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OpenLL
(2018)
Today's rendering APIs lack robust functionality and capabilities for dynamic, real-time text rendering and labeling, which represent key requirements for 3D application design in many fields. As a consequence, most rendering systems are barely or not at all equipped with respective capabilities. This paper drafts the unified text rendering and labeling API OpenLL intended to complement common rendering APIs, frameworks, and transmission formats. For it, various uses of static and dynamic placement of labels are showcased and a text interaction technique is presented. Furthermore, API design constraints with respect to state-of-the-art text rendering techniques are discussed. This contribution is intended to initiate a community-driven specification of a free and open label library.
A hybrid design approach of the hierarchical physical implementation design flow is presented and demonstrated on a fault-tolerant low-power multiprocessor system. The proposed flow allows to implement selected submodules in parallel with contrary requirements such as identical placement and individual block implementation. The overall system contains four Leon2 cores and communicates via the Waterbear framework and supports Adaptive Voltage Scaling (AVS) functionality. Three of the processor core variants are derived from the first baseline reference core but implemented individually at block level based on their clock tree specification. The chip is prepared for space applications and designed with triple modular redundancy (TMR) for control parts. The low-power performance is enabled by contemporary power and clock management control. An ASIC is fabricated in a low-power 0.13 mu m BiCMOS technology process node.
We compare the robustness of humans and current convolutional deep neural networks (DNNs) on object recognition under twelve different types of image degradations. First, using three well known DNNs (ResNet-152, VGG-19, GoogLeNet) we find the human visual system to be more robust to nearly all of the tested image manipulations, and we observe progressively diverging classification error-patterns between humans and DNNs when the signal gets weaker. Secondly, we show that DNNs trained directly on distorted images consistently surpass human performance on the exact distortion types they were trained on, yet they display extremely poor generalisation abilities when tested on other distortion types. For example, training on salt-and-pepper noise does not imply robustness on uniform white noise and vice versa. Thus, changes in the noise distribution between training and testing constitutes a crucial challenge to deep learning vision systems that can be systematically addressed in a lifelong machine learning approach. Our new dataset consisting of 83K carefully measured human psychophysical trials provide a useful reference for lifelong robustness against image degradations set by the human visual system.
The emergence of cloud computing allows users to easily host their Virtual Machines with no up-front investment and the guarantee of always available anytime anywhere. But with the Virtual Machine (VM) is hosted outside of user's premise, the user loses the physical control of the VM as it could be running on untrusted host machines in the cloud. Malicious host administrator could launch live memory dumping, Spectre, or Meltdown attacks in order to extract sensitive information from the VM's memory, e.g. passwords or cryptographic keys of applications running in the VM. In this paper, inspired by the moving target defense (MTD) scheme, we propose a novel approach to increase the security of application's sensitive data in the VM by continuously moving the sensitive data among several memory allocations (blocks) in Random Access Memory (RAM). A movement function is added into the application source code in order for the function to be running concurrently with the application's main function. Our approach could reduce the possibility of VM's sensitive data in the memory to be leaked into memory dump file by 2 5% and secure the sensitive data from Spectre and Meltdown attacks. Our approach's overhead depends on the number and the size of the sensitive data.
Comparative text mining extends from genre analysis and political bias detection to the revelation of cultural and geographic differences, through to the search for prior art across patents and scientific papers. These applications use cross-collection topic modeling for the exploration, clustering, and comparison of large sets of documents, such as digital libraries. However, topic modeling on documents from different collections is challenging because of domain-specific vocabulary. We present a cross-collection topic model combined with automatic domain term extraction and phrase segmentation. This model distinguishes collection-specific and collection-independent words based on information entropy and reveals commonalities and differences of multiple text collections. We evaluate our model on patents, scientific papers, newspaper articles, forum posts, and Wikipedia articles. In comparison to state-of-the-art cross-collection topic modeling, our model achieves up to 13% higher topic coherence, up to 4% lower perplexity, and up to 31% higher document classification accuracy. More importantly, our approach is the first topic model that ensures disjunct general and specific word distributions, resulting in clear-cut topic representations.
The collaboration during the modeling process is uncomfortable and characterized by various limitations. Faced with the successful transfer of first process modeling languages to the augmented world, non-transparent processes can be visualized in a more comprehensive way. With the aim to rise comfortability, speed, accuracy and manifoldness of real world process augmentations, a framework for the bidirectional interplay of the common process modeling world and the augmented world has been designed as morphologic box. Its demonstration proves the working of drawn AR integrations. Identified dimensions were derived from (1) a designed knowledge construction axiom, (2) a designed meta-model, (3) designed use cases and (4) designed directional interplay modes. Through a workshop-based survey, the so far best AR modeling configuration is identified, which can serve for benchmarks and implementations.
Microstructure Characterisation of Advanced Materials via 2D and 3D X-Ray Refraction Techniques
(2018)
3D imaging techniques have an enormous potential to understand the microstructure, its evolution, and its link to mechanical, thermal, and transport properties. In this conference paper we report the use of a powerful, yet not so wide-spread, set of X-ray techniques based on refraction effects. X-ray refraction allows determining internal specific surface (surface per unit volume) in a non-destructive fashion, position and orientation sensitive, and with a nanometric detectability. We demonstrate showcases of ceramics and composite materials, where microstructural parameters could be achieved in a way unrivalled even by high-resolution techniques such as electron microscopy or computed tomography. We present in situ analysis of the damage evolution in an Al/Al2O3 metal matrix composite during tensile load and the identification of void formation (different kinds of defects, particularly unsintered powder hidden in pores, and small inhomogeneity’s like cracks) in Ti64 parts produced by selective laser melting using synchrotron X-ray refraction radiography and tomography.
Precision fruticulture addresses site or tree-adapted crop management. In the present study, soil and tree status, as well as fruit quality at harvest were analysed in a commercial apple (Malus × domestica 'Gala Brookfield'/Pajam1) orchard in a temperate climate. Trees were irrigated in addition to precipitation. Three irrigation levels (0, 50 and 100%) were applied. Measurements included readings of apparent electrical conductivity of soil (ECa), stem water potential, canopy temperature obtained by infrared camera, and canopy volume estimated by LiDAR and RGB colour imaging. Laboratory analyses of 6 trees per treatment were done on fruit considering the pigment contents and quality parameters. Midday stem water potential (SWP), normalized crop water stress index (CWSI) calculated from thermal data, and fruit yield and quality at harvest were analysed. Spatial patterns of the variability of tree water status were estimated by CWSI imaging supported by SWP readings. CWSI ranged from 0.1 to 0.7 indicating high variability due to irrigation and precipitation. Canopy volume data were less variable. Soil ECa appeared homogeneous in the range of 0 to 4 mS m-1. Fruit harvested in a drought stress zone showed enhanced portion of pheophytin in the chlorophyll pool. Irrigation affected soluble solids content and, hence, the quality of fruit. Overall, results highlighted that spatial variation in orchards can be found even if marginal variability of soil properties can be assumed.
An Information System Supporting the Eliciting of Expert Knowledge for Successful IT Projects
(2018)
In order to guarantee the success of an IT project, it is necessary for a company to possess expert knowledge. The difficulty arises when experts no longer work for the company and it then becomes necessary to use their knowledge, in order to realise an IT project. In this paper, the ExKnowIT information system which supports the eliciting of expert knowledge for successful IT projects, is presented and consists of the following modules: (1) the identification of experts for successful IT projects, (2) the eliciting of expert knowledge on completed IT projects, (3) the expert knowledge base on completed IT projects, (4) the Group Method for Data Handling (GMDH) algorithm, (5) new knowledge in support of decisions regarding the selection of a manager for a new IT project. The added value of our system is that these three approaches, namely, the elicitation of expert knowledge, the success of an IT project and the discovery of new knowledge, gleaned from the expert knowledge base, otherwise known as the decision model, complement each other.
The relentless improvement of silicon photonics is making optical interconnects and networks appealing for use in miniaturized systems, where electrical interconnects cannot keep up with the growing levels of core integration due to bandwidth density and power efficiency limitations. At the same time, solutions such as 3D stacking or 2.5D integration open the door to a fully dedicated process optimization for the photonic die. However, an architecture-level integration challenge arises between the electronic network and the optical one in such tightly-integrated parallel systems. It consists of adapting signaling rates, matching the different levels of communication parallelism, handling cross-domain flow control, addressing re-synchronization concerns, and avoiding protocol-dependent deadlock. The associated energy and performance overhead may offset the inherent benefits of the emerging technology itself. This paper explores a hybrid CMOS-ECL bridge architecture between 3D-stacked technology-heterogeneous networks-on-chip (NoCs). The different ways of overcoming the serialization challenge (i.e., through an improvement of the signaling rate and/or through space-/wavelength division multiplexing options) give rise to a configuration space that the paper explores, in search for the most energy-efficient configuration for high-performance.