2018 5th NAFOSTED Conference on Information and Computer Science (NICS)最新文献_第8页

An Efficient Parallel Algorithm for Mining Both Frequent Closed and Generator Sequences on Multi-core Processors 多核处理器上频繁闭合序列和生成序列的高效并行挖掘算法

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/NICS.2018.8606896

Hai V. Duong, Tin C. Truong, Bac Le

{"title":"An Efficient Parallel Algorithm for Mining Both Frequent Closed and Generator Sequences on Multi-core Processors","authors":"Hai V. Duong, Tin C. Truong, Bac Le","doi":"10.1109/NICS.2018.8606896","DOIUrl":"https://doi.org/10.1109/NICS.2018.8606896","url":null,"abstract":"Compared to frequent sequence mining that is a computationally challenging task with many intermediate subsequences, frequent closed and generator sequence mining provides several benefits because it results in increased efficiency and concise representations while preserving all the information of all traditional patterns recovered from the representations. Besides, frequent closed sequences can be combined with generators to generate non-redundant sequential rules and to recover all sequential patterns as well as their frequencies quickly. However, most algorithms that have been proposed to discover either closed sequences or generators at a time and for large databases containing many long sequences are still too long to complete the work or run out of memory. Therefore, this paper, by exploiting the advantage of multi-core processor architectures, proposes a novel parallel algorithm called Par-GenCloSM for simultaneously mining both frequent closed and generator sequences in the same process. Par-GenCloSM is based on efficient techniques to quickly eliminate unpromising candidate branches and two novel strategies named EPUCloGen and GPPCloGen to reduce the global synchronization cost of the parallel model and speed up the mining process. Par-GenCloSM is the first parallel algorithm for mining frequent closed sequences and generators concurrently. Experimental results on many real-life and synthetic databases show that Par-GenCloSM outperforms state-of-the-art algorithms in terms of runtime and memory consumption, especially for long sequence databases with low minimum support thresholds.","PeriodicalId":137666,"journal":{"name":"2018 5th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126467266","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

A Predictive Model for ECG Signals Collected from Specialized IoT Devices using Deep Learning 使用深度学习的专用物联网设备收集的心电信号预测模型

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/NICS.2018.8606828

Duy Thanh Tran, H. Vo, Dung Duc Nguyen, Quan Anh Minh Nguyen, Liem T Huynh, Ly Thi Le, H. Trong, T. Quan

引用次数: 4

Integrating Grammatical Features into CNN Model for Emotion Classification 将语法特征整合到CNN模型中进行情感分类

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/NICS.2018.8606875

T. Huynh, Anh-Cuong Le

{"title":"Integrating Grammatical Features into CNN Model for Emotion Classification","authors":"T. Huynh, Anh-Cuong Le","doi":"10.1109/NICS.2018.8606875","DOIUrl":"https://doi.org/10.1109/NICS.2018.8606875","url":null,"abstract":"Emotion analysis is currently an attractive research topic in data mining and natural language processing. Along with the development of technology, people are also gradually evolving to post their emotional thinking on social media. Emotional information is useful for various aspects of business such as advertisement. Automatically classifying user emotions therefore becomes very important. In this paper we firstly formulate this problem under Convolutional Neural Network (CNN) framework. Actually language to express emotions is very diverse that make deep learning techniques such as CNN are ineffective in feature learning when the training data is not large enough. To solve this problem, we propose to use predefined grammatical patterns, which contain potential emotional information, to extract external features and integrate them into the CNN model. Our experiment are performed on two datasets, the ISEAR11http://affective-sciences.org/home/research/materials-and-onlineresearch/research-material/ (International Survey On Emotion Antecedents And Reactions) dataset and the Vietnamese emotion dataset. The experimental results show that the proposed model is very effective in comparison with previous studies.","PeriodicalId":137666,"journal":{"name":"2018 5th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114212652","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: 3

Time-Frequency Distribution for Undersampled Non-stationary Signals using Chirp-based Kernel 基于啁啾核的欠采样非平稳信号时频分布研究

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/NICS.2018.8606839

Y. Nguyen, D. McLernon, M. Ghogho, A. Zaidi

{"title":"Time-Frequency Distribution for Undersampled Non-stationary Signals using Chirp-based Kernel","authors":"Y. Nguyen, D. McLernon, M. Ghogho, A. Zaidi","doi":"10.1109/NICS.2018.8606839","DOIUrl":"https://doi.org/10.1109/NICS.2018.8606839","url":null,"abstract":"Missing samples and randomly sampled non-stationary signals give rise to artifacts that spread over both the time-frequency and the ambiguity domains. These two domains are related by a two-dimensional Fourier transform. As these artifacts resemble noise, the traditional reduced interference signal-independent kernels, which belong to Cohen’s class, cannot mitigate them efficiently. In this paper, a novel signal-independent kernel in the ambiguity domain is proposed. The proposed method is based on three important facts. Firstly, any windowed non-stationary signal can be approximated as a sum of chirps. Secondly, in the ambiguity domain, any chirp resides inside certain regions, which just occupy half of the ambiguity plane. Thirdly, the missing data artifacts always appear along the Doppler axis where the chirps auto-terms do not appear. Therefore, we propose using a chirp-based fixed kernel on windowed non-stationary signals in order to remove half of the noise-like artifacts in the ambiguity domain and compensate for the missing data effect located along the Doppler axis. It is shown that our method outperforms other reduced interference time-frequency distributions.","PeriodicalId":137666,"journal":{"name":"2018 5th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116996985","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

Energy-Efficient and Low Complexity Channel Coding for Wireless Body Area Networks 无线体域网络的高能效和低复杂度信道编码

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/NICS.2018.8606883

Hieu T. Nguyen, T. Nguyen

引用次数: 4

Improving Phonetic Recognition with Sequence-length Standardized MFCC Features and Deep Bi-Directional LSTM 利用序列长度标准化MFCC特征和深度双向LSTM改进语音识别

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/NICS.2018.8606886

Toan Pham Van, Hau Nguyen Thanh, Ta Minh Thanh

{"title":"Improving Phonetic Recognition with Sequence-length Standardized MFCC Features and Deep Bi-Directional LSTM","authors":"Toan Pham Van, Hau Nguyen Thanh, Ta Minh Thanh","doi":"10.1109/NICS.2018.8606886","DOIUrl":"https://doi.org/10.1109/NICS.2018.8606886","url":null,"abstract":"Phonetic recognition is one of the most challenging problems in the field of speech analysis. These applications can be mentioned such as dialect identification [1], mispronunciation detection [2], spoken document retrieval [3], and so on. There are different approaches to solve these problems such as improving the feature selection on input speech [4], applying deep learning technique [5] [6] [7] or combining both of them [8]. With the sequence data as the phonetics, the architecture which is based on recurrent neural network (RNN) is an appropriate approach [9]. It is even more powerful when combined with the improvement of features selection on input data. In our approach, we combine the Mel Frequency Cepstral Coefficients (MFCC) method with sequence-length to present the acoustic features of speech and use some RNN models to phonetic classification. Our experiments are implemented on the Texas Instruments Massachusetts Institute of Technology (TIMIT) [10] phone recognition dataset. Especially, our data processing and features selection method give consistently better results than other researches using the same neural network model. Currently, we have achieved the lowest error test rate (13.05%) by using Bidirectional LSTM, which is the best result in TIMIT dataset with the reduction of about 3.5% over the last best result [5] [6].","PeriodicalId":137666,"journal":{"name":"2018 5th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129527916","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

Fully Digital Background Calibration Technique for Channel Mismatches in TIADCs tiadc中信道失配的全数字背景校正技术

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/NICS.2018.8606871

Van-Thanh Ta, Yen Hoang Thi, Han Le Duc, Van‐Phuc Hoang

引用次数: 3

Invited Talk #2 Vietnamese Neural Language Model for NLP Tasks With Limited Resources 邀请演讲#2越南语神经语言模型在有限资源下的NLP任务

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/NICS.2018.8606865

Q. T. Tho

引用次数: 0

Occluded Image Recognition with Extended Nonnegative Matrix Factorization 基于扩展非负矩阵分解的遮挡图像识别

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/NICS.2018.8606869

Viet-Hang Duong, Manh-Quan Bui, Jia-Ching Wang

引用次数: 1

Deep Learning versus Traditional Classifiers on Vietnamese Students’ Feedback Corpus 越南学生反馈语料库上的深度学习与传统分类器之比较

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/NICS.2018.8606837

Phu X. V. Nguyen, T. T. T. Hong, Kiet Van Nguyen, N. Nguyen

{"title":"Deep Learning versus Traditional Classifiers on Vietnamese Students’ Feedback Corpus","authors":"Phu X. V. Nguyen, T. T. T. Hong, Kiet Van Nguyen, N. Nguyen","doi":"10.1109/NICS.2018.8606837","DOIUrl":"https://doi.org/10.1109/NICS.2018.8606837","url":null,"abstract":"Student’s feedback is an important source of collecting students’ opinions to improve quality of training activities. Implementing sentiment analysis into student feedback data, we can determine sentiments polarities which express all problems in the institution since changes necessary will be applied to improve the quality of teaching and learning. This study focused on the machine learning and natural language processing techniques (Naive Bayes, Maximum Entropy, Long Short-Term Memory, Bi-Directional Long Short-Term Memory) on the Vietnamese Students’ Feedback Corpus collected from a university. The final results were compared and evaluated to find the most effective model based on different evaluation criteria. The experimental results show that Bi-Directional Long Short-Term Memory algorithm outperformed than three other algorithms in term of the F1-score measurement with 92.0% on the sentiment classification task and 89.6% on the topic classification task. In addition, we developed a sentiment analysis application analyzing student feedback. The application will help the institution to recognize students’ opinions about a problem and identify shortcomings that still exist. With the use of this application, the institution can propose an appropriate method to improve the quality of training activities in the future.","PeriodicalId":137666,"journal":{"name":"2018 5th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131378004","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: 30