uu.seUppsala University Publications
Change search
Refine search result
1 - 31 of 31
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.
    Abdulla, Parosh Aziz
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Bouajjani, Ahmed
    Holík, Lukás
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Vojnar, Tomás
    Composed Bisimulation for Tree Automata2008In: Implementation and Application of Automata, Springer Berlin/Heidelberg, 2008, p. 212-222Conference paper (Refereed)
  • 2.
    Abdulla, Parosh Aziz
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Bouajjani, Ahmed
    Holík, Lukás
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Vojnar, Tomás
    Composed bisimulation for tree automata2009In: International Journal of Foundations of Computer Science, ISSN 0129-0541, Vol. 20, no 4, p. 685-700Article in journal (Refereed)
  • 3.
    Abdulla, Parosh Aziz
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Bouajjani, Ahmed
    Holík, Lukás
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Vojnar, Tomás
    Computing Simulations over Tree Automata: Efficient Techniques for Reducing Tree Automata2008In: Tools and Algorithms for the Construction and Analysis of Systems, Springer Berlin/Heidelberg, 2008, p. 93-108Conference paper (Refereed)
  • 4.
    Abdulla, Parosh Aziz
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Cederberg, Jonathan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Kaati, Lisa
    Analyzing the security in the GSM radio network using attack jungles2010In: Leveraging Applications of Formal Methods, Verification, and Validation: Part I, Berlin: Springer-Verlag , 2010, p. 60-74Conference paper (Refereed)
  • 5.
    Abdulla, Parosh Aziz
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Deneux, Johann
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Nilsson, Marcus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Minimization of Non-deterministic Automata with Large Alphabets2006In: Implementation and Application of Automata, Springer Berlin/Heidelberg, 2006, p. 31-42Conference paper (Refereed)
  • 6.
    Abdulla, Parosh Aziz
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Holík, Lukás
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Vojnar, Tomás
    A uniform (bi-)simulation-based framework for reducing tree automata2009In: Electronical Notes in Theoretical Computer Science, ISSN 1571-0661, E-ISSN 1571-0661, Vol. 251, p. 27-48Article in journal (Refereed)
  • 7.
    Abdulla, Parosh Aziz
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Högberg, Johanna
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Bisimulation minimization of tree automata2007In: International Journal of Foundations of Computer Science, ISSN 0129-0541, Vol. 18, no 4, p. 699-713Article in journal (Refereed)
  • 8.
    Abdulla, Parosh Aziz
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Högberg, Johanna
    Bisimulation Minimization of Tree Automata2006In: Implementation and Application of Automata, Berlin: Springer-Verlag , 2006, p. 173-185Conference paper (Refereed)
  • 9.
    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.

  • 10.
    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.

  • 11.
    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.

  • 12.
    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.

  • 13.
    Atig, Mohamed Faouzi
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Cassel, Sofia
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Activity profiles in online social media2014In: Proc. 6th International Conference on Advances in Social Networks Analysis and Mining, IEEE Computer Society, 2014, p. 850-855Conference paper (Refereed)
  • 14.
    Fernquist, Johan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Fängström, Torbjörn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems. Swedish Def Res Agcy, Stockholm, Sweden..
    IoT Data Profiles: The Routines of Your Life Reveals Who You Are2017In: 2017 European Intelligence and Security Informatics Conference (EISIC) / [ed] Brynielsson, J, IEEE, 2017, p. 61-67Conference paper (Refereed)
    Abstract [en]

    Preserving privacy is getting more and more important. The new EU general data protection regulation (GDPR) which will apply from May 2018 will introduce developments to some areas of EU data protection law and increase the privacy and personal integrity by strengthen and unify data protection for all individuals in EU. GDPR will most likely have an impact on many organizations and put pressure on many organizations that handle data. In this work, we investigate to what extent data profiles consisting of data from connected things can be used to identify a user. We use time and event profiles that can be created based on when, where and how a user communicates and uses digital devices. Our results show that such data profiles can be used to identify individuals and that collecting and creating data profiles of users can be seen as a serious threat towards privacy and personal integrity.

  • 15.
    Figea, Léo
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Scrivens, Ryan
    Measuring online affects in a white supremacy forum2016In: Proc. 14th International Conference on Intelligence and Security Informatics, IEEE, 2016, p. 85-90Conference paper (Refereed)
  • 16.
    Fängström, Torbjörn
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Internet of Things and Future Threats Towards our Society2015In: Proc. National Symposium on Technology and Methodology for Security and Crisis Management: TAMSEC 2015, 2015Conference paper (Refereed)
    Abstract [en]

    Internet of Things (IoT) is all things around us that are connected to the Internet. New technologies such as small and cheap sensors with wireless communication makes it possible to connect most of the electronic devices we use in our everyday life and according to analyst firm Gartner, close to 26 billion things to be connected to the Internet of Things in 2020. However, with new technology new threats arises. The Internet of Things in combination with the increasing number of internet users globally creates new possibilities for attacks for criminals to exploit. In this work we will investigate Internet of things and possible threats towards the security of the society.

  • 17.
    Isbister, Tim
    et al.
    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..
    Cohen, Katie
    Swedish Def Res Agcy FOI, Stockholm, Sweden..
    Gender Classification with Data Independent Features in Multiple Languages2017In: 2017 European Intelligence and Security Informatics Conference (EISIC) / [ed] Brynielsson, J, IEEE, 2017, p. 54-60Conference paper (Refereed)
    Abstract [en]

    Gender classification is a well-researched problem, and state-of-the-art implementations achieve an accuracy of over 85%. However, most previous work has focused on gender classification of texts written in the English language, and in many cases, the results cannot be transferred to different datasets since the features used to train the machine learning models are dependent on the data. In this work, we investigate the possibilities to classify the gender of an author on five different languages: English, Swedish, French, Spanish, and Russian. We use features of the word counting program Linguistic Inquiry and Word Count (LIWC) with the benefit that these features are independent of the dataset. Our results show that by using machine learning with features from LIWC, we can obtain an accuracy of 79% and 73% depending on the language. We also, show some interesting differences between the uses of certain categories among the genders in different languages.

  • 18. Johansson, Fredrik
    et al.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Detecting multiple aliases in social media2013In: Proc. 5th International Conference on Advances in Social Networks Analysis and Mining, New York: ACM Press, 2013, p. 1004-1011Conference paper (Refereed)
  • 19. Johansson, Fredrik
    et al.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Time profiles for identifying users in online environments2014In: Proc. 1st Joint Intelligence and Security Informatics Conference, IEEE Computer Society, 2014, p. 83-90Conference paper (Refereed)
  • 20. Johansson, Fredrik
    et al.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Timeprints for identifying social media users with multiple aliases2015In: Security Informatics, ISSN 2190-8532, Vol. 4, p. 7:1-11, article id 7Article in journal (Refereed)
  • 21.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Analysis and mining of tags, (micro)blogs, and virtual communities2014In: Encyclopedia of Social Network Analysis and Mining / [ed] Alhajj, Reda; Rokne, Jon, Springer, 2014, p. 19-25Chapter in book (Refereed)
  • 22.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Computer Systems. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Reduction Techniques for Finite (Tree) Automata2008Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Finite automata appear in almost every branch of computer science, for example in model checking, in natural language processing and in database theory. In many applications where finite automata occur, it is highly desirable to deal with automata that are as small as possible, in order to save memory as well as excecution time.

    Deterministic finite automata (DFAs) can be minimized efficiently, i.e., a DFA can be converted to an equivalent DFA that has a minimal number of states. This is not the case for non-deterministic finite automata (NFAs). To minimize an NFA we need to compute the corresponding DFA using subset construction and minimize the resulting automaton. However, subset construction may lead to an exponential blow-up in the size of the automaton and therefore even if the minimal DFA may be small, it might not be feasible to compute it in practice since we need to perform the expensive subset construction.

    To aviod subset construction we can reduce the size of an NFA using heuristic methods. This can be done by identifying and collapsing states that are equal with respect to some suitable equivalence relation that preserves the language of the automaton. The choice of an equivalence relation is a trade-off between the desired amount of reduction and the computation time since the coarser a relation is, the more expensive it is to compute. This way we obtain a reduction method for NFAs that is useful in practice.

    In this thesis we address the problem of reducing the size of non-deterministic automata. We consider two different computation models: finite tree automata and finite automata. Finite automata can be seen as a special case of finite tree automata and all of the previously mentioned results concerning finite automata are applicable to tree automata as well. For non-deterministic bottom-up tree automata, we present a broad spectrum of different relations that can be used to reduce their size. The relations differ in their computational complexity and reduction capabilities. We also provide efficient algorithms to compute the relations where we translate the problem of computing a given relation on a tree automaton to the problem of computing the relation on a finite automaton.

    For finite automata, we have extended and re-formulated two algorithms for computing bisimulation and simulation on transition systems to operate on finite automata with alphabets. In particular, we consider a model of automata where the labels are encoded symbolically and we provide an algorithm for computing bisimulation on this partial symbolic encoding.

    List of papers
    1. Minimization of Non-deterministic Automata with Large Alphabets
    Open this publication in new window or tab >>Minimization of Non-deterministic Automata with Large Alphabets
    2006 (English)In: Implementation and Application of Automata, Springer Berlin/Heidelberg, 2006, p. 31-42Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    Springer Berlin/Heidelberg, 2006
    Series
    Lecture Notes in Computer Science ; 3845
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:uu:diva-94525 (URN)10.1007/11605157_3 (DOI)3-540-31023-1 (ISBN)
    Conference
    CIAA 2005, June 27-29, Sophia Antipolis, France
    Available from: 2006-05-12 Created: 2006-05-12 Last updated: 2018-01-13Bibliographically approved
    2. Bisimulation minimization of tree automata
    Open this publication in new window or tab >>Bisimulation minimization of tree automata
    2007 (English)In: International Journal of Foundations of Computer Science, ISSN 0129-0541, Vol. 18, no 4, p. 699-713Article in journal (Refereed) Published
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:uu:diva-227791 (URN)10.1142/S0129054107004929 (DOI)000251316500004 ()
    Available from: 2008-10-31 Created: 2014-07-01 Last updated: 2018-01-11Bibliographically approved
    3. Computing Simulations over Tree Automata: Efficient Techniques for Reducing Tree Automata
    Open this publication in new window or tab >>Computing Simulations over Tree Automata: Efficient Techniques for Reducing Tree Automata
    Show others...
    2008 (English)In: Tools and Algorithms for the Construction and Analysis of Systems, Springer Berlin/Heidelberg, 2008, p. 93-108Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    Springer Berlin/Heidelberg, 2008
    Series
    Lecture Notes in Computer Science ; 4963
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:uu:diva-227795 (URN)10.1007/978-3-540-78800-3_8 (DOI)000254735100008 ()978-3-540-78799-0 (ISBN)
    Conference
    TACAS 2008, March 29 - April 6, Budapest, Hungary
    Available from: 2008-10-31 Created: 2014-07-01 Last updated: 2018-01-11Bibliographically approved
    4. Composed Bisimulation for Tree Automata
    Open this publication in new window or tab >>Composed Bisimulation for Tree Automata
    Show others...
    2008 (English)In: Implementation and Application of Automata, Springer Berlin/Heidelberg, 2008, p. 212-222Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    Springer Berlin/Heidelberg, 2008
    Series
    Lecture Notes in Computer Science ; 5148
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:uu:diva-224955 (URN)10.1007/978-3-540-70844-5_22 (DOI)000258311400022 ()978-3-540-70843-8 (ISBN)
    Conference
    CIAA 2008, July 21-24, San Francisco, CA
    Available from: 2008-10-31 Created: 2014-05-24 Last updated: 2018-01-11Bibliographically approved
    5. A uniform (bi-)simulation-based framework for reducing tree automata
    Open this publication in new window or tab >>A uniform (bi-)simulation-based framework for reducing tree automata
    2009 (English)In: Electronical Notes in Theoretical Computer Science, ISSN 1571-0661, E-ISSN 1571-0661, Vol. 251, p. 27-48Article in journal (Refereed) Published
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:uu:diva-227797 (URN)10.1016/j.entcs.2009.08.026 (DOI)
    Projects
    UPMARC
    Available from: 2008-10-31 Created: 2014-07-01 Last updated: 2018-01-11Bibliographically approved
  • 23.
    Kaati, Lisa
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Johansson, Fredrik
    Countering lone actor terrorism: Weak signals and online activities2016In: Understanding Lone Actor Terrorism: Past experience, future outlook, and response strategies, Abingdon, UK: Routledge, 2016, p. 266-279Chapter in book (Refereed)
  • 24.
    Kaati, Lisa
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Johansson, Fredrik
    Forsman, Elinor
    Semantic technologies for detecting names of new drugs on darknets2016In: Proc. 4th International Conference on Cybercrime and Computer Forensics, IEEE, 2016Conference paper (Refereed)
  • 25.
    Kaati, Lisa
    et al.
    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, Computer Systems.
    Prucha, Nico
    ICSR, London, England.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Detecting multipliers of jihadism on twitter2015In: Proc. 15th ICDM Workshops, IEEE Computer Society, 2015, p. 954-960Conference paper (Refereed)
    Abstract [en]

    Detecting terrorist related content on social media is a problem for law enforcement agency due to the large amount of information that is available. In this paper we describe a first step towards automatically classifying twitter user accounts (tweeps) as supporters of jihadist groups who disseminate propaganda content online. We use a machine learning approach with two set of features: data dependent features and data independent features. The data dependent features are features that are heavily influenced by the specific dataset while the data independent features are independent of the dataset and that can be used on other datasets with similar result. By using this approach we hope that our method can be used as a baseline to classify violent extremist content from different kind of sources since data dependent features from various domains can be added.

  • 26.
    Kaati, Lisa
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Cohen, Katie
    Linguistic analysis of lone offender manifestos2016In: Proc. 4th International Conference on Cybercrime and Computer Forensics, IEEE, 2016Conference paper (Refereed)
  • 27.
    Kaati, Lisa
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Cohen, Katie
    Lindquist, Sinna
    Automatic detection of xenophobic narratives: A case study on Swedish alternative media2016In: Proc. 14th International Conference on Intelligence and Security Informatics, IEEE, 2016, p. 121-126Conference paper (Refereed)
  • 28.
    Kaati, Lisa
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Sardella, Tony
    Washington Univ, St Louis, MO USA..
    Identifying Warning Behaviors of Violent Lone Offenders in Written Communication2016In: 2016 IEEE 16Th International Conference On Data Mining Workshops (ICDMW) / [ed] Domeniconi, C Gullo, F Bonchi, F DomingoFerrer, J BaezaYates, R Zhou, ZH Wu, X, New York: IEEE, 2016, p. 1053-1060Conference paper (Refereed)
    Abstract [en]

    Violent lone offenders such as school shooters and lone actor terrorists pose a threat to the modern society but since they act alone or with minimal help form others they are very difficult to detect. Previous research has shown that violent lone offenders show signs of certain psychological warning behaviors that can be viewed as indicators of an increasing or accelerating risk of committing targeted violence. In this work, we use a machine learning approach to identify potential violent lone offenders based on their written communication. The aim of this work is to capture psychological warning behaviors in written text and identify texts written by violent lone offenders. We use a set of features that are psychologically meaningful based on the different categories in the text analysis tool Linguistic Inquiry and Word Count (LIWC). Our study only contains a small number of known perpetrators and their written communication but the results are promising and there are many interesting directions for future work in this area.

  • 29.
    Kaati, Lisa
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Sardella, Tony
    Identifying warning behaviors of violent lone offenders in written communication2016In: Proc. 16th ICDM Workshops, IEEE Computer Society, 2016, p. 1053-1060Conference paper (Refereed)
  • 30.
    Shrestha, Amendra
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Cassel, Sofia
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Atig, Mohamed Faouzi
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Author recognition in discussion boards2013In: National Symposium on Technology and Methodology for Security and Crisis Management, 2013Conference paper (Refereed)
  • 31.
    Shrestha, Amendra
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems. FOI, Stockholm, Sweden..
    Cohen, Katie
    FOI, Stockholm, Sweden..
    A Machine Learning Approach Towards Detecting Extreme Adopters in Digital Communities2017In: 2017 28th International Workshop on Database and Expert Systems Applications (DEXA) / [ed] Tjoa, AM Wagner, RR, IEEE, 2017, p. 1-5Conference paper (Other academic)
    Abstract [en]

    In this study we try to identify extreme adopters on a discussion forum using machine learning. An extreme adopter is a user that has adopted a high level of a community-specific jargon and therefore can be seen as a user that has a high degree of identification with the community. The dataset that we consider consists of a Swedish xenophobic discussion forum where we use a machine learning approach to identify extreme adopters using a number of linguistic features that are independent on the dataset and the community. The results indicates that it is possible to separate these extreme adopters from the rest of the discussants on the discussion forum with more than 80% accuracy. Since the linguistic features that we use are highly domain independent, the results indicates that there is a possibility to use this kind of techniques to identify extreme adopters within other communities as well.

1 - 31 of 31
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