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

Service Attributes Available on Mobile Website

US m-commerce sales growth has been significant over the past few years. Total sales of products and services made using mobile phones and tablets reached $41 billion in 2013, accounting for 16% of total e-commerce sales. A majority of e-commerce consumers are expected to make purchases via mobile devices by 2017. Although mobile purchases are growing, most consumers still prefer to make actual purchases in physical stores or through retail websites (eMarketer, 2013)

Vulnerability assessment for the hazards of crosswinds when vehicles cross a bridge deck

AbstractA new procedure to assess the crosswind hazard of operating a vehicle over a bridge deck has been developed using a probabilistic approach that utilizes long-term wind data at bridge sites as well as the aerodynamic properties of bridge decks and vehicles. The proposed procedure for safety assessment considers the probabilities of two accident types: sideslip and overturning. The vulnerability of vehicles to crosswinds is represented by the number of days for traffic control that would be required to secure vehicle safety over a period of one year. The distribution of wind speed over a bridge deck was estimated from a section model wind tunnel test. A sea-crossing bridge was selected as an example, and a series of case studies were performed to identify the influential factors affecting vehicle vulnerability to crosswinds: vehicle type and loaded weight, the position of a running vehicle over a bridge deck, the bridge alignment relative to the dominant wind direction, and vehicle speed

Mental Imagery in an In-store Apparel Shopping Context: Do Women and Men Differ?

This study examined how mental imagery experienced during in-store shopping influences consumers’ affective (anticipatory emotion), cognitive (perceived ownership and decision satisfaction) responses and conative response (behavioral intentions) and further investigated how men and women differ in the way mental imagery influences consumer responses

SPANet: Frequency-balancing Token Mixer using Spectral Pooling Aggregation Modulation

Recent studies show that self-attentions behave like low-pass filters (as opposed to convolutions) and enhancing their high-pass filtering capability improves model performance. Contrary to this idea, we investigate existing convolution-based models with spectral analysis and observe that improving the low-pass filtering in convolution operations also leads to performance improvement. To account for this observation, we hypothesize that utilizing optimal token mixers that capture balanced representations of both high- and low-frequency components can enhance the performance of models. We verify this by decomposing visual features into the frequency domain and combining them in a balanced manner. To handle this, we replace the balancing problem with a mask filtering problem in the frequency domain. Then, we introduce a novel token-mixer named SPAM and leverage it to derive a MetaFormer model termed as SPANet. Experimental results show that the proposed method provides a way to achieve this balance, and the balanced representations of both high- and low-frequency components can improve the performance of models on multiple computer vision tasks. Our code is available at $\href{https://doranlyong.github.io/projects/spanet/}{\text{https://doranlyong.github.io/projects/spanet/}}$.Comment: Accepted paper at ICCV 202