Prognostic factor of emergency patients aged 90 years and older

学位記番号：医博甲146

Research Paper Recommender System with Serendipity Using Tweets vs. Diversification

21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, Kuala Lumpur, Malaysia, November 4–7, 2019. Part of the Lecture Notes in Computer Science book series (LNCS, volume 11853), also part of the Information Systems and Applications, incl. Internet/Web, and HCI book sub series (LNISA, volume 11853).So far, a lot of works have studied research paper recommender systems. However, most of them have focused only on the accuracy and ignored the serendipity, which is an important aspect for user satisfaction. The serendipity is concerned with the novelty of recommendations and to which extent recommendations positively surprise users. In this paper, we investigate a research paper recommender system focusing on serendipity. In particular, we examine (1) whether a user’s tweets lead to a generation of serendipitous recommendations and (2) whether the use of diversification on a recommendation list improves serendipity. We have conducted an online experiment with 22 subjects in the domain of computer science. The result of our experiment shows that tweets do not improve the serendipity, despite their heterogeneous nature. However, diversification delivers serendipitous research papers that cannot be generated by a traditional strategy

Personalized Filtering of the Twitter Stream

With the rapid growth in users on social networks, there is a corresponding increase in user-generated content, in turn resulting in information overload. On Twitter, for example, users tend to receive uninterested information due to their non-overlapping interests from the people whom they follow. In this paper we present a Semantic Web approach to filter public tweets matching interests from personalized user profiles. Our approach includes automatic generation of multi-domain and personalized user profiles, filtering Twitter stream based on the generated profiles and delivering them in real-time. Given that users interests and personalization needs change with time, we also discuss how our application can adapt with these changes

Hierarchical Interest Graph from Tweets

Industry and researchers have identified numerous ways to monetize microblogs for personalization and recommendation. A common challenge across these different works is the identification of user interests. Although techniques have been developed to address this challenge, a flexible approach that spans multiple levels of granularity in user interests has not been forthcoming. In this work, we focus on exploiting hierarchical semantics of concepts to infer richer user interests expressed as a Hierarchical Interest Graph. To create such graphs, we utilize users\u27 tweets to first ground potential user interests to structured background knowledge such as Wikipedia Category Graph. We then adapt spreading activation theory to assign user interest score to each category in the hierarchy. The Hierarchical Interest Graph not only comprises of users\u27 explicitly mentioned interests determined from Twitter, but also their implicit interest categories inferred from the background knowledge source

Location Prediction of Twitter Users using Wikipedia

The mining of user generated content in social media has proven very effective in domains ranging from personalization and recommendation systems to crisis management. The knowledge of online users locations makes their tweets more informative and adds another dimension to their analysis. Existing approaches to predict the location of Twitter users are purely data-driven and require large training data sets of geo-tagged tweets. The collection and modelling process of tweets can be time intensive. To overcome this drawback, we propose a novel knowledge based approach that does not require any training data. Our approach uses information in Wikipedia, about cities in the geographical area of our interest, to score entities most relevant to a city. By semantically matching the scored entities of a city and the entities mentioned by the user in his/her tweets, we predict the most likely location of the user. Using a publicly available benchmark dataset, we achieve 3% increase in accuracy and 80 miles drop in the average error distance with respect to the state-of-the-art approaches

Analytical simulating of pollutant dispersion during sunset transition time

In this work, an analytical Eulerian model was employed to simulate the pollutants concentration released from continuous point source during the sunset transition period. The analysis applies the dispersion model parameterized by the stable and decaying convective eddy diffusivities, representing the turbulent mixing in the stable boundary layer and pre-residual layer. The concentration simulations were calculated considering different times in the transition process through the sunset period and CLE height of 60m and 120m. The results presented In this work show similarity with ones reported by literature, where the mixing strong action caused by the decaying convective energy-containing eddies in the pre-residual layer causes an effective entrance of pollutants to the interior of the recently established SBL. Analyzing the concentrations for a height of 60m , we have to during the initial stage of the transition period, in which the SBL presents a small depth, the combination between residual convective and stable eddies acts efficiently to transport the pollutants in direction to the ground surface. For the later stages, the height of the SBL depth reaches the point source height so that the dispersion occurs in a large vertical extension that is dominated by a stable turbulence. For CLE height of 120m, happen same behavior for height of 60m, for the after phases there is not surface decrease concentration according as occurs the loss convective diffusion capacity inside pre-residual layer and the CLE height increase. The lack of an effective turbulent mixing, acting over the vertical extension of the SBL, prevents that pollutants do reach the surface. In the present contribution was focused on an analytical description of the pollutant dispersion occurring around the evening transition, which allows simulate the turbulent transport in a computationally efficient procedure.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESNeste trabalho, um modelo analítico Euleriano é utilizado para simular a concentração de poluentes emitidos a partir de uma fonte pontual contínua durante o período de transição dia-noite. A análise aplica o modelo de dispersão parametrizado por coeficientes de difusão da camada limite estável e camada pré-residual. As simulações da concentração são realizadas considerando diferentes intervalos de tempos no processo de transição durante o pôr do sol e altura da CLE de 60m e 120m. Os resultados apresentados neste trabalho mostram semelhanças com aqueles encontrados na literatura onde a ação da mistura turbulenta gerada pelo decaimento da energia convectiva na camada pré-residual causa uma transferência efetiva dos poluentes para o interior da camada limite estável. Analisando as concentrações para uma altura de 60m, temos que durante o estágio inicial do período de transição, no qual a camada estável apresenta pouca profundidade, a combinação entre turbilhões convectivos e estáveis que agem eficientemente para transportar os poluentes em direção a superfície. Para o estágio posterior, a altura da camada estável alcança a altura da fonte pontual tal que a dispersão ocorre numa extensão vertical mais profunda que é dominada pela turbulência estável. Para a altura da CLE de 120m, ocorre o mesmo comportamento que para a altura de 60m, para as fases posteriores não há diminuição da concentração na superfície à medida que ocorre a perda da capacidade de difusão convectiva dentro da camada pré-residual e o aumento da altura da CLE. A falta de uma mistura turbulenta efetiva, agindo na extensão vertical da camada limite estável, impede que os poluentes cheguem a superfície. Este trabalho tem no seu foco principal uma descrição analítica da dispersão de poluentes ocorrendo em torno do pôr do sol, a qual permite simular o transporte turbulento de forma computacionalmente eficiente