TY - CHAP A1 - Giurca, Adrian A1 - Baier, Daniel A1 - Schmitt, Ingo ED - Lausen, Berthold ED - Krolak-Schwerdt, Sabine ED - Böhmer, Matthias T1 - What is in a Like? : Preference Aggregation on the Social Web T2 - Data Science, Learning by Latent Structures, and Knowledge Discovery Y1 - 2015 SN - 978-3-662-44982-0 U6 - https://doi.org/10.1007/978-3-662-44983-7_38 SP - 435 EP - 444 PB - Springer CY - Heidelberg ER - TY - CHAP A1 - Zierenberg, Marcel A1 - Schmitt, Ingo ED - Amato, Giuseppe ED - Connor, Richard ED - Falchi, Fabrizio ED - Gennaro, Claudio T1 - Optimizing the Distance Computation Order of Multi-Feature Similarity Search Indexing T2 - Similarity Search and Applications - 8th International Conference, SISAP 2015, Glasgow, UK, October 12-14, 2015, Proceedings N2 - Multi-feature search is an effective approach to similarity search. Unfortunately, the search efficiency decreases with the number of features. Several indexing approaches aim to achieve efficiency by incrementally reducing the approximation error of aggregated distance bounds. They apply heuristics to determine the distance computations order and update the object's aggregated bounds after each computation. However, the existing indexing approaches suffer from several drawbacks. They use the same computation order for all objects, do not support important types of aggregation functions and do not take the varying CPU and I/O costs of different distance computations into account. To resolve these problems, we introduce a new heuristic to determine an efficient distance computation order for each individual object. Our heuristic supports various important aggregation functions and calculates cost-benefit-ratios to incorporate the varying computation costs of different distance functions. The experimental evaluation reveals that our heuristic outperforms state-of-the-art approaches in terms of the number of distance computations as well as search time. KW - combination of features KW - multi-feature search KW - metric indexing KW - nearest neighbor Y1 - 2015 UR - http://link.springer.com/chapter/10.1007/978-3-319-25087-8_8 SN - 978-3-319-25087-8 U6 - https://doi.org/10.1007/978-3-319-25087-8_8 SP - 90 EP - 96 ER - TY - CHAP A1 - Zellhöfer, David A1 - Böttcher, Thomas A1 - Bertram, Maria A1 - Schmidt, Christoph A1 - Tillmann, Claudius A1 - Uhlig, Markus A1 - Zierenberg, Marcel A1 - Schmitt, Ingo ED - Markl, Volker ED - Saake, Gunter ED - Sattler, Kai-Uwe ED - Hackenbroich, Gregor ED - Mitschang, Bernhard ED - Härder, Theo ED - Köppen, Veit T1 - PythiaSearch - Interaktives, Multimodales Multimedia-Retrieval T2 - Datenbanksysteme für Business, Technologie und Web (BTW), Proceedings der 15. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 2013, Magdeburg, Deutschland. N2 - PythiaSearch ist ein interaktives Multimedia-Retrieval-System. Es vereint verschiedene Suchstrategien, diverse Visualisierungen und erlaubt eine Personalisierung der Retrieval-Ergebnisse mittels eines Präferenz-basierten Relevance Feedbacks. Das System nutzt die probabilistische Anfragesprache CQQL und erlaubt eine multi-modale Anfragedefinition basierend auf Bildern, Texten oder Metadaten. Y1 - 2013 UR - http://subs.emis.de/LNI/Proceedings/Proceedings214/article6881.html UR - http://subs.emis.de/LNI/Proceedings/Proceedings214/495.pdf SN - 978-3-88579-608-4 SP - 495 EP - 498 PB - Gesellschaft für Informatik CY - Bonn ER - TY - CHAP A1 - Schmitt, Ingo A1 - Römer, Ronald A1 - Wirsching, Günther A1 - Wolff, Matthias ED - Trouvain, Jürgen ED - Steiner, Ingmar ED - Möbius, Bernd T1 - Semantische Repräsentation strukturierter Objekte im Hilbertraum und deren Verarbeitung nach quantenmechanischen Formalismen T2 - Tagungsband der 28. Konferenz Elektronische Sprachsignalverarbeitung (ESSV), 15.-17.03.2017, Saarbrücken Y1 - 2017 SN - 978-3-95908-094-1 SP - 270 EP - 277 PB - TUDpress CY - Dresden ER - TY - CHAP A1 - Schmitt, Ingo ED - Jokisch, Oliver T1 - Informationsdarstellung nach den Gesetzen der Quantenmechanik- und logik T2 - Elektronische Sprachsignalverarbeitung 2016, Tagungsband der 27. Konferenz, Leipzig, 2.-4. März 2016 N2 - Die 27. Konferenz "Elektronische Sprachsignalverarbeitung" ist der Sprach- und Audiosignalverarbeitung sowie angrenzenden Disziplinen in einer großen Breite gewidmet. Die ESSV 2016 bleibt der Tradition der Konferenzreihe verpflichtet und schlägt eine Brücke zwischen Forschung und Anwendung. Der vorliegende Tagungsband enthält 38 Beiträge von 95 Autorinnen und Autoren zu folgenden Themengruppen: Kognitive Systeme; Fremdspracherwerb, Dialekt- und Korpusanalyse; Spracherkennung und Dialogsysteme; Phonetik und Prosodie; Sprachproduktion, Therapie und Diagnostik; Sprechercharakteristik und Stimmanalyse; Audio- und Sprachkodierung, Sprachqualität; Musikanalyse, Sensorik und Signalverarbeitung. Die drei Hauptvorträge von Dietrich Dörner, Ingo Schmitt und Dirk Labudde beschäftigen sich mit künstlicher Intelligenz, Quantencomputing sowie forensischer Text- und Datenanalyse. Y1 - 2016 SN - 978-3-95908-040-8 SP - S. 13 PB - TUDpress CY - Dresden ER - TY - CHAP A1 - Schmitt, Ingo A1 - Römer, Ronald A1 - Wirsching, Günther A1 - Wolff, Matthias T1 - Denormalized Quantum Density Operators for Encoding Semantic Uncertainty in Cognitive Agents T2 - 8th IEEE International Conference on Cognitive Infocommunications CogInfoCom 2017 Proceedings, September 11-14, 2017 Debrecen, Hungary Y1 - 2017 SN - 978-1-5386-1264-4 N1 - IEEE Catalog Number: CFP1726R-ART SP - 165 EP - 170 PB - IEEE CY - Piscataway, NJ ER - TY - CHAP A1 - Beim Graben, Peter A1 - Huber, Markus A1 - Römer, Ronald A1 - Schmitt, Ingo A1 - Wolff, Matthias ED - Berton, André ED - Haiber, Udo ED - Minker, Wolfgang T1 - Der Fockraum als Labyrinth: Wissensrepräsentation und Problemlösungen am Beispiel des Mouse-Maze-Problems T2 - Elektronische Sprachsignalverarbeitung, Tagungsband der 29. Konferenz, Ulm, 7.-9. März 2018 Y1 - 2018 UR - http://essv2018.de/de/programm/ SN - 978-3-95908-128-3 SP - 167 EP - 174 PB - TUDpress CY - Dresden ER - TY - CHAP A1 - Wolff, Matthias A1 - Wirsching, Günther A1 - Huber, Markus A1 - Beim Graben, Peter A1 - Römer, Ronald A1 - Schmitt, Ingo ED - Karpov, Alexey ED - Jokisch, Oliver ED - Potapova, Rodmonga T1 - A Fock Space Toolbox and Some Applications in Computational Cognition T2 - Speech and computer : 20th International Conference, SPECOM 2018, Leipzig, Germany, September 18-22, 2018, proceedings N2 - We present a Matlab toolbox, called “FockBox”, handling Fock spaces and objects associated with Fock spaces: scalars, ket and bra vectors, and linear operators. We give brief application examples from computational linguistics, semantics processing, and quantum logic,demonstrating the use of the toolbox. KW - Fock space KW - Matlab KW - Computational linguistics KW - Semantics processing KW - Cognitive systems KW - Quantum logic KW - Computational cognition Y1 - 2018 SN - 978-3-319-99578-6 U6 - https://doi.org/10.1007/978-3-319-99579-3 PB - Springer International Publishing CY - Cham ER - TY - CHAP A1 - Wolff, Matthias A1 - Huber, Markus A1 - Wirsching, Günther A1 - Römer, Ronald A1 - Beim Graben, Peter A1 - Schmitt, Ingo T1 - Towards a Quantum Mechanical Model of the Inner Stage of Cognitive Agents T2 - 9th IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2018), proceedings, Aug. 22-24, 2018, Budapest, Hungary N2 - We present a model, inspired by quantum field theory, of the so-called inner stage of technical cognitive agents. The inner stage represents all knowledge of the agent. It allows for planning of actions and for higher cognitive functions like coping and fantasy. By the example of a cognitive mouse agent living in a maze wold, we discuss learning, action planning, and attention in a fully deterministic setting and assuming a totally observable world. We explain the relevance of our approach to cognitive infocommunications. KW - cognitive systems KW - cognitive agents KW - quantum field theory KW - machine learning KW - inner stage KW - veridicality KW - attention modeling KW - ontology inference Y1 - 2018 UR - https://ieeexplore.ieee.org/document/8639892 SN - 978-1-5386-7094-1 U6 - https://doi.org/10.1109/CogInfoCom.2018.8639892 N1 - Best Paper Award SP - 147 EP - 152 PB - IEEE CY - Piscataway, NJ ER - TY - GEN A1 - Klimczak, Peter A1 - Hofstedt, Petra A1 - Schmitt, Ingo A1 - Petersen, Christer T1 - Computergestützte Methoden der Interpretation. Perspektiven einer digitalen Medienwissenschaft T2 - Workshop INF-DH-2018 N2 - Zwar hat sich die elektronische Datenverarbeitung etwa im Rahmen der Archivierung, der Kategorisierung und der Suche von bzw. in Texten allgemein durchgesetzt, allerdings können deren Verfahren bisher nur bedingt auf eine Ebene des Textverständnisses vordringen. Es existieren keine Algorithmen, die menschliche Interpretation auf Subtextebene zufriedenstellend imitieren könnten. Unter Subtext wird hier eine Bedeutungsebene verstanden, die der expliziten Aussage eines Textes als zusätzliche Ausdrucksdimension unterlegt ist. Von Seiten der Computerphilologie sind bisher einzig Textanalyse und -interpretation unterstützende Verfahren entwickelt worden, die lediglich auf der Sprachoberfläche Anwendung finden (Jannidis, 2010). Von Seiten der Computerlinguistik und der Informatik existieren hingegen Text-Retrieval-Systeme, die den groben Inhalt von Texten erfassen (Manning et al., 2008). Dabei erfolgt jedoch keine ‚echte‘ Interpretation, die die impliziten Aussagen des Textes erfassen und damit die Ableitung neuen Wissens ermöglichen könnte. Aufbauend auf der 2016 vorgestellten formalen Subtextanalyse (Klimczak, 2016) erscheint aber ein algorithmisiertes Verfahren zur Rekonstruktion von komplexen Semantiken narrativer Gebrauchstexte möglich, welches die bestehenden Verfahren sowohl der Computerphilologie als auch der Computerlinguistik qualitativ übertreffen könnte, indem es hermeneutische Zugänge für informationstechnische Forschung erschließt. KW - Interpretation KW - Interdisziplinarität KW - Medienwissenschaft KW - Subtextanalyse KW - Narrativik KW - Hermeneutik KW - Semantik KW - Semiotik Y1 - 2018 U6 - https://doi.org/10.18420/infdh2018-06 PB - Gesellschaft für Informatik e.V. CY - Bonn ER - TY - CHAP A1 - Schmitt, Ingo A1 - Wirsching, Günther A1 - Wolff, Matthias ED - Khrennikov, Andrei ED - Melucci, Massimo ED - Bourama, Toni T1 - Quantum-Based Modelling of Database States T2 - Quantum-Like Models for Information Retrieval and Decision-Making Y1 - 2019 UR - https://www.springer.com/de/book/9783030259129 SN - 978-3-030-25912-9 SN - 978-3-030-25913-6 U6 - https://doi.org/10.1007/978-3-030-25913-6 SP - 115 EP - 127 PB - Springer Nature Switzerland AG, Springer International Publishing CY - Cham ET - 1. Auflage ER - TY - GEN A1 - Römer, Ronald A1 - beim Graben, Peter A1 - Huber, Markus A1 - Wolff, Matthias A1 - Wirsching, Günther A1 - Schmitt, Ingo T1 - Behavioral Control of Cognitive Agents Using Database Semantics and Minimalist Grammars T2 - Proceedings 10th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2019, Oct. 23-25 Y1 - 2019 SN - 978-1-7281-4793-2 SN - 978-1-7281-4792-5 U6 - https://doi.org/10.1109/CogInfoCom47531.2019.9089947 SN - 2380-7350 SP - 73 EP - 78 CY - Naples, Italy ER - TY - GEN A1 - Saha, Sanjit Kumar A1 - Schmitt, Ingo T1 - Non-TI Clustering in the Context of Social Networks T2 - Procedia Computer Science : The 11th International Conference on Ambient Systems, Networks and Technologies (ANT) / The 3rd International Conference on Emerging Data and Industry 4.0 (EDI40) / Affiliated Workshops N2 - Traditional clustering algorithms like K-medoids and DBSCAN take distances between objects as input and find clusters of objects. Distance functions need to satisfy the triangle inequality (TI) property, but sometimes TI is violated and, thus, may compromise the quality of resulting clusters. However, there are scenarios, for example in the context of social networks, where TI does not hold but a meaningful clustering is still possible. This paper investigates the consequences of TI violation with respect to different traditional clustering techniques and presents instead a clique guided approach to find meaningful clusters. In this paper, we use the quantum logic-based query language (CQQL) to measure the similarity value between objects instead of a distance function. The contribution of this paper is to propose an approach of non-TI clustering in the context of social network scenario. KW - Clustering KW - Social Network KW - Clique KW - Triangle Inequality Y1 - 2020 U6 - https://doi.org/10.1016/j.procs.2020.03.031 SN - 1877-0509 VL - 170 SP - 1186 EP - 1191 ER - TY - GEN A1 - Schmitt, Ingo A1 - Wang, Dongsheng A1 - Tiwari, Prayaq A1 - Shorfuzzaman, Mohammad T1 - Deep neural learning on weighted datasets utilizing label disagreement from crowdsourcing T2 - Computer Networks N2 - Experts and crowds can work together to generate high-quality datasets, but such collaboration is limited to a large-scale pool of data. In other words, training on a large-scale dataset depends more on crowdsourced datasets with aggregated labels than expert intensively checked labels. However, the limited amount of high-quality dataset can be used as an objective test dataset to build a connection between disagreement and aggregated labels. In this paper, we claim that the disagreement behind an aggregated label indicates more semantics (e.g. ambiguity or difficulty) of an instance than just spam or error assessment. We attempt to take advantage of the informativeness of disagreement to assist learning neural networks by computing a series of disagreement measurements and incorporating disagreement with distinct mechanisms. Experiments on two datasets demonstrate that the consideration of disagreement, treating training instances differently, can promisingly result in improved performance. KW - Neural networks KW - Assessed dataset KW - Instance weight Y1 - 2021 U6 - https://doi.org/10.1016/j.comnet.2021.108227 SN - 1389-1286 VL - 196 ER - TY - GEN A1 - Saha, Sanjit Kumar A1 - Schmitt, Ingo T1 - Quantitative Weighting Approach for Non-TI Clustering T2 - Procedia Computer Science N2 - To enable users to influence clusters of a social network by their external feedback, we present an adaptive clustering-based quantitative weighting approach. Intrinsically, Persons in a social network are connected and their homogeneity is reflected based on the similarity of their attributes. But all attributes do not have the same influence on the network and thus may affect to form the network and compromise the quality of resulting clusters. The introduced weighting approach is completely embedded in logic and has the capability of assigning query weights to atomic conditions in user interaction. Hence, the presented system supports users by offering an intuitive feedback formulation without deeper knowledge of the underlying attributes of objects. Experiments demonstrate the benefits of our approach. KW - Clustering KW - Social Network KW - Condition Weighting KW - Triangle Inequality KW - Clique Y1 - 2021 UR - https://www.sciencedirect.com/science/article/pii/S1877050921007675 U6 - https://doi.org/10.1016/j.procs.2021.03.119 SN - 1877-0509 VL - 184 SP - 966 EP - 971 ER - TY - CHAP A1 - Saha, Sanjit Kumar A1 - Schmitt, Ingo ED - Yu, Han ED - Fang, Fei ED - Guodong, Long ED - Xue, Li ED - Weitong, Chen ED - Jiang, Jing ED - Yue, Lin ED - Bohan, Li T1 - A Relevance Feedback-Based Approach for Non-TI Clustering T2 - Advanced Data Mining and Applications, 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2–4, 2022, Proceedings, Part II N2 - Homogeneity of persons in a social network is based on the similarity of their attributes. Traditional clustering algorithms like hierarchical (agglomerative) clustering or DBSCAN take distances between objects as input and find clusters of objects. Distance functions need to satisfy the triangle inequality (TI) property, but sometimes TI is violated and, in addition, not all attributes do have the same influence on the network and thus may affect the network and compromise the quality of resulting clusters. We present an adaptive clustering-based quantitative weighting approach that is completely embedded in logic. To facilitate the user interaction with the system, we exploit the concept of relevance feedback. The approach takes user feedback as input to improve the quality of clusters and finds meaningful clusters where TI does not hold. In addition, it has the capability of providing the user alternative possible feedbacks that can be fulfilled. To test the approach, we evaluate a clustering distance regarding an ideal solution. Experiments demonstrate the benefits of our approach. Y1 - 2022 UR - https://www.springerprofessional.de/en/advanced-data-mining-and-applications/20081846?tocPage=2 SN - 978-3-030-95408-6 SN - 978-3-030-95407-9 SP - 381 EP - 393 PB - Springer International Publishing CY - Cham ER - TY - RPRT A1 - Schmitt, Ingo T1 - Generating CQQL Conditions from Classifying CNNs N2 - Convolutional neural networks are often successfully used for clas- si cation problems. Usually, a huge number of weights need to be learnt by use of training data. However, the learnt weights give no insight how the cnn really works. Thus, a cnn can be seen as a black box solution. In our approach we develop a method to generate a commuting quantum query language (cqql) condition from a sample derived from a given cnn or from training input. The query language cqql is inspired by quantum logic and its conditions obey the rules of a Boolean algebra. The evaluation of a cqql condition provides values from the unit interval [0; 1] and establishes therefore an elegant bridge between logic and a cnn. The underlying assumption is that a condition (a logic expression) gives much more understanding than pure cnn weights. Furthermore, the rich theory of Boolean algebra can be used for manipulating logic expressions. After extracting a cqql condition from a cnn or its training data we can use logic as a way to predict classes alternatively to a cnn. KW - CNN KW - Erklärbarkeit KW - CQQL KW - Quantenlogik KW - Quantum Logic KW - CNN KW - Abfragesprache KW - Zellulares neuronales Netzwerk Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-55503 SN - 1437-7969 CY - Cottbus ER - TY - CHAP A1 - Schmitt, Ingo ED - Aerts, Diederik ED - Khrennikov, Andrei ED - Melucci, Massimo ED - Bourama, Toni T1 - Incorporating Weights into a Quantum-Logic-Based Query Language T2 - Quantum-Like Models for Information Retrieval and Decision-Making N2 - Traditional database query languages are based on set theory and crisp first order logic. However, many applications require imprecise conditions which return result objects associated with a degree of fulfillment. For example, a research paper should be reviewed by giving a degree of fulfillment for originality and relevance. Very often, imprecise conditions of a query are of different importance. Thus, a query language should allow the user to give conditions different weights. This paper proposes a weighting approach which is realized by means of conjunction, disjunction, and negation. Thus, our weighting is completely embedded within a logic. As a result, logical rules are preserved and can be used for query reformulation and optimization. As underlying logic-based query language, we use the CQQL query language. Furthermore we demonstrate that our weighting approach is applicable to further logic-based query languages. KW - Weights KW - Database query language KW - Information retrieval KW - DB&IR Y1 - 2019 UR - https://www.springer.com/de/book/9783030259129 SN - 978-3-030-25912-9 SN - 978-3-030-25913-6 U6 - https://doi.org/10.1007/978-3-030-25913-6_7 SN - 2520-1948 SP - 129 EP - 143 PB - Springer Nature Switzerland AG, Springer International Publishing CY - Cham ET - 1. Auflage ER - TY - GEN A1 - Schmitt, Ingo T1 - QLC: A Quantum-Logic-inspired Classifier T2 - IDEAS '22: Proceedings of the 26th International Database Engineered Applications Symposium N2 - Besides a good prediction a classifier is to give an explanation how the input data is related to the classification result. There is a general agreement that logic expressions provide a better explanation than other methods like SVM, logistic regression, and neural networks. However, a classifier based on Boolean logic needs to map continuous data to Boolean values which can cause a loss of information. In contrast, we design a quantum-logic-inspired classifier where continuous data are directly processed and the laws of the Boolean algebra are maintained. As a result from our approach we obtain a CQQL condition which provides good insights into the relation of input features to the class decision. Furthermore, our experiment shows a good prediction accuracy. Y1 - 2022 SN - 9781450397094 U6 - https://doi.org/10.1145/3548785.3548790 SP - 120 EP - 127 PB - Association of Computing Machinery CY - New York ER - TY - GEN A1 - Schmitt, Ingo T1 - QLDT: A Decision Tree Based on Quantum Logic T2 - New Trends in Database and Information Systems. ADBIS 2022 N2 - Besides a good prediction a classifier is to give an explanation how input data is related to the classification result. Decision trees are very popular classifiers and provide a good trade-off between accuracy and explainability for many scenarios. Its split decisions correspond to Boolean conditions on single attributes. In cases when for a class decision several attribute values interact gradually with each other, Boolean-logic-based decision trees are not appropriate. For such cases we propose a quantum-logic inspired decision tree (QLDT) which is based on sums and products on normalized attribute values. In contrast to decision trees based on fuzzy logic a QLDT obeys the rules of the Boolean algebra. KW - Quantum Logic KW - Decission tree KW - Interpretable AI Y1 - 2022 SN - 978-3-031-15743-1 SN - 978-3-031-15742-4 U6 - https://doi.org/10.1007/978-3-031-15743-1_28 SP - 299 EP - 308 PB - Springer CY - Switzerland ER -