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)

[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.
[Nov. 06, 2018] Call for papers about JIPS Award scheduled in 2018 are registered. Please refer to here for details.
[Jan. 01, 2018] Since January 01, 2018, the JIPS has started to manage the three manuscript tracks; 1) Regular Track, 2) Fast Track, and 3) Future Topic Track. Please refer to the details on the author information page.

Latest Publications

Journal of Information Processing Systems, Vol. 14, No.6, 2018

Artificial Intelligence for the Fourth Industrial Revolution
Young-Sik Jeong and Jong Hyuk Park
Page: 1301~1306, Vol. 14, No.6, 2018
Keywords: Artificial Intelligence, Super intelligence, Superconnection
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Artificial intelligence is one of the key technologies of the Fourth Industrial Revolution. This paper introduces the diverse kinds of approaches to subjects that tackle diverse kinds of research fields such as model-based MS approach, deep neural network model, image edge detection approach, cross-layer optimization model, LSSVM approach, screen design approach, CPU-GPU hybrid approach and so on. The research on Superintelligence and superconnection for IoT and big data is also described such as ‘superintelligence-based systems and infrastructures’, ‘superconnection-based IoT and big data systems’, ‘analysis of IoT-based data and big data’, ‘infrastructure design for IoT and big data’, ‘artificial intelligence applications’, and ‘superconnection-based IoT devices’.

Object tracking with the multi-templates regression model based MS algorithm
Hua Zhang and Lijia Wang
Page: 1307~1317, Vol. 14, No.6, 2018
Keywords: Mean Shift Algorithm, Multi-Templates, Object tracking, Regression Model
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To deal with the problems of occlusion, pose variations and illumination changes in the object tracking system, a regression model weighted multi-templates mean-shift (MS) algorithm is proposed in this paper. Target templates and occlusion templates are extracted to compose a multi-templates set. Then, the MS algorithm is applied to the multi-templates set for obtaining the candidate areas. Moreover, a regression model is trained to estimate the Bhattacharyya coefficients between the templates and candidate areas. Finally, the geometric center of the tracked areas is considered as the object’s position. The proposed algorithm is evaluated on several classical videos. The experimental results show that the regression model weighted multitemplates MS algorithm can track an object accurately in terms of occlusion, illumination changes and pose variations.

Video Captioning with Visual and Semantic Features
Sujin Lee and Incheol Kim
Page: 1318~1330, Vol. 14, No.6, 2018
Keywords: Attention-Based Caption Generation, Deep Neural Networks, Semantic Feature, Video Captioning
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Video captioning refers to the process of extracting features from a video and generating video captions using the extracted features. This paper introduces a deep neural network model and its learning method for effective video captioning. In this study, visual features as well as semantic features, which effectively express the video, are also used. The visual features of the video are extracted using convolutional neural networks, such as C3D and ResNet, while the semantic features are extracted using a semantic feature extraction network proposed in this paper. Further, an attention-based caption generation network is proposed for effective generation of video captions using the extracted features. The performance and effectiveness of the proposed model is verified through various experiments using two large-scale video benchmarks such as the Microsoft Video Description (MSVD) and the Microsoft Research Video-To-Text (MSR-VTT).

Triqubit-state Measurement-based Image Edge Detection Algorithm
Zhonghua Wang and Faliang Huang
Page: 1331~1346, Vol. 14, No.6, 2018
Keywords: Edge Detection, Partial Differential Equation, Pixel Saliency, Qubit State, Quantum Measurement
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Aiming at the problem that the gradient-based edge detection operators are sensitive to the noise, causing the pseudo edges, a triqubit-state measurement-based edge detection algorithm is presented in this paper. Combing the image local and global structure information, the triqubit superposition states are used to represent the pixel features, so as to locate the image edge. Our algorithm consists of three steps. Firstly, the improved partial differential method is used to smooth the defect image. Secondly, the triqubit-state is characterized by three elements of the pixel saliency, edge statistical characteristics and gray scale contrast to achieve the defect image from the gray space to the quantum space mapping. Thirdly, the edge image is outputted according to the quantum measurement, local gradient maximization and neighborhood chain code searching. Compared with other methods, the simulation experiments indicate that our algorithm has less pseudo edges and higher edge detection accuracy.

Energy efficient Cross Layer Multipath Routing for Image Delivery in Wireless Sensor Networks
Santhosha Rao, Kumara Shama and Pavan Kumar Rao
Page: 1347~1360, Vol. 14, No.6, 2018
Keywords: Castalia for OMNET++, Cross Layer Multimedia, Magick++, Sink-Based MMRE AOMDV
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Owing to limited energy in wireless devices power saving is very critical to prolong the lifetime of the networks. In this regard, we designed a cross-layer optimization mechanism based on power control in which source node broadcasts a Route Request Packet (RREQ) containing information such as node id, image size, end to end bit error rate (BER) and residual battery energy to its neighbor nodes to initiate a multimedia session. Each intermediate node appends its remaining battery energy, link gain, node id and average noise power to the RREQ packet. Upon receiving the RREQ packets, the sink node finds node disjoint paths and calculates the optimal power vectors for each disjoint path using cross layer optimization algorithm. Sink based cross-layer maximal minimal residual energy (MMRE) algorithm finds the number of image packets that can be sent on each path and sends the Route Reply Packet (RREP) to the source on each disjoint path which contains the information such as optimal power vector, remaining battery energy vector and number of packets that can be sent on the path by the source. Simulation results indicate that considerable energy saving can be accomplished with the proposed cross layer power control algorithm.

A Survey on Cyber Physical System Security for IoT: Issues, Challenges, Threats, Solutions
Nam Yong Kim, Shailendra Rathore, Jung Hyun Ryu, Jin Ho Park and Jong Hyuk Park
Page: 1361~1384, Vol. 14, No.6, 2018
Keywords: Cyber Physical System, Internet of Things, Security Analysis, Security Threats
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Recently, Cyber Physical System (CPS) is one of the core technologies for realizing Internet of Things (IoT). The CPS is a new paradigm that seeks to converge the physical and cyber worlds in which we live. However, the CPS suffers from certain CPS issues that could directly threaten our lives, while the CPS environment, including its various layers, is related to on-the-spot threats, making it necessary to study CPS security. Therefore, a survey-based in-depth understanding of the vulnerabilities, threats, and attacks is required of CPS security and privacy for IoT. In this paper, we analyze security issues, threats, and solutions for IoT-CPS, and evaluate the existing researches. The CPS raises a number challenges through current security markets and security issues. The study also addresses the CPS vulnerabilities and attacks and derives challenges. Finally, we recommend solutions for each system of CPS security threats, and discuss ways of resolving potential future issues.

Short-Term Wind Speed Forecast Based on Least Squares Support Vector Machine
Yanling Wang, Xing Zhou, Likai Liang, Mingjun Zhang, Qiang Zhang and Zhiqiang Niu
Page: 1385~1397, Vol. 14, No.6, 2018
Keywords: Cluster Analysis, Least Squares, Least Squares Support Vector Regression (LSSVR), Particle Swarm Optimization (PSO), Short-Time Wind Speed Forecasting
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There are many factors that affect the wind speed. In addition, the randomness of wind speed also leads to low prediction accuracy for wind speed. According to this situation, this paper constructs the short-time forecasting model based on the least squares support vector machines (LSSVM) to forecast the wind speed. The basis of the model used in this paper is support vector regression (SVR), which is used to calculate the regression relationships between the historical data and forecasting data of wind speed. In order to improve the forecast precision, historical data is clustered by cluster analysis so that the historical data whose changing trend is similar with the forecasting data can be filtered out. The filtered historical data is used as the training samples for SVR and the parameters would be optimized by particle swarm optimization (PSO). The forecasting model is tested by actual data and the forecast precision is more accurate than the industry standards. The results prove the feasibility and reliability of the model.

Proposal of Container-Based HPC Structures and Performance Analysis
Chanho Yong, Ga-Won Lee and Eui-Nam Huh
Page: 1398~1404, Vol. 14, No.6, 2018
Keywords: Container, Docker, High-Performance Computing, Singularity
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High-performance computing (HPC) provides to researchers a powerful ability to resolve problems with intensive computations, such as those in the math and medical fields. When an HPC platform is provided as a service, users may suffer from unexpected obstacles in developing and running applications due to restricted development environments and dependencies. In this context, operating system level virtualization can be a solution for HPC service to ensure lightweight virtualization and consistency in Dev-Ops environments. Therefore, this paper proposes three types of typical HPC structure for container environments built with HPC container and Docker. The three structures focus on smooth integration with existing HPC job framework, message passing interface (MPI). Lastly, the performance of the structures is analyzed with High Performance Linpack benchmark from the aspect of performance degradation in network communications under Docker.

Infrared and Visible Image Fusion Based on NSCT and Deep Learning
Xin Feng
Page: 1405~1419, Vol. 14, No.6, 2018
Keywords: Boltzmann Machine, Depth Model, Image Fusion, Split Bregman Iterative Algorithm
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An image fusion method is proposed on the basis of depth model segmentation to overcome the shortcomings of noise interference and artifacts caused by infrared and visible image fusion. Firstly, the deep Boltzmann machine is used to perform the priori learning of infrared and visible target and background contour, and the depth segmentation model of the contour is constructed. The Split Bregman iterative algorithm is employed to gain the optimal energy segmentation of infrared and visible image contours. Then, the nonsubsampled contourlet transform (NSCT) transform is taken to decompose the source image, and the corresponding rules are used to integrate the coefficients in the light of the segmented background contour. Finally, the NSCT inverse transform is used to reconstruct the fused image. The simulation results of MATLAB indicates that the proposed algorithm can obtain the fusion result of both target and background contours effectively, with a high contrast and noise suppression in subjective evaluation as well as great merits in objective quantitative indicators.

A Study on Business-Based Screen Design Techniques for Designing Efficient Applications
Tae-Woo Kim, Sun-Yi Park and Jeong-Mo Yeo
Page: 1420~1430, Vol. 14, No.6, 2018
Keywords: Applications, Business Processes, DFD, Screen Design, Screens
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To build a successful information system, design and development should be carried out from the enterprise perspective. A complicated business is represented in various ways as technology advances, and many development methodologies have been studied from the viewpoint of technology and development. Each domain is independently designed and developed from the enterprise perspective, but there would be inclusive parts due to the integrated process wherein the definition, design, and development of business are carried out, and the design is done based on the designer's experience. This study would like to address the technique of designing screens based on the business process of the applications derived from the business. It designs the screens that appear when actual applications are completed, including how the data transfer process in the derived business process is represented and operated on the relevant screens. It designs the screen which is displayed when the actual application is completed and how the data transfer process in the derived business process is represented and operated on the relevant screen. In addition, it designs the DFD representing the overall flow of data for each business to represent the movement procedure between screens in general. Through the design method proposed in this study, the client's requirement could be confirmed to reduce the cost for redevelopment, the problem of communication between designers and developers with various experiences could be reduced, and an efficient design procedure could be provided to persons who lack design experience.

Two-Phase Security Protection for the Internet of Things Object
Vera Suryani, Selo Sulistyo and Widyawan Widyawan
Page: 1431~1437, Vol. 14, No.6, 2018
Keywords: Attacks, Authentication, Internet of Things, Security, Statistic
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Securing objects in the Internet of Things (IoT) is essential. Authentication model is one candidate to secure an object, but it is only limited to handle a specific type of attack such as Sybil attack. The authentication model cannot handle other types of attack such as trust-based attacks. This paper proposed two-phase security protection for objects in IoT. The proposed method combined authentication and statistical models. The results showed that the proposed method could handle other attacks in addition to Sybil attacks, such as bad-mouthing attack, good-mouthing attack, and ballot stuffing attack.

Semantic-Based K-Means Clustering for Microblogs Exploiting Folksonomy
Jee-Uk Heu
Page: 1438~1444, Vol. 14, No.6, 2018
Keywords: Cluster, K-means, Microblog, Semantic, TagCluster
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Recently, with the development of Internet technologies and propagation of smart devices, use of microblogs such as Facebook, Twitter, and Instagram has been rapidly increasing. Many users check for new information on microblogs because the content on their timelines is continually updating. Therefore, clustering algorithms are necessary to arrange the content of microblogs by grouping them for a user who wants to get the newest information. However, microblogs have word limits, and it has there is not enough information to analyze for content clustering. In this paper, we propose a semantic-based K-means clustering algorithm that not only measures the similarity between the data represented as a vector space model, but also measures the semantic similarity between the data by exploiting the TagCluster for clustering. Through the experimental results on the RepLab2013 Twitter dataset, we show the effectiveness of the semantic-based K-means clustering algorithm.

A CPU-GPU Hybrid System of Environment Perception and 3D Terrain Reconstruction for Unmanned Ground Vehicle
Wei Song, Shuanghui Zou, Yifei Tian, Su Sun, Simon Fong, Kyungeun Cho and Lvyang Qiu
Page: 1445~1456, Vol. 14, No.6, 2018
Keywords: Driving Awareness, Environment Perception, Unmanned Ground Vehicle, 3D Reconstruction
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Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

Path Generation Method of UAV Autopilots using Max-Min Algorithm
Jeonghoon Kwak and Yunsick Sung
Page: 1457~1463, Vol. 14, No.6, 2018
Keywords: Autopilot, Max-Min Algorithm, Path Generation, Unmanned Aerial Vehicle
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In recent times, Natural User Interface/Natural User Experience (NUI/NUX) technology has found widespread application across a diverse range of fields and is also utilized for controlling unmanned aerial vehicles (UAVs). Even if the user controls the UAV by utilizing the NUI/NUX technology, it is difficult for the user to easily control the UAV. The user needs an autopilot to easily control the UAV. The user needs a flight path to use the autopilot. The user sets the flight path based on the waypoints. UAVs normally fly straight from one waypoint to another. However, if flight between two waypoints is in a straight line, UAVs may collide with obstacles. In order to solve collision problems, flight records can be utilized to adjust the generated path taking the locations of the obstacles into consideration. This paper proposes a natural path generation method between waypoints based on flight records collected through UAVs flown by users. Bayesian probability is utilized to select paths most similar to the flight records to connect two waypoints. These paths are generated by selection of the center path corresponding to the highest Bayesian probability. While the K-means algorithm-based straight-line method generated paths that led to UAV collisions, the proposed method generates paths that allow UAVs to avoid obstacles.

Small Sample Face Recognition Algorithm based on Novel Siamese Network
Jianming Zhang, Xiaokang Jin, Yukai Liu, Arun Kumar Sangaiah and Jin Wang
Page: 1464~1479, Vol. 14, No.6, 2018
Keywords: Convolutional Neural Network, Face Recognition, Loss Function, Siamese Network, Small Sample
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In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn’t need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFace1, which uses pairs of face images as inputs and maps them to target space so that the L2 norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.

An Intelligent Residual Resource Monitoring Scheme in Cloud Computing Environments
JongBeom Lim, HeonChang Yu and Joon-Min Gil
Page: 1480~1493, Vol. 14, No.6, 2018
Keywords: Cloud Computing, Clustering, Computational Intelligence, Resource Monitoring
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Recently, computational intelligence has received a lot of attention from researchers due to its potential applications to artificial intelligence. In computer science, computational intelligence refers to a machine’s ability to learn how to compete various tasks, such as making observations or carrying out experiments. We adopted a computational intelligence solution to monitoring residual resources in cloud computing environments. The proposed residual resource monitoring scheme periodically monitors the cloud-based host machines, so that the post migration performance of a virtual machine is as consistent with the pre-migration performance as possible. To this end, we use a novel similarity measure to find the best target host to migrate a virtual machine to. The design of the proposed residual resource monitoring scheme helps maintain the quality of service and service level agreement during the migration. We carried out a number of experimental evaluations to demonstrate the effectiveness of the proposed residual resource monitoring scheme. Our results show that the proposed scheme intelligently measures the similarities between virtual machines in cloud computing environments without causing performance degradation, whilst preserving the quality of service and service level agreement.

A Multi-Scale Parallel Convolutional Neural Network Based Intelligent Human Identification Using Face Information
Chen Li, Mengti Liang, Wei Song and Ke Xiao
Page: 1494~1507, Vol. 14, No.6, 2018
Keywords: Face Recognition, Intelligent Human Identification, MP-CNN, Robust Feature
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Intelligent human identification using face information has been the research hotspot ranging from Internet of Things (IoT) application, intelligent self-service bank, intelligent surveillance to public safety and intelligent access control. Since 2D face images are usually captured from a long distance in an unconstrained environment, to fully exploit this advantage and make human recognition appropriate for wider intelligent applications with higher security and convenience, the key difficulties here include gray scale change caused by illumination variance, occlusion caused by glasses, hair or scarf, self-occlusion and deformation caused by pose or expression variation. To conquer these, many solutions have been proposed. However, most of them only improve recognition performance under one influence factor, which still cannot meet the real face recognition scenario. In this paper we propose a multi-scale parallel convolutional neural network architecture to extract deep robust facial features with high discriminative ability. Abundant experiments are conducted on CMU-PIE, extended FERET and AR database. And the experiment results show that the proposed algorithm exhibits excellent discriminative ability compared with other existing algorithms.

CNN-LSTM Coupled Model for Prediction of Waterworks Operation Data
Kerang Cao, Hangyung Kim, Chulhyun Hwang and Hoekyung Jung
Page: 1508~1520, Vol. 14, No.6, 2018
Keywords: Big Data, CNN, Correlation Analysis, Deep-Learning, LSTM
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In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.

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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