52 research outputs found

    Twitter data analysis for studying communities of practice in the media industry

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    Today, more and more physical communities of practice, a concept that describes a group of people that share a passion and interact regularly at events to exchange knowledge, utilize social media, such as Twitter. Brotaru, for instance, is such a physical community of practice for media professionals in Brussels. It is a monthly meet-up of videogame developers in various locations in Brussels. Furthermore, Twitter becomes widely acknowledged as important instrument for learning and community formation in the virtual world. But, do these communities of practice use Twitter only to promote their physical activities of learning? Or, are the activities of the physical communities further extended into the virtual world meaning that virtual communities of practice emerge from them? This article suggests a novel mixed-methods approach based on qualitative and quantitative data to measure the role of Twitter for physical communities of practice. The method applies different statistical measures and analysis on harvested Twitter data and additionally brings two of the most used methods in Twitter analysis together, social network analysis and text data analysis (a.k.a., content analysis). Four different communities of practice in Brussels’ media industry and their activities and followers on Twitter have been analysed. The findings showed that the activities of the communities of practice extend into the Twitter sphere as the online communities are characterised by a shared domain, a lively community and shared practices. The analysis further revealed that Twitter offers three main opportunities for the activities of communities of practice: it offers geographical extension; it gives temporal autonomy; and, it can be used to diversify the practices

    HOMOE: A Memory-Based and Composition-Aware Framework for Zero-Shot Learning with Hopfield Network and Soft Mixture of Experts

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    Compositional Zero-Shot Learning (CZSL) has emerged as an essential paradigm in machine learning, aiming to overcome the constraints of traditional zero-shot learning by incorporating compositional thinking into its methodology. Conventional zero-shot learning has difficulty managing unfamiliar combinations of seen and unseen classes because it depends on pre-defined class embeddings. In contrast, Compositional Zero-Shot Learning uses the inherent hierarchies and structural connections among classes, creating new class representations by combining attributes, components, or other semantic elements. In our paper, we propose a novel framework that for the first time combines the Modern Hopfield Network with a Mixture of Experts (HOMOE) to classify the compositions of previously unseen objects. Specifically, the Modern Hopfield Network creates a memory that stores label prototypes and identifies relevant labels for a given input image. Following this, the Mixture of Expert models integrates the image with the fitting prototype to produce the final composition classification. Our approach achieves SOTA performance on several benchmarks, including MIT-States and UT-Zappos. We also examine how each component contributes to improved generalization

    Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference

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    Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e.g., meteorological and traffic information. In this work, we focus on street-level air quality inference by utilizing data collected by mobile stations. We formulate air quality inference in this setting as a graph-based matrix completion problem and propose a novel variational model based on graph convolutional autoencoders. Our model captures effectively the spatio-temporal correlation of the measurements and does not depend on the availability of additional information apart from the street-network topology. Experiments on a real air quality dataset, collected with mobile stations, shows that the proposed model outperforms state-of-the-art approaches

    Fed-LSAE: Thwarting Poisoning Attacks against Federated Cyber Threat Detection System via Autoencoder-based Latent Space Inspection

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    The significant rise of security concerns in conventional centralized learning has promoted federated learning (FL) adoption in building intelligent applications without privacy breaches. In cybersecurity, the sensitive data along with the contextual information and high-quality labeling in each enterprise organization play an essential role in constructing high-performance machine learning (ML) models for detecting cyber threats. Nonetheless, the risks coming from poisoning internal adversaries against FL systems have raised discussions about designing robust anti-poisoning frameworks. Whereas defensive mechanisms in the past were based on outlier detection, recent approaches tend to be more concerned with latent space representation. In this paper, we investigate a novel robust aggregation method for FL, namely Fed-LSAE, which takes advantage of latent space representation via the penultimate layer and Autoencoder to exclude malicious clients from the training process. The experimental results on the CIC-ToN-IoT and N-BaIoT datasets confirm the feasibility of our defensive mechanism against cutting-edge poisoning attacks for developing a robust FL-based threat detector in the context of IoT. More specifically, the FL evaluation witnesses an upward trend of approximately 98% across all metrics when integrating with our Fed-LSAE defense

    A Bibliometric Review on Realistic Mathematics Education in Scopus Database Between 1972-2019

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    Despite receiving increasing attention from mathematics education scholars, there has not yet been any overall understanding of the current state of realistic mathematics education (RME). To address this gap, this study aims to provide a review of 288 studies on realistic mathematics education from the Scopus database between 1972 and 2019. Using descriptive and bibliometric analyses, this study addresses four research issues as follows: (i) the total volume, growth trajectory, and geographic distribution; (ii) the most influencing authors and research groups; (iii) the most influencing sources (i.e., journals, books, conferences); and (iv) the most important topics. Several implications for not only mathematics education scholars but also other stakeholders, including policymakers, school managers, mathematics teachers, may not be considered in this study


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    A Particularly Sensitive Sea Area (PSSA) is an important management tool for biodiversity protection of a marine area. At the time of designation of a Particularly Sensitive Sea Area, an associated protective measure, which meets the requirements of the appropriate legal instrument establishing such measure, must have been approved or adopted by IMO to prevent, reduce, or eliminate the threat or identified vulnerability. Information on each of the Particularly Sensitive Sea Areas (PSSAs) that has been designated by IMO is available on the nautical chart. The Vietnam’s coastal zones and islands are the isolated oceanic habitat of extremely rich marine life in very good condition which is important to the maintenance and dispersal of the marine life of the western tropical Pacific. Vietnam coastal areas are very high risk areas affected by maritime activities, particularly international shipping, therefore in the future identification of some Particularly Sensitive Sea Areas (PSSAs) is necessary. The first Particularly Sensitive Sea Area for Vietnam in Ha Long - Cat Ba was initially proposed in this paper

    Host Transcription Profile in Nasal Epithelium and Whole Blood of Hospitalized Children Under 2 Years of Age With Respiratory Syncytial Virus Infection.

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    BACKGROUND: Most insights into the cascade of immune events after acute respiratory syncytial virus (RSV) infection have been obtained from animal experiments or in vitro models. METHODS: In this study, we investigated host gene expression profiles in nasopharyngeal (NP) swabs and whole blood samples during natural RSV and rhinovirus (hRV) infection (acute versus early recovery phase) in 83 hospitalized patients <2 years old with lower respiratory tract infections. RESULTS: Respiratory syncytial virus infection induced strong and persistent innate immune responses including interferon signaling and pathways related to chemokine/cytokine signaling in both compartments. Interferon-α/β, NOTCH1 signaling pathways and potential biomarkers HIST1H4E, IL7R, ISG15 in NP samples, or BCL6, HIST2H2AC, CCNA1 in blood are leading pathways and hub genes that were associated with both RSV load and severity. The observed RSV-induced gene expression patterns did not differ significantly in NP swab and blood specimens. In contrast, hRV infection did not as strongly induce expression of innate immunity pathways, and significant differences were observed between NP swab and blood specimens. CONCLUSIONS: We conclude that RSV induced strong and persistent innate immune responses and that RSV severity may be related to development of T follicular helper cells and antiviral inflammatory sequelae derived from high activation of BCL6
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