Publikationen

Although species are central units for biological research, recent findings in genomics are raising awareness that what we call species can be ill-founded entities due to solely morphology-based, regional species descriptions. This particularly applies to groups characterized by intricate evolutionary processes such as hybridization, polyploidy, or asexuality. Here, challenges of current integrative taxonomy (genetics/genomics + morphology + ecology, etc.) become apparent: different favored species concepts, lack of universal characters/markers, missing appropriate analytical tools for intricate evolutionary processes, and highly subjective ranking and fusion of datasets. Now, integrative taxonomy combined with artificial intelligence under a unified species concept can enable automated feature learning and data integration, and thus reduce subjectivity in species delimitation. This approach will likely accelerate revising and unraveling eukaryotic biodiversity.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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

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.

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.

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

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.