52 research outputs found
Decentralized Collective Learning for Self-managed Sharing Economies
The Internet of Things equips citizens with a phenomenal new means for online participation in sharing economies. When agents self-determine options from which they choose, for instance, their resource consumption and production, while these choices have a collective systemwide impact, optimal decision-making turns into a combinatorial optimization problem known as NP-hard. In such challenging computational problems, centrally managed (deep) learning systems often require personal data with implications on privacy and citizens’ autonomy. This article envisions an alternative unsupervised and decentralized collective learning approach that preserves privacy, autonomy, and participation of multi-agent systems self-organized into a hierarchical tree structure. Remote interactions orchestrate a highly efficient process for decentralized collective learning. This disruptive concept is realized by I-EPOS, the Iterative Economic Planning and Optimized Selections, accompanied by a paradigmatic software artifact. Strikingly, I-EPOS outperforms related algorithms that involve non-local brute-force operations or exchange full information. This article contributes new experimental findings about the influence of network topology and planning on learning efficiency as well as findings on techno-socio-economic tradeoffs and global optimality. Experimental evaluation with real-world data from energy and bike sharing pilots demonstrates the grand potential of collective learning to design ethically and socially responsible participatory sharing economies
Procurement of Covid-19 vaccines: why were legal liabilities transferred to the public sector?
he recent release of the Covid-19 vaccine supply contract between the European Commission and Astra Zeneca has caused a political and media storm about vaccine production logistics and supply issues. A lesser noticed but controversial issue revealed by the contract is that of where ultimate liabilities should lie, which has potentially far-reaching consequences for the public purse. Many commercial contracts include so-called indemnity clauses hereby one party contractually agrees to cover liabilities incurred by the other. The European Commission accepted in Article 14 of the agreement an extremely broad indemnity of the manufacturer covering almost any and every defect imaginable whether that be the vaccine’s inherent characteristics, manufacturing / distribution, and storage issues, labelling errors or even problems due to administration of the vaccine. This is a potentially significant burden to place on the state, and ultimately taxpayers
Driving Under the Influence of Drugs, Alcohol and Medicines in Europe — findings from the DRUID project
The ‘state of the art’ review presents the results of the largest research project ever carried out in the EU on Driving under the influence of drugs, alcohol and medicines (the ‘DRUID’ project), which ran between 2006 and 201
Privacy-enhancing Aggregation of Internet of Things Data via Sensors Grouping
Big data collection practices using Internet of Things (IoT) pervasive
technologies are often privacy-intrusive and result in surveillance, profiling,
and discriminatory actions over citizens that in turn undermine the
participation of citizens to the development of sustainable smart cities.
Nevertheless, real-time data analytics and aggregate information from IoT
devices open up tremendous opportunities for managing smart city
infrastructures. The privacy-enhancing aggregation of distributed sensor data,
such as residential energy consumption or traffic information, is the research
focus of this paper. Citizens have the option to choose their privacy level by
reducing the quality of the shared data at a cost of a lower accuracy in data
analytics services. A baseline scenario is considered in which IoT sensor data
are shared directly with an untrustworthy central aggregator. A grouping
mechanism is introduced that improves privacy by sharing data aggregated first
at a group level compared as opposed to sharing data directly to the central
aggregator. Group-level aggregation obfuscates sensor data of individuals, in a
similar fashion as differential privacy and homomorphic encryption schemes,
thus inference of privacy-sensitive information from single sensors becomes
computationally harder compared to the baseline scenario. The proposed system
is evaluated using real-world data from two smart city pilot projects. Privacy
under grouping increases, while preserving the accuracy of the baseline
scenario. Intra-group influences of privacy by one group member on the other
ones are measured and fairness on privacy is found to be maximized between
group members with similar privacy choices. Several grouping strategies are
compared. Grouping by proximity of privacy choices provides the highest privacy
gains. The implications of the strategy on the design of incentives mechanisms
are discussed
Identification of road user related risk factors, deliverable 4.1 of the H2020 project SafetyCube.
Safety CaUsation, Benefits and Efficiency (SafetyCube) is a European Commission supported Horizon 2020 project with the objective of developing an innovative road safety Decision Support System (DSS). The DSS will enable policy-makers and stakeholders to select and implement the
most appropriate strategies, measures, and cost-effective approaches to reduce casualties of all road user types and all severities. This document is the first deliverable (4.1) of work package 4 which is dedicated to identifying and assessing human related risk factors and corresponding countermeasures as well as their effect on
road safety. The focus of deliverable 4.1 is on identification and assessment of risk factors and describes the corresponding operational procedure and corresponding outcomes. The following steps have been carried out:
Identification of human related risk factors – creation of a taxonomy
Consultation of relevant stakeholders and policy papers for identification of topic with high priority (‘hot topics’)
Systematic literature search and selection of relevant studies on identified risk factors
•Coding of studies
•Analysis of risk factors on basis of coded studies
•Synopses of risk factors, including accident scenarios The core output of this task are synopses of risk factors which will be available through the DSS. Within the synopses, each risk factor was analysed systematically on basis of scientific studies and is further assigned to one of four levels of risk (marked with a colour code). Essential information of the more than 180 included studies were coded and will also be available in the database of the DSS. Furthermore, the synopses contain theoretical background on the risk factor and are prepared in different sections with different levels of detail for an academic as well as a non-academic audience. These sections are readable independently. It is important to note that the relationship between road safety and road user related risk factors is a difficult task. For some risk factors the available studies focused more on conditions of the behaviour (in which situations the behaviour is shown or which groups are more likely to show this
behaviour) rather than the risk factor itself. Therefore, it cannot be concluded that those risk factors that have not often been studied or have to rely more indirect and arguably weaker methodologies, e.g. self-reports , do not increase the chance of a crash occurring. The following analysed risk factors were assessed as ‘risky’, ‘probably risky’ or ‘unclear’. No risk
factors were identified as ‘probably not risky’.
Risky Probably risky Unclear
• Influenced driving – alcohol
• Influenced Driving – drugs
(legal & illegal)
• Speeding and inappropriate
speed
• Traffic rule violations – red
light running
• Distraction – cell phone use
(hand held)
• Distraction – cell phone use
(hands free)
• Distraction – cell phone use
(texting)
• Fatigue – sleep disorders –
sleep apnea
• Risk taking – overtaking
• Risk taking – close following
behaviour
• Insufficient knowledge and
skills
• Functional impairment –
cognitive impairment
• Functional impairment –
vision loss
• Diseases and disorders –
diabetes
• Personal factors – sensation
seeking
• Personal factors – ADHD
• Emotions – anger, aggression
• Fatigue – Not enough
sleep/driving while tired
• Distraction – conversation
with passengers
• Distraction – outside of
vehicle
• Distraction – cognitive
overload and inattention
• Functional impairment –
hearing loss (few studies)
• Observation errors (few studies)
• Distraction – music –
entertainment systems (many
studies, mixed results)
• Distraction – operating devices
(many studies, mixed results)
The next step in SafetyCube’s WP4 is to identify and assess the effectiveness of measures and to establish a link to the identified risk factors. The work of this first task indicates a set of risk factors
that should be centre of attention when identifying corresponding road safety measures (category ‘risky’)
Identification and safety effects of road user related measures. Deliverable 4.2 of the H2020 project SafetyCube
Safety CaUsation, Benefits and Efficiency (SafetyCube) is a European Commission supported
Horizon 2020 project with the objective of developing an innovative road safety Decision Support
System (DSS). The DSS will enable policy-makers and stakeholders to select and implement the
most appropriate strategies, measures, and cost-effective approaches to reduce casualties of all
road user types and all severities.
This document is the second deliverable (4.2) of work package 4, which is dedicated to identifying
and assessing road safety measures related to road users in terms of their effectiveness.
The focus of deliverable 4.2 is on the identification and assessment of countermeasures and
describes the corresponding operational procedure and outcomes. Measures which intend to
increase road safety of all kind of road user groups have been considered [...continues]
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