VNU Journal of Science: Computer Science and Communication Engineering最新文献

Aspect-Category based Sentiment Analysis with Unified Sequence-To-Sequence Transfer Transformers 基于方面分类的统一序对序传输变压器情感分析

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2023-08-07 DOI: 10.25073/2588-1086/vnucsce.662

D. Thin, N. Nguyen

{"title":"Aspect-Category based Sentiment Analysis with Unified Sequence-To-Sequence Transfer Transformers","authors":"D. Thin, N. Nguyen","doi":"10.25073/2588-1086/vnucsce.662","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.662","url":null,"abstract":"In recent years, Aspect-based sentiment analysis (ABSA) has received increasing attention from the scientific community for Vietnamese language. However, most previous studies solved various subtasks in ABSA based on machine learning, deep learning and transformer-based architectures in a classification way. Recently, the release of pre-trained sequence-to-sequence brings a new approach to address the ABSA as a text generation problem for Vietnamese ABSA tasks. In this paper, we formulate the Aspect-category based sentiment analysis task as the conditional text generation task and investigate different unified generative transformer-based models. To represent the labels in a natural sentence, we apply a simple statistical method and observation of the commenting style. We conduct experiments on two benchmark datasets. As a result, our model achieved new state-of-the-art results with the micro F1-score of 75.53% and 86.60% for the two datasets with different levels for the restaurant domain. In addition, our experimental results achieved the best score for the smartphone domain with the macro F1-score of 81.10%.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121241789","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 Bandwidth-Efficient High-Performance RTL-Microarchitecture of 2D-Convolution for Deep Neural Networks 一种带宽高效的深度神经网络二维卷积高性能rtl微架构

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2023-08-07 DOI: 10.25073/2588-1086/vnucsce.596

Hung K. Nguyen, Long Quoc Tran

{"title":"A Bandwidth-Efficient High-Performance RTL-Microarchitecture of 2D-Convolution for Deep Neural Networks","authors":"Hung K. Nguyen, Long Quoc Tran","doi":"10.25073/2588-1086/vnucsce.596","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.596","url":null,"abstract":"The computation complexity and huge memory access bandwidth of the convolutional layers in convolutional neural networks (CNNs) require specialized hardware architectures to accelerate CNN’s computations while keeping hardware costs reasonable for area-constrained embedded applications. This paper presents an RTL (Register Transfer Logic) level microarchitecture of hardware- and bandwidth-efficient high-performance 2D convolution unit for CNN in deep learning. The 2D convolution unit is made up of three main components including a dedicated Loader, a Circle Buffer, and a MAC (Multiplier-Accumulator) unit. The 2D convolution unit has a 2-stage pipeline structure that reduces latency, increases processing throughput, and reduces power consumption. The architecture proposed in the paper eliminates the reloading of both the weights as well as the input image data. The 2D convolution unit is configurable to support 2D convolution operations with different sizes of input image matrix and kernel filter. The architecture can reduce memory access time and power as well as execution time thanks to the efficient reuse of the preloaded input data, while simplifying hardware implementation. The 2D convolution unit has been simulated and implemented on Xilinx's FPGA platform to evaluate its superiority. Experimental results show that our design is 1.54× and 13.6× faster in performance than the design in [7] and [8], respectively, at lower hardware cost without using any FPGA’s dedicated hardware blocks. By reusing preloaded data, our design achieves a bandwidth reduction ratio between 66.4% and 90.5%.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125919106","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

Noisy-label propagation for Video Anomaly Detection with Graph Transformer Network 基于图变换网络的视频异常检测中的噪声标签传播

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2023-06-16 DOI: 10.25073/2588-1086/vnucsce.659

Viet-Cuong Ta, Thu Uyen Do

引用次数: 0

FRSL: A Domain Specific Language to Specify Functional Requirements FRSL:用于指定功能需求的领域特定语言

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2023-06-06 DOI: 10.25073/2588-1086/vnucsce.803

Duc-Hanh Dang

{"title":"FRSL: A Domain Specific Language to Specify Functional Requirements","authors":"Duc-Hanh Dang","doi":"10.25073/2588-1086/vnucsce.803","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.803","url":null,"abstract":"In software development, to obtain a precise specification of the software system's functional requirements is significant to ensure the software quality as well as to automate the development. Use cases are an effective way to capture functional requirements, however, the use of ambiguous or vague language in the use case can lead to imprecision. It is essential to ensure that use case specifications are clear, concise, and complete to avoid imprecision in requirements. This paper aims to develop a domain specific language called FRSL to precisely specify use cases and to provide a basis for transformations to generate software artifacts from the use case specification. We define a FRSL metamodel to capture the technical domain of use cases for FRSL's abstract syntax, and then provides a textual concrete syntax for this language. We also define a formal operational semantics for FRSL by characterizing the execution of a FRSL specification as sequences of system snapshot transitions. This formal semantics on the one hand allows us to precisely explain the meaning of use cases and their relationships, on the other hand provides a basis for transformations from the use case specification. We implement a tool support for this language and perform an evaluation of its expressiveness in comparison with current use case specification languages. This work brings out (1)~a DSL to specify use cases that is defined based on a formal semantics of use cases; and (2)~a tool support realized as an Eclipse plugin for this DSL. The use case specification language FRSL would help precisely specify the system's functional requirements and bring more automation in the software development.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132392629","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 Contract-Based Specification Method for Model Transformations 基于契约的模型转换规范方法

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2023-04-01 DOI: 10.25073/2588-1086/vnucsce.657

Duc-Hanh Dang, Thi-Hanh Nguyen

引用次数: 0

vnNLI - VLSP 2021: Vietnamese and English-Vietnamese Textual Entailment Based on Pre-trained Multilingual Language Models vnli - VLSP 2021:基于预训练多语言模型的越南语和英越语文本蕴涵

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2022-12-16 DOI: 10.25073/2588-1086/vnucsce.329

Ngan Nguyen Luu Thuy, Đặng Văn Thìn, Hoàng Xuân Vũ, Nguyễn Văn Tài, Khoa Thi-Kim Phan

{"title":"vnNLI - VLSP 2021: Vietnamese and English-Vietnamese Textual Entailment Based on Pre-trained Multilingual Language Models","authors":"Ngan Nguyen Luu Thuy, Đặng Văn Thìn, Hoàng Xuân Vũ, Nguyễn Văn Tài, Khoa Thi-Kim Phan","doi":"10.25073/2588-1086/vnucsce.329","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.329","url":null,"abstract":"Natural Language Inference (NLI) is a high-level semantic task in Natural Language Processing - NLP, and it extends further challenges if it is in the cross-lingual scenario. In recent years, pre-trained multilingual language models (e.g., mBERT ,XLM-R, InfoXLM) have greatly contributed to the success of dealing with these challenges. Based on the motivation behind these achievements, this paper describes our approach based on fine-tuning pretrained multilingual language models (XLM-R, InfoXLM) to tackle the shared task ``Vietnamese and English-Vietnamese Textual Entailment'' at the 8th International Workshop on Vietnamese Language and Speech Processing (VLSP 2021footnote{https://vlsp.org.vn/vlsp2021}). We investigate other techniques to improve the performance of our work: Cross-validation, Pseudo-labeling (PL), Learning rate adjustment, and Postagging. All experimental results demonstrated that our approach based on the InfoXLM model achieved competitive results, ranking 2nd for the task evaluation in VLSP 2021 with 0.89 in terms of F1-score on the private test set.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"59 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":"124389717","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

Early CTU Termination and Three-steps Mode Decision Method for Fast Versatile Video Coding 快速通用视频编码的早期CTU终止和三步模式判定方法

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2022-12-16 DOI: 10.25073/2588-1086/vnucsce.375

S. Q. Nguyen, Tien Huu Vu, Duong Trieu Dinh, Minh Bao Dinh, Minh N. Do, X. Hoang

{"title":"Early CTU Termination and Three-steps Mode Decision Method for Fast Versatile Video Coding","authors":"S. Q. Nguyen, Tien Huu Vu, Duong Trieu Dinh, Minh Bao Dinh, Minh N. Do, X. Hoang","doi":"10.25073/2588-1086/vnucsce.375","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.375","url":null,"abstract":"Versatile Video Coding (VVC) has been recently becoming popular in coding videos due to its compression efficiency. To reach this performance, Joint Video Experts Team (JVET) has introduced a number of improvement techniques to VVC coding model. Among them, VVC Intra coding introduces a new concept of quad-tree nested multi-type tree (QTMT) and extends the predicted modes with up to 67 options. As a result, the complexity of the VVC Intra encoding also greatly increases. To make VVC Intra coding more feasible in real-time applications, we propose in this paper a novel deep learning based fast QTMT and an early mode prediction method. At the first stage, we use a learned convolutional neural network (CNN) model to predict the coding unit map and then fed into the VVC encoder to early terminate the block partitioning process. After that, we design a statistical model to predict a list of most probable modes (MPM) for each selected Coding using (CU) size. Finally, we employ a so-called three-steps mode decision algorithm to estimate the optimal directional mode without sacrificing the compression performance. The proposed early CU splitting and fast intra prediction are integrated into the latest VTM reference software. Experimental results show that the proposed method can save 50.2% of encoding time with a negligible BD-Rate increase.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"29 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":"131282797","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

vnNLI - VLSP2021: An Empirical Study on Vietnamese-English Natural Language Inference Based on Pretrained Language Models with Data Augmentation vnli - VLSP2021:基于数据增强的预训练语言模型的越英自然语言推理实证研究

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2022-12-16 DOI: 10.25073/2588-1086/vnucsce.330

Thin Dang Van, D. Hao, N. Nguyen, Luân Đình Ngô, Kiến Lê Hiếu Ngô

引用次数: 0

A Hybrid Method for Test Data Generation for Unit Testing of C/C++ Projects C/ c++项目单元测试中测试数据生成的混合方法

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2022-12-16 DOI: 10.25073/2588-1086/vnucsce.354

Tran Nguyen Huong

{"title":"A Hybrid Method for Test Data Generation for Unit Testing of C/C++ Projects","authors":"Tran Nguyen Huong","doi":"10.25073/2588-1086/vnucsce.354","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.354","url":null,"abstract":"In recent years, automated test data generation from source code has gained a significantpopularity in software testing. This paper proposes a method, named Hybrid, to generate test datafor unit testing C/C++ projects. The method is a combination of two test data generation methodsnamed IBVGT and WCFT. In IBVGT method, the source code is analyzed to find simple conditions.Then, bases on these conditions, IBVGT generates test data for boundary values without having tosolve test paths constraints. This makes the method faster than BVTG method when generating testdata. In Hybrid method, while generating test data using WCFT, simple conditions are collected forboundary values test data generation. Test data generated by Hybrid are able to ensure both highsource code coverage and error detection ability. In addition, Hybrid is capable of finding infeasibleexecution paths and dead code. Experimental results with some popular unit functions show thatHybrid outperforms STCFG method in terms of test data generation time and boundary values relatederror detection. IBVGT is superior to BVTG in term of test data generation time whilst its boundaryvalues related error detection ability depends on the number of simple conditions inside each unitfunction.","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":"129205211","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

vieCap4H Challenge 2021: A transformer-based method for Healthcare Image Captioning in Vietnamese vieCap4H挑战2021:越南医疗保健图像字幕的基于转换器的方法

VNU Journal of Science: Computer Science and Communication Engineering Pub Date : 2022-12-16 DOI: 10.25073/2588-1086/vnucsce.371

Doanh Bui Cao, Truc Thi Thanh Trinh, Trong-Thuan Nguyen, V. Nguyen, Nguyen D. Vo

引用次数: 0