• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.369-376
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• 2021

One of the most active areas of research in the field of affective computing and signal processing is emotion recognition. This paper proposes emotion recognition of low-resource (Sindhi) language. This work's uniqueness is that it examines the emotions of languages for which there is currently no publicly accessible dataset. The proposed effort has provided a dataset named MAVDESS (Mehran Audio-Visual Dataset Mehran Audio-Visual Database of Emotional Speech in Sindhi) for the academic community of a significant Sindhi language that is mainly spoken in Pakistan; however, no generic data for such languages is accessible in machine learning except few. Furthermore, the analysis of various emotions of Sindhi language in MAVDESS has been carried out to annotate the emotions using line features such as pitch, volume, and base, as well as toolkits such as OpenSmile, Scikit-Learn, and some important classification schemes such as LR, SVC, DT, and KNN, which will be further classified and computed to the machine via Python language for training a machine. Meanwhile, the dataset can be accessed in future via https://doi.org/10.5281/zenodo.5213073.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.86-109
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• 2019

Cloud computing systems' performance is still a central focus of research for determining optimal resource utilization. Running several existing benchmarks simultaneously serves to acquire performance information from specific cloud system resources. However, the complexity of monitoring the existing performance of computing systems is a challenge requiring an efficient and interactive user directing performance-monitoring system. In this paper, we propose an effective multivariate control framework for monitoring cloud systems performance. The proposed framework utilizes the hardware cloud systems performance metrics, collects and displays the performance measurements in terms of meaningful graphics, stores the graphical information in a database, and provides the data on-demand without requiring a third party software. We present performance metrics in terms of CPU usage, RAM availability, number of cloud active machines, and number of running processes on the selected machines that can be monitored at a high control level by either using a cloud service customer or a cloud service provider. The experimental results show that the proposed framework is reliable, scalable, precise, and thus outperforming its counterparts in the field of monitoring cloud performance.

• Journal of Society of Preventive Korean Medicine
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• v.22 no.3
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• pp.73-81
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• 2018

Objectives : 'Geumnaeng method'' is a well-known folk remedy, but It has not been studied in academia. This study was conducted to review the chinese published articles on Geumnaeg method (Jinleng method) of Chinese traditional medicine. Methods : The author searched Chinese published papers from 2000 to 2018 via CNKI(China National Knowledge Infrastructure) database by using keyword 'Jinleng', 'Jinleng method', and analyzed the papers covered Jinleng method health preservation, and classified them including periods, type of study, target symptoms, and comparison between countries. Results : 17 studies were reviewed. The study of Jinleng method in China began in 2005. 8 articles (47.1%) were review articles, 4 articles (23.5%) of the case report and case series, and 5 articles (29.4%) were clinical studies. Clinical studies have increased since 2008. Most of the studies related to genital disorders and sexual function were mainly performed. There were differences between Japan, Korea and China in Jinleng method. Conclusions : Various disease and symptoms was researched with Jinleng method in China. Research in China is more active than other country. We need to increase the level of evidence of Jinleng method's effectiveness through additional studies in the future.

• Journal of Microbiology and Biotechnology
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• v.29 no.8
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• pp.1255-1265
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• 2019

To investigate the diversity of gastrointestinal microflora and lignocellulose-degrading enzymes in wild Asian elephants, three of these animals living in the same group were selected for study from the Wild Elephant Valley in the Xishuangbanna Nature Reserve of Yunnan Province, China. Fresh fecal samples from the three wild Asian elephants were analyzed by metagenomic sequencing to study the diversity of their gastrointestinal microbes and cellulolytic enzymes. There were a high abundance of Firmicutes and a higher abundance of hemicellulose-degrading hydrolases than cellulose-degrading hydrolases in the wild Asian elephants. Furthermore, there were a high abundance and a rich diversity of carbohydrate active enzymes (CAZymes) obtained from the gene set annotation of the three samples, with the majority of them showing low identity with the CAZy database entry. About half of the CAZymes had no species source at the phylum or genus level. These indicated that the wild Asian elephants might possess greater ability to digest hemicellulose than cellulose to provide energy, and moreover, the gastrointestinal tracts of these pachyderms might be a potential source of novel efficient lignocellulose-degrading enzymes. Therefore, the exploitation and utilization of these enzyme resources could help us to alleviate the current energy crisis and ensure food security.

• Computers and Concrete
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• v.23 no.6
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• pp.377-398
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• 2019

Behavior of RC beam-column joint is very complex as the composite material behaves differently in elastic and inelastic range. The approaches generally used for predicting joint shear strength are either based on theoretical, strut-and-tie or empirical methods. These approaches are incapable of predicting the accurate response of the joint for entire range of loading. In the present study a new generalized RC beam-column joint shear strength model based on hybrid approach i.e. combined strut-and-tie and empirical approach has been proposed. The contribution of governing parameters affecting the joint shear strength under compression has been derived from compressive strut approach whereas; the governing parameters active under tension has been extracted from empirical approach. The proposed model is applicable for various conditions such as, joints reinforced either with or without shear reinforcement, joints with wide beam or wide column, joints with transverse beams and slab, joints reinforced with X-bars, different anchorage of beam bar, and column subjected to various axial loading conditions. The joint shear strength prediction of the proposed model has been compared with 435 experimental results and with eleven popular models from literature. In comparison to other eleven models the prediction of the proposed model is found closest to the experimental results. Moreover, from statistical analysis of the results, the proposed model has the least coefficient of variation. The proposed model is simple in application and can be effectively used by designers.

• Safety and Health at Work
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• v.9 no.4
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• pp.479-485
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• 2018

The aim of this study is to validate an integrated air monitoring approach for assessing airborne formaldehyde (FA) in the workplace. An active sampling by silica gel impregnated with 2,4-dinitrophenylhydrazine, a passive solid phase microextraction technique using O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine as on-fiber derivatization reagent, an electrochemical direct-reading monitor, and an enzyme-based badge were evaluated and tested over a range of 0.020-5.12 ppm, using dynamically generated FA air concentrations. Simple linear regression analysis showed the four methods were suitable for evaluating airborne FA. Personal and area samplings in 12 anatomy pathology departments showed that the international occupational exposure limits in the GESTIS database were frequently exceeded. This monitoring approach would allow a fast, easy-to-use, and economical evaluation of both current work practices and eventual changes made to reduce FA vapor concentrations.

• Korea Trade Review
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• v.44 no.4
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• pp.87-100
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• 2019

A country's participation in international technical standards development directly relates to its trade competitiveness. Many countries adopt international standards or influence their development process through the ISO/IEC. This study applies the ABCD Model to assess such activities through a constructed ITS index, not only in terms of 'level' of contribution but also in terms of 'process' participation that can give important implications related to the future potential influence of countries in standards development. Having examined seven countries, Korea showed the lowest score implying the need to enhance its performance in both 'international standards adoption' and 'internationalization of domestic standards'. Korea needs to more actively participate in currently on-going standards development processes, establish more offices that improve accuracy in the development, and consider participating in additional committees where domestic interests may potentially be at stake. Although KATS has improved greatly in regards to its relevant activities and database construction, a more active and specific plan must be made to allow its efforts to successfully influence Korea in international standards development. Confronted with strong challenges from directly competing countries in trade like China and Japan that showed better ITS scores, there is a need for Korea to step up its research and participation in this field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.131-146
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• 2021

Multimodal biometric-based recognition has been an active topic because of its higher convenience in recent years. Due to high user convenience of finger, finger-based personal identification has been widely used in practice. Hence, taking Finger-Print (FP), Finger-Vein (FV) and Finger-Knuckle-Print (FKP) as the ingredients of characteristic, their feature representation were helpful for improving the universality and reliability in identification. To usefully fuse the multimodal finger-features together, a new robust representation algorithm was proposed based on hierarchical model. Firstly, to obtain more robust features, the feature maps were obtained by Gabor magnitude feature coding and then described by Local Binary Pattern (LBP). Secondly, the LGBP-based feature maps were processed hierarchically in bottom-up mode by variable rectangle and circle granules, respectively. Finally, the intension of each granule was represented by Local-invariant Gray Features (LGFs) and called Hierarchical Local-Gabor-based Gray Invariant Features (HLGGIFs). Experiment results revealed that the proposed algorithm is capable of improving rotation variation of finger-pose, and achieving lower Equal Error Rate (EER) in our homemade database.

• Convergence Security Journal
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• v.20 no.5
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• pp.41-52
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• 2020

This paper implements a priority algorithm for active response to security event risk, and implements an event scheduler that performs efficient event processing based on this. According to the standards that have global standards such as CVE and CVSS, standards for scoring when security events are executed are prepared and standardized so that priorities can be more objectively set. So, based on this, we build a security event database and use it to perform scheduling. In addition, by developing and applying the security event scheduling priority algorithm according to the situation of security events in Korea, it will contribute to securing the reliability of information protection and industrial development of domestic or ganizations and companies.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.63-68
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• 2020

This paper proposes a face authentication system based on deep learning framework. The proposed system is consisted of face region detection and feature extraction using deep learning algorithm, and performed the face authentication using joint-bayesian matrix learning algorithm. The performance of proposed paper is evaluated by various face database , and the face image of one person consists of 2 images. The face authentication algorithm was performed by measuring similarity by applying 2048 dimension characteristic and combined Bayesian algorithm through Deep Neural network and calculating the same error rate that failed face certification. The result of proposed paper shows that the proposed system using deep learning and joint bayesian algorithms showed the equal error rate of 1.2%, and have a good performance compared to previous approach.