Christophe Assens

Jean-Pierre Bouchez

Social Networks and Professional Communities: A Fair Governance?

Introduction

This research focuses on social relationship management within networks and workplace communities in which the softening of institutional boundaries and the development of a digital and collaborative economy becomes as important as financial resources and skills management. Yet relationship management is certainly the most problematic task as it is difficult to foresee and control another person's response. To minimize uncertainty, businesses tend to organize relationships among colleagues in a social network or a professional community in which the principles of trust and reciprocity prevail.

An Issue of Governance

One of the major topics of this research is the governance of a network or professional community, namely, how decisionmaking power and tasks are shared. In other words, what is the best way to manage the collective interest without threatening independence, individual responsibility, and equality among members? This is not an easy problem to resolve because traditional governance theory, such as Board of Directors' control of private companies or state supervision of public bodies, cannot be applied to the network; the parameters of a network's territorial, legal, or capital limits are not always clear. On the one hand, no clear legal, territorial, or capital boundaries have yet been established. On the other hand, the network may evolve into a hybrid public/private entity. Further, the network is not always represented by a collective agent with the legitimacy to discipline its members. Finally, the network does not always have a predetermined objective for its existence [START_REF] Kilduff | Social Networks and Organizations[END_REF].

In order to explore this issue of network and community governance, we shall base our analysis on the work of [START_REF] Provan | Modes of Network Governance: Structure, Management and Effectiveness[END_REF] by successively examining three forms: the participant-governed network, the lead organizationgoverned network, and the network administrative organization. This classification will enable us to determine, in the chapter's conclusion, whether governance is socially responsible based on certain criteria that could limit its implementation: transparency of information that is able to give legitimacy to a decision; equal treatment of members with equitable power sharing, which acts as a gauge of efficiency for democratic government; and equity in the distribution of tasks and payment, which effectively empowers each member.

Networks, Communities, and the Governance Model

In order to fully understand the challenges of our subject, it is important to establish the difference between the networks and communities that exist, particularly within organizations in the form of social networks (SNs) and communities of practice (CoP). 1A social network is defined by [START_REF] Powell | Neither Market Nor Hierarchy: Network Forms of Organization[END_REF] and [START_REF] Williamson | Comparative Economic Organization: The Analysis of Discrete Structural Alternatives[END_REF] as a collaborative organization of institutions or actors whose rules of exchange depend neither on market nor on hierarchy. The network is formed of fiscally and legally independent associates who self-govern and rely on one another for the achievement of a common goal.

Much like an ecosystem, as described by [START_REF] Moore | The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems[END_REF], the network thrives on an exchange with its environment, which urges it to further extend its sphere of influence and constantly challenge its own limits. However, the more the network expands, the more difficult it is to regulate, as the number of interacting components increases. Conversely, when the network stops growing, it loses flexibility and modularity, giving way to rigidity between its connecting parts, thus causing an imbalance of power between its members. Furthermore, by continuing to rely on the work of [START_REF] Lave | Situated Learning: Legitimate Peripheral Participation[END_REF], [START_REF] Wenger | Communities of Practice. Learning, Meaning, and Identity[END_REF]Cohendet et al. (2008), we will consider that a CoP is characterized by the voluntary and regular involvement of its members. They take part in the sharing and exchange of knowledge and practices, based on a shared interest in a given field. This in turn leads to the development of a shared identity while complying with the group's social norms. Also, a distinction between the two emerges through the deployment of a key variable based on the intensity of ties [START_REF] Bouchez | Vers l'émergence progressive d'un nouveau cycle de management hybride ? Le cas des communautés de pratique 'pilotées[END_REF][START_REF] Bouchez | L'entreprise à l'ère du digital. Les nouvelles pratiques collaboratives (Méthodes & Recherches)[END_REF], by referencing the work of [START_REF] Granovetter | The Strength of Weak Ties[END_REF] on "the strength of weak ties." Taking this perspective, SNs inherently rely on the logic of "weak ties." The internal rules of engagement and operation are-a priori-much looser and more relaxed, and concern peripheral relationships that fall outside the primary circle, such as neighbors, work colleagues, business associates (or sports buddies), friends of friends, and so on.

As Granovetter consistently illustrates, the strength of these ties is that they lead to a breakdown of boundaries, thanks to the potential for joining new circles and new communities, for example. This perspective also increases individuals' potential for accessing information that is quantitatively, and often qualitatively, superior to that offered by close relationships alone. In this way, these weak ties are "bridges" that provide members gateways to the members of other networks or social groups, thus building a greater social capital than that open to individuals with only strong ties. Similarly, we shall argue that CoPs more typically rely on "strong ties" in that they rely on a very close primary and direct network in the familial, intellectual, and cultural spheres, that is, those found in affective relationships. The strength of these ties in fact correlates with their closeness and the trust they generate. But very intense links can also contribute to the creation of relationships that isolate the members of a group, as shown below. Further, we will outline how [START_REF] Bogenrieder | Learning Groups: What Types Are There? A Theoretical Analysis and an Empirical Study in a Consultancy Firm[END_REF] present four criteria with which to assess the strength of ties. The first refers to their "intensity," explicitly their "strength," considering the amount of effort each party puts into the relationship. The second relates to the "frequency" of interactions. The third relates to the "degree of openness" of communication, and the fourth to the relationship's "longevity." These criteria highlight that the weak/strong ties polarity is graded. In this way, some digital CoPs within large firms bring together many thousands, sometimes tens of thousands of members (as with IBM). In this instance, the CoPs in reality resemble an SN.

It is however important to note from the outset, the network and the community frequently appear together within organizations. In this way, the largest digital network corporations, such as Facebook or LinkedIn, encourage their millions of members to organize themselves into "groups" that resemble communities of interest with minimal organizational effort. Equally, in large organizations that now have digitalized social networks, CoPs attach themselves to SNs, particularly as groups that share a professional interest.

With regard to the governance model, taking the work of Moreau Defarges (2003), we will examine three dimensions in order to understand the mechanics of governance. In other words, one has to address three dimensions that explain the power struggle between networks or communities of participants and the distribution of their bargaining power:

-The founding principle: the network is viewed as a regulated collaborative space based implicitly or explicitly on a founding principle of solidarity. In other words, it is necessary to understand the common denominator of its members and the sense of affiliation that unites them. This will help us to understand the strategic issues of governance.

-The network architecture: the issue is to examine how network boundaries change as members join and leave, whether by co-option or by "decree." This may prove useful for understanding network properties.

-The rules of relational games: this leads us to examine the informal or formal code that governs relationships within the network, guides collective action, and arbitrates conflicts between its members. This will help us understand how decision-making power is distributed and how the benefits of the common relational capital are shared.

Based on these dimensions, [START_REF] Provan | Modes of Network Governance: Structure, Management and Effectiveness[END_REF] recommend distinguishing three types of network, each with specific governance mechanisms that apply to communities: the participant-governed network, the lead organizationgoverned network, and the network administrative organization. We undertake to present these in the three sections that follow.

The Participant-Governed Network: Self-Governance Within this form, as with the lead organization-governed network that follows, we will sequentially present the network (RS) and community approach (CoPs), always bearing in mind that the third form, the network administrative organization, is more dependent on network than on community.

Network Perspective

This is based on a dense network, with members who occupy symmetrical and interchangeable positions. This means that no single member exercises control over another. In other words, no member is able to fill the network's structural gap by becoming an intermediary or pivotal connection. In this type of network, collective power is equally shared between all individuals.

Within the participant-governed network, governance has certain specific features. In so far as no member occupies a central position, governance is a self-regulatory process [START_REF] Accard | La dynamique des réseaux non hiérarchique[END_REF]. The self-governance process is a social structure based on exchange conventions. Through their interactions, the members together establish conventions that will either empower or restrict their subsequent interactions. These conventions also provide interactions with a spatial and temporal framework. In other words, the network develops a "structure" [START_REF] Giddens | The Constitution of Society: Outline of the Theory of Structuration[END_REF].

This type of self-governance exists mainly in networks with a founding principle based on corporatism, local rooting with a strong sense of belonging to a common territory, and shared working practices.

Participant-governed networks correspond, for instance, to a "community of practice" as defined by [START_REF] Wenger | Cultivating Communities of Practice: A Guide to Managing Knowledge[END_REF]. Specifically, this means that the network operates in a community-like way, regulated by peers who transmit the codes and adopt unwritten conventions [START_REF] Gomez | Qualité et théories des conventions[END_REF]. These conventions give meaning to collective action by ensuring legitimacy of action and equitable sharing of resources. Thus, deviant or over-opportunistic behavior is proscribed by peer pressure.

Community Perspective

Taken from a community perspective and its focus on selfgovernment, we will look at the case of Communities of Practice (CoPs), known as autonomous communities. The first CoPs, whose characteristics we highlighted above appeared progressively; thus, in some corporations from the 1990s on, they took on a spontaneous form. In the above contribution, the American anthropologist Julian E. Orr (1996) presents the community of maintenance technicians at Xerox, after having observed them closely in situ. These technicians, who worked on site at Xerox clients' premises, had the habit of meeting informally before and after work, as well as during their lunch break, to exchange information and share "war stories," which created a real narrative, generally around the subject of machinery malfunctions that "strangely" weren't covered by the company's very extensive documentation … These informal, professional exchanges gave technicians the opportunity to share their collective knowledge and practices in order to resolve problems created, in particular, by unforeseeable or unusual malfunctions.

In this way, the group effectively becomes a "self-organized" or "autonomous" CoP: one that is based on an area of common and shared interest as well as mutual, voluntary, "free" and virtually invisible interaction. Over time, it develops a collective memory based in particular on the sharing of an operational repertory of contextualized practices (under the form of a shared passion), thus effectively replacing the official manual.

Management's attitude was initially hostile, but evolved; particularly after finding that when the informal meetings were suspended, practices were no longer shared and client calls increased significantly, especially with regard to unforeseeable malfunctions … In order to overcome this problem, the firm launched a project called Eureka, designed to provide structure and supervise the diffusion of tacit practices relating to these technical repairs, and finally recognize the CoP by creating a database able to store and preserve useful ideas, making them accessible to all. After the Eureka project had been running for a few years, Xerox estimated that it had saved the company close to 100 million dollars.

Too Much Solidarity Kills Solidarity

The self-governed CoP provides the first real-life experience of a participantgoverned network thanks to its two-core governance-related characteristics: first, the equal treatment of members regardless of their social status, their size, or skills, and second, the limiting of individual opportunism through peer pressure.

More precisely, a lack of anonymity leads to a form of selfdiscipline for all the links in a chain of solidarity. In other words, in a participant-governed network, trust cannot be broken without negative consequences to the originator. By leaving the group, he loses access to the network resources on which he depends. Indeed, if one party tries to mislead another, the offending party is punished by all members of the network, not only by the victim or a transaction partner. Therefore, the transaction becomes equitable not because of the balance in interactions between parties, but rather the reciprocity, with its shared vision of solidarity.

However, this sense of solidarity is weakened when the network expands haphazardly and allows access to trespassers. Indeed, like any shared property that is used individually and paid for by the community, solidarity is beneficial for everyone and no one in particular [START_REF] Hardin | The Tragedy of the Commons[END_REF]). Yet, the management of a common asset is a sensitive issue, because its use cannot be refused to any member within the network, including those who exhibit trespassing behavior, thus depriving other users of the shared benefit of its use. This is why it is sometimes necessary to introduce a more formalized mode of governance to avoid deviant behavior among peers that may transform the network into a lead organizationgoverned network or a network administrative organization.

The Lead Organization-Governed Network: Hierarchical Governance

Network Perspective

The characteristic feature of a lead organization-governed network is a hierarchy that exists between a member who is at the pivot of exchanges and the group's peripheral members. The central member acts like a pilot regulating the behavior of other members [START_REF] Assens | Le réseau d'entreprises: vers une synthèse des connaissances[END_REF]. Specifically, the network pilot has three particular competencies: a strategic vision of the network's future through a set of specifications and contracts with various members of the network; the ability to create and consolidate an atmosphere of trust and reciprocity [START_REF] Mcevily | Architects of Trust: The Role of Network Facilitators in Geographical Clusters[END_REF]; and lastly, the means to canvass and select new associates. In this capacity, he defines the network's boundaries and pools its resources like a master architect. He secures solidarity within the network because, as project manager, his objective is to constantly consolidate relationships between entities. To do this, he monitors and contributes to the development of relationships in many ways: he shares information, educates newcomers, and disciplines members who fail to comply with the rules he introduces.

Thus, the unity of a lead organization-governed network relies on the intangible assets belonging to the pilot [START_REF] Assens | Mesurer la création de valeur réticulaire[END_REF]: brand image, relational capital, access to market players, and technological knowhow. They give meaning to the founding principle and explain the solidarity between partner manufacturers within the network. Ultimately these assets unite network members around a pilot situated at the core of all exchanges.

Community Perspective

Since the 2000s, there has been a progressive shift from "selforganizing" to "sponsored" communities. The main reason behind this is the common need of large, enlightened organizations to develop an original, hybrid form of governance that is advantageous for all participants. The sponsored communities of practice (SCoPs), via their lessremote leaders, gain official recognition from their sponsor leaders, as well as a certain amount of resources (time, meeting areas, financing), once their projects and work appear relevant.

SCoPs have in fact multiplied, sometimes bringing together many thousands of members through the use of digital supports.

In his annotated review, [START_REF] Bootz | L'évolution du manager: un pilote de communauté de pratique entre l'expert et l'intrapreneurs[END_REF] states that Siemens, British Petroleum, IBM, the Council of Europe, GDF-Suez (now Engie), Hydro-Quebec, the World Bank, HewlettPackard, EDF, Clarica, and Schlumberger have all undertaken a knowledge management initiative centered around sponsored communities of practice (SCoPs). For their part, [START_REF] Cohendet | Créer implanter et gérer une communauté de pratique[END_REF] confirm this trend by classifying the different types of sponsored communities: "apprenticeship groups" (Hewlett-Packard), "family groups" (Xerox), "peer groups" (British Petroleum), "knowledge networks" (IBM Global Services), "knowledge sharing groups" (Siemens), and "internal knowledge management group" (EDF GDF-former name). The objective remains the same: these companies recognize that communities are capable of making a big contribution to performance.

Too Much Monitoring Kills Practice Sharing

The sponsored communities referred to as "institutionalized," that is, those that form part of and are integrated into an organization, can be likened to a type of deviation. We have observed two approaches that must be differentiated from this point of view: "project communities" and "hierarchical communities."

The first case refers to a return to classic and formal functioning, particularly in the guise of qualified, collaborative "project" communities that in reality operate in a way that is analogous to traditional collaborative project groups. They function by mobilizing digital tools and constrained processes to the point of losing any identity and specificity. Managerial governance and procedural devices therefore strip them of all "community spirit" in a type of "recycling," hijack, or deviation. However, we have observed an increase in the existence of project manager communities. The second case can be seen in the deployment of what are referred to as "hierarchical" communities; inevitably closed, they extend a service or department's reach. But this fusion of a hierarchical team and a community (the team leader is also the community group leader), even when it includes exchanges that are both extraprofessional (sharing of holiday videos and curiosities, etc.) and professional (calendars, document libraries, information on current activities, capitalizing exchanges and document monitoring, etc.) carries a real risk of clouding boundaries and heightening embarrassment.

The Network Administrative Organization: "Democratic Governance"

By its very nature and extent, the administered form creates more of a network than a community, although this does not stop communities from forming or attaching themselves to these vast networks.

The network administrative organization has specific characteristics. Collaboration among members falls under the jurisdiction of institutional rules, that is, the charter of rights and duties required for network membership. The governance structure, led by elected members, is responsible for changing these rules and ensuring their implementation with compliant members.

This governance structure embodies the legal nature of the network. It engages the network's legal responsibility and has the legitimacy needed to resolve bottlenecks, mediate conflicts, and define the axes of expansion. Consequently, the network administrative organization is a real joint venture based on affectio societatis, that is, on the intention of its members to be considered equal within the governance structure [START_REF] Hatchuel | La société contre l'entreprise ? Vers une norme d'entreprise à progrès collectif[END_REF]. This model is dominant in the social and solidarity economies, associations, NGOs (non-governmental organizations); in the mutual banking sector, the social capital of which is distributed among member customers; in mutual health organizations where solidarity becomes apparent in the distribution of contributions made by the sick and the healthy; in the private sector with its economic interest groups; in the public sector through the example of public interest groups (PIG) which have a limited life span and a legal entity aimed at implementing a public interest project by pooling member resources.

We can complete this assessment by citing the cooperative entity, which appears in many production sectors such as banking, crafts, trade, industry, and services. It is likewise prevalent in the agricultural sector, which has a total turnover of 4 billion euros and employs 42,000 employees who are affected by this status.

One of the specific features of the network administrative organization, the examples of which we mentioned above, is the fair distribution of property rights, and, therefore, the democratic distribution of decision-making power within the governance structure. According to [START_REF] Parrat | La gouvernance d'entreprise[END_REF], the relational capital within the network is distributed in a "democratic" manner. The right of ownership over the governance structure is assigned solely to network members. Thus, no individual owner may appropriate the benefit of relational capital, which is a membership benefit, at the expense of other partners. The right to use social capital or a network membership benefit, in order to gain from it or transfer its ownership, therefore belongs to the network as a whole. Despite these theoretical arguments, the "democratic" dimension of a network's governance is often challenged in practice.

Too Much Democracy Kills Democracy

In associations or cooperatives, all members are entitled to an equal share of property rights in order to better apply the principles of solidarity and reciprocity. In theory, this rule is intended to reinforce the network's democratic nature, with the election of representatives to the Board of Directors being part of the governance structure. In practice, in the case of very large cooperatives or associations with several tens of thousands of voting members, the power of representation becomes so diluted and remote from its original purpose that elected representatives of the Board lose the legitimacy of their authority. This loss of legitimacy occurs most often in a technostructure in the case of the managing director, who enjoys the status of employee but not of elected member. In fact, in the end, the managing director, who remains untouched by the outcome of a vote, has the real power within a network, without ever being subject to the political alternation of elected representatives. In these circumstances, members of a network administrative organization may gradually lose "democratic power" in favor of a technostructure.

Conclusion

In theory, both networks and communities operate according to democratic standards, with a transparency and collegiality in decisionmaking that is in line with the universal principles of good governance [START_REF] Graham | Principles for Good Governance in the 21st Century[END_REF] as defined in the United Nations Development Programme (UNDP). On the one hand, it concerns the legitimacy that gives any actor a voice in decisionmaking in order to achieve consensus despite divergent interests, the orientation that it benefits all and not just some. On the other hand, it concerns the efficiency that results from a rational use of resources to produce the best possible result; the accountability practices that apply to members in charge of transparency of information; and the equity of coming together around the principles of equality and impartiality.

According to our framework of interpretation based on three forms of governance-participative, lead, and administrative-the network and community are not always untarnished examples of good governance that are unwaveringly respectful of democratic principles. We will review these principles to discuss the limits of their practical application within the network.

The legitimacy of network members to participate in collective decisions is at question, particularly in the lead organization-governed network where only the pilot has the legitimacy to make decisions on behalf of everyone, with the inherent risk that he will exploit the organization for his own interests. As for the legitimacy of a community of practice, and in particular that of its leader, the institutional integration into a classic organization amounts to a deviation that has the effect outlined above, specifically, a distortion of the "community spirit."

In practice, the orientation of a network, and of a community, is not always beneficial to the majority of network members. In the network administrative organization, the founding members retain the prerogative to steer the network's development and prioritize their own specific interests before considering the stakes of new members.

Efficiency is not always guaranteed either within the network or the community. Peer control as in a participant-governed network can cause adverse effects because of the crossbreeding of members acting as judge and judged, especially when network membership operates by the "reproductive cloning" of its existing members. This can lead to inertia or the desire for a soft consensus on collective decisions, thereby resulting in a loss of individual accountability and the paralysis of the network as a whole.

Accountability practices are not always established. In a participantgoverned network, for example, operating without a pilot or compliance with a charter of best practices, self-governance can lead to the appearance of "trespassers" who do not wish to get involved in the community but enjoy the benefits that it provides. Responsibility is no longer fairly distributed but is based on a compromise between active and inactive members. If there are no regulatory mechanisms to correct the perverse effects of this self-governance, the network may become discriminatory and rapidly lose efficiency.

Equity is another principle of good governance that can be jeopardized under certain circumstances. In a network administrative organization, the more one advocates equity with a fair distribution of decision-making power between all members of the network, the higher the risk of diluting this collegial power until it finally becomes a technostructure (too much democracy kills democracy). In the lead organization-governed network, particularly in the network form, the peripheral members' proximity to the pilot is more likely to sharpen relational asymmetry than reduce an imbalance of power (too much proximity kills proximity). Finally, in a participant-governed network, solidarity that is based on goodwill has its limitations because of the existence of active members and those who benefit from the structure without getting involved (too much solidarity kills solidarity).

Further, network governance does not depend on the nature of geographical boundaries. In network administrative organizations, local roots can be a good indicator of solidarity only if physical proximity is important for cognitive proximity-for the ability to exchange information and remain supportive. In lead organization-governed networks or participant-governed networks, territorial boundaries are not enough to understand the true limits of solidarity within a network. This solidarity can be extended by means of telecommunication tools, due to the geographical migration of agents (diasporas), without raising issues of sharing and pooling. Therefore, the true limits of a network or a community are often intangible; they are based on a sense of belonging to a club, with specific rules of co-optation and control. Governance issues are thus dependent on the rules of exchange that are established through conventions and based on a balance of bargaining power, for which a member's location is less important than his or her position within the network. This reflects his influence over others as an intermediate or a pilot, even if this influence is exercised remotely.

Table 12

 12

	.1

Table 12 .1

 12 An illustrative example: the originally "self-organizing" CoP of maintenance technicians at Xerox

It is worth noting that these concepts of community and network, which have come back into vogue over the last years, are probably as old as the history of mankind itself…. So that one could ask oneself whether digital communities are based on an intermediate form between traditional, human-sized communities and wide social networks.


Christian Lafforgue

Samuel Serna

Sylvain Guerber

Hongtao Lin

Joan Manel Ramirez

Guillaume Marcaud

Carlos Alonso-Ramos

Xavier Le Roux

Kathleen A Richardson

Nicolas Dubreuil

Delphine Marris-Morini

Juejun Hu

Frédéric Boeuf

Sébastien Cremer

Stéphane Monfray

Eric Cassan

Laurent Vivien
email: laurent.vivien@u-psud.fr

Nonlinear third order silicon photonics enabled by dispersion and subwavelength engineering

Keywords: Silicon photonics, nonlinear photonics, chalcogenides, slot waveguides

INTRODUCTION

Silicon photonics is now the preferred integration platform for the realization of complex circuits of high integration density [START_REF] Vivien | Handbook of silicon photonics[END_REF][START_REF] Liu | [END_REF][3][4][5]. At the same time, silicon, which is a material with a high Kerr index, has a bandgap energy (around 1,1eV) that makes it very sensitive to the phenomenon of two-photon absorption (TPA) in the telecom band around 1.55µm and at shorter wavelengths. To make concrete progress towards the realization of non-linear functions in CMOS technology, or at least compatible with an approach to the deposition of an optical substrate in back-end technology, the problem of silicon TPA must be addressed in the first approach [6].

We first report supercontinuum generation [7][8][9] in nitrogen-rich silicon nitride waveguides fabricated through back-end CMOS compatible processes on a 300mm platform. By pumping in the anomalous dispersion regime at a wavelength of 1200nm, two-octaves spanning spectra covering the visible and near-infrared ranges were obtained. Numerical calculations were performed and show that the nonlinear index of nitrogen-rich silicon nitride is the same order of magnitude than that of stoichiometric silicon nitride despite a decreasing of light confinement. Nitrogen-rich silicon nitride then appears to be a promising candidate for nonlinear devices compatible with back-end CMOS processes.

In a second approach, we demonstrate a novel class of highly nonlinear hybrid waveguide structures based on infiltration of As2S3 chalcogenide glass into silicon slot waveguides. The nonlinear properties of the hybrid waveguides were precisely quantified via a bidirectional top-hat D-Scan method, enabling a direct comparison between different device geometries. Furthermore, we experimentally verify that it is possible to have waveguides with TPA figure of merit larger than 1 (FOMTPA>1), which allows nonlinear phase shifts larger than π with minimal loss, opening the possibility for large scale, efficient and robust ultrafast all-optical devices and circuits.

SUPERCONTINUUM GENERATION IN NITROGEN-RICH SiN WAVEGUIDES PUMPED AT λ~1200nm

Stoiechiometric SiN (Si3N4) is an interesting nonlinear material that reconciles several of the above issues. It has a Kerr index n 2 =2,6×10 -19 m 2 W -1 and SiN on SiO2 waveguides can be characterized by low to ultralow propagation losses (<< 1dB/cm), yet strongly depending on the considered fabrication process. However, most of the reported SiN devices were fabricated using high temperature processes such as low pressure chemical vapor deposition (LPCVD) or annealing steps to avoid cracks in the films and achieve ultra-low linear losses. Such high temperature fabrication steps are not compatible with back-end CMOS processes, hindering the integration of SiN devices on electronic-photonic integrated circuits for large scale and low cost production. Plasma enhanced chemical vapor deposition (PECVD) addresses this issue since it is a low temperature deposition method (<500 ) widely used to deposit SiN films in CMOS foundaries. Nonetheless this deposition method involves the use of precursor gas such as silane resulting in N-H dangling bonds in the SiN film responsible for strong absorption at wavelengths around 1.5μm. Rece m. Recently results have been published indicating the possibility to reduce drastically the linear losses at 1550nm by employing deuterated SiN. This method can be a back-end compatible solution for nonlinear photonics in the C-band, but still no advances has been showed to cover the O-band (1260nm-1360nm) with similar processes. In addition, the development of shorter wavelength light sources in the visible is of great interest from an application point of view. Here we report a two octave spanning SCG covering the visible range and the O-band in N-rich SiN waveguides fabricated on a 300mm platform at STMicroelectronics. First a thermal oxidation of a silicon substrate was performed to obtain a 600nm thick silicon dioxide (SiO2) layer. Two tetraethyl orthosilicate (TEOS) deposition steps were carried out to form a 2μm. Rece m thick oxide layer. Then a 600nm thick N-rich SiN film was deposited with low-temperature PECVD. Finally a deep-UV lithography step followed by an etching to define the waveguide were operated.

The chromatic dispersion of the waveguides were optimized to show abnormal dispersion throughout the O band around 1300nm. We carried out a set of experiments to characterize the effective non-linear response of these waveguides. After measuring their linear transmission to around 0.6dB/cm for 700nm width and 0.4dB/cm for the 1200nm width waveguides at 1300nm wavelength, we performed nonlinear characterization on straight waveguides using a 130fs 1MHz pulsed laser with a tunable OPA to pump the device of pulse peak power ranging from 250W to 9.5kW. The pump wavelength was set to 1200nm. To estimate the nonlinear coefficient of N-rich SiN, numerical simulations were performed to fit the experimental data.

The simulations consisted in solving the generalized nonlinear Schroedinger equation using a 4th order Runge-Kutta in the interaction picture method. The carried out experiments demonstrated a two octave spanning supercontinuum generation covering the visible range and the O-band using N-rich SiN fabricated through low temperature back-end CMOS compatible processes (see Fig. 1). Experimental results were validated by numerical simulations and opened opportunities to integrated nonlinear photonics for telecommunications in the O-band, spectroscopy, or biosensing.

ENGINEERING THIRD ORDER OPTICAL NONLINEARITIES IN HYBRID CHALCOGENIDES-ON-SILICON WAVEGUIDES

We also explored the development of hybrid slot waveguides to partially bypass silicon's TPA in the telecom band around 1500nm. We experimentally demonstrated complete filling of nano-slot in silicon waveguides with As2S3 glass using a simple thermal reflow process. The devices were fabricated on SOI wafers (SOITEC) with a 220 nm-thick silicon layer over 2 μm. Rece m of buried SiO2. Patterns were defined in ZEP-520A resist (Zeon Chemical Co.) by 80 kV e-beam lithography Nanobeam NB-4 system followed. The resist patterns were then transferred to the Si layer by an inductively coupled plasma (ICP) reactive ion etching process using SF6 and C4F8 gases. Following this, thin films of As2S3 were prepared using single-source thermal evaporation. The film deposition was performed using a custom-designed system (PVD Products, Inc.) [START_REF] Du | Chip-scale broadband spectroscopic chemical sensing using an integrated supercontinuum source in a chalcogenide glass waveguide[END_REF]. The devices were then annealed for 1 min at 250• C in an inert gas atmosphere. Fig. 2 shows a typical view of two different hybrid slot waveguides characterized by 70nm and 200nm slot widths, respectively. To measure the figure of merit of different devices, we used our previously developed bidirectional top hat D-Scan technique [START_REF] Serna | Bi-directional top-hat d-scan: single beam accurate characterization of nonlinear waveguides[END_REF]. As a temporal domain analog of the well-known Z-scan technique, the D-scan method allows direct measurement of the waveguide coupling coefficients as well as the effective real and imaginary nonlinear coefficients. With this approach, we estimated the nonlinear effective waveguide factor of merit (FOMTPA) characterizing the Kerr over TPA compromise of third-order nonlinear effects and given in bulk materials by:

We showed experimentally that it is possible to have different effective nonlinearities by changing the slot sizes. Our results illustrated the potential for the development of dispersion engineered slotted structures for efficient ultra-fast all-optical nonlinear photonics in chip-scale platforms.

CONCLUSION.

As a conclusion, Silicon non-linear optics offers interesting opportunities in the 400nm-1700nm spectral range provided that the problem of two-photon absorption (TPA) can be solved or at least partially bypassed. We present here two approaches contributing to this objective, based either on an alternative materials, nitrogen-rich nitride on SiO2, or on the development of hybrid chalcogenide on silicon waveguides whose effective nonlinearity can be enhanced with benefit by the use of slot waveguide geometries.

These results will be presented in detail at the conference, as well as their perspectives.

FOM TPA = n 2

(β TPA ×λ) Fig. 3. Nonlinear factors of merit of the characterized hybrid chalcogenide on-Si slot waveguides [12]

Fig. 1 .Fig. 2 .

 12 Fig. 1. Experimental and simulated results for different peak powers.


Markus Benedikt

Helmut Manuel Scherf

Helmut Lammer

Emmanuel Marcq

Petra Odert

Martin Leitzinger

Escape and fractionation of elements from planetary embryos

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Escape and fractionation of elements from planetary embryos

Markus Benedikt (1), Manuel Scherf (1), Helmut Lammer (1), Emmanuel Marcq (2), Petra Odert (1), Martin Leitzinger (3)

(1) Space Research Institute, Austrian Academy of Sciences, Graz, Austria, (2) LATMOS, Université de Versailles Saint-Quentin-en-Yvelines, Guyancourt, France, (3) Institute of Physics/IGAM, University of Graz, Austria

Abstract

In the early evolution of planetary systems, protoplanets originate from the coagulation of dust and ice and initially reside embedded in the gas of the circumstellar disks. From isotope studies, it is expected that the Earth (and terrestrial planets) formed from pre-fractionated differentiated planetary embryos and a few percent carbonaceous chondrites. Additionally the importance of enstatite chondrites for the evolution of Earth can also be argued by isotope studies. Large planetesimals and planetary embryos are differentiated bodies with sizes of several hundred to a few thousand kilometers. Planetary embryos form protoplanets via mutual collisions, which can lead to the development of magma oceans. During their solidification, significant amounts of the mantles' volatile contents may be outgassed. We show that the resulting steam atmospheres can be lost efficiently via hydrodynamic escape that drag heavier elements like noble gases, K, Na, Si, Mg, Rb etc. into space so that they are also lost. Planetary embryos that are later involved in terrestrial planet formation can be drier than previously expected. We model the outgassing and subsequent hydrodynamic escape of steam atmospheres from such embryos, including the efficient outflow of H that drags along heavier species like O, CO2, CO, K, Na, Si, Mg, Rb etc., and noble gases (Ar, Ne) and their isotopes. The full range of possible EUV evolution tracks of a young solar-mass star is taken into account. We investigate the atmospheric/elemental escape from 0.5Mars-, Mars-and 1.5Mars-sized planetary embryos at different orbital distances and the fractionation of these planetary embryos. Finally, we discuss the implications of our findings in relation to elemental composition of the bulk silicate Earth composition.

Acknowledgements

We acknowledge support by the Austrian Fonds zur Förderung der Wissenschaftlichen Forschung, Nationales Forschungs Netzwerk (FWF NFN) project S116-N16 and the subprojects, S11606-N16, and S11607-N16. H.L, M.L. and P.O. acknowledge also support of the FWF project P27256-N27.


J Helbert
email: joern.helbert@dlr.de

M. D Dyar

A Maturilli

T Widemann

E Marcq

Y Rosas-Ortiz

I Walter

M. D 'amore

G Alemanno

N Müller

S Smrekar

Spectroscopy of the Surface of Venus -in the Laboratory and from Orbit

Interpretation of mineralogy using VNIR spectroscopy data from orbiters requires spectral libraries acquired under conditions matching those on the surfaces being studied. Recent advances in high-temperature laboratory spectroscopy at the Planetary Spectroscopy Laboratory at DLR provide the necessary data and enable novel instruments like the Venus Emissvity Mapper.

The instrument is currently on the payload of the ESA EnVision proposal as the VenSpec-M channel in the Ven-Spec spectrometer suite. It is also part of the VERITAS mission proposal for the NASA Discovery call.

Combining VEM with a high-resolution radar mapper will provide key insights into the divergent evolution of Venus and Earth. Flying VEM on more than one mission will enable a long timeline of monitoring for volcanic activity on Venus. Combined with the existing VenusExpress data [1-3], VEM enables detection and mapping of surface changes over decades.

Introduction

Many efforts have been made since Venera 9 and 10 [1] to obtain optical spectra of Venus analog materials at relevant temperatures. [2] provided a first set of reflectance measurements of basaltic materials in the spectral range of 0.4 to 0.8 µm. Since then, efforts to extend these measurements to longer wavelengths have stalled.

It was commonly accepted that compositional data could only be obtained by landed missions because Venus' permanent cloud cover prohibits observation of the surface with traditional imaging techniques over most of the visible spectral range. Fortuitously, Venus' CO2 atmosphere is actually partly transparent in small spectral windows near 1 µm. These windows have been used to obtain limited spectra of Venus' surface by ground observers, during a flyby of the Galileo mission at Jupiter, and from the VMC and VIRTIS instruments on the ESA VenusExpress spacecraft. In particular, the latter observations have revealed compositional variations correlated with geological features [START_REF] D'incecco | Planetary and Space Science[END_REF][START_REF] Helbert | The Venus Emissivity Mapper (VEM) concept[END_REF][START_REF] Helbert | Infrared Remote Sensing and Instrumentation XXV 10403[END_REF][START_REF] Helbert | Infrared Remote Sensing and Instrumentation XXVI[END_REF][START_REF] Mueller | [END_REF][8].

These observations challenge the notion that landed missions are needed to obtain mineralogical information. However, any interpretation in terms of mineralogy of VNIR spectroscopy data from orbiters requires spectral libraries acquired under conditions matching those on the surfaces being studied.

Venus facility at PSL

The Planetary Spectroscopy Laboratory (PSL) at DLR took up this challenge, building on nearly a decade of experience in high temperature emission spectroscopy in the mid-infrared [9][10][11]. After several years of development and extensive testing, PSL is now in routine operation for Venus-analog emissivity measurements from 0.7 to 1.5 µm, the whole surface temperature range.

PSL has begun a database of Venus analog spectra including measurements of rock and mineral samples covering a range from felsic to mafic rock and mineral samples [12]. This first set already shows the potential for mapping of Venus mineralogy and chemistry in situ from orbit with six-window VNIR spectroscopy [13][14][15].

The Venus facility at PSL is open to the community through the Europlanet Research Infrastructure (http://www.europlanet-2020-ri.eu/).

Venus Emissivity Mapper (VEM)

This instrument can provide a global map of rock type from orbit, assessing iron contents and the redox state of the surface by observing the surface with six narrow band filters, ranging from 0.86 to 1.18 µm. Three additional windows allow corrections for cloud composition and variability, two measure water abundance, and three compensate for stray light. Continuous observation of Venus' thermal emission will also place tighter constraints on current volcanic activity. Eight channels provide measurements of atmospheric water vapor abundance as well as cloud microphysics and dynamics, permitting accurate correction of atmospheric interference on surface data.

VEM Design

The VEM system design, discussed in details in [START_REF] Helbert | The Venus Emissivity Mapper (VEM) concept[END_REF][START_REF] Helbert | Infrared Remote Sensing and Instrumentation XXV 10403[END_REF][START_REF] Helbert | Infrared Remote Sensing and Instrumentation XXVI[END_REF], is a pushbroom multispectral imaging system. It leverages a proven measurement technique pioneered by VIRTIS on Venus Express (VEX) [1][2][START_REF] D'incecco | Planetary and Space Science[END_REF][START_REF] Mueller | [END_REF][8][9][10][11]. It also incorporates lessons learned from VIRTIS to achieve greatly improved sensitivity and spectral and spatial coverage:

• A filter array (rather than a grating) provides wavelength stability (band-center and width-scatter) ~5× more stable and maximizes signal to the focal-plane array (FPA).

• Spectral windows below 1µm are covered for the first time.

• A two-stage baffle decreases scattered light and improves sensitivity.

• Use of an InGaAs detector with an integrated thermal electric cooler (TEC) eliminates the need for cryogenic cooling.

VEM design draws on DLR's BepiColombo MERTIS instrument (launched and successfully commissioned in 2018). With this design maturity, combined with a standard camera optical design, development risk is low.

Methodology for retrieving surface emissivity is complex but well understood and demonstrated. To distinguish between surface and atmospheric contributions, VEM uses an updated version of the extensively tested pipeline developed to process VIRTIS data [2], combined with a radiative transfer model (RTM) [12][13][14][15]. Surface emissivity retrieval techniques were developed based on Galileo NIMS observations at 1700, 1800 and 2300 nm [16]. VEM cloud bands occur at 1195, 1310, and 1510 nm [17], the first on the flank of the 1180-nm surface windows [18]. VEM's cloud bands are close to surface bands, providing near-optimal correction. Only relative emissivity measurements are needed to calculate the spectral slope to meet our surface emissivity requirements [START_REF] Helbert | The Venus Emissivity Mapper (VEM) concept[END_REF]. We do not have a requirement on the accuracy of the retrieved emissivity. However, we can now tie an emissivity retrieval to in situ measurements to assess accuracy. For this comparison, the Venera 9 and 10 landing sites [19] (not observed by VIRTIS) will be observed by VEM both on EnVision and on VERITAS.

VEM observes each spot on the surface multiple times. Therefore both atmospheric and instrument noise are reduced by averaging image swaths acquired at different times. Applying the updated analysis of atmospheric error for VEM parameters [14] and taking multiple-look averaging into account, our capability for emissivity precision is better than 1.5% for all bands and better than 1% in most bands.

Conclusions

Venus is the only terrestrial planet for which we do not have a global map of the surface compostion. The Venus Emissvity Mapper will provide this much needed dataset, yielding new insights in comparative planetology of terrestrial planets in our solar system and beyond.

Figure 1 :

 1 Figure 1: High temperature emissivity spectra of rock types covering some of the expecting range of surface compositions on Venus

Figure 2

 2 Figure 2 VEM laboratory prototype incl. COTS version of flight detector, optics with radiation hard lenses and prototype filter array (top cover removed for display)

Acknowledgments: Europlanet 2020 RI has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 654208.


Christine Almunia

Yannick Cogne

Olivier Pible

Charlotte Lepleux

François Chevalier

Jean Armengaud

Yannick

Discriminating sub-population responses of a mixture of human cell lines by proteogenomics

come   L'archive ouverte pluridisciplinaire

Results and conclusion

Monitoring proteome dynamics from different human cell types present concomitantly in a given sample is of great interest and could be applied to ultra-precise molecular characterization of complex tissues. Here, we propose a proteogenomics-based strategy to point at cell line molecular signatures. For this, the proteome is analyzed by high-throughput shotgun mass spectrometry and specific bioinformatics search are performed. First, mRNA from chondrosarcoma cells (SW1353 cell line) and immortalized chondrocytes (T/C28A2 cell line) were sequenced by RNAseq for establishing the most appropriate protein sequence database. For this an innovative cascade search allows to conciliate de novo and mapping RNAseq assemblies and the Human swissprot databases (Cogne et al., 2018). A set of 2 million of discriminating peptide sequences of the two cell lines are then identified. From them, 480 peptide sequences were detected and monitored based on extracted ion chromatogram (XIC) signals recorded by tandem mass spectrometry. A list of 55 peptides were used for quantitating the ratio of each cell type in a given co-culture sample with high precision selected with cell lines mixed at 2:1, 1:1; and 1:2 ratio. This new methodology was used to analyze the bystander effect generated by irradiated chondrosarcoma cells (SW1353 cell line) on immortalized chondrocytes (T/C28A2 cell line) in co-culture conditions. Such strategy could be applied to investigate intercellular interactions between different cell types, paving the way to new insights into the molecular mechanisms of crosstalk between human cells.

  Database design and peptide quantification Merge of Multi database for a large identification TC28/A2 (Merge B1+B2+B3) Triplicates SW1353 (Merge B1+B2+B3) Triplicates 1

Discriminating sub-population responses of a mixture of human cell lines by proteogenomics Christine Almunia 1 , Cogne Yannick 1 , Olivier Pible 1 , Charlotte Lepleux 2 , François Chevalier 2 , Jean Armengaud 1 A new methodology based on proteogenomics approach including merged databases and extracted ion chromatogram (XIC) from selected peptides was developed for 1 =>discriminating two genetically closed cell lines in the mixture 2 =>evaluating in the mixture each cell line proportion This method can be applied to experiments done with co-cultured cell lines.


Noura Joudieh
email: noura.joudieh@univ-lr.fr

Mourad Rabah
email: mourad.rabah@univ-lr.fr

Εφαρμογή Προσέγγισης Εξόρυξης Διαδικασιών για Ανάπτυξη Εξατομικευμένων Προσαρμοστικών Διαδρομών Μάθησης σε Μικτά Περιβάλλοντα Μάθησης Employing a Process Mining Approach to Recommend Personalized Adaptive Learning Paths in Blended-Learning Environments 1

Keywords: E-Learning, Personalized Learning Paths, Ontology, Recommender Systems, Process Mining

In education, e-learning is highly adopted to improve the learning experience and increase learning efficiency and engagement. Yet, an explosion of online learning materials has overwhelmed learners, especially when trying to achieve their learning goals. In this scope, recommender systems are used to guide learners in their learning process by filtering out the available resources to best match their needs, i.e. to offer more personalized content and learning paths. Concurrently, process mining has emerged as a valuable tool for comprehending learner behavior during the learning journey. To synergize these disciplines and optimize learning outcomes, our paper introduces an ontology-based framework that aims to recommend an adaptive learning path, driven by a learner's learning objective, personalized to his learning style and enriched by the past learning experience of other learners extracted via process mining. The learning path considers pedagogical standards by employing Bloom's taxonomy within its structure. The framework establishes an Ontological Foundation, to model the Learner, Domain Knowledge, and Learning Path. Choosing Computer Science as a domain, we construct a knowledge base using synthesized data to underpin the framework. For past learning experience, we analyze Moodle log data

Introduction

In a rising world of technology, education obtained a decent share of development opening the door to enhanced learning techniques, environments, and technologies.

Traditional classrooms of a teacher-learner scenario in an academic setting have evolved to a wider concept, including various types of learning environments.

Distance, blended, open, flexible and personalized learning environments will govern the university settings in the future [START_REF] Kafa | Adapting to an Online Learning Environment in the Midst of the Global Pandemic: Insights from a Private Higher Institution in Cyprus[END_REF]. In the aforementioned environments, the exclusivity of education and learning materials is open to everyone, at anytime, anywhere [START_REF] Colace | E-Learning and Personalized Learning Path: A Proposal Based on the Adaptive Educational Hypermedia System[END_REF]. This scenario has changed a set of standards, starting with the identity of a learner that became anyone who wants to learn something rather than a student in an academic institution. Additionally, it provided learners with a vast scope of online available educational materials to nourish their knowledge curiosity at their own pace. Yet, this availability of learning resources can overwhelm a learner when trying to achieve his learning objective that may eventually lead him to a loss of motivation and a decline in the learning efficiency.

In this direction, Recommender Systems (RS), which are computer-based techniques that try to "intelligently" filter out the online content for a user based on his preferences, previous actions, needs, etc. [START_REF] Aggarwal | Recommender Systems[END_REF], gained interest in several domains, including the domain of e-learning [START_REF] Pireva | A Recommender System Based on Hierarchical Clustering for Cloud e-Learning[END_REF]. Using various data about the learner as his preferences, learning objective, restrictions, background knowledge, etc. these systems can rank the available learning resources according to their compatibility with the given learner data and recommend him the "best matches", thus offering a more personalized learning experience [START_REF] Singh | Recommender systems: An overview, research trends, and future directions[END_REF]. This personalization went further to consider more aspects about the learner in the recommendation process, as his learning style, cognitive abilities, personality, etc. [START_REF] Nabizadeh | Learning path personalization and recommendation methods: A survey of the state-of-the-art[END_REF]. Therefore, deviating from the "one-size-fits-all" models, RS in e-learning are used to tailor the learning experience to the uniqueness of each learner by personalized learning resources, and learning paths [START_REF] Raj | A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020[END_REF].

We define a learning path as a partially ordered sequence of learning objects that aims to achieve a learning objective. A learning object can be a learning resource, a course, an action or even a sub-process while a learning objective can vary among obtaining a degree, finishing a course, or acquiring a skill. For example, in Learning Management Systems (LMS) like Moodle, a student follows a sequence of learning resources provided by the instructor to finish an enrolled course of his.

In fact, learners' studying traces in an LMS or an educational platform are captured by integrated logging systems and recorded as an "event log" containing information about every action performed by any user in the system at each point in time.

Process Mining (PM) (van der Aalst, 2016), a discipline that combines data mining, machine learning and process modeling, then mines from this event log, a process model that reveals the real process followed by users in the system. The former has paved the way for a rich line of research to discover the behaviors of students while performing different learning activities like attending a course, or taking a quiz [START_REF] Cenka | Analysing student behaviour in a learning management system using a process mining approach[END_REF]. Also, the discovered process models can aid instructors and students to identify any bottlenecks, successful practices or patterns in the overall behavior, and thus making appropriate and informed decisions to enhance the learning process.

Using PM for a recommendation process is modestly used in e-learning and rarely harnessed to recommend a learning path [START_REF] Yari Eili | A survey on recommendation in process mining[END_REF]. Beyond existing literature on personalized learning path recommendations, this paper introduces an ontology-based framework that leverages PM to extract students' past learning experiences from event logs, integrating this insight into the personalized recommendation of an adaptive learning path tailored to each learner's profile. This approach can then be adopted in different learning scenarios to provide personalized learning paths. For example, it can serve distance or blended learning in enriching the experience, ameliorating the learning efficiency and emphasizing on the learners' engagement through personalized learning paths.

The subsequent sections offer an overview of related research in e-learning, encompassing recommendations, PM, and ontologies. This is followed by the articulation of the motivation and the main objective. The proposed framework and its ontological foundation are then explored, along with the knowledge base creation and data collection. Finally, the paper discusses some future perspectives.

Related Works

RS, a branch of information retrieval and filtering, are programs that try to understand the preferences and behavior of a user and predict him the most suitable item or service. In several domains like business, e-commerce social networks, finance, healthcare, and others, RS played an important role in improving the provided services and user experience by filtering the choices to the ones that mostly match the user's interest [START_REF] Beheshti | Towards Cognitive Recommender Systems[END_REF]. In education, RS gained ground for personalizing the learning experience within the various emerging types of learning environments, as they have shown positive impacts on increasing academic achievements, helping students facing learning difficulties and emphasizing learning engagement [START_REF] Nabizadeh | Learning path personalization and recommendation methods: A survey of the state-of-the-art[END_REF]. Several works addressed personalizing the recommendation of learning objects. In their work, [START_REF] Nafea | A Novel Algorithm for Course Learning Object Recommendation Based on Student Learning Styles[END_REF] implemented a personalized learning object recommender to learn a course in a LMS, tailored by the learner's learning style.

Course instructors set learning object (LO) profiles, while student profiles are collected through questionnaires. The process involves K-means clustering for LOs, repeated with each new addition of an LO. Recommendations are derived by calculating similarity between the student's learning style and cluster centroids, followed by ranking prediction and selection of top N learning objects. These are further presented to the learner in order of ranking. [START_REF] Tarus | A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining[END_REF] introduced a novel hybrid knowledge-based methodology for personalized learning resource recommendations, combining collaborative filtering with ontologies and Sequence Pattern Mining (SPM).

Learners assigned ratings to learning resources available on the LMS portal, assessing relevance to their courses on a 5-point scale. During the initial registration phase, they supplied fundamental information and rated some resources to enhance the ontology and their learner profiles. The RS subsequently predicts the learner's ratings of unseen learning resources and generates the list of top N learning items. Then, the learning patterns of the learner extracted from web logs by SPM are used to filter and generate the final list of learning items. Unlike PM that learns a model from a given log data, SPMs are used to discover frequent subsequences in a sequence dataset like web logs [START_REF] Mabroukeh | A taxonomy of sequential pattern mining algorithms[END_REF]. Ontologies defined by [START_REF] Gruber | A translation approach to portable ontology specifications[END_REF] as "a formal explicit specification of conceptualization" are a mechanism to model the knowledge where concepts and the relations among them are explicitly defined in a machine readable manner. They offer a reasoning mechanism that infers new knowledge from the already existing one. Being so, they are highly adopted by several works [START_REF] Yarandi | A personalized adaptive e-learning approach based on semantic web technology[END_REF][START_REF] Colace | Ontology for e-learning: A case study[END_REF], to model knowledge and context about the learner and the domain. [START_REF] Sudhana | An ontology-based framework for context-aware adaptive e-learning system[END_REF] proposed an ontology-driven framework for context-aware adaptive e-learning, with the primary aim of tailoring learning material to individual learner contexts. This initiative focuses on seeking learning resources within a web-based e-learning environment. Personalization parameters encompass background, learning style, and learning objectives. While, [START_REF] Wu | A Semantic Web-Based Recommendation Framework of Educational Resources in E-Learning[END_REF] leveraged the reasoning power of ontologies to personalize the learning material in a web-based environment via semantic rules applied on a knowledge base consisting the learner, learning resource and domain ontology.

When recommending a learning path, the problem is formulated while defining multiple aspects: (a) the structure / definition of the learning path, (b) the characteristics of the path (adaptive and/or personalized) (c) the personalization parameters if any, (d) the main context and involved actors, and (e) the recommendation technique. A learning path is a sequence of learning contents that guide the user to accomplish a learning goal. The learning content may vary among a course, a topic / a concept, a learning object, or an action, etc. [START_REF] Costa | Customized Pedagogical Recommendation Using Automated Planning for Sequencing Based on Bloom's Taxonomy[END_REF][START_REF] Nabizadeh | Learning path personalization and recommendation methods: A survey of the state-of-the-art[END_REF]. A learning object might be a video, a text file, an audio, or any other form of media. In a proposal of a RS for course learning, [START_REF] Cheng | Ontology-Based Personalized Learning Path Recommendation for Course Learning[END_REF] define a path as a sequence of video content personalized by the learner's knowledge mastery. The approach is based on three ontologies, namely the Learner ontology, the Video ontology and the Domain ontology which models the course curriculum and is designed by the help of the course instructors. As [START_REF] Nitchot | Personalized learning system for visualizing knowledge structures and recommending study materials links[END_REF] considers the learning path as a sequence of web links in a web-based environment, while it is a sequence of learning content for [START_REF] Yu | Ontology-Based Semantic Recommendation for Context-Aware E-Learning[END_REF] who proposed a semantic-based approach to recommend a learning path personalized by the contextual information of the learner. The former includes the learner prior knowledge and learning goal.

Ontologies are used to model the domain, the learning content and the learner.

Selecting a target learning content by the learner, the learning path is constructed using the pre-requisite relation among learning content and the knowledge extracted from the domain ontology. The goal in the work of [START_REF] Nabizadeh | RUTICO: Recommending Successful Learning Paths Under Time Constraints[END_REF] is to offer personalized adaptive learning paths for learning courses tailored by the learners' time constraints and knowledge background. Courses consist of lessons, each comprising multiple learning objects, and so is a learning path. A graph algorithm is employed to derive potential paths for studying a course which are then filtered to retain only those aligning with the student's time limitations while maximizing their score. Three approaches are employed to evaluate score and time for each path, with time and score estimated for individual learning objects. If the student falls short of the target score at a step, auxiliary learning objects are suggested to aid their progress.

Finally, [START_REF] Bian | Adaptive Learning Path Recommendation based on Graph Theory and an Improved Immune Algorithm[END_REF] proposes the use of graph theory and an improved immune algorithm to recommend a personalized learning path for each learner by their learning style and knowledge. A path is considered as a linear sequence of learning objects to finish a course. Yet, instructors are involved to construct the concept map of a course while modeling the prerequisites among the underlying concepts. The approach was tested in a flip classroom for a python programming course where students choose their learning goal from the concepts in the concept map to get a personalized path to their learning style.

On the other hand, information systems record the actions performed by their users in a form of an Event Log. The former records every activity taking place along with the time of its occurrence and a unique identifier that refers to the user of that activity, respectively known as the activity, the timestamp and the case id. Using the case id of a user, an ordered sequence of his performed activities can be extracted from the event log, namely a trace. As a single trace can tell about the behavior of one user in the system, from an event log, which is a set of traces, PM discovery algorithms are used to learn a process model that represents the general behavior of all the users in the system (van der Aalst, 2016). [START_REF] Trabelsi | User's Behavior in Digital Libraries: Process Mining Exploration[END_REF] applied PM to event logs from the Gallica digital library. Traces were initially grouped into three categories: lookup, borderline, and exploratory. Lookup traces represent users accessing documents with few manipulations, borderline traces pertain to those targeting a specific subject area, and exploratory traces involve wide-ranging document access. Subsequently, a process model was mined from each group's traces to comprehensively comprehend and analyze user journeys within the digital library. In e-learning, on the other hand, [START_REF] Juhaňák | Using process mining to analyze students' quiz-taking behavior patterns in a learning management system[END_REF] employed PM to extract the patterns and interactions of students in an LMS while taking online-based quizzes. While, [START_REF] Bey | Analyzing Student Programming Paths using Clustering and Process Mining[END_REF] conducted a study in a context of a programming course, to analyze the paths taken by students in 17 exercises. Students were first grouped into six clusters depending on their behavior. They were classified as "at-risk" or "good" using their final score on the course. Finally, the mined process model for each category revealed the trajectory patterns for high and low performing students while programming, thus determining which behaviors can contribute to the success or failure in a programming course. PM techniques are also used to discover and analyze learning strategies of learners in different learning environments as self-regulated learning [START_REF] Bannert | Process Mining Techniques for Analyzing Patterns and Strategies in Students' Self-Reg. Learning[END_REF] or MOOCs [START_REF] Rohani | Discovering Students' Learning Strategies in a Visual Programming MOOC Through Process Mining Techniques[END_REF].

Learning environment logs store student learning history for performance evaluation, grouping based on similarities, and detecting undesirable behavior. Yet, it can also be used to recommend them appropriate learning material [START_REF] Raj | A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020[END_REF]. In this scope, [START_REF] Wang | Sequence-Based Approaches to Course Recommender Systems[END_REF] proposed diverse sequence-based strategies for a Course Recommender System (CRS), including a PM approach. The CRS aims to select the optimal next course for individual undergraduates in higher education, guided by their past course history. This history encompasses course records, performance (grades), and sequencing. To recommend the next course, a similarity measure identifies successful students akin to the current one, with their subsequent courses forming candidates. These are ranked considering criteria like being mandatory and agility (average graduation time). The top-ranked course is recommended. This CRS enhanced instructors' understanding of prerequisites, accelerated students' graduation, and boosted their performance. PM was used for recommendation activities in few works in other domains. In fact, in the works of [START_REF] Huber | Next step recommendation and prediction based on process mining in adaptive case management[END_REF] and [START_REF] Schonenberg | Supporting Flexible Processes through Recommendations Based on History[END_REF], PM tools and algorithms were used to mine the model of a business process to enable the recommendation of the possible next steps during the execution of the former. Terragni andHassani (2018, 2019) employed web logs, transformed into event logs, to extract a process model encapsulating customer journeys on a website. Focusing on enhancing the "click product link" Key Performance Indicator (KPI), the process model analysis revealed that optimizing the "Visit Product Page" action positively influences this KPI. Leveraging user interactions with pages, they devised a RS that tailors links on the currently viewed page based on user behavior. This approach led to an increase in the "Visit Product Page" action, effectively optimizing the designated KPI. In healthcare, Pereira Detro et al. ( 2020

Motivation and Problem Statement

The literature extensively explores personalized strategies for presenting learning content and learning paths based on factors like learning style, time restrictions, knowledge level, and objectives. Predominantly, knowledge-based and ontologybased methods are favored, effectively addressing challenges such as RS cold-start issues. Ontologies, valued for their adeptness in knowledge representation and reasoning, have gained wide acceptance for their ability to infer new knowledge from existing one. In response, our framework revolves around ontologies as its core, integrating the less-explored in recommending a learning path, yet promising technique of PM to extract past learning experience.

Thus, the main objective becomes to personalize the learning process through the recommendation of an adaptive learning path driven by a learner's learning goal while using PM to extract past learning experience.

Proposed Framework

Considering the stated objective and the state-of-the-art, the following framework is derived and illustrated in this section. As Picture 1 shows, the learner, who is the main actor, interacts with the learning environment through the user interface. The framework takes as an input his profile communicated from the main interface, and gradually returns the learning path as an output. Thus, from the learner's perspective, first he chooses his learning goal, then takes a questionnaire to detect his learning style and pre-knowledge about the goal, after he starts receiving some learning contents. At each state, he gets assessed to decide whether to expand the current state or move to the next. The cycle repeats until reaching the goal. In what follows, the structure of the input and output, the ontological foundation and the role of the underlying modules of the framework are detailed.

Picture 1: The Proposed Framework

Input and Output

The structure of the learner profile, being the input and the learning path, being the output, is as follows.

Learner's Profile

There are several propositions to standardize a learner profile [START_REF] Dolog | Challenges and Benefits of the Semantic Web for User Modelling[END_REF] as PAPI -IEEE Personal and Private Information [START_REF] Farance | Draft standard for learning technology[END_REF] or IMS LIP -Learner Information Package (IMS, 2001). In this work, the learner profile is inspired by the existing standards yet customized to the context as presented in Picture 2.

The profile has the personal information along with the language preference. The knowledge is a collection of the learner's pre-knowledge, captured via a questionnaire prior to the learning process, and his mastered ones recorded throughout the process.

perceived and (d) understanding information (sequential, global) that questions whether a learner progresses into understanding through sequential detailing of the information or a global overview. Following the explanation of the results of the ILS given by [START_REF] Felder | Learning Styles and Strategies[END_REF], we define a learning style (S) as a vector of 4 values 𝑆 = {𝑑 ! , 𝑑 " , 𝑑 # , 𝑑 $ }, one for each dimension, where 𝑑 % 𝜖 [0,6] expresses to which style in this dimension is the learner more bias and to which extent as depicted in Picture 3. A zero value signifies no preference to any style. For example, a value 6 for the third dimension, means the learner has an extreme verbal style, thus it might be difficult for him to perceive information if it was not verbal.

Picture 3: Learning Style Encoding

Learning Path Structure

The recommended learning path is expected to be adaptive and personalized, which is translated in its structure. Thus, we define a learning path (LP) to reach a learning where 𝐿𝑂 % , is a learning object. The following assessment is initially a questionnaire that asks if the goals of the learning state are satisfied given its learning level.

goal as 𝐿𝑃 = {[𝐿𝑆 ! , 𝐴 ! ], [𝐿𝑆 " , 𝐴 " ], … [𝐿𝑆 & , 𝐴 & ]},
As seen in Picture 4, the learning path is delivered one learning state at a time, and according to the assessment results, the next state is created thus being adaptive.

While, the personalization takes place at the level of the learning objects chosen, where a compatibility value is computed for a learning object considering its learning style, covered concepts, and learning level with the learning style of the learner and the characteristics of the learning state.

Picture 4: Learning Path Structure

The proposed framework of the study has several important implications for the instructors, instructional designers, universities as well as learners. The instructors and instructional designers that are responsible for designing, developing and delivering courses are expected to take into consideration the important information to be provided in regards to learners' profile, style and expectations. Designing a personalized learning path involves tailoring the educational experience to the unique needs, preferences, and abilities of each learner. This approach requires a shift in the role of instructional designers and instructors, focusing on customization, flexibility, and active involvement. Implications for instructional designers when creating a personalized learning path for learners can be summarized as follows: understand learner profiles, customize learning content, develop flexible learning pathways, create adaptive assessment strategies, integrate technology, and monitor and analyze data. Implications for instructors when creating a personalized learning path for learners, differ at some level from the instructional designers, and can be summarized as follows: facilitate personalized learning, provide individualized support and feedback, build a learning community, promote self-regulation and reflection, continuous professional development trainings, and cultivate empathy and understanding towards diverse needs and backgrounds.

When learners have a personalized learning path, it means their educational experience is tailored to their specific needs, interests, learning styles, and pace. This approach has significant implications for learners, which can positively impact their engagement, motivation, understanding, and overall academic success. Some key implications of personalized learning paths for learners are the following: increased engagement and motivation, better understanding and retention, customized learning pace, addressing diverse learning styles, individualized support and feedback, empowerment and autonomy, enhanced critical thinking and problem-solving skills, flexibility and adaptability, long-term retention, and lifelong learning mindset.

When universities implement personalized learning paths, it signifies a shift towards a more student-centered and tailored educational experience. This approach has several implications for universities, affecting various aspects of their structure, processes, and outcomes, such as: restructure curriculum in order to be customizable, invest and integrate new, emergent and adaptive technologies, provide continuous development training for the faculty members, promote accessibility and inclusivity, develop appropriate quality assurance and accreditation mechanisms, etc.

Implementing personalized learning paths in universities requires a comprehensive approach involving technology, training, student support, assessment strategies, and an emphasis on flexibility and individualization. Universities should strive to create an educational environment that empowers students to take charge of their learning while maintaining academic standards and supporting their holistic development.

Finally, a personalized learning path can lead to a more engaging, effective, and fulfilling educational experience for learners, addressing their unique needs and preparing them for success in both academic and real-world settings.

The Ontological Foundation

The framework that results in the learning path starting from the learner's profile, An optional "Updating Module" might be added to the framework where the feedback on the recommended learning objects and path is recorded and considered in future recommendations. Also, where the history of the learner is used to enhance the process model initially built.

Data Collection and Primary Results

Following the objectives of the work (cf. section 3), at this point, the proposed framework was designed, with the learning path structure, the learner's model and the Ontological Foundation. To validate the former, Computer Science was chosen as a domain in which a synthesized data of learners, learning objects, domain knowledge and learning goals is created to build a knowledge base. Also, to initiate with the process mining module, a Moodle log data is collected and preprocessed. These two data-related phases are covered in this section along with a glimpse of the resulting knowledge base, followed by the next phases in the work.

Data-Related Phase

The data related phase has a part connected to the event logs collection, i.e. related to process mining module, and two other parts related to the knowledge base.

Process Mining Related: The Event Log Data

As the source data for process discovery algorithms is system logs, in this study, we needed to collect log data that records the behavior and paths taken by students while studying courses, i.e. while trying to achieve a learning objective. In what follows, the data collection and preprocessing processes are explained.

Event Logs Collection Process

Moodle, a well-known learning management system, widely used in universities and educational institutions, has a logging system that records the actions taken by any user in the system at each point in time. In regards to the current study, the event logs from Moodle were collected as an input source for the PM module. Thus, the Moodle The structure of a single log file is illustrated by Table 1. The "Regnum" is the registration number, used as a unique identifier to track the path of a student throughout different courses and different years, i.e. it is used as "case id". The "Timestamp" records the exact time of each event taken by the students, used to order the events. While the "Event Name" is used as the activity and the "Event

Context" gives information about the concerned learning resource (file, assignment, folder, etc.) affected by the event. Finally, the "Description" explains the event in a more detailed manner.

Data Preprocessing

The initial extracted logs included the actions taken by all users in the system (students, instructors, assisting instructors, manager, etc.). The goal is to transform the raw log data into abstract informative traces that will be then used as a source data to the process discovery step. Thus, two main steps were taken:

1. Anonymizing and cleaning the logs to keep only student logs using the roles given to users in the system.

2. Understanding Moodle events names to enrich the logs with semantics that serve the main goal

The initial number of event names was 65, including events related to course actions, quiz taking, assignments submissions, chats and discussions, profile viewing, and others. Only 14 events are kept, as shown in Table 2. These events were chosen as they particularly show actions like completion of an assignment or a learning resource, assessment, taking feedback, studying, and exploring. After choosing the events to keep, the log is cleaned again to exclude other recorded events to finally end with 471 students with a total of 4291 traces for the period of 2018 until 2022, where a trace is an ordered sequence of events taken by a student in one course. The trace is ordered by the timestamp in an ascending chronological order. At this step, the logs are ready for transformation.

The Knowledge Base Related: the synthetic data

The knowledge base is constructed by initializing data on top of the ontological foundation. As an initial approach, we built a synthetic knowledge base that includes a domain knowledge, learning objects with different metadata fields and learners with different learning styles and learning goals. In the domain ontology, we created 90 connected concepts related to the machine learning domain, some of which are previewed in Picture 6 using GraphDB.

Event Name

"A submission has been submitted." "Quiz attempt submitted" "Lesson started" "Course activity completion updated" "Course module viewed" "Lesson resumed" "Zip archive of folder downloaded" "Content page viewed" "Feedback viewed" "Clicked join meeting button " "Course summary viewed" "Course viewed" "Course module instance list viewed" "Sessions viewed" shown with the linked learning objects for some, from the Learning Object ontology.

Next Phases

Trace transformation is a non-trivial endeavor, yet it is performed to alleviate informational value and significance of the raw traces [START_REF] Ho | Trace-Based Multi-Cristeria Preselection Approach for Decision Making in Interactive Applications like Video Games[END_REF][START_REF] Settouti | A Trace-based system for TEL systems personalization[END_REF][START_REF] Clauzel | Enhancing synchronous collaboration by using interactive visualisation of modelled traces[END_REF]). Following, the transformed traces undergo analysis, then a PM discovery one. The resulting process model is modeled within the Process Ontology framework and incorporated into the knowledge base to become a part of the framework and serve as an input source for the recommendation process. Finally, the RS is programmed to construct the adaptive learning path based on the current context of the learner, that encompasses information about his current knowledge level, past learning experience, preferences and learning style and his learning goal.

Conclusion and Future Perspectives

In the realm of e-learning, where a plethora of learning resources exists, the imperative to recommend suitable content becomes essential to amplify learner engagement and performance. In response, our paper proposes an ontology-based framework to recommend an adaptive personalized learning path, tailored to learners' goals, learning style and knowledge state. Yet, we intend to integrate the insights extracted from other past learners' experiences via PM. Within the scope of this study, we elucidate the ontological foundation that constitutes the framework's core, validated against a knowledge base through the deployment of synthesized data of learners, learning paths, learning goals, and learning objects in the chosen domain of Computer Science. As we embark on the PM facet, we have gathered and preprocessed Moodle logs of 471 students which stand primed for transformation.

  ),[START_REF] Yu | Ontology-Based Semantic Recommendation for Context-Aware E-Learning[END_REF], and[START_REF] Yang | A Datadriven Process Recommender Framework[END_REF] used PM to discover the treatment process of patients and used this model to give recommendations on the next medical activity to perform in a treatment process. Despite the substantial progress in both fields of PM and RSs in different domains and research lines, using PM for a recommendation activity is yet modestly explored especially in e-learning. This paucity of exploration is particularly evident in the limited application of PM in recommending a learning path.Consequently, this work intends to propose an ontology-based framework that uses PM to extract and integrate learners' past educational experiences in the recommendation process for a personalized adaptive learning path for a learner aiming at a learning goal. The proposed approach can find ground in different types of learning environments like blended, distance, open learning, and others.

  constitutes three main modules and another optional. The main entities in the whole framework are the Learner, Learning Path, Learning Objects, Learning Process and a chosen Domain. To represent each of these entities with their metadata, an ontology is designed using Protégé. The created ontologies formed the Ontological Foundation of the framework, presented in Picture 5. The Learner and Learning Path ontologies represent the structure of the input and output as previously explained. The domain ontology, used to express the knowledge about the chosen domain, is SKOS-based rendering it understandable and easily reused. Also, inspired by the LOM standard (IEEE Standard for Learning Object Metadata, 2020) in the Learning Object ontology the chosen metadata to record for a learning object are specified. We added the learning level and learning style to serve the context. Finally, the Process Ontology is used to represent the PM related entities.This foundation is then used as a model to create the knowledge base of the framework, in which the data is saved. The Ontological Foundation together with the Knowledge Base make the Data Module of the framework.The Process Mining Module is responsible for extracting the past learning experience from the event logs and saving them in the Processes data storage. The procedure starts by preprocessing the event logs, then transforming them into high-level traces from which a process model is discovered. The model and the extracted information are then saved in the knowledge base and later used for recommendation.Picture 5: UML Class Diagram of the Ontological FoundationFollowing, the Recommender Module is responsible for the core functionality of constructing the adaptive personalized learning path. Starting with the learner profile, the learning goal indicates the goal knowledge and the target learning level. At first, the concepts to be covered are extracted from the domain ontology, then the plan of each learning state is created using the information about the past experience and the knowledge of the learner at the moment. After, the learning objects with the highest compatibility values with the state are chosen. An assessment then takes place before the next state is created. Finally, this cycle repeats until the learning goal is reached.

  event logs of 471 students taking courses in the Department of Computer Science and Engineering of Frederick University in Cyprus for the period of 2018 until 2022, were used. The log data records all the actions taken by students on Moodle while learning, including taking courses, exams, and assignments. The conventional and blended learning modes of delivery were employed.

Picture 6 :

 6 Part of the Domain OntologyPicture 7: Zoom into the Knowledge Base Picture 7, shows a complete example of the linked synthetic data from multiple ontologies in the knowledge base. From the Learner ontology, there is the learner "Jenny940" along with the data about her, including her pre-knowledge and learning style. Jenny is following a path (Learning Path ontology) that has a learning goal with an existing concept in the Domain ontology. For this concept, related concepts are

Table 1 :

 1 Event Log Structure

	Regnum	Timestamp	Event Context Event Name	Description

Table 2 :

 2 Chosen Event Names

Picture 2: Learner Profile

The learning paths (elaborated in the following subsection) have all the information about the learning goals and the progress of each, while the history records all the actions done by the user in the learning environment in the form of an event log. We define the learning goal (LG) via pedagogical means, as LG = {Knowledge, Learning Level}.

The Knowledge is what the learner wants to learn chosen from the domain, while the Learning Level could be either "Surface" (short term usage and retaining of information), "Intermediate" (average term usage and retaining of information), or "Profound" (active processing of information on long term with the ability to elaborate on the acquired information rather than only memorizing). To translate the learning levels into our context, we used the revised version of Bloom's taxonomy [START_REF] Fastiggi | Applying Bloom's Taxonomy to the Classroom-Technology for Learners[END_REF][START_REF] Shabatura | Using Bloom's Taxonomy to Write Effective Learning Outcomes | Teaching Innovation and Pedagogical Support[END_REF] that defines six successive levels of learning along with their associated verbs to represent the educational goals. Thus, we consider that the first two levels of "Remembering" and "Understanding" belong to the surface level, the next two levels of "Applying" and "Analyzing" belong to the intermediate level, and the last two of "Evaluating" and "Creating" belong to the profound level. For example, if the learning goal of a learner is "being able to apply clustering", it is translated to LG = {Clustering, Intermediate}.

For the learning style, being highly adopted and gaining a wide acceptability in literature, we chose Felder and Silverman's model [START_REF] Felder | Learning and Teaching Styles in Engineering Education[END_REF] captured by the ILS questionnaire [START_REF] Felder | Index of Learning Styles Questionnaire[END_REF] (Index of Learning Style Questionnaire) that comprises 44 questions. This model expresses the preferred style of learning on four dimensions, each with two styles within: (a) processing information (active, reflective), (b) perceiving information (sensing, intuitive), which shows the preferred type of information to be received (c) receiving information (visual, verbal), that spots the sensory channel through which external information is most effectively