scholarly journals Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

2020 ◽  
Vol 12 (6) ◽  
pp. 2373
Author(s):  
Seok-Woo Jang ◽  
Sang-Hong Lee
Keyword(s):  
High Speed ◽  
Learning Algorithm ◽  
Personal Information ◽  
Target Object ◽  
Wireless Internet ◽  
Neural Computing ◽  
Object Region ◽  
Application Fields ◽  
Human Faces ◽  
Media Data

High-speed wired and wireless Internet are one of the useful ways to acquire various types of media data easily. In this circumstance, people also can easily get media data including objects with exposed personal information through the Internet. Exposure of personal information emerges as a social issue. This paper proposes an effective blocking technique that makes it possible to robustly detect target objects with exposed personal information from various types of input images with the use of deep neural computing and to effectively block the detected objects’ regions. The proposed technique first utilizes the neural computing-based learning algorithm to robustly detect the target object including personal information from an image. It next generates a grid-type mosaic and lets the mosaic overlap the target object region detected in the previous step so as to effectively block the object region that includes personal information. Experimental results reveal that the proposed algorithm robustly detects the target object region with exposed personal information from a variety of input images and effectively blocks the detected region through grid-type mosaic processing. The object blocking technique proposed in this paper is expected to be applied to various application fields such as image security, sustainable anticipatory computing, object tracking, and target blocking.

High Speed Wireless Internet Using LiFi

2018 ◽  
Author(s):  
Chinmay Agarwal ◽  
Medhavini Kulshrestha ◽  
Himanshu Rathore ◽  
Kamalakannan J
Keyword(s):  
High Speed ◽  
Wireless Internet

Geometrical design of a crystal growth system guided by a machine learning algorithm

CrystEngComm ◽  
2021 ◽  
Author(s):  
Wancheng Yu ◽  
Can Zhu ◽  
Yosuke Tsunooka ◽  
Wei Huang ◽  
Yifan Dang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Crystal Growth ◽  
High Speed ◽  
Learning Algorithm ◽  
Computational Techniques ◽  
Machine Learning Algorithm ◽  
Geometrical Design ◽  
Large Numbers ◽  
Growth System ◽  
Speed Method

This study proposes a new high-speed method for designing crystal growth systems. It is capable of optimizing large numbers of parameters simultaneously which is difficult for traditional experimental and computational techniques.

scholarly journals Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining

2020 ◽  
Vol 17 (14) ◽  
pp. 4988 ◽  
Author(s):  
Diya Li ◽  
Harshita Chaudhary ◽  
Zhe Zhang
Keyword(s):  
Data Mining ◽  
Social Media ◽  
San Francisco ◽  
Learning Algorithm ◽  
The United States ◽  
Spatiotemporal Pattern ◽  
Social Media Data ◽  
Clinical Patient ◽  
Stress Symptoms ◽  
Media Data

By 29 May 2020, the coronavirus disease (COVID-19) caused by SARS-CoV-2 had spread to 188 countries, infecting more than 5.9 million people, and causing 361,249 deaths. Governments issued travel restrictions, gatherings of institutions were cancelled, and citizens were ordered to socially distance themselves in an effort to limit the spread of the virus. Fear of being infected by the virus and panic over job losses and missed education opportunities have increased people’s stress levels. Psychological studies using traditional surveys are time-consuming and contain cognitive and sampling biases, and therefore cannot be used to build large datasets for a real-time depression analysis. In this article, we propose a CorExQ9 algorithm that integrates a Correlation Explanation (CorEx) learning algorithm and clinical Patient Health Questionnaire (PHQ) lexicon to detect COVID-19 related stress symptoms at a spatiotemporal scale in the United States. The proposed algorithm overcomes the common limitations of traditional topic detection models and minimizes the ambiguity that is caused by human interventions in social media data mining. The results show a strong correlation between stress symptoms and the number of increased COVID-19 cases for major U.S. cities such as Chicago, San Francisco, Seattle, New York, and Miami. The results also show that people’s risk perception is sensitive to the release of COVID-19 related public news and media messages. Between January and March, fear of infection and unpredictability of the virus caused widespread panic and people began stockpiling supplies, but later in April, concerns shifted as financial worries in western and eastern coastal areas of the U.S. left people uncertain of the long-term effects of COVID-19 on their lives.

scholarly journals Attitudes and Knowledge of Adolescents in Jordan Regarding the Ethics of Social Media Data Use for Research Purposes

2020 ◽  
Vol 15 (1-2) ◽  
pp. 87-96
Author(s):  
Hiba Wazeer Al Zou’bi ◽  
Moawiah Khatatbeh ◽  
Karem H. Alzoubi ◽  
Omar F. Khabour ◽  
Wael K. Al-Delaimy
Keyword(s):  
Social Media ◽  
Informed Consent ◽  
Online Survey ◽  
Personal Information ◽  
Data Use ◽  
Ethical Aspects ◽  
Social Media Data ◽  
Attitudes And Knowledge ◽  
Media Data ◽  
Research Studies

This study assessed the awareness and attitudes of adolescents in Jordan concerning the ethics of using their social media data for scientific studies. Using an online survey, 393 adolescents were recruited (mean age: 17.2 years ± 1.8). The results showed that 88% of participants were using their real personal information on social media sites, with males more likely to provide their information than females. More than two thirds of participants (72.5%) were aware that researchers may use their data for research purposes, with the majority believing that informed consent must be obtained from both the adolescents and their parents. However, more than three quarters of those surveyed (76%) did not trust the results of research that depended on collecting data from social media. These findings suggest that adolescents in Jordan understood most of the ethical aspects related to the utilization of their data from social media websites for research studies.

scholarly journals Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6104
Author(s):  
Bernardo Calabrese ◽  
Ramiro Velázquez ◽  
Carolina Del-Valle-Soto ◽  
Roberto de Fazio ◽  
Nicola Ivan Giannoccaro ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Visually Impaired ◽  
High Speed ◽  
Low Cost ◽  
Recognition Rate ◽  
Object Identification ◽  
Time Interval ◽  
Solar Powered ◽  
Human Faces

This paper introduces a novel low-cost solar-powered wearable assistive technology (AT) device, whose aim is to provide continuous, real-time object recognition to ease the finding of the objects for visually impaired (VI) people in daily life. The system consists of three major components: a miniature low-cost camera, a system on module (SoM) computing unit, and an ultrasonic sensor. The first is worn on the user’s eyeglasses and acquires real-time video of the nearby space. The second is worn as a belt and runs deep learning-based methods and spatial algorithms which process the video coming from the camera performing objects’ detection and recognition. The third assists on positioning the objects found in the surrounding space. The developed device provides audible descriptive sentences as feedback to the user involving the objects recognized and their position referenced to the user gaze. After a proper power consumption analysis, a wearable solar harvesting system, integrated with the developed AT device, has been designed and tested to extend the energy autonomy in the different operating modes and scenarios. Experimental results obtained with the developed low-cost AT device have demonstrated an accurate and reliable real-time object identification with an 86% correct recognition rate and 215 ms average time interval (in case of high-speed SoM operating mode) for the image processing. The proposed system is capable of recognizing the 91 objects offered by the Microsoft Common Objects in Context (COCO) dataset plus several custom objects and human faces. In addition, a simple and scalable methodology for using image datasets and training of Convolutional Neural Networks (CNNs) is introduced to add objects to the system and increase its repertory. It is also demonstrated that comprehensive trainings involving 100 images per targeted object achieve 89% recognition rates, while fast trainings with only 12 images achieve acceptable recognition rates of 55%.

High Precision Automatic Alignment and its Computer Vision Technology

1989 ◽  
Vol 1 (3) ◽  
pp. 220-226
Author(s):  
Tohru Tanigawa ◽  
◽  
Toshitsugu Sawai ◽  
Tadashi Nakao
Keyword(s):  
Computer Vision ◽  
High Precision ◽  
Flexible Manufacturing ◽  
High Speed ◽  
Manufacturing Systems ◽  
Ultraviolet Rays ◽  
Human Ability ◽  
Automatic Alignment ◽  
Computer Vision Technology ◽  
Application Fields

Recently, industrial robotics and computer vision technology has become very important in flexible manufacturing systems and automated factories. Especially high precision automatic alignment technology beyond human ability is essential to some manufactures, and its application fields are extending rapidly. This paper describes the high precision automatic alignment system of large-sized LCD panels. The features of the system are (1) high precision and high speed detection of position using the special alignment mark, (2) high contrast image obtained by the use of ultraviolet rays, (3) new image-processing algorithms for improvement of system reliability.

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