Alzheimer's disease is one of the challenges to tackle in the coming aging era and is attempting to diagnose and predict through various biomarkers. While the application of various deep learning-based technologies as powerful imaging technologies has recently expanded across the medical industry, empirical design is not easy because there are various deep earning neural networks architecture and categorical hyperparameters that rely on problems and data to solve. In this paper, we show the possibility of optimizing a deep learning neural network structure and hyperparameters for Alzheimer's disease classification in amyloid brain images in a representative deep earning neural networks architecture using genetic algorithms. It was observed that the optimal deep learning neural network structure and hyperparameter were chosen as the values of the experiment were converging.

Face recognition has gained significant notice because of its application in many businesses: security, healthcare, and marketing. In this paper, we will present the recognition method using the combination of correlation filters (CF) and Support Vector Machine (SVM). Firstly, we evaluate the performance and compared four different correlation filters: minimum average correlation energy (MACE), maximum average correlation height (MACH), unconstrained minimum average correlation energy (UMACE), and optimal-tradeoff (OT). Secondly, we propose the machine learning approach by using the OT correlation filter for features extraction and SVM for classification. The numerical results on National Cheng Kung University (NCKU) and Pointing'04 face database show that the proposed method OT-SVM gets higher accuracy in face recognition compared to other machine learning methods. Our approach doesn't require graphics card to train the image. As a result, it could run well on a low hardware system like an embedded system.

In this paper, we introduce a technique for 360-degree panoramic video streaming with multiple virtual cameras in real-time. The proposed technique consists of generating 360-degree panoramic video data by ORB feature point detection, texture transformation, panoramic video data compression, and RTSP-based video streaming transmission. Especially, the generating process of 360-degree panoramic video data and texture transformation are accelerated by CUDA for complex processing such as camera calibration, stitching, blending, encoding. Our experiment evaluated the frames per second (fps) of the transmitted 360-degree panoramic video. Experimental results verified that our technique takes at least 30fps at 4K output resolution, which indicates that it can both generates and transmits 360-degree panoramic video data in real time.

Image steganalysis is a technique for detecting images with steganographic algorithms applied, called stego images. With state-of-the-art CNN-based steganalysis methods, we can detect stego images with high accuracy, but it is not possible to know which steganographic algorithm is used. Identifying stego images is essential for extracting embedded data. In this paper, as the first step for extracting data from stego images, we propose a hierarchical CNN structure for senary classification of steganographic algorithms. The hierarchical CNN structure consists of multiple CNN networks which are trained to classify each steganographic algorithm and performs binary or ternary classification. Thus, it classifies multiple steganogrphic algorithms hierarchically and stepwise, rather than classifying them at the same time. In experiments of comparing with several conventional methods, including those of classifying multiple steganographic algorithms at the same time, it is verified that using the hierarchical CNN structure can greatly improve the classification accuracy.

The digital image to be accepted as legal evidence, it is important to verify the authentication of the digital image. This study proposes a method of authenticating digital images through three steps of comparing the file structure of digital images taken with iPhone, analyzing the encoding information as well as media logs of the iPhone storing the digital images. For the experiment, digital image samples were acquired from nine iPhones through a camera application built into the iPhone. And the characteristics of file structure and media log were compared between digital images generated on the iPhone and digital images edited through a variety of image editing tools. As a result of examining those registered during the digital image creation process, it was confirmed that differences from the original characteristics occurred in file structure and media logs when manipulating digital images on the iPhone, and digital images take with the iPhone. In this way, it shows that it can prove its forensic authentication in iPhone.

Education emphasizes problem-solving skills based on convergent thinking power in an era of rising uncertainty and rapid progress. This paper proactively designed e-Learning team teaching convergence liberal arts courses for prospective teachers by these social needs. It analyzed the empirical effects on the operation of the subjects to foster future talent who can converge and apply knowledge in various fields. The curriculum consisted of professors of mathematics, practical Arts, computer, and education, and was operated to convey convergent knowledge of information technology and humanities, and consisted of 15 liberal arts courses at J University. Besides, textbooks and teaching materials were also developed by the faculty. As a result of the primary research, prospective teachers who took the course generally showed high satisfaction with the class, especially for the faculty. The students' overall convergent thinking ability has increased to a statistically significant level (p<.01), and the students' major has been found to be irrelevant. On the other hand, it can be seen that communication, content convergence, and caring factors, excluding creativity factor, have all risen to a significant level.

Obsessive-compulsive disorder (OCD) is a mental disorder in which a person repeated a particular thought or feels. The domain of beliefs and guilt predicted OCD symptoms. Although there were some neuroimaging studies investigating OCD symptoms, resting-state functional magnetic resonance imaging (rs-fMRI) study investigating intra-network functional connectivity associated with guilt for OCD is not reported yet. Therefore, in the current study, we assessed the differences between intra-network functional connectivity of healthy control group and OCD group using independent component analysis (ICA) method. In addition, we also aimed to investigate the correlation between changed functional connectivity and guilt score in OCD. Total 86 participants, which consisted of 42 healthy control volunteers and 44 OCD patients, acquired rs-fMRI data using the 3T MRI. After preprocessing the fMRI data, a functional connectivity was used for group independent component analysis. The results showed that OCD patients had higher score in emotion state in beliefs and lower functional connectivity in fronto-parietal network (FPN) than control group. A decrease of functional connectivity in FPN was negatively correlated with feelings of guilt in OCD. Our results suggest excessive increase in guilt negatively affect to process emotional state and behavior or cognitive processing by influencing intrinsic brain activity.

In this paper, we present the FlappyBird Competition System (FCS) implementation, a competition-based automated assessment system used in an entry-level artificial intelligence (AI) course at a university. The proposed system provides an evaluation method suitable for AI courses while taking advantage of automated assessment methods. Students are to design a neural network structure, train the weights, and tune hyperparameters using the given reinforcement learning code to improve the overall performance of game AI. Students participate using the resulting trained model during the competition, and the system automatically calculates the final score based on the ranking. The user evaluation conducted after the semester ends shows that our competition-based automated assessment system promotes active participation and inspires students to be interested and motivated to learn AI. Using FCS, the instructor significantly reduces the amount of time required for assessment.

This article combines literature and empirical research based on space perception theory and the case study of mixed reality game "The Fragment." It is concluded that the mixed reality scene under space perception has a three-level visual definition. This definition carries out a corresponding level analysis of the scenes of the "The Fragment" game and draws up the constituent factors of the mixed reality game scene characteristics. Finally, through questionnaire data investigation and analysis, it is verified that the three factors of virtual reality coexistence, human-computer interaction, and local serviceability can better explain the characteristics of mixed reality game scenes. At the end of the study, it is concluded that the definition of three levels of visual hierarchy and the constituent factors of mixed reality game scenes can provide reference and help for other mixed-reality game designs and a brief description of future research plans.

This thesis is based on the theory of multiple intelligences proposed by the american educator and psychologist Dr.Gardner. According to the definition and classification of children's intelligence development games by predecessors, 6 types of intelligence development suitable for children aged 3 to 6 are summarized games, fill in the questionnaire to understand children's personal preferences, the purpose is to understand whether children aged 3 to 6 have a preference for intelligent development games and whether the preference will be affected by gender and age, and to understand the reality of children aged 3 to 6 Preferences and intellectual development needs provide a factual basis for more scientifically launching intelligent development games.

With growing competition in the market, more product differentiation in visual perception is needed to enhance competitive power of products. The purpose of this paper is to have a research on designer's cognitive differences in aesthetic preferences of female consumers in product CMF design, and the deviation result in different female consumer groups will be obtained based on collected data of CMF design preferences of different female consumer groups. The research method adopted is to conduct matching experiment with professional products designers as participant to test the matching through correlation analysis between designers' cognition of female consumers and their preferences and female consumer preferences on the basis of the constructed typical user roles of female consumers. The results of the research show the correlation between designers' understanding of female consumer groups and their own real needs, and the surface processing of product surface decoration is the highest aesthetic preference of female consumer groups. The research provides reference for product design industry and designers of small and medium-sized enterprises who have substantial difficulty in surface design analysis.