Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams

In this work, we introduce a new algorithm for analyzing a diagram, which contains visual and textual information in an abstract and integrated way. Whereas diagrams contain richer information compared with individual image-based or language-based data, proper solutions for automatically understanding them have not been proposed due to their innate characteristics of multi-modality and arbitrariness of layouts. To tackle this problem, we propose a unified diagram-parsing network for generating knowledge from diagrams based on an object detector and a recurrent neural network designed for a graphical structure. Specifically, we propose a dynamic graph-generation network that is based on dynamic memory and graph theory. We explore the dynamics of information in a diagram with activation of gates in gated recurrent unit (GRU) cells. On publicly available diagram datasets, our model demonstrates a state-of-the-art result that outperforms other baselines. Moreover, further experiments on question answering shows potentials of the proposed method for various applications

A Design of MAC Model Based on the Separation of Duties and Data Coloring: DSDC-MAC

Among the access control methods for database security, there is Mandatory Access Control (MAC) model in which the security level is set to both the subject and the object to enhance the security control. Legacy MAC models have focused only on one thing, either confidentiality or integrity. Thus, it can cause collisions between security policies in supporting confidentiality and integrity simultaneously. In addition, they do not provide a granular security class policy of subjects and objects in terms of subjects\u27 roles or tasks. In this paper, we present the security policy of Bell_LaPadula Model (BLP) model and Biba model as one complemented policy. In addition, Duties Separation and Data Coloring (DSDC)-MAC model applying new data coloring security method is proposed to enable granular access control from the viewpoint of Segregation of Duty (SoD). The case study demonstrated that the proposed modeling work maintains the practicality through the design of Human Resources management System. The proposed model in this study is suitable for organizations like military forces or intelligence agencies where confidential information should be carefully handled. Furthermore, this model is expected to protect systems against malicious insiders and improve the confidentiality and integrity of data

Combining Reinforcement Learning With Genetic Algorithm for Many-To-Many Route Optimization of Autonomous Vehicles

This study introduces an approach for route optimization of many-to-many Demand-Responsive Transport (DRT) services. In contrast to conventional fixed-route transit systems, DRT provides dynamic, flexible, and cost-effective alternatives. We present an algorithm that integrates DRT with the autonomous shuttles at Korea National University of Transportation (KNUT), allowing dynamic route modifications in real-time to accommodate incoming service calls. The algorithm is designed to take into account the shuttle's current position, the destinations of passengers already on board, the current locations and destinations of individuals who have requested shuttle services, and the remaining capacity of the shuttle. The algorithm has been developed to combine genetic algorithms and reinforcement learning. The performance evaluation was conducted using a simulation model that emulates KNUT's campus and the adjoining local community area. The simulation results show significant improvements in two key metrics, specifically the 'Request to Pick-up Time' and 'Request to Drop-off Time' during high-demand periods over the single-shuttle operation. Additional simulation test with random service requests and stochastic passenger walking distances showed the potential adaptability across different settings

Learning by Doing: Evaluation of an Educational VR Application for the Care of Schizophrenic Patients

Nursing students learn a variety of skills to work in a clinic, such as dealing with patients with particular requirements, handling expensive equipment, and assisting doctors with treatments. However, a specific situation may require hands-on experience that cannot be easily conveyed through a textbook or a video, such as caring for a schizophrenic patient. Simulation has been considered as an effective method to replace observation-based clinical placement to overcome safety issues. In this paper, we investigate the possibility of employing a virtual reality (VR) learning platform for nursing students to learn how to care for schizophrenic patients. Using 360-degree video and a head-mounted display (HMD), students experienced virtual patients who have schizophrenia portrayed by professional actors. Our key contribution is in the insights about the design of educational VR applications, highlighting the potential value of VR for training students with non-technical backgrounds

Risk Factors and Control Strategies for the Rapidly Rising Rate of Breast Cancer in Korea

Due to the aging population and tremendous changes in life style over the past decades, cancer has been the leading cause of death in Korea. The incidence rate of breast cancer is the second highest in Korea, and it has shown an annual increase of 6.8% for the past 6 years. The major risk factors of breast cancer in Korean women are as follows: Early menarche, late menopause, late full-term pregnancy (FTP), and low numbers of FTP. Height and body mass index increased the risk of breast cancer in postmenopausal women only. There are ethnic variations in breast cancer due to the differences in genetic susceptibility or exposure to etiologic agent. With the epidemiological evidences on the possibility of further increase of breast cancer in Korea, the Korean Government began implementing the National Cancer Screening Program against breast cancer in 2002. Five-year survival rates for female breast cancer have improved significantly from 78.0% in early 1993-1995 to 90.0% in 2004-2008. This data indicate that improvement of the survival rate may be partially due to the early diagnosis of breast cancer as well as the increased public awareness about the significance of early detection and organized cancer screening program. The current primary prevention programs are geared towards strengthening national prevention campaigns. In accordance with the improvement in 5-year survival rate, the overall cancer mortality has started to decrease. However, breast cancer death rate and incidence rates are still increasing, which need further organized effort by the Korean Government

UBR2 of the N-end rule pathway is required for chromosome stability via histone ubiquitylation in spermatocytes and somatic cells

The N-end rule pathway is a proteolytic system in which its recognition components (N-recognins) recognize destabilizing N-terminal residues of short-lived proteins as an essential element of specific degrons, called N-degrons. The RING E3 ligases UBR2 and UBR1 are major N-recognins that share size (200 kDa), conserved domains and substrate specificities to N-degrons. Despite the known function of the N-end rule pathway in degradation of cytosolic proteins, the major phenotype of UBR2-deficient male mice is infertility caused by arrest of spermatocytes at meiotic prophase I. UBR2-deficient spermatocytes are impaired in transcriptional silencing of sex chromosome-linked genes and ubiquitylation of histone H2A. In this study we show that the recruitment of UBR2 to meiotic chromosomes spatiotemporally correlates to the induction of chromatin-associated ubiquitylation, which is significantly impaired in UBR2-deficient spermatocytes. UBR2 functions as a scaffold E3 that promotes HR6B/UbcH2-dependent ubiquitylation of H2A and H2B but not H3 and H4, through a mechanism distinct from typical polyubiquitylation. The E3 activity of UBR2 in histone ubiquitylation is allosterically activated by dipeptides bearing destabilizing N-terminal residues. Insufficient monoubiquitylation and polyubiquitylation on UBR2-deficient meiotic chromosomes correlate to defects in double strand break (DSB) repair and other meiotic processes, resulting in pachytene arrest at stage IV and apoptosis. Some of these functions of UBR2 are observed in somatic cells, in which UBR2 is a chromatin-binding protein involved in chromatin-associated ubiquitylation upon DNA damage. UBR2-deficient somatic cells show an array of chromosomal abnormalities, including hyperproliferation, chromosome instability, and hypersensitivity to DNA damage-inducing reagents. UBR2-deficient mice enriched in C57 background die upon birth with defects in lung expansion and neural development. Thus, UBR2, known as the recognition component of a major cellular proteolytic system, is associated with chromatin and controls chromatin dynamics and gene expression in both germ cells and somatic cells. © 2012 Kwon et al