The security of probabilistic data structures is increasingly important due to their wide adoption in many computing systems and applications. In particular, the security of approximate membership check filters such as Bloom or cuckoo filters has been recently studied showing how an attacker can degrade the filter performance in some settings. In this paper, we consider for the first time the security of another popular approximate membership check filter, the Quotient Filter (QF). Our analysis and simulations show that quotient filters are vulnerable to both white and black box attackers that can cause insertion failures and degrade the filter performance very significantly. An interesting finding is that quotient filters are vulnerable to a new type of attack, not applicable to Bloom or cuckoo filters, that can degrade the speed of queries dramatically. The paper also briefly discusses and evaluates potential countermeasures to detect and protect against those attacks.

Authoring tools are powerful systems in the area of e-Learning that make easier for teachers to create new learning objects by reusing or editing existing educational resources coming from learning repositories or content providers. However, due to the overwhelming number of resources these tools can access, sometimes it is difficult for teachers to find the most suitable resources taking into account their needs in terms of content (e.g. topic) or pedagogical aspects (e.g. target level associated to their students). Recommender systems can take an important role trying to mitigate this problem. In this paper we propose a new model to generate proactive context-aware recommendations on resources during the creation process of a new learning object that a teacher carries out by using an authoring tool. The common use cases covered by the model for having recommendations in online authoring tools and details about the recommender model itself are presented.

A proactive recommender system pushes recommendations to the user when the current situation seems appropriate, without explicit user request. This is suitable in e-Learning scenarios in which a great amount of learning objects are available but it is difficult to find them according to the user's needs. In this paper, we present a model for generating proactive context-aware recommendations in the Virtual Science Hub (ViSH), a educational platform related to the GLOBAL excursion European project. The model relies on domain-dependent context modeling in several categories to generate personalized recommendations to teachers and scientists that will produce the learning resources the students will consume. The recommendation process is divided into three phases. First, the generation of the social context information related to the users in the platform. Then, the current situation considering the social, location and user context is analyzed. Finally, the suitability of particular learning objects to be recommended is examined. Therefore, details about the recommendation model proposed and advantages related to applying the model in ViSH can be found in the paper, in addition to some conclusion remarks and outlook on future work.

This paper proposes the creation of an architectural layer for integration of all the resources and members participating in a given collaborative activity into a unified framework which is referred as the collaborative environment integration layer (CEIL). This layer is considered a necessary step for integrating with open standards all the collaborative resources or components into a tightly coupled collaborative environment which implements activity orientation. The proposal considers two architectural elements. The first element is the collaborative environment description language (CEDL), which is the formal language for describing all the elements and associated parameters relevant for modeling and managing a collaborative environment. The second one is the collaborative environment management interface (CEMI), which is the unified interface which provides uniform management and configuration of the collaborative components of an environment. This layer includes accurate descriptions of the virtual organizations associated to collaborative activity, including participants, roles, rights, hierarchies, etc being able to model the variety of existing virtual organization models

Isabel is a P2P like multipoint group collaboration tool for the Internet, which implements an innovative service concept for synchronous collaborations based on a flexible and programmable floor control. This approach leads to a more natural and effective management of collaboration sessions. The flexible and programmable floor control incorporates the experience gained in many years of service trials with real users in distributed conferences, classrooms or meetings. The main conclusion after all those trials is that services should use more or less the same media components (audio, video or application sharing), but differ in the floor control model used. The flexible floor control is especially well suited for multipoint audience interconnection in distributed classrooms, conferences, meetings, etc. The development of Isabel started in 1993 for the distribution of the RACE Summer Schools on Advanced Broadband Communication (ABC93-6) where the early versions of this service concept were developed and tuned. The effectiveness of the Isabel service concept has been proven and enhanced since then in many other distributed events, such as Global360x, IDC9x, Global IPv6 Summitts, Telecom I+D, etc. The adaptation of Isabel to the broadband Internet (including VPNs, IPv4/IPv6 transition scenarios, mixtures of unicast and multicast) has reached maturity recently