Miracle-dz/UniFAR-FTUs huggingface dataset:
A DWT-DCT-SVD based digital image watermarking scheme using particle swarm optimization

In this paper, we propose a hybrid watermarking scheme for digital images based on singular value decomposition (SVD) and particle swarm optimization (PSO). The two key aspects of watermarking schemes are copyright protected and robustness. In this, we are embedded the principal components of the watermark in the DCT domain of DWT subband of host image, for providing copyright protection as well as reliability. Since Scaling factor is an image dependent so we are providing it in the form of a matrix. PSO is used for finding suitable scaling factors such that to provide efficient robustness and fidelity of the scheme. Experimental results are provided to illustrate that the proposed scheme is able to withstand a variety of image processing attacks as well as imperceptibility.
Incidence and recurrence of late-life depression.

CONTEXT Depression is common in old age. Nevertheless, few incidence studies have established how often depression occurs in elderly persons with and without a history of depression. OBJECTIVES To determine the incidence and recurrence rates of depression in an elderly population. DESIGN, SETTING, AND PARTICIPANTS A cohort study of community-dwelling elderly persons aged 56 years or older residing in Rotterdam, the Netherlands, performed between September 1993 and October 2005 and encompassing baseline and 2 follow-up examinations as well as continuous procedures. The study population consisted of 5653 participants free of dementia. Depression was identified through standardized psychiatric examinations, monitoring of medical records, registration of antidepressant use, and self-reported histories of depression. We categorized the depression as depressive syndromes, including DSM-IV-defined major depression, or clinically relevant depressive symptoms. MAIN OUTCOME MEASURES Incidence and recurrence rates for depressive syndromes as well as for depressive syndromes and symptoms combined. In addition to overall rates, sex- and age-specific rates were calculated. RESULTS During the follow-up period of 8 years on average, 566 depressive syndromes and 1073 episodes of clinically relevant depressive symptoms occurred. For depressive syndromes, the incidence rate was 7.0 (95% confidence interval, 6.0-8.3) per 1000 person-years and the recurrence rate was 27.5 (95% confidence interval, 23.7-32.1) per 1000 person-years. The incidence and recurrence rates more than doubled when episodes of depressive symptoms were included. The recurrence rate of depressive syndromes was equal for women and men, but all other rates were almost twice as high for women compared with men. No rates seemed to change with age. CONCLUSIONS The incidence rate of depression in the elderly population is low except when episodes of clinically relevant depressive symptoms are accounted for. Most late-life depression occurs in persons with a history of depression. Moreover, the recurrence rate of depressive syndromes does not differ between men and women.
The TCP Authentication Option

This document specifies the TCP Authentication Option (TCP-AO), which obsoletes the TCP MD5 Signature option of RFC 2385 (TCP MD5). TCP-AO specifies the use of stronger Message Authentication Codes (MACs), protects against replays even for long-lived TCP connections, and provides more details on the association of security with TCP connections than TCP MD5. TCP-AO is compatible with either a static Master Key Tuple (MKT) configuration or an external, out-of-band MKT management mechanism; in either case, TCP-AO also protects connections when using the same MKT across repeated instances of a connection, using traffic keys derived from the MKT, and coordinates MKT changes between endpoints. The result is intended to support current infrastructure uses of TCP MD5, such as to protect long-lived connections (as used, e.g., in BGP and LDP), and to support a larger set of MACs with minimal other system and operational changes. TCP-AO uses a different option identifier than TCP MD5, even though TCP-AO and TCP MD5 are never permitted to be used simultaneously. TCP-AO supports IPv6, and is fully compatible with the proposed requirements for the replacement of TCP MD5.
Pingin' in the rain

Residential Internet connections are susceptible to weather-caused outages: Lightning and wind cause local power failures, direct lightning strikes destroy equipment, and water in the atmosphere degrades satellite links. Outages caused by severe events such as fires and undersea cable cuts are often reported upon by operators and studied by researchers. In contrast, outages cause by ordinary weather are typically limited in scope, and because of their small scale, there has not been comparable effort to understand how weather affects everyday last-mile Internet connectivity. We design and deploy a measurement tool called ThunderPing that measures the connectivity of residential Inter- net hosts before, during, and after forecast periods of severe weather. ThunderPing uses weather alerts from the US National Weather Service to choose a set of residential host addresses to ping from several vantage points on the Internet. We then process this ping data to determine when hosts lose connectivity, completely or partially, and categorize whether these failures occur during periods of severe weather or when the skies are clear. In our preliminary results, we find that compared to clear weather, failures are four times as likely during thunderstorms and two times as likely during rain. We also find that the duration of weather induced outages is relatively small for a satellite provider we focused on.
Budapest Working Papers on the Labour Market Detecting Wage Under-reporting Using a Double Hurdle Model Detecting Wage Under-reporting Using a Double Hurdle Model Detecting Wage Under-reporting Using a Double Hurdle Model

We estimate a double hurdle (DH) model of the Hungarian wage distribution assuming censoring at the minimum wage and wage under-reporting (i.e. compensation consisting of the minimum wage, subject to taxation, and an unreported cash supplement). We estimate the probability of under-reporting for minimum wage earners, simulate their genuine earnings and classify them and their employers as 'cheaters' and 'non-cheaters'. In the possession of the classification we check how cheaters and non-cheaters reacted to the introduction of a minimum social security contribution base, equal to 200 per cent of the
Electrical dimensions in cell science

Cells undergo a variety of physiological processes, including division, migration and differentiation, under the influence of endogenous electrical cues, which are generated physiologically and pathologically in the extracellular and sometimes intracellular spaces. These signals are transduced to regulate cell behaviours profoundly, both in vitro and in vivo. Bioelectricity influences cellular processes as fundamental as control of the cell cycle, cell proliferation, cancer-cell migration, electrical signalling in the adult brain, embryonic neuronal cell migration, axon outgrowth, spinal-cord repair, epithelial wound repair, tissue regeneration and establishment of left-right body asymmetry. In addition to direct effects on cells, electrical gradients interact with coexisting extracellular chemical gradients. Indeed, cells can integrate and respond to electrical and chemical cues in combination. This Commentary details how electrical signals control multiple cell behaviours and argues that study of the interplay between combined electrical and chemical gradients is underdeveloped yet necessary.
Estimating Saturated Hydraulic Conductivity Using Genetic Programming

Saturated hydraulic conductivity (K s ) is one of the key parameters in modeling solute and water movement in the vadose zone. Field and laboratory measurement of K s is time consuming, and hence is not practical for characterizing the large spatial and temporal variability of K s As an alternative to direct measurements, pedotransfer functions (PTFs), which estimate K s from readily available soil data, are being widely adopted. This study explores the utility of a promising data-driven method, namely, genetic programming (GP), to develop PTFs for estimating K s from sand, silt, and clay contents and bulk density (D b ). A data set from the Unsaturated Soil Hydraulic Database (UNSODA) was considered in this study. The performance of the GP models were compared with the neural networks (NNs) model, as it is the most widely adopted method for developing PTFs. The uncertainty of the PTFs was evaluated by combining the GP and the NN models, using the nonparametric bootstrap method. Results from the study indicate that GP appears to be a promising tool for developing PTFs for estimating K s The better performance of the GP model may be attributed to the ability of GP to optimize both the model structure and its parameters in unison. For the PTFs developed using GP, the uncertainty due to model structure is shown to be more than the uncertainty due to model parameters. Moreover, the results indicate that it is difficult, if not impossible, to achieve better prediction and less uncertainty simultaneously.
Radial basis function methods in computational finance

Radial basis function (RBF) based approximation methods for numerical solution of partial differential equations are interesting due to their potentially spectral accuracy and due to being meshfree. This could be especially beneficial for high dimensional problems, where meshing is non-trivial. In this work, we present different RBF approaches and evaluate them on a multi-asset option pricing problem. The conclusion is that the properties of the problem need to be taken into account in the solution method in order to have an approach that is viable for higher dimensions. Furthermore, we suggest to use an RBF based partition of unity approach in order to introduce locality and reduce the computational cost.
Biological interaction networks based on non-parametric estimation

Biological networks are often described as probabilistic graphs in the context of gene and protein sequence analysis in molecular biology. Microarrays and proteomics technologies facilitate the monitoring of expression levels over thousands of biological units over time. Several experimental efforts have appeared aiming to unveiling pairwise interactions, with many graphical models being introduced in order to discover associations from expression-data analysis. However, the small size of samples compared to the number of observed genes/proteins makes the inference of the network structure quite challenging. In this study, we generate gene–protein networks from sparse experimental temporal data using two methods, partial correlations and Kernel Density Estimation (KDE), in an attempt to capture genetic interactions. Applying KDE method we model the genetic associations as Gaussians approximations, while through the dynamic Gaussian analysis we aim to identify relationships between genes and proteins at di.
Wavelet packet coefficients weighted for frequency optimization in motor imagery based brain-computer interfaces

Common spatial patterns (CSP) is a successful algorithm in motor imagery (MI) based brain-computer interfaces (BCI). However, the performance of the algorithm in electroencephalography (EEG) classification depends largely on the subject's specific frequency band. In this paper, we proposed a new wavelet packet coefficients weighted CSP (WPW-CSP) algorithm to optimize the frequency band of the specific subject for motor imagery (MI) classification. The algorithm is applied to 5 MI data sets of two classes. The results suggest that the proposed algorithm achieves a 4% increase of classification accuracy in off-line analysis, compared to the original CSP algorithm using 8–30 Hz wide band.
Two-dimensional clustering-based discriminant analysis for SAR ATR

This paper gives a new image feature extraction technique coined two-dimensional clustering-based discriminant analysis (2DCDA), which is based on 2D image matrices for constructing the scatter matrices and assumes that the data obeys the multimodal distribution. The detailed procedure of 2DCDA is to first partition each class of the data into multiple clusters via fast 2D global k-means clustering algorithm, and then try to find some directions such that the projections of every pair of clusters from different classes are well separated while the within-cluster scatter is minimized. Therefore, it fully exploits the cluster information and alleviates the linearly unseparable problem to some extent. According to the projection fashion, we divide 2DCDA into two versions, the right 2DCDA (R-2DCDA) and the left 2DCDA (L-2DCDA). They compress the image row or column only, so they need more features. To solve this problem, two-directional 2DCDA ((2D)2CDA) is developed, which compresses the image row and column simultaneously. Experiments have been carried out for recognition of three types of ground vehicles in the Moving and Stationary Target Acquisition and Recognition (MSTAR) public database to evaluate and compare the performances of the proposed algorithms with other methods. Results demonstrate that 2DCDA and (2D)2CDA are effective. And, the highest recognition rate is up to 97.89%, which is the best ever reported in the literatures.
Using Digital Images to Enhance the Credibility of Information

With research revealing the importance of trust as a deciding factor for users to visit a website, designing websites that users find credible becomes important. Adding onscreen characters in various forms (e.g. photographs of a person) to represent the source of information is a simple and popular way to to increase the credibility of a website. However, despite its popularity, empirical studies have reported mixed results. This paper presents three experiments that explored the causes of this contradiction. In our experiments, the credibility of the source (as resulting from a photograph of a person) was found to be topic dependent. It was also found to positively correlate with and account for some 30% of the variability in the credibility of a website. These findings perhaps can explain the inconsistency of many previous studies. Finally, our study suggested that adding a user-selected photograph increases the credibility of the website, while the opposite occurs when adding a photograph that is lowly credible with respect to the topic of the website’s content.
A Survey of Dimensionality Reduction Techniques for Natural Language

Machine learning methods for natural language use features consisting of words or combinations of words to fit statistical models of linguistic phenomena. The discrete input spaces resulting from these features often have hundreds of thousands or millions of dimensions, and estimating reliable statistics of these features from limited amounts of training data is difficult. One technique for alleviating this data sparseness is to induce a low-dimensional representation of the original feature vector. Learning algorithms can then estimate statistics of the low-dimensional vectors more reliably than the original high-dimensional vectors. We begin with a brief summary of two standard techniques for dimensionality reduction of language: latent semantic analysis (Deerwester et al., 1990) and probabilistic latent semantic analysis (Hofmann, 1999). We then give an overview of three more recent models for dimensionality reduction of language. Sufficient dimensionality reduction (Globerson and Tishby, 2003) is an information-theoretic dimensionality reduction technique which learns a low-rank factorization of the natural parameters of a joint multinomial distribution. The neural probabilistic language model (Bengio et al., 2003) learns a nonlinear mapping in order to give a low-dimensional representation for whole phrases simultaneously. Finally structural learning (Ando and Zhang, 2005) is a dimensionality reduction technique for semisupervised learning. Given a target prediction task with labeled and unlabeled data, structural learning finds a low dimensional feature space using the unlabeled data. This new feature space in turn yields a better target predictor. We give a unified treatment of the five methods, focusing on their formulation as optimization problems. The three newer methods use more realistic loss functions and more flexible parameterizations than the two older ones, and as a result they are able to capture language phenomena more accurately. Finally, we discuss the disadvantages of these models and suggest directions for future research.
Experiments on Emergent Leadership , Lying Aversion , and Reciprocal Altruism : The Importance of Context ∗

From an experimental design, we nd that circumstance determines who takes the lead through communication. When the context varies exogenously, from containing mild to severe strategic con ict, initiative is taken by anyone in the former case, but by special individuals in the latter case. These special leaders dislike free-riding and lying. We show that initiative leads to cooperation even in games of severe strategic con ict such as the Prisoners' Dilemma.
Interactive and User-Friendly Construction of Multi-Segment Curved Handles

In this paper, we present a method to interactively create multi-segment, curved handles between two star-shaped faces of an orientable 2-manifold mesh or to connect two 2-manifold meshes along such faces. The presented algorithm combines a very simple 2D morping algorithm with a Hermite interpolation to construct the handle. Based on the method, we have developed a user interface tool that allows users to simply and easily create multi-segment curved
Serum Sphingolipids Reflect the Severity of Chronic HBV Infection and Predict the Mortality of HBV-Acute-on-Chronic Liver Failure

Patients with HBV-acute-on-chronic liver failure (HBV-ACLF) have high mortality and frequently require liver transplantation; few reliable prognostic markers are available. As a class of functional lipids, sphingolipids are extensively involved in the process of HBV infection. However, their role in chronic HBV infection remains unknown. The aim of this study was to determine the serum sphingolipid profile in a population of patients with chronic HBV infection, paying special attention to exploring novel prognostic markers in HBV-ACLF. High performance liquid chromatography tandem mass spectrometry was used to examine the levels of 41 sphingolipids in 156 serum samples prospectively collected from two independent cohorts. The training and validation cohorts comprised 20 and 28 healthy controls (CTRL), 29 and 23 patients with chronic hepatitis B (CHB), and 30 and 26 patients with HBV-ACLF, respectively. Biometric analysis was used to evaluate the association between sphingolipid levels and disease stages. Multivariate analysis revealed difference of sphingolipid profiles between CHB and HBV-ACLF was more drastic than that between CTRL and CHB, which indicated that serum sphingolipid levels were more likely to associate with the progression HBV-ACLF rather than CHB. Furthermore, a 3-month mortality evaluation of HBV-ACLF patients showed that dhCer(d18∶0/24∶0) was significantly higher in survivors than in non-survivors (including deceased patients and those undergoing liver transplantation, p<0.05), and showed a prognostic performance similar to that of the MELD score. The serum sphingolipid composition varies between CTRL and chronic HBV infection patients. In addition, dhCer(d18∶0/24∶0) may be a useful prognostic indicator for the early prediction of HBV-ACLF.
The Public Key Encryption to Improve the Security on Wireless Sensor Networks

Wireless sensor networks (WSN) grows and gains new in our lives. However security in WSN was not carefully carried out, since only some symmetric encryption based protocols are proposed in literature, under the assumption that the nature of sensor nodes does not support public key encryption due to the limitation in battery and CPU power. This paper present a scheme of Public Key Infrastructure(PKI) for wireless sensor networks. The scheme tries to solve the problem of security in WSN by the use of public key cryptography as a tool for ensuring the authenticity of the base station, and propose a RSA’s implementation of Public Key Encryption to improve the security on wireless sensor networks.
Selection of Material for Bicycle Chain in Indian Scenario using MADM Approach

India is the second largest bicycle manufacturer in the world, next only to China. The Chinese bicycle industry has already been successful in cornering almost 25 per cent share in fancy bicycle market in India which is a major cause of worry for the small-scale bicycle parts manufacturers in India. An attempt has been made to provide a comprehensive framework for the selection process of bicycle chain material in Indian manufacturing scenario for strategic success. Multiple attribute decision-making (MADM) methods have been applied to rank out the alternatives. Material AISI 1038 has been ranked first by using both the techniques. Index Terms — Material selection, MADM, Preference index value, TOPSIS
Overview of the ImageCLEF 2014 Domain Adaptation Task

This paper describes the rst edition of the Domain Adapta- tion Task at ImageCLEF 2014. Domain adaptation refers to the challenge of leveraging over knowledge acquired when learning to recognize given classes on a database, when using a dierent data collection. We de- scribe the scientic motivations behind the task, the research challenge on which the 2014 edition focused, the data and evaluation metric and results obtained by participants. After a discussion on the lesson learned during this rst edition, we conclude with possible ideas for future edi- tions of the task.
Quantitative Measurements of Forward Flapping Flight Using Image Processing

A method for automatically calculating the flight kinematics of a flapping bird flying on a relatively dense background environment was developed. The algorithm is able to calculate the bird’s flapping frequency, wingbeat phase, forward velocity, and forward acceleration with good accuracy with the use of only one camera. Image processing was used for extracting the bird’s silhouette from the background. An image matching algorithm, which made use of the Kullback–Leibler divergence, was used for matching a unique template frame to similar frames of the flight. The algorithm was tested on three full flights of one bird. The algorithm was able to detect similar wingbeat phases with 100% detection accuracy for every data set, giving good accuracy for the flight kinematics. Keywords—image processing, KL divergence, bird flight,
Off-Policy Policy Search

Gradient-based policy search is an alternative to value-function-based methods for reinforcement learning in non-Markovian domains. One apparent drawback of policy search is its requirement that all actions be “on-policy”; that is, that there be no explicit exploration. In this paper, we provide a method for using importance sampling to allow any wellbehaved directed exploration policy during learning. We show both theoretically and experimentally that using this method can achieve dramatic performance improvements.