Kaiserslautern - Fachbereich Informatik
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Distributed message-passing systems have become ubiquitous and essential for our daily lives. Hence, designing and implementing them correctly is of utmost importance. This is, however, very challenging at the same time. In fact, it is well-known that verifying such systems is algorithmically undecidable in general due to the interplay of asynchronous communication (messages are buffered) and concurrency. When designing communication in a system, it is natural to start with a global protocol specification of the desired communication behaviour. In such a top-down approach, the implementability problem asks, given such a global protocol, if the specified behaviour can be implemented in a distributed setting without additional synchronisation. This problem has been studied from two perspectives in the literature. On the one hand, there are Multiparty Session Types (MSTs) from process algebra, with global types to specify protocols. Key to the MST approach is a so-called projection operator, which takes a global type and tries to project it onto every participant: if successful, the local specifications are safe to use. This approach is efficient but brittle. On the other hand, High-level Message Sequence Charts (HMSCs) study the implementability problem from an automata-theoretic perspective. They employ very few restrictions on protocol specifications, making the implementability problem for HMSCs undecidable in general. The work in this thesis is the first to formally build a bridge between the world of MSTs and HMSCs. To start, we present a generalised projection operator for sender-driven choice. This allows a sender to send to different receivers when branching, which is crucial to handle common communication patterns from distributed computing. Despite this first step, we also show that the classical MST projection approach is inherently incomplete. We present the first formal encoding from global types to HMSCs. With this, we prove decidability of the implementability problem for global types with sender-driven choice. Furthermore, we develop the first direct and complete projection operator for global types with sender-driven choice, using automata-theoretic techniques, and show its effectiveness with a prototype implementation. We are the first to provide an upper bound for the implementability problem for global types with sender-driven (or directed) choice and show it to be in PSPACE. We also provide a session type system that uses the results from our projection operator. Last, we introduce protocol state machines (PSMs) – an automata-based protocol specification formalism – that subsume both global types from MSTs and HMSCs with regard to expressivity. We use transformations on PSMs to show that many of the syntactic restrictions of global types are not restrictive in terms of protocol expressivity. We prove that the implementability problem for PSMs with mixed choice, which requires no dedicated sender for a branch but solely all labels to be distinct, is undecidable in general. With our results on expressivity, this answers an open question: the implementability problem for mixed-choice global types is undecidable in general.
Machine learning algorithms are widely applied to create powerful prediction models. With increasingly complex models, humans' ability to understand the decision function (that maps from a high-dimensional input space) is quickly exceeded. To explain a model's decisions, black-box methods have been proposed that provide either non-linear maps of the global topology of the decision boundary, or samples that allow approximating it locally. The former loses information about distances in input space, while the latter only provides statements about given samples, but lacks a focus on the underlying model for precise ‘What-If'-reasoning. In this paper, we integrate both approaches and propose an interactive exploration method using local linear maps of the decision space. We create the maps on high-dimensional hyperplanes—2D-slices of the high-dimensional parameter space—based on statistical and personal feature mutability and guided by feature importance. We complement the proposed workflow with established model inspection techniques to provide orientation and guidance. We demonstrate our approach on real-world datasets and illustrate that it allows identification of instance-based decision boundary structures and can answer multi-dimensional ‘What-If'-questions, thereby identifying counterfactual scenarios visually.
Edit distances between merge trees of scalar fields have many applications in scientific visualization, such as ensemble analysis, feature tracking or symmetry detection. In this paper, we propose branch mappings, a novel approach to the construction of edit mappings for merge trees. Classic edit mappings match nodes or edges of two trees onto each other, and therefore have to either rely on branch decompositions of both trees or have to use auxiliary node properties to determine a matching. In contrast, branch mappings employ branch properties instead of node similarity information, and are independent of predetermined branch decompositions. Especially for topological features, which are typically based on branch properties, this allows a more intuitive distance measure which is also less susceptible to instabilities from small-scale perturbations. For trees with 𝒪(n) nodes, we describe an 𝒪(n4) algorithm for computing optimal branch mappings, which is faster than the only other branch decomposition-independent method in the literature by more than a linear factor. Furthermore, we compare the results of our method on synthetic and real-world examples to demonstrate its practicality and utility.
The development of algorithmic differentiation (AD) tools focuses mostly on handling floating point types in the target language. Taping optimizations in these tools mostly focus on specific operations like matrix vector products. Aggregated types like std::complex are usually handled by specifying the AD type as a template argument. This approach provides exact results, but prevents the use of expression templates. If AD tools are extended and specialized such that aggregated types can be added to the expression framework, then this will result in reduced memory utilization and improve the timing for applications where aggregated types such as complex number or matrix vector operations are used. Such an integration requires a reformulation of the stored data per expression and a rework of the tape evaluation process. We will demonstrate the overheads on a synthetic benchmark and show the improvement when aggregated types are handled properly by the expression framework of the AD tool.
To increase situational awareness of the crane operator, the aim of this thesis is to develop a vision-based deep learning object detection from crane load-view using an adaptive perception in the construction area. Conventional worker detection methods are based on simple shape or color features from the worker's appearances. Nonetheless, these methods can fail to recognize the workers who do not wear the protective gears. To find out an image representation of the object from the top view manually or handcrafted feature is crucial. We, therefore, employed deep learning methods to automatically learn those features.
To yield optimal results, deep learning methods require mass amount of data.
Due to the data deficit especially in the construction domain, we developed the photorealistic world to create the data in addition to our samples collected from the real construction area. The simulated platform does not benefit only from diverse data types, but also concurrent research development which speeds up the pipeline at a low cost.
Our research findings indicate that the combination of synthetic and real training samples improved the state-of-the-art detector. In line with previous studies to bridge the gap between synthetic and real data, the results of preprocessed synthetic images are substantially better than using the raw data by approximately 10%.
Finding the right deep learning model for load-view detection is challenging.
By investigating our training data, it becomes evident that the majority of bounding box sizes are very small with a complex background.
In addition, we gave the priority to speed over accuracy based on the construction safety criteria. Finally, RetinaNet is chosen out of the three primary object detection models.
Nevertheless, the data-driven detection algorithm can fail to handle scale invariance, especially for detectors whose input size is changed in an extremely wide range.
The adaptive zoom feature can enhance the quality of the worker detection.
To avoid further data gathering and extensive retraining, the proposed automatic zoom method of the load-view crane camera supports the deep learning algorithm, specifically in the high scale variant problem. The finite state machine is employed for control strategies to adapt the zoom level to cope not only with inconsistent detection but also abrupt camera movement during lifting operation. Consequently, the detector is able to detect a small size object by smooth continuous zoom control without additional training.
The adaptive zoom control not only enhances the performance of the top-view object detection but also reduces the interaction of the crane operator with camera system, reducing the risk of fatality during load lifting operation.
Turbulence models, which are a means to fix the closure problem arising from Reynolds averaging of Navier-Stokes equations, are economical stop-gaps but suffer from accuracy issues. Modifying turbulence models by incorporating corrections in their functional form is one approach to improve their accuracy. We estimate correction functionals for the Spalart - Allmaras turbulence model, based on an inverse problem with PDE constraints emphasizing the issue of regularization.
Algorithmic decision-making (ADM) systems have come to support, pre-empt or substitute for human decisions in manifold areas, with potentially significant impacts on individuals' lives. Achieving transparency and accountability has been formulated as a general goal regarding the use of these systems. However, concrete applications differ widely in the degree of risk and the accountability problems they entail for data subjects. The present paper addresses this variation and presents a framework that differentiates regulatory requirements for a range of ADM system uses. It draws on agency theory to conceptualize accountability challenges from the point of view of data subjects with the purpose to systematize instruments for safeguarding algorithmic accountability. The paper furthermore shows how such instruments can be matched to applications of ADM based on a risk matrix. The resulting comprehensive framework can guide the evaluation of ADM systems and the choice of suitable regulatory provisions.
We describe a novel technique for the simultaneous visualization of multiple scalar fields, e.g. representing the members of an ensemble, based on their contour trees. Using tree alignments, a graph-theoretic concept similar to edit distance mappings, we identify commonalities across multiple contour trees and leverage these to obtain a layout that can represent all trees simultaneously in an easy-to-interpret, minimally-cluttered manner. We describe a heuristic algorithm to compute tree alignments for a given similarity metric, and give an algorithm to compute a joint layout of the resulting aligned contour trees. We apply our approach to the visualization of scalar field ensembles, discuss basic visualization and interaction possibilities, and demonstrate results on several analytic and real-world examples.
Editorial
(2021)
In recent years, there has been a growing need for accurate 3D scene reconstruction. Recent developments in the automotive industry have led to the increased use of ADAS where 3D reconstruction techniques are used, for example, as part of a collision detection system. For such applications, scene geometry reconstruction is usually performed in the form of depth estimation, where distances to scene objects are obtained.
In general, depth estimation systems can be divided into active and passive. Both systems have their advantages and disadvantages, but passive systems are usually cheaper to produce and easier to assemble and integrate than active systems. Passive systems can be stereo- or multiple-view based. Up to a certain limit, increasing the number of views in multi-view systems usually results in improved depth estimation accuracy.
One potential problem for ensuring the reliability of multi-view systems is the need to accurately estimate the orientation of their optical sensors. One way to ensure sensor placement for multi-view systems is to rigidly fix the sensors at the manufacturing stage. Unlike arbitrary sensor placement, using of a simplified and known sensor placement geometry further simplifies the depth estimation.
We meet with the concept of light field, which parameterizes all visible light passing through all viewpoints by their intersection with angular and spatial planes. When applied to computer vision, this gives us a 2D set of 2D images, where the physical distances between each image are fixed and proportional to each other.
Existing light field depth estimation methods provide good accuracy, which is suitable for industrial applications. However, the main problems of these methods are related to their running time and resource requirements. Most of the algorithms presented in the literature are typically sharpened for accuracy, can only be run on high-performance machines and often require a significant amount of time to process and obtain results.
Real-world applications often have running time requirements. Also, often there is a power-consumption limitation. In this dissertation, we investigate the problem of building a depth estimation system with an light field camera that satisfies the operating time and power consumption constraints without significant loss of estimation accuracy.
First, an algorithm for calibrating light field cameras is proposed, together with an algorithm for automatic calibration refinement, that works on arbitrary captured scenes. An algorithm for classical geometric depth estimation using light field cameras is proposed. Ways to optimize the algorithm for real-time use without significant loss of accuracy are presented. Finally, the ways how the presented depth estimation methods can be extended using modern deep learning paradigms under the two previously mentioned constraints are shown.
In order to discuss the kinds of reasoning a visualization supports and the conclusions that can be drawn within the analysiscontext, a theoretical framework is needed that enables a formal treatment of the reasoning process. Such a model needs toencompass three stages of the visualization pipeline: encoding, decoding and interpretation. The encoding details how dataare transformed into a visualization and what can be seen in the visualization. The decoding explains how humans constructgraphical contexts inside the depicted visualization and how they interpret them assigning meaning to displayed structuresaccording to a formal reasoning strategy. In the presented model, we adapt and combine theories for the different steps intoa unified formal framework such that the analysis process is modelled as an assignment of meaning to displayed structuresaccording to a formal reasoning strategy. Additionally, we propose the ConceptGraph, a combined graph-based representationof the finite-state transducers resulting from the three stages, that can be used to formalize and understand the reasoning process.We apply the new model to several visualization types and investigate reasoning strategies for various tasks.
Knowledge workers face an ever increasing flood of information in their daily work. They live in a “multi-tasking craziness”, involving activities like creating, finding, processing, assessing or organizing information while constantly switching from one context to another, each being associated with different tasks, documents, mails, etc. Hence, their personal information sphere consisting of file, mail and bookmark folders as well as their content, calendar entries, etc. is cluttered with information that has become irrelevant. Finding important information thus gets harder and much of previously gained knowledge is practically lost.
This thesis explores new ways of solving this problem by investigating the potential of self-(re)organizing and especially forgetting-enabled personal knowledge assistants in the given scenario. It utilizes so-called Managed Forgetting, which is an escalating set of measures to overcome the binary keep-or-delete paradigm, ranging from temporal hiding, to condensation, to adaptive reorganization, synchronization, archiving and deletion. Managed Forgetting is combined with two other major ideas: First, it uses the Semantic Desktop as an ecosystem, which brings Semantic Web and thus knowledge graph technologies to a user’s desktop, making it possible to capture and represent major parts of a user’s personal mental model in a machine-understandable way and exploit it in many different applications. Second, the system uses explicated context information – so-called Context Spaces: context is seen as an explicit interaction element users can work with (i.e. a “tangible” object similar to a folder) and in (immersion). The thesis is structured according to the basic interaction cycle with such a system, ranging from evidence collection to information extraction and context elicitation, followed by information value assessment and the actual support measures consisting of self-(re)organization decisions (back-end) and user interface updates (front-end). The system’s data foundation are personal or group knowledge graphs as well as native data. This work makes contributions to all of these aspects, whereas several of them have been investigated and developed in interdisciplinary research with cognitive scientists. On a more general level, searching and trust in such highly autonomous assistants have also been investigated.
In summary, a self-(re)organizing and especially forgetting-enabled support system for information management and knowledge work has been realized. Its different features vary in maturity: the most mature ones are already in practical use (also in industry), while the latest are just well elaborated (position papers) or rough ideas. Different evaluation strategies have been applied ranging from mere data-driven experiments to various user studies. Some of them were rather short-term with controlled laboratory conditions, others less controlled but spanning several months. Different benefits of working with such a system could be quantified, e.g. cognitive offloading effects and reduced task switching/resumption time. Other benefits were gathered qualitatively, e.g. tidiness of the information sphere and its better alignment with the user’s mental model. The presented approach has been shown to hold a lot of potential. In some aspects, however, only first steps have been taken towards tapping it, e.g. several support measures can be further refined and automation further increased.
Editorial
(2020)
Editorial
(2020)
Several governmental organizations all over the world aim for algorithmic accountability of artificial intelligence systems. However, there are few specific proposals on how exactly to achieve it. This article provides an extensive overview of possible transparency and inspectability mechanisms that contribute to accountability for the technical components of an algorithmic decision-making system. Following the different phases of a generic software development process, we identify and discuss several such mechanisms. For each of them, we give an estimate of the cost with respect to time and money that might be associated with that measure.
Editorial
(2020)
This thesis focuses on the operation of reliability-constrained routes in wireless ad-hoc networks. A complete communication protocol that is capable of guaranteeing a statistical minimum reliability level would have to support several functionalities: first, routes that are capable of supporting the specified Quality of Service requirement have to be discovered. During operation of discovered routes, the current Quality of Service level has to be monitored continuously. Whenever significant deviations are detected and the required level of Quality of Service is endangered, route maintenance has to ensure continuous operation. All four functionalities, route discovery, route operation, route maintenance and collection and distribution of network status information, will be addressed in this thesis.
In the first part of the thesis, we propose a new approach for Quality-of- Service routing in wireless ad-hoc networks called rmin-routing, with the provision of statistical minimum route reliability as main route selection criterion. To achieve specified minimum route reliabilities, we improve the reliability of individual links by well-directed retransmissions, to be applied during the operation of routes. To select among a set of candidate routes, we define and apply route quality criteria concerning network load.
High-quality information about the network status is essential for the discovery and operation of routes and clusters in wireless ad-hoc networks. This requires permanent observation and assessment of nodes, links, and link metrics, and the exchange of gathered status data. In the second part of the thesis, we present cTEx, a configurable topology explorer for wireless ad-hoc networks that efficiently detects and exchanges high-quality network status information during operation.
In the third part, we propose a decentralized algorithm for the discovery and operation of reliability-constrained routes in wireless ad-hoc networks called dRmin-routing. The algorithm uses locally available network status information about network topology and link properties that is collected proactively in order to discover a preliminary route candidate. This is followed by a distributed, reactive search along this preselected route to remove imprecisions of the locally recorded network status before making a final route selection. During route operation, dRmin-routing monitors routes and performs different kinds of route repair actions to maintain route reliability in order to overcome varying link reliabilities.
Modeling and Simulation of Internet of Things Infrastructures for Cyber-Physical Energy Systems
(2024)
This dissertation presents a novel approach to the model-based development and simulation-based validation of Internet of Things (IoT) infrastructures within the context of Cyber-Physical Energy Systems (CPES). CPES represents an evolution in energy management, seamlessly blending physical and cyber components for efficient, secure, and dependable energy distribution. However, the intricate interplay of these components demands innovative modeling and simulation strategies.
The work begins by establishing a robust foundation, exploring essential background elements such as requirements engineering, model-based systems engineering, digitalization approaches, and the intricacies of IoT platforms. It introduces the novel concept of homomorphic encryption, a critical enabler for securing IoT data within CPES.
In the exploration of the state of the art, the dissertation delves into the multifaceted landscape of IoT simulation, emphasizing the significance of versatility, community support, scalability, and synchronization.
The core contribution emerges in the chapter on simulating IoT networks. It introduces a sophisticated framework that encompasses hardware-in-the-loop, software-in-the-loop, and human-in-the-loop simulation. This innovative framework extends the boundaries of conventional simulation, enabling holistic evaluations of IoT systems.
A practical case study on smart energy usage showcases the application of the framework. Detailed SysML models, including requirements, package diagrams, block definition diagrams, internal block diagrams, state machine diagrams, and activity diagrams, are meticulously examined. The performance evaluation encompasses diverse aspects, from hardware and software validation to human interaction.
In conclusion, this dissertation represents a significant leap forward in the integration of IoT infrastructures within CPES. Its contributions extend from a comprehensive understanding of foundational elements to the practical implementation of a holistic simulation framework. This work not only addresses the current challenges but also outlines a path for future research, shaping the landscape of IoT integration within the dynamic realm of CPES. It offers invaluable insights for researchers, engineers, and stakeholders working towards resilient, secure, and energy-efficient infrastructures.
In many applications, visual analytics (VA) has developed into a standard tool to ease data access and knowledge generation. VA describes a holistic cycle transforming data into hypothesis and visualization to generate insights that enhance the data. Unfortunately, many data sources used in the VA process are affected by uncertainty. In addition, the VA cycle itself can introduce uncertainty to the knowledge generation process but does not provide a mechanism to handle these sources of uncertainty. In this manuscript, we aim to provide an extended VA cycle that is capable of handling uncertainty by quantification, propagation, and visualization, defined as uncertainty-aware visual analytics (UAVA). Here, a recap of uncertainty definition and description is used as a starting point to insert novel components in the visual analytics cycle. These components assist in capturing uncertainty throughout the VA cycle. Further, different data types, hypothesis generation approaches, and uncertainty-aware visualization approaches are discussed that fit in the defined UAVA cycle. In addition, application scenarios that can be handled by such a cycle, examples, and a list of open challenges in the area of UAVA are provided.
Dataflow process networks (DPNs) are intrinsically data-driven, i.e., node actions are not synchronized among each other and may fire whenever sufficient input operands arrived at a node. While the general model of computation (MoC) of DPNs does not impose further restrictions, many different subclasses of DPNs representing different dataflow MoCs have been considered over time. These classes mainly differ in the kinds of behaviors of the processes. A DPN may be heterogeneous in that different processes in the network belong to different classes of DPNs. A heterogeneous DPN can therefore be effectively used to model and to implement different components of a system with different kinds of processes and, therefore, different dataflow MoCs. This paper presents a model-based design based on different dataflow MoCs including their heterogeneous combinations. In particular, it covers the automatic software synthesis of systems from DPN models. The main objective is to validate, evaluate and compare the artifacts exhibited by different dataflow MoCs at the implementation level of systems under the supervision of a common design tool. Moreover, this work also offers an efficient synthesis method that targets and exploits heterogeneity in DPNs by generating implementations based on the kinds of behaviors of the processes. The proposed synthesis method provides a tool chain including different specialized code generators for specific dataflow MoCs, and a runtime system that finally maps models using a combination of different dataflow MoCs on cross-vendor target hardware.