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Exploiting Title-Keywords Relation to Develop Concept Classifier for Scientific Community

引用

4th International Conference on Networked Digital Technologies, NDT 2012

作者： Khan, Sher Afgun Afzal, Muhammad Tanvir Qadir, Muhammad Abdul Centre for Distributed and Semantic Computing Mohammad Ali Jinnah University Pakistan

ISBN: (纸本)9783642305665

The exponential growth of scientific resources on the web has raised scalability issues like quick retrieval of relevant resource. The major obstacle is attributed to unstructured nature of information. It is estimated that the scientific knowledge is doubling in every five years. This information is being indexed by search engines, citation indexes, and digital libraries etc. Most of the time, the indexed information is unstructured or semi-structured. Therefore, the users are unable to find the relevant research papers. Users are given thousands and sometimes millions of irrelevant hits. This is mainly due to the unstructured nature of information being stored. For a query, the information retrieval may be restricted to the relevant resources if the resources are structured in a classification. Some classifications are being used in different domains such as in computer science, the ACM classification scheme is being used to structure the research papers. However, the ACM classification was majorly updated in 1998 and does not include evolving concepts and domains since then. Furthermore, only ACM papers are classified using ACM classification scheme while other publishers normally do not use this classification. Therefore, the automatic classification becomes a major challenge for scientific community. Several approaches are being used for creating automatic classification on the basis of co-occurrence analysis, keywords analysis, and using domain models, etc. However, the current approaches have their limitations. In this paper, we proposed and implemented a new approach of finding the evolving classification, termed as concept classifier for the scientific community by aggregating the correlation between title terms and keywords of the paper. Initially the experiment has been conducted over the dataset of journal i.e. Journal of Universal Computer Science (***). The resultant taxonomy has been verified and compared with the Growbag dataset (DBLP dataset of co

关键词： Classification (of information)

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Extraction and visualization of citation network

Extraction and visualization of citation network

引用

1st International Conference on Computer Networks and Information Technology, ICCNIT'11

作者： Naqvi, Mohsin Mailk, Ahmad Usman Razzaq, Sohail Afzal, Muhammad Tanvir Centre for Distributed and Semantic Computing Mohammad Ali Jinnah University Islamabad Pakistan

ISBN: (纸本)9781612849416

The digital information is expanding with exponential curve. It becomes difficult to find the relevant information. For example, in scientific domain, to identify the most relevant paper for the focused paper, one needs to explore the focused paper's citations using some citation index such as Google Scholar etc. However, the focused paper may receive 100's and sometimes 1000's of citations. With this huge number of citations, it becomes difficult for users to find the highly relevant papers with respect to the focused paper. While exploring the relevant papers from citations of the focused paper, the user may end up with finding only few. This is because of the fact that authors often cite a paper for providing a background study of the domain and hence cited and cited-by paper have no strong relationship. Currently users need to skim all citations to filter such papers. There is a dire need to develop a system that could automate the process of finding the most relevant papers from the citations. In the current work, we have proposed such a system which ranks the most relevant cited phapers for a cited-by paper. The system conceptualizes citations as a graph model. Furthermore, the system employs number of weights to find the most relevant papers. Such weights include topic weight, keyword weight, and author weight. The edge between two nodes in the graph gets a maximum Weight if both are the most relevant papers. Subsequently, a visualization technique has been designed to visualize the graph. We have used the dataset of Journal of Universal Computer Science having 1400 papers and 6090 citations. The manual inspection has revealed that the top ranked papers were actually the most relevant papers. © 2011 IEEE.

关键词： Visualization

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An efficient query matching algorithm for relational data semantic cache

An efficient query matching algorithm for relational data se...

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2009 2nd International Conference on Computer, Control and Communication, IC4 2009

作者： Ahmad, Munir Sanaullah, Muhammad Qadir, Muhammad Abdul Bashir, Muhammad Farhan Centre for Distributed and Semantic Computing Mohammad Ali Jinnah University Islamabad Pakistan

ISBN: (纸本)9781424433148

Data access latency can be reduced for databases by using caching. semantic caching enhances the performance of normal caching by locally answering the partially overlapped queries. Query processing (generation of probe and remainder query from the incoming queries) and cache management need to be addressed in its totality to really enjoy these benefits. That is, there is a need of correct, complete and efficient algorithms to process incoming queries and to manage semantic and data cache. In this paper, we address this issue in the context of query processing. We have observed that the algorithm proposed by Q. Ren and his colleagues has some inefficiencies and redundancies. To overcome these inefficiencies and redundancies, we have proposed an algorithm for query matching with hierarchal stored query semantics. Proposed algorithm performs matching of stored semantics in cache with semantics of incoming query. It also has capability to generate amending query efficiently and rejects incorrect queries at initial level. Comparison of proposed algorithm is made with existing algorithm. Complexity of proposed query matching algorithm is O(n) which is smaller then the existing which have 0(2n-l), n is number of attributes in a relation. Also, the proposed algorithm is capable to stop the useless processing as was done in the previous algorithms.

关键词： Query processing

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Throughput enhancement of simultaneous multipath communication using modified fast retransmit scheme

Throughput enhancement of simultaneous multipath communicati...

引用

1st International Conference on Computer Networks and Information Technology, ICCNIT'11

作者： Khan, Samiullah Ahmed, Syed Zubair Qadir, Mohammad Abdul Centre for Distributed and Semantic Computing Department of Computer Science Mohammad Ali Jinnah University Islamabad Pakistan

ISBN: (纸本)9781612849416

The increased availability of communication resource through simultaneous use of multiple network interfaces of mobile and fixed devices has enabled higher bandwidth for streaming applications. The out-of-sequence arrival on such multipath transport configurations remains the major challenge to be overcome before the available potential is maximized. In this paper, we propose Modified Fast Retransmit (MFR) Scheme for the multi-homed stream control transmission protocol (SCTP) that minimizes the impact of possible out-of-sequence (OOS) arrival at the destination. The proposed scheme isolates local and global OOS arrivals and adapts fast retransmission strategy for global OOS arrival, in accordance to the end-to-end communication path characteristics. The essence of higher availability of bandwidth is also signified through the modified congestion control mechanism. The proposed scheme produces large boost in throughput of the streams transported through multiple paths, as compared to other schemes;such as Concurrent Multipath Transmission. © 2011 IEEE.

关键词： Bandwidth

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MinG: An efficient algorithm to mine graphs for semantic associations

MinG: An efficient algorithm to mine graphs for semantic ass...

引用

1st International Conference on Computer Networks and Information Technology, ICCNIT'11

作者： Hassan, Zohaib Qadir, Mohammad Abdul Centre for Distributed and Semantic Computing Department of Computer Science Mohammad Ali Jinnah University Islamabad Pakistan

ISBN: (纸本)9781612849416

Data in semantic web is modelled in terms of directed labelled graph. Vertices of that graph represent entities and edges represent relationships between those entities. semantic web allows the discovery of relations between entities using the p-operators. In this paper an algorithm to answer p-operators, that is, to find all paths between any two nodes from a graph is proposed. The algorithm is based on p-index, an indexing scheme presented in the PhD thesis of Barton. Our algorithm reduces the computational and space complexity of indexing by not creating a special type of adjacency matrix called Path Type Matrix at each level of indexing which Barton's algorithm did. We only need Path Type Matrices at first and last level of indexing. Thus if an indexing has 100 levels, Barton requires Path Type Matrices at each level and we only require Path Type Matrices at level 1 and level 100. © 2011 IEEE.

关键词： Directed graphs

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Can Twitter be useful for e-learning recommendation? 33

Can Twitter be useful for e-learning recommendation?

引用

33rd Information Systems Education Conference, ISECON 2016

作者： Sharif, Nauman Afzal, Muhammad Tanvir Graz University of Technology Infeldgasse 21a Graz8010 Austria Centre for Distributed and Semantic Computing Capital University of Science and Technology Islamabad Pakistan

Twitter provides an opportunity to share various learning material along with short and precise text. Micro-blogging community is ever expanding. Discovery of the most relevant learning material becomes essential to solve the information overload problem. There is a potential need to provide twitter based learning recommendation for the learner without directly inferring the tool. E-learning recommender systems have been successfully supporting the individual leaning process. However there is a gap between the e-learning recommender system and twitter based recommendation such that no existing e-learning recommender systems are utilizing the twitter. This paper presents the work-in-progress with the aim to develop recommender system. Personalize learning activities and related tasks can be recommended to the learner according to the individual need of learner and as a result overall learning experience can be enhanced. In literature personalized recommender systems employs the characteristics of items, profiles of users and history of users and items interaction in order to recommend the related items. In this paper we shall investigate if the twitter can be useful for e-learning recommendation and what are the parameters that should be consider when recommending e-learning resources. © 2016 33rd Information Systems Education Conference. All Rights Reserved

关键词： Big data

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Metadata features evaluation for Twitter based e-learning recommendation 34

Metadata features evaluation for Twitter based e-learning re...

引用

34th Information Systems Education Conference, ISECON 2018

Social media has been already extensively used in e-learning context. Users produce a vast amount of learning resources on the web using technologies such as tweets, blogs and wikis. It is a daunting task for learners to select the appropriate resources according to their learning styles." One core area in Technology enhanced Learning deals with this information overload. Learning resources metadata features such as Title, Author, Category and keyword are used in literature to provide recommendation. However these metadata features have not been exploited for twitter based recommender systems. A comprehensive evaluations and experimentations are conducted to determine the effectiveness of resource metadata for twitter based e-learning recommendation. The detailed evaluation reveals the effectiveness of the resource metadata for twitter based e-learning recommendation. © 2018 34th Information Systems Education Conference. All Rights Reserved

关键词： E-learning

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Constructing experts profiles from Linked Open Data

Constructing experts profiles from Linked Open Data

引用

International Conference on Emerging Technologies

作者： Latif, Atif Afzal, Muhammad Tanvir Tochtermann, Klaus Graz University of Technology Knowledge Management Institute Know Center Graz Austria Centre for Distributed and Semantic Computing Mohammad Ali Jinnah University Islamabad Pakistan

ISBN: (纸本)9781424480586

Identification and assignment of (potential) experts to subject field is an important task in various settings and environments. In scientific domain, the identification of experts is normally based on number of factors like: number of publications, citation record, and experience etc. However, the discovered experts cannot be assigned reviewing duties immediately. One also need further information about expert like the country, university, service record, contributions, honors, and name of conferences/journals where the discovered expert is already serving as editor/reviewer. To some extent, this information can be found from search engines using heuristics, by applying Natural Language Processing, and Machine Learning techniques. However, the emergence of many semantically rich and structured datasets from Linked Open Data movement (LOD) can facilitate in more controlled search and fruitful results. This paper employs an automatic technique to find the required information about experts using LOD dataset. The expert profile is discovered, aggregated, clustered, structured, and visualized to the administration of peer-review system. The system has been implemented for an electronic journal such as Journal of Universal Computer Science (***). The proposed system facilitates *** administration to find potential reviewers for scientific papers to assign reviewing duties and to call new editors for computer science topics. © 2010 IEEE.

关键词： Linked data

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CFiTT - corrupt free file transfer technique

CFiTT - corrupt free file transfer technique

引用

International Conference on Information and Emerging Technologies, ICIET

作者： Amna Sultana Muhammad Farhan Bashir Muhammad Abdul Qadir Centre for Distributed and Semantic Computing (CDSC) Mohammad Ali Jinnah University Islamabad Pakistan Centre for Distributed and Semantic Computing (CDSC) Mohammad Ali Jinnah University Pakistan

Communication is one of the basic necessities of human beings. File transfer is one of the basic forms of communication. Reliability is the key issue raised due to complex nature of network and growth of computer science. In this paper we have devised a technique for file transfer which identifies whether some portion of the file is received corrupt or not, and if yes then exactly what portion is corrupt. This technique provides reliability by eliminating the corruption from a file, hence requiring less bandwidth of the network by reducing the amount of data to be re-sent in case of corruption. The reliability is ensured with the help of file signature generation method which we have devised in this paper. The beauty of this technique that it generates hashes which are not easy to break, hence ensuring security of the file. We have used TCP as the underlying protocol, whereas TCP is already considered to be reliable, but the fact is that it does not ensure the reliable transfer over the network due to the fact that it uses CRC which is still vulnerable to network conditions and malicious attacks. Our technique operates at the application layer and tries to finish the cope up with the reliability over the file transfer. We have also developed a prototype to test the integrity of our technique. Empirical results ensure the reliability of our technique. The emphasis of this paper is to provide users with the corrupt free file transfer over the network, so that their time and valuable resources might be saved.

关键词： Computer network reliability Humans Telecommunication network reliability Computer science Bandwidth Data security Protocols Cyclic redundancy check Prototypes Testing

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Recommendation approaches for E-learners - A survey 15

Recommendation approaches for E-learners - A survey

引用

7th ACM International Conference on Management of Computational and Collective IntElligence in Digital EcoSystems, MEDES 2015

作者： Sharif, Nauman Afzal, Muhammad Tanvir Institute for Knowledge Management Graz University of Technology Infeldgasse 21a Graz8010 Austria Centre for Distributed and Semantic Computing Mohammad Ali Jinnah University Islamabad Pakistan

ISBN: (纸本)9781450334808

Recommending relevant content to the learner is a challenging task for any e-Learning management system. This paper has critically reviewed the literature and identified the strategies being used to recommend relevant content to learners. This paper highlights the strength and limitations of prominent approaches and has presented challenging tasks which will be useful for the e-Learning research community to focus for the future research. © 2015 ACM.

关键词： Collaborative filtering

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