Anzahl der Treffer: 5
Erstellt: Sun, 23 Jun 2024 19:49:05 +0200 in 0.0431 sec

Nauber, Tristan; Hodač, Ladislav; Wäldchen, Jana; Mäder, Patrick
Parametrization of biological assumptions to simulate growth of tree branching architectures. - In: Tree physiology, ISSN 1758-4469, Bd. 44 (2024), 5, tpae045, S. 1-14

Modeling and simulating the growth of the branching of tree species remains a challenge. With existing approaches, we can reconstruct or rebuild the branching architectures of real tree species, but the simulation of the growth process remains unresolved. First, we present a tree growth model to generate branching architectures that resemble real tree species. Secondly, we use a quantitative morphometric approach to infer the shape similarity of the generated simulations and real tree species. Within a functional-structural plant model, we implement a set of biological parameters that affect the branching architecture of trees. By modifying the parameter values, we aim to generate basic shapes of spruce, pine, oak and poplar. Tree shapes are compared using geometric morphometrics of landmarks that capture crown and stem outline shapes. Five biological parameters, namely xylem flow, shedding rate, proprioception, gravitysense and lightsense, most influenced the generated tree branching patterns. Adjusting these five parameters resulted in the different tree shapes of spruce, pine, oak, and poplar. The largest effect was attributed to gravity, as phenotypic responses to this effect resulted in different growth directions of gymnosperm and angiosperm branching architectures. Since we were able to obtain branching architectures that resemble real tree species by adjusting only a few biological parameters, our model is extendable to other tree species. Furthermore, the model will also allow the simulation of structural tree-environment interactions. Our simplifying approach to shape comparison between tree species, landmark geometric morphometrics, showed that even the crown-trunk outlines capture species differences based on their contrasting branching architectures.

Fischer, Kai; Simon, Martin; Milz, Stefan; Mäder, Patrick
MagneticPillars: efficient point cloud registration through hierarchized Birds-Eye-View cell correspondence refinement. - In: IEEE Xplore digital library, ISSN 2473-2001, (2024), S. 7371-7380

Recent point cloud registration approaches often deal with a consecutive determination of coarse and fine feature correspondences for hierarchical pose refinement. Due to the unordered nature of point clouds, a common way to generate a subsampled representation for the coarse matching step is by applying 3D-sensitive convolution approaches. However, expensive grouping mechanisms such as nearest neighbour search have to be used to determine the associated fine features, generating individual associations for each point cloud and leading to an increased overall run-time. Furthermore current methods often tend to predict deficient point correspondences and rely on additional filtering by expensive registration backends like RANSAC impeding their application in time critical systems.To overcome these challenges, we present MagneticPillars utilizing a Birds-Eye-View (BEV) grid representation, entailing fixed affiliations between coarse and fine feature cells. We show that by extracting correspondences in this manner, a small amount of key points is already sufficient to achieve an accurate pose estimation without external optimization methods like RANSAC. We evaluate our approach on two autonomous driving datasets for the task of point cloud registration by applying SVD as the backend, where we outperform recent state-of-the-art methods, reducing the rotation and translation error by 12% and 40%, respectively, and to top it all off, cutting runtime in half.

Janke, Mario; Mäder, Patrick
7 dimensions of software change patterns. - In: Scientific reports, ISSN 2045-2322, Bd. 14 (2024), 6141, S. 1-17

Evolving software is a highly complex and creative problem in which a number of different strategies are used to solve the tasks at hand. These strategies and reoccurring coding patterns can offer insights into the process. However, they can be highly project or even task-specific. We aim to identify code change patterns in order to draw conclusions about the software development process. For this, we propose a novel way to calculate high-level file overarching diffs, and a novel way to parallelize pattern mining. In a study of 1000 Java projects, we mined and analyzed a total of 45,000 patterns. We present 13 patterns, showing extreme points of the 7 pattern categories we identified. We found that a large number of high-level change patterns exist and occur frequently. The majority of mined patterns were associated with a specific project and contributor, where and by whom it was more likely to be used. While a large number of different code change patterns are used, only a few, mostly unsurprising ones, are common under all circumstances. The majority of code change patterns are highly specific to different context factors that we further explore.

Walther, Dominik; Junger, Christina; Schmidt, Leander; Schricker, Klaus; Notni, Gunther; Bergmann, Jean Pierre; Mäder, Patrick
Recurrent autoencoder for weld discontinuity prediction. - In: Journal of advanced joining processes, ISSN 2666-3309, Bd. 9 (2024), 100203, S. 1-12

Laser beam butt welding is often the technique of choice for a wide range of industrial tasks. To achieve high quality welds, manufacturers often rely on heavy and expensive clamping systems to limit the sheet movement during the welding process, which can affect quality. Jiggless welding offers a cost-effective and highly flexible alternative to common clamping systems. In laser butt welding, the process-induced joint gap has to be monitored in order to counteract the effect by means of an active position control of the sheet metal. Various studies have shown that sheet metal displacement can be detected using inductive probes, allowing the prediction of weld quality by ML-based data analysis. The probes are dependent on the sheet metal geometry and are limited in their applicability to complex geometric structures. Camera systems such as long-wave infrared (LWIR) cameras can instead be mounted directly behind the laser to overcome a geometry dependent limitation of the jiggles system. In this study we will propose a deep learning approach that utilizes LWIR camera recordings to predict the remaining welding process to enable an early detection of weld interruptions. Our approach reaches 93.33% accuracy for time-wise prediction of the point of failure during the weld.

Tomova, Mihaela; Hofmann, Martin; Hütterer, Constantin; Mäder, Patrick
Assessing the utility of text-to-SQL approaches for satisfying software developer information needs. - In: Empirical software engineering, ISSN 1573-7616, Bd. 29 (2024), 1, 15, S. 1-48

Software analytics integrated with complex databases can deliver project intelligence into the hands of software engineering (SE) experts for satisfying their information needs. A new and promising machine learning technique known as text-to-SQL automatically extracts information for users of complex databases without the need to fully understand the database structure nor the accompanying query language. Users pose their request as so-called natural language utterance, i.e., question. Our goal was evaluating the performance and applicability of text-to-SQL approaches on data derived from tools typically used in the workflow of software engineers for satisfying their information needs. We carefully selected and discussed five seminal as well as state-of-the-art text-to-SQL approaches and conducted a comparative assessment using the large-scale, cross-domain Spider dataset and the SE domain-specific SEOSS-Queries dataset. Furthermore, we study via a survey how SE professionals perform in satisfying their information needs and how they perceive text-to-SQL approaches. For the best performing approach, we observe a high accuracy of 94% in query prediction when training specifically on SE data. This accuracy is almost independent of the query’s complexity. At the same time, we observe that SE professionals have substantial deficits in satisfying their information needs directly via SQL queries. Furthermore, SE professionals are open for utilizing text-to-SQL approaches in their daily work, considering them less time-consuming and helpful. We conclude that state-of-the-art text-to-SQL approaches are applicable in SE practice for day-to-day information needs.



Anzahl der Treffer: 12
Erstellt: Sun, 23 Jun 2024 19:48:58 +0200 in 0.1165 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.



Anzahl der Treffer: 29
Erstellt: Sun, 23 Jun 2024 19:48:42 +0200 in 0.2167 sec

Gotzig, Heinrich; Mohamed, Mohamed Elamir; Zöllner, Raoul; Mäder, Patrick
Improved ultrasonic sensing using machine learning. - In: AmE 2022, (2022), S. 25-26

We present a novel approach for using industrial grade ultrasonic sensors to perform echolocation by detecting ultrasonic echoes in a noisy environment using machine learning. Autonomous driving is expected to become a huge market and among other technical challenges, environmental perception will be the most critical one. For high automation level, classical technologies are limited. On the other hand, automotive is cost sensitive. The main part of the lecture starts with state of the art technology and then explains how we have used machine learning approaches to train a net for several classifications tasks: Distinguish whether the ultrasonic echo comes from the sensor or another noise source, distinguish whether the echo is relevant or not and finally a height classification. Results are presented in the form of F1-Score. In addition to this, a method will be presented to use CNN for noise suppression in real time. We demonstrate the potential of using the “bat principle” for perception and prove that by that we also achieve the low-cost targets.

Gao, Hui; Kuang, Hongyu; Sun, Kexin; Ma, Xiaoxing; Egyed, Alexander; Mäder, Patrick; Rong, Guoping; Shao, Dong; Zhang, He
Using consensual biterms from text structures of requirements and code to improve IR-based traceability recovery. - In: ASE '22, (2022), 114, insges. 13 S.

Traceability approves trace links among software artifacts based on whether two artifacts are related by system functionalities. The traces are valuable for software development, but are difficult to obtain manually. To cope with the costly and fallible manual recovery, automated approaches are proposed to recover traces through textual similarities among software artifacts, such as those based on Information Retrieval (IR). However, the low quality & quantity of artifact texts negatively impact the calculated IR values, thus greatly hindering the performance of IR-based approaches. In this study, we propose to extract co-occurred word pairs from the text structures of both requirements and code (i.e., consensual biterms) to improve IR-based traceability recovery. We first collect a set of biterms based on the part-of-speech of requirement texts, and then filter them through the code texts. We then use these consensual biterms to both enrich the input corpus for IR techniques and enhance the calculations of IR values. A nine-system-based evaluation shows that in general, when solely used to enhance IR techniques, our approach can outperform pure IR-based approaches and another baseline by 21.9% & 21.8% in AP, and 9.3% & 7.2% in MAP, respectively. Moreover, when used to collaborate with another enhancing strategy from different perspectives, it can outperform this baseline by 5.9% in AP and 4.8% in MAP.

Sonnekalb, Tim; Gruner, Bernd; Brust, Clemens-Alexander; Mäder, Patrick
Generalizability of code clone detection on CodeBERT. - In: ASE '22, (2022), 143, insges. 3 S.

Transformer networks such as CodeBERT already achieve outstanding results for code clone detection in benchmark datasets, so one could assume that this task has already been solved. However, code clone detection is not a trivial task. Semantic code clones, in particular, are challenging to detect. We show that the generalizability of CodeBERT decreases by evaluating two different subsets of Java code clones from BigCloneBench. We observe a significant drop in F1 score when we evaluate different code snippets and functionality IDs than those used for model building.

Schlarbaum, Laura; Forner, Frank; Bohn, Kristin; Amberg, Michael; Mäder, Patrick; Lorkowski, Stefan; Meier, Toni
Nutritional assessment of ready-to-eat salads in German supermarkets: comparison of the nutriRECIPE-Index and the Nutri-Score. - In: Foods, ISSN 2304-8158, Bd. 11 (2022), 24, 4011, S. 1-21

Globally, an unbalanced diet causes more deaths than any other factor. Due to a lack of knowledge, it is difficult for consumers to select healthy foods at the point of sale. Although different front-of-pack labeling schemes exist, their informative value is limited due to small sets of considered parameters and lacking information on ingredient composition. We developed and evalauated a manufacture-independent approach to quantify ingredient composition of 294 ready-to eat salads (distinguished into 73 subgroups) as test set. Nutritional quality was assessed by the nutriRECIPE-Index and compared to the Nutri-Score. The nutriRECIPE-Index comprises the calculation of energy-adjusted nutrient density of 16 desirable and three undesirable nutrients, which are weighted according to their degree of supply in the population. We show that the nutriRECIPE-Index has stronger discriminatory power compared to the Nutri-Score and discriminates as well or even better in 63 out of the 73 subgroups. This was evident in groups where seemingly similar products were compared, e.g., potato salads (Nutri-Score: C only, nutriRECIPE-Index: B, C and D). Moreover, the nutriRECIPE-Index is adjustable to any target population’s specific needs and supply situation, such as seniors, and children. Hence, a more sophisticated distinction between single food products is possible using the nutriRECIPE-Index.

Hammouch, Hajar; Mohapatra, Sambit; El Yacoubi, Mounim; Qin, Huafeng; Berbia, Hassan; Mäder, Patrick; Chikhaoui, Mohamed
GANSet - generating annnotated datasets using Generative Adversarial Networks. - In: Proceedings of the International Conference on Cyber-Physical Social Intelligence (ICCSI 2022), (2022), S. 615-620

The prediction of soil moisture for automated irrigation applications is a major challenge, as it is affected by various environmental parameters. The Application of Convolutional Neural Networks (CNN), to this end, has shown remarkable results for soil moisture prediction. These models, however, typically need large datasets, which are scarce in the agriculture field. To this end, this paper presents a Deep Convolutional Generative Adversarial Network (DCGAN) that can learn good data representations and generate highly realistic samples. Traditionally, Generative Adversarial Networks (GANs) have been used for generating data for segmentation and classification tasks or used in conjunction with CNNs or Multi Layer Perceptrons (MLPs) for regression tasks. In this paper, we propose a novel approach in which GANs are used to generate conjointly training images for plants as well as realistic regression values for their corresponding moisture levels without the use of any additional network. The generated images and regression vector targets, together with the training data, are then used to train a CNN which is then evaluated with actual test data from the dataset. We observe an improvement of error rate by 33 percent which shows the validity of our approach.

Teutsch, Philipp; Mäder, Patrick
Flipped classroom: effective teaching for time series forecasting. - New York, NY : TMLR. - 1 Online-Ressource (Seite 1-36)Sonderdruck aus: Transactions on Machine Learning Research (TMLR). - New York, NY : TMLR, 10/2022

Sequence-to-sequence models based on LSTM and GRU are a most popular choice for forecasting time series data reaching state-of-the-art performance. Training such models can be delicate though. The two most common training strategies within this context are teacher forcing (TF) and free running (FR). TF can be used to help the model to converge faster but may provoke an exposure bias issue due to a discrepancy between training and inference phase. FR helps to avoid this but does not necessarily lead to better results, since it tends to make the training slow and unstable instead. Scheduled sampling was the first approach tackling these issues by picking the best from both worlds and combining it into a curriculum learning (CL) strategy. Although scheduled sampling seems to be a convincing alternative to FR and TF, we found that, even if parametrized carefully, scheduled sampling may lead to premature termination of the training when applied for time series forecasting. To mitigate the problems of the above approaches we formalize CL strategies along the training as well as the training iteration scale. We propose several new curricula, and systematically evaluate their performance in two experimental sets. For our experiments, we utilize six datasets generated from prominent chaotic systems. We found that the newly proposed increasing training scale curricula with a probabilistic iteration scale curriculum consistently outperforms previous training strategies yielding an NRMSE improvement of up to 81% over FR or TF training. For some datasets we additionally observe a reduced number of training iterations. We observed that all models trained with the new curricula yield higher prediction stability allowing for longer prediction horizons.

Elamir, Mohamed Shawki; Gotzig, Heinrich; Zöllner, Raoul; Mäder, Patrick
A deep learning approach for direction of arrival estimation using automotive-grade ultrasonic sensors. - In: Journal of physics, ISSN 1742-6596, Bd. 2234 (2022), 012009, insges. 12 S.

In this paper, a deep learning approach is presented for direction of arrival estimation using automotive-grade ultrasonic sensors which are used for driving assistance systems such as automatic parking. A study and implementation of the state of the art deterministic direction of arrival estimation algorithms is used as a benchmark for the performance of the proposed approach. Analysis of the performance of the proposed algorithms against the existing algorithms is carried out over simulation data as well as data from a measurement campaign done using automotive-grade ultrasonic sensors. Both sets of results clearly show the superiority of the proposed approach under realistic conditions such as noise from the environment as well as eventual errors in measurements. It is demonstrated as well how the proposed approach can overcome some of the known limitations of the existing algorithms such as precision dilution of triangulation and aliasing.

Wäldchen, Jana; Wittich, Hans Christian; Rzanny, Michael Carsten; Fritz, Alice; Mäder, Patrick
Towards more effective identification keys: a study of people identifying plant species characters. - In: People and nature, ISSN 2575-8314, Bd. 4 (2022), 6, S. 1603-1615

Accurate species identification is essential for ecological monitoring and biodiversity conservation. Interactive plant identification keys have been considerably improved in recent years, mainly by providing iconic symbols, illustrations, or images for the users, as these keys are also commonly used by people with relatively little plant knowledge. Only a few studies have investigated how well morphological characteristics can be recognized and correctly identified by people, which is ultimately the basis of an identification key's success. This study consists of a systematic evaluation of people's abilities in identifying plant-specific morphological characters. We conducted an online survey where 484 participants were asked to identify 25 different plant character states on six images showing a plant from different perspectives. We found that survey participants correctly identified 79% of the plant characters, with botanical novices with little or no previous experience in plant identification performing slightly worse than experienced botanists. We also found that flower characters are more often correctly identified than leaf characteristics and that characters with more states resulted in higher identification errors. Additionally, the longer the time a participant needed for answering, the higher the probability of a wrong answer. Understanding what influences users' plant character identification abilities can improve the development of interactive identification keys, for example, by designing keys that adapt to novices as well as experts. Furthermore, our study can act as a blueprint for the empirical evaluation of identifications keys. Read the free Plain Language Summary for this article on the Journal blog.

Richter, Felix; Chen, Minqian; Schaub, Patrick; Wüst, Florian; Zhang, Di; Schneider, Steffen; Groß, Gregor Alexander; Mäder, Patrick; Dovzhenko, Oleksandr; Palme, Klaus; Köhler, Michael; Cao-Riehmer, Jialan
Induction of embryogenic development in haploid microspore stem cells in droplet-based microfluidics. - In: Lab on a chip, ISSN 1473-0189, Bd. 22 (2022), 22, S. 4292-4305

This work presents the application of droplet-based microfluidics for the cultivation of microspores from Brassica napus using the doubled haploid technology. Under stress conditions (e.g. heat shock) or by chemical induction a certain fraction of the microspores can be reprogrammed and androgenesis can be induced. This process is an important approach for plant breeding because desired plant properties can be anchored in the germline on a genetic level. However, the reprogramming rate of the microspores is generally very low, increasing it by specific stimulation is, therefore, both a necessary and challenging task. In order to accelerate the optimisation and development process, the application of droplet-based microfluidics can be a promising tool. Here, we used a tube-based microfluidic system for the generation and cultivation of microspores inside nL-droplets. Different factors like cell density, tube material and heat shock conditions were investigated to improve the yield of vital plant organoids. Evaluation and analysis of the stimuli response were done on an image base aided by an artificial intelligence cell detection algorithm. Droplet-based microfluidics allowed us to apply large concentration programs in small test volumes and to screen the best conditions for reprogramming cells by the histone deacetylase inhibitor trichostatin A and for enhancing the yield of vital microspores in droplets. An enhanced reprogramming rate was found under the heat shock conditions at 32 &ring;C for about 3 to 6 days. In addition, the comparative experiment with MTP showed that droplet cultivation with lower cell density (<10 cells per droplet) or adding media after 3 or 6 days significantly positively affects the microspore growth and embryo rate inside 120 nL droplets. Finally, the developed embryos could be removed from the droplets and further grown into mature plants. Overall, we demonstrated that the droplet-based tube system is suitable for implementation in an automated, miniaturized system to achieve the induction of embryogenic development in haploid microspore stem cells of Brassica napus.

Rath, Michael;
Utilizing traceable software artifacts to improve bug localization. - Ilmenau : Universitätsbibliothek, 2022. - 1 Online-Ressource (viii, 142, XXX Seiten)
Technische Universität Ilmenau, Dissertation 2022

Die Entwicklung von Softwaresystemen ist eine komplexe Aufgabe. Qualitätssicherung versucht auftretenden Softwarefehler (bugs) in Systemen zu vermeiden, jedoch können Fehler nie ausgeschlossen werden. Sobald ein Softwarefehler entdeckt wird, wird typischerweise ein Fehlerbericht (bug report) erstellt. Dieser dient als Ausgangspunkt für den Entwickler den Fehler im Quellcode der Software zu finden und zu beheben (bug fixing). Fehlerberichte sowie weitere Softwareartefakte, z.B. Anforderungen und der Quellcode selbst, werden in Software Repositories abgelegt. Diese erlauben die Artefakte mit trace links zur Nachvollziehbarkeit (traceability) zu verknüpfen. Oftmals ist die Erstellung der trace links im Entwicklungsprozess vorgeschrieben. Dazu zählen u.a. die Luftfahrt- und Automobilindustrie, sowie die Entwicklung von medizinischen Geräten. Das Auffinden von Softwarefehlern in großen Systemen mit tausenden Artefakten ist eine anspruchsvolle, zeitintensive und fehleranfällige Aufgabe, welche eine umfangreiche Projektkenntnis erfordert. Deswegen wird seit Jahren aktiv an der Automatisierung dieses Prozesses geforscht. Weiterhin wird die manuelle Erstellung und Pflege von trace links als Belastung empfunden und sollte weitgehend automatisiert werden. In dieser Arbeit wird ein neuartiger Algorithmus zum Auffinden von Softwarefehlern vorgestellt, der aktiv die erstellten trace links ausnutzt. Die Artefakte und deren Beziehungen dienen zur Erstellung eines Nachvollziehbarkeitsgraphen, welcher analysiert wird um fehlerhafte Quellcodedateien anhand eines Fehlerberichtes zu finden. Jedoch muss angenommen werden, dass nicht alle notwendigen trace links zwischen den Softwareartefakten eines Projektes erstellt wurden. Deswegen wird ein vollautomatisierter, projektunabhängiger Ansatz vorgestellt, der diese fehlenden trace links erstellt (augmentation). Die Grundlage zur Entwicklung dieses Algorithmus ist der typische Entwicklungsprozess eines Softwareprojektes. Die entwickelten Ansätze wurden mit mehr als 32.000 Fehlerberichten von 27 Open-Source Projekten evaluiert und die Ergebnisse zeigen, dass die Einbeziehung von traceability signifikant das Auffinden von Fehlern im Quellcode verbessert. Weiterhin kann der entwickelte Augmentation Algorithmus zuverlässig fehlende trace links erstellen.

Mohapatra, Sambit; Mesquida, Thomas; Hodaei, Mona; Yogamani, Senthil; Gotzig, Heinrich; Mäder, Patrick
SpikiLi: a spiking simulation of LiDAR based real-time object detection for autonomous driving. - In: 2022 8th International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP), (2022), insges. 5 S.

Spiking Neural Networks are a recent and new neural network design approach that promises tremendous improvements in power efficiency, computation efficiency, and processing latency. They do so by using asynchronous spike-based data flow, event-based signal generation, processing, and modifying the neuron model to resemble biological neurons closely. While some initial works have shown significant initial evidence of applicability to common deep learning tasks, their applications in complex real-world tasks have been relatively low. In this work, we first illustrate the applicability of spiking neural networks to a complex deep learning task, namely LiDAR based 3D object detection for automated driving. Secondly, we make a step-by-step demonstration of simulating spiking behavior using a pre-trained Convolutional Neural Network. We closely model essential aspects of spiking neural networks in simulation and achieve equivalent run-time and accuracy on a GPU. We expect significant improvements in power efficiency when the model is implemented on neuromorphic hardware.

Sennlaub, Rabea; Hofmann, Martin; Hankey, Mike; Ennes, Mario; Müller, Thomas; Kroll, Peter; Mäder, Patrick
Object classification on video data of meteors and meteor-like phenomena: algorithm and data. - In: Monthly notices of the Royal Astronomical Society, ISSN 1365-2966, Bd. 516 (2022), 1, S. 811-823

Every moment, countless meteoroids enter our atmosphere unseen. The detection and measurement of meteors offer the unique opportunity to gain insights into the composition of our solar systems’ celestial bodies. Researchers therefore carry out a wide-area-sky-monitoring to secure 360-degree video material, saving every single entry of a meteor. Existing machine intelligence cannot accurately recognize events of meteors intersecting the earth’s atmosphere due to a lack of high-quality training data publicly available. This work presents four reusable open source solutions for researchers trained on data we collected due to the lack of available labelled high-quality training data. We refer to the proposed data set as the NightSkyUCP data set, consisting of a balanced set of 10 000 meteor- and 10 000 non-meteor-events. Our solutions apply various machine-learning techniques, namely classification, feature learning, anomaly detection, and extrapolation. For the classification task, a mean accuracy of 99.1 per cent is achieved. The code and data are made public at figshare with DOI 10.6084/m9.figshare.16451625.

Dunker, Susanne; Boyd, Matthew; Durka, Walter; Erler, Silvio; Harpole, Stan; Henning, Silvia; Herzschuh, Ulrike; Hornick, Thomas; Knight, Tiffany M.; Lips, Stefan; Mäder, Patrick; Švara, Elena Motivans; Mozarowski, Steven; Rakosy, Demetra; Römermann, Christine; Schmitt-Jansen, Mechthild; Stoof-Leichsenring, Kathleen R.; Stratmann, Frank; Treudler, Regina; Virtanen, Risto; Wendt-Potthoff, Katrin; Wilhelm, Christian
The potential of multispectral imaging flow cytometry for environmental monitoring. - In: Cytometry, ISSN 1552-4930, Bd. 101 (2022), 9, S. 782-799

Environmental monitoring involves the quantification of microscopic cells and particles such as algae, plant cells, pollen, or fungal spores. Traditional methods using conventional microscopy require expert knowledge, are time-intensive and not well-suited for automated high throughput. Multispectral imaging flow cytometry (MIFC) allows measurement of up to 5000 particles per second from a fluid suspension and can simultaneously capture up to 12 images of every single particle for brightfield and different spectral ranges, with up to 60x magnification. The high throughput of MIFC has high potential for increasing the amount and accuracy of environmental monitoring, such as for plant-pollinator interactions, fossil samples, air, water or food quality that currently rely on manual microscopic methods. Automated recognition of particles and cells is also possible, when MIFC is combined with deep-learning computational techniques. Furthermore, various fluorescence dyes can be used to stain specific parts of the cell to highlight physiological and chemical features including: vitality of pollen or algae, allergen content of individual pollen, surface chemical composition (carbohydrate coating) of cells, DNA- or enzyme-activity staining. Here, we outline the great potential for MIFC in environmental research for a variety of research fields and focal organisms. In addition, we provide best practice recommendations.

Bohn, Kristin; Amberg, Michael; Mäder, Patrick; Forner, Frank; Meier, Toni
Back to the Roots - Bewertung und Vergleich der Nachhaltigkeit von Nahrungsmitteln im Lebensmitteleinzelhandel : Abschlussbericht für die Deutsche Bundesstiftung Umwelt. - Ilmenau. - 1 Online Ressource (37 Seiten)Literaturverzeichnis: Seite 34-36

Gegenwärtig ist es nahezu unmöglich die vielfältigen Nachhaltigkeitsleistungen von Lebensmitteln einzusehen oder zu vergleichen, da Informationen über die gesamte Prozesskette nicht umfänglich verfügbar sind. Um das steigende Informationsinteresse der Konsumierenden zu bedienen und damit einen verantwortungsvollen Konsum (Ziel 12 der Sustainable Development Goals, UN 2016) von Lebensmitteln zu ermöglichen, sollten entsprechende Informationen in einer übersichtlichen Form am point of sale für die Verbraucher_innen verfügbar sein. Vor diesem Hintergrund entwickelten wir im Projekt einen Bewertungsansatz und darauf aufbauend eine mobile App, welche eine produktspezifische Ausweisung von Nachhaltigkeitsinformationen (Fokus: Umwelt + Gesundheit) beim Einkauf ermöglicht.

Ravi Kumar, Varun; Klingner, Marvin; Yogamani, Senthil; Bach, Markus; Milz, Stefan; Fingscheidt, Tim; Mäder, Patrick
SVDistNet: self-supervised near-field distance estimation on surround view fisheye cameras. - In: IEEE transactions on intelligent transportation systems, Bd. 23 (2022), 8, S. 10252-10261

A 360&ring; perception of scene geometry is essential for automated driving, notably for parking and urban driving scenarios. Typically, it is achieved using surround-view fisheye cameras, focusing on the near-field area around the vehicle. The majority of current depth estimation approaches focus on employing just a single camera, which cannot be straightforwardly generalized to multiple cameras. The depth estimation model must be tested on a variety of cameras equipped to millions of cars with varying camera geometries. Even within a single car, intrinsics vary due to manufacturing tolerances. Deep learning models are sensitive to these changes, and it is practically infeasible to train and test on each camera variant. As a result, we present novel camera-geometry adaptive multi-scale convolutions which utilize the camera parameters as a conditional input, enabling the model to generalize to previously unseen fisheye cameras. Additionally, we improve the distance estimation by pairwise and patchwise vector-based self-attention encoder networks. We evaluate our approach on the Fisheye WoodScape surround-view dataset, significantly improving over previous approaches. We also show a generalization of our approach across different camera viewing angles and perform extensive experiments to support our contributions. To enable comparison with other approaches, we evaluate the front camera data on the KITTI dataset (pinhole camera images) and achieve state-of-the-art performance among self-supervised monocular methods. An overview video with qualitative results is provided at https://youtu.be/bmX0UcU9wtA. Baseline code and dataset will be made public.

Mohapatra, Sambit; Hodaei, Mona; Yogamani, Senthil; Milz, Stefan; Gotzig, Heinrich; Simon, Martin; Rashed, Hazem; Mäder, Patrick
LiMoSeg: real-time Bird's Eye View based LiDAR motion segmentation. - In: , (2022), S. 828-835
LiDAR = Light Detection and Ranging

Moving object detection and segmentation is an essential task in the Autonomous Driving pipeline. Detecting and isolating static and moving components of a vehicle’s surroundings are particularly crucial in path planning and localization tasks. This paper proposes a novel real-time architecture for motion segmentation of Light Detection and Ranging (LiDAR) data. We use two successive scans of LiDAR data in 2D Bird’s Eye View (BEV) representation to perform pixel-wise classification as static or moving. Furthermore, we propose a novel data augmentation technique to reduce the significant class imbalance between static and moving objects. We achieve this by artificially synthesizing moving objects by cutting and pasting static vehicles. We demonstrate a low latency of 8 ms on a commonly used automotive embedded platform, namely Nvidia Jetson Xavier. To the best of our knowledge, this is the first work directly performing motion segmentation in LiDAR BEV space. We provide quantitative r esults on the challenging SemanticKITTI dataset, and qualitative results are provided in https://youtu.be/2aJ-cL8b0LI.

Walther, Dominik; Schmidt, Leander; Schricker, Klaus; Junger, Christina; Bergmann, Jean Pierre; Notni, Gunther; Mäder, Patrick
Automatic detection and prediction of discontinuities in laser beam butt welding utilizing deep learning. - In: Journal of advanced joining processes, ISSN 2666-3309, Bd. 6 (2022), 100119, S. 1-11

Laser beam butt welding of thin sheets of high-alloy steel can be really challenging due to the formation of joint gaps, affecting weld seam quality. Industrial approaches rely on massive clamping systems to limit joint gap formation. However, those systems have to be adapted for each individually component geometry, making them very cost-intensive and leading to a limited flexibility. In contrast, jigless welding can be a high flexible alternative to substitute conventionally used clamping systems. Based on the collaboration of different actuators, motions systems or robots, the approach allows an almost free workpiece positioning. As a result, jigless welding gives the possibility for influencing the formation of the joint gap by realizing an active position control. However, the realization of an active position control requires an early and reliable error prediction to counteract the formation of joint gaps during laser beam welding. This paper proposes different approaches to predict the formation of joint gaps and gap induced weld discontinuities in terms of lack of fusion based on optical and tactile sensor data. Our approach achieves 97.4 % accuracy for video-based weld discontinuity detection and a mean absolute error of 0.02 mm to predict the formation of joint gaps based on tactile length measurements by using inductive probes.

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, 045106, 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: 2022 IEEE Winter Conference on Applications of Computer Vision, (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: 2022 IEEE Winter Conference on Applications of Computer Vision, (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.

Hofmann, Martin; Mäder, Patrick
Synaptic scaling - an artificial neural network regularization inspired by nature. - In: IEEE transactions on neural networks and learning systems, ISSN 2162-2388, Bd. 33 (2022), 7, S. 3094-3108

Nature has always inspired the human spirit and scientists frequently developed new methods based on observations from nature. Recent advances in imaging and sensing technology allow fascinating insights into biological neural processes. With the objective of finding new strategies to enhance the learning capabilities of neural networks, we focus on a phenomenon that is closely related to learning tasks and neural stability in biological neural networks, called homeostatic plasticity. Among the theories that have been developed to describe homeostatic plasticity, synaptic scaling has been found to be the most mature and applicable. We systematically discuss previous studies on the synaptic scaling theory and how they could be applied to artificial neural networks. Therefore, we utilize information theory to analytically evaluate how mutual information is affected by synaptic scaling. Based on these analytic findings, we propose two flavors in which synaptic scaling can be applied in the training process of simple and complex, feedforward, and recurrent neural networks. We compare our approach with state-of-the-art regularization techniques on standard benchmarks. We found that the proposed method yields the lowest error in both regression and classification tasks compared to previous regularization approaches in our experiments across a wide range of network feedforward and recurrent topologies and data sets.

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&ring; 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: 18
Erstellt: Sun, 23 Jun 2024 19:48:31 +0200 in 0.1449 sec

Cierpka, Christian; Barnkob, Rune; Sachs, Sebastian; Chen, Minqian; Mäder, Patrick; Rossi, Massimiliano
On the uncertainty of defocus methods for 3D particle tracking velocimetry. - In: International Symposium on Particle Image Velocimetry, ISSN 2769-7576, Bd. 1 (2021), 1, insges. 2 S.

Defocus methods have become more and more popular for the estimation of the 3D position of particles in flows (Cierpka and Kähler, 2011; Rossi and Kähler, 2014). Typically the depth positions of particles are determined by the defocused particle images using image processing algorithms. As these methods allow the determination of all components of the velocity vector in a volume using only a single optical access and a single camera, they are often used in, but not limited to microfluidics. Since almost no additional equipment is necessary they are low-cost methods that are meanwhile widely applied in different fields. To overcome the ambiguity of perfect optical systems, often a cylindrical lens is introduced in the optical system which enhances the differences of the obtained particle images for different depth positions. However, various methods are emerging and it is difficult for non-experienced users to judge what method might be best suited for a given experimental setup. Therefore, the aim of the presentation is a thorough evaluation of the performance of general advanced methods, including also recently presented neural networks (Franchini and Krevor, 2020; König et al., 2020) based on typical images.

Mäder, Patrick; Poll, Constanze; Hüther, Jonas; Jeschke, Sebastian; Otto, Henning; Cierpka, Christian
SmartPIV - an app for flow visualization by cross-correlation and optical flow using smartphones. - In: International Symposium on Particle Image Velocimetry, ISSN 2769-7576, Bd. 1 (2021), 1, insges. 2 S.

In recent years smartphones considerably changed our communication and are used on a daily (or even every minute) basis especially by students without any difficulties. Fluid flows also belong to our daily experiences. However, the education of the basic principles of fluid mechanics is sometimes cumbersome due to its non-linear nature. This problem may be tackled in practical sessions applying flow visualization techniques in wind or water tunnels and directly learn from own observations. Nowadays, often optical methods like particle imaging velocimetry (PIV) or particle tracking velocimetry (PTV) are used for these purposes. A typical PIV/PTV setup consists of a (double)pulse laser, a scientific camera and a synchronization device. The costs for this equipment can easily add up to more than 100,000 euros and the installations and set up of the systems requires experiences and is complex. For these reasons Universities often only offer practical courses for a small amount of students and the students may not be allowed to use and set up the systems by their own as the equipment is also needed for scientific research. Due to the COVID-19 pandemic it is also often not allowed to share equipment or even to work in larger groups during practical sessions.

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&ring;). 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&ring; 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.



Anzahl der Treffer: 13
Erstellt: Sun, 23 Jun 2024 19:48:24 +0200 in 0.1039 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: Sun, 23 Jun 2024 19:48:13 +0200 in 0.1537 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: Sun, 23 Jun 2024 19:48:05 +0200 in 0.1130 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

Dunker, 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: Sun, 23 Jun 2024 19:47:59 +0200 in 0.0834 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

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.



Anzahl der Treffer: 14
Erstellt: Sun, 23 Jun 2024 19:47:51 +0200 in 0.1194 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, (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