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An essential measure of autonomy in assistive service robots is adaptivity to the various contexts of human-oriented tasks, which are subject to subtle variations in task parameters that determine optimal behaviour. In this work, we propose an apprenticeship learning approach to achieving context-aware action generalization on the task of robot-to-human object hand-over. The procedure combines learning from demonstration and reinforcement learning: a robot first imitates a demonstrator’s execution of the task and then learns contextualized variants of the demonstrated action through experience. We use dynamic movement primitives as compact motion representations, and a model-based C-REPS algorithm for learning policies that can specify hand-over position, conditioned on context variables. Policies are learned using simulated task executions, before transferring them to the robot and evaluating emergent behaviours. We additionally conduct a user study involving participants assuming different postures and receiving an object from a robot, which executes hand-overs by either imitating a demonstrated motion, or adapting its motion to hand-over positions suggested by the learned policy. The results confirm the hypothesized improvements in the robot’s perceived behaviour when it is context-aware and adaptive, and provide useful insights that can inform future developments.
Airborne and spaceborne platforms are the primary data sources for large-scale forest mapping, but visual interpretation for individual species determination is labor-intensive. Hence, various studies focusing on forests have investigated the benefits of multiple sensors for automated tree species classification. However, transferable deep learning approaches for large-scale applications are still lacking. This gap motivated us to create a novel dataset for tree species classification in central Europe based on multi-sensor data from aerial, Sentinel-1 and Sentinel-2 imagery. In this paper, we introduce the TreeSatAI Benchmark Archive, which contains labels of 20 European tree species (i.e., 15 tree genera) derived from forest administration data of the federal state of Lower Saxony, Germany. We propose models and guidelines for the application of the latest machine learning techniques for the task of tree species classification with multi-label data. Finally, we provide various benchmark experiments showcasing the information which can be derived from the different sensors including artificial neural networks and tree-based machine learning methods. We found that residual neural networks (ResNet) perform sufficiently well with weighted precision scores up to 79 % only by using the RGB bands of aerial imagery. This result indicates that the spatial content present within the 0.2 m resolution data is very informative for tree species classification. With the incorporation of Sentinel-1 and Sentinel-2 imagery, performance improved marginally. However, the sole use of Sentinel-2 still allows for weighted precision scores of up to 74 % using either multi-layer perceptron (MLP) or Light Gradient Boosting Machine (LightGBM) models. Since the dataset is derived from real-world reference data, it contains high class imbalances. We found that this dataset attribute negatively affects the models' performances for many of the underrepresented classes (i.e., scarce tree species). However, the class-wise precision of the best-performing late fusion model still reached values ranging from 54 % (Acer) to 88 % (Pinus). Based on our results, we conclude that deep learning techniques using aerial imagery could considerably support forestry administration in the provision of large-scale tree species maps at a very high resolution to plan for challenges driven by global environmental change. The original dataset used in this paper is shared via Zenodo (https://doi.org/10.5281/zenodo.6598390, Schulz et al., 2022). For citation of the dataset, we refer to this article.
A company's financial documents use tables along with text to organize the data containing key performance indicators (KPIs) (such as profit and loss) and a financial quantity linked to them. The KPI’s linked quantity in a table might not be equal to the similarly described KPI's quantity in a text. Auditors take substantial time to manually audit these financial mistakes and this process is called consistency checking. As compared to existing work, this paper attempts to automate this task with the help of transformer-based models. Furthermore, for consistency checking it is essential for the table's KPIs embeddings to encode the semantic knowledge of the KPIs and the structural knowledge of the table. Therefore, this paper proposes a pipeline that uses a tabular model to get the table's KPIs embeddings. The pipeline takes input table and text KPIs, generates their embeddings, and then checks whether these KPIs are identical. The pipeline is evaluated on the financial documents in the German language and a comparative analysis of the cell embeddings' quality from the three tabular models is also presented. From the evaluation results, the experiment that used the English-translated text and table KPIs and Tabbie model to generate table KPIs’ embeddings achieved an accuracy of 72.81% on the consistency checking task, outperforming the benchmark, and other tabular models.
Herein we report an update to ACPYPE, a Python3 tool that now properly converts AMBER to GROMACS topologies for force fields that utilize nondefault and nonuniform 1–4 electrostatic and nonbonded scaling factors or negative dihedral force constants. Prior to this work, ACPYPE only converted AMBER topologies that used uniform, default 1–4 scaling factors and positive dihedral force constants. We demonstrate that the updated ACPYPE accurately transfers the GLYCAM06 force field from AMBER to GROMACS topology files, which employs non-uniform 1–4 scaling factors as well as negative dihedral force constants. Validation was performed using β-d-GlcNAc through gas-phase analysis of dihedral energy curves and probability density functions. The updated ACPYPE retains all of its original functionality, but now allows the simulation of complex glycomolecular systems in GROMACS using AMBER-originated force fields. ACPYPE is available for download at https://github.com/alanwilter/acpype.
Less is Often More: Header Whitelisting as Semantic Gap Mitigation in HTTP-Based Software Systems
(2021)
The web is the most wide-spread digital system in the world and is used for many crucial applications. This makes web application security extremely important and, although there are already many security measures, new vulnerabilities are constantly being discovered. One reason for some of the recent discoveries lies in the presence of intermediate systems—e.g. caches, message routers, and load balancers—on the way between a client and a web application server. The implementations of such intermediaries may interpret HTTP messages differently, which leads to a semantically different understanding of the same message. This so-called semantic gap can cause weaknesses in the entire HTTP message processing chain.
In this paper we introduce the header whitelisting (HWL) approach to address the semantic gap in HTTP message processing pipelines. The basic idea is to normalize and reduce an HTTP request header to the minimum required fields using a whitelist before processing it in an intermediary or on the server, and then restore the original request for the next hop. Our results show that HWL can avoid misinterpretations of HTTP messages in the different components and thus prevent many attacks rooted in a semantic gap including request smuggling, cache poisoning, and authentication bypass.
Is It Really You Who Forgot the Password? When Account Recovery Meets Risk-Based Authentication
(2024)
Components and Architecture for the Implementation of Technology-Driven Employee Data Protection
(2021)
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.
Software developers build complex systems using plenty of third-party libraries. Documentation is key to understand and use the functionality provided via the libraries’ APIs. Therefore, functionality is the main focus of contemporary API documentation, while cross-cutting concerns such as security are almost never considered at all, especially when the API itself does not provide security features. Documentations of JavaScript libraries for use in web applications, e.g., do not specify how to add or adapt a Content Security Policy (CSP) to mitigate content injection attacks like Cross-Site Scripting (XSS). This is unfortunate, as security-relevant API documentation might have an influence on secure coding practices and prevailing major vulnerabilities such as XSS. For the first time, we study the effects of integrating security-relevant information in non-security API documentation. For this purpose, we took CSP as an exemplary study object and extended the official Google Maps JavaScript API documentation with security-relevant CSP information in three distinct manners. Then, we evaluated the usage of these variations in a between-group eye-tracking lab study involving N=49 participants. Our observations suggest: (1) Developers are focused on elements with code examples. They mostly skim the documentation while searching for a quick solution to their programming task. This finding gives further evidence to results of related studies. (2) The location where CSP-related code examples are placed in non-security API documentation significantly impacts the time it takes to find this security-relevant information. In particular, the study results showed that the proximity to functional-related code examples in documentation is a decisive factor. (3) Examples significantly help to produce secure CSP solutions. (4) Developers have additional information needs that our approach cannot meet.
Overall, our study contributes to a first understanding of the impact of security-relevant information in non-security API documentation on CSP implementation. Although further research is required, our findings emphasize that API producers should take responsibility for adequately documenting security aspects and thus supporting the sensibility and training of developers to implement secure systems. This responsibility also holds in seemingly non-security relevant contexts.
Publikation von Umweltdaten
(2010)
Deployment of modern data-driven machine learning methods, most often realized by deep neural networks (DNNs), in safety-critical applications such as health care, industrial plant control, or autonomous driving is highly challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability and implausible predictions to directed attacks by means of malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from so-called safety concerns, properties that preclude their deployment as no argument or experimental setup can help to assess the remaining risk. In recent years, an abundance of state-of-the-art techniques aiming to address these safety concerns has emerged. This chapter provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our work addresses machine learning experts and safety engineers alike: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern machine learning methods. We hope that this contribution fuels discussions on desiderata for machine learning systems and strategies on how to help to advance existing approaches accordingly.
XML Signature Wrapping (XSW) has been a relevant threat to web services for 15 years until today. Using the Personal Health Record (PHR), which is currently under development in Germany, we investigate a current SOAP-based web services system as a case study. In doing so, we highlight several deficiencies in defending against XSW. Using this real-world contemporary example as motivation, we introduce a guideline for more secure XML signature processing that provides practitioners with easier access to the effective countermeasures identified in the current state of research.
Die Blockchain-Technologie ist einer der großen Innovationstreiber der letzten Jahre. Mit einer zugrundeliegenden Blockchain-Technologie ist auch der Betrieb von verteilten Anwendungen, sogenannter Decentralized Applications (DApps), bereits technisch umsetzbar. Dieser Beitrag verfolgt das Ziel, Gestaltungsmöglichkeiten der digitalen Verbraucherteilhabe an Blockchain-Anwendungen zu untersuchen. Hierzu enthält der Beitrag eine Einführung in die digitale Verbraucherteilhabe und die technischen Grundlagen und Eigenschaften der Blockchain-Technologie, einschließlich darauf basierender DApps. Abschließend werden technische, ethisch-organisatorische, rechtliche und sonstige Anforderungsbereiche für die Umsetzung von digitaler Verbraucherteilhabe in Blockchain-Anwendungen adressiert.
Quantum mechanical theories are used to search and optimized the conformations of proposed small molecule candidates for treatment of SARS-CoV-2. These candidate compounds are taken from what is reported in the news and in other pre-peer-reviewed literature (e.g. ChemRxiv, bioRxiv). The goal herein is to provided predicted structures and relative conformational stabilities for selected drug and ligand candidates, in the hopes that other research groups can make use of them for developing a treatment.
Threats to passwords are still very relevant due to attacks like phishing or credential stuffing. One way to solve this problem is to remove passwords completely. User studies on passwordless FIDO2 authentication using security tokens demonstrated the potential to replace passwords. However, widespread acceptance of FIDO2 depends, among other things, on how user accounts can be recovered when the security token becomes permanently unavailable. For this reason, we provide a heuristic evaluation of 12 account recovery mechanisms regarding their properties for FIDO2 passwordless authentication. Our results show that the currently used methods have many drawbacks. Some even rely on passwords, taking passwordless authentication ad absurdum. Still, our evaluation identifies promising account recovery solutions and provides recommendations for further studies.
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.
Dieser Beitrag betrachtet den Stand der Entwicklung bei der Vernetzung von Fahrzeugen aus Sicht der IT-Sicherheit. Etablierte Kommunikationssysteme und Verkehrstelematikanwendungen im Automobil werden ebenso vorgestellt und diskutiert wie auch zukünftige Kommunikationstechnologien Car-2-Car und Car-2-X. IT-Sicherheit im Automobil ist ein schwieriges Feld, da es hier um eine Integration von neuen innovativen Anwendungen in eine hochkomplexe bestehende Fahrzeugarchitektur geht, die zu keinen neuen Gefährdungen für die Fahrzeuginsassen führen darf. Zudem bleibt die Funktionsweise dieser Anwendungen mit ihren Auswirkungen auf das informationelle Selbstbestimmungsrecht oft intransparent. Die abschließende Diskussion gibt Handlungsempfehlungen aus Sicht der Verbraucher.