Fakultät Informatik und Mathematik
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Fast and Reliable Update Protocols in WSNs During Software Development, Testing and Deployment
(2018)
A lot of research has been done in the area of Wireless Sensor Networks during the past years. Today, Wireless Sensor Networks are in field in many different ways and applications (e.g. energy management services, heat and water billing, smoke detectors). Nevertheless, research and development is continued in this area. After the network is deployed, software updates are performed very rarely, but during development and testing one typical, high frequented task is to deploy a new firmware to thousands of nodes. In this paper, we consider such a software update for a special, but well-known and frequently used sensor network platform. There exist some interesting research papers about updating sensor nodes, but we have a special focus on the technical update process. In this context, we show the reasons why these existing update processes do not cover our challenges. Our goal is to allow a developer to update thousands of nodes reliably and very fast during development and testing. Fo r this purpose, it is not so important to perform the best update with regard to energy consumption. We do not need a multi hop protocol, because all devices are in range, e.g., in a laboratory. In our work, we present a model of the update process and give very fast protocols to solve it. The results of our extensive simulations show that the developed protocols do a fast, scalable and reliable update.
Increasing user participation or changing behavior are key goals when applying gamification. Existing studies in domains such as education, health, and enterprise show that gamification can have a positive impact on meeting these goals. However, there is still a lack of detailed insights into how certain game design elements affect user behavior and motivation. To gain further insight, this paper presents a user study in the field with 20, 000 participants of a mobile e-commerce application over a one-month time period to analyze the impact of gamification in the e-commerce domain and to compare the effectiveness of tangible versus intangible rewards. Results show that gamification has a positive impact in the e-commerce domain. The study also reveals that tangible rewards increase the user activity substantially more than intangible rewards. We further show how tangible rewards affect certain user types and provide a first discussion on the lastingness of these rewards.
We present jHound, a tool for profiling large collections of JSON data, and apply it to thousands of data sets holding open government data. jHound reports key characteristics of JSON documents, such as their nesting depth. As we show, jHound can help detect structural outliers, and most importantly, badly encoded documents: jHound can pinpoint certain cases of documents that use string-typed values where other native JSON datatypes would have been a better match. Moreover, we can detect certain cases of maladaptively structured JSON documents, which obviously do not comply with good data modeling practices. By interactively exploring particular example documents, we hope to inspire discussions in the community about what makes a good JSON encoding.
Failure to pay attention to ethical, legal, and social aspects or impacts (ELSA/ELSI) of technology can have considerable negative repercussions like lack of acceptance of a new technology, product, or service among prospective users and thus economic failure. Furthermore, funding institutions expect researchers and developers taking ELSA into account; in the EU, this is called Responsible Research & Innovation. In order to implement this concept in R&D, tools are needed; in what follows MEESTAR and its successor MEESTAR2 shall be presented. Initially developed for the ethical evaluation of ambient assisted living systems, MEESTAR2 can also be used to evaluate other technologies.
The relation of syntax and prosody (the syntax-prosody interface) has been an active area of research, mostly in linguistics and typically studied under controlled conditions. More recently, prosody has also been successfully used in the data-based training of syntax parsers. However, there is a gap between the controlled and detailed study of the individual effects between syntax and prosody and the large-scale application of prosody in syntactic parsing with only a shallow analysis of the respective influences. In this paper, we close the gap by investigating the significance of correlations of prosodic realization with specific syntactic functions using linear mixed effects models in a very large corpus of read-out German encyclopedic texts. Using this corpus, we are able to analyze prosodic structuring performed by a diverse set of speakers while they try to optimize factual content delivery. After normalization by speaker, we obtain significant effects, e.g. confirming that the subject function, as compared to the object function, has a positive effect on pitch and duration of a word, but a negative effect on loudness.
After overcoming the traditional metrics, modern and postmodern poetry developed a large variety of ‘free verse prosodies’ that falls along a spectrum from a more fluent to a more disfluent and choppy style. We present a method, grounded in philological analysis and theories on cognitive (dis)fluency, to analyze this ‘free verse spectrum’ into six classes of poetic styles as well as to differentiate three types of poems with enjambments. We use a model for automatic prosodic analysis of spoken free verse poetry which uses deep hierarchical attention networks to integrate the source text and audio and predict the assigned class. We then analyze and fine-tune the model with a particular focus on enjambments and in two ways: we drill down on classification performance by analyzing whether the model focuses on similar traits of poems as humans would, specifically, whether it internally builds a notion of enjambment. We find that our model is similarly good as humans in finding enjambments; however, when we employ the model for classifying enjambment-dominated poem types, it does not pay particular attention to those lines. Adding enjambment labels to the training only marginally improves performance, indicating that all other lines are similarly informative for the model.
We show how to classify the phrasing of readout poems with the help of machine learning algorithms that use manually engineered features or automatically learn representations. We investigate modern and postmodern poems from the webpage lyrikline, and focus on two exemplary rhythmical patterns in order to detect the rhythmic phrasing: The Parlando and the Variable Foot. These rhythmical patterns have been compared by using two important theoretical works: The Generative Theory of Tonal Music and the Rhythmic Phrasing in English Verse. Using both, we focus on a combination of four different features: The grouping structure, the metrical structure, the time-span-variation, and the prolongation in order to detect the rhythmic phrasing in the two rhythmical types. We use manually engineered features based on text-speech alignment and parsing for classification. We also train a neural network to learn its own representation based on text, speech and audio during pauses. The neural network outperforms manual feature engineering, reaching an f-measure of 0.85.
One of the most important patterns in ancient as well as modern poetry is the enjambment, the continuation of a sentence beyond the end of a line, couplet, or stanza. The paper reports first activities towards the development of a digital tool to analyze the accentuation of poetic enjambments in readout poetry. The aim in this contribution is to recognize two forms of enjambment (emphasized and unemphasized) in poems using audio and text data. We use data from lyrikline which is a major online portal for spoken poetry whereas poems are read aloud by the original authors. We identified by hermeneutical means based on literary analysis a total of 69 poems being characteristic for the use of enjambments in modern and postmodern German poetry and train classifiers to differentiate the emphasized/unemphasized ategorization. A remarkable result of our automated analyses (and to our knowledge the first data-driven analysis of this kind) is the identification of a cultural difference in the accentuation of enjambments: statistically speaking, poets from the former GDR tend to emphasize the enjambment, whereas poets from the FRG do not. We use features derived from speech-to-text alignment and statistical parsing information such as pause lengths, number of lines with verbs, and number of lines with punctuation. The best classification results, calculated by the F-measure, for the both types of enjambment (emphasized/unemphasized) is 0.69.
We present the open-source extensible dialog manager DialogOS.
DialogOS features simple finite-state based dialog management
(which can be expanded to more complex DM strategies via a full-fledged scripting language) in combination with integrated speech recognition and synthesis in multiple languages.
DialogOS runs on all major platforms, provides a simple-to-use
graphical interface and can easily be extended via well-defined
plugin and client interfaces, or can be integrated server-side into
larger existing software infrastructures. We hope that DialogOS
will help foster research and teaching given that it lowers the bar
of entry into building and testing spoken dialog systems and provides paths to extend one’s system as development progresses.