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In any natural language there are two linguistic variations: dialect and accent. In any standard natural language, the presence of various dialects can be seen due to the pattern of pronunciation and the vocabularies used by a particular community because of geographical differences. Assamese Language also has several dialect variations based on the phonology and morphology. Recent studies show that there are four dialect groups in this language. Since very little work has been done in the regional dialect of Assamese language, it has become necessary to discriminate these dialects as it seems no significant and systematic study is carried out on the dialects of Assamese language. In this paper a study has been done to show the works that have been done in the area of Automatic dialect classification/recognition of some Foreign and Indian languages till date. Also the phonology and morphology of Assamese language and the need to study the dialects present in the language has been discussed.

Accent, Dialect, Phonology, Morphology

[1] Behravan, Hamid. “Dialect and Accent Recognition”, Master's Thesis, University of Eastern Finland, School of Computing, December, 2012
[2] Rao, K. Sreenivasa, Saurav Nandy, and Shashidhar G. Koolagudi. "Identification of Hindi dialects using speech." WMSCI-2010 (2010).
[3] www.iitg.ernet.in/rcilts/pdf/assamese.pdf
[4] Zissman, Marc A., and Kay M. Berkling. "Automatic language identification."Speech Communication 35, no. 1 (2001): 115-124.
[5] Tong, Rong, Bin Ma, Donglai Zhu, Haizhou Li, and EngSiongChng. "Integrating acoustic, prosodic and phonotactic features for spoken language identification." In Acoustics, Speech and Signal Processing, 2006.ICASSP 2006 Proceedings.2006 IEEE International Conference on, vol. 1, pp. I-I.IEEE, 2006.
[6] Mary, Leena, and BayyaYegnanarayana. "Extraction and representation of prosodic features for language and speaker recognition." Speech communication 50, no. 10 (2008): 782-796.
[7] Sinha Sweta, Jain Aruna, Agawam Sham.”Speech processing for Hindi dialect recognition.”Advances in Signal Processing and Intelligent Recognition Systems, Advances in Intelligent Systems and Computing Volume 264, 2014, pp 161-169
[8] Sethi Dhabal Prasad “Morphological Analyzer for Sambalpuri Odia Dialect Inflected Verbal Forms.”International Journal of Advanced Research in Computer Science and Software Engineering: Volume 3, Issue 10, October 2013 ISSN: 2277 128X.
[9] Muchahary, Rujab. "Variation in the lexicon of the Mech (Boro) dialect of North Bengal and standard Boro language spoken in Assam." International Journal of Scientific and Research Publications: 616.
[10] Patgiri, Bipasha. “Stress in the Nalbaria Dialect of Assamese”. PhD Student, JNU
[11] Das, Harish.“A Regional Dialect of Assamese Language”. IJCA SPECIAL ISSUE ON BASIC, APPLIED & SOCIAL SCIENCES, VOLUME II, JULY 2012 [ISSN: 2231-4946] 159
[12] Sharma, Utpal.”Unsupervised Learning of Morphology of a Highly Inflectional Language”,2006.Ph.DThesis,Supervisors:Rajib K Das and Jugal K Kalita
[13] http://ielanguages.com/linguist.html
[14] Saharia, Navanath, Utpal Sharma, and Jugal Kalita. "Analysis and evaluation of stemming algorithms: a case study with Assamese." In Proceedings of the International Conference on Advances in Computing, Communications and Informatics, pp.842-846.ACM, 2012.

Jahnabi Borah and Dr. Uzzal Sharma, "Significance of the Study on Regional Dialects in Assamese Language", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.145-149, 2015.

Detection of word boundary in continuous speech is an important issue in speech recognition. This paper presents different methods for detecting word boundary in Assamese language and also discusses the possible future development to improve the accuracy. These methods are based on the behavior of pitch frequency across the sentences. Here we use Scilab, Praat and Wavesurfer software.

Word boundary detection(WBD),Hidden markov model( HMM), LPC, SOM, MLP

[1] Srichand "Word Boundary Detection in Indian Languages and Application to Keyword Spotting," Department of Computer Science and Engineering Indian Institute of Technology, Madras- 600 03G, India, August 1996.
[2] Ramana Rao G.V. and Srichand J. ,"Word Boundary Detection Using Pitch Variation," Department of Computer Science and Engineering, Indian Institute of Technology, May 1996.
[3] Anurag Jain, S.S. Agrawal, Nupur Prakash ,"Performance evaluation of word boundary detection for Hindi speech database," GGSIP University, Delhi India ,2CDAC Noida India.
[4] G.V.Ramana Rao "Detection of word boundaries in continuous hindi speech using pitch and duration,",Department of computer science and engineering,Indian Institute of Technology,Madras 600036,India.
[5] Colin Keng-Yan TAN, Kim-Teng LUA,"Learning of Word Boundaries In Continuous Speech Using Time Delay Neural Networks," Laboratory for Computational Linguistics, Department of Computer Science, School of Computing, National University of Singapore.
[6] Andreas Tsiartas, Prasanta Kumar Ghosh, Panayiotis Georgiou and Shrikanth Narayanan ,"Robust Word Boundary Detection in Spontaneous Speech using Acoustic and Lexical Cues,"Speech Analysis and Interpretation Laboratory,Department of Electrical Engineering, University of Southern California, Los Angeles,CA90089.
[7] Syed Abbas Ali,Najmi Ghani Haider and Mahmood Khan Pathan, "A LPC-PEV Based VAD for Word Boundary Detection,", Faculty of Computer &Information Systems Engineering, N.E.D University of Engg.&Tech.,Karachi.
[8] Jan Bartoˇsek and V´aclav Hanˇzl ,"Foot Detection in Czech Using Pitch Information and HMM,",Department of Circuit Theory, FEE CTU in Prague, Technick´a 2, 166 27 Praha 6 - Dejvice, Czech Republic.
[9] Manash Pratim Sarma and Kandarpa Kumar Sarma, "Speech Recognition of Assamese Numerals Using Combinations of LPC - Features and Heterogenous ANNs", Department of Electronics And Communication Technology,Gauhati University, Assam, India, Springer-Verlag Berlin Heidelberg 2010, pp. 8–12.
[10] Robert Togneri department of Electrical and Electronics Engineering , M.D. Alder , "Speech processing using artificial neural network", Department of mathematics Yianni Attikiouzel Department of electrical and electronics engineering, the university of western Australia.
[11] Archana Agarwal, Anurag Jain, Nupur Prakash and S.S.Agrawal," Word Boundary Detection in Continuous Speech based on Suprasegmental Features for Hindi Language,"University School of Information Technology, G.G.S Indraprastha University, Delhi, India, 2010 2nd International Conference on Signal Processing Systems (ICSPS).
[12] Francesco Beritelli, "Robust Word Boundary Detection Using Fuzzy Logic,"(Istituto di Informatica e Telecomunicazioni -University of Catania, V.le A. Doria 6, 95125 Catania, Italy).
[13] Khurram Waheed, Kim Weaver and Fathi M. Salam, "A Robust Algorithm for Detecting Speech Segments using an Entropic Contrast," Circuits, Systems and Artificial Neural Networks Laboratory ,Michigan State University, East Lansing, MI 48824-1226.
[14] Joe Tebelskis, "Speech Recognition using Neural Networks",School of Computer Science,Carnegie Mellon University,Pittsburgh, Pennsylvania 15213-3890,May 1995.

Sudarshana Sarma and Uzzal Sharma, "Word Boundary Detection in Assamese Language", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.150-156, 2015.

In the recent years, Bioinformatics and computational biology are two of some important and active research disciplines. Finding insights into biology, information technology tools in the form of programming languages suitable for biology along with data mining tools and techniques are deployed. The open source programming languages used in bioinformatics are informally called Bio* projects. This work explores the performances of BioPerl, Biojava, BioPython, BioRuby, BioSmalltalk under Bio* projects for executing bioinformatics tasks.

Bioinformatics, Bio* projects , BioPerl, Biojava, BioPython, BioRuby, BioSmalltalk

[1] James Tisdall, Beginning Perl for Bioinformatics, First Edition, O’Reilly, October 2001, ISBN: 0-596-00080-4
[2] Jason E.Stajich, Hilmar Lapp, “Open Source tools and toolkits for bioinformatics: significance, and where are we?”, Briefings in Bioinformatics, Oxford University Press, Vol. 7 No. 3, Pp. 287-296, 2006
[3] Dat Tran, Christopher Dubay, Paul Gorman, William Hersh, “Applying Task Analysis to Describe & Facilitate Bioinformatics Tasks”, MEDINFO, 2004, M. Fieschi et al. (Eds), Amsterdam: IOS Press, IMIA
[4] Lutz Prechelt, “An empirical Comparison of C, C++, Java, Perl, Python, Rexx and Tcl for a search/string-processing program”, University at Karlsruhe, Technical Report 2000-5, March 10, 2000
[5] Mathieu Fourment, Michael R Gillings, “A comparison of common programming languages used in bioinformatics”, BMC Bioinformatics, 9:82, 2008
[6] Open Source Initiative, http://www.opensource.org/, accessed December, 2014
[7] Open Bioinformatics Foundation, http://www.open-bio.org/, accessed December, 2014
[8] M. Rahmania, D. Bastola, L. Najjar, “Comparative Analysis of Software Repository Metrics in BioPerl, BioJava and BioRuby”, International Conference on Computational Science, ICCS 2012, Procedia Computer Science 9 ( 2012 ) 518 – 521, 1877-0509, Published by Elsevier Ltd.
[9] T. Ryu, “Benchmarking of BioPerl, Perl, BioJava, Java, BioPython, and Python for primitive bioinformatics tasks and choosing a suitable language”, International Journal of Contents, Vol.5, No.2, June 2000
[10] Hernán F. Morales, Guillermo Giovambattista, “BioSmalltalk: A pure object system and library for bioinformatics”, Bioinformatics Application Note, Oxford University Press, vol. 29, No. 18, Pp. 2355-2356, 2013
[11] K. Cara Woodwark, “Meeting Review: The Bioinformatics Open Source Conference 2001 (BOSC 2001)”, Comparative and Functional Genomics, 2001; 2: 327–329
[12] BioPerl Web Information, http://bioperl.org, accessed December, 2014
[13] The Open Source Network, http://openhub.net, accessed December, 2014
[14] BioPerl Web Information, http://www.biopython.org, accessed December, 2014
[15] Ruby Programming Language, http://www.ruby-lang.org, accessed December, 2014
[16] IEEE Spectrum, http://spectrum.ieee.org/ns/IEEE_TPL/methods.html, accessed December, 2014
[17] Wiki Definition of Google Trends, http://en.wikipedia.org/wiki/Google_Trends, accessed December, 2014
[18] Hyunyoung Choi, Hal Varian “Predicting the Present with Google Trends”, Google Inc, April 10, 2009
[19] Liguo Yu, Stephen R. Schach, Kai Chen, “Measuring the Maintainability of Open-Source Software”, 0-7803-9508-5/05, IEEE
[20] Wiki Definition of Open Source Community Support, http://en.wikipedia.org/wiki/Open-source_movement, accessed December, 2014
[21] Carsten Kolassa, Dirk Riehle, Michel A. Salim, “The Empirical Commit Frequency Distribution of Open Source Projects”, ACM 978-1-4503-1852-5/13/08
[22] Carsten Kolassa, Dirk Riehle, Michel A. Salim, “A Model of the Commit Size Distribution of Open Source”, Proceedings of the 39th International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM 2013), LNCS 7741. Page 52-66. Springer Verlag, 2013

Dipanjan Moitra and R. K. Samanta, "Performance Evaluation of BioPerl, Biojava, BioPython, BioRuby and BioSmalltalk for Executing Bioinformatics Tasks", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.157-164, 2015.

User Authentication process is the essential integral part of any secure or collaborative system. Where, Knowledge-based Authentication is convenient and low cost among currently used authentication processes. But, today, password or PIN is not limited in knowledge-based user authentication due to off-line guessing attacks; it demands higher level of security and performance together with low cost. Here, Keystroke Dynamics is the best possible solution, where the users are not only indentified by their password or PIN, their regular typing style is also accounted for. But this technique, as is now, suffers from accuracy level and performance. Thus, in order to realize this technique in practice a higher level of security and performance together with low cost version is demanded with an error to an accepted level. Hence, it is highly needed to identify the controlling parameters and optimize the accuracy and performance. In this paper we investigated typing style of 15 different individuals with 3 different texts and analyzed the collected data. Here, we introduced some effective factors which can optimize the accuracy and performance and at the end, we concluded by suggesting some future plans that also can be effectively implemented by this technique.

Keystroke Dynamics, Behavioral biometric, Computer Security, Manhattan Distance, Euclidean Distance, Mahanobolis Distance, Z Score, EER, FAR, FRR, Knowledge-based Authentication

[1] Hafiz, Z. U. K. (2010). Comparative Study of Authentication Techniques. International Journal of Video & Image Processing and Network Security, IJVIPNSIJENS Vol: 10, No: 04.
[2] Gaines, R. et al. (1980). Authentication by keystroke timing: some preliminary results. Rand Rep. R-2560-NSF, Rand Corporation.
[3] Bleha, S. et al. (1990). Computer-access security systems using keystroke dynamics. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 1217–1222.
[4] Killourhy, K. S. (2012). A Scientific Understanding of Keystroke Dynamics. PhD thesis, Computer Science Department, Carnegie Mellon University, Pittsburgh, US.
[5] Joyce, R. & Gupta, G. (1990). Identity authorization based on keystroke latencies. Communication of ACM 33 (2) 168–176.
[6] Monrose, F. & Rubin, A. D. (2000). Keystroke dynamics as a biometric for authentication. Future Generation Computer Systems, Vol. 16, No. 4, pp. 351–359.
[7] Shimaa, I. H. et al. (2013). User Authentication with Adaptive Keystroke Dynamics. IJCSI, Vol. 10, Issue 4, July 2013.
[8] Pin, S. T. (2013). A Survey of Keystroke Dynamics Biometrics. The Scientific World Journal, Vol-2013, Article ID 408280.
[9] Giot, R. et al. (2011). Analysis of template updates strategies for keystroke dynamics. Computational Intelligence in Biometrics and Identity Management (CIBIM), 2011 IEEE Workshop, pp.21,28, 11-15.

Soumen Pal, Utpal Roy, D. D. Sinha, "Text-based Analysis of Keystroke Dynamics in User Authentication", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.165-173, 2015.

Library is a store-house of information, documentation, audio-visual and graphic materials stored in a variety of media ranging from printed books, periodicals, posters and report, microforms, slides, films, videos, audio, discs, audio-tapes, optical discs, magnetic tapes, floppy discs etc. Electronic Library is likely to be part of a network. An electronic Library deals with both house-keeping operations like acquisitions, catalogue creation, circulation control, serial control, Online Public Access Catalogue (OPAC) and the generation of management information and information retrieval systems like external databases and associated services and products and internal or local databases and their associated services and products. These are Current Awareness Services (CAS), Selective Dissemination of Information (SDI) etc. Application of new information technology has brought in dramatic changes in the library and information field. With technological advancement, libraries and information centres around the world have computerised their library routines and have developed databases for shared use on computer networks. Besides improving services and operations for improving performance, libraries have also been able to evolve effective computer networks with an aim to optimize utilization of resources and facilities. The library and information networks have potential to improve library services in several ways. It brings down the cost of information products and services in the network environment in shared mode. It enables libraries to offer need based services to the end users eliminating the limitation of size, distance and language barriers among them. With evolution in library networks the emphasis has moved from the networks as physical entities to the resources available through the networks. These network accessible resources as include databases of library holdings, journal articles, electronic text, images, video and audio files, scientific and technical data etc.

Library, Network, Information and Communication Technology (ICT), Databases, Online Public Access Catalogue (OPAC), Current Awareness Services (CAS), Selective Dissemination of Information (SDI)

[1] Harries, Steve (1993). Networking and Telecommunication for Information System, London. Library Association Publishing.
[2] India: Planning Commission; Working group on libraries and information for the Ninth Five Year Plan, 1997-2000. (1996) Report.
[3] Kaul, H.K. (1999). Library Resource Sharing and Networks. New Delhi: Virgo Publication.
[4] University Grants Commission (1988) Development of an INFormation LIBrary NETwork (INFLIBNET) Report. Delhi:UGC.
[5] JANET: An Introduction. London:JNT ASSO., 2005.
[6] Martin, S.K. (1986). Library Networks, 1986-87: Libraries in partnership. White plan, New York:Knowledge Industry.
[7] Prasad, ARD (2000). Design and developing academic library websites, Theme paper. In: CALIBER-2000 held at Chennai, Feb14-16.
[8] Subha Rao, Siriginidi (2001). Networking of Libraries and information centre: challenges in India. Library Hi-Tech, 19 (?), pp.166-174.
[9] Subha Rao, Siriginidi (1999). Networking Scenario in India. New Library World, pp. 160-164.

Arup Jana, "Contemporary Library Networks in India: A Survey", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.174-179, 2015.