Pub Date : 2021-11-11DOI: 10.25073/2588-1086/vnucsce.280
Nguyen Ngoc Khai, Trương Anh Hoàng, Dang Duc Hanh
Estimating memory required by complex programs is a well-known research topic. In this work, we build a type system to statically estimate the memory bounds required by shared variables in software transactional memory (STM) programs. This work extends our previous works with additional language features such as explicitly declared shared variables, introduction of primitive types, and allowing loop body to contain any statement, not required to be well-typed as in our previous works. Also, the new type system has better compositionality compared to available type systems.
{"title":"Estimate the Memory Bounds Required by Shared Variables in Software Transactional Memory Programs","authors":"Nguyen Ngoc Khai, Trương Anh Hoàng, Dang Duc Hanh","doi":"10.25073/2588-1086/vnucsce.280","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.280","url":null,"abstract":"Estimating memory required by complex programs is a well-known research topic. In this work, we build a type system to statically estimate the memory bounds required by shared variables in software transactional memory (STM) programs. This work extends our previous works with additional language features such as explicitly declared shared variables, introduction of primitive types, and allowing loop body to contain any statement, not required to be well-typed as in our previous works. Also, the new type system has better compositionality compared to available type systems.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124647831","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 : 2021-11-11DOI: 10.25073/2588-1086/vnucsce.296
Phi Hoang Nha, Phạm Hùng Phi, Dao Quang Thuy, Lê Xuân Hải, Pham Xuan Dat, N. N. Linh
The paper presents a new approach to design a nonlinear controller for switched reluctance motors (SRMs) based on backstepping technique and artificial neuron network (ANN) in flux estimator. Backstepping controller with an ANN flux estimator will be applied for controlling SRMs which have a nonlinear drive model. The ANN flux estimator was trained off-line using backpropagation algorithm. The stability of the closed control loop was analyzed and proved accroding to the Lyapunov stability standard. The numerical simulation results confirmed the accuracy of the estimator and the quality of the backstepping control system.
{"title":"Backstepping Control of Switched Reluctance Motor with Artificial Neural Network based Flux Estimator","authors":"Phi Hoang Nha, Phạm Hùng Phi, Dao Quang Thuy, Lê Xuân Hải, Pham Xuan Dat, N. N. Linh","doi":"10.25073/2588-1086/vnucsce.296","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.296","url":null,"abstract":"The paper presents a new approach to design a nonlinear controller for switched reluctance motors (SRMs) based on backstepping technique and artificial neuron network (ANN) in flux estimator. Backstepping controller with an ANN flux estimator will be applied for controlling SRMs which have a nonlinear drive model. The ANN flux estimator was trained off-line using backpropagation algorithm. The stability of the closed control loop was analyzed and proved accroding to the Lyapunov stability standard. The numerical simulation results confirmed the accuracy of the estimator and the quality of the backstepping control system.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123212342","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 : 2021-06-28DOI: 10.25073/2588-1086/vnucsce.295
N. V. Tu, L. Cuong
The community Question Answering (cQA) problem requires the task that given a question it aims at selecting the most related question-answer tuples (a question and its answers) from the stored question-answer tuples data set. The core mission of this task is to measure the similarity (or relationship) between an input question and questions from the given question-answer data set. Under our observation, there are either various information sources as well as di erent measurement models which can provide complementary knowledge for indicating the relationship between questions and question-answer tuples. In this paper we address the problem of modeling and combining multiple knowledge sources for determining and ranking the most related question-answer tuples given an input question for cQA problem. Our proposed model will generate di erent features based on di erent representations of the data as well as on di erent methods and then integrate this information into the BERT model for similarity measurement in cQA problem. We evaluate our proposed model on the SemEval 2016 data set and achieve the state-of-the-art result. Keywords Community question answering, Multi knowledge sources, Deep learning, The BERT model References [1] C. Alberto, D. Bonadiman, G. D. S. Martino, Answer and Question Selection for Question Answering on Arabic and English Fora, in Proceedings of SemEval-2016, 2016, pp. 896-903. [2] Filice, D. Croce, A. Moschitti, R. Basili, Learning Semantic Relations between Questions and Answers, in Proceedings of SemEval-2016, 2016, pp. 1116-1123. [3] Wang, Z. Ming, T. S. Chua, A Syntactic Tree Matching Approach to Finding Similar Questions in Community-based qa Services, in SIGIR, 2009, pp. 187-194. [4] Pengfei, Q. Xipeng, C. Jifan, H. Xuanjing, Deep Fusion lstms for Text Semantic Matching, in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Vol. 1, 2016, pp. 1034-1043, https://doi.org/ 10.18653/v1/P16-1098. [5] Jonas, T. Aditya, Siamese Recurrent Architectures for Learning Sentence Similarity, in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), 2016, pp. 2786-2792. [6] Jacob, C. M. Wei, L. Kenton, T. Kristina, Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding, in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019, pp. 4171-4186. [7] Wissam, B. Fady, H. Hazem, Arabert: Transformer-based Model for Arabic Language Understanding, in Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on O ensive Language Detection, 2020, pp. 9-15. [8] Lukovnikov, A. Fischer, J. Lehmann, Pretrained Transformers for Simple Question Answering Over Knowledge Graphs, ArXiv, abs/2001.11985, 2019. [9] V. Aken, B. Winter, A. Loser, F. Gers, How Does BERT Answer Questions?: A Layer-Wise Analysis of Trans
社区问答(cQA)问题要求任务给出一个问题，它的目标是从存储的问答元组数据集中选择最相关的问答元组(问题及其答案)。该任务的核心任务是测量输入问题与给定问答数据集中的问题之间的相似性(或关系)。根据我们的观察，既有各种各样的信息来源，也有不同的测量模型，可以为表明问题和问答元组之间的关系提供互补的知识。在本文中，我们解决了建模和组合多个知识来源的问题，以确定最相关的问答元组并对其进行排序。我们提出的模型将基于数据的不同表示以及不同的方法生成不同的特征，然后将这些信息集成到BERT模型中用于cQA问题的相似性度量。我们在SemEval 2016数据集上评估了我们提出的模型，并获得了最先进的结果。关键词社区问答，多知识来源，深度学习，BERT模型[1]C. Alberto, D. Bonadiman, G. D. S. Martino，阿拉伯语和英语论坛问答的答案和问题选择，semeval2016, 2016, pp. 896-903。[2]王晓明，王晓明，王晓明，基于语义的问答关系学习，中文信息学报，2016,pp. 391 - 391。[3]王志明，蔡廷生，基于句法树的社区问答服务问题搜索方法，中文信息学报，2009,pp. 187-194。[4]彭飞，祁希鹏，陈吉凡，宣静，基于深度融合的文本语义匹配方法，计算语言学年会论文集，2016,Vol. 1, pp. 1034-1043, https://doi.org/ 10.18653/v1/P16-1098。[5]杨建军，陈建军，基于重复结构的句子相似度学习方法，中文信息学报，2016,pp. 391 - 391。[6]刘志强，魏志明，李晓明，李晓明。基于深度双向变换的语言理解预训练方法，中文信息学报，2019，第6期，第1-4页。[7]王晓明，王晓明，王晓明。基于变换的阿拉伯语语言理解模型，中文信息学报，2014，第4期，第9-15页。[8]张晓明，张晓明，基于知识图谱的简单问答方法，计算机应用学报，2001,11(1)，2019。[9]王晓东，王晓东，王晓东，《BERT如何回答问题》。:变压器表示的分层分析，第28届ACM信息与知识管理国际会议论文集，2019。[10]陈勇，范勇，一种基于bert的问题生成模型，中文信息学报，2019,pp. 154-162。[11]陈晓明，陈晓明，陈晓明。基于自监督学习的语言表征方法研究[j] .中文信息学报，2014(4):1102 - 1102,2020。[12]陈建军，陈建军，陈建军，基于自适应的新型冠状病毒肺炎问答模型，计算机应用与控制学报，2016,32(1):1 - 4。[13]于磊，吴丽，邓勇，马洪度，曾庆强，蒋明，一种基于迁移学习的技术问答系统，应用科学学报，2020,pp. 92-99。[14]张晓明，张晓明，张晓明，基于bert的问答模型，中文信息学报，2019。[15]张晓明，张晓明，基于深度神经网络的中文社区问答方法，中文信息学报，Vol. 59, 2020, pp. 357 - 357。[16]张晓明，张晓明，张晓明，张晓明。基于神经网络的神经网络信息处理研究，计算机科学，2017，第1期，第1 -6页。[17]阮涛，李爱春，阮洪宁，基于卷积神经网络的社区问答系统问题相似度度量模型，中文信息学报，Vol. 11, No. 3, 2021, pp. 194-201, https://doi.org/ 10.18178/ijmlc.2021.11.3.1035。
{"title":"A Deep Learning Model of Multiple Knowledge Sources Integration for Community Question Answering","authors":"N. V. Tu, L. Cuong","doi":"10.25073/2588-1086/vnucsce.295","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.295","url":null,"abstract":"The community Question Answering (cQA) problem requires the task that given a question it aims at selecting the most related question-answer tuples (a question and its answers) from the stored question-answer tuples data set. The core mission of this task is to measure the similarity (or relationship) between an input question and questions from the given question-answer data set. Under our observation, there are either various information sources as well as di erent measurement models which can provide complementary knowledge for indicating the relationship between questions and question-answer tuples. In this paper we address the problem of modeling and combining multiple knowledge sources for determining and ranking the most related question-answer tuples given an input question for cQA problem. Our proposed model will generate di erent features based on di erent representations of the data as well as on di erent methods and then integrate this information into the BERT model for similarity measurement in cQA problem. We evaluate our proposed model on the SemEval 2016 data set and achieve the state-of-the-art result. \u0000Keywords \u0000Community question answering, Multi knowledge sources, Deep learning, The BERT model \u0000References \u0000[1] C. Alberto, D. Bonadiman, G. D. S. Martino, Answer and Question Selection for Question Answering on Arabic and English Fora, in Proceedings of SemEval-2016, 2016, pp. 896-903. \u0000[2] Filice, D. Croce, A. Moschitti, R. Basili, Learning Semantic Relations between Questions and Answers, in Proceedings of SemEval-2016, 2016, pp. 1116-1123. \u0000[3] Wang, Z. Ming, T. S. Chua, A Syntactic Tree Matching Approach to Finding Similar Questions in Community-based qa Services, in SIGIR, 2009, pp. 187-194. \u0000[4] Pengfei, Q. Xipeng, C. Jifan, H. Xuanjing, Deep Fusion lstms for Text Semantic Matching, in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Vol. 1, 2016, pp. 1034-1043, \u0000https://doi.org/ 10.18653/v1/P16-1098. \u0000[5] Jonas, T. Aditya, Siamese Recurrent Architectures for Learning Sentence Similarity, in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), 2016, pp. 2786-2792. \u0000[6] Jacob, C. M. Wei, L. Kenton, T. Kristina, Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding, in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019, pp. 4171-4186. \u0000[7] Wissam, B. Fady, H. Hazem, Arabert: Transformer-based Model for Arabic Language Understanding, in Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on O ensive Language Detection, 2020, pp. 9-15. \u0000[8] Lukovnikov, A. Fischer, J. Lehmann, Pretrained Transformers for Simple Question Answering Over Knowledge Graphs, ArXiv, abs/2001.11985, 2019. \u0000[9] V. Aken, B. Winter, A. Loser, F. Gers, How Does BERT Answer Questions?: A Layer-Wise Analysis of Trans","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122038246","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 : 2020-06-24DOI: 10.25073/2588-1086/VNUCSCE.245
K. Dang, Akram Ben Ahmed, Abderazek Ben Abdallah, Xuan-Tu Tran
As one of the most promising technologies to reduce footprint, power consumption and wire latency, Three Dimensional Integrated Circuits (3D-ICs) is considered as the near future for VLSI system. Combining with the Network-on-Chip infrastructure to obtain 3D Networks-on-Chip (3D-NoCs), the new on-chip communication paradigm brings several advantages. However, thermal dissipation is one of the most critical challenges for 3D-ICs, where the heat cannot easily transfer through several layers of silicon. Consequently, the high-temperature area also confronts the reliability threat as the Mean Time to Failure (MTTF) decreases exponentially with the operating temperature as in Black’s model. Apparently, 3D-NoCs and 3D ICs must tackle this fundamental problem in order to be widely used. However, the thermal analyses usually require complicated simulation and might cost an enormous execution time. As a closed-loop design flow, designers may take several times to optimize their designs which significantly increase the thermal analyzing time. Furthermore, reliability prediction also requires both completed design and thermal prediction, and designer can use the result as a feedback for their optimization. As we can observe two big gaps in the design flow, it is difficult to obtain both of them which put 3D-NoCs under thermal throttling and reliability threats. Therefore, in this work, we investigate the thermal distribution and reliability prediction of 3D-NoCs. We first present a new method to help simulate the temperature (both steady and transient) using traffic values from realistic and synthetic benchmarks and the power consumption from standard VLSI design flow. Then, based on the proposed method, we further predict the relative reliability between different parts of the network. Experimental results show that the method has an extremely fast execution time in comparison to the acceleration lifetime test. Furthermore, we compare the thermal behavior and reliability between Monolithic design and TSV-based design. We also explore the ability to implement the thermal via a mechanism to help reduce the operating temperature.
{"title":"Thermal distribution and reliability prediction for 3D Networks-on-Chip","authors":"K. Dang, Akram Ben Ahmed, Abderazek Ben Abdallah, Xuan-Tu Tran","doi":"10.25073/2588-1086/VNUCSCE.245","DOIUrl":"https://doi.org/10.25073/2588-1086/VNUCSCE.245","url":null,"abstract":"As one of the most promising technologies to reduce footprint, power consumption and wire latency, Three Dimensional Integrated Circuits (3D-ICs) is considered as the near future for VLSI system. Combining with the Network-on-Chip infrastructure to obtain 3D Networks-on-Chip (3D-NoCs), the new on-chip communication paradigm brings several advantages. However, thermal dissipation is one of the most critical challenges for 3D-ICs, where the heat cannot easily transfer through several layers of silicon. Consequently, the high-temperature area also confronts the reliability threat as the Mean Time to Failure (MTTF) decreases exponentially with the operating temperature as in Black’s model. Apparently, 3D-NoCs and 3D ICs must tackle this fundamental problem in order to be widely used. However, the thermal analyses usually require complicated simulation and might cost an enormous execution time. As a closed-loop design flow, designers may take several times to optimize their designs which significantly increase the thermal analyzing time. Furthermore, reliability prediction also requires both completed design and thermal prediction, and designer can use the result as a feedback for their optimization. As we can observe two big gaps in the design flow, it is difficult to obtain both of them which put 3D-NoCs under thermal throttling and reliability threats. Therefore, in this work, we investigate the thermal distribution and reliability prediction of 3D-NoCs. We first present a new method to help simulate the temperature (both steady and transient) using traffic values from realistic and synthetic benchmarks and the power consumption from standard VLSI design flow. Then, based on the proposed method, we further predict the relative reliability between different parts of the network. Experimental results show that the method has an extremely fast execution time in comparison to the acceleration lifetime test. Furthermore, we compare the thermal behavior and reliability between Monolithic design and TSV-based design. We also explore the ability to implement the thermal via a mechanism to help reduce the operating temperature.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132704317","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 : 2020-06-03DOI: 10.25073/2588-1086/VNUCSCE.240
Duc-Tai Truong, Q. Nguyen, T. Dinh
Currently, there are a lot of secure communication schemes have been proposed to hide secret contents. In this work, one of the methods deploying encryption to cipher data is represented. The primary object of this project is applying Advanced Encryption Standard (AES) in communications based Orthogonal Frequency Division Multiplexing (OFDM). This article discusses the security of the method encrypting directly QAM symbols instead of input bit-stream. This leads to improving the security of transmitting data by utilization of authentication key between the mobile and base station. The archived results demonstrate that the performance of the AES-OFDM system is completely acceptable to compare with the criteria for 4G. Keywords: Orthogonal Frequency Division Multiplexing (OFDM), Advanced Encryption Standard (AES), Quadrature Amplitude Modulation (QAM), Authentication Key, Cellular Network, Encryption, Physical Layer, 4G, LTE. References [1] M.A. Jessen, “Wireless communication security: Physical-Layer techniques exploiting radio and propagation characteristics”, Wireless Information Technology and Systems (ICWITS), IEEE International Conference, 2012.[2] M. Kim, M. Lee, S. Kim, D. Won, “Weakness and Improvements of a One-time Password Authentication Scheme”, International Journal of Future Generation Communication and Networking, 2009. [3] Alabaichi, Ashwaq, Salih, Adnan, “Enhance security of advance encryption standard algorithm based on key-dependent S-box”, 2015, pp. 44-53. [4] S. Xiao, W. Gong, D. Towsley, “Secure Wireless Communication with Dynamic secrets”, IEEE INFOCOM, 2010.[5] N.U. Rehman, L. Zhang, M.Z. Hammad, “ICI cancellation in OFDM system by frequency offset reduction”, Journal of Information Engineering and Applications, 2014. [6] Nikita Agrawal, Neelesh Gupta, “Security of OFDM through Steganography”, International Journal of Computer Applications 121(20) (2015) 41-43. [7] A. Al-Dweik, M. Mirahmadi, A. Sharmi, Z. Ding, R. Hamila, “Joint Secured and Robust technique for OFDM systems”, Western University, Canada, IEEE ICC 2013. [8] G.R. Tsouri, D. Wulich, “Securing OFDM over Wireless Time-varying channel using subcarrier overloading with Joint signal constellations”, Hindawi Publishing Corporation, EURASIP Journal on Wireless Communication and Networking, 2009. [9] D. Rajaveerappa, A. Almarimi, A., “RSA/Shift secured IFFT/FFT based OFDM wireless system,” Fifth International Conference on Information Assurance and Security, 2009. [10] M. Hilmey, S. Elhalafwy, M. Zein Eldin, “Efficient transmission of chaotic and AES encrypted images with OFDM over an AWGN channel”, 2009 International Conference on Computer Engineering & Systems, Cairo, 2009, pp. 353-358. [11] B.V. Naik, N.L.K. Sai, C.M. Kumar, “Efficient transmission of encrypted images with OFDM system”, 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Chennai, 2017, pp. 2383-2388. [12] S.M.S. Eldin, “Optimized OFDM
{"title":"Performance of Orthogonal Frequency Division Multiplexing Based Advanced Encryption Standard","authors":"Duc-Tai Truong, Q. Nguyen, T. Dinh","doi":"10.25073/2588-1086/VNUCSCE.240","DOIUrl":"https://doi.org/10.25073/2588-1086/VNUCSCE.240","url":null,"abstract":"Currently, there are a lot of secure communication schemes have been proposed to hide secret contents. In this work, one of the methods deploying encryption to cipher data is represented. The primary object of this project is applying Advanced Encryption Standard (AES) in communications based Orthogonal Frequency Division Multiplexing (OFDM). This article discusses the security of the method encrypting directly QAM symbols instead of input bit-stream. This leads to improving the security of transmitting data by utilization of authentication key between the mobile and base station. The archived results demonstrate that the performance of the AES-OFDM system is completely acceptable to compare with the criteria for 4G. \u0000Keywords: \u0000Orthogonal Frequency Division Multiplexing (OFDM), Advanced Encryption Standard (AES), Quadrature Amplitude Modulation (QAM), Authentication Key, Cellular Network, Encryption, Physical Layer, 4G, LTE. \u0000References \u0000[1] M.A. Jessen, “Wireless communication security: Physical-Layer techniques exploiting radio and propagation characteristics”, Wireless Information Technology and Systems (ICWITS), IEEE International Conference, 2012.[2] M. Kim, M. Lee, S. Kim, D. Won, “Weakness and Improvements of a One-time Password Authentication Scheme”, International Journal of Future Generation Communication and Networking, 2009. \u0000[3] Alabaichi, Ashwaq, Salih, Adnan, “Enhance security of advance encryption standard algorithm based on key-dependent S-box”, 2015, pp. 44-53. \u0000[4] S. Xiao, W. Gong, D. Towsley, “Secure Wireless Communication with Dynamic secrets”, IEEE INFOCOM, 2010.[5] N.U. Rehman, L. Zhang, M.Z. Hammad, “ICI cancellation in OFDM system by frequency offset reduction”, Journal of Information Engineering and Applications, 2014. \u0000[6] Nikita Agrawal, Neelesh Gupta, “Security of OFDM through Steganography”, International Journal of Computer Applications 121(20) (2015) 41-43. \u0000[7] A. Al-Dweik, M. Mirahmadi, A. Sharmi, Z. Ding, R. Hamila, “Joint Secured and Robust technique for OFDM systems”, Western University, Canada, IEEE ICC 2013. \u0000[8] G.R. Tsouri, D. Wulich, “Securing OFDM over Wireless Time-varying channel using subcarrier overloading with Joint signal constellations”, Hindawi Publishing Corporation, EURASIP Journal on Wireless Communication and Networking, 2009. \u0000[9] D. Rajaveerappa, A. Almarimi, A., “RSA/Shift secured IFFT/FFT based OFDM wireless system,” Fifth International Conference on Information Assurance and Security, 2009. \u0000[10] M. Hilmey, S. Elhalafwy, M. Zein Eldin, “Efficient transmission of chaotic and AES encrypted images with OFDM over an AWGN channel”, 2009 International Conference on Computer Engineering & Systems, Cairo, 2009, pp. 353-358. \u0000[11] B.V. Naik, N.L.K. Sai, C.M. Kumar, “Efficient transmission of encrypted images with OFDM system”, 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Chennai, 2017, pp. 2383-2388. \u0000[12] S.M.S. Eldin, “Optimized OFDM","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126759326","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 : 2020-05-30DOI: 10.25073/2588-1086/VNUCSCE.238
P. Huyen, Ho Thuan
The paper aims to improve the multi-label classification performance using the feature reduction technique. According to the determination of the dependency among features based on fuzzy rough relation, features with the highest dependency score will be retained in the reduction set. The set is subsequently applied to enhance the performance of the multi-label classifier. We investigate the effectiveness of the proposed model againts the baseline via time complexity. Keywords: Fuzzy rough relation, label-specific feature, feature reduction set References [1] Richard Jensen, Chris Cornelis, Fuzzy-Rough Nearest Neighbor Classification and Prediction. Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing, 2011, 310-319. [2] Y.H. Qian, Q. Wang, H.H. Cheng, J.Y. Liang, C.Y. Dang, Fuzzy-Rough feature selection accelerator, Fuzzy Sets Syst. 258 (2014) 61-78. [3] Quang-Thuy Ha, Thi-Ngan Pham, Van-Quang Nguyen, Minh-Chau Nguyen, Thanh-Huyen Pham, Tri-Thanh Nguyen, A New Text Semi-supervised Multi-label Learning Model Based on Using the Label-Feature Relations, International Conference on Computational Collective Intelligence, LNAI 11055, Springer, 2018, pp. 403-413. [4] Daniel Kostrzewa, Robert Brzeski, The data Dimensionality Reduction and Feature Weighting in the Classification Process Using Forest Optimization Algorithm, ACIIDS, 2019, pp. 97-108. [5] Nele Verbiest, Fuzzy Rough and Evolutionary Approaches to Instance Selection, PhD Thesis, Ghent University, 2014. [6] Y. Yu, W. Pedrycz, D.Q. Miao, Multi-label classification by exploiting label correlations, Expert syst, Appl. 41 (2014) 2989-3004. [7] M.L. Zhang, LIFT: Multi-label learning with label-specific features, IEEE Trans, Pattern Anal, Mach, Intell 37 (2015) 107-120. [8] Suping Xu, Xibei Yang, Hualong Yu, Dong-Jun Yu, Jingyu Yang, Eric CC Tsang, Multi-label learning with label-specific feature reduction, Knowledge-Based Systems 104 (2016) 52-61. https://doi.org/10.1080/24751839.2017.1364925. [9] Thi-Ngan Pham, Van-Quang Nguyen, Van-Hien Tran, Tri-Thanh Nguyen, Quang-Thuy Ha, A Semi-supervised multi-label classification framework with feature reduction and enrichment, Journal of Information and Telecommunication 1(4) (2017) 305-318. [10] M. Ghaemi, M.R. Feizi-Derakhshi, Feature selection using forest optimization algorithm, Pattern Recognition 60 (2016) 121-129. [11] M.L. Zhang, Z.H. Zhou, ML-KNN: A lazy learning approach to multi-label learning, Pattern Recognition 40 (2007) 2038-2048. [12] M.Z. Ahmad, M.K. Hasan, A New Approach for Computing Zadeh's Extension Principle, MATEMATIKA. 26(1) (2010) 71-81. [13] Richard Jensen, Neil Mac Parthaláin and Qiang Shen. Fuzzy-rough data mining (using the Weka data mining suite), A Tutorial, IEEE WCCI 2014, Beijing, China, July 6, 2014. [14] D. Dubois, H. Prade, Rough fuzzy sets and fuzzy rough sets, Int. J. Gen. Syst. 17 (1990) 191-209.
本文旨在利用特征约简技术提高多标签分类性能。根据模糊粗糙关系确定特征之间的依赖关系，将依赖分数最高的特征保留在约简集中。随后应用该集合来增强多标签分类器的性能。我们通过时间复杂度来考察所提出的模型对基线的有效性。关键词:模糊粗糙关系，标签特定特征，特征约简集参考文献b[1] Richard Jensen, Chris Cornelis，模糊粗糙近邻分类与预测。第六届粗糙集国际会议论文集，2011,310-319。[10]钱永辉，王琪，程红辉，梁建银，党长云，模糊粗糙特征选择加速器，模糊集系统，2014,31(1):61-78。[1]胡广辉，范志刚，范文光，阮明周，一种基于标签-特征关系的文本半监督多标签学习模型，中文信息学报，2018,pp. 407 -413。[10]张晓明，张晓明，基于森林优化算法的数据降维和特征加权，林业科学学报，2019,pp. 97-108。[10] Nele Verbiest，模糊粗糙进化方法在实例选择中的应用，博士论文，根特大学，2014。[10]于玉玉，缪德清，基于标签相关性的多标签分类，中文信息学报，41(2014):2989-3004。[10]张明亮，基于多标签学习的多标签学习，中文信息学报，vol . 32(2015): 107-120。[10]徐素平，杨西贝，于华龙，于东军，杨景宇，曾志成，基于多标签特征约简的多标签学习，中文信息学报，2016,32(1):52-61。https://doi.org/10.1080/24751839.2017.1364925。[10]范世彦，陈文贤，陈文光，一种基于特征约简的半监督多标签分类框架，信息通信学报(4)(2017)305-318。[10] M. Ghaemi, M. Feizi-Derakhshi，基于森林优化算法的特征选择，模式识别，60(2016)121-129。[10]张明亮，周志辉，周志明，张志明:一种多标签学习的懒惰学习方法，模式识别，40(2007):2038-2048。[10] M.Z. Ahmad, M.K. Hasan，一种计算Zadeh扩展原理的新方法，数学学报。26(1)(2010) 71-81。[13] Richard Jensen, Neil Mac Parthaláin和沈强。模糊粗糙数据挖掘(使用Weka数据挖掘套件)，IEEE WCCI 2014 A教程，2014年7月6日，中国北京。[10] D.杜波依斯，H.普拉德，模糊粗糙集与模糊粗糙集，英。司法总编17(1990)191-209。
{"title":"A new Feature Reduction Algorithm Based on Fuzzy Rough Relation for the Multi-label Classification","authors":"P. Huyen, Ho Thuan","doi":"10.25073/2588-1086/VNUCSCE.238","DOIUrl":"https://doi.org/10.25073/2588-1086/VNUCSCE.238","url":null,"abstract":"The paper aims to improve the multi-label classification performance using the feature reduction technique. According to the determination of the dependency among features based on fuzzy rough relation, features with the highest dependency score will be retained in the reduction set. The set is subsequently applied to enhance the performance of the multi-label classifier. We investigate the effectiveness of the proposed model againts the baseline via time complexity. \u0000Keywords: \u0000Fuzzy rough relation, label-specific feature, feature reduction set \u0000References \u0000[1] Richard Jensen, Chris Cornelis, Fuzzy-Rough Nearest Neighbor Classification and Prediction. Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing, 2011, 310-319. \u0000[2] Y.H. Qian, Q. Wang, H.H. Cheng, J.Y. Liang, C.Y. Dang, Fuzzy-Rough feature selection accelerator, Fuzzy Sets Syst. 258 (2014) 61-78. \u0000[3] Quang-Thuy Ha, Thi-Ngan Pham, Van-Quang Nguyen, Minh-Chau Nguyen, Thanh-Huyen Pham, Tri-Thanh Nguyen, A New Text Semi-supervised Multi-label Learning Model Based on Using the Label-Feature Relations, International Conference on Computational Collective Intelligence, LNAI 11055, Springer, 2018, pp. 403-413. \u0000[4] Daniel Kostrzewa, Robert Brzeski, The data Dimensionality Reduction and Feature Weighting in the Classification Process Using Forest Optimization Algorithm, ACIIDS, 2019, pp. 97-108. \u0000[5] Nele Verbiest, Fuzzy Rough and Evolutionary Approaches to Instance Selection, PhD Thesis, Ghent University, 2014. \u0000[6] Y. Yu, W. Pedrycz, D.Q. Miao, Multi-label classification by exploiting label correlations, Expert syst, Appl. 41 (2014) 2989-3004. \u0000[7] M.L. Zhang, LIFT: Multi-label learning with label-specific features, IEEE Trans, Pattern Anal, Mach, Intell 37 (2015) 107-120. \u0000[8] Suping Xu, Xibei Yang, Hualong Yu, Dong-Jun Yu, Jingyu Yang, Eric CC Tsang, Multi-label learning with label-specific feature reduction, Knowledge-Based Systems 104 (2016) 52-61. https://doi.org/10.1080/24751839.2017.1364925. \u0000[9] Thi-Ngan Pham, Van-Quang Nguyen, Van-Hien Tran, Tri-Thanh Nguyen, Quang-Thuy Ha, A Semi-supervised multi-label classification framework with feature reduction and enrichment, Journal of Information and Telecommunication 1(4) (2017) 305-318. \u0000[10] M. Ghaemi, M.R. Feizi-Derakhshi, Feature selection using forest optimization algorithm, Pattern Recognition 60 (2016) 121-129. \u0000[11] M.L. Zhang, Z.H. Zhou, ML-KNN: A lazy learning approach to multi-label learning, Pattern Recognition 40 (2007) 2038-2048. \u0000[12] M.Z. Ahmad, M.K. Hasan, A New Approach for Computing Zadeh's Extension Principle, MATEMATIKA. 26(1) (2010) 71-81. \u0000[13] Richard Jensen, Neil Mac Parthaláin and Qiang Shen. Fuzzy-rough data mining (using the Weka data mining suite), A Tutorial, IEEE WCCI 2014, Beijing, China, July 6, 2014. \u0000[14] D. Dubois, H. Prade, Rough fuzzy sets and fuzzy rough sets, Int. J. Gen. Syst. 17 (1990) 191-209.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125754148","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 : 2020-05-30DOI: 10.25073/2588-1086/VNUCSCE.237
P. Trang, Bui Manh Thang, Dang Thanh Hai
Chemical compounds (drugs) and diseases are among top searched keywords on the PubMed database of biomedical literature by biomedical researchers all over the world (according to a study in 2009). Working with PubMed is essential for researchers to get insights into drugs’ side effects (chemical-induced disease relations (CDR), which is essential for drug safety and toxicity. It is, however, a catastrophic burden for them as PubMed is a huge database of unstructured texts, growing steadily very fast (~28 millions scientific articles currently, approximately two deposited per minute). As a result, biomedical text mining has been empirically demonstrated its great implications in biomedical research communities. Biomedical text has its own distinct challenging properties, attracting much attetion from natural language processing communities. A large-scale study recently in 2018 showed that incorporating information into indenpendent multiple-input layers outperforms concatenating them into a single input layer (for biLSTM), producing better performance when compared to state-of-the-art CDR classifying models. This paper demonstrates that for a CNN it is vice-versa, in which concatenation is better for CDR classification. To this end, we develop a CNN based model with multiple input concatenated for CDR classification. Experimental results on the benchmark dataset demonstrate its outperformance over other recent state-of-the-art CDR classification models. Keywords: Chemical disease relation prediction, Convolutional neural network, Biomedical text mining References [1] Paul SM, S. Mytelka, C.T. Dunwiddie, C.C. Persinger, B.H. Munos, S.R. Lindborg, A.L. Schacht, How to improve R&D productivity: The pharmaceutical industry's grand challenge, Nat Rev Drug Discov. 9(3) (2010) 203-14. https://doi.org/10.1038/nrd3078. [2] J.A. DiMasi, New drug development in the United States from 1963 to 1999, Clinical pharmacology and therapeutics 69 (2001) 286-296. https://doi.org/10.1067/mcp.2001.115132. [3] C.P. Adams, V. Van Brantner, Estimating the cost of new drug development: Is it really $802 million? Health Affairs 25 (2006) 420-428. https://doi.org/10.1377/hlthaff.25.2.420. [4] R.I. Doğan, G.C. Murray, A. Névéol et al., "Understanding PubMed user search behavior through log analysis", Oxford Database, 2009. [5] G.K. Savova, J.J. Masanz, P.V. Ogren et al., "Mayo clinical text analysis and knowledge extraction system (cTAKES): Architecture, component evaluation and applications", Journal of the American Medical Informatics Association, 2010. [6] T.C. Wiegers, A.P. Davis, C.J. Mattingly, Collaborative biocuration-text mining development task for document prioritization for curation, Database 22 (2012) pp. bas037. [7] N. Kang, B. Singh, C. Bui et al., "Knowledge-based extraction of adverse drug events from biomedical text", BMC Bioinformatics 15, 2014. [8] A. Névéol, R.L. Doğan, Z. Lu, "Semi-automatic semantic annotation of PubMed queries: A study on qu
{"title":"Single Concatenated Input is Better than Indenpendent Multiple-input for CNNs to Predict Chemical-induced Disease Relation from Literature","authors":"P. Trang, Bui Manh Thang, Dang Thanh Hai","doi":"10.25073/2588-1086/VNUCSCE.237","DOIUrl":"https://doi.org/10.25073/2588-1086/VNUCSCE.237","url":null,"abstract":"Chemical compounds (drugs) and diseases are among top searched keywords on the PubMed database of biomedical literature by biomedical researchers all over the world (according to a study in 2009). Working with PubMed is essential for researchers to get insights into drugs’ side effects (chemical-induced disease relations (CDR), which is essential for drug safety and toxicity. It is, however, a catastrophic burden for them as PubMed is a huge database of unstructured texts, growing steadily very fast (~28 millions scientific articles currently, approximately two deposited per minute). As a result, biomedical text mining has been empirically demonstrated its great implications in biomedical research communities. Biomedical text has its own distinct challenging properties, attracting much attetion from natural language processing communities. A large-scale study recently in 2018 showed that incorporating information into indenpendent multiple-input layers outperforms concatenating them into a single input layer (for biLSTM), producing better performance when compared to state-of-the-art CDR classifying models. This paper demonstrates that for a CNN it is vice-versa, in which concatenation is better for CDR classification. To this end, we develop a CNN based model with multiple input concatenated for CDR classification. Experimental results on the benchmark dataset demonstrate its outperformance over other recent state-of-the-art CDR classification models. \u0000Keywords: \u0000Chemical disease relation prediction, Convolutional neural network, Biomedical text mining \u0000References \u0000[1] Paul SM, S. Mytelka, C.T. Dunwiddie, C.C. Persinger, B.H. Munos, S.R. Lindborg, A.L. Schacht, How to improve R&D productivity: The pharmaceutical industry's grand challenge, Nat Rev Drug Discov. 9(3) (2010) 203-14. https://doi.org/10.1038/nrd3078. \u0000[2] J.A. DiMasi, New drug development in the United States from 1963 to 1999, Clinical pharmacology and therapeutics 69 (2001) 286-296. https://doi.org/10.1067/mcp.2001.115132. \u0000[3] C.P. Adams, V. Van Brantner, Estimating the cost of new drug development: Is it really $802 million? Health Affairs 25 (2006) 420-428. https://doi.org/10.1377/hlthaff.25.2.420. \u0000[4] R.I. Doğan, G.C. Murray, A. Névéol et al., \"Understanding PubMed user search behavior through log analysis\", Oxford Database, 2009. \u0000[5] G.K. Savova, J.J. Masanz, P.V. Ogren et al., \"Mayo clinical text analysis and knowledge extraction system (cTAKES): Architecture, component evaluation and applications\", Journal of the American Medical Informatics Association, 2010. \u0000[6] T.C. Wiegers, A.P. Davis, C.J. Mattingly, Collaborative biocuration-text mining development task for document prioritization for curation, Database 22 (2012) pp. bas037. \u0000[7] N. Kang, B. Singh, C. Bui et al., \"Knowledge-based extraction of adverse drug events from biomedical text\", BMC Bioinformatics 15, 2014. \u0000[8] A. Névéol, R.L. Doğan, Z. Lu, \"Semi-automatic semantic annotation of PubMed queries: A study on qu","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124058330","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 : 2020-05-30DOI: 10.25073/2588-1086/VNUCSCE.221
L. S. Cong, N. Q. Tuan, K. Sandrasegaran
Fractional Frequency Reuse (FFR) is a promising to improve the spectrum e ciency in the LongTerm Evolution (LTE) cellular network. In the literature, various research works have been conducted to evaluate the performance of FFR. However, the presented analytical approach only dealt with the special cases in which the users are divided into 2 groups and only two power levels are utilised. In this paper, we consider a general case of FFR in which the users are classified intogroups and each group is assigned a serving power level. The mathematical model of the general FFR is presented and analysed through a stochastic geometry approach. The derived analytical results in terms of average coverage probability can covered all the related well-known results in the literature. Keywords: Fractional Frequency Reuse, LongTerm Evolution, coverage probability, stochastic geometry References [1] Cisco, Cisco visual networking index: Global mobile data traffic forecast update, 2015 - 2020, 2016. [2] A.S. Hamza, S.S. Khalifa, H.S. Hamza, K. Elsayed, A Survey on Inter-Cell Interference Coordination Techniques in OFDMA-Based Cellular Networks, IEEE Commun, Surveys & Tutorials 15(4) (2013) 1642-1670 [3] 3GPP TR 36.819 V11.1.0, Coordinated multi-point operation for LTE physical layer aspects, 2011. [4] 3GPP Release 10 V0.2.1, LTE-Advanced (3GPP Release 10 and beyond), 2014. [5] 3GPP TS 36.211 V14.1.0, E-UTRA Physical Channels and Modulation, 2016. [6] R. Ghaffar, R. Knopp, Fractional frequency reuse and interference suppression for ofdma networks, in: 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, 2010, pp. 273-277. [7] Y. Kwon, O. Lee, J. Lee, M. Chung, Power Control for Soft Fractional Frequency Reuse in OFDMA System, Vol. 6018 of Lecture Notes in Comput.Science, Springer Berlin Heidelberg, 2010, book section 7 (2010) 63-71. [8] Enhancing LTE Cell-Edge Performance via PDCCH ICIC, in: FUJITSU NETWORK COMMUNICATIONS INC., 2011 [9] A.S. Hamza, S.S. Khalifa, H.S. Hamza, K. Elsayed, A Survey on Inter-Cell Interference Coordination Techniques in OFDMA-Based Cellular Networks, IEEE Commun, Surveys & Tutorials 15(4) (2013) 1642-1670. https://doi.org/10.1109/SURV.2013.013013.00028. [10] A. Busson1, I. Lahsen-Cherif2, Impact of resource blocks allocation strategies on downlink interference and sir distributions in lte networks: A stochastic geometry approach, Wireless Communications and Mobile Computing. [11] H. ElSawy, E. Hossain, M. Haenggi, Stochastic Geometry for Modeling, Analysis and Design of Multi-Tier and Cognitive Cellular Wireless Networks: A Survey, IEEE Commun, Surveys Tutorials 15(3) (2013) 996-1019. https://doi.org/10.1109/SURV.2013.052213.00000. [12] W. Bao, B. Liang, Stochastic Analysis of Uplink Interference in Two-Tier Femtocell Networks: Open Versus Closed Access, IEEE Trans, Wireless Commun. 14(11) (2015) 6200-6215. https://doi.org/10.1109/TWC.2015.2450216 . [13] H. Tabassum, Z. Dawy, E
{"title":"A General Model of Fractional Frequency Reuse: Modelling and Performance Analysis","authors":"L. S. Cong, N. Q. Tuan, K. Sandrasegaran","doi":"10.25073/2588-1086/VNUCSCE.221","DOIUrl":"https://doi.org/10.25073/2588-1086/VNUCSCE.221","url":null,"abstract":"Fractional Frequency Reuse (FFR) is a promising to improve the spectrum e ciency in the LongTerm Evolution (LTE) cellular network. In the literature, various research works have been conducted to evaluate the performance of FFR. However, the presented analytical approach only dealt with the special cases in which the users are divided into 2 groups and only two power levels are utilised. In this paper, we consider a general case of FFR in which the users are classified intogroups and each group is assigned a serving power level. The mathematical model of the general FFR is presented and analysed through a stochastic geometry approach. The derived analytical results in terms of average coverage probability can covered all the related well-known results in the literature. \u0000Keywords: \u0000Fractional Frequency Reuse, LongTerm Evolution, coverage probability, stochastic geometry \u0000References \u0000[1] Cisco, Cisco visual networking index: Global mobile data traffic forecast update, 2015 - 2020, 2016. \u0000[2] A.S. Hamza, S.S. Khalifa, H.S. Hamza, K. Elsayed, A Survey on Inter-Cell Interference Coordination Techniques in OFDMA-Based Cellular Networks, IEEE Commun, Surveys & Tutorials 15(4) (2013) 1642-1670 \u0000[3] 3GPP TR 36.819 V11.1.0, Coordinated multi-point operation for LTE physical layer aspects, 2011. \u0000[4] 3GPP Release 10 V0.2.1, LTE-Advanced (3GPP Release 10 and beyond), 2014. \u0000[5] 3GPP TS 36.211 V14.1.0, E-UTRA Physical Channels and Modulation, 2016. \u0000[6] R. Ghaffar, R. Knopp, Fractional frequency reuse and interference suppression for ofdma networks, in: 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, 2010, pp. 273-277. \u0000[7] Y. Kwon, O. Lee, J. Lee, M. Chung, Power Control for Soft Fractional Frequency Reuse in OFDMA System, Vol. 6018 of Lecture Notes in Comput.Science, Springer Berlin Heidelberg, 2010, book section 7 (2010) 63-71. \u0000[8] Enhancing LTE Cell-Edge Performance via PDCCH ICIC, in: FUJITSU NETWORK COMMUNICATIONS INC., 2011 \u0000[9] A.S. Hamza, S.S. Khalifa, H.S. Hamza, K. Elsayed, A Survey on Inter-Cell Interference Coordination Techniques in OFDMA-Based Cellular Networks, IEEE Commun, Surveys & Tutorials 15(4) (2013) 1642-1670. https://doi.org/10.1109/SURV.2013.013013.00028. \u0000[10] A. Busson1, I. Lahsen-Cherif2, Impact of resource blocks allocation strategies on downlink interference and sir distributions in lte networks: A stochastic geometry approach, Wireless Communications and Mobile Computing. \u0000[11] H. ElSawy, E. Hossain, M. Haenggi, Stochastic Geometry for Modeling, Analysis and Design of Multi-Tier and Cognitive Cellular Wireless Networks: A Survey, IEEE Commun, Surveys Tutorials 15(3) (2013) 996-1019. https://doi.org/10.1109/SURV.2013.052213.00000. \u0000[12] W. Bao, B. Liang, Stochastic Analysis of Uplink Interference in Two-Tier Femtocell Networks: Open Versus Closed Access, IEEE Trans, Wireless Commun. 14(11) (2015) 6200-6215. https://doi.org/10.1109/TWC.2015.2450216 . \u0000[13] H. Tabassum, Z. Dawy, E","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123858120","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 : 2020-05-30DOI: 10.25073/2588-1086/VNUCSCE.241
Hoang Hong Son, P. C. Phuong, T. Walsum, Luu Manh Ha
Liver segmentation is relevant for several clinical applications. Automatic liver segmentation using convolutional neural networks (CNNs) has been recently investigated. In this paper, we propose a new approach of combining a largest connected component (LCC) algorithm, as a post-processing step, with CNN approaches to improve liver segmentation accuracy. Specifically, in this study, the algorithm is combined with three well-known CNNs for liver segmentation: FCN-CRF, DRIU and V-net. We perform the experiment on a variety of liver CT images, ranging from non-contrast enhanced CT images to low-dose contrast enhanced CT images. The methods are evaluated using Dice score, Haudorff distance, mean surface distance, and false positive rate between the liver segmentation and the ground truth. The quantitative results demonstrate that the LCC algorithm statistically significantly improves results of the liver segmentation on non-contrast enhanced and low-dose images for all three CNNs. The combination with V-net shows the best performance in Dice score (higher than 90%), while the DRIU network achieves the smallest computation time (2 to 6 seconds) for a single segmentation on average. The source code of this study is publicly available at https://github.com/kennyha85/Liver-segmentation. Keywords: Liver segmentations, CNNs, Connected Components, Post processing Reference [1] K.A. McGlynn, J.L. Petrick, W.T. London, Global epidemiology of hepatocellular carcinoma: An emphasis on demographic and regional variability. Clinics in liver disease 19(2) (2015) 223-238. [2] M. Mohammadian, N. Mahdavifar, A. Mohammadian-Hafshejani, H. Salehiniya, Liver cancer in the world: epidemiology, incidence, mortality and risk factors, World Cancer Res J. 5(2) (2018) e1082. [3] T.T. Hong, N. Phuong Hoa, S.M. Walker, P.S. Hill, C. Rao, Completeness and reliability of mortality data in Viet Nam: Implications for the national routine health management information system, PloS one 13(1) 2018) e0190755. https://doi.org/10.1371/journal.pone.0190755. [4] T. Pham, L. Bui, G. Kim, D. Hoang, T. Tran, M. Hoang, Cancers in Vietnam-Burden and Control Efforts: A Narrative Scoping Review. Cancer Control 26(1) (2019) 1073274819863802. [5] M. Borner, M. Castiglione, J. Triller, H.U. Baer, M. Soucek, L. Blumgart, K. Brunner, Arena: Considerable side effects of chemoembolization for colorectal carcinoma metastatic to the liver, Annals of oncology 3(2) (1992) 113-115. [6] K. Memon, R.J. Lewandowski, L. Kulik, A. Riaz, M.F. Mulcahy, R. Salem, Radioembolization for primary and metastatic liver cancer, In Seminars in radiation oncology, WB Saunders. 21(4) (2011) 294-302. [7] I. Gory, M. Fink, S. Bell, P. Gow, A. Nicoll, V. Knight, W. Kemp, Radiofrequency ablation versus resection for the treatment of early stage hepatocellular carcinoma: A multicenter Australian study, Scandinavian journal of gastroenterology 50(5) (2015) 567-576. [8] H.M. Luu, C. Klink, W. Niessen, A. Moelker, T. Van Wa
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Pub Date : 2020-05-30DOI: 10.25073/2588-1086/VNUCSCE.231
N. Pham, V. Nguyen
In this paper, we propose a new method for domain adaptation in Statistical Machine Translation for low-resource domains in English-Vietnamese language. Specifically, our method only uses monolingual data to adapt the translation phrase-table, our system brings improvements over the SMT baseline system. We propose two steps to improve the quality of SMT system: (i) classify phrases on the target side of the translation phrase-table use the probability classifier model, and (ii) adapt to the phrase-table translation by recomputing the direct translation probability of phrases. Our experiments are conducted with translation direction from English to Vietnamese on two very different domains that are legal domain (out-of-domain) and general domain (in-of-domain). The English-Vietnamese parallel corpus is provided by the IWSLT 2015 organizers and the experimental results showed that our method significantly outperformed the baseline system. Our system improved on the quality of machine translation in the legal domain up to 0.9 BLEU scores over the baseline system,… Keywords: Machine Translation, Statistical Machine Translation, Domain Adaptation References [1] Philipp Koehn, Franz Josef Och, Daniel Marcu, Statistical phrase-based translation, In Proceedings of HLT-NAACL, Edmonton, Canada, 2003, 127-133. [2] Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes and Jeffrey Dean, Google’s neural machine translation system: Bridging the gap between human and machine translation, CoRR, abs/1609.08144, 2016. [3] Luisa Bentivogli, Arianna Bisazza, Mauro Cettolo and Marcello Federico, Neural versus phrase-based machine translation quality: A case study, 2016. [4] Barry Haddow, Philipp Koehn, Analysing the effect of out-of-domain data on smt systems, In Proceedings of the Seventh Workshop on Statistical Machine Translation, 2012, 422-432. [5] Boxing Chen, Roland Kuhn and George Foster, Vector space model for adaptation in statistical machine translation, Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, 2013, pp. 1285-1293. [6] Daniel Dahlmeier, Hwee Tou Ng, Siew Mei Wu4, Building a large annotated corpus of learner english: The nus corpus of learner english, In Proceedings of the NAACL Workshop on Innovative Use of NLP for Building Educational Appli-cations, 2013. [7] Eva Hasler, Phil Blunsom, Philipp Koehn and Barry Haddow, Dynamic topic adaptation for phrase-based mt, In Proceedings of the 14th Conference of the European Chapter of The Association for Computational Linguistics, 2014, pp. 328-337. [8] George Foster, Roland Kuhn, Mixture-model
[18]刘建军，吴建军，李建军，基于n-gram语言模型的跨域n-gram语言建模方法，中文信息学报，2016,pp. 137-142。[19]张志强，张志强，《科学》，第8期。约翰·威利和儿子们，2011年。[20]刘志强，《越南语词汇问题》，《国际语言学报》第19卷第1期(1963)，39-52。https:// doi.org/1080/00437956.1963.11659787。[21]吴平，《越南语学习框架》，《东南亚语言教学》第10期(2001):1-24。[22]李洪芳，阮氏明辉，何东荣，一种基于多学科的越南语文本分词方法，2008。[23] philip Koehn, Hieu Hoang, Alexandra Birch, Chris callson - burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondřej Bojar, Alexandra Constantin, Evan Herbst, Moses:统计机器翻译的开源工具包，In ACL-2007:演示和招展会议，Prague, Czech Republic, 2007, pp.177-180。[24]王志强，李志强，统计机器翻译中的最小错误率训练，中文信息学报，2003,pp.160-167。[25]李建平，李建平，李建平，等。一种基于语言模型的语言建模方法，中文信息学报，2002。[26]朱伟静，朱伟静，朱伟静。一种机器翻译的自动评价方法，中文信息学报，2002。[27]李建军，李建军，李建军，等Rush, OpenNMT:神经机器翻译的开源工具包。ArXiv预印本。[28]李保利，杜金华，李保利，李保利，基于自动分类器的多域统计机器翻译模型，中文信息学报，2010。, 2010年。
{"title":"Adaptation in Statistical Machine Translation for Low-resource Domains in English-Vietnamese Language","authors":"N. Pham, V. Nguyen","doi":"10.25073/2588-1086/VNUCSCE.231","DOIUrl":"https://doi.org/10.25073/2588-1086/VNUCSCE.231","url":null,"abstract":"In this paper, we propose a new method for domain adaptation in Statistical Machine Translation for low-resource domains in English-Vietnamese language. Specifically, our method only uses monolingual data to adapt the translation phrase-table, our system brings improvements over the SMT baseline system. We propose two steps to improve the quality of SMT system: (i) classify phrases on the target side of the translation phrase-table use the probability classifier model, and (ii) adapt to the phrase-table translation by recomputing the direct translation probability of phrases. \u0000 \u0000Our experiments are conducted with translation direction from English to Vietnamese on two very different domains that are legal domain (out-of-domain) and general domain (in-of-domain). The English-Vietnamese parallel corpus is provided by the IWSLT 2015 organizers and the experimental results showed that our method significantly outperformed the baseline system. Our system improved on the quality of machine translation in the legal domain up to 0.9 BLEU scores over the baseline system,… \u0000Keywords: \u0000Machine Translation, Statistical Machine Translation, Domain Adaptation \u0000References \u0000[1] Philipp Koehn, Franz Josef Och, Daniel Marcu, Statistical phrase-based translation, In Proceedings of HLT-NAACL, Edmonton, Canada, 2003, 127-133. \u0000[2] Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes and Jeffrey Dean, Google’s neural machine translation system: Bridging the gap between human and machine translation, CoRR, abs/1609.08144, 2016. \u0000[3] Luisa Bentivogli, Arianna Bisazza, Mauro Cettolo and Marcello Federico, Neural versus phrase-based machine translation quality: A case study, 2016. \u0000[4] Barry Haddow, Philipp Koehn, Analysing the effect of out-of-domain data on smt systems, In Proceedings of the Seventh Workshop on Statistical Machine Translation, 2012, 422-432. \u0000[5] Boxing Chen, Roland Kuhn and George Foster, Vector space model for adaptation in statistical machine translation, Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, 2013, pp. 1285-1293. \u0000[6] Daniel Dahlmeier, Hwee Tou Ng, Siew Mei Wu4, Building a large annotated corpus of learner english: The nus corpus of learner english, In Proceedings of the NAACL Workshop on Innovative Use of NLP for Building Educational Appli-cations, 2013. \u0000[7] Eva Hasler, Phil Blunsom, Philipp Koehn and Barry Haddow, Dynamic topic adaptation for phrase-based mt, In Proceedings of the 14th Conference of the European Chapter of The Association for Computational Linguistics, 2014, pp. 328-337. \u0000[8] George Foster, Roland Kuhn, Mixture-model","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114734263","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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