IOSR Journal of Computer Engineering (IOSR-JCE)

Jul - Aug 2017 Volume 19 - Issue 4

Version 1 Version 2 Version 3 Version 4 Version 5 Version 6 Version 7


Paper Type : Research Paper
Title : Exploration of the Advanced Metering Infrastructure and its Application in Smart Grids
Country : South Africa
Authors : Poolo, IJ
: 10.9790/0661-1904030106     logo

Abstract: In this exploration emphasis is on the Smart Grid (SG), the role of the Advanced Metering Infrastructure (AMI) in SG and security concerns in the AMI. This work is primarily on AMI, which is responsible for gathering all the data and information from loads and consumers, as the benchmarks for SG. AMI is also concerned with therealization of control signals and instructions to effect necessary regulatorytasks, Security in line with threats to the AMI. In this assessmentfocus is on SG and some of its structures, establish the link between SG and AMI, explain three main subsystems of AMI and discuss related security issues.

Keyword: Smart Grid; Advanced Metering Infrastructure; smart metering

[1]. SAMSET – Supporting Sub-Saharan African Municipalities with Sustainable Energy Transitions: http://samsetproject.net/wp-content/uploads/2016/02/Smart-metering-overview-and-consideration-for-South-African-municipalities.pdf [accessed May, 2017]]
[2]. Patel, U. M and Modi, M. M. 2015. A Review on Smart Meter System. IJIREEICE, 3(12): 70-73.
[3]. Momoh, J.A.2009. "Smart Grid Design for Efficient and Flexible Power Networks Operation and Control," Power Systems Conference and Exposition-PSCE'09, 1-8.
[4]. Ramyar, R. M, Fung. A.S, Mohammadi, F and Raahemifar, K.2014. A Survey on Advanced Metering Infrastructure and its Application in Smart Grids.CCECE IEEE, Toronto, Canada
[5]. "The History of Electrification: The Birth of our Power Grid," Edison Tech Centre. [online] Available at: http://edisontechcenter.org/ HistElectPowTrans.html, [accessed May, 2017].


Paper Type : Research Paper
Title : Application of Fuzzy m*x Oscillation in the Field of face recognition using Rough Set
Country : India
Authors : Sumanta Saha || Sharmistha Bhattacharya(Halder)
: 10.9790/0661-1904030713      logo

Abstract: The aim of this paper is to introduce the new mathematical concept of Fuzzy Oscillation using rough sets. Fuzzy-rough
Oscillation is a new concept which has been applied on some set of images. With the he lp of Fuzzy-rough Oscillation an unknown face image can be distinguished from a set of known face images. In this paper we introduce a new algorithm based on the theory of Fuzzy-rough Oscillation and using MATLAB 7.9 software we implemented this algorithm. Experiments are performed to test the proposed algorithm on Face fix Database and ORL database. Accuracy of the results describes the application of Fuzzy-rough Oscillation in the field of face recognition.

Keywords:Face recognition , Fuzzy Oscillation, Fuzzy-rough Oscillation, Rough set, etc

[1] M. Alimohammady, M.Roohi, "Fuzzy Minimal Structure and Fuzzy Minimal vector spaces",Chaos, Solitons and Fractals," Vol.27,No.3,p-599-605 No.3,(2006),.
[2] M. Alimohammady, M.Roohi, "Compactness in Fuzzy Minimal Spaces," Chaos,Solitons and Fractals,Vol.28,No.4, p-906-912 ,(2006).
[3] M. Alimohammady, M.Roohi, "Extreme points in minimal spaces," Chaos,Solitons and Fractals,Vol.39 ,No.3,p-1480-1485, 2009).
[4] Alimohammady, M.Roohi, "Transfer closed and transfer open multimaps in minimal space," Chaos,Solitons and Fractals,Vol.40,No.3,(2009),p-1162-1168.
[5] Abbas Hussien Miry , "Face Detection Based on Multi Facial Feature using Fuzzy Logic," Al-Mansour Journal Issue(21)..


Paper Type : Research Paper
Title : Speech to text conversion & display using Raspberry Pi
Country : India
Authors : M. Sudhakar || Vandana Khare || D Vijay Krishna Kanth
: 10.9790/0661-1904031418     logo

Abstract: There has been a relentless effort to process the speech for a wide range of applications. Speech recognition and conversion to text is extremely useful in many applications. Speech has not been used much in the field of electronics and computers due to the complexity and variety of speech signals and sounds. However, with modern processors, complex algorithms and methods we can process speech signals to convert to text. This paper deals with display of text from speech on a monitor using Android mobile, Bluetooth and Raspberry Pi. This application is quite useful in classrooms and presentations...............

Keywords: Speech to text display, AMR Voice App, Raspberry Pi.

[1]. K. Altun, B. Barshan, and O. Tunçel, "Comparative study on classifying human activities with miniature inertial and magnetic sensors," Pattern Recognit., vol. 43, no. 10, pp. 3605–3620, 2010.

[2]. Card, Stuart K.; Thomas P. Moran; Allen Newell (July 1980). "The keystroke-level model for user performance time with interactive systems". Communications of the ACM. 23 (7): 396–410.
[3]. G. Bailador, C. Sanchez-Avila, J. Guerra-Casanova, and A. de Santos Sierra, "Analysis of pattern recognition techniques for in-air signature biometrics," Pattern Recognit., vol. 44, nos. 10–11, pp. 2468–2478,2011.

[4]. S. Kallio, J. Kela, P. Korpipää, and J. Mäntyjärvi, "User independent gesture interaction for small handheld devices," Int. J. Pattern
Recognit Artif. Intell., vol. 20, no. 4, pp. 505–524, 2006.
[5]. S. Katsura and K. Ohishi, "Acquisition and analysis of finger motions by skill preservation system," IEEE Trans. Ind. Electron.,
vol. 54, no. 6, pp. 3353–3361, Dec. 2007.


Paper Type : Research Paper
Title : (1, 2) Burst-Correcting Optimal Codes Over GF (3)
Country : India
Authors : Tarun Lata
: 10.9790/0661-1904031923     logo

Abstract: :.In this paper we obtain a lower bound on the number of parity-check digits in an (n, k) linear codes over GF(3) which are optimal in a specific sense i.e. the codes are capable to correcting single errors in the first sub-block of length n1 and bursts of length 2 or less in the second sub-block of length n2; n = n1 + n2

Keywords: Parity-check matrix, Syndromes, Burst error, Optimal codes.

[1] Buccimazza, B., Dass, B.K. and Jain, S., Ternary (1,2)- optimal linear codes, Journal of Interdisciplinary Mathematics, 7(1), 2004,
71-77
[2] Chein, R.T. and Tang, D.T., On definition of a Burst, IBM Journal Research Development, 9 (4), 1965, 292-293.
[3] Dass, B.K., Iembo, R. and Jain, S., (1,2)- optimal linear codes over GF(5), Journal of Interdisciplinary Mathematics, 9(2), 2006,
319-326.
[4] Dass, B.K., Iembo, R. and Jain, S., (1,2)- optimal linear codes over GF(7), Reliability and Information Technology: Trends and
Future Directions, Narosa Pub. House, Delhi, India, 2005, 371-376.
[5] Dass, B.K. and Das, P.K., On perfect like binary and non binary perfect codes- A brief survey, Bull. Malays. Maths. Sci. Soc.(2),
32(2), 2009, 187-210..


Paper Type : Research Paper
Title : ICT in Teaching Learning Process for Higher Education: Challenges and Opportunities
Country : India
Authors : Girish SR || DR. C. SureshKumar
: 10.9790/0661-1904032428     logo

Abstract: In present trend the students of Higher Education are getting information from many sources it includes Internet, Social Media, Multimedia, Animations, Web applications and list goes on, and base for this is Information and Communication Technology (ICT) and in current trend ICT has become a common platform for majority population all over the globe. Web application and multimedia technologies have revolutionized educational field. Hence ICT plays a vital role in the field of Education especially in Higher Education, since implementation of ICT in higher Education is more...............

Keywords: Higher Education, ICT, Multimedia, Technology.

[1]. The Constitution (Eighty-sixth Amendment) Act, 2002 inserted Article 21-A in the Constitution of India.
[2]. Higher Education in India: the need for change working paper no 180 Indian council for research on international economic relation (PAWAN AGARWAL)
[3]. Kirsebom, B. (1998). Universiteteni IT-aldern – frontlinjeellerbakgard?in Bauer, M. (eds.), Kraften ligger idetokanda. EtfestskrifttilStigHagstrom, universitetskansler 1992 1998, Stockholm: Hogskoleverket.
[4]. Schmidtlein, F.A. and Taylor, A.L. (2000).Identifying costs of instructional technology in higher education, Tertiary Education and Management 6(4), 289–304.
[5]. ICT in Higher Education – A Study A.R.Nadira Banu Kamal and A Thahira Banu (Canadian Journal on Data, Information and Knowledge Engineering Vol. 1, No. 1, April 2010)


Paper Type : Research Paper
Title : Real-time Big Data Analytics and parallel processing using Hadoop on Remote Sensing data
Country : India
Authors : Ms. D.Prema swarupa rani
: 10.9790/0661-1904032932     logo

Abstract: At present applications like Internet, mobile devices, social media, geospatial devices, sensors will generate massive volume of data. Processing and extracting the useful information in an efficient manner leads a system toward major computational challenges, such as to analyze, aggregate, and store data. For these Big data analytical architecture is proposed. The architecture comprises three main units, such as 1) remote sensing Big Data acquisition unit (RSDU); 2) data processing unit (DPU); and 3) data analysis decision unit (DADU). RSDU acquires data from the sensors and sends this data to the Base Station. DPU provides an efficient processing of Data by providing filtration, load balancing, and parallel processing. DADU is responsible for compilation, storage of the results, and generation of decision based on the results received from DPU and find Frequency occurrences.

[1]. D. Agrawal, S. Das, and A. E. Abbadi, "Big Data and cloud computing: Current state and future opportunities," in Proc. Int. Conf. ExtendingDatabase Technol. (EDBT), 2011, pp. 530–533.
[2]. J. Cohen, B. Dolan, M. Dunlap, J. M. Hellerstein, and C. Welton, "Mad skills: New analysis practices for Big Data," PVLDB, vol. 2, no. 2, pp. 1481–1492, 2009.
[3]. J. Dean and S. Ghemawat, "Mapreduce: Simplified data processing on large clusters," Commun. ACM, vol. 51, no. 1, pp. 107–113, 2008.
[4]. H. Herodotouet al., "Starfish: A self-tuning system for Big Data analytics," in Proc. 5th Int. Conf. Innovative Data Syst. Res. (CIDR), 2011, pp. 261–272.
[5]. K. Michael and K. W. Miller, "Big Data: New opportunities and new challenges [guest editors' introduction]," IEEE Comput., vol. 46, no. 6, pp. 22–24, Jun. 2013..


Paper Type : Research Paper
Title : Discovering Periodic high-utility item sets from transactional databases
Country : India
Authors : Kilaru Gowthami || Divvela.Srinivasa Rao
: 10.9790/0661-1904033342     logo

Abstract: High-utility item set mining is the task of finding high-utility item sets, i.e. sets of things that return a high benefit in a client exchange database. High-utility item sets are helpful, as they give data about profitable set of items purchased by clients to retail store administrators, which can then utilize this data to take strategic marketing decisions. An inherent limitation of customary high-utility item set mining calculations is that they are inappropriate to find repeating client buy conduct, although such conduct is normal all things considered, circumstances (for instance, a client may get a few items consistently, week or month). In this paper, we address this limitation by proposing the task of high-utility item set mining..............

Keywords: high-utility item set, periodic item set, Average periodicity

[1]. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proc. Int. Conf. Very Large Databases, pp. 487–499, (1994)
[2]. Amphawan, K., Lenca, P., Surarerks, and A.: Mining top-k periodic-frequent pattern from transactional databases without support threshold. In: Proc. 3rd Intern. Conf. on Advances in Information Technology, pp. 18–29 (2009)
[3]. Amphawan, K., Surarerks, A., Lenca, and P.: Mining periodic-frequent item sets with approximate periodicity using interval transaction-ids list tree. In: Proc. 2010 Third Intern. Conf. on Knowledge Discovery and Data Mining, pp. 245-248 (2010)
[4]. Fournier-Viger, P., Wu, C.-W., Zida, S., Tseng, V. S.: FHM: Faster high-utility item set mining using estimated utility co-occurrence pruning. In: Proc. 21st Intern. Symp. On Methodologies for Intel. Syst., pp. 83–92 (2014)
[5]. Lan, G. C., Hong, T. P., and Tseng, V. S.: An efficient projection-based indexing approach for mining high utility item sets. Knowl. And Inform. Syst. 38(1), 85–107 (2014)


Paper Type : Research Paper
Title : Privacy-Preserving Data Mining with Random decision tree framework
Country : India
Authors : Ms. Ch.Likitha Sravya || Mrs. G.V Rajya Lakshmi
: 10.9790/0661-1904034349     logo

Abstract: Data mining is the useful tool to discovering the knowledge from large data. Different methods & algorithms are available in data mining. Classification is most common method used for finding the mine rule from the large database. Decision tree method generally used for the Classification, because it is the simple hierarchical structure for the user understanding & decision making. Various data mining algorithms available for classification based on Artificial Neural Network, Nearest Neighbour Rule & Bayesian classifiers but decision tree mining is simple one. ID3 and C4.5..............

Keywords: Data mining, Privacy-preserving data mining, classification horizontal partitioning, vertical partitioning

[1] J. Vaidya, C. Clifton, and M. Zhu, Privacy-Preserving Data Mining.ser. Advances in Information Security first ed., vol. 19, Springer-Verlag, 2005.
[2] W. Fan, H. Wang, P.S. Yu, and S. Ma, "Is Random Model Better? On Its Accuracy and Efficiency," Proc. Third IEEE Int'l Conf. Data Mining (ICDM '03), pp. 51-58, 2003.
[3] W. Fan, J. McCloskey, and P. S. Yu, "A General Framework for Accurate and Fast Regression by Data Summarization in Random Decision Trees," Proc. 12th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '06), pp. 136-146, 2006.
[4] X. Zhang, Q. Yuan, S. Zhao, W. Fan, W. Zheng, and Z. Wang, "Multi-Label Classification without the Multi-Label Cost," Proc. SIAM Int'l Conf. Data Mining (SDM '10), pp. 778-789, 2010.
[5] A. Dhurandhar and A. Dobra, "Probabilistic Characterization of Random Decision Trees," J. Machine Learning Research, vol. 9,
2321-2348, 2008.


Paper Type : Research Paper
Title : An Efficient General Decentralized Clustering Exploiting Hierarchy
Country : India
Authors : I.GeetaSowmya || K.NagaPrashanthi
: 10.9790/0661-1904035055     logo

Abstract: Clustering or unsupervised learning is important for analyzing large data sets. Large amounts of data are distributed among multiple sources. Examination of this data and identifying clusters is challenging due to processing storage and transmission costs. In this project we are implementing GD cluster, General Decentralized Clustering (GD) method which is capable of clustering dynamic and disturbed data sets. Nodes store and share a set of data items from other nodes. Data items represent internal data which may change over time and external data which may also store attribute vectors of data items from other nodes...............

Keywords: Distributedsystems, clustering, hierarchical clustering, dynamic system

[1] K. M. Hammouda and M. S. Kamel, "Models of distributed data clustering in peer-to-peer environments," Knowl. Inf. Syst., vol. 38, no. 2, pp. 303–329, 2014.
[2] E. Januzaj, H.-P. Kriegel, and M. Pfeifle, "Scalable density-based distributed clustering," in Proc. 8th Eur. Conf. Principles Pract. Knowl. Discovery Databases, 2004, pp. 231–244.
[3] S. Lodi, G. Moro, and C. Sartori, "Distributed data clustering in multi-dimensional peer-to-peer networks," in Proc. 21st Austral-asian Conf. Database Technol., 2010, vol. 104, pp. 171–178.
[4] S. Datta, C. R. Giannella, and H. Kargupta, "Approximate distrib-uted k-means clustering over a peer-to-peer network," IEEE Trans. Knowl. Data Eng., vol. 21, no. 10, pp. 1372–1388, Oct. 2009.
[5] A. Elgohary and M. A. Ismail, "Efficient data clustering over peer-to-peer networks," in Proc. 11th Int. Conf. Intell. Syst. Des. Appl., 2011, pp. 208–212


Paper Type : Research Paper
Title : Comparative Analysis of Data Mining Techniques, Tools and Machine Learning Algorithms for Efficient Data Analytics
Country : India
Authors : Keshav Singh Rawat
: 10.9790/0661-1904035661     logo

Abstract: Data mining is more demanding now due to large amount of data are created by web, social media like- twitter, face book, web, and other sources. Data mining is Extracting knowledge from raw data available on large data sets on computer. Extracting knowledge from large data set requires decision making algorithms, machine learning is a process to classification in data mining the data. Many free and open source data mining tools are available on World Wide Web and they are...............

Keywords: Data mining, Data classification, free and open source, Machine learning.

[1] Sumit Garg,Arvind K. Sharma , "Comparative Analysis of Data Mining Techniques on Educa-tional Dataset" ,International Journal of Computer Applications (0975 – 8887),Volume 74– No.5, July 2013

[2] Rangra et al., International Journal of Advanced Research in Computer Science and Software Engineering 4(6), June - 2014, pp. 216-223

[3] Chun-Wei Tsai, Chin-Feng Lai, Han-Chieh Chao and Athanasios V. Vasilakos, "Big data analytics: a survey", Journal of Big Data , springer open ,2015

[4] Micó L, Oncina J, Carrasco RC. A fast branch and bound nearest neighbour classifier in metric spaces. Pattern Recogn Lett. 1996;17(7):731–9.

[5] Mehta M, Agrawal R, Rissanen J. SLIQ: a fast scalable classifier for data mining. In: Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology. 1996. pp 18–32..


Paper Type : Research Paper
Title : A Comprehensive Review: Internet of Things (IOT)
Country : India
Authors : Neha Mangla || Priya Rathod
: 10.9790/0661-1904036272     logo

Abstract: "Internet of things"– is a subject of great interest for many in today's world. IoT is the future that scholars and researchers anticipate and work for. IoT tries to bring everything under one umbrella with cross disciplinary collaboration. The unification of everything in the world, making use of a common infrastructure that can, not only provide the users with the control but also helps them understand the state of it is the zenith of IoT. Keeping this in mind, this study addresses various IoT concepts through professional discussion with experts, systematic review of scholarly research papers and online databases. This research paper consists of definitions, evolution, IoT hardware, software, cloud services, and tools for analysing data sets. The prime objective is to provide thumbnail information about the Internet of Things and technologies used in day to day life.

[1] http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=40524&copyownerid=6818
[2] Mashood Mukhtar, "GPS based Advanced Vehicle Tracking and Vehicle Control System", I.J. Intelligent Systems and Applications, 2015, 03, 1-12

[3] AK Srivastava, C Kumar, N Mangla," Analysis of Diabetic Dataset and Developing Prediction Model by using Hive and R", Indian Journal of Science and Technology, 2017
[4] Kevin Ashton,"That 'Internet of Things' Thing", RFID Journal, 22 June 2009
[5] Kosmatos, E.A., Tselikas, N.D. and Boucouvalas, A.C. (2011) Integrating RFIDs and Smart Objects into a Unified Internet of Things Architecture. Advances in Internet of Things: Scientific Research, 1, 5-12. http://dx.doi.org/10.4236/ait.2011.11002.


Paper Type : Research Paper
Title : Model of Cloud Computing Platform as a Service to VR/AR Military Cyber Simulation Operation Problem
Country : Korea
Authors : Jungho Kang
: 10.9790/0661-1904037376     logo

Abstract: Recently, This paper we propose the placement of Cloud Computing to solve considering the effective range of rifles and suggest the algorithm to delete the program when VR/AR Military Cyber Simulation Operation Problem. we analyze various problems that arise when using AR (Augmented Reality) technology for military Simulation Operations and suggest ways to improve them. In order to solve cyber attacks and threats (physical attacks, technical attacks) that may arise when.............

Keywords: Virtual Reality, Augmented Reality, Cloud Computing, Cyber Warfare, Scalability, Security

[1] Kim Hyo Koon, Son Young Joo, Kim Myung Seok, Lee Seon Jim (2017). The Present and Future of "AR (Augmented
Reality) versus VR (Virtual Reality) versus MR (Mixed Reality)". Defense & Technology, (455), 76-87.
[2] Ho-Kyun Park (2013). Types and Information Security Technology on Cyber Warfare. The Korea Contenta Association Review,
11(4), 41-44
[3] Worl-Su Jang, Jumh-Younh Choi, Jong-in Lim (2012). A Study on adopting cloud computing in the military. Journal of the Korea
Institute of Information Security & Cryptoloogy, 22(3), 645-654.
[4] Kyoung-a Shin, Sang-jim Lee (2012). Information Securtiy Management System on Cloud Computing Service. Journal of the Korea
Institute of Information Security & Cryptology, 22(1), 155-167.
[5] Wylie Wong (2013) , The Army Brings the Cloud to the Battlefield, July, Summer 2013 Issue ..


Paper Type : Research Paper
Title : Extensive survey on Virtual Machine Migration techniques in Cloud Environment
Country : India
Authors : Sunil Kumar
: 10.9790/0661-1904037779     logo

Abstract: In today's competitive environment, cloud computing gain more popularity due to its capability to provide the various services at the minimum cost. To achieve the minimum computation cost, virtualization of cloud datacenter has been performed for better resource utilization and further Migration of Virtual Machines helps to balance the workload, energy efficiency and fault tolerance. Migration techniques make capable to system for server consolidation and ease of Management............

Keywords: VM Migration, Cloud Computing, Load Balancing, Pre-Copy Migration, Post –Copy Migration.

[1] R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, "Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility," Future Generation computer systems, vol. 25, no. 6, pp.599-616, 2009.
[2] Zhang Q, Cheng L, Boutaba R. Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications. 2010 May 1;1(1):7-18.
[3] Mell P, Grance T. The NIST definition of cloud computing. Communications of the ACM. 2010 Dec 10; 53(6):50.
[4] A. Desai, "Virtual Machine." (2012), [online]. Available: http://searchservervirtualization.techtarget.com/definition/virtualmachin
[5] Glazer DW, Tropper C (1993) On process migration and load balancing in time warp. IEEE Trans Parallel Distrib Syst 4:318–327 ..


Paper Type : Research Paper
Title : To Improve Accuracy in Movies Reviews Using Sentiment Analysis
Country : India
Authors : Rasika Wankhede || Prof. A. N. Thakare
: 10.9790/0661-1904038088     logo

Abstract: Opinion mining is one of the new concepts of data mining. As World Wide Web is growing at higher rate, this has resulted in enormous increase in online communications. The online communication data consist of feedback, comments and reviews on particular topic that are posted on internet by internet users. Sentiment analysis is a sub-domain of opinion mining where the analysis is focused on the extraction of emotions, specific view or judgment on certain topic. Sentiment analysis system classifies text data into their respective sentiments based on polarity............

Keywords: Classifier, Feature extraction, Movie reviews, Opinion mining, Polarity, Sentiment Analysis

[1] P.Nagamma, Pruthvi H.R, Nisha K.K, Carlos Soares," An ImprovedSentiment Analysis of Online Movie Reviews", IEEE 2015, International conference on Computer and Inforamation Technology.
[2] B. Pang, L. Lee, and S. Vaithyanathan, "Thumbs up?:sentiment classification using machine learning techniques," in Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10. Association for Computational Linguistics, 2002, pp. 79–86.
[3] J. Erman, M. Arlitt, and A. Mahanti, "Traffic classification using clustering algorithms," in Proceedings of the 2006 SIGCOMM workshop on Mining network data. ACM, 2006, pp. 281–286 A. Baloglu, Mehmat A. Aktas, "An Automated Framework for Mining Reviews from Blogosphere," International Journal on Advances in Internet Technology, vol. 3, 2010.
[4] Turney, Peter, and Michael L. Littman. "Unsupervised learning of semantic orientation from a hundred-billion-word corpus." (2002).
[5] Baccianella, Stefano, Andrea Esuli and Fabrizio Sebastiani. "SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining." LREC. Vol. 10. 2010. ..


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