VNU Journal of Science: Computer Science and Communication Engineering

In the clinical diagnosis of epilepsy using electroencephalogram (EEG) data, an accurate automatic epileptic spikes  detection system is highly useful and  meaningful in that the conventional manual process is not only very tedious and time-consuming, but also subjective since it depends on the knowledge and experience of the doctors. In this paper, motivated by significant advantages and lots of achieved successes of deep learning in  data mining, we apply Deep Belief Network (DBN), which is one of the breakthrough models laid the foundation for deep learning, to detect epileptic spikes in EEG data. It is really useful in practice because the promising quality evaluation of the spike detection system is higher than $90$\%.  In particular, to construct  accurate detection model for non-spikes and spikes, a new set of detailed features of epileptic spikes is proposed. These features were then fed to the DBN which is modified from a generative model into a discriminative model to aim at classification accuracy. The experiment results indicate that it is possible to use deep learning models for epileptic spike detection with very high performance in item of sensitivity, selectivity, specificity and accuracy  92.82%,  97.83% , 96.41%, and 96.87%, respectively.

This paper presents a human action recognition method using Dynamic Time Warping and voting algorithms on 3D human skeletal models. In this method, human actions which are the combinations of multiple body part movements are described by feature matrices in concerning with both spatial and temporal domains. The feature matrices are created based on the spatial selection of time series of relative angles between body parts. Then, action recognition is done by applying a classifier based on the combination of Dynamic Time Warping (DTW) and a Voting algorithm to the feature matrices. Experimental results show that the performance of our action recognition method obtains high recognition accuracy and reliable computation speed in order to be applied in real time human action recognition systems. 

The Internet of Things (IoT) is an evolution of connected networks including million chatty embedded devices. A huge amount of data generated day by day by things must be aggregated and analyzed with technologies of the "Big Data Analytics". It requires coordination of complex components deployed both on premises and Cloud platforms. This article proposes BDAaaS, a flexibly adaptive cloud-based framework for real-time Big Data analytics. The framework collects and analyzes data for IoT applications reusing existing components such as IoT gateways, Message brokers and Big Data Analytics platforms which are deployed automatically. We demonstrate and evaluate BDAaaS with the implementation of a smart-grid usecase using dataset originating from a practical source. The results show that our approach can generate predictive power consumption fitting well with real consumption curve, which proves its soundness.

In this paper, we first introduce notion of linguistic intuitionistic fuzzy relations. This notion may be useful in situations when each correspondence of objects is presented as two labels such that the first expresses degree of membership, and the second expresses degree of non-membership as in the intuitionistic fuzzy theory. Sanchez's approach for medical diagnosis is extended using linguistic intuitionistic fuzzy relation.

In this paper, we study performances of multi-hop transmission protocols in underlay cognitive radio (CR) networks under impact of transceiver hardware impairment. In the considered protocols, cooperative communication is used to enhance reliability of data transmission at each hop on an established route between a secondary source and a secondary destination. For performance evaluation, we derive exact and asymptotic closed-form expressions of outage probability and average number of time slots over Rayleigh fading channel. Then, computer simulations are performed to verify the derivations. Results present that the cooperative -based multi-hop transmission protocols can obtain better performance and diversity gain, as compared with multi-hop scheme using direct transmission (DT). However, with the same number of hops, these protocols use more time slots than the DT protocol.

One of the emerging research trends in natural language understanding is machine reading comprehension (MRC) which is the task to find answers to human questions based on textual data. While many datasets have been developed for MRC research for other languages, there is a lack of such resources for the Vietnamese language. Although many datasets and methodologies have been developed for English and Chinese, many Vietnamese machine reading comprehension limitations need to be solved further. Existing Vietnamese datasets for MRC research concentrate solely on answerable questions. However, in reality, questions can be unanswerable for which the correct answer is not stated in the given textual data. To address the weakness, we provide the research community with a benchmark dataset named UIT-ViQuAD 2.0 for evaluating the MRC task and question answering systems for the Vietnamese language. We use UIT-ViQuAD 2.0 as a benchmark dataset for the shared task on Vietnamese machine reading comprehension (VLSP2021-MRC) at the Eighth Workshop on Vietnamese Language and Speech Processing (VLSP 2021). This task attracted 77 participant teams from 34 universities and other organizations. Each participant was provided with the training data, including 28,457 annotated question-answer pairs, and returned the result on a public test set of more than 3,821 questions and a private test set of 3,712 questions. In this article, we present details of the organization of the shared task, an overview of the methods employed by shared-task participants, and the results. The highest performances in this competition are 77.24% (in EM) and 67.43% (in F1-score) on the private test set. The Vietnamese MRC systems proposed by the top 3 teams use XLM-RoBERTa, a powerful pre-trained language model based on the transformer architecture that has achieved state-of-the-art results on many natural language processing tasks. We believe that releasing the UIT-ViQuAD 2.0 dataset motivates more researchers to improve Vietnamese machine reading comprehension.

Artificial Immune System (AIS) is a multidisciplinary research area that combines the principles of immunologyand computation. Negative Selection Algorithms (NSA) is one of the most popular models of AIS mainly designed for one-class learning problems such as anomaly detection. Positive Selection Algorithms (PSA) is the twin brother of NSA with quite similar performance for AIS. Both NSAs and PSAs comprise of two phases: generating a set D of detectors from a given set S of selves (detector generation phase); and then detecting if a given cell (new data instace) is self or non-self using the generated detector set (detection phase). In this paper, we propose a novel approach to combining NSAs and PSAs that employ binary representation and r-chunk matching rule. The new algorithm achieves smaller detector storage complexity and potentially better detection time in comparison with single NSAs or PSAs.

The soft error rates per single-bit due to alpha particles in sub-micron technology is expectedly reducedas the feature size is shrinking. On the other hand, the complexity and density of integrated systems are accelerating which demand ecient soft error protection mechanisms, especially for on-chip communication. Using soft error protection method has to satisfy tight requirements for the area and energy consumption, therefore a low complexity and low redundancy coding method is necessary. In this work, we propose a method to enhance Parity Product Code (PPC) and provide adaptation methods for this code. First, PPC is improved as forward error correcting using transposable retransmissions. Then, to adapt with dierent error rates, an augmented algorithm for configuring PPC is introduced. The evaluation results show that the proposed mechanism has coding rates similar to Parity check’s and outperforms the original PPC.Keywords Error Correction Code, Fault-Tolerance, Network-on-Chip. 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Liver segmentation is relevant for several clinical applications. Automatic liver segmentation using convolutional neural networks (CNNs) has been recently investigated. In this paper, we propose a new approach of combining a largest connected component (LCC) algorithm, as a post-processing step, with CNN approaches to improve liver segmentation accuracy. Specifically, in this study, the algorithm is combined with three well-known CNNs for liver segmentation: FCN-CRF, DRIU and V-net. We perform the experiment on a variety of liver CT images, ranging from non-contrast enhanced CT images to low-dose contrast enhanced CT images. The methods are evaluated using Dice score, Haudorff distance, mean surface distance, and false positive rate between the liver segmentation and the ground truth. The quantitative results demonstrate that the LCC algorithm statistically significantly improves results of the liver segmentation on non-contrast enhanced and low-dose images for all three CNNs. 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