@article{Hobohm, author = {Hobohm, Hans-Christoph}, title = {The impact of new technology on libraries : an introductory note}, series = {Inspel}, volume = {30}, journal = {Inspel}, number = {4}, publisher = {Weinert}, address = {Berlin}, issn = {0019-0217}, pages = {303 -- 307}, subject = {Bibliothek}, language = {en} } @inproceedings{DaesslerOtto, author = {D{\"a}ßler, Rolf and Otto, Anne}, title = {3-D-navigation in virtual information spaces : from text-based retrieval to cognitive user interaction}, series = {Classification and knowledge organization}, booktitle = {Classification and knowledge organization}, editor = {Klar, R{\"u}diger and Opitz, Otto}, publisher = {Springer}, address = {Berlin}, isbn = {3-540-62981-5}, doi = {10.1007/978-3-642-59051-1_34}, pages = {324 -- 334}, subject = {Informationsmanagement}, language = {en} } @inproceedings{StangeDoerk, author = {Stange, Jan-Erik and D{\"o}rk, Marian}, title = {Visualizing the spatiality in fictional narratives}, abstract = {This work is part of ongoing research on the visualization of spatial relationships in fictional works. Our aim is to arrive at aesthetic representations of fictional narratives set in actual places such as cities. Novel City Maps offers two map views, one inspired by transit maps and the other by conventional street maps. The former uses the aesthetic of abstract transit maps to reveal the co-occurrence structures between important places in a story. The street map view is designed as a spatial fingerprint of a novel by highlighting the places occurring often in the story.}, subject = {Informationsvisualisierung}, language = {en} } @article{EhmelBrueggemannDoerk, author = {Ehmel, Fabian and Br{\"u}ggemann, Viktoria and D{\"o}rk, Marian}, title = {Topography of Violence}, series = {Computer Graphics Forum}, volume = {40}, journal = {Computer Graphics Forum}, number = {3}, publisher = {Wiley}, address = {Oxford}, issn = {1467-8659}, doi = {10.1111/cgf.14285}, pages = {13 -- 24}, abstract = {Based on a collaborative visualization design process involving sensitive historical data and historiographical expertise, we investigate the relevance of ethical principles in visualization design. While fundamental ethical norms like truthfulness and accuracy are already well-described and common goals in visualization design, datasets that are accompanied by specific ethical concerns need to be processed and visualized with an additional level of carefulness and thought. There has been little research on adequate visualization design incorporating such considerations. To address this gap we present insights from Topography of Violence, a visualization project with the Jewish Museum Berlin that focuses on a dataset of more than 4,500 acts of violence against Jews in Germany between 1930 and 1938. Drawing from the joint project, we develop an approach to the visualization of sensitive data, which features both conceptual and procedural considerations for visualization design. Our findings provide value for both visualization researchers and practitioners by highlighting challenges and opportunities for ethical data visualization.}, subject = {Daten}, language = {en} } @article{HoeySchroederMorganetal., author = {Hoey, Jesse and Schr{\"o}der, Tobias and Morgan, Jonathan Howard and Rogers, Kimberly B. and Rishi, Deepak and Nagappan, Meiyappan}, title = {Artificial Intelligence and Social Simulation}, series = {Small Group Research}, volume = {49}, journal = {Small Group Research}, number = {6}, publisher = {Sage Publications}, address = {London}, issn = {1552-8278}, doi = {10.1177/1046496418802362}, pages = {647 -- 683}, abstract = {Recent advances in artificial intelligence and computer science can be used by social scientists in their study of groups and teams. Here, we explain how developments in machine learning and simulations with artificially intelligent agents can help group and team scholars to overcome two major problems they face when studying group dynamics. First, because empirical research on groups relies on manual coding, it is hard to study groups in large numbers (the scaling problem). Second, conventional statistical methods in behavioral science often fail to capture the nonlinear interaction dynamics occurring in small groups (the dynamics problem). Machine learning helps to address the scaling problem, as massive computing power can be harnessed to multiply manual codings of group interactions. Computer simulations with artificially intelligent agents help to address the dynamics problem by implementing social psychological theory in data-generating algorithms that allow for sophisticated statements and tests of theory. We describe an ongoing research project aimed at computational analysis of virtual software development teams.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @inproceedings{OttenHildebrandNageletal., author = {Otten, Heike and Hildebrand, Lennart and Nagel, Till and D{\"o}rk, Marian and M{\"u}ller, Boris}, title = {Shifted Maps}, series = {2018 IEEE VIS Arts Program}, booktitle = {2018 IEEE VIS Arts Program}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {Berlin}, organization = {Institute of Electrical and Electronics Engineers (IEEE)}, isbn = {978-1-7281-2805-4}, doi = {10.1109/VISAP45312.2018.9046054}, pages = {1 -- 10}, abstract = {We present a hybrid visualization technique that integrates maps into network visualizations to reveal and analyze diverse topologies in geospatial movement data. With the rise of GPS tracking in various contexts such as smartphones and vehicles there has been a drastic increase in geospatial data being collect for personal reflection and organizational optimization. The generated movement datasets contain both geographical and temporal information, from which rich relational information can be derived. Common map visualizations perform especially well in revealing basic spatial patterns, but pay less attention to more nuanced relational properties. In contrast, network visualizations represent the specific topological structure of a dataset through the visual connections of nodes and their positioning. So far there has been relatively little research on combining these two approaches. Shifted Maps aims to bring maps and network visualizations together as equals. The visualization of places shown as circular map extracts and movements between places shown as edges, can be analyzed in different network arrangements, which reveal spatial and temporal topologies of movement data. We implemented a web-based prototype and report on challenges and opportunities about a novel network layout of places gathered during a qualitative evaluation.}, subject = {Karte}, language = {en} } @inproceedings{KauerJoglekarRedietal., author = {Kauer, Tobias and Joglekar, Sagar and Redi, Miriam and Aiello, Luca Maria and Quercia, Daniele}, title = {Mapping and Visualizing Deep-Learning Urban Beautification}, series = {IEEE Computer Graphics and Applications}, volume = {38}, booktitle = {IEEE Computer Graphics and Applications}, number = {5}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {New York}, organization = {Institute of Electrical and Electronics Engineers (IEEE)}, issn = {1558-1756}, doi = {10.1109/MCG.2018.053491732}, pages = {70 -- 83}, abstract = {Information visualization has great potential to make sense of the increasing amount of data generated by complex machine-learning algorithms. We design a set of visualizations for a new deep-learning algorithm called FaceLift (goodcitylife.org/facelift). This algorithm is able to generate a beautified version of a given urban image (such as from Google Street View), and our visualizations compare pairs of original and beautified images. With those visualizations, we aim at helping practitioners understand what happened during the algorithmic beautification without requiring them to be machine-learning experts. We evaluate the effectiveness of our visualizations to do just that with a survey among practitioners. From the survey results, we derive general design guidelines on how information visualization makes complex machine-learning algorithms more understandable to a general audience.}, subject = {Visualisierung}, language = {en} } @inproceedings{NakovDaSanMartinoElsayedetal., author = {Nakov, Preslav and Da San Martino, Giovanni and Elsayed, Tamer and Barr{\´o}n-Cede{\~n}o, Alberto and M{\´i}guez, Rub{\´e}n and Shaar, Shaden and Alam, Firoj and Haouari, Fatima and Hasanain, Maram and Mansour, Watheq and Hamdan, Bayan and Sheikh Ali, Zien and Babulkov, Nikolay and Nikolov, Alex and Koshore Shahi, Gautam and Struß, Julia Maria and Mandl, Thomas and Kutlu, Mucahid and Selim Kartal, Yavuz}, title = {Overview of the CLEF-2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News}, series = {Experimental IR Meets Multilinguality, Multimodality, and Interaction}, booktitle = {Experimental IR Meets Multilinguality, Multimodality, and Interaction}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-85251-1}, doi = {10.1007/978-3-030-85251-1_19}, pages = {264 -- 291}, abstract = {We describe the fourth edition of the CheckThat! Lab, part of the 2021 Conference and Labs of the Evaluation Forum (CLEF). The lab evaluates technology supporting tasks related to factuality, and covers Arabic, Bulgarian, English, Spanish, and Turkish. Task 1 asks to predict which posts in a Twitter stream are worth fact-checking, focusing on COVID-19 and politics (in all five languages). Task 2 asks to determine whether a claim in a tweet can be verified using a set of previously fact-checked claims (in Arabic and English). Task 3 asks to predict the veracity of a news article and its topical domain (in English). The evaluation is based on mean average precision or precision at rank k for the ranking tasks, and macro-F1 for the classification tasks. This was the most popular CLEF-2021 lab in terms of team registrations: 132 teams. Nearly one-third of them participated: 15, 5, and 25 teams submitted official runs for tasks 1, 2, and 3, respectively.}, subject = {Desinformation}, language = {en} } @incollection{Distelmeyer, author = {Distelmeyer, Jan}, title = {IT sees : speculations on the technologization of the view and its distribution}, series = {Versatile camcorders : looking at the GoPro movement}, booktitle = {Versatile camcorders : looking at the GoPro movement}, editor = {Gerling, Winfried and Krautkr{\"a}mer, Florian}, publisher = {Kulturverlag Kadmos}, address = {Berlin}, isbn = {978-3-86599-461-5}, pages = {63 -- 77}, subject = {Digitalkamera}, language = {en} } @inproceedings{ScheidtPulver, author = {Scheidt, Alexander and Pulver, Tim}, title = {Any-Cubes}, series = {MuC'19: Proceedings of Mensch und Computer 2019}, booktitle = {MuC'19: Proceedings of Mensch und Computer 2019}, publisher = {Association for Computing Machinery}, address = {New York}, isbn = {978-1-4503-7198-8}, doi = {10.1145/3340764.3345375}, pages = {893 -- 895}, abstract = {Here we present Any-Cubes, a prototype toy with which children can intuitively and playfully explore and understand machine learning as well as Internet of Things technology. Our prototype is a combination of deep learning-based image classification [12] and machine-to-machine (m2m) communication via MQTT. The system consists of two physical and tangible wooden cubes. Cube 1 ("sensor cube") is inspired by Google's teachable machine [14,15]. The sensor cube can be trained on any object or scenery. The machine learning functionality is directly implemented on the microcontroller (Raspberry Pi) by a Google Edge TPU Stick. Via MQTT protocol, the microcontroller sends its current status to Cube 2, the actuator cube. The actuator cube provides three switches (relays controlled by an Arduino board) to which peripheral devices can be connected. This allows simple if-then functions to be executed in real time, regardless of location. We envision our system as an intuitive didactic tool for schools and maker spaces.}, subject = {Maschinelles Lernen}, language = {en} }