Miracle-dz/UniFAR-FTUs huggingface dataset:
Evidence from major tax reforms in India

Taxes on transactions are a common way of raising tax revenue, mostly in the form of a sales tax or a value-added tax (VAT). We analyze the effect of a switch from a sales tax to a VAT on output and tax evasion. States in India gradually transitioned from a sales tax to a VAT system. We digitize and harmonize all of India’s state-level consumption tax systems, which feature tax rates on hundreds of categories of goods that vary across states. Exploiting stateand product-specific tax variation and the staggered implementation of VAT across states, we show that by five years after the reform, gross sales increase by 16%. This increase in output is a result of the VAT lowering tax rates and reducing distortionary effects of double taxation. Furthermore, in a sample of relatively large manufacturing firms, we find limited evidence of bunching at registration thresholds indicating limited tax evasion. Our study has implications for India’s more recent reforms aimed at simplifying the tax law and for the consequences of a similar move in other countries.
INTEGRATION OF CROATIAN AND SELECTED CEE EQUITY MARKETS: COINTEGRATION APPROACH MARUŠKA ČENIĆ AND TAJANA DADIĆ This paper examines bilateral and multilateral cointegration of equity markets of selected Central and Eastern European countries including Croatia, and German equity

This paper examines bilateral and multilateral cointegration of equity markets of selected Central and Eastern European countries including Croatia, and German equity market for the period of January 2, 1997 to June 10 , 2005. Application of the Johansen – Juselius cointegration procedure indicates existence of multilateral integration between equity markets of analyzed CEE economies, as well as between the group of CEE equity markets and German equity market. This study offers compelling evidence that the forces driving financial integration are quite powerful, and that we are likely to see more substantial movement in the same direction with time and once these countries join EMU, because evidence from this study suggests that bilateral integration between particular CEE equity markets and German equity market is still missing.
Mapping and Semantic Modeling of Underground Parking Lots Using a Backpack LiDAR System

Presented in this paper is a novel method for the mapping and semantic modeling of an underground parking lot using 3D point clouds collected by a low-cost Backpack Laser Scanning (BLS) or LiDAR system. Our method consists of two parts: a Simultaneous Localization and Mapping (SLAM) algorithm based on Sparse Point Clouds (SPC) and a semantic modeling algorithm based on a modified PointNet model. The main contributions of this paper are as follows: (1) a probability frontend framework for the alignment of point clouds using the local point cloud surface variance as the weight of registration, which modifies registration failure caused by the lack of features in sparse point clouds, (2) a robust submap-based strategy for loop closure detection and back-end optimization under sparse point clouds, and (3) a modified PointNet model for classifying the point clouds of underground parking lots into four categories: ceiling, floor, wall, others. Experimental results show that our SPC-SLAM algorithm achieves centimeter-level accuracy (0.09% trajectory error rate) after closed loop processing in a Global Navigation Satellite System (GNSS)-denied underground parking lot, and precision of 84.8% in semantic segmentation.
STUDYING THE EVOLUTION OF THE LANDSCAPE IN THE ROMAGNA COASTAL ZONE (NORTHERN ITALY). THE CONTRIBUTION OF HISTORICAL CARTOGRAPHY

This article focuses its attention on the key-role of historical cartography in the study of landscape evolution and man-environment interactions, in particular in the framework of fast evolutional and natural/man-induced processes sensitive areas, such as sandy coastal systems. In the case-study of the Romagna Coastline (Northern Italy), facing the Adriatic Sea, a step-by-step reconstruction of littoral trends during the last four centuries has been possible thanks to historical maps. On the basis of these visual sources, the progressive transition from a coastal zone under sedimentation to the present-day one under erosion is recognizable. Other landscape-related themes visible on the maps are coastal woods deforestation and large land-reclamation programs aimed at creating new lands for agriculture and mitigate the demographic growth according to a malthusian view of problems.
Communications of the Association for Information Systems Communications of the Association for Information Systems

: Today, many organizations use personal data and algorithms for ads, recommendations, and decisions. However, some have expressed concern that this use negatively impacts individual privacy and poses a risk to individuals and society. In response, many have called for greater algorithmic transparency; that is, for organizations to be more public and open about their use of personal data and algorithms. To better understand algorithmic transparency, we reviewed the literature and interviewed 10 experts. We identified the factors that influence algorithmic transparency, the Association for Computing Machinery’s principles for ensuring that one uses personal data and algorithms fairly, and recommendations for company best practices. We also speculate about how personal data and algorithms may be used in the future and suggest research opportunities.
Alopecia areata: medical treatments

Alopecia areata (AA) is a non-scarring, autoimmune, inflammatory, relapsing hair loss affecting the scalp and/or body. In acute-phase AA, CD4+ and CD8+ T cells infiltrated in the juxta-follicular area. In chronic-phase AACD8+ T cells dominated the infiltrate around hair bulbs which contributes to the prolonged state of hair loss. Treatments include mainly corticosteroids, topical irritants, minoxidil, cytotoxic drugs and biologicals. This review highlights mainly the pathomechanism and pathology, classifications and associated diseases with regard to their importance for current and future treatment.
Interstate Migration and Employer-to-Employer Transitions in the U.S.: New Evidence From Administrative Records Data

Recent evidence has suggested that interstate migration is in decline in the United States, which might imply that the labor market is becoming more rigid. However, the sharp post-2000 decline in the non-imputed interstate migration rate in the Current Population Survey (CPS), which has received considerable attention, is not reflected in other available data. In this paper, we use administrative records data to investigate labor mobility and migration within the U.S. We investigate the discrepancy in recent migration trends in the CPS and migration rates derived from administrative records sources using CPS micro data linked to administrative records on residential location. We find that a substantial fraction of CPS respondents who are cross-state migrants in the administrative records data do not report a cross-state move in the CPS, and that this disagreement has grown over time. Despite this disagreement in recent trends in overall interstate migration, rates and trends related to economic migration are remarkably similar in available data sources.
HEMATOLOGICAL AND BIOCHEMICAL PARAMETERS DURING THE EARLY STAGES OF N-NITROSODIETHYLAMINE- INDUCED HEPATOCARCINOGENESIS IN TURKEYS

Some haematological and biochemical parameters in turkeys, hatched from embryonated eggs inoculated with the proven hepatocarcinogen N-nitrosodiethylamine were studied. Histopathology confirmed the presence of clear-cell and basophilic foci of altered hepatocytes and hyperplasia of cholangiocytes. The application of the chemical carcinogen affected both haematological and biochemical parameters. The established conditions such as thrombocytopenia and increased levels of the major liver enzymes were associated with the process of malignancy. In addition, leukogram abnormalities (leukocytosis, lymphocytosis and neutropenia) as well as hypoproteinaemia, hypoalbuminaemia and hypoglycemia were also observed.
Comparing Advanced Machine Learning Techniques for Classification Problems

This research compares (and combines) multiple advanced machine learning methods: the multi-layer support vector machine (ML-SVM), extreme gradient boosting (XGB), principal component analysis (PCA) and ensemble learning methods (bagging and stacking). These methods are compared on a large dataset for classification. On this dataset, XGB combined with an ensemble method achieved the highest accuracy. The eigenvectors obtained using PCA did not contribute to any promising results for XGB. Combining the dataset with principal components also did not contribute to promising results for XGB. Training the ML-SVM is a time consuming computation, especially on large datasets. The ML-SVM uses particle swarm optimization (PSO) in order to optimize its algorithm. In this research the complexity of the PSO on the MLSVM is reduced by decreasing the search space of the PSO. These results are compared to the original results of the ML-SVM and the overall accuracy of the less parameter tuned ML-SVM was slightly less than the original, but higher than a single SVM. Also XGB is compared to these results: XGB performed worse than the ML-SVM on smaller datasets.
Facial Emoticons Expression Recognition as a Means of Interaction for Physically Challenged People

Facial expressions are highly effective nonverbal means to share emotions among human beings. Effectively, many of these expressions are universally understandable. Nowadays, in the cybernetic panorama, emoticons are used as surrogate facial expressions in electronic-mediated communication. As a new modality to more easily interact with computers, it provides a natural means of interaction that may be particularly useful for physically challenged people, since it opens a feedback channel that makes it possible to interact without using a keyboard or a mouse. We present FacialEmoticons, a facial expression recognition library that can be used as the basis for meaningful interactions with motor-impaired users. After face tracking and feature detection, Bayesian Classifiers are used to infer the expression on the user’s face. Our methodology requires low computational power yielding good results with low-latency rates and has recognition rates comparable to those of humans. Our results show the viability for facial-expression-based real-time interaction in the context of people with severe
A 2.35/2.4/2.45/2.55 GHz Low-Noise Amplifier Design Using Body Self-Biasing Technique for ISM and LTE Band Application

paper presents a quadruple-band low noise amplifier (LNA) which utilizes a differential pair common-source (CS) cascode amplifier to drive a LC-tank loading. The capacitors array are parallel with the LC-tank to implement the central frequency selection. The band-selection switch employs the binary voltage controlling to alter the equivalent capacitance of capacitors array of the loading LC-tank, which results in the central frequency of the LNA is switched. The body self-biasing technique is designed to minimize the noise contribution caused by the body effect of the MOS devices. In addition, the analysis of the transistors dimension ratio versus output referred 1-dB compression point (OP $_{1-dB}$ ) is presented to describe the design of the linearity optimization in CS cascode LNA. The |S21| is 16.8, 16.63, 16.78, 16.39 dB at 2.35, 2.4, 2.45, 2.55 GHz, respectively. The noise figure (NF) is under 2.75 dB between the quadruple-band mode. This proposed LNA is simulated by 55 nm RF CMOS process and consumes 3.75 mW excluding output buffer from 1.25 V supply.
Sensor Fusion in Human Cyber Sensor System for Motion Artifact Removal from NIRS Signal

Near-Infrared Spectroscopy (NIRS) signals have been widely used to monitor hemodynamic changes in clinical and psychological investigations as well as human system interfacing such as brain computer interfacing (BCI) for gait and rehabilitation. However, the estimation of hemodynamic changes might be blurred due to the presence of motion artifacts in a moving human in the loop system, which should be removed for a more accurate estimation. To register the motion information more accurately, a wearable wireless NIRS cyber sensor system was developed capable of registering motion-related signals from a multisensory integrated Inertia Measurement Unit (IMU) placed close to the NIR optical sensor. Although multi-axis accelerometer, gyroscope and magnetometer signals that are highly correlated to the motion at the optical sensor may provide a good estimation of the motion artifacts in the NIRS signal, the motion fusion algorithms might provide more accurate estimation of motion artefacts in the NIR signal by overcoming the intrinsic limitations of individual sensors such as imprecision and drifts. This study was purposed to determine whether the combination of motion fusion algorithm-based signal and individual sensor readings from IMU could provide a more accurate correction of the motion artifacts in the NIRS signal. The results revealed that the signal-to-noise ratio (SNR) increased significantly when motion fusion signals were used in the estimation and removal of the motion artifacts. The results suggest that the motion fusion algorithm can provide a more accurate estimation and removal of motion artifacts and thus, supporting a better detection of hemodynamic changes.
Sub-6GHz 4G/5G Conformal Glasses Antennas

The difficulty of antenna design applied to glasses is that the structure of glasses is too single, and the space available for antenna design is greatly limited. In this background, the integrated design of 4G antennas and 5G antennas applied to glasses is proposed in this paper. The most important highlight of this design is that it makes full use of the limited three-dimensional space structure provided by glasses and achieves the perfect combination of the antenna and glasses in the physical structure. Specifically, two antennas for 4G communication are arranged on two glasses frames, and four antennas for 5G communication are arranged on two glasses legs. In this way, we can make full use of the space provided by the glasses to design antennas and ensure that there is a certain distance between the 4G antennas and 5G antennas so that the performance of both 4G antennas and 5G antennas can be guaranteed. The 4G antenna consists of a loop structure printed on the frame and leg of the glasses and a parasitic branch strip printed on the front of the leg of the glasses. The resonance modes of the 4G antenna are mainly loop, monopole, and dipole modes, which can cover two 4G bands of 0.824-0.96 GHz and 1.71-2.69 GHz. Each 5G antenna mainly comes from the open slot mode etched on the metal ground surface of an FR4 substrate of the glasses leg. In addition, the slot antennas operate in two 5G bands of 3.3-3.6 GHz and 4.8-5.0 GHz. Finally, the glasses and the antennas are fabricated based on FR4 substrates and measured. The measured results show that the proposed antennas perform well and have the potential to be used in 4G/5G communications through glasses.
Statistics-Based Music Generation Approach Considering Both Rhythm and Melody Coherence

This paper presents a music generation method which is an extension of a previously presented method that generates coherent melodies using a melodic coherence structure extracted from a template piece. This extension, which has been applied for generating bertso melodies, adds the generation of the rhythmic content of the melodies, for which a rhythmic coherence structure of the template piece is also created. To do so, a pattern discovery and ranking method is used to discover the rhythmically repeated segments that are interesting, and create a rhythmic coherence structure which can have several levels of nesting. Independent sampling processes have been developed for melodic and rhythmic content, using an adapted optimization method for sampling the rhythmic content of the new pieces. An evaluation process has been carried out to evaluate some of the generated pieces, considering on one hand how the listeners perceive them and on the other hand whether they share the features with bertso melodies. It has been concluded from this evaluation that the method is capable of generating good coherent bertso melodies.
Intelligent Environmental Monitoring System based on LoRa Long Range Technology

The main purpose of this study is to establish an intelligent environmental monitoring system based on Long Range (LoRa) transmission technology with low power consumption to be installed in large-scaled campus with high population density. First of all, sensors at different locations will be configured to provide statistics such as temperature, humidity, suspended particles, carbon monoxide, and carbon dioxide in indoor environment, and temperature, humidity, wind speed, apparent temperature, suspended particles, and ultraviolet light in outdoor environment to serve as the basis for analysis. LoRa 915 MHz serves as the communication protocol of the transmission technology of this system via the transmission module LRM001 developed by Liyatech. An industrial computer is used as the Gateway, and C# Windows Forms is installed for analyzing data from various detection points. The data will be uploaded to Microsoft Azure SQL Server by the unit of hour. In the end the Server end will publish the website via ASP.NET technology and position different detection stations via Google Map API such that users can use the webpage to inquire the trend of environmental quality and the further Big Data analysis.
Chinese Micro-Blog Sentiment Analysis Based on Multiple Sentiment Dictionaries and Semantic Rule Sets

Sentiment analysis of Chinese micro-blog based on sentiment dictionary has become a challenging research subject in the field of artificial intelligence. However, due to insufficient sentiment words, Chinese micro-blog sentiment analysis is difficult to process high accuracy. Aimed at this issue, we propose a method for constructing multiple sentiment dictionaries, which mainly constructs original sentiment dictionary, emoji dictionary, and other related dictionaries. Among them, we have innovatively constructed a Chinese micro-blog new word sentiment dictionary. Multiple sentiment dictionaries increase the coverage of sentiment words. At the same time, we further analyze semantic rule sets between Chinese micro-blog texts and take the inter-sentence analysis rules and sentence pattern analysis rules into the sentiment analysis of Chinese micro-blog, which further improves the accuracy of Chinese micro-blog sentiment analysis. Finally, based on the method of multiple sentiment dictionaries and semantic rule sets, we propose an algorithm for Chinese micro-blog sentiment calculation from complex sentences to clauses and then from clauses to words, and finally combined with the emoji. This algorithm can accurately classify Chinese micro-blog into positive Chinese micro-blog, negative Chinese micro-blog, and neutral Chinese micro-blog. The experimental results show that this method has greatly improved the sentiment analysis of Chinese micro-blog.
Deadline Sensitive Task Assignment in Mobile Crowd Sensing: A greedy Approach

In mobile crowd-sensing, the diversity of worker and task locations and trajectories of worker makes the optimal task allocation problem more challenging. The different trajectory of worker leads to different response time and traveling cost. In this paper, we compute the trajectory of the worker by applying an asymmetric traveling salesman problem (ATSP) heuristic algorithm. Furthermore, We formulate Deadline Sensitive Task Assignment problem (DSTA) mathematically and propose a greedy strategy with the aim of maximizing the remaining budget while considering several constraints. The experiment shows the performance of our proposed method for different parameters.
Music Classification using Multiclass Support Vector Machine and Multilevel Wasserstein Means

Music classification is a challenging task in music information retrieval. In this article, we compare the performance of the two types of models. The first category is classified by Support Vector Machine (SVM). We use the feature extraction from audio as the basis of classification. Firstly, a total of 500 pieces of music by five famous classical music composers were selected, 400 of which were regarded as the training set of music genre classification, and the remaining pieces were regarded as the testing set. The second method is Multilevel Wasserstein Means(MWMs). From the experimental results, Multilevel Wasserstein Means, as an unsupervised learning, has been able to approach SVM in classification results，and has achieved 85% accuracy.
Automatic Crowdsourcing Demand and Supply Management for Sustainability of Pedestrian Navigation Service

In order to provide location-based services including pedestrian route guidance in numerous indoor and outdoor spaces of various structures, it is essential to maintain a low-cost platform that continuously improves accuracy. In particular, in order for the elderly, and the disabled to receive universal services, maintenance should be guaranteed along with social welfare. A realistic way to do this is an open platform based on crowdsourcing and crowdsensing for indoor positioning. However, it can only be sustained if the public is easily involved and rewarded with appropriate rewards or contributions. In this paper, we propose a method to realize the crowd contribution method based on the collection of a large number of location resources using a smartphone is realized by automatic management of spatial crowdsourcing demand and supply.
Energy-aware Routing Scheme for Large-scale Industrial Internet of Things (IIoT)

This paper proposes a tactical routing scheme that minimizes power consumption for large-scale Industrial Internet of Things (IIOT). Minimizing energy consumption and real-time routing has become a very tedious and near impossible job owing to the larger-scale and complex deployment nature of IIOT today. In order to improve scalability of IIOT elements, a hierarchical system framework is employed. The proposed algorithm arranges similar devices of the system into chains called clusters, managed by a cluster head. The simulation results indicates that this algorithm can reduce energy consumption in IIoT elements.
Player Behavior Analysis for Validation of Mobile Game Update Effectiveness

Most current mobile games adopt F2P revenue model. In this case, game update such as patch or event is important in increasing the revenue, and it usually generates environmental change. Once the actual effect of this environmental change on players is measured, it can be helpful to plan future updates. In this paper, we propose the various approach to validate the effectiveness of conducted game update.
Using CNNs For Users Segmentation In Video See-Through Augmented Virtuality

In this paper, we present preliminary results on the use of deep learning techniques to integrate the user's self-body and other participants into a head-mounted video see-through augmented virtuality scenario. It has been previously shown that seeing user's bodies in such simulations may improve the feeling of both self and social presence in the virtual environment, as well as user performance. We propose to use a convolutional neural network for real time semantic segmentation of users' bodies in the stereoscopic RGB video streams acquired from the perspective of the user. We describe design issues as well as implementation details of the system and demonstrate the feasibility of using such neural networks for merging users' bodies in an augmented virtuality simulation.