A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging

In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; thus allowing systematic control of the interaction between the rules. Experimental results on 13 languages show that our approach is fast in terms of training time and tagging speed. Furthermore, our approach obtains very competitive accuracy in comparison to state-of-the-art POS and morphological taggers.Comment: Version 1: 13 pages. Version 2: Submitted to AI Communications - the European Journal on Artificial Intelligence. Version 3: Resubmitted after major revisions. Version 4: Resubmitted after minor revisions. Version 5: to appear in AI Communications (accepted for publication on 3/12/2015

Ripple Down Rules for Question Answering

Recent years have witnessed a new trend of building ontology-based question answering systems. These systems use semantic web information to produce more precise answers to users' queries. However, these systems are mostly designed for English. In this paper, we introduce an ontology-based question answering system named KbQAS which, to the best of our knowledge, is the first one made for Vietnamese. KbQAS employs our question analysis approach that systematically constructs a knowledge base of grammar rules to convert each input question into an intermediate representation element. KbQAS then takes the intermediate representation element with respect to a target ontology and applies concept-matching techniques to return an answer. On a wide range of Vietnamese questions, experimental results show that the performance of KbQAS is promising with accuracies of 84.1% and 82.4% for analyzing input questions and retrieving output answers, respectively. Furthermore, our question analysis approach can easily be applied to new domains and new languages, thus saving time and human effort.Comment: V1: 21 pages, 7 figures, 10 tables. V2: 8 figures, 10 tables; shorten section 2; change sections 4.3 and 5.1.2. V3: Accepted for publication in the Semantic Web journal. V4 (Author's manuscript): camera ready version, available from the Semantic Web journal at http://www.semantic-web-journal.ne

The Current Status of Historical Preservation Law in Regularory Takings Jurisprudence: Has the Lucas Missile Dismantled Preservation Programs?

This paper describes our NIHRIO system for SemEval-2018 Task 3 "Irony detection in English tweets". We propose to use a simple neural network architecture of Multilayer Perceptron with various types of input features including: lexical, syntactic, semantic and polarity features.  Our system achieves very high performance in both subtasks of binary and multi-class irony detection in tweets. In particular, we rank at fifth in terms of the accuracy metric and the F1 metric. Our code is available at: https://github.com/NIHRIO/IronyDetectionInTwitte

Sentiment classification on polarity reviews: an empirical study using rating-based features

We present a new feature type named rating-based feature and evaluate the contribution of this feature to the task of document-level sentiment analysis. We achieve state-of-the-art results on two publicly available standard polarity movie datasets: on the dataset consisting of 2000 reviews produced by Pang and Lee (2004) we obtain an accuracy of 91.6% while it is 89.87% evaluated on the dataset of 50000 reviews created by Maas et al. (2011). We also get a performance at 93.24% on our own dataset consisting of 233600 movie reviews, and we aim to share this dataset for further research in sentiment polarity analysis task

Développement des capteurs sans fil basés sur les tags RFID uhf passifs pour la détection de la qualité des aliments

Le but de cette thèse est de développer des capteurs sur la base des tags RFID, des technologies et matériaux disponibles au Vietnam afin de contribuer à résoudre la problématique du contrôle de la qualité des produits alimentaires. En effet la technologie RFID s est affirmée en importance pour ses applications dans de nombreux domaines. Dans ce contexte, l identification des produits alimentaires expirés, sans les endommager, est une orientation de recherche très prometteuse. Un tag RFID UHF passif peut aussi être composé de plusieurs puces et plusieurs antennes, chaque couple puce/antenne conçu pour travailler sur un intervalle déterminé de valeur de permittivité. Donc, à partir de l ensemble des permittivités définies pour chaque couple puce/antenne et les signaux réfléchis vers le lecteur, nous pourrons mesurer la permittivité de l objet tracé. Ainsi la connaissance de la permittivité des aliments et la conception spécifique de l antenne, nous développerons un tag capteur de type "multi puce/antenne" qui sera utilisé comme un capteur sans fil pour la détection de la qualité des alimentsIn recent years, RFID technology has established itself in importance, particularly for applications in the civil sector. In this context, identification of expired products without damage is a very promising direction of research. However, the price of these sensors is still too high especially compared to living in Vietnam. A passive UHF RFID tag chip can include many antennae and many chips on a same substrate in which each pair of chip/antenna is designed to be activated on a determined interval value of permittivity. So from designed permittivities for each pair of chip/antenna and the reflected signals to the reader, we can define the value of permittivity of the object that is labelled with RFID tag. From the characterization of food permittivity and the background of antenna design, we developped a sensor tag "multi chip/antenna" to be used as a wireless sensor for the detection of food quality. The aim of this thesis intends to develop a new family of wireless sensors based on RFID technology and available technology of fabrication in Vietnam to solve this problem.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Molecular identification of three Habenaria species from Binh Chau-Phuoc Buu Nature Reserve, Vietnam

The present provides molecular data for species of Habenaria diphylla (Nimmo) Dalzell, H. khasiana Hook.f. and H. rostellifera Rchb.f. collected from Binh Chau-Phuoc Buu Nature Reserve, Vietnam for the first time. Along with other DNA sequences from GenBank database, the phylogenetic trees for Habenaria species from Vietnam have been established

Full-duplex transmission of multi-Gb/s subcarrier multiplexing and 5G NR signals in 39 GHz band over fiber and space

[EN] We propose a stable full-duplex transmission of millimeter-wave signals over a hybrid single-mode fiber (SMF) and free-space optics (FSO) link for the fifth-generation (5G) radio access networks to accelerate the Industry 4.0 transformation. For the downlink (DL), we transmit 39 GHz subcarrier multiplexing (SCM) signals using variable quadrature amplitude modulation (QAM) allocations for multi-user services. As a proof of operation, we experimentally demonstrate the transmission of 3 Gb/s SCM signals (1 Gb/s per user) over a hybrid system consisting of a 10 km SMF and 1.2 m FSO link. For the uplink (UL), satisfactory performance for the transmission of 2.4 Gb/s 5G new radio (NR) signal at 37 GHz over the hybrid system is experimentally confirmed for the first time, to the best of our knowledge. The measured error vector magnitudes for both DL and UL signals using 4/16/64-QAM formats are well below the third generation partnership project (3GPP) requirements. We also further evaluate by simulation the full-duplex transmission over the system in terms of received optical and RF powers and bit error rate performance. A wireless radio distance of approximately 200 m, which is sufficient for 5G small-cell networks, is estimated for both DL and UL direction under the heavy rain condition, based on the available data from Spain. Furthermore, simulation for the DL direction is conducted to verify the superior performance of the system using variable QAM allocation over uniform QAM allocation. Using a variable modulation allocation, up to five users (2 Gb/s per user) can be transmitted over a hybrid 10 km SMF and 150 m FSO link.Ceske Vysoke Uceni Technicke v Praze (SGS20/166/OHK3/3T/13); European Cooperation in Science and Technology (CA19111 NEWFOCUS).Nguyen, D.; Vallejo-Castro, L.; Almenar Terre, V.; Ortega Tamarit, B.; Dat, PT.; Le, ST.; Bohata, J.... (2022). Full-duplex transmission of multi-Gb/s subcarrier multiplexing and 5G NR signals in 39 GHz band over fiber and space. Applied Optics. 61(5):1183-1193. https://doi.org/10.1364/AO.4475291183119361

HYBRID END-TO-END APPROACH INTEGRATING ONLINE LEARNING WITH FACE-IDENTIFICATION SYSTEM

peer reviewedFacial recognition has been one of the most intriguing and exciting research topics over the last few years. It involves multiple face-based algorithms such as facial detection, facial alignment, facial representation, and facial recognition. However, all of these algorithms are derived from large deep-learning architectures, leading to limitations in development, scalability, accuracy, and deployment for public use with mere CPU servers. Also, large data sets that contain hundreds of thousands of records are often required for training purposes. In this paper, we propose a complete pipeline for an effective face-recognition application that requires only a small dataset of Vietnamese celebrities and a CPU for training, solving the problem of data leakage, and the need for GPU devices.The pipeline is based on the combination of a conversion algorithm from face vectors to string tokens and the indexing & retrieval process by Elasticsearch, thereby tackling the problem of online learning in facial recognition. Compared with other popular algorithms on the same data set, our proposed pipeline not only outperforms the counterpart in terms of accuracy but also delivers faster inference, which is essential to real-time applications