Abstract: With the advance of research in human computation, applications and software systems are increasingly being designed to include the human aspect of computation. We work with Social Compute Units (SCUs) that are computational constructs with people as their core resources. They are collaborative units, and have a cloud-like behavior in the sense that they may be elastically adapted at runtime. Systems that utilize the concept of SCUs bring challenges that are associated with the highly dynamic and unpredictable human-centric behavior. Thus, trust in the human based services that execute tasks is of paramount importance. While there is related work on social trust in the social networking and crowdsourcing areas, trust in highly coordinated team-based systems such as SCUs remains as a significant challenge. Thus, in this paper we provide a trust model that considers merging social trust with performance based trust of human based services into an integrated trust model. We illustrate the models’ application in concrete strategies, such as for elastic management of SCUs and incentives for SCU members.

Citation: Mirela Riveni, Hong-Linh Truong, Schahram Dustdar, “Trust-aware Elastic Social Compute Units”, The 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-15), Helsinki, Finland, 20-22 August 2015.

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Abstract: Hybrid Diversity-aware Collective Adaptive Systems (HDA-CAS) is a new generation of socio-technical systems where both humans and machine peers complement each other and operate collectively to achieve their goals. These systems are characterized by the fundamental properties of hybridity and collectiveness, hiding from users the complexities associated with managing the collaboration and coordination of hybrid human/machine teams. In this paper we present the key programming elements of the SmartSociety HDA-CAS platform. We first describe the overall platform’s architecture and functionality and then present concrete programming model elements – Collective-based Tasks (CBTs) and Collectives, describe their properties and show how they meet the hybridity and collectiveness requirements. We also describe the associated Java language constructs, and show how concrete use-cases can be encoded with the introduced constructs.

Citation: O. Scekic, T. Schiavinotto, D. I. Diochnos, M. Rovatsos, H.-L. Truong, I. Carreras, S. Dustdar, Programming Model Elements for Hybrid Collaborative Adaptive Systems, 1st IEEE International Conference on Collaboration and Internet Computing (CIC’15), 27-30 October 2015, Hangzhou, China.

Citation: http://bit.ly/1p8SJOP

Abstract: Society is moving towards a socio-technical ecosystem in which physical and virtual dimensions of life are intertwined and where people interactions ever more take place with or are mediated by machines. Hybrid Diversity-aware Collective Adaptive Systems (HDA-CAS) is a new generation of sociotechnical systems where humans and machines synergetically complement each other and operate collectively to achieve their goals. HDA-CAS introduce the fundamental properties of hybridity and collectiveness, hiding from the users the complexities associated with managing the collaboration and coordination of machine and human computing elements. In this paper we present an HDA-CAS system called SmartSociety, supporting computations with hybrid human/machine collectives. We describe the platform’s architecture and functionality, validate it on two real-world scenarios involving human and machine elements and present a performance evaluation.

Citation: O. Scekic, D. Miorandi, T. Schiavinotto, D. I. Diochnos, A. Hume, R. Chenu-Abente, H.-L. Truong, M. Rovatsos, I. Carreras, S. Dustdar, F. Giunchiglia, SmartSociety — A Platform for Collaborative People-Machine Computation, The 8th IEEE International Conference on Service Oriented Computing & Applications (SOCA’15), 19-21 October 2015, Rome, Italy.

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Abstract. Advances in human computation bring the feasibility of utilizing human capabilities as services. On the other hand, we have witnessed emerging collective adaptive systems which are formed from heterogeneous types of compute units to solve complex problems. The recently introduced Social Compute Units (SCUs) present one type of these systems, which have human-based services as their core fundamental compute units. While, there is related work on forming SCUs and optimizing their performance with adaptation techniques, most of it is focused on static structures of SCUs. To provide better runtime performance and exibility management for SCUs, we present an elasticity model for SCUs and mechanisms for their elastic management which allow for certain uctuations in size, structure, performance and quality. We model states of elastic SCUs, present APIs for managing SCUs as well as metrics for controlling their elasticity with which it is possible to tailor their performance parameters at runtime within the customer-set constraints. We illustrate our contribution with an example algorithm.

Keywords: social compute units, elasticity, adaptation, collective adaptive systems

doi: http://link.springer.com/chapter/10.1007%2F978-3-319-07881-6_25

Citation: Mirela Riveni, Hong-Linh Truong, Schahram Dustdar. On the Elasticity of Social Compute Units, Springer-Verlag, 26th International Conference on Advanced Information Systems Engineering (CAiSE 2014), 16-20 June 2014, Thessaloniki, Greece. Accepted.

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Abstract. Information systems are becoming ever more reliant on different forms of social computing, employing individuals, crowds or assembled teams of professionals. With humans as first-class elements, the success of such systems depends heavily on how well we can motivate people to act in a planned fashion. Incentives are an important part of human resource management, manifesting selective and motivating effects. However, support for defining and executing incentives in today’s information systems is underdeveloped, often being limited to simple, per-task cash rewards. Furthermore, no systematic approach to program incentive functionalities for this type of platforms exists.

In this paper we present fundamental elements of a framework for programmable incentive management in information systems. These elements form the basis necessary to support modeling, programming, and execution of various incentive mechanisms. They can be integrated with different underlying systems, promoting portability and reuse of proven incentive strategies. We carry out a functional design evaluation by illustrating modeling and composing capabilities of a prototype implementation on realistic incentive scenarios.

Keywords: rewards, incentives, social computing, crowdsourcing.

doi: http://dx.doi.org/10.1007/978-3-642-38709-8_44

Citation: Ognjen Scekic, Hong-Linh Truong, Schahram Dustdar, “Programming Incentives in Information Systems”, 25th International Conference on Advanced Information Systems Engineering(CAiSE’13), Springer-Verlag, Valencia, Spain, 17-21 June, 2013.

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Abstract. Conventional incentive mechanisms were designed for business environments involving static business processes and a limited number of actors. They are not easily applicable to crowdsourcing and other social computing platforms, characterized by dynamic collaboration patterns and high numbers of actors, because the effects of incentives in these environments are often unforeseen and more costly than in a well-controlled environment of a traditional company.

In this paper we investigate how to design and calibrate incentive schemes for crowdsourcing processes by simulating joint effects of a combination of different participation and incentive mechanisms applied to a working crowd. More specifically, we present a simulation model of incentive schemes and evaluate it on a relevant real-world scenario. We show how the model is used to simulate different compositions of incentive mechanisms and model parameters, and how these choices influence the costs on the system provider side and the number of malicious workers.

Keywords: rewards, incentives, crowdsourcing, social computing, collective adaptive systems.

doi: http://dx.doi.org/10.1007/978-3-642-41030-7_11

Citation: Ognjen Scekic, Christoph Dorn, Schahram Dustdar, “Simulation-Based Modeling and Evaluation of Incentive Schemes in Crowdsourcing Environments”, 21st International Conference on Cooperative Information Systems (CoopIS’13), September 11-13, 2013, Graz, Austria.

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Abstract. To date, on-demand provisioning models of human-based services in the cloud are mainly used to deal with simple human tasks solvable by individual compute units (ICU). In this paper, we propose a framework allowing the provisioning of a group of people as an execution service unit, a so-called Social Compute Unit (SCU), by utilizing clouds of ICUs. Our model allows service consumers to specify quality requirements, which contain constraints and objectives with respect to skills, connectedness, response time, and cost. We propose a solution model for tackling the problem in quality-aware SCUs provisioning and employ some metaheuristic techniques to solve the problem. A prototype of the framework is implemented, and experiments using data from simulated
clouds and consumers are conducted to evaluate the model.

Keywords: human-based service, social compute unit, quality of service, service cloud management.

doi: http://dx.doi.org/10.1007/978-3-642-45005-1_22

Citation: Muhammad Zuhri Catur Candra, Hong-Linh Truong, Schahram Dustdar, “Provisioning Quality-Aware Social Compute Units in the Cloud”, the 11th International Conference on Service Oriented Computing. Berlin, Germany, 2-5 December, 2013.

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Introduction. Incentives and rewards help align the interests of employees and organizations. They first appeared with the division of labor and have since followed the increasing complexity of human labor and organizations. As a single incentive measure always targets a specific behavior and sometimes additionally induces unwanted responses from workers, multiple incentives are usually combined to counteract the dysfunctional behavior and produce  desired results. Numerous studies have shown the effectiveness of different incentive mechanisms and their selective and motivational effects. Their importance is reflected in the fact that most big and mid-size companies employ some kind of incentive measures.

Expansion of social computing will include not only better exploitation of crowdsourcing but also solutions that extend traditional business processes; increasing research interest seems to confirm the trend. Several frameworks aiming to support such new collaboration models are being developed (such as socially enhanced computing). These new forms of social computing are intended to support greater task complexity, more intelligent task division, complex organizational and managerial structures for virtual teams, and virtual “careers.” With envisioned changes, incentives will also gain importance and complexity to address workers’ dysfunctional behavior. This new emphasis calls for automated ways of handling incentives and rewards. However, the social computing market is dominated by flat and short-lived organizational structures, employing a limited number of simple incentive mechanisms. That is why we view the state of the social computing market as an opportunity to add novel ways of handling incentives and rewards.

Here, we analyze incentive mechanisms and suggest how they can be used for next-generation social computing. We start with a classification of incentive mechanisms in the literature and in traditional business organizations, then identify elements that can be used as building blocks for any composite incentive mechanism and show the same elements are also used in social computing, even though the resulting schemes lack the complexity needed to support advanced business processes; we conclude with our vision
for future developments.

doi: http://doi.acm.org/10.1145/2461256.2461275

Citation: Ognjen Scekic, Hong-Linh Truong, Schahram Dustdar, “Incentives and Rewarding in Social Computing”, Communications of the ACM, Vol. 65, No. 6, pp. 72-82.

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Abstract. For solving complex problems, in many cases, software alone might not be sufficient and we need hybrid systems of software and humans in which humans not only direct the software performance but also perform computing and vice versa. Therefore, we advocate constructing “social computers” which combine software and human services. However, to date, human capabilities cannot be easily programmed into complex applications in a similar way like software capabilities. There is a lack of techniques to conceptualize and program human and software capabilities in a unified way. In this paper, we explore a new way to virtualize, provision and program human capabilities using cloud computing concepts and service delivery models. We propose novel methods for conceptualizing and modeling clouds of human-based services and combine human-based services with software-based services to establish clouds of hybrid services. In our model, we present common APIs, similar to well-developed APIs for software services, to access individual and team-based compute units in clouds of humanbased services. Based on that, we propose a framework for utilizing software-based and human-based services to solve complex problems. We present several programming primitives for hybrid services, also covering forming hybrid solutions consisting of software and humans. We illustrate our concepts via some examples of using our cloud APIs and existing cloud APIs for software.

Keywords: hybrid services, cloud computing, human-based computation, service computing.

doi: http://www.worldscientific.com/doi/abs/10.1142/S0218843013410037

Citation: Hong-Linh Truong, Schahram Dustdar,Kamal Bhattacharya “Conceptualizing and Programming Hybrid Services in the Cloud”, International Journal of Cooperative Information Systems, (c)World Scientific Publishing, 2013, Accepted.

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