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

SDP-Based Quality Adaptation and Performance Prediction in Adaptive Streaming of VBR Videos

Recently, various adaptation methods have been proposed to cope with throughput fluctuations in HTTP adaptive streaming (HAS). However, these methods have mostly focused on constant bitrate (CBR) videos. Moreover, most of them are qualitative in the sense that performance metrics could only be obtained after a streaming session. In this paper, we propose a new adaptation method for streaming variable bitrate (VBR) videos using stochastic dynamic programming (SDP). With this approach, the system should have a probabilistic characterization along with the definition of a cost function that is minimized by a control strategy. Our solution is based on a new statistical model where the future streaming performance is directly related to the past bandwidth statistics. We develop mathematical models to predict and develop simulation models to measure the average performance of the adaptation policy. The experimental results show that the prediction models can provide accurate performance prediction which is useful in planning adaptation policy and that our proposed adaptation method outperforms the existing ones in terms of average quality and average quality switch

A Knowledge-Based Model For Context-Aware Smart Service Systems

The advancement of the Internet of Things, big data, and mobile computing leads to the need for smart services that enable the context awareness and the adaptability to their changing contexts. Today, designing a smart service system is a complex task due to the lack of an adequate model support in awareness and pervasive environment. In this paper, we present the concept of a context-aware smart service system and propose a knowledge model for context-aware smart service systems. The proposed model organizes the domain and context-aware knowledge into knowledge components based on the three levels of services: Services, Service system, and Network of service systems. The knowledge model for context-aware smart service systems integrates all the information and knowledge related to smart services, knowledge components, and context awareness that can play a key role for any framework, infrastructure, or applications deploying smart services. In order to demonstrate the approach, two case studies about chatbot as context-aware smart services for customer support are presented

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

Ecpoc: an evolutionary computation-based proof of criteria consensus protocol

Recently, blockchain technology has been applied in many domains in our life. Blockchain networks typically utilize a consensus protocol to achieve consistency among network nodes in a decentralized environment. Delegated Proof of Stake (DPoS) is a popular mechanism adopted in many networks such as BitShares, EOS, and Cardano because of its speed and scalability advantages. However, votes that come from nodes on a DPoS network tend to support a set of specific nodes that have a greater chance of becoming block producers after voting rounds. Therefore, only a small group of nodes can be selected to become block producers. To address this issue, we propose a new protocol called Evolutionary Computation-based Proof of Criteria (ECPoC), which uses ten criteria to evaluate and select a new block procedure in each round. Next, a set of optimal weights used for maximizing the network’s decentralization level is identified through the use of evolutionary computation algorithms. The experimental results show that our consensus significantly enhances the degree of decentralization in the selection process of witness nodes compared to DPoS. As a result, ECPoC facilitates fairness between nodes and creates momentum for blockchain network developmen

The effect of polyamine 70000 (BT70) on the zinc plating process in the non-cyanide alkaline plating bath

The effect of polyamine having molecular weight 70,000u (BT70) in the non-cyanide alkaline plating bath on the morphological zinc electrodeposited coating was investigated. The obtained results from the reflectance of electrodeposited zinc coating by a suitable range of electro-current density and SEM images showed that BT70 had effect on the zinc plating in comparison with the zinc deposits from plating bath without BT70. The electrodeposited coating surface was rough and poor adhesion. However, adding BT70 into the plating bath led to the surface roughness of electrodeposited coating and grand size being reduced. Zinc electrodeposited coating from a plating bath containing BT70 at 0.5 g/L, respectively, experienced the highest reflectance, equal 46 % of the electrodeposited coating from the bath containing commercial additives. Hull method showed that the zinc deposited coating surface became smoother with the presence of polyamine in non-cyanide alkaline zinc plating solution. The zinc deposited coating had a semi-gloss scope at 10 A/dm2. If the BT70 content increased, the semi-gloss scop and the gloss of samples’ surfaces also grew up. Keywords. Polyamine, additive, non-cyanide alkaline zinc plating, zinc coating

Lung Volume Reduction Surgery in Patients with Heterogenous Emphysema: Selecting Perspective

BACKGROUND: Lung volume reduction surgery (LVRS) was introduced to alleviate clinical conditions in selected patients with heterogenous emphysema. Clarifying the most suitable patients for LVRS remained unclear. AIM: This study was undertaken to specifically analyze the preoperative factor affecting to LVRS. METHODS: The prospective study was conducted at 103 Military Hospital between July 2014 and April 2016. Severe heterogenous emphysema patients were selected to participate in the study. The information, spirometry, and body plethysmographic pulmonary function tests in 31 patients who underwent LVRS were compared with postoperative outcomes (changing in FEV1 and CAT scale). RESULTS: Of the 31 patients, there was statistically significant difference in the outcome of functional capacity, lung function between two groups (FEV1 ≤ 50% and > 50%) (∆FEV1: 22.46 vs 18.32%; p = 0.042. ∆CAT: 6.85 vs 5.07; p = 0.048). Changes of the FEV1 and CAT scale were no statistically significant differences in three groups residual volume. Patients with total lung capacity < 140% had more improved than others (∆FEV1: 23.81 vs 15.1%; p = 0.031). CONCLUSION: Preoperative spirometry and body plethysmographic pulmonary function tests were useful measures to selected severe heterogenous emphysema patients for LVRS. Patients with FEV1 ≤ 50%, TLC in the range of 100-140% should be selected

The results of deep magnetotelluric sounding for studying the Nha Trang - Tanh Linh fault

The profile of deep magnetotelluric sounding (MT) from Duc Trong - Tuy Phong has been carried out in Lam Dong and Binh Thuan  provinces. The length of the Duc Trong - Tuy Phong profile is about 80 km with 15 stations and the distance between the stations measures about 5 km. Two-dimensional MT inversion was used to find a resistivity model that fits the data. The 2D resistivity model allows determining position and development formation of the Nha Trang - Tanh Linh  fault. This is the deep fault, which is showed by the boundaries of remarkable change of resistivity. In the near surface of the Earth (from ground to the depth of 6 km), the angle of inclination of this fault is about 60o; in the next part, the direction of the Nha Trang - Tanh Linh  faut is vertical. Geoelectrical section of the Nha Trang - Tanh Linh  profile shows that the resistivity of mid-crust is higher than that of lower-crust and of upper-crust