Reviewerly/exHarmony huggingface dataset:
Efficient Multi-Organ Segmentation Using HRNet And OCRNet

The research literature on medical images, driven by the fast growth of deep learning, has proved their robust-ness while dealing with medical datasets. But the available methods meet difficulties in reaching an efficient speed and memory cost. In this work, we present our method used in the MICCAI 2021 FLARE Challenge, which aims to solve the requirements of generalization on unseen data and efficiency which is evaluated by computation time and memory cost. Our work focuses mainly on three points, i.e, segmentation model, data augmentation and post-processing. (1) Efficient 2D segmentation methods can balance accuracy and efficiency. (2) Using augmented data can help the model generalize better. (3) Floating-point number format conversion and resizing images significantly improve the result. With these techniques, we yielded sixth place in the competition. Codes are available at https://github.com/quoccuonglqd/mmsegmentation.
Is word segmentation necessary for Vietnamese sentiment classification?

To the best of our knowledge, this paper made the first attempt to answer whether word segmentation is necessary for Vietnamese sentiment classification. To do this, we presented five pre-trained monolingual S4-based language models for Vietnamese, including one model without word segmentation, and four models using RDRsegmenter, uitnlp, pyvi, or underthesea toolkits in the pre-processing data phase. According to comprehensive experimental results on two corpora, including the VLSP2016-SA corpus of technical article reviews from the news and social media and the UIT-VSFC corpus of the educational survey, we have two suggestions. Firstly, using traditional classifiers like Naive Bayes or Support Vector Machines, word segmentation maybe not be necessary for the Vietnamese sentiment classification corpus, which comes from the social domain. Secondly, word segmentation is necessary for Vietnamese sentiment classification when word segmentation is used before using the BPE method and feeding into the deep learning model. In this way, the RDRsegmenter is the stable toolkit for word segmentation among the uitnlp, pyvi, and underthesea toolkits.
Building 3 levels of security for traceability using Blockchain 3.0 Generation

Nowadays, tracking the origin of a product as it goes from raw material to finished product is an essential need of consumers, which determines whether consumers choose to buy that product or not. At present, the most common method of product traceability is to scan the QR code stamp on the goods. These stamps, however, are still being counterfeited, resulting in inaccurate product information, not ensuring product integrity and transparency for consumers. Therefore, this research suggests a variety of blockchain-based solutions with different security levels to improve the security, integrity, and transparency of product data. In this paper, we propose to use blockchain technology 3.0 which has more advanced features than blockchain 1.0 and 2.0, including a transaction processing time of just roughly 6 seconds.
A Method for Flexible Definition and Automatic Implementation of Laboratory Environment in Online Training Platforms

Cloud infrastructure enables individuals, organizations, and enterprises to offer scalable and elastic resources to support business operations remotely. The demand for digital transformation encourages communities and technical professionals to adopt cloud computing and automation platforms for facilitating their resource capacity, including operating systems, networks, and applications. Of cloud-based applications for social good, virtual education platforms play an important role to re-duce the cost and effort for trainees and trainers during practical courses, especially in the context of pandemics such as Covid-19. Nonetheless, the task of setting up practical environments with virtual machines, network elements, and software programs is the burden of the system that hosts many training courses with numerous trainees or resources. Hence, this research provides the mechanism for defining and automatically implementing the hands-on laboratory environments for information technology (IT) training. Specifically, we design and implement a concurrent scheme and local repository for deploying multiple environments with high performance in large virtual classrooms. The total time to finish environmental settings for learners is kept stable to meet the satisfaction of users in case of the remarkable growth in the number of environments and trainees.
An Ensemble Method for Sentiment Classification of Long Vietnamese Documents

Sentiment classification of text is one of the major tasks of natural language processing which has has gained significant interest recently. While there are many researches in this field dealing with many other languages, it is quite chal-lenging to handle Vietnamese text, especially in case of specific domains such as social security protection domain where the classification requires and depends much on expertise knowledge of that field to collect and to label the dataset. In this work, we collect 2541 Vietnamese news (a paragraph or a whole text document) from websites and social networks such as Facebook to build our dataset and propose an ensemble model of Support Vector Machine (SVM) and Light Gradient Boosting Machine (LightGBM) to classify whether the news is normal, negative or anti-social. To evaluate the performance of our propose model, we also experiment on the same dataset with other state of the art classification models such as Naive Bayes, Maximum Entropy, PhoBERT, XLM-RoBERTa. Preliminary results show that our propose model outperforms those model in term of accuracy and f1 score.
ECPASurv: Exploring Career Path with Attention Mechanism and Survival Analysis

Employees' career paths can be predicted by their historical experience in order to understand employee intention, accurate predictions permit organizations to make the right decisions for the retention or recruitment planning of employees. Survival analysis is widely used in clinical research but receives little attention in other domains despite its superiority in handling censored data, which frequently appear in real-world datasets. To take advantage of the survival analysis, we propose a new model, named as ECPASurv. The proposed model is built to handle two problems, with the first one being the prediction of the risk score of an employee and the second one related to the forecast of the next job title that an employee may want to apply for. Unlike previous work, ECPASurv is the combination of transformer encoder and survival analysis that can effectively utilize the hidden information of employee experience's transition. The use of the transformer encoder gives better results for both survival function prediction and next job title prediction. For the case studied and proved the efficiency of the proposed model, we created some experiments with more than 10.000 profiles; each has one or multiple job experiences and a subset of skills.
Vietnamese Fact Checking based on the Knowledge Graph and Deep Learning

Fact checking is the assessment of the correctness of a claim in a particular context. This is usually done by experts, by drawing on evidences such as speeches, arguments, legal information and previously published figures or known facts, with inference to draw conclusions about the claim. In the world, there have been many researches on the topic of fact checking, mostly based on datasets supporting the English language, using natural language processing techniques to analyze the sentence components in the claim and the evidences, from which there is a basis for comparison to draw conclusions. In Vietnam, researches on checking facts on Vietnamese documents have not been done much, due to the supporting tools for analyzing Vietnamese sentence structures and semantics is still limited and the performance is not high. Therefore, we propose a model to help checking facts with Vietnamese content by combining knowledge graph (KG) with Bidirectional Encoder Representation from Transformer (BERT) deep learning technique. These are advanced techniques that are being used by many researchers. Experimental results on the Vietnamese dataset of 129045 triples extracted from Wikipedia show that our model has a high accuracy up to 96%, and is capable of supporting inference while checking facts.
A Graph and PhoBERT based Vietnamese Extractive and Abstractive Multi-Document Summarization Frame

Although many methods of solving the Multi-Document Summarization (MDS) problem have been proposed, which belong to both extractive and abstractive summarization, models using only one of the two types of summarization still bring their own disadvantages. One of the good and potential approaches to the MDS problem is the combined approach of extractive and abstractive summarization. Currently, with many languages and especially Vietnamese, the studies that propose a combination of extractive and abstractive summarization are still very limited and have not been deeply exploited. In this paper, we propose a new MDS frame which contains two components in a pipeline architecture combining extractive and abstractive approaches for Vietnamese MDS. We use extractive approach in the first component to select the most important sentences in each document by constructing graphs with the edges representing sentences' relationship, nodes illustrating sentences of input documents. The selected sentences will be clustered to groups of sentences with similar meaning, then combined into documents corresponding to each group. The abstractive approach is used in second component, which uses the PhoBERT2PhoBERT model to generate final summary document. The results of the frame achieved a positive evaluation with the ROUGE-2 measure on two datasets ViMs and VN-MDS are 36.42 and 34.89 percent respectively.
A Novel Two-Stage Framework for 2D/3D Registration in Neurological Interventions

2D/3D medical image registration of pre-operative volumes and intra-operative images plays an important role in neurological interventions. However, vast space of transformation parameters makes this task incredibly challenging. In this paper, a novel two-stage framework for 2D/3D registration is proposed. It consists of two basic modules: CNN regression for preliminarily estimating transformation parameters, and centroid alignment for further reducing estimation errors to get refined images. Experimental results on two patients show the proposed framework can achieve superior performances to baseline methods with an inference rate of 22 FPS, satisfying real-time requirements (6~12 FPS) in clinical practice. Extensive ablation studies prove the effectiveness of centroid alignment in significantly improving registration accuracies. These promising results indicate the framework has the potential to be integrated into navigation frameworks for neurological interventions, facilitating treatments of physicians.
Text Classification Using Deep Learning Methods

Text classification is a fundamental task in multiple practical scenarios of natural language processing (NLP). During the last few decades, many text classification methods based on deep learning (DL) models have been proposed and adopted in various fields. This article provides an overview description of the mainstream deep learning approaches that are applied in text classification in recent years, including recurrent neural networks (RNN), convolutional neural networks (CNN), the attention mechanism, transformer based pre-trained models, and graph neural networks (GNN). Moreover, in order to give more intuitive comparison, the most popular datasets that are used to evaluate the above models, as well as their respective performance, are also analyzed. In the end, the potential research directions in the future are discussed.
Intelligent System of Lightning Monitoring and Defense Based on Intelligent Algorithm

With the maturity of lightning detection technology, meteorological departments have successively established lightning monitoring systems for lightning monitoring and lightning protection. In order to reduce the losses caused by lightning disasters to people, this paper uses Web servers and the Internet to build the network structure of the lightning detection and defense intelligent system, and transmits monitoring data through the system network. Lightning data information can be used to realize lightning early warning and take defensive measures. In the system test experiment, the server side and the user side of the system are tested respectively. The test results show that the security of the system designed in this paper meets the requirements, and the user interface has good interactivity, which can realize the intelligent management of lightning monitoring data.
Solving Multiple Variable Problems by Regression Models

There are a lot of Regression models to solve multiple variable problems especially category, predict problems. In this paper, we propose some of regression models to predict the price of diamonds. Known as the “Four C's,” carat, clarity, color and cut collectively inform the price of a diamond. No single characteristic outweighs another. They all contribute and influence the final retail value. The data we used for testing our approach was obtained from the Data and Story Library. Results generated show that our proposed approach achieves high precision.
A Model of Vietnamese Optical Character Recognition

Optical Character Recognition (OCR) is a method to transform images in words into digital documents in the computer vision field. This helps digital or hand-written words and characters in images to be recognized by reading a document file with a monitor. However, a computer can only understand a picture as pixels or a tree-dimension array with values from 0 to 255. OCR applications can help translate nearby pixels into characters, words, and sentences. In this paper, we propose a transformer model to solve the Vietnamese OCR problem which is optimized or shortened to fit on the GPU while still producing solid results and loaded pre-trained from the HuggingFace Hub [1]. The proposed model achieves the maximum level of accuracy, 96.2%, with a CER of 0.8% in case of training on a labeled dataset that could not discriminate between single and compound words. The simulations result also proves that the training and assessment losses are reduced quickly in the first half and steadily in the second half. Due to the complexity of the Vietnamese and a few studies related to identifying Vietnamese through images, our study can be considered as an effective and supportive model for optical character recognition and a basis for related research.
Chinese Sentiment Analysis Using Deep Learning Methods

Sentiment analysis has attracted increasing attention and applied widely in various fields over the past couple of decades. Meanwhile, the rapid development of deep learning methods makes it possible to achieve giant leaps on the performance of sentiment analysis. However, most of the current state-of-the-art sentiment analysis results are designed for English and other alphabetic-based languages, while attention paid on sentiment analysis on Chinese are much less. This article provides an overview on recent development of Chinese sentiment analysis using deep learning methods and makes comparisons between them. Finally, future possible directions on this topic are discussed.
English-Chinese Translation using Neural Machine Models

During the last few years, neural machine translation (NMT) as a notable branch of machine translation has been increasing its popularity both in research and in practice. In particular, neural machine translation between English and Chinese, which are two of the most widely used languages all over the world, is receiving more and more attention. In this review, we discuss current mainstream models for neural machine translation, including recurrent neural network (RNN), convolution neural network (CNN), and self-attention network (SAN) or transformer. The mechanisms of these models are illustrated. Moreover, examples of studies and applications of them are analyzed. In addition, comparisons on the performance of different models are implemented.
Memory-Based Method using Prototype Augmentation for Continual Relation Extraction

Continual relation extraction is a field that applies continual learning in relation extraction, which aims to train the model with new relations without losing the accurate classification of old ones. There are two significant challenges in continual Learning: catastrophic forgetting and knowledge transfer. Some previous work has shown that the memory-based method, which stores a few training examples in old tasks and retrains them while training new tasks, can solve these problems of continual learning, and it is well-performed in natural language processing tasks. However, memory-based methods tend to be overfitting and low-performed on imbalanced datasets. A combination of consistent representation learning and the prototype augmentation mechanism is proposed to solve this problem. We also reduce the real data storage by using the virtual data points, instead of using all data are the real ones. These virtual data points are generated by the prototypes of relations obtained through the data augmentation process. Experiments on FewRel and TACRED datasets reveal that our method performs better than the latest baselines in catastrophic forgetting.
A Hardware Implementation for Deep Reinforcement Learning Machine

Deep Reinforcement Learning (DRL) algorithm is used in many areas of life where an agent learns how to interact with the environment to achieve a certain goal. The Deep Q-Network (DQN) has become a benchmark and building point for many DRL researchers. The DQN maps input states to (action, Q-value) pairs. In this paper, we propose a hardware architecture to implement the DQN algorithm, suitable for real-time applications. Its main features are low power and suitable for limited hardware resources. We have implemented the design on Xilinx Virtex 7 (XC7VX485tffg1927-1) with a Mountain Car simulation environment. Performance results are evaluated in terms of hardware resources, frequency, and power consumption with other reinforcement learning algorithms. Our work has been processed at 131MHz, the neural is 2x24x24x3 and the power consumption is 0.921 w.
A Real-time Border Surveillance System using Deep Learning and Edge Computing

Border security has always been one of the top priority obligations and responsibilities in protecting the peace of nations. Most nations have thousands of kilometers of long borders, where illegal activities occur frequently. To ensure safety, robust and timely border surveillance systems are in high demand. However, current border surveillance systems using cloud architecture have faced several problems with high latency, bandwidth consumption, and security risks. In this paper, we introduce a real-time border surveillance system based on edge computing that shifts computation tasks from cloud to edge, resulting in alleviating existing problems. In detail, we produce a lightweight human detection model based on the MobileNet architecture, namely BorderEdge, to operate on resource-constrained devices effectively. Our experiment results on Raspberry Pi 4 show that our system could achieve high accuracy with 29.6 frames per second (FPS) and a low memory footprint.
Approaching semi-supervised collaborative learning model for remote sensing image analysis

Remote sensing image data often has many advantages in mapping, target recognition, object tracking, and so on. However, remote sensing image data often face problems such as big data, multiple dimensions, and time series. These things greatly affect the problem of remote sensing image analysis. In this paper, we extend the collaborative clustering algorithm for remote sensing image analysis by using a fuzzy semi-supervised technique. The proposed algorithm applies in the case of very few labeled data. In particular, the collaborative learning model can help reduce centralized computation at one site and instead process decentralized data at different sites. Thereby, it is possible to optimize the calculation ability with big data problems such as remote sensing image data. Moreover, by using a semi-supervised technique, it is possible to take advantage of a supervised clustering technique with a small amount of labeled data to improve the accuracy of clustering the results. Experiments on some actual remote sensing image data show that the proposed method gives significantly better results than the previously proposed methods. This result shows the potential of collaborative clustering in big data processing and improves the clustering quality by using semi-supervised fuzzy clustering with a small amount of labeled data.
Research on Learning from Demonstration System of Manipulator Based on the Improved Soft Actor-Critic Algorithm

With the promotion of artificial intelligence technology, service robots play an increasingly important role in human life. Compared with industrial robots, the audience of service robots is mostly non-professionals, so robots need to have stronger learning ability and can perform related tasks through human demonstration. This paper designs and develops a robot Learning from Demonstration (LFD) system based on the improved Soft Actor-Critic algorithm. Through continuous maximum entropy inverse reinforcement learning, the human demonstration data can be transformed into a robot executable task model by training. Finally, it is verified via simulation and experiments that the robot can effectively reproduce the human demonstration task through LFD system training.
Research on General Terminal of Intelligent Measurement and Control of Distribution Network Based on Open Architecture

With the continuous advancement of the construction of new power systems, the data information of the power system is increasing exponentially, and the spatiotemporal distribution of the use of smart terminals and electrical equipment is more scattered. Data collection and analysis of traditional power systems will be a huge challenge. In order to meet the actual needs of multi-scenario, multi-service, and changeable measurement and control objects of intelligent distribution network, this paper firstly studies the design of general terminal open architecture for distribution network multi-measurement and control scenarios, proposes a data service architecture based on open architecture, and establishes a self-description-based open architecture design. The unified information model of the Internet of Things protocol, research and development of intelligent measurement, and the controlling of universal terminals meet multiple communication methods, and realize the deployment verification of intelligent measurement.