Best of both worlds: Combining deep neural networks with statistical state estimators

Thursday, June 22, 2023 | 09:00 am (CET) | Room: B04.1.06 | Lakeside Science & Technology Park

Ass.-Prof. Dr. Jan Steinbrener | Department of Smart System Technologies (Control of Networked Systems group) at Alpen-Adria-Universität Klagenfurt

Abstract: Deep neural networks (DNNs) have become an important tool in many fields of applications from image recognition to natural language processing and beyond, often outperforming human experts in their domains. Compared to heuristic, expert algorithms or shallow machine learning models, DNNs benefit from better prediction accuracy and better generalizability to unseen data. This comes at the cost of resource and data-intensive training of these models and a black-box-like behavior that does not provide information about underlying reasoning or uncertainty of the predictions. In robotics, DNNs have been successfully applied to diverse tasks such as state estimation, path planning, and control for various different platforms. This talk will explore the application of deep neural networks for sensor data processing with a particular focus on state estimation for robotic applications. End-to-end trainable deep neural networks that directly predict the desired state based on raw sensory inputs as well as hybrid models where the predictions of the DNNs are fused with other sensor data in a statistical state estimator will be discussed. Finally, strategies how to quantify model and task-based uncertainties of DNN predictions with the goal to improve the consistency of DNN-based state estimators will be presented.

Bio: Jan Steinbrener is an assistant professor on a tenure track position in the Control of Networked Systems group (CNS) at the University of Klagenfurt. He obtained his PhD in Physics in 2010 from Stony Brook University in Stony Brook, NY USA. After his PhD, he worked as a postdoctoral researcher at the Max-Planck Institute for medical research in Heidelberg, Germany, and then spent 5 years working in industry developing medical x-ray machines at Siemens Healthcare in Erlangen, Germany. Before joining CNS in 2019, he worked as a senior researcher at the Carinthian Tech Research Centre (now Silicon Austria Labs) in Villach Austria.

His current research focuses on combining machine learning approaches with classical methods for state estimation and navigation of autonomous systems. He has authored or co-authored more than 40 peer reviewed publications on novel imaging systems, image processing and reconstruction techniques, applied machine learning, machine learning algorithm development, and combination of machine learning with classical filters for state estimation. He currently holds 2 patents on image processing techniques.

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Life Cycle Assessments and Ecodesign to reduce CO2 Footprints

Tuesday, May 16, 2023 | 01:00 pm (CET) | Room: Z.1.09 | | Alpen-Adria-Universität Klagenfurt

Dr. Michael Has | University of Grenoble

Abstract: The impact of climate change to biosphere and the human civilization living therein are widely discussed as is the need to reduce the consumption of energy and materials – measured in footprints. Despite of that the means on how to reduce footprints and the difficulties going along with that are lesser discussed.
The assessment of CO2 Footprints during all phases of its production and existence (Life Cycle Assessment) is embedded in the legally required of non-financial reporting for companies. This talk is intended to provide background on non-financial reporting and how that relates to footprints and risks. Of course reporting is not l´art pour l´art. It is intended to reduce the impact of an activity – Ecodesign and it impacts the value of companies. The intent is to discuss in more detail how reporting is done, how (CO2-) Footprints are derived, ways to reduce footprints and how the related activities influence the value of companies. 

Bio: Following apprenticeships Has studied physics at the University of Regensburg. In 1992 he received his doctorate on his work in the field of biophysics.  Has also attended the INSEAD Business School in Fontainebleau, France. He spent the initial part of his industrial career at FOGRA delivering industrial research in the field of new technology analysis, consulting, offset and digital print, digital workflows and Color Management. Has founded the predecessor organization of the International Color Consortium (ICC, Reston, Virginia), whose first technical secretary he became.

In 1998 he received his habilitation from the University of Grenoble. Since 1998 he serves as Distinguished Professor in the fields of future technology, business – and portfolio strategy, circular economy and sustainability at University of Grenoble. From 1998 until 2020 Has worked with Canon where he has held senior and executive positions in R & D, marketing and strategic planning and product design.  

Has was active in various scientific advisory boards, held board positions and worked as business advisor in start-up companies. His work on industrial development led to numerous publications and patents. 

Stimulated by a long stay with indigenous populations in Canada Has was and is active in the field of human rights for minorities: He was board member of the Society for Threatened Peoples, Göttingen, and the World Uranium Hearing, Munich. Today he chairs the board of trustees of the foundation Vielfalt der Kulturen der Welt, Göttingen. As such he dealt with the impact tourism, resource conservation and consequences of mining and waste disposal. In connection with his professional work in the printing industry, these interests led him to work, publications and research in the fields of sustainability and the circular economy (eg for the World Economic Forum, Davos, and scientific and commercial conferences on circular economy). 

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Communications and Power: Two Sides of One Tapestry

Monday, April 17, 2023 | 04:00 pm (CET) | Room: HS 3 | | Alpen-Adria-Universität Klagenfurt

Prof. Bruno Clerckx | Faculty of Engineering, Department of Electrical and Electronic Engineering | Imperial College London

Abstract: Radio waves carry both energy and information simultaneously. Nevertheless, radio-frequency (RF) transmissions of these quantities have traditionally been treated separately. Future wireless networks will experience a paradigm shift, namely, unifying wireless transmission of information and power to make the best use of the RF spectrum and radiation as well as the network infrastructure for the dual purpose of communicating and energizing. Such networks will enable trillions of future low-power devices to sense, compute, connect, and energize anywhere, anytime, and on the move. The design of such future networks brings new challenges and opportunities for RF, communications, signal processing, machine learning, sensing, and computing. In this talk, I give an overview progress in laying the foundations of the envisioned dual-purpose networks by establishing a signal theory and design for wireless information and power transmission (WIPT) and identifying the fundamental tradeoff between conveying information and power wirelessly.

Bio: Bruno Clerckx is a (Full) Professor, the Head of the Wireless Communications and Signal Processing Lab, and the Deputy Head of the Communications and Signal Processing Group, within the Electrical and Electronic Engineering Department, Imperial College London, London, U.K. He is also the Chief Technology Officer (CTO) of Silicon Austria Labs (SAL) where he is responsible for all research areas of Austria’s top research center for electronic based systems.

He received the MSc and Ph.D. degrees in Electrical Engineering from Université Catholique de Louvain, Belgium, and the Doctor of Science (DSc) degree from Imperial College London, U.K. Prior to joining Imperial College in 2011, he was with Samsung Electronics, Suwon, South Korea, where he actively contributed to 4G (3GPP LTE/LTE-A and IEEE 802.16m). He has authored two books on “MIMO Wireless Communications” and “MIMO Wireless Networks”, 250 peer-reviewed international research papers, and 150 standards contributions, and is the inventor of 80 issued or pending patents among which 15 have been adopted in the specifications of 4G standards and are used by billions of devices worldwide. His research spans the general area of wireless communications and signal processing for wireless networks. He received the prestigious Blondel Medal 2021 from France for exceptional work contributing to the progress of Science and Electrical and Electronic Industries, the 2021 Adolphe Wetrems Prize in mathematical and physical sciences from Royal Academy of Belgium, multiple awards from Samsung, IEEE best student paper award, and the EURASIP (European Association for Signal Processing) best paper award 2022. He is a Fellow of the IEEE and the IET, and an IEEE Communications Society Distinguished Lecturer.

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How to Optimize Dynamic Adaptive Video Streaming? Challenges and Solutions

Monday, February 27, 2023 | 02:00 pm (CET) | Room: S.2.42 | | Alpen-Adria-Universität Klagenfurt

Dr. Farzad Tashtarian | Department of Information Technology (ATHENA Christian Doppler (CD) Laboratory)

Abstract: Empowered by today’s rich tools for media generation and collaborative production and convenient network access to the Internet, video streaming has become very popular. Dynamic adaptive video streaming is a technique used to deliver video content to users over the Internet, where the quality of the video adapts in real time based on the network conditions and the capabilities of the user’s device. HTTP Adaptive Streaming (HAS) has become the de-facto standard to provide a smooth and uninterrupted viewing experience, especially when network conditions frequently change. Improving the QoE of users concerning various applications‘ requirements presents several challenges, such as network variability, limited resources, and device heterogeneity. For example, the available network bandwidth can vary over time, leading to frequent changes in the video quality. In addition, different users have different preferences and viewing habits, which can further complicate live streaming optimization. Researchers and engineers have developed various approaches to optimize dynamic adaptive streaming, such as QoE-driven adaptation, machine learning-based approaches, and multi-objective optimization, to address these challenges. In this talk, we will give an introduction to the topic of video streaming and point out the significant challenges in the field. We will present a layered architecture for video streaming and then discuss a selection of approaches from our research addressing these challenges. For instance, we will present approaches to improve the  QoE of clients in User-generated content applications in centralized and distributed fashions. Moreover, we will present a novel architecture for low-latency live streaming that is agnostic to the protocol and codecs that can work equally with existing HAS-based approaches.

Bio: Farzad Tashtarian (M’15) is a post-doctoral researcher in the ATHENA project at the Institute of Information Technology (ITEC), Alpen-AdriaUniversitat Klagenfurt (AAU). Before joining ATHENA, he was an assistant professor at the Azad University of Mashhad, Iran. He received his Ph.D. from the Ferdowsi University of Mashhad in Computer Engineering.

As a researcher, he co-authored more than 60 papers published in prestigious journals such as  IEEE Transactions of Vehicular Technology, IEEE Transactions on Network Service and Management, IEEE Transactions on Multimedia,  Elsevier Computer Communication, and IEEE Access and difference conferences. He is a member of the Technical Program Committee of several international conferences. His current research areas of interest are end-to-end latency and QoE in video streaming, video networking, software-defined networking, network function virtualization, mathematical modeling, and distributed optimization. Further information is at

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Distributed systems in the Post-Moore era

Tuesday, March 14, 2023 | 02:00 pm (CET) | Room: HS 8 | | Alpen-Adria-Universität Klagenfurt

Dr. Vincenzo De Maio | Vienna University of Technology

Abstract: In recent years, we have experienced an exponential growth in the amount of data generated by IoT devices. Data have to be processed strict low latency constraints, that cannot be addressed by conventional computing paradigm and architectures. On top of this, if we consider that we recently hit the limit codified by the Moore’s law, satisfying low-latency requirements of modern applications will become even more challenging in the future. In this talk, we discuss challenges and possibilities of heterogeneous distributed systems in the Post-Moore era.

Bio: Dr. Vincenzo De Maio has a Ph.D. in Computer science by the Institute of Computer Science at the University of Innsbruck, (Austria). He received his Ph.D in November 2016 under the supervision of Univ. Prof. Dr. Radu Prodan and Dr. Gabor Kecskemeti.  

Since 2017 he is working as a postdoctoral researcher at the Institute of Information Systems Engineering of the Vienna University of Technology, whose leader is Univ. Prof. Dr. Ivona Brandic. He has been author of different conference and journal publications on the topic of energy efficiency and modelling for Cloud and Edge computing. His research in the area of parallel and distributed systems includes energy-aware Cloud/Edge computing and Post-Moore computing.

Since 2023, he also collaborates with the Department of Experimental Physics of the University of Innsbruck and Dr. Thomas Monz as CO-PI of the HPQC FFG Leitprojekt Project, whose goal is the integration of quantum hardware in HPC systems.

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Conceptualizations of Software and Data Quality

January 30, 2023 | 02:00 pm (CET) | Room: S.0.05 | | Alpen-Adria-Universität Klagenfurt

Dr.Phil. Volodymyr Shekhovtsov | Department of Informatics Systems

Abstract: Conceptual modeling activities need to integrate software and data quality at the same level of abstraction as the functionality of the system or the data itself (a conceptualization of quality). This need is related to the fact that looking at the problem domain to obtain its conceptual model only from the view of the system’s functionality or the structure of the associated data restricts the analyst and can be the source of mistakes. The reason for such mistakes is that many quality-related issues become only evident when the system is put into use; on the other hand, such mistakes are parts of the early design decisions and can be difficult and costly to fix. This thesis summarizes several selected publications of a research program towards elaborating conceptualizations of software and data quality on different stages of the software development process and for different domains. This program addresses five research challenges. The first challenge is related to the need for the unified classification of the quality conceptualization techniques based on the agreed-upon concept of the quality itself. Without such a classification it is difficult for analysts to decide which quality conceptualization solution is better suited for their problem. This is addressed in the thesis by proposing the classification of such techniques, which can serve as a foundation for the evaluation framework for quality conceptualizations; the rest of the thesis is aligned with this classification. The second challenge is positioned at the intersection of the research fields of static code analysis and requirement engineering. It is related to the problem that the rationale of applying code-checking rules in static code analysis is not captured explicitly, which leads to the problems of rule reuse in similar development contexts. In this thesis, it is proposed to trace possible sources of such rules back to design decisions and quality requirements and to conceptualize the quality[1]related rationale information along with specific code-checking rules to serve as a schema for a rule repository. The third challenge is positioned in the research field of quality-aware requirements engineering. It is related to the problem that more attention should be paid to conceptualizing software quality requirements before performing design-time activities. It is addressed in the thesis by the quality requirements conceptualization activity positioned between the activities of quality requirements elicitation and conceptual design. The results of this activity can be used at different stages of the software process, capturing the quality requirements semantics in a way that can be easily understood and verified by the system users and can be mapped into different design notations. The fourth challenge is positioned in the research field of software process improvement. It is related to the problem that organizing quality-related interaction between business stakeholders and software developers is difficult as they need a common set of concepts. In this thesis, this challenge is addressed by three approaches. First, the thesis proposes to conceptualize the stakeholder interaction process on two levels: a coarse-grained level defining the set of generic quality-related activities and the conditions of launching these activities and a fine-grained level describing specific interaction steps in detail. Second, the thesis proposes to conceptualize such a process as a process of quality view harmonization on three levels: terminology harmonization (agreeing on the common quality-related terminology), view harmonization (agreeing on the sets of objects and the types of their qualities to be assessed, as well as the assessment procedures), and quality harmonization (agreeing on the evaluation schemes and the specific qualities to be used). Third, the thesis introduces a model-centered software solution that facilitates quality-related stakeholder communication in the software development process based on these concepts. The fifth challenge is positioned in the research field of data modeling and management for a specific domain (biobanking). It is related to the problem that the quality of data and metadata in biobanks is not properly defined on the conceptual level and that such conceptualizations are not used to facilitate the biobank search. In this thesis, it is again addressed by three approaches. First, the thesis conceptualizes the data item quality in biobanks and defines a set of data item quality characteristics and metrics. Second, the thesis proposes a definition and a conceptualization for the metadata in biobanks, which covers stored data item quality values, and defines a set of metadata quality characteristics and metrics. Third, the thesis proposes an architecture to support researchers in identifying relevant collections of material and data with documented quality for their research projects while observing strict privacy rules. It is based on the conceptualization of the biobank metadata (including its data item quality values) to serve as a schema for a metadata repository to be queried in a search for collections.

Bio: Volodymyr Shekhovtsov is a postdoctoral researcher at the Department of Informatics Systems (ISYS), University of Klagenfurt. He received his PhD degree in Computerized Control Systems and Progressive Information Technology in 1998 from the National Technical University “Kharkiv Polytechnic Institute”, Kharkiv, Ukraine. As a researcher, since 2012 he participated in total in 5 research projects at the University of Klagenfurt.

He co-authored more than 45 papers, published in journals such as Applied Sciences, Internet of Things, International Journal of Web Information Systems, and gave 27 talks at venues such as ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS), International Working Conference on Requirements Engineering (REFSQ), Dagstuhl seminars “Next Generation Domain Specific Conceptual Modeling: Principles and Methods”, and “The Evolution of Conceptual Modeling”. As a teacher, since 1994 he was responsible for 14 university lecture and lab courses in both Ukraine and Austria. His research interests include software, data, and metadata quality, Internet of Things and Industry 4.0, behavior modeling and prediction, methods and tools for information system modeling, analysis, and design, source code analysis and quality, and applying decision theory and optimization techniques to software engineering problems.

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Engineering Serverless Workflow Applications in Federated FaaS

November 29, 2022 | 10:00 am (CET) | Room: S.2.69 | | Alpen-Adria-Universität Klagenfurt

Ass. Prof. Sashko Ristov | Computer Science Department, University of Innsbruck

Abstract: Function-as-a-Service (FaaS) is the latest paradigm of cloud computing in which developers deploy their codes as serverless functions, while the entire underlying platform and infrastructure is completely managed by cloud providers. Each cloud provider offers a huge set of cloud services and many libraries to simplify development and deployment, but only inside their clouds, often in a single cloud region. With such „help“ of cloud providers, users are locked to use resources and services of the selected cloud provider, which are often limited. Moreover, such heterogeneous and distributed environment of multiple cloud regions and providers challenge scientists to engineer cloud applications, often in a form of serverless workflows. In this talk, I will present our design principle „code once, run everywhere, with everything“. In particular, I will present challenges and our approaches and techniques how to program, model, orchestrate, and run distributed serverless workflow applications in federated FaaS.

Bio: Dr. Sashko Ristov is Assistant Professor at the Computer Science Department of University of Innsbruck, Austria. His research interests include engineering distributed applications and systems in federated clouds, in particular automation, simulation, performance modeling, and optimization of serverless workflow applications.

Dr. Ristov has a PhD degree in computer science in 2012 from Ss. Cyril and Methodius University, Skopje, North Macedonia, where he was Assistant professor (2013-2016). He was also Postdoctoral University Assistant at the Distributed and Parallel Systems Group, Computer Science Department, University of Innsbruck, in the period 2016-2022.

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Don’t Treat the Symptom, Find the Cause! Efficient AI Methods for (Interactive) Debugging

October 25, 2022 | 09:00 – 11:00 am (CET) | HS 11 | Patrick Rodler | Alpen-Adria-Universität Klagenfurt

Abstract: In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in eCommerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play.

Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above. It exploits and orchestrates techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, learning, stochastics, statistics, decision making under uncertainty, as well as combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems.   

In this talk, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these challenges. For instance, we will present methods for the optimization of the time and memory performance of diagnosis systems, show efficient techniques for a semi-automatic debugging by interacting with a user or expert, and demonstrate how our algorithms can be effectively leveraged in important application domains such as scheduling or the Semantic Web.

Bio: Patrick Rodler is a postdoctoral researcher at the Department of Artificial Intelligence and Cybersecurity (AICS), University of Klagenfurt. He holds MSc degrees in Technical Mathematics and Computer Science, and received his PhD degree in Computer Science in 2015 from the University of Klagenfurt. As a researcher, he co-authored more than 50 papers, published in prestigious journals such as Web Semantics, Knowledge-Based Systems, Artificial Intelligence, or Information Sciences, and gave 30 talks at renowned venues such as the AAAI Conference on Artificial Intelligence (AAAI), the European Conference on Artificial Intelligence (ECAI), or the Int’l Conference on Knowledge Representation and Reasoning (KR). As a teacher, he was responsible for 26 university courses and lectures, and in 2018 he was awarded a university-wide prize for excellent teaching by the University of Klagenfurt. His research interests include artificial intelligence in general, and model-based diagnosis, intelligent search, heuristic problem solving, as well as knowledge representation and reasoning in particular.

Slides are animated. Please view in Powerpoint presentation mode.

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The Role of Machine Learning in Fluid Network Control and Data Planes

Thursday, October 20, 2022 | 04:00 pm (CET) | Online via Zoom, please register here:

Prof. Dr. Christian Rothenberg | University of Campinas, Brazil

Abstract: As the network softwarization trend started by SDN and NFV keeps evolving, the hardware/software continuum becomes more relevant than ever, offering new offloading/acceleration opportunities at node and network-wide scales. This talk will review evolving transformations behind network softwarization with a special focus on network refactoring and offloading trends leading to “fluid networks planes”, characterized by multiple candidate options for the specific HW/SW embodiment and the location of chained network functions, from the edge to core, from one administrative provider to another, from programmable silicon to portable lightweight virtualized containers. The talk will overview concrete examples from the literature with a special focus on the role of Machine Learning to assist key (automated) decision-making steps.  Lastly, the talk will conclude with a glimpse on ongoing ML work applied to Youtube video QoE prediction in live 5G networks.

Bio: Christian Rothenberg is Associate Professor (tenure-track) and head of the Information & Networking Technologies Research & Innovation Group (INTRIG) at the School of Electrical and Computer Engineering (FEEC) of the University of Campinas (UNICAMP), where he received his Ph.D. in Electrical and Computer Engineering in 2010. From 2010 to 2013, he worked as Senior Research Scientist in the areas of IP systems and networking, leading SDN research at CPQD R&D Center in Telecommunications, Campinas, Brazil. He holds the Telecommunication Engineering degree from the Technical University of Madrid (ETSIT – UPM), Spain, and the M.Sc. (Dipl. Ing.) degree in Electrical Engineering and Information Technology from the Darmstadt University of Technology (TUD), Germany, 2006. Christian has contributed to 07 international patents, co-authored three books, and over 200 scientific publications, including top-tier scientific journals and networking conferences such as SIGCOMM and INFOCOM, altogether featuring 10 000+ citations (h-index: 30+, i10-index: 70+). 

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Edge Intelligence and Protocols for IoT Applications

Friday, July 15, 2022 | 02:00 pm (CET) | Room: S.2.42

Dr. Shajulin Benedict | Indian Institute of Information Technology Kottayam

Abstract: IoT-enabled applications increase tremendously in various sectors, such as transportation, healthcare, education, agriculture, and so forth. These applications sense properties using sensors, perform intelligence, and apply the findings using actuators. Instead of submitting sensor data directly to the cloud, intelligence could be performed with the inclusion of several edge/fog nodes. This improves the privacy and computation time of applications. This talk will provide insights on edge intelligence techniques for such IoT-enabled applications. In addition, a few protocols that are involved in such applications are discussed. 

Bio: Dr. Shajulin Benedict graduated in 2001 from Manonmaniam Sunderanar University, India, with Distinction. In 2004, he received M.E Degree in Digital Communication and Computer Networking from A.K.C.E, Anna University, Chennai. He is the University second rank holder for his masters. He did his Ph.D degree in the area of Grid scheduling under Anna University, Chennai (Supervisor – Dr. V. Vasudevan, Director, Software Technologies Group of TIFAC Core in Network Engineering). After his Ph.D award, he joined a research team in Germany to pursue PostDoctorate under the guidance of Prof. Gerndt. He served as Professor at SXCCE Research Centre of Anna University-Chennai. Later, he visited TUM Germany for teaching Cloud Computing as Guest Professor of TUM-Germany.

Currently, he teaches Internet of Things at the Technical University Munich, Germany; he is affiliated to TUM Germany and to the Indian Institute of Information Technology Kottayam, Kerala, India, an institute of national importance of India. He serves as Director/PI/Representative Officer of AIC-IIITKottayam (Sec.8 Company) for nourishing young entrepreneurs of India. His research interests include IoT Cloud, Performance Analysis of IoT Applications, Cloud Scheduling, Edge Analytics, and so forth.

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