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The European General Data Protection Regulation requires the implementation of Technical and Organizational Measures (TOMs) to reduce the risk of illegitimate processing of personal data. For these measures to be effective, they must be applied correctly by employees who process personal data under the authority of their organization. However, even data processing employees often have limited knowledge of data protection policies and regulations, which increases the likelihood of misconduct and privacy breaches. To lower the likelihood of unintentional privacy breaches, TOMs must be developed with employees’ needs, capabilities, and usability requirements in mind. To reduce implementation costs and help organizations and IT engineers with the implementation, privacy patterns have proven to be effective for this purpose. In this chapter, we introduce the privacy pattern Data Cart, which specifically helps to develop TOMs for data processing employees. Based on a user-centered design approach with employees from two public organizations in Germany, we present a concept that illustrates how Privacy by Design can be effectively implemented. Organizations, IT engineers, and researchers will gain insight on how to improve the usability of privacy-compliant tools for managing personal data.
Users should always play a central role in the development of (software) solutions. The human-centered design (HCD) process in the ISO 9241-210 standard proposes a procedure for systematically involving users. However, due to its abstraction level, the HCD process provides little guidance for how it should be implemented in practice. In this chapter, we propose three concrete practical methods that enable the reader to develop usable security and privacy (USP) solutions using the HCD process. This chapter equips the reader with the procedural knowledge and recommendations to: (1) derive mental models with regard to security and privacy, (2) analyze USP needs and privacy-related requirements, and (3) collect user characteristics on privacy and structure them by user group profiles and into privacy personas. Together, these approaches help to design measures for a user-friendly implementation of security and privacy measures based on a firm understanding of the key stakeholders.
This paper presents the b-it-bots RoboCup@Work team and its current hardware and functional architecture for the KUKA youBot robot. We describe the underlying software framework and the developed capabilities required for operating in industrial environments including features such as reliable and precise navigation, flexible manipulation, robust object recognition and task planning. New developments include an approach to grasp vertical objects, placement of objects by considering the empty space on a workstation, and the process of porting our code to ROS2.
Neuromorphic computing aims to mimic the computational principles of the brain in silico and has motivated research into event-based vision and spiking neural networks (SNNs). Event cameras (ECs) capture local, independent changes in brightness, and offer superior power consumption, response latencies, and dynamic ranges compared to frame-based cameras. SNNs replicate neuronal dynamics observed in biological neurons and propagate information in sparse sequences of ”spikes”. Apart from biological fidelity, SNNs have demonstrated potential as an alternative to conventional artificial neural networks (ANNs), such as in reducing energy expenditure and inference time in visual classification. Although potentially beneficial for robotics, the novel event-driven and spike-based paradigms remain scarcely explored outside the domain of aerial robots.
To investigate the utility of brain-inspired sensing and data processing in a robotics application, we developed a neuromorphic approach to real-time, online obstacle avoidance on a manipulator with an onboard camera. Our approach adapts high-level trajectory plans with reactive maneuvers by processing emulated event data in a convolutional SNN, decoding neural activations into avoidance motions, and adjusting plans in a dynamic motion primitive formulation. We conducted simulated and real experiments with a Kinova Gen3 arm performing simple reaching tasks involving static and dynamic obstacles. Our implementation was systematically tuned, validated, and tested in sets of distinct task scenarios, and compared to a non-adaptive baseline through formalized quantitative metrics and qualitative criteria.
The neuromorphic implementation facilitated reliable avoidance of imminent collisions in most scenarios, with 84% and 92% median success rates in simulated and real experiments, where the baseline consistently failed. Adapted trajectories were qualitatively similar to baseline trajectories, indicating low impacts on safety, predictability and smoothness criteria. Among notable properties of the SNN were the correlation of processing time with the magnitude of perceived motions (captured in events) and robustness to different event emulation methods. Preliminary tests with a DAVIS346 EC showed similar performance, validating our experimental event emulation method. These results motivate future efforts to incorporate SNN learning, utilize neuromorphic processors, and target other robot tasks to further explore this approach.
Der Programmier-Trainingsplan für alle, die weiter kommen wollen.
In diesem Übungsbuch trainierst du anhand von kurzweiligen und praxisnahen Aufgaben deine Programmierfähigkeiten. Jedes Kapitel beginnt mit einem kurzen Warmup zum behandelten Programmierkonzept; die Umsetzung übst du dann anhand von zahlreichen Workout-Aufgaben. Du startest mit einfachen Aufgaben und steigerst dich hin zu komplexeren Fragestellungen. Damit dir nicht langweilig wird, gibt es über 150 praxisnahe Übungen. So lernst du z. B. einen BMI-Rechner oder einen PIN-Generator zu programmieren oder wie du eine Zeitangabe mit einer analogen Uhr anzeigen kannst. (Verlagsangaben)
Ziel der neunten Ausgabe des wissenschaftlichen Workshops "Usable Security und Privacy" auf der Mensch und Computer 2023 ist es, aktuelle Forschungs- und Praxisbeiträge auf diesem Gebiet zu präsentieren und mit den Teilnehmer:innen zu diskutieren. Getreu dem Konferenzmotto "Building Bridges" soll mit dem Workshop ein etabliertes Forum fortgeführt und weiterentwickelt werden, in dem sich Expert:innen, Forscher:innen und Praktiker:innen aus unterschiedlichen Domänen transdisziplinär zum Thema Usable Security und Privacy austauschen können. Das Thema betrifft neben dem Usability- und Security-Engineering unterschiedliche Forschungsgebiete und Berufsfelder, z. B. Informatik, Ingenieurwissenschaften, Mediengestaltung und Psychologie. Der Workshop richtet sich an interessierte Wissenschaftler:innen aus all diesen Bereichen, aber auch ausdrücklich an Vertreter:innen der Wirtschaft, Industrie und öffentlichen Verwaltung.
The non-filarial and non-communicable disease podoconiosis affects around 4 million people and is characterized by severe leg lymphedema accompanied with painful intermittent acute inflammatory episodes, called acute dermatolymphangioadenitis (ADLA) attacks. Risk factors have been associated with the disease but the mechanisms of pathophysiology remain uncertain. Lymphedema can lead to skin lesions, which can serve as entry points for bacteria that may cause ADLA attacks leading to progression of the lymphedema. However, the microbiome of the skin of affected legs from podoconiosis individuals remains unclear. Thus, we analysed the skin microbiome of podoconiosis legs using next generation sequencing. We revealed a positive correlation between increasing lymphedema severity and non-commensal anaerobic bacteria, especially Anaerococcus provencensis, as well as a negative correlation with the presence of Corynebacterium, a constituent of normal skin flora. Disease symptoms were generally linked to higher microbial diversity and richness, which deviated from the normal composition of the skin. These findings show an association of distinct bacterial taxa with lymphedema stages, highlighting the important role of bacteria for the pathogenesis of podoconiosis and might enable a selection of better treatment regimens to manage ADLA attacks and disease progression.
Eine Überprüfung der Leistungsentwicklung im Radsport geht bis heute mit der Durchführung einer spezifischen Leistungsdiagnostik unter Verwendung vorgegebener Testprotokolle einher. Durch die zwischenzeitlich stark gestiegene Popularität von »wearable devices« ist es gleichzeitig heutzutage sehr einfach, die Herzfrequenz im Alltag und bei sportlichen Aktivitäten aufzuzeichnen. Doch eine geeignete Modellierung der Herzfrequenz, die es ermöglicht, Rückschlüsse über die Leistungsentwicklung ziehen zu können, fehlt bislang. Die Herzfrequenzaufzeichnungen in Kombination mit einer phänomenologisch interpretierbaren Modellierung zu nutzen, um auf möglichst direkte Weise und ohne spezifische Anforderungen an die Trainingsfahrten Rückschlüsse über die Leistungsentwicklung ziehen zu können, bietet die Chance, sowohl im professionellen Radsport wie auch in der ambitionierten Radsportpraxis den Erkenntnisgewinn über die eigene Leistungsentwicklung maßgeblich zu vereinfachen. In der vorliegenden Arbeit wird ein neuartiges und phänomenologisch interpretierbares Modell zur Simulation und Prädiktion der Herzfrequenz beim Radsport vorgestellt und im Rahmen einer empirischen Studie validiert. Dieses Modell ermöglicht es, die Herzfrequenz (sowie andere Beanspruchungsparameter aus Atemgasanalysen) mit adäquater Genauigkeit zu simulieren und bei vorgegebener Wattbelastung zu prognostizieren. Weiterhin wird eine Methode zur Reduktion der Anzahl der kalibrierbaren freien Modellparameter vorgestellt und in zwei empirischen Studien validiert. Nach einer individualisierten Parameterreduktion kann das Modell mit lediglich einem einzigen freien Parameter verwendet werden. Dieser verbleibende freie Parameter bietet schließlich die Möglichkeit, im zeitlichen Verlauf mit dem Verlauf der Leistungsentwicklung verglichen zu werden. In zwei unterschiedlichen Studien zeigt sich, dass der freie Modellparameter grundsätzlich in der Lage zu sein scheint, den Verlauf der Leistungsentwicklung über die Zeit abzubilden.
This paper addresses the classification of Arabic text data in the field of Natural Language Processing (NLP), with a particular focus on Natural Language Inference (NLI) and Contradiction Detection (CD). Arabic is considered a resource-poor language, meaning that there are few data sets available, which leads to limited availability of NLP methods. To overcome this limitation, we create a dedicated data set from publicly available resources. Subsequently, transformer-based machine learning models are being trained and evaluated. We find that a language-specific model (AraBERT) performs competitively with state-of-the-art multilingual approaches, when we apply linguistically informed pre-training methods such as Named Entity Recognition (NER). To our knowledge, this is the first large-scale evaluation for this task in Arabic, as well as the first application of multi-task pre-training in this context.
Loading of shipping containers for dairy products often includes a press-fit task, which involves manually stacking milk cartons in a container without using pallets or packaging. Automating this task with a mobile manipulator can reduce worker strain, and also enhance the efficiency and safety of the container loading process. This paper proposes an approach called Adaptive Compliant Control with Integrated Failure Recovery (ACCIFR), which enables a mobile manipulator to reliably perform the press-fit task. We base the approach on a demonstration learning-based compliant control framework, such that we integrate a monitoring and failure recovery mechanism for successful task execution. Concretely, we monitor the execution through distance and force feedback, detect collisions while the robot is performing the press-fit task, and use wrench measurements to classify the direction of collision; this information informs the subsequent recovery process. We evaluate the method on a miniature container setup, considering variations in the (i) starting position of the end effector, (ii) goal configuration, and (iii) object grasping position. The results demonstrate that the proposed approach outperforms the baseline demonstration-based learning framework regarding adaptability to environmental variations and the ability to recover from collision failures, making it a promising solution for practical press-fit applications.
Question Answering (QA) has gained significant attention in recent years, with transformer-based models improving natural language processing. However, issues of explainability remain, as it is difficult to determine whether an answer is based on a true fact or a hallucination. Knowledge-based question answering (KBQA) methods can address this problem by retrieving answers from a knowledge graph. This paper proposes a hybrid approach to KBQA called FRED, which combines pattern-based entity retrieval with a transformer-based question encoder. The method uses an evolutionary approach to learn SPARQL patterns, which retrieve candidate entities from a knowledge base. The transformer-based regressor is then trained to estimate each pattern’s expected F1 score for answering the question, resulting in a ranking ofcandidate entities. Unlike other approaches, FRED can attribute results to learned SPARQL patterns, making them more interpretable. The method is evaluated on two datasets and yields MAP scores of up to 73 percent, with the transformer-based interpretation falling only 4 pp short of an oracle run. Additionally, the learned patterns successfully complement manually generated ones and generalize well to novel questions.
Although climate-induced liquidity risks can cause significant disruptions and instabilities in the financial sector, they are frequently overlooked in current debates and policy discussions. This paper proposes a macro-financial agent-based integrated assessment model to investigate the transmission channels of climate risks to financial instability and study the emergence of liquidity crises through interbank market dynamics. Our simulations show that the financial system could experience serious funding and market liquidity shortages due to climate-induced liquidity crises. Our investigation contributes to our understanding of the impact - and possible solutions - to climate-induced liquidity crises, besides the issue of asset stranding related to transition risks usually considered in the existing studies.
This research investigates the efficacy of multisensory cues for locating targets in Augmented Reality (AR). Sensory constraints can impair perception and attention in AR, leading to reduced performance due to factors such as conflicting visual cues or a restricted field of view. To address these limitations, the research proposes head-based multisensory guidance methods that leverage audio-tactile cues to direct users' attention towards target locations. The research findings demonstrate that this approach can effectively reduce the influence of sensory constraints, resulting in improved search performance in AR. Additionally, the thesis discusses the limitations of the proposed methods and provides recommendations for future research.
Microbiome analyses are essential for understanding microorganism composition and diversity, but interpretation is often challenging due to biological and technical variables. DNA extraction is a critical step that can significantly bias results, particularly in samples containing a high abundance of challenging-to-lyse microorganisms. Taking into consideration the distinctive microenvironments observed in different bodily locations, our study sought to assess the extent of bias introduced by suboptimal bead-beating during DNA extraction across diverse clinical sample types. The question was whether complex targeted extraction methods are always necessary for reliable taxonomic abundance estimation through amplicon sequencing or if simpler alternatives are effective for some sample types. Hence, for four different clinical sample types (stool, cervical swab, skin swab, and hospital surface swab samples), we compared the results achieved from extracting targeted manual protocols routinely used in our research lab for each sample type with automated protocols specifically not designed for that purpose. Unsurprisingly, we found that for the stool samples, manual extraction protocols with vigorous bead-beating were necessary in order to avoid erroneous taxa proportions on all investigated taxonomic levels and, in particular, false under- or overrepresentation of important genera such as Blautia, Faecalibacterium, and Parabacteroides. However, interestingly, we found that the skin and cervical swab samples had similar results with all tested protocols. Our results suggest that the level of practical automation largely depends on the expected microenvironment, with skin and cervical swabs being much easier to process than stool samples. Prudent consideration is necessary when extending the conclusions of this study to applications beyond rough estimations of taxonomic abundance.
LiDAR-based Indoor Localization with Optimal Particle Filters using Surface Normal Constraints
(2023)
The perceptual upright results from the multisensory integration of the directions indicated by vision and gravity as well as a prior assumption that upright is towards the head. The direction of gravity is signalled by multiple cues, the predominant of which are the otoliths of the vestibular system and somatosensory information from contact with the support surface. Here, we used neutral buoyancy to remove somatosensory information while retaining vestibular cues, thus "splitting the gravity vector" leaving only the vestibular component. In this way, neutral buoyancy can be used as a microgravity analogue. We assessed spatial orientation using the oriented character recognition test (OChaRT, which yields the perceptual upright, PU) under both neutrally buoyant and terrestrial conditions. The effect of visual cues to upright (the visual effect) was reduced under neutral buoyancy compared to on land but the influence of gravity was unaffected. We found no significant change in the relative weighting of vision, gravity, or body cues, in contrast to results found both in long-duration microgravity and during head-down bed rest. These results indicate a relatively minor role for somatosensation in determining the perceptual upright in the presence of vestibular cues. Short-duration neutral buoyancy is a weak analogue for microgravity exposure in terms of its perceptual consequences compared to long-duration head-down bed rest.
Dieses Buch wurde im Rahmen eines Wirtschaftsinformatik-Projektes an der Hochschule Bonn-Rhein-Sieg unter Aufsicht von Prof. Dr. Alexandra Kees geschrieben. Ziel des Projektes war die Erstellung eines Funktionsreferenzmodells für Enterprise Resource Planning (ERP-) Software, welches in Form eines Buches veröffentlicht werden sollte. Die Studierenden haben für das Projekt jeweils verschiedene Teilbereiche, die in einem ERP-System gewöhnlich Anwendung finden, zugeteilt bekommen. In diesem Teil wird der Bereich Lagerverwaltung näher betrachtet.
Risikobasierte Authentifizierung (RBA) ist ein adaptiver Ansatz zur Stärkung der Passwortauthentifizierung. Er überwacht eine Reihe von Merkmalen, die sich auf das Loginverhalten während der Passworteingabe beziehen. Wenn sich die beobachteten Merkmalswerte signifikant von denen früherer Logins unterscheiden, fordert RBA zusätzliche Identitätsnachweise an. Regierungsbehörden und ein Erlass des US-Präsidenten empfehlen RBA, um Onlineaccounts vor Angriffen mit gestohlenen Passwörtern zu schützen. Trotz dieser Tatsachen litt RBA unter einem Mangel an offenem Wissen. Es gab nur wenige bis keine Untersuchungen über die Usability, Sicherheit und Privatsphäre von RBA. Das Verständnis dieser Aspekte ist jedoch wichtig für eine breite Akzeptanz.
Diese Arbeit soll ein umfassendes Verständnis von RBA mit einer Reihe von Studien vermitteln. Die Ergebnisse ermöglichen es, datenschutzfreundliche RBA-Lösungen zu schaffen, die die Authentifizierung stärken bei gleichzeitig hoher Menschenakzeptanz.
Saliency methods are frequently used to explain Deep Neural Network-based models. Adebayo et al.'s work on evaluating saliency methods for classification models illustrate certain explanation methods fail the model and data randomization tests. However, on extending the tests for various state of the art object detectors we illustrate that the ability to explain a model is more dependent on the model itself than the explanation method. We perform sanity checks for object detection and define new qualitative criteria to evaluate the saliency explanations, both for object classification and bounding box decisions, using Guided Backpropagation, Integrated Gradients, and their Smoothgrad versions, together with Faster R-CNN, SSD, and EfficientDet-D0, trained on COCO. In addition, the sensitivity of the explanation method to model parameters and data labels varies class-wise motivating to perform the sanity checks for each class. We find that EfficientDet-D0 is the most interpretable method independent of the saliency method, which passes the sanity checks with little problems.
Intelligent virtual agents provide a framework for simulating more life-like behavior and increasing plausibility in virtual training environments. They can improve the learning process if they portray believable behavior that can also be controlled to support the training objectives. In the context of this thesis, cognitive agents are considered a subset of intelligent virtual agents (IVA) with the focus on emulating cognitive processes to achieve believable behavior. The complexity of employed algorithms, however, is often limited since multiple agents need to be simulated in real-time. Available solutions focus on a subset of the indicated aspects: plausibility, controllability, or real-time capability (scalability). Within this thesis project, an agent architecture for attentive cognitive agents is developed that considers all three aspects at once. The result is a lightweight cognitive agent architecture that is customizable to application-specific requirements. A generic trait-based personality model influences all cognitive processes, facilitating the generation of consistent and individual behavior. An additional mapping process provides a formalized mechanism to transfer results of psychological studies to the architecture. Personality profiles are combined with an emotion model to achieve situational behavior adaptation. Which action an agent selects in a situation also influences plausibility. An integral element of this selection process is an agent's knowledge about its world. Therefore, synthetic perception is modeled and integrated into the architecture to provide a credible knowledge base. The developed perception module includes a unified sensor interface, a memory hierarchy, and an attention process. With the presented realization of the architecture (CAARVE), it is possible for the first time to simulate cognitive agents, whose behaviors are simultaneously computable in real-time and controllable. The architecture's applicability is demonstrated by integrating an agent-based traffic simulation built with CAARVE into a bicycle simulator for road-safety education. The developed ideas and their realization are evaluated within this work using different strategies and scenarios. For example, it is shown how CAARVE agents utilize personality profiles and emotions to plausibly resolve deadlocks in traffic simulations. Controllability and adaptability are demonstrated in additional scenarios. Using the realization, 200 agents can be simulated in real-time (50 FPS), illustrating scalability. The achieved results verify that the developed architecture can generate plausible and controllable agent behavior in real-time. The presented concepts and realizations provide sound fundamentals to everyone interested in simulating IVA in real-time environments.
The representation, or encoding, utilized in evolutionary algorithms has a substantial effect on their performance. Examination of the suitability of widely used representations for quality diversity optimization (QD) in robotic domains has yielded inconsistent results regarding the most appropriate encoding method. Given the domain-dependent nature of QD, additional evidence from other domains is necessary. This study compares the impact of several representations, including direct encoding, a dictionary-based representation, parametric encoding, compositional pattern producing networks, and cellular automata, on the generation of voxelized meshes in an architecture setting. The results reveal that some indirect encodings outperform direct encodings and can generate more diverse solution sets, especially when considering full phenotypic diversity. The paper introduces a multi-encoding QD approach that incorporates all evaluated representations in the same archive. Species of encodings compete on the basis of phenotypic features, leading to an approach that demonstrates similar performance to the best single-encoding QD approach. This is noteworthy, as it does not always require the contribution of the best-performing single encoding.