Universität Stuttgart
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Item Open Access Visual prediction of quantitative information using social media data(2017) Fatehi Ebrahimzadeh, HamedIn recent years, the availability of a vast amount of user-generated data via social media, has given an opportunity to researchers for analyzing these data sources and discovering meaningful information. However, processing and understanding this immense amount of data is challenging and calls for automated approaches, and involvement of field experts to use their field knowledge and experience to enhance the data analysis. So far, existing approaches only enable the detection of indicative information from the data such as the occurrence of critical incidents, relevant situation reports etc. Consequently, the next step would be to better relate the user provided information to the real-world quantities. In this work, a predictive visual analytics approach is developed that offers semi-automated methods to estimate quantitative information (e.g. number of people who participate in a public event). At first, the approach provides interactive visual tools to explore social media data in time and space and select features required as input for training and prediction interactively. Next, a suitable model can be trained based on these feature sets and applied for prediction. Finally, the approach also allows to visually explore prediction results and measure quality of predictions with respect to the ground truth information obtained from past observations. The result of this work is a generic visual analytics approach, that provides expert user with visual tools for a constant interaction between human and machine, for producing quantitative predictions based on social media data. The results of predictions are promising, especially in cases that the location, time and other related information to public events are considered together with the content of user-generated data.Item Open Access Integration of IoT devices via a blockchain-based decentralized application(2017) Ahmad, AfzaalBlockchains are shared, immutable ledgers for recording the history of transactions. They foster a new generation of transactional applications that establish trust, accountability, and transparency. It enables contract partners to secure a deal without involving a trusted third party. Initially, the focus was on financial industry for digital assets trading like Bitcoin, but with the emergence of Smart Contracts, blockchain becomes a complete programmable platform. Many research and commercial organization start diving into blockchain world, bringing new ideas of its application in different sectors like supply chain, Health, and autonomous shopping. This thesis presents an idea to integrate Internet of Things (IoT) devices via a blockchain based decentralize application based on Ethereum. The application consists of front-end application which can be deployed to any web server, and a smart contract which will be deployed on a private blockchain network comprises of Peer-to-Peer (P2P) connected IoT devices acting as full Ethereum node. The application emulates the digital transport ticketing system where the asset is a ticket which can be purchased and paid by the user using ether in their Ethereum account on the blockchain. Once the purchase transaction is mined, it is propagated to all the peers. Ticket can now be accessed locally without requesting any centralized system, which makes the system easily accessible and safe because of the security, data integrity and decentralization of the blockchain-based systems.Item Open Access Modeling recommendations for pattern-based mashup plans(2018) Das, SomeshData mashups are modeled as pipelines. The pipelines are basically a chain of data processing steps in order to integrate data from different data sources into a single one. These processing steps include data operations, such as join, filter, extraction, integration or alteration. To create and execute data mashups, modelers need to have technical knowledge in order to understand these data operations. In order to solve this issue, an extended data mashup approach was created - FlexMash developed at the University of Stuttgart - which allows users to define data mashups without technical knowledge about any execution details. Consquently, modelers with no or limited technical knowledge can design their own domain-specific mashup based on their use case scenarios. However, designing data mashups graphically is still difficult for non-IT users. When users design a model graphically, it is hard to understand which patterns or nodes should be modeled and connected in the data flow graph. In order to cope with this issue, this master thesis aims to provide users modeling recommendations during modeling time. At each modeling step, user can query for recommendations. The recommendations are generated by analyzing the existing models. To generate the recommendations from existing models, association rule mining algorithms are used in this thesis. If users accept a recommendation, the recommended node is automatically added to the partial model and connected with the node for which recommendations were given.Item Open Access Das Ordnungsproblem für Automatengruppen und verwandte Fragestellungen(2019) Bühler, AndreasIn dieser Arbeit werden Problemstellungen in der Klasse der Automatenhalbgruppen untersucht. Ein besonderer Augenmerk gilt dabei dem Ordnungsproblem welches im Allgemeinen sowohl für Automatenhalbgruppen als auch für Automatengruppen unentscheidbar ist. Es wird dann für die Klasse der Automatenhalbgruppen mit beschränkter Aktivität ein Algorithmus mit überraschend geringem Platzbedarf vorgestellt. Danach wird ein Entscheidungsalgorithmus für das Mitgliedschaftsproblem in ultimativ periodischen Teilmengen von Automatenhalbgruppen beschränkter Aktivität erarbeitet. Dieses Problem beinhaltet insbesondere das Mitgliedschaftsproblem in monogenen Unterhalbgruppen, welches dadurch ebenfalls in Automatenhalbgruppen beschränkter Aktivität entscheidbar ist.Item Open Access REST compliant clients for REST APIs(2014) Jaber, MustafaIn today's distributed systems, REST services play a centric role in defining applications' architecture. Current technologies and literature focus on building server-side REST applications. But they fail to build generic and REST compliant client solutions. Therefore, most offered services and especially client applications rarely comply to the constraints that constitute the REST architecture. In this thesis, the architecture of a new generic framework for building REST compliant client applications is introduced. In addition, a new description language that conforms to REST's constraints and helps reduce development time is presented. We describe in this work the building-blocks of the proposed solutions and show a software implementation of a library that leverages the solutions' architectures. Using the proposed framework and description language, client applications that conform to the full set of REST's constraints can be built in an easy and optimized way. In addition, REST service providers can rely on the proposed description language to eliminate the complexity of repetitively building customized solutions for different technologies or platforms.Item Open Access A MATLAB toolbox for the Scintrex CG-5 gravimeter at GIS(2017) Gu, SiyunThis thesis is about a MATLAB toolbox for the Scintrex CG-5 gravimeter. The aim of this toolbox is to offer a basic data process for gravity measurement, which is compatible for most applications in geodesy. In particular, the toolbox covers: 1. data selection, 2. adjustment, 3. gravity gradient computation, 4. gravity visualization, 5. calibration factor estimation. A graphical user interface enables users without deeper programming knowledge to operate this toolbox and obtain the results like adjusted values or figures.Item Open Access Inferring object hypotheses based on feature motion from different sources(2015) Fuchs, SteffenPerception systems in robotics are typically closely tailored to the given task, e.g., in typical pick-and-place tasks the perception systems only recognizes the mugs that are supposed to be moved and the table the mugs are placed on. The obvious limitation of those systems is that for a new task a new vision system must be designed and implemented. This master's thesis proposes a method that allows to identify entities in the world based on motion of various features from various sources. This is without relying on strong prior assumptions and to provide an important piece towards a more general perception system. While entities are rigid bodies in the world, the sources can be anything that allows to track certain features over time in order to create trajectories. For example, these feature trajectories can be obtained from RGB and RGB-D sensors of a robot, from external cameras, or even the end effector of the robot (proprioception). The core conceptual elements are: the distance variance between trajectory pairs is computed to construct an affinity matrix. This matrix is then used as input for a divisive k-means algorithm in order to cluster trajectories into object hypotheses. In a final step these hypotheses are combined with previously observed hypotheses by computing the correlations between the current and the updated sets. This approach has been evaluated on both simulated and real world data. Generating simulated data provides an elegant way for a qualitative analysis of various scenarios. The real world data was obtained by tracking Shi-Tomasi corners using the Lucas-Kanade optical flow estimation of RGB image sequences and projecting the features into range image space.Item Open Access Erweiterung und Evaluation einer lupenbasierten Technik zur Exploration von Textsammlungen(2017) Assenov, IvanIn recent years there has been a sharp increase in the amount of text publicly accessible in digital form. The primary cause for this is widespread access to the Internet, the popularity of e-mail and social networking websites and collaborative efforts to preserve and share knowledge. These developments have inspired the creation of a wide variety of information visualization techniques that focus on large-scale text data and facilitate its exploration and analysis. One popular approach represents individual documents as glyphs on a 2D surface, with pairwise distances corresponding to semantic similarities. The metaphor of a moveable lens that summarizes the contents of texts underneath it has been proposed as a method of interaction targeted at free exploration tasks. The main goal of this master’s thesis project is to extend the basic technique by adding labels to the visualization that guide its users towards regions of interest more quickly without negatively impacting the lens’ usefulness. Also, an automatic framework that determines the tool’s effectiveness under different parameter settings is developed. Finally, the proposed improvements and the overall technique are evaluated by means of a think-aloud user study.Item Open Access Indefinite linear quadratic optimal control: periodic dissipativity and turnpike properties(2018) Berberich, JulianItem Open Access Vision assisted biasing for robot manipulation planning(2018) Puang, En YenSampling efficiency has been one of the major bottlenecks of sampling-based motion planner. Although being more reliable in complex environments, Rapidly-exploring Random Tree for example often requires longer planning time than its optimisation-based counterpart. Recent developments have introduced numerous methods to bias sampling in configuration-space. Gaussian mixture model, in particular, was proposed to estimate feasible regions in configuration-space for low-variance task. Unfortunately this method does not adapt its biases according to individual planning scene during inference. Therefore, this work proposes vision assisted biasing to adapt biases by changing the weights of Gaussian components upon query. It uses autoencoder to extract features directly from depth image, and the resulted latent code is then used for either nearest neighbours search or direct weights prediction. With a modified pipeline, these extensions show improvements on not only the sampling efficiency but also path optimality of simple motion planner.