29 research outputs found

    Designing a Patient-Centered Clinical Workflow to Assess Cyberbully Experiences of Youths in the U.S. Healthcare System

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    Cyberbullying or online harassment is often defined as when someone repeatedly and intentionally harasses, mistreats, or makes fun of others aiming to scare, anger or shame them using electronic devices [296]. Youths experiencing cyberbullying report higher levels of anxiety and depression, mental distress, suicide thoughts, and substance abuse than their non-bullied peers [360, 605, 261, 354]. Even though bullying is associated with significant health problems, to date, very little youth anti-bullying efforts are initiated and directed in clinical settings. There is presently no standardized procedure or workflow across health systems for systematically assessing cyberbullying or other equally dangerous online activities among vulnerable groups like children or adolescents [599]. Therefore, I developed a series of research projects to link digital indicators of cyberbullying or online harassment to clinical practices by advocating design considerations for a patient-centered clinical assessment and workflow that addresses patients’ needs and expectations to ensure quality care. Through this dissertation, I aim to answer these high-level research questions:RQ1. How does the presence of severe online harassment on online platforms contribute to negative experiences and risky behaviors within vulnerable populations? RQ2. How efficient is the current mechanism of screening these risky online negative experiences and behaviors, specifically related to cyberbully, within at-risk populations like adolescent in clinical settings? RQ3. How might evidence of activities and negative harassing experiences on online platforms best be integrated into electronic health records during clinical treatment? I first explore how harassment is presented within different social media platforms from diverse contexts and cultural norms (study 1,2, and 3); next, by analyzing actual patient data, I address current limitations in the screening process in clinical settings that fail to efficiently address core aspect of cyberbullying and their consequences within adolescent patients (study 4 and 5); finally, connecting all my findings, I recommend specific design guidelines for a refined screening tool and structured processes for implementation and integration of the screened data into patients’ electronic health records (EHRs) for better patient assessment and treatment outcomes around cyberbully within adolescent patients (study 6)

    Slums on Screen

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    From Jacob Riis’ How The Other Half Lives (1890) to Danny Boyle’s Slumdog Millionaire (2008), Igor Krstić outlines a transnational history of films that either document or fictionalise the favelas, shantytowns, barrios poulares or chawls of our ‘planet of slums’

    Raphtory: Modelling, Maintenance and Analysis of Distributed Temporal Graphs.

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    PhD ThesesTemporal graphs capture the development of relationships within data throughout time. This model ts naturally within a streaming architecture, where new events can be inserted directly into the graph upon arrival from a data source and be compared to related entities or historical state. However, the majority of graph processing systems only consider traditional graph analysis on static data, whilst those which do expand past this often only support batched updating and delta analysis across graph snapshots. In this work we de ne a temporal property graph model and the semantics for updating it in both a distributed and non-distributed context. We have built Raphtory, a distributed temporal graph analytics platform which maintains the full graph history in memory, leveraging the de ned update semantics to insert streamed events directly into the model without batching or centralised ordering. In parallel with the ingestion, traditional and time-aware analytics may be performed on the most up-to-date version of the graph, as well as any point throughout its history. The depth of history viewed from the perspective of a time point may also be varied to explore both short and long term patterns within the data. Through this we extract novel insights over a variety of use cases, including phenomena never seen before in social networks. Finally, we demonstrate Raphtory's ability to scale both vertically and horizontally, handling consistent throughput in excess of 100,000 updates a second alongside the ingestion and maintenance of graphs built from billions of events

    KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation

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    International audienceWe present KADABRA, a new algorithm to approximate betweenness centrality in directed and undirected graphs, which significantly outperforms all previous approaches on real-world complex networks. The efficiency of the new algorithm relies on two new theoretical contributions, of independent interest. The first contribution focuses on sampling shortest paths, a subroutine used by most algorithms that approximate betweenness centrality. We show that, on realistic random graph models, we can perform this task in time |E| 1 2 +o (1) with high probability, obtaining a significant speedup with respect to the Θ(|E|) worst-case performance. We experimentally show that this new technique achieves similar speedups on real-world complex networks, as well. The second contribution is a new rigorous application of the adaptive sampling technique. This approach decreases the total number of shortest paths that need to be sampled to compute all betweenness centralities with a given absolute error, and it also handles more general problems, such as computing the k most central nodes. Furthermore, our analysis is general, and it might be extended to other settings

    A METHOD TO IMPROVE THE TIME OF COMPUTING BETWEENNESS CENTRALITY IN SOCIAL NETWORK GRAPH

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    The Betweenness centrality is an important metric in the graph theory and can be applied in the analyzing social network. The main researches about Betweenness centrality often focus on reducing the complexity. Nowadays, the number of users in the social networks is huge. Thus, improving the computing time of Betweenness centrality to apply in the social network is neccessary. In this paper, we propose the algorithm of computing Betweenness centrality by reduce the similar nodes in the graph in order to reducing computing time. Our experiments with graph networks result shows that the computing time of the proposed algorithm is less than Brandes algorithm. The proposed algorithm is compared with the Brandes algorithm [3] in term of execution time

    Association of Researchers in Construction Management (ARCOM) Proceedings Of The 35th Annual Conference

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