Post-Adoption Behavior of Mobile Internet Users: A Model-Based Comparison between Continuers and Discontinuers

Many mobile Internet users are not continuing to use mobile Internet services after initial use. This study aims to explore how such users (discontinuers) differ from ongoing users (continuers) in terms of accepting mobile Internet technology. We propose an adoption model for the mobile Internet consisting of seven critical factors. An on-line survey was conducted on the basis of this model to compare continuers and discontinuers. The survey results show that discontinuers are more sensitive to usefulness and social influences in using mobile Internet services, while continuers are more sensitive to ubiquitous connectivity

A study on improvement local waterworks business with public private partnership(PPP)

Thesis(Master) --KDI School:Master of Public Management,2018.This research paper aims to suggest a public-private partnership(PPP) scheme for local government to improve aged water supply pipe lines in local waterworks business. In 2015, 691 million tons of tap water was lost by leakage of aged water supply pipes and in terms of money, it was 60.6 billion won loss. The water flow rate, showing how much proportion of supplied tap water turns to revenue without loss while transmitting water through the pipe line is much lower in small sized cities than metropolitan cities. Though low water flow rate is continuing problems to local governments which take charge of local waterworks business, deteriorated waterworks networks has not been improved due to poor financial condition and low expertise in local governments which is identified as main factors to affect low water flow rate among other variables In this respect, PPP prescribed in Private Invest Act, would be good scheme to improve local waterworks by attracting private capital and advanced technologies into local waterworks business. Especially BTL is considered to be advantageous way in terms of inelastic demand of water and stability for investors. Finally, central government should reform subsidy policy as stopgap measure, and attract voluntary participation in sustainable development of local waterworks business from local governments.1. Introduction 2. Literature review 3. Current status of the local waterworks business 4. Study on PPP of the local waterworks business under Private Investment Act 5. Study on the feasibility of the local waterworks business through PPP 6. ConclusionmasterpublishedHoyoung, KIM

Discrete Double Hilbert Transforms Along Polynomial Surfaces

We obtain a necessary and sufficient condition on a polynomial $P(t_1,t_2)$ for the $\ell^{p}$ boundedness of the discrete double Hilbert transforms associated with $P(t)$ for $1 < p < \infty$. The proof is based on the multi-parameter circle method treating the cases of $|t_1|\not\approx |t_2|$ arising from $1/t_1$ and $1/t_2$

High dimensional discriminant rules with shrinkage estimators of covariance matrix and mean vector

Linear discriminant analysis is a typical method used in the case of large dimension and small samples. There are various types of linear discriminant analysis methods, which are based on the estimations of the covariance matrix and mean vectors. Although there are many methods for estimating the inverse matrix of covariance and the mean vectors, we consider shrinkage methods based on non-parametric approach. In the case of the precision matrix, the methods based on either the sparsity structure or the data splitting are considered. Regarding the estimation of mean vectors, nonparametric empirical Bayes (NPEB) estimator and nonparametric maximum likelihood estimation (NPMLE) methods are adopted which are also called f-modeling and g-modeling, respectively. We analyzed the performances of linear discriminant rules which are based on combined estimation strategies of the covariance matrix and mean vectors. In particular, we present a theoretical result on the performance of the NPEB method and compare that with the results from other methods in previous studies. We provide simulation studies for various structures of covariance matrices and mean vectors to evaluate the methods considered in this paper. In addition, real data examples such as gene expressions and EEG data are presented.Comment: 39 pages, 3 figure

Experimental study of pipe-pile-based micro-scale compressed air energy storage (PPMS-CAES) for a building

Compressed air energy storage (CAES) technology has been re-emerging as one of the promising options to address the challenge coming from the intermittency of renewable energy resources. Unlike the large-scale CAES, which is limited by the geologic location, small- and micro-scale CAES that uses a human-made pressure vessel is adaptable for both grid-connected and standalone distributed units equipped with the energy generation capacity. The research team recently suggested a new concept of pipe- pile-based micro-scale CAES (PPMS-CAES) that uses pipe-pile foundations of a building as compressed air storage vessels. To ascertain the mechanical feasibility of the new concept, we conducted lab-scale pile loading tests with a model test pile in both a loose and dense soil chamber that emulates an actual closed- ended pipe pile. The test pile was subjected to a repeated cycle of compressed air charge (to Pmax=10 MPa) and discharge (to Pmin=0.1 MPa) during the experimental study. The displacement at the top of the test pile, with and without a structural loading, in loose and dense sand, was closely monitored during the repetitive air pressurization-and-depressurization. It was observed that the vertical displacement at the pile head under different conditions was accumulated during the extended cycle of air charge and discharge, but the rate of displacement gradually attenuates during the cycle. And, the presence of structural load and density of soil affected the magnitude of the accumulated vertical displacement. From the analysis, it can be concluded that the concept of PPMS-CAES is not likely to compromise the mechanical integrity of pipe piles while showing a promising capacity for energy storage

Empirical Research of Fans Consumption in Talent Show Economy of China

In recent years, with the continuous development of China's draft industry, Internet drafts have become common throughout China. The talent show industry has not only won the love of the audience, but also the attention of capital. A lot of capital began to invest in the talent show industry to cultivate new popular idols. Through social media platforms, fans can purchase products recommended by idols and vote for idols to increase their ranking. Idols also gain more popularity and sell more endorsed products through interaction with fans. This article starts with the talent economy, through questionnaire surveys and empirical methods, explores the factors that affect fans' consumption on idols. Diversified participation, idol abilities, social influence and empathy have been proved to have positive impacts on fans’ intention to consuming. This research provides detailed empirical research conclusions for China's talent show market, which has a strong role in promoting the sustainable development of the talent show economy

Adaptive Superpixel for Active Learning in Semantic Segmentation

Learning semantic segmentation requires pixel-wise annotations, which can be time-consuming and expensive. To reduce the annotation cost, we propose a superpixel-based active learning (AL) framework, which collects a dominant label per superpixel instead. To be specific, it consists of adaptive superpixel and sieving mechanisms, fully dedicated to AL. At each round of AL, we adaptively merge neighboring pixels of similar learned features into superpixels. We then query a selected subset of these superpixels using an acquisition function assuming no uniform superpixel size. This approach is more efficient than existing methods, which rely only on innate features such as RGB color and assume uniform superpixel sizes. Obtaining a dominant label per superpixel drastically reduces annotators' burden as it requires fewer clicks. However, it inevitably introduces noisy annotations due to mismatches between superpixel and ground truth segmentation. To address this issue, we further devise a sieving mechanism that identifies and excludes potentially noisy annotations from learning. Our experiments on both Cityscapes and PASCAL VOC datasets demonstrate the efficacy of adaptive superpixel and sieving mechanisms

Instrumentation and Axial Load Testing of Displacement Piles

Despite the fact that results of many instrumented pile load tests have been reported in the literature, it is difficult to find well-documented instrumentation procedures that can be used when planning a load testing programme. A load test programme designed to investigate various aspects of the design and behaviour of driven steel piles is discussed in the present paper. Although the literature contains information on load testing of instrumented piles driven in either sand or clay, limited information is available regarding their axial load response in transitional soils (soils composed of various amounts of clay, silt and sand). Results are presented for fully instrumented axial load tests performed on an H pile and a closed-ended pipe pile driven into a multilayered soil profile consisting of transitional soils. In addition, the load testing planning, the instrumentation of the piles, the testing methods and the interpretation of the pile testing data are discussed in detail in the context of this and other load testing programmes described in the literature, in order to illustrate the various steps

Active Learning for Semantic Segmentation with Multi-class Label Query

This paper proposes a new active learning method for semantic segmentation. The core of our method lies in a new annotation query design. It samples informative local image regions (e.g., superpixels), and for each of such regions, asks an oracle for a multi-hot vector indicating all classes existing in the region. This multi-class labeling strategy is substantially more efficient than existing ones like segmentation, polygon, and even dominant class labeling in terms of annotation time per click. However, it introduces the class ambiguity issue in training since it assigns partial labels (i.e., a set of candidate classes) to individual pixels. We thus propose a new algorithm for learning semantic segmentation while disambiguating the partial labels in two stages. In the first stage, it trains a segmentation model directly with the partial labels through two new loss functions motivated by partial label learning and multiple instance learning. In the second stage, it disambiguates the partial labels by generating pixel-wise pseudo labels, which are used for supervised learning of the model. Equipped with a new acquisition function dedicated to the multi-class labeling, our method outperformed previous work on Cityscapes and PASCAL VOC 2012 while spending less annotation cost

ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision

The recent advances in representation learning inspire us to take on the challenging problem of unsupervised image classification tasks in a principled way. We propose ContraCluster, an unsupervised image classification method that combines clustering with the power of contrastive self-supervised learning. ContraCluster consists of three stages: (1) contrastive self-supervised pre-training (CPT), (2) contrastive prototype sampling (CPS), and (3) prototype-based semi-supervised fine-tuning (PB-SFT). CPS can select highly accurate, categorically prototypical images in an embedding space learned by contrastive learning. We use sampled prototypes as noisy labeled data to perform semi-supervised fine-tuning (PB-SFT), leveraging small prototypes and large unlabeled data to further enhance the accuracy. We demonstrate empirically that ContraCluster achieves new state-of-the-art results for standard benchmark datasets including CIFAR-10, STL-10, and ImageNet-10. For example, ContraCluster achieves about 90.8% accuracy for CIFAR-10, which outperforms DAC (52.2%), IIC (61.7%), and SCAN (87.6%) by a large margin. Without any labels, ContraCluster can achieve a 90.8% accuracy that is comparable to 95.8% by the best supervised counterpart.Comment: Accepted at ICPR 202