Artem A Nazarenko
email: a.nazarenko@campus.fct.unl.pt

Luis M Camarinha-Matos

Basis for an Approach to Design Collaborative Cyber-Physical Systems

Keywords: Collaborative Cyber-Physical Systems, Industry 4.0, design methodology

Nowadays Cyber-Physical Systems gain more and more attention in regard to the Industry 4.0 or Digital Transformation in general. These systems imply the tight integration of physical and software components and are becoming more complex, forming highly inter-connected systems-of-systems. Furthermore, as components and subsystems are becoming more intelligent, there is a need for a paradigm shift towards considering them as ecosystems of collaborative entities with growing levels of autonomy. There is, however, the lack of proper methodologies and support frameworks for the design of such systems. In this context a contribution to an approach for the development of Collaborative Cyber-Physical Systems is proposed. It introduces some core definitions, organizational and architectural aspects. The proposed approach is in line with the design science research methodology and is illustrated with some examples.

Introduction

Cyber-Physical Systems (CPS) are becoming complex systems, based on interconnected and tightly coupled hardware and software components with growing level of intelligence. Modern CPS might contain a large number of components, in fact, being a system-of-systems. These new systems are characterized by high levels of intelligence and autonomy of their components and requiring collaboration among those components as well as interaction with outer systems, which led to emergence of the Collaborative Cyber-Physical Systems (CCPS) concept [START_REF] Nazarenko | Towards collaborative Cyber-Physical Systems[END_REF]. CCPS focus on improving the networking among components and their joint efforts for accomplishing complex tasks, bridging not only the cyber and physical parts, but also the social one. The physical part of a CCPS consists of sensing and actuating capabilities deployed in a certain environment, while the cyber part ensures the needed abstraction level to integrate the physical devices together and provide highlevel functionalities, while also allowing users to be an active part of CCPS.

Modern CCPS face not only technical challenges, but also architectural and organizational issues. Thus the borders of modern systems are extending such that often a CPS can be considered as a complex system or a collaborative ecosystem incorporating various elements with different aims, characteristics and even nature. While the smart objects become smarter and more able to accomplish complex tasks, conventional centralized approaches to organize them might limit their autonomy and thus their capabilities. This raises several issues in terms of organization and management of the smart autonomous entities that are combined to form a system. It also requires paying attention to the aspects of collaboration between them and users.

As some smart objects are mobile, their movements will affect the conditions of the whole system or environment, making it difficult to define strict borders for the smart environment to which they are assigned to, leading to a notion of changing or "fluid" borders. Moreover, for the cases of complex structures containing several autonomous environments/systems an approach to manage the mobility of objects, not only in terms of physical positioning, but also in terms of logical belonging is of particular importance. This also raises the issue of organizing collaboration at different scales and hierarchical layers. Another aspect is that systems need to adapt to changing environments through periodic analysis of those changes and context, developing an appropriate adaptation strategy. One small change may cause a "domino" effect on the other systems within the same ecosystem or affecting the successful accomplishment of a certain task.

Those topics are not well-developed in the literature, as most of the research works on CPS and Internet of Things are focused on low-level aspects, such as protocols, communication technologies, energy optimization, etc., and also on specific applications. Due to the variety of possible organizational approaches and involved technologies, the design process of complex CPS might be very challenging. As such there is a need for a methodological and framework support to help designing new CPS/CCPS. In fact, nowadays each time developers build a new system they start from scratch, possibly facing the same difficulties as in other systems. As an example, [START_REF] Jezewski | Selected design issues of the medical cyber-physical system for telemonitoring pregnancy at home[END_REF] identifies such difficulties in the telemedicine CPS solutions, stating the need for an environment that supports multirole distributed teams with "interface units" being used to connect monitoring devices to a "surveillance centre" which serves as hub. The proposed approach in this example goes back again to the classical master-slave paradigm, limiting the system's capabilities. In another work devoted to CPS implementation in the energy sector [START_REF] Munoz | Energy-Aware Environments for the Development of Green Applications for Cyber-Physical Systems[END_REF], the same approach with one aggregation point collecting all the data from devices deployed all over a building is presented. In these examples, selected just for illustration purposes, developers went through similar difficulties, namely determining how the devices have to be managed, how to support the human-system interaction, coping with different hierarchical layers, etc. In most cases mobility is not even considered. In this context, this work is a part of initiative to provide CPS designers with supportive techniques, strategies, and tools for the development, integration and evaluation of CPS. The aimed methodology and support framework needs to be general in order to be applicable to different application areas.

Based on above mentioned aspects, the following research question is defined:

What could be a suitable set of models and organizational structures to support the increasing complexity of evolving CCPSs?

In order to answer this question, the following hypothesis is set for this work:

The increasing complexity of evolving CCPS can be effectively supported if an appropriate collaborative ecosystem is provided that combines a suitable organizational structure with intelligent adaptation and integration methods. Moreover, a hierarchical organization, supporting horizontal and vertical collaboration can increase autonomy and provide logical sections with higher level of independence, context awareness, enhanced responsibilities and mobility support.

The following sections of the paper present preliminary results in this direction and some discussion on ongoing and future research.

Relation to Innovation in Industrial and Service Systems

Cyber-Physical Systems are an integral part of the Industrial and Service systems being utilized in a wide variety of application areas. One of the main innovation trends here is observed in the area of Smart Manufacturing, which strongly relies on the adoption of CPS. From the market point of view, the implementation of CPSs in manufacturing allows more flexible adaptation to demand changes, to product lifetime reduction, as well as support for the collaboration processes among all parties involved in manufacturing and related supply chains [START_REF] Atos | Connecting data, machines, people and processes -delivering the next generation of manufacturing[END_REF]. Furthermore, using CPS in manufacturing offers new capabilities for companies to provide services throughout the life cycle of products [START_REF] Herterich | The Impact of Cyber-Physical Systems on Industrial Services in Manufacturing[END_REF], e.g. post sale support will take new and more effective forms with the introduction of CPSs, which will also induce new business models for these companies. In particular, the notion of smart supply chain covers the entire range of issues related to both provision of the necessary materials and delivery of products to the customers or distributors, which requires effective CPS. Many related research works appear more focused on industrial use cases, covering for instance the challenges of smart predictive systems [START_REF] Chen | Predictive offloading in mobile-fog-cloud enabled cyber-manufacturing systems[END_REF], human-based [START_REF] Pacaux-Lemoine | Towards human-based industrial cyber-physical systems[END_REF], fog industrial computing systems [START_REF] O'donovan | A fog computing industrial cyber-physical system for embedded low-latency machine learning Industry 4.0 applications[END_REF] and other relevant topics responding to principles set by the Industry 4.0 paradigm. The concept of Industry 4.0 can be described by a number of dimensions, including: vertical integration, horizontal integration, throughengineering, acceleration of manufacturing, digitalization of products and services, and customer involvement in co-creation process [START_REF] Camarinha-Matos | Collaborative Networks as a Core Enabler of Industry 4.0[END_REF]. CPS and collaboration aspects are present in all these dimensions, thus a clear case for CCPS.

Another application area for the ideas proposed in this work is the smart home, which is a good example of Service System. A smart home can be seen as an ecosystem containing various services provided for its inhabitants and visitors, while also facilitating the interaction with other service systems in its vicinity, eventually connected to the concept of smart city, smart energy grid, etc.

Design Approach

As CPS become larger and more complex, various authors have pointed out the need for proper design methodologies and support tools [START_REF] Sangiovanni-Vincentelli | Frankenstein: Contract-Based Design for Cyber-physical Systems[END_REF], [START_REF] Seshia | Design Automation of Cyber-Physical Systems: Challenges, Advances, and Opportunities[END_REF], [START_REF] Stokic | Engineering Methods and Tools for Collaborative Development of Industrial Cyber-Physical Based Products and Services[END_REF], [START_REF] La | A Service-based Approach to Designing Cyber Physical Systems[END_REF]. As such, various proposals have been put forward for classical CPS design, with origin in different areas, e.g. embedded systems, software engineering, control, and thus reflecting the concerns of those areas. Service-based design, platform-based design, contract-based approach, waterfall and spiral models, hardware-software co-design, V-model, model-based development, standards-based design, are just a few examples.

Of special relevance is the identification of design challenges [START_REF] Seshia | Design Automation of Cyber-Physical Systems: Challenges, Advances, and Opportunities[END_REF], [START_REF] Sangiovanni-Vincentelli | Frankenstein: Contract-Based Design for Cyber-physical Systems[END_REF] namely coping with the following characteristics: hybridity, heterogeneity, interoperability, distributed nature, dynamicity, complexity, adaptiveness / evolving nature, humancentricity, etc. According to [START_REF] Seshia | Design Automation of Cyber-Physical Systems: Challenges, Advances, and Opportunities[END_REF], a design methodology should support the crossdomain developments and be component-based, learning-based, time-aware, trustaware and human-centric. As we move towards cognitive and collaborative CPS, other challenges become relevant e.g. (i) learning capability, already identified in [START_REF] Seshia | Design Automation of Cyber-Physical Systems: Challenges, Advances, and Opportunities[END_REF], but becoming more important in the context of big data, and (ii) collaborative facets, for which it is relevant to seek contributions in the area of Collaborative Networks / Business Ecosystems [START_REF] Nazarenko | Towards collaborative Cyber-Physical Systems[END_REF].

On the other hand, cognitive and collaborative CPS represent a new "unexplored territory" and thus engineering such systems becomes more and more like an engineering research activity. As such, our proposed approach for Collaborative Cyber-Physical system design is based on the Design Science Research Methodology for Information Systems Projects [START_REF] Hevner | [END_REF]. The underlying conceptual framework considers three main cycles Relevance, Rigor and Design, and three pillars (Figure 1):

-Application Domain (e.g. Smart Manufacturing, Smart Home, Elderly Care, etc.), which contains needs, limitations and challenges that need to be considered throughout the design cycle. It also identifies the main actors and the scenario where these actors are living in and the way they are communicating and organised within the CCPS. -Research pillar, which concentrates on the implementation of the given scenario by designing the cyber and physical artefacts with further evaluation and testing. -CCPS Knowledge Base, which provides access to base concepts, theories, and best practices for artefacts building. To interconnect the pillars of the CCPS Design Methodology, and following the design science approach three cycles are adopted:

-Relevance cycle, which is responsible for bridging the Application Domain and the CCPS Research in terms of providing the input for the design phase, but also in terms of identifying formal criteria for assessing the output of the design Cycle inside the application area. -Design Cycle, during which the artefact's design undergoes constant checking for concordance with stated requirements and needs, in order to reach a satisfying solution to the stated problem.

-Rigor Cycle, that assures the necessary link between the research process and CCPS Knowledge Base containing both the application-based expertise as well as existing generic solutions or artefacts. Moreover, this cycle accumulates the knowledge generated during the artefacts' design phase to be considered in further iterations.

Of special relevance in this context is the identification of the key concepts for the CCPS knowledge base that should reflect the collaborative perspective, which will be addressed in the following sections.

Base Concepts

In this section some of the main concepts that will populate the CCPS Knowledge Base are introduced. To be able to design a complex CCPS there is a need to consider various abstractions levels [START_REF] Gerostathopoulos | Tuning Self-Adaptation in Cyber-Physical Systems through Architectural Homeostasis[END_REF]. Moreover some inspiration for describing and designing complex CPS can also be found in the Nature [START_REF] Camarinha-Matos | Roots of Collaboration: Nature-Inspired Solutions for Collaborative Networks[END_REF], which leads to adoption of the concepts of Ecosystem, Environment and Users Community.

Thus, a definition of a Smart Environment is proposed as follows:

"Smart Environment is a surrounding, with changing characteristics enriched with sensing, actuating and computing capabilities focusing on improving its inhabitants' experiences through increased contextual awareness and extended interaction capabilities."

A Smart Environment comprises physical as well as cyber artefacts which are deployed in the environment and provide a variety of services. Physical artefacts are represented, in fact, by Smart Objects: "Smart object is a physical entity with a cyber identity, some computing power, storage, and communication capabilities. It might be equipped with sensing and/or actuation capabilities, able to monitor the surrounding environment and/or change it." Cyber artefacts might be of a several types, as for instance, the applications which are discovering and enabling the capabilities of the physical artefacts and ensuring bidirectional communication. Another important cyber artefact is a digital twin: "Digital twin is a virtual representation, in the cyberspace, of a physical asset, process or system, possessing real-time information about the represented entity and a model of its behavior. Each smart object can have a digital twin in the cyber part of a CCPS."

As the CCPS often presume that the end-users of a provided service can be humans and not only other sub-systems, these systems might be assigned to a certain group or "Users Community": "Users Community is a group of humans tied together by some social bond that creates in its members a "sense of belonging". The cohesion provided by this social bond usually leads to some sharing of intent or common interest, resources, preferences, risks, etc., materializing some notion of collective identity."

Both "Smart Environment" and "Users Community" are the constituents of a Cyber-Physical Ecosystem:

"Cyber-Physical Ecosystem is a complex structure composed of a group of interconnected entities forming communities of users and sets of digital twins, the environment where they live in and the complex types of relationships between them considering mutual influence on each other both on cyber as well as physical layers."

It also considers collaboration between humans and systems, including identification of users' needs and response with corresponding service, conflicts resolution, policy implementation, and adaptation to changing circumstances. Moreover the CPS Ecosystem as well as the Smart Environment can be considered under a hierarchical composition point of view: "A Hierarchy assumes decomposition of some entity on smaller structural blocks/units which are characterised by some common aspect (e.g. location, devices types) or function/aim (e.g. energy management, air control). Those in turn might contain further structural units organised following the same principle while remaining affiliated to a parent entity and thus inheriting its attributes, rules and constraints."

As the complexity and autonomy of CPS components increase, the CPS can be considered as System of Systems, consisting of a variety of building blocks or modules, which can be combined together in order to respond to users' needs bringing different functionalities in run time to create an "integrated service". This view is aligned with the notion of "CPS components being freely composable" as stated in [START_REF]Framework for Cyber-Physical Systems[END_REF]. Through combining resources and functionalities of smart, distributed entities we might achieve the concentration of necessary competencies to solve a certain problem which is not possible by a single component. This process needs to be dynamic, namely possessing the ability to quickly respond to the need with an appropriate "solution" and, if necessary, to add new components or replace those which do not deliver the required functionality. This idea is similar to the earlier notion of "coalition" of manufacturing components introduced in [START_REF] Barata | Coalitions of manufacturing components for shop floor agility -The CoBaSA architecture[END_REF] and later revisited in other works such as [START_REF] Wang | Current status and advancement of cyber-physical systems in manufacturing[END_REF]. While that initial idea faced several obstacles due to the lack of modularity of the physical manufacturing components, recent progress towards smart objects, represented by digital twins, makes it a promising approach. The notion of coalition or consortium gets its inspiration in the area of Collaborative Networks (CN) [START_REF] Camarinha-Matos | Collaborative networks: a new scientific discipline[END_REF], namely from the concept of Dynamic Virtual Organisation (VO). Dynamic VOs are able to quickly respond to a changing market situation and are formed for a limited time. This idea presents a good potential in terms of application within CPS, as the components can be reused lately in order to respond to another need, thus this will allow to spare deployment of additional components with similar functionality. Thereby combining principles from CN, Coalition of Manufacturing Agents and the CPS background we define a notion of Coalition of Smart Components as: "Coalition of Smart Components is a temporary association of a set of cyber entities -digital twins, representing physical components, which can be dynamically configured and adjusted to a changing surroundings/demand in order to provide an integrated solution."

While in CNs partners for a VO are typically chosen from a pool of members of a Virtual Organizations Breeding Environment (VBE), here the components to create the coalition are selected from the pool of digital twins associated to a smart Environment.

Some of the definitions given above might be refined during the next stages of this ongoing research and some new might be added. However, these ones allow understanding the scope of the research and establishing the common basis. Three key concepts, Cyber-Physical Ecosystem, Smart Environment and Users Community, are intended to give the necessary abstraction level during the design and deployment phases. Cyber-Physical Ecosystems may also be small or large scale. An example of a small scale ecosystem can be a Smart Home, while a large scale example can be a Smart City. It is important to mention that not all smart objects necessarily require a digital twin, as simple sensors providing some measurements can be deployed as a part of more complex objects, while complex robots containing various actuators and sensors controlling and reacting on a variety of events might benefit from introducing the digital twin representing functionality on higher abstraction levels. Nevertheless, higher abstraction levels allow focusing on the collaborative and organizational aspects paying less attention to the low level technical details, which have been thoroughly addressed by other works.

High-level Modeling of Basic Concepts

This section provides a model for some relevant concepts, such as Cyber-Physical Ecosystem, Users Community and Smart Environment, which were defined in the previous section. The UML diagram (Fig. 2) shows the main relations among these concepts. The Cyber Physical Ecosystem is a "bridging point" between the Smart Environment, responsible for managing the cyber and physical components, and the Users Community, focusing on users' organization. It is important to mention, that the Cyber-Physical Ecosystems, as well as the Smart Environments can consist of several sub-ecosystems or sub-environments building a hierarchy.

Each Users Community has a number of members; some of them, however, can be at the same time members of the other Users Communities. For instance, in a Smart House a person providing some repair services might be a member of the visitors' community and of the service providers' community. Being a member of a Users Community allows getting access to certain capabilities with privileges which depend on the assigned role. Thus, in fact, the role defines the access rights for each particular member. One open issue related to Users Community is the problem of "ownership", since although members may be owners of components, also a particular component might be the property of a Users Community and not of a specific member. This is not yet considered in the UML diagram.

The Smart Environment includes software entities being digital replicas of the physical components, the Digital Twins. Physical components are of two types, namely Composed entities and Smart Objects such as sensors and actuators. Furthermore, components can be of two types: mobile or static. Mobile components can move among different Smart Environments, as well as among Cyber-Physical Ecosystems.

Fig. 2 -A partial UML model of relevant concepts

The mobility of components sets another challenge regarding "borders", which means, when the mobile component leaves the host environment it does not possess a link to this environment anymore, but to the new one. One possible representation of this aspect is presented in Fig. 3:

Fig. 3 -Mobile and stationary components

Complementarily, and for further clarification of the stated concepts, the i* modeling language [START_REF] Yu | Social Modeling and i*[END_REF] is also used. It supports two modeling concepts, namely:

-Strategic Dependency Model, reflecting the agents or actors and the relationships among them; -Strategic Rationale Model, which allows capturing the context behind the agents or actors through some basic elements, as tasks, goals, resources and soft goals.

In Fig. 3 the key concepts are presented in a Strategic Dependency model as agents showing the main relations among them. There are three main types of relations in this type of diagram: Goal, Resource and Soft Goal. This model allows giving an answer to some basic issues: "Which goals need to be achieved by an agent?", "Which goals an agent asks to be achieved by the other agents?", "Which resources are required?"

Collaborative Facets in CCPS Design

As mentioned above, the convergence of ideas developed within the area of Collaborative Networks and the foundations of the CPS is central point for this work. Namely the aspects related to VBEs and VOs are particularly relevant. Virtual Organisations Breeding Environment can be described as a pool of organisations possessing the "potential and will to cooperate with each other and that are prepared to rapidly join a VO in response to a business opportunity" [START_REF] Camarinha-Matos | Collaborative networks: a new scientific discipline[END_REF]. Analogously, a Cyber-Physical Ecosystem contains a pool of ready to cooperate entities/components. The counterpart to a VO is a Coalition of Smart Components (CSC). In order to define the stages of the life cycle of the CSC, the corresponding model developed for VO creation can be adopted. In the simplest case, the VO creation process involves: establishing the VBE, partners search and suggestion, negotiation, launching and dissolution. Table 1 attempts to map the steps of VO creation process to the creation of CSC:

VO Creation CSC Creation

VBE -is "a strategic alliance of organisations which adhere to long term cooperation and adopting common operating principles having the goal of increasing their chances towards collaboration in potential VO".

In the proposed approach, the Cyber Physical Ecosystem is analogous to a VBE representing a pool of Components (through their corresponding digital representations or Digital Twins), which provides a basis for building coalitions. Partners' search and suggestionfinding partners based on some criteria, Formalizing the challenges and identifying necessary competencies to respond to the such as fitness of competences, trust level, etc. problem/collaborative opportunity. Build a taxonomy/structure of needed capabilities and how they can be combined. Discovery of components with defined capabilities (e.g. offered services) and selection based on some criteria, such as availability (free/occupied), location, (if in the required location or if it can move to desired location), etc. Negotiation -an iterative process aiming at reaching agreements among partners and adjust needs with offers.

Negotiations can be made within a Cyber-Physical Ecosystem considering the needs of components owners, scheduling plans and the desired output. VO launching involves ICT infrastructure configuration, orchestration of collaboration spaces, resources allocation, informing of involved members and manifestation of new VO in the VBE space.

On this stage the responsibilities for each component are set, the structure of the Coalition and its representation in the ecosystems are properly defined.

VO Dissolution -dissolution occurs when the VO has fulfilled its goal or the fulfilment seems not possible anymore due to various circumstances.

When the task is finished for which the Coalition was established it is either dissolved or evolved to respond to another collaborative opportunity.

As evaluation scenario, our ongoing work focuses on the area of home automation or smart home. Home automation includes a variety of possible applications. For instance, let us consider one of the core tasks of smart home, which is the provision of a comfortable living environment. For ensuring the comfortable living environment various components, with sensing and actuating capabilities, are involved and for this purpose a Coalition of Smart Components needs to be established. This CSC will include components such as temperature, humidity, air quality, and smoke sensors, and corresponding actuators as air conditioner, thermostat, lights, etc. Within the CSC these components are cooperating providing an integrated impact to the Users Community, i.e. jointly contributing to provide the service desired by the user. Moreover, as different users can have different preferences considering temperature, humidity, etc., other components possesing the ability to recognise the presence of a particular user may join the CSC.

For the stated scenario we have already started initial developments, for which Prolog language and a frame engine developed on top of it are used as the development environment. Following the previously introduced CCPS methodology, the approach for further developments is illustrated in Fig. 6. The framework considers 3 segments, the first one focuses on the target scenario (in this example a smart home), in the centre is the design space supported by a Frame Engine and Prolog which allows usage of the object-oriented and logic-based approach, and the left side represents the knowledge base, which provides the meta-model of relevant concepts. Frames are used as the base modelling formalism.

Conclusion and Further Work

The convergence of theories and ideas developed in the area of Collaborative Networks with the base CPS background led to emergence of the concept of Collaborative CPS which is intended to improve the potential of conventional CPS to adapt to changing users' needs and benefit from increasing intelligence level of CPS components. This collaborative perspective facilitates the development of Service Systems, as they require dynamic configuration of resources in order to produce an integrated impact. In this regard, one challenging aspect is to establish a CCPS design methodology for which an approach is highlighted in this work. Basic concepts and definitions for CCPS, combining ideas from CN and CPS, were also proposed in the scope of this work. Moreover, the idea of establishing Coalitions of Smart Components, based on background for VO creation, was proposed and a mapping of the VO creation to the CSC creation was established. Some of open challenges are the aspects related to components mobility and corresponding issues of ownership / belonging and borders for the Cyber-Physical Ecosystem. Ongoing work focuses on the application of the proposed concepts and approach to a real use-case in the area of home automation.

Fig. 1 .

 1 Fig. 1. -CCPS Design Methodology inspired on Design Science

Fig. 4 -

 4 Fig. 4 -Partial Strategic Dependency Model of key concepts

Fig. 5 -

 5 Fig. 5 -SRM Model of membership acquiring

Fig. 6 -

 6 Fig. 6 -General view for the planned developments

Table 1 .

 1 Mapping the steps for VO Creation to CSC Creation

Acknowledgments. This work was funded in part by the Center of Technology and Systems (CTS) and the Portuguese Foundation for Science and Technology (FCT) through the Strategic Program UID/EEA/00066/2013.


Ivan Pisa

Ramon Vilanova

Ignacio Santín

Jose Lopez Vicario

Antoni Morell
email: antoni.morell@uab.cat

Ignacio Santin

Jose Vicario

Artificial Neural Networks Application to Support Plant Operation in the Wastewater Industry

Keywords: Wastewater Industries, Data Driven methods, Artificial Neural Networks

This communication presents the main aim, contextual and development framework of the PhD that is being conducted by the first author. In this PhD, main aim is the application of data driven methods to industrial processes in order to improve and support industrial operations. In this case, Wastewater Treatment Plants (WWTPs) are adopted as the industry where data driven methods will be applied. WWTPs are industries devoted to managing and process residual water coming from urban and industrial areas. Those type of industries apply highly-complex and nonlinear processes to reduce the contamination of water. Therefore, among the different data driven methods, in this PhD we will focus on the application of Artificial Neural Networks (ANNs) in order to improve and support the operations performed in this type of industries. ANNs are considered due to their ability in the modeling of highlycomplex and nonlinear processes such as the WWTPs processes.

Introduction

Nowadays, the incursion of Industry 4.0 has arisen as a paradigm becoming true, industrial systems are becoming able to communicate and cooperate with other systems and also with humans by means of Wireless Sensor Networks (WSN) in order to take decentralized decisions to manage the industrial plants [START_REF] Sarvari | Technology Roadmap for Industry 4.0[END_REF]. Consequently, large amount of measurements and data from industrial processes will be stored and processed [START_REF] Rashid | Applications of wireless sensor networks for urban areas: A survey[END_REF]. Thus, the aim of this research is to support and improve the performance of certain industrial processes by means of data driven methods and especially Artificial Neural Networks (ANNs). In addition, these methods will be applied over data coming from Wastewater Treatment Plants (WWTPs) which are industrial plants devoted to reducing the pollutant levels of residual waters.

As introduced, investigated prediction schemes will be based on ANNs, whose most popular tasks when dealing with industrial data correspond to predictive tasks such as predictive maintenance or anomaly detection [START_REF] Martí | Anomaly detection base on sensor data in petroleum industry applications[END_REF]. Thus, predictions can support industrial processes operations by providing them with information about future industrial events or measurements. In this work, predictions will support the WWTP plant operations by feeding control strategies providing information to them to anticipate actuations and improve the overall WWTP performance.

Relationship to Industrial and Service Systems

As it has been stated, Industry 4.0 is becoming a reality. Industries have been provided with systems able to communicate and cooperate with others. Thus, the connectivity and communication among industrial systems will be performed by means of WSNs which will generate thousands of bytes of information per day. Consequently, data can be processed in order to extract and generate value from it.

In terms of WWTPs industries, the ones in which this work is based, WSN are adopted in order to measure incoming water ratios, pollutant concentrations, oxygen demand and other parameters which characterize the WWTPs' behavior [START_REF] Fernandez De Canete | Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network[END_REF]. Therefore, large amount of concentration measurements is stored during a day and consequently, the application of data driven methods to these measurements is widely encouraged in order to extract certain characteristics of the plant. For instance, in [START_REF] Qiao | Adaptive fuzzy neural network control of wastewater treatment process with multiobjective operation[END_REF] the WWTP's influent ratio profile is processed to determine the expected weather and therefore prepare the WWTP to deal with all the incoming flow rate.

In that sense, ANNs are widely adopted as service systems which feed industrial systems or simply obtain certain measurements which gives information about the industry where they are deployed. In addition, their adoption is motivated by the fact that they only require input and output information of the industrial processes to generate a mathematical model able to yield information of the industrial process. This information will be used to complement other systems and consequently, generate value from the stored data. Thus, ANNs can be seen as soft-sensors adopting data-driven methods which work as service systems. Their purpose is to model highcomplex and nonlinear systems in order to yield either prediction or classification of process outputs which will fulfill other industrial processes or drive monitoring and decision modules. In that manner, the overall complexity of the industrial processes can be simplified just requiring input and output data of these processes.

State-of-the Art

The increasing interest in the Internet of Things (IoT) and Industry 4.0 [START_REF] Wollschlaeger | The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0[END_REF] has motivated the adoption of ANNs for forecasting purposes in industrial systems. ANNs have been considered in different works where predictions are adopted to complement different industrial processes and systems. For instance, the adoption of neural networks as service systems dealing with sewer systems' data is proposed in [START_REF] Zhang | Manage Sewer In-Line Storage Control Using Hydraulic Model and Recurrent Neural Network[END_REF], [START_REF] Zhang | Hydraulic modeling and deep learning based flow forecasting for optimizing inter catchment wastewater transfer[END_REF].

In WWTPs context, ANNs have been considered to perform different tasks. For instance, ANNs have been considered as soft-sensors predicting hard-to-measure WWTPs' values. Predictions of measurements such as Chemical Oxygen on Demand (COD) and aeration tank's dissolved oxygen concentration (SO) are performed in [START_REF] Güçlü | Artificial neural network modelling of a large-scale wastewater treatment plant operation[END_REF] showing a Percentage Error (MAPE) in the prediction around 4.48%.

Other approaches are based on the adoption of ANN in the reduction of pollution effluent concentrations. This process is performed by means of highly-complex and nonlinear biochemical and biological processes which are defined by the Activated Sludge Model No.1 (ASM1) [START_REF] Henze | Activated sludge model No.1[END_REF]. Those are devoted to reducing the pollutants derived from nitrogen and phosphorus in order to reduce the pollution of the environment where treated water flows. Besides, local administrations have established limits to pollutants such as ammonium (SNH,e) and total nitrogen (SNtot,e) present at the WWTP effluent. Violations of them implies economical punishments to the WWTP [START_REF] Vilanova | Control y Operación de Estaciones Depuradoras de Aguas Residuales: Modelado y Simulación[END_REF].

In order to avoid violation, predictions can be adopted by different control to let them actuate before violations are really produced. For instance, in [START_REF] Santín | Advanced decision control system for effluent violations removal in wastewater treatment plants[END_REF] a hierarchical control strategy based on Model Predictive Control (MPC) and Fuzzy Logic control are proposed to reduce the concentrations of ammonium (SNH,e) and total nitrogen (SNtot,e) concentrations. Both strategies are fed with effluent concentration predictions performed by Multilayer Perceptron (MLP) ANN. They predict the maximum effluent concentration that will be observed during a day. Moreover, MLPs do not consider the time correlation which characterize industrial signals. In [START_REF] Foscoliano | Predictive control of an activated sludge process for long term operation[END_REF], the adoption of ANNs to forecast WWTPs effluent concentrations and feed control strategies is presented. However, the considered ANN does not take into account the time correlation between influent and effluent measurements. Instead, it adopts the previously observed effluent concentration as an input.

Research Problem

Aforementioned, ANNs predictions feeding some control strategies have been applied in previous works. However, there exist pollutant violations as shown in [START_REF] Santín | Advanced decision control system for effluent violations removal in wastewater treatment plants[END_REF], where the hierarchical control strategy is not able to totally remove the violations of total nitrogen. They are produced during a total of 8.72 days during a year. Some of these violations are produced due to the fact that the control strategy does not have enough time to process the pollutant.

In order to solve this, data driven methods and specially ANNs considering the time correlation between influent and effluent data are proposed in the realization of this PhD. In such a context, those ANNs are adopted at WWTPs industries to complement the control strategies and, therefore improve the performance of the overall WWTP operability exploiting the time-correlation of data. In the case of this work, we will focus on the nitrogen removal process. Thus, SNH,e and SNtot,e concentrations in the effluent are the process outputs to be forecasted by the considered ANNs. A violation is prone to occur whenever the prediction exceeds the limits shown in Table 1.

Table 1. Effluent limits. Exceeding these limits is punished with high economical fines.

Concentration

Value Ammonium (SNH,e) 4 mg/L Total Nitrogen (SNtot,e) 18 mg/L

Materials and Methods

In accordance to what has been seen in the previous section, the purpose of this work is to adopt ANNs to perform predictions of the WWTP effluent pollutant levels to complement the control strategies. Thus, the required materials and methods are described in this section: (i) Benchmark Simulation No.2 will be considered in the generation of input and output data, whereas (ii) ANNs especially Long-Short Term Memory cells will be adopted to perform predictions.

Benchmark Simulation Model No.2

BSM No.2 corresponds to a simulation scenario which models the default structure of a WWTP [START_REF] Jeppsson | Benchmark simulation model no 2: general protocol and exploratory case studies[END_REF]. This default structure can be split into two differentiated parts: (i) the water line and (ii) the sludge treatment. The water line corresponds to five biological reactor tanks (Activated Sludge Reactors) where pollutant concentrations reduction process is performed (See Fig. 1). First two tanks are anoxic (working without oxygen) whereas the remaining three correspond to aerated ones. An internal recycle flow transporting sludge from the last aerobic tank to the first anoxic one is considered. In addition, two clarifiers where the sludge sedimentation process is performed are considered. Concerning the Sludge treatment part, it corresponds to those elements devoted to treat the WWTPs' sludge. This part encompasses elements such as the Thickener, the Storage tank, the Dewatering and the Anaerobic Digester elements. Different flows are considered in BSM No.2 scenario: Qin corresponds to the influent, Qpo is the primary clarifier overflow, Qa is the internal recycle flow rate, Qr is the sludge internal recycle flow rate and finally Qe is the effluent flow rate.

Activated Sludge reactors Primary Clarifier

Secondary

Long-Short Term Memory Cells

ANNs consist in a set of layers where each layer considers a certain number of hidden neurons. These neurons are characterized by an activation function which relates the input of the neuron with the output. Each layer is connected to its neighbors by means of weights and biases which are obtained in the ANNs' training process.

Moreover, among the different ANNs, we will consider the of Recurrent Neural Networks (RNN) and specially the Long-Short Term Memories (LSTM) due to their performance dealing with time series and highly-time correlated signals [START_REF] Goodfellow | Deep Learning[END_REF]. Thus, if influent and effluent profiles are considered as highly-time correlated parameters, the application of LSTM is widely suitable. LSTMs, as other gated RNNs, are designed to alleviate the well-known RNN's problem: the vanishing and exploding gradients [START_REF] Bengio | Learning Long-Term Dependencies with Gradient Descent is Difficult[END_REF]. They replace the RNN's hidden neurons by memory cells with four gates: the forget, the input the state update and the output gate. Forget gate is in charge of resetting the LSTM's memory cell which will be filled with the information coming from the input gate (2) and the state update [START_REF] Martí | Anomaly detection base on sensor data in petroleum industry applications[END_REF].

ft = (Wf T xt + Uf T ht-1 + bf) .
(

) 1
it = (Wi T xt + Ui T ht-1 + bi) . (2)
c't = tanh(Wc T xt + Uc T ht-1 + bc) . (3)
Output gate determines if the output of the memory cell will influence on another LSTM cell or not [18, Chapter 10].

ot = (Wo T xt + Uo T ht + bo) . (4)
In all cases W, U correspond to the weights of each gate, b to their biases and x and h to the input and cell state values. LSTM structure is observable in Fig. 2. Its training process is performed by means of the backpropagation through time algorithm, which is based on the iterative update of the network weights towards the opposite of the gradient of the loss function w.r.t the weights. Training process is performed considering the well-known split of data (70% for training the ANN and 30% for testing purposes).

Expected Outcomes

Prediction Approach

This PhD is based on the application of ANNs to improve and support WWTPs. Therefore, the reduction of violations and the overall operational cost is sought. A first approach adopting WWTP's predictions corresponds to an ANN-based system, which is able to predict the WWTP's effluent concentrations. It will generate an alarm whenever a violation is predicted. Further details on the ANN-based system can be found in [START_REF] Pisa | A Recurrent Neural Network for Wastewater Treatment Plant effluents' prediction[END_REF]. The steps followed by the system are:

1. Gather available measurements of the WWTP plant (in real time) 2. Normalize the data to reach ANN converge towards an optimal solution 3. The system will order measurements considering the time correlation 4. Effluent concentrations will be predicted by means of LSTM-based ANNs 5. Predictions will be contrasted with the effluent limits to determine is violations will be performed or not. 6. The effluent limits can be modified by WWTP's operator to set the accuracy of alarm detection process by means of ROC Curves.

Prediction Results

Predictions of the proposed ANN-based system [START_REF] Pisa | A Recurrent Neural Network for Wastewater Treatment Plant effluents' prediction[END_REF] are performed by means of two LSTM-based prediction structures: (i) Ammonium prediction structure which considers two stacked LSTM cells with 50 hidden neurons per gate; (ii) and the Total nitrogen prediction structure which considers two stacked LSTM cells with 10 hidden neurons per gate. Table 2 shows the performance of both prediction structures where Mean Absolute Percentage Error (MAPE) and the false positive (Pfa) and missdetection (Pmiss) probabilities are computed [START_REF] Pisa | A Recurrent Neural Network for Wastewater Treatment Plant effluents' prediction[END_REF]. Predictions are observable in Fig. 3 Table Results show that LSTM cells predict accurate effluent concentrations in real time considering the time correlation between values. Thus, predictions performed in previously commented work [START_REF] Santín | Advanced decision control system for effluent violations removal in wastewater treatment plants[END_REF] are improved with the deployment of the proposed system by considering the time correlation between input and output data.

Future Work

The future research will be oriented along the following main streams:

• On one side, predictions improvement by adopting K-Fold as the training algorithm in order to deal with the data unbalanced problem (the largest part of values is below the effluent limits). Moreover, predictions will be adopted to feed control strategies and consequently reduce the number of violations. • Direct use of predicted output data for computing of predictive control actions.

In this case, the use of data driven predictive control algorithms is sought • The third step will be to explore the possibility to directly generate control action on the basis of ANN. In this case, ANN could be trained to do the work of the predictive controller or, instead, explore the use of the actor-critic scenario where all the players are implemented as ANN.

Conclusions

The

Fig. 1 .

 1 Fig. 1. BSM No.2 model where the different WWTP elements are observable.

Fig. 2 .

 2 Fig.2. LSTM Structure. ft, it and ot correspond to the forget, input and output gate.  and tanh corresponds to the activation function adopted at each gate. Each gate is formed by different ANNs with the same number of hidden neurons. ct corresponds to the hidden state of the memory cell. ht is the cell's output.

Fig. 3 .

 3 Fig. 3. Performed predictions. First figure corresponds to Ammonium predictions whereas second figure corresponds to the Total Nitrogen predictions.

2 .

 2 Prediction structures' performance.

	Prediction Structure	MAPE	Pfa	Pmiss
	Ammonium	7.93%	0.03	29.85
	Total Nitrogen	4.69%	0.44	31.75
	Although performance shows Pfa and Pmiss around 29.85% and 31.75%, they can be
	enhanced lowering the limits at which effluent predictions are considered a violation.
	Receiver Operating Characteristic (ROC) curves, where Pfa and Pmiss are faced,
	showing an Area under the Curve (AuC) around 0.99 (1 means perfect performance).

  adoption of ANNs and especially LSTM cells as service systems applied at industrial environments has been studied in this work. Here, two LSTM-based structures to predict effluent concentrations at WWTPs have been considered. They show a good performance yielding low MAPE values around 4.69% for SNtot,e and 7.93% for SNH,e concentrations. Moreover, Pfa and Pmiss probabilities and ROC curves have been also computed showing an AuC around 0.99 (1 is perfect performance). from La Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya i del Fons Social Europeu under FI grant. Authors belong to the recognized research groups SGR 1202 and SGR 1670 by the Catalan Government.

Acknowledgments

This work has received the support from the Spanish Ministry of Economy and Competitiveness program under MINECO/FEDER grant DPI2016-77271-R and also


Majid Zamiri
email: zamiri_majid@yahoo.com

Luis M Camarinha-Matos

Organizational Structure for Mass Collaboration and Learning

Keywords: Mass Collaboration and Learning, Knowledge Creation and Sharing, Community, Innovation

The rapid emergence of collaborative communities supported by Internet has led to unprecedented waves of novelty in the ways people create and share knowledge. In this framework, the notion of mass collaboration has opened new windows of opportunity for collective learning. Mass collaborative learning is a new paradigm, through which large numbers of people engage in collaborative initiatives to learn from each other and alter the nature of formal education. Even though mass collaboration opens up an apparently limitless field for promoting social inclusion in effective learning, not all aspects, features, and characteristics of this phenomenon such as the organizational structures are quite clear at present. Therefore, this study is conducted to review the organizational structures of 14 real examples of mass collaboration. Through the analysis of the most suitable features of those structures we expect to be able to propose a general organizational structure for mass collaborative learning purpose. It is expected that such organizational structure could help developing a better insight into this field of study.

Introduction

Advances in information and communication technologies (ICT) and the emergence of IoT (Internet of Thing) have enabled people to redefine the boundaries of collaboration. This trend provides the possibility for large scale groups of distributed humans to join into mass collective projects and harness their potential joint power to deal with multi-faceted problems in social, economic, and environmental contexts. The emergence of the mass collaboration paradigm and its application to different domains is now reshaping the landscape of a wide variety of tasks, both locally and globally. Evidences clearly show that mass collaboration, by exploiting the capabilities of thousands of people, can create a kind of agile problem-solving system which is almost superior to any type of intelligent artefact that is made to serve similar purpose [START_REF] Ramaley | Collaboration in an Era of Change: New Forms of Community Problem-Solving[END_REF].

Mass collaboration brings together multitudes of individuals that may have not had the opportunity to work together before and may remain anonymous. It brings the opportunity to utilize the brainpower of participants in a collective effort and orchestrate their attempts in order to reach a common goal. In this context, Internet and ICT have a facilitating role to play. In such collective action participants can efficiently and quickly contribute in developing an idea, plan, action, process, project, or artefact, to help solving a grand challenge [START_REF] Zamiri | Learning Through Mass Collaboration -Issues and Challenges[END_REF].

There are many interesting applications of mass collaboration. For instance, its application in social learning occurs at a wider scale than the individual or group learning. In this case a large number of interested people capitalize on one another's resources, skills, and knowledge aiming to learn something new, and create lasting impact together. Mass collaborative learning, indeed, refers to a method of learning that can take place at community level where thousands of participants collectively and proactively engage in the process of knowledge acquisition, building, sharing, and developing, and where they can add their own contributions or even revise others' contributions. As opposed to traditional and formal learning methods delivered by instructors and utilizing systematic learning approaches within educational settings, mass collaborative learning stands upon the contribution of decentralized and selfdirected participants who produce knowledge in an informal way [START_REF] Ulrike | Mass collaboration and education[END_REF].

In this case, knowledge creation and sharing can be considered as the core of learning that relies on the participation of a variety of people in learning networks helping to reach the community objectives. In addition, the ability to manage such knowledge is key to community success, which secures its competitive advantage and capability to achieve a sustainable superior performance. In this regard, it is significant to promote knowledge building and sharing that drives communities to create and/or add more value, thus engaging in effective innovation [START_REF] Chiangmai | Creating efficient collaboration for knowledge creation in area-based rural development[END_REF].

There are a number of factors that differentiate small organizations and communities from large entities, such as the type of organizational structure. An organizational structure determines how power, roles, and duties can be defined, controlled, and coordinated toward reaching community goals. It also specifies the way in which knowledge, information or data flow across different layers of the organization. Every organization or community certainly needs a structure (even if self-organizing) in order to survive, take actions, and grow [START_REF] Galbraith | Organization design[END_REF]. Every community should select its structure based on its requirements and priorities. The type of organizational structure implicitly indicates in which ways internal works can be carried out.

In the past, the structures of communities were mostly designed for effectiveness and efficiency although they are nowadays designed for agility, speed, and adaptability to be able to compete and win in today's global competitive environments. As organizations or communities are becoming more and more digitalbased and there is a transformation towards performing projects collaboratively, they are also facing with an imperative to redesign their structures in order to learn more rapidly, quickly respond to demands, and adapt to the characteristics of new workforces and workplaces. While the business environment, customer needs, technology capabilities and the nature of work in organizations and communities are likely to change, the organizational structure needs to reshape as well in a deliberate and strategic way. As such, the design of structures for adaptability is a shift away from traditional organizational structures like the hierarchical, centralized and bureaucratic models, towards unconventional models where projects are fulfilled collectively by network participants [START_REF] Yaragarla | Scenarios of collaborative approach in mass collaboration[END_REF].

However, thus far there have been very few attempts to report on the role of organizational structures in the context of mass collaboration and learning. Furthermore, there are no clear evidences in the literature that show how mass collaborative projects can define, design, implement, and develop appropriate structures. Therefore, gaining some insight on what kinds of organizational structures have more chances for being adopted in mass collaborative learning projects is the foremost motivation for conducting this research work. Thus, a key research question that emerges is: What kind of organizational structure within a community should be established to help developing learning through mass collaboration? The proposed hypothesis to address this research question is:

Community learning through mass collaboration could be helped if existing models of organizational structures for long-term strategic networks are extended to allow more fluid borders and new roles, incentives and internal subgroups are defined to focus on learning and knowledge generation.

For this study, in order to search, choose, and review relevant papers, databases such as, SCOPUS, IEEE Xplore, Web of Science and Google scholar were used, being the goal to identify relevant examples and evaluate their organizational structures.

Relationship to Innovation in Industrial and Service Systems

From an organizational perspective, knowledge communities can speed knowledge creation, transfer, and utilization on an ongoing basis, as well as facilitate knowledge mobilization (for example, through providing suitable spaces for discussion in order to narrow the gaps between research and practice) [START_REF] Lindkvist | Knowledge Communities and Knowledge Collectivities: A Typology of Knowledge Work in Groups[END_REF].

Knowledge communities are often found to introduce changes to a system, and promote the culture of innovation. Such kind of communities can be called communities of innovation which are dedicated to support innovation. Communities of innovation are creative and dynamic entities that pursue innovative solutions to societal challenges. Communities of innovation are not only responsible for a growing number of innovations, but can also provide a common ground for learning. In this subject, they can freely and efficiently impart information and knowledge to the wider public. The literature shows that some examples of communities of innovation [START_REF] Brogi | Online Brand Communities: A Literature Review[END_REF], [START_REF] Moser | The influence of social norms on community learning -a theoretical framework to understand learning processes in online innovation communities[END_REF] have successfully influenced the learning process.

Furthermore, "online innovation communities have an ability to learn in a dispersed setting without any formal involvement, their learning capability is actually very remarkable, making it even more striking that we lack academic insight how these learning competences come about" [START_REF] Moser | The influence of social norms on community learning -a theoretical framework to understand learning processes in online innovation communities[END_REF]. Mass collaboration through a large online community can be applied in various domains and fields of study. For instance, it can foster learning and optimization of the innovation portfolio through:

• Increasing the flow of new ideas, knowledge, or information generation,

• Boosting the chance of association between ideas, knowledge, or information,

• Improving the quality of ideas, knowledge, or information,

• Speeding up the collaborative feedbacks,

• Developing connection between members,

• Reaping the power of collective intelligence,

• Etc. However, it is vital in this new context to be able to evaluate how much trustworthy the acquired knowledge is, because knowledge is power, and it serves as a basis for making choices and decisions in communities. One fundamental step in the process of learning, particularly in an online environment, is to ensure that the created and shared knowledge or information is reliable, as well as the accuracy and credibility of the materials that people encounter with, are high.

It is largely evidenced that in World Wide Web, and specifically in networkedcollaborative activities neither all delivered materials are reliable nor will all stay stable. In addition, on one hand, the quality and value of various types of Internet sources (that are available in different formats) are not all high, and on the other hand not all Internet users are able to accurately evaluate the appropriateness of all types of online sources [START_REF] Kaushik | Evaluation of Internet Resources: A Review of Selected Literature[END_REF]. Thus, along with informal learning in such communities, it is essential to assess the quality and reliability of knowledge or information created in whatever format, particularly in mass collaborative projects. To cope with this challenge, approaches such as machine learning [START_REF] Gilda | Evaluating Machine Learning Algorithms for Fake News Detection[END_REF], digital audio and video output [START_REF] Arnold | Assessing Student Learning Online: Overcoming Reliability Issues[END_REF], BS detector [13], and linguistic and network-based approaches [START_REF] Conroy | Automatic deception detection: methods for finding fake news[END_REF] can play a relevant role.

Analysis of Selected Cases

In this study, in order to gain a clear understanding of the organizational structure of mass collaboration, and propose an appropriate structure for mass collaborative learning projects, 14 relevant examples of mass collaboration in different domains were selected from the literature including, 1) Wikipedia (a well-known case of a web-based encyclopedia that is written collaboratively by its users), 2) Digg (a social networking website that aggregates interesting online news, pictures, and videos), 3) Yahoo! Answer (a community-driven question-and-answer website that allows users to ask questions and answer questions), 4) SETI@home (a computing project and scientific experiment that benefits of Internet-connected computers in the search for signs of Extraterrestrial Intelligence), 5) Scratch (an online community that enables children to program and share interactive media with other people), 6) Galaxyzoo (a crowdsourced and on-line astronomy project which classifies the morphology of galaxies, and then analyze their pictures and rate them), 7) Foldit (an online puzzle video game that uses the power of distributed computing to create and design the primary structure of chosen proteins), 8) Applications of the Delphi method (a structured communication technique based on the results of several rounds of questionnaires sent to a panel of experts), 9) Climate Colab (an open problem-solving platform where a community of experts on climate change evaluate plans to reach global climate change goals), 10) Assignment Zero (an experiment in crowd-sourced journalism, allowing collaboration between lots of people to work on a publishable story, with many parts), 11) DonationCoder (a community of programmers who develop and finance their own free software), 12) Experts Exchange (a trusted global online community offering millions of verified solutions from industry experts), 13) Waze (a navigation app that runs on smartphones and tablet computers, through which users help each other to find directions and avoid traffic jams), and 14) Makerspaces (which are physical or digital spaces for open collaboration, where people have access to resources for developing projects with the aim of creating products or services).

In Table 1 and Table 2, the organizational structure and main characteristics of two of the above-mentioned examples are summarized as an instance.

Table. 1. Organizational Structure of Wikipedia

What is Wikipedia: it is a web-based, free and open content encyclopedia based on a model of openly editable content -a wiki. As a general reference, it has been the most popular on the World Wide Web. It is written collaboratively by the people that use it. Many people from around the world are willingly contributing in Wikipedia development.

Wikipedia Organizational Structure Wikipedia Membership Method

• Wikipedia is open to public to use, read, write, edit, and make changes in articles.

▪ People from any age group, gender, race, culture, and background can participate in. • Wikipedia community includes all anonymous editors, supporters, current and potential readers.

• Wikipedia contributors consist of two main groups, those who create an identity, and those who communicate with other contributors. Wikipedia Roles Wikipedians (or editors) are the volunteer who write and edit Wikipedia's articles. Anyone can become a Wikipedian. Wikipedians do a wide variety of tasks, being free to pick and complete their tasks anytime anywhere. Wikipedians through collaboration and discussion can gain a sense of collective purpose and connectedness. While there are disagreements about an issue, a consensus can be reached through open and friendly discussion. While unresolved disputes or conflict arise, no matter the reason, it can be pursued in related talk page, either through comprehensive dispute resolution process of Wikipedia or, requesting comments from other Wikipedians.

No

Roles Descriptions 1

Account creator Gets access to a tool that allows trusted Wikipedians to create a high number of accounts for other people who request them. 2

Editor (from expert scholar to casual reader)

A volunteer who writes and edits Wikipedia's articles.

3 Bureaucrat Permitted to perform particular actions on Wikipedians' accounts. 4

Volunteer Response Team Group of volunteers who answer most email sent to Wikipedia. Digg Content Management • All the content-related decisions are made by site's users.

• When a user submits a story, its validity will be checked by the system.

• When a submitted article is up for promotion to a category homepage or the front page, the system (karma) checks it to make sure the Diggs are valid. • Digg does not have editorial control on submissions, promotions, or burying.

• Digg manages all things with a proprietary algorithm (de-promotion algorithm). When a story is Digged by certain number of users (at least 40 persons), the Digg system automatically will move it to the front page of the website. • The most popular of Digg are placed in the "Top News" section of the website.

• The top news can be anything (e.g., fun content or serious news).

• Digg is classified into different groups based on topics (e.g., business, technology, videos, and entertainment news). • Digg has tabs that let users filter or sort contents into news stories, videos, images and podcasts.

In summary, elements for a typical organizational structure for mass collaborative learning projects, as derived from the 14 studied examples, are displayed in Figure 1. Having critically analyzed the organizational structure of 14 studied examples of mass collaboration and also reviewed related papers, it is concluded that each organizational structure stands upon some building blocks and fundamental elements. In this sense, it is therefore suggested that the organizational structure of mass collaborative learning (for creating, developing, and servicing) should take into account at least four core elements and three supplementary elements. Core elements including, A) the required mechanism for members to join the community, B) the roles that can be taken and played by members, C) the methods of governing the community, and D) the way that possessed knowledge or information can be managed properly and efficiently. Supplementary element consists of, a) the ways and levels that members can engage in different activities, b) the ways that different roles can built and involved in interrelationships, and c) the power, rights, and responsibilities that members can take. In this structure the role of supplementary elements is augmenting, clarifying, and facilitating core elements. This organizational structure depends on the situations and conditions of application. It is expected that the proposed structure in this work can contribute to the development of this field of study, and enrich the understanding of the complex organizational structures for mass collaboration and learning.

Conclusions and Future Work

Mass collaborative learning provides greater opportunities for distributed contributors to engage in virtual global learning and take advantage of powerful social communities of experts and peers to develop innovative solutions to major challenges. Despite of successful outcomes that mass collaborative learning already gained, we still need to clarify our understanding about the required organizational structures for this emerging phenomenon.

Evidences demonstrate that collaboration and innovation are not mutually exclusive; on the contrary, they feed and build upon each other. That is, collaboration brings and drives innovation, and innovation happens through collaboration [START_REF] Gressga˚rd | Virtual team collaboration and innovation in organizations[END_REF], [START_REF] Levine | Open Collaboration for Innovation: Principles and Performance[END_REF], [START_REF] Camarinha-Matos | Collaborative Networked Organizations -Concepts and practice in Manufacturing Enterprises[END_REF], [START_REF] Rosas | Open Innovation Participants Profiling: An Archetypes Approach[END_REF]. Considering that fact, in this study, in order to identify appropriate organizational structures for mass collaborative learning projects, the organizational structure of 14 real examples of mass collaboration are reviewed.

This work is still ongoing, but it is expected that the preliminary findings of this review and the proposed organizational structure can provide communities and learners with helpful guidelines and directions for achieving effective mass collaborative learning.

Fig. 1 .

 1 Fig. 1. Elements for an Organizational Structure for Mass Collaborative Learning

  Digg provides a place for lively conversation, discussion, inquiry, and debate. Digg community can discuss the topics that they're passionate about.• Users can add friends and develop their relationships.• A user can block another user if doesn't like his submissions or comments. The blocked user may get banned from Digg if he is blocked by enough number of Digg users. • Users can create or develop a "Digg game" by submitting stories and digging them.

			report those are not good.
			Relationship Between Roles in Digg
	•
	5	Event coordinator	For a short time, can add new created accounts to verified user
			groups, hence such accounts could write new articles.
	6	Edit filter manager	Can make, change, enable, disable, and omit edit filters. Moreover,
			can check private filters and also their related logs.
	7	Arbitration Committee	Handles those conflicts which remain unresolved while all the efforts
			in dispute resolution have failed.
	8	Steward	Can give and revoke any authority to or from any Wikipedian on any
			wiki operated by the Foundation of Wikimedia that permits creating
			open account.
	9	Oversight	Authorized to delete pages and revisions, and block function pages
			that it makes possible to hide logs or modify pages from any form of
			usual access by other Wikipedians.
	10	CheckUser	Permitted to check the list of all IP addresses, the list of all edits, and
			all user accounts.

Acknowledgements

This work was funded in part by the Center of Technology and Systems of Uninova and the Portuguese FCT-PEST program UID/EEA/00066/2013.

Administrator A volunteer editor who is granted some technical authority to make particular decision and actions about blocking and unblocking user accounts and IP addresses; editing, modifying, and removing the protected pages, deleting and undeleting pages, protecting and unprotecting pages from editing; and etc. [START_REF] Arnold | Assessing Student Learning Online: Overcoming Reliability Issues[END_REF] Interface administrator The only local user group who authorized to edit Cascading Style Sheets, JavaScript, and Javascript Object Notation pages.

Relationship Between Roles in Wikipedia

• The community of Wikipedia is divided into large number of "spheres" which categories members based on their area of interest, expertise, background, age, etc. • The conversations and debates among members will be facilitated by means of Discussion Pages.

• There is possibility for members to nominate each other for awards of Wikipedia.

• Neither the quantity or frequency of contributions can be controlled, nor will members be fired.

• There are additional administrative responsibilities that can be taken to serve the community better.

• It has been attempted to Wikipedians be treated equally with no "power structure" although a hierarchy of positions and permissions is there (from simple editor to Jimmy Wales, the founder of Wikipedia).

Wikipedia Content Management

• Wikipedia is a free online encyclopedia and its entire contents are written by unpaid volunteers.

• Wikipedia is continually developed and updated. Its articles are intended to be realistic, reliable, and verifiable with cited external sources. • Wikipedia has variety of procedurs (e.g., peer review, good article assessment, and the featured article process) for continual article review and improvement. • Feedback about articles, in the first instance, raise on the related discussion pages.

• Quality constantly improves via removing or repairing the misinformation and other errors.

Table. 2. Organizational Structure of Digg

What is Digg: it is a website that aggregates interesting online news, pictures, and videos through compiling links to the relevant webpages. Users submit stories for promotion, and they are subsequently either voted for (digged) or against (buried). The most popular contents are posted to the front page for making it easier to identify and consume the most important stories, videos, and trends of the day.

Digg Organizational Structure

Digg Membership Method • Digg is a social networking and user-driven website that anybody can participate.

• In Digg, nothing is written by paid editors. Contents are made by hundreds of thousands of users.

• All users need to create a Digg user account to access the features of the website. They should play active role in both presenting and Digging stories. Log in is mandatory to get to the website. It provides kind of security assurance for each visit. • After registration, it is possible for users to give comment and vote on others contribution.

• All users' information (e.g., past Diggs, friends, feedbacks) will be stored in their Digg profile.

• Users can submit their stories and also benefit of all provided features in the website. Bad stories will be ignored, and good ones promoted. Thus, the stories that got positive votes and selected as the best will be cross-pollinated across other channels.