A new paper from our group that provides a framework to structure arising issues of computational law while bridging the gap between theoretical literature and practical implementation has been published in the August 2022 issue of Artificial Intelligence and Law.

Abstract: The field of computational law has increasingly moved into the focus of the scientific community, with recent research analysing its issues and risks. In this article, we seek to draw a structured and comprehensive list of societal issues that the deployment of automatically processable regulation could entail. We do this by systematically exploring attributes of the law that are being challenged through its encoding and by taking stock of what issues current projects in this field raise. This article adds to the current literature not only by providing a needed framework to structure arising issues of computational law but also by bridging the gap between theoretical literature and practical implementation. Key findings of this article are: (1) The primary benefit (efficiency vs. accessibility) sought after when encoding law matters with respect to the issues such an endeavor triggers; (2) Specific characteristics of a project—project type, degree of mediation by computers, and potential for divergence of interests—each impact the overall number of societal issues arising from the implementation of automatically processable regulation.

Access the full paper via Alexandria or in Artificial Intelligence and Law.

A new paper from our group and together with colleagues from across the University of St.Gallen and ETH Zurich has been published in the July 2022 issue of the Journal of Medical Internet Research.

Abstract: In the paper, we aimed to investigate the impact of a future-self avatar mHealth intervention on physical activity and food purchasing behavior and examine the feasibility of a novel automated nutrition tracking system. We also aimed to understand how this intervention impacts related attitudinal and motivational constructs. We recruited 167 participants, and 95 eligible participants were randomized into either the intervention (n=42) or control group (n=53). The FutureMe intervention led to small statistically insignificant increases in physical activity (median +242 steps/day) and small insignificant improvements in the nutritional quality of food purchases (median −1.28 British Food Standards Agency Nutrient Profiling System Dietary Index points) at the end of the intervention. Intrinsic motivation significantly increased (P=.03) in the FutureMe group, but decreased in the control group. Outcome expectancy directionally increased in the FutureMe group, but decreased in the control group. Leveraging loyalty card data to track the nutritional quality of food purchases was found to be a feasible and accepted fully automated nutrition tracking system.

Link to the full paper on Alexandria and on JMIR

A new paper from our group on the integration of autonomous agents (software agents as well as humans) to perform complex agricultural tasks while integrating heterogeneous devices and services across multiple vendors will be presented at the 7th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture.

Abstract: A higher degree of automation – and autonomization – of agricultural processes is expected to lead to productivity gains, especially in light of more environmentally-friendly farming practices, while improving the safety of agricultural processes. To exploit the potential of this development, it should be possible to flexibly integrate devices and services within service mashups, and thereby enable them to provide higher-value services together. However, current farm automation tools instead tend to reinforce vertical functional silos and tight coupling within often proprietary systems that manage the farm environment information. We propose to describe capabilities of individual devices and services and interlink them across components and with the description of the farm environment. We posit that this will better enable autonomous agents – software agents as well as humans – to perform complex agricultural tasks while integrating heterogeneous devices and services across multiple vendors. Concretely, we describe – and demonstrate in a laboratory setting – the usage of a Knowledge Graph to describe the environment and equipment used to perform farming tasks. We show how a multi-agent-based automation system for smart farming uses this graph to reason about the state of the environment and the agents to plan the achievement of user-specified goals. Furthermore, we show how such knowledge-driven autonomous systems may include human agents alongside artificial agents as first-class citizens, towards realizing “Social Machines” in the agriculture domain.

Link to the full paper

A new paper from our group and with colleagues from ETH Zurich and KU Leuven was published at the 11th Mediterranean Conference on Embedded Computing.

Abstract: Batteryless sensors have recently been proposed as an energy-efficient and cost-effective alternative to battery-powered sensors. By harvesting and immediately consuming ambient energy, it becomes unnecessary to design systems with large energy storages, which significantly increases their form factor, cost, and environmental impact. Many works have focused on designing batteryless systems that sense data, process it, and wirelessly broadcast processed data. Yet wireless security aspects of batteryless applications are only now receiving attention. In this work, we propose a secure communication system based on symmetric key encryption, that enables batteryless sensors to securely broadcast data. Furthermore, we demonstrate this system on a batteryless smartcard in two application scenarios: static sensor deployment for secure data gathering, and mobile device for identification purposes. Our experimental results demonstrate not only the feasibility of secure communication with batteryless devices but also the small overheads of introducing security in wireless beaconing applications.

Link to the full paper

A new paper from our group on the explainability of cyber-physical systems was presented at the FLAIRS-35 Conference.

Abstract: The increase in automating complicated physical processes using Cyber-Physical Systems (CPS) raises the complexity of CPS and their behavior. It creates the necessity to make them explainable. The popular Explainable Artificial Intelligence (XAI) methodologies employed to explain the behavior of CPS usually overlook the impact of physical and virtual context when explaining the outputs of decision-making software models, which are essential factors in explaining CPS’ behavior to stakeholders. Hence in this article, we survey the most relevant XAI methods to identify their shortcomings and applicability in explaining the behavior of CPS. Our main findings are (i) Several papers emphasize the relevance of context in describing CPS. However, the approaches for explaining CPS fall short of being context-aware; (ii) the explanation delivery mechanisms use low-level visualization tools that make the explanations unintelligible. Finally (iii), these unintelligible explanations lack actionability. Therefore, we propose to enrich the explanations further with contextual information using Semantic Technologies, user feedback, and enhanced explanation visualization techniques to improve their understandability. To that end, context-aware explanation and better explanation presentation based on knowledge graphs might be a promising research direction for explainable CPS.

Link to the full paper

A new paper from our group and with colleagues from OST – Eastern Switzerland University of Applied Sciences was published at the Workshop AutomationXP22: Engaging with Automation which took place during the ACM CHI Conference on Human Factors in Computing Systems (CHI 2022).

Abstract: Mixed Reality (MR) is becoming an integral part of many context-aware industrial applications. In maintenance and remote support operations, the individual steps of computer-supported (cooperative) work can be defined and presented to human operators through MR headsets. Tracking of eye movements can provide valuable insights into a user’s decision-making and interaction processes. Thus, our overarching goal is to better understand the visual inspection behavior of machine operators on shopfloors and to find ways to provide them with attention-aware and context-aware assistance through MR headsets that increasingly come with eye tracking (ET) as a default feature. Toward this goal, in two industrial scenarios, we used two mobile eye tracking devices and systematically compared the visual inspection behavior of novice and expert operators. In this paper we present our preliminary findings and lessons learned.

Link to the full paper

An impression from the workshop:

The Refashion Collection, a project by Solve Studio that was supported by members from our group, has been unveiled today. We are looking forward to exploring the potential of industrializing this circular design strategy together with Solve!

A new paper with involvement of a member of our group was awarded Best Demo at the International Conference on Information Processing in Sensor Networks (IPSN).

The work, titled “DPP3e: A Harvesting-based Dual Processor Platform for Advanced Indoor Environmental Sensing“, has been a collaboration with researchers from ETH Zurich. The demonstrator showcases the DPP3e dual-processor indoor energy-harvesting platform. The DPP3e (and its advanced sensors for indoor environment sensing) can be integrated via its BLE interface (with the ability to send BLE packets every 5 seconds while consuming only 37 𝜇W) and also features a sub-GHz LoRa transceiver to facilitate data collection over long distances.

Link to the full paper.