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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Rizi, F S"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.07252">arXiv:2002.07252</a> <span> [<a href="https://arxiv.org/pdf/2002.07252">pdf</a>, <a href="https://arxiv.org/ps/2002.07252">ps</a>, <a href="https://arxiv.org/format/2002.07252">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/ICCC.2019.00026">10.1109/ICCC.2019.00026 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Investigating Extensions to Random Walk Based Graph Embedding </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Schloetterer%2C+J">Joerg Schloetterer</a>, <a href="/search/cs?searchtype=author&query=Wehking%2C+M">Martin Wehking</a>, <a href="/search/cs?searchtype=author&query=Rizi%2C+F+S">Fatemeh Salehi Rizi</a>, <a href="/search/cs?searchtype=author&query=Granitzer%2C+M">Michael Granitzer</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2002.07252v1-abstract-short" style="display: inline;"> Graph embedding has recently gained momentum in the research community, in particular after the introduction of random walk and neural network based approaches. However, most of the embedding approaches focus on representing the local neighborhood of nodes and fail to capture the global graph structure, i.e. to retain the relations to distant nodes. To counter that problem, we propose a novel exte… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.07252v1-abstract-full').style.display = 'inline'; document.getElementById('2002.07252v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.07252v1-abstract-full" style="display: none;"> Graph embedding has recently gained momentum in the research community, in particular after the introduction of random walk and neural network based approaches. However, most of the embedding approaches focus on representing the local neighborhood of nodes and fail to capture the global graph structure, i.e. to retain the relations to distant nodes. To counter that problem, we propose a novel extension to random walk based graph embedding, which removes a percentage of least frequent nodes from the walks at different levels. By this removal, we simulate farther distant nodes to reside in the close neighborhood of a node and hence explicitly represent their connection. Besides the common evaluation tasks for graph embeddings, such as node classification and link prediction, we evaluate and compare our approach against related methods on shortest path approximation. The results indicate, that extensions to random walk based methods (including our own) improve the predictive performance only slightly - if at all. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.07252v1-abstract-full').style.display = 'none'; document.getElementById('2002.07252v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.06685">arXiv:2002.06685</a> <span> [<a href="https://arxiv.org/pdf/2002.06685">pdf</a>, <a href="https://arxiv.org/format/2002.06685">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/DEXA.2017.36">10.1109/DEXA.2017.36 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Global and Local Feature Learning for Ego-Network Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rizi%2C+F+S">Fatemeh Salehi Rizi</a>, <a href="/search/cs?searchtype=author&query=Granitzer%2C+M">Michael Granitzer</a>, <a href="/search/cs?searchtype=author&query=Ziegler%2C+K">Konstantin Ziegler</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2002.06685v1-abstract-short" style="display: inline;"> In an ego-network, an individual (ego) organizes its friends (alters) in different groups (social circles). This social network can be efficiently analyzed after learning representations of the ego and its alters in a low-dimensional, real vector space. These representations are then easily exploited via statistical models for tasks such as social circle detection and prediction. Recent advances i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.06685v1-abstract-full').style.display = 'inline'; document.getElementById('2002.06685v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.06685v1-abstract-full" style="display: none;"> In an ego-network, an individual (ego) organizes its friends (alters) in different groups (social circles). This social network can be efficiently analyzed after learning representations of the ego and its alters in a low-dimensional, real vector space. These representations are then easily exploited via statistical models for tasks such as social circle detection and prediction. Recent advances in language modeling via deep learning have inspired new methods for learning network representations. These methods can capture the global structure of networks. In this paper, we evolve these techniques to also encode the local structure of neighborhoods. Therefore, our local representations capture network features that are hidden in the global representation of large networks. We show that the task of social circle prediction benefits from a combination of global and local features generated by our technique. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.06685v1-abstract-full').style.display = 'none'; document.getElementById('2002.06685v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.06665">arXiv:2002.06665</a> <span> [<a href="https://arxiv.org/pdf/2002.06665">pdf</a>, <a href="https://arxiv.org/format/2002.06665">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3297280.3297622">10.1145/3297280.3297622 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Predicting event attendance exploring social influence </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rizi%2C+F+S">Fatemeh Salehi Rizi</a>, <a href="/search/cs?searchtype=author&query=Granitzer%2C+M">Michael Granitzer</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2002.06665v1-abstract-short" style="display: inline;"> The problem of predicting people's participation in real-world events has received considerable attention as it offers valuable insights for human behavior analysis and event-related advertisement. Today social networks (e.g. Twitter) widely reflect large popular events where people discuss their interest with friends. Event participants usually stimulate friends to join the event which propagates… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.06665v1-abstract-full').style.display = 'inline'; document.getElementById('2002.06665v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.06665v1-abstract-full" style="display: none;"> The problem of predicting people's participation in real-world events has received considerable attention as it offers valuable insights for human behavior analysis and event-related advertisement. Today social networks (e.g. Twitter) widely reflect large popular events where people discuss their interest with friends. Event participants usually stimulate friends to join the event which propagates a social influence in the network. In this paper, we propose to model the social influence of friends on event attendance. We consider non-geotagged posts besides structures of social groups to infer users' attendance. To leverage the information on network topology we apply some of recent graph embedding techniques such as node2vec, HARP and Poincar`e. We describe the approach followed to design the feature space and feed it to a neural network. The performance evaluation is conducted using two large music festivals datasets, namely the VFestival and Creamfields. The experimental results show that our classifier outperforms the state-of-the-art baseline with 89% accuracy observed for the VFestival dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.06665v1-abstract-full').style.display = 'none'; document.getElementById('2002.06665v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.05257">arXiv:2002.05257</a> <span> [<a href="https://arxiv.org/pdf/2002.05257">pdf</a>, <a href="https://arxiv.org/format/2002.05257">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/ASONAM.2018.8508763">10.1109/ASONAM.2018.8508763 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Shortest path distance approximation using deep learning techniques </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rizi%2C+F+S">Fatemeh Salehi Rizi</a>, <a href="/search/cs?searchtype=author&query=Schloetterer%2C+J">Joerg Schloetterer</a>, <a href="/search/cs?searchtype=author&query=Granitzer%2C+M">Michael Granitzer</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2002.05257v1-abstract-short" style="display: inline;"> Computing shortest path distances between nodes lies at the heart of many graph algorithms and applications. Traditional exact methods such as breadth-first-search (BFS) do not scale up to contemporary, rapidly evolving today's massive networks. Therefore, it is required to find approximation methods to enable scalable graph processing with a significant speedup. In this paper, we utilize vector e… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.05257v1-abstract-full').style.display = 'inline'; document.getElementById('2002.05257v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.05257v1-abstract-full" style="display: none;"> Computing shortest path distances between nodes lies at the heart of many graph algorithms and applications. Traditional exact methods such as breadth-first-search (BFS) do not scale up to contemporary, rapidly evolving today's massive networks. Therefore, it is required to find approximation methods to enable scalable graph processing with a significant speedup. In this paper, we utilize vector embeddings learnt by deep learning techniques to approximate the shortest paths distances in large graphs. We show that a feedforward neural network fed with embeddings can approximate distances with relatively low distortion error. The suggested method is evaluated on the Facebook, BlogCatalog, Youtube and Flickr social networks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.05257v1-abstract-full').style.display = 'none'; document.getElementById('2002.05257v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1510.00805">arXiv:1510.00805</a> <span> [<a href="https://arxiv.org/pdf/1510.00805">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> A Fast Survey on Methods for Classification Anonymity Requirements </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kharaji%2C+M+Y">Morteza Yousefi Kharaji</a>, <a href="/search/cs?searchtype=author&query=Rizi%2C+F+S">Fatemeh Salehi Rizi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1510.00805v1-abstract-short" style="display: inline;"> Anonymity has become a significant issue in security field by recent advances in information technology and internet. The main objective of anonymity is hiding and concealing entities privacy inside a system. Many methods and protocols have been proposed with different anonymity services to provide anonymity requirements in various fields until now. Each anonymity method or protocol is developed u… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1510.00805v1-abstract-full').style.display = 'inline'; document.getElementById('1510.00805v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1510.00805v1-abstract-full" style="display: none;"> Anonymity has become a significant issue in security field by recent advances in information technology and internet. The main objective of anonymity is hiding and concealing entities privacy inside a system. Many methods and protocols have been proposed with different anonymity services to provide anonymity requirements in various fields until now. Each anonymity method or protocol is developed using particular approach. In this paper, first, accurate and perfect definitions of privacy and anonymity are presented then most important problems in anonymity field are investigated. Afterwards, the numbers of main anonymity protocols are described with necessary details. Finally, all findings are concluded and some more future perspectives are discussed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1510.00805v1-abstract-full').style.display = 'none'; document.getElementById('1510.00805v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 October, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2015. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 2 figures, 1 table, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 12, No. 4, April 2014</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1406.7377">arXiv:1406.7377</a> <span> [<a href="https://arxiv.org/pdf/1406.7377">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> A New Approach for Finding Cloned Profiles in Online Social Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kharaji%2C+M+Y">Morteza Yousefi Kharaji</a>, <a href="/search/cs?searchtype=author&query=Rizi%2C+F+S">Fatemeh Salehi Rizi</a>, <a href="/search/cs?searchtype=author&query=Khayyambashi%2C+M+R">Mohammad Reza Khayyambashi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1406.7377v1-abstract-short" style="display: inline;"> Today, Online Social Networks such as Facebook, LinkedIn and Twitter are the most popular platforms on the Internet, on which millions of users register to share personal information with their friends. A large amount of data, social links and statistics about users are collected by Online Social Networks services and they create big digital mines of various statistical data. Leakage of personal i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1406.7377v1-abstract-full').style.display = 'inline'; document.getElementById('1406.7377v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1406.7377v1-abstract-full" style="display: none;"> Today, Online Social Networks such as Facebook, LinkedIn and Twitter are the most popular platforms on the Internet, on which millions of users register to share personal information with their friends. A large amount of data, social links and statistics about users are collected by Online Social Networks services and they create big digital mines of various statistical data. Leakage of personal information is a significant concern for social network users. Besides information propagation, some new attacks on Online Social Networks such as Identity Clone attack (ICA) have been identified. ICA attempts to create a fake online identity of a victim to fool their friends into believing the authenticity of the fake identity to establish social links in order to reap the private information of the victims friends which is not shared in their public profiles. There are some identity validation services that perform users identity validation, but they are passive services and they only protect users who are informed on privacy concerns and online identity issues. This paper starts with an explanation of two types of profile cloning attacks are explained and a new approach for detecting clone identities is proposed by defining profile similarity and strength of relationship measures. According to similar attributes and strength of relationship among users which are computed in detection steps, it will be decided which profile is clone and which one is genuine by a predetermined threshold. Finally, the experimental results are presented to demonstrate the effectiveness of the proposed approach. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1406.7377v1-abstract-full').style.display = 'none'; document.getElementById('1406.7377v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 June, 2014; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2014. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1403.2006">arXiv:1403.2006</a> <span> [<a href="https://arxiv.org/pdf/1403.2006">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> An IAC Approach for Detecting Profile Cloning in Online Social Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kharaji%2C+M+Y">Morteza Yousefi Kharaji</a>, <a href="/search/cs?searchtype=author&query=Rizi%2C+F+S">Fatemeh Salehi Rizi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1403.2006v1-abstract-short" style="display: inline;"> Nowadays, Online Social Networks are popular websites on the internet, which millions of users register on and share their own personal information with others. Privacy threats and disclosing personal information are the most important concerns of OSNs users. Recently, a new attack which is named Identity Cloned Attack is detected on OSNs. In this attack the attacker tries to make a fake identity… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1403.2006v1-abstract-full').style.display = 'inline'; document.getElementById('1403.2006v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1403.2006v1-abstract-full" style="display: none;"> Nowadays, Online Social Networks are popular websites on the internet, which millions of users register on and share their own personal information with others. Privacy threats and disclosing personal information are the most important concerns of OSNs users. Recently, a new attack which is named Identity Cloned Attack is detected on OSNs. In this attack the attacker tries to make a fake identity of a real user in order to access to private information of the users friends which they do not publish on the public profiles. In today OSNs, there are some verification services, but they are not active services and they are useful for users who are familiar with online identity issues. In this paper, Identity cloned attacks are explained in more details and a new and precise method to detect profile cloning in online social networks is proposed. In this method, first, the social network is shown in a form of graph, then, according to similarities among users, this graph is divided into smaller communities. Afterwards, all of the similar profiles to the real profile are gathered (from the same community), then strength of relationship (among all selected profiles and the real profile) is calculated, and those which have the less strength of relationship will be verified by mutual friend system. In this study, in order to evaluate the effectiveness of proposed method, all steps are applied on a dataset of Facebook, and finally this work is compared with two previous works by applying them on the dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1403.2006v1-abstract-full').style.display = 'none'; document.getElementById('1403.2006v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 March, 2014; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2014. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div 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