Starting from spring 2020, the last project from the Vienna Science, Research and Technology Fund (WWTF) Information and Communication Technologies 2019 call has successfully set off in July 2020. Out of 96 submitted short proposals, 26 have been invited to submit a full proposal, and 9 of those have been finally selected by an international jury of experts to be funded—7 of those projects are located at the Faculty of Informatics. 

ICT and Digital Humanism

Information and Communication Technology 2019 was the fifth project call within the Vienna Science and Technology Fund (WWTF) ICT Programme. It invited researchers and scientists at universities and non-university research institutions in Vienna who want to conduct a multi-annual cutting-edge research project in ICT and its application in other fields. Projects aim to improve the understanding of substantial current scientific research questions (disciplinary, interdisciplinary, or transdisciplinary) in ICT with potential medium-term economic or social benefit. A total of €5,525,150 million is dedicated to this call. The funding range per project is €200,000 to €800,000.

The current special call on Digital Humanism 2020, also within the thematic program ICT, is open and aims at facilitating interdisciplinary research between social sciences & humanities (SSH) and ICT: WWTF wants to position Vienna as an internationally strong location for developing Digital Humanism as a movement and paradigm. The proposed projects should improve the understanding of current fundamental digitalization questions in its social and human dimensions. 

The Successful Projects

From the smartphone that wakes us in the morning to the algorithm that suggests television series we might like in the evenings—with information and communication technology, we are working almost around the clock today. The Vienna Science, Research, and Technology Fund (WWTF) promotes research in this area with a highly-paid funding invitation. It is not about applying information and communication technology in other areas, but about the further development of the technology itself. The seven successful projects from TU Wien Informatics result in more than 4 million euros for our faculty’s research projects.

Modeling the World at Scale: Stefan Ohrhallinger, Michael Wimmer—€ 578.450

“The goal is to integrate different sources such as images of road traffic, and depth images from high-end 3D scanners and low-cost end-user devices into a 3D model that can be queried in real-time at different resolution levels, for example, to display changes as augmented reality on mobile devices, or to navigate autonomous vehicles and avoid collisions,” explains Stefan Ohrhallinger. The vision of the project is to reconstruct a model of the world that permits online level-of-detail extraction. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

ProbInG: Distribution Recovery for Invariant Generation of Probabilistic Programs, Ezio Bartocci, Laura Kovacs, Efstathia Bura—€ 782.100

Probabilistic programming is a new emerging paradigm adopted by high-tech giants, such as Google, Amazon, and Uber, to simplify the development of AI and machine learning-based applications, such as route planning and detecting cyber intrusions. Probabilistic programming languages include native constructs for sampling distributions allowing to mix deterministic and stochastic elements freely. The resulting flexible framework comes at the price of programs with behaviors hard to analyze, leading to unpredictable adverse consequences in safety-critical applications. One of the main challenges in analyzing these programs is to compute invariant properties that summarize loop behaviors. Despite recent results, full automation of invariant generation is at its infancy, and only targets expected values of the program variables, which is insufficient to recover the full probabilistic program behavior. “Our project aims at developing novel and fully automated approaches to study and formalize the distributions of probabilistic program variables, without any user guidance. Our results will reduce the need for expert knowledge in ensuring the safety and security of computer systems and will cut the design costs of applications based on probabilistic programs, bringing crucial intellectual and economic benefits to our society,” says principal investigator Ezio Bartocci.

Engineering Linear Ordering Algorithms for Optimizing Data Visualizations , Martin Nöllenburg—€ 463.710

Optimizing linear orderings of objects is a fundamental problem for many types of data visualizations ranging from graph layouts over geospatial data to abstract sets and sequences or time-series data. Yet a systematic investigation of algorithms for solving novel constrained and application-specific ordering problems that go beyond well-studied and NP-hard classic ordering problems are missing. Practical work in visualization often resorts to heuristics without rigorous performance and quality guarantees for solving these algorithmic problems. “Data have been called the new oil, but they are abstract, and unlike oil, you cannot see, touch, or smell them. In our project, we develop algorithms to make complex data visible and understandable to humans by combinatorially optimizing and tidying their data visualizations,” explains Martin Nöllenburg the approach of crossing the gap between fundamental theory and practical applications. 

Guidance-Enriched Visual Analytics for Temporal Data (GuidedVA) , Silvia Miksch, Davide Ceneda—€ 664.770 

This proposal will show new ways in Visual Analytics (VA)—how users can be supported in their exploration and decision-making processes and continuous analysis by integrating orientation aids. Many of the social and economic decisions are based on visualizations that change over time. However, the process of analyzing and visualizing large amounts of data is complex and error-prone, especially when exploring temporal data, including uncertainty. Although VA has helped to provide advanced methodologies in recent years, the gap between the expertise required for their application and the dissemination of sound VA for decision making is widening. “GuidedVA will break new ground in Visual Analytics (VA) by incorporating guidance to support users in their exploration and decision-making processes and make continuous progress. We will apply GuidedVA to payment fraud detection—supporting users finding a needle in a haystack over time with changing conditions and uncertainty,” Silvia Miksch is convinced.

IoTIO: Analyzing and Understanding the Internet of Insecure Things, Martina Lindorfer, Kevin Borgolte—€ 783.940

Consumer devices, from door locks to light bulbs, are becoming increasingly smart. They are linked with other devices as part of smart homes and offices, usually Internet-connected, and may be publicly accessible through misconfiguration or IPv6. The related security and privacy implications have yet to be explored in-depth, and their analysis is complicated by device type and architecture diversity. “We are developing novel analysis techniques for mobile companion apps, which are freely available in app stores, to discover potential security and privacy issues in communication protocols and cloud backends of IoT devices—without requiring access to the devices themselves,“ Martina Lindorfer from our research unit Security and Privacy points out. Prior work focused on case studies of specific device types or analyzed devices’ firmware in isolation, requiring substantial manual effort. In this project, scalable techniques are proposed to analyze smart devices for potential vulnerabilities based on how they collect, process, and share data by interacting with their mobile companion app or smart hubs. The project is a multi-disciplinary research effort enabling security and privacy analyses. It also has a societal impact by enabling informed decision making by manufacturers, lawmakers, and users.

Learning to Solve Quantified Boolean Formulas, Friedrich Slivovsky, Stefan Szeider—€ 330.890

The ongoing digitalization of all areas of life poses new challenges for our society ever. The security and reliability of complex and continuously changing software systems are difficult to check, and new security gaps are becoming known almost daily. In the case of conventional software, these weaknesses can gradually be eliminated by regular updates. Such an approach is unacceptable for safety-critical systems. This project deals with quantified problems located high up in the complexity hierarchy. These are problems where the solution does not depend only on some static input data, but where you have a back and forth between the solver and a possible antagonist, like in a chess play. Stefan Szeider explains, “Such problems are extremely challenging from the algorithmic point of view. They arise, for instance, when one wants to verify that a computer network protocol is secure. This project’s idea is a novel way of approaching such quantified problems, like reimagining the solving algorithm as an algorithm that learns a winning strategy for a game.”

Revealing and Utilizing the Hidden Structure for Solving Hard Problems in AI, Stefan Szeider, Stefan Woltran—€ 566.900

Over the last few years, Artificial Intelligence has taken an essential role in our everyday professional and private life. One quickly forgets that AI-systems strongly depend on efficient algorithms that deal with large data sets and solve challenging computational problems. The problems are often intractable and admit, in general, only a costly solution by brute force. Since such computations occur in the cloud, on some server farms in remote places, one is not aware of the massive amount of energy used for the computation. “We aim at utilizing the structure that is hidden in the data. Such a hidden structure can provide us a shortcut through the search space. So, in some sense, the impossible becomes possible, solving an intractable problem with an efficient algorithm,” Stefan Szeider describes the project’s aim. 

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