Exploring Factors That Influence Students’ Behaviors in Information Security

Due to the ever-increasing use of the Internet, information security has become a critical issue in society. This is especially the case for young adults who have different attitudes towards information security practices. In this research, we examine factors that motivate college students’ information security behaviors. Based on the concept of fear arousal in the presence of a threatened event, a well-founded theory known as Protection Motivation Theory (PMT) is adopted in the research model. Social norms and habit factors are integrated to the model as a means to assess students’ behaviors of information security. A survey of 202 responses is used to test the designed model using structural equation modeling to analyze relationships among variables. Results indicated that students are very motivated to practice information security if they perceive high levels of severity, response efficacy, response costs and self-efficacy. Their intentions, however, are not affected by perceived vulnerability or by social influence. Our findings suggest that PMT is a valuable model for predicting students’ attitudes towards information security and that their motivation is influenced by education in security awareness and understanding severity of such issues

Bottom-up estimation and top-down prediction: Solar energy prediction combining information from multiple sources

Accurately forecasting solar power using the data from multiple sources is an important but challenging problem. Our goal is to combine two different physics model forecasting outputs with real measurements from an automated monitoring network so as to better predict solar power in a timely manner. To this end, we propose a new approach of analyzing large-scale multilevel models with great computational efficiency requiring minimum monitoring and intervention. This approach features a division of the large scale data set into smaller ones with manageable sizes, based on their physical locations, and fit a local model in each area. The local model estimates are then combined sequentially from the specified multilevel models using our novel bottom-up approach for parameter estimation. The prediction, on the other hand, is implemented in a top-down matter. The proposed method is applied to the solar energy prediction problem for the U.S. Department of Energy’s SunShot Initiative

The Interaction of Phospholipase C-{beta}3 with Shank2 Regulates mGluR-mediated Calcium Signal

Phospholipase C-{beta} isozymes that are activated by G protein-coupled receptors (GPCR) and heterotrimeric G proteins carry a PSD-95/Dlg/ZO-1 (PDZ) domain binding motif at their C terminus. Through interactions with PDZ domains, this motif may endow the PLC-{beta} isozyme with specific roles in GPCR signaling events that occur in compartmentalized regions of the plasma membrane. In this study, we identified the interaction of PLC-{beta}3 with Shank2, a PDZ domain-containing multimodular scaffold in the postsynaptic density (PSD). The C terminus of PLC-{beta}3, but not other PLC-{beta} isotypes, specifically interacts with the PDZ domain of Shank2. Homer 1b, a Shank-interacting protein that is linked to group I metabotropic glutamate receptors and IP3 receptors, forms a multiple complex with Shank2 and PLC-{beta}3. Importantly, microinjection of a synthetic peptide specifically mimicking the C terminus of PLC-{beta}3 markedly reduces the mGluR-mediated intracellular calcium response. These results demonstrate that Shank2 brings PLC-{beta}3 closer to Homer 1b and constitutes an efficient mGluR-coupled signaling pathway in the PSD region of neuronal synapses

Factorization of the 3d superconformal index

We prove that 3d superconformal index for general =2 U(N) gauge group with fundamentals and anti-fundmentals with/without Chern-Simons terms is factorized into vortex and anti-vortex partition function. We show that for simple cases, 3d vortex partition function coincides with a suitable topological open string partition function. We provide much more elegant derivation at the index level for =2 Seiberg-like dualities of unitary gauge groups with fundamantal matters and =4 mirror symmetry1114sciescopu

Piper retrofractum

Photoaging occurs by UVB-irradiation and involves production of reactive oxygen species (ROS) and overexpression of matrix metalloproteinases (MMPs), leading to extracellular matrix damage. Piper retrofractum Vahl. is used as a traditional medicine for antiflatulence, expectorant, sedative, and anti-irritant; however, its antiphotoaging effect has not yet been studied. The current study investigated the antiphotoaging effect of standardized Piper retrofractum extract (PRE) on UVB-damaged human dermal fibroblasts and hairless mouse skin. PRE treatment activated the peroxisome proliferator-activated receptor delta (PPARδ) and the adenosine monophosphate-activated protein kinase (AMPK), consequently upregulating mitochondrial synthesis and reducing ROS production. Additionally, PRE inhibited MMPs expression via suppressing mitogen-activated protein kinase (MAPK) and activator protein-1 (AP-1). PRE downregulated UVB-induced inflammatory reactions by inhibiting the nuclear factor-kappa B (NF-κB) activity. PRE also enhanced transforming growth factor-beta (TGF-β) and the Smad signaling pathway, thereby promoting procollagen gene transcription. Furthermore, oral administration of PRE (300 mg/kg/day) similarly regulated the signaling pathways and increased antioxidant enzyme expression, thus attenuating physiological deformations, such as wrinkle formation and erythema response. Collectively, these results suggest that PRE acts as a potent antiphotoaging agent via PPARδ and AMPK activation

Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images

Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test datasets, CNN-based segmentation models trained by a training dataset fail to segment buildings for the test dataset. In this paper, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-dataset experiments and the ablation study are conducted for the three different datasets: the Inria aerial image labeling dataset, the Massachusetts building dataset, and the WHU East Asia dataset. Compared to the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall IoU. Moreover, it is verified that the proposed method outperforms even when compared to feature adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure