An Investigation of Decoding Complexity and Coding Rate Performance of Raptor Codes

This thesis examines two aspects of wireless transmissions using Raptor codes: (i) decoding complexity and (ii) rate performance. First, observing that the high complexity of Raptor decoding process is mainly due to the required number of decoding attempts, a strategy is proposed to reduce the decoding complexity by choosing an appropriate time to start the first decoding attempt and thus keeping a small number of decoding attempts. Simulations results show that the proposed strategy, when combined with a decoding algorithm, can achieve a significant reduction in Raptor decoding complexity. Another threshold strategy is also investigated, aiming to further reduce the decoding complexity by providing only "reliable" bits for Raptor decoding process. The effect of this considered strategy can be interpreted as simulating a better transmission channel and techniques to estimate its effective channel quality improvement are developed and evaluated. Second, the Raptor coding rate performance over Nakagami-m fading channels and in a cooperative relaying network using Binary Phase Shift Keying (BPSK) is studied. The simulation results show that the Raptor-coded BPSK scheme can provide a transmission rate closely approaching the channel capacity for different fading conditions at low SNR. For cooperative relaying network using Raptor-coded BPSK scheme, two cooperative protocols are considered: the existing Time Division (TD) and the modified Phase-2 Simultaneous Transmission (PST). Their performance is investigated in terms of average time and energy required for a successful transmission under various conditions of the Relay-Destination (RD) link. The simulation results show that the PST protocol often outperforms the TD protocol in terms of average transmission time and the TD protocol only has lower average transmission energy when the RD link's quality is better that of the Source-Destination (SD) link

Link Prediction for Wikipedia Articles as a Natural Language Inference Task

Link prediction task is vital to automatically understanding the structure of large knowledge bases. In this paper, we present our system to solve this task at the Data Science and Advanced Analytics 2023 Competition "Efficient and Effective Link Prediction" (DSAA-2023 Competition) with a corpus containing 948,233 training and 238,265 for public testing. This paper introduces an approach to link prediction in Wikipedia articles by formulating it as a natural language inference (NLI) task. Drawing inspiration from recent advancements in natural language processing and understanding, we cast link prediction as an NLI task, wherein the presence of a link between two articles is treated as a premise, and the task is to determine whether this premise holds based on the information presented in the articles. We implemented our system based on the Sentence Pair Classification for Link Prediction for the Wikipedia Articles task. Our system achieved 0.99996 Macro F1-score and 1.00000 Macro F1-score for the public and private test sets, respectively. Our team UIT-NLP ranked 3rd in performance on the private test set, equal to the scores of the first and second places. Our code is publicly for research purposes.Comment: Accepted at the 10th IEEE International Conference On Data Science And Advanced Analytics (DSAA 2023

ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing

English and Chinese, known as resource-rich languages, have witnessed the strong development of transformer-based language models for natural language processing tasks. Although Vietnam has approximately 100M people speaking Vietnamese, several pre-trained models, e.g., PhoBERT, ViBERT, and vELECTRA, performed well on general Vietnamese NLP tasks, including POS tagging and named entity recognition. These pre-trained language models are still limited to Vietnamese social media tasks. In this paper, we present the first monolingual pre-trained language model for Vietnamese social media texts, ViSoBERT, which is pre-trained on a large-scale corpus of high-quality and diverse Vietnamese social media texts using XLM-R architecture. Moreover, we explored our pre-trained model on five important natural language downstream tasks on Vietnamese social media texts: emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech spans detection. Our experiments demonstrate that ViSoBERT, with far fewer parameters, surpasses the previous state-of-the-art models on multiple Vietnamese social media tasks. Our ViSoBERT model is available only for research purposes.Comment: Accepted at EMNLP'2023 Main Conferenc

Improving bottleneck features for Vietnamese large vocabulary continuous speech recognition system using deep neural networks

In this paper, the pre-training method based on denoising auto-encoder is investigated and proved to be good models for initializing bottleneck networks of Vietnamese speech recognition system that result in better recognition performance compared to base bottleneck features reported previously. The experiments are carried out on the dataset containing speeches on Voice of Vietnam channel (VOV). The results show that the DBNF extraction for Vietnamese recognition decreases relative word error rate by 14 % and 39 % compared to the base bottleneck features and MFCC baseline, respectively

An in vivo biosensor for neurotransmitter release and in situ receptor activity.

Tools from molecular biology, combined with in vivo optical imaging techniques, provide new mechanisms for noninvasively observing brain processes. Current approaches primarily probe cell-based variables, such as cytosolic calcium or membrane potential, but not cell-to-cell signaling. We devised cell-based neurotransmitter fluorescent engineered reporters (CNiFERs) to address this challenge and monitor in situ neurotransmitter receptor activation. CNiFERs are cultured cells that are engineered to express a chosen metabotropic receptor, use the G(q) protein-coupled receptor cascade to transform receptor activity into a rise in cytosolic [Ca(2+)] and report [Ca(2+)] with a genetically encoded fluorescent Ca(2+) sensor. The initial realization of CNiFERs detected acetylcholine release via activation of M1 muscarinic receptors. We used chronic implantation of M1-CNiFERs in frontal cortex of the adult rat to elucidate the muscarinic action of the atypical neuroleptics clozapine and olanzapine. We found that these drugs potently inhibited in situ muscarinic receptor activity

Motorized measurement device for automatic registration of cutting edges

Sheet metal products can be found in almost all household-, electrical appliances, vehicles and industrial machines. One of the widely spread methods of sheet metal processing is the shearing process which separates large sheets into smaller sections (stampings) for subsequent operations. Cutting edges of stampings are substantial for the evaluation of the product quality. For this purpose the significant parameters of cutting edges such as rollover, burnish, fracture and burr are standardized in VDI norm 2906. Currently, the most popular methods of evaluation of cutting edges are metallography, confocal microscopy and tactile measuring systems, which are still time-consuming and cost-intensive, since the stamped parts need to be analyzed by qualified personnel. This work is based on objectives aimed at developing a motorized measurement device, which registry edge profiles automatically by means of optical sensors and calculate parameters of cutting edges by means of an algorithm. Therefore no specific knowledges of user are required for the evaluation

ViCGCN: Graph Convolutional Network with Contextualized Language Models for Social Media Mining in Vietnamese

Social media processing is a fundamental task in natural language processing with numerous applications. As Vietnamese social media and information science have grown rapidly, the necessity of information-based mining on Vietnamese social media has become crucial. However, state-of-the-art research faces several significant drawbacks, including imbalanced data and noisy data on social media platforms. Imbalanced and noisy are two essential issues that need to be addressed in Vietnamese social media texts. Graph Convolutional Networks can address the problems of imbalanced and noisy data in text classification on social media by taking advantage of the graph structure of the data. This study presents a novel approach based on contextualized language model (PhoBERT) and graph-based method (Graph Convolutional Networks). In particular, the proposed approach, ViCGCN, jointly trained the power of Contextualized embeddings with the ability of Graph Convolutional Networks, GCN, to capture more syntactic and semantic dependencies to address those drawbacks. Extensive experiments on various Vietnamese benchmark datasets were conducted to verify our approach. The observation shows that applying GCN to BERTology models as the final layer significantly improves performance. Moreover, the experiments demonstrate that ViCGCN outperforms 13 powerful baseline models, including BERTology models, fusion BERTology and GCN models, other baselines, and SOTA on three benchmark social media datasets. Our proposed ViCGCN approach demonstrates a significant improvement of up to 6.21%, 4.61%, and 2.63% over the best Contextualized Language Models, including multilingual and monolingual, on three benchmark datasets, UIT-VSMEC, UIT-ViCTSD, and UIT-VSFC, respectively. Additionally, our integrated model ViCGCN achieves the best performance compared to other BERTology integrated with GCN models