Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy Preserving Third Party Public Auditing Scheme for Secure Cloud Storage

Cloud storage is one of the service provided by Cloud computing in which data is maintained, managed, backed up remotely and made available to users over a network (typically the Internet). The user is concerned about the integrity of data stored in the cloud as the user’s data can be attacked or modified by outside attacker. Therefore, a new concept called data auditing is introduced which check the integrity of data with the help of an entity called Third Party Auditor (TPA). The purpose of this work is to develop an auditing scheme which is secure, efficient to use and possess the capabilities such as privacy preserving, public auditing, maintaining the data integrity along with confidentiality. Thus the new auditing scheme has been developed by considering all these requirements. It consist of three entities: data owner, TPA and cloud server. The data owner performs various operations such as splitting the file to blocks, encrypting them, generating a hash value for each, concatenating it and generating a signature on it. The TPA performs the main role of data integrity check. It performs activities like generating hash value for encrypted blocks received from cloud server, concatenating them and generates signature on it. It later compares both the signatures to verify whether the data stored on cloud is tampered or not. It verifies the integrity of data on demand of the users. Thus no additional burden is provided on the cloud server. It is used only to save the encrypted blocks of data. All the task for the scheme is performed by the TPA and data owner. This proposed auditing scheme make use of AES algorithm for encryption, SHA-2 for integrity check and RSA signature for digital signature calculation.
A novel normalization method for active parameters in MPEG

This paper presents novel normalization method for active parameters in MPEG. In this work, final normalizable active parameters can be obtained by the normalization function which adopt auto-changing factor. In order to obtain the auto-changing factor which represents the adjusting amplitude of over or under one, the activity of video image will be described such as TMS in MPEG-2. And then according to the properties such as size or fluctuation of the spatial and temporal activity, the auto-changing factor in normalization function can be calculated by the activity of last video frame and adjacent macroblock. Simulation results demonstrate that this method can do the normalization processing automatically and obtain the uniform property of quantization parameters.
Diffusion-Based Adaptive Distributed Detection: Steady-State Performance in the Slow Adaptation Regime

This paper examines the close interplay between cooperation and adaptation for distributed detection schemes over fully decentralized networks. The combined attributes of cooperation and adaptation are necessary to enable networks of detectors to continually learn from streaming data and to continually track drifts in the state of nature when deciding in favor of one hypothesis or another. The results in this paper establish a fundamental scaling law for the steady-state probabilities of miss detection and false alarm in the slow adaptation regime, when the agents interact with each other according to distributed strategies that employ small constant step-sizes. The latter are critical to enable continuous adaptation and learning. This paper establishes three key results. First, it is shown that the output of the collaborative process at each agent has a steady-state distribution. Second, it is shown that this distribution is asymptotically Gaussian in the slow adaptation regime of small step-sizes. Third, by carrying out a detailed large deviations analysis, closed-form expressions are derived for the decaying rates of the false-alarm and miss-detection probabilities. Interesting insights are gained from these expressions. In particular, it is verified that as the step-size μ decreases, the error probabilities are driven to zero exponentially fast as functions of 1μ, and that the exponents governing the decay increase linearly in the number of agents. It is also verified that the scaling laws governing the errors of detection and the errors of estimation over the network behave very differently, with the former having exponential decay proportional to 1μ, while the latter scales linearly with decay proportional to μ. Moreover, and interestingly, it is shown that the cooperative strategy allows each agent to reach the same detection performance, in terms of detection error exponents, of a centralized stochastic-gradient solution. The results of this paper are illustrated by applying them to canonical distributed detection problems.
International Journal of C 2009 Institute for Scientific Numerical Analysis and Modeling Computing and Information a Three-stage Operator-splitting/finite Element Method for the Numerical Simulation of Liquid Crystal Flow

In this article, we investigate the application of an operator-splitting/finite element method to the numerical simulation of a liquid crystal flow. The operator-splitting is achieved through three stages, so that each stage is simpler and easier to deal with than the step of any un-split implicit scheme. The first stage deals with the system coupling a Stokes equation for velocity with an equation modeling the diffusion of the liquid crystal director field. The second stage deals with the convection of both the velocity and director field; a wave-like equation approach is used to treat this advection part and proves being quite efficient. Finally, the third stage deals with the nonlin-ear terms; a (quasi) closed form solution can be derived for this stage. Overall, with this type of splitting, the nonlinear terms in the liquid crystal model can be treated quite easily. The results of several numerical experiments show the good performances of the three-stage splitting method discussed in this article.
Modeling and Optimization of Compact Microwave Bandpass Filters

This paper presents the modeling and optimization of compact microwave bandpass filters whose compactness leads to complex and strong stray coupling paths, thereby making the identification of a simple and sparse coupling topology difficult and even impossible. The strong coupling coefficients needed for a broadband response can also cause an ambiguity in identifying the spatial extent of local resonances. An equivalent circuit, which is extracted directly from Maxwell's equations, is used in optimizing these filters. The filter is represented by its global resonances instead of individual resonators. The extraction of the parameters of the equivalent circuit is carried out in the physical frequency and not in the normalized frequency in order to preserve the physicality of the equivalent circuit, especially for asymmetric responses. The technique is successfully applied to the optimization of second-order suspended stripline bandpass filters with one transmission zero either below or above the passband, as well as fourth-order filters with three transmission zeros. A fourth-order filter with three transmission zeros is fabricated and measured.
Perceived Stress, Behavior, and Body Mass Index Among Adults Participating in a Worksite Obesity Prevention Program, Seattle, 2005–2007

Introduction Stress in numerous contexts may affect the risk for obesity through biobehavioral processes. Acute stress has been associated with diet and physical activity in some studies; the relationship between everyday stress and such behavior is not clear. The objective of this study was to examine associations between perceived stress, dietary behavior, physical activity, eating awareness, self-efficacy, and body mass index (BMI) among healthy working adults. Secondary objectives were to explore whether eating awareness modified the relationship between perceived stress and dietary behavior and perceived stress and BMI. Methods Promoting Activity and Changes in Eating (PACE) was a group-randomized worksite intervention to prevent weight gain in the Seattle metropolitan area from 2005 through 2007. A subset of 621 participants at 33 worksites provided complete information on perceived stress at baseline. Linear mixed models evaluated cross-sectional associations. Results The mean (standard deviation [SD]) Perceived Stress Scale-10 score among all participants was 12.7 (6.4), and the mean (SD) BMI was 29.2 kg/m2 (6.3 kg/m2). Higher levels of perceived stress were associated with lower levels of eating awareness, physical activity, and walking. Among participants who had low levels of eating awareness, higher levels of perceived stress were associated with fewer servings of fruit and vegetables and greater consumption of fast food meals. Conclusion Dietary and physical activity behaviors of workers may be associated with average levels of perceived stress. Longitudinal studies are needed, however, to support inclusion of stress management or mindfulness techniques in workplace obesity prevention efforts.
Swarm intelligence based multi-phase OPF for peak power loss reduction in a smart grid

Recently there has been increasing interest in improving smart grids efficiency using computational intelligence. A key challenge in future smart grid is designing Optimal Power Flow tool to solve important planning problems including optimal DG capacities. Although, a number of OPF tools exists for balanced networks there is a lack of research for unbalanced multi-phase distribution networks. In this paper, a new OPF technique has been proposed for the DG capacity planning of a smart grid. During the formulation of the proposed algorithm, multi-phase power distribution system is considered which has unbalanced loadings, voltage control and reactive power compensation devices. The proposed algorithm is built upon a co-simulation framework that optimizes the objective by adapting a constriction factor Particle Swarm optimization. The proposed multi-phase OPF technique is validated using IEEE 8500-node benchmark distribution system.
Photorealistic 3D mapping of indoors by RGB-D scanning process

In this work, a RGB-D input stream is utilized for GPU-boosted 3D reconstruction of textured indoor environments. The goal is to develop a process which produces standard 3D models from indoors to explore them virtually. Camera motion is tracked in 3D space by registering the current view with a reference view. Depending on the trajectory shape, the reference is either fetched from a concurrently built keyframe model or from a previous RGB-D measurement. Realtime tracking (30Hz) is executed on a low-end GPU, which is possible because structural data is not fused concurrently. After camera poses have been estimated, both trajectory and structure are refined in post-processing. The global point cloud is compressed into a watertight polygon mesh by using Poisson reconstruction method. The Poisson method is well-suited, because it compressed the raw data without introducing multiple geometries and also fills holes efficiently. Holes are typically introduced at occluded regions. Texturing is generated by backprojecting the nearest RGB image onto the mesh. The final model is stored in a standard 3D model format to allow easy user exploration and navigation in virtual 3D environment.
The Regiment Macroprogramming System

The development of high-level programming environments is essential if wireless sensor networks are to be accessible to nonexperts. In this paper, we present the Regiment system, which consists of a high-level language for spatiotemporal macroprogramming, along with a compiler that translates global programs into node-level code. In Regiment, the programmer views the network as a set of spatially-distributed data streams. The programmer can manipulate sets of these streams that may be defined by topological or geographic relationships between nodes. Regiment provides a rich set of primitives for processing data on individual streams, manipulating regions, performing aggregation over a region, and triggering new computation within the network. In this paper, we describe the design and implementation of the Regiment language and compiler. We describe the deglobalization process that compiles a network-wide representation of the program into a node-level, event-driven program. Deglobalization maps region operations onto associated spanning trees that establish region membership and permit efficient in-network aggregation. We evaluate Regiment in the context of a complex distributed application involving rapid detection of spatially-distributed events, such as wildfires or chemical plumes. Our results show that Regiment makes it possible to develop complex sensor network applications at a global level.
Reducing forecast errors by logarithmic transformations for complex time series

Both nonlinear theories and Fourier analysis predicate theoretically that low-order nonlinear transformation has three advantages. First, it can increase the signal-to-noise ratio of a complex time series that has a large fluctuation. Second, it can insulate negative effects of outliers or bad data. Last, it can improve the distinction between the signal and white noise. The trend in the classical decomposition of “trend plus seasonals plus residuals” of a complex time series is equivalent to a non-periodic signal with continuous frequency spectrum, and is inevitably lost partly by the discrete Fourier transform for the discrete finite time point. Our numerical experiments with natural logarithmic transformation confirm these theoretical deductions.
Metabolic Stabilization of Muscle Nicotinic Acetylcholine Receptor by Rapsyn

Although the metabolic half-life of muscle endplate acetylcholine receptor (AChR) changes during development and after denervation in the adult, little is known about the molecular mechanisms that influence receptor stability. We have investigated the effect on AChR turnover of its interaction with rapsyn, a 43 kDa peripheral membrane protein that is closely associated with the AChR in muscle cells and is required for its clustering at endplates. Both in transfected COS cells and in cultured myotubes from rapsyn-negative and rapsyn-positive mice, we have found that the presence of rapsyn slows the turnover of AChRs by as much as twofold. The effect was similar for both embryonic (α2βδγ) and adult (α2βδε) AChRs and for AChRs whose β subunit lacked a putative tyrosine phosphorylation site. Neither colchicine nor cytochalasin D altered AChR turnover or prevented the rapsyn effect. Mutant rapsyn proteins whose N-terminal myristoylation signal was eliminated, or whose C terminus or zinc-finger domains were deleted, failed to change the rate of receptor turnover. Each of these mutations affects the association of the AChR with rapsyn, suggesting that AChR stability is altered by interaction between the two proteins. Our results suggest that, in addition to its role in AChR clustering, rapsyn also functions to metabolically stabilize the AChR.
Role of CYP2E1 and NQO1 polymorphisms in oxidative stress derived cancer in Thais with and without dyslipidemia

Abstract Background Hyperlipidemia can induce the endogenous production of reactive oxygen species (ROS), which may cause carcinogenesis. Cytochrome P450 (CYP) 2E1 activity, induced by various factors including polyunsaturated fatty acids, effects the incidence of cancers, whereas NQO1, a flavoprotein, may protect against ROS. Objectives To investigate the effect of CYP2E1 and NQO1 polymorphism on oxidative stress status in Thais with and without dyslipidemia. Methods We included 1380 apparently healthy employees of the Electricity Generating Authority of Thailand in this study. We determined their CYP2E1 and NQO1 genotypes and related these to blood lipid profiles, and circulating levels of antioxidant enzymes, malondialdehyde (MDA), and reduced glutathione (GSH). Lifestyle-related factors were determined from questionnaires. Results All tested genotype frequencies were in Hardy-Weinberg equilibrium. The heterozygous and variant genotype distribution and allele frequency of CYP2E1 *5B were less common than CYP2E1 *6. Heterozygous NQO1 was the most prevalent form. The frequency of the mutated allele CYP2E1 *5B was 0.16, CYP2E1 *6 was 0.22, and NQO1 *2 was 0.43. Significant differences were observed for blood cholesterol, triglyceride, low-density lipoprotein-cholesterol, and high-density lipoprotein-cholesterol between normolipidemic participants, and those with hypercholesterolemia, hypertriglyceridemia, and combined hyperlipidemia. Participants in the hyperlipidemic subgroup who bore any variant alleles of genes had higher plasma MDA and GSH levels, and superoxide dismutase and glutathione peroxidase activity, but lower catalase activity when compared with normolipidemic participants bearing wild-type alleles. Conclusions Variations in genetic disposition and dyslipidemia can modify oxidative stress status. Relatively more free radicals may be generated in individuals in subgroups with hyperlipidemia bearing any variant alleles.
Exploiting Host Name Locality for Reduced Stretch P2P Routing

Structured P2P networks are a promising alternative for engineering new distributed services and for replacing existing distributed services like DNS. Providing competitive performance with traditional distributed services is however very difficult because existing services like DNS are highly tuned using a combination of caching and localized communication. Typically, P2P systems use randomized host IDs which destroys any locality that might have been inherent in the IP addresses or the names of the hosts. In this way, P2P communication can result in a high stretch. We propose a locality preserving structured P2P system that supports efficient local communication and low stretch. While this system was optimized for resolving domain names, it will also provide a low stretch to other applications and it can be combined with existing replication schemes to optimize the response times even further.
Model-based optimization revisited: Towards real-world processes

The application of empirically determined surrogate models provides a standard solution to expensive optimization problems. Over the last decades several variants based on DACE (design and analysis of computer experiments) have provided excellent optimization results in cases where only a few evaluations could be made. In this paper these approaches are revisited with respect to their applicability in the optimization of production processes, which are in general multiobjective and allow no exact evaluations. The comparison to standard methods of experimental design shows significant improvements with respect to prediction quality and accuracy in detecting the optimum even if the experimental outcomes are highly distorted by noise. The universally assumed sensitivity of DACE models to nondeterministic data can therefore be refuted. Additionally, a practical example points out the potential of applying EC-methods to production processes by means of these models.
Demographic and socio-economic influences on community-based care and caregivers of people with dementia in China

Background Dementia is a major public health challenge and China has the largest population with dementia in the world. However, dementia care and caregivers for Chinese are less investigated. Objectives and design To evaluate demographic and socio-economic influences on dementia care, management patterns and caregiver burden in a household community-dwelling-based survey, using participants’ care receipts and Zarit scale. Setting and participants Rural and urban communities across six provinces of China comprising 4837 residents aged ≥60 years, in whom 398 had dementia and 1312 non-dementia diseases. Results People with dementia were less likely to receive care if they were living in rural compared to urban areas (Odd ratio (OR) = 0.20; 95%CI: 0.10–0.41), having education level below compared to above secondary school (OR = 0.24; 95%CI: 0.08–0.70), manual labourer compared to non-manual workers (OR = 0.27; 95%CI: 0.13–0.55), having personal annual income below RMB 10,000 yuan (£1000) compared to above (OR = 0.37; 95%CI: 0.13–0.74) or having four or more than compared to less four children (OR = 0.52; 95%CI: 0.27–1.00). Caregivers for dementia compared with those for non-dementia diseases were younger and more likely to be patients’ children or children in-law, had lower education and spent more caring time. Caregiver burden increased with low education, cutback on work and caring for patients who were younger or living in rural areas, and this caregiver burden was three-fold greater than that for non-dementia diseases. Conclusions There are a number of inequalities in dementia care and caregiver burden in China. Reducing the socio-economic gap and increasing education may improve community care for people with dementia and preserve caregivers’ well-being.
AUTOMATIC ROAD EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGES USING NEURAL NETWORKS , TEXTURE ANALYSIS , FUZZY CLUSTERING AND GENETIC ALGORITHMS

In this article, a new method for road extraction from high resolution Quick Bird and IKONOS pan-sharpened satellite images is presented. The proposed methodology consists of two separate stages of road detection and road vectorization. Neural networks are applied on high resolution IKONOS and Quick-Bird images for road detection. This paper has endeavoured to optimize neural networks’ functionality, using a variety of texture parameters. These texture parameters had different window sizes and grey level numbers, not only from source but also from pre-classified image. Road vectorization is based on the idea of road raster map clustering obtained from the previous road detection stage. In this step, despite of genetically guided clustering, a new flexible clustering methodology is proposed for road key point identification. The last step of road key point connecting is carried out based on the obtained information from a fuzzy shell based clustering. The accuracy assessment of the obtained vectorized road network proved the ability of the proposed method in sub-pixel road extraction. * Corresponding author
Graph Construction with Label Information for Semi-Supervised Learning

In the literature, most existing graph-based semi-supervised learning (SSL) methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the Low-Rank Representation (LRR), and propose a novel semi-supervised graph learning method called Semi-Supervised Low-Rank Representation (SSLRR). This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real datasets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.