Determination of Tax Payers Behavior on Tax Reporting with E-Filing System

This study aims to obtain empirical evidence of the factors in TAM and TPB which explain the behavior of taxpayers in reporting taxes by e-filing. The location of the study was conducted at the Singaraja Pratama Tax Service Office. The research method used is quantitative research methods with primary data obtained from questionnaire data. The population in this study were all individual taxpayers registered at the Pratama Singaraja Tax Service Office, totaling 70,592 people. Based on this population using the Slovin formula the number of samples in this study was 400 respondents. The sampling technique in this study was snowball sampling. Data analysis techniques using Variance (Partial Least Square) Structural Equation Modeling Path Analysis. The results of the study show that the factors that influence the behavior of taxpayers' acceptance of e-filing are the perceptions of ease of use, perceptions of usefulness, tax justice, attitudes toward use, perceptions of control behavior, subjective norms, and intentions to use. Other factors, namely gender, education level, income level, and risk perception have no effect on the behavior to use e-filing. Keywords: tax reporting, e-filing, structural equation modeling, partial least square, acceptance behavior

Finding Optimal Views for 3D Face Shape Modeling

A fundamental problem in multi-view 3D face modeling is the determination of the set of optimal views (poses) required for accurate 3D shape estimation of a generic face. There is no analytical solution to this problem, instead (partial) solutions require (near) exhaustive combinatorial search, hence the inherent computational difficulty of this task. We build on our previous modeling framework [Silhouette-based 3D face shape recovery, Model-based 3D face capture using shape-from-silhouettes] which uses an efficient contour-based silhouette method and extend it by aggressive pruning of the view-sphere with view clustering and various imaging constraints. A multi-view optimization search is performed using both model-based (eigenheads) and data-driven (visual hull) methods, yielding comparable best views. These constitute the first reported set of optimal views for 3D face shape capture and provide useful empirical guidelines for the design of 3D face recognition systems.Engineering and Applied Science

Beyond Econometrics: Using Google Trends and Social Media Data to Forecast Unemployment - OECD analysis of accuracy gains and robustness of predictions

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsGoogle Trends has been used for less than two decades in academia to forecast outcomes, using various techniques. While most research has focused on developed countries, there are clear information gaps that have not been fully addressed. Previous studies in this field indicate that non-linear algorithms with feature set selection while using a large set of queries can yield better results across more countries. However, it is unlikely that these methods will be widely and rapidly adopted given the skills required. Therefore, the objective of this research is to explore whether the abundance of digital data sources, specifically Google searches, can aid agents as institutions and policy makers in their modeling efforts. The aim is to fill the gap in analysis for less influential countries and explore whether the use of Google searches data can be extended to multiple countries using a simple and agile methodology based on a widely used statistics-based modeling approach (ARIMAX). For this use we selected unemployment rate as the variable of interest. However, our findings show that only 30% of countries had promising results using Google-augmented ARIMAs. Thus, more computationally intensive empirical strategies would be needed to extract more predictive power out of Google queries information pool for unemployment rate modelling

Molecular modeling for physical property prediction

Multiscale modeling is becoming the standard approach for process study in a broader framework that promotes computer aided integrated product and process design. In addition to usual purity requirements, end products must meet new constraints in terms of environmental impact, safety of goods and people, specific properties. This chapter adresses the use of molecular modeling tools for the prediction of physical property usefull for chemical engineering practice

Image Segmentation Using Weak Shape Priors

The problem of image segmentation is known to become particularly challenging in the case of partial occlusion of the object(s) of interest, background clutter, and the presence of strong noise. To overcome this problem, the present paper introduces a novel approach segmentation through the use of "weak" shape priors. Specifically, in the proposed method, an segmenting active contour is constrained to converge to a configuration at which its geometric parameters attain their empirical probability densities closely matching the corresponding model densities that are learned based on training samples. It is shown through numerical experiments that the proposed shape modeling can be regarded as "weak" in the sense that it minimally influences the segmentation, which is allowed to be dominated by data-related forces. On the other hand, the priors provide sufficient constraints to regularize the convergence of segmentation, while requiring substantially smaller training sets to yield less biased results as compared to the case of PCA-based regularization methods. The main advantages of the proposed technique over some existing alternatives is demonstrated in a series of experiments.Comment: 27 pages, 8 figure

Entropy balancing is doubly robust

Covariate balance is a conventional key diagnostic for methods used estimating causal effects from observational studies. Recently, there is an emerging interest in directly incorporating covariate balance in the estimation. We study a recently proposed entropy maximization method called Entropy Balancing (EB), which exactly matches the covariate moments for the different experimental groups in its optimization problem. We show EB is doubly robust with respect to linear outcome regression and logistic propensity score regression, and it reaches the asymptotic semiparametric variance bound when both regressions are correctly specified. This is surprising to us because there is no attempt to model the outcome or the treatment assignment in the original proposal of EB. Our theoretical results and simulations suggest that EB is a very appealing alternative to the conventional weighting estimators that estimate the propensity score by maximum likelihood.Comment: 23 pages, 6 figures, Journal of Causal Inference 201

Do tolerant societies demand better institutions?

The increasing ethnic heterogeneity that many societies are experiencing could be interpreted as a detrimental phenomenon, since empirical literature exists that indicates that higher levels of ethnic fractionalization induce higher levels of corruption. This paper aims to show the role of tolerance in overcoming this harmful effect of ethnic heterogeneity. To this end, a sample of 86 countries is tested for a positive association between ethnic fractionalization and corruption. It is then shown that tolerance offsets this effect through both direct and indirect effects on corruption. In order to analyse the indirect effects, the level of income and the freedom of the press are selected as channels, since these represent two determinants of corruption that are linked to tolerance. Moreover, tolerance and corruption have been modelled as composites. Consequently, Partial Least Squares path modelling (PLS-PM) has been used. For our sample, an index of tolerance towards immigrants and people of different race and an index of corruption are constructed, for which several sources are jointly utilised. Our results appear to indicate that the adverse effect of ethnic fractionalization on corruption is offset by tolerance, which reduces corruption not only directly but also indirectly through the level of income and the freedom of the press