About the project

Objective
By delivering reliable, local and nearly real-time data about wildlife, the data gathered by FLOX Robotics drones provide insights for data-based wildlife-related decisions to veterinary institutes, nature conservationists, hunting associations, insurance companies and many others. Through AI-assisted identification of wildlife species, the stakeholders have the possibility to track the animal species which are injured, bearing diseases or have been involved in an incident.

The project demonstrates an integrated solution for automated mapping, identification, tracking and, when required, repelling wild animals using autonomous drones with AI-assisted computer vision and ultrasound repellent technology combined with a geographic information system (GIS)-like for data visualization, analysis and decision making.

Background
The problem of wildlife damage is widespread all over the world, from Sweden to Italy, in the US, India and many other countries. Historically, there has been limited means for quantifying the wild animal population, their moving patterns, and the damages they cause. Damage by wild animals to cultivated fields is a major cause of profit loss for farmers in Europe. In Sweden, in 2020, damage caused by wildlife occurred on 17% of the cultivated area for cereals and nearly 28% for starch potatoes. Temporary grasses are Sweden’s largest crop in acreage, and 17% of the cultivated area had some form of wildlife damage in 2020 [www.scb.se]. In Sweden, around 50% of agricultural companies reported damage from the wildstock in 2020, and more than one-third of farmers stated that the wildstock affects their choice of crops.

Wildlife-related damages are widely present not only in agriculture but also in forest areas. The “rooting” and “wallowing” by wild boars also has an environmental impact, destroying vegetation and degrading water quality. For wildlife-related insurance cases, there is often no physical evidence of species involved in the damage. The verification requires too high a burden of proof to be met to receive payments.

The project demonstrates an integrated solution for mapping and, when required, repelling wild animals using autonomous drones with AI-assisted computer vision and ultrasound repellent technology combined with a geographic information system-like (GIS) for data visualization, analysis and decision making. The project solution will help public authorities and decision making bodies to access site-specific identification of wildlife in larger areas, aggregated from separate fields to regional, national and even international levels.

Crossdisciplinary collaboration
The researchers in the team represent RISE Digital Systems and the Department of Computer and System Sciences at Stockholm University.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

About the project

Objective
In the DataLEASH project, practically, we develop and test machine learning models, among other methods, to ensure the use of data without the risk of revealing people’s identities or allowing unwanted inferences about them. In a more theoretical approach, we aim at provable guarantees for privacy and take a holistic approach to the legal implications. This implies a quest for finding relevant rules and regulations and illuminating interpretation and application.

The project consortium from KTH, SU, and RISE has a unique set-up in terms of an interdisciplinary and multidisciplinary profile among the researchers, combining perspectives from information theory, legal informatics, language processing, machine learning, cryptography, and systems security.

Background
Digitalization has resulted in more and more data being generated and collected from various sources (such as health care, customer service, surveillance cameras, etc.). The data is valuable for processing and additional analysis to improve predictions and planning. Advances in machine learning have improved this kind of data analysis, while data-protection regulation such as the GDPR has introduced constraints, limiting what data can be used and for what purpose. There is, thus a tension between the utility of data and the privacy of the individuals the data is about.

Cross-disciplinary collaboration
DataLEASH brings together researchers from the School of Electrical Engineering and Computer Science (EECS, KTH), the Department of Computer and Systems Sciences (DSV) and the Department of Law both at Stockholm University and from the Decisions, Network, and Analytics lab at RISE.

Watch the recorded presentation at Digitalize in Stockholm 2022 event:

Link to DataLEASH website on Department of Computer and System Sciences (DSV) at Stockholm University

Activities & Results

Activities, awards, and other outputs

• Speakers at workshops on “AI inom medicinteknik,” session “Vad minns en högparametriserad modell? Organized by Läkemedelsverket, April 6, online with more than 150 participants from industry and regulatory bodies.
• “Tillgängliggörande av hälsodata,” Dec 2021 online with more than 50 participants from four regions participating
• “Digital innovation i samverkan stad, region och akademi,” Oct 2021 online with about 20 participants from KTH, Region and City of Stockholm, plus some KTH internal events.
• Organisation and participation of panel at Nordic Privacy Forum 2022 panel discussing calculated privacy and the interplay between law and tech.
• DataLEASH organizes regular seminars every two months for three years with the City of Stockholm and Region Stockholm about requirements from the stakeholders and the results from our research project.
• SAIS 2022, Swedish AI Society workshop, is organised and paper [BFLSSR22] is presented in this workshop.
• Award: Rise solution for Encrypted Health AI was announced the winner of the Vinnova Vinter competition in the infrastructure category.

Click here for Demo HB Deid

Results

Research objectives of DataLEASH are: (i) develop and study privacy measures suitable for privacy risk assessment and utility optimization; (ii) characterization of fundamental bounds on data disclosure mechanisms; (iii) design and study of efficient data disclosure mechanisms with privacy guarantees; (iv) demonstration and testing of algorithms using real-data repositories; (v) study of the cross-disciplinary privacy aspects between law and information technology.

Research achievements and main results of DataLEASH:

• Pointwise Maximal Leakage (PML) has been proposed as a new privacy measure framework. PML has an operational meaning and is robust. Using the framework, several other privacy measures have been derived and their properties have been characterized as well as the relation to existing privacy measures have been established.
• The privacy-preserving learning mechanism PATE has been studied using conditional maximal leakage explaining the cost of privacy. PATE approach has been extended to deal with high-dimensional targets such as in segmentation tasks of MRI brain scans.
• Fundamental bounds on data disclosure mechanisms have been derived considering various pointwise privacy measures. Furthermore, approximate solutions to optimal data disclosure mechanisms have been derived using concepts from Euclidian Information Theory.
• In a cross-disciplinary study between law and tech, we propose and discuss how to relate the legal data protection principles of data minimization to the mathematical concept of a sufficient statistic to be able to deal from a regulatory perspective with the rapid advancements in machine learning.
• Health Bank, a large health data repository of 2 million patient record texts in Swedish has been de-identified. A deep learning BERT model, SweDeClin-BERT, has been created and obtained permission from the Swedish Ethical Review Authority to be shared among academic users. The model SweDeClin-BERT has been used at the University Hospital of Linköping with promising results. Handling sensitive health-related data is often challenging. Proposed Fully Homomorphic Encryption (FHE) to encrypt diabetes data. The proposed approach won the pilot Winter competition 2021–22 organized by Vinnova.
• We created a systematization of knowledge on ambient assisted living (combining the challenges of mobile and smart-home monitoring for health) from a privacy perspective to map out potential issues and intervention points.
• Using a cryptographic approach, we developed distance-bounding attribute-based credentials, which provide anonymity for location-based services, provably resisting attacks.
• We investigated the uses and limitations of synthetic data as a privacy-preservation mechanism. For image data, we developed a framework of clustering and synthesizing facial images for privacy-preserving data analysis with privacy guarantees from k-anonymity and found trade-off choice points with analysis utility. In a different work on facial images, we proposed a novel approach for the privacy preservation of attributes using adversarial representation learning. This work removes the sensitive facial expressions and replaces them with an independent random expression while preserving facial features. For tabular data, we investigated across several datasets whether different methods of generating fully synthetic data vary in their utility a priori (when the specific analyses to be performed on the data are not known yet), how closely their results conform to analyses on original data a posteriori, and whether these two effects are correlated. We found classification tasks when using synthetic data for training machine-learning models more promising in terms of consistent accuracy than statistical analysis.

In the interplay between information technology and law, the project itself has been a testbed, given the personal data processing in research of this kind. Quite often, there is a challenge merely to find the governing legal framework. Practical experiences and theoretical studies can be a sign of this. However, much research today is concentrated on specific data protection regulations. The reasoning above boils down to a broadened approach to GDPR.

Publications

We like to inspire and share interesting knowledge…

• Vakili, T., Hullmann T., Henriksson A. and H. Dalianis. 2024. When Is a Name Sensitive? Eponyms in Clinical Text and Implications for De-Identification. To be presented at the CALD-pseudo Workshop at the 18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2024, Malta.
• Ngo, P., Tejedor M., Olsen Svenning T., Chomutare T., Budrionis A. and H. Dalianis. 2024. Deidentifying a Norwegian clinical corpus – An effort to create a privacy-preserving Norwegian large clinical language model. To be presented at the CALD-pseudo Workshop at the 18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2024, Malta.
• Lamproudis, A., Mora, S., Olsen Svenning T., Torsvik T., Chomutare T., Dinh Ngo P. and H. Dalianis. 2023. De-identifying Norwegian Clinical Text using Resources from Swedish and Danish. Proceedings of AMIA 2023, Annual Symposium, November 11-15. New Orleans, LA, USA, link.
• Vakili, T. and H. Dalianis. 2023. Using Membership Inference Attacks to Evaluate Privacy-Preserving Language Modeling Fails for Pseudonymizing Data. Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa 2023). Faroe Islands, May 22-24, 2023, link.
• Vakili, T., Lamproudis, A., Henriksson, A. and H. Dalianis. 2022. Downstream Task Performance of BERT Models Pre-Trained Using Automatically De-Identified Clinical Data. In the Proceedings of the 13th International Conference on Language Resources and Evaluation, LREC 2022, Marseille, France, pp. 4245–4252, link.
• Vakili, T. and H. Dalianis 2022, Utility Preservation of Clinical Text After De-Identification. In the Proceedings of the 21st Workshop on Biomedical Language Processing (pp. 383-388) in conjunction with ACL 2022, Dublin, Ireland, link.
• Sara Saeidian, Giulia Cervia, Tobias J. Oechtering, Mikael Skoglund, Quantifying Membership Privacy via Information Leakage, IEEE Transactions Information Forensics and Security. Vol.16, pp. 3096-3108, 2021, link.
• Sara Saeidian, Giulia Cervia, Tobias J. Oechtering, Mikael Skoglund, Optimal Maximal Leakage-Distortion Tradeoff. Information Theory Workshop (ITW) 2021 IEEE, pp. 1-6, 2021, link.
• Vakili, T. and H. Dalianis. 2021. Are Clinical BERT Models Privacy-Preserving? The Difficulty of Extracting Patient-Condition Associations. In the Proceedings of the Association for the Advancement of Artificial Intelligence AAAI Fall 2021 Symposium in HUman partnership with Medical Artificial iNtelligence (HUMAN.AI), November 4-6, 2021, pdf.
• Lamproudis, A., Henriksson, A. and H. Dalianis. 2021. Developing a Clinical Language Model for Swedish: Continued Pretraining of Generic BERT with In-Domain Data. In the Proceeding of RANLP 21: Recent Advances in Natural Language Processing, 1-3 Sept 2021, Varna, Bulgaria, pdf.
• Grancharova, M. and H. Dalianis. 2021. Applying and Sharing pre-trained BERT-models for Named Entity Recognition and Classification in Swedish Electronic Patient Records. In the Proceedings of the 23rd Nordic Conference on Computational Linguistics, NoDaLiDa 2021, Iceland, May 31 – June 2, 2021, pdf.
• Dalianis, H. and H. Berg. 2021. HB Deid – HB De-identification tool demonstrator. In the Proceedings of the 23rd Nordic Conference on Computational Linguistics, NoDaLiDa 2021, Iceland, May 31 – June 2, 2021, pdf.
• Berg, H., Henriksson, A., Fors, U. and H. Dalianis. 2021. De-identification of Clinical Text for Secondary Use: Research Issues. In the proceedings of HEALTHINF 2021, 14th International Conference on Health Informatics Feb 11-13, 2021, pdf.
• Grancharova, M., Berg, H. and H. Dalianis. 2020. Improving Named Entity Recognition and Classification in Class Imbalanced Swedish Electronic Patient Records through Resampling. Compilation of abstracts in The Eight Swedish Language Technology Conference (SLTC-2020), Göteborg, pdf.
• Berg, H., A.Henriksson and H. Dalianis. 2020. The Impact of De-identification on Downstream Named Entity Recognition in Clinical Text. In Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis, Louhi 2020, in conjunction with EMNLP 2020, (pp. 1-11), pdf.
• Berg, H., Henriksson, A., Fors, U. and H. Dalianis. De-identification of Clinical Text for Secondary Use: Research Issues. Presented at the Healthcare Text Analytics Conference HealTAC 2020, April 23, London.
• Berg, H. and H. Dalianis. 2020. A Semi-supervised Approach for De-identification of Swedish Clinical Text. Proceedings of 12th Conference on Language Resources and Evaluation, LREC 2020, May 13-15, Marseille, pp. 4444‑4450, pdf.
• Berg, H., T. Chomutare and H. Dalianis. 2019. Building a De-identification System for Real Swedish Clinical Text Using Pseudonymised Clinical Text. In the Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis, Louhi 2019, in conjunction with Conference on Empirical Methods in Natural Language Processing, (EMNLP) November 2019, Hongkong, ACL, pp 118-125, pdf.
• Berg, H. and H. Dalianis. 2019. Augmenting a De-identification System for Swedish Clinical Text Using Open Resources (and Deep learning). In the Proceedings of the Workshop on NLP and Pseudonymisation, in conjunction with the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), Turku, Finland, September 30, 2019, pdf.
• Dalianis, H. 2019. Pseudonymisation of Swedish Electronic Patient Records Using a Rule-based Approach. In the Proceedings of the Workshop on NLP and Pseudonymisation, in conjunction with the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), Turku, Finland, September 30, 2019, pdf.

Videos & Presentations

Watch recorded videos and download the presentations…

VIDEO RECORDINGS
Presentation at Digitalize in Stockholm 2022

Research: Privacy-preserving data analysis. We apply tools from information theory to problems related to privacy-preserving data analysis
Speaker: Sara Saeidian, PhD student, saeidian@kth.se
Supervisors: Tobias J. Oechtering, Mikael Skoglund

Click here to watch the recorded video presentation on “Privacy-preserving data analysis”

OUR PRESENTATIONS
Quantifying Membership Privacy via Information Leakage

Sara Saeidian, Giulia Cervia, Tobias J. Oechtering, Mikael Skoglund, “Quantifying Membership Privacy via Information Leakage, IEEE Transactions Information Forensics and Security, Vol.16, pp. 3096-3108, 2021.

Machine learning models are known to memorize the unique properties of individual data points in a training set. This memorization capability can be exploited by several types of attacks to infer information about the training data, most notably, membership inference attacks. In this work, we propose an approach based on information leakage for guaranteeing membership privacy. Specifically, we propose to use a conditional form of the notion of maximal leakage to quantify the information leaking about individual data entries in a dataset, i.e., the entrywise information leakage.

We apply our privacy analysis to the Private Aggregation of Teacher Ensembles (PATE) framework for privacy-preserving classification of sensitive data and prove that the entrywise information leakage of its aggregation mechanism is Schur-concave when the injected noise has a log-concave probability density. The Schur-concavity of this leakage implies that increased consensus among teachers in labelling a query reduces its associated privacy cost. We also derive upper bounds on the entrywise information leakage when the aggregation mechanism uses Laplace distributed noise.

DOWNLOAD THE PRESENTATION HERE: Quantifying Membership Privacy via Information Leakage

About the project

Objective
The research team’s ambition is to develop a new research area in urban development (studies). Well-being in smart cities is the defined research area, focusing on interactions of human-machine-computers or “cyber-physical-human systems” based on human decision-making on an institutional, individual and neurological abstraction level. The smart city of the future is our main application area, as these are complex cyber-physical-human systems. The project will develop a framework for capturing interactions and dynamics in these systems and demonstrate the applications in user case studies.

Background
The health condition of a human being is the basis of individual and social well-being. The driving force for human-social behaviour and many choices individuals make is the desire for well-being, which will manifest in the future of smart cities. Networks, human agents, cyber agents, and physical infrastructure perform feedback and interactions in smart cities. Smart cities can efficiently and sustainably increase human well-being.

Cross-disciplinary collaboration
The research team represents the School of Electrical Engineering and Computer Science (EECS, KTH), the School of Industrial Engineering and Management (ITM, KTH) and the School of Architecture and the Built Environment (ABE, KTH).

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

Find out more on HiSS webpage

Activities & Results

Find out what’s going on!

Activities, awards, and other outputs

• HiSS Workshop 2021: From smart to intelligent cities: A human-social choice? Smart cities of a digitalized society are envisioned as cyber-physical-human systems of sustainable economic growth that enhance human well-being. The grand challenge for designing and developing smart cities is to achieve mutually beneficial interactions between cyber and human systems where machines learn from humans, and humans learn from machines. Theories of human behaviour are traditionally context-dependent and specialized in economics, education, games, social interactions, management, etc. Smart cities, as cyber-physical-human systems, set a new context for modelling and understanding human-social behaviour at different levels – from neuro-cognition of individual choices to collective decisions and emergence in social networks at multiple time scales.
• A Workshop was held in September 2021 with three online sessions:
  • Session 1: 6/9 Interactional Intelligence with speakers Assoc. Prof. Sarah Williams, MIT, Univ.-Prof. Dr. Marcel Schweiker, University Hospital RWTH Aachen, Dr. Umberto
  • Fugiglando, MIT.
  • Session 2: 13/9 Reflective Intelligence with speakers Prof. Katina Michael, Arizona State University, Assoc. Prof. Esteban Moro, Universidad Carlos III de Madrid and MIT, Prof. Angelia Nedich, Arizona State University,
  • Session 3: 20/9 Neuro-cognition Prof. Jerome Busemeyer, Univ of Indiana, Prof. Alan Safney, Radboud University, Prof. Scott Huettel, Duke University

Results

A general objective of the project is to link dominant mechanisms of decision-making and choice between the micro, meso and macro scales that are most relevant for advancing the sustainability agenda in smart cities. The specific objectives are related to theoretical and experimental studies of different aspects of decision-making at micro, meso and macro scales that help answer the following questions:

• Which human choice/decision models are suitable for understanding decision processes, policy making, etc., in the sustainable smart city context
• What aspects of human choices and decisions in the present context can be related to and explained by neuro-cognitive processes?
• How do social network interactions (e.g. collaboration and competition) affect human choices from smart homes to smart cities?

Publications

We like to inspire and share interesting knowledge!

1. M. Lenninger, M. Skoglund, P. Herman & A. Kumar. Are single-peaked tuning curves tuned for speed rather than accuracy? Nature Communications (in review).
2. M. Lundqvist, S.L. Brincat, M.R. Warden, T.J. Buschman, E.K. Miller & P. Herman. Working memory control dynamics follow principles of spatial computing. Nature Communications (in review).
3. M. Molinari, J. Anund Vogel, D. Rolando. Using Living Labs to tackle innovation bottlenecks: the KTH Live-In Lab case study,Applied energy(Extension under review).
4. N. Chrysanthidis, F. Fiebig, A. Lansner & P. Herman. “Traces of semantization-from episodic to semantic memory in a spiking cortical network model”, eNeuro, July 2022, 9 (4). https://doi.org/10.1523/ENEURO.0062-22.2022.
5. Fontan, V. Cvetkovic, K. H. Johansson. On behavioral changes towards sustainability for connected individuals: a dynamic decision-making approach, in 4th IFAC Workshop on Cyber-Physical Human Systems, Houston, Texas, December 1-2, 2022.
6. Taras Kucherenko, Rajmund Nagy, Michael Neff, Hedvig Kjellström, and Gustav Eje Henter. Multimodal analysis of the predictability of hand-gesture properties. In
7. International Conference on Autonomous Agents and Multi-Agent Systems, 2022.
8. M. Lundqvist, J. Rose, S.L. Brincat, M.R. Warden, T.J. Buschman, P. Herman, & E.K. Miller. “Reduced variability of bursting activity during working memory.” Scientific Reports 12, no. 1 (2022): 1-10.
9. N.B. Ravichandran, A. Lansner & P. Herman. “Brain-like combination of feedforward and recurrent network components achieves prototype extraction and robust pattern recognition”. In: Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, Springer, Cham
10. D. Rolando, W. Mazzotti, M. Molinari. Long-Term Evaluation of Comfort, Indoor Air Quality and Energy Performance in Buildings: The Case of the KTH Live-In Lab Testbeds, Energies, vol. 15, no. 14, pp. 4955, 2022.
11. N. Chrysanthidis, F. Fiebig, A. Lansner & P. Herman. “Semantization of episodic memory in a spiking cortical attractor network model”, Journal of Computational Neuroscience, vol. 49, no. SUPPL 1, pp. S86–S87, 2021.
12. A. Karvonen, V. Cvetkovic, P. Herman, K.H. Johansson, H. Kjellström, M. Molinari & M. Skoglund. “The ‘New Urban Science’: towards the interdisciplinary and transdisciplinary pursuit of sustainable transformations.” Urban Transformations 3, no. 1 (2021): 1-13.
13. M. Molinari, J. Anund Vogel, D. Rolando. Using Living Labs to tackle innovation bottlenecks: the KTH Live-In Lab case study, in Energy Proceedings – Applied Energy Symposium: MIT A+B, 2021.
14. N.B. Ravichandran, A. Lansner & P. Herman. “Semi-supervised learning with Bayesian Confidence Propagation Neural Network”, in Proc. European Symposium on Artificial Neural Networks (ESANN) 2021. doi.org/10.14428/esann/2021.es2021-156.
15. Ruibo Tu, Kun Zhang, Hedvig Kjellström, and Cheng Zhang. Optimal transport for causal discovery. In International Conference on Learning Representations, 2022.
16. Carles Balsells Rodas, Ruibo Tu, and Hedvig Kjellström. Causal discovery from conditionally stationary time-series, arXiv:2110.06257, 2021.
17. M. Lenninger, M. Skoglund, P. Herman and A. Kumar. Bandwidth expansion in the brain: Optimal encoding manifolds for population coding. In Cosyne, 2021.
18. S. Molavipour, G. Bassi, and M. Skoglund. On neural estimators for conditional mutual information using nearest neighbors sampling. IEEE Transactions on Signal Processing 69:766-780, 2021.
19. M. Sorkhei, G. Eje Henter, and H. Kjellström. Full-Glow: Fully conditional Glow for more realistic image generation. In DAGM German Conference on Pattern Recognition, 2021.
20. Chenda Zhang and Hedvig Kjellström. A subjective model of human decision making
21. based on Quantum Decision Theory, arXiv:2101.05851, 2021.
22. M. Molinari and D. Rolando. Digital twin of the Live-In Lab Testbed KTH: Development and calibration. In Buildsim Nordic, 2020.
23. D. Rolando and M. Molinari. Development of a comfort platform for user feedback: The experience of the KTH Live-In Lab. In International Conference on Applied Energy, 2020.
24. E. Stefansson, F. J. Jiang, E. Nekouei, H. Nilsson, and K. H. Johansson. Modeling the decision-making in human driver overtaking. In IFAC World Congress, 2020.
25. Y. Yi, L. Shan, P. E. Paré, and K. H. Johansson. Edge deletion algorithms for minimizing spread in SIR epidemic models. arXiv preprint arXiv:2011.11087, 2020.

About the project

Objective
This impact project focuses on one of the main areas of the Democritus collaborative project, the digitization of drinking water and wastewater networks. Based on the results of the Democritus project, the objectives of this impact project are the following:

• Demonstrate the viability of theoretical modelling to the water industry by applying the project results to address practical water network problems defined by SVOA and Stockholm City. The collaboration will increase digitalization-related know-how in the Swedish water sector.
• Demonstrate the theoretical results on pollutants and leak localization in small-scale water network testbeds within the international collaboration. This way, we connect the communities of experimental and theoretical research on water distribution networks.

The results will be disseminated through stakeholder workshops, open-source software and accessible video material.

Background
The Smart Society critically depends on large infrastructures like electrical grids or water distribution networks that need to operate efficiently, with predictable performance and meet strict safety and security requirements. In the Democritus collaborative project, we have investigated the problems of monitoring and managing these large critical infrastructures with the help of digitization. Within this large area, we focused on three main topics: (a) distributed learning over wireless networks, (b) learning accuracy and security in large systems, and (c) learning for detection and localization with application to water networks.

Cross-disciplinary collaboration
The team consists of experts from the School of Electrical Engineering and Computer Science and the School of Engineering Sciences at KTH, from Stockholm University and RISE, with research experience in network design and optimization, learning and decision making, security of cyber-physical systems, and large-scale experimentation. The project collaborates with SVOA, the Stockholm Water company.

About the project

Objective
The main goal of the extension HiSSx is to plan for long-term scientific and societal impact by establishing a new transdisciplinary research centre on a human-centric smart built environment. Three specific tasks have been identified to reach this goal:

• Societal and industrial stakeholders engagement, based on active dissemination of the project HiSS and the identification of the most relevant stakeholders to create a reference group to maximize the exploitation of the results and the impact of the planned research centre;
• Research program development, aiming at the definition of innovative research areas and a strategic research plan;
• Consortium creation and centre implementation, shaping a consortium for a centre on human-centric cyber-physical systems for a smart built environment.

Background
The building sector accounts for over one-third of global energy consumption and emissions; therefore, transforming the built environment is urgent to achieve the societal climate and sustainability goals by 2030. Traditional solutions for smart and sustainable buildings and cities have focused and prioritized the technological perspective, overlooking the human dimension and leading to sub-optimization. The project HiSS tackled this complex, unresolved, and cross-disciplinary challenge addressing human decision-making and behaviour in complex environments like smart buildings. Using a multi-layered system perspective approach, the project HiSS aimed to integrate knowledge, tools, and methodologies from cognitive and behavioural sciences, control, building, and energy technology.

Among multiple success factors, HiSS developed a novel model for choice probability; the project also delved into and made notable contributions to human behavioural modelling, learning and modelling social networks and addressing key limitations in behavioural modelling and control in buildings. These advancements in the study of human behaviour further inspired us to investigate cognitive brain processes and simulate human decision-making in neuroeconomic game scenarios using computational brain models. Joint interest in brain function also sparked a collaborative effort towards a deeper theoretical understanding of how the brain processes input information at the level of neural circuits.

These modelling advancements, together with an innovative data-driven control architecture and the development of a digital twin for smart building testbeds, provided the proof-of-concept of the feasibility of scalable human-centric controls in buildings. These concepts have proven and demonstrated the technical feasibility of the exploitation of human-centric networks and controls in the KTH Live-In Lab, a platform of smart building testbeds and have been integrated into the analysis of policy expectations of households’ role in the smart grid, resulting in identified gaps, where social issues are not accounted for.

Cross-disciplinary collaboration
The cross-disciplinary research team is formed by seven PIs representing three KTH Schools: Electrical Engineering and Computer Science (EECS), Architecture and the Build Environment (ABE), and Industrial Engineering and Management (ITM). The project team will also involve key industrial and societal stakeholders and our external academic collaborators from Technion, Imperial College London and Uppsala University.

About the project

Objective
The collaborative project DataLEASH in Action aims to develop novel methods that enable the sharing and learning from data. Legal privacy concerns often prevent implementations of technical solutions so that case studies (sandbox pilots) involving legal and technical competences as proposed in this impact project are seen as the most promising strategy forward. These case studies are pivotal in understanding the nuances of legal requirements and developing technically feasible solutions. The objective is to strike a balance where legal requests are not overly demanding yet necessitate state-of-the-art technical solutions.

Background
Digitalization has resulted in more and more data being generated and collected from various sources (such as health care, customer service, surveillance cameras, etc.). The data is valuable for processing and additional analysis to improve predictions and planning. Advances in machine learning have improved this kind of data analysis, while data-protection regulation such as the GDPR has introduced constraints, limiting what data can be used and for what purpose. There is, thus a tension between the utility of data and the privacy of the individuals the data is about.

Cross-disciplinary collaboration
DataLEASH in Action brings together researchers from the School of Electrical Engineering and Computer Science (EECS, KTH), the Department of Computer and Systems Sciences (DSV) and the Department of Law both at Stockholm University

About the project

Objective
The Decision-making in Critical Societal Infrastructures (DEMOCRITUS) project develops methods for monitoring and controlling large-scale infrastructures with the help of digitalization. We design new methods for learning over large datasets, propose networking solutions that support monitoring, learning and control, and construct data-driven models of the monitored physical processes. As an application, Democritus focuses on the water distribution systems, which exhibit many unsolved challenges for future societal systems. We study real-time leak detection, detection and mitigation of possible contamination or attacks, global decision-making while observing local data privacy, and the efficient utilization of smart meters.

Background
The Smart Society critically depends on large infrastructures like electrical grids or water distribution networks that need to operate efficiently, with predictable performance and meet strict safety and security requirements. They must also be able to make informed decisions under constraints and in real time – one simple error can have devastating consequences. Despite their technological diversity, the digitalization of these infrastructures can follow a common set of novel design principles.

Cross-disciplinary collaboration
The team consists of experts from the School of Electrical Engineering and Computer Science and the School of Engineering Sciences at KTH, from Stockholm University and from RISE with research experience in network design and optimization, learning and decision making, security of cyber-physical systems, and large-scale experimentation.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

Activities & Results

Find out what’s going on!

Activities, awards, and other outputs

• Viktoria Fodor and Carlo Fischione gave invited tutorials on the project result at several top IEEE conferences, including IEEE ICC 2022 and IEEE Globecom 2022
• Henrik Sandberg gave plenary talks on the security and safety of networked control systems at SAFEPROCESS 2022 and the DISC Summer School 2022.
• Digital Futures Seminar – Decision Making in Critical Societal Infrastructures, 2020, Oct 20, https://www.digitalfutures.kth.se/event/df-seminar-decision-making-in-critical- societal-infrastructures/
• Democritus Stakeholder Workshop, 2021, Nov 24, https://www.digitalfutures.kth.se/event/decision-making-for-water-distribution-networks- open-workshop-of-the-democritus-project/
• Workshop co-organized by Digital Futures – KTH WaterCentre, on Sensor Technologies for Clean Water, 2022 April 6, www.digitalfutures.kth.se/event/workshop-on-sensor-technologies-for-cleaner-water/
• Presentation at the Digital Futures Research Day, 2022, April 21, https://www.digitalfutures.kth.se/event/digital-futures-open-research-day-21-april-2022/
• Digitalize in Stockholm, presentations in 2021 and 2022, https://digitalizeinsthlm21.se/, https://digitalizeinsthlm22.se/
• Bengt Ahlgren and Viktoria Fodor organized the 2022 Swedish National Computer Networking Workshop, sponsored by Digital Futures. 2022 June 16/17. Several of the project results were also presented, http://www.sncnw.se/2022/
• Viktoria Fodor chaired IEEE International Conference on Sensing, Communication, and Networking (SECON) 2022.
• Carlo Fischione chairs the Wireless Communications for Distributed Intelligence workshop at IEEE Global Communications Conference (GLOBECOM) 2022.
• Carlo Fischione chairs the Symposium on Selected Areas in Communications: Machine Learning for Communications and Networking Track at IEEE International Conference on Communications (ICC) 2023.

Results

Results from three main areas of research include dimensioning of network and learning resources for distributed learning, wireless protocols for supporting machine learning, and methods to localise leaks in water networks from passively collected data. A stakeholder workshop on the project’s topic was organised in 2021 with participation from the public and private sectors, and collaboration with water distribution companies and the KTH Water Centre has been strengthened.

Publications

We like to inspire and share interesting knowledge!

Water distribution networks

DP Souza, R Du, B da Silva Jr, J Mairton, CC Cavalcante, C Fischione, Leakage Detection In Water Distribution Networks: Efficient Training By Data Clustering, IWA World Water Congress & Exhibition 2022.

DP Sousa, R Du, J Mairton Barros da Silva Jr, CC Cavalcante, C. Fischione, “Leakage detection in water distribution networks using machine-learning strategies”, Water Supply 23 (3), 1115-1126, 2023.

L. Lindström, S. Gracy, S. Magnússon and H. Sandberg, “Leakage Localization in Water Distribution Networks: A Model-Based Approach”, European Control Conference, 2022.

M. Mascherpa, I. Haasler, B. Ahlgren, J. Karlsson: “Estimating pollution spread in water networks as a Schrödinger bridge problem with partial information.” Accepted for publication in European Journal of Control.

Cyber-physical systems

M, Zhang, Q. Xu, S Magnússon, R. Pilawa-Podgurski and G. Guo, “Multi-Agent Deep Reinforcement Learning for Decentralized Voltage-Var Control in Distribution Power System”, IEEE Energy Conversion Congress and Exposition, 2022.

D Weinberg, Q Wang, TO Timoudas, C Fischione, “A review of reinforcement learning for controlling Building Energy Systems from a computer science perspective”, Sustainable Cities and Society, 104351,3, 2022.

Bengt Ahlgren. Simulator Framework for Developing Decision-Making Methods for Critical Infrastructure. In 18th Swedish National Computer Networking and Cloud Computing Workshop (SNCNW 2023), Kristianstad University, Kristianstad, June 14-15, 2023.

Large-scale and distributed learning and optimization

A. Alanwar, A. Berndt, K.H. Johansson and H. Sandberg, “Data-Driven Set-Based Estimation Using Matrix Zonotopes with Set Containment Guarantees”, European Control Conference, 2022.

X. Wu, S. Magnússon, H. Reza Feyzmahdavian, M. Johansson, “Optimal convergence rates of totally asynchronous optimization”, IEEE Conference on Decision and Control (CDC), 2022.

X. Wu, S. Magnússon, H. Reza Feyzmahdavian, and M. Johansson, “Delay-adaptive step-sizes for asynchronous learning” International Conference on Machine Learning (ICML), 2022.

TO Timoudas, S Zhang, S Magnússon, C Fischione, ”A General Framework to Distribute Iterative Algorithms with Localized Information over Networks”, IEEE Transactions on Automatic Control, 2023.

S. Vaishnav and S. Magnússon, “Energy-Efficient and Adaptive Gradient Sparsification for Federated Learning” 2023 IEEE International Conference on Communications (ICC), Rome, Italy, 2023.

S. Vaishnav and S. Magnússon, “Intelligent Processing of Data Streams on the Edge Using Reinforcement Learning” 2023 IEEE International Conference on Communications Workshops (ICC), Rome, Italy, 2023

I. Haasler, A. Ringh, Y. Chen, J. Karlsson, “Multi-marginal optimal transport with a tree-structured cost and the Schroedinger bridge problem,” SIAM Journal on Control and Optimization, 59(4), 2428-2453, 2021.

I. Haasler, J. Karlsson, and A. Ringh, “Control and estimation of ensembles via structured optimal transport, A computational approach based on entropy-regularized multi-marginal optimal transport,” IEEE Control Systems Magazine 41 (4), 50-69, 2021.

I. Haasler, A. Ringh, Y. Chen, and J. Karlsson, “Efficient computations of multi-species mean field games via graph-structured optimal transport,” IEEE Conference on Decision and Control, 2021.

J. Fan, I. Haasler, J. Karlsson, and Y. Chen, “On the complexity of the optimal transport problem with graph-structured cost,” AISTATS, 2022.

I. Haasler, A. Ringh, Y. Chen, J. Karlsson, “Scalable computation of dynamic flow problems via multi-marginal graph-structured optimal transport” Accepted for publication in Mathematics of Operations Research.

Networking for/with machine learning

H. Hellström, V. Fodor and C. Fischione, “Over-the-Air Federated Learning with Retransmissions,” IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2021.

H. Hellström, V. Fodor, C. Fischione, “Unbiased Over-the-Air Computation via Retransmissions,” IEEE , Global Communications Conference (GLOBECOM), 2022.

S Razavikia, JA Peris, JMB Da Silva, C Fischione, “Blind Asynchronous Over-the-Air Federated Edge Learning”, 2022 IEEE Globecom Workshops (GC Wkshps), 1834-1839, 2, 2022.

H Hellström, JMB da Silva Jr, MM Amiri, M Chen, V Fodor, HV Poor, C. Fischione, “Wireless for machine learning: A survey”, Foundations and Trends® in Signal Processing 15 (4), 290-39916, 2022.

P Park, P Di Marco, C Fischione, Optimized over-the-air computation for wireless control systems, IEEE Communications Letters 26 (2), 2022.

Z Chen, EG Larsson, C Fischione, M Johansson, Y Malitsky, Over-the-Air Computation for Distributed Systems: Something Old and Something New, IEEE Networking Magazine, 2023.

H Hellström, V Fodor, C Fischione, “Federated Learning Over-the-Air by Retransmissions”, IEEE Transactions on Wireless Communications, 2023.

S Razavikia, JMB Silva Jr, C Fischione, “Computing Functions Over-the-Air Using Digital Modulations”, IEEE ICC 2023.

Y Kim, E Al Hakim, J Haraldson, H Eriksson, JMB da Silva, C Fischione, Dynamic clustering in federated learning, ICC 2021-IEEE International Conference on Communications, 1-610, 2021.

R Du, S Magnusson, C Fischione, The Internet of Things as a deep neural network, IEEE Communications Magazine 58 (9), 20-2511, 2020.

R Du, S Magnússon, C Fischione, The IoT as a Deep Neural Network, IEEE Communications Magazine, 2, 2020.

A Mahmoudi, HS Ghadikolaei, C Fischione, Cost-efficient distributed optimization in machine learning over wireless networks, ICC 2020-2020 IEEE International Conference on Communications (ICC), 1-77, 2020.

A Mahmoudi, HS Ghadikolaei, C Fischione, Machine learning over networks: co-design of distributed optimization and communications, 2020 IEEE 21st International Workshop on Signal Processing Advances in Communications.

X. Wang, F. S. Samani, and R. Stadler, “Online feature selection for rapid, low-overhead learning in networked systems,” 16th International Conference on Network and Service Management (CNSM 2020). IEEE, 2020.

R. S. Villaca and R. Stadler, “Online learning under resources constraints,” in 2021 17th IFIP/IEEE Symposium on Integrated Network and Service Management (IM 2021). IEEE, 2021.

X. Wang, FS. Samani, A. Johnsson,R. Stadler, “Online Feature Selection for Low-overhead Learning in Networked Systems (Demonstration),” In 2021 17th International Conference on Network and Service Management (CNSM) 2021 Oct 25 (pp. 527-529). IEEE, 2021.

X. Wang, R. Stadler, “Online Feature Selection for Efficient Learning in Networked System,” IEEE Transactions on Network and Service Management. 2022 Jun 8.

Network protocol design

P Park, P Di Marco, C Fischione, Wireless for control: Over-the-air controller, IEEE Communications Letters 25 (10), 3437-34411, 2021.

TO Timoudas, R Du, C Fischione, Enabling massive IoT in ambient backscatter communication systems, ICC 2020-2020 IEEE International Conference on Communications (ICC), 1-64 2020.

R Du, TO Timoudas, C Fischione, Comparing backscatter communication and energy harvesting in massive IoT networks, IEEE Transactions on Wireless Communications 21 (1), 429-4431, 2021.

P Park, HS Ghadikolaei, C Fischione, Proactive fault-tolerant wireless mesh networks for mission-critical control systems, Journal of Network and Computer Applications 186, 1030824, 2021.

M. Zeng, V. Fodor, Energy minimization for delay constrained mobile edge computing with orthogonal and non-orthogonal multiple access, Ad Hoc Networks, Vol. 98, 2020.

M. Zeng and V. Fodor, “Parallel Processing at the Edge in Dense Wireless Networks,” in IEEE Open Journal of the Communications Society, vol. 3, 2022.

J. A. Peris and V. Fodor, “Modelling multi-cell edge video analytics,” ICC 2022 – IEEE International Conference on Communications, 2022.

J. A. Peris and V. Fodor, “Distributed Join-the-Shortest-Queue with Sparse and Unreliable Information Updates,” ICC 2022 – IEEE International Conference on Communications, 2022.

B. Ahlgren, and K-J Grinnemo, “ZQTRTT: a multipath scheduler for heterogeneous traffic in ICNs based on zero queueing time ratio”, In Proceedings of the 9th ACM Conference on Information-Centric Networking (ICN ’22), 2022.