1,721,080 research outputs found
Specifications, models and standards for personalisation features and inquiry learning apps
Full text linkPersonalisation of the Go-Lab front end solutions is critical to improve usability, findability and user experience. In this deliverable, we analyse the requirements for personalisation through use cases that detail how teachers can be assisted during their activities by these functionalities. Afterwards, we provide comprehensive surveys on existing standards and specifications that enable personalisation. We first discuss the data needed to enable personalisation. Labs, inquiry learning spaces (ILS), apps and learning resources will include rich metadata on top of their content that can be used for effective filtering and recommendation. For apps, Go-Lab will follow the OpenSocial metadata specification and the ROLE Ontology. For resources, ILS and labs, metadata specifications are still under dicussion. Then, we present specifications for personalisation in Go-Lab. More specifically, personalisation in Go-Lab will be centered around internationalisation and recommendation. For internationalisation, Go-Lab will support the personalisation of languages at ILS creation time. For recommendation we propose to use a hybrid recommender system using collaborative-filtering and a multi-relational graph used in Graasp. We also propose to investigate federated and time-sensitive recommender systems. Finally, we present specifications for inquiry-learning apps, which will be based on OpenSocial standards for communication and data storage & retrieval. The apps will target both desktop and mobile devices. Discussions on software libraries that enable the use of apps on both desktop and mobile clients are still on-going. This deliverable prepares the specifications, models, and standards for personalisation which are the foundation for the specification of the Go-Lab Portal (D5.2)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A gossip learning approach to urban trajectory nowcasting for anticipatory RAN management
No full textIn future radio access networks, machine learning (ML) based strategies for short-term forecasting of vehicular trajectories will be key for anticipatory resource allocation and management at the mobile edge. However, training ML models in a centralized fashion, over data collected from a massive heterogeneous and dynamic set of devices, poses significant scalability, reliability, and efficiency challenges, which are still open to date. In this paper, we look at the specific issue of scalable and resource-efficient training of ML models in a vehicular environment. To address such a challenge, we propose a new Gossip Learning scheme, i.e., a fully distributed, collaborative training approach based on direct, opportunistic model exchanges via wireless device-to-device (D2D) communications with no centralized support. Our approach is based on constantly improving each node's own model instance through knowledge transfer among nodes, and on different strategies for estimating the potential contribution of neighboring nodes to the training process at a node. Extensive numerical assessments on a variety of measurement-based dynamic urban scenarios suggest that our schemes are able to converge rapidly and provide sufficiently accurate forecasts of vehicle position for time horizons which are typical of future 5G/6G dynamic resource allocation algorithms
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Context-Aware Orchestration of Energy-Efficient Gossip Learning Schemes
No full textFully distributed learning schemes such as Gossip Learning (GL) are gaining momentum due to their scalability and effectiveness even in dynamic settings. However, they often imply a high utilization of communication and computing resources, whose energy footprint may jeopardize the learning process, particularly on battery-operated IoT devices. To address this issue, we present optimized Gossip Learning (OGL), a distributed training approach based on the combination of GL with adaptive optimization of the learning process, which allows for achieving a target accuracy while minimizing the energy consumption of the learning process. We propose a data-driven approach to OGL management that relies on optimizing in real-time for each node the number of training epochs and the choice of which model to exchange with neighbors based on patterns of node contacts, models’ quality, and available resources at each node. Our approach employs a DNN model for dynamic tuning of the aforementioned parameters, trained by an infrastructure-based orchestrator function. We performed our assessments on two different datasets, leveraging time-varying random graphs and a measurement-based dynamic urban scenario. Results suggest that our approach is highly efficient and effective in a broad spectrum of network scenarios
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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