Search

Ever-increasingly complex business processes are enabled by loosely coupled cloud-native systems. In such fast-paced development environments, data controllers face the challenge of capturing and updating all personal data processing activities due to considerable communication overhead between development teams and data protection staff. To date, established business process management methods generate valuable insights about systems, however, they do not account for all regulatory transparency obligations. For instance, data controllers need to record all information about data categories, legal purpose specifications, third-country transfers, etc. Therefore, we propose to bridge the gap between business processes and application systems by providing three contributions that assist in modeling, discovering, and checking personal data transparency through a process-oriented perspective. We enable transparency modeling for relevant business activities by providing a plug-in extension to BPMN featuring regulatory transparency information. Furthermore, we utilize event logs to record regulatory transparency information in realistic cloud-native systems. On this basis, we leverage process mining techniques to discover and analyze personal data flows in business processes, e.g., through transparency conformance checking. We design and implement prototypes for all contributions, emphasizing the appropriate integration and modeling effort required to create business-process-oriented transparency. Altogether, we connect current business process engineering techniques with regulatory needs as imposed by the GDPR and other legal frameworks., Comment: Preprint, accepted to the BPM Forum 2024

Strain-free GaAs/AlGaAs semiconductor quantum dots (QDs) grown by droplet etching and nanohole infilling (DENI) are highly promising candidates for the on-demand generation of indistinguishable and entangled photon sources. The spectroscopic fingerprint and quantum optical properties of QDs are significantly influenced by their morphology. The effects of nanohole geometry and infilled material on the exciton binding energies and fine structure splitting are well understood. However, a comprehensive understanding of GaAs/AlGaAs QD morphology remains elusive. To address this, we employ high-resolution scanning transmission electron microscopy (STEM) and reverse engineering through selective chemical etching and atomic force microscopy (AFM). Cross-sectional STEM of uncapped QDs reveals an inverted conical nanohole with Al-rich sidewalls and defect-free interfaces. Subsequent selective chemical etching and AFM measurements further reveal asymmetries in element distribution. This study enhances the understanding of DENI QD morphology and provides a fundamental three-dimensional structural model for simulating and optimizing their optoelectronic properties.

The crystallographic texture is a key organization feature of many technical and biological materials. In these materials, especially hierarchically structured ones, the preferential alignment of the nano constituents is heavily influencing the macroscopic behaviour of the material. In order to study local crystallographic texture with both high spatial and angular resolution, we developed Texture tomography (TexTOM). This approach allows to model the diffraction data of polycrystalline materials by using the full reciprocal space of the ensemble of crystals and describe the texture in each voxel via a orientation distribution function. This means, it provides 3D reconstructions of the local texture by measuring the probabilities of all crystal orientations. The TexTOM approach addresses limitations associated with existing models: It correlates the intensities from several Bragg reflections, thus reduces ambiguities resulting from symmetry. Further, it yields quantitative probability distributions of local real space crystal orientations without further assumptions on the sample structure. Finally, its efficient mathematical formulation enables reconstructions faster than the time-scale of the experiment. In this manuscript, we present the mathematical model, the inversion strategy and its current experimental implementation. We show characterizations of simulated data as well as experimental data obtained from a synthetic, inorganic model sample, the silica-witherite biomorph. In conclusion, Tex-TOM provides a versatile framework to reconstruct 3D quantitative texture information for polycrystalline samples. In this way, it opens the door for unprecedented insights into the nanostructural makeup of natural and technical materials.

gRPC is at the heart of modern distributed system architectures. Based on HTTP/2 and Protocol Buffers, it provides highly performant, standardized, and polyglot communication across loosely coupled microservices and is increasingly preferred over REST- or GraphQL-based service APIs in practice. Despite its widespread adoption, gRPC lacks any advanced privacy techniques beyond transport encryption and basic token-based authentication. Such advanced techniques are, however, increasingly important for fulfilling regulatory requirements. For instance, anonymizing or otherwise minimizing (personal) data before responding to requests, or pre-processing data based on the purpose of the access may be crucial in certain usecases. In this paper, we therefore propose a novel approach for integrating such advanced privacy techniques into the gRPC framework in a practically viable way. Specifically, we present a general approach along with a working prototype that implements privacy techniques, such as data minimization and purpose limitation, in a configurable, extensible, and gRPC-native way utilizing a gRPC interceptor. We also showcase how to integrate this contribution into a realistic example of a food delivery use case. Alongside these implementations, a preliminary performance evaluation shows practical applicability with reasonable overheads. Altogether, we present a viable solution for integrating advanced privacy techniques into real-world gRPC-based microservice architectures, thereby facilitating regulatory compliance ``by design''., Comment: 15 pages, accepted for the ICWE, International Conference on Web Engineering, 2024, research paper

This work proposes an innovative approach to improve Bragg coherent diffraction imaging (BCDI) microscopy applied to time evolving crystals and/or non-homogeneous crystalline strain fields, identified as two major limitations of BCDI microscopy. Speckle BCDI (spBCDI), introduced here, rests on the ability of a strongly non-uniform illumination to induce a convolution of the three-dimensional (3D) frequency content associated with the finite-size crystal and a kernel acting perpendicularly to the illumination beam. In the framework of Bragg diffraction geometry, this convolution is beneficial as it encodes some 3D information about the sample in a single two-dimensional (2D) measurement, i.e., in the detector plane. With this approach, we demonstrate that we can drastically reduce the sampling frequency along the rocking curve direction and still obtain data sets with enough information to be inverted by a traditional phase retrieval algorithm. Numerical simulations, performed for a highly distorted crystal, show that spBCDI allows a gain in the sampling ratio ranging between 4 and 20 along the rocking curve scan, for a speckle illumination with individual speckle size of 50 nm. Furthermore, spBCDI allows working at low intensity levels, leading to an additional gain for the total scanning time. Reductions of a factor of about 32 were numerically observed. Thus, measurements in the 0.3 s time scale at 4th generation synchrotrons become feasible, with a remarkable performance for the imaging of strongly distorted crystals. Practical details on the implementation of the method are also discussed., Comment: 17 pages, 11 figures

In van der Waals materials, coupling between adjacent layers is weak, and consequently interlayer interactions are weakly screened. This opens the possibility to profoundly modify the electronic structure, e.g., by applying electric fields or with adsorbates. Here, we show for the case of the topologically trivial semimetal 1Tprime-MoTe2 that potassium dosing at room temperature significantly transforms its band structure. With a combination of angle-resolved photoemission spectroscopy, scanning tunneling microscopy, x-ray photoemission spectroscopy, and density functional theory we show that i) for small concentrations of K, 1Tprime-MoTe2 undergoes a Lifshitz transition with the electronic structure shifting rigidly, and ii) for larger K concentrations 1Tprime-MoTe2 undergoes significant band structure transformation. Our results demonstrate that the origin of this electronic structure change stems from alkali metal intercalation.

Keeping track of all relevant recent publications and experimental results for a research area is a challenging task. Prior work has demonstrated the efficacy of information extraction models in various scientific areas. Recently, several datasets have been released for the yet understudied materials science domain. However, these datasets focus on sub-problems such as parsing synthesis procedures or on sub-domains, e.g., solid oxide fuel cells. In this resource paper, we present MuLMS, a new dataset of 50 open-access articles, spanning seven sub-domains of materials science. The corpus has been annotated by domain experts with several layers ranging from named entities over relations to frame structures. We present competitive neural models for all tasks and demonstrate that multi-task training with existing related resources leads to benefits., Comment: 17 pages, 2 figures, 28 tables, to be published in "Proceedings of the second Workshop on Information Extraction from Scientific Publications"

Transparency and accountability are indispensable principles for modern data protection, from both, legal and technical viewpoints. Regulations such as the GDPR, therefore, require specific transparency information to be provided including, e.g., purpose specifications, storage periods, or legal bases for personal data processing. However, it has repeatedly been shown that all too often, this information is practically hidden in legalese privacy policies, hindering data subjects from exercising their rights. This paper presents a novel approach to enable large-scale transparency information analysis across service providers, leveraging machine-readable formats and graph data science methods. More specifically, we propose a general approach for building a transparency analysis platform (TAP) that is used to identify data transfers empirically, provide evidence-based analyses of sharing clusters of more than 70 real-world data controllers, or even to simulate network dynamics using synthetic transparency information for large-scale data-sharing scenarios. We provide the general approach for advanced transparency information analysis, an open source architecture and implementation in the form of a queryable analysis platform, and versatile analysis examples. These contributions pave the way for more transparent data processing for data subjects, and evidence-based enforcement processes for data protection authorities. Future work can build upon our contributions to gain more insights into so-far hidden data-sharing practices., Comment: technical report

Microplasmas can be used for a wide range of technological applications and to improve our understanding of fundamental physics. Scanning electron microscopy, on the other hand, provides insights into the sample morphology and chemistry of materials from the mm-down to the nm-scale. Combining both would provide direct insight into plasma-sample interactions in real-time and at high spatial resolution. Up till now, very few attempts in this direction have been made, and significant challenges remain. This work presents a stable direct current glow discharge microplasma setup built inside a scanning electron microscope. The experimental setup is capable of real-time in-situ imaging of the sample evolution during plasma operation and it demonstrates localized sputtering and sample oxidation. Further, the experimental parameters such as varying gas mixtures, electrode polarity, and field strength are explored and experimental $V$-$I$ curves under various conditions are provided. These results demonstrate the capabilities of this setup in potential investigations of plasma physics, plasma-surface interactions, and materials science and its practical applications. The presented setup shows the potential to have several technological applications, e.g., to locally modify the sample surface (e.g., local oxidation and ion implantation for nanotechnology applications) on the $\mu$m-scale., Comment: LG, DC, and RDM contributed equally to this work. The videos mentioned in the manuscript can be found in the Zenodo repository linked in the paper

The compatibility of W-boson mass measurements performed by the ATLAS, LHCb, CDF, and D0 experiments is studied using a coherent framework with theory uncertainty correlations. The measurements are combined using a number of recent sets of parton distribution functions (PDF), and are further combined with the average value of measurements from the Large Electron-Positron collider. The considered PDF sets generally have a low compatibility with a suite of global rapidity-sensitive Drell-Yan measurements. The most compatible set is CT18 due to its larger uncertainties. A combination of all mW measurements yields a value of mW = 80394.6 +- 11.5 MeV with the CT18 set, but has a probability of compatibility of 0.5% and is therefore disfavoured. Combinations are performed removing each measurement individually, and a 91% probability of compatibility is obtained when the CDF measurement is removed. The corresponding value of the W boson mass is 80369.2 +- 13.3 MeV, which differs by 3.6 sigma from the CDF value determined using the same PDF set., Comment: Submitted to EPJC

Scientific publications follow conventionalized rhetorical structures. Classifying the Argumentative Zone (AZ), e.g., identifying whether a sentence states a Motivation, a Result or Background information, has been proposed to improve processing of scholarly documents. In this work, we adapt and extend this idea to the domain of materials science research. We present and release a new dataset of 50 manually annotated research articles. The dataset spans seven sub-topics and is annotated with a materials-science focused multi-label annotation scheme for AZ. We detail corpus statistics and demonstrate high inter-annotator agreement. Our computational experiments show that using domain-specific pre-trained transformer-based text encoders is key to high classification performance. We also find that AZ categories from existing datasets in other domains are transferable to varying degrees., Comment: 15 pages, 2 figures, 14 tables, to be published in "Proceedings of the 4th Workshop on Computational Approaches to Discourse"

Transparency is one of the most important principles of modern privacy regulations, such as the GDPR or CCPA. To be compliant with such regulatory frameworks, data controllers must provide data subjects with precise information about the collection, processing, storage, and transfer of personal data. To do so, respective facts and details must be compiled and always kept up to date. In traditional, rather static system environments, this inventory (including details such as the purposes of processing or the storage duration for each system component) could be done manually. In current circumstances of agile, DevOps-driven, and cloud-native information systems engineering, however, such manual practices do not suit anymore, making it increasingly hard for data controllers to achieve regulatory compliance. To allow for proper collection and maintenance of always up-to-date transparency information smoothly integrating into DevOps practices, we herein propose a set of novel approaches explicitly tailored to specific phases of the DevOps lifecycle most relevant in matters of privacy-related transparency and accountability at runtime: Release, Operation, and Monitoring. For each of these phases, we examine the specific challenges arising in determining the details of personal data processing, develop a distinct approach and provide respective proof of concept implementations that can easily be applied in cloud native systems. We also demonstrate how these components can be integrated with each other to establish transparency information comprising design- and runtime-elements. Furthermore, our experimental evaluation indicates reasonable overheads. On this basis, data controllers can fulfill their regulatory transparency obligations in line with actual engineering practices., Comment: preprint, accepted for the 16th IEEE International Conference on Cloud Computing 2023, IEEE Cloud 2023

Machine-readable representations of privacy policies are door openers for a broad variety of novel privacy-enhancing and, in particular, transparency-enhancing technologies (TETs). In order to generate such representations, transparency information needs to be extracted from written privacy policies. However, respective manual annotation and extraction processes are laborious and require expert knowledge. Approaches for fully automated annotation, in turn, have so far not succeeded due to overly high error rates in the specific domain of privacy policies. In the end, a lack of properly annotated privacy policies and respective machine-readable representations persists and enduringly hinders the development and establishment of novel technical approaches fostering policy perception and data subject informedness. In this work, we present a prototype system for a `Human-in-the-Loop' approach to privacy policy annotation that integrates ML-generated suggestions and ultimately human annotation decisions. We propose an ML-based suggestion system specifically tailored to the constraint of data scarcity prevalent in the domain of privacy policy annotation. On this basis, we provide meaningful predictions to users thereby streamlining the annotation process. Additionally, we also evaluate our approach through a prototypical implementation to show that our ML-based extraction approach provides superior performance over other recently used extraction models for legal documents., Comment: Accepted for 2023 IEEE European Symposium on Security and Privacy Workshops (EuroS&P)

Transparency and data portability are two core principles of modern privacy legislations such as the GDPR. From the regulatory perspective, providing individuals (data subjects) with access to their data is a main building block for implementing these. Different from other privacy principles and respective regulatory provisions, however, this right to data access has so far only seen marginal technical reflection. Processes related to performing data subject access requests (DSARs) are thus still to be executed manually, hindering the concept of data access from unfolding its full potential. To tackle this problem, we present an automated approach to the execution of DSARs, employing modern techniques of web automation. In particular, we propose a generic DSAR workflow model, a corresponding formal language for representing the particular workflows of different service providers (controllers), a publicly accessible and extendable workflow repository, and a browser-based execution engine, altogether providing ``one-click'' DSARs. To validate our approach and technical concepts, we examine, formalize and make publicly available the DSAR workflows of 15 widely used service providers and implement the execution engine in a publicly available browser extension. Altogether, we thereby pave the way for automated data subject access requests and lay the groundwork for a broad variety of subsequent technical means helping web users to better understand their privacy-related exposure to different service providers., Comment: Accepted for publication at the 23rd International Conference on Web Engineering (ICWE 2023) to appear in https://link.springer.com/book/9783031344459. This is a preprint manuscript (authors' own version before final copy-editing)

Electrically controlled rotation of spins in a semiconducting channel is a prerequisite for the successful realization of many spintronic devices, like, e.g., the spin field effect transistor (sFET). To date, there have been only a few reports on electrically controlled spin precession in sFET-like devices. These devices operated in the ballistic regime, as postulated in the original sFET proposal, and hence need high SOC channel materials in practice. Here, we demonstrate gate-controlled precession of spins in a non-ballistic sFET using an array of narrow diffusive wires as a channel between a spin source and a spin drain. Our study shows that spins traveling in a semiconducting channel can be coherently rotated on a distance far exceeding the electrons mean free path, and spin-transistor functionality can be thus achieved in non-ballistic channels with relatively low SOC, relaxing two major constraints of the original sFET proposal., Comment: Main text: 21 pages, 4 figures. Supplementary Information:11 pages, 4 figures

Transparency regarding the processing of personal data in online services is a necessary precondition for informed decisions on whether or not to share personal data. In this paper, we argue that privacy interfaces shall incorporate the context of display, personal preferences, and individual competences of data subjects following the principles of universal design and usable privacy. Doing so requires -- among others -- to consciously decouple the provision of transparency information from their ultimate presentation. To this end, we provide a general model of how transparency information can be provided from a data controller to data subjects, effectively leveraging machine-readable transparency information and facilitating versatile presentation interfaces. We contribute two actual implementations of said model: 1) a GDPR-aligned privacy dashboard and 2) a chatbot and virtual voice assistant enabled by conversational AI. We evaluate our model and implementations with a user study and find that these approaches provide effective and time-efficient transparency. Consequently, we illustrate how transparency can be enhanced using machine-readable transparency information and how data controllers can meet respective regulatory obligations., Comment: Preprint, accepted to the Privacy Symposium 2023 https://privacysymposium.org/

Heterostructures increasingly attracted attention over the past several years to enable various optoelectronic and photonic applications. In this work, atomically thin interfaces of Ir/Al2O3 heterostructures compatible with micro-optoelectronic technologies are reported. Their structural and optical properties were determined by spectroscopic and microscopic techniques (XRR, XPS, HRTEM, spectroscopic ellipsometry, and UV/VIS/NIR spectrophotometry). The XRR and HRTEM analyses reveal a layer-by-layer growth mechanism of Ir in atomic scale heterostructures, which is different from the typical island-type growth of metals on dielectrics. Alongside, XPS investigations imply the formation of Ir-O-Al bonding at the interfaces for lower Ir concentrations, in contrast to the nanoparticle core-shell structure formation. Precisely tuning the ratio of the constituents ensures the control of the dispersion profile along with a transition from effective dielectric to metallic heterostructures. The Ir coating thickness was varied ranging from a few {\AA} to films of about 7 nm in the heterostructures. The transition has been observed in the structures containing individual Ir coating thicknesses of about 2-4 nm. Following this, show epsilon-near-zero metamaterials with tunable dielectric constants by precisely varying the composition of such heterostructures. Overall, a comprehensive study on structural and optical properties of the metal-dielectric interface of Ir/Al2O3 heterostructures was addressed indicating an extension of the material portfolio available for optical system design.

Modal verbs (e.g., "can", "should", or "must") occur highly frequently in scientific articles. Decoding their function is not straightforward: they are often used for hedging, but they may also denote abilities and restrictions. Understanding their meaning is important for various NLP tasks such as writing assistance or accurate information extraction from scientific text. To foster research on the usage of modals in this genre, we introduce the MIST (Modals In Scientific Text) dataset, which contains 3737 modal instances in five scientific domains annotated for their semantic, pragmatic, or rhetorical function. We systematically evaluate a set of competitive neural architectures on MIST. Transfer experiments reveal that leveraging non-scientific data is of limited benefit for modeling the distinctions in MIST. Our corpus analysis provides evidence that scientific communities differ in their usage of modal verbs, yet, classifiers trained on scientific data generalize to some extent to unseen scientific domains., Comment: 20 pages, 7 figures. Accepted to EMNLP Findings 2022; typesetting of this version slightly differs from conference version

Ongoing advances in force field and computer hardware development enable the use of molecular dynamics (MD) to simulate increasingly complex systems with the ultimate goal of reaching cellular complexity. At the same time, rational design by high-throughput (HT) simulations is another forefront of MD. In these areas, the Martini coarse-grained force field, especially the latest version (i.e. v3), is being actively explored because it offers enhanced spatial-temporal resolution. However, the automation tools for preparing simulations with the Martini force field, accompanying the previous version, were not designed for HT simulations or studies of complex cellular systems. Therefore, they become a major limiting factor. To address these shortcomings, we present the open-source vermouth python library. Vermouth is designed to become the unified framework for developing programs, which prepare, run, and analyze Martini simulations of complex systems. To demonstrate the power of the vermouth library, the martinize2 program is showcased as a generalization of the martinize script, originally aimed to set up simulations of proteins. In contrast to the previous version, martinize2 automatically handles protonation states in proteins and post-translation modifications, offers more options to fine-tune structural biases such as the elastic network, and can convert non-protein molecules such as ligands. Finally, martinize2 is used in two high-complexity benchmarks. The entire I-TASSER protein template database as well as a subset of 200,000 structures from the AlphaFold Protein Structure Database are converted to CG resolution and we illustrate how the checks on input structure quality can safeguard HT applications., Comment: corresponding authors: F. Gr\"unewald f.grunewald[at]rug.nl; S. J. Marrink s.j.marrink[at]rug.nl; changes made in v2: new benchmark test-case; data availability statement; language corrections; changes made in v3: additional test cases for non-protein molecules; updated explanation of the graph matching algorithm; language corrections; link to extended and updated documentation

Cloud native systems are processing large amounts of personal data through numerous and possibly multi-paradigmatic data stores (e.g., relational and non-relational databases). From a privacy engineering perspective, a core challenge is to keep track of all exact locations, where personal data is being stored, as required by regulatory frameworks such as the European General Data Protection Regulation. In this paper, we present Teiresias, comprising i) a workflow pattern for scalable discovery of personal data at rest, and ii) a cloud native system architecture and open source prototype implementation of said workflow pattern. To this end, we enable a continuous inventory of personal data featuring transparency and accountability following DevOps/DevPrivOps practices. In particular, we scope version-controlled Infrastructure as Code definitions, cloud-based storages, and how to integrate the process into CI/CD pipelines. Thereafter, we provide iii) a comparative performance evaluation demonstrating both appropriate execution times for real-world settings, and a promising personal data detection accuracy outperforming existing proprietary tools in public clouds., Comment: Preprint of 2022-09-09 before final copy-editing of an accepted peer-reviewed paper to appear in the Proceedings of the 20th International Conference on Service-Oriented Computing ICSOC 2022

The Martini 3 force field is a full re-parametrization of the Martini coarse-grained model for biomolecular simulations. Due to the improved interaction balance it allows for more accurate description of condensed phase systems. In the present work we develop a consistent strategy to parametrize carbohydrate molecules accurately within the framework of Martini 3. In particular, we develop a canonical mapping scheme that decomposes arbitrarily large carbohydrates into a limited number of fragments. Bead types for these fragments have been assigned by matching physicochemical properties of mono- and disaccharides. In addition, guidelines for assigning bonds, angles, and dihedrals are developed. These guidelines enable a more accurate description of carbohydrate conformations than in the Martini 2 force field. We show that models obtained with this approach are able to accurately reproduce osmotic pressures of carbohydrate water solutions. Furthermore, we provide evidence that the model differentiates correctly the solubility of the poly-glucoses dextran (water soluble) and cellulose (water insoluble, but soluble in ionic-liquids). Finally, we demonstrate that the new building blocks can be applied to glycolipids, being able to reproduce membrane properties and to induce binding of peripheral membrane proteins. These test cases demonstrate the validity and transferability of our approach.

Federated learning may be subject to both global aggregation attacks and distributed poisoning attacks. Blockchain technology along with incentive and penalty mechanisms have been suggested to counter these. In this paper, we explore verifiable off-chain computations using zero-knowledge proofs as an alternative to incentive and penalty mechanisms in blockchain-based federated learning. In our solution, learning nodes, in addition to their computational duties, act as off-chain provers submitting proofs to attest computational correctness of parameters that can be verified on the blockchain. We demonstrate and evaluate our solution through a health monitoring use case and proof-of-concept implementation leveraging the ZoKrates language and tools for smart contract-based on-chain model management. Our research introduces verifiability of correctness of learning processes, thus advancing blockchain-based federated learning.

The purpose of regulatory data minimization obligations is to limit personal data to the absolute minimum necessary for a given context. Beyond the initial data collection, storage, and processing, data minimization is also required for subsequent data releases, as it is the case when data are provided using query-capable Web APIs. Data-providing Web APIs, however, typically lack sophisticated data minimization features, leaving the task open to manual and all too often missing implementations. In this paper, we address the problem of data minimization for data-providing, query-capable Web APIs. Based on a careful analysis of functional and non-functional requirements, we introduce Janus, an easy-to-use, highly configurable solution for implementing legally compliant data minimization in GraphQL Web APIs. Janus provides a rich set of information reduction functionalities that can be configured for different client roles accessing the API. We present a technical proof-of-concept along with experimental measurements that indicate reasonable overheads. Janus is thus a practical solution for implementing GraphQL APIs in line with the regulatory principle of data minimization., Comment: Preprint version (2022-03-18) This version of the contribution has been accepted for publication at the 22nd International Conference on Web Engineering (ICWE 2022), Bari, Italy