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The smart home of the future is typically researched in lab settings or apartments that have been built from scratch. However, comparing the lifecycle of buildings and information technology, it is evident that modernization strategies and technologies are needed to empower residents to modify and extend their homes to make it smarter. In this paper, we describe a case study about the deployment, adaption to and adoption of tailorable home energy management systems in 7 private households. Based on this experience, we want to discuss how hardware and software technologies should be designed so that people could build their own smart home with a high usability and user experience.
This paper introduces our robotic system named UGAV (Unmanned Ground-Air Vehicle) consisting of two semi-autonomous robot platforms, an Unmanned Ground Vehicle (UGV) and an Unmanned Aerial Vehicles (UAV). The paper focuses on three topics of the inspection with the combined UGV and UAV: (A) teleoperated control by means of cell or smart phones with a new concept of automatic configuration of the smart phone based on a RKI-XML description of the vehicles control capabilities, (B) the camera and vision system with the focus to real time feature extraction e.g. for the tracking of the UAV and (C) the architecture and hardware of the UAV.
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique to 'illuminate' the problem space through the lens of chosen features has the potential to be a powerful tool for exploring design spaces, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination (SAIL) algorithm, introduced here, integrates approximative models and intelligent sampling of the objective function to minimize the number of evaluations required by MAP-Elites.
The ability of SAIL to efficiently produce both accurate models and diverse high-performing solutions is illustrated on a 2D airfoil design problem. The search space is divided into bins, each holding a design with a different combination of features. In each bin SAIL produces a better performing solution than MAP-Elites, and requires several orders of magnitude fewer evaluations. The CMA-ES algorithm was used to produce an optimal design in each bin: with the same number of evaluations required by CMA-ES to find a near-optimal solution in a single bin, SAIL finds solutions of similar quality in every bin.
A new method for design space exploration and optimization, Surrogate-Assisted Illumination (SAIL), is presented. Inspired by robotics techniques designed to produce diverse repertoires of behaviors for use in damage recovery, SAIL produces diverse designs that vary according to features specified by the designer. By producing high-performing designs with varied combinations of user-defined features a map of the design space is created. This map illuminates the relationship between the chosen features and performance, and can aid designers in identifying promising design concepts. SAIL is designed for use with compu-tationally expensive design problems, such as fluid or structural dynamics, and integrates approximative models and intelligent sampling of the objective function to minimize the number of function evaluations required. On a 2D airfoil optimization problem SAIL is shown to produce hundreds of diverse designs which perform competitively with those found by state-of-the-art black box optimization. Its capabilities are further illustrated in a more expensive 3D aerodynamic optimization task.
Multiple myeloma is the second most common hematological malignancy. Despite all the progress made in treating multiple myeloma, it still remains an incurable disease. Patients are left with a median survival of 4-5 years. The combined treatment of multiple myeloma with histone deacetylase inhibitors and cytokine-induced killer cells provides a promising targeted treatment option for patients. This study investigated the impact of a combined treatment compared to treatment with histone deacetylase inhibitors. The experiments revealed that a treatment with histone deacetylase (HDAC) inhibitors could reduce cell viability to 59% for KMS 18 cell line and 46% for the U-266 cell line. The combined treatment led to a decrease of cell viability to 33% for KMS 18 and 27% for the U-266 cell line, thus showing a significantly better efficacy than the single treatment.
WiFi-based Long Distance (WiLD) networks have emerged as a promising alternative approach for Internet in rural areas. The main hardware components of these networks are commercial off-the-shelf WiFi radios and directional antennas. During our experiences with real-world WiLD networks, we encountered that interference among long-distance links is a major issue even with high gain directional antennas. In this work, we are providing an in-depth analysis of these interference effects by conducting simulations in ns-3. To closely match the real-world interference effects, we implemented a module to load radiation pattern of commonly used antennas. We analyze two different interference scenarios typically present as a part of larger networks. The results show that side-lobes of directional antennas significantly influence the throughput of long-distance WiFi links depending on the orientation. This work emphasizes that the usage of simple directional antenna models needs to be considered carefully.
This study contributes to the growing body of research concerning management consultancies by linking two previously disparate fields of study: (1) the examination of the effectiveness of consulting interventions and (2) the examination of the social processes that aim to create and legitimize the insights, knowledge and capabilities of management consultancies. We propose that consulting firms accumulate social authority in the course of pre-intervention discourse processes that is reflected in their reputation and celebrity. With respect to intervention, this social authority affects change recipients’ commitment to and compliance with the requirements of change implementation. We test the proposed relationships by conducting a measured variable path analysis of 117 change initiatives in German companies that were set up and implemented with the assistance of external consultancies. Our findings indicate that a consulting firm’s levels of both celebrity and reputation affect the change recipients’ commitment to proposed change strategies and thus, indirectly affect their behavioral compliance with the explicit requirements of change implementation.
The work done in this thesis enhances the MMD algorithm in multi-core environments. The MMD algorithm, a transformation based algorithm for reversible logic synthesis, is based on the works introduced by Maslov, Miller and Dueck and their original, sequential implementation. It synthesises a formal function specification, provided by a truth table, into a reversible network and is able to perform several optimization steps after the synthesis. This work concentrates on one of these optimization steps, the template matching. This approach is used to reduce the size of the reversible circuit by replacing a number of gates that match a template which implements the same function and uses less gates. Smaller circuits have several benefits since they need less area and are not as costly. The template matching approach introduced in the original works is computationally expensive since it tries to match a library of templates against the given circuit. For each template at each position in the circuit, a number of different combinations have to be calculated during runtime resulting in high execution times, especially for large circuits. In order to make the template matching approach more efficient and usable, it has been reimplemented in order to take advantage of modern multi-core architectures such as the Cell Broadband Engine or a Graphics Processing Unit. For this work, two algorithmically different approaches that try to consider each multi-core architecture’s strengths, have been analyzed and improved. For the analysis these approaches have been cross-implemented on the two target hardware architectures and compared to the original parallel versions. Important metrics for this analysis are the execution time of the algorithm and the result of the minimization with the template matching approach. It could be shown that the algorithmically different approaches produce the same minimization results, independent of the used hardware architecture. However, both cross-implementations also show a significantly higher execution time which makes them practically irrelevant. The results of the first analysis and comparison lead to the decision to enhance only the original parallel approaches. Using the same metrics for successful enhancements as mentioned above, it could be shown that improving the algorithmic concepts and exploiting the capabilities of the hardware lead to better results for the execution time and the minimization results compared to their original implementations.
This thesis presents an approach to automatically adjust the parameters of a Java application run on the IBM J9 Virtual Machine in order to improve its performance. It works by analyzing the logfile the VM generates and searching for specific behavioral patterns. These patterns are matched against a list of known patterns for which rules exist that specify how to adapt the VM to the given application. Adapting the application is done by adding parameters and changing existing ones, for example to achieve a better heap usage. The process is fully automated and carried out by a toolkit developed for this thesis. The toolkit iteratively cycles through multiple possible parameter sets, benchmarks them and proposes the best alternative to the user. The user can, without any prior knowledge about the Java application or the VM improve the performance of the deployed application.
The Java Virtual Machine (JVM) executes the compiled bytecode version of a Java program and acts as a layer between the program and the operating system. The JVM provides additional features such as Process, Thread, and Memory Management to manage the execution of these programs. The Garbage Collection (GC) is part of the memory management and has an impact on the overall runtime performance because it is responsible for removing dead objects from the heap. Currently, the execution of a program needs to be halted during every GC run. The problem of this stop-the-world approach is that all threads in the JVM need to be suspended. It would be desirable to have a thread-local GC that only blocks the current thread and does not affect any other threads. In particular, this would improve the execution of multi-threaded Java programs. An object that is accessible by more than one thread is called escaped. It is not possible to thread-locally determine if escaped objects are still alive so that they cannot be handled in a thread-local GC. To gain significant performance improvements with a thread-local GC, it is therefore necessary to determine if it is possible to reliably predict if a given object will escape. Experimental results show that the escaping of objects can be predicted with high accuracy based on the line of code the object was allocated from. A thread-local GC was developed to minimize the number of stop-the-world GCs. The prototype implementation delivers a proof-of-concept that shows that this goal can be achieved in certain scenarios.
An analysis of sharing string objects with the Java Virtual Machine was conducted; they are the most used objects in Java programs and they are immutable - thus they are read-only and easily identified. While the results are promising, it is clear that sharing more objects would result in better performance. Automatic object selection for sharing is non-trivial, because in the current state only read-only objects can be shared. This attribute can not be easily determined during runtime by an algorithm; the developer on the other hand can. This thesis presents the development of an Application Programmer Interface (API) that allows programmers to use the Java Virtual Machine (JVM) internal sharing functionality. Furthermore, we present the usage of the sharing API. Open-source software was used as real-world test cases. Afterwards the evaluation shows that the ratio between memory savings and start-up time overhead is reasonable.
RNA is one of the most important molecules in living organisms. One of its main functions is to regulate gene expression. This involves binding to and forming a joint structure with a messenger RNA. An RNAs functions is determined by its sequence and the structure it folds into. Accordingly, the prediction of individual as well as joint structures is an important area of research. In this thesis a method for the prediction of RNA-RNA joint structure using their minimum free energy (mfe) structures was developed. It is able to extensively explore the joint structural landscape of two interacting RNAs by taking advantage of the locality of changes in the RNAs structures as well as natural and energetic constraints. The method predicts the mfe joint structure as well as alternative stable joint structures while also computing non-optimal folding pathways from the unbound individual mfe structures to the predicted joint structures. It is shown how an enumeration approach is used which is able to deal with the enormous search space as well as to avoid any cyclic behaviour. The method is evaluated using two standard datasets of known interacting RNAs and shows good results.
Evolutionary illumination is a recent technique that allows producing many diverse, optimal solutions in a map of manually defined features. To support the large amount of objective function evaluations, surrogate model assistance was recently introduced. Illumination models need to represent many more, diverse optimal regions than classical surrogate models. In this PhD thesis, we propose to decompose the sample set, decreasing model complexity, by hierarchically segmenting the training set according to their coordinates in feature space. An ensemble of diverse models can then be trained to serve as a surrogate to illumination.
Neuroevolution methods evolve the weights of a neural network, and in some cases the topology, but little work has been done to analyze the effect of evolving the activation functions of individual nodes on network size, an important factor when training networks with a small number of samples. In this work we extend the neuroevolution algorithm NEAT to evolve the activation function of neurons in addition to the topology and weights of the network. The size and performance of networks produced using NEAT with uniform activation in all nodes, or homogenous networks, is compared to networks which contain a mixture of activation functions, or heterogenous networks. For a number of regression and classification benchmarks it is shown that, (1) qualitatively different activation functions lead to different results in homogeneous networks, (2) the heterogeneous version of NEAT is able to select well performing activation functions, (3) the produced heterogeneous networks are significantly smaller than homogeneous networks.
While executing actions, service robots may experience external faults because of insufficient knowledge about the actions' preconditions. The possibility of encountering such faults can be minimised if symbolic and geometric precondition models are combined into a representation that specifies how and where actions should be executed. This work investigates the problem of learning such action execution models and the manner in which those models can be generalised. In particular, we develop a template-based representation of execution models, which we call delta models, and describe how symbolic template representations and geometric success probability distributions can be combined for generalising the templates beyond the problem instances on which they are created. Our experimental analysis, which is performed with two physical robot platforms, shows that delta models can describe execution-specific knowledge reliably, thus serving as a viable model for avoiding the occurrence of external faults.
From video games to mobile augmented reality, 3D interaction is everywhere. But simply choosing to use 3D input or 3D displays isn't enough: 3D user interfaces (3D UIs) must be carefully designed for optimal user experience. 3D User Interfaces: Theory and Practice, Second Edition is today's most comprehensive primary reference to building outstanding 3D UIs. Four pioneers in 3D user interface research and practice have extensively expanded and updated this book, making it today's definitive source for all things related to state-of-the-art 3D interaction.
This paper describes the security mechanisms of several wireless building automation technologies, namely ZigBee, EnOcean, ZWave, KNX, FS20, and Home-Matic. It is shown that none of the technologies provides the necessary measure ofsecurity that should be expected in building automation systems. One of the conclusions drawn is that software embedded in systems that are build for a lifetime of twenty years or more needs to be updatable.
The knowledge of Software Features (SFs) is vital for software developers and requirements specialists during all software engineering phases: to understand and derive software requirements, to plan and prioritize implementation tasks, to update documentation, or to test whether the final product correctly implements the requested SF. In most software projects, SFs are managed in conjunction with other information such as bug reports, programming tasks, or refactoring tasks with the aid of Issue Tracking Systems (ITSs). Hence ITSs contains a variety of information that is only partly related to SFs. In practice, however, the usage of ITSs to store SFs comes with two major problems: (1) ITSs are neither designed nor used as documentation systems. Therefore, the data inside an ITS is often uncategorized and SF descriptions are concealed in rather lengthy. (2) Although an SF is often requested in a single sentence, related information can be scattered among many issues. E.g. implementation tasks related to an SF are often reported in additional issues. Hence, the detection of SFs in ITSs is complicated: a manual search for the SFs implies reading, understanding and exploiting the Natural Language (NL) in many issues in detail. This is cumbersome and labor intensive, especially if related information is spread over more than one issue. This thesis investigates whether SF detection can be supported automatically. First the problem is analyzed: (i) An empirical study shows that requests for important SFs reside in ITSs, making ITSs a good tar- get for SF detection. (ii) A second study identifies characteristics of the information and related NL in issues. These characteristics repre- sent opportunities as well as challenges for the automatic detection of SFs. Based on these problem studies, the Issue Tracking Software Feature Detection Method (ITSoFD), is proposed. The method has two main components and includes an approach to preprocess issues. Both components address one of the problems associated with storing SFs in ITSs. ITSoFD is validated in three solution studies: (I) An empirical study researches how NL that describes SFs can be detected with techniques from Natural Language Processing (NLP) and Machine Learning. Issues are parsed and different characteristics of the issue and its NL are extracted. These characteristics are used to clas- sify the issue’s content and identify SF description candidates, thereby approaching problem (1). (II) An empirical study researches how issues that carry information potentially related to an SF can be detected with techniques from NLP and Information Retrieval. Characteristics of the issue’s NL are utilized to create a traceability network vii of related issues, thereby approaching problem (2). (III) An empirical study researches how NL data in issues can be preprocessed using heuristics and hierarchical clustering. Code, stack traces, and other technical information is separated from NL. Heuristics are used to identify candidates for technical information and clustering improves the heuristic’s results. The technique can be applied to support components, I. and II.
p53 is a crucial regulator of cell response to DNA damage. MDM4 and MDM2 are the two main negative regulators of p53 activity. Upon DNA damage, their constraint is released and p53 becomes activated and exerts its safeguard function by arresting cell growth or by killing excessively damaged cells. Under these conditions, increasing data suggest that MDM4 and MDM2 play novel roles. In this respect, we recently published that MDM4 exerts a positive activity towards p53 mitochondrial apoptosis. We observed that a fraction of MDM4 stably localizes at the mitochondria where upon lethal stress conditions, promotes the mitochondrial localization of p53 phosphorylated at Ser46 (p53Ser46(P)) and facilitates its binding to BCL2, cytochrome C release and apoptosis. Most importantly, we observed a correlation of MDM4 expression with cisplatin-resistance in a group of human ovarian cancers suggesting that MDM4 proapoptotic activity may have in vivo relevance. Here, we discuss about these and some new findings and compare them with previous data trying to settle some apparent contradictions. In addition, this review discusses the potential relevance of our data to the field of human cancer.