Data Driven Computational Sciences 2019 (DDCS) Session 2

Time and Date: 16:50 - 18:30 on 12th June 2019

Room: 0.4

Chair: Craig Douglas

141 An Implementation of Coupled Dual-Porosity-Stokes Model with FEniCS [abstract]
Abstract: Porous media and conduit coupled systems are heavily used in a variety of areas. A coupled dual-porosity-Stokes model has been proposed to simulate the fluid flow in a dual-porosity media and conduits coupled system. In this paper, we propose an implementation of this multi-physics model. We solve the system with the automated high performance differential equation solving environment FEniCS. Tests of the convergence rate of our implementation in both 2D and 3D are conducted in this paper. We also give tests on performance and scalability of our implementation.
Xiukun Hu and Craig C. Douglas
443 Anomaly Detection in Social Media using Recurrent Neural Network [abstract]
Abstract: In today’s information environment there is an increasing reliance on online and social media in the acquisition, dissemination and consumption of news. Specifically, the utilization of social media platforms such as Facebook and Twitter has increased as a cutting edge medium for breaking news. On the other hand, the low cost, easy access and rapid propagation of news through so-cial media makes the platform more sensitive to fake and anomalous reporting. The propagation of fake and anomalous news is not some benign exercise. The extensive spread of fake news has the potential to do serious and real damage to individuals and society. As a result, the detection of fake news in social media has become a vibrant and important field of research. In this paper, a novel ap-plication of machine learning approaches to the detection and classification of fake and anomalous data are considered. An initial clustering step with the K-Nearest Neighbor (KNN) algorithm is proposed before training the result with a Recurrent Neural Network (RNN). The results of a preliminary application of the KNN phase before the RNN phase produces a quantitative and measureable im-provement in the detection of outliers, and as such is more effective in detecting anomalies or outliers against the test dataset of 2016 US Presidential Election predictions.
Madhu Goyal
539 Conditional BERT Contextual Augmentation [abstract]
Abstract: We propose a novel data augmentation method for labeled sentences called con- ditional BERT contextual augmentation. Data augmentation methods are often ap- plied to prevent overfitting and improve generalization of deep neural network models. Recently proposed contextual augmentation augments labeled sentences by randomly replacing words with more varied substitutions predicted by language model. BERT demonstrates that a deep bidirectional language model is more pow- erful than either an unidirectional lan- guage model or the shallow concatena- tion of a forward and backward model. We retrofit BERT to conditional BERT by introducing a new conditional masked language model 1 task. The well trained conditional BERT can be applied to en- hance contextual augmentation. Experi- ments on six various different text classi- fication tasks show that our method can be easily applied to both convolutional or re- current neural networks classifier to obtain obvious improvement.
Xing Wu, Shangwen Lv, Liangjun Zang, Jizhong Han and Songlin Hu
552 An innovative and reliable water leak detection service supported by data-intensive remote sensing processing [abstract]
Abstract: In the scope of the H2020 WADI project, an airborne water leak detection surveillance service, based on manned and unmanned aerial vehicles, is being developed to provide water utilities with adequate information on leaks in large water distribution infrastructures outside urban areas. Given the high cost associated with water infrastructure networks repairs, a reliability layer is necessary to improve the trustworthiness of the WADI leak identification, based on complementary technologies for leak detection. Herein, a methodology based on the combined use of Sentinel remote sensing data and a water leak pathways model is presented, based on data-intensive computing. The resulting water leak detection reliability service, provided to the users through a web interface, targets prompt and cost-effective infrastructure repairs with the required degree of confidence on the detected leaks. The web platform allows for both data analysis and visualization of Sentinel images and relevant leak indicators at the sites selected by the user. The user can provide aerial imagery inputs, to be processed together with Sentinel remote sensing data at the satellite acquisition dates identified by the user. The platform provides information about the detected leaks location and time evolution, and will be linked in the future with the outputs from water pathway models.
Ricardo Martins, Anabela Oliveira, André Fortunato, Alberto Azevedo, Elsa Alves and Alexandra Carvalho