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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Einleitung:
Multiple-Choice-Klausuren spielen immer noch eine herausragende Rolle für fakultätsinterne medizinische Prüfungen. Neben inhaltlichen Arbeiten stellt sich die Frage, wie die technische Abwicklung optimiert werden kann. Für Dozenten in der Medizin gibt es zunehmend drei Optionen zur Durchführung von MC-Klausuren: Papierklausuren mit oder ohne Computerunterstützung oder vollständig elektronische Klausuren. Kritische Faktoren sind der Aufwand für die Formatierung der Klausur, der logistische Aufwand bei der Klausurdurchführung, die Qualität, Schnelligkeit und der Aufwand der Klausurkorrektur, die Bereitstellung der Dokumente für die Einsichtnahme, und die statistische Analyse der Klausurergebnisse.
Methoden:
An der Universität Würzburg wird seit drei Semestern ein Computerprogramm zur Eingabe und Formatierung der MC-Fragen in medizinischen und anderen Papierklausuren verwendet und optimiert, mit dem im Wintersemester (WS) 2009/2010 elf, im Sommersemester (SS) 2010 zwölf und im WS 2010/11 dreizehn medizinische Klausuren erstellt und anschließend die eingescannten Antwortblätter automatisch ausgewertet wurden. In den letzten beiden Semestern wurden die Aufwände protokolliert.
Ergebnisse:
Der Aufwand der Formatierung und der Auswertung einschl. nachträglicher Anpassung der Auswertung einer Durchschnittsklausur mit ca. 140 Teilnehmern und ca. 35 Fragen ist von 5-7 Stunden für Klausuren ohne Komplikation im WS 2009/2010 über ca. 2 Stunden im SS 2010 auf ca. 1,5 Stunden im WS 2010/11 gefallen. Einschließlich der Klausuren mit Komplikationen bei der Auswertung betrug die durchschnittliche Zeit im SS 2010 ca. 3 Stunden und im WS 10/11 ca. 2,67 Stunden pro Klausur.
Diskussion:
Für konventionelle Multiple-Choice-Klausuren bietet die computergestützte Formatierung und Auswertung von Papierklausuren einen beträchtlichen Zeitvorteil für die Dozenten im Vergleich zur manuellen Korrektur von Papierklausuren und benötigt im Vergleich zu rein elektronischen Klausuren eine deutlich einfachere technische Infrastruktur und weniger Personal bei der Klausurdurchführung.
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.
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.
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.
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.
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.
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.
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.
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%.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Die künstliche Intelligenz (KI) entwickelt sich rasant und hat bereits eindrucksvolle Erfolge zu verzeichnen, darunter übermenschliche Kompetenz in den meisten Spielen und vielen Quizshows, intelligente Suchmaschinen, individualisierte Werbung, Spracherkennung, -ausgabe und -übersetzung auf sehr hohem Niveau und hervorragende Leistungen bei der Bildverarbeitung, u. a. in der Medizin, der optischen Zeichenerkennung, beim autonomen Fahren, aber auch beim Erkennen von Menschen auf Bildern und Videos oder bei Deep Fakes für Fotos und Videos. Es ist zu erwarten, dass die KI auch in der Entscheidungsfindung Menschen übertreffen wird; ein alter Traum der Expertensysteme, der durch Lernverfahren, Big Data und Zugang zu dem gesammelten Wissen im Web in greifbare Nähe rückt. Gegenstand dieses Beitrags sind aber weniger die technischen Entwicklungen, sondern mögliche gesellschaftliche Auswirkungen einer spezialisierten, kompetenten KI für verschiedene Bereiche der autonomen, d. h. nicht nur unterstützenden Entscheidungsfindung: als Fußballschiedsrichter, in der Medizin, für richterliche Entscheidungen und sehr spekulativ auch im politischen Bereich. Dabei werden Vor- und Nachteile dieser Szenarien aus gesellschaftlicher Sicht diskutiert.
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.