. This task is also significative since opponent strategies recognition is a crucial component of creating high-level AI system that can defeat high-level human players in RTS game. Most previous approaches focus on predicting tech tree, building order and strategies through game logs, where perfect information is utilized. Accordingly, these prediction methods cannot be applied to real AI systems confronting the fog of war. Furthermore, conventional approaches use machine learning techniques such as Hidden Markov Model (HMM) and Bayesian network, which is difficult to deal with higher-dimensional state spaces. Besides, the hand-crafted features are commonly used instead of high-dimensional feature of the complex environment, which leads to loss of information of the environment. To address these problems, we propose a deep feature fusion neural network to handle the above imperfect and complex information of the environment for opponent strategies recognition in RTS game. We test our method on the canonical RTS game, i.e., Starcraft II, and promising performance has been obtained.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127882386","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 : 2020-05-01DOI: 10.1109/ICCCS49078.2020.9118576
U. Choi, Sung-Jin Cho, S. Kang
Due to the development of the Internet and the communication network environment, image transmissions occur very frequently. In such an environment, the color image is likely to be distorted by noise. Image shuffling is a technique which resists distortion and deletion attacks. In this paper, we propose a speed-up method for shuffling pixels in the color image encryption system. In that step of shuffling, we use 1-D MLCAs to shuffle the pixel position of an image. In fact 1-D MLCA is higher in randomness than the conventional chaotic map and faster than the 3-D generalized chaotic cat map. The comparison of the execution time between the proposed method and the existing method is made.
{"title":"High Speed Color Image Encryption Using Pixel Shuffling With 1-D MLCA","authors":"U. Choi, Sung-Jin Cho, S. Kang","doi":"10.1109/ICCCS49078.2020.9118576","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118576","url":null,"abstract":"Due to the development of the Internet and the communication network environment, image transmissions occur very frequently. In such an environment, the color image is likely to be distorted by noise. Image shuffling is a technique which resists distortion and deletion attacks. In this paper, we propose a speed-up method for shuffling pixels in the color image encryption system. In that step of shuffling, we use 1-D MLCAs to shuffle the pixel position of an image. In fact 1-D MLCA is higher in randomness than the conventional chaotic map and faster than the 3-D generalized chaotic cat map. The comparison of the execution time between the proposed method and the existing method is made.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132868886","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 : 2020-05-01DOI: 10.1109/ICCCS49078.2020.9118433
Zhengbo Chen, Liu Xiu, Xing Yafei, Hu Miao, Xiaoming Ju
In order to improve the buffering performance of the data encrypted by CP-ABE (ciphertext policy attribute based encryption), this paper proposed a Markov prefetching model based on attribute classification. The prefetching model combines the access strategy of CP-ABE encrypted file, establishes the user relationship network according to the attribute value of the user, classifies the user by the modularity-based community partitioning algorithm, and establishes a Markov prefetching model based on attribute classification. In comparison with the traditional Markov prefetching model and the classification-based Markov prefetching model, the attribute-based Markov prefetching model is proposed in this paper has higher prefetch accuracy and coverage.
{"title":"Markov Encrypted Data Prefetching Model Based On Attribute Classification","authors":"Zhengbo Chen, Liu Xiu, Xing Yafei, Hu Miao, Xiaoming Ju","doi":"10.1109/ICCCS49078.2020.9118433","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118433","url":null,"abstract":"In order to improve the buffering performance of the data encrypted by CP-ABE (ciphertext policy attribute based encryption), this paper proposed a Markov prefetching model based on attribute classification. The prefetching model combines the access strategy of CP-ABE encrypted file, establishes the user relationship network according to the attribute value of the user, classifies the user by the modularity-based community partitioning algorithm, and establishes a Markov prefetching model based on attribute classification. In comparison with the traditional Markov prefetching model and the classification-based Markov prefetching model, the attribute-based Markov prefetching model is proposed in this paper has higher prefetch accuracy and coverage.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130118239","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 : 2020-05-01DOI: 10.1109/ICCCS49078.2020.9118444
Kun Yue, Jiahui Wang, Xinbai Li, Kuang Hu
The method of knowledge graph completion (KGC) by adding external knowledge with new entities was discussed in this paper. Adopting the TransE-based representation of relations and triples in Knowledge Graph, we extract triples from open-world data and evaluate their correctness to fulfill KGC, where vectors are used for similarity evaluation. From the “structural” point of view, triples were first built from open-world data according to the similarity between TransE-based representation of pairs of entities and that of relations in KG. From the “semantic” point of view, the correctness of each external triple was evaluated by measuring the distance in the triple locally and ranking in the entire KG globally. ON the FreeBase and DBPedia KGs by different KG representation models and KGC methods, experimental results show that our proposal outperforms some state-of-the-art methods.
{"title":"Representation-Based Completion of Knowledge Graph with Open-World Data","authors":"Kun Yue, Jiahui Wang, Xinbai Li, Kuang Hu","doi":"10.1109/ICCCS49078.2020.9118444","DOIUrl":"https://doi.org/10.1109/ICCCS49078.2020.9118444","url":null,"abstract":"The method of knowledge graph completion (KGC) by adding external knowledge with new entities was discussed in this paper. Adopting the TransE-based representation of relations and triples in Knowledge Graph, we extract triples from open-world data and evaluate their correctness to fulfill KGC, where vectors are used for similarity evaluation. From the “structural” point of view, triples were first built from open-world data according to the similarity between TransE-based representation of pairs of entities and that of relations in KG. From the “semantic” point of view, the correctness of each external triple was evaluated by measuring the distance in the triple locally and ranking in the entire KG globally. ON the FreeBase and DBPedia KGs by different KG representation models and KGC methods, experimental results show that our proposal outperforms some state-of-the-art methods.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127864820","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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