nils-herrmann/clean_scirepeval huggingface dataset:
dijiteolneitibeu(Digital Native)ceogdo gaebal mic geomjeung
Year: 2018

bon yeonguneun Don Tapscott(2008)yi dijiteolneitibeu(Digital Native)yi 8gaji teugseongin jayu, majcumjejag, ceoljeohan josa, seongsilseong, hyeobeob, enteoteinmeonteu, sogdo, hyeogsine daehan ceogdoreul gaebalhayeo, hanguggwa junggugyi jeolmeun sedaedeuleul daesangeuro geomjeunghae boassda. seolmuneun hangug, junggugyi 10dae~30dae, namnyeo 506myeongi eungdabhayeosseumyeo, sujibdoen seolmunjireul tonghae dijiteolneitibeuyi 8gaji caweoneul hwaginhayeosseumyeo, ceugjeongyi sinroedowa tadangseongeul geomjeunghayeossda. ddohan hanguggwa junggugyi dijiteolneitibeu seonghyang caireul salpyeobogo ITsobi sanggwangwangyee daehae alaboassda. geu gyeolgwa dijiteolneitibeu(Digital Native)yi 8gaji yoineun IT sobiwa yuyihan sanggwangwangyereul natanaeeosseumyeo, teughi enteoteinmeonteu, jayu, hyeogsin sogseongeseo bigyojeog nopeun sanggwangwangyega issneun geoseuro natanassda. ddohan dijiteolneitibeu seonghyangi nopeun jibdangwa najeun jibdan ganeneun IT sobi myeoneseo geoyi modu yuyihan caiga natanassda. bon yeongugyeolgwaneun hanguggwa junggugyi dijiteolneitibeu(Digital Native) sedaeyi saeroun sobi teugjinggwa sotong bangbeobeul yeonguhayeo geudeulege majneun saeroun maketing banghyangeul jesihal su isseul geosida.
Beginning Hybrid Mobile Application Development
Venue: Apress
Year: 2016

With this hands on book, you'll learn how to create hybrid mobile apps using HTML 5, JQuery, and CSS3 that can be deployed on multiple platforms such as Android, iOS Windows Phone. Beginning Hybrid Mobile Application Development explains the top frameworks for hybrid app development along with the available APIs. You'll learn the internals of how hybrid apps work, why to choose JSON over XML, and how to access data in a hybrid app. And also you'll learn about the security issues in hybrid apps and secure coding guidelines.
Inclusive Block Chain Protocols
Venue: Financial Cryptography
Year: 2015

Distributed cryptographic protocols such as Bitcoin and Ethereum use a data structure known as the block chain to synchronize a global log of events between nodes in their network. Blocks, which are batches of updates to the log, reference the parent they are extending, and thus form the structure of a chain. Previous research has shown that the mechanics of the block chain and block propagation are constrained: if blocks are created at a high rate compared to their propagation time in the network, many conflicting blocks are created and performance suffers greatly. As a result of the low block creation rate required to keep the system within safe parameters, transactions take long to securely confirm, and their throughput is greatly limited.
Reduction Sensitive Self Aggregates as a Novel Delivery System
Year: 2010

Methoxy PEG amine with molecular weight of 5k and e caprolactone with molecular weight of 1960 were conjugated to a peptide comprising three cysteine residues. The shift of peak molecular weight and narrow molecular weight distribution in GPC trace without any noticeable shoulder as well as 1 H NMR analysis confirmed the successful synthesis of the copolymer. A modified O/W dialysis system was employed to prepare self aggregates having the size around 210 nm. During the dialysis, stabilized aggregates were obtained by intermolecular disulfide bonds via oxidation. Critical aggregate concentration (CAC) of the copolymer was determined as 0.07 mg*mL 1 and disulfide stabilized self aggregates remained stable regardless of the concentration without displaying CAC. Doxorubicin loading amount and efficiency was 8.7 and 26.0% respectively. Release profile of doxorubicin below CAC at 37 degC showed a sustained release and the addition of D L dithiothreitol (DTT) after 24 h triggered a burst release of doxorubicin. Intermolecular disulfide bonds via oxidation stabilized the polymeric aggregates even in the diluted condition similar to that in the bloodstream and addition of DTT destabilized the aggregates to burst encapsulated doxorubicin in the reductive condition.
Determinants of Tax Non Compliance Behavior in Ethiopia: The Case of Hosanna Town Taxpayers
Venue: Research Journal of Finance and Accounting
Year: 2019

In the Ethiopian government the second five year Growth and Transformation Plan, it has been clearly stated that efforts will be geared towards promoting compliance and equipping tax collection institutions with adequate enforcement power which will further boost revenue mobilization at federal, regional, Zonal and woreds levels. The country's tax mobilization was also the lowest among sub Saharan African countries and thus, objectives if the study the identifying the factors that determine tax non compliance behavior has been open for empirical investigation. There are seven determinates included in this study, to be researched upon and are categorized into non economic factors and economic factors. Authors used a cross sectional survey method of research design. The target population of the study was category "C" tax payers in purposefully selected three category taxpayers of Hosanna town, Ethiopia ,this group are considered hard to tax because the law doesn't require them to declare their income or keep books of account. A sample of 247 Category "C" tax payers was randomly taken from the selected cities. The number of questionnaires completed and returned was two hundred and twenty eight (247) constituting 76% response rate. Both primary and secondary data were collected. Seven explanatory variables were used in the ordered logistic regression model. The p value was used to test the significant at 5 significance level of the study variables. The results showed that rate of tax, future tax costs, tax education/tax knowledge and Gross of sales are factors that positively significantly affect tax non compliance behavior. However, Individual Tax Morale, report of income tax, and public quality governance were not significantly at 5 significance level affect non tax compliance. Therefore, researcher recommended, the outcomes of the study may inform policymakers about the determinants of non tax compliance behavior of tax payers in the Hosanna town and helps to formulate better policy decisions related to non tax compliance behavior.  Tax Non Compliance Behavior, Tax Payers, Ethiopia DOI 10.7176/RJFA/10 21 02 Publication date: November 30 th 2019
The influence of a first language: training nonnative listeners on voicing contrasts
Year: 2018

Learning how to perceive speech contrasts in a second language (L2) is influenced by many factors, including similarity to the first language (L1) Over five days, we trained native English and native Spanish speakers to perceive differences in the voiced Hindi contrast /p/ /ph/ while recording their mismatch negativity (MMN) response before, during, and after training. Our results show that only native Spanish speakers showed a decrease in MMN amplitude following training, suggesting that the nonnative contrast was learned successfully, but this was not the case for native English speakers. This suggests that the acoustic and phonetic organisation of L1 differentially affects how we perceive and learn L2 sounds. We examine our results in the context of L2 learning and speech models, suggesting that further development on the difficulty of learning to perceive L2 speech should include more details on similarity to L1.
MobileFace: 3D Face Reconstruction with Efficient CNN Regression
Venue: ECCV Workshops
Year: 2018

Estimation of facial shapes plays a central role for face transfer and animation. Accurate 3D face reconstruction, however, often deploys iterative and costly methods preventing real time applications. In this work we design a compact and fast CNN model enabling real time face reconstruction on mobile devices. For this purpose, we first study more traditional but slow morphable face models and use them to automatically annotate a large set of images for CNN training. We then investigate a class of efficient MobileNet CNNs and adapt such models for the task of shape regression. Our evaluation on three datasets demonstrates significant improvements in the speed and the size of our model while maintaining state of the art reconstruction accuracy.
Enantioselective remote meta C H arylation and alkylation via a chiral transient mediator
Venue: Nature
Year: 2018

Enantioselective carbon hydrogen (C H) activation reactions by asymmetric metallation could provide new routes for the construction of chiral molecules1,2. However, current methods are typically limited to the formation of five or six membered metallacycles, thereby preventing the asymmetric functionalization of C H bonds at positions remote to existing functional groups. Here we report enantioselective remote C H activation using a catalytic amount of a chiral norbornene as a transient mediator, which relays initial ortho C H activation to the meta position. This was used in the enantioselective meta C H arylation of benzylamines, as well as the arylation and alkylation of homobenzylamines. The enantioselectivities obtained using the chiral transient mediator are comparable across different classes of substrates containing either neutral s donor or anionic coordinating groups. This relay strategy could provide an alternative means to remote chiral induction, one of the most challenging problems in asymmetric catalysis3,4.Remote, enantioselective C H activation reactions can be achieved by relaying ortho C H activation to the meta position, using a chiral norbornene as the mediator.
Generalized Labeled Multi Bernoulli Approximation of Multi Object Densities
Venue: IEEE Transactions on Signal Processing
Year: 2015

In multiobject inference, the multiobject probability density captures the uncertainty in the number and the states of the objects as well as the statistical dependence between the objects. Exact computation of the multiobject density is generally intractable and tractable implementations usually require statistical independence assumptions between objects. In this paper we propose a tractable multiobject density approximation that can capture statistical dependence between objects. In particular, we derive a tractable Generalized Labeled Multi Bernoulli (GLMB) density that matches the cardinality distribution and the first moment of the labeled multiobject distribution of interest. It is also shown that the proposed approximation minimizes the Kullback Leibler divergence over a special tractable class of GLMB densities. Based on the proposed GLMB approximation we further demonstrate a tractable multiobject tracking algorithm for generic measurement models. Simulation results for a multiobject Track Before Detect example using radar measurements in low signal to noise ratio (SNR) scenarios verify the applicability of the proposed approach.
Reasoning with Heterogeneous Graph Alignment for Video Question Answering
Venue: AAAI
Year: 2020

The dominant video question answering methods are based on fine grained representation or model specific attention mechanism. They usually process video and question separately, then feed the representations of different modalities into following late fusion networks. Although these methods use information of one modality to boost the other, they neglect to integrate correlations of both inter and intra modality in an uniform module. We propose a deep heterogeneous graph alignment network over the video shots and question words. Furthermore, we explore the network architecture from four steps: representation, fusion, alignment, and reasoning. Within our network, the inter and intra modality information can be aligned and interacted simultaneously over the heterogeneous graph and used for cross modal reasoning. We evaluate our method on three benchmark datasets and conduct extensive ablation study to the effectiveness of the network architecture. Experiments show the network to be superior in quality.
VALAN: Vision and Language Agent Navigation
Venue: ArXiv
Year: 2019

VALAN is a lightweight and scalable software framework for deep reinforcement learning based on the SEED RL architecture. The framework facilitates the development and evaluation of embodied agents for solving grounded language understanding tasks, such as Vision and Language Navigation and Vision and Dialog Navigation, in photo realistic environments, such as Matterport3D and Google StreetView. We have added a minimal set of abstractions on top of SEED RL allowing us to generalize the architecture to solve a variety of other RL problems. In this article, we will describe VALAN's software abstraction and architecture, and also present an example of using VALAN to design agents for instruction conditioned indoor navigation.
Organocatalytic Mitsunobu Reactionswith 3,5 Dinitrobenzoic Acid
Year: 2010

A second generation procedure for organocatalytic Mitsunobu reactions using 3,5 dinitrobenzoic acid as the pro nucleophile has been developed. The increased acidity of this acid compared to 4 nitrobenzoic acid, which was used in the original procedure, allowed for higher isolated yields of the desired products and eliminated formation of undesired acetate byproducts.
Research on the Financial Sustainable Growth of the Listed Companies on GEM
Year: 2015

Currently high risk assumed by a listed company on GEM makes numerous operators, investors and creditors pay close attention to the sustainable growth ability of an enterprise. In view of this, this paper makes an empirical analysis on the current status of the financial sustainable growth of the listed companies on GEM and the main factors that might affect their sustainable growth separately through the Wilcoxon signed ranks test and the factor analysis. The research result turns out that the listed companies on GEM have failed to achieve the financial sustainable growth with its actual growth rate greater than the sustainable growth rate to indicate an excessive growth. Meanwhile since the factors that influence the enterprise sustainable growth include the profitability, the cash generating ability, the debt paying ability, the operation capacity and the growth ability, then the managers should take the following measures to achieve the financial sustainable growth of the enterprise through the cost control, the reinforcement of cash management, the cultivation of core competence, the optimization of the capital structure and the enhancement of the management level etc.  Listed companies on GEM; Financial sustainable growth; Empirical analysis
Mechanisms of Macrophage Polarization in Insulin Signaling and Sensitivity
Venue: Frontiers in Endocrinology
Year: 2020

Type 2 diabetes (T2D) is a disease of two etiologies: metabolic and inflammatory. At the cross section of these etiologies lays the phenomenon of metabolic inflammation. Whilst metabolic inflammation is characterized as systemic, a common starting point is the tissue resident macrophage, who's successful physiological or aberrant pathological adaptation to its microenvironment determines disease course and severity. This review will highlight the key mechanisms in macrophage polarization, inflammatory and non inflammatory signaling that dictates the development and progression of insulin resistance and T2D. We first describe the known homeostatic functions of tissue macrophages in insulin secreting and major insulin sensitive tissues. Importantly we highlight the known mechanisms of aberrant macrophage activation in these tissues and the ways in which this leads to impairment of insulin sensitivity/secretion and the development of T2D. We next describe the cellular mechanisms that are known to dictate macrophage polarization. We review recent progress in macrophage bio energetics, an emerging field of research that places cellular metabolism at the center of immune effector function. Importantly, following the advent of the metabolically activated macrophage, we cover the known transcriptional and epigenetic factors that canonically and non canonically dictate macrophage differentiation and inflammatory polarization. In closing perspectives, we discuss emerging research themes and highlight novel non inflammatory or non immune roles that tissue macrophages have in maintaining microenvironmental and systemic homeostasis.
Hubungan tingkat aktivitas fisik dan fungsi kognitif lansia di Posyandu Lansia Mekar Sari Kota Surabaya Tahun 2016
Year: 2016

Populasi lansia terus mengalami peningkatan sesuai dengan data yang diterbitkan oleh World Health Organization (WHO) dan Departemen Kesehatan (DEPKES RI) Seiring dengan meningkatnya kelompok umur ini, ditemukan berbagai masalah kesehatan. Salah satu masalah kesehatan tersebut adalah penurunan fungsi sistem saraf yang dapat memicu terjadinya penurunan fungsi kognitif. Peningkatan aktivitas fisik disebutkan dapat menghambat proses penurunan fungsi kognitif. Tujuan dari penelitian ini adalah untuk menganalisis hubungan tingkat aktivitas fisik dan fungsi kognitif lansia di Posyandu Lansia Mekar Sari Kota Surabaya Tahun 2016. Jenis penelitian ini adalah analisis obsevasional dengan rancangan penelitian berupa cross sectional. Kuesioner yang digunakan adalah Mini Mental State Examination (MMSE) dan International Physical Activity Questionare (IPAQ) Berdasarkan uji koefisien kontingensi didapatkan adanya hubungan yang signifikan antara tingkat aktivitas fisik dan fungsi kognitif lansia dengan p=0,002.
Mediating effects of affective organizational commitment and psychological contract in the relationship between strategic compensation practices and knowledge sharing
Year: 2014

This study contributes to the development of the knowledge management and strategic compensation literatures through developing the linkages between them. The study sample comprised of 301 employees from universities of medical sciences in Iran. Multiple and simple linear regression and path analysis were used to test the direct and mediated relationships among the variables. Results highlighted significant relationships between (a) strategic compensation practices and affective organizational commitment, (b) affective organizational commitment and knowledge sharing, (c) strategic compensation practices and psychological contract, and (d) psychological contract and knowledge sharing. Results revealed that strategic compensation practices are positively related to affective organizational commitment and psychological contract. In addition, the results provided evidence that affective organizational commitment and psychological contract have a significant mediating effect on the relationship between strategic compensation practices and knowledge sharing. Managerial and practical implications of the findings are highlighted.
Taxes, Private Equity, and Evolution of Income Inequality in the US*
Year: 2018

From 1980 to 2012 the share of U.S. business receipts from businesses organized as pass through entities (for example LLCs and S corporations) rather than traditional C corporations nearly triples following a sequence of tax reforms that reduced the effective tax rate on pass through business income. We show this shift in the pattern of business organization has significant economic implications for firm dynamics and income inequality. First, using firm level administrative data we find that the tax reform dampened the employment growth of the US firms. Next, using a reduced form decomposition of data from the Survey of Consumer Finances, we find the increase in pass through entities explains over 50 percent of the increase in the share of pre tax income for the top 1 percent of households. Finally, to illustrate the mechanism and quantify the effects of policy shifts we develop a heterogeneous agent equilibrium model with workers, entrepreneurs and endogenous choice of legal forms, which captures a key trade off between tax benefits and diversification of investment risk. In the calibrated model, we quantify the contribution of tax reforms through the business reorganization channel to the evolution of income, wealth and consumption inequality of workers and entrepreneurs. Our initial estimates indicate that 1 percentage point reduction in the marginal personal income tax translates into the 0.3 percentage point increase in the top 1 percent pre tax income share and significant increase in the number of the pass through businesses.
CatBoost: unbiased boosting with categorical features
Venue: NeurIPS
Year: 2018

This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit. Their combination leads to CatBoost outperforming other publicly available boosting implementations in terms of quality on a variety of datasets. Two critical algorithmic advances introduced in CatBoost are the implementation of ordered boosting, a permutation driven alternative to the classic algorithm, and an innovative algorithm for processing categorical features. Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing implementations of gradient boosting algorithms. In this paper, we provide a detailed analysis of this problem and demonstrate that proposed algorithms solve it effectively, leading to excellent empirical results.
Optimizing Vulnerability Driven Honey Traffic Using Game Theory
Venue: ArXiv
Year: 2020

Enterprises are increasingly concerned about adversaries that slowly and deliberately exploit resources over the course of months or even years. A key step in this kill chain is network reconnaissance, which has historically been active (e.g. network scans) and therefore detectable. However, new networking technology increases the possibility of passive network reconnaissance, which will be largely undetectable by defenders. In this paper, we propose Snaz, a technique that uses deceptively crafted honey traffic to confound the knowledge gained through passive network reconnaissance. We present a two player non zero sum Stackelberg game model that characterizes how a defender should deploy honey traffic in the presence of an adversary who is aware of Snaz. In doing so, we demonstrate the existence of optimal defender strategies that will either dissuade an adversary from acting on the existence of real vulnerabilities observed within network traffic, or reveal the adversary's presence when it attempts to unknowingly attack an intrusion detection node.
Thermal Based Pedestrian Detection Using Faster R CNN and Region Decomposition Branch
Venue: 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)
Year: 2019

In this paper, we present an infrared thermal based pedestrian detection method that can be applied in nighttime intelligent surveillance systems. Pedestrian detection plays an important role in computer vision and automation industry applications, which include video surveillance, automotive robot, and smart vehicles. Recently, the improvement in deep learning techniques, such as convolutional neural networks (CNNs) have significantly increased the accuracy of pedestrian detection. Normally, the optical cameras, e.g. charge coupled device cameras, are the device used to capture images. However, considering the dark environments and the luminance variation issues, infrared thermal camera would be an effective alternative solution to nighttime pedestrian detection. On the other hand, occlusion is one of the commonest problems, which makes nighttime pedestrian detection more challenging. To address the abovementioned problems, this work presents a pedestrian detection framework which consists of Faster R CNN and a region decomposition branch. The proposed region decomposition branch allows us to detect wider range of the pedestrian appearances including partial body poses and occlusions. From the experimental results, this work demonstrates better detection accuracy than the currently developed CNN based detection method because of combining the multi region features.