Journal of Information Processing Systems

The Journal of Information Processing Systems (JIPS) is the official international journal of the Korea Information Processing Society. As information processing systems are progressing at a rapid pace, the Korea Information Processing Society is committed to providing researchers and other professionals with the academic information and resources they need to keep abreast with ongoing developments. The JIPS aims to be a premier source that enables researchers and professionals all over the world to promote, share, and discuss all major research issues and developments in the field of information processing systems and other related fields.

ISSN: 1976-913X (Print), ISSN: 2092-805X (Online)

[April 23, 2019] We announced The 2nd JIPS Survey Paper Awards. Please refer to here for details.
[Jan. 23, 2018] Call for papers about JIPS Future Topic Track - Special Section scheduled in 2019 are registered. Please refer to here for details.
[Nov. 16, 2018] JIPS committee has made a decision for the article processing charge (APC), thus the new policy applies to all published papers after January 1, 2019. For more information, click here.

Latest Publications

Journal of Information Processing Systems, Vol. 15, No.5, 2019

Learning Algorithms in AI System and Services
Young-Sik Jeong and Jong Hyuk Park
Page: 1029~1035, Vol. 15, No.5, 2019

Keywords: Blockchain and Crypto Currency, Cloud Computing, Internet of Things, Sentiment Analysis
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In 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.

Privacy-Preservation Using Group Signature for Incentive Mechanisms in Mobile Crowd Sensing
Mihui Kim, Younghee Park and Pankaj Balasaheb Dighe
Page: 1036~1054, Vol. 15, No.5, 2019

Keywords: Incentive Method, Internet of Things (IoT) Model, Mobile Crowd Sensing (MCS), Privacy-Preserving, Using Group Signature
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Recently, 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.

Community Discovery in Weighted Networks Based on the Similarity of Common Neighbors
Miaomiao Liu, Jingfeng Guo and Jing Chen
Page: 1055~1067, Vol. 15, No.5, 2019

Keywords: Common Neighbors, Community Discovery, Similarity, Weighted Networks
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In 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.

Lung Sound Classification Using Hjorth Descriptor Measurement on Wavelet Sub-bands
Achmad Rizal, Risanuri Hidayat and Hanung Adi Nugroho
Page: 1068~1081, Vol. 15, No.5, 2019

Keywords: Activity, Complexity, Hjorth Descriptor, Lung Sound, Mobility, Wavelet Transform
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Signal 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.

An Improved Fast Camera Calibration Method for Mobile Terminals
Fang-li Guan, Ai-jun Xu and Guang-yu Jiang
Page: 1082~1095, Vol. 15, No.5, 2019

Keywords: Camera Calibration Technique, Camera Distortion Correction, Close-Range Photogrammetry, Machine Vision, Mobile Terminals, Pinhole Model
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Camera 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.

An Ontology-Based Labeling of Influential Topics Using Topic Network Analysis
Hyon Hee Kim and Hey Young Rhee
Page: 1096~1107, Vol. 15, No.5, 2019

Keywords: Data Mining Ontology, Labeling of Topic Models, Ontology-based Interpretation of Topics, Topic Network Analysis
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In 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.

Image Denoising via Fast and Fuzzy Non-local Means Algorithm
Junrui Lv and Xuegang Luo
Page: 1108~1118, Vol. 15, No.5, 2019

Keywords: Fuzzy Metric, Image Denoising, Non-local Means Algorithm, Visual Similarity
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Non-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.

Fake News Detection Using Deep Learning
Dong-Ho Lee, Yu-Ri Kim, Hyeong-Jun Kim, Seung-Myun Park and Yu-Jun Yang
Page: 1119~1130, Vol. 15, No.5, 2019

Keywords: Artificial Intelligence, Fake News Detection, Natural Language Processing
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With 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.

Research on the Variable Rate Spraying System Based on Canopy Volume Measurement
Kaiqun Hu and Xin Feng
Page: 1131~1140, Vol. 15, No.5, 2019

Keywords: Canopy Volume Measurement, Tracer Deposition Device, Ultrasonic Sensor, Variable Rate Spraying System
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Characteristics 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.

Personalized Movie Recommendation System Combining Data Mining with the k-Clique Method
Phonexay Vilakone, Khamphaphone Xinchang and Doo-Soon Park
Page: 1141~1155, Vol. 15, No.5, 2019

Keywords: Association Rule Mining, k-Cliques, Recommendation System
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Today, 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 DBSCAN
Hongyi Cao, Qiaomu Ren, Xiuguo Zou, Shuaitang Zhang and Yan Qian
Page: 1156~1170, Vol. 15, No.5, 2019

Keywords: Boxplot Method, DBSCAN Clustering Algorithm, Main Grid, SCADA, Theoretical Line Loss
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In 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.

Image Understanding for Visual Dialog
Yeongsu Cho and Incheol Kim
Page: 1171~1178, Vol. 15, No.5, 2019

Keywords: Attribute Recognition, Image Understanding, Visual Dialog
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This 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 Retrieval
Hui Zeng, Qi Wang, Chen Li and Wei Song
Page: 1179~1191, Vol. 15, No.5, 2019

Keywords: Learning-Based Multiple Pooling Fusion, Multi-View Convolutional Neural Network, 3D Model Classification, 3D Model Retrieval
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We 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 Intelligence
JongBeom Lim, DaeWon Lee, Kwang-Sik Chung and HeonChang Yu
Page: 1192~1200, Vol. 15, No.5, 2019

Keywords: Artificial Intelligence, Cloud Computing, Edge-Cloud Systems, Fog Computing, Resource Management
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Recently, 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 Markets
Ximei Liu, Zahid Latif, Daoqi Xiong, Sehrish Khan Saddozai and Kaif Ul Wara
Page: 1201~1210, Vol. 15, No.5, 2019

Keywords: ARIMA Model, Neural Network, Non-linear Sequence, Stock Price
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Stock 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.

New Construction of Order-Preserving Encryption Based on Order-Revealing Encryption
Kee Sung Kim
Page: 1211~1217, Vol. 15, No.5, 2019

Keywords: Database Encryption, Order-Preserving Encryption, Order-Revealing Encryption
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Developing 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.

A Video Traffic Flow Detection System Based on Machine Vision
Xin-Xin Wang, Xiao-Ming Zhao and Yu Shen
Page: 1218~1230, Vol. 15, No.5, 2019

Keywords: Background Difference Method, Intelligent Traffic System, Motion Object Location, Object Detection, Vehicle Location
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This 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.

Two-Dimensional Attention-Based LSTM Model for Stock Index Prediction
Yeonguk Yu and Yoon-Joong Kim
Page: 1231~1242, Vol. 15, No.5, 2019

Keywords: Attention Mechanism, LSTM, Stock Index Prediction, Two-Dimensional Attention
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This 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.

Multi-Level Fusion Processing Algorithm for Complex Radar Signals Based on Evidence Theory
Runlan Tian, Rupeng Zhao and Xiaofeng Wang
Page: 1243~1257, Vol. 15, No.5, 2019

Keywords: Complex Radar Signal, Evidence Theory, Multi-Level Fusion, Similarity
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As 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.

Featured Papers

A Survey on Asynchronous Quorum-Based Power Saving Protocols in Multi-Hop Networks
Mehdi Imani, Majid Joudaki, Hamid R. Arabnia and Niloofar Mazhari
Pages: 1436~1458, Vol. 13, No.6, 2017
Keywords: Ad Hoc Networks, Asynchronous Sleep Scheduling Protocols, Power Saving Protocols, Quorum Based Systems
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Fuzzy Linguistic Recommender Systems for the Selective Diffusion of Information in Digital Libraries
Carlos Porcel, Alberto Ching-López, Juan Bernabé-Moreno, Alvaro Tejeda-Lorente and Enrique Herrera-Viedma
Pages: 653~667, Vol. 13, No.4, 2017
Keywords: Digital Libraries, Dissemination of Information, Fuzzy Linguistic Modeling, Recommender Systems
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Granular Bidirectional and Multidirectional Associative Memories: Towards a Collaborative Buildup of Granular Mappings
Witold Pedrycz
Pages: 435~447, Vol. 13, No.3, 2017
Keywords: Allocation of Information Granularity and Optimization, Bidirectional Associative Memory, Collaborative Clustering, Granular Computing, Multi-directional Associative Memory, Prototypes
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Deep Learning in Genomic and Medical Image Data Analysis: Challenges and Approaches
Ning Yu, Zeng Yu, Feng Gu, Tianrui Li, Xinmin Tian and Yi Pan
Pages: 204~214, Vol. 13, No.2, 2017
Keywords: Bioinformatics, Deep Learning, Deep Neural Networks, DNA Genome Analysis, Image Data Analysis, Machine Learning, lincRNA
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A Survey of Multimodal Systems and Techniques for Motor Learning
Ramin Tadayon, Troy McDaniel and Sethuraman Panchanathan
Pages: 8~25, Vol. 13, No.1, 2017
Keywords: Augmented Motor Learning and Training, Multimodal Systems and Feedback, Rehabilitative Technologies
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Survey on 3D Surface Reconstruction
Alireza Khatamian and Hamid R. Arabnia
Pages: 338~357, Vol. 12, No.3, 2016

Keywords: Explicit Surfaces, Implicit Surfaces, Point Cloud, Surface Reconstruction
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A Comprehensive Review of Emerging Computational Methods for Gene Identification
Ning Yu, Zeng Yu, Bing Li, Feng Gu and Yi Pan
Pages: 1~34, Vol. 12, No.1, 2016

Keywords: Cloud Computing, Comparative Methods, Deep Learning, Fourier Transform, Gene Identification, Gene Prediction, Hidden Markov Model, Machine Learning, Protein-Coding Region, Support Vector Machine
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On the Performance of Oracle Grid Engine Queuing System for Computing Intensive Applications
Vladi Kolici, Albert Herrero and Fatos Xhafa
Pages: 491~502, Vol. 10, No.4, 2014
Keywords: Benchmarking, Cloud Computing, Computing Intensive Applications, Genetic Algorithms, Grid Computing, Oracle Grid Engine, Scheduling, Simulation
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Training-Free Fuzzy Logic Based Human Activity Recognition
Eunju Kim and Sumi Helal
Pages: 335~354, Vol. 10, No.3, 2014
Keywords: Activity Semantic Knowledge, Fuzzy Logic, Human Activity Recognition, Multi-Layer Neural Network
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Janus - Multi Source Event Detection and Collection System for Effective Surveillance of Criminal Activity
Cyrus Shahabi, Seon Ho Kim, Luciano Nocera, Giorgos Constantinou, Ying Lu, Yinghao Cai, Gérard Medioni, Ramakant Nevatia and Farnoush Banaei-Kashani
Pages: 1~22, Vol. 10, No.1, 2014
Keywords: Multi-source, Multi-modal Event Detection, Law Enforcement, Criminal Activity, Surveillance, Security, Safety
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The Confinement Problem: 40 Years Later
Alex Crowell, Beng Heng Ng, Earlence Fernandes and Atul Prakash
Pages: 189~204, Vol. 9, No.2, 2013
Keywords: Confinement Problem, Covert Channels, Virtualization, Isolation, Taint Tracking
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An Adaptive Approach to Learning the Preferences of Users in a Social Network Using Weak Estimators
B. John Oommen, Anis Yazidi and Ole-Christoffer Granmo
Pages: 191~212, Vol. 8, No.2, 2012
Keywords: Weak es timators, User's Profiling, Time Varying Preferences
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Indoor Link Quality Comparison of IEEE 802.11a Channels in a Multi-radio Mesh Network Testbed
Asitha U Bandaranayake, Vaibhav Pandit and Dharma P. Agrawal
Pages: 1~20, Vol. 8, No.1, 2012
Keywords: IEEE 802.11a, Indoor Test Bed, Link Quality, Wireless Mesh Networks
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A Survey of RFID Deployment and Security Issues
Amit Grover and Hal Berghel
Pages: 561~580, Vol. 7, No.4, 2011
Keywords: RFID, RFID Standards, RFID Protocols, RFID Security, EPC structure, RFID Applications, RFID Classification
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The Principle of Justifiable Granularity and an Optimization of Information Granularity Allocation as Fundamentals of Granular Computing
Witold Pedrycz
Pages: 397~412, Vol. 7, No.3, 2011
Keywords: Information Granularity, Principle of Justifiable Granularity, Knowledge Management, Optimal Granularity Allocation
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CASPER: Congestion Aware Selection of Path with Efficient Routing in Multimedia Networks
Mohammad S. Obaidat, Sanjay K. Dhurandher and Khushboo Diwakar
Pages: 241~260, Vol. 7, No.2, 2011
Keywords: Routing, Multimedia Networks, Congestion-aware Selection, MANET, CASPER, Performance Evaluation
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An Efficient Broadcast Technique for Vehicular Networks
Ai Hua Ho, Yao H. Ho, Kien A. Hua, Roy Villafane and Han-Chieh Chao
Pages: 221~240, Vol. 7, No.2, 2011
Keywords: V2V Communication Protocols, Vehicular Network, Ad Hoc Network, Broadcast, Broadcasting Storm, Routing
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Security Properties of Domain Extenders for Cryptographic Hash Functions
Elena Andreeva, Bart Mennink and Bart Preneel
Pages: 453~480, Vol. 6, No.4, 2010
Keywords: Hash Functions, Domain Extenders, Security Properties
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Hiding Secret Data in an Image Using Codeword Imitation
Zhi-Hui Wang, Chin-Chen Chang and Pei-Yu Tsai
Pages: 435~452, Vol. 6, No.4, 2010
Keywords: Data Hiding, Steganography, Vector Quantization
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DEESR: Dynamic Energy Efficient and Secure Routing Protocol for Wireless Sensor Networks in Urban Environments
Mohammad S. Obaidat, Sanjay K. Dhurandher, Deepank Gupta, Nidhi Gupta and Anupriya Asthana
Pages: 269~294, Vol. 6, No.3, 2010
Keywords: Sensor Network, Security, Energy Efficiency, Routing, Dynamic Trust Factor
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Challenges to Next Generation Services in IP Multimedia Subsystem
Kai-Di Chang, Chi-Yuan Chen, Jiann-Liang Chen and Han-Chieh Chao
Pages: 129~146, Vol. 6, No.2, 2010
Keywords: IP Multimedia Subsystems, Peer-to-Peer, Web Services, SCIM
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TOSS: Telecom Operations Support Systems for Broadband Services
Yuan-Kai Chen, Chang-Ping Hsu, Chung-Hua Hu, Rong-Syh Lin, Yi-Bing Lin, Jian-Zhi Lyu, Wudy Wu and Heychyi Young
Pages: 1~20, Vol. 6, No.1, 2010
Keywords: Operations Support System (OSS), New Generation Operations Systems and Software (NGOSS), enhanced Telecom Operations Map (eTOM), Internet Protocol Television (IPTV), IP-Virtual Private Network (IP-VPN)
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Providing Efficient Secured Mobile IPv6 by SAG and Robust Header Compression
Tin-Yu Wu, Han-Chieh Chao and Chi-Hsiang Lo
Pages: 117~130, Vol. 5, No.3, 2009
10.3745/JIPS.2009.5.3. 117
Keywords: SAG, RoHC, MIPv6, Handoff Latency, Early Binding Update
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A Survey of Face Recognition Techniques
Rabia Jafri and Hamid R Arabnia
Pages: 41~68, Vol. 5, No.2, 2009
Keywords: Face Recognition, Person Identification, Biometrics
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