Tourists' Attitudes Towards Tea Tourism: A Case Study in Xinyang, China

Tea tourism as a new niche market has become more and more popular. Through a case study in Xinyang, China, this research explores tourists' attitudes and perceptions toward tea and tea tourism, identifies who the potential tea tourists are, and compares their attitudes with others. One hundred seventy-nine questionnaires were administered; one-way ANOVA and chi-square test were used based on their willingness of tea tourism. The results suggest that tea tourists and non-tea tourists have significant differences in terms of their attitudes toward tea drinking and their willingness of buying tea as souvenir. Tea tourists are mainly tea lovers driven by their high interest in tea and tea culture; they tend to be both males and females (yet females show a significant higher percentage than males), between ages 31-40, who have a positive attitude toward tea drinking, and who often drink tea. This research also provides some marketing suggestions for this niche market

Group Membership Prediction

The group membership prediction (GMP) problem involves predicting whether or not a collection of instances share a certain semantic property. For instance, in kinship verification given a collection of images, the goal is to predict whether or not they share a {\it familial} relationship. In this context we propose a novel probability model and introduce latent {\em view-specific} and {\em view-shared} random variables to jointly account for the view-specific appearance and cross-view similarities among data instances. Our model posits that data from each view is independent conditioned on the shared variables. This postulate leads to a parametric probability model that decomposes group membership likelihood into a tensor product of data-independent parameters and data-dependent factors. We propose learning the data-independent parameters in a discriminative way with bilinear classifiers, and test our prediction algorithm on challenging visual recognition tasks such as multi-camera person re-identification and kinship verification. On most benchmark datasets, our method can significantly outperform the current state-of-the-art.Comment: accepted for ICCV 201

Employment Policy and Sustainable Livelihoods of Landless Peasants in China: A Study in Zhengdong New Area

Landless peasants are peasants who have been deprived of their land in the process of urbanization in China and other countries. This thesis focuses on the impact of employment policies on the sustainable livelihoods of landless peasants in one of China's newly developed urban districts, Zhengdong New Area. I first provide the background of Zhengdong New Area, followed by a literature review of sustainable livelihoods, and of the role of government in the employment of landless peasants. In the analysis section, I develop an employment-related policy evaluation framework for examining the ability of landless peasants to maintain a sustainable livelihood, by detailing relevant indicators, including material and non-material aspects. I apply the policy evaluation framework to both policy design and implementation, finding that the non-material aspect of sustainable livelihoods has been largely neglected, and that the promises made to LPs in various policies have not been fulfilled. Lastly, I provide policy suggestions from a planner's perspective. In terms of policy design, I propose to define specific actions and hard numbers in which they can be measured, to give more considerations to LPs'non-material satisfaction, and to focus on the actual effects of policies. Regarding policy implementation, I propose a strengthening of supervisions, trying to cover the majority during the implementation process, and to achieve a balance between inadequate regulation and over-regulation

Discovering user mobility and activity in smart lighting environments

"Smart lighting" environments seek to improve energy efficiency, human productivity and health by combining sensors, controls, and Internet-enabled lights with emerging “Internet-of-Things” technology. Interesting and potentially impactful applications involve adaptive lighting that responds to individual occupants' location, mobility and activity. In this dissertation, we focus on the recognition of user mobility and activity using sensing modalities and analytical techniques. This dissertation encompasses prior work using body-worn inertial sensors in one study, followed by smart-lighting inspired infrastructure sensors deployed with lights. The first approach employs wearable inertial sensors and body area networks that monitor human activities with a user's smart devices. Real-time algorithms are developed to (1) estimate angles of excess forward lean to prevent risk of falls, (2) identify functional activities, including postures, locomotion, and transitions, and (3) capture gait parameters. Two human activity datasets are collected from 10 healthy young adults and 297 elder subjects, respectively, for laboratory validation and real-world evaluation. Results show that these algorithms can identify all functional activities accurately with a sensitivity of 98.96% on the 10-subject dataset, and can detect walking activities and gait parameters consistently with high test-retest reliability (p-value < 0.001) on the 297-subject dataset. The second approach leverages pervasive "smart lighting" infrastructure to track human location and predict activities. A use case oriented design methodology is considered to guide the design of sensor operation parameters for localization performance metrics from a system perspective. Integrating a network of low-resolution time-of-flight sensors in ceiling fixtures, a recursive 3D location estimation formulation is established that links a physical indoor space to an analytical simulation framework. Based on indoor location information, a label-free clustering-based method is developed to learn user behaviors and activity patterns. Location datasets are collected when users are performing unconstrained and uninstructed activities in the smart lighting testbed under different layout configurations. Results show that the activity recognition performance measured in terms of CCR ranges from approximately 90% to 100% throughout a wide range of spatio-temporal resolutions on these location datasets, insensitive to the reconfiguration of environment layout and the presence of multiple users.2017-02-17T00:00:00

Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction

Object detection systems based on the deep convolutional neural network (CNN) have recently made ground- breaking advances on several object detection benchmarks. While the features learned by these high-capacity neural networks are discriminative for categorization, inaccurate localization is still a major source of error for detection. Building upon high-capacity CNN architectures, we address the localization problem by 1) using a search algorithm based on Bayesian optimization that sequentially proposes candidate regions for an object bounding box, and 2) training the CNN with a structured loss that explicitly penalizes the localization inaccuracy. In experiments, we demonstrated that each of the proposed methods improves the detection performance over the baseline method on PASCAL VOC 2007 and 2012 datasets. Furthermore, two methods are complementary and significantly outperform the previous state-of-the-art when combined.Comment: CVPR 201

Probing dynamics of dark energy with latest observations

We examine the validity of the $\Lambda$CDM model, and probe for the dynamics of dark energy using latest astronomical observations. Using the $Om(z)$ diagnosis, we find that different kinds of observational data are in tension within the $\Lambda$CDM framework. We then allow for dynamics of dark energy and investigate the constraint on dark energy parameters. We find that for two different kinds of parametrisations of the equation of state parameter $w$, a combination of current data mildly favours an evolving $w$, although the significance is not sufficient for it to be supported by the Bayesian evidence. A forecast of the DESI survey shows that the dynamics of dark energy could be detected at $7\sigma$ confidence level, and will be decisively supported by the Bayesian evidence, if the best fit model of $w$ derived from current data is the true model.Comment: 4.5 pages, 3 figures, 1 table; references adde