Are People Really Concerned About Their Privacy?: Privacy Paradox In Mobile Environment

The wide spread of mobile devices enables people to use the Internet everywhere. It provides people convenience in various aspects. However, they also are exposed to the risk of personal information leakage and privacy invasion. No previous study has examined whether the behaviors of people are influenced by their awareness of privacy in a mobile environment. With the ever-increasing importance of privacy issues, our study examines the critical relationship between individual privacy concerns and its behavior. The data is the media diary or 10,174 individuals’ media usage for three days, collected by the Korea Information Society Development Institute (KISDI) in 2014. Our result suggests that privacy concern has a positive influence on the smartphone usage, mobile application purchase and in-app purchase. It implies that the individual privacy concern does not correspond to his or her actual behaviors, which is paradoxical

A Review System Based On Product Features In A Mobile Environment

With the rapid growth of the mobile commerce, firms have been trying to get their online channels optimized for the mobile devices. However, many contents on online shopping sites are still focused on a desktop PC environment. Especially, consumer reviews are difficult to browse and grasp via a mobile device. Usually, it is not helpful to simply reduce the size of fonts or photos to fit to mobile devices without a fundamental transformation of the review presentation. In this study, we suggest a feature-based summarization process of consumer reviews in mobile environment. Further, we illustrate an implementation of the process by applying opinion mining techniques to product reviews crawled from a major shopping site in Korean. Finally, a plan for a controlled laboratory experiment is proposed to validate the effectiveness of the suggested review framework in this study

NOVEL TWO-INTERCONNECTED FLUIDIZED BED SYSTEM FOR SELECTIVE SOLID CIRCULATION

A novel two-interconnected fluidized bed system was developed to separate fine and coarse particles by means of particle size difference. Coarse (212~300 μm) and fine (63~106 μm) particles were separated perfectly using the solid separator. The effects of the fluidizing velocity, solid injection velocity, diameter of solid injection nozzle, and solid height on the solid separation rate were investigated. Moreover, continuous solid separation and circulation test up to 20 hours was performed to check feasibility of stable operation

THE EFFECT OF GAS TEMPERATURE AND VELOCITY ON COAL DRYING IN FLUIDIZED BED DRYER

The objective of this research work is to develop fluidized bed coal dryer to overcome the disadvantages of low rank coal with high moisture such as low calorific values, costly transportation, high emissions of pollutants, and operational problem. In this paper, laboratory scale bubbling fluidized bed was used to dry high moisture, low-rank Indonesian coal to produce low moisture, high-rank coal. The effects of temperature, gas velocity and bed height to diameter ratio (L/D) on drying rate were studied to obtain information relating to optimum operating conditions. Coal characterizations (proximate analysis, ultimate analysis, Thermogravimetric Analysis (TGA), BET, Higher Heating Value (HHV), Lower Heating Value (LHV)) were performed to identify the effect of the change of moisture content. This investigation aims to study the drying process under moderated heating conditions. As a result of the experiments the conclusion is that the thermal fluidized bed process can be successfully applied to reducing moisture in Indonesian coal. Results also indicate that about 80~90% of total moisture could be reduced, including some of the inherent moisture, yielding high heating value product. The drying rate of coal in a fluidized bed is increased by increasing the temperature and velocity of the drying gas. However gas temperature had limitations causing from the spontaneous combustion and gas velocity has to be decided considering energy efficiency

Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations

Long-term time series forecasting (LTSF) is a challenging task that has been investigated in various domains such as finance investment, health care, traffic, and weather forecasting. In recent years, Linear-based LTSF models showed better performance, pointing out the problem of Transformer-based approaches causing temporal information loss. However, Linear-based approach has also limitations that the model is too simple to comprehensively exploit the characteristics of the dataset. To solve these limitations, we propose LTSF-DNODE, which applies a model based on linear ordinary differential equations (ODEs) and a time series decomposition method according to data statistical characteristics. We show that LTSF-DNODE outperforms the baselines on various real-world datasets. In addition, for each dataset, we explore the impacts of regularization in the neural ordinary differential equation (NODE) framework.Comment: Accepted at IEEE BigData 202

Adaptive Segmentation and Stitching on 8K UHD Video

Transmission technology is necessary to display contents stored on the Cloud server. When excessive compression is performed to the transmission of ultra-high resolution image, tangibility is reduced. Therefore, in this paper, we proposed an algorithm that divides the image into a number of sub-images. The sub-images were restored to the original one by stitching at the receiver. In the existing study, important objects were located at the center of the images, but an exception occurred when they are at the edge of the images. The saliency map was used to detect their main part so that the region of interest will not be divided. The images were divided depending on the position of saliency map. It is expected that users will be able to provide realistic signage by displaying ultra-high resolution images with a large screen

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049