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 Vol. 10 (2014)
 Vol. 9 (2013)
 Vol. 8 (2012)
 Vol. 7 (2011)
 Vol. 6 (2010)
 Vol. 5 (2009)
 Vol. 4 (2008)
 Vol. 3 (2007)
 Vol. 2 (2006)
 Vol. 1 (2005)


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All publications of JIPS are indexed in SCOPUS, DOI, DBLP, EBSCO, Google and Google scholar.
JIPS is also selected as the Journal for Accreditation by NRF (National Research Foundation of Korea).
The Journal of Information Processing Systems (ISSN: 1976-913X(Print), ISSN: 2092-805X(Online)) is the official international journal of the Korea Information Processing Society (KIPS). It is committed to publishing high-quality papers on the state-of-the-art of information processing systems. Theoretical research contributions presenting new techniques, concepts, or analyses, reports on experiences and experiments of implementation and application of theories, and tutorials on new technologies and trends are all welcome. The subjects covered by the journal include all topics related to a) computer system and theory; b) multimedia system and graphics; c) communication system and security; d) software system and application. All submitted manuscripts are treated consistently, fairly, and with a minimum of delay from submission to final decision (less than 6 months!)

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 Vol. 10, No.1, 2014

The Confinement Problem: 40 Years Later
Alex Crowell*, Beng Heng Ng*, Earlence Fernandes* and Atul Prakash* Vol. 9, No.2, 2013

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 Vol. 8, No.2, 2012

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 Vol. 8, No.1, 2012

A Survey of RFID Deployment and Security Issues
Amit Grover and Hal Berghel Vol. 7, No.4, 2011

The Principle of Justifiable Granularity and an Optimization of Information Granularity Allocation as Fundamentals of Granular Computing
Witold Pedrycz Vol. 7, No.3, 2011

CASPER: Congestion Aware Selection of Path with Efficient Routing in Multimedia Networks
Mohammad S. Obaidat, Sanjay K. Dhurandher and Khushboo Diwakar Vol. 7, No.2, 2011

An Efficient Broadcast Technique for Vehicular Networks
Ai Hua Ho, Yao H. Ho, Kien A. Hua, Roy Villafane and Han-Chieh Chao Vol. 7, No.2, 2010

Security Properties of Domain Extenders for Cryptographic Hash Functions
Elena Andreeva, Bart Mennink and Bart Preneel Vol. 6, No.4, 2010

Hiding Secret Data in an Image Using Codeword Imitation
Zhi-Hui Wang, Chin-Chen Chang and Pei-Yu Tsai Vol. 6, No.4, 2010

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 Vol. 6, No.3, 2010

Challenges to Next Generation Services in IP Multimedia Subsystem
Kai-Di Chang, Chi-Yuan Chen, Jiann-Liang Chen and Han-Chieh Chao Vol. 6, No.2, 2010

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 Vol. 6, No.1, 2010

Providing Efficient Secured Mobile IPv6 by SAG and Robust Header Compression
Tin-Yu Wu, Han-Chieh Chao and Chi-Hsiang Lo Vol. 5, No.3, 2009

A Survey of Face Recognition Techniques
Rabia Jafri and Hamid R Arabnia Vol. 5, No.2, 2009


Journal of Information Processing Systems, Vol. 10, No.3, 2014

Training-Free Fuzzy Logic Based Human Activity Recognition
Eunju Kim* and Sumi Helal*
Page: 335~354
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The accuracy of training-based activity recognition depends on the training procedure and the extent to which the training dataset comprehensively represents the activity and its varieties. Additionally, training incurs substantial cost and effort in the process of collecting training data. To address these limitations, we have developed a training-free activity recognition approach based on a fuzzy logic algorithm that utilizes a generic activity model and an associated activity semantic knowledge. The approach is validated through experimentation with real activity datasets. Results show that the fuzzy logic based algorithms exhibit comparable or better accuracy than other trainingbased approaches.
Improving Database System Performance by Applying NoSQL
Yong-Lak Choi*, Woo-Seong Jeon** and Seok-Hwan Yoon***
Page: 355~364
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Internet accessibility has been growing due to the diffusion of smartphones in today’s society. Therefore, people can generate data anywhere and are confronted with the challenge that they should process a large amount of data. Since the appearance of relational database management system (RDBMS), most of the recent information systems are built by utilizing it. RDBMS uses foreign-keys to avoid data duplication. The transactions in the database use attributes, such as atomicity, consistency, isolation, durability (ACID), which ensures that data integrity and processing results are stably managed. The characteristic of RDBMS is that there is high data reliability. However, this results in performance degradation. Meanwhile, from among these information systems, some systems only require high-performance rather than high reliability. In this case, if we only consider performance, the use of NoSQL provides many advantages. It is possible to reduce the maintenance cost of the information system that continues to increase in the use of open source software based NoSQL. And has a huge advantage that is easy to use NoSQL. Therefore, in this study, we prove that the leverage of NoSQL will ensure high performance than RDBMS by applying NoSQL to database systems that implement RDBMS.
A Novel Framework for Defining and Submitting Workflows to Service-Oriented Systems
Hayat Bendoukha*, Yahya Slimani** and Abdelkader Benyettou*
Page: 365~383
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Service-oriented computing offers efficient solutions for executing complex applications in an acceptable amount of time. These solutions provide important computing and storage resources, but they are too difficult for individual users to handle. In fact, Service-oriented architectures are usually sophisticated in terms of design, specifications, and deployment. On the other hand, workflow management systems provide frameworks that help users to manage cooperative and interdependent processes in a convivial manner. In this paper, we propose a workflow-based approach to fully take advantage of new service-oriented architectures that take the users’ skills and the internal complexity of their applications into account. To get to this point, we defined a novel framework named JASMIN, which is responsible for managing service-oriented workflows on distributed systems. JASMIN has two main components: unified modeling language (UML) to specify workflow models and business process execution language (BPEL) to generate and compose Web services. In order to cover both workflow and service concepts, we describe in this paper a refinement of UML activity diagrams and present a set of rules for mapping UML activity diagrams into BPEL specifications.
Femtocell Subband Selection Method for Managing Cross- and Co-tier Interference in a Femtocell Overlaid Cellular Network
Young Min Kwon*, Hyunseung Choo*, Tae-Jin Lee*, Min Young Chung* and Mihui Kim**
Page: 384~394
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The femtocell overlaid cellular network (FOCN) has been used to enhance the capacity of existing cellular systems. To obtain the desired system performance, both cross-tier interference and co-tier interference in an FOCN need to be managed. This paper proposes an interference management scheme that adaptively constructs a femtocell cluster, which is a group of femtocell base stations that share the same frequency band. The performance evaluation shows that the proposed scheme can enhance the performance of the macrocell-tier and maintain a greater signal to interference-plus-noise ratio than the outage level can for about 99% of femtocell users.
Imputation of Medical Data Using Subspace Condition Order Degree Polynomials
Klaokanlaya Silachan* and Panjai Tantatsanawong**
Page: 395~411
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Temporal medical data is often collected during patient treatments that require personal analysis. Each observation recorded in the temporal medical data is associated with measurements and time treatments. A major problem in the analysis of temporal medical data are the missing values that are caused, for example, by patients dropping out of a study before completion. Therefore, the imputation of missing data is an important step during pre-processing and can provide useful information before the data is mined. For each patient and each variable, this imputation replaces the missing data with a value drawn from an estimated distribution of that variable. In this paper, we propose a new method, called Newton’s finite divided difference polynomial interpolation with condition order degree, for dealing with missing values in temporal medical data related to obesity. We compared the new imputation method with three existing subspace estimation techniques, including the k-nearest neighbor, local least squares, and natural cubic spline approaches. The performance of each approach was then evaluated by using the normalized root mean square error and the statistically significant test results. The experimental results have demonstrated that the proposed method provides the best fit with the smallest error and is more accurate than the other methods.
Ultra Low Power Data Aggregation for Request Oriented Sensor Networks
Kwang-il Hwang* and In Jang*
Page: 412~428
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Request oriented sensor networks have stricter requirements than conventional event-driven or periodic report models. Therefore, in this paper we propose a minimum energy data aggregation (MEDA), which meets the requirements for request oriented sensor networks by exploiting a low power real-time scheduler, ondemand time synchronization, variable response frame structure, and adaptive retransmission. In addition we introduce a test bed consisting of a number of MEDA prototypes, which support near real-time bidirectional sensor networks. The experimental results also demonstrate that the MEDA guarantees deterministic aggregation time, enables minimum energy operation, and provides a reliable data aggregation service.
Fault Detection in the Semiconductor Etch Process Using the Seasonal Autoregressive Integrated Moving Average Modeling
Muhammad Zeeshan Arshad*, Javeria Muhammad Nawaz* and Sang Jeen Hong*
Page: 429~442
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In this paper, we investigated the use of seasonal autoregressive integrated moving average (SARIMA) time series models for fault detection in semiconductor etch equipment data. The derivative dynamic time warping algorithm was employed for the synchronization of data. The models were generated using a set of data from healthy runs, and the established models were compared with the experimental runs to find the faulty runs. It has been shown that the SARIMA modeling for this data can detect faults in the etch tool data from the semiconductor industry with an accuracy of 80% and 90% using the parameter-wise error computation and the step-wise error computation, respectively. We found that SARIMA is useful to detect incipient faults in semiconductor fabrication.
Extreme Learning Machine Ensemble Using Bagging for Facial Expression Recognition
Deepak Ghimire* and Joonwhoan Lee*
Page: 443~458
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An extreme learning machine (ELM) is a recently proposed learning algorithm for a single-layer feed forward neural network. In this paper we studied the ensemble of ELM by using a bagging algorithm for facial expression recognition (FER). Facial expression analysis is widely used in the behavior interpretation of emotions, for cognitive science, and social interactions. This paper presents a method for FER based on the histogram of orientation gradient (HOG) features using an ELM ensemble. First, the HOG features were extracted from the face image by dividing it into a number of small cells. A bagging algorithm was then used to construct many different bags of training data and each of them was trained by using separate ELMs. To recognize the expression of the input face image, HOG features were fed to each trained ELM and the results were combined by using a majority voting scheme. The ELM ensemble using bagging improves the generalized capability of the network significantly. The two available datasets (JAFFE and CK+) of facial expressions were used to evaluate the performance of the proposed classification system. Even the performance of individual ELM was smaller and the ELM ensemble using a bagging algorithm improved the recognition performance significantly.
Spectrum Sensing and Data Transmission in a Cognitive Relay Network Considering Spatial False Alarms
Tasnina A. Tishita*, Sumiya Akhter*, Md. Imdadul Islam** and M. R. Amin*
Page: 459~470
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In this paper, the average probability of the symbol error rate (SER) and throughput are studied in the presence of joint spectrum sensing and data transmission in a cognitive relay network, which is in the environment of an optimal power allocation strategy. In this investigation, the main component in calculating the secondary throughput is the inclusion of the spatial false alarms, in addition to the conventional false alarms. It has been shown that there exists an optimal secondary power amplification factor at which the probability of SER has a minimum value, whereas the throughput has a maximum value. We performed a Monte-Carlo simulation to validate the analytical results.
Efficient Greedy Algorithms for Influence Maximization in Social Networks
Jiaguo Lv*, **, Jingfeng Guo* and Huixiao Ren*
Page: 471~482
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Influence maximization is an important problem of finding a small subset of nodes in a social network, such that by targeting this set, one will maximize the expected spread of influence in the network. To improve the efficiency of algorithm KK_Greedy proposed by Kempe et al., we propose two improved algorithms, Lv_NewGreedy and Lv_CELF. By combining all of advantages of these two algorithms, we propose a mixed algorithm Lv_MixedGreedy. We conducted experiments on two synthetically datasets and show that our improved algorithms have a matching influence with their benchmark algorithms, while being faster than them.
Multifactor Authentication Using a QR Code and a One-Time Password
Jyoti Malik*, Dhiraj Girdhar**, Ratna Dahiya*** and G. Sainarayanan****
Page: 483~490
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In today’s world, communication, the sharing of information, and money transactions are all possible to conduct via the Internet, but it is important that it these things are done by the actual person. It is possible via several means that an intruder can access user information. As such, several precautionary measures have to be taken to avoid such instances. The purpose of this paper is to introduce the idea of a one-time password (OTP), which makes unauthorized access difficult for unauthorized users. A OTP can be implemented using smart cards, time-based tokens, and short message service, but hardware based methodologies require maintenance costs and can be misplaced Therefore, the quick response code technique and personal assurance message has been added along with the OTP authentication.

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