Miracle-dz/UniFAR-FTUs huggingface dataset:
Does Transparency Reduce Corruption?

Does a better monitoring (transparency) of officials lowers the incidence of corruption ? Using a common agency game with imperfect information, we show that the answer depends on the measure of corruption that one uses. More transparency lowers the prevalence of corruption but it may raise the average bribe as it motivates the corruptor to bid more aggressively for the agent’s favour. We show that transparency affects the prevalence of corruption at the margin through a competitive effect and an efficiency effect.
Cannon--Thurston maps for CAT(0) groups with isolated flats

Mahan Mitra (Mj) proved Cannon--Thurston maps exist for normal hyperbolic subgroups of a hyperbolic group. We prove that Cannon--Thurston maps do not exist for infinite normal hyperbolic subgroups of non-hyperbolic CAT(0) groups with isolated flats with respect to the visual boundaries. We also show Cannon--Thurston maps do not exist for infinite infinite-index normal CAT(0) subgroups with isolated flats in non-hyperbolic CAT(0) groups with isolated flats. We obtain a structure theorem for the normal subgroups in these settings and show that outer automorphism groups of hyperbolic groups have no purely atoroidal $\mathbb{Z}^2$ subgroups.
Automatic Functionality Verification of Hybrid Set-Top Boxes With Dynamic User Interface

Modern set-top-box (STB) user interfaces (UI) contain increased amount of animations/effects compared to standard UIs, thus demanding a new approach for accurate and cost-efficient STB functionality verification. In this paper, a fully automated system for functional failure detection in hybrid STBs with dynamic UI is proposed. It consists of image capturing unit, testing tool, and hybrid STB being verified. The automatic verification of hybrid STB functionality is performed by comparing the captured image of the dynamic UI to the referent image captured from referent STB. The system incorporates a new algorithm for image similarity measurement, adjusted for image alterations which often appear in dynamic UIs. Algorithm utilizes image feature extraction and comparison to determine captured and referent image similarity and to make decision regarding STB functionality. The proposed system was compared with existing fully automated system for functionality verification of an STB device with static UI. Tests were performed on both computer generated and live stream cases. For this purpose a new freely available computer generated image alterations database is created. The results show that the proposed system achieves higher performance and robustness, in terms of measuring image similarity despite alterations caused by dynamic UI effects.
The Effect of Ownership Structure on Audit Quality: Evidence from Jordan

Audit quality is getting very important due to the role of an audit in enhancing the quality and reliability of financial statements. The studying of audit quality and the factors that are affecting it has captured the attention and imagination of researchers, especially those in developed countries. This study aims at investigating the effect of ownership structure among Jordanian listed firms based on their audit quality. The sample study consists of one hundred ninety eight (198) companies out of the two hundred sixty two (262) listed companies on the Amman Stock Exchange (ASE). The analysis of logistic regression was used to investigate the relationship between the audit quality measured based on the audit firms size as a dependent variable, and ownership structure as independent variables. The results show a significant positive relationship between the audit quality and that of companies both with foreign and institutional ownership. Whereas ownership concentration was shown to have a negative relationship with quality, that relationship was not significant. These results indicate that foreign and institutional investors tend to hire high quality auditors.
A chemometric model applied to fatty acid determination in blood

Mudancas no perfil de acidos graxos tem sido associadas a varios processos patofisiologicos. Dados obtidos por cromatografia gasosa-espectrometria de massas (GC-MS) usando monitoramento seletivo de ions foram empregados para revelarem, apos analise por analise de componentes principais (PCA), um conjunto relevante de ions para quantificacao e caracterizacao de acidos graxos. Este protocolo foi aplicado com sucesso na analise de acidos graxos presentes em diversas fracoes lipidicas de sangue, permitindo a quantificacao de diversos acidos graxos e revelando seus numeros de insaturacoes. Adicionalmente, a presenca de contaminantes, artefatos e co-eluicoes no cromatograma tambem foram reveladas sem analises adicionais. Por fim, acidos graxos presentes em triacilglicerideos, fosfolipidios e esteres de colesterol presentes em plasma e membrana de eritrocitos foram determinados com exatidao adequada, repetibilidade e baixos limites de deteccao e de quantificacao. Changes in fatty acid profiles have been associated with several pathophysiological processes. Gas chromatography-mass spectrometry (GC-MS) data monitoring of selected ions was used with principal components analysis (PCA), revealing a set of relevant ions for quantification and characterization of fatty acids. This protocol was successfully applied to the analyses of fatty acids in different human blood lipids, allowing the quantification of several fatty acids and revealing their unsaturation numbers. Moreover the presences of contaminants, artifacts and co-eluitions in the chromatogram were also revealed without any additional analyses. Thus, fatty acid constituents of triglycerides, phospholipids and esters of cholesterol present in plasma and erythrocyte membranes were accurately determined, with repeatability, low limits of determination and of quantification.
Optimal Intelligent Control of Plug-in Fuel Cell Electric Vehicles in Smart Electric Grids

In this paper, Plug-in Fuel Cell Electric Vehicle (PFCEV) is considered with dual power sources including Fuel Cell (FC) and battery Energy Storage. In order to respond to a transient power demand, usually battery energy storage device is combined with fuel cell to create a hybrid system with high energy density of fuel cell and the high power density of super capacitor. In order to simulate the PEV model, dynamic state space models of bidirectional DC-DC converter and grid connected voltage source converter are considered to connect the PFCEV to the main grid. In order to stabilize the DC-link power and distribute the power between dual energy storage sources in PEV during normal and disturbance conditions on the parameters of fuzzy logic controller, the PSO (particle swarm optimization) algorithm has been used. This controller determines the super capacitor and fuel cell powers that should be generated according to the amount of available energy in DC-link. Moreover, a robust sliding mode control strategy for three phase power electronic converter based on the positive and negative symmetrical components is presented to investigate the voltage disturbance ride-through capability.
The Empirical Research on Independent Innovation Competencies of Enterprise R&D Departments

The strength of the independent innovation competencies of enterprise R&D departments (hereinafter referred to as ERDD) decides the market competitiveness of enterprises. It is of vital importance to evaluate the innovation competencies of ERDD. Given to the characteristics of ERDD in China, this paper builds the evaluation index system of the independent innovation competencies of ERDD which is based on factor analysis and aimed to understand the corresponding strength of industrial enterprises of the whole country and every province and city clearly. In this paper, 16417 ERDD in Jiangsu province are analyzed. Firstly, their overall situation and dynamic changes of quantities, innovation input and output, platform stimulating effect are analyzed. Secondly, the dynamic changes of constituent elements on their independent innovation competencies are researched. Thirdly, the comparison and evaluation of their independent innovation competencies are carried out by the results of 31 provinces' ERDD in mainland China.
Self-localization of Anonymous Mobile Robots from Aerial Images

This paper presents three methods for anonymous mobile robot localization within a global frame. An aerial camera takes, at regular time intervals, pictures of the area in which robots are moving. The camera determines the coordinates of each robot. Each robot receives the whole set of coordinates extracted from each picture. Mobile Robots are all identical, they do not have any identifier and they can neither communicate with each other nor they can detect themselves. The first localization method is based on the analysis of the angular variation between two images. The second method relies on the analysis of the distances stemmed from three successive pictures. The last one determines if there exists an orientation allowing a specific robot to travel the path between two successive positions. A simulation plateform using augmented reality and the multi-robot software Player-Stage are presented. This plateform is used for validating the different localization methods. Tests and results are presented and compared.
Simulation, modeling and analysis of the eICIC/ABS in H-CRAN

In this paper, we propose mathematical models to evaluate the performances of the interference remediation technique eICIC/ABS (enhanced Inter- Cell Interference Coordination / Almost Blank Sub-frame) in the context of Heterogeneous Cloud based Radio Access Networks (H-CRAN) architecture and 5G networks. The objective is to propose a dynamic resource management tool to ease decisions on the activation/deactivation of micro cells as well as for the distributions of sub-frames among macro and micro cells. First, we propose a Markov chain based model that fits the behavior of the considered scheme and allows the analysis of the cell throughput according to traffic load, radio conditions and the distribution of available resources among macro and micro cells. Then, we propose an approximation model with a closed form formula. The two models are validated and evaluated in terms of accuracy and computation time. Numerical results are compared to Matlab simulations reproducing realistic radio conditions. Results show that both models are accurate, while the closed form approximation is less complex and provides faster results.
Wavelet Based Intentional Blurring Variance Scheme for Blur Detection in Barcode Images

Blur is an undesirable phenomenon which appears as one of the most frequent causes of image degradation. Automatic blur detection is extremely enviable to restore barcode image or simply utilize them. That is to assess whether a given image is blurred or not. To detect blur, many algorithms have been proposed. These algorithms are different in their performance, time complexity, precision, and robustness in noisy environments. In this paper, we present an efficient method blur detection in barcode images, with no reference perceptual blur metric using wavelets. Index Terms—Blur, Intentional blur detection, Wavelet, Barcode images.
MAKING THE CONNECTION: PROCEDURAL AND CONCEPTUAL STUDENTS' USE OF LINKING WORDS IN SOLVING PROBLEMS

The Malaysian educational system tends to take a clear instructionally-focused approach to the teaching of mathematics. This means that many students gain a good procedural command of areas of mathematics. This paper explores one outcome of a teaching experiment in which one area of mathematics (differentiation) was taught in a radically different way which emphasized key concepts rather than procedures. The paper examines how students appear to access their mathematical knowledge in solving problems in a non-mathematical context and demonstrates that there is a strong relationship between an aspect of their language use (transitional words of consequence or contrast) and the form of their mathematical understanding in this area. We discuss the relevance of this finding to the possible structure students’ mental spaces.
A review of the application of RFM model

RFM (Recency, Frequency and Monetary) model has been widely applied in many practical areas in a long history, particularly in direct marketing. By adopting RFM model, decision makers can effectively identify valuable customers and then develop effective marketing strategy. This paper aims to provide a comprehensive review on the application of RFM model. In addition, this paper depicts the definition and the scoring scheme of RFM and summarizes how RFM model has been effectively applied in a wide variety of areas. Furthermore, this paper presents the advantages and disadvantages of the RFM model. The relative advantages and disadvantages of RFM and other models are also exploited. Finally, this paper describes the extended RFM model via a presentation of how RFM combines with other variables and models. Key words: RFM model, literature review, customers, direct marketing, customer value, customer relationship management.
Primary and secondary organic aerosol from heated cooking oil emissions

Abstract. Cooking emissions have been identified as a source of both primary organic aerosol (POA) and secondary organic aerosol (SOA). To examine the characteristics of SOA from cooking emissions, emissions from seven vegetable oils (sunflower, olive, peanut, corn, canola, soybean, and palm oils) heated at 200 ∘C were photooxidized under high-NOx conditions in a smog chamber. OA was characterized using a high-resolution time-of-flight aerosol mass spectrometer (HR-TOF-AMS). Sunflower, peanut, corn, canola, and soybean oil generated relatively low concentrations of POA (≤0.5 µg m−3) in the chamber. For palm and olive oil, positive matrix factorization (PMF) analysis separated POA and SOA better than the residual spectrum method. Temporal trends in concentrations of POA from heated palm oil were accurately predicted assuming first-order POA wall loss. However, this assumption overestimated POA concentrations from heated olive oil, which was attributed to the heterogeneous oxidation of POA. The mass spectra of the PMF resolved POA factor for palm oil, and the average POA from sunflower, peanut, corn, and canola oils were in better agreement (θ=8–12∘) with ambient cooking organic aerosol (COA) factors resolved in select Chinese megacities than those found in given European cities in the literature. The mass spectra of SOA formed from heated cooking oils had high abundances of m∕zs 27, 28, 29, 39, 41, 44, and 55 and displayed limited similarity (θ > 20∘) with ambient semi-volatile oxygenated OA (SV-OOA) factors. The entire OA data set measured herein follows a linear trend with a slope of approximately −0.4 in the Van Krevelen diagram, which may indicate oxidation mechanisms involving the addition of both carboxylic acid and alcohol or peroxide functional groups without fragmentation and/or the addition of carboxylic acid functional groups with fragmentation.
Carbon and water vapor balance in a subtropical pine plantation

Abstract: Afforestation has been proposed as an effective tool for protecting primary and/or secondary forests and for mitigating atmospheric CO2. However, the dynamics of primary productivity differs between plantations and natural forests. The objective of this work was to evaluate the potential for carbon storage of a commercial pine plantation by determining its carbon balance. Measurements started when trees were aged 6 and ended when they were older than 8 years. We measured CO2 and water vapor concentrations using the Eddy covariance method. Gross primary productivity in 2010 and 2011 was 4290 ± 473 g C m-2 and 4015 ± 485 g C m-2, respectively. Ecosystem respiration ranged between 7 and 20 g C m-2 d-1, reaching peaks in all Februaries. Of the 30 months monitored, the plantation acted as carbon source for 21 months and as carbon sink for 6 months, while values close to neutrality were obtained during 3 months. The positive balance representing CO2 loss by the system was most likely due to the cut branches left on the ground following pruning activities. The plantation was subjected to pruning in January and September 2008 and to sanitary pruning in October 2010. In all cases, cut branches were not removed but remained on the ground. Residue management seems to have a very important impact on carbon balance.
Energy-Efficient Secure Transmission for UAV-enabled Wireless Powered Communication

This paper investigates an uplink transmission of Internet of Things (IoT) in an unmanned aerial vehicle (UAV)enabled wireless powered communication (WPC) network, where a source sensor and multiple intermediate sensors harvest energy from a dedicated power transmitter deployed at the UAV. In order to help the source sensor transmit the data packet to the access point, an intermediate sensor is selected as a relay based on their links with source. Then, we derive the closed-form expressions of secrecy outage probability (SOP) and secure energy efficiency (SEE). Furthermore, we formulate the SEE optimization problem to find the optimal transmit power and location for the UAV power transmitter. Simulation results show that the best position of UAV is closer to the relays with the increasing of the number of relays. In addition, the decreasing of the UAV height contributes to the improvement of SEE.
MITK Global Tractography-Application to the Corticospinal Tract

In the DTI Tractography Challenge MICCAI 2011 different tractography algorithms compete in tracking the corticospinal tract. In this paper we present an implementation of the global tractography algorithm proposed by Reisert et.al. [1] using the open source Medical Imaging Interaction Toolkit (MITK) developed and maintained by the Division of Medical and Biological Informatics at the German Cancer Research Center (DKFZ). The MITK diffusion imaging application combines all the steps necessary for a successful tractography: preprocessing, reconstruction of the images, the actual tracking, live monitoring of intermediate results, postprocessing and visualization of the final tracking results.
Fluctuations in net doping and lifetime in Cu(In,Ga)Se2 solar cells

By extracting profiles perpendicular to the substrate from images obtained by electron-beam-induced current measurements on Cu(In,Ga)Se2 (CIGS) solar cells, fluctuations in the width of the space-charge region as well as in the diffusion length in the quasi-neutral region were studied. Devices with various solution-grown buffer layers as well as different [Ga]/([Ga]+[In]) ratios in the CIGS absorber were investigated. Complementary analyses by means of energy-dispersive X-ray spectrometry, and cathodoluminescence provide additional insight into the local optoelectronic and compositional properties of investigated cells.
Supersymmetry and Inflation

Theories with elementary scalar degrees of freedom seem nowadays required for simple descriptions of the Standard Model and of the Early Universe. It is then natural to embed theories of inflation in supergravity, also in view of their possible ultraviolet completion in String Theory. After some general remarks on inflation in supergravity, we describe examples of minimal inflaton dynamics which are compatible with recent observations, including higher-curvature ones inspired by the Starobinsky model. We also discuss different scenarios for supersymmetry breaking during and after inflation, which include a revived role for non-linear realizations. In this spirit, we conclude with a discussion of the link, in four dimensions, between "brane supersymmetry breaking" and the super--Higgs effect in supergravity.
Multi-label Semantic Decoding from Human Brain Activity

It is meaningful to decode the semantic information from functional magnetic resonance imaging (fMRI) brain signals evoked by natural images. Semantic decoding can be viewed as a classification problem. Since a natural image may contain many semantic information of different objects, the single label classification model is not appropriate to cope with semantic decoding problem, which motivates the multi-label classification model. However, most multi-label models always treat each label equally. Actually, if dataset is associated with a large number of semantic labels, it will be difficult to get an accurate prediction of semantic label when the label appears with a low frequency in this dataset. So we should increase the relative importance degree to the labels that associate with little instances. In order to improve multi-label prediction performance, in this paper, we firstly propose a multinomial label distribution to estimate the importance degree of each associated label for an instance by using conditional probability, and then establish a deep neural network (DNN) based model which contains both multinomial label distribution and label co-occurrence information to realize the multi-label classification of semantic information in fMRI brain signals. Experiments on three fMRI recording datasets demonstrate that our approach performs better than the state-of-the-art methods on semantic information prediction.
Word Level Language Identification in Code-Mixed Data using Word Embedding Methods for Indian Languages

In recent years, social media networking has grown to be a marvel of technology in our way of life. Facebook operates the world's leading web-based social networking system with over 2.19 billion clients(as of the first quarter of 2018). As its popularity increased, more individuals from all age demographics, have been accessing this growing phenomenon. Resultant usage of code-mixed data has become an all too common practice in the context of social media. The aim of our project was to identify different languages in the processing of code-mixed data. A comparison of different word embedding methods like Continuous Bag of Words (CBOW) and Skip-Gram models was used to generate feature vectors. These vectors are given as input to the machine learning algorithms like Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Gauss Naive Bayes, Adaboost, and Random Forest which yielded in good cross-validation scores. The paper also reveals that Precision, Recall, F-Score, Micro and Macro averaging were used as evaluation measures.
MultiDimensional Sparse Super-Resolution

This paper studies sparse super-resolution in arbitrary dimensions. More precisely, it develops a theoretical analysis of support recovery for the so-called BLASSO method, which is an off-the-grid generalisation of l1 regularization (also known as the LASSO). While super-resolution is of paramount importance in overcoming the limitations of many imaging devices, its theoretical analysis is still lacking beyond the 1-dimensional (1-D) case. The reason is that in the 2-dimensional (2-D) case and beyond, the relative position of the spikes enters the picture, and different geometrical configurations lead to different stability properties. Our first main contribution is a connection, in the limit where the spikes cluster at a given point, between solutions of the dual of the BLASSO problem and Hermite polynomial interpolation ideals. Polynomial bases for these ideals, introduced by De Boor, can be computed by Gaussian elimination, and lead to an algorithmic description of limiting solutions to the dual problem. With this construction at hand, our second main contribution is a detailed analysis of the support stability and super-resolution effect in the case of a pair of spikes. This includes in particular a sharp analysis of how the signal-to-noise ratio should scale with respect to the separation distance between the spikes. Lastly, numerical simulations on different classes of kernels show the applicability of this theory and highlight the richness of super-resolution in 2-D.