Anzahl der Treffer: 12
Erstellt: Fri, 23 Feb 2024 04:08:53 +0100 in 0.1435 sec

Scheliga, Daniel; Mäder, Patrick; Seeland, Marco
Dropout is NOT all you need to prevent gradient leakage. - In: 37th AAAI Conference on Artificial Intelligence (AAAI-23), (2023), S. 9733-9741

Gradient inversion attacks on federated learning systems reconstruct client training data from exchanged gradient information. To defend against such attacks, a variety of defense mechanisms were proposed. However, they usually lead to an unacceptable trade-off between privacy and model utility. Recent observations suggest that dropout could mitigate gradient leakage and improve model utility if added to neural networks. Unfortunately, this phenomenon has not been systematically researched yet. In this work, we thoroughly analyze the effect of dropout on iterative gradient inversion attacks. We find that state of the art attacks are not able to reconstruct the client data due to the stochasticity induced by dropout during model training. Nonetheless, we argue that dropout does not offer reliable protection if the dropout induced stochasticity is adequately modeled during attack optimization. Consequently, we propose a novel Dropout Inversion Attack (DIA) that jointly optimizes for client data and dropout masks to approximate the stochastic client model. We conduct an extensive systematic evaluation of our attack on four seminal model architectures and three image classification datasets of increasing complexity. We find that our proposed attack bypasses the protection seemingly induced by dropout and reconstructs client data with high fidelity. Our work demonstrates that privacy inducing changes to model architectures alone cannot be assumed to reliably protect from gradient leakage and therefore should be combined with complementary defense mechanisms.

Altschaffel, Robert; Dittmann, Jana; Scheliga, Daniel; Seeland, Marco; Mäder, Patrick
Model-based data generation for the evaluation of functional reliability and resilience of distributed machine learning systems against abnormal cases. - In: Engineering for a changing world, (2023), 5.3.128, S. 1-6

Future production technologies will comprise a multitude of systems whose core functionality is closely related to machine-learned models. Such systems require reliable components to ensure the safety of workers and their trust in the systems. The evaluation of the functional reliability and resilience of systems based on machine-learned models is generally challenging. For this purpose, appropriate test data must be available, which also includes abnormal cases. These abnormal cases can be unexpected usage scenarios, erroneous inputs, accidents during operation or even the failure of certain subcomponents. In this work, approaches to the model-based generation of an arbitrary abundance of data representing such abnormal cases are explored. Such computer-based generation requires domain-specific approaches, especially with respect to the nature and distribution of the data, protocols used, or domain-specific communication structures. In previous work, we found that different use cases impose different requirements on synthetic data, and the requirements in turn imply different generation methods [1]. Based on this, various use cases are identified and different methods for computer-based generation of realistic data, as well as for the quality assessment of such data, are explored. Ultimately we explore the use of Federated Learning (FL) to address data privacy and security challenges in Industrial Control Systems. FL enables local model training while keeping sensitive information decentralized and private to their owners. In detail, we investigate whether FL can benefit clients with limited knowledge by leveraging collaboratively trained models that aggregate client-specific knowledge distributions. We found that in such scenarios federated training results in a significant increase in classification accuracy by 31.3% compared to isolated local training. Furthermore, as we introduce Differential Privacy, the resulting model achieves on par accuracy of 99.62% to an idealized case where data is independent and identically distributed across clients.

Teutsch, Philipp; Käufer, Theo; Mäder, Patrick; Cierpka, Christian
Data-driven estimation of scalar quantities from planar velocity measurements by deep learning applied to temperature in thermal convection. - In: Experiments in fluids, ISSN 1432-1114, Bd. 64 (2023), 12, 191, S. 1-18

The measurement of the transport of scalar quantities within flows is oftentimes laborious, difficult or even unfeasible. On the other hand, velocity measurement techniques are very advanced and give high-resolution, high-fidelity experimental data. Hence, we explore the capabilities of a deep learning model to predict the scalar quantity, in our case temperature, from measured velocity data. Our method is purely data-driven and based on the u-net architecture and, therefore, well-suited for planar experimental data. We demonstrate the applicability of the u-net on experimental temperature and velocity data, measured in large aspect ratio Rayleigh-Bénard convection at Pr = 7.1 and Ra = 2 x 10^5, 4 x 10^5, 7 x 10^5. We conduct a hyper-parameter optimization and ablation study to ensure appropriate training convergence and test different architectural variations for the u-net. We test two application scenarios that are of interest to experimentalists. One, in which the u-net is trained with data of the same experimental run and one in which the u-net is trained on data of different Ra. Our analysis shows that the u-net can predict temperature fields similar to the measurement data and preserves typical spatial structure sizes. Moreover, the analysis of the heat transfer associated with the temperature showed good agreement when the u-net is trained with data of the same experimental run. The relative difference between measured and reconstructed local heat transfer of the system characterized by the Nusselt number Nu is between 0.3 and 14.1% depending on Ra. We conclude that deep learning has the potential to supplement measurements and can partially alleviate the expense of additional measurement of the scalar quantity.

Katal, Negin; Rzanny, Michael Carsten; Mäder, Patrick; Römermann, Christine; Wittich, Hans Christian; Boho, David; Musavi, Talie Sadat; Wäldchen, Jana
Bridging the gap: how to adopt opportunistic plant observations for phenology monitoring. - In: Frontiers in plant science, ISSN 1664-462X, Bd. 14 (2023), 1150956, S. 1-13

Plant phenology plays a vital role in assessing climate change. To monitor this, individual plants are traditionally visited and observed by trained volunteers organized in national or international networks - in Germany, for example, by the German Weather Service, DWD. However, their number of observers is continuously decreasing. In this study, we explore the feasibility of using opportunistically captured plant observations, collected via the plant identification app Flora Incognita to determine the onset of flowering and, based on that, create interpolation maps comparable to those of the DWD. Therefore, the opportunistic observations of 17 species collected in 2020 and 2021 were assigned to “Flora Incognita stations” based on location and altitude in order to mimic the network of stations forming the data basis for the interpolation conducted by the DWD. From the distribution of observations, the percentile representing onset of flowering date was calculated using a parametric bootstrapping approach and then interpolated following the same process as applied by the DWD. Our results show that for frequently observed, herbaceous and conspicuous species, the patterns of onset of flowering were similar and comparable between both data sources. We argue that a prominent flowering stage is crucial for accurately determining the onset of flowering from opportunistic plant observations, and we discuss additional factors, such as species distribution, location bias and societal events contributing to the differences among species and phenology data. In conclusion, our study demonstrates that the phenological monitoring of certain species can benefit from incorporating opportunistic plant observations. Furthermore, we highlight the potential to expand the taxonomic range of monitored species for phenological stage assessment through opportunistic plant observation data.

Amthor, Peter; Döring, Ulf; Fischer, Daniel; Genath, Jonas; Kreuzberger, Gunther
Erfahrungen bei der Integration des Autograding-Systems CodeOcean in die universitäre Programmierausbildung. - In: Proceedings of the Sixth Workshop "Automatische Bewertung von Programmieraufgaben" (ABP 2023), (2023), S. 67-74

Eine effektive und effiziente universitäre Programmierausbildung erfordert zunehmend den Einsatz automatisierter Bewertungssysteme. Im Rahmen des Projekts examING erprobt das Teilprojekt AutoPING den Einsatz des quelloffenen Autograding-Systems CodeOcean für übergreifende Lehrangebote und Prüfungen an der TU Ilmenau mit dem Ziel, selbstgesteuertes und kompetenzorientiertes Lernen zu ermöglichen und zu fördern. Der Beitrag gibt einen Überblick über erste Projekterfahrungen bei der Adaption didaktischer Szenarien in der Programmierausbildung hin zu testgetriebener Softwareentwicklung sowie der Generierung von Feedback. Es werden wesentliche Erkenntnisse aus Sicht der Studierenden und Lehrenden erörtert, Herausforderungen und Lösungsansätze zur Integration und Erweiterung von CodeOcean für neue Anwendungsfelder diskutiert sowie zukünftige Perspektiven eröffnet.

Janke, Mario; Mäder, Patrick
FS3change: a scalable method for change pattern mining. - In: IEEE transactions on software engineering, ISSN 1939-3520, Bd. 49 (2023), 6, S. 3616-3629

Mining change patterns can give unique understanding on the evolution of dynamically changing systems like social relation graphs, weblinks, hardware descriptions and models. A more recent focus is source code change pattern mining that may qualitatively justify expected or uncover unexpected patterns. These patterns then offer a basis, e.g., for program language evolution or auto-completion support. We present a change pattern mining method that greatly expands the limits of input data and pattern complexity, over existing methods. We propose scalability solutions on conceptual and algorithmic level, thereby evolving the state-of-the-art sampling-based frequent subgraph mining method FS3, resulting in 75% reduction in memory consumption and a speedup of 6500 for a large scale dataset. Patterns can have 100,000 s of occurrences for which manual review is impossible and may lead to misinterpretation. We propose the novel content track approach for interactively exploring pattern contents in context, based on marginal distributions. We evaluate our approach by mining 1,000 open source projects contributing a total of 558 million changes and 2 billion contextual connections among them, thereby, demonstrating its scalability. A manual interpretation of 19 patterns shows sensible mined patterns allowing to deduct implications for language design and demonstrating the soundness of the approach.

Schneider, Christian; Wäldchen, Jana; Mäder, Patrick
Artificial intelligence in nature conservation :
Künstliche Intelligenz im Naturschutz. - In: Natur und Landschaft, ISSN 0028-0615, Bd. 98 (2023), 6/7, S. 304-311

SW: Naturschutz ; maschinelles Lernen ; künstliche Intelligenz (KI) ; automatische Artenerkennung ; Vorhersagemodelle ; nachvollziehbare KI ; Reproduzierbarkeit

Sieg, Miriam; Roselló Atanet, Iván; Tomova, Mihaela Todorova; Schoeneberg, Uwe; Sehy, Victoria; Mäder, Patrick; März, Maren
Discovering unknown response patterns in progress test data to improve the estimation of student performance. - In: BMC medical education, ISSN 1472-6920, Bd. 23 (2023), 1, 193, S. 1-12

Background: The Progress Test Medizin (PTM) is a 200-question formative test that is administered to approximately 11,000 students at medical universities (Germany, Austria, Switzerland) each term. Students receive feedback on their knowledge (development) mostly in comparison to their own cohort. In this study, we use the data of the PTM to find groups with similar response patterns. Methods: We performed k-means clustering with a dataset of 5,444 students, selected cluster number k = 5, and answers as features. Subsequently, the data was passed to XGBoost with the cluster assignment as target enabling the identification of cluster-relevant questions for each cluster with SHAP. Clusters were examined by total scores, response patterns, and confidence level. Relevant questions were evaluated for difficulty index, discriminatory index, and competence levels. Results: Three of the five clusters can be seen as “performance” clusters: cluster 0 (n = 761) consisted predominantly of students close to graduation. Relevant questions tend to be difficult, but students answered confidently and correctly. Students in cluster 1 (n = 1,357) were advanced, cluster 3 (n = 1,453) consisted mainly of beginners. Relevant questions for these clusters were rather easy. The number of guessed answers increased. There were two “drop-out” clusters: students in cluster 2 (n = 384) dropped out of the test about halfway through after initially performing well; cluster 4 (n = 1,489) included students from the first semesters as well as “non-serious” students both with mostly incorrect guesses or no answers. Conclusion: Clusters placed performance in the context of participating universities. Relevant questions served as good cluster separators and further supported our “performance” cluster groupings.

Milz, Stefan; Wäldchen, Jana; Abouee, Amin; Ravichandran, Ashwanth A.; Schall, Peter; Hagen, Chris; Borer, John; Lewandowski, Benjamin; Wittich, Hans-Christian; Mäder, Patrick
The HAInich: a multidisciplinary vision data-set for a better understanding of the forest ecosystem. - In: Scientific data, ISSN 2052-4463, Bd. 10 (2023), 1, 168, S. 1-11

We present a multidisciplinary forest ecosystem 3D perception dataset. The dataset was collected in the Hainich-Dün region in central Germany, which includes two dedicated areas, which are part of the Biodiversity Exploratories - a long term research platform for comparative and experimental biodiversity and ecosystem research. The dataset combines several disciplines, including computer science and robotics, biology, bio-geochemistry, and forestry science. We present results for common 3D perception tasks, including classification, depth estimation, localization, and path planning. We combine the full suite of modern perception sensors, including high-resolution fisheye cameras, 3D dense LiDAR, differential GPS, and an inertial measurement unit, with ecological metadata of the area, including stand age, diameter, exact 3D position, and species. The dataset consists of three hand held measurement series taken from sensors mounted on a UAV during each of three seasons: winter, spring, and early summer. This enables new research opportunities and paves the way for testing forest environment 3D perception tasks and mission set automation for robotics.

Walther, Dominik; Viehweg, Johannes; Haueisen, Jens; Mäder, Patrick
A systematic comparison of deep learning methods for EEG time series analysis. - In: Frontiers in neuroinformatics, ISSN 1662-5196, Bd. 17 (2023), 1067095, S. 01-17

Analyzing time series data like EEG or MEG is challenging due to noisy, high-dimensional, and patient-specific signals. Deep learning methods have been demonstrated to be superior in analyzing time series data compared to shallow learning methods which utilize handcrafted and often subjective features. Especially, recurrent deep neural networks (RNN) are considered suitable to analyze such continuous data. However, previous studies show that they are computationally expensive and difficult to train. In contrast, feed-forward networks (FFN) have previously mostly been considered in combination with hand-crafted and problem-specific feature extractions, such as short time Fourier and discrete wavelet transform. A sought-after are easily applicable methods that efficiently analyze raw data to remove the need for problem-specific adaptations. In this work, we systematically compare RNN and FFN topologies as well as advanced architectural concepts on multiple datasets with the same data preprocessing pipeline. We examine the behavior of those approaches to provide an update and guideline for researchers who deal with automated analysis of EEG time series data. To ensure that the results are meaningful, it is important to compare the presented approaches while keeping the same experimental setup, which to our knowledge was never done before. This paper is a first step toward a fairer comparison of different methodologies with EEG time series data. Our results indicate that a recurrent LSTM architecture with attention performs best on less complex tasks, while the temporal convolutional network (TCN) outperforms all the recurrent architectures on the most complex dataset yielding a 8.61% accuracy improvement. In general, we found the attention mechanism to substantially improve classification results of RNNs. Toward a light-weight and online learning-ready approach, we found extreme learning machines (ELM) to yield comparable results for the less complex tasks.

Sachs, Sebastian; Ratz, Manuel; Mäder, Patrick; König, Jörg; Cierpka, Christian
Particle detection and size recognition based on defocused particle images: a comparison of a deterministic algorithm and a deep neural network. - In: Experiments in fluids, ISSN 1432-1114, Bd. 64 (2023), 2, 21, S. 1-16

The systematic manipulation of components of multimodal particle solutions is a key for the design of modern industrial products and pharmaceuticals with highly customized properties. In order to optimize innovative particle separation devices on microfluidic scales, a particle size recognition with simultaneous volumetric position determination is essential. In the present study, the astigmatism particle tracking velocimetry is extended by a deterministic algorithm and a deep neural network (DNN) to include size classification of particles of multimodal size distribution. Without any adaptation of the existing measurement setup, a reliable classification of bimodal particle solutions in the size range of 1.14 μm–5.03 μm is demonstrated with a precision of up to 99.9 %. Concurrently, the high detection rate of the particles, suspended in a laminar fluid flow, is quantified by a recall of 99.0 %. By extracting particle images from the experimentally acquired images and placing them on a synthetic background, semi-synthetic images with consistent ground truth are generated. These contain labeled overlapping particle images that are correctly detected and classified by the DNN. The study is complemented by employing the presented algorithms for simultaneous size recognition of up to four particle species with a particle diameter in between 1.14 μm and 5.03 μm. With the very high precision of up to 99.3 % at a recall of 94.8 %, the applicability to classify multimodal particle mixtures even in dense solutions is confirmed. The present contribution thus paves the way for quantitative evaluation of microfluidic separation and mixing processes.

Viehweg, Johannes; Worthmann, Karl; Mäder, Patrick
Parameterizing echo state networks for multi-step time series prediction. - In: Neurocomputing, ISSN 1872-8286, Bd. 522 (2023), S. 214-228

Prediction of multi-dimensional time-series data, which may represent such diverse phenomena as climate changes or financial markets, remains a challenging task in view of inherent nonlinearities and non-periodic behavior In contrast to other recurrent neural networks, echo state networks (ESNs) are attractive for (online) learning due to lower requirements w.r.t.training data and computational power. However, the randomly-generated reservoir renders the choice of suitable hyper-parameters as an open research topic. We systematically derive and exemplarily demonstrate design guidelines for the hyper-parameter optimization of ESNs. For the evaluation, we focus on the prediction of chaotic time series, an especially challenging problem in machine learning. Our findings demonstrate the power of a hyper-parameter-tuned ESN when auto-regressively predicting time series over several hundred steps. We found that ESNs’ performance improved by 85.1%-99.8% over an already wisely chosen default parameter initialization. In addition, the fluctuation range is considerably reduced such that significantly worse performance becomes very unlikely across random reservoir seeds. Moreover, we report individual findings per hyper-parameter partly contradicting common knowledge to further, help researchers when training new models.



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Anzahl der Treffer: 16
Erstellt: Fri, 23 Feb 2024 01:25:23 +0100 in 0.1360 sec

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

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.

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. 33 (2021), 17, art00011, 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, S. 1-13

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.

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.



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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.



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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

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.



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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
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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

Requirements traceability has long been recognized as an important quality of a well-engineered system. Among stakeholders, traceability is often unpopular due to the unclear benefits. In fact, little evidence exists regarding the expected traceability benefits. There is a need for empirical work that studies the effect of traceability. In this paper, we focus on the four main requirements implementation supporting activities that utilize traceability. For each activity, we propose generalized traceability completeness measures. In a defined process, we selected 24 medium to large-scale open-source projects. For each software project, we quantified the degree to which a studied development activity was enabled by existing traceability with the proposed measures. We analyzed that data in a multi-level Poisson regression analysis. We found that the degree of traceability completeness for three of the studied activities significantly affects software quality, which we quantified as defect rate. Our results provide for the first time empirical evidence that more complete traceability decreases the expected defect rate in the developed software. The strong impact of traceability completeness on the defect rate suggests that traceability is of great practical value for any kind of software development project, even if traceability is not mandated by a standard or regulation

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.



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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, (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, (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