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Nowadays, with the evolution of e-learning technologies, there is an urgent need for the design, development, reuse and sharing of online courses and contents as standard learning objects. To build this courses, this requires the use of creation tools, called “authoring tools”, which manifest, in most cases, a complexity of use for teachers authors. The aim of this work is to develop an authoring tool for constructing learning objects, tool targeted at supporting teachers instructors in implementing educational contents. The authoring environment, called “e-LD (e-Learning Design) Authoring Tool” is based on the methods of Instructional Design and Learning Design, particularly the IMS-LD specification that offers an explicit separation of "activities" and "resources" by specifying the semantic relations linking them. In addition, e-LD tools introduces an aspect of online and collaborative Learning Design : the process of e-Learning Design is done collaboratively by integrating interaction tools inspired from collaboration on Web 2.0 applications. Firstly, the paper justify the choice of IMS-LD as basic model to construct learning objects. Then, this work presents the design and development process of learning objects and describes the steps of the development process. Finally, the paper exposes the adopted technical choices and a first prototype is set up to provide a subjective evaluation of the new framework.

e-LD; e-Learning; Learning Design; IMS-LD; Authoring tool; Collaboration

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[3] D. A. Wiley, “Connecting learning objects to Instructional design theory: a definition, a methaphor, and taxonomy”. In Wiley (Ed), The Instructional Use of Learning Objects. Agency for Instructional Technology and Association for Educational Communications of Technology, Bloomington, Indiana, 2002.
[4] A. Battou, "Approche granulaire des objets pédagogiques en vue de l’adaptabilité dans le cadre des Environnements Informatiques pour l'Apprentissage Humain", Doctoral dissertation, Ibn Zohr University, Faculty of Sciences, Agadir – Morocco, 2012.
[5] N. Zniber and C. Cauvet, "Modèle orienté service pour la conception de parcours pédagogiques personnalisés", Doctoraal dissertation, Aix-Marseille University, France, 2009.
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[7] R. Koper and B. Olivier, “Representing the learning design of units of learning”, Educational Technology & Society, Volume-7, Issue-3, Page No (97–111), 2004,.
[8] R. Koper and Y Miao, “Using the IMS LD standard to describe learning designs”, 2008. In: Lockyer, L., Bennet, S., Agostinho, S. and Harper, B. (Eds.), Handbook of Research on Learning Design and Learning Objects Issues, Applications and Technologies. IGI Global, Hersey, Page No (41-86), 2008.
[9] IMS-LD (IMS Learning Design specification).
Available at:
http://www.imsglobal.org/learningdesign/index.cfm
Access date: December 2015.
[10] D. Burgos, C. Tattersall, M. Dougiamas, H. Vogten, and R. Koper, “A First StepMapping IMS Learning Design and Moodle”. Journal of Universal Computer Science, Volume- 13, Issue-7, Page No (924-931), 2007,.

Abderrahim El Mhouti, Azeddine Nasseh and Mohamed Erradi, "e-LD : A Collaborative and Online e-Learning Design Authoring Tool Based on IMS-LD Specification," International Journal of Computer Sciences and Engineering, Vol.4, Issue.1, pp.61-65, 2016.

In this world of providing effective interface to the user for accomplishing the requirements needed to perform information gathering for the purpose of implementing Penetration testing in a network we need an adaptive scenario of carrying out the same task. Ruby on Rails provides an interactive way of dealing with the user’s inputs. This kind of Web application allows a user to perform the basic information gathering, regarding possible threats in its network without having prior knowledge of Penetration testing.

Penetration Testing, Ruby on Rails, Information Gathering

[1] An Overview of Penetration Testing, International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.6, November 2011
[2] Why Johnny Can’t Pentest: An Analysis of Black-box Web Vulnerability Scanners, University of California, Santa Barbara
[3] Improving penetration testing through static and dynamic analysis, Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/stvr.450
[4] State of the Art: Automated Black-Box Web Application Vulnerability Testing, Stanford University
[5] PENETRATION TESTING AND VULNERABILITY ASSESSMENTS: A PROFESSIONAL APPROACH, Published in the Proceedings of the 1st International Cyber Resilience Conference, Edith Cowan University, Perth Western Australia, 23rd August 2010
[6] Penetration Testing: Assessing Your Overall Security Before Attackers Do, SANS Institute InfoSec Reading Room
[7] Arkin, B., Stender, S., McGraw, G. (2005). “Software Penetration Testing”, IEEE Security and Privacy, Volume 3, Issue 1
[8] Network Penetration Testing and Research, Brandon F. Murphy North Carolina Agricultural and Technical State University, Greensboro, North Carolina, 27411
[9] Ruby on Rails Tutorials 3rd Edition – Michael Hartl, 2nd Edition, Addison-Wesley Professional Ruby Series
[10] Certified Ethical Hacker – Kimberly Graves, 1st Edition, Wiley Publising Inc.
[11] Core Security Technologies, http://www.coresecurity.com/content/intro-pen-test
[12] Hacking Articles by Raj Chandel, http://www.hackingarticles.in/

Harsh Bhardwaj, Manish Aggarwal, Neha Gupta, "Information Gathering on a Web Application deployed in Ruby on Rails," International Journal of Computer Sciences and Engineering, Vol.4, Issue.1, pp.60-69, 2016.

Load balancing is usually a technique used for enhancing the performance of a parallel as well as distributed system. The performance from the systems might be enhanced by means of redistribution regarding load over the different cell phone networks. As a result, load balancing plays a significant role pertaining to increasing your efficiency regarding distributed program, but this is a very challenging and difficult task inside large scale distributed system as the global condition of spread systems is usually changes dynamically. Regarding various factors like greatest throughput, outage possibility, availability along with scalability your distributed program needs productive load evening out. In this paper various load balancing techniques have been studied and out of these dynamic load balancing techniques are seem to be good for load balancing. Also, the present load balancing algorithms are reviewed along with the comparative analysis is usually performed.

Load Balancing, Cell Networks, Fuzzy Logic, fuzzy based dynamic load balancing algorithm

[1] Yang Xu; Rui Yin; Guanding Yu, "Adaptive biasing scheme for load balancing in backhaul constrained small cell networks," in Communications, IET , vol.9, no.7, pp.999-1005,57 2015 doi: 10.1049/iet-com.2014.074
[2] Collotta, Mario, "FLBA: A fuzzy algorithm for load balancing in IEEE 802.11 networks." Journal of Network and Computer Applications 53 (2015): 183-192.
[3] Muñoz, P., R. Barco, and I. de la Bandera, "Load balancing and handover joint optimization in LTE networks using Fuzzy Logic and Reinforcement Learning." Computer Networks 76 (2015): 112-125.
[4] Gódor, Győző, et al, "A survey of handover management in LTE-based multi-tier femtocell networks: Requirements, challenges and solutions." Computer Networks 76 (2015): 17-41.
[5] Singh, Aarti, Dimple Juneja, and Manisha Malhotra, "Autonomous Agent Based Load Balancing Algorithm in Cloud Computing." Procedia Computer Science 45 (2015): 832-841.
[6] Krishan, Ram, and Vijay Laxmi. "IEEE 802.11 WLAN Load Balancing for Network Performance Enhancement." Procedia Computer Science 57 (2015): 493-499.
[7] Sreenivas, Velagapudi, M. Prathap, and Mohammed Kemal, "Load balancing techniques: Major challenge in Cloud Computing-a systematic review." Electronics and Communication Systems (ICECS), 2014 International Conference on. IEEE, 2014.
[8] Domanal, Shridhar G., and G. Ram Mohana Reddy, "Optimal load balancing in cloud computing by efficient utilization of virtual machines." Communication Systems and Networks (COMSNETS), 2014 Sixth International Conference on. IEEE, 2014.
[9] Kumar, Vikas, and Shiva Prakash, "A Load Balancing Based Cloud Computing Techniques and Challenges."
[10] Yang, Yufei, Tony QS Quek, and Lingjie Duan. "Backhaul-constrained small cell networks: Refunding and QoS provisioning." Wireless Communications, IEEE Transactions on 13.9 (2014): 5148-5161.
[11] Zhang, Haijun, et al, "Cooperative Interference Mitigation and Handover Management for Heterogeneous Cloud Small Cell Networks."arXiv preprint arXiv:1504.08076 (2015).
[12] Zhang, Haijun, et al, "Coexistence of Wi-Fi and heterogeneous small cell networks sharing unlicensed spectrum." Communications Magazine, IEEE 53.3 (2015): 158-164.
[13] Ge, Xiaohu, et al, "Energy efficiency of small cell backhaul networks based on Gauss–Markov mobile models." Networks, IET 4.2 (2014): 158-167.
[14] Zou, Kingsley Jun, and Kristo Wenjie Yang. "Network synchronization for dense small cell networks." Wireless Communications, IEEE 22.2 (2015): 108-117.
[15] Mao, Yuyi, et al. "Energy Harvesting Small Cell Networks: Feasibility, Deployment and Operation." arXiv preprint arXiv:1501.02620 (2015).
[16]http://www.tutorialspoint.com/cloud_computing/cloud_computing_tutorial.pdf
[17] Begum, Suriya, and Dr Prashanth CSR. "Review of load balancing in cloud computing." IJCSI International Journal of Computer Science Issues 10.1 (2013): 1694-0784
[18] Foram Kherani, Jignesh Vania. "Load Balancing in Cloud Computing", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Vol.2, Issue 1, pp.907-912, March 2014,
[19] Kaur, Rajwinder, and Pawan Luthra. "Load Balancing in Cloud Computing." Proceedings of International Conference on Recent Trends in Information, Telecommunication and Computing, ITC. 2012.
[20] Saxena, S.; Khan, M.Z.; Singh, R., "Performance Analysis in Distributed System of Dynamic Load Balancing Using Fuzzy Logic," in Engineering and Technology (S-CET), 2012 Spring Congress on , vol., no., pp.1-5, 27-30 May2012 doi: 10.1109/SCET.2012.6341923
[21] Qi-Ye Zhang; Ze-Ming Sun; Feng Zhang, "A clustering routing protocol for wireless sensor networks based on type-2 fuzzy logic and ACO," in Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on , vol., no., pp.1060-1067,6-11 July 2014 doi: 10.1109/FUZZ-IEEE.2014.6891

Jasween Kaur, Kiranbir Kaur, "A Study of Fuzzy Based Dynamic Load Balancing for Cell Networks," International Journal of Computer Sciences and Engineering, Vol.4, Issue.1, pp.70-75, 2016.

Understanding the characteristics of queries wherever a search engine is failing is very important for improving search engine performance. Previous work for the most part depends on user-interaction options (e.g., click through statistics) to spot such underperforming queries. This paper evaluates the techniques used for users log history query grouping in automatic manner. Automatic query grouping is very useful for lots of software and web application. In this paper we proposes new method for calculating similarity between query using various log record attributes like time, clicked url, text similarity and frequently occurring queries using association rules. This work introduces another strong method for similar query grouping to make web browsing easy and efficient by query recommendation. A comparative evaluation of proposed method with existing work available in literature has also been carried out and the result shows that the proposed method is more effective.

Query Reformulation, Click Graph, Web Mining, Association Rules, Text Similarity

[1] Ageev, M., Guo, Q., Lagun, D., and Agichtein, E. “Find it if you can: a game for modeling different types of web search success using interaction data”. SIGIR, pp-345 –354 , 2011.
[2] Feild, H., Allan, J., and Jones, R. Predicting searcher frustration. SIGIR,pp- 34–41, 2010.
[3] Guo, Q., White, R.W., Zhang, Y., Anderson, B., and Dumais, S.T. Why searchers switch: understanding and pre-dicting engine switching rationales. SIGIR, pp-335–344, 2011.
[4] Hassan, A., Song, Y., and He, L. A task level user satisfaction model and its application on improving relevance estimation. CIKM,pp- 125–134,2011.
[5] Spink, M. Park, B.J. Jansen, and J. Pedersen,“Multitasking during Web Search Sessions” Information Processing and Management, vol. 42, no. 1, pp. 264-275, 2006.
[6] Fuxman, P. Tsaparas, K. Achan, and R. Agrawal, “Using the Wisdom of the Crowds for Keyword Generation” Proc. the 17th Int’l Conf. World Wide Web (WWW ’08), 2008.
[7] Heasoo Hwang, Hady W. Lauw, Lise Getoor, and Alexandros Ntoulas, “Organizing User Search Histories”, IEEE Transactions On Knowledge And Data Engineering, Vol. 24, NO. 5, Page 912-925.2012.
[8] A. Fuxman, P. Tsaparas, K. Achan, and R. Agrawal, “Using the Wisdom of the Crowds for Keyword Generation” Proc. the 17th Int’l Conf. World Wide Web (WWW ’08),2008.
[9] W.M. Rand, “Objective Criteria for the Evaluation of Clustering Methods” J. the Am. Statistical Assoc.,vol. 66, no. 336, pp. 846-850, 1971.
[10] Jaideep Srivastava, Robert Cooley, Mukund Deshpande Pang-Ning Tan, “Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data”, SIGKDD Explorations, Vol. 1, No. 2, 2000.
[11] Spink, M. Park, B.J. Jansen, and J. Pedersen, “Multitasking during Web Search sessions,” Information Processing and Management, vol. 42, no. 1, pp. 264-275, 2006.
[12] H.C. Ozmutlu and F. C¸ avdur, “Application of Automatic Topic Identification on Excite Web Search Engine Data Logs,” Information Processing and Management, vol. 41, no. 5, pp. 1243-1262, 2005
[13] F. Radlinski and T. Joachims, “Query Chains: Learning to Rank from Implicit Feedback,” Proc. ACM Conf. Knowledge Discovery and Data Mining (KDD), 2005.
[14] J. Yi and F. Maghoul, “Query Clustering Using Click-through Graph,” Proc. the 18th Int’l Conf. World Wide Web (WWW ’09), 2009.
[15] E. Sadikov, J. Madhavan, L. Wang, and A. Halevy, “Clustering Query Refinements by User Intent,” Proc. the 19th Int’l Conf. World Wide Web (WWW ’10), 2010.
[16] R. Baeza-Yates and A. Tiberi, “Extracting Semantic Relations from Query Logs,” Proc. 13th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD), 2007.
[17] M. Spiliopoulou, C. Pohle, and L.C. F aulstich. Improving the effectiveness of a website with web usage mining. In Advances in Web Usage Analysis and User Profiling, Berlin, Springer, pp. 141-62, 2000
[18] K. Collins-Thompson and J. Callan, “Query Expansion Using Random Walk Models,” Proc. 14th ACM Int’l Conf. Information and Knowledge Management (CIKM), 2005.
[19] N. Craswell and M. Szummer, “Random Walks on the Click Graph,” Proc. 30th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR ’07), 2007.
[20] J.-R. Wen, J.-Y. Nie, and H.-J. Zhang, “Query Clustering Using User Logs,” ACM Trans. in Information Systems, vol. 20, no. 1, pp. 59-81,2002.
[21] Tahira Tabassum, Amit Dubey, “User Search Query Grouping using Association Fusion Graph”,International Journal of Advanced Research in Computer Science and Software Engineering, Volume4, ,Issue4, Page 259-267, April 2014.

Divakar Pandey, "Search Engine Query Grouping using the combination of Time, Text and URL Similarity with Association Rules," International Journal of Computer Sciences and Engineering, Vol.4, Issue.1, pp.76-81, 2016.

String matching is a conventional problem in computer science. For a known text string ‘T’, the problem of string matching is to locate whether a pattern string ‘P’ occurs in ‘T’ or not, and if ‘P’ occurs then the position of ‘P’ in ‘T’ is reported. String matching sometimes called string searching has become an important aspect of the real world because it is being used in many applications where the string algorithm tries to find a location of one or several strings (also called Patterns) within a larger string or text (Text Data Set). A few of its essential applications are Spell Checkers, Spam Filters, Intrusion Detection System, Search Engines, Plagiarism Detection, Bioinformatics, Digital Forensics and Information Retrieval Systems, etc. The paper includes various string matching methodologies along with its historical contributory details in a variety of needful real world applications.

String Matching, Spell checkers, Spam Filter, Intrusion Detection System, Search Engines, Plagiarism Detection, Bioinformatics

[1] Thomas H Corman, Charles E. Leiserson, Oonald L. Rivest and Clifforf Stein, “Introduction to Algorithms – String Matching”, IEEE Edition, 2nd Edition, PP No. 906-907.
[2] Leena Salmela, J. Tarhio and J. Kytojoki “MultiPattern String Matching with Very Large Pattern Sets”, ACM Journal Algorithmic, Volume 11, 2006.
[3] Nimisha Singla, Deepak Garg, “String Matching Algorithms and their Applicability in Various Applications” IJSCE, ISSN 2231-2307 Vol I, PP No. 6, January 2012.
[4] Simone Faro and Thierry Lecroq, “The exact online string matching problem: A review of the most recent results” ACM computing surveys Vol .V, PP .N, Article A, January 2011.
[5] Gonzalo Navarro, “A Guided Tour to Approximate String”, ACM Computing Surveys, Vol 33 No. 1, PP No. 31-88, March 2001.
[6] Christian Charras and Thierry Lecroq, “Handbook of Exact String Matching Algorithms”, Published in King’s college publication, Feb 2004.
[7] Alberto Apostolico and ZviGalil,” Pattern Matching Algorithms” Published in Oxford University Press, USA, 1st edition, May 29, 1997.
[8] Morris J.H., Pratt V.R., 1970, “A Linear Pattern-Matching Algorithm”, Technical Report40, University of California, Berkeley 1970.
[9] Donald Knuth; James H. Morris, Jr, Vaughaz Pratt (1977). "Fast Pattern Matching in Strings". SIAM Journal on Computing 6 (2): 323–350. Doi: 10.1137/0206024.
[10] BOYER, R. S. AND MOORE, J. S,”A fast string searching algorithm”, Communication of ACM 20, Vol. 10, pp. 762–772, 1977.
[11] Alfred v. Aho and Margaret J. Corasick,”Efficient String Matching: An aid to Bibliographic Search” communication of ACM, vol. 18, june 1975.
[12] Cormen, Thomas H.; Leiserson, Charles E.; Rivest, Ronald L.; Stein, Clifford (2001-09-01). "The Rabin–Karp algorithm". Introduction to Algorithms (2nd ed.). Cambridge, Massachusetts: MIT Press. pp. 911–916.
[13] V. Saikrishna, A. Rasool, N. Khare, “String Matching and its Applications in Diversified Fields”, IJCSI Jan 2012, Volume 9- PP No 1.

Kapil Kumar Soni, "Contributing Efforts of Various String Matching Methodologies in Real World Applications," International Journal of Computer Sciences and Engineering, Vol.4, Issue.1, pp.82-85, 2016.

Images taken in foggy weather condition often suffer from poor visibility and clarity. Images of the outdoor scene which are captured under bad weather conditions contain atmospheric degradation such as haze, fog, smoke caused by the particles present in the atmosphere resulting in the absorption and scattering of the light, which travels from the scene point of the observer. In this paper, we define Dark Channel Prior Model, Gamma Transformation and Bilateral Filtering for fog removal and show better result.In this paper, to visibility increase with only single hazy image, a haze removal algorithm type is proposed. Firstly, the raw atmospheric transmission map is estimated with dark channel prior use.The experimental outcome shows that the good result as compared to previous gamma transformation and median filtering. The result based on the contrast gain ratio, execution time and entropy.

Bilateral Filtering, Dark Channel Prior, Gamma Transformation

[1] Garima Yadav, Saurabh Maheshwari and Anjali Agarwal,” Fog Removal Techniques from Images: A Comparative Review and Future Directions”, International Conference on Signal Propagation and Computer Technology (ICSPCT), IEEE, pp: 44-52,2014.
[2] Apurva Kumari, Philip Joseph Thomas and S.K.Sahoo,” Single Image Fog Removal Using Gamma transformation and median filtering”,Annual IEEE India Conference (INDICON),2014.
[3] Nirali Pambhar and Prof.Priyanka Buch,”Analysis and Survey of Various Methods of Fog Removal”, International Journal of Emerging Trends in Engineering and Development Issue 4, Vol.2 Page 363-368(March 2014).
[4] Yadwinder Singh, Er. Rajan Goyal,” A Study Of Various Haze Removal Algorithms”, IJIRT | Volume 1 Issue 3, pp:1-7,2014.
[5] V. Agarwal, S. Khandelwal, D. Goyal, J. Sharma2 and A. Tiwari3,” Two-Pass Adaptive Histogram Based Method For Restoration Of Foggy Images”, International Conference on Pattern Recognition and Image Analysis ,pp: 139- 142,2014.
[6] Gagandeep Singh , Gagandeep Singh,” Evaluation Of Various Digital Image Fog Removal Algorithms”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, pp: 7536-7540, Issue 7, July 2014
[7] Atul Gujral, Shailender Gupta and Bharat Bhushan,” A Novel Defogging Technique for Dehazing Images”,International Journal of Hybrid Information Technology Vol.7, No.4, pp.235-248 http://dx.doi.org/10.14257/ijhit.2014.7.4.20, 2014.
[8] Jun Mao, Uthai Phommasak, Shinya Watanabe and Hiroyuki Shioya,” Detecting Foggy Images and Estimating the Haze Degree Factor”, Mao et al., J Comput Sci Syst Biol , 7:6 http://dx.doi.org/10.4172/jcsb.1000161,Volume 7(6) 226-228 (014),2014.
[9] Hiroshi Kawarabuki and Kazunori Onoguchi,” Snowfall Detection in a Foggy Scene”, 22nd International Conference on Pattern Recognition, IEEE, pp: 877-882, 2014.
[10] Naman Chopra, Mr. Anshul Anand,”Despeckling of Images Using Wiener Filter in Dual Wavelet Transform Domain”, Naman Chopra et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 4069-4071,2014.
[11] S. Bronte, L. M. Bergasa, P. F. Alcantarilla,” Fog Detection System Based on Computer Vision Techniques”ITSC,2009.
[12] Shota Furukawa, Takahiro Fukuda, Takanori Koga, Noriaki Suetakeand Eiji Uchin,”High-Speed Min-Max Bilateral Filter-Based Image Dehazing by Using GPGPU”, Proceedings of the 2014 International Conference on Advanced Mechatronic Systems, Kumamoto, Japan, IEEE,pp: 459-462, August 10-12, 2014.

Suman Tyagi, Nirupma Tiwari, "Fog Image Restoration Using Dark Channel Prior Model with Gamma Transformation and Bilateral Filtering," International Journal of Computer Sciences and Engineering, Vol.4, Issue.1, pp.86-92, 2016.

This paper focuses on key insights of big data architecture which somehow lead to top 5 big data security risks and the use of top 5 best practices that should be considered while designing big data solution which can thereby surmount with these risks. Big data architecture, being distributive in nature can undergo partition, replication and distribution among thousands of data and processing nodes for distributed computation thus supporting multiple features associated with big data analytics like real time, streaming and continuous data computation along with massive parallel and powerful programming framework. These series of characteristics are put into effect via a key setup that somehow leads to certain crucial security implications. The challenges induced by this can be handled via big data technologies and solutions that exist inside big data architecture compound characterized for specific big data problems. Big data solutions should provide effective ways to be more proactive against fraud, management and consolidation of data, proper security against data intrusion, malicious attacks and many other fraudulent activities. In particular, this paper discusses the issues and key features that should be taken into consideration while undergoing development of secured big data solutions and technologies that will handle the risks and privacy concerns (e.g. Data security, insecure computation and data storage, invasive marketing etc.) associated with big data analysis in an effective way to increase the performance impact, considering that these risks are somehow a result of characteristics of big data architecture.

Big Data; Hadoop; MapReduce; Secure Computation

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Renu Bhandari, Vaibhav Hans and Neelu Jyothi Ahuja, "Big Data Security – Challenges and Recommendations," International Journal of Computer Sciences and Engineering, Vol.4, Issue.1, pp.93-98, 2016.