Brief descriptions of the projects follow:

SmartNurse is a FETLaunchpad winning proposal originated in the SmartSociety project that aims at developing (a) smart teaching assistant(s) for individualised training of student nurses. Such systems will offer information on demand (e.g. instant feedback, regulations or quick-check information, hints, …), and also provide feedback if the activities performed by the trainee during training sessions are not performed to required training goals. This application will allow student nurses to learn faster and get specific individual support.

We have released the video above which demonstrates the practical applications of our research in emergency care situations. In this case study, nurses or student nurses wearing a Smart-Assistant (in this example Smart-Eye-Ware) are attempting to resuscitate a patient (doll). Besides offering information on demand in their HMD (e.g. instant feedback, regulations or quick-check information, hints), the Smart-Assistant also detects specific activities like performing chest compressions and provides feedback if the activity is not performed to required standards.

This research expands on work presented in the award winning papers: Smart-Watch Life Saver: Smart-Watch Interactive-Feedback System for Improving Bystander CPR and Recognizing Hospital Care Activities with a Pocket Worn Smartphone (award details here).

 
AGENDA

12.30-13.30 : Light lunch & networking

13.30-13.35 : Welcome from EIT Digital (U. Bub)

13.35-13.45 : Blue sky talk: Beyond Sharing Economy: Service Provisioning in the Computational Humanism Era (F. Giunchiglia)

13.45-14.05 : Keynote: Towards a responsible sharing economy (A. Cañigueral)

14.05-14.35 : Startup Pitches – featuring WhiteRabbit, The Incentive Server & Augmented Collective Training

14.35-15.05 : High-impact verticals – panel with S. Anderson, S. Laepple.

15.05-15.35 : Coffee break & networking

15.35-15.55 : Keynote: The future of trust in the sharing economy (S. Green Brodersen)

15.55-16.05 : Open source corner: The SmartCollectives Toolkit (D. Miorandi)

16.05-16.30 : Innovation pilots and case studies – panel with L. Pannese, A. Grueberl, R. Chenu and K. Gal

16.30-17.00 : Fireside chat: Sharing economy, investors and a world without money – with D. Mazzella and R. van Kleji

17.00-19.00 : Beers, demo booths and networking

VENUE

EIT Digital Berlin CLC, Ernst-Reuter-Platz 7, 10587 Berlin, Germany

Directions and information: http://www.eitdigital.eu/about-us/locations/berlin-node/

Abstract: Healthcare Information Systems typically fall into the group of systems in which the need of data sharing conflicts with the privacy. A myriad of these systems have to, however, constantly communicate among each other. One of the ways to address the dilemma between data sharing and privacy is to use data obfuscation by lowering data accuracy to guarantee patient’s privacy while retaining its usefulness. Even though many obfuscation methods are able to handle numerical values, the obfuscation of non-numerical values (e.g., textual information) is not as trivial, yet extremely important to preserve data utility along the process. In this paper, we preliminary investigate how to exploit ontologies to create obfuscation mechanism for releasing personal and electronic health records (PHR and EHR) to selected audiences with different degrees of obfuscation. Data minimisation and access control should be supported to enforce different actors, e.g., doctors, nurses and managers, will get access to no more information than needed for their tasks. Besides that, ontology-based obfuscation can also be used for the particular case of data anonymisation. In such case, the obfuscation has to comply with a specific criteria to provide anonymity, so that the data set could be safely released. This research contributes to: state the problems in the area; review related privacy and data protection legal requirements; discuss ontology-based obfuscation and anonymisation methods; and define relevant healthcare use cases. As a result, we present the early concept of our Ontology-based Data Sharing Service (O-DSS) that enforces patient’s privacy by means of obfuscation and anonymisation functions.

Citation: Iwaya, Leonardo H. and Giunchiglia, Fausto and Martucci, Leonardo A. and Hume, Alethia and Fischer-H{\”u}bner, Simone and Chenu-Abente, Ronald, “Ontology-Based Obfuscation and Anonymisation for Privacy”, In “Privacy and Identity Management. Time for a Revolution? 10th IFIP WG 9.2, 9.5, 9.6/11.7, 11.4, 11.6/SIG 9.2.2 International Summer School, Edinburgh, UK, August 16-21, 2015, Revised Selected Papers”, 2016, Springer International Publishing, Cham, pages 343–358, isbn 978-3-319-41763-9, doi 10.1007/978-3-319-41763-9_23, http://dx.doi.org/10.1007/978-3-319-41763-9_23. New York, USA, July 2016.

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Abstract: The flâneur is the urban vagabond in search of experiences and inspirations from serendipitously exploring a city environment. This construct is put beside post-modern stances about the suburban areas built and populated after the Second World War industrialization, along with considerations about ecological psychology, cultural materialism, and sound theory. The main concept is to provide those places with a communication level that would be pleasant to discover while wandering without a destination. Therefore it is desirable to conceive a meta-design tool able to incorporate creativity, ownership, and conviviality.

Citation: Torsi, Silvia. “City of Beats: Analysing Flânerie as a Practice for Living the Physical Space.” Cultural Influences on Architecture. IGI Global, 2017. 157-180. Web. 11 Jan. 2017. doi:10.4018/978-1-5225-1744-3.ch006

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Abstract: In computing science, much attention has been paid to generic methods for sharing data in secure infrastructures. These sorts of methods and infrastructures are, of course, necessary for sharing healthcare data. The authors are, however, a long way away from being able to realise the potential of medical and healthcare data to support the sorts of extensive, data-intensive experiments being demanded by precision and stratified medicine. A key architectural problem remaining to be solved is how to maintain control of patient data within the governance of local data jurisdictions, while also allowing these jurisdictions to engage with experiment designs that (because of the need to scale to large population sizes) may require analyses across several jurisdictions. This study provides a snapshot of architectural work underway to provide a clear, effective structure of data safe havens within jurisdictions. It then describes how formally specified experiment designs can be used to enable jurisdictions to work together on experiments that no single jurisdiction could tackle alone. The authors’ current work relates to two jurisdictions (in Scotland and in Italy), but the architecture and methods are general across similar jurisdictions.

Citation: David Robertson, Fausto Giunchiglia, Stephen Pavis, Ettore Turra, Gabor Bella, Elizabeth Elliot, Andrew Morris, Malcolm Atkinson, Gordon McAllister, Areti Manataki, Petros Papapanagiotou, and Mark Parsons (2016). Healthcare data safe havens: towards a logical architecture and experiment automation. The Journal of Engineering, Institution of Engineering and Technology, October, 2016. This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), http://digital-library.theiet.org/content/journals/10.1049/joe.2016.0170.

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Abstract: Public institutions, such as universities, maintain data in several information silos, each of them engineered to serve a specific vertical application. Data about key entities—such as people, publications, courses, projects—is scattered across them and difficult to correlate due to the diversity in format, metadata, conventions, and terminology used. In such a scenario, nowadays it is practically impossible to correlate data and support advanced search and analytics facilities, in turn vital to identify institutional priorities and support institutional strategic goals, as well as to offer effective data visualization and navigation services to their users (e.g., researchers, students, alumni, companies).

Citation: Vincenzo Maltese and Fausto Giunchiglia. 2016. Search and Analytics Challenges in Digital Libraries and Archives. J. Data and Information Quality 7, 3, Article 10 (August 2016), 3 pages. DOI: http://dx.doi.org/10.1145/2939377

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Abstract: As the European Union (EU) funded SmartSociety project aims to create a toolset for rapidly and systematically engineering collective intelligence systems to support daily living, it simultaneously wants to ameliorate the risks to individuals of participating in these types of hyper-connected digital systems. This paper reports on a panel session at the close at of the 2015 IFIP summer school that reflected upon a keynote speech covering SmartSociety concepts, technologies and ethical dilemmas. The panel session was conceived as a consultative exercise as part of the ongoing Responsible Research and Innovation (RRI) approach embedded within the SmartSociety project. In this chapter we present an analysis of the panel session discussion, which touched on several key issues, including the relationships between technology and society, what we should expect from a ‘SmartSociety’, barriers and horizons in managing ethical issues, and brokerage as a methodological approach to weaving multiple perspectives into design.

Citation: Hartswood, Mark, and Marina Jirotka. “SmartSociety: Collaboration Between Humans and Machines, Promises and Perils.” In Privacy and Identity Management. Time for a Revolution?, Aspinall, D., Camenisch, J., Hansen, M., Fischer-Hübner, S.,Raab, C. (Eds.) pp. 30-48. Springer.

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Imaginary has released a series of videos on our work here at SmartSociety. The videos offer an everyday-human perspective of the challenges faced by a society entering an era marked by an increased digital presence. As our 3 years FP7 EU project nears its end, we are reminded of the diverse and impactful work delivered by all SmartSociety members at the University of Trento, the University of Edinburgh, U-hopper s.r.l., the German Research Centre for Artificial Intelligence, the University of Oxford, Ben-Gurion University of the Negev, Imaginary s.r.l., the University of Karlstad, the Vienna University of Technology, and the University of Southampton.

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Abstract: Plan recognition algorithms need to maintain all candidate hypotheses which are consistent with the observations, even though there is only a single hypothesis that is the correct one. Unfortunately, the number of possible hypotheses can be exponentially large in practice. This paper addresses the problem of how to disambiguate between many possible hypotheses that are all consistent with the actions of the observed agent. One way to reduce the number of hypotheses is to consult a domain expert or the acting agent directly about its intentions. This process can be performed sequentially, updating the set of hypotheses during the recognition process. The paper specifically addresses the problem of how to minimize the number of queries made that are required to find the correct hypothesis. It adapts a number of probing techniques for choosing which plan to query, such as maximal information gain and maximum likelihood. These approaches were evaluated on a domain from the literature using a well known plan recognition algorithm. The results showed that the information gain approach was able to find the correct plan using significantly fewer queries than the maximum likelihood approach as well as a baseline approach choosing random plans. Our technique can inform the design of future plan recognition systems that interleave the recognition process with intelligent interventions of their users.

Citation: Reuth Mirsky, Ya’akov Gal, Roni Stern, Meir Kalech. Sequential Plan Recognition. International Joint Conference on Artificial Intelligence (IJCAI), New York, USA, July 2016.

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Abstract: Plan Recognition algorithms require to recognize a complete hierarchy explaining the agent’s actions and goals. While the output of such algorithms is informative to the recognizer, the cost of its calculation is high in run-time, space, and completeness. Moreover, performing plan recognition on-line requires the observing agent to reason about future actions that have not yet been seen and maintain a set of hypotheses to support all possible options. This paper presents a new and efficient algorithm for online plan recognition called SLIM (Semi-Lazy Inference Mechanism). It combines both a bottom-up and top-down parsing processes, which allow it to commit only to the minimum necessary actions in real-time, but still provide complete hypotheses post factum. We show both theoretically and empirically that although the computational cost of this process is still exponential, there is a significant improvement in run-time when compared to a state of the art of plan recognition algorithm.

Citation: Reuth Mirsky, Ya’akov Gal. SLIM: Semi-Lazy Inference Mechanism for Plan Recognition. International Joint Conference on Artificial Intelligence (IJCAI), New York, USA, July 2016.

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Abstract: Volunteer-based crowdsourcing depend critically on maintaining the engagement of participants. We explore a methodology for extending engagement in citizen science by combining machine learning with intervention design. We first present a platform for using real-time predictions about forthcoming disengagement to guide interventions. Then we discuss a set of experiments with delivering different messages to users based on the proximity to the predicted time of disengagement. The messages address motivational factors that were found in prior studies to influence users’ engagements. We evaluate this approach on Galaxy Zoo, one of the largest citizen science application on the web, where we traced the behavior and contributions of thousands of users who received intervention messages over a period of a few months. We found sensitivity of the amount of user contributions to both the timing and nature of the message. Specifically, we found that a message emphasizing the helpfulness of individual users significantly increased users’ contributions when delivered according to predicted times of disengagement, but not when delivered at random times. The influence of the message on users’ contributions was more pronounced as additional user data was collected and made available to the classifier.

Citation: Avi Segal, Ya’akov Gal, Ece Kamar, Eric Horvitz, Alex Bower, Grant Miller. Intervention Strategies for Increasing Engagement in Volunteer-Based Crowdsourcing. International Joint Conference on Artificial Intelligence (IJCAI), New York, USA, July 2016.

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Abstract: What is a fair way to assign rooms to several housemates, and divide the rent between them? This is not just a theoretical question: many people have used the Spliddit website to obtain envy-free solutions to rent division instances. But envy freeness, in and of itself, is insufficient to guarantee outcomes that people view as intuitive and acceptable. We therefore focus on solutions that optimize a criterion of social justice, subject to the envy freeness constraint, in order to pinpoint the “fairest” solutions. We develop a general algorithmic framework that enables the computation of such solutions in polynomial time. We then study the relations between natural optimization objectives, and identify the maximin solution, which maximizes the minimum utility subject to envy freeness, as the most attractive. We demonstrate, in theory and using experiments on real data from Spliddit, that the maximin solution gives rise to significant gains in terms of our optimization objectives. Finally, a user study with Spliddit users as subjects demonstrates that people find the maximin solution to be significantly fairer than arbitrary envy-free solutions; this user study is unprecedented in that it asks people about their real-world rent division instances. Based on these results, the maximin solution has been deployed on Spliddit since April 2015.

Citation: Ya’akov Gal, Moshe Mash, Ariel D. Procaccia, Yair Zick. Which Is the Fairest (Rent Division) of Them All? ACM Conference on Economics and Computation (EC), July, Maasticht, The Netherlands. Best Paper Award.

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Abstract: Researchers that study Human-Like computing mainly aim to understand how systems can emulate human cognitive performance. However, when Human-Like systems are designed for sharing economy applications in which humans have to collaborate in order to achieve a desired task, there are several problems that needs to be addressed before asking how cognitive performance of a single human can be emulated. In this paper, we highlight these problems, provide examples related to the ridesharing scenario, and introduce how we approach these problems and which techniques we are using to tackle them.

Citation: S.Ceppi. The Dilemma of Human-Like Collective Systems. In S. Muggleton et al, editor, Twentieth Workshop on Machine Intelligence (MI 20), Windsor Park, UK, 23-25 October, 2016. In press.

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Abstract: Vehicle-to-grid (V2G) is a promising approach whereby electric vehicles (EVs) are used to store excess electricity supply (e.g., from renewable sources), which is sold back to the grid in times of scarcity. In this paper we consider the setting of a smart car park, where EVs come and go, and can be used for V2G while parked. We develop novel allocation and payment mechanisms which truthfully elicit the EV owners’ preferences and constraints, including arrival, departure, required charge, as well as the costs of discharging due to loss of efficiency of the battery. The car park will schedule the charging and discharging of each EV, ensuring the constraints of the EVs are met, and taking into consideration predictions about future electricity prices. Optimally solving the global problem is intractable, and we present three novel heuristic online scheduling algorithms. We show that, under certain conditions, two of these satisfy monotonicity and are therefore truthful. We furthermore evaluate the algorithms using simulations, and we show that some of our algorithms benefit significantly from V2G, achieving positive benefit for the car park even when agents do not pay for using it.

Citation: Enrico H. Gerding, Sebastian Stein, Sofia Ceppi and Valentin Robu. Online Mechanism Design for Vehicle-to-Grid Car Parks. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI). New York, USA, July 2016.

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Abstract: While AI has recently produced impressive systems that achieve human-like performance at challenging tasks, these systems tell us very little about how human intelligence works. In particular, they do not address the problem of composing knowledge and behaviour incrementally – a phenomenon that is pervasive in individual and collective human intelligence. We argue that achieving more human-like AI requires focusing on diversity in reasoning and behaviour among humans and artificial agents, and that developing systems capable of dealing with such diversity is key to achieving more human-like AI. In these systems intelligence should not only be measured in terms of how a system performs at a certain task, but also in terms of the properties of the process by which each component combines its knowledge and behaviour with that of others, just like humans do.

Citation: M. Rovatsos. Diversity-Awareness – The Key to Human-Like Computing? In S. Muggleton et al, editor, Twentieth Workshop on Machine Intelligence (MI 20), Windsor Park, UK, 23-25 October, 2016. In press.

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Abstract: Sharing economy applications need to coordinate humans, each of whom may have different preferences over the provided service. Traditional approaches model this as a resource allocation problem and solve it by identifying matches between users and resources. These require knowledge of user preferences and, crucially, assume that they act deterministically or, equivalently, that each of them is expected to accept the proposed match. This assumption is unrealistic for applications like ridesharing and house sharing (like airbnb), where user coordination requires handling of the diversity and uncertainty in human behaviour.
We address this shortcoming by proposing a diversity-aware recommender system that leaves the decision-power to users but still assists them in coordinating their activities. We achieve this through taxation, which indirectly modifies users’ preferences over options by imposing a penalty on them. This is applied on options that, if selected, are expected to lead to less favourable outcomes, from the perspective of the collective. The framework we used to identify the options to recommend is composed by three optimisation steps, each of which has a mixed integer linear program at its core. Using a combination of these three programs, we are also able to compute solutions that permit a good trade-off between satisfying the global goals of the collective and the individual users’ interests. We demonstrate the effectiveness of our approach with two experiments in a simulated ridesharing scenario, showing: (a) significantly better coordination results with the approach we propose, than with a set of recommendations in which taxation is not applied and each solution maximises the goal of the collective, (b) that we can propose a recommendation set to users instead of imposing them a single allocation at no loss to the collective, and (c) that our system allows for an adaptive trade-off between conflicting criteria.

Citation: P. Andreadis, S. Ceppi, M. Rovatsos, and S. Ramamoorthy. Diversity-Aware Recommendation for Human Collectives. In Proceedings of the 1st International Workshop on Diversity-Aware Artificial Intelligence (DIVERSITY 2016), The Hague, The Netherlands, 2016

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Abstract: Web-based collaborative systems, where most computation is performed by human collectives, have distinctly different requirements from traditional workflow orchestration systems, as humans have to be mobilised to perform computations and the system has to adapt to their collective behaviour at runtime. In this paper, we present a social orchestration system called SmartOrch, which has been designed specifically for collective adaptive systems in which human participation is at the core of the overall distributed computation. SmartOrch provides a flexible and customisable workflow composition framework that has multi-level optimisation capabilities. These features allow us to manage the uncertainty that collective adaptive systems need to deal with in a principled way.
We demonstrate the benefits of SmartOrch with simulation experiments in a ridesharing domain. Our experiments show that SmartOrch is able to respond flexibly to variation
in collective human behaviour, and to adapt to observed behaviour at different levels. This is accomplished by learning how to propose and route human-based tasks, how to allocate computational resources when managing these tasks, and how to adapt the overall interaction model of the platform based on past performance. By proposing novel, solid engineering principles for these kinds of systems, SmartOrch addresses shortcomings of previous work that mostly focused on application-specific, non-adaptive solutions.

Citation: M. Rovatsos, D. Diochnos, Z. Wen, S. Ceppi, and P. Andreadis. SmartOrch: An Adaptive Orchestration System for Human-Machine Collectives. In Proceedings of the Special Track on Collective Adaptive Systems of the 32nd ACM Symposium on Applied Computing (SAC2017), Marrakech, Morocco, 2017. In Press

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Abstract: Large-scale IoT services such as healthcare, smart cities and marine monitoring are pervasive in Cyber-physical environments strongly supported by Internet technologies and Fog computing. Complex IoT services are increasingly composed of sensors, devices, and compute resources within Fog computing infrastructures. The orchestration of such applications can be leveraged to alleviate the difficulties of maintenance and enhance data security and system reliability. However, how to efficiently deal with dynamic variations and transient operational behavior is a crucial challenge within the context of choreographing complex services. Furthermore, with the rapid increase of the scale of IoT deployments, the heterogeneity, dynamicity, and uncertainty within Fog environments and increased computational complexity further dramatically aggravate this challenge. This article provides an overview of the core issues, challenges and future research directions in Fog-enabled orchestration for IoT services. Additionally, we present early experiences of an orchestration scenario, demonstrating the feasibility and initial results of using a distributed genetic algorithm in this context.

Citation: Z. Wen, R. Yang, P. Garraghan, T. Lin, J. Xu, and M. Rovatsos. Fog Orchestration for IoT Services: Issues, Challenges and Directions. IEEE Internet Computing, 2017. In press

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Abstract: Human participation in socio-technical systems is overgrowing conventional crowdsourcing where humans solve simple, independent tasks. Novel systems are attempting to leverage humans for more intellectually challenging tasks, involving longer lasting worker engagement and complex collaboration patterns. Controllability of such systems requires different direct and indirect methods of influencing the participating humans. Conventional human organizations, such as companies or institutions, have been using incentives for decades to align the interests of workers and organizations. With the collaborations managed by the socio-technical platforms growing ever more complex and resembling, or even surpassing in complexity, the conventional ones, there is a need to apply advanced incentivizing techniques in the virtual environment as well. However, existing incentive management techniques in use in crowdsourcing/sociotechnical platforms are not suitable for the described (complex or intellectually-challenging) tasks. In addition, existing platforms currently use custom-developed solutions. This approach is not portable, and effectively prevents reuse of common incentive logic and reputation transfer. Consequently, this prevents workers from comparing different platforms, hindering the competitiveness of the virtual labor market and making it less attractive to skilled workers.
This research presents a complete set of models and tools for programmable incentive management for social computing platforms. In particular, it introduces:
(i) A comprehensive, multidisciplinary review of existing literature on incentives as well as an extensive survey of real-world incentive practices in social computing milieu,
(ii) A low-level model of incentives suitable for use in socio-technical systems
(iii) princ – a model and framework for execution of programmable incentive mechanisms, allowing the offering of incentives through a service model.
(iv) pringl – a high-level domain-specific language for encoding complex incentive strategies for socio-technical systems, encouraging a modular approach in building
incentive strategies, cutting down development and adjustment time and creating a basis for development of standardized but tweakable incentives.
The tools are meant to allow system and incentive designers a complete environment for modeling, administering/executing and adjusting a whole spectrum of realistic incentive mechanisms in a privacy-preserving manner. No known comparable systems were known to exist at the time of writing of the thesis.

Citation: PhD Thesis: Ognjen Scekic: Automated Incentive Management for Social Computing – Foundations, Models, Tools and Algorithms, TU Wien, March 2016.

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Abstract: Modern advances of computing systems allow humans to participate not only as service consumers but also as service providers, yielding the so-called human-based computation. In this paradigm, some computational steps to solve a problem can be outsourced to humans. Such an interweaving of humans and machines as compute units can be observed in various computing systems, such as collective intelligence systems, Process-Aware Information Systems (PAISs) with human tasks, and Cyber-Physical-Social Systems (CPSSs). Even with the multitude realizations of such systems — herein we refer to as Hybrid Human-Machine Computing System (HCS) — yet we still lack important building blocks to develop an HCS, where humans and machines are both considered as first class problem solvers from the ground up. These building blocks should tackle issues arise from different phases of an HCS’ lifecycle, i.e., pre-runtime, runtime, and post-runtime. Each phase introduces unique challenges, mainly due to the diversity of the involved compute units, which bring in different characteristics and behaviors that need to be taken into consideration. This thesis contributes to some important building blocks in managing HCSs’ lifecycle: the provisioning of compute units, the monitoring of the running system, and the reliability analysis of the task executions.
Our first contribution deals with the quality-aware provisioning of a group of compute units, a so-called compute units collective, by discovering and composing compute units obtained from various sources either on-premise or in the Cloud. We propose a novel solution model for tackling the problem in the quality-aware provisioning of compute units collectives, and employ some heuristic techniques to solve the problem. Our approach allows service consumers to specify quality requirements, which contain constraints and optimization objectives with respect to functional capabilities and non-functional properties.
In our second contribution, we develop a monitoring framework for capturing and analyzing runtime metrics occurring on various facets of HCSs. This framework is developed based on metric models, which deals with diverse compute units. Our approach also utilizes Quality of Data (QoD) to enable elastic monitoring catering different monitoring needs.
While the reliability analysis for machine-based compute units has been widely developed, the reliability analysis for HCSs has not been extensively studied. In our final
contribution, we present models and a framework for analyzing the reliability of compute units collectives.

Citation: PhD Thesis: Muhammad Z. C. Candra: Hybrid Human-Machine Computing Systems – Provisioning, Monitoring, and Reliability Analysis, TU Wien, June 2016.

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Abstract: Recent developments of computing systems allow humans to participate not only as service consumers but also as service providers. The interweaving of human-based computing into machine-based computing systems becomes apparent in smart city settings, where human-based services together with software-based services and thing-based services (e.g., sensor-as-a-service) are orchestrated for solving complex problems, leading to the creation of the so-called Cyber-PhySical-Social Systems (CPSSs). Monitoring such CPSSs is essential for system planning, management, and governance. However, due to the diversity of the involved building blocks, it is challenging to monitor such systems. In this paper, we present metric models and the associated Quality of Data (QoD) to elastically monitor the execution metrics of a centralized coordinated CPSS. We develop a monitoring framework for capturing and analyzing runtime metrics occurring on various facets of the coordinated CPSS. Furthermore, we present the implementation of our monitoring framework, and showcase monitoring features in a simulated system using real world infrastructure maintenance scenarios.

Citation: Z.C. Muhammad Candra, Hong-Linh Truong, Schahram Dustdar: On Monitoring Cyber-Physical-Social Systems. SERVICES 2016: 56-63.

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Abstract: Today’s crowdsourcing systems are predominantly used for processing independent tasks with simplistic coordination. As such, they offer limited support for handling complex, intellectually and organizationally challenging labour types, such as software development. In order to support crowdsourcing of the software development processes, the system needs to enact coordination mechanisms which integrate human creativity with machine support. While workflows can be used to handle highly-structured and predictable labour processes, they are less suitable for software development methodologies where unpredictability is an unavoidable part the process. This is especially true in phases of requirement elicitation and feature development, when both the client and development communities change with time. In this paper we present models and techniques for coordination of human workers in crowdsourced software development environments. The techniques augment the existing Social Compute Unit (SCU) concept-a general framework for management of ad-hoc human worker teams-with versatile coordination protocols expressed in the Lightweight Social Calculus (LSC). This approach allows us to combine coordination and quality constraints with dynamic assessments of software-user’s desires, while dynamically choosing appropriate software development coordination models.

Citation: Dave Murray-Rust, Ognjen Scekic, Petros Papapanagiotou, Hong-Linh Truong, Dave Robertson, Schahram Dustdar: A Collaboration Model for Community-Based Software Development with Social Machines. EAI Endorsed Trans. Collaborative Computing 1(5): e6 (2015).

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Abstract: Novel types of crowdsourcing systems require a wider spectrum of incentives for efficient motivation and management of human workers taking part in complex collaborations. Incentive management techniques used in conventional crowdsourcing platforms are not suitable for more intellectually-challenging tasks. Currently, incentives are custom-developed and managed by each particular platform. This prevents incentive portability and cross-platform comparison. In this paper we present PRINGL – a domain-specific language for programming and managing complex incentive strategies for socio-technical platforms in general. It promotes re-use of proven incentive logic and simplifies modeling, adjustment and enactment of complex incentives for socio-technical systems. We demonstrate its applicability and expressiveness on a set of realistic use-cases and discuss its properties.

Citation: Ognjen Scekic, Hong Linh Truong, Schahram Dustdar: PRINGL – A domain-specific language for incentive management in crowdsourcing. Computer Networks 90: 14-33(2015).

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Abstract: Computer interfaces provide an environment that allows for multiple objectively optimal solutions but individuals will, over time, use a smaller number of subjectively optimal solutions, developed as habits that have been formed and tuned by repetition. Designing an interface agent to provide assistance in this environment thus requires not only knowledge of the objectively optimal solutions, but also recognition that users act from habit and that adaptation to an individual’s subjectively optimal solutions is required. We present a dynamic Bayesian network model for predicting a user’s actions by inferring whether a decision is being made by deliberation or through habit. The model adapts to individuals in a principled manner by incorporating observed actions using Bayesian probabilistic techniques. We demonstrate the model’s effectiveness using specific implementations of deliberation and habitual decision making, that are simple enough to transparently expose the mechanisms of our estimation procedure. We show that this implementation achieves > 90% prediction accuracy in a task with a large number of optimal solutions and a high degree of freedom in selecting actions.

Citation: A.H. Cruickshank, R. Shillcock, S. Ramamoorthy, Predicting actions using an adaptive probabilistic model of human decision behaviours, Poster, In Ext. Proc. Conference on User Modelling, Adaptation and Personalization (UMAP), 2015.

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Abstract: The key for effective interaction in many multiagent applications is to reason explicitly about the behaviour of other agents, in the form of a hypothesised behaviour. While there exist several methods for the construction of a behavioural hypothesis, there is currently no universal theory which would allow an agent to contemplate the correctness of a hypothesis. In this work, we present a novel algorithm which decides this question in the form of a frequentist hypothesis test. The algorithm allows for multiple metrics in the construction of the test statistic and learns its distribution during the interaction process, with asymptotic correctness guarantees. We present results from a comprehensive set of experiments, demonstrating that the algorithm achieves high accuracy and scalability at low computational costs.

Citation: S. Albrecht, S. Ramamoorthy, Are you doing what I think you are doing? Criticising uncertain agent models, In Proc. Conference on Uncertainty in Artificial Intelligence (UAI), 2015.

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Abstract: Modern development of computing systems caters the collaboration of human-based resources together with machine-based resources as active compute units. Those units can be dynamically provisioned on-demand for solving complex tasks, such as observed in collaborative applications, crowd sourced applications, and human task workflows. Such collaborations involve very diverse compute units, which have different capabilities and reliability. While the reliability analysis for machine-based compute units has been widely developed, the reliability analysis for the hybrid human-machine collaborations has not been extensively studied. In this paper we present models and a framework for analyzing the reliability of hybrid compute units (HCU), which represent on-demand collectives of humans collaboration supported by machines (hardware and software units) for performing tasks. We present the implementation of our models and study the reliability of HCUs in a simulated system for infrastructure maintenance scenarios. Our evaluation shows that the proposed framework is effective for measuring the reliability of the collaboration collectives, and beneficial to obtain insights for improvements.

Citation: Candra, Muhammad ZC, Hong-Linh Truong, and Schahram Dustdar. “Analyzing Reliability in Hybrid Compute Units.” In: Collaboration and Internet Computing (CIC), IEEE Conference on, 2015.

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Abstract: Many multiagent applications require an agent to learn quickly how to interact with previously unknown other agents. To address this problem, researchers have studied learning algorithms which compute posterior beliefs over a hypothesised set of policies, based on the observed actions of the other agents. The posterior belief is complemented by the prior belief, which specifies the subjective likelihood of policies before any actions are observed. In this paper, we present the first comprehensive empirical study on the practical impact of prior beliefs over policies in repeated interactions. We show that prior beliefs can have a significant impact on the long-term performance of such methods, and that the magnitude of the impact depends on the depth of the planning horizon. Moreover, our results demonstrate that automatic methods can be used to compute prior beliefs with consistent performance effects. This indicates that prior beliefs could be eliminated as a manual parameter and instead be computed automatically.

Citation: S. Albrecht, J. Crandall, S. Ramamoorthy, An Empirical Study on the Practical Impact of Prior Beliefs over Policy Types, In Proc. AAAI Conference on Artificial Intelligence (AAAI), 2015.

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Abstract: Past research has studied two approaches to utilise pre-defined policy sets in repeated interactions: as experts, to dictate our own actions, and as types, to characterise the behaviour of other agents. In this work, we bring these complementary views together in the form of a novel meta-algorithm, called Expert-HBA (E-HBA), which can be applied to any expert algorithm that considers the average (or total) payoff an expert has yielded in the past. E-HBA gradually mixes the past payoff with a predicted future payoff, which is computed using the type-based characterisation. We present results from a comprehensive set of repeated matrix games, comparing the performance of several well-known expert algorithms with and without the aid of E-HBA. Our results show that E-HBA has the potential to significantly improve the performance of expert algorithms.

Citation: S. Albrecht, J. Crandall, S. Ramamoorthy, E-HBA: Using Action Policies for Expert Advice and Agent Typification, In Proc. AAAI-Workshop on Multiagent Interaction without Prior Coordination (MIPC), 2015.

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Abstract: Despite the fact that social computation systems involve interaction mechanisms that closely resemble well-known models of agent coordination, current applications in this area make little or no use of the techniques the agent-based systems literature has to offer. In order to bridge this gap, this paper proposes a data-driven method for defining and deploying agent interaction protocols that is entirely based on using the standard architecture of the World Wide Web. This obviates the need of bespoke message passing mechanisms and agent platforms, thereby facilitating the use of agent coordination principles in standard Web-based applications. We describe a prototypical implementation of the architecture and experimental results that prove it can deliver the scalability and robustness required of modern social computation applications while maintaining the expressiveness and versatility of agent interaction protocols.

Citation: M. Rovatsos, D. Diochnos, and M. Craciun. Agent Protocols for Social Computation. Advances in Social Computing and Multiagent Systems, CCIS 541, Springer, 2015.

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Abstract: Ridesharing, that is, the problem of finding parts of routes that can be shared by several travelers with different points of departure and destinations, is a complex, multiagent decision-making problem. The problem has been widely studied but only for the case of ridesharing using freely moving vehicles not bound to fixed routes and/or schedules—ridesharing on timetabled public transport services has not been previously considered. In this article, we address this problem and propose a solution employing strategic multiagent planning that guarantees that for any shared journey plan found, each individual is better off taking the shared ride rather than traveling alone, thus providing a clear incentive to participate in it. We evaluate the proposed solution on real-world scenarios in terms of the algorithm’s scalability and the ability to address the inherent trade-off between cost savings and the prolongation of journey duration. The results show that under a wide range of circumstances our algorithm finds attractive shared journey plans. In addition to serving as a basis for traveler-oriented ridesharing service, our system allows stakeholders to determine appropriate pricing policies to incentivize group travel and to predict the effects of potential service changes.

Citation: J. Hrncir, M. Rovatsos, and M. Jakob. Ridesharing on Timetabled Transport Services: A Multiagent Planning Approach, Journal of Intelligent Transportation Systems, 19(1):89-105, 2015.

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Abstract: The recent media focus on Smart City services, particularly ride sharing, that provide ordinary users with the ability to advertise their resources has highlighted society’s need for transparent and accountable systems. Current systems offer little transparency behind their processes that claim to provide accountability to and for their users. To address such a concern, some applications provide a static, textual description of the automated algorithms used, with a view to promote transparency. However, this is not sufficient to inform users exactly how information is derived. These descriptions can be enhanced by explaining the actual execution of the algorithm, the data it operated on, and the parameters it was configured with. Such descriptions about a system’s execution and its information flow can be expressed using PROV, a standardised provenance data model. However, given its generic and domain-agnostic nature, PROV only provides limited information about the relationship between provenance elements. Combined with semantic information, a PROV instance becomes a rich resource, which can be exploited to provide users with understandable accounts of automated processes, thereby promoting transparency and accountability. Thus, this paper contributes, a vocabulary for Smart City resource sharing applications, an architecture for accountable systems, and a set of use cases that demonstrate and quantify how the semantics enrich an account in a ride share scenario.

Citation: Heather Packer, Dimitris Diochnos, Michael Rovatsos, Ya’akov Gal, Luc Moreau, Semantics and Provenance for Accountable Smart City Applications, The Role of Semantics in Smart Cities, Semantic Web Journal special issue, 2014.

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Abstract: We propose a novel approach for Ad-Hoc WiFi based distribution of information within large crowds of mobile users. The work is motivated by civil protection scenarios where infrastructure based communication often breaks down in cases of emergency. We follow a basic opportunistic networking approach by making use of the smartphones’ built-in WiFi hotspot functionality which in combination with the devices switching between access point and client modes facilitates the propagation of messages on a multi-hop basis. We make three contributions with respect to previous work on this topic. First, we empirically determine core boundary conditions given by the performance of modern smartphones. To maximize system performance under such circumstances we propose novel heuristics for a mode switching strategy based on client mobility instead of random strategies that have mainly been utilized so far. Finally, we compare its performance to a random role switching strategy in a large-scale simulation based on a real dataset consisting of movement traces from 28’000 people during a three day festival in Zurich. Within the simulation we investigate the influence of various parameters on the system’s behavior.

Citation: Franke, T., Negele, S., Kampis, G. and Lukowicz, P. (2015): Leveraging Human Mobility in Smartphone Based Ad-Hoc Information Distribution in Crowd Management Scenarios, submitted to MobiSys 2015.

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Abstract: Collaborative localization is a special case for knowledge fusion where information is exchanged in order to attain improved global and local knowledge. We propose analytical as well as agent based simulation models for pedestrian dead reckoning (PDR) systems in agents collaborating to improve their location estimate by exchanging subjective position information when two agents are detected close to each other. The basis of improvement is the fact that two agents are at approximately the same position when they meet, and this can be used to update local position information. In analytical models we find that the localization error remains asymptotically finite in infinite systems or when there is at least one immobile agent (i.e. an agent with a zero localization error) in the system. In the agent model we tested finite systems under realistic (that is, inexact) meeting conditions and tested localization errors as function of several parameters. We found that a large finite system comprising hundreds of users is capable of collaborative localization with an essentially constant error under various conditions. The presented models can be used for predicting the improvement in localization that can be achieved by a collaboration among several mobile computers. Besides, our results can be considered as first steps toward a more general collaborative (incremental) form of knowledge fusion.

Citation: Kampis, G., Kantelhardt, J.W, Kloch, K., and Lukowicz, P. (2014): Analytical and Simulation Models for Collaborative Localization, J. Computational Science 6 (2015) 1–10.

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Abstract: In this paper, we introduce a privacy-enhanced Peer Manager, which is a fundamental building block for the implementation of a privacy-preserving collective adaptive systems computing platform. The Peer Manager is a user-centered identity management platform that keeps information owned by a user private and is built upon an attribute based privacy policy. Furthermore, this paper explores the ethical, privacy and social values aspects of collective adaptive systems and their extensive capacity to transform lives. We discuss the privacy, social and ethical issues around profiles and present their legal privacy requirements from the European legislation perspective. © IFIP International Federation for Information Processing 2015.

Citation: Mark Hartswood, Marina Jirotka, Ronald Chenu-Abente, Alethia Hume, Fausto Giunchiglia, Leonardo A. Martucci, Simone Fischer-Hübner. “Privacy for Peer Profiling in Collective Adaptive Systems.” Privacy and Identity Management for the Future Internet in the Age of Globalisation. Springer International Publishing, 2014. 237-252.

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Abstract: Collaborative localization is the computation of improved spatial coordinates in mobile agents based on their physical meetings in a pedestrian dead reckoning (PDR) system. Upon meeting the agents can exchange information about their subjective position and update it based on a simple algorithm. We show in a simulation model that the localization error diverges unless this algorithm is introduced in which case it remains bounded. We consider collaborative localization as an example of broader incremental knowledge fusion and discuss its various implications such as the importance of well-informed agents.

Citation: Kampis, G. and Lukowicz, P. (2014): Collaborative Localization as a Paradigm for Incremental Knowledge Fusion, 5th IEEE CogInfoCom 2014 Conference

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Abstract: In this paper we present a novel approach to multiagent planning in domains with concurrent actions and associated concurrent action constraints. In these domains, we associate the actions of individual agents with subsets of objects, which allows for a transformation of the problems into single-agent planning problems that are considerably easier to solve. The transformation forces agents to select joint actions associated with a single subset of objects at a time, and ensures that the concurrency constraints on this subset are satisfied. Joint actions are serialised such that each agent performs their part of the action separately. The number of actions in the resulting single-agent planning problem turns out to be manageable in many real-world domains, thus allowing the problem to be solved efficiently using a standard single-agent planner. We also describe a cost-optimal algorithm for compressing the resulting plan, i.e. merging individual actions in order to reduce the total number of joint actions. Results show that our approach can handle large problems that are impossible to solve for most multiagent planners.

Citation: M. Crosby, A. Jonsson, M. Rovatsos. A Single-Agent Approach to Multiagent Planning. Proceedings of the 21st European Conference on Artificial Intelligence (ECAI 2014), Prague, Czech Republic, August 18-22, 2014.

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Abstract: Novel web-based socio-technical systems require incentives for efficient management and motivation of human workers taking part in complex collaborations. Incentive management techniques used in existing crowdsourcing platforms are not suitable for intellectually-challenging tasks; platform-specific solutions prevent both workers from comparing working conditions across different platforms as well as platform owners from attracting skilled workers. In this paper we present PRINGL, a domain-specific language for programming complex incentive strategies. It promotes re-use of proven incentive logic and allows composing of complex incentives suitable for novel types of socio-technical systems. We illustrate its applicability and expressiveness and discuss its properties and limitations.

Citation: Ognjen Scekic, Hong-Linh Truong, Schahram Dustdar, “Managing Incentives in Social Computing Systems with PRINGL”, 15th Intl. Conf. on Web Information Systems Engineering (WISE), Thessaloniki, Greece, October, 2014.

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Abstract: Collective adaptive systems (CASs) have been researched intensively since many years. However, the recent emerging developments and advanced models in service-oriented computing, cloud computing and human computation have fostered several new forms of CASs. Among them, Hybrid and Diversity-aware CASs (HDA-CASs) characterize new types of CASs in which a collective is composed of hybrid machines and humans that collaborate together with different complementary roles. This emerging HDA-CAS poses several research challenges in terms of programming, management and provisioning. In this paper, we investigate the main issues in programming HDA-CASs. First, we analyze context characterizing HDA-CASs. Second, we propose to use the concept of hybrid compute units to implement HDA-CASs that can be elastic. We call this type of HDA-CASs h2h2 CAS (Hybrid Compute Unit-based HDA-CAS). We then discuss a meta-view of h2h2 CAS that describes a h2h2 CAS program. We analyze and present program features for h2h2 CAS in four main different contexts.

Citation: Hong-Linh Truong, Schahram Dustdar, “Context-aware Programming for Hybrid and Diversity-aware Collective Adaptive Systems”, Springer, International Workshop on Business Processes in Collective Adaptive Systems (BPCAS 2014), 12th Intl. Conf. on Business Process Management (BPM14), Eindhoven, The Netherlands, September 7-12, 2014

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Abstract: In this work, we show how a smart-phone worn unobtrusively in a nurses coat pocket can be used to document the patient care activities performed during a regular morning routine. The main contribution is to show how, taking into account certain domain specific boundary conditions, a single sensor node worn in such an (from the sensing point of view) unfavorable location can still recognize complex, sometimes subtle activities. We evaluate our approach in a large real life dataset from day to day hospital operation. In total, 4 runs of patient care per day were collected for 14 days at a geriatric ward and annotated in high detail by following the performing nurses for the entire duration. This amounts to over 800 hours of sensor data including acceleration, gyroscope, compass, wifi and sound annotated with groundtruth at less than 1min resolution.

Citation: Gernot Bahle, Agnes Gruenerbl, Enrico Bignotti, Mattia Zeni, Fausto Giunchiglia and Paul Lukowicz (2014): “Recognizing Hospital Care Activities with a Pocket Worn Smartphone”, 6th International Conference on Mobile Computing, Applications and Services (MobiCASE 2014)

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Abstract: Interactive interfaces are a common feature of many systems ranging from field robotics to video games. In most applications, these interfaces must be used by a heterogeneous set of users, with substantial variety in effectiveness with the same interface when configured differently. We address the issue of personalizing such an interface, adapting parameters to present the user with an environment that is optimal with respect to their individual traits – enabling that particular user to achieve their personal optimum. We introduce anew class of problem in interface personalization where the task of the adaptive interface is to choose the subset of actions of the full interface to present to the user. In formalizing this problem, we model the user as a Markov decision process (MDP), wherein the transition dynamics within a task depends on the type (e.g., skill or dexterity) of the user, where the type parametrizes the MDP. The action set of the MDP is divided into disjoint set of actions, with different action-sets optimal for different type (transition dynamics). The task of the adaptive interface is then to choose the right action-set.Given this formalization, we present experiments with simulated and human users in a video game domain to show that (a) action set selection is an interesting class of problems(b) adaptively choosing the right action set improves performance over sticking to a fixed action set and (c) immediately applicable approaches such as bandits can be improved upon.

Citation: M.M.H. Mahmud, B. Rosman, S. Ramamoorthy, P. Kohli. Adapting interaction environments to diverse users through online action set selection. In Proc. AAAI Workshop on Machine Learning for Interactive Systems (AAAI-MLIS), 2014.

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Abstract: Digital societies increasingly rely on secure communication between parties. Certificate enrollment protocols are used by certificate authorities to issue public key certificates to clients. Key agreement protocols, such as Diffie-Hellman, are used to compute secret keys, using public keys as input, for establishing secure communication channels. Whenever the keys are generated by clients, the bootstrap process requires either (a) an out-of-band verification for certification of keys when those are generated by the clients themselves, or (b) a trusted server to generate both the public and secret parameters. This paper presents a novel constrained key agreement protocol, built upon a constrained Diffie-Hellman, which is used to generate a secure public-private key pair, and to set up a certification environment without disclosing the private keys. In this way, the servers can guarantee that the generated key parameters are safe, and the clients do not disclose any secret information to the servers.

Citation: F{\’a}bio Borges and Leonardo A. Martucci and Filipe Beato and and Max M{\”u}hlh{\”a}user (2014). Secure and Privacy-Friendly Public Key Generation and Certification. In Proceedings of the 13th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 24–26 September, Beijing, China, TrustCom 2014.

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Abstract: Privacy-enhancing technologies for the Smart Grid usually address either the consolidation of users’ energy consumption or the verification of billing information. The goal of this paper is to introduce iKUP, a protocol that addresses both problems simultaneously. iKUP is an efficient privacy-enhancing protocol based on DC-Nets and Elliptic Curve Cryptography as Commitment. It covers the entire cycle of power provisioning, consumption, billing, and verification. iKUP allows: (i) utility providers to obtain a consolidated energy consumption value that relates to the consumption of a user set, (ii) utility providers to verify the correctness of this consolidated value, and (iii) the verification of the correctness of the billing information by both utility providers and users. iKUP prevents utility providers from identifying individual contributions to the consolidated value and, therefore, protects the users’ privacy. The analytical performance evaluation of iKUP is validated through simulation using as input a real-world data set with over 157 million measurements collected from 6,345 smart meters. Our results show that iKUP has a worse performance than other protocols in aggregation and decryption, which are operations that happen only once per round of measurements and, thus, have a low impact in the total protocol performance. iKUP heavily outperforms other protocols in encryption, which is the most demanded cryptographic function, has the highest impact on the overall protocol performance, and it is executed in the smart meters.

Citation: F{\’a}bio Borges and Leonardo A. Martucci (2014). {iKUP} Keeps Users’ Privacy in the Smart Grid. In Proceedings of the IEEE Conference on Communications and Network Security (CNS 2014), 29–31 Oct, San Francisco, CA, USA.

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Abstract: Corruption frequently occurs in many aspects of multi-party interaction between private agencies and government employees. Past works studying corruption in a lab context have explicitly included covert or illegal activities in participants’ strategy space or have relied on surveys like the Corruption Perception Index (CPI). This paper studies corruption in ecologically realistic settings in which corruption is not suggested to the players a priori but evolves during repeated interaction. We ran studies involving hundreds of subjects in three countries: China, Israel, and the United States. Subjects interacted using a four-player board game in which three bidders compete to win contracts by submitting bids in repeated auctions, and a single auctioneer determines the winner of each auction. The winning bid was paid to an external “government” entity, and was not distributed among the players. The game logs were analyzed posthoc for cases in which the auctioneer was bribed to choose a bidder who did not submit the highest bid. We found that although China exhibited the highest corruption level of the three countries, there were surprisingly more cases of corruption in the U.S. than in Israel, despite the higher PCI in Israel as compared to the U.S. We also found that bribes in the U.S. were at times excessively high, resulting in bribing players not being able to complete their winning contracts. We were able to predict the occurrence of corruption in the game using machine learning. The significance of this work is in providing a novel paradigm for investigating covert activities in the lab without priming subjects, and it represents a first step in the design of intelligent agents for detecting and reducing corruption activities in such settings.

Citation: Ya’akov Gal, Avi Rosenfeld, Sarit Kraus, Michele Gelfand, Bo An and Jun Lin. A new paradigm for the study of corruption across cultures. International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction (SBP), Maryland, MD, April 2014.

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Abstract: Crowdsourcing is generally used for tasks with minimal coordination, providing limited support for dynamic reconfiguration. Modern systems, exemplified by social machines, are subject to continual flux in both the client and development communities and their needs. To support crowd sourcing of open-ended development, systems must dynamically integrate human creativity with machine support. While workflows can be used to handle structured, predictable processes, they are less suitable for social machine development and its attendant uncertainty. We present models and techniques for coordination of human workers in crowd sourced software development environments. We combine the Social Compute Unit—a model of ad-hoc human worker teams—with versatile coordination protocols expressed in the Lightweight Social Calculus. This allows us to combine coordination and quality constraints with dynamic assessments of end-user desires, dynamically discovering and applying development protocols.

Citation: Dave Murray-Rust, Ognjen Scekic, Hong-Linh Truong, Dave Robertson and Schahram Dustdar. A Collaboration Model for Community-Based Software Development with Social Machines. 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, 22-25 Oct, Miami, FL, USA, 2014.

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