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2020 7th NAFOSTED Conference on Information and Computer Science (NICS)最新文献

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Secured Scheme for RF Energy Harvesting Mobile Edge Computing Networks based on NOMA and Access Point Selection 基于NOMA和接入点选择的射频能量收集移动边缘计算网络安全方案
2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335833
Van-Truong Truong, Dac-Binh Ha
{"title":"Secured Scheme for RF Energy Harvesting Mobile Edge Computing Networks based on NOMA and Access Point Selection","authors":"Van-Truong Truong, Dac-Binh Ha","doi":"10.1109/NICS51282.2020.9335833","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335833","url":null,"abstract":"In this paper, we study an RF energy harvesting mobile edge computing network where an energy-constrained user harvests the RF energy from the power station and of-floading its tasks to two access points based on non-orthogonal multiple access (NOMA). In particular, the user can offload its confidential tasks to the trusted access point, and nonconfidential tasks can be offload to both trusted and untrusted access points by using the harvested energy. We propose two protocols based on NOMA, non-access point selection (NAPS), and access point selection (APS) schemes, namely NOMA-NAPS and NOMA-APS. The closed-form exact expressions of successful computation probability (SCP) are derived to evaluate these proposed protocols' system performance. Numerical results are shown that the NOMA-APS protocol outperforms the NOMA-NAPS one. The Monte-Carlo simulation results also verify the correctness of our analysis.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125402612","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}
引用次数: 4
Multi-Branch Network with Dynamically Matching Algorithm in Person Re-Identification 基于动态匹配算法的多分支网络人物再识别
2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335832
T. V. Dao, T. Dinh, T. Dinh
{"title":"Multi-Branch Network with Dynamically Matching Algorithm in Person Re-Identification","authors":"T. V. Dao, T. Dinh, T. Dinh","doi":"10.1109/NICS51282.2020.9335832","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335832","url":null,"abstract":"In person re-identification problem, learning distinctive features to differentiate one person from the others is one of the key factors to improve the result. In this paper, we propose the Multi-Branch Network with Dynamically Matching (MNDM) algorithm, which has a multi-branch deep network architecture consisting of three branches: one for learning global features, and two for local features. In the one branch learning local features, where the bounding box is split into horizontal stripes, the model applies a dynamically matching algorithm to efficiently matching these parts addressing the misalignment issue caused by imperfect detection bounding boxes. Several experiments are conducted on Market1501, CUHK03 and DukeMTMC. All the results that demonstrate the proposed model significantly outperforms the previous state-of-the-art.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123710572","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}
引用次数: 0
Private Identity-Based Encryption For Key Management 用于密钥管理的基于私有身份的加密
2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335831
Van-Quang-Huy Nguyen, Dinh-Hy Ngo
{"title":"Private Identity-Based Encryption For Key Management","authors":"Van-Quang-Huy Nguyen, Dinh-Hy Ngo","doi":"10.1109/NICS51282.2020.9335831","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335831","url":null,"abstract":"An Identity-Based Encryption (IBE) scheme uses public identities of entities for cryptographic purposes. Unlike that, we introduce a new scheme which is based on private identities, and we call it Private Identity-Based Encryption. A Private IBE scheme makes sure the adversaries cannot get the information that somebody uses for encryption in order to decrypt the data. Moreover, thanks to using identities as secret keys, an user-friendly system can be designed to support users in protecting data without storing any keys privately. This allows builds decentralized applications to manage keys that is often long and difficult to remember.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127874448","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}
引用次数: 0
Transfer Learning for Macroeconomic Forecasting 宏观经济预测的迁移学习
2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335848
Hien T. Nguyen, D. Nguyen
{"title":"Transfer Learning for Macroeconomic Forecasting","authors":"Hien T. Nguyen, D. Nguyen","doi":"10.1109/NICS51282.2020.9335848","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335848","url":null,"abstract":"In this paper we present a novel approach to macroeconomic forecasting based on transfer learning using normalizing flows conditioned on LSTM-based encoder-decoder as building blocks. The approach consists of two steps: (1) pretraining and (2) fine-tuning. At the pre-training step, we train a model based on macroeconomic data of many different countries. The obtained pre-trained model can capture hidden patterns in temporal changes of macroeconomic indicators. The pre-trained model is then fine-tuned on macroeconomic data of the target country. In the approach, LSTM-based encoder-decoder aims at learning vector representations of the input data. The obtained representations are then transformed by using conditional normalizing flows so that the distribution of the data encoded in the representations is transformed into a more complex distribution. We evaluate the proposed approach on seventeen macroeconomic variables of a public dataset. The experimental results show that transfer learning using normalizing flows conditioned on LSTM-based encoder-decoder as building blocks significantly improves the performance of macroeconomic forecasting with one-step ahead forecasts. To the best of our knowledge, this is the first time neural transfer learning has been successfully applied to forecast many macroeconomic variables simultaneously.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129263564","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}
引用次数: 1
Solve Systems of Ordinary Differential Equations Using Deep Neural Networks 用深度神经网络求解常微分方程组
2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335885
B. Pham, Thanh P. Nguyen, Trung T. Nguyen, Binh T. Nguyen
{"title":"Solve Systems of Ordinary Differential Equations Using Deep Neural Networks","authors":"B. Pham, Thanh P. Nguyen, Trung T. Nguyen, Binh T. Nguyen","doi":"10.1109/NICS51282.2020.9335885","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335885","url":null,"abstract":"The systems of ordinary differential equations have been ubiquitously investigated and had many applications for various areas in real life. This paper investigates a deep learning method to solve the systems of ordinary differential equations (ODEs). We formulate the original problem with the initial conditions as an optimization problem. By minimizing a loss function associated with the optimization problem, we can construct an appropriate neural network to estimate the exact solutions of the systems of equations. We do experiments by considering two types of ODEs, Lotka-Volterra and Biochemical Oscillator equations. The experimental results show that we can obtain accurate results in solving these two systems of ODEs, where the numerical errors (mean square errors) vary from 10−6 to 10−11 for different neural networks, compared to the traditional approaches.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115078575","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}
引用次数: 1
Integrated On-Silicon and On-glass Antennas for Mm-Wave Applications: (Invited) 用于毫米波应用的集成硅和玻璃天线:(特邀)
2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335849
Nguyen Ngoc Mai-Khanh, Kuhiniro Asada
{"title":"Integrated On-Silicon and On-glass Antennas for Mm-Wave Applications: (Invited)","authors":"Nguyen Ngoc Mai-Khanh, Kuhiniro Asada","doi":"10.1109/NICS51282.2020.9335849","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335849","url":null,"abstract":"The paper presents several integrated high frequency antenna prototypes based on Si/CMOS and on-glass technologies for millimeter-wave (mm-wave) applications. On-chip loop antenna and dipole radiator are presented. In addition, a wide-band dipole-patch antenna design for the range of 74 – 104 GHz is integrated into a CMOS chip with an on-chip pulse generator. In addition, an implementation of a fully on-Silicon antenna array integrated with a timed-array transmitter. To control the beam-forming of this array, a digital-based time adjustment circuit is integrated together with the antenna array. Simulated and measured data including return loss, and radiation patterns are presented. This paper also introduces an on-glass antenna prototypes fabricated on quartz substrate. The on-glass antenna is to demonstrate for handset or automobile's windshield/windows applications where radio waves could be transmitted and received from various directions. The results show several compact antenna candidates integrated by both Silicon and quartz substrates towards mm-Wave/sub-mm-Wave sensing and communication applications.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"50 28","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113957701","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}
引用次数: 0
Exploring Document-Level Neural Machine Translation for English-Vietnamese 英语-越南语的文档级神经机器翻译探索
2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335843
D. Truong, Thang H. Nguyen-Vo, Long H. B. Nguyen, D. Dinh
{"title":"Exploring Document-Level Neural Machine Translation for English-Vietnamese","authors":"D. Truong, Thang H. Nguyen-Vo, Long H. B. Nguyen, D. Dinh","doi":"10.1109/NICS51282.2020.9335843","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335843","url":null,"abstract":"In Neural Machine Translation, the Transformer model has proven to be the state-of-the-art in multiple translation tasks. However, as a Seq2seq model, it can not abstract the contextual information when translating a document from one to another language. In the translation process, there are cases where, without the surrounding contextual information from consecutive sentences, an individual sentence causes ambiguity translations. The document-level approach makes the translation much more coherent and fluent by conserving the connectivity between sentences in the whole document to improve the quality of translation and human readability. Recent works show that models that are able to encapsulate these contextual information gain better results and evaluation than conventional sentence-level models. This paper conducts experiments and analyzes various context-aware models specifically in English-Vietnamese translation tasks.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"76 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132846073","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}
引用次数: 0
Fine-Tuning BERT for Sentiment Analysis of Vietnamese Reviews 基于BERT的越南语评论情感分析
2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-20 DOI: 10.1109/NICS51282.2020.9335899
Quoc Thai Nguyen, Thoaī Nguyen, N. H. Luong, Quoc Hung Ngo
{"title":"Fine-Tuning BERT for Sentiment Analysis of Vietnamese Reviews","authors":"Quoc Thai Nguyen, Thoaī Nguyen, N. H. Luong, Quoc Hung Ngo","doi":"10.1109/NICS51282.2020.9335899","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335899","url":null,"abstract":"Sentiment analysis is an important task in the field of Nature Language Processing (NLP), in which users' feedback data on a specific issue are evaluated and analyzed. Many deep learning models have been proposed to tackle this task, including the recently-introduced Bidirectional Encoder Representations from Transformers (BERT) model. In this paper, we experiment with two BERT fine-tuning methods for the sentiment analysis task on datasets of Vietnamese reviews: 1) a method that uses only the [CLS] token as the input for an attached feed-forward neural network, and 2) another method in which all BERT output vectors are used as the input for classification. Experimental results on two datasets show that models using BERT slightly outperform other models using GloVe and FastText. Also, regarding the datasets employed in this study, our proposed BERT fine-tuning method produces a model with better performance than the original BERT fine-tuning method.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115548313","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}
引用次数: 27
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