Pub Date : 2019-08-01DOI: 10.1109/UCET.2019.8881884
I. Bibi, Adnan Akhunzada, Jahanzaib Malik, Ghufran Ahmed, M. Raza
with the increasing diversity of Android malware, the effectiveness of conventional defense mechanisms are at risk. This situation has endorsed a notable interest in the improvement of the exactitude and scalability of malware detection for smart devices. In this study, we have proposed an effective deep learning-based malware detection model for competent and improved ransomware detection in Android environment by looking at the algorithm of Long Short-Term Memory (LSTM). The feature selection has been done using 8 different feature selection algorithms. The 19 important features are selected through simple majority voting process by comparing results of all feature filtration techniques. The proposed algorithm is evaluated using android malware dataset (CI-CAndMal2017) and standard performance parameters. The proposed model outperforms with 97.08% detection accuracy. Based on outstanding performance, we endorse our proposed algorithm to be efficient in malware and forensic analysis.
{"title":"An Effective Android Ransomware Detection Through Multi-Factor Feature Filtration and Recurrent Neural Network","authors":"I. Bibi, Adnan Akhunzada, Jahanzaib Malik, Ghufran Ahmed, M. Raza","doi":"10.1109/UCET.2019.8881884","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881884","url":null,"abstract":"with the increasing diversity of Android malware, the effectiveness of conventional defense mechanisms are at risk. This situation has endorsed a notable interest in the improvement of the exactitude and scalability of malware detection for smart devices. In this study, we have proposed an effective deep learning-based malware detection model for competent and improved ransomware detection in Android environment by looking at the algorithm of Long Short-Term Memory (LSTM). The feature selection has been done using 8 different feature selection algorithms. The 19 important features are selected through simple majority voting process by comparing results of all feature filtration techniques. The proposed algorithm is evaluated using android malware dataset (CI-CAndMal2017) and standard performance parameters. The proposed model outperforms with 97.08% detection accuracy. Based on outstanding performance, we endorse our proposed algorithm to be efficient in malware and forensic analysis.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127238453","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 : 2019-08-01DOI: 10.1109/UCET.2019.8881859
Yong Xiao, Lei Zhang, M. Imran
Traditional active constellation extension (ACE) techniques iterate under a further and further away from decision boundary constraint to find distortions for peak-to-average power ratio (PAPR) reduction, which may stop the solution on suboptimal points because it's not permitted to go back when running into a suboptimum direction. In this paper, we present a novel ACE technique by iterating in both positive and negative directions, referring to distortions found in the last iteration. During iterations, optimization variations are changed from normally used extra distortions on the last estimates to the primitive OFDM signal, which can eliminate correlations between magnitudes and phases of complex distortions and finally give an analytic solution based on orthogonal projection. By making iterations run in positive and negative directions, this algorithm can find distortions to reduce PAPR more, compared with existing methods. Simulation results show that significant improvement can be achieved either for pure ACE or TR assisted ACE method, especially under higher-order modulation schemes.
{"title":"Active Constellation Extension for Peak Power Reduction Based on Positive and Negative Iterations in OFDM Systems","authors":"Yong Xiao, Lei Zhang, M. Imran","doi":"10.1109/UCET.2019.8881859","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881859","url":null,"abstract":"Traditional active constellation extension (ACE) techniques iterate under a further and further away from decision boundary constraint to find distortions for peak-to-average power ratio (PAPR) reduction, which may stop the solution on suboptimal points because it's not permitted to go back when running into a suboptimum direction. In this paper, we present a novel ACE technique by iterating in both positive and negative directions, referring to distortions found in the last iteration. During iterations, optimization variations are changed from normally used extra distortions on the last estimates to the primitive OFDM signal, which can eliminate correlations between magnitudes and phases of complex distortions and finally give an analytic solution based on orthogonal projection. By making iterations run in positive and negative directions, this algorithm can find distortions to reduce PAPR more, compared with existing methods. Simulation results show that significant improvement can be achieved either for pure ACE or TR assisted ACE method, especially under higher-order modulation schemes.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116464972","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 : 2019-08-01DOI: 10.1109/UCET.2019.8881874
Mohamed Abdur Rahman, Md Mamunur Rashid, S. Barnes, Syed Maruf Abdullah
In this paper, we propose a secure internet of vehicles (IoV) framework that can handle the transportation ecosystem of a very large and dynamic crowd. The framework will allow personalized and location-aware vehicle IoT data to store in blockchain and off-chain repositories for secure sharing with one's community of interest. As a test case of our proposed application, we have developed distributed smartphone applications that can be interfaced with the OBD-II interface to collect in-vehicle data from the CAN bus of a vehicle and an ambient intelligent environment consisting of IoT devices. The in-vehicle environment can collect vehicle sensory information, process the sensory data within the mobile edge network and store the transactions and the raw sensory data to blockchain and off-chain repositories through secure digital wallets. Finally, we will present our implemented framework and initial test results.
{"title":"A Blockchain-based Secure Internet of Vehicles Management Framework","authors":"Mohamed Abdur Rahman, Md Mamunur Rashid, S. Barnes, Syed Maruf Abdullah","doi":"10.1109/UCET.2019.8881874","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881874","url":null,"abstract":"In this paper, we propose a secure internet of vehicles (IoV) framework that can handle the transportation ecosystem of a very large and dynamic crowd. The framework will allow personalized and location-aware vehicle IoT data to store in blockchain and off-chain repositories for secure sharing with one's community of interest. As a test case of our proposed application, we have developed distributed smartphone applications that can be interfaced with the OBD-II interface to collect in-vehicle data from the CAN bus of a vehicle and an ambient intelligent environment consisting of IoT devices. The in-vehicle environment can collect vehicle sensory information, process the sensory data within the mobile edge network and store the transactions and the raw sensory data to blockchain and off-chain repositories through secure digital wallets. Finally, we will present our implemented framework and initial test results.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123946796","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 : 2019-08-01DOI: 10.1109/UCET.2019.8881842
Salah-ud-din Farooq, M. Usama, Junaid Qadir, M. Imran
Deep Neural Networks (DNN) have been widely adopted in self-organizing networks (SON) for automating different networking tasks. Recently, it has been shown that DNN lack robustness against adversarial examples where an adversary can fool the DNN model into incorrect classification by introducing a small imperceptible perturbation to the original example. SON is expected to use DNN for multiple fundamental cellular tasks and many DNN-based solutions for performing SON tasks have been proposed in the literature have not been tested against adversarial examples. In this paper, we have tested and explained the robustness of SON against adversarial example and investigated the performance of an important SON use case in the face of adversarial attacks. We have also generated explanations of incorrect classifications by utilizing an explainable artificial intelligence (AI) technique.
{"title":"Adversarial ML Attack on Self Organizing Cellular Networks","authors":"Salah-ud-din Farooq, M. Usama, Junaid Qadir, M. Imran","doi":"10.1109/UCET.2019.8881842","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881842","url":null,"abstract":"Deep Neural Networks (DNN) have been widely adopted in self-organizing networks (SON) for automating different networking tasks. Recently, it has been shown that DNN lack robustness against adversarial examples where an adversary can fool the DNN model into incorrect classification by introducing a small imperceptible perturbation to the original example. SON is expected to use DNN for multiple fundamental cellular tasks and many DNN-based solutions for performing SON tasks have been proposed in the literature have not been tested against adversarial examples. In this paper, we have tested and explained the robustness of SON against adversarial example and investigated the performance of an important SON use case in the face of adversarial attacks. We have also generated explanations of incorrect classifications by utilizing an explainable artificial intelligence (AI) technique.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125828896","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 : 2019-08-01DOI: 10.1109/UCET.2019.8881836
Fawad, Muhammad Adeel Asghar, A. Saeed, Muhammad Jamil Khan, Muhammad Zahid, M. Rehman
In this paper, we have proposed a hybrid descriptor for the texture classification task. The feature variables are extracted from the approximation coefficients of the image, through a combination of deep neural network and handcrafted feature. The AlexNet along with completed joint scale local binary pattern (CJLBP) is used for illumination, scaling, and orientation invariance description. The wavelet decomposition layer provides robustness against additive white Gaussian noise. The feature dimensionality is reduced by using Principal Component Analysis. We have evaluated our proposed descriptor on the images of Outex texture databases. The experimental results presented in the paper in term of classification accuracy show that our proposed descriptor outperforms state-of-the-art feature extraction scheme.
{"title":"Texture Classification using a Hybrid Deep and Handcrafted Features","authors":"Fawad, Muhammad Adeel Asghar, A. Saeed, Muhammad Jamil Khan, Muhammad Zahid, M. Rehman","doi":"10.1109/UCET.2019.8881836","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881836","url":null,"abstract":"In this paper, we have proposed a hybrid descriptor for the texture classification task. The feature variables are extracted from the approximation coefficients of the image, through a combination of deep neural network and handcrafted feature. The AlexNet along with completed joint scale local binary pattern (CJLBP) is used for illumination, scaling, and orientation invariance description. The wavelet decomposition layer provides robustness against additive white Gaussian noise. The feature dimensionality is reduced by using Principal Component Analysis. We have evaluated our proposed descriptor on the images of Outex texture databases. The experimental results presented in the paper in term of classification accuracy show that our proposed descriptor outperforms state-of-the-art feature extraction scheme.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124962978","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 : 2019-08-01DOI: 10.1109/UCET.2019.8881838
Jie Su, Xiaohai He, L. Qing, Yanmei Yu, Shengyu Xu, Yonghong Peng
In this paper, a new end-to-end framework is proposed for person re-identification (re-ID) by combining metric learning and classification. In this new framework, the Additive Angular Margin Softmax is used which imposes an additive angular margin constraint to the target logit on hypersphere manifold. This is aimed to improve the similarity of the intra-class features and the dissimilarity of the inter-class features simultaneously. Compard with the three popular used softmax-based-loss methods, the experiments show that the proposed approach has achieved improved performance on Market1501 and DukeMTMC-reID datasets for person re-ID.
{"title":"A New Discriminative Feature Learning for Person Re-Identification Using Additive Angular Margin Softmax Loss","authors":"Jie Su, Xiaohai He, L. Qing, Yanmei Yu, Shengyu Xu, Yonghong Peng","doi":"10.1109/UCET.2019.8881838","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881838","url":null,"abstract":"In this paper, a new end-to-end framework is proposed for person re-identification (re-ID) by combining metric learning and classification. In this new framework, the Additive Angular Margin Softmax is used which imposes an additive angular margin constraint to the target logit on hypersphere manifold. This is aimed to improve the similarity of the intra-class features and the dissimilarity of the inter-class features simultaneously. Compard with the three popular used softmax-based-loss methods, the experiments show that the proposed approach has achieved improved performance on Market1501 and DukeMTMC-reID datasets for person re-ID.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128503008","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 : 2019-08-01DOI: 10.1109/UCET.2019.8881862
Stefano Bolis, Davide Scazzoli, L. Reggiani, M. Magarini, M. Alam
In this paper we present a study on the use of beamforming (BF) for devices discovery from flying platforms, typically Unmanned Aerial Vehicles (UAVs). This type of application is meant to be exploited in emergency scenarios characterized by the absence of a network infrastructure; the purpose is to search and identify the devices (and consequently the persons) spread in a limited area without the possibility of connecting to a mobile network. The use of an antenna array from the UAV is supposed to increase the sensitivity towards devices with weak signals and/or difficult propagation conditions. Our preliminary results indicate the effectiveness of a scanning method based on BF techniques for discovering and detecting terminals on the ground. The numerical results provide an insight on the capability level of BF solutions in these conditions w.r.t. to the size of the area to be covered.
{"title":"A Study on Beamforming for Coverage of Emergency Areas from UAVs","authors":"Stefano Bolis, Davide Scazzoli, L. Reggiani, M. Magarini, M. Alam","doi":"10.1109/UCET.2019.8881862","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881862","url":null,"abstract":"In this paper we present a study on the use of beamforming (BF) for devices discovery from flying platforms, typically Unmanned Aerial Vehicles (UAVs). This type of application is meant to be exploited in emergency scenarios characterized by the absence of a network infrastructure; the purpose is to search and identify the devices (and consequently the persons) spread in a limited area without the possibility of connecting to a mobile network. The use of an antenna array from the UAV is supposed to increase the sensitivity towards devices with weak signals and/or difficult propagation conditions. Our preliminary results indicate the effectiveness of a scanning method based on BF techniques for discovering and detecting terminals on the ground. The numerical results provide an insight on the capability level of BF solutions in these conditions w.r.t. to the size of the area to be covered.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130032825","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 : 2019-08-01DOI: 10.1109/UCET.2019.8881852
Fengling Jiang, K. Dashtipour, A. Hussain
With recent achievements in deep learning over the past year, many computer and network applications actively used deep learning architectures including convolution neural network and long short-term memory to improve the performance of their approach through them. The computer network used a complex and dynamic system. For example, routing is the main networking tasks in the fields of the communication network and it is widely used to optimize the optimal routing from the original host to the destination host. However, most of the traditional routing protocol is based on the experience of experts. In this paper, we present an overview of deep learning methods for the routing layer in the computer network. Furthermore, this paper discusses reinforcement learning methods about network routing. Finally, we outline a summary of the current state-of-the-art approaches along with some future research directions.
{"title":"A Survey on Deep Learning for the Routing Layer of Computer Network","authors":"Fengling Jiang, K. Dashtipour, A. Hussain","doi":"10.1109/UCET.2019.8881852","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881852","url":null,"abstract":"With recent achievements in deep learning over the past year, many computer and network applications actively used deep learning architectures including convolution neural network and long short-term memory to improve the performance of their approach through them. The computer network used a complex and dynamic system. For example, routing is the main networking tasks in the fields of the communication network and it is widely used to optimize the optimal routing from the original host to the destination host. However, most of the traditional routing protocol is based on the experience of experts. In this paper, we present an overview of deep learning methods for the routing layer in the computer network. Furthermore, this paper discusses reinforcement learning methods about network routing. Finally, we outline a summary of the current state-of-the-art approaches along with some future research directions.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130920049","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 : 2019-08-01DOI: 10.1109/UCET.2019.8881868
Xiaoyan Shi, J. Thompson, M. Safari, Shenjie Huang, Rongke Liu
Emerging applications in the radio access network (RAN), such as proactive caching and the massive machine type communications, will generate traffic with a Large Volume of data but can tolerate long Transmission Delay (LVTD). Together with a rapid growth of overall traffic demand, traffic with LVTD features brings lots of challenges to the backhaul in RANs. In this paper, we study how to schedule traffic in multi-hop backhaul networks with the help of unmanned aerial vehicles (UAVs), In the proposed system, the UAVs are employed to establish broadband connections with the ground terminals through free-space optical links and serve as data collectors between terrestrial access points so as to alleviate the communication burden on the terrestrial network. Through numerical simulations, it is demonstrated that the novel network scheduling scheme combined with dynamic UAV path planning can provide significant performance gain.
{"title":"UAV Aided Data Dissemination for Multi-hop Backhauling in RAN","authors":"Xiaoyan Shi, J. Thompson, M. Safari, Shenjie Huang, Rongke Liu","doi":"10.1109/UCET.2019.8881868","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881868","url":null,"abstract":"Emerging applications in the radio access network (RAN), such as proactive caching and the massive machine type communications, will generate traffic with a Large Volume of data but can tolerate long Transmission Delay (LVTD). Together with a rapid growth of overall traffic demand, traffic with LVTD features brings lots of challenges to the backhaul in RANs. In this paper, we study how to schedule traffic in multi-hop backhaul networks with the help of unmanned aerial vehicles (UAVs), In the proposed system, the UAVs are employed to establish broadband connections with the ground terminals through free-space optical links and serve as data collectors between terrestrial access points so as to alleviate the communication burden on the terrestrial network. Through numerical simulations, it is demonstrated that the novel network scheduling scheme combined with dynamic UAV path planning can provide significant performance gain.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121498872","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 : 2019-08-01DOI: 10.1109/UCET.2019.8881869
Lun Ma
This paper considers the problem of estimating gain-phase and position errors for multi-channel synthetic aperture radar (SAR) systems. In multi-channel SAR systems, the clutter spectrum components within a Doppler bin can be used as calibration sources with known directions. Conventional eigen-structure methods, which iterates alternatively between the self-calibration method for gain-phase error estimation and the least squares method for estimating position errors, may converge to a local optimal solution. In this paper, it is observed that the steering vectors corresponding to a pair of Doppler bins within the same range bin have a rotational relationship. We obtain a new array-error estimation method based on this observation. The method, which obtain the position errors in terms of extracting the rotational matrix in a least square's framework, is proposed via combining the projection matrices corresponding to the paired Doppler bins. The validity of the proposed method is verified by the experimental results of measured four-channel SAR data.
{"title":"An free of iteration array-error estimation method for multi-channel SAR systems*","authors":"Lun Ma","doi":"10.1109/UCET.2019.8881869","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881869","url":null,"abstract":"This paper considers the problem of estimating gain-phase and position errors for multi-channel synthetic aperture radar (SAR) systems. In multi-channel SAR systems, the clutter spectrum components within a Doppler bin can be used as calibration sources with known directions. Conventional eigen-structure methods, which iterates alternatively between the self-calibration method for gain-phase error estimation and the least squares method for estimating position errors, may converge to a local optimal solution. In this paper, it is observed that the steering vectors corresponding to a pair of Doppler bins within the same range bin have a rotational relationship. We obtain a new array-error estimation method based on this observation. The method, which obtain the position errors in terms of extracting the rotational matrix in a least square's framework, is proposed via combining the projection matrices corresponding to the paired Doppler bins. The validity of the proposed method is verified by the experimental results of measured four-channel SAR data.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125620267","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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