TY - BOOK A1 - Kuban, Robert A1 - Rotta, Randolf A1 - Nolte, Jörg A1 - Chromik, Jonas A1 - Beilharz, Jossekin Jakob A1 - Pirl, Lukas A1 - Friedrich, Tobias A1 - Lenzner, Pascal A1 - Weyand, Christopher A1 - Juiz, Carlos A1 - Bermejo, Belen A1 - Sauer, Joao A1 - Coelh, Leandro dos Santos A1 - Najafi, Pejman A1 - Pünter, Wenzel A1 - Cheng, Feng A1 - Meinel, Christoph A1 - Sidorova, Julia A1 - Lundberg, Lars A1 - Vogel, Thomas A1 - Tran, Chinh A1 - Moser, Irene A1 - Grunske, Lars A1 - Elsaid, Mohamed Esameldin Mohamed A1 - Abbas, Hazem M. A1 - Rula, Anisa A1 - Sejdiu, Gezim A1 - Maurino, Andrea A1 - Schmidt, Christopher A1 - Hügle, Johannes A1 - Uflacker, Matthias A1 - Nozza, Debora A1 - Messina, Enza A1 - Hoorn, André van A1 - Frank, Markus A1 - Schulz, Henning A1 - Alhosseini Almodarresi Yasin, Seyed Ali A1 - Nowicki, Marek A1 - Muite, Benson K. A1 - Boysan, Mehmet Can A1 - Bianchi, Federico A1 - Cremaschi, Marco A1 - Moussa, Rim A1 - Abdel-Karim, Benjamin M. A1 - Pfeuffer, Nicolas A1 - Hinz, Oliver A1 - Plauth, Max A1 - Polze, Andreas A1 - Huo, Da A1 - Melo, Gerard de A1 - Mendes Soares, Fábio A1 - Oliveira, Roberto Célio Limão de A1 - Benson, Lawrence A1 - Paul, Fabian A1 - Werling, Christian A1 - Windheuser, Fabian A1 - Stojanovic, Dragan A1 - Djordjevic, Igor A1 - Stojanovic, Natalija A1 - Stojnev Ilic, Aleksandra A1 - Weidmann, Vera A1 - Lowitzki, Leon A1 - Wagner, Markus A1 - Ifa, Abdessatar Ben A1 - Arlos, Patrik A1 - Megia, Ana A1 - Vendrell, Joan A1 - Pfitzner, Bjarne A1 - Redondo, Alberto A1 - Ríos Insua, David A1 - Albert, Justin Amadeus A1 - Zhou, Lin A1 - Arnrich, Bert A1 - Szabó, Ildikó A1 - Fodor, Szabina A1 - Ternai, Katalin A1 - Bhowmik, Rajarshi A1 - Campero Durand, Gabriel A1 - Shevchenko, Pavlo A1 - Malysheva, Milena A1 - Prymak, Ivan A1 - Saake, Gunter ED - Meinel, Christoph ED - Polze, Andreas ED - Beins, Karsten ED - Strotmann, Rolf ED - Seibold, Ulrich ED - Rödszus, Kurt ED - Müller, Jürgen T1 - HPI Future SOC Lab – Proceedings 2019 N2 - The “HPI Future SOC Lab” is a cooperation of the Hasso Plattner Institute (HPI) and industry partners. Its mission is to enable and promote exchange and interaction between the research community and the industry partners. The HPI Future SOC Lab provides researchers with free of charge access to a complete infrastructure of state of the art hard and software. This infrastructure includes components, which might be too expensive for an ordinary research environment, such as servers with up to 64 cores and 2 TB main memory. The offerings address researchers particularly from but not limited to the areas of computer science and business information systems. Main areas of research include cloud computing, parallelization, and In-Memory technologies. This technical report presents results of research projects executed in 2019. Selected projects have presented their results on April 9th and November 12th 2019 at the Future SOC Lab Day events. N2 - Das Future SOC Lab am HPI ist eine Kooperation des Hasso-Plattner-Instituts mit verschiedenen Industriepartnern. Seine Aufgabe ist die Ermöglichung und Förderung des Austausches zwischen Forschungsgemeinschaft und Industrie. Am Lab wird interessierten Wissenschaftlern eine Infrastruktur von neuester Hard- und Software kostenfrei für Forschungszwecke zur Verfügung gestellt. Dazu zählen teilweise noch nicht am Markt verfügbare Technologien, die im normalen Hochschulbereich in der Regel nicht zu finanzieren wären, bspw. Server mit bis zu 64 Cores und 2 TB Hauptspeicher. Diese Angebote richten sich insbesondere an Wissenschaftler in den Gebieten Informatik und Wirtschaftsinformatik. Einige der Schwerpunkte sind Cloud Computing, Parallelisierung und In-Memory Technologien. In diesem Technischen Bericht werden die Ergebnisse der Forschungsprojekte des Jahres 2019 vorgestellt. Ausgewählte Projekte stellten ihre Ergebnisse am 09. April und 12. November 2019 im Rahmen des Future SOC Lab Tags vor. T3 - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 158 KW - Future SOC Lab KW - research projects KW - multicore architectures KW - in-memory technology KW - cloud computing KW - machine learning KW - artifical intelligence KW - Future SOC Lab KW - Forschungsprojekte KW - Multicore Architekturen KW - In-Memory Technologie KW - Cloud Computing KW - maschinelles Lernen KW - künstliche Intelligenz Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-597915 SN - 978-3-86956-564-4 SN - 1613-5652 SN - 2191-1665 IS - 158 PB - Universitätsverlag Potsdam CY - Potsdam ER - TY - JOUR A1 - Long, Xiang A1 - de Melo, Gerard A1 - He, Dongliang A1 - Li, Fu A1 - Chi, Zhizhen A1 - Wen, Shilei A1 - Gan, Chuang T1 - Purely attention based local feature integration for video classification JF - IEEE Transactions on Pattern Analysis and Machine Intelligence N2 - Recently, substantial research effort has focused on how to apply CNNs or RNNs to better capture temporal patterns in videos, so as to improve the accuracy of video classification. In this paper, we investigate the potential of a purely attention based local feature integration. Accounting for the characteristics of such features in video classification, we first propose Basic Attention Clusters (BAC), which concatenates the output of multiple attention units applied in parallel, and introduce a shifting operation to capture more diverse signals. Experiments show that BAC can achieve excellent results on multiple datasets. However, BAC treats all feature channels as an indivisible whole, which is suboptimal for achieving a finer-grained local feature integration over the channel dimension. Additionally, it treats the entire local feature sequence as an unordered set, thus ignoring the sequential relationships. To improve over BAC, we further propose the channel pyramid attention schema by splitting features into sub-features at multiple scales for coarse-to-fine sub-feature interaction modeling, and propose the temporal pyramid attention schema by dividing the feature sequences into ordered sub-sequences of multiple lengths to account for the sequential order. Our final model pyramidxpyramid attention clusters (PPAC) combines both channel pyramid attention and temporal pyramid attention to focus on the most important sub-features, while also preserving the temporal information of the video. We demonstrate the effectiveness of PPAC on seven real-world video classification datasets. Our model achieves competitive results across all of these, showing that our proposed framework can consistently outperform the existing local feature integration methods across a range of different scenarios. KW - Feature extraction KW - Convolution KW - Computational modeling KW - Plugs KW - Three-dimensional displays KW - Task analysis KW - Two dimensional displays KW - Video classification KW - action recognition KW - attention mechanism KW - computer vision KW - Algorithms KW - Neural Networks KW - Computer Y1 - 2020 U6 - https://doi.org/10.1109/TPAMI.2020.3029554 SN - 0162-8828 SN - 1939-3539 SN - 2160-9292 VL - 44 IS - 4 SP - 2140 EP - 2154 PB - Inst. of Electr. and Electronics Engineers CY - Los Alamitos ER - TY - JOUR A1 - Kulahcioglu, Tugba A1 - Melo, Gerard de T1 - Affect-aware word clouds JF - ACM transactions on interactive intelligent systems N2 - Word clouds are widely used for non-analytic purposes, such as introducing a topic to students, or creating a gift with personally meaningful text. Surveys show that users prefer tools that yield word clouds with a stronger emotional impact. Fonts and color palettes are powerful typographical signals that may determine this impact. Typically, these signals are assigned randomly, or expected to be chosen by the users. We present an affect-aware font and color palette selection methodology that aims to facilitate more informed choices. We infer associations of fonts with a set of eight affects, and evaluate the resulting data in a series of user studies both on individual words as well as in word clouds. Relying on a recent study to procure affective color palettes, we carry out a similar user study to understand the impact of color choices on word clouds. Our findings suggest that both fonts and color palettes are powerful tools contributing to the affects evoked by a word cloud. The experiments further confirm that the novel datasets we propose are successful in enabling this. We also find that, for the majority of the affects, both signals need to be congruent to create a stronger impact. Based on this data, we implement a prototype that allows users to specify a desired affect and recommends congruent fonts and color palettes for the word. KW - affective interfaces KW - word clouds KW - typography KW - color palettes Y1 - 2020 U6 - https://doi.org/10.1145/3370928 SN - 2160-6455 SN - 2160-6463 VL - 10 IS - 4 PB - Association for Computing Machinery CY - New York, NY ER - TY - JOUR A1 - Puri, Manish A1 - Varde, Aparna S. A1 - Melo, Gerard de T1 - Commonsense based text mining on urban policy JF - Language resources and evaluation N2 - Local laws on urban policy, i.e., ordinances directly affect our daily life in various ways (health, business etc.), yet in practice, for many citizens they remain impervious and complex. This article focuses on an approach to make urban policy more accessible and comprehensible to the general public and to government officials, while also addressing pertinent social media postings. Due to the intricacies of the natural language, ranging from complex legalese in ordinances to informal lingo in tweets, it is practical to harness human judgment here. To this end, we mine ordinances and tweets via reasoning based on commonsense knowledge so as to better account for pragmatics and semantics in the text. Ours is pioneering work in ordinance mining, and thus there is no prior labeled training data available for learning. This gap is filled by commonsense knowledge, a prudent choice in situations involving a lack of adequate training data. The ordinance mining can be beneficial to the public in fathoming policies and to officials in assessing policy effectiveness based on public reactions. This work contributes to smart governance, leveraging transparency in governing processes via public involvement. We focus significantly on ordinances contributing to smart cities, hence an important goal is to assess how well an urban region heads towards a smart city as per its policies mapping with smart city characteristics, and the corresponding public satisfaction. KW - Commonsense reasoning KW - Opinion mining KW - Ordinances KW - Smart cities KW - Social KW - media KW - Text mining Y1 - 2022 U6 - https://doi.org/10.1007/s10579-022-09584-6 SN - 1574-020X SN - 1574-0218 VL - 57 SP - 733 EP - 763 PB - Springer CY - Dordrecht [u.a.] ER -