VNU Journal of Science: Computer Science and Communication Engineering最新文献_第3页

Stabilizing Techniques for Secure On-chip Key Generation Based on RO-PUF 基于RO-PUF的安全片上密钥生成稳定技术

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2022-12-16 DOI: 10.25073/2588-1086/vnucsce.306

Van‐Phuc Hoang, Van-Toan Tran, Quang-Kien Trinh

{"title":"Stabilizing Techniques for Secure On-chip Key Generation Based on RO-PUF","authors":"Van‐Phuc Hoang, Van-Toan Tran, Quang-Kien Trinh","doi":"10.25073/2588-1086/vnucsce.306","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.306","url":null,"abstract":"Based on intrinsic physical characteristics of devices, Physically Unclonable Functions (PUFs) provide the high reliability while maintaining the sufficient uniqueness. However, in the practical implementation based on PUFs, the extracted bit-string normally exhibits the unavoidable small fluctuation. Hence, PUFs can be used for the application of chip identification, but not suitable for the application that strictly requires an exact generated number. In this work, we propose several techniques to stabilize the generated value based on the existing Ring Oscillator (RO)-PUF circuit so that the stable unique number can be directly used for high-profile hardware security applications. In detail, we design a specialized on-chip key generation circuit that repeatedly samples the RO frequency values for statistical analysis and dynamically phases out the unstable bits, resulting in a unique and stable output bit-string. The experiments are conducted for the actual data measured from Xilinx Artix-7 FPGA devices. The generated key is proven to be relatively stable and can be readily used for the emerging security applications.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132520356","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

ViMRC - VLSP 2021: An empirical study of Vietnamese Machine Reading Comprehension with Unsupervised Context Selector and Adversarial Learning ViMRC - VLSP 2021:基于无监督上下文选择器和对抗学习的越南语机器阅读理解实证研究

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2022-12-16 DOI: 10.25073/2588-1086/vnucsce.344

Minh Le Nguyen

引用次数: 0

VLSP 2021 - VieCap4H Challenge: Automatic Image Caption Generation for Healthcare Domain in Vietnamese VLSP 2021 - VieCap4H挑战:越南医疗保健领域的自动图像标题生成

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2022-12-16 DOI: 10.25073/2588-1086/vnucsce.364

P. Phan

{"title":"VLSP 2021 - VieCap4H Challenge: Automatic Image Caption Generation for Healthcare Domain in Vietnamese","authors":"P. Phan","doi":"10.25073/2588-1086/vnucsce.364","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.364","url":null,"abstract":"Machine reading comprehension (MRC) is a challenging Natural Language Processing (NLP) research fieldand wide real-world applications. The great progress of this field in recents is mainly due to the emergence offew datasets for machine reading comprehension tasks with large sizes and deep learning. For the Vietnameselanguage, some datasets, such as UIT-ViQuAD [1] and UIT-ViNewsQA [2], most recently, UIT-ViQuAD 2.0 [3] - adataset of the competitive VLSP 2021-MRC Shared Task 1 . MRC systems must not only answer questions whennecessary but also tactfully abstain from answering when no answer is available according to the given passage.In this paper, we proposed two types of joint models for answerability prediction and pure-MRC prediction with/without a dependency mechanism to learn the correlation between a start position and end position in pure-MRCoutput prediction. Besides, we use ensemble models and a verification strategy by voting the best answer from thetop K answers of different models. Our proposed approach is evaluated on the benchmark VLSP 2021-MRC SharedTask challenge dataset UIT-ViQuAD 2.0 [3] shows that our approach is significantly better than the baseline.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134316037","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

N/A Modern Approaches in Natural Language Processing 自然语言处理中的现代方法

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2022-12-16 DOI: 10.25073/2588-1086/vnucsce.302

T. Quan

{"title":"N/A Modern Approaches in Natural Language Processing","authors":"T. Quan","doi":"10.25073/2588-1086/vnucsce.302","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.302","url":null,"abstract":"Natural Language Processing (NLP) is one of the major branches in the emerging field of Artificial Intelligence (AI). Classical approaches in this area were mostly based on parsing and information extraction techniques, which suffered from great difficulty when dealing with very large textual datasets available in practical applications. This issue can potentially be addressed with the recent advancement of the Deep Learning (DL) techniques, which are naturally assuming very large datasets for training. In fact, NLP research has witnessed a remarkable achievement with the introduction of Word Embedding techniques, which allows a document to be represented meaningfully as a matrix, on which major DL models like CNN or RNN can be deployed effectively to accomplish common NLP tasks. Gradually, NLP scholars keep developing specific models for their areas, notably attention-enhanced BiLSTM, Transformer and BERT. The births of those models have introduced a new wave of modern approaches which frequently report new breaking results and open much novel research directions. The aim of this paper is to give readers a roadmap of those modern approaches in NLP, including their ideas, theories and applications. This would hopefully offer a solid background for further research in this area.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130790134","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

gPartition: An Efficient Alignment Partitioning Program for Genome Datasets gPartition:一个高效的基因组数据集比对分区程序

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2022-12-16 DOI: 10.25073/2588-1086/vnucsce.353

Le Kim Thu, Do Duc Dong, Bui Ngoc Thang, Hoang Thi Diep, Nguyen Phuong Thao, L. Vinh

{"title":"gPartition: An Efficient Alignment Partitioning Program for Genome Datasets","authors":"Le Kim Thu, Do Duc Dong, Bui Ngoc Thang, Hoang Thi Diep, Nguyen Phuong Thao, L. Vinh","doi":"10.25073/2588-1086/vnucsce.353","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.353","url":null,"abstract":"Phylogenomics, or evolutionary inference based on genome alignment, is becoming prominent thanks to next-generation sequencing technologies. In model-based phylogenomics, the partition scheme has a significant impact on inference performance, both in terms of log-likelihoods and computation time. Therefore, finding an optimal partition scheme, or partitioning, is critical in a phylogenomic inference pipeline. To accomplish this, one needs to divide the alignment sites into disjoint partitions so that the sites of similar evolutionary models are in the same partition. Computational partitioning is a recent approach of increasing interest due to its capability of modeling the site-rate heterogeneity within a single gene. State-of-the-art computational partitioning methods, such as mPartition or RatePartition, are, however, ineffective on long alignments of millions of sites. In this paper, we introduce gPartition, a new computational partitioning method leveraging both the site rate and the best-fit substitution model. We conducted experiments on recently published alignments to compare gPartition with mPartition and RatePartition. gPartition was orders of magnitude faster than other methods. The AIC score demonstrated that gPartition produced partition schemes that were better or comparable to mPartition. gPartition outperformed RatePartition on all examined alignments. We implemented our proposed method in the gPartition program to help researchers partition genome alignments with millions of sites more efficiently.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115075615","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

NER - VLSP 2021: Two Stage Model for Nested Named Entity Recognition NER - VLSP 2021:嵌套命名实体识别的两阶段模型

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2022-06-30 DOI: 10.25073/2588-1086/vnucsce.368

Quan Chu Quoc, Viola Van

引用次数: 1

SV - VLSP 2021: Combine Attentive Statistical Pooling-based Xvector and Pretrained ECAPA-TDNN for Vietnamese Text-Independent Speaker Verification SV - VLSP 2021:结合细心统计池的Xvector和预训练ECAPA-TDNN的越南语文本独立说话人验证

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2022-06-30 DOI: 10.25073/2588-1086/vnucsce.320

T. Thang, Huynh Thi Thanh Binh

引用次数: 1

TTS - VLSP 2021: The Thunder Text-To-Speech System TTS - VLSP 2021:迅雷文本转语音系统

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2022-06-30 DOI: 10.25073/2588-1086/vnucsce.342

N. Ngoc Anh, Nguyen Tien Thanh, Le Dang Linh

引用次数: 1

VLSP 2021 - NER Challenge: Named Entity Recognition for Vietnamese VLSP 2021 - NER挑战:越南命名实体识别

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2022-06-30 DOI: 10.25073/2588-1086/vnucsce.362

Ha My Linh, Do Duy Dao, Nguyen Thi Minh Huyen, Ngo The Quyen, Doan Xuan Dung

引用次数: 1

TTS - VLSP 2021: The NAVI’s Text-To-Speech System for Vietnamese TTS - VLSP 2021: NAVI的越南语文本转语音系统

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2022-06-30 DOI: 10.25073/2588-1086/vnucsce.347

Nguyen Le Minh, An Quoc Do, Viet Q. Vu, Huyen Thuc Khanh Vo

{"title":"TTS - VLSP 2021: The NAVI’s Text-To-Speech System for Vietnamese","authors":"Nguyen Le Minh, An Quoc Do, Viet Q. Vu, Huyen Thuc Khanh Vo","doi":"10.25073/2588-1086/vnucsce.347","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.347","url":null,"abstract":"The Association for Vietnamese Language and Speech Processing (VLSP) has organized a series of workshops intending to bring together researchers and professionals working in NLP and attempt a synthesis of research in the Vietnamese language. One of the shared tasks held at the eighth workshop is TTS [14] using a dataset that only consists of spontaneous audio. This poses a challenge for current TTS models since they only perform well constructing reading-style speech (e.g, audiobook). Not only that, the quality of the audio provided by the dataset has a huge impact on the performance of the model. Specifically, samples with noisy backgrounds or with multiple voices speaking at the same time will deteriorate the performance of our model. In this paper, we describe our approach to tackle this problem: we first preprocess the training data then use it to train a FastSpeech2 [10] acoustic model with some replacements in the external aligner model, finally we use HiFiGAN [4] vocoder to construct the waveform. According to the official evaluation of VLSP 2021 competition in the TTS task, our approach achieves 3.729 in-domain MOS, 3.557 out-of-domain MOS, and 79.70% SUS score. Audio samples are available at https://navi-tts.github.io/.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131040199","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