Anzahl der Treffer: 11
Erstellt: Sat, 02 Jul 2022 23:14:48 +0200 in 0.1027 sec

Bohn, Kristin; Amberg, Michael; Meier, Toni; Forner, Frank; Stangl, Gabriele I.; Mäder, Patrick;
Estimating food ingredient compositions based on mandatory product labeling. - In: Journal of food composition and analysis, ISSN 0889-1575, Bd. 110 (2022), 104508, S. 1-9

Having a specific understanding of the actual ingredient composition of products helps to calculate additional nutritional information, such as containing fatty and amino acids, minerals and vitamins, as well as to determine its environmental impacts. Unfortunately, producers rarely provide information on how much of each ingredient is in a product. Food manufacturers are, however, required to declare their products in terms of a label comprising an ingredient list (in descending order) and Big7 nutrient values. In this paper, we propose an automated approach for estimating ingredient contents in food products. First, we parse product labels to extract declared ingredients. Next, we exert mathematical formulations on the assumption that the weighted sum of Big7 ingredients as available from food compositional tables should resemble the product’s declared overall Big7 composition. We apply mathematical optimization techniques to find the best fitting ingredient composition estimate. We apply the proposed method to a dataset of 1804 food products spanning 11 product categories. We find that 76% of these products could be analyzed by our approach, and a composition within the prescribed nutrient tolerances could be calculated, using 20% of the allowed tolerances per Big7 ingredient on average. The remaining 24% of the food products could still be estimated when relaxing one or multiple nutrient tolerances. A study with known ingredient compositions shows that estimates are within a 0.9% difference of products’ actual recipes. Hence, the automated approach presented here allows for further analysis of large product quantities and provides possibilities for more intensive nutritional and ecological evaluations of food.

Tomova, Mihaela Todorova; Hofmann, Martin; Mäder, Patrick;
SEOSS-Queries - a software engineering dataset for text-to-SQL and question answering tasks. - In: Data in Brief, ISSN 2352-3409, Bd. 42 (2022), 108211, S. 1-11

Katal, Negin; Rzanny, Michael Carsten; Mäder, Patrick; Wäldchen, Jana;
Deep learning in plant phenological research: a systematic literature review. - In: Frontiers in plant science, ISSN 1664-462X, Bd. 13 (2022), 805738, S. 1-18

Climate change represents one of the most critical threats to biodiversity with far-reaching consequences for species interactions, the functioning of ecosystems, or the assembly of biotic communities. Plant phenology research has gained increasing attention as the timing of periodic events in plants is strongly affected by seasonal and interannual climate variation. Recent technological development allowed us to gather invaluable data at a variety of spatial and ecological scales. The feasibility of phenological monitoring today and in the future depends heavily on developing tools capable of efficiently analyzing these enormous amounts of data. Deep Neural Networks learn representations from data with impressive accuracy and lead to significant breakthroughs in, e.g., image processing. This article is the first systematic literature review aiming to thoroughly analyze all primary studies on deep learning approaches in plant phenology research. In a multi-stage process, we selected 24 peer-reviewed studies published in the last five years (2016-2021). After carefully analyzing these studies, we describe the applied methods categorized according to the studied phenological stages, vegetation type, spatial scale, data acquisition- and deep learning methods. Furthermore, we identify and discuss research trends and highlight promising future directions. We present a systematic overview of previously applied methods on different tasks that can guide this emerging complex research field.

Pandey, Sandeep; Teutsch, Philipp; Mäder, Patrick; Schumacher, Jörg;
Direct data-driven forecast of local turbulent heat flux in Rayleigh-Bénard convection. - In: Physics of fluids, ISSN 1089-7666, Bd. 34 (2022), 4, S. 045106-1-045106-14

A combined convolutional autoencoder-recurrent neural network machine learning model is presented to directly analyze and forecast the dynamics and low-order statistics of the local convective heat flux field in a two-dimensional turbulent Rayleigh-Bénard convection flow at Prandtl number Pr=7 and Rayleigh number Ra=10^7. Two recurrent neural networks are applied for the temporal advancement of turbulent heat transfer data in the reduced latent data space, an echo state network, and a recurrent gated unit. Thereby, our work exploits the modular combination of three different machine learning algorithms to build a fully data-driven and reduced model for the dynamics of the turbulent heat transfer in a complex thermally driven flow. The convolutional autoencoder with 12 hidden layers is able to reduce the dimensionality of the turbulence data to about 0.2% of their original size. Our results indicate a fairly good accuracy in the first- and second-order statistics of the convective heat flux. The algorithm is also able to reproduce the intermittent plume-mixing dynamics at the upper edges of the thermal boundary layers with some deviations. The same holds for the probability density function of the local convective heat flux with differences in the far tails. Furthermore, we demonstrate the noise resilience of the framework. This suggests that the present model might be applicable as a reduced dynamical model that delivers transport fluxes and their variations to coarse grids of larger-scale computational models, such as global circulation models for atmosphere and ocean.

Janke, Mario; Mäder, Patrick;
Graph based mining of code change patterns from version control commits. - In: IEEE transactions on software engineering, ISSN 1939-3520, Bd. 48 (2022), 3, S. 848-863

Detailed knowledge of frequently recurring code changes can be beneficial for a variety of software engineering activities. For example, it is a key step to understand the process of software evolution, but is also necessary when developing more sophisticated code completion features predicting likely changes. Previous attempts on automatically finding such code change patterns were mainly based on frequent itemset mining, which essentially finds sets of edits occurring in close proximity. However, these approaches do not analyze the interplay among code elements, e.g., two code objects being named similarly, and thereby neglect great potential in identifying a number of meaningful patterns. We present a novel method for the automated mining of code change patterns from Git repositories that captures these context relations between individual edits. Our approach relies on a transformation of source code into a graph representation, while keeping relevant relations present. We then apply graph mining techniques to extract frequent subgraphs, which can be used for further analysis of development projects. We suggest multiple usage scenarios for the resulting pattern type. Additionally, we propose a transformation into complex event processing (CEP) rules which allows for easier application, especially for event-based auto-completion recommenders or similar tools. For evaluation, we mined seven open-source code repositories. We present 25 frequent change patterns occurring across these projects. We found these patterns to be meaningful, easy to interpret and mostly persistent across project borders. On average, a pattern from our set appeared in 45 percent of the analyzed code changes.

Fischer, Kai; Simon, Martin; Milz, Stefan; Mäder, Patrick;
StickyLocalization: robust end-to-end relocalization on point clouds using graph neural networks. - In: IEEE Xplore digital library, ISSN 2473-2001, (2022), S. 307-316

Relocalization inside pre-built maps provides a big benefit in the course of today’s autonomous driving tasks where the map can be considered as an additional sensor for refining the estimated current pose of the vehicle. Due to potentially large drifts in the initial pose guess as well as maps containing unfiltered dynamic and temporal static objects (e.g. parking cars), traditional methods like ICP tend to fail and show high computation times. We propose a novel and fast relocalization method for accurate pose estimation inside a pre-built map based on 3D point clouds. The method is robust against inaccurate initialization caused by low performance GPS systems and tolerates the presence of unfiltered objects by specifically learning to extract significant features from current scans and adjacent map sections. More specifically, we introduce a novel distance-based matching loss enabling us to simultaneously extract important information from raw point clouds and aggregating inner- and inter-cloud context by utilizing self- and cross-attention inside a Graph Neural Network. We evaluate StickyLocalization’s (SL) performance through an extensive series of experiments using two benchmark datasets in terms of Relocalization on NuScenes and Loop Closing using KITTI’s Odometry dataset. We found that SL outperforms state-of-the art point cloud registration and relocalization methods in terms of transformation errors and runtime.

Scheliga, Daniel; Mäder, Patrick; Seeland, Marco;
PRECODE - a generic model extension to prevent deep gradient leakage. - In: IEEE Xplore digital library, ISSN 2473-2001, (2022), S. 3605-3614

Collaborative training of neural networks leverages distributed data by exchanging gradient information between different clients. Although training data entirely resides with the clients, recent work shows that training data can be reconstructed from such exchanged gradient information. To enhance privacy, gradient perturbation techniques have been proposed. However, they come at the cost of reduced model performance, increased convergence time, or increased data demand. In this paper, we introduce PRECODE, a PRivacy EnhanCing mODulE that can be used as generic extension for arbitrary model architectures. We propose a simple yet effective realization of PRECODE using variational modeling. The stochastic sampling induced by variational modeling effectively prevents privacy leakage from gradients and in turn preserves privacy of data owners. We evaluate PRECODE using state of the art gradient inversion attacks on two different model architectures trained on three datasets. In contrast to commonly used defense mechanisms, we find that our proposed modification consistently reduces the attack success rate to 0% while having almost no negative impact on model training and final performance. As a result, PRECODE reveals a promising path towards privacy enhancing model extensions.

Rzanny, Michael Carsten; Wittich, Hans Christian; Mäder, Patrick; Deggelmann, Alice; Boho, David; Wäldchen, Jana;
Image-based automated recognition of 31 Poaceae species: the most relevant perspectives. - In: Frontiers in plant science, ISSN 1664-462X, Bd. 12 (2022), 804140, S. 1-12

Poaceae represent one of the largest plant families in the world. Many species are of great economic importance as food and forage plants while others represent important weeds in agriculture. Although a large number of studies currently address the question of how plants can be best recognized on images, there is a lack of studies evaluating specific approaches for uniform species groups considered difficult to identify because they lack obvious visual characteristics. Poaceae represent an example of such a species group, especially when they are non-flowering. Here we present the results from an experiment to automatically identify Poaceae species based on images depicting six well-defined perspectives. One perspective shows the inflorescence while the others show vegetative parts of the plant such as the collar region with the ligule, adaxial and abaxial side of the leaf and culm nodes. For each species we collected 80 observations, each representing a series of six images taken with a smartphone camera. We extract feature representations from the images using five different convolutional neural networks (CNN) trained on objects from different domains and classify them using four state-of-the art classification algorithms. We combine these perspectives via score level fusion. In order to evaluate the potential of identifying non-flowering Poaceae we separately compared perspective combinations either comprising inflorescences or not. We find that for a fusion of all six perspectives, using the best combination of feature extraction CNN and classifier, an accuracy of 96.1% can be achieved. Without the inflorescence, the overall accuracy is still as high as 90.3%. In all but one case the perspective conveying the most information about the species (excluding inflorescence) is the ligule in frontal view. Our results show that even species considered very difficult to identify can achieve high accuracies in automatic identification as long as images depicting suitable perspectives are available. We suggest that our approach could be transferred to other difficult-to-distinguish species groups in order to identify the most relevant perspectives.

Gao, Hui; Kuang, Hongyu; Ma, Xiaoxing; Hu, Hao; Lü, Jian; Mäder, Patrick; Egyed, Alexander;
Propagating frugal user feedback through closeness of code dependencies to improve IR-based traceability recovery. - In: Empirical software engineering, ISSN 1573-7616, Bd. 27 (2022), 2, 41, insges. 53 S.

Traceability recovery captures trace links among different software artifacts (e.g., requirements and code) when two artifacts cover the same part of system functionalities. These trace links provide important support for developers in software maintenance and evolution tasks. Information Retrieval (IR) is now the mainstream technique for semi-automatic approaches to recover candidate trace links based on textual similarities among artifacts. The performance of IR-based traceability recovery is evaluated by the ranking of relevant traces in the generated lists of candidate links. Unfortunately, this performance is greatly hindered by the vocabulary mismatch problem between different software artifacts. To address this issue, a growing body of enhancing strategies based on user feedback is proposed to adjust the calculated IR values of candidate links after the user verifies part of these links. However, the improvement brought by this kind of strategies requires a large amount of user feedback, which could be infeasible in practice. In this paper, we propose to improve IR-based traceability recovery by propagating a small amount of user feedback through the closeness analysis on call and data dependencies in the code. Specifically, our approach first iteratively asks users to verify a small set of candidate links. The collected frugal feedback is then composed with the quantified functional similarity for each code dependency (called closeness) and the generated IR values to improve the ranking of unverified links. An empirical evaluation based on nine real-world systems with three mainstream IR models shows that our approach can outperform five baseline approaches by using only a small amount of user feedback.

Sonnekalb, Tim; Heinze, Thomas S.; Mäder, Patrick;
Deep security analysis of program code : a systematic literature review. - In: Empirical software engineering, ISSN 1573-7616, Bd. 27 (2022), 1, 2, insges. 39 S.

Due to the continuous digitalization of our society, distributed and web-based applications become omnipresent and making them more secure gains paramount relevance. Deep learning (DL) and its representation learning approach are increasingly been proposed for program code analysis potentially providing a powerful means in making software systems less vulnerable. This systematic literature review (SLR) is aiming for a thorough analysis and comparison of 32 primary studies on DL-based vulnerability analysis of program code. We found a rich variety of proposed analysis approaches, code embeddings and network topologies. We discuss these techniques and alternatives in detail. By compiling commonalities and differences in the approaches, we identify the current state of research in this area and discuss future directions. We also provide an overview of publicly available datasets in order to foster a stronger benchmarking of approaches. This SLR provides an overview and starting point for researchers interested in deep vulnerability analysis on program code.

Ravi Kumar, Varun;
Multi-task near-field perception for autonomous driving using surround-view fisheye cameras. - Ilmenau : Universitätsbibliothek, 2021. - 1 Online-Ressource (xxv, 219 Seiten)
Technische Universität Ilmenau, Dissertation 2021

Literaturverzeichnis: Seite 183-219

Die Bildung der Augen führte zum Urknall der Evolution. Die Dynamik änderte sich von einem primitiven Organismus, der auf den Kontakt mit der Nahrung wartete, zu einem Organismus, der durch visuelle Sensoren gesucht wurde. Das menschliche Auge ist eine der raffiniertesten Entwicklungen der Evolution, aber es hat immer noch Mängel. Der Mensch hat über Millionen von Jahren einen biologischen Wahrnehmungsalgorithmus entwickelt, der in der Lage ist, Autos zu fahren, Maschinen zu bedienen, Flugzeuge zu steuern und Schiffe zu navigieren. Die Automatisierung dieser Fähigkeiten für Computer ist entscheidend für verschiedene Anwendungen, darunter selbstfahrende Autos, Augmented Realität und architektonische Vermessung. Die visuelle Nahfeldwahrnehmung im Kontext von selbstfahrenden Autos kann die Umgebung in einem Bereich von 0-10 Metern und 360˚ Abdeckung um das Fahrzeug herum wahrnehmen. Sie ist eine entscheidende Entscheidungskomponente bei der Entwicklung eines sichereren automatisierten Fahrens. Jüngste Fortschritte im Bereich Computer Vision und Deep Learning in Verbindung mit hochwertigen Sensoren wie Kameras und LiDARs haben ausgereifte Lösungen für die visuelle Wahrnehmung hervorgebracht. Bisher stand die Fernfeldwahrnehmung im Vordergrund. Ein weiteres wichtiges Problem ist die begrenzte Rechenleistung, die für die Entwicklung von Echtzeit-Anwendungen zur Verfügung steht. Aufgrund dieses Engpasses kommt es häufig zu einem Kompromiss zwischen Leistung und Laufzeiteffizienz. Wir konzentrieren uns auf die folgenden Themen, um diese anzugehen: 1) Entwicklung von Nahfeld-Wahrnehmungsalgorithmen mit hoher Leistung und geringer Rechenkomplexität für verschiedene visuelle Wahrnehmungsaufgaben wie geometrische und semantische Aufgaben unter Verwendung von faltbaren neuronalen Netzen. 2) Verwendung von Multi-Task-Learning zur Überwindung von Rechenengpässen durch die gemeinsame Nutzung von initialen Faltungsschichten zwischen den Aufgaben und die Entwicklung von Optimierungsstrategien, die die Aufgaben ausbalancieren.



Anzahl der Treffer: 17
Erstellt: Sat, 02 Jul 2022 23:14:48 +0200 in 0.1302 sec

Bortz, Luisa; Döring, Ulf;
Web tool for the comparison of multiple-choice scoring schemes. - In: INTED 2021, (2021), S. 4105-4113

Sobh, Ibrahim; Hamed, Ahmed; Ravi Kumar, Varun; Yogamani, Senthil;
Adversarial attacks on multi-task visual perception for autonomous driving. - In: The journal of imaging science & technology, ISSN 1943-3522, Bd. 65 (2021), 6, S. 60408-1-60408-9

In recent years, deep neural networks (DNNs) have accomplished impressive success in various applications, including autonomous driving perception tasks. However, current deep neural networks are easily deceived by adversarial attacks. This vulnerability raises significant - concerns, particularly in safety-critical applications. As a result, research into attacking and defending DNNs has gained much coverage. In this work, detailed adversarial attacks are applied on a diverse multi-task visual perception deep network across distance estimation, semantic segmentation, - motion detection, and object detection. The experiments consider both white and black box attacks for targeted and un-targeted cases, while attacking a task and inspecting the effect on all others, in addition to inspecting the effect of applying a simple defense method. We conclude this paper - by comparing and discussing the experimental results, proposing insights and future work. The visualizations of the attacks are available at https://youtu.be/6AixN90budY.

Franke, Henning; Kucera, Paul; Kuners, Julian; Reinhold, Tom; Grabmann, Martin; Mäder, Patrick; Seeland, Marco; Gläser, Georg;
Trash or treasure? Machine-learning based PCB layout anomaly detection with AnoPCB. - In: SMACD/ PRIME 2021, (2021), S. 48-51

Ravi Kumar, Varun; Yogamani, Senthil; Milz, Stefan; Mäder, Patrick;
FisheyeDistanceNet++: self-supervised fisheye distance estimation with self-attention, robust loss function and camera view generalization. - In: Electronic imaging, ISSN 2470-1173, Bd. 2021 (2021), S. 181-1-181-10

FisheyeDistanceNet [1] proposed a self-supervised monocular depth estimation method for fisheye cameras with a large field of view (> 180˚). To achieve scale-invariant depth estimation, FisheyeDistanceNet supervises depth map predictions over multiple scales during training. To overcome this bottleneck, we incorporate self-attention layers and robust loss function [2] to FisheyeDistanceNet. A general adaptive robust loss function helps obtain sharp depth maps without a need to train over multiple scales and allows us to learn hyperparameters in loss function to aid in better optimization in terms of convergence speed and accuracy. We also ablate the importance of Instance Normalization over Batch Normalization in the network architecture. Finally, we generalize the network to be invariant to camera views by training multiple perspectives using front, rear, and side cameras. Proposed algorithm improvements, FisheyeDistanceNet++, result in 30% relative improvement in RMSE while reducing the training time by 25% on the WoodScape dataset. We also obtain state-of-the-art results on the KITTI dataset, in comparison to other self-supervised monocular methods.

Fischer, Kai; Simon, Martin; Ölsner, Florian; Milz, Stefan; Groß, Horst-Michael; Mäder, Patrick;
StickyPillars: robust and efficient feature matching on point clouds using graph neural networks. - In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2021), S. 313-323

Robust point cloud registration in real-time is an important prerequisite for many mapping and localization algorithms. Traditional methods like ICP tend to fail without good initialization, insufficient overlap or in the presence of dynamic objects. Modern deep learning based registration approaches present much better results, but suffer from a heavy runtime. We overcome these drawbacks by introducing StickyPillars, a fast, accurate and extremely robust deep middle-end 3D feature matching method on point clouds. It uses graph neural networks and performs context aggregation on sparse 3D key-points with the aid of transformer based multi-head self and cross-attention. The network output is used as the cost for an optimal transport problem whose solution yields the final matching probabilities. The system does not rely on hand crafted feature descriptors or heuristic matching strategies. We present state-of-art art accuracy results on the registration problem demonstrated on the KITTI dataset while being four times faster then leading deep methods. Furthermore, we integrate our matching system into a LiDAR odometry pipeline yielding most accurate results on the KITTI odometry dataset. Finally, we demonstrate robustness on KITTI odometry. Our method remains stable in accuracy where state-of-the-art procedures fail on frame drops and higher speeds.

Mohapatra, Sambit; Yogamani, Senthil; Gotzig, Heinrich; Milz, Stefan; Mäder, Patrick;
BEVDetNet: Bird's Eye View LiDAR point cloud based real-time 3D object detection for autonomous driving. - In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), (2021), S. 2809-2915

3D object detection based on LiDAR point clouds is a crucial module in autonomous driving particularly for long range sensing. Most of the research is focused on achieving higher accuracy and these models are not optimized for deployment on embedded systems from the perspective of latency and power efficiency. For high speed driving scenarios, latency is a crucial parameter as it provides more time to react to dangerous situations. Typically a voxel or point-cloud based 3D convolution approach is utilized for this module. Firstly, they are inefficient on embedded platforms as they are not suitable for efficient parallelization. Secondly, they have a variable runtime due to level of sparsity of the scene which is against the determinism needed in a safety system. In this work, we aim to develop a very low latency algorithm with fixed runtime. We propose a novel semantic segmentation architecture as a single unified model for object center detection using key points, box predictions and orientation prediction using binned classification in a simpler Bird's Eye View (BEV) 2D representation. The proposed architecture can be trivially extended to include semantic segmentation classes like road without any additional computation. The proposed model has a latency of 4 ms on the embedded Nvidia Xavier platform. The model is 5X faster than other top accuracy models with a minimal accuracy degradation of 2% in Average Precision at IoU = 0.5 on KITTI dataset.

Rabe, Martin; Milz, Stefan; Mäder, Patrick;
Development methodologies for safety critical machine learning applications in the automotive domain: a survey. - In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition workshops, (2021), S. 129-141

Enabled by recent advances in the field of machine learning, the automotive industry pushes towards automated driving. The development of traditional safety-critical automotive software is subject to rigorous processes, ensuring its dependability while decreasing the probability of failures. However, the development and training of machine learning applications substantially differs from traditional software development. The processes and methodologies traditionally prescribed are unfit to account for specifics like, e.g., the importance of datasets for a development. We perform a systematic mapping study surveying methodologies proposed for the development of machine learning applications in the automotive domain. We map the identified primary publications to a general machine learning-based development process and preliminary assess their maturity. The reviews's goal is providing a holistic view of current and previous research contributing to ML-aware development processes and identifying challenges that need more attention. Additionally, we list methods, network architectures, and datasets used within these publications. Our meta-study identifies that model training and model V&V received by far the most research attention accompanied by the most mature evaluations. The remaining development phases, concerning domain specification, data management, and model integration, appear underrepresented and in need of more thorough research. Additionally, we identify and aggregate typically methods applied when developing automated driving applications like models, datasets and simulators showing the state of practice in this field.

Mahecha, Miguel; Rzanny, Michael Carsten; Kraemer, Guido; Mäder, Patrick; Seeland, Marco; Wäldchen, Jana;
Crowd-sourced plant occurrence data provide a reliable description of macroecological gradients. - In: Ecography, ISSN 1600-0587, Bd. 44 (2021), 8, S. 1131-1142

Deep learning algorithms classify plant species with high accuracy, and smartphone applications leverage this technology to enable users to identify plant species in the field. The question we address here is whether such crowd-sourced data contain substantial macroecological information. In particular, we aim to understand if we can detect known environmental gradients shaping plant co-occurrences. In this study we analysed 1 million data points collected through the use of the mobile app Flora Incognita between 2018 and 2019 in Germany and compared them with Florkart, containing plant occurrence data collected by more than 5000 floristic experts over a 70-year period. The direct comparison of the two data sets reveals that the crowd-sourced data particularly undersample areas of low population density. However, using nonlinear dimensionality reduction we were able to uncover macroecological patterns in both data sets that correspond well to each other. Mean annual temperature, temperature seasonality and wind dynamics as well as soil water content and soil texture represent the most important gradients shaping species composition in both data collections. Our analysis describes one way of how automated species identification could soon enable near real-time monitoring of macroecological patterns and their changes, but also discusses biases that must be carefully considered before crowd-sourced biodiversity data can effectively guide conservation measures.

Cierpka, Christian; Otto, Henning; Poll, Constanze; Hüther, Jonas; Jeschke, Sebastian; Mäder, Patrick;
SmartPIV: flow velocity estimates by smartphones for education and field studies. - In: Experiments in fluids, ISSN 1432-1114, Bd. 62 (2021), 8, 172, insges. 13 S.

In this paper, a smartphone application is presented that was developed to lower the barrier to introduce particle image velocimetry (PIV) in lab courses. The first benefit is that a PIV system using smartphones and a continuous wave (cw-) laser is much cheaper than a conventional system and thus much more affordable for universities. The second benefit is that the design of the menus follows that of modern camera apps, which are intuitively used. Thus, the system is much less complex and costly than typical systems, and our experience showed that students have much less reservations to work with the system and to try different parameters. Last but not least the app can be applied in the field. The relative uncertainty was shown to be less than 8%, which is reasonable for quick velocity estimates. An analysis of the computational time necessary for the data evaluation showed that with the current implementation the app is capable of providing smooth live display vector fields of the flow. This might further increase the use of modern measurement techniques in industry and education.

Mäder, Patrick; Kuschke, Tobias; Janke, Mario;
Reactive auto-completion of modeling activities. - In: IEEE transactions on software engineering, ISSN 1939-3520, Bd. 47 (2021), 7, S. 1431-1451

Assisting and automating software engineering tasks is a state-of-the-art way to support stakeholders of development projects. A common assistance function of IDEs is the auto-completion of source code. Assistance functions, such as auto-completion, are almost entirely missing in modeling tools though auto-completion in general gains continuously more importance in software development. We analyze a user’s performed editing operations in order to anticipate modeling activities and to recommend appropriate auto-completions for them. Editing operations are captured as events and modeling activities are defined as complex event patterns, facilitating the matching by complex-event-processing. The approach provides adapted auto-completions reactively upon each editing operation of the user. We implemented the RapMOD prototype as add-in for the modeling tool Sparx Enterprise Architect™ . A controlled user experiment with 37 participants performing modeling tasks demonstrated the approach's potential to reduce modeling effort significantly. Users having auto-completions available for a modeling scenario performed the task 27 percent faster, needed to perform 56 percent less actions, and perceived the task 29 percent less difficult.

Mäder, Patrick; Boho, David; Rzanny, Michael Carsten; Seeland, Marco; Wittich, Hans Christian; Deggelmann, Alice; Wäldchen, Jana;
The Flora Incognita app - interactive plant species identification. - In: Methods in ecology and evolution, ISSN 2041-210X, Bd. 12 (2021), 7, S. 1335-1342

Being able to identify plant species is an important factor for understanding biodiversity and its change due to natural and anthropogenic drivers. We discuss the freely available Flora Incognita app for Android, iOS and Harmony OS devices that allows users to interactively identify plant species and capture their observations. Specifically developed deep learning algorithms, trained on an extensive repository of plant observations, classify plant images with yet unprecedented accuracy. By using this technology in a context-adaptive and interactive identification process, users are now able to reliably identify plants regardless of their botanical knowledge level. Users benefit from an intuitive interface and supplementary educational materials. The captured observations in combination with their metadata provide a rich resource for researching, monitoring and understanding plant diversity. Mobile applications such as Flora Incognita stimulate the successful interplay of citizen science, conservation and education.

Barnkob, Rune; Cierpka, Christian; Chen, Minqian; Sachs, Sebastian; Mäder, Patrick; Rossi, Massimiliano;
Defocus particle tracking : a comparison of methods based on model functions, cross-correlation, and neural networks. - In: Measurement science and technology, ISSN 1361-6501, Bd. 32 (2021), 9, 094011, insges. 14 S.

Defocus particle tracking (DPT) has gained increasing importance for its use to determine particle trajectories in all three dimensions with a single-camera system, as typical for a standard microscope, the workhorse of todays ongoing biomedical revolution. DPT methods derive the depth coordinates of particle images from the different defocusing patterns that they show when observed in a volume much larger than the respective depth of field. Therefore it has become common for state-of-the-art methods to apply image recognition techniques. Two of the most commonly and widely used DPT approaches are the application of (astigmatism) particle image model functions (MF methods) and the normalized cross-correlations between measured particle images and reference templates (CC methods). Though still young in the field, the use of neural networks (NN methods) is expected to play a significant role in future and more complex defocus tracking applications. To assess the different strengths of such defocus tracking approaches, we present in this work a general and objective assessment of their performances when applied to synthetic and experimental images of different degrees of astigmatism, noise levels, and particle image overlapping. We show that MF methods work very well in low-concentration cases, while CC methods are more robust and provide better performance in cases of larger particle concentration and thus stronger particle image overlap. The tested NN methods generally showed the lowest performance, however, in comparison to the MF and CC methods, they are yet in an early stage and have still great potential to develop within the field of DPT.

Ravi Kumar, Varun; Klingner, Marvin; Yogamani, Senthil; Milz, Stefan; Fingscheidt, Tim; Mäder, Patrick;
SynDistNet: self-supervised monocular fisheye camera distance estimation synergized with semantic segmentation for autonomous driving. - In: 2021 IEEE Winter Conference on Applications of Computer Vision, (2021), S. 61-71

State-of-the-art self-supervised learning approaches for monocular depth estimation usually suffer from scale ambiguity. They do not generalize well when applied on distance estimation for complex projection models such as in fisheye and omnidirectional cameras. This paper introduces a novel multi-task learning strategy to improve self-supervised monocular distance estimation on fisheye and pinhole camera images. Our contribution to this work is threefold: Firstly, we introduce a novel distance estimation network architecture using a self-attention based encoder coupled with robust semantic feature guidance to the decoder that can be trained in a one-stage fashion. Secondly, we integrate a generalized robust loss function, which improves performance significantly while removing the need for hyperparameter tuning with the reprojection loss. Finally, we reduce the artifacts caused by dynamic objects violating static world assumptions using a semantic masking strategy. We significantly improve upon the RMSE of previous work on fisheye by 25% reduction in RMSE. As there is little work on fisheye cameras, we evaluated the proposed method on KITTI using a pinhole model. We achieved state-of-the-art performance among self-supervised methods without requiring an external scale estimation.

Ravi Kumar, Varun; Yogamani, Senthil; Rashed, Hazem; Sitsu, Ganesh; Witt, Christian; Leang, Isabelle; Milz, Stefan; Mäder, Patrick;
OmniDet: surround view cameras based multi-task visual perception network for autonomous driving. - In: IEEE Robotics and automation letters, ISSN 2377-3766, Bd. 6 (2021), 2, S. 2830-2837

Surround View fisheye cameras are commonly deployed in automated driving for 360˚ near-field sensing around the vehicle. This work presents a multi-task visual perception network on unrectified fisheye images to enable the vehicle to sense its surrounding environment. It consists of six primary tasks necessary for an autonomous driving system: depth estimation, visual odometry, semantic segmentation, motion segmentation, object detection, and lens soiling detection. We demonstrate that the jointly trained model performs better than the respective single task versions. Our multi-task model has a shared encoder providing a significant computational advantage and has synergized decoders where tasks support each other. We propose a novel camera geometry based adaptation mechanism to encode the fisheye distortion model both at training and inference. This was crucial to enable training on the WoodScape dataset, comprised of data from different parts of the world collected by 12 different cameras mounted on three different cars with different intrinsics and viewpoints. Given that bounding boxes is not a good representation for distorted fisheye images, we also extend object detection to use a polygon with non-uniformly sampled vertices. We additionally evaluate our model on standard automotive datasets, namely KITTI and Cityscapes. We obtain the state-of-the-art results on KITTI for depth estimation and pose estimation tasks and competitive performance on the other tasks. We perform extensive ablation studies on various architecture choices and task weighting methodologies. A short video at https://youtu.be/xbSjZ5OfPes provides qualitative results.

Hofmann, Martin; Mäder, Patrick;
Synaptic scaling - an artificial neural network regularization inspired by nature. - In: IEEE transactions on neural networks and learning systems, ISSN 2162-237X, (2021), S. 1-15

Seeland, Marco; Mäder, Patrick;
Multi-view classification with convolutional neural networks. - In: PLOS ONE, ISSN 1932-6203, Bd. 16 (2021), 1, e0245230, insges. 17 S.

Dunker, Susanne; Motivans, Elena; Rakosy, Demetra; Boho, David; Mäder, Patrick; Hornick, Thomas; Knight, Tiffany M.;
Pollen analysis using multispectral imaging flow cytometry and deep learning. - In: The new phytologist, ISSN 1469-8137, Bd. 229 (2021), 1, S. 593-606

Pollen identification and quantification are crucial but challenging tasks in addressing a variety of evolutionary and ecological questions (pollination, paleobotany), but also for other fields of research (e.g. allergology, honey analysis or forensics). Researchers are exploring alternative methods to automate these tasks but, for several reasons, manual microscopy is still the gold standard. In this study, we present a new method for pollen analysis using multispectral imaging flow cytometry in combination with deep learning. We demonstrate that our method allows fast measurement while delivering high accuracy pollen identification. A dataset of 426 876 images depicting pollen from 35 plant species was used to train a convolutional neural network classifier. We found the best-performing classifier to yield a species-averaged accuracy of 96%. Even species that are difficult to differentiate using microscopy could be clearly separated. Our approach also allows a detailed determination of morphological pollen traits, such as size, symmetry or structure. Our phylogenetic analyses suggest phylogenetic conservatism in some of these traits. Given a comprehensive pollen reference database, we provide a powerful tool to be used in any pollen study with a need for rapid and accurate species identification, pollen grain quantification and trait extraction of recent pollen.



Anzahl der Treffer: 13
Erstellt: Sat, 02 Jul 2022 23:14:47 +0200 in 0.1018 sec

Mäder, Patrick; Wäldchen, Jana;
Flora Incognita - interactive, semi-automatic species identification with mobile devices :
Flora Incognita - interaktive, halbautomatische Artenbestimmung mit mobilen Endgeräten und vollautomatischer Kartierung : Bundesprogramm Biologische Vielfalt : Projektlaufzeit: 1. August 2014-31. Juli 2020. - Ilmenau : Technische Universität Ilmenau. - 1 Online-Ressource (24 Seiten, 728,64 KB)Förderkennzeichen BMBF 01LC1319A+B

Döring, Ulf; Sommer, Oliver; Fincke, Sabine;
First experiences in the generation of reasonable feedback for Java beginners. - In: INTED 2020, (2020), S. 4383-4389

Ravi Kumar, Varun; Yogamani, Senthil; Bach, Markus; Witt, Christian; Milz, Stefan; Mäder, Patrick;
UnRectDepthNet: self-supervised monocular depth estimation using a generic framework for handling common camera distortion models. - In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (2020), S. 8177-8183

Boho, David; Rzanny, Michael Carsten; Wäldchen, Jana; Nitsche, Fabian; Deggelmann, Alice; Wittich, Hans Christian; Seeland, Marco; Mäder, Patrick;
Flora Capture: a citizen science application for collecting structured plant observations. - In: BMC bioinformatics, ISSN 1471-2105, Bd. 21 (2020), 576, insges. 11 S.

Digital plant images are becoming increasingly important. First, given a large number of images deep learning algorithms can be trained to automatically identify plants. Second, structured image-based observations provide information about plant morphological characteristics. Finally in the course of digitalization, digital plant collections receive more and more interest in schools and universities.

Reinhold, Tom; Seeland, Marco; Grabmann, Martin; Paintz, Christian; Mäder, Patrick; Gläser, Georg;
Ain't got time for this? : reducing manual evaluation effort with Machine Learning based Grouping of Analog Waveform Test Data. - In: ANALOG 2020, (2020), S. 47-52

Gonzalez, Danielle; Rath, Michael; Mirakhorli, Mehdi;
Did you remember to test your tokens?. - In: 2020 IEEE/ACM 17th International Conference on Mining Software Repositories, (2020), S. 232-242

Authentication is a critical security feature for confirming the identity of a system's users, typically implemented with help from frameworks like Spring Security. It is a complex feature which should be robustly tested at all stages of development. Unit testing is an effective technique for fine-grained verification of feature behaviors that is not widely-used to test authentication. Part of the problem is that resources to help developers unit test security features are limited. Most security testing guides recommend test cases in a "black box" or penetration testing perspective. These resources are not easily applicable to developers writing new unit tests, or who want a security-focused perspective on coverage. In this paper, we address these issues by applying a grounded theory-based approach to identify common (unit) test cases for token authentication through analysis of 481 JUnit tests exercising Spring Security-based authentication implementations from 53 open source Java projects. The outcome of this study is a developer-friendly unit testing guide organized as a catalog of 53 test cases for token authentication, representing unique combinations of 17 scenarios, 40 conditions, and 30 expected outcomes learned from the data set in our analysis. We supplement the test guide with common test smells to avoid. To verify the accuracy and usefulness of our testing guide, we sought feedback from selected developers, some of whom authored unit tests in our dataset.

Holtmann, Jörg; Steghöfer, Jan-Philipp; Rath, Michael; Schmelter, David;
Cutting through the jungle: disambiguating model-based traceability terminology. - In: 28th IEEE International Requirements Engineering Conference, (2020), S. 8-19

Ravi Kumar, Varun; Hiremath, Sandesh Athni; Bach, Markus; Milz, Stefan; Witt, Christian; Pinard, Clément; Yogamani, Senthil; Mäder, Patrick;
FisheyeDistanceNet : self-supervised scale-aware distance estimation using monocular fisheye camera for autonomous driving. - In: 2020 IEEE International Conference on Robotics and Automation (ICRA), (2020), S. 574-581

Rath, Michael; Mäder, Patrick;
Request for comments: conversation patterns in issue tracking systems of open-source projects. - In: The 35th Annual ACM Symposium on Applied Computing, (2020), S. 1414-1417

Rath, Michael; Tomova, Mihaela Todorova; Mäder, Patrick;
SpojitR: intelligently link development artifacts. - In: SANER '20, (2020), S. 652-656

König, Jörg; Chen, Minqian; Rösing, Wiebke; Boho, David; Mäder, Patrick; Cierpka, Christian;
On the use of a cascaded convolutional neural network for three-dimensional flow measurements using astigmatic PTV. - In: Measurement science and technology, ISSN 1361-6501, Volume 31 (2020), number 7, 074015, 14 Seiten

Many applications in chemistry, biology and medicine use microfluidic devices to separate, detect and analyze samples on a miniaturized size-level. Fluid flows evolving in channels of only several tens to hundreds of micrometers in size are often of a 3D nature, affecting the tailored transport of cells and particles. To analyze flow phenomena and local distributions of particles within those channels, astigmatic particle tracking velocimetry (APTV) has become a valuable tool, on condition that basic requirements like low optical aberrations and particles with a very narrow size distribution are fulfilled. Making use of the progress made in the field of machine vision, deep neural networks may help to overcome these limiting requirements, opening new fields of applications for APTV and allowing them to be used by nonexpert users. To qualify the use of a cascaded deep convolutional neural network (CNN) for particle detection and position regression, a detailed investigation was carried out starting from artificial particle images with known ground truth to real flow measurements inside a microchannel, using particles with uni- and bimodal size distributions. In the case of monodisperse particles, the mean absolute error and standard deviation of particle depth-position of less than and about 1 [my]m were determined, employing the deep neural network and the classical evaluation method based on the minimum Euclidean distance approach. While these values apply to all particle size distributions using the neural network, they continuously increase towards the margins of the measurement volume of about one order of magnitude for the classical method, if nonmonodisperse particles are used. Nevertheless, limiting the depth of measurement volume in between the two focal points of APTV, reliable flow measurements with low uncertainty are also possible with the classical evaluation method and polydisperse tracer particles. The results of the flow measurements presented herein confirm this finding. The source code of the deep neural network used here is available on https://github.com/SECSY-Group/DNN-APTV.

Janke, Mario; Kuschke, Tobias; Mäder, Patrick;
A definition-by-example approach and visual language for activity patterns in engineering disciplines. - In: PLOS ONE, ISSN 1932-6203, Bd. 15 (2020), 1, e0226877, insges. 28 S.



Anzahl der Treffer: 20
Erstellt: Sat, 02 Jul 2022 23:14:47 +0200 in 0.1535 sec

Döring, Ulf; Fincke, Sabine;
Scoring schemes for multiple-choice tests. - In: INTED 2019, (2019), S. 5835-5844

Döring, Ulf;
Aktuelle Möglichkeiten des Einsatzes von Struktogrammen als anschauliches Hilfsmittel beim Programmierenlernen. - In: Diversität und kulturelle Vielfalt - Differenzieren, Individualisieren - oder Integrieren?, (2019), S. 249-254

Döring, Ulf;
Ansatz zur automatischen Generierung von Java-OOP-Aufgaben inkl. Bewertungsschemen. - Bonn : Gesellschaft für Informatik e.V. (GI). - 1 Online-Ressource (4 Seiten)Publikation entstand im Rahmen der Veranstaltung: Proceedings of the Fourth Workshop "Automatische Bewertung von Programmieraufgaben" (ABP 2019) : 8. und 9. Oktober 2019, Essen / Sven Strickroth, Michael Striewe, Oliver Rod (Hrsg.). - Bonn : Gesellschaft für Informatik e.V. (GI), 2019. - Seite 63-66

Mohamed, Mohamed Elamir; Gotzig, Heinrich; Zöllner, Raoul; Mäder, Patrick;
A convolution neural network based machine learning approach for ultrasonic noise suppression with minimal distortion. - In: 2019 IEEE International Ultrasonics Symposium (IUS), (2019), S. 1629-1634

Vehar, Darko; Nestler, Rico; Franke, Karl-Heinz;
Scene based camera pose estimation in Manhattan worlds. - In: Photonics and education in measurement science 2019, (2019), S. 111440L-1-111440L-9

Rath, Michael; Mäder, Patrick;
Structured information in bug report descriptions - influence on IR-based bug localization and developers. - In: Software quality journal, ISSN 1573-1367, Bd. 27 (2019), 3, S. 1315-1337

Rath, Michael; Tomova, Mihaela Todorova; Mäder, Patrick;
Selecting open source projects for traceability case studies. - In: Requirements engineering: foundation for software quality, (2019), S. 229-242

Brix, Torsten; Döring, Ulf;
15 Jahre Digitale Mechanismen- und Getriebebibliothek. - In: Tagungsband 13. Kolloquium Getriebetechnik, (2019), S. 3-18

Döring, Ulf; Artelt, Benedikt;
On the usefulness of animated structograms in teaching algorithms and programming. - In: Mobile technologies and applications for the internet of things, (2019), S. 34-46

Döring, Ulf; Artelt, Benedikt;
Web-based tools for interactive training in implementing JAVA methods. - In: INTED 2019, (2019), S. 3974-3979

Cierpka, Christian; Mäder, Patrick;
SmartPIV - Smartphone-based flow visualization for education :
SmartPIV - Strömungsvisualisierung mit dem Smartphone in der Lehre. - In: Experimentelle Strömungsmechanik, (2019), S. 23.1-23.7

Kuang, Hongyu; Gao, Hui; Hu, Hao; Ma, Xiaoxing; Lü, Jian; Mäder, Patrick; Egyed, Alexander;
Using frugal user feedback with closeness analysis on code to improve IR-based traceability recovery. - In: ICPC 2019, (2019), S. 369-379

Artelt, Benedikt; Brix, Torsten; Döring, Ulf;
THEDI - the first online editor for the IFToMM dictionary. - In: Advances in mechanism and machine science, (2019), S. 3511-3519

Döring, Ulf; Brix, Torsten; Artelt, Benedikt; Brandt-Salloum, Christiane;
Patents from the age of Prussian industrialization revived. - In: Advances in mechanism and machine science, (2019), S. 1223-1232

Rzanny, Michael Carsten; Mäder, Patrick; Degelmann, Alice; Chen, Minqian; Wäldchen, Jana;
Flowers, leaves or both? How to obtain suitable images for automated plant identification. - In: Plant methods, ISSN 1746-4811, 15 (2019), article number 77, Seite 1-11

Mohamed, Mohamed Elamir; Gotzig, Heinrich; Zöllner, Raoul; Mäder, Patrick;
A machine learning approach for detecting ultrasonic echoes in noisy environments. - In: 2019 IEEE 89th Vehicular Technology Conference (VTC Spring), (2019), insges. 6 S.

Cierpka, Christian; König, Jörg; Chen, Minqian; Boho, David; Mäder, Patrick;
On the use of machine learning algorithms for the calibration of astigmatism PTV. - In: 13th International Symposium on Particle Image Velocimetry, (2019), S. 772-781

Rath, Michael; Mäder, Patrick;
The SEOSS 33 dataset - requirements, bug reports, code history, and trace links for entire projects. - In: Data in Brief, ISSN 2352-3409, Bd. 25 (2019), 104005, S. 1-12

Hofmann, Martin; Seeland, Marco; Mäder, Patrick;
Efficiently annotating object images with absolute size information using mobile devices. - In: International journal of computer vision, ISSN 1573-1405, Bd. 127 (2019), 2, S. 207-224

Seeland, Marco; Rzanny, Michael Carsten; Boho, David; Wäldchen, Jana; Mäder, Patrick;
Image-based classification of plant genus and family for trained and untrained plant species. - In: BMC bioinformatics, ISSN 1471-2105, Bd. 20 (2019), 4, insges. 13 S.



Anzahl der Treffer: 14
Erstellt: Sat, 02 Jul 2022 23:14:46 +0200 in 0.1082 sec

Meder, Julian; Brüderlin, Beat;
Screen Space Approximate Gaussian Hulls. - In: Eurographics Symposium on Rendering 2018 - Experimental Ideas & Implementations, (2018), S. 107-115

Meder, Julian; Brüderlin, Beat;
Hemispherical Gaussians for accurate light integration. - In: Computer vision and graphics, (2018), S. 3-15

Vehar, Darko; Nestler, Rico; Franke, Karl-Heinz;
Präzise Berechnung von Kameraposen in Manhattan-Welten. - In: 3D-NordOst 2018, (2018), S. 15-24

Dunke, Susanne; Boho, David; Wäldchen, Jana; Mäder, Patrick;
Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplankton. - In: BMC ecology, ISSN 1472-6785, Bd. 18 (2018), 51, insges. 15 S.

Rath, Michael; Mäder, Patrick;
Influence of structured information in bug report descriptions on IR-based bug localization. - In: SEAA 2018, ISBN 978-1-5386-7383-6, (2018), S. 26-32

Wäldchen, Jana; Mäder, Patrick;
Machine learning for image based species identification. - In: Methods in ecology and evolution, ISSN 2041-210X, Bd. 9 (2018), 11, S. 2216-2225

Mäder, Patrick; Baumann, Tommy; Grüner, David;
Tool-supported methodology for the development of complex systems and complex software (SimDesign) :
Werkzeugunterstützte Methodik zur simulationsgetriebenen Entwicklung von komplexen Systemen und komplexer Software (SimDesign) : KMU-Innovativ Verbundvorhaben : Projektlaufzeit: 1. September 2014 - 31.Dezember 2017. - [Ilmenau] : [Technische Universität Ilmenau, Fachgebiet Softwaretechnik für sicherheitskritische Systeme]. - 1 Online-Ressource (17 Seiten, 1,30 MB)Förderkennzeichen BMBF 01IS14026A-B. - Verbund-Nummer 01155480

Krishnamurthy, Rohan; Meinel, Michael; Haupt, Carina; Schreiber, Andreas; Mäder, Patrick;
DLR secure software engineering. - In: 2018 ACM/IEEE 1st International Workshop on Security Awareness from Design to Deployment, ISBN 978-1-4503-5727-2, (2018), S. 49-50
Position and vision paper

Rath, Michael; Lo, David; Mäder, Patrick;
Analyzing requirements and traceability information to improve bug localization. - In: 2018 ACM/IEEE 15th International Conference on Mining Software Repositories, ISBN 978-1-4503-5716-6, (2018), S. 442-453

Tomova, Mihaela Todorova; Rath, Michael; Mäder, Patrick;
Poster: use of trace link types in issue tracking systems. - In: 2018 ACM/IEEE 40th International Conference on Software Engineering: companion proceeedings, ISBN 978-1-4503-5663-3, (2018), S. 181-182

Rath, Michael; Rendall, Jacob; Gio, Jin L. C.; Cleland-Huang, Jane; Mäder, Patrick;
Traceability in the wild: automatically augmenting incomplete trace links. - In: 2018 ACM/IEEE 40th International Conference on Software Engineering, ISBN 978-1-4503-5638-1, (2018), S. 834-845

Wäldchen, Jana; Rzanny, Michael Carsten; Seeland, Marco; Mäder, Patrick;
Automated plant species identification - trends and future directions. - In: PLoS Computational Biology, ISSN 1553-7358, Bd. 14 (2018), 4, e1005993, insges. 19 S.

Wittich, Hans Christian; Seeland, Marco; Wäldchen, Jana; Rzanny, Michael Carsten; Mäder, Patrick;
Recommending plant taxa for supporting on-site species identification. - In: BMC bioinformatics, ISSN 1471-2105, Bd. 19 (2018), 190, insges. 17 S.

Wäldchen, Jana; Mäder, Patrick;
Plant species identification using computer vision techniques: a systematic literature review. - In: Archives of computational methods in engineering, ISSN 1886-1784, Bd. 25 (2018), 2, S. 507-543



Anzahl der Treffer: 11
Erstellt: Sat, 02 Jul 2022 23:14:46 +0200 in 0.0797 sec

Döring, Ulf; Fincke, Sabine;
Interaktive Ansätze zur Vermittlung von Programmierfähigkeiten im Rahmen des Ingenieurstudiums. - In: Digitalisierung in der Techniklehre, (2017), S. 171-176

Antakli, André; Moya, Pablo Alvarado; Brüderlin, Beat; Canzler, Ulrich;
Virtuelle Techniken und Semantic-Web : Stand der Wissenschaft und Technik. - In: Web-basierte Anwendungen Virtueller Techniken, (2017), S. 17-116

Jahn, Rainer; Kapusi, Daniel; Vehar, Darko; Nestler, Rico; Franke, Karl-Heinz;
Bewertung von Farbmustercodes zur flächigen, aktiven 3D-Erfassung. - In: 3D-NordOst 2017, (2017), S. 23-32

Färber, Markus; Ghiletiuc, Johannes; Schwarz, Peter; Brüderlin, Beat;
Echtzeit-Visualisierung von sehr großen Virtual- und Augmented-Reality-Szenen auf Smartphones und mobilen Tablet-Computern. - In: Entwerfen Entwickeln Erleben - Methoden und Werkzeuge in der Produktentwicklung, (2017), S. 251-265

Rzanny, Michael Carsten; Seeland, Marco; Wäldchen, Jana; Mäder, Patrick;
Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain. - In: Plant methods, ISSN 1746-4811, 13 (2017), article number 97, Seite 1-11

Rath, Michael; Rempel, Patrick; Mäder, Patrick;
The IlmSeven dataset. - In: 2017 IEEE 25th International Requirements Engineering Conference, (2017), S. 516-519

Rempel, Patrick; Mäder, Patrick;
Preventing defects: the impact of requirements traceability completeness on software quality. - In: IEEE transactions on software engineering, ISSN 1939-3520, Bd. 43 (2017), 8, S. 777-797

Kuschke, Tobias; Mäder, Patrick;
Poster: RapMOD - in situ auto-completion for graphical models. - In: 2017 IEEE/ACM 39th International Conference on Software Engineering companion, ISBN 978-1-5386-1589-8, (2017), S. 303-304

Kuang, Hongyu; Nie, Jia; Hu, Hao; Rempel, Patrick; Lü, Jian; Egyed, Alexander; Mäder, Patrick;
Analyzing closeness of code dependencies for improving IR-based traceability recovery. - In: SANER 2017, ISBN 978-1-5090-5501-2, (2017), S. 68-78

Seeland, Marco; Rzanny, Michael Carsten; Alaqraa, Nedal; Wäldchen, Jana; Mäder, Patrick;
Plant species classification using flower images - a comparative study of local feature representations. - In: PLOS ONE, ISSN 1932-6203, Bd. 12 (2017), 2, e0170629, insges. 29 S.



Anzahl der Treffer: 14
Erstellt: Sat, 02 Jul 2022 23:14:45 +0200 in 0.1142 sec

Meder, Julian; Brüderlin, Beat;
Fast depth image based rendering for synthetic frame extrapolation. - In: Journal of theoretical and applied computer science, ISSN 2299-2634, Bd. 10 (2016), 3, S. 3-18

Jahn, Rainer; Kapusi, Daniel; Franke, Karl-Heinz; Nestler, Rico;
Aktive Stereoskopie mittels Farbmusterkodierung. - In: 22. Workshop Farbbildverarbeitung, (2016), S. 183-196

Junger, Stephan; Nestler, Rico; Gäbler, Daniel;
Multispektraler CMOS-Sensor und dessen Eignungsbewertung für Lichtanwendungen. - In: 22. Workshop Farbbildverarbeitung, (2016), S. 9-21

Seeland, Marco; Rzanny, Michael Carsten; Alaqraa, Nedal; Thuille, Angelika; Wiesner, David; Wäldchen, Jana; Mäder, Patrick;
Description of flower colors for image based plant species classification. - In: 22. Workshop Farbbildverarbeitung, (2016), S. 145-154

Meder, Julian; Brüderlin, Beat;
Decoupling rendering and display using fast depth image based rendering on the GPU. - In: Computer Vision and Graphics, (2016), S. 61-72

Rempel, Patrick;
Continuous assessment of software traceability. - Ilmenau : Universitätsbibliothek, 2016. - 1 Online-Ressource (xi, 153 Seiten)
Technische Universität Ilmenau, Dissertation 2016

Die Nachvollziehbarkeit von Anforderungen ist wichtiges Qualitätsmerkmal der Softwareentwicklung. Für eine Vielzahl von Softwareentwicklungsaktivitäten ist die Nachvollziehbarkeit von Anforderungen eine notwenige Voraussetzung. Dazu gehören unter anderem die Analyse funktionaler Sicherheit, die Einflussanalyse, die Analyse des Abdeckungsgrades oder die Compliance. Für die Entwicklung sicherheitskritischer Softwaresysteme ist dieses Qualitätsmerkmal von besonderer Bedeutung. Daher wird dieses von entsprechenden Richtlinien zur Entwicklung sicherheitskritischer Software explizit vorgeschrieben. Obwohl die Relevanz der Nachvollziehbarkeit in Softwareprojekten allgemein bekannt ist, findet nur in wenigen Fällen eine systematische Planung zur Erreichung dieses Qualitätsmerkmals Anwendung. Häufig wird Nachvollziehbarkeit erst nachträglich umgesetzt. Daraus resultieren oft unvollständige Implementierungen der Nachvollziehbarkeit, die trotzdem als Grundlage für schwerwiegende Entscheidungen herangezogen werden. Aus diesem Grunde sollten die entsprechenden Implementierungen einer eingehenden Prüfung unterzogen werden, besonders im Rahmen der Entwicklung sicherheitskritischer Systeme. Dazu ist jedoch eine Vielzahl von Herausforderungen zu meistern. Zum einen hängt die Nachvollziehbarkeit von den projektspezifischen Zielen ab. Bei sicherheitskritischen Systemen müssen oft Vorgaben aus Richtlinien erfüllt werden. Auch die Nutzung der Nachvollziehbarkeit ist sehr stark von den jeweiligen Zielen abhängig. In dieser Arbeit wird ein Ansatz zur systematischen Prüfung von Softwareprojekten im Hinblick auf deren Nachvollziehbarkeit der Anforderungen vorgeschlagen. Eine notwendige Voraussetzung für den Prüfansatz ist die präzise Planung und Definition der Nachvollziehbarkeit von Anforderungen in einem Softwareprojekt. Daher wird im Rahmen dieser Arbeit ein entsprechender Planungsansatz präsentiert. Weiterhin wird ein analytisches Modell zur systematischen Prüfung der Nachvollziehbarkeit in Softwareprojekten präsentiert. Dieses Modell umfasst eine vollständige Klassifikation möglicher Fehlertypen. Außerdem werden Kriterien zur systematischen Erkennung dieser Fehler vorgeschlagen. Die Ergebnisse einer Expertenbefragung bestätigen die Vollständigkeit des analytischen Prüfmodells. Zudem wurde der vorgeschlagene Ansatz zur systematischen Prüfung der Nachvollziehbarkeit von Anforderungen in zwei Studien evaluiert. Dabei konnte der Nutzen des Ansatzes für die Entwicklung von sicherheitskritischer und nicht sicherheitskritischer Software nachgewiesen werden.

Kramer, Rüdiger; Döring, Ulf
Tool zur Unterstützung der bildorientierten Selektion von Patentdokumenten am Beispiel des XPAT Patent Viewers$HRüdiger Kramer, Ulf Döring. - In: Big Data - Chancen und Herausforderungen, ISBN 978-3-932488-20-7, (2016), S. 209-219

Wenzel, Kay; Fischer, Kai; Nestler, Rico; Machleidt, Torsten; Franke, Karl-Heinz;
Konzept einer LED-Lichtquelle zum Synthetisieren von spektral definierten Lichtstimmungen. - In: Proceedings des FWS 2015, (2016), S. 65-73

Nestler, Rico; Jahn, Rainer; Franke, Karl-Heinz; Junger, Stephan; Gäbler, Daniel;
Einsatz hyperspektraler Sensoren zur Überwachung intelligenter LED-basierter Lichtquellen - eine Fallstudie. - In: Proceedings des FWS 2015, (2016), S. 50-64

Prinke, Philipp; Nestler, Rico; Franke, Karl-Heinz;
Umsetzung einer farbwertbezogenen Kontrastanhebung zur robusten Segmentierung von medizinischen Hautbildern. - In: Proceedings des FWS 2015, (2016), S. 39-49

Linåker, Johan; Rempel, Patrick; Regnell, Björn; Mäder, Patrick;
How firms adapt and interact in open source ecosystems: analyzing stakeholder influence and collaboration patterns. - In: Requirements engineering: foundation for software quality, ISBN 978-3-319-30282-9, (2016), S. 63-81

Rempel, Patrick; Mäder, Patrick;
Continuous assessment of software traceability. - In: 2016 IEEE/ACM 38th IEEE International Conference on Software Engineering Companion, ISBN 978-1-4503-4205-6, (2016), S. 747-748

Mäder, Patrick; Egyed, Alexander;
The benefit of requirements traceability when evolving a software product: a controlled experiment. - In: Software Engineering 2016, (2016), S. 109-110

Rempel, Patrick; Mäder, Patrick;
A quality model for the systematic assessment of requirements traceability. - In: Software Engineering 2016, (2016), S. 37-38