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Browsing by Author "Xiao, Luo"
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Item ASPER: Attention-based Approach to Extract Syntactic Patterns denoting Semantic Relations in Sentential Context(2021) Md. Ahsanul, Kabir; Typer, Philips; Xiao, Luo; Mohammed, Al Hasan; Computer and Information Science, School of ScienceSemantic relationships, such as hyponym-hypernym, cause-effect, meronym-holonym etc., between a pair of entities in a sentence are usually reflected through syntactic patterns. Automatic extraction of such patterns benefits several downstream tasks, including, entity extraction, ontology building, and question answering. Unfortunately, automatic extraction of such patterns has not yet received much attention from NLP and information retrieval researchers. In this work, we propose an attentionbased supervised deep learning model, ASPER, which extracts syntactic patterns between entities exhibiting a given semantic relation in the sentential context. We validate the performance of ASPER on three distinct semantic relations—hyponym-hypernym, cause-effect, and meronym-holonym on six datasets. Experimental results show that for all these semantic relations, ASPER can automatically identify a collection of syntactic patterns reflecting the existence of such a relation between a pair of entities in a sentence. In comparison to the existing methodologies of syntactic pattern extraction, ASPER’s performance is substantially superior.Item A Machine Learning Based Visible Light Communication Model Leveraging Complementary Color Channel(2020-08) Jiang, Ruizhe; King, Brian; Guo, Xiaonan; Xiao, LuoRecently witnessed a great popularity of unobtrusive Visible Light Communication (VLC) using screen-camera channels. They overcomes the inherent drawbacks of traditional approaches based on coded images like bar codes. One popular unobtrusive method is the utilizing of alpha channel or color channels to encode bits into the pixel translucency or color intensity changes with over-the-shelf smart devices. Specifically, Uber-in-light proves to be an successful model encoding data into the color intensity changes that only requires over-the-shelf devices. However, Uber-in-light only exploit Multi Frequency Shift Keying (MFSK), which limits the overall throughput of the system since each data segment is only 3-digit long. Motivated by some previous works like Inframe++ or Uber-in-light, in this thesis, we proposes a new VLC model encoding data into color intensity changes on red and blue channels of video frames. Multi-Phase-Shift-Keying (MPSK) along with MFSK are used to match 4-digit and 5-digit long data segments to specific transmission frequencies and phases. To ensure the transmission accuracy, a modified correlation-based demodulation method and two learning-based methods using SVM and Random Forest are also developed.