Refine
H-BRS Bibliography
- yes (2497) (remove)
Departments, institutes and facilities
- Fachbereich Informatik (979)
- Fachbereich Angewandte Naturwissenschaften (630)
- Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) (388)
- Fachbereich Ingenieurwissenschaften und Kommunikation (386)
- Fachbereich Wirtschaftswissenschaften (319)
- Institute of Visual Computing (IVC) (286)
- Institut für funktionale Gen-Analytik (IFGA) (231)
- Internationales Zentrum für Nachhaltige Entwicklung (IZNE) (126)
- Institut für Cyber Security & Privacy (ICSP) (107)
- Institut für Verbraucherinformatik (IVI) (104)
Document Type
- Article (1021)
- Conference Object (918)
- Part of a Book (190)
- Preprint (86)
- Doctoral Thesis (52)
- Report (51)
- Book (monograph, edited volume) (43)
- Master's Thesis (30)
- Working Paper (28)
- Research Data (22)
Year of publication
Language
- English (2497) (remove)
Keywords
- Virtual Reality (15)
- FPGA (14)
- Machine Learning (14)
- GC/MS (13)
- Robotics (13)
- Sustainability (11)
- virtual reality (11)
- Augmented Reality (9)
- Lignin (9)
- Social Protection (9)
After more than twenty years of research, the molecular events of apoptotic cell death can be succinctly stated; different pathways, activated by diverse signals, increase the activity of proteases called caspases that rapidly and irreversibly dismantle condemned cell by cleaving specific substrates. In this time the ideas that apoptosis protects us from tumourigenesis and that cancer chemotherapy works by inducing apoptosis also emerged. Currently, apoptosis research is shifting away from the intracellular events within the dying cell to focus on the effect of apoptotic cells on surrounding tissues. This is producing counterintuitive data showing that our understanding of the role of apoptosis in tumourigenesis and cancer therapy is too simple, with some interesting and provocative implications. Here, we will consider evidence supporting the idea that dying cells signal their presence to the surrounding tissue and, in doing so, elicit repair and regeneration that compensates for any loss of function caused by cell death. We will discuss evidence suggesting that cancer cell proliferation may be driven by inappropriate or corrupted tissue-repair programmes that are initiated by signals from apoptotic cells and show how this may dramatically modify how we view the role of apoptosis in both tumourigenesis and cancer therapy.
Software developers build complex systems using plenty of third-party libraries. Documentation is key to understand and use the functionality provided via the libraries’ APIs. Therefore, functionality is the main focus of contemporary API documentation, while cross-cutting concerns such as security are almost never considered at all, especially when the API itself does not provide security features. Documentations of JavaScript libraries for use in web applications, e.g., do not specify how to add or adapt a Content Security Policy (CSP) to mitigate content injection attacks like Cross-Site Scripting (XSS). This is unfortunate, as security-relevant API documentation might have an influence on secure coding practices and prevailing major vulnerabilities such as XSS. For the first time, we study the effects of integrating security-relevant information in non-security API documentation. For this purpose, we took CSP as an exemplary study object and extended the official Google Maps JavaScript API documentation with security-relevant CSP information in three distinct manners. Then, we evaluated the usage of these variations in a between-group eye-tracking lab study involving N=49 participants. Our observations suggest: (1) Developers are focused on elements with code examples. They mostly skim the documentation while searching for a quick solution to their programming task. This finding gives further evidence to results of related studies. (2) The location where CSP-related code examples are placed in non-security API documentation significantly impacts the time it takes to find this security-relevant information. In particular, the study results showed that the proximity to functional-related code examples in documentation is a decisive factor. (3) Examples significantly help to produce secure CSP solutions. (4) Developers have additional information needs that our approach cannot meet.
Overall, our study contributes to a first understanding of the impact of security-relevant information in non-security API documentation on CSP implementation. Although further research is required, our findings emphasize that API producers should take responsibility for adequately documenting security aspects and thus supporting the sensibility and training of developers to implement secure systems. This responsibility also holds in seemingly non-security relevant contexts.
For robots acting - and failing - in everyday environments, a predictable behaviour representation is important so that it can be utilised for failure analysis, recovery, and subsequent improvement. Learning from demonstration combined with dynamic motion primitives is one commonly used technique for creating models that are easy to analyse and interpret; however, mobile manipulators complicate such models since they need the ability to synchronise arm and base motions for performing purposeful tasks. In this paper, we analyse dynamic motion primitives in the context of a mobile manipulator - a Toyota Human Support Robot (HSR)- and introduce a small extension of dynamic motion primitives that makes it possible to perform whole body motion with a mobile manipulator. We then present an extensive set of experiments in which our robot was grasping various everyday objects in a domestic environment, where a sequence of object detection, pose estimation, and manipulation was required for successfully completing the task. Our experiments demonstrate the feasibility of the proposed whole body motion framework for everyday object manipulation, but also illustrate the necessity for highly adaptive manipulation strategies that make better use of a robot's perceptual capabilities.
This ICB Research Report constitutes the proceedings of the following four workshops which were held on Tuesday, 29th June 2010 as part of the Requirements Engineering: Foundation for Software Quality (REFSQ) conference 2010 at the University of Duisburg-Essen. First Workshop on Creativity in Requirements Engineering (CreaRE). First International Workshop on Product Line Requirements Engineering and Quality (PLREQ). First Workshop on Requirements Prioritization for customer-oriented Software-Development (RePriCo). First Workshop on Requirements Engineering in Small Companies (RESC).
2-methylacetoacetyl-coenzyme A thiolase (beta-ketothiolase) deficiency: one disease - two pathways
(2020)
Background: 2-methylacetoacetyl-coenzyme A thiolase deficiency (MATD; deficiency of mitochondrial acetoacetyl-coenzyme A thiolase T2/ “beta-ketothiolase”) is an autosomal recessive disorder of ketone body utilization and isoleucine degradation due to mutations in ACAT1.
Methods: We performed a systematic literature search for all available clinical descriptions of patients with MATD. Two hundred forty-four patients were identified and included in this analysis. Clinical course and biochemical data are presented and discussed.
Results: For 89.6% of patients at least one acute metabolic decompensation was reported. Age at first symptoms ranged from 2 days to 8 years (median 12 months). More than 82% of patients presented in the first 2 years of life, while manifestation in the neonatal period was the exception (3.4%). 77.0% (157 of 204 patients) of patients showed normal psychomotor development without neurologic abnormalities. Conclusion: This comprehensive data analysis provides a systematic overview on all cases with MATD identified in the literature. It demonstrates that MATD is a rather benign disorder with often favourable outcome, when compared with many other organic acidurias.
2-methylacetoacetyl-coenzyme A thiolase (beta-ketothiolase) deficiency: one disease - two pathways
(2019)
Background: 2-methylacetoacetyl-coenzyme A thiolase deficiency (MATD; deficiency of mitochondrial acetoacetyl-coenzyme A thiolase T2/ “beta-ketothiolase”) is an autosomal recessive disorder of ketone body utilization and isoleucine degradation due to mutations in ACAT1.
Methods: We performed a systematic literature search for all available clinical descriptions of patients with MATD. 244 patients were identified and included in this analysis. Clinical course and biochemical data are presented and discussed.
Results: For 89.6 % of patients at least one acute metabolic decompensation was reported. Age at first symptoms ranged from 2 days to 8 years (median 12 months). More than 82% of patients presented in the first two years of life, while manifestation in the neonatal period was the exception (3.4%). 77.0% (157 of 204 patients) of patients showed normal psychomotor development without neurologic abnormalities.
Conclusion: This comprehensive data analysis provides a systematic overview on all cases with MATD identified in the literature. It demonstrates that MATD is a rather benign disorder with often favourable outcome, when compared with many other organic acidurias.
Sharing economies enabled by technical platforms have been studied regarding their economic, legal, and social effects, as well as with regard to their possible influences on CSCW topics such as work, collaboration, and trust. While a lot current research is focusing on the sharing economy and related communities, there is little work addressing the phenomenon from a socio-technical point of view. Our workshop is meant to address this gap. Building on research themes and discussion from last year’s ECSCW, we seek to engage deeper with topics such as novel socio-technical approaches for enabling sharing communities, discussing issues around digital consumer and worker protection, as well as emerging challenges and opportunities of existing platforms and approaches.
Nitrile-type inhibitors are known to interact with cysteine proteases in a covalent-reversible manner. The chemotype of 3-cyano-3-aza-β-amino acid derivatives was designed in which the N-cyano group is centrally arranged in the molecule to allow for interactions with the nonprimed and primed binding regions of the target enzymes. These compounds were evaluated as inhibitors of the human cysteine cathepsins K, S, B, and L. They exhibited slow-binding behavior and were found to be exceptionally potent, in particular toward cathepsin K, with second-order rate constants up to 52 900 × 103 M–1 s–1.
Background: 3-hydroxy-3-methylglutaryl-coenzyme A lyase deficiency (HMGCLD) is an autosomal recessive disorder of ketogenesis and leucine degradation due to mutations in HMGCL.
Method: We performed a systematic literature search to identify all published cases. Two hundred eleven patients of whom relevant clinical data were available were included in this analysis. Clinical course, biochemical findings and mutation data are highlighted and discussed. An overview on all published HMGCL variants is provided.
Results: More than 95% of patients presented with acute metabolic decompensation. Most patients manifested within the first year of life, 42.4% already neonatally. Very few individuals remained asymptomatic. The neurologic long-term outcome was favorable with 62.6% of patients showing normal development.
Conclusion: This comprehensive data analysis provides a systematic overview on all published cases with HMGCLD including a list of all known HMGCL mutations.
Background 3-hydroxy-3-methylglutaryl-coenzyme A lyase deficiency (HMGCLD) is an autosomal recessive disorder of ketogenesis and leucine degradation due to mutations in HMGCL .
Method We performed a systematic literature search to identify all published cases. 211 patients of whom relevant clinical data were available were included in this analysis. Clinical course, biochemical findings and mutation data are highlighted and discussed. An overview on all published HMGCL variants is provided.
Results More than 95% of patients presented with acute metabolic decompensation. Most patients manifested within the first year of life, 42.4% already neonatally. Very few individuals remained asymptomatic. The neurologic long-term outcome was favorable with 62.6% of patients showing normal development.
Conclusion This comprehensive data analysis provides a systematic overview on all published cases with HMGCLD including a list of all known HMGCL mutations.
3-Hydroxyisobutyrate Dehydrogenase (HIBADH) deficiency - a novel disorder of valine metabolism
(2021)
3-Hydroxyisobutyric acid (3HiB) is an intermediate in the degradation of the branched-chain amino acid valine. Disorders in valine degradation can lead to 3HiB accumulation and its excretion in the urine. This article describes the first two patients with a new metabolic disorder, 3-hydroxyisobutyrate dehydrogenase (HIBADH) deficiency, its phenotype and its treatment with a low-valine diet. The detected mutation in the HIBADH gene leads to nonsense-mediated mRNA decay of the mutant allele and to a complete loss-of-function of the enzyme. Under strict adherence to a low-valine diet a rapid decrease of 3HiB excretion in the urine was observed. Due to limited patient numbers and intrafamilial differences in phenotype with one affected and one unaffected individual, the clinical phenotype of HIBADH deficiency needs further evaluation.
In service robotics, tasks without the involvement of objects are barely applicable, like in searching, fetching or delivering tasks. Service robots are supposed to capture efficiently object related information in real world scenes while for instance considering clutter and noise, and also being flexible and scalable to memorize a large set of objects. Besides object perception tasks like object recognition where the object’s identity is analyzed, object categorization is an important visual object perception cue that associates unknown object instances based on their e.g. appearance or shape to a corresponding category. We present a pipeline from the detection of object candidates in a domestic scene over the description to the final shape categorization of detected candidates. In order to detect object related information in cluttered domestic environments an object detection method is proposed that copes with multiple plane and object occurrences like in cluttered scenes with shelves. Further a surface reconstruction method based on Growing Neural Gas (GNG) in combination with a shape distribution-based descriptor is proposed to reflect shape characteristics of object candidates. Beneficial properties provided by the GNG such as smoothing and denoising effects support a stable description of the object candidates which also leads towards a more stable learning of categories. Based on the presented descriptor a dictionary approach combined with a supervised shape learner is presented to learn prediction models of shape categories.
Experimental results, of different shapes related to domestically appearing object shape categories such as cup, can, box, bottle, bowl, plate and ball, are shown. A classification accuracy of about 90% and a sequential execution time of lesser than two seconds for the categorization of an unknown object is achieved which proves the reasonableness of the proposed system design. Additional results are shown towards object tracking and false positive handling to enhance the robustness of the categorization. Also an initial approach towards incremental shape category learning is proposed that learns a new category based on the set of previously learned shape categories.
The ability of detecting people has become a crucial subtask, especially in robotic systems which aim an application in public or domestic environments. Robots already provide their services e.g. in real home improvement markets and guide people to a desired product. In such a scenario many robot internal tasks would benefit from the knowledge of knowing the number and positions of people in the vicinity. The navigation for example could treat them as dynamical moving objects and also predict their next motion directions in order to compute a much safer path. Or the robot could specifically approach customers and offer its services. This requires to detect a person or even a group of people in a reasonable range in front of the robot. Challenges of such a real-world task are e.g. changing lightning conditions, a dynamic environment and different people shapes. In this thesis a 3D people detection approach based on point cloud data provided by the Microsoft Kinect is implemented and integrated on mobile service robot. A Top-Down/Bottom-Up segmentation is applied to increase the systems flexibility and provided the capability to the detect people even if they are partially occluded. A feature set is proposed to detect people in various pose configurations and motions using a machine learning technique. The system can detect people up to a distance of 5 meters. The experimental evaluation compared different machine learning techniques and showed that standing people can be detected with a rate of 87.29% and sitting people with 74.94% using a Random Forest classifier. Certain objects caused several false detections. To elimante those a verification is proposed which further evaluates the persons shape in the 2D space. The detection component has been implemented as s sequential (frame rate of 10 Hz) and a parallel application (frame rate of 16 Hz). Finally, the component has been embedded into complete people search task which explorates the environment, find all people and approach each detected person.
3D tracking using multiple Nintendo Wii Remotes: a simple consumer hardware tracking approach
(2009)
An easy to build and cost-effective 3D tracking solution is presented, using Nintendo Wii Remotes acting as cameras. As the hardware differs from usual tracking cameras, the calibration and tracking process has to be adapted accordingly. The tracking approach described could be used for tracking the user's motions in video games based upon physical activity (sports, fighting or dancing games), allowing the player to interact with the game in a more intuitive way than by just pressing buttons.
3D user interfaces for virtual reality and games: 3D selection, manipulation, and spatial navigation
(2018)
In this course, we will take a detailed look at different topics in the field of 3D user interfaces (3DUIs) for Virtual Reality and Gaming. With the advent of Augmented and Virtual Reality in numerous application areas, the need and interest in more effective interfaces becomes prevalent, among others driven forward by improved technologies, increasing application complexity and user experience requirements. Within this course, we highlight key issues in the design of diverse 3DUIs by looking closely into both simple and advanced 3D selection/manipulation and spatial navigation interface design topics. These topics are highly relevant, as they form the basis for most 3DUI-driven application, yet also can cause major issues (performance, usability, experience. motion sickness) when not designed properly as they can be difficult to handle. Within this course, we build on top of a general understanding of 3DUIs to discuss typical pitfalls by looking closely at theoretical and practical aspects of selection, manipulation, and navigation and highlight guidelines for their use.
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.
Animal models are often needed in cancer research but some research questions may be answered with other models, e.g., 3D replicas of patient-specific data, as these mirror the anatomy in more detail. We, therefore, developed a simple eight-step process to fabricate a 3D replica from computer tomography (CT) data using solely open access software and described the method in detail. For evaluation, we performed experiments regarding endoscopic tumor treatment with magnetic nanoparticles by magnetic hyperthermia and local drug release. For this, the magnetic nanoparticles need to be accumulated at the tumor site via a magnetic field trap. Using the developed eight-step process, we printed a replica of a locally advanced pancreatic cancer and used it to find the best position for the magnetic field trap. In addition, we described a method to hold these magnetic field traps stably in place. The results are highly important for the development of endoscopic tumor treatment with magnetic nanoparticles as the handling and the stable positioning of the magnetic field trap at the stomach wall in close proximity to the pancreatic tumor could be defined and practiced. Finally, the detailed description of the workflow and use of open access software allows for a wide range of possible uses.
The objective of the presented approach is to develop a 3D-reconstruction method for micro organisms from sequences of microscopic images by varying the level-of-focus. The approach is limited to translucent silicatebased marine and freshwater organisms (e.g. radiolarians). The proposed 3D-reconstruction method exploits the connectivity of similarly oriented and spatially adjacent edge elements in consecutive image layers. This yields a 3D-mesh representing the global shape of the objects together with details of the inner structure. Possible applications can be found in comparative morphology or hydrobiology, where e.g. deficiencies in growth and structure during incubation in toxic water or gravity effects on metabolism have to be determined.
4GREAT is an extension of the German Receiver for Astronomy at Terahertz frequencies (GREAT) operated aboard the Stratospheric Observatory for Infrared Astronomy (SOFIA). The spectrometer comprises four different detector bands and their associated subsystems for simultaneous and fully independent science operation. All detector beams are co-aligned on the sky. The frequency bands of 4GREAT cover 491-635, 890-1090, 1240-1525 and 2490-2590 GHz, respectively. This paper presents the design and characterization of the instrument, and its in-flight performance. 4GREAT saw first light in June 2018, and has been offered to the interested SOFIA communities starting with observing cycle 6.
The objective of this research project is to develop a user-friendly and cost-effective interactive input device that allows intuitive and efficient manipulation of 3D objects (6 DoF) in virtual reality (VR) visualization environments with flat projections walls. During this project, it was planned to develop an extended version of a laser pointer with multiple laser beams arranged in specific patterns. Using stationary cameras observing projections of these patterns from behind the screens, it is planned to develop an algorithm for reconstruction of the emitter’s absolute position and orientation in space. Laser pointer concept is an intuitive way of interaction that would provide user with a familiar, mobile and efficient navigation though a 3D environment. In order to navigate in a 3D world, it is required to know the absolute position (x, y and z position) and orientation (roll, pitch and yaw angles) of the device, a total of 6 degrees of freedom (DoF). Ordinary laser-based pointers when captured on a flat surface with a video camera system and then processed, will only provide x and y coordinates effectively reducing available input to 2 DoF only. In order to overcome this problem, an additional set of multiple (invisible) laser pointers should be used in the pointing device. These laser pointers should be arranged in a way that the projection of their rays will form one fixed dot pattern when intersected with the flat surface of projection screens. Images of such a pattern will be captured via a real-time camera-based system and then processed using mathematical re-projection algorithms. This would allow the reconstruction of the full absolute 3D pose (6 DoF) of the input device. Additionally, multi-user or collaborative work should be supported by the system, would allow several users to interact with a virtual environment at the same time. Possibilities to port processing algorithms into embedded processors or FPGAs will be investigated during this project as well.
Safety-critical applications like autonomous driving use Deep Neural Networks (DNNs) for object detection and segmentation. The DNNs fail to predict when they observe an Out-of-Distribution (OOD) input leading to catastrophic consequences. Existing OOD detection methods were extensively studied for image inputs but have not been explored much for LiDAR inputs. So in this study, we proposed two datasets for benchmarking OOD detection in 3D semantic segmentation. We used Maximum Softmax Probability and Entropy scores generated using Deep Ensembles and Flipout versions of RandLA-Net as OOD scores. We observed that Deep Ensembles out perform Flipout model in OOD detection with greater AUROC scores for both datasets.
A Bicycle Simulator Based on a Motion Platform in a Virtual Reality Environment - FIVIS Project
(2007)
Background: Cancer heterogeneity poses a serious challenge concerning the toxicity and adverse effects of therapeutic inhibitors, especially when it comes to combinatorial therapies that involve multiple targeted inhibitors. In particular, in non-small cell lung cancer (NSCLC), a number of studies have reported synergistic effects of drug combinations in the preclinical models, while they were only partially successful in the clinical setup, suggesting those alternative clinical strategies (with genetic background and immune response) should be considered. Herein, we investigated the antitumor effect
of cytokine-induced killer (CIK) cells in combination with ALK and PD-1 inhibitors in vitro on genetically variable NSCLC cell lines.
Methods: We co-cultured the three genetically different NSCLC cell lines NCI-H2228 (EML4-ALK), A549 (KRAS mutation), and HCC-78 (ROS1 rearrangement) with and without nivolumab (PD-1 inhibitor) and crizotinib (ALK inhibitor). Additionally, we profiled the variability of surface expression multiple immune checkpoints, the concentration of absolute dead cells, intracellular granzyme B on CIK cells using flow cytometry as well as RT-qPCR. ELISA and Western blot were performed to verify the activation of CIK cells.
Results: Our analysis showed that (a) nivolumab significantly weakened PD-1 surface expression on CIK cells without impacting other immune checkpoints or PD-1 mRNA expression, (b) this combination strategy showed an effective response on cell viability, IFN-g production, and intracellular release of granzyme B in CD3+ CD56+ CIK cells, but solely in NCI-H2228, (c) the intrinsic expression of Fas ligand (FasL) as a T-cell activation marker in CIK cells was upregulated by this additive effect, and (d) nivolumab induced Foxp3 expression in CD4+CD25+ subpopulation of CIK cells significantly increased. Taken together, we could show that CIK cells in combination with crizotinib and nivolumab can enhance the anti-tumor immune response through FasL activation, leading to increased IFN-g and granzyme B, but only in NCI-H2228 cells with EML4-ALK rearrangement. Therefore, we hypothesize that CIK therapy may be a potential alternative in NSCLC patients harboring EML4-ALK rearrangement, in addition, we support the idea that combination therapies offer significant potential when they are optimized on a patient-by-patient basis.
The simultaneous operation of multiple different semiconducting metal oxide (MOX) gas sensors is demanding for the readout circuitry. The challenge results from the strongly varying signal intensities of the various sensor types to the target gas. While some sensors change their resistance only slightly, other types can react with a resistive change over a range of several decades. Therefore, a suitable readout circuit has to be able to capture all these resistive variations, requiring it to have a very large dynamic range. This work presents a compact embedded system that provides a full, high range input interface (readout and heater management) for MOX sensor operation. The system is modular and consists of a central mainboard that holds up to eight sensor-modules, each capable of supporting up to two MOX sensors, therefore supporting a total maximum of 16 different sensors. Its wide input range is archived using the resistance-to-time measurement method. The system is solely built with commercial off-the-shelf components and tested over a range spanning from 100Ω to 5 GΩ (9.7 decades) with an average measurement error of 0.27% and a maximum error of 2.11%. The heater management uses a well-tested power-circuit and supports multiple modes of operation, hence enabling the system to be used in highly automated measurement applications. The experimental part of this work presents the results of an exemplary screening of 16 sensors, which was performed to evaluate the system’s performance.
The choice of suitable semiconducting metal oxide (MOX) gas sensors for the detection of a specific gas or gas mixture is time-consuming since the sensor’s sensitivity needs to be characterized at multiple temperatures to find its optimal operating conditions. To obtain reliable measurement results, it is very important that the power for the sensor’s integrated heater is stable, regulated and error-free (or error-tolerant). Especially the error-free requirement can be only be achieved if the power supply implements failure-avoiding and failure-detection methods. The biggest challenge is deriving multiple different voltages from a common supply in an efficient way while keeping the system as small and lightweight as possible. This work presents a reliable, compact, embedded system that addresses the power supply requirements for fully automated simultaneous sensor characterization for up to 16 sensors at multiple temperatures. The system implements efficient (avg. 83.3% efficiency) voltage conversion with low ripple output (<32 mV) and supports static or temperature-cycled heating modes. Voltage and current of each channel are constantly monitored and regulated to guarantee reliable operation. To evaluate the proposed design, 16 sensors were screened. The results are shown in the experimental part of this work.
A Comparative Study of Uncertainty Estimation Methods in Deep Learning Based Classification Models
(2020)
Deep learning models produce overconfident predictions even for misclassified data. This work aims to improve the safety guarantees of software-intensive systems that use deep learning based classification models for decision making by performing comparative evaluation of different uncertainty estimation methods to identify possible misclassifications.
In this work, uncertainty estimation methods applicable to deep learning models are reviewed and those which can be seamlessly integrated to existing deployed deep learning architectures are selected for evaluation. The different uncertainty estimation methods, deep ensembles, test-time data augmentation and Monte Carlo dropout with its variants, are empirically evaluated on two standard datasets (CIFAR-10 and CIFAR-100) and two custom classification datasets (optical inspection and RoboCup@Work dataset). A relative ranking between the methods is provided by evaluating the deep learning classifiers on various aspects such as uncertainty quality, classifier performance and calibration. Standard metrics like entropy, cross-entropy, mutual information, and variance, combined with a rank histogram based method to identify uncertain predictions by thresholding on these metrics, are used to evaluate uncertainty quality.
The results indicate that Monte Carlo dropout combined with test-time data augmentation outperforms all other methods by identifying more than 95% of the misclassifications and representing uncertainty in the highest number of samples in the test set. It also yields a better classifier performance and calibration in terms of higher accuracy and lower Expected Calibration Error (ECE), respectively. A python based uncertainty estimation library for training and real-time uncertainty estimation of deep learning based classification models is also developed.
This paper gives an overview of the development of Fair Trade in six European countries: Austria, France, Germany, the Netherlands, Switzerland and the United Kingdom. After the description of the food retail industry and its market structures in these countries, the main European Fair Trade organizations are analyzed regarding their role within the Fair Trade system. The following part deals with the development of Fair Trade sales in general and with respect to the products coffee, tea, bananas, fruit juice and sugar. An overview of the main activities of national Fair Trade organizations, e.g. public relation activities, completes the analysis. This study shows the enormous upswing of Fair Trade during the last decade and the reasons for this development. Nevertheless, it comes to the conclusion that Fair Trade is still far away from being an essential part of the food retail industry in Europe.
This paper compares the memory allocation of two Java virtual machines, namely Oracle Java HotSpot VM 32-bit (OJVM) and Jamaica JamaicaVM (JJVM). The basic difference of the architectures in both machines is that the JamaicaVM uses fixed-size blocks for allocating objects on the heap. The basic difference of the architectures is that the JJVM uses fixed size block allocation on the heap. This means that objects have to be split into several connected blocks if they are bigger than the specified block-size. On the other hand, for small objects a full block must be allocated. The paper contains both theoretical and experimental analysis on the memory-overhead. The theoretical analysis is based on specifications of the two virtual machines. The experimental analysis is done with a modified JVMTI Agent together with the SPECjvm2008 Benchmark.
The continuously increasing number of biomedical scholarly publications makes it challenging to construct document recommendation algorithms that can efficiently navigate through literature. Such algorithms would help researchers in finding similar, relevant, and related publications that align with their research interests. Natural Language Processing offers various alternatives to compare publications, ranging from entity recognition to document embeddings. In this paper, we present the results of a comparative analysis of vector-based approaches to assess document similarity in the RELISH corpus. We aim to determine the best approach that resembles relevance without the need for further training. Specifically, we employ five different techniques to generate vectors representing the text in the documents. These techniques employ a combination of various Natural Language Processing frameworks such as Word2Vec, Doc2Vec, dictionary-based Named Entity Recognition, and state-of-the-art models based on BERT. To evaluate the document similarity obtained by these approaches, we utilize different evaluation metrics that account for relevance judgment, relevance search, and re-ranking of the relevance search. Our results demonstrate that the most promising approach is an in-house version of document embeddings, starting with word embeddings and using centroids to aggregate them by document.
The research of autonomous artificial agents that adapt to and survive in changing, possibly hostile environments, has gained momentum in recent years. Many of such agents incorporate mechanisms to learn and acquire new knowledge from its environment, a feature that becomes fundamental to enable the desired adaptation, and account for the challenges that the environment poses. The issue of how to trigger such learning, however, has not been as thoroughly studied as its significance suggest. The solution explored is based on the use of surprise (the reaction to unexpected events), as the mechanism that triggers learning. This thesis introduces a computational model of surprise that enables the robotic learner to experience surprise and start the acquisition of knowledge to explain it. A measure of surprise that combines elements from information and probability theory, is presented. Such measure offers a response to surprising situations faced by the robot, that is proportional to the degree of unexpectedness of such event. The concepts of short- and long-term memory are investigated as factors that influence the resulting surprise. Short-term memory enables the robot to habituate to new, repeated surprises, and to “forget” about old ones, allowing them to become surprising again. Long-term memory contains knowledge that is known a priori or that has been previously learned by the robot. Such knowledge influences the surprise mechanism, by applying a subsumption principle: if the available knowledge is able to explain the surprising event, suppress any trigger of surprise. The computational model of robotic surprise has been successfully applied to the domain of a robotic learner, specifically one that learns by experimentation. A brief introduction to the context of such application is provided, as well as a discussion on related issues like the relationship of the surprise mechanism with other components of the robot conceptual architecture, the challenges presented by the specific learning paradigm used, and other components of the motivational structure of the agent.
The objective of this thesis is to implement a computer game based motivation system for maximal strength testing on the Biodex System 3 Isokinetic Dynamometer. The prototype game has been designed to improve the peak torque produced in an isometric knee extensor strength test. An extensive analysis is performed on a torque data set from a previous study. The torque responses for five second long maximal voluntary contractions of the knee extensor are analyzed to understand torque response characteristics of different subjects. The parameters identifed in the data analysis are used in the implementation of the 'Shark and School of Fish' game. The behavior of the game for different torque responses is analyzed on a different torque data set from the previous study. The evaluation shows that the game rewards and motivates continuously over a repetition to reach the peak torque value. The evaluation also shows that the game rewards the user more if he overcomes a baseline torque value within the first second and then gradually increase the torque to reach peak torque.
New cars are increasingly "connected" by default. Since not having a car is not an option for many people, understanding the privacy implications of driving connected cars and using their data-based services is an even more pressing issue than for expendable consumer products. While risk-based approaches to privacy are well established in law, they have only begun to gain traction in HCI. These approaches are understood not only to increase acceptance but also to help consumers make choices that meet their needs. To the best of our knowledge, perceived risks in the context of connected cars have not been studied before. To address this gap, our study reports on the analysis of a survey with 18 open-ended questions distributed to 1,000 households in a medium-sized German city. Our findings provide qualitative insights into existing attitudes and use cases of connected car features and, most importantly, a list of perceived risks themselves. Taking the perspective of consumers, we argue that these can help inform consumers about data use in connected cars in a user-friendly way. Finally, we show how these risks fit into and extend existing risk taxonomies from other contexts with a stronger social perspective on risks of data use.
During exercise, heart rate has proven to be a good measure in planning workouts. It is not only simple to measure but also well understood and has been used for many years for workout planning. To use heart rate to control physical exercise, a model which predicts future heart rate dependent on a given strain can be utilized. In this paper, we present a mathematical model based on convolution for predicting the heart rate response to strain with four physiologically explainable parameters. This model is based on the general idea of the Fitness-Fatigue model for performance analysis, but is revised here for heart rate analysis. Comparisons show that the Convolution model can compete with other known heart rate models. Furthermore, this new model can be improved by reducing the number of parameters. The remaining parameter seems to be a promising indicator of the actual subject’s fitness.
Computers can help us to trigger our intuition about how to solve a problem. But how does a computer take into account what a user wants and update these triggers? User preferences are hard to model as they are by nature vague, depend on the user’s background and are not always deterministic, changing depending on the context and process under which they were established. We pose that the process of preference discovery should be the object of interest in computer aided design or ideation. The process should be transparent, informative, interactive and intuitive. We formulate Hyper-Pref, a cyclic co-creative process between human and computer, which triggers the user’s intuition about what is possible and is updated according to what the user wants based on their decisions. We combine quality diversity algorithms, a divergent optimization method that can produce many, diverse solutions, with variational autoencoders to both model that diversity as well as the user’s preferences, discovering the preference hypervolume within large search spaces.
In Sensor-based Fault Detection and Diagnosis (SFDD) methods, spatial and temporal dependencies among the sensor signals can be modeled to detect faults in the sensors, if the defined dependencies change over time. In this work, we model Granger causal relationships between pairs of sensor data streams to detect changes in their dependencies. We compare the method on simulated signals with the Pearson correlation, and show that the method elegantly handles noise and lags in the signals and provides appreciable dependency detection. We further evaluate the method using sensor data from a mobile robot by injecting both internal and external faults during operation of the robot. The results show that the method is able to detect changes in the system when faults are injected, but is also prone to detecting false positives. This suggests that this method can be used as a weak detection of faults, but other methods, such as the use of a structural model, are required to reliably detect and diagnose faults.
Electrical signal transmission in power electronic devices takes place through high-purity aluminum bonding wires. Cyclic mechanical and thermal stresses during operation lead to fatigue loads, resulting in premature failure of the wires, which cannot be reliably predicted. The following work presents two fatigue lifetime models calibrated and validated based on experimental fatigue results of an aluminum bonding wire and subsequently transferred and applied to other wire types. The lifetime modeling of Wöhler curves for different load ratios shows good but limited applicability for the linear model. The model can only be applied above 10,000 cycles and within the investigated load range of R = 0.1 to R = 0.7. The nonlinear model shows very good agreement between model prediction and experimental results over the entire investigated cycle range. Furthermore, the predicted Smith diagram is not only consistent in the investigated load range but also in the extrapolated load range from R = −1.0 to R = 0.8. A transfer of both model approaches to other wire types by using their tensile strengths can be implemented as well, although the nonlinear model is more suitable since it covers the entire load and cycle range.
As from the beginning of the late 70's an impressive number of innovative electronic payment systems have been developed and tested commercially. However, the resulting variety and complexity of the systems have turned out to be one of the obstacles for the broad acceptance of electronic payment. In this paper we propose a process and service oriented framework, which offers a structural and conceptual orientation in the field of electronic payment. It renders possible an integral view on electronic payment that goes beyond the frame of an individual system. To do this, we have generalized the systems-oriented approaches to a phase-oriented payment model. Using this model, requirements and supporting services for electronic payment can be sorted systematically and described from both the customers' and the merchants' viewpoint. Providing integrated payment processes and services is proving to be a difficult task. With this paper we would like to outline the necessity for a Payment Service Provider to act as a mediator for suppliers and users of electronic payment systems.
Climate change is increasingly affecting vulnerable groups and resulting in dire social and economic consequences, especially for those in the Global South. Managing current and emerging climate-related risks will require increasing individual’s and communities’ resilience, including enhancing absorptive, adaptive, and transformative capacities. Policymakers are now considering the role that social protection policies and programmes can play in building climate resilience by contributing to these capacities. However, there is a limited understanding of the extent to which social protection instruments can influence these three resilience-related capacities. Lack of assessment tools or frameworks might contribute to limited evidence of social protection’s ability to increase climate resilience. In particular, there appear to be no frameworks or tools that help assess the role of social cash transfers (SCT) in building adaptive capacity. Based on a multi-staged literature review, we develop an adaptive capacity outcomes framework (ACOF) that can help assess SCT’s contribution to building adaptive capacity, and, consequently, resilience. The framework is then tested using impact evaluation and assessment reports from SCT programmes in Indonesia, Zambia, Ethiopia, Bangladesh, and Tanzania. The exercise finds that SCTs alone have a limited contribution to adaptive capacity outcomes, but interventions that combine cash transfers with other components such as nutrition or livelihood training show positive impacts. We find that the ACOF can support assessments of SCT’s contribution towards adaptive capacity. It can help build evidence, evaluate impacts, and through further research, can facilitate learning on SCTs' role in increasing climate resilience.
Failure prognostic builds up on constant data acquisition and processing and fault diagnosis and is an essential part of predictive maintenance of smart manufacturing systems enabling condition based maintenance, optimised use of plant equipment, improved uptime and yield and to prevent safety problems. Given known control inputs into a plant and real sensor outputs or simulated measurements, the model-based part of the proposed hybrid method provides numerical values of unknown parameter degradation functions at sampling time points by the evaluation of equations that have been derived offline from a bicausal diagnostic bond graph. These numerical values are computed concurrently to the constant monitoring of a system and are stored in a buffer of fixed length. The data-driven part of the method provides a sequence of remaining useful life estimates by repeated projection of the parameter degradation into the future based on the use of values in a sliding time window. Existing software can be used to determine the best fitting function and can account for its random parameters. The continuous parameter estimation and their projection into the future can be performed in parallel for multiple isolated simultaneous parametric faults on a multicore, multiprocessor computer.
The proposed hybrid bond graph model-based, data-driven method is verified by an offline simulation case study of a typical power electronic circuit. It can be used to implement embedded systems that enable cooperating machines in smart manufacturing to perform prognostic themselves.
In the design of robot skills, the focus generally lies on increasing the flexibility and reliability of the robot execution process; however, typical skill representations are not designed for analysing execution failures if they occur or for explicitly learning from failures. In this paper, we describe a learning-based hybrid representation for skill parameterisation called an execution model, which considers execution failures to be a natural part of the execution process. We then (i) demonstrate how execution contexts can be included in execution models, (ii) introduce a technique for generalising models between object categories by combining generalisation attempts performed by a robot with knowledge about object similarities represented in an ontology, and (iii) describe a procedure that uses an execution model for identifying a likely hypothesis of a parameterisation failure. The feasibility of the proposed methods is evaluated in multiple experiments performed with a physical robot in the context of handle grasping, object grasping, and object pulling. The experimental results suggest that execution models contribute towards avoiding execution failures, but also represent a first step towards more introspective robots that are able to analyse some of their execution failures in an explicit manner.
Computers will soon be powerful enough to simulate consciousness. The artificial life community should start to try to understand how consciousness could be simulated. The proposal is to build an artificial life system in which consciousness might be able to evolve. The idea is to develop internet-wide artificial universe in which the agents can evolve. Users play games by defining agents that form communities. The communities have to perform tasks, or compete, or whatever the specific game demands. The demands should be such that agents that are more aware of their universe are more likely to succeed. The agents reproduce and evolve within their user’s machine, but can also sometimes transfer to other machine across the internet. Users will be able to choose the capabilities of their agents from a fixed list, but may also write their own powers for their agents.
A Low-Cost Based 6 DoF Head Tracker for Usability Application Studies in Virtual Environments
(2008)
In Mixed Reality (MR) Environments, the user's view is augmented with virtual, artificial objects. To visualize virtual objects, the position and orientation of the user's view or the camera is needed. Tracking of the user's viewpoint is an essential area in MR applications, especially for interaction and navigation. In present systems, the initialization is often complex. For this reason, we introduce a new method for fast initialization of markerless object tracking. This method is based on Speed Up Robust Features and paradoxically on a traditional marker-based library. Most markerless tracking algorithms can be divided into two parts: an offline and an online stage. The focus of this paper is optimization of the offline stage, which is often time-consuming.
A Method for the Sustainable Documentation of Operations Processes in Parcel Distribution Centers
(2018)
There is often no common understanding on operational processes in logistics companies as they are not properly documented. Hence, people execute the same process differently and training is conducted by experienced operators on an ad-hoc basis. Furthermore, continuous process improvement is hampered as neither the ideal process nor current issues in as-is processes are visible. A major reason for the missing documentation is the complexity of existing business process modelling languages. Modelling experts are required for initially describing the processes and also for updating the models after process changes. Furthermore, operations people are usually not used to read complex process models in EPCs or BPMN diagrams. In order to overcome these limitations, a domain-specific modelling language which facilitates maintaining up-to-date process models has been designed with a large logistics company in Germany. The paper at hand briefly describes this language and illustrates the method on how to apply it in operations environments.
Integrating physical simulation data into data ecosystems challenges the compatibility and interoperability of data management tools. Semantic web technologies and relational databases mostly use other data types, such as measurement or manufacturing design data. Standardizing simulation data storage and harmonizing the data structures with other domains is still a challenge, as current standards such as the ISO standard STEP (ISO 10303 ”Standard for the Exchange of Product model data”) fail to bridge the gap between design and simulation data. This challenge requires new methods, such as ontologies, to rethink simulation results integration. This research describes a new software architecture and application methodology based on the industrial standard ”Virtual Material Modelling in Manufacturing” (VMAP). The architecture integrates large quantities of structured simulation data and their analyses into a semantic data structure. It is capable of providing data permeability from the global digital twin level to the detailed numerical values of data entries and even new key indicators in a three-step approach: It represents a file as an instance in a knowledge graph, queries the file’s metadata, and finds a semantically represented process that enables new metadata to be created and instantiated.
Recessive mutations in the MPV17 gene cause mitochondrial DNA depletion syndrome, a fatal infantile genetic liver disease in humans. Loss of function in mice leads to glomerulosclerosis and sensineural deafness accompanied with mitochondrial DNA depletion. Mutations in the yeast homolog Sym1, and in the zebra fish homolog tra cause interesting, but not obviously related phenotypes, although the human gene can complement the yeast Sym1 mutation. The MPV17 protein is a hydrophobic membrane protein of 176 amino acids and unknown function. Initially localised in murine peroxisomes, it was later reported to be a mitochondrial inner membrane protein in humans and in yeast. To resolve this contradiction we tested two new mouse monoclonal antibodies directed against the human MPV17 protein in Western blots and immunohistochemistry on human U2OS cells. One of these monoclonal antibodies showed specific reactivity to a protein of 20 kD absent in MPV17 negative mouse cells. Immunofluorescence studies revealed colocalisation with peroxisomal, endosomal and lysosomal markers, but not with mitochondria. This data reveal a novel connection between a possible peroxisomal/endosomal/lysosomal function and mitochondrial DNA depletion.
Cognitive robotics aims at understanding biological processes, though it has also the potential to improve future robotics systems. Here we show how a biologically inspired model of motor control with neural fields can be augmented with additional components such that it is able to solve a basic robotics task, that of obstacle avoidance. While obstacle avoidance is a well researched area, the focus here is on the extensibility of a biologically inspired framework. This work demonstrates how easily the biological inspired system can be used to adapt to new tasks. This flexibility is thought to be a major hallmark of biological agents.
Neuromorphic computing aims to mimic the computational principles of the brain in silico and has motivated research into event-based vision and spiking neural networks (SNNs). Event cameras (ECs) capture local, independent changes in brightness, and offer superior power consumption, response latencies, and dynamic ranges compared to frame-based cameras. SNNs replicate neuronal dynamics observed in biological neurons and propagate information in sparse sequences of ”spikes”. Apart from biological fidelity, SNNs have demonstrated potential as an alternative to conventional artificial neural networks (ANNs), such as in reducing energy expenditure and inference time in visual classification. Although potentially beneficial for robotics, the novel event-driven and spike-based paradigms remain scarcely explored outside the domain of aerial robots.
To investigate the utility of brain-inspired sensing and data processing in a robotics application, we developed a neuromorphic approach to real-time, online obstacle avoidance on a manipulator with an onboard camera. Our approach adapts high-level trajectory plans with reactive maneuvers by processing emulated event data in a convolutional SNN, decoding neural activations into avoidance motions, and adjusting plans in a dynamic motion primitive formulation. We conducted simulated and real experiments with a Kinova Gen3 arm performing simple reaching tasks involving static and dynamic obstacles. Our implementation was systematically tuned, validated, and tested in sets of distinct task scenarios, and compared to a non-adaptive baseline through formalized quantitative metrics and qualitative criteria.
The neuromorphic implementation facilitated reliable avoidance of imminent collisions in most scenarios, with 84% and 92% median success rates in simulated and real experiments, where the baseline consistently failed. Adapted trajectories were qualitatively similar to baseline trajectories, indicating low impacts on safety, predictability and smoothness criteria. Among notable properties of the SNN were the correlation of processing time with the magnitude of perceived motions (captured in events) and robustness to different event emulation methods. Preliminary tests with a DAVIS346 EC showed similar performance, validating our experimental event emulation method. These results motivate future efforts to incorporate SNN learning, utilize neuromorphic processors, and target other robot tasks to further explore this approach.
A New Approach of Using Two Wireless Tracking Systems in Mobile Augmented Reality Applications
(2003)
This paper proposes a novel approach to the generation of state equations from a bond graph (BG) of a mode switching linear time invariant model. Fast state transitions are modelled by ideal or non-ideal switches. Fixed causalities are assigned following the Standard Causality Assignment Procedure such that the number of storage elements in integral causality is maximised. A system of differential and algebraic equations (DAEs) is derived from the BG that holds for all system modes. It is distinguished between storage elements with mode independent causality and those that change causality due to switch state changes.
In the last 5 years a close co-operation between the Bonn-Rhein-Sieg University of Applied Sciences and the Philips Research Laboratories in Aachen has been established. In this article I want to report on the co-operation of the Department of Electrical Engineering, Mechanical Engineering and Technical Journalism with Philips. Besides a number of diploma theses on the field of water treatment with new discharge lamps, power electronics and modelling of electromagnetic field configurations, there is running also an activity on a new generation of highly efficient light sources based on molecular discharges.
With the increasing demand for ultrapure water in the pharmaceutical and semiconductor industry, the need for precise measuring instruments for those applications is also growing. One critical parameter of water quality is the amount of total organic carbon (TOC). This work presents a system that uses the advantage of the increased oxidation power achieved with UV/O3 advanced oxidation process (AOP) for TOC measurement in combination with a significant miniaturization compared to the state of the art. The miniaturization is achieved by using polymer-electrolyte membrane (PEM) electrolysis cells for ozone generation in combination with UV-LEDs for irradiation of the measuring solution, as both components are significantly smaller than standard equipment. Conductivity measurement after oxidation is the measuring principle and measurements were carried out in the TOC range between 10 and 1000 ppb TOC. The suitability of the system for TOC measurement is demonstrated using the oxidation by ozonation combined with UV irradiation of defined concentrations of isopropyl alcohol (IPA).
The increasing complexity of tasks that are required to be executed by robots demands higher reliability of robotic platforms. For this, it is crucial for robot developers to consider fault diagnosis. In this study, a general non-intrusive fault diagnosis system for robotic platforms is proposed. A mini-PC is non-intrusively attached to a robot that is used to detect and diagnose faults. The health data and diagnosis produced by the mini-PC is then standardized and transmitted to a remote-PC. A storage device is also attached to the mini-PC for data logging of health data in case of loss of communication with the remote-PC. In this study, a hybrid fault diagnosis method is compared to consistency-based diagnosis (CBD), and CBD is selected to be deployed on the system. The proposed system is modular and can be deployed on different robotic platforms with minimum setup.
This paper presents a novel approach to address noise, vibration, and harshness (NVH) issues in electrically assisted bicycles (e-bikes) caused by the drive unit. By investigating and optimising the structural dynamics during early product development, NVH can decisively be improved and valuable resources can be saved, emphasising its significance for enhancing riding performance. The paper offers a comprehensive analysis of the e-bike drive unit’s mechanical interactions among relevant components, culminating—to the best of our knowledge—in the development of the first high-fidelity model of an entire e-bike drive unit. The proposed model uses the principles of elastic multi body dynamics (eMBD) to elucidate the structural dynamics in dynamic-transient calculations. Comparing power spectra between measured and simulated motion variables validates the chosen model assumptions. The measurements of physical samples utilise accelerometers, contactless laser Doppler vibrometry (LDV) and various test arrangements, which are replicated in simulations and provide accessibility to measure vibrations onto rotating shafts and stationary structures. In summary, this integrated system-level approach can serve as a viable starting point for comprehending and managing the NVH behaviour of e-bikes.
In this paper, a gas-to-power (GtoP) system for power outages is digitally modeled and experimentally developed. The design includes a solid-state hydrogen storage system composed of TiFeMn as a hydride forming alloy (6.7 kg of alloy in five tanks) and an air-cooled fuel cell (maximum power: 1.6 kW). The hydrogen storage system is charged under room temperature and 40 bar of hydrogen pressure, reaching about 110 g of hydrogen capacity. In an emergency use case of the system, hydrogen is supplied to the fuel cell, and the waste heat coming from the exhaust air of the fuel cell is used for the endothermic dehydrogenation reaction of the metal hydride. This GtoP system demonstrates fast, stable, and reliable responses, providing from 149 W to 596 W under different constant as well as dynamic conditions. A comprehensive and novel simulation approach based on a network model is also applied. The developed model is validated under static and dynamic power load scenarios, demonstrating excellent agreement with the experimental results.
In the context of the Franco-German research project Re(h)strain, this work focuses on a global system analysis integrating both safety and security analysis of international and/or urban railway stations. The Re(h)strain project focuses on terrorist attacks on high speed train systems and investigates prevention and mitigation measures to reduce the overall vulnerability and strengthen the system resilience. One main criterion regarding public transport issues is the number of passengers. For example, the railway station of Paris “Gare du Nord” deals with a bigger number of passengers than the biggest airport in the world (SNCF open Data 2014), the Atlanta airport, but in terms of passengers, it is only around the 23rd rank railway station in the world. Due to the enormous mass of people, this leads to the system approach of breaking out the station into several classes of zones, e.g. entrance, main hall, quays, trains, etc. All classes are analysed considering state-of-the-art parameters, like targets attractiveness, feasibility of attack, possible damage, possible mitigation and defences. Then, safety incidence of security defence is discussed in order to refine security requirement with regard to the considered zone. Finally, global requirements of security defence correlated to the corresponding class of zones are proposed.
The development of robot control programs is a complex task. Many robots are different in their electrical and mechanical structure which is also reflected in the software. Specific robot software environments support the program development, but are mainly text-based and usually applied by experts in the field with profound knowledge of the target robot. This paper presents a graphical programming environment which aims to ease the development of robot control programs. In contrast to existing graphical robot programming environments, our approach focuses on the composition of parallel action sequences. The developed environment allows to schedule independent robot actions on parallel execution lines and provides mechanism to avoid side-effects of parallel actions. The developed environment is platform-independent and based on the model-driven paradigm. The feasibility of our approach is shown by the application of the sequencer to a simulated service robot and a robot for educational purpose.
A PM2.5 concentration prediction framework with vehicle tracking system: From cause to effect
(2023)
AI systems pose unknown challenges for designers, policymakers, and users which aggravates the assessment of potential harms and outcomes. Although understanding risks is a requirement for building trust in technology, users are often excluded from legal assessments and explanations of AI hazards. To address this issue we conducted three focus groups with 18 participants in total and discussed the European proposal for a legal framework for AI. Based on this, we aim to build a (conceptual) model that guides policymakers, designers, and researchers in understanding users’ risk perception of AI systems. In this paper, we provide selected examples based on our preliminary results. Moreover, we argue for the benefits of such a perspective.
A qualitative study of Machine Learning practices and engineering challenges in Earth Observation
(2021)
Machine Learning (ML) is ubiquitously on the advance. Like many domains, Earth Observation (EO) also increasingly relies on ML applications, where ML methods are applied to process vast amounts of heterogeneous and continuous data streams to answer socially and environmentally relevant questions. However, developing such ML- based EO systems remains challenging: Development processes and employed workflows are often barely structured and poorly reported. The application of ML methods and techniques is considered to be opaque and the lack of transparency is contradictory to the responsible development of ML-based EO applications. To improve this situation a better understanding of the current practices and engineering-related challenges in developing ML-based EO applications is required. In this paper, we report observations from an exploratory study where five experts shared their view on ML engineering in semi-structured interviews. We analysed these interviews with coding techniques as often applied in the domain of empirical software engineering. The interviews provide informative insights into the practical development of ML applications and reveal several engineering challenges. In addition, interviewees participated in a novel workflow sketching task, which provided a tangible reflection of implicit processes. Overall, the results confirm a gap between theoretical conceptions and real practices in ML development even though workflows were sketched abstractly as textbook-like. The results pave the way for a large-scale investigation on requirements for ML engineering in EO.
This work presents the analysis of data recorded by an eye tracking device in the course of evaluating a foveated rendering approach for head-mounted displays (HMDs). Foveated rendering methods adapt the image synthesis process to the user’s gaze and exploiting the human visual system’s limitations to increase rendering performance. Especially, foveated rendering has great potential when certain requirements have to be fulfilled, like low-latency rendering to cope with high display refresh rates. This is crucial for virtual reality (VR), as a high level of immersion, which can only be achieved with high rendering performance and also helps to reduce nausea, is an important factor in this field. We put things in context by first providing basic information about our rendering system, followed by a description of the user study and the collected data. This data stems from fixation tasks that subjects had to perform while being shown fly-through sequences of virtual scenes on an HMD. These fixation tasks consisted of a combination of various scenes and fixation modes. Besides static fixation targets, moving tar- gets on randomized paths as well as a free focus mode were tested. Using this data, we estimate the precision of the utilized eye tracker and analyze the participants’ accuracy in focusing the displayed fixation targets. Here, we also take a look at eccentricity-dependent quality ratings. Comparing this information with the users’ quality ratings given for the displayed sequences then reveals an interesting connection between fixation modes, fixation accuracy and quality ratings.
This paper introduces a random number generator (RNG) based on the avalanche noise of two diodes. A true random number generator (TRNG) generates true random numbers with the use of the electronic noise produced by two avalanche diodes. The amplified outputs of the diodes are sampled and digitized. The difference between the two concurrently sampled and digitized outputs is calculated and used to select a seed and to drive a pseudo-random number generator (PRNG). The PRNG is an xorshift generator that generates 1024 bits in each cycle. Every sequence of 1024 bits is moderately modified and output. The TRNG delivers the next seed and the next cycle begins. The statistical behavior of the generator is analyzed and presented.