Vol. 15, No. 5, Oct. 2019
Young-Sik Jeong, Jong Hyuk Park
Vol. 15, No. 5, pp. 1029-1035, Oct. 2019
Keywords: Blockchain and Crypto Currency, Cloud Computing, Internet of Things, Sentiment Analysis
Show / Hide AbstractIn recent years, artificial intelligence (AI) services have become one of the most essential parts to extend human capabilities in various fields such as face recognition for security, weather prediction, and so on. Various learning algorithms for existing AI services are utilized, such as classification, regression, and deep learning, to increase accuracy and efficiency for humans. Nonetheless, these services face many challenges such as fake news spread on social media, stock selection, and volatility delay in stock prediction systems and inaccurate moviebased recommendation systems. In this paper, various algorithms are presented to mitigate these issues in different systems and services. Convolutional neural network algorithms are used for detecting fake news in Korean language with a Word-Embedded model. It is based on k-clique and data mining and increased accuracy in personalized recommendation-based services stock selection and volatility delay in stock prediction. Other algorithms like multi-level fusion processing address problems of lack of real-time database.
Mihui Kim, Younghee Park, Pankaj Balasaheb Dighe
Vol. 15, No. 5, pp. 1036-1054, Oct. 2019
Keywords: Incentive Method, Internet of Things (IoT) Model, Mobile Crowd Sensing (MCS), Privacy-Preserving, Using Group Signature
Show / Hide AbstractRecently, concomitant with a surge in numbers of Internet of Things (IoT) devices with various sensors, mobile crowdsensing (MCS) has provided a new business model for IoT. For example, a person can share road traffic pictures taken with their smartphone via a cloud computing system and the MCS data can provide benefits to other consumers. In this service model, to encourage people to actively engage in sensing activities and to voluntarily share their sensing data, providing appropriate incentives is very important. However, the sensing data from personal devices can be sensitive to privacy, and thus the privacy issue can suppress data sharing. Therefore, the development of an appropriate privacy protection system is essential for successful MCS. In this study, we address this problem due to the conflicting objectives of privacy preservation and incentive payment. We propose a privacy-preserving mechanism that protects identity and location privacy of sensing users through an on-demand incentive payment and group signatures methods. Subsequently, we apply the proposed mechanism to one example of MCS—an intelligent parking system—and demonstrate the feasibility and efficiency of our mechanism through emulation.
Miaomiao Liu, Jingfeng Guo, Jing Chen
Vol. 15, No. 5, pp. 1055-1067, Oct. 2019
Keywords: Common Neighbors, Community Discovery, Similarity, Weighted Networks
Show / Hide AbstractIn view of the deficiencies of existing weighted similarity indexes, a hierarchical clustering method initializeexpand- merge (IEM) is proposed based on the similarity of common neighbors for community discovery in weighted networks. Firstly, the similarity of the node pair is defined based on the attributes of their common neighbors. Secondly, the most closely related nodes are fast clustered according to their similarity to form initial communities and expand the communities. Finally, communities are merged through maximizing the modularity so as to optimize division results. Experiments are carried out on many weighted networks, which have verified the effectiveness of the proposed algorithm. And results show that IEM is superior to weighted common neighbor (CN), weighted Adamic-Adar (AA) and weighted resources allocation (RA) when using the weighted modularity as evaluation index. Moreover, the proposed algorithm can achieve more reasonable community division for weighted networks compared with cluster-recluster-merge-algorithm (CRMA) algorithm.
Achmad Rizal, Risanuri Hidayat, Hanung Adi Nugroho
Vol. 15, No. 5, pp. 1068-1081, Oct. 2019
Keywords: Activity, Complexity, Hjorth Descriptor, Lung Sound, Mobility, Wavelet transform
Show / Hide AbstractSignal complexity is one point of view to analyze the biological signal. It arises as a result of the physiological signal produced by biological systems. Signal complexity can be used as a method in extracting the feature for a biological signal to differentiate a pathological signal from a normal signal. In this research, Hjorth descriptors, one of the signal complexity measurement techniques, were measured on signal sub-band as the features for lung sounds classification. Lung sound signal was decomposed using two wavelet analyses: discrete wavelet transform (DWT) and wavelet packet decomposition (WPD). Meanwhile, multi-layer perceptron and N-fold cross-validation were used in the classification stage. Using DWT, the highest accuracy was obtained at 97.98%, while using WPD, the highest one was found at 98.99%. This result was found better than the multiscale Hjorth descriptor as in previous studies.
Fang-li Guan, Ai-jun Xu, Guang-yu Jiang
Vol. 15, No. 5, pp. 1082-1095, Oct. 2019
Keywords: Camera Calibration Technique, Camera Distortion Correction, Close-Range Photogrammetry, Machine vision, Mobile Terminals, Pinhole Model
Show / Hide AbstractCamera calibration is an important part of machine vision and close-range photogrammetry. Since current calibration methods fail to obtain ideal internal and external camera parameters with limited computing resources on mobile terminals efficiently, this paper proposes an improved fast camera calibration method for mobile terminals. Based on traditional camera calibration method, the new method introduces two-order radial distortion and tangential distortion models to establish the camera model with nonlinear distortion items. Meanwhile, the nonlinear least square L-M algorithm is used to optimize parameters iteration, the new method can quickly obtain high-precise internal and external camera parameters. The experimental results show that the new method improves the efficiency and precision of camera calibration. Terminals simulation experiment on PC indicates that the time consuming of parameter iteration reduced from 0.220 seconds to 0.063 seconds (0.234 seconds on mobile terminals) and the average reprojection error reduced from 0.25 pixel to 0.15 pixel. Therefore, the new method is an ideal mobile terminals camera calibration method which can expand the application range of 3D reconstruction and close-range photogrammetry technology on mobile terminals.
Hyon Hee Kim, Hey Young Rhee
Vol. 15, No. 5, pp. 1096-1107, Oct. 2019
Keywords: Data Mining Ontology, Labeling of Topic Models, Ontology-based Interpretation of Topics, Topic Network Analysis
Show / Hide AbstractIn this paper, we present an ontology-based approach to labeling influential topics of scientific articles. First, to look for influential topics from scientific article, topic modeling is performed, and then social network analysis is applied to the selected topic models. Abstracts of research papers related to data mining published over the 20 years from 1995 to 2015 are collected and analyzed in this research. Second, to interpret and to explain selected influential topics, the UniDM ontology is constructed from Wikipedia and serves as concept hierarchies of topic models. Our experimental results show that the subjects of data management and queries are identified in the most interrelated topic among other topics, which is followed by that of recommender systems and text mining. Also, the subjects of recommender systems and context-aware systems belong to the most influential topic, and the subject of k-nearest neighbor classifier belongs to the closest topic to other topics. The proposed framework provides a general model for interpreting topics in topic models, which plays an important role in overcoming ambiguous and arbitrary interpretation of topics in topic modeling.
Junrui Lv, Xuegang Luo
Vol. 15, No. 5, pp. 1108-1118, Oct. 2019
Keywords: Fuzzy Metric, Image Denoising, Non-local Means Algorithm, Visual Similarity
Show / Hide AbstractNon-local means (NLM) algorithm is an effective and successful denoising method, but it is computationally heavy. To deal with this obstacle, we propose a novel NLM algorithm with fuzzy metric (FM-NLM) for image denoising in this paper. A new feature metric of visual features with fuzzy metric is utilized to measure the similarity between image pixels in the presence of Gaussian noise. Similarity measures of luminance and structure information are calculated using a fuzzy metric. A smooth kernel is constructed with the proposed fuzzy metric instead of the Gaussian weighted L2 norm kernel. The fuzzy metric and smooth kernel computationally simplify the NLM algorithm and avoid the filter parameters. Meanwhile, the proposed FMNLM using visual structure preferably preserves the original undistorted image structures. The performance of the improved method is visually and quantitatively comparable with or better than that of the current state-ofthe- art NLM-based denoising algorithms.
Dong-Ho Lee, Yu-Ri Kim, Hyeong-Jun Kim, Seung-Myun Park, Yu-Jun Yang
Vol. 15, No. 5, pp. 1119-1130, Oct. 2019
Keywords: Artificial intelligence, Fake News Detection, Natural Language Processing
Show / Hide AbstractWith the wide spread of Social Network Services (SNS), fake news—which is a way of disguising false information as legitimate media—has become a big social issue. This paper proposes a deep learning architecture for detecting fake news that is written in Korean. Previous works proposed appropriate fake news detection models for English, but Korean has two issues that cannot apply existing models: Korean can be expressed in shorter sentences than English even with the same meaning; therefore, it is difficult to operate a deep neural network because of the feature scarcity for deep learning. Difficulty in semantic analysis due to morpheme ambiguity. We worked to resolve these issues by implementing a system using various convolutional neural network-based deep learning architectures and “Fasttext” which is a word-embedding model learned by syllable unit. After training and testing its implementation, we could achieve meaningful accuracy for classification of the body and context discrepancies, but the accuracy was low for classification of the headline and body discrepancies.
Kaiqun Hu, Xin Feng
Vol. 15, No. 5, pp. 1131-1140, Oct. 2019
Keywords: Canopy Volume Measurement, Tracer Deposition Device, Ultrasonic Sensor, Variable Rate Spraying System
Show / Hide AbstractCharacteristics of fruit tree canopies are important target information for adjusting the pesticide application rate in variable rate spraying in orchards. Therefore, the target detection of the canopy characteristics is very important. In this study, a canopy volume measurement method for peach trees was presented and a variable rate spraying system based on canopy volume measurement was developed using the ultrasonic sensing, one of the most effective target detection method. Ten ultrasonic sensors and two flow control units were mounted on the orchard air-assisted sprayer. The ultrasonic sensors were used to detect the canopy diameters and the flow controls were used to modify the flow rate of the nozzles in real time. Two treatments were established: a constant application rate of 300 Lha-1 was set as the control treatment for the comparison with the variable rate application at a 0.095 Lm-3 canopy. The tracer deposition at different parts of peach trees and the tracer losses to the ground (between rows and within rows) were analyzed in detail under constant rate and variable rate application. The results showed that there were no significant differences between two treatments in the liquid distribution and the capability to reach the inner parts of the crop canopies.
Phonexay Vilakone, Khamphaphone Xinchang, Doo-Soon Park
Vol. 15, No. 5, pp. 1141-1155, Oct. 2019
Keywords: association rule mining, k-Cliques, Recommendation System
Show / Hide AbstractToday, most approaches used in the recommendation system provide correct data prediction similar to the data that users need. The method that researchers are paying attention and apply as a model in the recommendation system is the communities’ detection in the big social network. The outputted result of this approach is effective in improving the exactness. Therefore, in this paper, the personalized movie recommendation system that combines data mining for the k-clique method is proposed as the best exactness data to the users. The proposed approach was compared with the existing approaches like k-clique, collaborative filtering, and collaborative filtering using k-nearest neighbor. The outputted result guarantees that the proposed method gives significant exactness data compared to the existing approach. In the experiment, the MovieLens data were used as practice and test data.
An Optimization Method for the Calculation of SCADA Main Grid's Theoretical Line Loss Based on DBSCANHongyi Cao, Qiaomu Ren, Xiuguo Zou, Shuaitang Zhang, Yan Qian
Vol. 15, No. 5, pp. 1156-1170, Oct. 2019
Keywords: Boxplot Method, DBSCAN Clustering Algorithm, Main Grid, SCADA, Theoretical Line Loss
Show / Hide AbstractIn recent years, the problem of data drifted of the smart grid due to manual operation has been widely studied by researchers in the related domain areas. It has become an important research topic to effectively and reliably find the reasonable data needed in the Supervisory Control and Data Acquisition (SCADA) system has become an important research topic. This paper analyzes the data composition of the smart grid, and explains the power model in two smart grid applications, followed by an analysis on the application of each parameter in densitybased spatial clustering of applications with noise (DBSCAN) algorithm. Then a comparison is carried out for the processing effects of the boxplot method, probability weight analysis method and DBSCAN clustering algorithm on the big data driven power grid. According to the comparison results, the performance of the DBSCAN algorithm outperforming other methods in processing effect. The experimental verification shows that the DBSCAN clustering algorithm can effectively screen the power grid data, thereby significantly improving the accuracy and reliability of the calculation result of the main grid’s theoretical line loss.
Yeongsu Cho, Incheol Kim
Vol. 15, No. 5, pp. 1171-1178, Oct. 2019
Keywords: Attribute Recognition, Image Understanding, Visual Dialog
Show / Hide AbstractThis study proposes a deep neural network model based on an encoder–decoder structure for visual dialogs. Ongoing linguistic understanding of the dialog history and context is important to generate correct answers to questions in visual dialogs followed by questions and answers regarding images. Nevertheless, in many cases, a visual understanding that can identify scenes or object attributes contained in images is beneficial. Hence, in the proposed model, by employing a separate person detector and an attribute recognizer in addition to visual features extracted from the entire input image at the encoding stage using a convolutional neural network, we emphasize attributes, such as gender, age, and dress concept of the people in the corresponding image and use them to generate answers. The results of the experiments conducted using VisDial v0.9, a large benchmark dataset, confirmed that the proposed model performed well.
Learning-Based Multiple Pooling Fusion in Multi-View Convolutional Neural Network for 3D Model Classification and RetrievalHui Zeng, Qi Wang, Chen Li, Wei Song
Vol. 15, No. 5, pp. 1179-1191, Oct. 2019
Keywords: Learning-Based Multiple Pooling Fusion, Multi-View Convolutional Neural Network, 3D Model Classification, 3D Model Retrieval
Show / Hide AbstractWe design an ingenious view-pooling method named learning-based multiple pooling fusion (LMPF), and apply it to multi-view convolutional neural network (MVCNN) for 3D model classification or retrieval. By this means, multi-view feature maps projected from a 3D model can be compiled as a simple and effective feature descriptor. The LMPF method fuses the max pooling method and the mean pooling method by learning a set of optimal weights. Compared with the hand-crafted approaches such as max pooling and mean pooling, the LMPF method can decrease the information loss effectively because of its “learning” ability. Experiments on ModelNet40 dataset and McGill dataset are presented and the results verify that LMPF can outperform those previous methods to a great extent.
Intelligent Resource Management Schemes for Systems, Services, and Applications of Cloud Computing Based on Artificial IntelligenceJongBeom Lim, DaeWon Lee, Kwang-Sik Chung, HeonChang Yu
Vol. 15, No. 5, pp. 1192-1200, Oct. 2019
Keywords: Artificial intelligence, Cloud Computing, Edge-Cloud Systems, Fog Computing, Resource Management
Show / Hide AbstractRecently, artificial intelligence techniques have been widely used in the computer science field, such as the Internet of Things, big data, cloud computing, and mobile computing. In particular, resource management is of utmost importance for maintaining the quality of services, service-level agreements, and the availability of the system. In this paper, we review and analyze various ways to meet the requirements of cloud resource management based on artificial intelligence. We divide cloud resource management techniques based on artificial intelligence into three categories: fog computing systems, edge-cloud systems, and intelligent cloud computing systems. The aim of the paper is to propose an intelligent resource management scheme that manages mobile resources by monitoring devices' statuses and predicting their future stability based on one of the artificial intelligence techniques. We explore how our proposed resource management scheme can be extended to various cloud-based systems.
Mean-VaR Portfolio: An Empirical Analysis of Price Forecasting of the Shanghai and Shenzhen Stock MarketsXimei Liu, Zahid Latif, Daoqi Xiong, Sehrish Khan Saddozai, Kaif Ul Wara
Vol. 15, No. 5, pp. 1201-1210, Oct. 2019
Keywords: ARIMA Model, Neural Network, Non-linear Sequence, Stock Price
Show / Hide AbstractStock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.
Kee Sung Kim
Vol. 15, No. 5, pp. 1211-1217, Oct. 2019
Keywords: Database Encryption, Order-Preserving Encryption, Order-Revealing Encryption
Show / Hide AbstractDeveloping methods to search over an encrypted database (EDB) have received a lot of attention in the last few years. Among them, order-revealing encryption (OREnc) and order-preserving encryption (OPEnc) are the core parts in the case of range queries. Recently, some ideally-secure OPEnc schemes whose ciphertexts reveal no additional information beyond the order of the underlying plaintexts have been proposed. However, these schemes either require a large round complexity or a large persistent client-side storage of size O(n) where n denotes the number of encrypted items stored in EDB. In this work, we propose a new construction of an efficient OPEnc scheme based on an OREnc scheme. Security of our construction inherits the security of the underlying OREnc scheme. Moreover, we also show that the construction of a non-interactive ideally-secure OPEnc scheme with a constant client-side storage is theoretically possible from our construction.
Xin-Xin Wang, Xiao-Ming Zhao, Yu Shen
Vol. 15, No. 5, pp. 1218-1230, Oct. 2019
Keywords: Background Difference Method, Intelligent Traffic System, Motion Object Location, Object Detection, Vehicle Location
Show / Hide AbstractThis study proposes a novel video traffic flow detection method based on machine vision technology. The threeframe difference method, which is one kind of a motion evaluation method, is used to establish initial background image, and then a statistical scoring strategy is chosen to update background image in real time. Finally, the background difference method is used for detecting the moving objects. Meanwhile, a simple but effective shadow elimination method is introduced to improve the accuracy of the detection for moving objects. Furthermore, the study also proposes a vehicle matching and tracking strategy by combining characteristics, such as vehicle’s location information, color information and fractal dimension information. Experimental results show that this detection method could quickly and effectively detect various traffic flow parameters, laying a solid foundation for enhancing the degree of automation for traffic management.
Yeonguk Yu, Yoon-Joong Kim
Vol. 15, No. 5, pp. 1231-1242, Oct. 2019
Keywords: Attention Mechanism, LSTM, Stock Index Prediction, Two-Dimensional Attention
Show / Hide AbstractThis paper presents a two-dimensional attention-based long short-memory (2D-ALSTM) model for stock index prediction, incorporating input attention and temporal attention mechanisms for weighting of important stocks and important time steps, respectively. The proposed model is designed to overcome the long-term dependency, stock selection, and stock volatility delay problems that negatively affect existing models. The 2DALSTM model is validated in a comparative experiment involving the two attention-based models multi-input LSTM (MI-LSTM) and dual-stage attention-based recurrent neural network (DARNN), with real stock data being used for training and evaluation. The model achieves superior performance compared to MI-LSTM and DARNN for stock index prediction on a KOSPI100 dataset.
Runlan Tian, Rupeng Zhao, Xiaofeng Wang
Vol. 15, No. 5, pp. 1243-1257, Oct. 2019
Keywords: Complex Radar Signal, Evidence Theory, Multi-Level Fusion, Similarity
Show / Hide AbstractAs current algorithms unable to perform effective fusion processing of unknown complex radar signals lacking database, and the result is unstable, this paper presents a multi-level fusion processing algorithm for complex radar signals based on evidence theory as a solution to this problem. Specifically, the real-time database is initially established, accompanied by similarity model based on parameter type, and then similarity matrix is calculated. D-S evidence theory is subsequently applied to exercise fusion processing on the similarity of parameters concerning each signal and the trust value concerning target framework of each signal in order. The signals are ultimately combined and perfected. The results of simulation experiment reveal that the proposed algorithm can exert favorable effect on the fusion of unknown complex radar signals, with higher efficiency and less time, maintaining stable processing even of considerable samples.