Cloud-based Image Processing System with Priority-based Data Distribution Mechanism

[[abstract]]Most users process short tasks using MapReduce. In other words, most tasks handled by the Map and Reduce functions require low response time. Currently, quite few users use MapReduce for 2D to 3D image processing, which is highly complicated and requires long execution time. However, in our opinion, MapReduce is exactly suitable for processing applications of high complexity and high computation. This paper implements MapReduce on an integrated 2D to 3D multi-user system, in which Map is responsible for image processing procedures of high complexity and high computation, and Reduce is responsible for integrating the intermediate data processed by Map for the final output. Different from short tasks, when several users compete simultaneously to acquire data from MapReduce for 2D to 3D applications, data that waits to be processed by Map will be delayed by the current user and Reduce has to wait until the completion of all Map tasks to generate the final result. Therefore, a novel scheduling scheme, Dynamic Switch of Reduce Function (DSRF) Algorithm, is proposed in this paper for MapReduce to switch dynamically to the next task according to the achieved percentage of tasks and reduce the idle time of Reduce. By using Hadoop to implement our MapReduce platform, we compare the performance of traditional Hadoop with our proposed scheme. The experimental results reveal that our proposed scheduling scheme efficiently enhances MapReduce performance in running 2D to 3D applications.[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Privacy Preserving Utility Mining: A Survey

In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and applications, such as market basket analysis, retail, click-stream analysis, medical analysis, and bioinformatics. However, analysis of these data with sensitive private information raises privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent years. In this paper, we provide a comprehensive overview of PPUM. We first present the background of utility mining, privacy-preserving data mining and PPUM, then introduce the related preliminaries and problem formulation of PPUM, as well as some key evaluation criteria for PPUM. In particular, we present and discuss the current state-of-the-art PPUM algorithms, as well as their advantages and deficiencies in detail. Finally, we highlight and discuss some technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page

When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks

Discovering and exploiting the causality in deep neural networks (DNNs) are crucial challenges for understanding and reasoning causal effects (CE) on an explainable visual model. "Intervention" has been widely used for recognizing a causal relation ontologically. In this paper, we propose a causal inference framework for visual reasoning via do-calculus. To study the intervention effects on pixel-level features for causal reasoning, we introduce pixel-wise masking and adversarial perturbation. In our framework, CE is calculated using features in a latent space and perturbed prediction from a DNN-based model. We further provide the first look into the characteristics of discovered CE of adversarially perturbed images generated by gradient-based methods \footnote{~~https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvImg}. Experimental results show that CE is a competitive and robust index for understanding DNNs when compared with conventional methods such as class-activation mappings (CAMs) on the Chest X-Ray-14 dataset for human-interpretable feature(s) (e.g., symptom) reasoning. Moreover, CE holds promises for detecting adversarial examples as it possesses distinct characteristics in the presence of adversarial perturbations.Comment: Noted our camera-ready version has changed the title. "When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks" as the v3 official paper title in IEEE Proceeding. Please use it in your formal reference. Accepted at IEEE ICIP 2019. Pytorch code has released on https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvIm

Mitochondrial targeting of human NADH dehydrogenase (ubiquinone) flavoprotein 2 (NDUFV2) and its association with early-onset hypertrophic cardiomyopathy and encephalopathy

<p>Abstract</p> <p>Background</p> <p>NADH dehydrogenase (ubiquinone) flavoprotein 2 (NDUFV2), containing one iron sulfur cluster ([2Fe-2S] binuclear cluster N1a), is one of the core nuclear-encoded subunits existing in human mitochondrial complex I. Defects in this subunit have been associated with Parkinson's disease, Alzheimer's disease, Bipolar disorder, and Schizophrenia. The aim of this study is to examine the mitochondrial targeting of NDUFV2 and dissect the pathogenetic mechanism of one human deletion mutation present in patients with early-onset hypertrophic cardiomyopathy and encephalopathy.</p> <p>Methods</p> <p>A series of deletion and point-mutated constructs with the <it>c-myc </it>epitope tag were generated to identify the location and sequence features of mitochondrial targeting sequence for NDUFV2 in human cells using the confocal microscopy. In addition, various lengths of the NDUFV2 N-terminal and C-terminal fragments were fused with enhanced green fluorescent protein to investigate the minimal region required for correct mitochondrial import. Finally, a deletion construct that mimicked the IVS2+5_+8delGTAA mutation in <it>NDUFV2 </it>gene and would eventually produce a shortened NDUFV2 lacking 19-40 residues was generated to explore the connection between human gene mutation and disease.</p> <p>Results</p> <p>We identified that the cleavage site of NDUFV2 was located around amino acid 32 of the precursor protein, and the first 22 residues of NDUFV2 were enough to function as an efficient mitochondrial targeting sequence to carry the passenger protein into mitochondria. A site-directed mutagenesis study showed that none of the single-point mutations derived from basic, hydroxylated and hydrophobic residues in the NDUFV2 presequence had a significant effect on mitochondrial targeting, while increasing number of mutations in basic and hydrophobic residues gradually decreased the mitochondrial import efficacy of the protein. The deletion mutant mimicking the human early-onset hypertrophic cardiomyopathy and encephalopathy lacked 19-40 residues in NDUFV2 and exhibited a significant reduction in its mitochondrial targeting ability.</p> <p>Conclusions</p> <p>The mitochondrial targeting sequence of NDUFV2 is located at the N-terminus of the precursor protein. Maintaining a net positive charge and an amphiphilic structure with the overall balance and distribution of basic and hydrophobic amino acids in the N-terminus of NDUFV2 is important for mitochondrial targeting. The results of human disease cell model established that the impairment of mitochondrial localization of NDUFV2 as a mechanistic basis for early-onset hypertrophic cardiomyopathy and encephalopathy.</p

Preparation and Characterization of Carrot Nanocellulose and Ethylene/Vinyl Acetate Copolymer-Based Green Composites

This study aims to investigate the effect of nanocellulose on the properties and physical foaming of ethylene/vinyl acetate (EVA) copolymer. The nanocellulose is prepared from waste carrot residue using the 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) oxidation method (CT) and is further modified through suspension polymerization of methyl methacrylate (MMA) monomer (CM). The obtained nanocellulose samples (CT or CM) are added to EVA to create a series of nanocomposites. Moreover, the EVA and CM/EVA composite were further foamed using supercritical carbon dioxide physical foaming. TEM results show that the average diameters of CT and CM are 24.35 ± 3.15 nm and 30.45 ± 1.86 nm, respectively. The analysis of mechanical properties demonstrated that the tensile strength of pure EVA increased from 10.02 MPa to 13.01 MPa with the addition of only 0.2 wt% of CM. Furthermore, the addition of CM to EVA enhanced the melt strength of the polymer, leading to improvements in the physical foaming properties of the material. The results demonstrate that the pore size of the CM/EVA foam material is smaller than that of pure EVA foam. Additionally, the cell density of the CM/EVA foam material can reach 3.23 × 1011 cells/cm3