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Learning is a central component of human life and essential for personal development. Therefore, utilizing new technologies in the learning context and exploring their combined potential are considered essential to support self-directed learning in a digital age. A learning environment can be expanded by various technical and content-related aspects. Gamification in the form of elements from video games offers a potential concept to support the learning process. This can be supplemented by technology-supported learning. While the use of tablets is already widespread in the learning context, the integration of a social robot can provide new perspectives on the learning process. However, simply adding new technologies such as social robots or gamification to existing systems may not automatically result in a better learning environment. In the present study, game elements as well as a social robot were integrated separately and conjointly into a learning environment for basic Spanish skills, with a follow-up on retained knowledge. This allowed us to investigate the respective and combined effects of both expansions on motivation, engagement and learning effect. This approach should provide insights into the integration of both additions in an adult learning context. We found that the additions of game elements and the robot did not significantly improve learning, engagement or motivation. Based on these results and a literature review, we outline relevant factors for meaningful integration of gamification and social robots in learning environments in adult learning.
Practical optimization problems often comprise several incomparable and conflicting objectives. When booking a trip using several means of transport, for instance, it should be fast and at the same time not too expensive. The first part of this thesis is concerned with the algorithmic solvability of such multiobjective optimization problems. Several solution notions are discussed and compared with respect to their difficulty. Interestingly, these solution notions are always equally difficulty for a single-objective problem and they differ considerably already for two objectives (unless P = NP). In this context, the difference between search and decision problems is also investigated in general. Furthermore, new and improved approximation algorithms for several variants of the traveling salesperson problem are presented. Using tools from discrepancy theory, a general technique is developed that helps to avoid an obstacle that is often hindering in multiobjective approximation: The problem of combining two solutions such that the new solution is balanced in all objectives and also mostly retains the structure of the original solutions. The second part of this thesis is dedicated to several aspects of systems of equations for (formal) languages. Firstly, conjunctive and Boolean grammars are studied, which are extensions of context-free grammars by explicit intersection and complementation operations, respectively. Among other results, it is shown that one can considerably restrict the union operation on conjunctive grammars without changing the generated language. Secondly, certain circuits are investigated whose gates do not compute Boolean values but sets of natural numbers. For these circuits, the equivalence problem is studied, i.\,e.\ the problem of deciding whether two given circuits compute the same set or not. It is shown that, depending on the allowed types of gates, this problem is complete for several different complexity classes and can thus be seen as a parametrized) representative for all those classes.
In the last 40 years, complexity theory has grown to a rich and powerful field in theoretical computer science. The main task of complexity theory is the classification of problems with respect to their consumption of resources (e.g., running time or required memory). To study the computational complexity (i.e., consumption of resources) of problems, similar problems are grouped into so called complexity classes. During the systematic study of numerous problems of practical relevance, no efficient algorithm for a great number of studied problems was found. Moreover, it was unclear whether such algorithms exist. A major breakthrough in this situation was the introduction of the complexity classes P and NP and the identification of hardest problems in NP. These hardest problems of NP are nowadays known as NP-complete problems. One prominent example of an NP-complete problem is the satisfiability problem of propositional formulas (SAT). Here we get a propositional formula as an input and it must be decided whether an assignment for the propositional variables exists, such that this assignment satisfies the given formula. The intensive study of NP led to numerous related classes, e.g., the classes of the polynomial-time hierarchy PH, P, #P, PP, NL, L and #L. During the study of these classes, problems related to propositional formulas were often identified to be complete problems for these classes. Hence some questions arise: Why is SAT so hard to solve? Are there modifications of SAT which are complete for other well-known complexity classes? In the context of these questions a result by E. Post is extremely useful. He identified and characterized all classes of Boolean functions being closed under superposition. It is possible to study problems which are connected to generalized propositional logic by using this result, which was done in this thesis. Hence, many different problems connected to propositional logic were studied and classified with respect to their computational complexity, clearing the borderline between easy and hard problems.
Background
Localization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms are based on fitting parametric models of the point spread function (PSF) to a measured photon distribution. These algorithms make assumptions about the symmetry of the PSF and thus, do not work well with irregular, non-linear PSFs that occur for example in confocal lifetime imaging, where a laser is scanned across the sample. An alternative method for reconstructing sparse emitter sets from noisy, diffraction-limited images is compressed sensing, but due to its high computational cost it has not yet been widely adopted. Deep neural network fitters have recently emerged as a new competitive method for localization microscopy. They can learn to fit arbitrary PSFs, but require extensive simulated training data and do not generalize well. A method to efficiently fit the irregular PSFs from confocal lifetime localization microscopy combining the advantages of deep learning and compressed sensing would greatly improve the acquisition speed and throughput of this method.
Results
Here we introduce ReCSAI, a compressed sensing neural network to reconstruct localizations for confocal dSTORM, together with a simulation tool to generate training data. We implemented and compared different artificial network architectures, aiming to combine the advantages of compressed sensing and deep learning. We found that a U-Net with a recursive structure inspired by iterative compressed sensing showed the best results on realistic simulated datasets with noise, as well as on real experimentally measured confocal lifetime scanning data. Adding a trainable wavelet denoising layer as prior step further improved the reconstruction quality.
Conclusions
Our deep learning approach can reach a similar reconstruction accuracy for confocal dSTORM as frame binning with traditional fitting without requiring the acquisition of multiple frames. In addition, our work offers generic insights on the reconstruction of sparse measurements from noisy experimental data by combining compressed sensing and deep learning. We provide the trained networks, the code for network training and inference as well as the simulation tool as python code and Jupyter notebooks for easy reproducibility.
The introduction of new types of frequency spectrum in 6G technology facilitates the convergence of conventional mobile communications and radar functions. Thus, the mobile network itself becomes a versatile sensor system. This enables mobile network operators to offer a sensing service in addition to conventional data and telephony services. The potential benefits are expected to accrue to various stakeholders, including individuals, the environment, and society in general. The paper discusses technological development, possible integration, and use cases, as well as future development areas.
The Fifth Generation (5G) communication technology, its infrastructure and architecture, though already deployed in campus and small scale networks, is still undergoing continuous changes and research. Especially, in the light of future large scale deployments and industrial use cases, a detailed analysis of the performance and utilization with regard to latency and service times constraints is crucial. To this end, a fine granular investigation of the Network Function (NF) based core system and the duration for all the tasks performed by these services is necessary. This work presents the first steps towards analyzing the signaling traffic in 5G core networks, and introduces a tool to automatically extract sequence diagrams and service times for NF tasks from traffic traces.
This work proposes a novel approach to disperse dense transmission intervals and reduce bursty traffic patterns without the need for centralized control. Furthermore, by keeping the mechanism as close to the Long Range Wide Area Network (LoRaWAN) standard as possible the suggested mechanism can be deployed within existing networks and can even be co-deployed with other devices.
Future broadband wireless networks should be able to support not only best effort traffic but also real-time traffic with strict Quality of Service (QoS) constraints. In addition, their available resources are scare and limit the number of users. To facilitate QoS guarantees and increase the maximum number of concurrent users, wireless networks require careful planning and optimization. In this monograph, we studied three aspects of performance optimization in wireless networks: resource optimization in WLAN infrastructure networks, quality of experience control in wireless mesh networks, and planning and optimization of wireless mesh networks. An adaptive resource management system is required to effectively utilize the limited resources on the air interface and to guarantee QoS for real-time applications. Thereby, both WLAN infrastructure and WLAN mesh networks have to be considered. An a-priori setting of the access parameters is not meaningful due to the contention-based medium access and the high dynamics of the system. Thus, a management system is required which dynamically adjusts the channel access parameters based on the network load. While this is sufficient for wireless infrastructure networks, interferences on neighboring paths and self-interferences have to be considered for wireless mesh networks. In addition, a careful channel allocation and route assignment is needed. Due to the large parameter space, standard optimization techniques fail for optimizing large wireless mesh networks. In this monograph, we reveal that biology-inspired optimization techniques, namely genetic algorithms, are well-suitable for the planning and optimization of wireless mesh networks. Although genetic algorithms generally do not always find the optimal solution, we show that with a good parameter set for the genetic algorithm, the overall throughput of the wireless mesh network can be significantly improved while still sharing the resources fairly among the users.
A key feature for Internet of Things (IoT) is to control what content is available to each user. To handle this access management, encryption schemes can be used. Due to the diverse usage of encryption schemes, there are various realizations of 1-to-1, 1-to-n, and n-to-n schemes in the literature. This multitude of encryption methods with a wide variety of properties presents developers with the challenge of selecting the optimal method for a particular use case, which is further complicated by the fact that there is no overview of existing encryption schemes. To fill this gap, we envision a cryptography encyclopedia providing such an overview of existing encryption schemes. In this survey paper, we take a first step towards such an encyclopedia by creating a sub-encyclopedia for secure group communication (SGC) schemes, which belong to the n-to-n category. We extensively surveyed the state-of-the-art and classified 47 different schemes. More precisely, we provide (i) a comprehensive overview of the relevant security features, (ii) a set of relevant performance metrics, (iii) a classification for secure group communication schemes, and (iv) workflow descriptions of the 47 schemes. Moreover, we perform a detailed performance and security evaluation of the 47 secure group communication schemes. Based on this evaluation, we create a guideline for the selection of secure group communication schemes.
Interactive system for similarity-based inspection and assessment of the well-being of mHealth users
(2021)
Recent digitization technologies empower mHealth users to conveniently record their Ecological Momentary Assessments (EMA) through web applications, smartphones, and wearable devices. These recordings can help clinicians understand how the users' condition changes, but appropriate learning and visualization mechanisms are required for this purpose. We propose a web-based visual analytics tool, which processes clinical data as well as EMAs that were recorded through a mHealth application. The goals we pursue are (1) to predict the condition of the user in the near and the far future, while also identifying the clinical data that mostly contribute to EMA predictions, (2) to identify users with outlier EMA, and (3) to show to what extent the EMAs of a user are in line with or diverge from those users similar to him/her. We report our findings based on a pilot study on patient empowerment, involving tinnitus patients who recorded EMAs with the mHealth app TinnitusTips. To validate our method, we also derived synthetic data from the same pilot study. Based on this setting, results for different use cases are reported.
This paper describes the estimation of the body weight of a person in front of an RGB-D camera. A survey of different methods for body weight estimation based on depth sensors is given. First, an estimation of people standing in front of a camera is presented. Second, an approach based on a stream of depth images is used to obtain the body weight of a person walking towards a sensor. The algorithm first extracts features from a point cloud and forwards them to an artificial neural network (ANN) to obtain an estimation of body weight. Besides the algorithm for the estimation, this paper further presents an open-access dataset based on measurements from a trauma room in a hospital as well as data from visitors of a public event. In total, the dataset contains 439 measurements. The article illustrates the efficiency of the approach with experiments with persons lying down in a hospital, standing persons, and walking persons. Applicable scenarios for the presented algorithm are body weight-related dosing of emergency patients.
DLTPulseGenerator: a library for the simulation of lifetime spectra based on detector-output pulses
(2018)
The quantitative analysis of lifetime spectra relevant in both life and materials sciences presents one of the ill-posed inverse problems and, hence, leads to most stringent requirements on the hardware specifications and the analysis algorithms. Here we present DLTPulseGenerator, a library written in native C++ 11, which provides a simulation of lifetime spectra according to the measurement setup. The simulation is based on pairs of non-TTL detector output-pulses. Those pulses require the Constant Fraction Principle (CFD) for the determination of the exact timing signal and, thus, the calculation of the time difference i.e. the lifetime. To verify the functionality, simulation results were compared to experimentally obtained data using Positron Annihilation Lifetime Spectroscopy (PALS) on pure tin.
Lifetime techniques are applied to diverse fields of study including materials sciences, semiconductor physics, biology, molecular biophysics and photochemistry.
Here we present DDRS4PALS, a software for the acquisition and simulation of lifetime spectra using the DRS4 evaluation board (Paul Scherrer Institute, Switzerland) for time resolved measurements and digitization of detector output pulses. Artifact afflicted pulses can be corrected or rejected prior to the lifetime calculation to provide the generation of high-quality lifetime spectra, which are crucial for a profound analysis, i.e. the decomposition of the true information. Moreover, the pulses can be streamed on an (external) hard drive during the measurement and subsequently downloaded in the offline mode without being connected to the hardware. This allows the generation of various lifetime spectra at different configurations from one single measurement and, hence, a meaningful comparison in terms of analyzability and quality. Parallel processing and an integrated JavaScript based language provide convenient options to accelerate and automate time consuming processes such as lifetime spectra simulations.
Maps are the main tool to represent geographical information. Users often zoom in and out to access maps at different scales. Continuous map generalization tries to make the changes between different scales smooth, which is essential to provide users with comfortable zooming experience.
In order to achieve continuous map generalization with high quality, we optimize some important aspects of maps. In this book, we have used optimization in the generalization of land-cover areas, administrative boundaries, buildings, and coastlines. According to our experiments, continuous map generalization indeed benefits from optimization.
The charged aerosol detector (CAD) is the latest representative of aerosol-based detectors that generate a response independent of the analytes' chemical structure. This study was aimed at accurately predicting the CAD response of homologous fatty acids under varying experimental conditions. Fatty acids from C12 to C18 were used as model substances due to semivolatile characterics that caused non-uniform CAD behaviour. Considering both experimental conditions and molecular descriptors, a mixed quantitative structure-property relationship (QSPR) modeling was performed using Gradient Boosted Trees (GBT). The ensemble of 10 decisions trees (learning rate set at 0.55, the maximal depth set at 5, and the sample rate set at 1.0) was able to explain approximately 99% (Q\(^2\): 0.987, RMSE: 0.051) of the observed variance in CAD responses. Validation using an external test compound confirmed the high predictive ability of the model established (R-2: 0.990, RMSEP: 0.050). With respect to the intrinsic attribute selection strategy, GBT used almost all independent variables during model building. Finally, it attributed the highest importance to the power function value, the flow rate of the mobile phase, evaporation temperature, the content of the organic solvent in the mobile phase and the molecular descriptors such as molecular weight (MW), Radial Distribution Function-080/weighted by mass (RDF080m) and average coefficient of the last eigenvector from distance/detour matrix (Ve2_D/Dt). The identification of the factors most relevant to the CAD responsiveness has contributed to a better understanding of the underlying mechanisms of signal generation. An increased CAD response that was obtained for acetone as organic modifier demonstrated its potential to replace the more expensive and environmentally harmful acetonitrile.
Modern software is often realized as a modular combination of subsystems for, e. g.,
knowledge management, visualization, verification, or the interaction with users. As
a result, software libraries from possibly different programming languages have to
work together. Even more complex the case is if different programming paradigms
have to be combined. This type of diversification of programming languages and
paradigms in just one software application can only be mastered by mechanisms
for a seamless integration of the involved programming languages. However, the
integration of the common logic programming language Prolog and the popular
object-oriented programming language Java is complicated by various interoperability
problems which stem on the one hand from the paradigmatic gap between the
programming languages, and on the other hand, from the diversity of the available
Prolog systems.
The subject of the thesis is the investigation of novel mechanisms for the integration
of logic programming in Prolog and object–oriented programming in Java. We are
particularly interested in an object–oriented, uniform approach which is not specific
to just one Prolog system. Therefore, we have first identified several important
criteria for the seamless integration of Prolog and Java from the object–oriented
perspective. The main contribution of the thesis is a novel integration framework
called the Connector Architecture for Prolog and Java (CAPJa). The framework is
completely implemented in Java and imposes no modifications to the Java Virtual
Machine or Prolog. CAPJa provides a semi–automated mechanism for the integration
of Prolog predicates into Java. For compact, readable, and object–oriented
queries to Prolog, CAPJa exploits lambda expressions with conditional and relational
operators in Java. The communication between Java and Prolog is based
on a fully automated mapping of Java objects to Prolog terms, and vice versa. In
Java, an extensible system of gateways provides connectivity with various Prolog
system and, moreover, makes any connected Prolog system easily interchangeable,
without major adaption in Java.
Synthetically designed alternative photorespiratory pathways increase the biomass of tobacco and rice plants. Likewise, some in planta–tested synthetic carbon-concentrating cycles (CCCs) hold promise to increase plant biomass while diminishing atmospheric carbon dioxide burden. Taking these individual contributions into account, we hypothesize that the integration of bypasses and CCCs will further increase plant productivity. To test this in silico, we reconstructed a metabolic model by integrating photorespiration and photosynthesis with the synthetically designed alternative pathway 3 (AP3) enzymes and transporters. We calculated fluxes of the native plant system and those of AP3 combined with the inhibition of the glycolate/glycerate transporter by using the YANAsquare package. The activity values corresponding to each enzyme in photosynthesis, photorespiration, and for synthetically designed alternative pathways were estimated. Next, we modeled the effect of the crotonyl-CoA/ethylmalonyl-CoA/hydroxybutyryl-CoA cycle (CETCH), which is a set of natural and synthetically designed enzymes that fix CO₂ manifold more than the native Calvin–Benson–Bassham (CBB) cycle. We compared estimated fluxes across various pathways in the native model and under an introduced CETCH cycle. Moreover, we combined CETCH and AP3-w/plgg1RNAi, and calculated the fluxes. We anticipate higher carbon dioxide–harvesting potential in plants with an AP3 bypass and CETCH–AP3 combination. We discuss the in vivo implementation of these strategies for the improvement of C3 plants and in natural high carbon harvesters.
In today's Internet, building overlay structures to provide a service is becoming more and more common. This approach allows for the utilization of client resources, thus being more scalable than a client-server model in this respect. However, in these architectures the quality of the provided service depends on the clients and is therefore more complex to manage. Resource utilization, both at the clients themselves and in the underlying network, determine the efficiency of the overlay application. Here, a trade-off exists between the resource providers and the end users that can be tuned via overlay mechanisms. Thus, resource management and traffic management is always quality-of-service management as well. In this monograph, the three currently significant and most widely used overlay types in the Internet are considered. These overlays are implemented in popular applications which only recently have gained importance. Thus, these overlay networks still face real-world technical challenges which are of high practical relevance. We identify the specific issues for each of the considered overlays, and show how their optimization affects the trade-offs between resource efficiency and service quality. Thus, we supply new insights and system knowledge that is not provided by previous work.
Shannon channel capacity estimation, based on large packet length is used in traditional Radio Resource Management (RRM) optimization. This is good for the normal transmission of data in a wired or wireless system. For industrial automation and control, rather short packages are used due to the short-latency requirements. Using Shannon’s formula leads in this case to inaccurate RRM solutions, thus another formula should be used to optimize radio resources in short block-length packet transmission, which is the basic of Ultra-Reliable Low-Latency Communications (URLLCs). The stringent requirement of delay Quality of Service (QoS) for URLLCs requires a link-level channel model rather than a physical level channel model. After finding the basic and accurate formula of the achievable rate of short block-length packet transmission, the RRM optimization problem can be accurately formulated and solved under the new constraints of URLLCs. In this short paper, the current mathematical models, which are used in formulating the effective transmission rate of URLLCs, will be briefly explained. Then, using this rate in RRM for URLLC will be discussed.
Having a mixed-cultural membership becomes increasingly common in our modern society. It is thus beneficial in several ways to create Intelligent Virtual Agents (IVAs) that reflect a mixed-cultural background as well, e.g., for educational settings. For research with such IVAs, it is essential that they are classified as non-native by members of a target culture. In this paper, we focus on variations of IVAs’ speech to create the impression of non-native speakers that are identified as such by speakers of two different mother tongues. In particular, we investigate grammatical mistakes and identify thresholds beyond which the agents is clearly categorised as a non-native speaker. Therefore, we conducted two experiments: one for native speakers of German, and one for native speakers of English. Results of the German study indicate that beyond 10% of word order mistakes and 25% of infinitive mistakes German-speaking IVAs are perceived as non-native speakers. Results of the English study indicate that beyond 50% of omission mistakes and 50% of infinitive mistakes English-speaking IVAs are perceived as non-native speakers. We believe these thresholds constitute helpful guidelines for computational approaches of non-native speaker generation, simplifying research with IVAs in mixed-cultural settings.
Despite the fact that mixed-cultural backgrounds become of increasing importance in our daily life, the representation of multiple cultural backgrounds in one entity is still rare in socially interactive agents (SIAs). This paper’s contribution is twofold. First, it provides a survey of research on mixed-cultured SIAs. Second, it presents a study investigating how mixed-cultural speech (in this case, non-native accent) influences how a virtual robot is perceived in terms of personality, warmth, competence and credibility. Participants with English or German respectively as their first language watched a video of a virtual robot speaking in either standard English or German-accented English. It was expected that the German-accented speech would be rated more positively by native German participants as well as elicit the German stereotypes credibility and conscientiousness for both German and English participants. Contrary to the expectations, German participants rated the virtual robot lower in terms of competence and credibility when it spoke with a German accent, whereas English participants perceived the virtual robot with a German accent as more credible compared to the version without an accent. Both the native English and native German listeners classified the virtual robot with a German accent as significantly more neurotic than the virtual robot speaking standard English. This work shows that by solely implementing a non-native accent in a virtual robot, stereotypes are partly transferred. It also shows that the implementation of a non-native accent leads to differences in the perception of the virtual robot.
Knowledge encoding in game mechanics: transfer-oriented knowledge learning in desktop-3D and VR
(2019)
Affine Transformations (ATs) are a complex and abstract learning content. Encoding the AT knowledge in Game Mechanics (GMs) achieves a repetitive knowledge application and audiovisual demonstration. Playing a serious game providing these GMs leads to motivating and effective knowledge learning. Using immersive Virtual Reality (VR) has the potential to even further increase the serious game’s learning outcome and learning quality. This paper compares the effectiveness and efficiency of desktop-3D and VR in respect to the achieved learning outcome. Also, the present study analyzes the effectiveness of an enhanced audiovisual knowledge encoding and the provision of a debriefing system. The results validate the effectiveness of the knowledge encoding in GMs to achieve knowledge learning. The study also indicates that VR is beneficial for the overall learning quality and that an enhanced audiovisual encoding has only a limited effect on the learning outcome.
Impaired decision-making leads to the inability to distinguish between advantageous and disadvantageous choices. The impairment of a person’s decision-making is a common goal of gambling games. Given the recent trend of gambling using immersive Virtual Reality it is crucial to investigate the effects of both immersion and the virtual environment (VE) on decision-making. In a novel user study, we measured decision-making using three virtual versions of the Iowa Gambling Task (IGT). The versions differed with regard to the degree of immersion and design of the virtual environment. While emotions affect decision-making, we further measured the positive and negative affect of participants. A higher visual angle on a stimulus leads to an increased emotional response. Thus, we kept the visual angle on the Iowa Gambling Task the same between our conditions. Our results revealed no significant impact of immersion or the VE on the IGT. We further found no significant difference between the conditions with regard to positive and negative affect. This suggests that neither the medium used nor the design of the VE causes an impairment of decision-making. However, in combination with a recent study, we provide first evidence that a higher visual angle on the IGT leads to an effect of impairment.
The successful development and classroom integration of Virtual (VR) and Augmented Reality (AR) learning environments requires competencies and content knowledge with respect to media didactics and the respective technologies. The paper discusses a pedagogical concept specifically aiming at the interdisciplinary education of pre-service teachers in collaboration with human-computer interaction students. The students’ overarching goal is the interdisciplinary realization and integration of VR/AR learning environments in teaching and learning concepts. To assist this approach, we developed a specific tutorial guiding the developmental process. We evaluate and validate the effectiveness of the overall pedagogical concept by analyzing the change in attitudes regarding 1) the use of VR/AR for educational purposes and in competencies and content knowledge regarding 2) media didactics and 3) technology. Our results indicate a significant improvement in the knowledge of media didactics and technology. We further report on four STEM learning environments that have been developed during the seminar.
The landscape of today’s programming languages is manifold. With the diversity of applications, the difficulty of adequately addressing and specifying the used programs increases. This often leads to newly designed and implemented domain-specific languages. They enable domain experts to express knowledge in their preferred format, resulting in more readable and concise programs. Due to its flexible and declarative syntax without reserved keywords, the logic programming language Prolog is particularly suitable for defining and embedding domain-specific languages.
This thesis addresses the questions and challenges that arise when integrating domain-specific languages into Prolog. We compare the two approaches to define them either externally or internally, and provide assisting tools for each. The grammar of a formal language is usually defined in the extended Backus–Naur form. In this work, we handle this formalism as a domain-specific language in Prolog, and define term expansions that allow to translate it into equivalent definite clause grammars. We present the package library(dcg4pt) for SWI-Prolog, which enriches them by an additional argument to automatically process the term’s corresponding parse tree. To simplify the work with definite clause grammars, we visualise their application by a web-based tracer.
The external integration of domain-specific languages requires the programmer to keep the grammar, parser, and interpreter in sync. In many cases, domain-specific languages can instead be directly embedded into Prolog by providing appropriate operator definitions. In addition, we propose syntactic extensions for Prolog to expand its expressiveness, for instance to state logic formulas with their connectives verbatim. This allows to use all tools that were originally written for Prolog, for instance code linters and editors with syntax highlighting. We present the package library(plammar), a standard-compliant parser for Prolog source code, written in Prolog. It is able to automatically infer from example sentences the required operator definitions with their classes and precedences as well as the required Prolog language extensions. As a result, we can automatically answer the question: Is it possible to model these example sentences as valid Prolog clauses, and how?
We discuss and apply the two approaches to internal and external integrations for several domain-specific languages, namely the extended Backus–Naur form, GraphQL, XPath, and a controlled natural language to represent expert rules in if-then form. The created toolchain with library(dcg4pt) and library(plammar) yields new application opportunities for static Prolog source code analysis, which we also present.
Making machines understand natural language is a dream of mankind that existed
since a very long time. Early attempts at programming machines to converse with
humans in a supposedly intelligent way with humans relied on phrase lists and simple
keyword matching. However, such approaches cannot provide semantically adequate
answers, as they do not consider the specific meaning of the conversation. Thus, if we
want to enable machines to actually understand language, we need to be able to access
semantically relevant background knowledge. For this, it is possible to query so-called
ontologies, which are large networks containing knowledge about real-world entities
and their semantic relations. However, creating such ontologies is a tedious task, as often
extensive expert knowledge is required. Thus, we need to find ways to automatically
construct and update ontologies that fit human intuition of semantics and semantic
relations. More specifically, we need to determine semantic entities and find relations
between them. While this is usually done on large corpora of unstructured text, previous
work has shown that we can at least facilitate the first issue of extracting entities by
considering special data such as tagging data or human navigational paths. Here, we do
not need to detect the actual semantic entities, as they are already provided because of
the way those data are collected. Thus we can mainly focus on the problem of assessing
the degree of semantic relatedness between tags or web pages. However, there exist
several issues which need to be overcome, if we want to approximate human intuition of
semantic relatedness. For this, it is necessary to represent words and concepts in a way
that allows easy and highly precise semantic characterization. This also largely depends
on the quality of data from which these representations are constructed.
In this thesis, we extract semantic information from both tagging data created by users
of social tagging systems and human navigation data in different semantic-driven social
web systems. Our main goal is to construct high quality and robust vector representations
of words which can the be used to measure the relatedness of semantic concepts.
First, we show that navigation in the social media systems Wikipedia and BibSonomy is
driven by a semantic component. After this, we discuss and extend methods to model
the semantic information in tagging data as low-dimensional vectors. Furthermore, we
show that tagging pragmatics influences different facets of tagging semantics. We then
investigate the usefulness of human navigational paths in several different settings on
Wikipedia and BibSonomy for measuring semantic relatedness. Finally, we propose
a metric-learning based algorithm in adapt pre-trained word embeddings to datasets
containing human judgment of semantic relatedness.
This work contributes to the field of studying semantic relatedness between words
by proposing methods to extract semantic relatedness from web navigation, learn highquality
and low-dimensional word representations from tagging data, and to learn
semantic relatedness from any kind of vector representation by exploiting human
feedback. Applications first and foremest lie in ontology learning for the Semantic Web,
but also semantic search or query expansion.
The emerging serverless computing may meet Edge Cloud in a beneficial manner as the two offer flexibility and dynamicity in optimizing finite hardware resources. However, the lack of proper study of a joint platform leaves a gap in literature about consumption and performance of such integration. To this end, this paper identifies the key questions and proposes a methodology to answer them.
This paper discusses the problem of finding multiple shortest disjoint paths in modern communication networks, which is essential for ultra-reliable and time-sensitive applications. Dijkstra’s algorithm has been a popular solution for the shortest path problem, but repetitive use of it to find multiple paths is not scalable. The Multiple Disjoint Path Algorithm (MDPAlg), published in 2021, proposes the use of a single full graph to construct multiple disjoint paths. This paper proposes modifications to the algorithm to include a delay constraint, which is important in time-sensitive applications. Different delay constraint least-cost routing algorithms are compared in a comprehensive manner to evaluate the benefits of the adapted MDPAlg algorithm. Fault tolerance, and thereby reliability, is ensured by generating multiple link-disjoint paths from source to destination.
Given a collection of diverging documents about some lost original text, any person interested in the text would try reconstructing it from the diverging documents. Whether it is eclecticism, stemmatics, or copy-text, one is expected to explicitly or indirectly select one of the documents as a starting point or as a base text, which could be emended through comparison with remaining documents, so that a text that could be designated as the original document is generated. Unfortunately the process of giving priority to one of the documents also known as witnesses is a subjective approach. In fact even Cladistics, which could be considered as a computer-based approach of implementing stemmatics, does not present or recommend users to select a certain witness as a starting point for the process of reconstructing the original document. In this study, a computational method using a rule-based Bayesian classifier is used, to assist text scholars in their attempts of reconstructing a non-existing document from some available witnesses. The method developed in this study consists of selecting a base text successively and collating it with remaining documents. Each completed collation cycle stores the selected base text and its closest witness, along with a weighted score of their similarities and differences. At the end of the collation process, a witness selected more often by majority of base texts is considered as the probable base text of the collection. Witnesses’ scores are weighted using a weighting system, based on effects of types of textual modifications on the process of reconstructing original documents. Users have the possibility to select between baseless and base text collation. If a base text is selected, the task is reduced to ranking the witnesses with respect to the base text, otherwise a base text as well as ranking of the witnesses with respect to the base text are computed and displayed on a histogram.
Learning a book in general involves reading it, underlining important words, adding comments, summarizing some passages, and marking up some text or concepts. Once deeper understanding is achieved, one would like to organize and manage her/his knowledge in such a way that, it could be easily remembered and efficiently transmitted to others. In this paper, books organized in terms of chapters consisting of verses, are considered as the source of knowledge to be modeled. The knowledge model consists of verses with their metadata and semantic annotations. The metadata represent the multiple perspectives of knowledge modeling. Verses with their metadata and annotations form a meta-model, which will be published on a web Mashup. The meta-model with linking between its elements constitute a knowledge base. An XML-based annotation system breaking down the learning process into specific tasks, helps constructing the desired meta-model. The system is made up of user interfaces for creating metadata, annotating chapters’ contents according to user selected semantics, and templates for publishing the generated knowledge on the Internet. The proposed software system improves comprehension and retention of knowledge contained in religious texts through modeling and visualization. The system has been applied to the Quran, and the result obtained shows that multiple perspectives of information modeling can be successfully applied to religious texts. It is expected that this short ongoing study would motivate others to engage in devising and offering software systems for cross-religions learning.
Design and Implementation of Architectures for Interactive Textual Documents Collation Systems
(2011)
One of the main purposes of textual documents collation is to identify a base text or closest witness to the base text, by analyzing and interpreting differences also known as types of changes that might exist between those documents. Based on this fact, it is reasonable to argue that, explicit identification of types of changes such as deletions, additions, transpositions, and mutations should be part of the collation process. The identification could be carried out by an interpretation module after alignment has taken place. Unfortunately existing collation software such as CollateX1 and Juxta2’s collation engine do not have interpretation modules. In fact they implement the Gothenburg model [1] for collation process which does not include an interpretation unit. Currently both CollateX and Juxta’s collation engine do not distinguish in their critical apparatus between the types of changes, and do not offer statistics about those changes. This paper presents a model for both integrated and distributed collation processes that improves the Gothenburg model. The model introduces an interpretation component for computing and distinguishing between the types of changes that documents could have undergone. Moreover two architectures implementing the model in order to solve the problem of interactive collation are discussed as well. Each architecture uses CollateX library, and provides on the one hand preprocessing functions for transforming input documents into CollateX input format, and on the other hand a post-processing module for enabling interactive collation. Finally simple algorithms for distinguishing between types of changes, and linking collated source documents with the collation results are also introduced.
The Quran is the holy book of Islam consisting of 6236 verses divided into 114 chapters called suras. Many verses are similar and even identical. Searching for similar texts (e.g verses) could return thousands of verses, that when displayed completely or partly as textual list would make analysis and understanding difficult and confusing. Moreover it would be visually impossible to instantly figure out the overall distribution of the retrieved verses in the Quran. As consequence reading and analyzing the verses would be tedious and unintuitive. In this study a combination of interactive scatter plots and tables has been developed to assist analysis and understanding of the search result. Retrieved verses are clustered by chapters, and a weight is assigned to each cluster according to number of verses it contains, so that users could visually identify most relevant areas, and figure out the places of revelation of the verses. Users visualize the complete result and can select a region of the plot to zoom in, click on a marker to display a table containing verses with English translation side by side.
A Knowledge-based Hybrid Statistical Classifier for Reconstructing the Chronology of the Quran
(2011)
Computationally categorizing Quran’s chapters has been mainly confined to the determination of chapters’ revelation places. However this broad classification is not sufficient to effectively and thoroughly understand and interpret the Quran. The chronology of revelation would not only improve comprehending the philosophy of Islam, but also the easiness of implementing and memorizing its laws and recommendations. This paper attempts estimating possible chapters’ dates of revelation through their lexical frequency profiles. A hybrid statistical classifier consisting of stemming and clustering algorithms for comparing lexical frequency profiles of chapters, and deriving dates of revelation has been developed. The classifier is trained using some chapters with known dates of revelation. Then it classifies chapters with uncertain dates of revelation by computing their proximity to the training ones. The results reported here indicate that the proposed methodology yields usable results in estimating dates of revelation of the Quran’s chapters based on their lexical contents.
Overlapping is a common word used to describe documents whose structural dimensions cannot be adequately represented using tree structure. For instance a quotation that starts in one verse and ends in another verse. The problem of overlapping hierarchies is a recurring one, which has been addressed by a variety of approaches. There are XML based solutions as well as Non-XML ones. The XML-based solutions are: multiple documents, empty elements, fragmentation, out-of-line markup, JITT and BUVH. And the Non-XML approaches comprise CONCUR/XCONCUR, MECS, LMNL ...etc. This paper presents shortly state-of-the-art in overlapping hierarchies, and introduces two variations on the TEI fragmentation markup that have several advantages.
The Visual Editor for XML (Vex)[1] used by TextGrid [2]and other applications has got rendering and layout engines. The layout engine is well documented but the rendering engine is not. This lack of documenting the rendering engine has made refactoring and extending the editor hard and tedious. For instance many CSS2.1 and upcoming CSS3 properties have not been implemented. Software developers in different projects such as TextGrid using Vex would like to update its CSS rendering engine in order to provide advanced user interfaces as well as support different document types. In order to minimize the effort of extending Vex functionality, I found it beneficial to write a basic documentation about Vex software architecture in general and its CSS rendering engine in particular. The documentation is mainly based on the idea of architectural layered diagrams. In fact layered diagrams can help developers understand software’s source code faster and easier in order to alter it, and fix errors. This paper is written for the purpose of providing direct support for exploration in the comprehension process of Vex source code. It discusses Vex software architecture. The organization of packages that make up the software, the architecture of its CSS rendering engine, an algorithm explaining the working principle of its rendering engine are described.
The technique of using Cascading Style Sheets (CSS) to format and present structured data is called CSS processing model. For instance a CSS processing model for XML documents describes steps involved in formatting and presenting XML documents on screens or papers. Many software applications such as browsers and XML editors have their own CSS processing models which are part of their rendering engines. For instance each browser based on its CSS processing model renders CSS layout differently, as a result an inconsistency in the support of CSS features arises. Some browsers support more CSS features than others, and the rendering itself varies. Moreover the W3C standards are not even adhered by some browsers such as Internet Explorer. Test suites and other hacks and filters cannot definitely solve these problems, because these solutions are temporary and fragile. To palliate this inconsistency and browser compatibility issues with respect to CSS, a reference CSS processing model is needed. By extension it could even allow interoperability across CSS rendering engines. A reference architecture would provide common software architecture and interfaces, and facilitate refactoring, reuse, and automated unit testing. In [2] a reference architecture for browsers has been proposed. However this reference architecture is a macro reference model which does not consider separately individual components of rendering and layout engines. In this paper an attempt to develop a reference architecture for CSS processing models is discussed. In addition the Vex editor [3] rendering and layout engines, as well as an extended version of the editor used in TextGrid project [5] are also presented in order to validate the proposed reference architecture.
Empirical Study on Screen Scraping Web Service Creation: Case of Rhein-Main-Verkehrsverbund (RMV)
(2010)
Internet is the biggest database that science and technology have ever produced. The world wide web is a large repository of information that cannot be used for automation by many applications due to its limited target audience. One of the solutions to the automation problem is to develop wrappers. Wrapping is a process whereby unstructured extracted information is transformed into a more structured one such as XML, which could be provided as webservice to other applications. A web service is a web page whose content is well structured so that a computer program can consume it automatically. This paper describes steps involved in constructing wrappers manually in order to automatically generate web services.
This article discusses web frameworks that are available to a software developer in Java language. It introduces MVC paradigm and some frameworks that implement it. The article presents an overview of Struts, Spring MVC, JSF Frameworks, as well as guidelines for selecting one of them as development environment.
Webservices composition is traditionally carried out using composition technologies such as Business Process Execution Language (BPEL) [1] and Web Service Choreography Interface (WSCI) [2]. The composition technology involves the process of web service discovery, invocation, and composition. However these technologies are not easy and flexible enough because they are mainly developer-centric. Moreover majority of websites have not yet embarked into the world of web service, although they have very important and useful information to offer. Is it because they have not understood the usefulness of web services or is it because of the costs? Whatever might be the answers to these questions, time and money are definitely required in order to create and offer web services. To avoid these expenditures, wrappers [7] to automatically generate webservices from websites would be a cheaper and easier solution. Mashups offer a different way of doing webservices composition. In web environment a Mashup is a web application that brings together data from several sources using webservices, APIs, wrappers and so on, in order to create entirely a new application that was not provided before. This paper presents first an overview of Mashups and the process of web service invocation and composition based on Mashup, then describes an example of a web-based simulator for navigation system in Germany.
This paper discusses the categorization of Quranic chapters by major phases of Prophet Mohammad’s messengership using machine learning algorithms. First, the chapters were categorized by places of revelation using Support Vector Machine and naïve Bayesian classifiers separately, and their results were compared to each other, as well as to the existing traditional Islamic and western orientalists classifications. The chapters were categorized into Meccan (revealed in Mecca) and Medinan (revealed in Medina). After that, chapters of each category were clustered using a kind of fuzzy-single linkage clustering approach, in order to correspond to the major phases of Prophet Mohammad’s life. The major phases of the Prophet’s life were manually derived from the Quranic text, as well as from the secondary Islamic literature e.g hadiths, exegesis. Previous studies on computing the places of revelation of Quranic chapters relied heavily on features extracted from existing background knowledge of the chapters. For instance, it is known that Meccan chapters contain mostly verses about faith and related problems, while Medinan ones encompass verses dealing with social issues, battles…etc. These features are by themselves insufficient as a basis for assigning the chapters to their respective places of revelation. In fact, there are exceptions, since some chapters do contain both Meccan and Medinan features. In this study, features of each category were automatically created from very few chapters, whose places of revelation have been determined through identification of historical facts and events such as battles, migration to Medina…etc. Chapters having unanimously agreed places of revelation were used as the initial training set, while the remaining chapters formed the testing set. The classification process was made recursive by regularly augmenting the training set with correctly classified chapters, in order to classify the whole testing set. Each chapter was preprocessed by removing unimportant words, stemming, and representation with vector space model. The result of this study shows that, the two classifiers have produced useable results, with an outperformance of the support vector machine classifier. This study indicates that, the proposed methodology yields encouraging results for arranging Quranic chapters by phases of Prophet Mohammad’s messengership.
In this research, an attempt to create a knowledge-based learning system for the Quranic text has been performed. The knowledge base is made up of the Quranic text along with detailed information about each chapter and verse, and some rules. The system offers the possibility to study the Quran through web-based interfaces, implementing novel visualization techniques for browsing, querying, consulting, and testing the acquired knowledge. Additionally the system possesses knowledge acquisition facilities for maintaining the knowledge base.
Computing Generic Causes of Revelation of the Quranic Verses Using Machine Learning Techniques
(2011)
Because many verses of the holy Quran are similar, there is high probability that, similar verses addressing same issues share same generic causes of revelation. In this study, machine learning techniques have been employed in order to automatically derive causes of revelation of Quranic verses. The derivation of the causes of revelation is viewed as a classification problem. Initially the categories are based on the verses with known causes of revelation, and the testing set consists of the remaining verses. Based on a computed threshold value, a naïve Bayesian classifier is used to categorize some verses. After that, using a decision tree classifier the remaining uncategorized verses are separated into verses that contain indicators (resultative connectors, causative expressions…), and those that do not. As for those verses having indicators, each one is segmented into its constituent clauses by identification of the linking indicators. Then a dominant clause is extracted and considered either as the cause of revelation, or post-processed by adding or subtracting some terms to form a causal clause that constitutes the cause of revelation. Concerning remaining unclassified verses without indicators, a naive Bayesian classifier is again used to assign each one of them to one of the existing classes based on features and topics similarity. As for verses that could not be classified so far, manual classification was made by considering each verse as a category on its own. The result obtained in this study is encouraging, and shows that automatic derivation of Quranic verses’ generic causes of revelation is achievable, and reasonably reliable for understanding and implementing the teachings of the Quran.
Learning a book in general involves reading it, underlining important words, adding comments, summarizing some passages, and marking up some text or concepts. Once deeper understanding is achieved, one would like to organize and manage her/his knowledge in such a way that, it could be easily remembered and efficiently transmitted to others. This paper discusses about modeling religious texts using semantic XML markup based on frame-based knowledge representation, with the purpose of assisting understanding, retention, and sharing of knowledge they contain. In this study, books organized in terms of chapters made up of verses are considered as the source of knowledge to model. Some metadata representing the multiple perspectives of knowledge modeling are assigned to each chapter and verse. Chapters and verses with their metadata form a meta-model, which is represented using frames, and published on a web mashup. An XML-based annotation and visualization system equipped with user interfaces for creating static and dynamic metadata, annotating chapters’ contents according to user selected semantics, and templates for publishing generated knowledge on the Internet, has been developed. The system has been applied to the Quran, and the result obtained shows that multiple perspectives of information modeling can be successfully applied to religious texts, in order to support analysis, understanding, and retention of the texts.
Given a collection of diverging documents about some lost original text, any person interested in the text would try reconstructing it from the diverging documents. Whether it is eclecticism, stemmatics, or copy-text, one is expected to explicitly or indirectly select one of the documents as a starting point or as a base text, which could be emended through comparison with remaining documents, so that a text that could be designated as the original document is generated. Unfortunately the process of giving priority to one of the documents also known as witnesses is a subjective approach. In fact even Cladistics, which could be considered as a computer-based approach of implementing stemmatics, does not present or recommend users to select a certain witness as a starting point for the process of reconstructing the original document. In this study, a computational method using a rule-based Bayesian classifier is used, to assist text scholars in their attempts of reconstructing a non-existing document from some available witnesses. The method developed in this study consists of selecting a base text successively and collating it with remaining documents. Each completed collation cycle stores the selected base text and its closest witness, along with a weighted score of their similarities and differences. At the end of the collation process, a witness selected more often by majority of base texts is considered as the probable base text of the collection. Witnesses’ scores are weighted using a weighting system, based on effects of types of textual modifications on the process of reconstructing original documents. Users have the possibility to select between baseless and base text collation. If a base text is selected, the task is reduced to ranking the witnesses with respect to the base text, otherwise a base text as well as ranking of the witnesses with respect to the base text are computed and displayed on a bar diagram. Additionally this study includes a recursive algorithm for automatically reconstructing the original text from the identified base text and ranked witnesses.
The question of why the Quran structure does not follow its chronology of revelation is a recurring one. Some Islamic scholars such as [1] have answered the question using hadiths, as well as other philosophical reasons based on internal evidences of the Quran itself. Unfortunately till today many are still wondering about this issue. Muslims believe that the Quran is a summary and a copy of the content of a preserved tablet called Lawhul-Mahfuz located in the heaven. Logically speaking, this suggests that the arrangement of the verses and chapters is expected to be similar to that of the Lawhul-Mahfuz. As for the arrangement of the verses in each chapter, there is unanimity that it was carried out by the Prophet himself under the guidance of Angel Gabriel with the recommendation of God. But concerning the ordering of the chapters, there are reports about some divergences [3] among the Prophet’s companions as to which chapter should precede which one. This paper argues that Quranic chapters might have been arranged according to months and seasons of revelation. In fact, based on some verses of the Quran, it is defendable that the Lawhul-Mahfuz itself is understood to have been structured in terms of the months of the year. In this study, philosophical and mathematical arguments for computing chapters’ months of revelation are discussed, and the result is displayed on an interactive scatter plot.
Experimental high-throughput analysis of molecular networks is a central approach to characterize the adaptation of plant metabolism to the environment. However, recent studies have demonstrated that it is hardly possible to predict in situ metabolic phenotypes from experiments under controlled conditions, such as growth chambers or greenhouses. This is particularly due to the high molecular variance of in situ samples induced by environmental fluctuations. An approach of functional metabolome interpretation of field samples would be desirable in order to be able to identify and trace back the impact of environmental changes on plant metabolism. To test the applicability of metabolomics studies for a characterization of plant populations in the field, we have identified and analyzed in situ samples of nearby grown natural populations of Arabidopsis thaliana in Austria. A. thaliana is the primary molecular biological model system in plant biology with one of the best functionally annotated genomes representing a reference system for all other plant genome projects. The genomes of these novel natural populations were sequenced and phylogenetically compared to a comprehensive genome database of A. thaliana ecotypes. Experimental results on primary and secondary metabolite profiling and genotypic variation were functionally integrated by a data mining strategy, which combines statistical output of metabolomics data with genome-derived biochemical pathway reconstruction and metabolic modeling. Correlations of biochemical model predictions and population-specific genetic variation indicated varying strategies of metabolic regulation on a population level which enabled the direct comparison, differentiation, and prediction of metabolic adaptation of the same species to different habitats. These differences were most pronounced at organic and amino acid metabolism as well as at the interface of primary and secondary metabolism and allowed for the direct classification of population-specific metabolic phenotypes within geographically contiguous sampling sites.
In recent history, normalized digital surface models (nDSMs) have been constantly gaining importance as a means to solve large-scale geographic problems. High-resolution surface models are precious, as they can provide detailed information for a specific area. However, measurements with a high resolution are time consuming and costly. Only a few approaches exist to create high-resolution nDSMs for extensive areas. This article explores approaches to extract high-resolution nDSMs from low-resolution Sentinel-2 data, allowing us to derive large-scale models. We thereby utilize the advantages of Sentinel 2 being open access, having global coverage, and providing steady updates through a high repetition rate. Several deep learning models are trained to overcome the gap in producing high-resolution surface maps from low-resolution input data. With U-Net as a base architecture, we extend the capabilities of our model by integrating tailored multiscale encoders with differently sized kernels in the convolution as well as conformed self-attention inside the skip connection gates. Using pixelwise regression, our U-Net base models can achieve a mean height error of approximately 2 m. Moreover, through our enhancements to the model architecture, we reduce the model error by more than 7%.
Mobile telecommunication systems of the 3.5th generation (3.5G) constitute a first step towards the requirements of an all-IP world. As the denotation suggests, 3.5G systems are not completely new designed from scratch. Instead, they are evolved from existing 3G systems like UMTS or cdma2000. 3.5G systems are primarily designed and optimized for packet-switched best-effort traffic, but they are also intended to increase system capacity by exploiting available radio resources more efficiently. Systems based on cdma2000 are enhanced with 1xEV-DO (EV-DO: evolution, data-optimized). In the UMTS domain, the 3G partnership project (3GPP) specified the High Speed Packet Access (HSPA) family, consisting of High Speed Downlink Packet Access (HSDPA) and its counterpart High Speed Uplink Packet Access (HSUPA) or Enhanced Uplink. The focus of this monograph is on HSPA systems, although the operation principles of other 3.5G systems are similar. One of the main contributions of our work are performance models which allow a holistic view on the system. The models consider user traffic on flow-level, such that only on significant changes of the system state a recalculation of parameters like bandwidth is necessary. The impact of lower layers is captured by stochastic models. This approach combines accurate modeling and the ability to cope with computational complexity. Adopting this approach to HSDPA, we develop a new physical layer abstraction model that takes radio resources, scheduling discipline, radio propagation and mobile device capabilities into account. Together with models for the calculation of network-wide interference and transmit powers, a discrete-event simulation and an analytical model based on a queuing-theoretical approach are proposed. For the Enhanced Uplink, we develop analytical models considering independent and correlated other-cell interference.
Performance Evaluation of Efficient Resource Management Concepts for Next Generation IP Networks
(2007)
Next generation networks (NGNs) must integrate the services of current circuit-switched telephone networks and packet-switched data networks. This convergence towards a unified communication infrastructure necessitates from the high capital expenditures (CAPEX) and operational expenditures (OPEX) due to the coexistence of separate networks for voice and data. In the end, NGNs must offer the same services as these legacy networks and, therefore, they must provide a low-cost packet-switched solution with real-time transport capabilities for telephony and multimedia applications. In addition, NGNs must be fault-tolerant to guarantee user satisfaction and to support business-critical processes also in case of network failures. A key technology for the operation of NGNs is the Internet Protocol (IP) which evolved to a common and well accepted standard for networking in the Internet during the last 25 years. There are two basically different approaches to achieve QoS in IP networks. With capacity overprovisioning (CO), an IP network is equipped with sufficient bandwidth such that network congestion becomes very unlikely and QoS is maintained most of the time. The second option to achieve QoS in IP networks is admission control (AC). AC represents a network-inherent intelligence that admits real-time traffic flows to a single link or an entire network only if enough resources are available such that the requirements on packet loss and delay can be met. Otherwise, the request of a new flow is blocked. This work focuses on resource management and control mechanisms for NGNs, in particular on AC and associated bandwidth allocation methods. The first contribution consists of a new link-oriented AC method called experience-based admission control (EBAC) which is a hybrid approach dealing with the problems inherent to conventional AC mechanisms like parameter-based or measurement-based AC (PBAC/MBAC). PBAC provides good QoS but suffers from poor resource utilization and, vice versa, MBAC uses resources efficiently but is susceptible to QoS violations. Hence, EBAC aims at increasing the resource efficiency while maintaining the QoS which increases the revenues of ISPs and postpones their CAPEX for infrastructure upgrades. To show the advantages of EBAC, we first review today’s AC approaches and then develop the concept of EBAC. EBAC is a simple mechanism that safely overbooks the capacity of a single link to increase its resource utilization. We evaluate the performance of EBAC by its simulation under various traffic conditions. The second contribution concerns dynamic resource allocation in transport networks which implement a specific network admission control (NAC) architecture. In general, the performance of different NAC systems may be evaluated by conventional methods such as call blocking analysis which has often been applied in the context of multi-service asynchronous transfer mode (ATM) networks. However, to yield more practical results than abstract blocking probabilities, we propose a new method to compare different AC approaches by their respective bandwidth requirements. To present our new method for comparing different AC systems, we first give an overview of network resource management (NRM) in general. Then we present the concept of adaptive bandwidth allocation (ABA) in capacity tunnels and illustrate the analytical performance evaluation framework to compare different AC systems by their capacity requirements. Different network characteristics influence the performance of ABA. Therefore, the impact of various traffic demand models and tunnel implementations, and the influence of resilience requirements is investigated. In conclusion, the resources in NGNs must be exclusively dedicated to admitted traffic to guarantee QoS. For that purpose, robust and efficient concepts for NRM are required to control the requested bandwidth with regard to the available transmission capacity. Sophisticated AC will be a key function for NRM in NGNs and, therefore, efficient resource management concepts like experience-based admission control and adaptive bandwidth allocation for admission-controlled capacity tunnels, as presented in this work are appealing for NGN solutions.
In recent years several community testbeds as well as participatory sensing platforms have successfully established themselves to provide open data to everyone interested. Each of them with a specific goal in mind, ranging from collecting radio coverage data up to environmental and radiation data. Such data can be used by the community in their decision making, whether to subscribe to a specific mobile phone service that provides good coverage in an area or in finding a sunny and warm region for the summer holidays.
However, the existing platforms are usually limiting themselves to directly measurable network QoS. If such a crowdsourced data set provides more in-depth derived measures, this would enable an even better decision making. A community-driven crowdsensing platform that derives spatial application-layer user experience from resource-friendly bandwidth estimates would be such a case, video streaming services come to mind as a prime example. In this paper we present a concept for such a system based on an initial prototype that eases the collection of data necessary to determine mobile-specific QoE at large scale. In addition we reason why the simple quality metric proposed here can hold its own.
The ITS2 Database
(2012)
The internal transcribed spacer 2 (ITS2) has been used as a phylogenetic marker for more than two decades. As ITS2 research mainly focused on the very variable ITS2 sequence, it confined this marker to low-level phylogenetics only. However, the combination of the ITS2 sequence and its highly conserved secondary structure improves the phylogenetic resolution1 and allows phylogenetic inference at multiple taxonomic ranks, including species delimitation.
The ITS2 Database presents an exhaustive dataset of internal transcribed spacer 2 sequences from NCBI GenBank accurately reannotated. Following an annotation by profile Hidden Markov Models (HMMs), the secondary structure of each sequence is predicted. First, it is tested whether a minimum energy based fold (direct fold) results in a correct, four helix conformation. If this is not the case, the structure is predicted by homology modeling. In homology modeling, an already known secondary structure is transferred to another ITS2 sequence, whose secondary structure was not able to fold correctly in a direct fold.
The ITS2 Database is not only a database for storage and retrieval of ITS2 sequence-structures. It also provides several tools to process your own ITS2 sequences, including annotation, structural prediction, motif detection and BLAST search on the combined sequence-structure information. Moreover, it integrates trimmed versions of 4SALE and ProfDistS for multiple sequence-structure alignment calculation and Neighbor Joining tree reconstruction. Together they form a coherent analysis pipeline from an initial set of sequences to a phylogeny based on sequence and secondary structure.
In a nutshell, this workbench simplifies first phylogenetic analyses to only a few mouse-clicks, while additionally providing tools and data for comprehensive large-scale analyses.
This work is subdivided into two main areas: resilient admission control and resilient routing. The work gives an overview of the state of the art of quality of service mechanisms in communication networks and proposes a categorization of admission control (AC) methods. These approaches are investigated regarding performance, more precisely, regarding the potential resource utilization by dimensioning the capacity for a network with a given topology, traffic matrix, and a required flow blocking probability. In case of a failure, the affected traffic is rerouted over backup paths which increases the traffic rate on the respective links. To guarantee the effectiveness of admission control also in failure scenarios, the increased traffic rate must be taken into account for capacity dimensioning and leads to resilient AC. Capacity dimensioning is not feasible for existing networks with already given link capacities. For the application of resilient NAC in this case, the size of distributed AC budgets must be adapted according to the traffic matrix in such a way that the maximum blocking probability for all flows is minimized and that the capacity of all links is not exceeded by the admissible traffic rate in any failure scenario. Several algorithms for the solution of that problem are presented and compared regarding their efficiency and fairness. A prototype for resilient AC was implemented in the laboratories of Siemens AG in Munich within the scope of the project KING. Resilience requires additional capacity on the backup paths for failure scenarios. The amount of this backup capacity depends on the routing and can be minimized by routing optimization. New protection switching mechanisms are presented that deviate the traffic quickly around outage locations. They are simple and can be implemented, e.g, by MPLS technology. The Self-Protecting Multi-Path (SPM) is a multi-path consisting of disjoint partial paths. The traffic is distributed over all faultless partial paths according to an optimized load balancing function both in the working case and in failure scenarios. Performance studies show that the network topology and the traffic matrix also influence the amount of required backup capacity significantly. The example of the COST-239 network illustrates that conventional shortest path routing may need 50% more capacity than the optimized SPM if all single link and node failures are protected.
This paper presents a prototypical implementation of the In-band Network Telemetry (INT) specification in P4 and demonstrates a use case, where a Tofino Switch is used to measure device and network performance in a lab setting. This work is based on research activities in the area of P4 data plane programming conducted at the network lab of HTW Berlin.
In recent years, satellite communication has been expanding its field of application in the world of computer networks. This paper aims to provide an overview of how a typical scenario involving 5G Non-Terrestrial Networks (NTNs) for vehicle to everything (V2X) applications is characterized. In particular, a first implementation of a system that integrates them together will be described. Such a framework will later be used to evaluate the performance of applications such as Vehicle Monitoring (VM), Remote Driving (RD), Voice Over IP (VoIP), and others. Different configuration scenarios such as Low Earth Orbit and Geostationary Orbit will be considered.
The Internet sees an ongoing transformation process from a single best-effort service network into a multi-service network. In addition to traditional applications like e-mail,WWW-traffic, or file transfer, future generation networks (FGNs) will carry services with real-time constraints and stringent availability and reliability requirements like Voice over IP (VoIP), video conferencing, virtual private networks (VPNs) for finance, other real-time business applications, tele-medicine, or tele-robotics. Hence, quality of service (QoS) guarantees and resilience to failures are crucial characteristics of an FGN architecture. At the same time, network operations must be efficient. This necessitates sophisticated mechanisms for the provisioning and the control of future communication infrastructures. In this work we investigate such echanisms for resilient FGNs. There are many aspects of the provisioning and control of resilient FGNs such as traffic matrix estimation, traffic characterization, traffic forecasting, mechanisms for QoS enforcement also during failure cases, resilient routing, or calability concerns for future routing and addressing mechanisms. In this work we focus on three important aspects for which performance analysis can deliver substantial insights: load balancing for multipath Internet routing, fast resilience concepts, and advanced dimensioning techniques for resilient networks. Routing in modern communication networks is often based on multipath structures, e.g., equal-cost multipath routing (ECMP) in IP networks, to facilitate traffic engineering and resiliency. When multipath routing is applied, load balancing algorithms distribute the traffic over available paths towards the destination according to pre-configured distribution values. State-of-the-art load balancing algorithms operate either on the packet or the flow level. Packet level mechanisms achieve highly accurate traffic distributions, but are known to have negative effects on the performance of transport protocols and should not be applied. Flow level mechanisms avoid performance degradations, but at the expense of reduced accuracy. These inaccuracies may have unpredictable effects on link capacity requirements and complicate resource management. Thus, it is important to exactly understand the accuracy and dynamics of load balancing algorithms in order to be able to exercise better network control. Knowing about their weaknesses, it is also important to look for alternatives and to assess their applicability in different networking scenarios. This is the first aspect of this work. Component failures are inevitable during the operation of communication networks and lead to routing disruptions if no special precautions are taken. In case of a failure, the robust shortest-path routing of the Internet reconverges after some time to a state where all nodes are again reachable – provided physical connectivity still exists. But stringent availability and reliability criteria of new services make a fast reaction to failures obligatory for resilient FGNs. This led to the development of fast reroute (FRR) concepts for MPLS and IP routing. The operations of MPLS-FRR have already been standardized. Still, the standards leave some degrees of freedom for the resilient path layout and it is important to understand the tradeoffs between different options for the path layout to efficiently provision resilient FGNs. In contrast, the standardization for IP-FRR is an ongoing process. The applicability and possible combinations of different concepts still are open issues. IP-FRR also facilitates a comprehensive resilience framework for IP routing covering all steps of the failure recovery cycle. These points constitute another aspect of this work. Finally, communication networks are usually over-provisioned, i.e., they have much more capacity installed than actually required during normal operation. This is a precaution for various challenges such as network element failures. An alternative to this capacity overprovisioning (CO) approach is admission control (AC). AC blocks new flows in case of imminent overload due to unanticipated events to protect the QoS for already admitted flows. On the one hand, CO is generally viewed as a simple mechanism, AC as a more complex mechanism that complicates the network control plane and raises interoperability issues. On the other hand, AC appears more cost-efficient than CO. To obtain advanced provisioning methods for resilient FGNs, it is important to find suitable models for irregular events, such as failures and different sources of overload, and to incorporate them into capacity dimensioning methods. This allows for a fair comparison between CO and AC in various situations and yields a better understanding of the strengths and weaknesses of both concepts. Such an advanced capacity dimensioning method for resilient FGNs represents the third aspect of this work.
The ongoing digitization of historical photographs in archives allows investigating the quality, quantity, and distribution of these images. However, the exact interior and exterior camera orientations of these photographs are usually lost during the digitization process. The proposed method uses content-based image retrieval (CBIR) to filter exterior images of single buildings in combination with metadata information. The retrieved photographs are automatically processed in an adapted structure-from-motion (SfM) pipeline to determine the camera parameters. In an interactive georeferencing process, the calculated camera positions are transferred into a global coordinate system. As all image and camera data are efficiently stored in the proposed 4D database, they can be conveniently accessed afterward to georeference newly digitized images by using photogrammetric triangulation and spatial resection. The results show that the CBIR and the subsequent SfM are robust methods for various kinds of buildings and different quantity of data. The absolute accuracy of the camera positions after georeferencing lies in the range of a few meters likely introduced by the inaccurate LOD2 models used for transformation. The proposed photogrammetric method, the database structure, and the 4D visualization interface enable adding historical urban photographs and 3D models from other locations.
Effects of Acrophobic Fear and Trait Anxiety on Human Behavior in a Virtual Elevated Plus-Maze
(2021)
The Elevated Plus-Maze (EPM) is a well-established apparatus to measure anxiety in rodents, i.e., animals exhibiting an increased relative time spent in the closed vs. the open arms are considered anxious. To examine whether such anxiety-modulated behaviors are conserved in humans, we re-translated this paradigm to a human setting using virtual reality in a Cave Automatic Virtual Environment (CAVE) system. In two studies, we examined whether the EPM exploration behavior of humans is modulated by their trait anxiety and also assessed the individuals’ levels of acrophobia (fear of height), claustrophobia (fear of confined spaces), sensation seeking, and the reported anxiety when on the maze. First, we constructed an exact virtual copy of the animal EPM adjusted to human proportions. In analogy to animal EPM studies, participants (N = 30) freely explored the EPM for 5 min. In the second study (N = 61), we redesigned the EPM to make it more human-adapted and to differentiate influences of trait anxiety and acrophobia by introducing various floor textures and lower walls of closed arms to the height of standard handrails. In the first experiment, hierarchical regression analyses of exploration behavior revealed the expected association between open arm avoidance and Trait Anxiety, an even stronger association with acrophobic fear. In the second study, results revealed that acrophobia was associated with avoidance of open arms with mesh-floor texture, whereas for trait anxiety, claustrophobia, and sensation seeking, no effect was detected. Also, subjects’ fear rating was moderated by all psychometrics but trait anxiety. In sum, both studies consistently indicate that humans show no general open arm avoidance analogous to rodents and that human EPM behavior is modulated strongest by acrophobic fear, whereas trait anxiety plays a subordinate role. Thus, we conclude that the criteria for cross-species validity are met insufficiently in this case. Despite the exploratory nature, our studies provide in-depth insights into human exploration behavior on the virtual EPM.
Constraining graph layouts - that is, restricting the placement of vertices and the routing of edges to obey certain constraints - is common practice in graph drawing.
In this book, we discuss algorithmic results on two different restriction types:
placing vertices on the outer face and on the integer grid.
For the first type, we look into the outer k-planar and outer k-quasi-planar graphs, as well as giving a linear-time algorithm to recognize full and closed outer k-planar graphs Monadic Second-order Logic.
For the second type, we consider the problem of transferring a given planar drawing onto the integer grid while perserving the original drawings topology;
we also generalize a variant of Cauchy's rigidity theorem for orthogonal polyhedra of genus 0 to those of arbitrary genus.
This article presents an immersive virtual reality (VR) system for training classroom management skills, with a specific focus on learning to manage disruptive student behavior in face-to-face, one-to-many teaching scenarios. The core of the system is a real-time 3D virtual simulation of a classroom populated by twenty-four semi-autonomous virtual students. The system has been designed as a companion tool for classroom management seminars in a syllabus for primary and secondary school teachers. This will allow lecturers to link theory with practice using the medium of VR. The system is therefore designed for two users: a trainee teacher and an instructor supervising the training session. The teacher is immersed in a real-time 3D simulation of a classroom by means of a head-mounted display and headphone. The instructor operates a graphical desktop console, which renders a view of the class and the teacher whose avatar movements are captured by a marker less tracking system. This console includes a 2D graphics menu with convenient behavior and feedback control mechanisms to provide human-guided training sessions. The system is built using low-cost consumer hardware and software. Its architecture and technical design are described in detail. A first evaluation confirms its conformance to critical usability requirements (i.e., safety and comfort, believability, simplicity, acceptability, extensibility, affordability, and mobility). Our initial results are promising and constitute the necessary first step toward a possible investigation of the efficiency and effectiveness of such a system in terms of learning outcomes and experience.
This short letter proposes more consolidated explicit solutions for the forces and torques acting on typical rover wheels, that can be used as a method to determine their average mobility characteristics in planetary soils. The closed loop solutions stand in one of the verified methods, but at difference of the previous, observables are decoupled requiring a less amount of physical parameters to measure. As a result, we show that with knowledge of terrain properties, wheel driving performance rely in a single observable only. Because of their generality, the formulated equations established here can have further implications in autonomy and control of rovers or planetary soil characterization.
Around 4.9 billion Internet users worldwide watch billions of hours of online video every day. As a result, streaming is by far the predominant type of traffic in communication networks. According to Google statistics, three out of five video views come from mobile devices. Thus, in view of the continuous technological advances in end devices and increasing mobile use, datasets for mobile streaming are indispensable in research but only sparsely dealt with in literature so far. With this public dataset, we provide 1,081 hours of time-synchronous video measurements at network, transport, and application layer with the native YouTube streaming client on mobile devices. The dataset includes 80 network scenarios with 171 different individual bandwidth settings measured in 5,181 runs with limited bandwidth, 1,939 runs with emulated 3 G/4 G traces, and 4,022 runs with pre-defined bandwidth changes. This corresponds to 332 GB video payload. We present the most relevant quality indicators for scientific use, i.e., initial playback delay, streaming video quality, adaptive video quality changes, video rebuffering events, and streaming phases.
In this work, we describe the network from data collection to data processing and storage as a system based on different layers. We outline the different layers and highlight major tasks and dependencies with regard to energy consumption and energy efficiency. With this view, we can outwork challenges and questions a future system architect must answer to provide a more sustainable, green, resource friendly, and energy efficient application or system. Therefore, all system layers must be considered individually but also altogether for future IoT solutions. This requires, in particular, novel sustainability metrics in addition to current Quality of Service and Quality of Experience metrics to provide a high power, user satisfying, and sustainable network.
Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming
(2021)
Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance.
Towards LoRaWAN without data loss: studying the performance of different channel access approaches
(2022)
The Long Range Wide Area Network (LoRaWAN) is one of the fastest growing Internet of Things (IoT) access protocols. It operates in the license free 868 MHz band and gives everyone the possibility to create their own small sensor networks. The drawback of this technology is often unscheduled or random channel access, which leads to message collisions and potential data loss. For that reason, recent literature studies alternative approaches for LoRaWAN channel access. In this work, state-of-the-art random channel access is compared with alternative approaches from the literature by means of collision probability. Furthermore, a time scheduled channel access methodology is presented to completely avoid collisions in LoRaWAN. For this approach, an exhaustive simulation study was conducted and the performance was evaluated with random access cross-traffic. In a general theoretical analysis the limits of the time scheduled approach are discussed to comply with duty cycle regulations in LoRaWAN.
LoRaWAN Network Planning in Smart Environments: Towards Reliability, Scalability, and Cost Reduction
(2022)
The goal in this work is to present a guidance for LoRaWAN planning to improve overall reliability for message transmissions and scalability. At the end, the cost component is discussed. Therefore, a five step approach is presented that helps to plan a LoRaWAN deployment step by step: Based on the device locations, an initial gateway placement is suggested followed by in-depth frequency and channel access planning. After an initial planning phase, updates for channel access and the initial gateway planning is suggested that should also be done periodically during network operation. Since current gateway placement approaches are only studied with random channel access, there is a lot of potential in the cell planning phase. Furthermore, the performance of different channel access approaches is highly related on network load, and thus cell size and sensor density. Last, the influence of different cell planning ideas on expected costs are discussed.
Background: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented “A Rule-based Information Extraction System” (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM. Results: Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98. Conclusions: Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice.
Mobile 3D fluoroscopes have become increasingly available in neurosurgical operating rooms. We recently reported its use for imaging cerebral vascular malformations and aneurysms. This study was conducted to evaluate various radiation settings for the imaging of cerebral aneurysms before and after surgical occlusion. Eighteen patients with cerebral aneurysms with the indication for surgical clipping were included in this prospective analysis. Before surgery the patients were randomized into one of three different scan protocols according (default settings of the 3D fluoroscope): Group 1: 110 kV, 80 mA (enhanced cranial mode), group 2: 120 kV, 64 mA (lumbar spine mode), group 3: 120 kV, 25 mA (head/neck settings). Prior to surgery, a rotational fluoroscopy scan (duration 24 s) was performed without contrast agent followed by another scan with 50 ml of intravenous iodine contrast agent. The image files of both scans were transferred to an Apple PowerMac(R) workstation, subtracted and reconstructed using OsiriX(R) MD 10.0 software. The procedure was repeated after clip placement. The image quality regarding preoperative aneurysm configuration and postoperative assessment of aneurysm occlusion and vessel patency was analyzed by 2 independent reviewers using a 6-grade scale. This technique quickly supplies images of adequate quality to depict intracranial aneurysms and distal vessel patency after aneurysm clipping. Regarding these features, a further optimization to our previous protocol seems possible lowering the voltage and increasing tube current. For quick intraoperative assessment, image subtraction seems not necessary. Thus, a native scan without a contrast agent is not necessary. Further optimization may be possible using a different contrast injection protocol.
Background: Due to the importance of radiologic examinations, such as X-rays or computed tomography scans, for many clinical diagnoses, the optimal use of the radiology department is 1 of the primary goals of many hospitals.
Objective: This study aims to calculate the key metrics of this use by creating a radiology data warehouse solution, where data from radiology information systems (RISs) can be imported and then queried using a query language as well as a graphical user interface (GUI).
Methods: Using a simple configuration file, the developed system allowed for the processing of radiology data exported from any kind of RIS into a Microsoft Excel, comma-separated value (CSV), or JavaScript Object Notation (JSON) file. These data were then imported into a clinical data warehouse. Additional values based on the radiology data were calculated during this import process by implementing 1 of several provided interfaces. Afterward, the query language and GUI of the data warehouse were used to configure and calculate reports on these data. For the most common types of requested reports, a web interface was created to view their numbers as graphics.
Results: The tool was successfully tested with the data of 4 different German hospitals from 2018 to 2021, with a total of 1,436,111 examinations. The user feedback was good, since all their queries could be answered if the available data were sufficient. The initial processing of the radiology data for using them with the clinical data warehouse took (depending on the amount of data provided by each hospital) between 7 minutes and 1 hour 11 minutes. Calculating 3 reports of different complexities on the data of each hospital was possible in 1-3 seconds for reports with up to 200 individual calculations and in up to 1.5 minutes for reports with up to 8200 individual calculations.
Conclusions: A system was developed with the main advantage of being generic concerning the export of different RISs as well as concerning the configuration of queries for various reports. The queries could be configured easily using the GUI of the data warehouse, and their results could be exported into the standard formats Excel and CSV for further processing.
Global Navigation Satellite System (GNSS) provides accurate positioning data for vehicular navigation in open outdoor environment. In an indoor environment, Light Detection and Ranging (LIDAR) Simultaneous Localization and Mapping (SLAM) establishes a two-dimensional map and provides positioning data. However, LIDAR can only provide relative positioning data and it cannot directly provide the latitude and longitude of the current position. As a consequence, GNSS/Inertial Navigation System (INS) integrated navigation could be employed in outdoors, while the indoors part makes use of INS/LIDAR integrated navigation and the corresponding switching navigation will make the indoor and outdoor positioning consistent. In addition, when the vehicle enters the garage, the GNSS signal will be blurred for a while and then disappeared. Ambiguous GNSS satellite signals will lead to the continuous distortion or overall drift of the positioning trajectory in the indoor condition. Therefore, an INS/LIDAR seamless integrated navigation algorithm and a switching algorithm based on vehicle navigation system are designed. According to the experimental data, the positioning accuracy of the INS/LIDAR navigation algorithm in the simulated environmental experiment is 50% higher than that of the Dead Reckoning (DR) algorithm. Besides, the switching algorithm developed based on the INS/LIDAR integrated navigation algorithm can achieve 80% success rate in navigation mode switching.
Tactile Internet aims at allowing perceived real-time interactions between humans and machines. This requires satisfying a stringent latency requirement of haptic data streams whose data rates vary drastically as the results of perceptual codecs. This introduces a complex problem for the underlying network infrastructure to fulfill the pre-defined level of Quality of Service (QoS). However, novel networking hardware with data plane programming capability allows processing packets differently and opens up a new opportunity. For example, a dynamic and network-aware resource management strategy can help satisfy the QoS requirements of different priority flows without wasting precious bandwidth. This paper introduces virtual queues for service differentiation between different types of traffic streams, leveraging protocol independent switch architecture (PISA). We propose coordinating the management of all the queues and dynamically adapting their sizes to minimize packet loss and delay due to network congestion and ensure QoS compliance.
In the last decades, the classical Vehicle Routing Problem (VRP), i.e., assigning a set of orders to vehicles and planning their routes has been intensively researched. As only the assignment of order to vehicles and their routes is already an NP-complete problem, the application of these algorithms in practice often fails to take into account the constraints and restrictions that apply in real-world applications, the so called rich VRP (rVRP) and are limited to single aspects. In this work, we incorporate the main relevant real-world constraints and requirements. We propose a two-stage strategy and a Timeline algorithm for time windows and pause times, and apply a Genetic Algorithm (GA) and Ant Colony Optimization (ACO) individually to the problem to find optimal solutions. Our evaluation of eight different problem instances against four state-of-the-art algorithms shows that our approach handles all given constraints in a reasonable time.
The work presents a performance evaluation and optimization of so-called overlay networks for content distribution in the Internet. Chapter 1 describes the importance which have such networks in today's Internet, for example, for the transmission of video content. The focus of this work is on overlay networks based on the peer-to-peer principle. These are characterized by the fact that users who download content, also contribute to the distribution process by sharing parts of the data to other users. This enables efficient content distribution because each user not only consumes resources in the system, but also provides its own resources. Chapter 2 of the monograph contains a detailed description of the functionality of today's most popular overlay network BitTorrent. It explains the various components and their interaction. This is followed by an illustration of why such overlay networks for Internet service providers (ISPs) are problematic. The reason lies in the large amount of inter-ISP traffic that is produced by these overlay networks. Since this inter-ISP traffic leads to high costs for ISPs, they try to reduce it by improved mechanisms for overlay networks. One optimization approach is the use of topology awareness within the overlay networks. It provides users of the overlay networks with information about the underlying physical network topology. This allows them to avoid inter-ISP traffic by exchanging data preferrentially with other users that are connected to the same ISP. Another approach to save inter-ISP traffic is caching. In this case the ISP provides additional computers in its network, called caches, which store copies of popular content. The users of this ISP can then obtain such content from the cache. This prevents that the content must be retrieved from locations outside of the ISP's network, and saves costly inter-ISP traffic in this way. In the third chapter of the thesis, the results of a comprehensive measurement study of overlay networks, which can be found in today's Internet, are presented. After a short description of the measurement methodology, the results of the measurements are described. These results contain data on a variety of characteristics of current P2P overlay networks in the Internet. These include the popularity of content, i.e., how many users are interested in specific content, the evolution of the popularity and the size of the files. The distribution of users within the Internet is investigated in detail. Special attention is given to the number of users that exchange a particular file within the same ISP. On the basis of these measurement results, an estimation of the traffic savings that can achieved by topology awareness is derived. This new estimation is of scientific and practical importance, since it is not limited to individual ISPs and files, but considers the whole Internet and the total amount of data exchanged in overlay networks. Finally, the characteristics of regional content are considered, in which the popularity is limited to certain parts of the Internet. This is for example the case of videos in German, Italian or French language. Chapter 4 of the thesis is devoted to the optimization of overlay networks for content distribution through caching. It presents a deterministic flow model that describes the influence of caches. On the basis of this model, it derives an estimate of the inter-ISP traffic that is generated by an overlay network, and which part can be saved by caches. The results show that the influence of the cache depends on the structure of the overlay networks, and that caches can also lead to an increase in inter-ISP traffic under certain circumstances. The described model is thus an important tool for ISPs to decide for which overlay networks caches are useful and to dimension them. Chapter 5 summarizes the content of the work and emphasizes the importance of the findings. In addition, it explains how the findings can be applied to the optimization of future overlay networks. Special attention is given to the growing importance of video-on-demand and real-time video transmissions.
Today’s advanced Internet-of-Things applications raise technical challenges on cloud, edge, and fog computing. The design of an efficient, virtualized, context-aware, self-configuring orchestration system of a fog computing system constitutes a major development effort within this very innovative area of research. In this paper we describe the architecture and relevant implementation aspects of a cloudless resource monitoring system interworking with an SDN/NFV infrastructure. It realizes the basic monitoring component of the fundamental MAPE-K principles employed in autonomic computing. Here we present the hierarchical layering and functionality within the underlying fog nodes to generate a working prototype of an intelligent, self-managed orchestrator for advanced IoT applications and services. The latter system has the capability to monitor automatically various performance aspects of the resource allocation among multiple hosts of a fog computing system interconnected by SDN.
Presence is often considered the most important quale describing the subjective feeling of being in a computer-generated and/or computer-mediated virtual environment. The identification and separation of orthogonal presence components, i.e., the place illusion and the plausibility illusion, has been an accepted theoretical model describing Virtual Reality (VR) experiences for some time. This perspective article challenges this presence-oriented VR theory. First, we argue that a place illusion cannot be the major construct to describe the much wider scope of virtual, augmented, and mixed reality (VR, AR, MR: or XR for short). Second, we argue that there is no plausibility illusion but merely plausibility, and we derive the place illusion caused by the congruent and plausible generation of spatial cues and similarly for all the current model’s so-defined illusions. Finally, we propose congruence and plausibility to become the central essential conditions in a novel theoretical model describing XR experiences and effects.
This paper examines the relationship between time and motion perception in virtual environments. Previous work has shown that the perception of motion can affect the perception of time. We developed a virtual environment that simulates motion in a tunnel and measured its effects on the estimation of the duration of time, the speed at which perceived time passes, and the illusion of self-motion, also known as vection. When large areas of the visual field move in the same direction, vection can occur; observers often perceive this as self-motion rather than motion of the environment. To generate different levels of vection and investigate its effects on time perception, we developed an abstract procedural tunnel generator. The generator can simulate different speeds and densities of tunnel sections (visibly distinguishable sections that form the virtual tunnel), as well as the degree of embodiment of the user avatar (with or without virtual hands). We exposed participants to various tunnel simulations with different durations, speeds, and densities in a remote desktop and a virtual reality (VR) laboratory study. Time passed subjectively faster under high-speed and high-density conditions in both studies. The experience of self-motion was also stronger under high-speed and high-density conditions. Both studies revealed a significant correlation between the perceived passage of time and perceived self-motion. Subjects in the virtual reality study reported a stronger self-motion experience, a faster perceived passage of time, and shorter time estimates than subjects in the desktop study. Our results suggest that a virtual tunnel simulation can manipulate time perception in virtual reality. We will explore these results for the development of virtual reality applications for therapeutic approaches in our future work. This could be particularly useful in treating disorders like depression, autism, and schizophrenia, which are known to be associated with distortions in time perception. For example, the tunnel could be therapeutically applied by resetting patients’ time perceptions by exposing them to the tunnel under different conditions, such as increasing or decreasing perceived time.
The drug-minded protein interaction database (DrumPID) has been designed to provide fast, tailored information on drugs and their protein networks including indications, protein targets and side-targets. Starting queries include compound, target and protein interactions and organism-specific protein families. Furthermore, drug name, chemical structures and their SMILES notation, affected proteins (potential drug targets), organisms as well as diseases can be queried including various combinations and refinement of searches. Drugs and protein interactions are analyzed in detail with reference to protein structures and catalytic domains, related compound structures as well as potential targets in other organisms. DrumPID considers drug functionality, compound similarity, target structure, interactome analysis and organismic range for a compound, useful for drug development, predicting drug side-effects and structure–activity relationships.
Introduction The fast, precise, and accurate measurement of the new generation of oral anticoagulants such as dabigatran and rivaroxaban in patients' plasma my provide important information in different clinical circumstances such as in the case of suspicion of overdose, when patients switch from existing oral anticoagulant, in patients with hepatic or renal impairment, by concomitant use of interaction drugs, or to assess anticoagulant concentration in patients' blood before major surgery. Methods Here, we describe a quick and precise method to measure the coagulation inhibitors dabigatran and rivaroxaban using ultra-performance liquid chromatography electrospray ionization-tandem mass spectrometry in multiple reactions monitoring (MRM) mode (UPLC-MRM MS). Internal standards (ISs) were added to the sample and after protein precipitation; the sample was separated on a reverse phase column. After ionization of the analytes the ions were detected using electrospray ionization-tandem mass spectrometry. Run time was 2.5 minutes per injection. Ion suppression was characterized by means of post-column infusion. Results The calibration curves of dabigatran and rivaroxaban were linear over the working range between 0.8 and 800 mu g/L (r > 0.99). Limits of detection (LOD) in the plasma matrix were 0.21 mu g/L for dabigatran and 0.34 mu g/L for rivaroxaban, and lower limits of quantification (LLOQ) in the plasma matrix were 0.46 mu g/L for dabigatran and 0.54 mu g/L for rivaroxaban. The intraassay coefficients of variation (CVs) for dabigatran and rivaroxaban were < 4% and 6%; respectively, the interassay CVs were < 6% for dabigatran and < 9% for rivaroxaban. Inaccuracy was < 5% for both substances. The mean recovery was 104.5% (range 83.8-113.0%) for dabigatran and 87.0%(range 73.6-105.4%) for rivaroxaban. No significant ion suppressions were detected at the elution times of dabigatran or rivaroxaban. Both coagulation inhibitors were stable in citrate plasma at -20 degrees C, 4 degrees C and even at RT for at least one week. A method comparison between our UPLC-MRM MS method, the commercially available automated Direct Thrombin Inhibitor assay (DTI assay) for dabigatran measurement from CoaChrom Diagnostica, as well as the automated anti-Xa assay for rivaroxaban measurement from Chromogenix both performed by ACL-TOP showed a high degree of correlation. However, UPLC-MRM MS measurement of dabigatran and rivaroxaban has a much better selectivity than classical functional assays measuring activities of various coagulation factors which are susceptible to interference by other coagulant drugs. Conclusions Overall, we developed and validated a sensitive and specific UPLC-MRM MS assay for the quick and specific measurement of dabigatran and rivaroxaban in human plasma.
The joint 1st Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC 2019) and Workshop on Self-Aware Computing (SeAC) was held as part of the FAS* conference alliance in conjunction with the 16th IEEE International Conference on Autonomic Computing (ICAC) and the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) in Umeå, Sweden on 20 June 2019. The goal of this one-day workshop was to bring together researchers and practitioners from academic environments and from the industry to share their solutions, ideas, visions, and doubts in self-aware computing systems in general and in the evaluation and measurements of such systems in particular. The workshop aimed to enable discussions, partnerships, and collaborations among the participants. This special issue follows the theme of the workshop. It contains extended versions of workshop presentations as well as additional contributions.
Two-component systems (TCS) are short signalling pathways generally occurring in prokaryotes. They frequently regulate prokaryotic stimulus responses and thus are also of interest for engineering in biotechnology and synthetic biology. The aim of this study is to better understand and describe rewiring of TCS while investigating different evolutionary scenarios. Based on large-scale screens of TCS in different organisms, this study gives detailed data, concrete alignments, and structure analysis on three general modification scenarios, where TCS were rewired for new responses and functions: (i) exchanges in the sequence within single TCS domains, (ii) exchange of whole TCS domains; (iii) addition of new components modulating TCS function. As a result, the replacement of stimulus and promotor cassettes to rewire TCS is well defined exploiting the alignments given here. The diverged TCS examples are non-trivial and the design is challenging. Designed connector proteins may also be useful to modify TCS in selected cases.
Background
Machine learning, especially deep learning, is becoming more and more relevant in research and development in the medical domain. For all the supervised deep learning applications, data is the most critical factor in securing successful implementation and sustaining the progress of the machine learning model. Especially gastroenterological data, which often involves endoscopic videos, are cumbersome to annotate. Domain experts are needed to interpret and annotate the videos. To support those domain experts, we generated a framework. With this framework, instead of annotating every frame in the video sequence, experts are just performing key annotations at the beginning and the end of sequences with pathologies, e.g., visible polyps. Subsequently, non-expert annotators supported by machine learning add the missing annotations for the frames in-between.
Methods
In our framework, an expert reviews the video and annotates a few video frames to verify the object’s annotations for the non-expert. In a second step, a non-expert has visual confirmation of the given object and can annotate all following and preceding frames with AI assistance. After the expert has finished, relevant frames will be selected and passed on to an AI model. This information allows the AI model to detect and mark the desired object on all following and preceding frames with an annotation. Therefore, the non-expert can adjust and modify the AI predictions and export the results, which can then be used to train the AI model.
Results
Using this framework, we were able to reduce workload of domain experts on average by a factor of 20 on our data. This is primarily due to the structure of the framework, which is designed to minimize the workload of the domain expert. Pairing this framework with a state-of-the-art semi-automated AI model enhances the annotation speed further. Through a prospective study with 10 participants, we show that semi-automated annotation using our tool doubles the annotation speed of non-expert annotators compared to a well-known state-of-the-art annotation tool.
Conclusion
In summary, we introduce a framework for fast expert annotation for gastroenterologists, which reduces the workload of the domain expert considerably while maintaining a very high annotation quality. The framework incorporates a semi-automated annotation system utilizing trained object detection models. The software and framework are open-source.
Background
Colorectal cancer is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is a colonoscopy. However, not all colon polyps have the risk of becoming cancerous. Therefore, polyps are classified using different classification systems. After the classification, further treatment and procedures are based on the classification of the polyp. Nevertheless, classification is not easy. Therefore, we suggest two novel automated classifications system assisting gastroenterologists in classifying polyps based on the NICE and Paris classification.
Methods
We build two classification systems. One is classifying polyps based on their shape (Paris). The other classifies polyps based on their texture and surface patterns (NICE). A two-step process for the Paris classification is introduced: First, detecting and cropping the polyp on the image, and secondly, classifying the polyp based on the cropped area with a transformer network. For the NICE classification, we design a few-shot learning algorithm based on the Deep Metric Learning approach. The algorithm creates an embedding space for polyps, which allows classification from a few examples to account for the data scarcity of NICE annotated images in our database.
Results
For the Paris classification, we achieve an accuracy of 89.35 %, surpassing all papers in the literature and establishing a new state-of-the-art and baseline accuracy for other publications on a public data set. For the NICE classification, we achieve a competitive accuracy of 81.13 % and demonstrate thereby the viability of the few-shot learning paradigm in polyp classification in data-scarce environments. Additionally, we show different ablations of the algorithms. Finally, we further elaborate on the explainability of the system by showing heat maps of the neural network explaining neural activations.
Conclusion
Overall we introduce two polyp classification systems to assist gastroenterologists. We achieve state-of-the-art performance in the Paris classification and demonstrate the viability of the few-shot learning paradigm in the NICE classification, addressing the prevalent data scarcity issues faced in medical machine learning.
Deep learning enables enormous progress in many computer vision-related tasks. Artificial Intel- ligence (AI) steadily yields new state-of-the-art results in the field of detection and classification. Thereby AI performance equals or exceeds human performance. Those achievements impacted many domains, including medical applications.
One particular field of medical applications is gastroenterology. In gastroenterology, machine learning algorithms are used to assist examiners during interventions. One of the most critical concerns for gastroenterologists is the development of Colorectal Cancer (CRC), which is one of the leading causes of cancer-related deaths worldwide. Detecting polyps in screening colonoscopies is the essential procedure to prevent CRC. Thereby, the gastroenterologist uses an endoscope to screen the whole colon to find polyps during a colonoscopy. Polyps are mucosal growths that can vary in severity.
This thesis supports gastroenterologists in their examinations with automated detection and clas- sification systems for polyps. The main contribution is a real-time polyp detection system. This system is ready to be installed in any gastroenterology practice worldwide using open-source soft- ware. The system achieves state-of-the-art detection results and is currently evaluated in a clinical trial in four different centers in Germany.
The thesis presents two additional key contributions: One is a polyp detection system with ex- tended vision tested in an animal trial. Polyps often hide behind folds or in uninvestigated areas. Therefore, the polyp detection system with extended vision uses an endoscope assisted by two additional cameras to see behind those folds. If a polyp is detected, the endoscopist receives a vi- sual signal. While the detection system handles the additional two camera inputs, the endoscopist focuses on the main camera as usual.
The second one are two polyp classification models, one for the classification based on shape (Paris) and the other on surface and texture (NBI International Colorectal Endoscopic (NICE) classification). Both classifications help the endoscopist with the treatment of and the decisions about the detected polyp.
The key algorithms of the thesis achieve state-of-the-art performance. Outstandingly, the polyp detection system tested on a highly demanding video data set shows an F1 score of 90.25 % while working in real-time. The results exceed all real-time systems in the literature. Furthermore, the first preliminary results of the clinical trial of the polyp detection system suggest a high Adenoma Detection Rate (ADR). In the preliminary study, all polyps were detected by the polyp detection system, and the system achieved a high usability score of 96.3 (max 100). The Paris classification model achieved an F1 score of 89.35 % which is state-of-the-art. The NICE classification model achieved an F1 score of 81.13 %.
Furthermore, a large data set for polyp detection and classification was created during this thesis. Therefore a fast and robust annotation system called Fast Colonoscopy Annotation Tool (FastCAT) was developed. The system simplifies the annotation process for gastroenterologists. Thereby the
i
gastroenterologists only annotate key parts of the endoscopic video. Afterward, those video parts are pre-labeled by a polyp detection AI to speed up the process. After the AI has pre-labeled the frames, non-experts correct and finish the annotation. This annotation process is fast and ensures high quality. FastCAT reduces the overall workload of the gastroenterologist on average by a factor of 20 compared to an open-source state-of-art annotation tool.
The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users' individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.
Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case.
This technical report introduces the Descartes Modeling Language (DML), a new architecture-level modeling language for modeling Quality-of-Service (QoS) and resource management related aspects of modern dynamic IT systems, infrastructures and services. DML is designed to serve as a basis for self-aware resource management during operation ensuring that system QoS requirements are continuously satisfied while infrastructure resources are utilized as efficiently as possible.
Complexity and Partitions
(2001)
Computational complexity theory usually investigates the complexity of sets, i.e., the complexity of partitions into two parts. But often it is more appropriate to represent natural problems by partitions into more than two parts. A particularly interesting class of such problems consists of classification problems for relations. For instance, a binary relation R typically defines a partitioning of the set of all pairs (x,y) into four parts, classifiable according to the cases where R(x,y) and R(y,x) hold, only R(x,y) or only R(y,x) holds or even neither R(x,y) nor R(y,x) is true. By means of concrete classification problems such as Graph Embedding or Entailment (for propositional logic), this thesis systematically develops tools, in shape of the boolean hierarchy of NP-partitions and its refinements, for the qualitative analysis of the complexity of partitions generated by NP-relations. The Boolean hierarchy of NP-partitions is introduced as a generalization of the well-known and well-studied Boolean hierarchy (of sets) over NP. Whereas the latter hierarchy has a very simple structure, the situation is much more complicated for the case of partitions into at least three parts. To get an idea of this hierarchy, alternative descriptions of the partition classes are given in terms of finite, labeled lattices. Based on these characterizations the Embedding Conjecture is established providing the complete information on the structure of the hierarchy. This conjecture is supported by several results. A natural extension of the Boolean hierarchy of NP-partitions emerges from the lattice-characterization of its classes by considering partition classes generated by finite, labeled posets. It turns out that all significant ideas translate from the case of lattices. The induced refined Boolean hierarchy of NP-partitions enables us more accuratly capturing the complexity of certain relations (such as Graph Embedding) and a description of projectively closed partition classes.
Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research
(2021)
Creation and exchange of knowledge depends on collaboration. Recent work has suggested that the emergence of collaboration frequently relies on geographic proximity. However, being co-located tends to be associated with other dimensions of proximity, such as social ties or a shared organizational environment. To account for such factors, multiple dimensions of proximity have been proposed, including cognitive, institutional, organizational, social and geographical proximity. Since they strongly interrelate, disentangling these dimensions and their respective impact on collaboration is challenging. To address this issue, we propose various methods for measuring different dimensions of proximity. We then present an approach to compare and rank them with respect to the extent to which they indicate co-publications and co-inventions. We adapt the HypTrails approach, which was originally developed to explain human navigation, to co-author and co-inventor graphs. We evaluate this approach on a subset of the German research community, specifically academic authors and inventors active in research on artificial intelligence (AI). We find that social proximity and cognitive proximity are more important for the emergence of collaboration than geographic proximity.
A bipartite graph G=(U,V,E) is convex if the vertices in V can be linearly ordered such that for each vertex u∈U, the neighbors of u are consecutive in the ordering of V. An induced matching H of G is a matching for which no edge of E connects endpoints of two different edges of H. We show that in a convex bipartite graph with n vertices and m weighted edges, an induced matching of maximum total weight can be computed in O(n+m) time. An unweighted convex bipartite graph has a representation of size O(n) that records for each vertex u∈U the first and last neighbor in the ordering of V. Given such a compact representation, we compute an induced matching of maximum cardinality in O(n) time. In convex bipartite graphs, maximum-cardinality induced matchings are dual to minimum chain covers. A chain cover is a covering of the edge set by chain subgraphs, that is, subgraphs that do not contain induced matchings of more than one edge. Given a compact representation, we compute a representation of a minimum chain cover in O(n) time. If no compact representation is given, the cover can be computed in O(n+m) time. All of our algorithms achieve optimal linear running time for the respective problem and model, and they improve and generalize the previous results in several ways: The best algorithms for the unweighted problem versions had a running time of O(n\(^{2}\)) (Brandstädt et al. in Theor. Comput. Sci. 381(1–3):260–265, 2007. https://doi.org/10.1016/j.tcs.2007.04.006). The weighted case has not been considered before.
Today’s Internet architecture was not designed from scratch but was driven by new services that emerged during its development. Hence, it is often described as patchwork where additional patches are applied in case new services require modifications to the existing architecture. This process however is rather slow and hinders the development of innovative network services with certain architecture or network requirements. Currently discussed technologies like Software-Defined Networking (SDN) or Network Virtualization (NV) are seen as key enabling technologies to overcome this rigid best effort legacy of the Internet. Both technologies offer the possibility to create virtual networks that accommodate the specific needs of certain services. These logical networks are operated on top of a physical substrate and facilitate flexible network resource allocation as physical resources can be added and removed depending on the current network and load situation. In addition, the clear separation and isolation of networks foster the development of application-aware networks that fulfill the special requirements of emerging applications. A prominent use case that benefits from these extended capabilities of the network is denoted with service component mobility. Services hosted on Virtual Machines (VMs) follow their consuming mobile endpoints, so that access latency as well as consumed network resources are reduced. Especially for applications like video streaming, which consume a large fraction of the available resources, is this an important means to relieve the resource constraints and eventually provide better service quality. Service and endpoint mobility both allow an adaptation of the used paths between an offered service, i.e., video streaming and the consuming users in case the service quality drops due to network problems. To make evidence-based adaptations in case of quality drops, a scalable monitoring component is required that is able to monitor the service quality for video streaming applications with reliable accuracy. This monograph details challenges that arise when deploying a certain service, i.e., video streaming, in a future virtualized network architecture and discusses possible solutions. In particular, this work evaluates the performance of mechanisms enabling service mobility and presents an optimized architecture for service mobility. Concerning endpoint mobility, improvements are developed that reduce the latency between endpoints and consumed services and ensure connectivity regardless of the used mobile access network. In the last part, a network-based video quality monitoring solution is developed and its accuracy is evaluated.
The focus of this work lies on the communication issues of Medium Access Control (MAC) and routing protocols in the context of WSNs. The communication challenges in these networks mainly result from high node density, low bandwidth, low energy constraints and the hardware limitations in terms of memory, computational power and sensing capabilities of low-power transceivers. For this reason, the structure of WSNs is always kept as simple as possible to minimize the impact of communication issues. Thus, the majority of WSNs apply a simple one hop star topology since multi-hop communication has high demands on the routing protocol since it increases the bandwidth requirements of the network. Moreover, medium access becomes a challenging problem due to the fact that low-power transceivers are very limited in their sensing capabilities. The first contribution is represented by the Backoff Preamble-based MAC Protocol with Sequential Contention Resolution (BPS-MAC) which is designed to overcome the limitations of low-power transceivers. Two communication issues, namely the Clear Channel Assessment (CCA) delay and the turnaround time, are directly addressed by the protocol. The CCA delay represents the period of time which is required by the transceiver to detect a busy radio channel while the turnaround time specifies the period of time which is required to switch between receive and transmit mode. Standard Carrier Sense Multiple Access (CSMA) protocols do not achieve high performance in terms of packet loss if the traffic is highly correlated due to the fact that the transceiver is not able to sense the medium during the switching phase. Therefore, a node may start to transmit data while another node is already transmitting since it has sensed an idle medium right before it started to switch its transceiver from receive to transmit mode. The BPS-MAC protocol uses a new sequential preamble-based medium access strategy which can be adapted to the hardware capabilities of the transceivers. The protocol achieves a very low packet loss rate even in wireless networks with high node density and event-driven traffic without the need of synchronization. This makes the protocol attractive to applications such as structural health monitoring, where event suppression is not an option. Moreover, acknowledgments or complex retransmission strategies become almost unnecessary since the sequential preamble-based contention resolution mechanism minimizes the collision probability. However, packets can still be lost as a consequence of interference or other issues which affect signal propagation. The second contribution consists of a new routing protocol which is able to quickly detect topology changes without generating a large amount of overhead. The key characteristics of the Statistic-Based Routing (SBR) protocol are high end-to-end reliability (in fixed and mobile networks), load balancing capabilities, a smooth continuous routing metric, quick adaptation to changing network conditions, low processing and memory requirements, low overhead, support of unidirectional links and simplicity. The protocol can establish routes in a hybrid or a proactive mode and uses an adaptive continuous routing metric which makes it very flexible in terms of scalability while maintaining stable routes. The hybrid mode is optimized for low-power WSNs since routes are only established on demand. The difference of the hybrid mode to reactive routing strategies is that routing messages are periodically transmitted to maintain already established routes. However, the protocol stops the transmission of routing messages if no data packets are transmitted for a certain time period in order to minimize the routing overhead and the energy consumption. The proactive mode is designed for high data rate networks which have less energy constraints. In this mode, the protocol periodically transmits routing messages to establish routes in a proactive way even in the absence of data traffic. Thus, nodes in the network can immediately transmit data since the route to the destination is already established in advance. In addition, a new delay-based routing message forwarding strategy is introduced. The forwarding strategy is part of SBR but can also be applied to many routing protocols in order to modify the established topology. The strategy can be used, e.g. in mobile networks, to decrease the packet loss by deferring routing messages with respect to the neighbor change rate. Thus, nodes with a stable neighborhood forward messages faster than nodes within a fast changing neighborhood. As a result, routes are established through nodes with correlated movement which results in fewer topology changes due to higher link durations.
The Internet of Things (IoT) enables a variety of smart applications, including smart home, smart manufacturing, and smart city. By enhancing Business Process Management Systems with IoT capabilities, the execution and monitoring of business processes can be significantly improved. Providing a holistic support for modeling, executing and monitoring IoT-driven processes, however, constitutes a challenge. Existing process modeling and process execution languages, such as BPMN 2.0, are unable to fully meet the IoT characteristics (e.g., asynchronicity and parallelism) of IoT-driven processes. In this article, we present BPMNE4IoT—A holistic framework for modeling, executing and monitoring IoT-driven processes. We introduce various artifacts and events based on the BPMN 2.0 metamodel that allow realizing the desired IoT awareness of business processes. The framework is evaluated along two real-world scenarios from two different domains. Moreover, we present a user study for comparing BPMNE4IoT and BPMN 2.0. In particular, this study has confirmed that the BPMNE4IoT framework facilitates the support of IoT-driven processes.
Graphs are a frequently used tool to model relationships among entities. A graph is a binary relation between objects, that is, it consists of a set of objects (vertices) and a set of pairs of objects (edges).
Networks are common examples of modeling data as a graph. For example, relationships between persons in a social network, or network links between computers in a telecommunication network can be represented by a graph.
The clearest way to illustrate the modeled data is to visualize the graphs. The field of Graph Drawing deals with the problem of finding algorithms to automatically generate graph visualizations. The task is to find a "good" drawing, which can be measured by different criteria such as number of crossings between edges or the used area. In this thesis, we study Angular Schematization in Graph Drawing. By this, we mean drawings
with large angles (for example, between the edges at common vertices or at crossing points).
The thesis consists of three parts. First, we deal with the placement of boxes. Boxes are axis-parallel rectangles that can, for example, contain text.
They can be placed on a map to label important sites, or can be used to describe semantic relationships between words in a word network. In the second part of the thesis, we consider graph drawings visually guide the
viewer. These drawings generally induce large angles between edges that meet at a vertex. Furthermore, the edges are drawn crossing-free and in a way that
makes them easy to follow for the human eye. The third and final part is devoted to crossings with large angles. In drawings with crossings, it is important to have large angles between edges at their crossing point, preferably right angles.
This article introduces the Off-The-Shelf Stylus (OTSS), a framework for 2D interaction (in 3D) as well as for handwriting and sketching with digital pen, ink, and paper on physically aligned virtual surfaces in Virtual, Augmented, and Mixed Reality (VR, AR, MR: XR for short). OTSS supports self-made XR styluses based on consumer-grade six-degrees-of-freedom XR controllers and commercially available styluses. The framework provides separate modules for three basic but vital features: 1) The stylus module provides stylus construction and calibration features. 2) The surface module provides surface calibration and visual feedback features for virtual-physical 2D surface alignment using our so-called 3ViSuAl procedure, and surface interaction features. 3) The evaluation suite provides a comprehensive test bed combining technical measurements for precision, accuracy, and latency with extensive usability evaluations including handwriting and sketching tasks based on established visuomotor, graphomotor, and handwriting research. The framework’s development is accompanied by an extensive open source reference implementation targeting the Unity game engine using an Oculus Rift S headset and Oculus Touch controllers. The development compares three low-cost and low-tech options to equip controllers with a tip and includes a web browser-based surface providing support for interacting, handwriting, and sketching. The evaluation of the reference implementation based on the OTSS framework identified an average stylus precision of 0.98 mm (SD = 0.54 mm) and an average surface accuracy of 0.60 mm (SD = 0.32 mm) in a seated VR environment. The time for displaying the stylus movement as digital ink on the web browser surface in VR was 79.40 ms on average (SD = 23.26 ms), including the physical controller’s motion-to-photon latency visualized by its virtual representation (M = 42.57 ms, SD = 15.70 ms). The usability evaluation (N = 10) revealed a low task load, high usability, and high user experience. Participants successfully reproduced given shapes and created legible handwriting, indicating that the OTSS and it’s reference implementation is ready for everyday use. We provide source code access to our implementation, including stylus and surface calibration and surface interaction features, making it easy to reuse, extend, adapt and/or replicate previous results (https://go.uniwue.de/hci-otss).
An important but very time consuming part of the research process is literature review. An already large and nevertheless growing ground set of publications as well as a steadily increasing publication rate continue to worsen the situation. Consequently, automating this task as far as possible is desirable. Experimental results of systems are key-insights of high importance during literature review and usually represented in form of tables. Our pipeline KIETA exploits these tables to contribute to the endeavor of automation by extracting them and their contained knowledge from scientific publications. The pipeline is split into multiple steps to guarantee modularity as well as analyzability, and agnosticim regarding the specific scientific domain up until the knowledge extraction step, which is based upon an ontology. Additionally, a dataset of corresponding articles has been manually annotated with information regarding table and knowledge extraction. Experiments show promising results that signal the possibility of an automated system, while also indicating limits of extracting knowledge from tables without any context.
Background: Boolean networks capture switching behavior of many naturally occurring regulatory networks. For semi-quantitative modeling, interpolation between ON and OFF states is necessary. The high degree polynomial interpolation of Boolean genetic regulatory networks (GRNs) in cellular processes such as apoptosis or proliferation allows for the modeling of a wider range of node interactions than continuous activator-inhibitor models, but suffers from scaling problems for networks which contain nodes with more than ~10 inputs. Many GRNs from literature or new gene expression experiments exceed those limitations and a new approach was developed.
Results: (i) As a part of our new GRN simulation framework Jimena we introduce and setup Boolean-tree-based data structures; (ii) corresponding algorithms greatly expedite the calculation of the polynomial interpolation in almost all cases, thereby expanding the range of networks which can be simulated by this model in reasonable time. (iii) Stable states for discrete models are efficiently counted and identified using binary decision diagrams. As application example, we show how system states can now be sampled efficiently in small up to large scale hormone disease networks (Arabidopsis thaliana development and immunity, pathogen Pseudomonas syringae and modulation by cytokinins and plant hormones).
Conclusions: Jimena simulates currently available GRNs about 10-100 times faster than the previous implementation of the polynomial interpolation model and even greater gains are achieved for large scale-free networks. This speed-up also facilitates a much more thorough sampling of continuous state spaces which may lead to the identification of new stable states. Mutants of large networks can be constructed and analyzed very quickly enabling new insights into network robustness and behavior.
Nowadays, robotics plays an important role in increasing fields of application. There exist many environments or situations where mobile robots instead of human beings are used, since the tasks are too hazardous, uncomfortable, repetitive, or costly for humans to perform. The autonomy and the mobility of the robot are often essential for a good solution of these problems. Thus, such a robot should at least be able to answer the question "Where am I?". This thesis investigates the problem of self-localizing a robot in an indoor environment using range measurements. That is, a robot equipped with a range sensor wakes up inside a building and has to determine its position using only its sensor data and a map of its environment. We examine this problem from an idealizing point of view (reducing it into a pure geometric one) and further investigate a method of Guibas, Motwani, and Raghavan from the field of computational geometry to solving it. Here, so-called visibility skeletons, which can be seen as coarsened representations of visibility polygons, play a decisive role. In the major part of this thesis we analyze the structures and the occurring complexities in the framework of this scheme. It turns out that the main source of complication are so-called overlapping embeddings of skeletons into the map polygon, for which we derive some restrictive visibility constraints. Based on these results we are able to improve one of the occurring complexity bounds in the sense that we can formulate it with respect to the number of reflex vertices instead of the total number of map vertices. This also affects the worst-case bound on the preprocessing complexity of the method. The second part of this thesis compares the previous idealizing assumptions with the properties of real-world environments and discusses the occurring problems. In order to circumvent these problems, we use the concept of distance functions, which model the resemblance between the sensor data and the map, and appropriately adapt the above method to the needs of realistic scenarios. In particular, we introduce a distance function, namely the polar coordinate metric, which seems to be well suited to the localization problem. Finally, we present the RoLoPro software where most of the discussed algorithms are implemented (including the polar coordinate metric).
For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance. However, appropriate data sources are needed on which digital services can be technically based. As many powerful and cheap sensors have been introduced over the last years, their integration into complex machines is promising for developing digital services for various scenarios. It is apparent that for components handling recorded data of these sensors they must usually deal with large amounts of data. In particular, the labeling of raw sensor data must be furthered by a technical solution. To deal with these data handling challenges in a generic way, a sensor processing pipeline (SPP) was developed, which provides effective methods to capture, process, store, and visualize raw sensor data based on a processing chain. Based on the example of a machine manufacturing company, the SPP approach is presented in this work. For the company involved, the approach has revealed promising results.
Ambalytics: a scalable and distributed system architecture concept for bibliometric network analyses
(2021)
A deep understanding about a field of research is valuable for academic researchers. In addition to technical knowledge, this includes knowledge about subareas, open research questions, and social communities (networks) of individuals and organizations within a given field. With bibliometric analyses, researchers can acquire quantitatively valuable knowledge about a research area by using bibliographic information on academic publications provided by bibliographic data providers. Bibliometric analyses include the calculation of bibliometric networks to describe affiliations or similarities of bibliometric entities (e.g., authors) and group them into clusters representing subareas or communities. Calculating and visualizing bibliometric networks is a nontrivial and time-consuming data science task that requires highly skilled individuals. In addition to domain knowledge, researchers must often provide statistical knowledge and programming skills or use software tools having limited functionality and usability. In this paper, we present the ambalytics bibliometric platform, which reduces the complexity of bibliometric network analysis and the visualization of results. It accompanies users through the process of bibliometric analysis and eliminates the need for individuals to have programming skills and statistical knowledge, while preserving advanced functionality, such as algorithm parameterization, for experts. As a proof-of-concept, and as an example of bibliometric analyses outcomes, the calculation of research fronts networks based on a hybrid similarity approach is shown. Being designed to scale, ambalytics makes use of distributed systems concepts and technologies. It is based on the microservice architecture concept and uses the Kubernetes framework for orchestration. This paper presents the initial building block of a comprehensive bibliometric analysis platform called ambalytics, which aims at a high usability for users as well as scalability.
Automatic image reconstruction is critical to cope with steadily increasing data from advanced microscopy. We describe here the Fiji macro 3D ART VeSElecT which we developed to study synaptic vesicles in electron tomograms. We apply this tool to quantify vesicle properties (i) in embryonic Danio rerio 4 and 8 days past fertilization (dpf) and (ii) to compare Caenorhabditis elegans N2 neuromuscular junctions (NMJ) wild-type and its septin mutant (unc-59(e261)). We demonstrate development-specific and mutant-specific changes in synaptic vesicle pools in both models. We confirm the functionality of our macro by applying our 3D ART VeSElecT on zebrafish NMJ showing smaller vesicles in 8 dpf embryos then 4 dpf, which was validated by manual reconstruction of the vesicle pool. Furthermore, we analyze the impact of C. elegans septin mutant unc-59(e261) on vesicle pool formation and vesicle size. Automated vesicle registration and characterization was implemented in Fiji as two macros (registration and measurement). This flexible arrangement allows in particular reducing false positives by an optional manual revision step. Preprocessing and contrast enhancement work on image-stacks of 1nm/pixel in x and y direction. Semi-automated cell selection was integrated. 3D ART VeSElecT removes interfering components, detects vesicles by 3D segmentation and calculates vesicle volume and diameter (spherical approximation, inner/outer diameter). Results are collected in color using the RoiManager plugin including the possibility of manual removal of non-matching confounder vesicles. Detailed evaluation considered performance (detected vesicles) and specificity (true vesicles) as well as precision and recall. We furthermore show gain in segmentation and morphological filtering compared to learning based methods and a large time gain compared to manual segmentation. 3D ART VeSElecT shows small error rates and its speed gain can be up to 68 times faster in comparison to manual annotation. Both automatic and semi-automatic modes are explained including a tutorial.