Kategorier
Bridge project

Low-Code Programming of Spatial Contexts for Logistic Tasks in Mobile Robotics

Project type: Bridge Project

Low-Code Programming of Spatial Contexts for Logistic Tasks in Mobile Robotics

An unmet need in industry is flexibility and adaptability of manufacturing processes in low-volume production. Low-volume production represents a large share of the Danish manufacturing industry. Existing solutions for automating industrial logistics tasks include combinations of automated storage, conveyor belts, and mobile robots with special loading and unloading docks. However, these solutions require major investments and are not cost efficient for low-volume production.

Therefore, low-volume production is today labor intensive, as automation technology and software are not yet cost effective for such production scenarios where a machine can be operated by untrained personnel. The need for flexibility, ease of programming, and fast adaptability of manufacturing processes is recognized in both Europe and USA. EuRobotics highlights the need for systems that can be easily re-programmed without the use of skilled system configuration personnel. Furthermore, the American roadmap for robotics  highlights adaptable and reconfigurable assembly and manipulation as an important capability for manufacturing.

The company Enabled Robotics (ER) aims to provide easy programming as an integral part of their products. Their mobile manipulator ER-FLEX consists of a robot arm and a mobile platform. The ER-FLEX mobile collaborative robot provides an opportunity to automate logistic tasks in low-volume production. This includes manipulation of objects in production in a less invasive and more cost-efficient way, reusing existing machinery and traditional storage racks. However, this setting also challenges the robots due to the variability in rack locations, shelf locations, box types, object types, and drop off points.

Today the ER-FLEX can be programmed by means of block-based features, which can be configured to high-level robot behaviors. While this approach offers an easier programming experience, the operator must still have a good knowledge of robotics and programming to define the desired behavior. In order to enable the product to be accessible to a wider audience of users in low-volume production companies, robot behavior programming has to be defined in a simpler and intuitive manner. In addition, a solution is needed that address the variability in a time-efficient and adaptive way to program the 3D spatial context.

Low-code software development is an emerging research topic in software engineering. Research in this area has investigated the development of software platforms that allow non-technical people to develop fully functional application software without having to make use of a general-purpose programming language. The scope of most low-code development platforms, however, has been limited to create software-only solutions for business processes automation of low-to-moderate complexity.

Programming of robot tasks still relies on dedicated personnel with special training. In recent years, the emergence of digital twins, block-based programming languages, and collaborative robots that can be programmed by demonstration, has made a breakthrough in this field. However, existing solutions still lack the ability to address variability for programming logistics and manipulation tasks in an everchanging environment.

Current low-code development platforms do not support robotic systems. The extensive use of hardware components and sensorial data in robotics makes it challenging to translate low-level manipulations into a high-level language that is understandable for non-programmers. In this project we will tackle this by constraining the problem focusing on the spatial dimension and by using machine learning for adaptability. Therefore, the first research question we want to investigate in this project is whether and how the low-code development paradigm can support robot programming of spatial logistic task in indoor environments. The second research question will address how to apply ML-based methods for remapping between high-level instructions and the physical world to derive and execute new task-specific robot manipulation and logistic actions.

Therefore, the overall aim of this project is to investigate the use of low-code development for adaptive and re-configurable robot programming of logistic tasks. Through a case study proposed by ER, the project builds on SDU’s previous work on domain-specific languages (DSLs) to propose a solution for high-level programming of the 3D spatial context in natural language and work on using machine learning for adaptable programming of robotic skills. RUC will participate in the project with interaction competences to optimize the usability of the approach.

Our research methodology to solve this problem is oriented towards design science, which provides a concrete framework for dynamic validation in an industrial setting. For the problem investigation, we are planning a systematic literature review around existing solutions to address the issues of 3D space mapping and variability of logistic tasks. For the design and implementation, we will first address the requirement of building a spatial representation of the task conditions and the environment using external sensors, which will give us a map for deploying the ER platform. Furthermore, to minimizing the input that the users need to provide to link the programming parameters to the physical world we will investigate and apply sensor-based user interface technologies and machine learning. The designed solutions will be combined into the low-code development platform that will allow for the high-level robot programming.

Finally, for validation the resultant low-code development platform will be tested for logistics-manipulation tasks with the industry partner Enabled Robotics, both at a mockup test setup which will be established in the SDU I4.0 lab and at a customer site with increasing difficulty in terms of variability.

Making it easier to program robotic solutions enables both new users of the technology and new use cases. This contributes to the DIREC’s long-term goal of building up research capacity as this project focuses on building the competences necessary to address challenges within software engineering, cyber-physical systems (robotics), interaction design, and machine learning.

Scientific value
The project’s scientific value is to develop new methods and techniques for low-code programming of robotic systems with novel user interface technologies and machine learning approaches to address variability. This addresses the lack of approaches for low-code development of robotic skills for logistic tasks. We expect to publish at least four high-quality research articles and to demonstrate the potential of the developed technologies in concrete real-world applications.

Capacity building
The project will build and strengthen the research capacity in Denmark directly through the education of one PhD candidate, and through the collaboration between researchers, domain experts, and end-users that will lead to R&D growth in the industrial sector. In particular, research competences in the intersection of software engineering and robotics to support the digital foundation for this sector.

Societal and business value
The project will create societal and business value by providing new solutions for programming robotic systems. A 2020 market report predicts that the market for autonomous mobile robots will grow from 310M DKK in 2021 to 3,327M DKK in 2024 with inquiries from segments such as the semiconductor manufacturers, automotive, automotive suppliers, pharma, and manufacturing in general. ER wants to tap into these market opportunities by providing an efficient and flexible solution for internal logistics. ER would like to position its solution with benefits such as making logistics smoother and programmable by a wide customer base while alleviating problems with shortage of labor. This project enables ER to improve their product in regard to key parameters. The project will provide significant societal value and directly contribute to SDGs 9 (Build resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation).

In conclusion, the project will provide a strong contribution to the digital foundation for robotics based on software competences and support Denmark being a digital frontrunner in this area.

September 1, 2022 – December 31, 2025 – 3,5 years.

Total budget DKK 7,15 million / DIREC investment DKK 1,97 million

Participants

Project Manager

Thiago Rocha Silva

Associate Professor

University of Southern Denmark
Maersk Mc-Kinney Moller Institute

E: trsi@mmmi.sdu.dk

Aljaz Kramberger

Associate Professor

University of Southern Denmark
Maersk Mc-Kinney Moller Institute

Mikkel Baun Kjærgaard

Professor

University of Southern Denmark
Maersk Mc-Kinney Moller Institute

Mads Hobye

Associate Professor

Roskilde University
Department of People and Technology

Lars Peter Ellekilde

Chief Executive Officer

Enabled Robotics ApS

Partners

Kategorier
Bridge project

Privacy-Preserving and Software-Independent Voting Protocols

Project type: Bridge Project

Privacy-Preserving and Software-Independent Voting Protocols

Here are five considerations that explain the unmet needs of this proposed project.
  1. Voting protocols, both in form of Voting Governance Protocols and Internet Voting Protocols have become increasingly popular and will be more widely deployed, as a result of an ongoing digitalization effort of democratic processes and also driven by the current pandemic.
  2. Elections are based on trust, which means that election systems ideally should be based on algorithms and data structures that are already trusted. Blockchains provide such a technology. They provide a trusted bulletin board, which can be used as part of voting.
  3. Voting crucially depends on establishing the identity of the voter to avoid fraud and to establish eligibility veri ability.
  4. Any implementation created by a programmer, be it a Voting Governance Protocol or an Internet Voting Protocol can have bugs that quickly erode public confidence. Proof assistants are established tools that help to avoid large classes of common programming mistakes.
  5. Greenland laws were recently changed to allow for Internet Voting.
Having said all of this, tackling these unmet needs is a real research challenge. Decades of research in voting protocols have shown how diffcult it is to combine the privacy of the vote with the auditability of the election outcome. It is easy to achieve one without the other, but hard to combine both into one protocol. Thus, the topic of this proposed research proposal is to study voting protocols that are privacy-preserving and software-independent in the sense of Rivest and Wack’s definition. “A voting system is software-independent if an undetected change or error in its software cannot cause an undetectable change or error in an election outcome.” No such protocol is known to exist for online voting. In future work, we expect to apply the knowledge gained of this proposed research project more broadly to other security protocols.

The proposed research project aims to shed more light on the overall research question, if and what role blockchain technologies should play in the design of software-independent Voting Governance Protocols and Internet Voting Protocols in theory and practice. Affirming this research question in the positive would lead to a new generation of voting protocols that derive trust in the election outcome from trust in the blockchain, they would increase public con dence in the proper treatment of voter eligibility, and they would deliver technology for post-conflict and developing countries, where the population has little trust in paper evidence. This would trigger further innovation in the Voting Governance Protocol and Internet Voting markets. To answer this research question, we structure the research into two research objectives, which we elaborate on next.

  • (RO1) Explore the notion of software-independence, verifiability, and accountability in the context of blockchain voting protocols.
  • (RO2) Mapping the concept of vote privacy to the privacy-preservation in blockchains and how to scale this to a formally-verified and software-independent voting protocol.

Research methodology. In order to achieve (RO1), we will consider two theories of what constitutes software-independence. There is the game-theoretic view, which, similar to proof by reduction and simulation in cryptography, reduces software-independence of one protocol to another. The genesis protocol that was originally advocated by Rivest bases trust entirely on paper evidence, but there are alternatives, based on digital evidence, testing, and statistics. We plan to understand what software-independence actually means for blockchain voting protocols. We plan to consider these formal models of software-independence that we plan to study using proof assistants, to give even stronger software-independence guarantees. For all voting protocols that we design within this project, we will develop formal proofs of software independence, verifiability, and accountability.

To achieve (RO2), we start from the assumption that the blockchain provides sufficient privacy guarantees. We piggy-bag on blockchains that have a clear formal definition of what is meant by privacy, and that are mechanically proven correct. Based on results, we will then reconsider the designs of existing voting protocols, and design new voting protocols by choose-pick the best elements with the goal to achieve a software-independent protocol. A formal de nition of software-independence and a mechanized proof of correctness will be done in this work-package. Time permitting, we will extend our notion of software independence to other guarantees, including receipt freeness, coercion mitigation, and dispute resolution.

For a secure implementation, one needs to make sure that the deployed code correctly implements the protocol. We aim to automatically extract an executable verified smart contract from the formal model developed. The Concordium blockchain provides a secure and private way to put credentials, such as passport information, on the internet. We will investigate how to reuse such blockchain based identities for voting. Based on the results, we propose to develop an open-source library that makes our verified blockchain voting technology available for use in third-party products. We envision to release a product similar to Election Guard (which is provided by Microsoft), but with a blockchain functioning as a public bulletin board.

Scientific value Internet voting provides a unique collection of challenges, such as, for example, vote privacy, software independence, receipt freeness, coercion resistance, and dispute resolution. Subsets of them can be solved separately, here we aim to guarantee vote privacy and software independence by the means of a privacy-preserving and accountable blockchain and formally verify the resulting voting protocol. The resulting voting protocol will be di erent from the existing ones, because they build on formally verified properties that are guaranteed by the choice of blockchain. Capacity building The proposed project pursues two kinds of capacity building. First, by training the PhD student and university students affiliated with the project, making Denmark a leading place for secure Internet voting. Second, if successful, the results of the project will contribute to the Greenland voting project and to international capacity building in the sense that they will strengthen democratic institutions. Business value The project is highly interesting to and relevant for the industry. There are two reasons why it is interesting for Concordium. On the one hand voting is an excellent application demonstrating the vision of the blockchain, and on the other hand Concordium will as part of the project implement a voting scheme to be used for decentralized governance of the blockchain. More precisely, the Concordium blockchain is designed to support applications where users can act privately while maintaining accountability and meeting regulatory requirements. Furthermore, it is an explicit goal of Concordium to support formally verified smart contracts. Obviously all these goals fit nicely with the proposed project, and it will be important for Concordium to demonstrate that the blockchain actually supports the secure voting schemes developed in the project. With respect to governance, Concordium has a need to develop a strong voting scheme allowing members of our community to vote on proposed features and to elect members of the Governance Committee. The project is of great interest to the Alexandra Institute to apply and improve in-house capacity for implementing cryptographic algorithms. The involvement of Alexandra will guarantee that the theoretical findings of the proposed project will we translated into usable real world products and disseminated further to Internet Voting providers that may eventually provide a voting solution to Greenland. Societal value Some nations are rethinking their respective electoral processes and they ways they hold elections. Since the start of the pandemic, approximately a third of all nations scheduled to hold a national election, have postponed them. It is therefore not surprising that countries are exploring Internet Voting as an additional voting channel. The result of this project would contribute to making Internet election more credible, and therefore strengthen developing and post-conflict democracies around the world. The election commission in Greenland, a partner in this proposed project, is currently actively pursuing the development and deployment of an Internet Voting system.

January 1, 2023 – December 31, 2025 – 3 years.

Total budget DKK 12,09 million / DIREC investment DKK 3,6 million

Participants

Project Manager

Carsten Schürmann

Professor

IT University of Copenhagen
Department of Computer Science

E: carsten@itu.dk

Bas Spitters

Associate Professor

Aarhus University
Department of Computer Science

Gert Læssøe Mikkelsen

Head of Security Lab

The Alexandra Institute

Kåre Kjelstrøm

Chief Technology Officer

Concordium ApS

Klaus Georg Hansen

Head of Division

Government of Greenland

Bernardo David

Associate Professor

IT University of Copenhagen

Diego Aranha

Associate Professor

Aarhus University
Department of Computer Science

Tore Kasper Frederiksen

Senior Cryptography Engineer

The Alexandra Institute

Ron Rivest

Professor

MIT

Philip Stark

Professor

University of California, Berkeley

Peter Ryan

Professor, Dr.

University of Luxembourg

Partners

Kategorier
Bridge project

Multimodal Data Processing of Earth Observation Data

Project type: Bridge Project

Multimodal Data Processing of Earth Observation Data

The Danish partnership for digitalization has concluded that there is a need to support the digital acceleration of the green transition. This includes strengthening efforts to establish a stronger data foundation for environmental data. Based on observations of the Earth a range of Danish public organizations build and maintain important data foundations. Such foundations are used for decision making, e.g., for executing environmental law or making planning decisions in both private and public organizations in Denmark.

The increasing possibilities of automated data collection and processing can decrease the cost of creating and maintaining such data foundations and provide service improvements to provide more accurate and rich information. To realize such benefits, public organizations need to be able to utilize the new data sources that become available, e.g., to automize manual data curation tasks and increase the accuracy and richness of data. However, the organizations are challenged by the available methods ability to efficiently combine the different sources of data for their use cases. This is particularly the case when user-facing tools must be constructed on top of the data foundation. The availability of better data for end-users will among others help the user decrease the cost of executing environmental law and making planning decisions. In addition, the ability of public data sources to provide more value to end-users, improves the societal return-on-investment for publishing these data, which is in the interest of the public data providers as well as their end-users and the society at large.

The Danish Environmental Protection Agency (EPA) has the option to receive data from many data sources but today does not utilize this because today’s lack of infrastructure makes it cost prohibitive to take advantage of the data. Therefore, they are expressing a need for methods to enable a data hub that provide data products combining satellite, orthophoto and IoT data. The Danish GeoData Agency (GDA) collects very large quantities of Automatic Identification System (AIS) data from ships sailing in Denmark. However, they are only to a very limited degree using this data today. The GDA has a need for methods to enable a data hub that combines multiple sources of ship-based data including AIS data, ocean observation data (sea level and sea temperature) and metrological data. There is a need for analytics on top that can provide services for estimating travel-time at sea or finding the most fuel-efficient routes. This includes estimating the potential of lowering CO2 emissions at sea by following efficient routes.

Geo supports professional users in performing analysis of subsurface conditions based on their own extensive data, gathered from tens of thousands of geotechnical and environmental drilling operations, and on public sources. They deliver a professional software tool that presents this multi modal data in novel ways and are actively working on creating an educational platform giving high school students access to the same data. Geo has an interest in and need for methods for adding live, multi modal data to their platform, to support both professional decision makers and students. Furthermore, they have a need for novel new ways of querying and representing such data, to make it accessible to professionals and students alike. Creating a testbed for combining Geo’s data with satellite feeds, combined with automated processing to interpret this data, will create new synergies and has the potential to greatly improve the visualizations of the subsurface by building detailed, regional and national 3D voxel models.

Therefore, the key challenges that this project will address are how to construct scalable data warehouses for Earth observation data, how to design systems for combining and enriching multimodal data at scale and how to design user-oriented data interfaces and analytics to support domain experts. Thereby, helping the organizations to produce better data for the benefit of the green transition of the Danish society.

The aim of the project is to do use-inspired basic research on methods for multimodal processing of Earth observation data. The research will cover the areas of advanced and efficient big data management, software engineering, Internet of Things and machine learning. The project will research in these areas in the context of three domain cases with GDA on sea data and EPA/GEO on environmental data.

Scalable data warehousing is the key challenge that work within advanced and efficient big data management will address. The primary research question is how to build a data warehouse with billions of rows of all relevant domain data. AIS data from GDA will be studied and in addition to storage also data cleaning will be addressed. On top of the data warehouse, machine learning algorithms must be enabled to compute the fastest and most fuel-efficient route between two arbitrary destinations.

Processing pipelines for multimodal data processing is the key topic for work within software engineering, Internet of Things and machine learning. The primary research question is how to engineer data processing pipelines that allows for enriching data through processes of transformation and combination. In the EPA case there is a need for enriching data by combining data sources, both from multiple sources (e.g., satellite and drone) and modality (e.g., the NDVI index for quantifying vegetation greenness is a function over a green and a near infrared band). Furthermore, we will research methods for easing the process of bringing disparate data into a form that can be inspected both by a human and an AI user. For example, data sources are automatically cropped to a polygon representing a given area of interest (such as a city, municipality or country), normalized for comparability and subjected to data augmentation, in order to improve machine learning performance. We will leverage existing knowledge on graph databases. We aim to facilitate the combination of satellite data with other sources like sensor recordings at specific geo locations. This allows for advanced data analysis of a wide variety of phenomena, like detection and quantification of objects and changes over time, which again allows for prediction of future occurrences.

User-oriented data hubs and analytics is a cross cutting topic with the aim to design interfaces and user-oriented analytics on top of data warehouses and processing pipelines. In the EPA case the focus is on developing a Danish data hub with Earth observation data. The solution must provide a uniform interface to working with the data providing a user-centric view to data representation. This will then enable decision support systems, which will be worked on in the GEO case, that may be augmented by artificial intelligence and understandable to the human users through explorative graph-based user interfaces and data visualizations. For the GPA case the focus is on a web-frontend for querying AIS data as a trajectory and heat maps and estimating the travel time between two points in Danish waters. As part of the validation the data warehouse and related services will be deployed at GDA and serve as the foundation for future GDA services.

Advancing means to process, store and use Earth observation data has many potential domain applications. To build the world class computer science research and innovation centres, as per the long-term goal of DIREC, this project focuses on building the competencies necessary to address challenges with Earth observation data building on advances in advanced and efficient big data management, software engineering, Internet of Things and machine learning.

Scientific value
The project’s scientific value is the development of new methods and techniques for scalable data warehousing, processing pipelines for multimodal data and user-oriented data hubs and analytics. We expect to publish at least seven rank A research articles and to demonstrate the potential of the developed technologies in concrete real-world applications.

Capacity building
The project will build and strengthen the research capacity in Denmark directly through the education of two PhDs, and through the collaboration between researchers, domain experts, and end-users that will lead to R&D growth in the public and industrial sectors. Research competences to address a stronger digital foundation for the green transformation is important for the Danish society and associated industrial sectors.

Societal and business value
The project will create societal and business value by providing the foundation for the Blue Denmark to reduce environmental and climate impact in Danish and Greenlandic waters to help support the green transformation. With ever-increasing human activity at sea, growing transportation of goods where 90% is being transported by shipping and a goal of a European economy based on carbon neutrality there is a need for activating marine data to support this transformation. For the environmental protection sector the project will provide the foundation for efforts to increase the biodiversity in Denmark by better protection of fauna types and data-supported execution of environmental law. The project will provide significant societal value and directly contribute to SDGs 13 (climate action), 14 (life under water) and 15 (life on land).

In conclusion, the project will provide a strong contribution to the digital foundation for the green transition and support Denmark being a digital frontrunner in this area.

September 1, 2022 – September 31, 2025 – 3 years.

Total budget DKK 12,27 million / DIREC investment DKK 3,6 million

Participants

Project Manager

Kristian Torp

Professor

Aalborg University
Department of Computer Science

E: torp@cs.aau.dk

Christian S. Jensen

Professor

Aalborg University
Department of Computer Science

Thiago Rocha Silva

Associate Professor

University of Southern Denmark
Maersk Mc-Kinney Moller Institute

Aslak Johansen

Associate Professor

University of Southern Denmark
Maersk Mc-Kinney Moller Institute

Sarah Lønholt

Special consultant

Danish Environmental Protection Agency

Mads Darø Kristensen

Principal Application Architect

The Alexandra Institute

Søren Krogh Sørensen

Software Developer

The Alexandra Institute

Oliver Hjermitslev

Visual Computing Specialist

The Alexandra Institute

Mads Robenhagen Mølgaard

Department Director

GEO
Geodata & Subsurface Models

Ove Andersen

Special Consultant

Danish Geodata Agency

Niels Tvilling Larsen

Head of Department

Danish Geodata Agency
Danish Hydrographic Office

Partners

Kategorier
Bridge project

REWORK – The future of hybrid work

Project type: Bridge Project

REWORK – The future of hybrid work

The recent COVID-19 pandemic, and the attendant lockdown, have demonstrated the potential benefits and possibilities of remote work practices, as well as the glaring deficiencies such practices bring. Zoom fatigue, resulting from high cognitive loads and intense amounts of eye contact, is just the tip of an uncomfortable iceberg where the problem of embodied presence remains a stubborn limitation. Remote and hybrid work will certainly be part of the future of most work practices, but what should these future work practices look like? Should we merely attempt to fix what we already have or can we be bolder and speculate different kinds of workplace futures? We seek a vision of the future that integrates hybrid work experiences with grace and decency. This project will focus on the following research question: what are the possible futures of embodied presence in hybrid and remote work conditions?

There are a multitude of reasons to embrace remote and hybrid work. Climate concerns are increasing, borders are difficult to cross, work/life balance may be easier to attain, power distributions in society could potentially be redressed, to name a few. This means that the demand for Computer Supported Cooperative Work (CSCW) systems that support hybrid work will increase significantly. At the same time, we consistently observe and collectively experience that current digital technologies struggle to mediate the intricacies of collaborative work of many kinds. Even when everything works, from network connectivity to people being present and willing to engage, there are aspects of embodied co-presence that are almost impossible to achieve digitally.

We argue that one major weakness in current remote work technologies is the lack of support for relation work and articulation work, caused by limited embodiment. The concept of relation work denotes the fundamental activities of creating socio-technical connections between people and artefacts during collaborative activities, enabling actors in a global collaborative setting to engage each other in activities such as articulation work. We know that articulation work cannot be handled in the same way in hybrid remote environments. The fundamental difference is that strategies of awareness and coordination mechanisms are embedded in the physical surroundings, and use of artefacts cannot simply be applied to the hybrid setting, but instead requires translation.

Actors in hybrid settings must create and connect the foundational network of globally distributed people and artefacts in a multitude of ways.

In REWORK, we focus on enriching digital technologies for hybrid work. We will investigate ways to strengthen relation work and articulation work through explorations of embodiment and presence. To imagine futures and technologies that can be otherwise, we look to artistic interventions, getting at the core of engagement and reflection on the future of remote and hybrid work by imagining and making alternatives through aesthetic speculations and prototyping of novel multimodal interactions (using the audio, haptic, visual, and even olfactory modalities). We will explore the limits of embodiment in remote settings by uncovering the challenges and limitations of existing technical solutions, following a similar approach as some of our previous research.

Scientific value
REWORK will develop speculative techniques and ideas that can help rethink the practices and infrastructures of remote work and its future. REWORK focuses on more than just the efficiency of task completion in hybrid work. Rather, we seek to foreground and productively support the invisible relation and articulation work that is necessary to ensure overall wellbeing and productivity.

Specifically, REWORK will contribute:

  1. Speculative techniques for thinking about the future of remote work;
  2. Multimodal prototypes to inspire a rethink of remote work;
  3. Design Fictions anchoring future visions in practice;
  4. Socio-technical framework for the future of hybrid remote work practices;
  5. Toolkits for industry.

The research conducted as part of REWORK will produce substantial scientific contributions disseminated through scientific publications in top international journals and conferences relevant to the topic. The scientific contributions will constitute both substantive insights and methodological innovations. These will be targeting venues such as the Journal of Human-Computer Interaction, ACM TOCHI, Journal of Computer Supported Cooperative Work, the ACM CHI conference, NordiCHI, UIST, DIS, Ubicomp, ICMI, CSCW, and others of a similar level.

The project will also engage directly and closely with industries of different kinds, from startups that are actively envisioning new technology to support different types of hybrid work (Cadpeople, Synergy XR, and Studio Koh) to organizations that are trying to find new solutions to accommodate changes in work practices (Arla, Bankdata, Keyloop, BEC).

Part of the intent of engagement with the artistic collaboratory is to create bridges between artistic explorations and practical needs articulated by relevant industry actors. REWORK will enable the creation of hybrid fora to enable such bridging. The artistic collaboratory will enable the project to engage with the general public through an art exhibit at Catch, public talks, and workshops. It is our goal to exhibit some of the artistic output at a venue, such as Ars Electronica, that crosses artistic and scientific audiences.

Societal value
The results of REWORK have the potential to change everybody’s work life broadly. We all know that “returning to work after COVID-19” will not be the same – and the combined situation of hybrid work will be a challenge. Through the research conducted in REWORK, individuals that must navigate the demands of hybrid work and the organizations that must develop policies and practices to support such work will benefit from the improved sense of embodiment and awareness, leading to more effective collaboration.

REWORK will take broadening participation and public engagement seriously, by offering online and in-person workshops/events through a close collaboration with the arts organization Catch (catch.dk). The workshops will be oriented towards particular stakeholder groups – artists interested in exploring the future of hybrid work, industry organizations interested in reconfiguring their existing practices – and open public events.

Capacity building
There are several ways in which REWORK contributes to capacity building. Firstly, by collaborating with the Alexandra Institute, we will create a multimodal toolbox/demonstrator facility that can be used in education, and in industry.

REWORK will work closely with both industry partners (through the Alexandra Institute) and cultural (e.g. catch.dk)/public institutions for collaboration and knowledge dissemination, in the general spirit of DIREC.

We will include the findings from REWORK in our research-based teaching at all three universities. Furthermore, we plan to host a PhD course, or a summer school, on the topic in Year 2 or Year 3. Participants will be recruited nationally and internationally.

Lastly, in terms of public engagement, HCI and collaborative technologies are disciplines that can be attractive to the public at large, so there will be at least one REWORK Open Day where we will invite interested participants, and the DIREC industrial collaborators.

January 1, 2022 – December 31, 2024 – 3 years.

Participants

Project Manager

Eve Hoggan

Professor

Aarhus University
Department of Computer Science

E: eve.hoggan@cs.au.dk

Susanne Bødker

Professor

Aarhus University
Department of Computer Science

Irina Shklovski

Professor

University of Copenhagen
Department of Computer Science

Pernille Bjørn

Professor

University of Copenhagen
Department of Computer Science

Louise Barkhuus

Professor

IT University of Copenhagen
Department of Computer Science

Naja Holten Møller

Assistant Professor

University of Copenhagen
Department of Computer Science

Nina Boulus-Rødje

Associate Professor

Roskilde University
Department of People and Technology

Allan Hansen

Head of Digital Experience and Solutions Lab

The Alexandra Institute

Mads Darø Kristensen

Principal Application Architect

The Alexandra Institute

Partners

Kategorier
Bridge project

SIOT – Secure Internet of Things – risk analysis in design and operation

Project type: Bridge Project

SIOT – Secure Internet of Things – risk analysis in design and operation

When developing novel IoT services or products today, it is essential to consider the potential security implications of the system and to take those into account before deployment. Due to the criticality and widespread deployment of many IoT systems, the need for security in these systems has even been recognised at the government and legislative level, e.g., in the US and the UK, resulting in proposed legislation to enforce at least a minimum of security consideration in deployed IoT products.

However, developing secure IoT systems is notoriously difficult, not least due to the characteristics of many such systems: they often operate in unknown and frequently in privacy‐sensitive environments, engage in communication using a wide variety of protocols and technologies, and must perform essential tasks such as monitoring and controlling (physical) entities. In addition, IoT systems must often perform within real‐ time bounds on limited computing platforms and at times even with a limited energy budget. Moreover, with the increasing number of safety‐critical IoT devices (such as medical devices and industrial IoT devices), IoT security has become a public safety issue. To develop a secure IoT system, one should take into account all of the factors and characteristics mentioned above, and balance them against functionality and performance requirements. Such a risk analysis must be performed not only at the design stage, but also throughout the lifetime of the product. Besides technical aspects, the analysis should also take into account the human and organizational aspects. This type of analysis will form an essential activity for standardization and certification purposes.

In this project, we will develop a modelling formalism with automated tool support, for performing such risk assessments and allowing for extensive “what‐if” scenario analysis. The starting point will be the well‐ known and widely used formalism of attack‐defense trees extended to include various quantities, e.g., cost or energy consumption, as well as game features, for modelling collaboration and competition between systems and between a system and its environment.


In summary, the project will deliver:

  • a modeling method for a systematic description of the relevant IoT system/service aspects
  • a special focus on their security, interaction, performance, and cost aspects
  • a systematic approach, through a new concept of attack‐defense‐games
  • algorithms to compute optimal strategies and trade‐offs between performance, cost and security
  • a tool to carry out quantitative risk assessment of secure IoT systems
  • a tool to carry out “what‐if” scenario analysis, to harden a secure IoT system’s design and/or operation
  • usability studies and design for usability of the tools within organizations around IoT services
  • design of training material to enforce security policies for employees within these organizations.

The main research problems are:

  1. To identify safety and security requirements (including threats, attacker models and counter measures) for IoT systems, as well as the inherent design limitations in the IoT problem domain (e.g., limited computing resources and a limited energy budget).
  2. To organize the knowledge in a comprehensive model. We propose to extend attack‐defense trees with strategic game features and quantitative aspects (time, cost, energy, probability).
  3. To transform this new model into existing “computer models” (automata and games) that are amenable to automatic analysis algorithms. We consider stochastic priced timed games as an underlying framework for such models due to their generality and existing tool support.
  4. To develop/extend the algorithms needed to perform analysis and synthesis of optimal response strategies, which form the basis of quantitative risk assessment and decision‐making.
  5. To translate the findings into instruments and recommendations for the partner companies, addressing both technical and organizational needs.
  6. To design, evaluate, and assess the user interface of the IoT security tools, which serve as important backbones supporting to design and certify IoT security training programs for stakeholder organizations.

Throughout the project, we focus on the challenges and needs of the partner companies. The concrete results and outcomes of the project will also be evaluated in the contexts of these companies. The project will combine the expertise of five partners of DIREC (AAU, AU, Alexandra, CBS and DTU) and four Work Streams from DIREC (WS7: Verification, WS6: CPS and IoT systems, WS8: Cybersecurity and WS5: HCI, CSCW and InfoVis) in a synergistic and collaborative way.

Business value
While it is difficult to make a precise estimate of the number of IoT devices, most estimates are in the range 7‐15 billion connected devices and expected to increase dramatically over the next 5‐10 years. The impact of a successful attack on IoT systems can range from nuisance, e.g., when baby monitors or thermostats are hacked, over potentially expensive DDoS attacks, e.g., when the Mirai malware turned many IoT devices into a DDoS botnet, to life‐threatening, e.g., when pacemakers are not secure. Gartner predicted that the worldwide spending on IoT security will increase from roughly USD 900M to USD 3.1B in 2021 out of a total IoT market up to USD 745B.

The SIOT project will concretely contribute to the agility of the Danish IoT industry. By applying the risk analysis and secure design technologies developed in the project, these companies get a fast path to certification of secure IoT devices. Hence, this project will give Danish companies a head‐start for the near future where the US and UK markets will demand security certification for IoT devices. Also, EU is already working on security regulation for IoT devices. Furthermore, it is well known that the earlier in the development process a security vulnerability or programming error is found, the cheaper it is to fix it. This is even more important for IoT products that may not be updatable “over‐the‐air” and thus require a product recall or physical update process. The methods and technologies developed in this project will help companies find and fix security vulnerabilities already from the design phase and exploration phase, thus reducing long‐term cost of maintenance.

Societal value
It is an academic duty to contribute to safer and more secure IoT systems, since they are permeating the society. Security issues quickly become safety incidents, for instance since IoT systems are monitoring against dangerous physical conditions. In addition, compromised IoT devices can be detrimental for our privacy, since they are measuring all aspects of human life. DTU and Alexandra Institute will disseminate the knowledge and expertise through the network built in the joint CIDI project (Cybersecure IoT in Danish Industry, ending in 2021), in particular a network of Danish IoT companies interested in security, with a clear understanding of companies’ needs for security concerns.

We will strengthen the cybersecurity level of Danish companies in relation to Industry 4.0 and Internet of Things (IoT) security, which are key technological pillars of digital transformation. We will do this by means of research and lectures on several aspects of IoT security, with emphasis on security‐by‐design, risk analysis, and remote attestation techniques as a counter measure.

Capacity building
The education of PhD students itself already contributes to “capacity building”. We will organize a PhD Summer school towards the end of the project, to disseminate the results, across the PhD students from DIREC and students abroad.

We will also prepare learning materials to be integrated in existing course offerings (e.g., existing university courses, and the PhD and Master training networks of DIREC) to ensure that the findings of the project are injected into the current capacity building processes.

Through this education, we will also attract more students for the Danish labor market. The lack of skilled people is even larger in the security area than in other parts of computer science and engineering.

February 1, 2022 – January 31, 2025 – 3 years.

Total budget DKK 25,10 million / DIREC investment DKK 6,74 million

Participants

Project Manager

Jaco van de Pol

Professor

Aarhus University
Department of Computer Science

E: jaco@cs.au.dk

Torkil Clemmensen

Professor

Copenhagen Business School
Department of Digitalization

Qiqi Jiang

Associate Professor

Copenhagen Business School
Department of Digitalization

Kim Guldstrand Larsen

Professor

Aalborg University
Department of Computer Science

René Rydhof Hansen

Associate Professor

Aalborg University
Department of Computer Science

Flemming Nielson

Professor

Technical University of Denmark
DTU Compute

Alberto Lluch Lafuente

Associate Professor

Technical University of Denmark
DTU Compute

Nicola Dragoni

Professor

Technical University of Denmark
DTU Compute

Gert Læssøe Mikkelsen

Head of Security Lab

The Alexandra Institute

Laura Lynggaard Nielsen

Senior Anthropologist

The Alexandra Institute

Zaruhi Aslanyan

Security Architect

The Alexandra Institute

Claus Riber

Senior Manager, Software Cybersecurity

Beumer Group

Poul Møller Eriksen

CTO

Develco Products

Mike Aarup

senior quality engineer

Grundfos

Mads Pii

Chief Technical Officer

Logos Payment Solutions

Anders Qvistgaard Sørensen

R&D Manager

Micro Technic

Jørgen Hartig

CEO & Strategic Advisor

SecuriOT

Daniel Lux

Chief Technology Officer

Seluxit

Samant Khajuria

Chief Specialist Cybersecurity

Terma

Alyzia-Maria Konsta

PhD Student

Technical University of Denmark
DTU Compute

Mikael Bisgaard Dahlsen-Jensen

PhD Student

Aarhus University
Department of Computer Science

Partners

Kategorier
Bridge project

Embedded AI

Project type: Bridge Project

Embedded AI

AI is currently limited by the need for massive data centres and centralized architectures, as well as the need to move this data to algorithms. To overcome this key limitation, AI will evolve from today’s highly structured, controlled, and centralized architecture to a more flexible, adaptive, and distributed network of devices. This transformation will bring algorithms to the data, made possible by algorithmic agility and autonomous data discovery, and it will drastically reduce the need for high-bandwidth connectivity, which is required to transport massive data sets, and eliminate any potential sacrifice of the data’s security and privacy. Furthermore, it will eventually allow true real-time learning at the edge.

This transformation is enabled by the merging of AI and IoT into “Artificial Intelligence of Things” (AIoT), and has created an emerging sector of Embedded AI (eAI), where all or parts of the AI processing is done on the sensor devices at the edge, rather than sent to the cloud. The major drivers for Embedded AI are, increased responsiveness and functionality, reduced data transfer, and increased resilience, security, and privacy. To deliver these benefits, development engineers need to acquire new skills in embedded development and systems design.

To enter and compete in the AI era, companies are hiring data scientists to build expertise in AI and create value from data. This is true for many companies developing embedded systems, for instance to control water, heat and air flow in large facilities, large ship engines or industrial robots, all with the aim to optimize their products and services.
However, there is a challenging gap between programming AI in the cloud using tools like Tensorflow, and programming at the edge, where resources are extremely constrained. This project will develop methods and tools to migrate AI algorithms from the cloud to a distributed network of AI enabled edge-devices. The methods will be demonstrated on several use-cases from the industrial partners.

In a traditional, centralized AI architecture, all the technology blocks would be combined in the cloud or at a single cluster (Edge computing) to enable AI. Data collected by IoT, i.e., individual edge-devices, will be sent towards the cloud. To limit the amount of data needed to be sent, data aggregation may be performed along the way to the cloud. The AI stack, the training, and the later inference, will be performed in the cloud, and results for actions will be transferred back to the relevant edge-devices. While the cloud provides complex AI algorithms which can analyse huge datasets fast and efficiently, it cannot deliver true real-time response and data security and privacy may be challenged.

When it comes to Embedded AI, where AI algorithms are moved to the edge, there is a need to transform the foundation of the AI Stack by enabling transformational advances, algorithmic agility and distributed processing will enable AI to perceive and learn in real-time by mirroring critical AI functions across multiple disparate systems, platforms, sensors, and devices operating at the edge. We propose to address these challenges in the following steps, starting with single edge-devices.

  1. Tiny inference engines – Algorithmic agility of the inference engines will require new AI algorithms and new processing architectures and connectivity. We will explore suitable microcontroller architectures and reconfigurable platform technologies, such as Microchip’s low power FPGA’s, for implementing optimized inference engines. Focus will be on achieving real-time performance and robustness. This will be tested on cases from the industry partners.
  2. µBrains – Extending the edge-devices from pure inference engines to also provide local learning. This will allow local devices to provide continuous improvements. We will explore suitable reconfigurable platform technologies with ultra-low power consumption, such as Renesas’ DRP’s using 1/10 of the power budget of current solutions, and Microchip’s low power FPGA’s for optimizing neural networks. Focus will be on ensuring the performance, scheduling, and resource allocation of the new AI algorithms running on very resource constrained edge-devices.
  3. Collective intelligence – The full potential of Embedded AI will require distributed algorithmic processing of the AI algorithms. This will be based on federated learning and computing (microelectronics) optimized for neural networks, but new models of distributed systems and stochastic analysis, is necessary to ensure the performance,
    prioritization, scheduling, resource allocation, and security of the new AI algorithms—especially with the very dynamic and opportunistic communications associated with IoT.

The expected outcome is an AI framework which supports autonomous discovery and processing of disparate data from a distributed collection of AI-enabled edge-devices. All three presented steps will be tested on cases from the industry partners.

 

Deep neural networks have changed the capabilities of machine learning reaching higher accuracy than hitherto. They are in all cases on learning from unstructured data now the de facto standard. These networks often include millions of parameters and may take months to train on dedicated hardware in terms of GPUs in the cloud. This has resulted in high demand of data scientists with AI skills and hence, an increased demand for educating such profiles. However, an increased use of IoT to collect data at the edge, have created a wish for training and executing deep neural networks at the edge rather than transferring all data to the cloud for processing. As IoT end- or edge-devices are characterized by low memory, low processing power, and low energy (powered by battery or energy harvesting), training or executing deep neural networks is considered infeasible. However, developing dedicated accelerators, novel hardware circuits and architectures, or executing smaller discretized networks may provide feasible solutions for the edge.

The academic partners DTU, KU, AU and CBS, will not only create the scientific value from the results disseminated through the four PhDs, but will also create important knowledge, experience, and real-life cases to be included in the education, and hence, create capacity building in this important merging field of embedded AI or AIoT.

The industry partners Indesmatech, Grundfos, MAN ES, and VELUX are all strong examples of companies who will benefit from mastering embedded AI, i.e., being able to select the right tools and execution platforms for implementing and deploying embedded AI in their products.

  • Indesmatech expect to gain leading edge knowledge about how AI can be implemented on various chip processing platforms, with a focus on finding the best and most efficient path to build cost and performance effective industrial solutions across industries as their customers are represented from most industries.
  • Grundfos will create value in applications like condition monitoring of pump and pump systems, predictive maintenance, heat energy optimization in buildings and waste-water treatment where very complex tasks can be optimized significant by AI. The possibility to deploy embedded AI directly on low cost and low power End and Edge devices instead of large cloud platforms, will give Grundfos a significant competitive advantage by reducing total energy consumption, data traffic, product cost, while at the same time increase real time application performance and secure customers data privacy.
  • MAN ES will create value from using embedded AI to predict problems faster than today. Features such as condition monitoring and dynamic engine optimization will give MAN ES competitive advantages, and
    the exploitation of embedded AI together with their large amount of data collected in the cloud will in the long run create marked advantages for MAN ES.
  • VELUX will increase their competitive edge by attaining a better understanding of the ability to implement the right level of embedded AI into their products. The design of new digital smart products with embedded intelligence, will create value from driving the digital product transformation of VELUX.

January 1, 2022 – December 31, 2024 – 3 years.

Total budget DKK 22,5 million / DIREC investment DKK 6,54 million.

Participants

Project Manager

Jan Madsen

Professor

Technical University of Denmark
DTU Compute

E: jama@dtu.dk

Peter Gorm Larsen

Professor

Aarhus University
Dept. of Electrical and Computer Engineering

Mads Nielsen

Professor

University of Copenhagen
Department of Computer Science

Jan Damsgaard

Professor

Copenhagen Business School
Department of Digitalization

Thorkild Kvisgaard

Head of Electronics

Grundfos

Henrik R. Olesen

Senior Manager

MAN Energy Solutions

Thomas S. Toftegaard

Director, Smart Product Technology

Velux

Rune Domsten

Co-founder & CEO

Indesmatech

Partners

Kategorier
Bridge project

HERD: Human-AI collaboration: Engaging and controlling swarms of Robots and Drones

Project type: Bridge Project

HERD: Human-AI collaboration: Engaging and controlling swarms of Robots and Drones

Robots and drones take on an increasingly broad set of tasks, such as AgroIntelli’s autonomous farming robot and the drone-based emergency response systems from Robotto. Currently, however, such robots are limited in their capacity to cooperate with one another and with humans. In the case of AgroIntelli, for instance, only one robot can currently be deployed on a field at any time and is unable to respond effectively to the presence of a human-driven tractor or even another farming robot working in the same field. In the future, AgroIntelli wants to leverage the potential benefits of having multiple robots working in parallel on the same field to reduce time to completion. A straightforward way to achieve this is to partition the field into several distinct areas corresponding to the number of robots available and then assign each robot its own area. However, such an approach is inflexible and requires detailed a priori planning. If, instead, the robots were given the task collectively as a swarm, they could potentially coordinate their operation on the fly and adapt based on local conditions to achieve optimal or near-optimal task performance.

Similarly, Robotto’s system architecture currently requires one control unit to manage each deployed drone. In large area search scenarios and operations with complex terrain, the coverage provided by a single drone is insufficient. Multiple drones can provide real-time data on a larger surface area and from multiple perspectives – thereby aiding emergency response teams in their time-critical operations. In the current system, however, additional drones each requires a dedicated operator and control unit. Coordination between operators introduces an overhead and it can become a struggle to maintain a shared understanding of the rapidly evolving situation. There is thus a need to develop control algorithms for drone-to-drone coordination and interfaces that enable high-level management of the swarm from a single control console. The complexity requires advanced interactions to keep the data actionable, simple, and yet support the critical demands of the operation. This challenge is relevant to search & rescue (SAR) as well as other service offerings in the roadmap, including firefighting, inspections, and first responder missions.

For both of our industrial partners, AgroIntelli and Robotto, and for similar companies that are pushing robotics technology toward real-world application, there is a clear unmet need for approaches that enable human operators to effectively engage and control systems composed of multiple autonomous robots. This raises a whole new set of challenges compared to the current paradigm where there is a one-to-one mapping between operator and robot. The operator must be able to interact with the system at the swarm level as a single entity to set mission priorities and constraints, and at the same time, be able to intervene and take control of a single robot or a subset of robots. An emergency responder may, for instance, want to take control over a drone to follow a civilian or a group of personnel close to a search area, while a farmer may wish to reassign one or more of her farming robots to another field.

HERD will build an understanding of the challenges in multi-robot collaboration, and design and evaluate technological solutions that enable end-users to engage and control autonomous multi-robot systems. The project will build on use cases in agriculture and search & rescue supported by the industrial partners’ domain knowledge and robotic hardware. Through the research problems and aims outlined below, we seek to enable the next generation of human-swarm collaboration.

Pre-operation and on-the-fly mission planning for robot swarms: An increase in the number of robots under the user’s control has the potential to lead to faster task completion and/or a higher quality. However, the increase in unit count significantly increases the complexity of both end-user-to-robot communication and coordination between robots. As such, it is critical to support the user in efficient and effective task allocation between robots. We will answer the following research questions: (i) What are the functionalities required for humans to effectively define mission priorities and constraints at the swarm level? (ii) How can robotic systems autonomously divide tasks based on location, context, and capability, and under the constraints defined by the end-user? (iii) How does the use of autonomous multi-robot technologies change existing organizational routines, and which new ones are required?

Situational awareness under uncertainty in multi-robot tasks: Users of AI-driven (multi-)robot systems often wish to simulate robot behaviour across multiple options to determine the best possible approach to the task at hand. Given the context-dependent and algorithm-driven nature of these robots, simulation accuracy can only be achieved up to a limited degree. This inherent uncertainty negatively impacts the user’s ability to make an informed decision on the best approach to task completion. We will support situational awareness in the control of multi-robot systems by studying: (i) How to determine and visualise levels of uncertainty in robot navigation scenarios to optimise user understanding and control? (ii) What are the implications of the digital representation of the operational environment for organizational sensemaking? (iii) How can live, predictive visualisations of multi-robot trajectories and task performance support the steering and directing of robot swarms from afar?

User intervention and control of swarm subsets: Given the potentially (rapidly) changing contexts in which the robots operate, human operators will have to regularly adapt from a predetermined plan for a subset of robots. This raises novel research questions both in terms of robot control, in which the swarm might depend on a sufficient number of nearby robots to maintain communication, and in terms of user interaction, in which accurate robot selection and information overload can quickly raise issues. We will therefore answer the following research questions: (i) When a user takes low-level control of a single robot or subset of a robot swarm, how should that be done, and how should the rest of the system respond? (ii) How can the user interfaces help the user to understand the potential impact when they wish to intervene or deviate from the mission plans?

Validation of solutions in real-world applications: Based on the real-world applications of adaptive herbicide spraying by farming robots and search & rescue as provided by our industrial partners, we will validate the solutions developed in the project. While both industrial partners deal with robotic systems, their difference in both application area and technical solution (in-the-air vs. on land) allows us to assess the generalisability and efficiency of our solutions in real-world applications. We will answer the following research questions: (i) What common solutions should be validated in both scenarios and which domain-specific solutions are relevant in the respective types of scenarios? (ii) What business and organisational adaptation and innovation are necessary for swarm robotics technology to be successfully adopted in the public sector and in the private sector.

Advances in AI, computer science, and mechatronics mean that robots can be applied to an increasingly broader set of domains. To build the world class computer science research and innovation centres, as per the long-term goal of DIREC, this project focuses on building the competencies necessary to address the complex relationship between humans, artificial intelligence, and autonomous robots.

The project’s scientific value is the development of new methods and techniques to facilitate effective interaction between humans and complex AI systems and the empirical validation in two distinct use cases. The use cases provide opportunities to engage with swarm interactions across varying demands, including domains where careful a priori planning is possible (agricultural context) and chaotic and fast-paced domains (search & rescue with drones). HERD will thus lead to significant contributions in the areas of autonomous multi-robot coordination and human-robot interaction. We expect to publish at least ten rank A research articles and to demonstrate the potential of the developed technologies in concrete real-world applications. This project also gears up the partners to participate in project proposals to the EU Framework Programme on specific topics in agricultural robotics, nature conservation, emergency response, security, and so on, and in general topics related to developing key enabling technologies.

HERD will build and strengthen the research capacity in Denmark directly through the education of three PhDs, and through the collaboration between researchers, domain experts, and end-users that will lead to industrial R&D growth. Denmark has been a thought leader in robotics, innovating how humans collaborate with robots in manufacturing and architecture, e.g. Universal Robots, MiR, Odico, among others. Through HERD, we support not only the named partners in developing and improving their products and services, but the novel collaboration between the academic partners, who have not previously worked together, helps to ensure that the Danish institutes of higher education build the competencies and the workforce that are needed to ensure continued growth in the sectors of robotics and artificial intelligence. HERD will thus contribute to building the capacity required to facilitate effective interaction between end-users and complex AI systems.

HERD will create business value through the development of technologies that enable end-users to effectively engage and control systems composed of multiple robots. These technologies will significantly increase the value of the industrial partners’ products, since current tasks can be done faster and at a lower cost, and entirely new tasks that require multiple coordinated robots can be addressed. The value increase will, in turn, increase sales and exports. Furthermore, multi-robot systems have numerous potential application domains in addition to those addressed in this project, such as infrastructure inspection, construction, environmental monitoring, and logistics. The inclusion of DTI as partner will directly help explore these opportunities through a broader range of anticipated tech transfer, future market and project possibilities.

HERD will create significant societal value and directly contribute to SDGs 1 (no poverty), 2 (zero hunger), 13 (climate action), and 15 (life on land). Increased use of agricultural robots can, for instance, lead to less soil compaction and enable the adoption of precision agriculture techniques, such as mechanical weeding that eliminates the need for pesticides. Similarly, increased use of drones in search & rescue can reduce the time needed to save people in critical situations.

November 1, 2021 – July 31, 2025 – 3,75 years.

Total budget DKK 17,08 million / DIREC investment DKK 4,59 million

Participants

Project Manager

Anders Lyhne Christensen

Professor

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

E: andc@mmmi.sdu.dk

Ulrik Pagh Schultz

Professor

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Mikael B. Skov

Professor

Aalborg University
Department of Computer Science

Timothy Robert Merritt

Associate Professor

Aalborg University
Department of Computer Science

Niels van Berkel

Associate Professor

Aalborg University
Department of Computer Science

Ionna Constantiou

Professor

Copenhagen Business School
Department of Digitalization

Christiane Lehrer

Associate Professor

Copenhagen Business School
Department of Digitalization

Kenneth Richard Geipel

Chief Executive Officer

Robotto

Alea Scovill

Strategic Project Manager

Agro Intelligence ApS

Lars Dalgaard

Head of Section

Danish Technological Institute
Robot Technology

Partners

Kategorier
Bridge project

EXPLAIN-ME: Learning to Collaborate via Explainable AI in Medical Education

Project type: Bridge Project

EXPLAIN-ME: Learning to Collaborate via Explainable AI in Medical Education

AI is widely deployed in assistive medical technologies, such as image-based diagnosis, to solve highly specific tasks with feasible model optimization. However, AI is rarely designed as a collaborator for the healthcare professionals, but rather as a mechanical substitute for part of a diagnostic workflow. From the AI researcher’s point of view, the goal of development is to beat state-of-the-art on narrow performance parameters, which the AI may solve with superhuman accuracy. However, for more general problems such as full diagnosis, treatment execution, or explaining the background for a diagnosis, the AI is still not to be trusted. Hence, clinicians do not always perceive AI solutions as helpful in solving their clinical tasks, as they only solve part of the problem sufficiently well. The EXPLAIN-ME initiative seeks to create AIs that help solve the overall general tasks in collaboration with the human health care professional. To do so, we need not only to provide interpretability in the form of explainable AI models — we need to provide models whose explanations are easy to understand and utilize during the clinician’s workflow. Put simply, we need to provide good explanations.

The EXPLAIN-ME initiative seeks to optimize the utility of feedback provided by healthcare explainable AI (XAI). We will approach this problem both in static healthcare applications, where clinical decisions are based on data already collected, and in dynamic applications, where data is collected on the fly to continually improve confidence in the clinical decision.

Case 1: Renal tumor classification

Classification of a renal tumor as malign or benign is an example of a decision that needs to be taken under time pressure. If malign, the patient should be operated immediately to prevent cancer from spreading to the rest of the body, and thus a false positive diagnosis may lead to the unnecessary destruction of a kidney and other complications. While AI methods can be shown statistically to be more precise than an expert physician, there is a need for extending it with explanation for a decision– and only the physicians know what “a good explanation” is. This motivates a collaborative design and development process to find the best balance between what is technically possible and what is clinically needed.

Case 2: Ultrasound Screening.
Even before birth, patients suffer from erroneous decisions made by healthcare workers. In Denmark, 95% of all pregnant women participate in the national ultrasound screening program aimed at detecting severe maternal-fetal disease. Correct diagnosis is directly linked to the skills of the clinicians, and only about half of all serious conditions are detected before birth. AI feedback, therefore, comes with the potential to standardize care across clinicians and hospitals. At DTU, KU and CAMES, ultrasound imaging will be the main case for development, as data access and management, as well as manual annotations, are already in place. We seek to give the clinician feedback during scanning, such as whether the current image is a standard ultrasound plane (see figure); whether it has sufficient quality; whether the image can be used to predict clinical outcomes, or how to move the probe to improve image quality.

Case 3: Robotic Surgery.
AAU and NordSim will collaborate on the assessment and development of robotic surgeons’ skills, associated with an existing clinical PhD project. Robotic surgery allows surgeons to do their work with more precision and control than traditional surgical tools, thereby reducing errors and increasing efficiency. AI-based decision support is expected to have a further positive effect on outcomes. The usability of AI decision support is critical, and this project will study temporal aspects of the human-AI collaboration, such as how to present AI suggestions in a timely manner without interrupting the clinician; how to hand over tasks between a member of the medical team and an AI system; and how to handle disagreement between the medical expert and the AI system.

In current healthcare AI research and development, there is often a gap between the needs of clinicians and the developed solutions. This comes with a lost opportunity for added value: We miss out on potential clinical value for creating standardized, high quality care across demographic groups. Just as importantly, we miss out on added business value: If the first, research-based step in the development food chain is unsuccessful, then there will also be fewer spin-offs and start-ups, less knowledge dissemination to industry, and overall less innovation in healthcare AI.

The EXPLAIN-ME initiative will address this problem:

  • We will improve clinical interpretability of healthcare AI by developing XAI methods and workflows that allow us to optimize XAI feedback for clinical utility, measured both on clinical performance and clinical outcomes.
  • We will improve clinical technology acceptance by introducing these XAI models in clinical training via simulation-laboratories.
  • We will improve business value by creating a prototype for collaborative, simulation-based deployment of healthcare AI. This comes with great potential for speeding up industrial development of healthcare AI: Simulation-based testing of algorithms can begin while algorithms still make mistakes, because there is no risk of harming patients. This, in particular, can speed up the timeline from idea to clinical implementation, as the simulation-based testing is realistic while not requiring the usual ethical approvals.

This comes with great potential value: While AI has transformed many aspects of society, its impact on the healthcare sector is so far limited. Diagnostic AI is a key topic in healthcare research, but only marginally deployed in clinical care. This is partly explained by the low interpretability of state-of-the-art AI, which negatively affects both patient safety and clinicians’ technology acceptance. This is also explained by the typical workflow in healthcare AI research and development, which is often structured as parallel tracks where AI researchers independently develop technical solutions to a predefined clinical problem, while only occasionally interacting with the clinical end-users. This often results in a gap between the clinicians’ needs and the developed solution. The EXPLAIN-ME initiative aims to close this gap by developing AI solutions that are designed to interact with clinicians in every step of the design-, training-, and implementation process.

October 1, 2021 – April 30, 2025 – 3,5 years.

Participants

Project Manager

Aasa Feragen

Professor

Technical University of Denmark
DTU Compute

E: afhar@dtu.dk

Anders Nymark Christensen

Asscociate Professor

Technical University of Denmark
DTU Compute

Mads Nielsen

Professor

University of Copenhagen
Department of Computer Science

Mikael B. Skov

Professor

Aalborg University
Department of Computer Science

Niels van Berkel

Associate Professor

Aalborg University
Department of Computer Science

Henning Christiansen

Professor

Roskilde University
Department of People and Technology

Jesper Simonsen

Professor

Roskilde University
Department of People and Technology

Henrik Bulskov Styltsvig

Associate Professor

Roskilde University
Department of People and Technology

Martin Tolsgaard

Associate Professor

CAMES Rigshopitalet
University of Copenhagen

Morten Bo Svendsen

Chief Engineer

CAMES Rigshospitalet
University of Copenhagen

Sten Rasmussen

Professor, Head

Dept. of Clinical Medicine
Aalborg University

Mikkel Lønborg Friis

Director

NordSim
Aalborg University

Nessn Htum Azawi

Associate Professor, Head of Research Unit & Renal Cancer team

Department of Urology
Zealand University Hospital

Partners

Kategorier
Bridge project

Business Transformation and Organisational AI-based Decision Making

Project type: Bridge Project

Business Transformation and Organisational AI-based Decision Making

Business processes in private companies and public organisations are today widely supported by Enterprise Resource Planning, Business Process Management  and Electronic Case Management systems, put into use with the aim to improve efficiency of the business processes.

The combined result is however often an increasingly elaborate information systems landscape, leading to ineffectiveness, limited understanding of business processes, inability to predict and find the root cause of losses, errors and fraud, and inability to adapt the business processes. This lack of understanding, agility and control over business processes places a major burden on the organisations. For instance, a recent report concludes that the Danish Ministry of Taxation’s control of the state’s annual revenue of one trillion DKK is so “deficient and weak” that there is a clear “increased risk” that employees can cheat and abuse for their own gain in the same style as the recent Britta Nielsen and Armed forces cases.

Enterprise systems generate a plethora of highly granular data recording their operation. Machine learning has a great potential to aid in the analysis of this data in order to predict errors, detect fraud and improve their efficiency. Knowledge of business processes can also be used to support the needed transformation of old and heterogeneous it landscapes to new platforms. Application areas include Anti-Money-Laundering (AML) and Know-Your-Customer (KYC) supervision of business processes in the financial sector, supply chain management in agriculture and foodstuff supply, and compliance and optimisation of workflow processes in the public sector.

The research aim of the project to develop methods and tools that enable industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise systems, with specific focus on tools and responsible methods for the use of process insights for business intelligence and transformation. Through field studies in organizations that are using AI, BPM and process mining techniques it will be investigated how organizations implement, use and create value (both operational and strategic) through AI, BPM and process mining techniques. In particular, the project will focus on how organizational decision-making changes with the implementation of AI-based algorithms in terms of decision making skills (intuitive + analytical) of the decision makers, their roles and responsibilities, their decision rights and authority and the decision context.

The scientific value of the project is new methods and user interfaces for decision support and business transformation and associated knowledge of their performance and properties in case studies. These are important contributions to provide excellent knowledge to Danish companies and education programs within AI for business innovation and processes.

For capacity building the value of the project is to educate 1 industrial PhD in close collaboration between CBS, DIKU and the industrial partner DCR Solutions. The project will also provide on-line course material that can be used in existing and new courses for industry, MSc and PhD.

For the business and societal value, the project has very broad applicability, targeting improvements in terms of effectiveness and control of process aware information systems across the private and public sector. Concretely, the project considers cases of customers of the participating industry partners within the financial sector, the public sector and within energy and building management. All sectors that have vital societal role. The industry partner will create business value of estimated 10-20MDkr increased turnaround and 2-3 new employees in 5-7 years through the generation of IP by the industrial researcher and the development of state- of-the-art proprietary process analysis and decision support tools.

July 1, 2021 – December 31, 2025 – 3,5 years

Total budget DKK 16,77 million / DIREC investment DKK4,95 million

Participants

Project Manager

Arisa Shollo

Associate Professor

Copenhagen Business School
Department of Digitalization

E: ash.digi@cbs.dk

Thomas Hildebrandt

Professor

University of Copenhagen
Department of Computer Science

Raghava Mukkamala

Associate Professor

Copenhagen Business School
Department of Digitalization

Morten Marquard

Founder & CEO

DCR Solutions

Søren Debois

CTO

DCR Solutions

Partners

Kategorier
Bridge project

AI and Blockchains for Complex Business Processes

Project type: Bridge Project

AI and Blockchains for Complex Business Processes

Business processes in private companies and public organisations are today widely supported by Enterprise Resource Planning (ERP), Business Process Management (BPM) and Electronic Case Management (ECM) systems [1], put into use with the aim to improve efficiency of the business processes. Recently, also blockchain technologies are being proposed as a means to provide guarantees for security, computational integrity and pseudonymous agency.

The combined result is however often an increasingly elaborate information systems landscape, leading to ineffectiveness, limited understanding of business processes, inability to predict and find the root cause of losses, errors and fraud, and inability to adapt the business processes [1]. This lack of understanding, agility and control over business processes places a major burden on the organisations. For instance, a recent report concludes that the Danish Ministry of Taxation’s control of the state’s annual revenue of one trillion DKK is so “deficient and weak” that there is a clear “increased risk” that employees can cheat and abuse for their own gain in the same style as the recent Britta Nielsen and Armed forces cases.

Enterprise and block chain systems generate a plethora of highly granular data recording their operation. Machine learning has a great potential to aid in the analysis of this data in order to predict errors, detect fraud and improve their efficiency. Knowledge of business processes can also be used to support the needed transformation of old and heterogeneous it landscapes to new platforms. Application areas include Anti-Money-Laundering (AML) and Know-Your-Customer (KYC) supervision of business processes in the financial sector, supply chain management in agriculture and foodstuff supply, and compliance and optimisation of workflow processes in the public sector.

The research aim of the AI and Blockchain for Complex Business Processes project is methods and tools that enable industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise and blockchain systems, from techniques for automatic identification of business events, via the development of new rule based process mining technologies to tools for the use of process insights for business intelligence and transformation. The project will do this through a unique bridge between industry and academia, involving two innovative, complementary industrial partners and researchers across disciplines of AI, software engineering and business intelligence from three DIREC partner universities. Open source release (under the LGPL 3.0 license) of the rule-based mining algorithms developed by the PhD assigned task 2 will ensure future enhancement and development by the research community, while simultaneously providing businesses the opportunity to include them in proprietary software.
The scientific value of the project is new methods and tools for process mining, decision support and business transformation and associated knowledge of their performance and properties in case studies. These are important contributions to provide excellent knowledge to Danish companies and education programs within AI and Blockchain technology for business innovation and processes. For capacity building the value of the project is to educate 2 PhD and 1 industrial Post Doc in close collaboration with industry. Open source availability of general project outcomes and industry collaboration enable several exploitation paths. The project will also provide on-line course material for existing and new courses for industry, MSc and PhD. For the business and societal value, the project has very broad applicability, targeting improvements in terms of effectiveness and control of process aware information systems across the private and public sector. Concretely, the project considers cases of customers of the participating industry partners within the financial sector, the public sector and within the operations and supply chains for agriculture and foodstuffs supply. All sectors that have vital societal role. The industrial partners will create business value of estimated 155MDkr increased turnaround and 10-12 new employees in 5-7 years through the generation of IP by the two industrial researchers and the development of state- of-the-art proprietary process analysis and decision support tools.

July 1, 2021 – December 31, 2025 – 3,5 years

Participants

Project Manager

Tijs Slaats

Associate Professor

University of Copenhagen
Department of Computer Science

E: hilde@di.ku.dk

Jakob Grue Simonsen

Professor

University of Copenhagen
Department of Computer Science

Thomas Hildebrandt

Professor

University of Copenhagen
Department of Computer Science

Michel Avital

Professor

Copenhagen Business School
Department of Digitalization

Henrik Axelsen

PHD Fellow

University of Copenhagen
Department of Computer Science

Christoffer Olling Back

Staff Machine Learning Engineer

ServiceNow

Hugo López

Assistant Professor

University of Copenhagen
Department of Computer Science

Søren Debois

Associate Professor

IT University of Copenhagen
Department of Computer Science

Jens Strandbygaard

Senior Director

ServiceNow

Omri Ross

Chief Blockchain Scientist

eToro

Axel Fjelrad Christfort

PhD Fellow

University of Copenhagen
Dept. of Computer Science

Partners