Unveiling the frontiers of deep learning: innovations shaping diverse domains

Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

Data science for tax administration

In this PhD-thesis several new and existing data science application are described that are particularly focused on applications for tax administrations. The thesis contains a chapter on the managerial side of analytics with a balanced overview of the pros and cons of applying analytics within taxpayer supervision. Another topic is (tax) fraud detection with unsupervised anomaly detection techniques. Here a new type of outliers is described (singular outliers) and an algorithm is provided for finding them. Attention is also paid to improving risk selection models. It is noted that most current algorithms cannot treat interactions of categorical variables with many levels very well. An extension of logistic regression is provided that uses Factorization Machines, which resulted in a ten percent improvement in precision. A fourth topic is statistical testing on similar treatment of similar cases. A contribution is made by providing an algorithm to statistically test on similar treatment based on process logs. The thesis contains further a benchmark study of different anomaly detection algorithms. Finally HR Analytics, Reinforcement Learning and applications of fuzzy sets are shortly described. Algorithms and the Foundations of Software technolog

THREE ESSAYS ON CONSUMPTION TAXATION IN INDONESIA

Consumption taxes play a major role in revenue generation for many developing countries. They are also used to encourage or discourage certain consumption behaviors. However, the administrative aspects of implementing these taxes can create challenges for the tax authorities, and lead to distortions in taxpayer behavior. To overcome these challenges, efforts have been made to simplify policies and utilize technology to improve administration and compliance. My dissertation aims to provide empirical evidence on the effects of three consumption or commodity tax policy reforms in Indonesia. The dissertation consists of three essays as follows. In my first essay, titled “The effect of modernizing the self-enforcing “paper” trail in Value Added Tax”, I study a modernization policy under Indonesia\u27s Value Added Tax (VAT) regime, where the government switched from a paper-based VAT invoice system to an online electronic system. To measure how the digitalization of the self-enforcing “paper” trail affects firms\u27 behavior and revenue mobilization, I exploit a two-stage implementation that mandated the new electronic invoice system to start in July 2015 for taxpayers in 7 provinces (similar to states) and then in the rest of 27 provinces one year later. This policy creates a quasi-experimental variation that allows me to use the two-stage introduction in a generalized differences-in-differences estimation following Malkova (2018). I utilize the Indonesian tax administrative data from the monthly VAT returns filed from 2014 to 2017. I use the firms in the 7 provinces that adopted the electronic invoices early as the treatment group and the rest of firms in the 27 provinces that adopted the electronic invoices later. I find that the electronic invoices system leads to an immediate increase in taxable sales but no effect on taxable inputs, and it further translates to increased VAT liability. The impact is driven by firms’ sizes, particularly smaller firms. The effect of electronic invoices are also heterogenous under different institutional environment (tax office type) and firm’s sectors. The second essay in my dissertation, titled “Toward fewer tax audit: Evidence from Indonesia“, utilizes another VAT administrative policy reform. The existing empirical literature has primarily focused on examining the impact of a higher probability of detection or audit in a tax system. However, we have little empirical evidence on the opposite situation when the probability of detection or audit gets lower. This essay fills the gap by studying firms\u27 behavioral response when the tax authority in Indonesia removes a mandatory audit requirement for firms claiming a refund on VAT excess payments below IDR 1 billion (around $70,000). I use a difference-in-differences estimation by exploiting the variation in the treatment at the sector level to estimate the impact of this reform. I use firms in the sectors concentrating on export activity as the treatment group and firms in the sector less concentrated on export as the control group. For this essay, I also use the Indonesian tax administrative data from the monthly VAT returns filed from 2016 to 2020. The result show that the fast-track refund led to an 11.66 percent increased taxable inputs (partially significant), but no impact on productivity (export and domestic sales) and VAT liability. Consistent with the standard tax compliance model, this increases in taxable inputs indicates that fewer audit activity may facilitate tax evasion. The third essay, titled “Tax stimulus during the COVID-19 pandemic: Evidence from Indonesia” investigates the impact of a commodity tax stimulus program on new car purchases introduced in response to the COVID-19 economic downturn. This program provides a luxury sales tax (LST) exemption for new car purchases where the eligibility requirement is based on engine sizes and domestic content ratio. My empirical design exploits comparable vehicles similar in price, type, market segment, engine sizes, and engine power but with variations in their eligibility for the program. Using event study and multi period difference-in-differences, I analyze the shift in sales number between the incentivized cars and their comparable before and during the COVID-19 pandemic. I use national-wholesale level data of monthly new vehicle purchases from January 2019 to December 2021. I find that tax stimulus increases purchases on eligible cars by 80 percent but only statistically significant at 10 percent confidence interval, which may be due to the small number of observations in my data. This translates to approximately 40 thousand additional units of eligible vehicles being purchase from April 2021 to December 2021

Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.

This report gives an overview of the most relevant organisational and\ud behavioural aspects regarding user profiling. It discusses not only the\ud most important aims of user profiling from both an organisation’s as\ud well as a user’s perspective, it will also discuss organisational motives\ud and barriers for user profiling and the most important conditions for\ud the success of user profiling. Finally recommendations are made and\ud suggestions for further research are given

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.