@misc{XenikoudakisAhmedHarrisetal.2020, author = {Xenikoudakis, Georgios and Ahmed, Mayeesha and Harris, Jacob Colt and Wadleigh, Rachel and Paijmans, Johanna L. A. and Hartmann, Stefanie and Barlow, Axel and Lerner, Heather and Hofreiter, Michael}, title = {Ancient DNA reveals twenty million years of aquatic life in beavers}, series = {Current biology : CB}, volume = {30}, journal = {Current biology : CB}, number = {3}, publisher = {Current Biology Ltd.}, address = {London}, issn = {0960-9822}, doi = {10.1016/j.cub.2019.12.041}, pages = {R110 -- R111}, year = {2020}, abstract = {Xenikoudakis et al. report a partial mitochondrial genome of the extinct giant beaver Castoroides and estimate the origin of aquatic behavior in beavers to approximately 20 million years. This time estimate coincides with the extinction of terrestrial beavers and raises the question whether the two events had a common cause.}, language = {en} } @misc{PatilHaiderPopeetal.2011, author = {Patil, Kaustubh R. and Haider, Peter and Pope, Phillip B. and Turnbaugh, Peter J. and Morrison, Mark and Scheffer, Tobias and McHardy, Alice C.}, title = {Taxonomic metagenome sequence assignment with structured output models}, series = {Nature methods : techniques for life scientists and chemists}, volume = {8}, journal = {Nature methods : techniques for life scientists and chemists}, number = {3}, publisher = {Nature Publ. Group}, address = {London}, issn = {1548-7091}, doi = {10.1038/nmeth0311-191}, pages = {191 -- 192}, year = {2011}, language = {en} } @misc{MarweckiBaudisch2018, author = {Marwecki, Sebastian and Baudisch, Patrick}, title = {Scenograph}, series = {UIST '18: Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology}, journal = {UIST '18: Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology}, publisher = {Association for Computing Machinery}, address = {New York}, isbn = {978-1-4503-5948-1}, doi = {10.1145/3242587.3242648}, pages = {511 -- 520}, year = {2018}, abstract = {When developing a real-walking virtual reality experience, designers generally create virtual locations to fit a specific tracking volume. Unfortunately, this prevents the resulting experience from running on a smaller or differently shaped tracking volume. To address this, we present a software system called Scenograph. The core of Scenograph is a tracking volume-independent representation of real-walking experiences. Scenograph instantiates the experience to a tracking volume of given size and shape by splitting the locations into smaller ones while maintaining narrative structure. In our user study, participants' ratings of realism decreased significantly when existing techniques were used to map a 25m2 experience to 9m2 and an L-shaped 8m2 tracking volume. In contrast, ratings did not differ when Scenograph was used to instantiate the experience.}, language = {en} } @misc{KliemeTietzMeinel2018, author = {Klieme, Eric and Tietz, Christian and Meinel, Christoph}, title = {Beware of SMOMBIES}, series = {The 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2018)/the 12th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE 2018)}, journal = {The 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2018)/the 12th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE 2018)}, publisher = {IEEE}, address = {New York}, isbn = {978-1-5386-4387-7}, issn = {2324-9013}, doi = {10.1109/TrustCom/BigDataSE.2018.00096}, pages = {651 -- 660}, year = {2018}, abstract = {Several research evaluated the user's style of walking for the verification of a claimed identity and showed high authentication accuracies in many settings. In this paper we present a system that successfully verifies a user's identity based on many real world smartphone placements and yet not regarded interactions while walking. Our contribution is the distinction of all considered activities into three distinct subsets and a specific one-class Support Vector Machine per subset. Using sensor data of 30 participants collected in a semi-supervised study approach, we prove that unsupervised verification is possible with very low false-acceptance and false-rejection rates. We furthermore show that these subsets can be distinguished with a high accuracy and demonstrate that this system can be deployed on off-the-shelf smartphones.}, language = {en} } @misc{MeinelSack2004, author = {Meinel, Christoph and Sack, Harald}, title = {WWW : Kommunikation, Internetworking, Web-Technologien}, publisher = {Springer}, address = {Berlin}, isbn = {3-540-44276-6}, issn = {1439-5428}, pages = {1179 S.}, year = {2004}, language = {de} } @misc{AlhosseiniAlmodarresiYasinBinTareafNajafietal.2019, author = {Alhosseini Almodarresi Yasin, Seyed Ali and Bin Tareaf, Raad and Najafi, Pejman and Meinel, Christoph}, title = {Detect me if you can}, series = {Companion Proceedings of The 2019 World Wide Web Conference}, journal = {Companion Proceedings of The 2019 World Wide Web Conference}, publisher = {Association for Computing Machinery}, address = {New York}, isbn = {978-1-4503-6675-5}, doi = {10.1145/3308560.3316504}, pages = {148 -- 153}, year = {2019}, abstract = {Spam Bots have become a threat to online social networks with their malicious behavior, posting misinformation messages and influencing online platforms to fulfill their motives. As spam bots have become more advanced over time, creating algorithms to identify bots remains an open challenge. Learning low-dimensional embeddings for nodes in graph structured data has proven to be useful in various domains. In this paper, we propose a model based on graph convolutional neural networks (GCNN) for spam bot detection. Our hypothesis is that to better detect spam bots, in addition to defining a features set, the social graph must also be taken into consideration. GCNNs are able to leverage both the features of a node and aggregate the features of a node's neighborhood. We compare our approach, with two methods that work solely on a features set and on the structure of the graph. To our knowledge, this work is the first attempt of using graph convolutional neural networks in spam bot detection.}, language = {en} } @misc{OPUS4-33848, title = {Design thinking : understand - improve - apply}, editor = {Plattner, Hasso and Meinel, Christoph and Leifer, Larry}, publisher = {Springer-Verlag Berlin Heidelberg}, address = {Berlin, Heidelberg}, isbn = {978-3-642-13756-3}, pages = {236 S.}, year = {2011}, language = {en} } @misc{KrstićJentzsch2018, author = {Krstić, Miloš and Jentzsch, Anne-Kristin}, title = {Reliability, safety and security of the electronics in automated driving vehicles - joint lab lecturing approach}, series = {2018 12TH European Workshop on Microelectronics Education (EWME)}, journal = {2018 12TH European Workshop on Microelectronics Education (EWME)}, publisher = {IEEE}, address = {New York}, isbn = {978-1-5386-1157-9}, pages = {21 -- 22}, year = {2018}, abstract = {This paper proposes an education approach for master and bachelor students to enhance their skills in the area of reliability, safety and security of the electronic components in automated driving. The approach is based on the active synergetic work of research institutes, academia and industry in the frame of joint lab. As an example, the jointly organized summer school with the respective focus is organized and elaborated.}, language = {en} }