TY - RPRT A1 - Schmitt, Ingo T1 - ANN-Partitionen und CQQL-Ausdrücke T1 - ANN-partitions and CQQL-expressions N2 - Gegeben sei für ein binäres Klassifikationsproblem ein künstliches, neuronales Netzwerk ann bestehend aus ReLUKnoten und linearen Schichten (convolution, pooling, fully connected). Das Netzwerk ann sei mit hinreichender Genauigkeit an Hand von Trainingsdaten trainiert. Wir werden zeigen, dass ein solches Netzwerk in verschiedene Partitionen des Eingaberaums zerlegt werden kann, wobei jede Partition eine lineare Abbildung der Eingabewerte auf einen klassifizierenden Ausgabewert repräsentiert. Im Weiteren gehen wir von einem einfachen Netzwerk ann aus, bei dem die Eingangswerte Mintermen von Attributwerten entsprechen. Einfach ist ein Netzwerk, wenn es für eine geringe Anzahl von Attributen trainiert wurde und die Anzahl der ReLU-Knoten ebenfalls gering ist. In der Arbeit wird gezeigt, dass jede lineare Partition durch einen CQQL-Ausdruck beschrieben werden kann. Ein CQQL-Ausdruck lässt sich mit Hilfe von Quantenlogik-inspirierten Entscheidungsbäumen beschreiben. N2 - Let for a binary classification problem an artificial, neuronal network be given, which consists of ReLU-nodes and linear layers (convolution, pooling, fully connected). Assume, the network ann is trained by training data with sufficient accuracy. We will show how to decompose such a network into several partitions of the input space. Every partition represents a linear function of the input values to a classifying output value. Furthermore, we assume the network announced is simple and the input values correspond to minterm values of attribute values. A network is simple if it is trained for a small number of attributes and it contains a small number of ReLU-nodes. Our work shows that every linear partition can be described by a CQQL-expression. A CQQL-expression can be represented by quantum-logic-inspired decision trees. T3 - Computer science reports / Institut für Informatik - 2024, 1 KW - Quantum logic KW - Artificial neural networks KW - Quantenlogik KW - Künstliche neuronale Netze KW - Neuronales Netz KW - Quantenlogik Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-66069 ER - TY - RPRT A1 - Schmitt, Ingo T1 - Generating CQQL conditions from classifying CNNs T1 - Generierung von CQQL-Bedingungen aus klassifizierenden CNNs N2 - Convolutional neural networks are often successfully used for classification 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. CQQL is inspired by quantum logic and its conditions obey the rules of 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. N2 - Convolutional neural networks (CNN) werden oft erfolgreich zur Lösung von Klassifikationsproblemen verwendet. Üblicherweise muss für ein CNN eine sehr große Anzahl von Gewichten aus vorgegebenen Trainingsdaten gelernt werden. Die so gelernten Gewichte jedoch erklären nicht, wie das CNN wirklich funktioniert. Daher kann man ein CNN als eine Black-Box-Lösung ansehen. In unserem Ansatz entwickeln wir eine Methode, um einen Logikausdruck der commuting quantum query language (CQQL) aus einer Stichprobe eines gegebenen CNNs oder aus den Trainingsdaten zu generieren. CQQL basiert auf der Quantenlogik und deren Ausdrücke folgen den Regeln der Booleschen Algebra. Die Auswertung eines CQQL-Ausdrucks liefert Werte aus dem Einheitsintervall und baut damit eine elegante Brücke zwischen Logik und einem CNN. Es wird angenommen, dass ein Logikausdruck ein besseres Verständnis eines CNNs als dessen reinen Gewichte gibt. Außerdem kann die umfangreiche Theorie der Booleschen Algebra genutzt werden, um Logikausdrücke zu manipulieren. Nach der Extraktion eines CQQL-Logikausdrucks aus einem CNN oder seinen Trainingsdaten kann Logik alternativ zum CNN verwendet werden, um Klassen vorherzusagen. T3 - Computer science reports / Institut für Informatik - 2021, 1 KW - CQQL KW - Quantum Logic KW - CNN KW - CQQL KW - Quantenlogik KW - CNN KW - Erklärbarkeit KW - Zellulares neuronales Netzwerk KW - Abfragesprache KW - Quantenlogik Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-55503 ER - TY - RPRT A1 - Schmitt, Ingo ED - Schmitt, Ingo T1 - Quantum-based construction of a probability measure N2 - From Gleason's theorem we know that in principle every probability measure can be expressed by Hermitian operators in a separable Hilbert space and the Born rule as part of a quantum mechanical system. However, that theorem is not constructive. For a given discrete and additive probability measure based on a σ-algebra we construct a quantum system with projectors expressing that probability measure. T3 - Computer science reports / Institut für Informatik - 2019, 1 KW - Quantum modelling KW - Probability measure KW - Wahrscheinlichkeitsmetrik KW - Mathematische Modellierung KW - Quanteninformatik Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-50760 ER - TY - RPRT A1 - Herajy, Mostafa A1 - Liu, Fei A1 - Rohr, Christian A1 - Heiner, Monika T1 - (Coloured) Hybrid Petri nets in Snoopy – user manual N2 - Hybrid simulation of biological processes becomes widely used to overcome the limitations of the pure stochastic or the complete deterministic simulation. In this manual, we present easy-to-follow steps for constructing and executing hybrid models via Snoopy [HHL+12]. Snoopy is a tool to design and animate or simulate hierarchical graphs, i.e., qualitative, stochastic, continuous, and hybrid Petri nets. This manual is concerned with hybrid Petri nets (HPN) [HH12] as well as their coloured counterpart (HPNC) [HLR14]. HPN combine the merits of stochastic and continuous Petri nets into one single class. Moreover, HPN in Snoopy supports state of the art hybrid simulation algorithms (e.g., [HH16]) to execute the constructed HPN models. Simulating a model using Snoopy's hybrid simulation involves first constructing the reaction network via HPN notations and afterwards executing such model. T3 - Computer science reports / Institut für Informatik - 2017,01 KW - Petri net KW - Hierarchical graph KW - Gefärbtes Petri-Netz KW - Hybridsimulation Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-41572 ER - TY - RPRT A1 - Schwarick, Martin A1 - Rohr, Christian A1 - Heiner, Monika T1 - MARCIE manual N2 - This manual gives an overview on MARCIE – Model Checking And Reachability analysis done effiCIEntly. MARCIE was originally developed as a symbolic model checker for stochastic Petri nets, building on its predecessor – IDDMC – Interval Decision Diagram based Model Checking – which has been previously developed for the qualitative analysis of bounded Place/Transition nets extended by special arcs. Over the last years the tool has been enriched to allow also quantitative analysis of extended stochastic Petri nets. We concentrate here on the user viewpoint. For a detailed introduction to the relevant formalisms, formal definitions and algorithms we refer to related literature. T3 - Computer science reports / Institut für Informatik - 2016,02 KW - Petri nets KW - Model checking KW - Model Checking KW - Petri-Netz Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-40568 ER - TY - RPRT A1 - Lieske, Henry A1 - Noack, Daniel A1 - Schwalbe, Christoph A1 - Vogel, Elisabeth A1 - Hinze, Thomas T1 - Faszination Molekulares Rechnen: Studentische Beiträge vermitteln Erkenntnisse interdisziplinärer Forschung N2 - Molekulares Rechnen etabliert sich als interdisziplinär geprägtes Wissensgebiet mit hohem Anwendungspotential. Es beschäftigt sich mit Prinzipien biologischer Informationsverarbeitung. Moleküle bzw. molekulare Systeme übernehmen die Rolle eines Datenträgers, auf dem biochemische Prozesse oder physische Wechselwirkungen operieren. Facetten sind unter anderem DNA-Computing, chemisches Rechnen, Membrane Computing und ameisenbasiertes Computing. Vier studentische Beiträge stellen diese Forschungsrichtungen anhand eingängiger Beispiele anschaulich vor. N2 - Molecular Computing emerges as a highly interdisciplinary field of research with numerous applications. It is mainly focussed on principles of biological and biologically inspired information processing. Molecules or molecular systems act as data carrier operated by biochemical processes and physical interactions. There are various facets like DNA computing, chemical computing, membrane computing, and ant-based computing. Four contributions made by students illustrate these facets along with plausible examples. T3 - Computer science reports / Institut für Informatik - 2016,01 KW - Molekulares Rechnen KW - DNA Computing KW - Chemisches Rechnen KW - Membrane Computing KW - Ameisenbasiertes Computing KW - DNA computing KW - Chemical computing KW - Membrane computing KW - Molecular computing KW - Ant-based computing KW - Biocomputer KW - Molekulare Bioinformatik Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-40328 ER - TY - RPRT A1 - Swapnil, Chiru T1 - Stochastic simulation efficiency N2 - The work described in this report can be broadly divided into two sections. The first section considers two export features. We describe how the export for stochastic Petri nets to SBML level 1 has been added to the Petri net modelling and simulation tool Snoopy. This task was accomplished by making appropriate changes to the existing export code to generate SBML level 2. Also we demonstrate in detail, how the direct export for coloured Petri nets to both levels (i.e. 1 and 2) of SBML was realised. The next section summarises the performed comparison of different stochastic simulation tools for biochemical reaction networks. We first compare BioNetGen and SSC with each other by performing simulations on non-coloured Petri nets. Then, we compare the remaining four tools, i.e. Cain, Marcie, Snoopy and Stochkit with each other by performing simulation on coloured Petri nets. This work builds on results by Aman Sinha [19]. T3 - Computer science reports / Institut für Informatik - 2014,02 KW - Petri Nets KW - SBML KW - Stochastic simulation KW - Biochemical reaction networks KW - Export KW - MathML KW - Stochastisches Modell KW - Petri-Netz KW - Export Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-31479 ER - TY - RPRT A1 - Sinha, Aman T1 - Comparison of stochastic simulation tools N2 - This report compares some stochastic simulation tools for biochemical reaction networks. The stochastic simulation tools are selected on the basis of some selection criteria. Simulations are performed for the different stochastic simulation tools on different benchmark models. This report gives an overview of how the comparison is carried out for the chosen tools. The tools are compared on a common evaluation protocol. The evaluation protocol comprises a set of benchmark models along with the parameters which are provided as input to the tools. The benchmark models are represented as Petri nets and fed in SBML (System Biology Markup Language) to the different tools. Experiments are performed on each tool and the results are recorded. The tools are finally compared based on the comparison criteria. T3 - Computer science reports / Institut für Informatik - 2014,01 KW - Benchmarks KW - Biochemical reaction networks KW - Evaluation protocol KW - SBML KW - Stochastic simulation KW - Stochastisches Modell KW - Petri-Netz Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-31460 ER - TY - RPRT A1 - Nolte, Jörg T1 - 12. GI/ITG Fachgespräch Sensornetze N2 - Drahtlose Sensornetze stellen eine vielversprechende Technologie zur Beobachtung und Beeinflussung von Vorgängen in der realen Welt dar. Autonome Sensorknoten nehmen dabei Parameter der Umwelt durch Sensoren wahr und können diese durch Aktoren beeinflussen. Viele solcher autonomen und ressourcenbeschränkten Knoten kooperieren dabei mittels drahtloser Kommunikation. Die Eigenschaften dieser Knoten und Netze implizieren eine Vielzahl von neuartigen Herausforderungen, die sich in einer regen Forschungsaktivität widerspiegeln. Ziel dieser Reihe von Fachgesprächen ist es, Wissenschaftlerinnen und Wissenschaftlern aus Hochschule und Industrie die Möglichkeit zu einem informellen Gedankenaustausch zu geben und die Kooperation in diesem multidisziplinären Forschungsbereich zu verstärken. T3 - Computer science reports / Institut für Informatik - 2013,03 KW - Drahtloses Sensorsystem Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus-28986 ER - TY - RPRT A1 - Herajy, Mostafa A1 - Heiner, Monika T1 - Snoopy computational steering framework - user manual version 1.0 N2 - In this manual we discuss the use of Snoopy’s computational steering framework to simulate and interactively steer (stochastic, continuous, hybrid) Petri nets, e.g., biochemical network models. In a typical application scenario, a user constructs a model using a Petri net editing tool (e.g., Snoopy). Afterwards, the Petri net model is submitted to one of the running servers to quantitatively simulate it. Later, other users can adapt their steering GUIs to connect to this model. One of the connected users initialises the simulation while others could stop, pause, or restart it. When the simulator initially starts, it uses the current model settings to run the simulation. Later, other users can remotely join the running simulation and change on the fly parameters and the current marking. T3 - Computer science reports / Institut für Informatik - 2013,02 KW - Datenstruktur KW - Zuverlässigkeit Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus-29034 ER - TY - RPRT A1 - Noack, Tino A1 - Schmitt, Ingo A1 - Saretz, Sascha T1 - OVA-based multi-class classification for data stream anomaly detection N2 - Mobile cyber-physical systems (MCPSs), such as the International Space Station, are equipped with sensors which produce sensor data streams. Continuous changes like wear and tear influence the system states of a MCPS continually during runtime. Hence, monitoring is necessary to provide reliability and to avoid critical damage. Although, the monitoring process is limited by resource restrictions. Therefore, the focal point of the present paper is on time-efficient multi-class data stream anomaly detection. Our contribution is bifid. First, we use a one-versusall classification model to combine a set of heterogeneous one-class classifiers consecutively. Such a chain of one-class classifiers provides a very flexible structure while the administrative overhead is reasonably low. Second, based on the classifier chain, we introduce classifier pre-selection. T3 - Computer science reports / Institut für Informatik - 2013,01 KW - Informatik Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus-28187 ER -