Doctor of Philosophy

dissertationThe theme of my dissertation is users' opinion learning. We propose three different studies to learn users' opinion using various approaches and to address several important research questions. Firstly, in order to discover the significant factors that induce the rating differences from user-generated reviews, we first extract possible specific influences from the review, known as aspects, and then we propose an unsupervised aspect-based sentiment learning system that assigns sentiment scores to potential aspects. Based on the sentiment scores, we adopt linear regression models to identify the aspects that lead to the rating differences. Food quality, service, dessert and drink quality, location, value, and general opinion toward the restaurants are recognized as the main influential factors that cause the Yelp rating differences among chain restaurants. Secondly, to understand the impact of time reminder designs such as counting down clock, progressing bar indicator, and remaining number of advertisements reminder embedded in specific long and short advertisement videos, we propose a 4 by 2 between-subject experimental study with follow-up survey questions to collect user's opinions toward different temporal designs in the video. Thirdly, our study analyzes the advertisement video designs from the content level. We design the advertisement video with high and low content relevance levels with the desired video. A 2 by 2 betweensubject experimental study with follow-up survey questions is proposed. Results point out that advertisement videos with high content relevance levels can lead to shorter video iv duration perception and less negative attitudes toward the video, but can also diminish the effectiveness of the advertisement with users recalling fewer products and brands promoted in both longer and shorter advertisement videos

How do Aspects of Chain Restaurants Affect the Overall Rating: Trip-Advisor Multi-dimensional Rating System Analysis

In this study, we analyze the aspect ratings and overall ratings of chain restaurants retrieved from the TripAdvisor multi-dimensional rating system. We gain aspect ratings including food aspect rating, value aspect rating, service aspect rating, and atmosphere aspect rating and their corresponding overall restaurant rating from individual reviews and each restaurant. We build three econometric models to examine how overall rating is affected and found that the food aspect has a significant positive impact on the overall rating. Another interesting finding of our analysis is that the service aspect negatively affects the overall rating. This is explainable under the chain restaurant domain because when these restaurants share the standard menu, close price range, and other similar features, service becomes the most diverse aspect for restaurants under the same brand name. When receiving good service, customers would expect other aspects of this restaurant perceived are of the same quality as the service. However, this is hard to achieve because of the similar evaluation of other aspects from the same chain. Therefore, the overall rating would decrease. In addition, we understand and interpret the value aspect of the chain restaurants. Service, food, and atmosphere ratings influence the value aspect ratings significantly and positively

User-Centered Intelligent Interface of Vending Machines Modeling

Convenience and speed of service makes vending machines popular world-wide.However, the development and use of vending machines in China have not kept pace with global markets. In this paper, in order to determine the key design factors, interface elements and parameters which affect the convenience of user-machine interaction, the author analyzes the interaction problems in current vending machine design and finds out that unreasonable design results from machine-centered logic design. Then, with user-centered design principles, a new user-centered intelligent interaction model of vending machines is developed.The result of the test shows that the user-centered interface system can effectively reduce the operational time and decrease the mistake type and mistake rate. The process followed in the present study can also serve as a general framework for the analysis and development of UCD interfaces for other self-service systems

Predicting Medication Prescription Rankings with Medication Relation Network

Medication prescription rankings and demands prediction could benefit both medication consumers and pharmaceutical companies from various aspects. Our study predicts the medication prescription rankings focusing on patients’ medication switch and combination behavior, which is an innovative genre of medication knowledge that could be learned from unstructured patient generated contents. We first construct two supervised machine learning systems for medication references identification and medication relations classification from unstructured patient’s reviews. We further map the medication switch and combination relations into directed and undirected networks respectively. An adjusted transition in and out (ATIO) system is proposed for medication prescription rankings prediction. The proposed system demonstrates the highest positive correlation with actual medication prescription amounts comparing to other network-based measures. In order to predict the prescription demand changes, we compare four predictive regression models. The model incorporated the network-based measure from ATIO system achieve the lowest mean square errors

Free Ω\Omega-Rota-Baxter systems and Gr\"obner-Shirshov bases

In this paper, we propose the concept of an $\Omega$-Rota-Baxter system, which is a generalization of a Rota-Baxter system and an $\Omega$-Rota-Baxter algebra of weight zero. In the framework of operated algebras, we obtain a linear basis of a free $\Omega$-Rota-Baxter system for an extended diassociative semigroup $\Omega$, in terms of bracketed words and the method of Gr\"obner-Shirshov bases. As applications, we introduce the concepts of Rota-Baxter system family algebras and matching Rota-Baxter systems as special cases of $\Omega$-Rota-Baxter systems, and construct their free objects. Meanwhile, free $\Omega$-Rota-Baxter algebras of weight zero, free Rota-Baxter systems, free Rota-Baxter family algebras and free matching Rota-Baxter algebras are reconstructed via new method.Comment: 18 page

Graphics processing unit accelerating compressed sensing photoacoustic computed tomography with total variation

Photoacoustic computed tomography with compressed sensing (CS-PACT) is a commonly used imaging strategy for sparse-sampling PACT. However, it is very time-consuming because of the iterative process involved in the image reconstruction. In this paper, we present a graphics processing unit (GPU)-based parallel computation framework for total-variation-based CS-PACT and adapted into a custom-made PACT system. Specifically, five compute-intensive operators are extracted from the iteration algorithm and are redesigned for parallel performance on a GPU. We achieved an image reconstruction speed 24–31 times faster than the CPU performance. We performed in vivo experiments on human hands to verify the feasibility of our developed method

SUVH1, a Su(var)3-9 family member, promotes the expression of genes targeted by DNA methylation.

Transposable elements are found throughout the genomes of all organisms. Repressive marks such as DNA methylation and histone H3 lysine 9 (H3K9) methylation silence these elements and maintain genome integrity. However, how silencing mechanisms are themselves regulated to avoid the silencing of genes remains unclear. Here, an anti-silencing factor was identified using a forward genetic screen on a reporter line that harbors a LUCIFERASE (LUC) gene driven by a promoter that undergoes DNA methylation. SUVH1, a Su(var)3-9 homolog, was identified as a factor promoting the expression of the LUC gene. Treatment with a cytosine methylation inhibitor completely suppressed the LUC expression defects of suvh1, indicating that SUVH1 is dispensable for LUC expression in the absence of DNA methylation. SUVH1 also promotes the expression of several endogenous genes with promoter DNA methylation. However, the suvh1 mutation did not alter DNA methylation levels at the LUC transgene or on a genome-wide scale; thus, SUVH1 functions downstream of DNA methylation. Histone H3 lysine 4 (H3K4) trimethylation was reduced in suvh1; in contrast, H3K9 methylation levels remained unchanged. This work has uncovered a novel, anti-silencing function for a member of the Su(var)3-9 family that has previously been associated with silencing through H3K9 methylation

Multi-Attention Fusion Drowsy Driving Detection Model

Drowsy driving represents a major contributor to traffic accidents, and the implementation of driver drowsy driving detection systems has been proven to significantly reduce the occurrence of such accidents. Despite the development of numerous drowsy driving detection algorithms, many of them impose specific prerequisites such as the availability of complete facial images, optimal lighting conditions, and the use of RGB images. In our study, we introduce a novel approach called the Multi-Attention Fusion Drowsy Driving Detection Model (MAF). MAF is aimed at significantly enhancing classification performance, especially in scenarios involving partial facial occlusion and low lighting conditions. It accomplishes this by capitalizing on the local feature extraction capabilities provided by multi-attention fusion, thereby enhancing the algorithm's overall robustness. To enhance our dataset, we collected real-world data that includes both occluded and unoccluded faces captured under nighttime and daytime lighting conditions. We conducted a comprehensive series of experiments using both publicly available datasets and our self-built data. The results of these experiments demonstrate that our proposed model achieves an impressive driver drowsiness detection accuracy of 96.8%.Comment: 8 pages, 6 figure

Application of DMSP/OLS nighttime light images : a meta-analysis and a systematic literature review

© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remote Sensing 6 (2014): 6844-6866, doi:10.3390/rs6086844.Since the release of the digital archives of Defense Meteorological Satellite Program Operational Line Scanner (DMSP/OLS) nighttime light data in 1992, a variety of datasets based on this database have been produced and applied to monitor and analyze human activities and natural phenomena. However, differences among these datasets and how they have been applied may potentially confuse researchers working with these data. In this paper, we review the ways in which data from DMSP/OLS nighttime light images have been applied over the past two decades, focusing on differences in data processing, research trends, and the methods used among the different application areas. Five main datasets extracted from this database have led to many studies in various research areas over the last 20 years, and each dataset has its own strengths and limitations. The number of publications based on this database and the diversity of authors and institutions involved have shown promising growth. In addition, researchers have accumulated vast experience retrieving data on the spatial and temporal dynamics of settlement, demographics, and socioeconomic parameters, which are “hotspot” applications in this field. Researchers continue to develop novel ways to extract more information from the DMSP/OLS database and apply the data to interdisciplinary research topics. We believe that DMSP/OLS nighttime light data will play an important role in monitoring and analyzing human activities and natural phenomena from space in the future, particularly over the long term. A transparent platform that encourages data sharing, communication, and discussion of extraction methods and synthesis activities will benefit researchers as well as public and political stakeholders.This work is supported by the 111 project “Hazard and Risk Science Base at Beijing Normal University” under Grant B08008 (Ministry of Education and State Administration of Foreign Experts Affairs, PRC), the State Key Laboratory of Earth Surface Processes and Resource Ecology of Beijing Normal University (No. 2013-RC-03), and the Fundamental Research Funds for the Central Universities (Grant No. 201413037)