Vergi reformunun çerçevesi ve Türkiye’ deki gelişimi

Devletin vatandaşlarından karşılıksız ve belli kurallar dahilinde sosyal, mali ve ekonomik amaçlar ile kamu giderlerini karşılamak için başvurduğu en iyi kaynak vergilerdir. Devletin günümüzde kamu ihtiyaçlarını karşılamak için kullandığı en önemli finansal kaynak vergilerdir. Devletin bu önemli kaynakları sağlıklı bir şekilde toplayabilmesi için vergi etkin ve adil olması çok önemlidir. Vergi sistemimizin etkin ve adaletli olmasının en önemli nedenlerinden biri vergi reformları gelmektedir. Herhangi bir vergi sisteminde yapılan radikal değişiklikler vergi reformudur. Vergile teorisi vergi reformu yapılırken öncelikli olarak dikkate alınmalıdır. Bu sistem içerisinde yer alan teorik bilgiler dikkate alınmaz ise etkili bir veri reformu gerçekleştirilemez. Geçmiş yıllarda vergi reformu yapılırken elde edilmiş bilgiler yeni vergi reformunda kullanılırsa daha etkili sonuçlar alınabilir. Birçok gelişmiş ülkede vergi reformları oluşturulmuştur. Bunlar incelendiğinde bunların birçoğunun ortak özellikleri mevcuttur. Türkiye’de 2005 yılı başından itibaren önemli bir vergi reformu çalışması yapılmaya başlanmıştır. Fakat hazırlanan bu vergi reform çalışmaları uygulanmakta olan sisteme ve ekonomiye cevap verebilecek düzeyde olmadığı anlaşılmıştır. Şu an sürdürülmekte olan çalışmalar neticesinde bir “vergi reformuna” ihtiyaç duyan Türkiye’nin kısa dönemde bu beklentilere cevap veremeyeceği görülmektedir. Bu amaçla; çalışmamda Türkiye’nin Vergi Reformu konusunu kapsayan ve vergi reformunun ekonomideki etkileri nedir, nasıl yapılmıştır ve nasıl yapmalıyız ya da yapabiliriz, sorularının cevapları aranmıştır.It is the best source of taxes that the State applies to meet its social, financial and economic objectives and public expenditure without compensation from its citizens and within certain rules. The most important financial resources that the state uses today to meet public needs are taxpayers. It is very important that the tax is efficient and fair so that the state can gather these important resources in a healthy way. Tax reforms are one of the most important reasons why our tax system is effective and just. The radical changes in any tax system are tax reform. The theory of tax revenues should be considered as a priority for tax reform. Effective data reform can not be achieved if the theoretical information contained in this system is not taken into account. More effective results can be obtained if the information gained during tax reforms in the past years is used in the new tax reform. Tax reforms have been established in many developed countries. When examined, some of them have common characteristics. An important tax reform study has been started in Turkey since the beginning of 2005. However, it is understood that these tax reform studies are not at a level where the system and the economy can be responded to. Turkey, which is in need of a "tax reform" in the light of the ongoing studies, is not expected to respond to these expectations in the short term. For this purpose; I have searched for the answers to the questions about Turkey's Tax Reform issue and the effects of tax reform on economy, how it was done and how we should do or can do it

Transferring Synthetic Elementary Learning Tasks to Classification of Complex Targets

Deep learning has a promising impact on target classification performance at the expense of huge training data requirements. Therefore, the use of simulated data is inevitable for convergence of deep models (DMs). However, generating synthetic data for real-life complex targets can be quite tedious and is not always possible. In this study, DMs trained with synthetic one-dimensional scattered data of elementary targets are transferred to classify complex targets from measured signals for the first time. For this purpose, a novel system is proposed by combining three strategies: first, initial training of DMs using analytical and simulated time domain scattered data obtained from the basic targets; second, the last layers of initial DMs are fine-tuned by transfer learning using measured signals of the real targets; and third, an ensemble model is developed to generate a model that can completely represent real target characteristics by combining diverse and complementary properties of the fine-tuned DMs. The proposed system provides higher accuracy, sensitivity, and specificity performances compared to the existing methods

Utilizing Resonant Scattering Signal Characteristics of Magnetic Spheres via Deep Learning for Improved Target Classification

Object classification using LAte-time Resonant Scattering Electromagnetic Signals (LARSESs) is a significant problem found in different areas of application. Due to their special properties, spherical objects play an important role in this field both as a challenging target and analytical LARSES source. Although many studies focus on their detailed analysis, the challenges associated with target classification by resonant LARSESs from multi-layer spheres have not been investigated in detail. Moreover, existing studies made the simplifying assumption that the objects having (one or more) layers constitute equal permeability values at the core and coatings. However, especially for metamaterials, magneto-dielectric inclusions require consideration of magnetic properties as well as dielectric ones. In this respect, this study shows that the utilization LARSESs of magnetic spheres provides diverse information and features, which result with superior object classification performance. For this purpose, first, time domain LARSESs are generated numerically for single and multi-layer radially symmetrical dielectric and magnetic spheres. Then, by using emerging deep learning tools, particularly Convolutional Neural Network (CNNs), which are trained with spheres having different material properties, a high multi-layer object classification performance is achieved. Moreover, by extending the proposed strategy to measured data via modern data augmentation and transfer learning techniques, an improved classification performance is also obtained for more complex targets

Utilizing resonant scattering signal characteristics via deep learning for improved classification of complex targets

Object classification using late-time resonant scattering electromagnetic signals is a significant problem found in different areas of application. Due to their unique properties, spherical objects play an essential role in this field both as a challenging target and a resource of analytical late-time resonant scattering electromagnetic signals. Although many studies focus on their detailed analysis, the challenges associated with target classification by resonant late-time resonant scattering electromagnetic signals from multilayer spheres have not been investigated in detail. Moreover, existing studies made the simplifying assumption that the objects having (one or more) layers constitute equal permeability values at the core and coatings. However, especially for metamaterials, magneto-dielectric inclusions require consideration of magnetic properties as well as dielectric ones. In this respect, this study shows that the utilization late-time resonant scattering electromagnetic signals of magnetic spheres provide diverse information and features, which result in superior object classification performance. For this purpose, first, time-domain late-time resonant scattering electromagnetic signals are generated numerically for single and multilayer radially symmetrical dielectric and magnetic spheres. Then, by using emerging deep learning tools, particularly convolutional neural networks trained with spheres having different material properties, a high multilayer object classification performance is achieved. Furthermore, by incorporating the frequency characteristics of the late-time resonant scattering electromagnetic signals to the classification process through Fourier transform and convolutional neural network layers for feature extraction, a convolutional neural network with long short term memory algorithm is developed. The outcome of the proposed algorithm design is shown to be particularly successful even in the case of limited available data on challenging targets. This extended strategy is also shown to outperform modern data augmentation and transfer learning techniques in terms of accuracy as well as the computational cost

Machine Learning Based Bounding Box Regression for Improved Pedestrian Detection

The most of the studies on pedestrian and passenger detection focus on end-to-end learning by considering either improvement of features to be used or the enhancement of the detectors. One of the important steps of these systems is non-maximum suppression (NMS), which aims reducing proposed bounding boxes that supposed to belong the same target through a greedy regional search and clustering. In order to improve the performance of NMS, recent approaches consider using only bounding boxes and their scores. By following this path with a novel approach, in this study, a machine learning based bounding box regression approach is proposed. During the training phase, proposed system uses position, size and confidence scores of bounding boxes as features and the same information of the corresponding ground truth (except score) as the desired output. By this way, a pattern between initially generated bounding boxes and the ground truth is revealed. Several tests and experiments have been performed and the results show that the developed system can be particularly effective when correct decisions are needed with low overlapping ratios (such as applications with strong occlusion) without increasing false positives