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These cookies are set through our site by our analytic and marketing partners. They may be used by those companies to build a profile of your interests so we can provide the best possible user experience for you through our website and marketing campaigns. They work by uniquely identifying your browser, operating system, IP and location.
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By default, our tag manager code is set up to track page views using the Google's IP Anonymization feature turned on. Also, all the cookies that are set up from Google by default, are set up to expire after the browsing session ends. Turning on this cookie type, will tell Google that the full IP can be used to track your behavior on our website.
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If set to 1, will tell google that it can track the full IP and it will set up it's cookies to expire in 365 days, not 0 days as set up by default.
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When the Facebook pixel is installed on a website, and the pixel uses first-party cookies, the pixel automatically saves a unique identifier to an _fbp cookie for the website domain if one does not already exist.
_fbc
When a user clicks on an ad on Facebook, the link sometimes includes a fbclid query parameter. When the user lands on the target website, if the website has a Facebook pixel that uses first-party cookies, the pixel automatically saves the fbclid query parameter to an _fbc cookie for that website domain.
The LinkedIn Insight Tag is a piece of lightweight JavaScript code that you can add to your website to enable in-depth campaign reporting and unlock valuable insights about your website visitors.
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Dalia Sulieman
Visiting Assistant Professor - Department of Mathematics, Computer Science, and Data Science
Biography
I joined °ÅÀÖ´óÈË°æappÔ¼ÅÚ in the Fall of 2022 to teach computer science. Prior to that I spent seven years, teaching computer science, as assistant professor at Metropolitan State University, in Saint Paul Minnesota. I am passionate about teaching Computer Science and dedicated to students’ success. I strive to make Computer Science feel an incredibly motivating field to explore and study, for all of my students. Besides to my passion about teaching, I enjoy designing new courses and developing curriculums. My research focus is on search algorithms, machine learning, knowledge graphs and their applications on social network analysis and recommender systems. Since 2019, I have been the vice-chair of the Twin Cities ACM professional chapter. The mission of this Chapter is to organize and support events and activities related to computing and its applications in Minnesota, and across the Upper Midwest region.
Research interests
Search algorithms, machine learning, knowledge graphs and their applications on social network analysis and recommender systems.
Selected Publications
- Ismail Bile Hassan, Thanaa M. Ghanem, David Jacobson, Simon Jin, Katherine Johnson, Dalia Sulieman, Wei Wei: Data Science Curriculum Design: A Case Study. SIGCSE 2021: 529-534
- Mehdi Mekni, Zakaria Baani, Dalia Sulieman: A Smart Virtual Assistant for Students. APPIS 2020: 15:1-15:6
- Dalia Sulieman, Maria Malek, Hubert Kadima, Dominique Laurent: Toward Social-Semantic Recommender Systems. Int. J. Inf. Syst. Soc. Chang. 7(1): 1-30 (2016)
- Dalia Sulieman, Maria Malek, Hubert Kadima, Dominique Laurent: Graph Searching Algorithms for Semantic-Social Recommendation. ASONAM 2012: 733- 738
Degrees
- Ph.D. in Computer Science, Cergy-Pontoise University, France, 2014
- M.S. in Computer Science, Lille 1 University of Science and Technology, Lille France, 2009
- B.S. in Computer Engineering, Aleppo University Syria, 2007