Vol. 16, No. 2, Apr. 2020
Ji Su Park, Jong Hyuk Park
Vol. 16, No. 2, pp. 239-245, Apr. 2020
Keywords: Big data, Blockchain, Machine Learning
Show / Hide AbstractBlockchain, machine learning, and big data are among the key components of the future IT track. These technologies are used in various fields; hence their increasing application. This paper discusses the technologies developed in various research fields, such as data representation, Blockchain application, 3D shape recognition and classification, query method, classification method, and search algorithm, to provide insights into the future paradigm. In this paper, we present a summary of 18 high-quality accepted articles following a rigorous review process in the fields of Blockchain, machine learning, and big data.
Hanchul Woo, Suk-Jae Jeong, and Jun-Ho Huh
Vol. 16, No. 2, pp. 246-260, Apr. 2020
Keywords: Defense Acquisition Program, ITSM, Military Electronic Service, Satisfaction Level, software engineering
Show / Hide AbstractIT Service Management (ITSM) achieves consolidated management for the IT services supporting the acquisition system, and no outside connections can be established with such ITSM. Issues pertaining to the D2B can be addressed to System Q&A or a Call Center for problem-solving. In other words, internal staff can take the necessary measures for problems by directly connecting with ITSM. Currently, diverse innovative technologies are being used in electronics and ubiquitous computing environments. This allows us to create a better world by providing the backbone for remarkable development in our human society in the fields of electronics, devices, computer science, and engineering. Following the expansion of IT services in the military acquisition sector such as Defense Electronic Procurement, military export/import support system, etc., customers’ dependence on IT for conducting business with the military or related companies is increasing, including the military’s dependence on the same technology for services to the public. Nonetheless, issues pertaining to the simplified/integrated management of complex IT service management systems, including slow system recovery, lack of integrated customer service window, and insufficient information sharing, have become the priority problems that IT managers are required to solve. Therefore, this study conducted research on the integrated management of IT services provided by Korea’s national defense acquisition system, which was developed based on the existing system IT Infrastructure Library (ITIL) v2, and investigated the level of satisfaction with services with focus on ensuring that it can be used for understanding the necessity of the system and its advancement in the future.
Khamis Abdul-Latif Khamis, Huazhu Song, Xian Zhong
Vol. 16, No. 2, pp. 261-276, Apr. 2020
Keywords: Digital Contents, Ontology Formal Representation, Ontology Query Model, Ontology Storage Model
Show / Hide Abstract"Digital contents services are one of the topics that have been intensively studied in the media industry, where various semantic and ontology techniques are applied. However, query execution for ontology data is still inefficient, lack of sufficient extensible definitions for node relationships, and there is no specific semantic method fit for media data representation. In order to make the machine understand digital contents (DCs) data well, we analyze DCs data, including static data and dynamic data, and use ontology to specify and classify objects and the events of the particular objects. Then the formal representation method is proposed which not only redefines DCs data based on the technology of OWL/RDF, but is also combined with media segmentation methods. At the same time, to speed up the access mechanism of DCs data stored under the persistent database, an ontology-based DCs query solution is proposed, which uses the specified distance vector associated to a surveillance of semantic label (annotation) to detect and track a moving or static object."
Supavit Kongwudhikunakorn, Kitsana Waiyamai
Vol. 16, No. 2, pp. 277-300, Apr. 2020
Keywords: Document Clustering, Document Distance, Short Text Documents, Short Text Document Clustering
Show / Hide Abstract"This paper presents a method for clustering short text documents, such as news headlines, social media statuses, or instant messages. Due to the characteristics of these documents, which are usually short and sparse, an appropriate technique is required to discover hidden knowledge. The objective of this paper is to identify the combination of document representation, document distance, and document clustering that yields the best clustering quality. Document representations are expanded by external knowledge sources represented by a Distributed Representation. To cluster documents, a K-means partitioning-based clustering technique is applied, where the similarities of documents are measured by word mover’s distance. To validate the effectiveness of the proposed method, experiments were conducted to compare the clustering quality against several leading methods. The proposed method produced clusters of documents that resulted in higher precision, recall, F1- score, and adjusted Rand index for both real-world and standard data sets. Furthermore, manual inspection of the clustering results was conducted to observe the efficacy of the proposed method. The topics of each document cluster are undoubtedly reflected by members in the cluster."
Kyoung-Tack Song, Shee-Ihn Kim, Seung-Hee Kim
Vol. 16, No. 2, pp. 301-317, Apr. 2020
Keywords: Blockchain, Chaincode, Deployment Service, Hyperledger Fabric, Patch
Show / Hide Abstract"An enterprise patch-management system (PMS) typically supplies a single point of failure (SPOF) of centralization structure. However, a Blockchain system offers features of decentralization, transaction integrity, user certification, and a smart chaincode. This study proposes a Hyperledger Fabric Blockchain-based distributed patch-management system and verifies its technological feasibility through prototyping, so that all participating users can be protected from various threats. In particular, by adopting a private chain for patch file set management, it is designed as a Blockchain system that can enhance security, log management, latest status supervision and monitoring functions. In addition, it uses a Hyperledger Fabric that owns a practical Byzantine fault tolerant consensus algorithm, and implements the functions of upload patch file set, download patch file set, and audit patch file history, which are major features of PMS, as a smart contract (chaincode), and verified this operation. The distributed ledger structure of Blockchain-based PMS can be a solution for distributor and client authentication and forgery problems, SPOF problem, and distribution record reliability problem. It not only presents an alternative to dealing with central management server loads and failures, but it also provides a higher level of security and availability."
Risk Assessment and Decision-Making of a Listed Enterprise’s L/C Settlement Based on Fuzzy Probability and Bayesian Game TheoryZhang Cheng, Nanni Huang
Vol. 16, No. 2, pp. 318-328, Apr. 2020
Keywords: Bayesian Game Theory, Fuzzy Probability, Listed Enterprise, L/C Settlement
Show / Hide Abstract"Letter of Credit (L/C) is currently a very popular international settlement method frequently used in international trade processes amongst countries around the globe. Compared with other international settlement methods, however, L/C has some obvious shortcomings. Firstly, it is not easy to use due to the sophisticated processes its usage involves. Secondly, it is sometimes accompanied by a few risks and some uncertainty. Thus, highly efficient methods need to be used to assess and control these risks. To begin with, FAHP and KMV methods are used to resolve the problem of incomplete information associated with L/C and then, on this basis, Bayesian game theory is used in order to make more scientific and reasonable decisions with respect to international trade."
Vol. 16, No. 2, pp. 329-342, Apr. 2020
Keywords: Comment Text Set, Emotional Classification, LDA Topic Model, Support Vector Machine
Show / Hide Abstract"In recent years, emotional text classification is one of the essential research contents in the field of natural language processing. It has been widely used in the sentiment analysis of commodities like hotels, and other commentary corpus. This paper proposes an improved W-LDA (weighted latent Dirichlet allocation) topic model to improve the shortcomings of traditional LDA topic models. In the process of the topic of word sampling and its word distribution expectation calculation of the Gibbs of the W-LDA topic model. An average weighted value is adopted to avoid topic-related words from being submerged by high-frequency words, to improve the distinction of the topic. It further integrates the highest classification of the algorithm of support vector machine based on the extracted high-quality document-topic distribution and topic-word vectors. Finally, an efficient integration method is constructed for the analysis and extraction of emotional words, topic distribution calculations, and sentiment classification. Through tests on real teaching evaluation data and test set of public comment set, the results show that the method proposed in the paper has distinct advantages compared with other two typical algorithms in terms of subject differentiation, classification precision, and F1- measure."
Three-Dimensional Shape Recognition and Classification Using Local Features of Model Views and Sparse Representation of Shape DescriptorsHussein Kanaan, Alireza Behrad
Vol. 16, No. 2, pp. 343-359, Apr. 2020
Keywords: Shape Classification, sparse representation, 3D Local Features, 3D Shape Recognition, View Cube
Show / Hide Abstract"In this paper, a new algorithm is proposed for three-dimensional (3D) shape recognition using local features of model views and its sparse representation. The algorithm starts with the normalization of 3D models and the extraction of 2D views from uniformly distributed viewpoints. Consequently, the 2D views are stacked over each other to from view cubes. The algorithm employs the descriptors of 3D local features in the view cubes after applying Gabor filters in various directions as the initial features for 3D shape recognition. In the training stage, we store some 3D local features to build the prototype dictionary of local features. To extract an intermediate feature vector, we measure the similarity between the local descriptors of a shape model and the local features of the prototype dictionary. We represent the intermediate feature vectors of 3D models in the sparse domain to obtain the final descriptors of the models. Finally, support vector machine classifiers are used to recognize the 3D models. Experimental results using the Princeton Shape Benchmark database showed the average recognition rate of 89.7% using 20 views. We compared the proposed approach with state-of-the-art approaches and the results showed the effectiveness of the proposed algorithm."
Min Li, Shaobo Deng, Lei Wang
Vol. 16, No. 2, pp. 360-376, Apr. 2020
Keywords: Class-Oriented Attribute Reduction, Ensemble learning, Multiclass Datasets, Probabilistic Rough Sets
Show / Hide Abstract"Many heuristic attribute reduction algorithms have been proposed to find a single reduct that functions as the entire set of original attributes without loss of classification capability; however, the proposed reducts are not always perfect for these multiclass datasets. In this study, based on a probabilistic rough set model, we propose the class-oriented attribute reduction (COAR) algorithm, which separately finds a reduct for each target class. Thus, there is a strong dependence between a reduct and its target class. Consequently, we propose a type of ensemble constructed on a group of classifiers based on class-oriented reducts with a customized weighted majority voting strategy. We evaluated the performance of our proposed algorithm based on five real multiclass datasets. Experimental results confirm the superiority of the proposed method in terms of four general evaluation metrics."
Suganya Selvaraj, Hanjun Kim, Eunmi Choi
Vol. 16, No. 2, pp. 377-393, Apr. 2020
Keywords: Big Data Analysis, Freight Management System, O2O service, System Architecture
Show / Hide Abstract"Freight management systems require a new business model for rapid decision making to improve their business processes by dynamically analyzing the previous experience data. Moreover, the amount of data generated by daily business activities to be analyzed for making better decisions is enormous. Online-to-offline or offlineto- online (O2O) is an electronic commerce (e-commerce) model used to combine the online and physical services. Data analysis is usually performed offline. In the present paper, to extend its benefits to online and to efficiently apply the big data analysis to the freight management system, we suggested a system architecture based on O2O services. We analyzed and extracted the useful knowledge from the real-time freight data for the period 2014–2017 aiming at further business development. The proposed system was deemed useful for truck management companies as it allowed dynamically obtaining the big data analysis results based on O2O services, which were used to optimize logistic freight, improve customer services, predict customer expectation, reduce costs and overhead by improving profit margins, and perform load balancing."
Zidan Sun, Xiaofeng Zhou, Likai Liang, Yang Mo
Vol. 16, No. 2, pp. 394-405, Apr. 2020
Keywords: Current Capacity, Electromagnetic Environment, Line Parameter, Power Grid, Transmission Line
Show / Hide Abstract"The parameters of transmission lines have an influence on the electromagnetic environment surrounding the line. This paper proposes a method based on phasor measurement unit (PMU) and supervisory control and data acquisition (SCADA) to achieve online estimation of transmission line full parameters, such as resistance, reactance and susceptance. The proposed full parameter estimation method is compared with the traditional method of estimating resistance independently based on SCADA system. Then, the electromagnetic environment is analyzed based on the different parameter estimation methods. The example results illustrate that online estimation of transmission line full parameters is more accurate in the analysis of electromagnetic environment, which further confirms its necessity and significance in engineering application."
Liping Zhang, Song Li, Yingying Guo, Xiaohong Hao
Vol. 16, No. 2, pp. 406-420, Apr. 2020
Keywords: Line Segment k Nearest Neighbor, Line Segment Obstacle Distance, Line Segment Voronoi Diagram, Nearest Neighbor Query, spatial database
Show / Hide Abstract"In order to make up the deficiencies of the existing research results which cannot effectively deal with the nearest neighbor query based on the line segments in obstacle space, the k nearest neighbor query method of line segment in obstacle space is proposed and the STA_OLkNN algorithm under the circumstance of static obstacle data set is put forward. The query process is divided into two stages, including the filtering process and refining process. In the filtration process, according to the properties of the line segment Voronoi diagram, the corresponding pruning rules are proposed and the filtering algorithm is presented. In the refining process, according to the relationship of the position between the line segments, the corresponding distance expression method is put forward and the final result is obtained by comparing the distance. Theoretical research and experimental results show that the proposed algorithm can effectively deal with the problem of k nearest neighbor query of the line segment in the obstacle environment."
Chang-Hyun Roh, Im-Yeong Lee
Vol. 16, No. 2, pp. 421-434, Apr. 2020
Keywords: Blockchain, Distributed Ledger System, electronic voting
Show / Hide AbstractThe development of digital technology has changed the lives of many people in terms of the velocity and convenience of completing tasks. This technology has also been applied to the process of voting, yet electronic voting is seldom used. The existing electronic voting scheme operates by applying various encryption algorithms. This type of electronic voting can be problematic since the administrator is given full authority. The administrator cannot always be trusted, and the contents of the ballot could be forged or tampered by a single point of failure. To resolve these problems, researchers continue to apply blockchain technology to electronic voting. Blockchain technology provides reliability and data integrity because all untrusted network participants have the same data. In this paper, we propose an electronic voting system that secures reliability by applying blockchain technology to electronic voting and ensures secret voting.
Xiuguo Zou, Qiaomu Ren, Hongyi Cao, Yan Qian, Shuaitang Zhang
Vol. 16, No. 2, pp. 435-446, Apr. 2020
Keywords: High Dimensional Data, Machine Learning, Spectral Reflectance, Tea Diseases
Show / Hide AbstractWith the ability to learn rules from training data, the machine learning model can classify unknown objects. At the same time, the dimension of hyperspectral data is usually large, which may cause an over-fitting problem. In this research, an identification methodology of tea diseases was proposed based on spectral reflectance and machine learning, including the feature selector based on the decision tree and the tea disease recognizer based on random forest. The proposed identification methodology was evaluated through experiments. The experimental results showed that the recall rate and the F1 score were significantly improved by the proposed methodology in the identification accuracy of tea disease, with average values of 15%, 7%, and 11%, respectively. Therefore, the proposed identification methodology could make relatively better feature selection and learn from high dimensional data so as to achieve the non-destructive and efficient identification of different tea diseases. This research provides a new idea for the feature selection of high dimensional data and the nondestructive identification of crop diseases.
Samuel Sangkon Lee
Vol. 16, No. 2, pp. 447-459, Apr. 2020
Keywords: Concept Word with Co-occurrence, Importance of the Keyword Candidate, Keyword Extraction, Keyword Pattern, Production Rule, Relation of Sentential Distance and Conceptual Distance
Show / Hide AbstractAfter reading a document, people construct a concept about the information they consumed and merge multiple words to set up keywords that represent the material. With that in mind, this study suggests a smarter and more efficient keyword extraction method wherein scholarly journals are used as the basis for the establishment of production rules based on a concept information of words appearing in a document in a way in which authorprovided keywords are functional although they do not appear in the body of the document. This study presents a new way to determine the importance of each keyword, excluding non-relevant keywords. To identify the validity of extracted keywords, titles and abstracts of journals about natural language and auditory language were collected for analysis. The comparison of author-provided keywords with the keyword results of the developed system showed that the developed system was highly useful, with an accuracy rate as good as up to 96%.
Xiao Ma, Zhongbao Zhang, Sen Su
Vol. 16, No. 2, pp. 460-477, Apr. 2020
Keywords: Energy-Aware, Particle Swarm Optimization, Virtual Data Center Embedding, Virtual Link, Virtual Node
Show / Hide AbstractAs one of the most significant challenges in the virtual data center, the virtual data center embedding has attracted extensive attention from researchers. The existing research works mainly focus on how to design algorithms to increase operating revenue. However, they ignore the energy consumption issue of the physical data center in virtual data center embedding. In this paper, we focus on studying the energy-aware virtual data center embedding problem. Specifically, we first propose an energy consumption model. It includes the energy consumption models of the virtual machine node and the virtual switch node, aiming to quantitatively measure the energy consumption in virtual data center embedding. Based on such a model, we propose two algorithms regarding virtual data center embedding: one is heuristic, and the other is based on particle swarm optimization. The second algorithm provides a better solution to virtual data center embedding by leveraging the evolution process of particle swarm optimization. Finally, experiment results show that our proposed algorithms can effectively save energy while guaranteeing the embedding success rate.
Yongli Liu, Renjie Li
Vol. 16, No. 2, pp. 478-493, Apr. 2020
Keywords: Evolutionary Algorithm, genetic algorithm, Meta Heuristic, Physical Properties, Photon Search
Show / Hide AbstractWe designed a new meta-heuristic algorithm named Photon Search Algorithm (PSA) in this paper, which is motivated by photon properties in the field of physics. The physical knowledge involved in this paper includes three main concepts: Principle of Constancy of Light Velocity, Uncertainty Principle and Pauli Exclusion Principle. Based on these physical knowledges, we developed mathematical formulations and models of the proposed algorithm. Moreover, in order to confirm the convergence capability of the algorithm proposed, we compared it with 7 unimodal benchmark functions and 23 multimodal benchmark functions. Experimental results indicate that PSA has better global convergence and higher searching efficiency. Although the performance of the algorithm in solving the optimal solution of certain functions is slightly inferior to that of the existing heuristic algorithm, it is better than the existing algorithm in solving most functions. On balance, PSA has relatively better convergence performance than the existing metaheuristic algorithms.
Movie Recommendation System Based on Users’ Personal Information and Movies Rated Using the Method of k-Clique and Normalized Discounted Cumulative GainPhonexay Vilakone, Khamphaphone Xinchang, Doo-Soon Park
Vol. 16, No. 2, pp. 494-507, Apr. 2020
Keywords: association rule mining, k-Cliques, Normalized Discounted Cumulative Gain, Recommendation System
Show / Hide AbstractThis study proposed the movie recommendation system based on the user’s personal information and movies rated using the method of k-clique and normalized discounted cumulative gain. The main idea is to solve the problem of cold-start and to increase the accuracy in the recommendation system further instead of using the basic technique that is commonly based on the behavior information of the users or based on the best-selling product. The personal information of the users and their relationship in the social network will divide into the various community with the help of the k-clique method. Later, the ranking measure method that is widely used in the searching engine will be used to check the top ranking movie and then recommend it to the new users. We strongly believe that this idea will prove to be significant and meaningful in predicting demand for new users. Ultimately, the result of the experiment in this paper serves as a guarantee that the proposed method offers substantial finding in raw data sets by increasing accuracy to 87.28% compared to the three most successful methods used in this experiment, and that it can solve the problem of cold-start.
Nonlinear Quality Indices Based on a Novel Lempel-Ziv Complexity for Assessing Quality of Multi-Lead ECGs Collected in Real TimeYatao Zhang, Zhenguo Ma, Wentao Dong
Vol. 16, No. 2, pp. 508-521, Apr. 2020
Keywords: Complexity, ECG Quality Assessment, Encoding LZ Complexity, Entropy
Show / Hide AbstractWe compared a novel encoding Lempel-Ziv complexity (ELZC) with three common complexity algorithms i.e., approximate entropy (ApEn), sample entropy (SampEn), and classic Lempel-Ziv complexity (CLZC) so as to determine a satisfied complexity and its corresponding quality indices for assessing quality of multi-lead electrocardiogram (ECG). First, we calculated the aforementioned algorithms on six artificial time series in order to compare their performance in terms of discerning randomness and the inherent irregularity within time series. Then, for analyzing sensitivity of the algorithms to content level of different noises within the ECG, we investigated their change trend in five artificial synthetic noisy ECGs containing different noises at several signal noise ratios. Finally, three quality indices based on the ELZC of the multi-lead ECG were proposed to assess the quality of 862 real 12-lead ECGs from the MIT databases. The results showed the ELZC could discern randomness and the inherent irregularity within six artificial time series, and also reflect content level of different noises within five artificial synthetic ECGs. The results indicated the AUCs of three quality indices of the ELZC had statistical significance (>0.500). The ELZC and its corresponding three indices were more suitable for multi-lead ECG quality assessment than the other three algorithms.