Enabling collaboration in virtual reality navigators

In this paper we characterize a feature superset for Collaborative Virtual Reality Environments (CVRE), and derive a component framework to transform stand-alone VR navigators into full-fledged multithreaded collaborative environments. The contributions of our approach rely on a cost-effective and extensible technique for loading software components into separate POSIX threads for rendering, user interaction and network communications, and adding a top layer for managing session collaboration. The framework recasts a VR navigator under a distributed peer-to-peer topology for scene and object sharing, using callback hooks for broadcasting remote events and multicamera perspective sharing with avatar interaction. We validate the framework by applying it to our own ALICE VR Navigator. Experimental results show that our approach has good performance in the collaborative inspection of complex models.Postprint (published version

"Go make your face known": collaborative working through the lens of personal relationships

Background: Collaborative working between professionals is a key component of integrated care. The academic literature on it largely focuses either on integration between health and social care or on the dynamics of power and identity between doctors and nurses. With the proliferation and extension of nursing roles, there is a need to examine collaborative working amongst different types of nurses. Method: This study explored experiences of collaborative working amongst generalist and specialist nurses, in community and acute settings. We carried out semi-structured interviews, incorporating the Pictor technique, with 45 nurses, plus 33 other key stakeholders. Transcripts were analysed using Template Analysis. This article focuses on one major thematic area that emerged from the analysis: the significance of interpersonal relationships amongst nurses, and between them and other professionals, patients and carers. Results: Relationship issues were ubiquitous in participants’ accounts of collaborative working. Good personal relationships facilitated collaboration; face-to-face interaction was especially valued. Relationships were recognized as requiring effort, especially in new roles. Organisational changes could disrupt productive personal networks. Conclusion: Relationship issues are integral to successful collaborative working. Policy and practice leaders must take this into account in future service developments. Further research into collaborative relationships in different settings is needed

IMPROVING THE QUALITY OFPRACTICE LEARNING THROUGH COMPETENCY-BASED LEARNING WITH COLLABORATIVE SKILL APPROACH ON VOCATIONAL EDUCATION

Learning is the core of education. It means solving the problem on technology and vocational education cannot be separated from the demand of innovations that focus on the improvement of learning quality. Competency-based learning of practice with a collaborative approach is one of learning innovations which is relevant to be conducted in vocational education. It is in line with the main objective of vocational education i.e. to provide productive competence for the learners to become graduates who are ready to competein the corporate world. Principally, the implementation of competency-based learning of practice with a collaborative approach is by dividing the students into groups. Each member of the group has the task to workon one component,which is then combined into its group to be one unit. The advantages of this learning model were: 1) the existence of positive interdependence among learners, 2) promoting intensive face-to-face interaction, 3) developing a sense of personal responsibility, and 4) stimulating students’ collaborative skills

A Graph-Neural-Network-Based Social Network Recommendation Algorithm Using High-Order Neighbor Information

Social-network-based recommendation algorithms leverage rich social network information to alleviate the problem of data sparsity and boost the recommendation performance. However, traditional social-network-based recommendation algorithms ignore high-order collaborative signals or only consider the first-order collaborative signal when learning users’ and items’ latent representations, resulting in suboptimal recommendation performance. In this paper, we propose a graph neural network (GNN)-based social recommendation model that utilizes the GNN framework to capture high-order collaborative signals in the process of learning the latent representations of users and items. Specifically, we formulate the representations of entities, i.e., users and items, by stacking multiple embedding propagation layers to recursively aggregate multi-hop neighborhood information on both the user–item interaction graph and the social network graph. Hence, the collaborative signals hidden in both the user–item interaction graph and the social network graph are explicitly injected into the final representations of entities. Moreover, we ease the training process of the proposed GNN-based social recommendation model and alleviate overfitting by adopting a lightweight GNN framework that only retains the neighborhood aggregation component and abandons the feature transformation and nonlinear activation components. The experimental results on two real-world datasets show that our proposed GNN-based social recommendation method outperforms the state-of-the-art recommendation algorithms

Enhancing knowledge management in online collaborative learning

This study aims to explore two crucial aspects of collaborative work and learning: on the one hand, the importance of enabling collaborative learning applications to capture and structure the information generated by group activity and, on the other hand, to extract the relevant knowledge in order to provide learners and tutors with efficient awareness, feedback and support as regards group performance and collaboration. To this end, in this paper we first propose a conceptual model for data analysis and management that identifies and classifies the many kinds of indicators that describe collaboration and learning into high-level aspects of collaboration. Then, we provide a computational platform that, at a first step, collects and classifies both the event information generated asynchronously from the users' actions and the labeled dialogues from the synchronous collaboration according to these indicators. This information is then analyzed in next steps to eventually extract and present to participants the relevant knowledge about the collaboration. The ultimate aim of this platform is to efficiently embed information and knowledge into collaborative learning applications. We eventually suggest a generalization of our approach to be used in diverse collaborative learning situations and domains

Tools for user interaction in immersive environments

REVERIE -- REal and Virtual Engagement in Realistic Immersive Environments -- is a large scale collaborative project co-funded by the European Commission targeting novel research in the general domain of Networked Media and Search Systems. The project aims to bring about a revolution in 3D media and virtual reality by developing technologies for safe, collaborative, online environments that can enable realistic interpersonal communication and interaction in immersive environments. To date, project partners have been developing component technologies for a variety of functionalities related to the aims of REVERIE prior to integration into an end-to-end system. In this demo submission, we first introduce the project in general terms, outlining the high-level concept and vision before briefly describing the suite of demonstrations that we intend to present at MMM 2014

Analysis and control of complex collaborative design systems

This paper presents a novel method for modelling the complexity of collaborative design systems based on its analysis and proposes a solution to reducing complexity and improving performance of such systems. The interaction and interfacing properties among many components of a complex design system are analysed from different viewpoints and then a complexity model for collaborative design is established accordingly. In order to simplify complexity and improve performance of collaborative design, a general solution of decomposing a whole system into sub-systems and using unified interface mechanism between them has been proposed. This proposed solution has been tested with a case study. It has been shown that the proposed solution is meaningful and practical

Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text

Collaborative filtering (CF) is the key technique for recommender systems (RSs). CF exploits user-item behavior interactions (e.g., clicks) only and hence suffers from the data sparsity issue. One research thread is to integrate auxiliary information such as product reviews and news titles, leading to hybrid filtering methods. Another thread is to transfer knowledge from other source domains such as improving the movie recommendation with the knowledge from the book domain, leading to transfer learning methods. In real-world life, no single service can satisfy a user's all information needs. Thus it motivates us to exploit both auxiliary and source information for RSs in this paper. We propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH) methods for cross-domain recommendation with unstructured text in an end-to-end manner. TMH attentively extracts useful content from unstructured text via a memory module and selectively transfers knowledge from a source domain via a transfer network. On two real-world datasets, TMH shows better performance in terms of three ranking metrics by comparing with various baselines. We conduct thorough analyses to understand how the text content and transferred knowledge help the proposed model.Comment: 11 pages, 7 figures, a full version for the WWW 2019 short pape