uu.seUppsala University Publications
Change search
Refine search result
1 - 9 of 9
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Ashcroft, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    An Introduction To Bayesian Networks in Systems and Control2012Conference paper (Refereed)
  • 2.
    Ashcroft, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Bayesian Networks in Business Analytics2012In: 2012 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS), 2012, p. 955-961Conference paper (Refereed)
  • 3.
    Ashcroft, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Performing Decision-Theoretic Inference in Bayesian Network Ensemble Models2013In: Twelfth Scandinavian Conference on Artificial Intelligence / [ed] Jaeger, M; Nielsen, TD; Viappiani, P, 2013, Vol. 257, p. 25-34Conference paper (Refereed)
    Abstract [en]

    The purpose of this paper is to present a simple extension to an existing inference algorithm on influence diagrams (i.e. decision theoretic extensions to Bayesian networks) that permits these algorithms to be applied to ensemble models. The extension, though simple, is important because of the power and robustness that such ensemble models provide [1]. This paper is intended principally as a 'recipe' that can be used even by those unfamiliar with the algorithms extended. Accordingly, I present the algorithms that the original contribution builds upon in full, though references are given to less concise renditions. Those familiar with these algorithms are invited to skip the elucidation. The consequence is a useful paper with more background and less original input than usual.

  • 4.
    Ashcroft, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Using Bayesian networks in business analytics: Overview and short case study2012In: Business Informatics, ISSN 1507-3858, Vol. 3, no 25Article in journal (Refereed)
  • 5.
    Ashcroft, Michael
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Fisher, Ali
    Univ Vienna, VORTEX, Vienna, Austria.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Omer, Enghin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Prucha, Nico
    Kings Coll London, ICSR, London, England.
    Detecting jihadist messages on twitter2015In: Proc. 5th European Intelligence and Security Informatics Conference, IEEE Computer Society, 2015, p. 161-164Conference paper (Refereed)
    Abstract [en]

    Jihadist groups such as ISIS are spreading online propaganda using various forms of social media such as Twitter and YouTube. One of the most common approaches to stop these groups is to suspend accounts that spread propaganda when they are discovered. This approach requires that human analysts manually read and analyze an enormous amount of information on social media. In this work we make a first attempt to automatically detect messages released by jihadist groups on Twitter. We use a machine learning approach that classifies a tweet as containing material that is supporting jihadists groups or not. Even tough our results are preliminary and more tests needs to be carried out we believe that results indicate that an automated approach to aid analysts in their work with detecting radical content on social media is a promising way forward. It should be noted that an automatic approach to detect radical content should only be used as a support tool for human analysts in their work.

  • 6.
    Ashcroft, Michael
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Johansson, Fredrik
    Swedish Def Res Agcy FOI, Stockholm, Sweden..
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems. Swedish Def Res Agcy FOI, Stockholm, Sweden..
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Multi-domain alias matching using machine learning2016In: Proc. 3rd European Network Intelligence Conference, IEEE, 2016, p. 77-84Conference paper (Refereed)
    Abstract [en]

    We describe a methodology for linking aliases belonging to the same individual based on a user's writing style (stylometric features extracted from the user generated content) and her time patterns (time-based features extracted from the publishing times of the user generated content). While most previous research on social media identity linkage relies on matching usernames, our methodology can also be used for users who actively try to choose dissimilar usernames when creating their aliases. In our experiments on a discussion forum dataset and a Twitter dataset, we evaluate the performance of three different classifiers. We use the best classifier (AdaBoost) to evaluate how well it works on different datasets using different features. Experiments show that combining stylometric and time based features yield good results on our synthetic datasets and a small-scale evaluation on real-world blog data confirm these results, yielding a precision over 95%. The use of emotion-related and Twitter-related features yield no significant impact on the results.

  • 7.
    Ashcroft, Michael
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems. FOI, Stockholm, Sweden.
    Meyer, Maxime
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    A step towards detecting online grooming: Identifying adults pretending to be children2015In: Proc. 5th European Intelligence and Security Informatics Conference, IEEE Computer Society, 2015, p. 98-104Conference paper (Refereed)
    Abstract [en]

    Online grooming is a major problem in todays society where more and more time is spent online. To become friends and establish a relationship with their young victims in online communities, groomers often pretend to be children. In this paper we describe an approach that can be used to detect if an adult is pretending to be a child in a chat room conversation. The approach involves a two step process wherein authors are first classified as being children or adults, and then each child is being examined and false children distinguished from genuine children. Our results show that even if it is hard to separate ordinary adults from children in chat logs it is possible to distinguish real children from adults pretending to be children with a high accuracy. In this paper we will discuss the accuracy of the methods proposed, as well as the features that were important in their success. We believe that this work is an important step towards automated analysis of chat room conversation to detect and possible attempts of grooming. Our approach where we use text analysis to distinguish adults who are pretending to be children from actual children could be used to inform children about the true age of the person that they are communicating. This would be a step towards making the Internet more secure for young children and eliminate grooming.

  • 8.
    Ashcroft, Michael
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Meyer, Maxime
    Are You Really a Child?: A Machine Learning Approach To Protect Children from Online Grooming2015In: Proc. National Symposium on Technology and Methodology for Security and Crisis Management: TAMSEC 2015, 2015Conference paper (Refereed)
    Abstract [en]

    Online grooming and sexual abuse of children is a major threat towards the security of todays society where more and more time is spent online. To become friends and establish a relationship with their young victims in online communities, groomers often pretend to be children. In this work we describe an approach that can be used to detect if an adult is pretending to be a child in a chat room conversation. Our results show that even if it is hard to separate ordinary adults from children in chat logs it is possible to distinguish real children from adults pretending to be children with a high accuracy.

  • 9.
    Ashcroft, Michael
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Magnani, Matteo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Vega, Davide
    Univ Bologna, Bologna, Italy..
    Montesi, Danilo
    Univ Bologna, Bologna, Italy..
    Rossi, Luca
    IT Univ Copenhagen, Copenhagen, Denmark..
    Multilayer Analysis of Online Illicit Marketplaces2016In: 2016 European Intelligence And Security Informatics Conference (EISIC) / [ed] Brynielsson, J Johansson, F, IEEE , 2016, p. 199-199Conference paper (Refereed)
1 - 9 of 9
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf