Dr. Georgios K. PItsilis

@ntnu.edu

Research Associate, Department of Computer Science
Norwegian University of Science and Technology

Dr. Georgios K. PItsilis


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https://researchid.co/pitsilis
21

Scopus Publications

1200

Scholar Citations

14

Scholar h-index

20

Scholar i10-index

Scopus Publications

  • Effective hate-speech detection in Twitter data using recurrent neural networks
    Georgios K. Pitsilis, Heri Ramampiaro, Helge Langseth
    Applied Intelligence, 2018
    This paper addresses the important problem of discerning hateful content in social media. We propose a detection scheme that is an ensemble of Recurrent Neural Network (RNN) classifiers, and it incorporates various features associated with userrelated information, such as the users’ tendency towards racism or sexism. These data are fed as input to the above classifiers along with the word frequency vectors derived from the textual content. Our approach has been evaluated on a publicly available corpus of 16k tweets, and the results demonstrate its effectiveness in comparison to existing state of the art solutions. More specifically, our scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms.
  • Abstracting massive data for lightweight intrusion detection in computer networks
    Wei Wang, Jiqiang Liu, Georgios Pitsilis, Xiangliang Zhang
    Information Sciences, 2018
    Anomaly intrusion detection in big data environments calls for lightweight models that are able to achieve real-time performance during detection. Abstracting audit data provides a solution to improve the efficiency of data processing in intrusion detection. Data abstraction refers to abstract or extract the most relevant information from the massive dataset. In this work, we propose three strategies of data abstraction, namely, exemplar extraction, attribute selection and attribute abstraction. We first propose an effective method called exemplar extraction to extract representative subsets from the original massive data prior to building the detection models. Two clustering algorithms, Affinity Propagation (AP) and traditional k-means, are employed to find the exemplars from the audit data. k-Nearest Neighbor (k-NN), Principal Component Analysis (PCA) and one-class Support Vector Machine (SVM) are used for the detection. We then employ another two strategies, attribute selection and attribute extraction, to abstract audit data for anomaly intrusion detection. Two http streams collected from a real computing environment as well as the KDD’99 benchmark data set are used to validate these three strategies of data abstraction. The comprehensive experimental results show that while all the three strategies improve the detection efficiency, the AP-based exemplar extraction achieves the best performance of data abstraction.
  • Harnessing the power of social bookmarking for improving tag-based recommendations
    Georgios Pitsilis, Wei Wang
    Computers in Human Behavior, 2015
    Used the collaborative tagging idea to produce personalized recommendations.Attempted utilization of the power of taxonomies through clustering of annotations.Experimental evaluation of the model using data from public annotation system, citeUlike.The approach improved prediction quality without compromising computational efficiency. Social bookmarking and tagging has emerged a new era in user collaboration. Collaborative Tagging allows users to annotate content of their liking, which via the appropriate algorithms can render useful for the provision of product recommendations. It is the case today for tag-based algorithms to work complementary to rating-based recommendation mechanisms to predict the user liking to various products. In this paper we propose an alternative algorithm for computing personalized recommendations of products, that uses exclusively the tags provided by the users. Our approach is based on the idea of using the semantic similarity of the user-provided tags for clustering them into groups of similar meaning. Afterwards, some measurable characteristics of users' Annotation Competency are combined with other metrics, such as user similarity, for computing predictions. The evaluation on data used from a real-world collaborative tagging system, citeUlike, confirmed that our approach outperforms the baseline Vector Space model, as well as other state of the art algorithms, predicting the user liking more accurately.
  • Securing recommender systems against shilling attacks using social-based clustering
    Xiang-Liang Zhang, Tak Man Desmond Lee, Georgios Pitsilis
    Journal of Computer Science and Technology, 2013
    Recommender systems (RS) have been found supportive and practical in e-commerce and been established as useful aiding services. Despite their great adoption in the user communities, RS are still vulnerable to unscrupulous producers who try to promote their products by shilling the systems. With the advent of social networks new sources of information have been made available which can potentially render RS more resistant to attacks. In this paper we explore the information provided in the form of social links with clustering for diminishing the impact of attacks. We propose two algorithms, CluTr and WCluTr, to combine clustering with \\trust" among users. We demonstrate that CluTr and WCluTr enhance the robustness of RS by experimentally evaluating them on data from a public consumer recommender system Epinions.com.
  • An efficient authorship protection scheme for shared multimedia content
    Mohamed El-Hadedy, Georgios Pitsilis, Svein J. Knapskog
    Proceedings 6th International Conference on Image and Graphics Icig 2011, 2011
    Many electronic content providers today like Flickr and Google, offer space to users to publish their electronic media(e.g. photos and videos) in their cloud infrastructures so that they can be publicly accessed. Features like including other information, such as keywords or owner information into the digital material is already offered by existing providers. Despite the useful features made available to users by such infrastructures, the authorship of the published content is not protected against various attacks such as compression. In this paper we propose a robust scheme that uses digital invisible watermarking and hashing to protect the authorship of the digital content and provide resistance against malicious manipulation of multimedia content. The scheme is enhanced by an algorithm called MMBEC, that is an extension of an established scheme MBEC towards higher resistance.
  • Exploring the use of explicit trust links for filtering recommenders: A study on epinions.com
    Pern Hui Chia, Georgios Pitsilis
    Journal of Information Processing, 2011
    The majority of recommender systems predict user preferences by relating users with similar attributes or taste. Prior research has shown that trust networks improve the accuracy of recommender systems, predominantly using algorithms devised by individual researchers. In this work, omitting any specific trust inference algorithm, we investigate how useful it might be if explicit trust relationships are used to select the best neighbors or predictors, to generate accurate recommendations. We conducted a series of evaluations using data from Epinions.com, a popular collaborative reviewing system. We find that, for highly active users, using trusted sources as predictors does not give more accurate recommendations compared to the classic similarity-based collaborative filtering scheme, except in improving the precision to recommend items that are of users' liking. This cautions against the intuition that inputs from trusted sources would always be more accurate or helpful. The use of explicit trust links, however, provides a slight gain in prediction accuracy when it comes to the less active users. These findings highlight the need and potential to adapt the use of trust information for different groups of users, besides to better understand trust when employing it in the recommender systems. Parallel to the trust criterion, we also investigated the effects of requiring the candidate predictors to have an equal or higher experience level.
  • Clustering recommenders in collaborative filtering using explicit trust information
    Georgios Pitsilis, Xiangliang Zhang, Wei Wang
    IFIP Advances in Information and Communication Technology, 2011
    In this work, we explore the benefits of combining clustering and social trust information for Recommender Systems. We demonstrate the performance advantages of traditional clustering algorithms like k-Means and we explore the use of new ones like Affinity Propagation (AP). Contrary to what has been used before, we investigate possible ways that social-oriented information like explicit trust could be exploited with AP for forming clusters of high quality. We conducted a series of evaluation tests using data from a real Recommender system Epinions.com from which we derived conclusions about the usefulness of trust information in forming clusters of Recommenders. Moreover, from our results we conclude that the potential advantages in using clustering can be enlarged by making use of the information that Social Networks can provide.
  • Abstracting audit data for lightweight intrusion detection
    Wei Wang, Xiangliang Zhang, Georgios Pitsilis
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2010
    High speed of processing massive audit data is crucial for an anomaly Intrusion Detection System (IDS) to achieve real-time performance during the detection. Abstracting audit data is a potential solution to improve the efficiency of data processing. In this work, we propose two strategies of data abstraction in order to build a lightweight detection model. The first strategy is exemplar extraction and the second is attribute abstraction. Two clustering algorithms, Affinity Propagation (AP) as well as traditional k-means, are employed to extract the exemplars, and Principal Component Analysis (PCA) is employed to abstract important attributes (a.k.a. features) from the audit data. Real HTTP traffic data collected in our institute as well as KDD 1999 data are used to validate the two strategies of data abstraction. The extensive test results show that the process of exemplar extraction significantly improves the detection efficiency and has a better detection performance than PCA in data abstraction.
  • Does trust matter for user preferences? A study on epinions ratings
    Georgios Pitsilis, Pern Hui Chia
    IFIP Advances in Information and Communication Technology, 2010
    Recommender systems have evolved during the last few years into useful online tools for assisting the daily e-commerce activities. The majority of recommender systems predict user preferences relating users with similar taste. Prior research has shown that trust networks improve the performance of recommender systems, predominantly using algorithms devised by individual researchers. In this work, omitting any specific trust inference algorithm, we investigate how useful it might be if explicit trust relationships (expressed by users for others) are used to select the best neighbours (or predictors), for the provision of accurate recommendations. We conducted our experiments using data from Epinions.com, a popular recommender system. Our analysis indicates that trust information can be helpful to provide a slight performance gain in a few cases especially when it comes to the less active users.
  • Social trust as a solution to address sparsity-inherent problems of recommender systems
    Ceur Workshop Proceedings, 2009
  • Trust-enhanced recommender systems for efficient on-line collaboration
    Georgios Pitsilis
    IFIP Advances in Information and Communication Technology, 2009
  • Modeling trust for recommender systems using similarity metrics
    Georgios Pitsilis, Lindsay F. Marshall
    IFIP International Federation for Information Processing, 2008
  • Energy software programs for educational use
    Petros Axaopoulos, Georgios Pitsilis
    Renewable Energy, 2007
  • A Trust-enabled P2P recommender system
    Georgios Pitsilis, Lindsay Marshall
    Proceedings of the Workshop on Enabling Technologies Infrastructure for Collaborative Enterprises Wetice, 2006
  • Exploiting the TTL rule in unstructured peer-to-peer networks
    Georgios Pitsilis, Panayiotis Periorellis
    Proceedings of the Workshop on Enabling Technologies Infrastructure for Collaborative Enterprises Wetice, 2006
  • A policy for electing super-nodes in unstructured P2P networks
    Georgios Pitsilis, Panayiotis Periorellis, Lindsay Marshall
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2005
  • Trust as a key to improving recommendation systems
    Georgios Pitsilis, Lindsay Marshall
    Lecture Notes in Computer Science, 2005
  • Choosing reputable resources in atomistic peer-to-peer environments
    Georgios Pitsilis
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2003
  • Multimedia education program for an active solar hot water system
    Agricultural History, 2002
  • Multimedia education program for an active solar hot water system
    P. Axaopoulos, G. Pitsilis, P. Panagakis
    International Journal of Solar Energy, 2002
  • AGRISIM: A PC user-friendly transient simulation program for growing-finishing swine buildings
    Applied Engineering in Agriculture, 1994

RECENT SCHOLAR PUBLICATIONS

  • Improved two-stage hate speech classification for twitter based on Deep Neural Networks
    GK Pitsilis
    arXiv preprint arXiv:2206.04162 , 2022
    2022
  • Securing Tag-based recommender systems against profile injection attacks: A comparative study. (Extended Report)
    GK Pitsilis, H Ramampiaro, H Langseth
    https://arxiv.org/pdf/1901.08422.pdf , 2019
    2019
    Citations: 3
  • Effective hate-speech detection in Twitter data using recurrent neural networks
    GK Pitsilis, H Ramampiaro, H Langseth
    Applied Intelligence 48 (12), 4730-4742 , 2018
    2018
    Citations: 358
  • Securing Tag-based recommender systems against profile injection attacks: A comparative study.
    GK Pitsilis, H Ramampiaro, H Langseth
    LBRS@RecSys ’18, Vancouver, BC, Canada : arXiv preprint arXiv:1808.10550 , 2018
    2018
    Citations: 3
  • Abstracting massive data for lightweight intrusion detection in computer networks
    W Wang, J Liu, G Pitsilis, X Zhang
    Information Sciences 433, 417-430 , 2018
    2018
    Citations: 89
  • Detecting offensive language in tweets using deep learning
    GK Pitsilis, H Ramampiaro, H Langseth
    arXiv preprint arXiv:1801.04433 , 2018
    2018
    Citations: 173
  • Detecting offensive language in tweets using deep learning
    KP Georgios, H Ramampiaro, H Langseth
    arXiv preprint arXiv:1801.04433, 1-17 , 2018
    2018
    Citations: 70
  • Posting with credibility in Micro-blogging systems using Digital Signatures and Watermarks: A case study on Twitter
    GK Pitsilis, M El-Hadedy
    arXiv preprint arXiv:1612.09480 , 2016
    2016
    Citations: 1
  • Harnessing the power of social bookmarking for improving tag-based recommendations
    G Pitsilis, W Wang
    Computers in Human Behavior 50, 239-251 , 2015
    2015
    Citations: 14
  • Securing recommender systems against shilling attacks using social-based clustering
    XL Zhang, TMD Lee, G Pitsilis
    Journal of Computer Science and Technology 28, 616-624 , 2013
    2013
    Citations: 33
  • Social Trust as a solution to address sparsity-inherent problems of Recommender systems
    G Pitsilis, SJ Knapskog
    arXiv preprint arXiv:1208.1004 , 2012
    2012
    Citations: 60
  • Building Robust Recommender Systems Using Social-based Clustering
    X Zhang, TM Desmond-Lee, G Pitsilis
    EDB 2012, 4th International Conference on Emerging Databases, Technologies … , 2012
    2012
  • An efficient authorship protection scheme for shared multimedia content
    M El-Hadedy, G Pitsilis, SJ Knapskog
    2011 Sixth International Conference on Image and Graphics, 914-919 , 2011
    2011
    Citations: 4
  • Clustering Recommenders in Collaborative Filtering Using Explicit Trust Information
    G Pitsilis, X Zhang, W Wang
    Trust Management V, 82-97 , 2011
    2011
    Citations: 47
  • Exploring the use of explicit trust links for filtering recommenders: a study on epinions. com
    PH Chia, G Pitsilis
    Journal of information processing 19 (Special Issue on Trust Management … , 2011
    2011
    Citations: 10
  • Abstracting audit data for lightweight intrusion detection
    W Wang, X Zhang, G Pitsilis
    Information Systems Security, 201-215 , 2011
    2011
    Citations: 11
  • Does Trust Matter for User Preferences? A Study on Epinions Ratings
    G Pitsilis, P Chia
    Trust Management IV, 232-247 , 2010
    2010
    Citations: 9
  • Social Trust as a solution to address sparsity-inherent problems of Recommender systems
    GK Pitsilis, SJ Knapskog
    RSWeb Workshop: 3rd ACM Conference on Recommender Systems RecSys'2009 531 … , 2009
    2009
    Citations: 60
  • Trust-enhanced Recommender Systems for efficient on-line collaboration
    GK Pitsilis
    IFIP International Conference on Trust Management, 30-46 , 2009
    2009
  • Cloud Computing for e-Science with CARMEN
    P Watson, P Lord, F Gibson, P Periorellis, G Pitsilis
    Proceedings of the 2nd Iberian Grid Infrastructure Conference Proceedings … , 2008
    2008
    Citations: 90

MOST CITED SCHOLAR PUBLICATIONS

  • Effective hate-speech detection in Twitter data using recurrent neural networks
    GK Pitsilis, H Ramampiaro, H Langseth
    Applied Intelligence 48 (12), 4730-4742 , 2018
    2018
    Citations: 358
  • Detecting offensive language in tweets using deep learning
    GK Pitsilis, H Ramampiaro, H Langseth
    arXiv preprint arXiv:1801.04433 , 2018
    2018
    Citations: 173
  • Cloud Computing for e-Science with CARMEN
    P Watson, P Lord, F Gibson, P Periorellis, G Pitsilis
    Proceedings of the 2nd Iberian Grid Infrastructure Conference Proceedings … , 2008
    2008
    Citations: 90
  • Abstracting massive data for lightweight intrusion detection in computer networks
    W Wang, J Liu, G Pitsilis, X Zhang
    Information Sciences 433, 417-430 , 2018
    2018
    Citations: 89
  • Detecting offensive language in tweets using deep learning
    KP Georgios, H Ramampiaro, H Langseth
    arXiv preprint arXiv:1801.04433, 1-17 , 2018
    2018
    Citations: 70
  • Social Trust as a solution to address sparsity-inherent problems of Recommender systems
    G Pitsilis, SJ Knapskog
    arXiv preprint arXiv:1208.1004 , 2012
    2012
    Citations: 60
  • Social Trust as a solution to address sparsity-inherent problems of Recommender systems
    GK Pitsilis, SJ Knapskog
    RSWeb Workshop: 3rd ACM Conference on Recommender Systems RecSys'2009 531 … , 2009
    2009
    Citations: 60
  • Clustering Recommenders in Collaborative Filtering Using Explicit Trust Information
    G Pitsilis, X Zhang, W Wang
    Trust Management V, 82-97 , 2011
    2011
    Citations: 47
  • A model of trust derivation from evidence for use in recommendation systems.
    G Pitsilis, LF Marshall
    School of Computer Science; University of Newcastle upon Tyne , 2004
    2004
    Citations: 40
  • A model of Trust derivation from Evidence for Use in Recommendation systems
    G Pitsilis, LF Marshall, UNTC Science
    University of Newcastle upon Tyne, Computing Science , 2004
    2004
    Citations: 40
  • Energy software programs for educational use
    P Axaopoulos, G Pitsilis
    Renewable energy 32 (6), 1045-1058 , 2007
    2007
    Citations: 37
  • Securing recommender systems against shilling attacks using social-based clustering
    XL Zhang, TMD Lee, G Pitsilis
    Journal of Computer Science and Technology 28, 616-624 , 2013
    2013
    Citations: 33
  • Trust as a key to improving recommendation systems
    G Pitsilis, L Marshall
    International Conference on Trust Management, 210-223 , 2005
    2005
    Citations: 29
  • Trust as a key to improving recommendation systems
    G Pitsilis, L Marshall
    Trust Management: Third International Conference, iTrust 2005, Paris, France … , 2005
    2005
    Citations: 29
  • A trust-enabled P2P recommender system
    G Pitsilis, L Marshall
    Enabling Technologies: Infrastructure for Collaborative Enterprises, 2006 … , 2006
    2006
    Citations: 25
  • The CARMEN neuroscience server
    P Watson, T Jackson, G Pitsilis, F Gibson, J Austin, M Fletcher, B Liang, ...
    UK e-Science All Hands Meeting 2007 , 2007
    2007
    Citations: 20
  • Modeling trust for recommender systems using similarity metrics
    G Pitsilis, L Marshall
    Trust Management II, 103-118 , 2008
    2008
    Citations: 17
  • Harnessing the power of social bookmarking for improving tag-based recommendations
    G Pitsilis, W Wang
    Computers in Human Behavior 50, 239-251 , 2015
    2015
    Citations: 14
  • Agrisim: A PC user-friendly transient simulation program for growing-finishing swine buildings
    P Axaopoulos, P Panagakis, G Pitsilis, S Kyritsis
    Applied engineering in agriculture 10 (5), 735-742 , 1994
    1994
    Citations: 14
  • A model of trust derivation from evidence for use in recommendation systems. University of Newcastle upon Tyne
    G Pitsilis, LF Marshall
    Computing Science , 2004
    2004
    Citations: 12