Assumption Of Errors Research Articles

Lattice-based signcryption with equality test in standard model

A signcryption, which is an integration of a public key encryption and a digital signature, can provide confidentiality and authenticity simultaneously. Additionally, a signcryption associated with equality test allows a third party (e.g., a cloud server) to check whether or not two ciphertexts are encrypted from the same message without knowing the message. This application plays an important role especially in computing on encrypted data. In this paper, we propose the first lattice-based signcryption scheme equipped with a solution to testing the message equality in the standard model. The proposed signcryption scheme is proven to be secure against insider attacks under the learning with errors assumption and the intractability of the short integer solution problem. As a by-product, we also show that some existing lattice-based signcryptions either is insecure or does not work correctly.

Misclassification and measurement error - planning a study and interpreting results.

Measurement error must always be considered when planning a research project and interpreting its results. The accuracy of some data collected during a study can often be confidently assured, but more than one measurement or observer is needed to assess exposure and outcomes status in cases where clinical measurement is prone to measurement error. Little attention is paid in nursing research to misclassification and measurement error. Bias is often discussed in nursing research education, but not its potential consequences or measures that can be taken to improve the study's quality. To present examples of random measurement error - misclassification of a binary outcome - in a continuous exposure and outcomes variable, to address this gap in nurses' research training. The article discusses the relationship between exposure and outcome in the absence and presence of measurement error using risk (relative risk) and association using correlation. It provides methods to estimate the true value of these measures of risk and association, when only given the clinical measurements with errors. If the assumption of random error holds, attenuation of risk or association towards the null will occur. Understanding the effect of measurement error including misclassification will enable researchers to interpret the results of their studies, and to take into consideration this potential error when planning and conducting a study.

Into Hyperspace: A Critique of Hypernudge

The term “hypernudge” has been offered by legal scholar Karen Yeung (2017) as a name for a growing phenomenon which sits at the intersection of behavioural science and computer science. Hypernudges are frequently described as personalised nudges, Big Data nudges, or data-driven nudges. Yet, at present, the concept of hypernudging has not robustly been integrated into the taxonomy of nudging described by nudge theory and behavioural science at-large. In this paper, I offer a critique of the hypernudging concept from a behavioural science and nudge theory perspective. The primary intention of this critique is to situate hypernudge as a distinct category within the existing nudge theory taxonomy, as well as to relate several ideas given by Yeung (2017) and associated authors to contemporary nudge theory. In doing so, I argue hypernudges produce, or at the very least magnify, three burdens which a decision-maker must overcome: the burden of avoidance, the burden of understanding, and the burden of experimentation. The burden of avoidance captures the costs facing a decision-maker who does not want to follow the hypernudge. The burden of understanding captures the costs facing a decision-maker who wishes to understand the dynamics of a hypernudge. Finally, the burden of experimentation captures the costs facing a decision-maker when merely interacting with a hypernudge, given the technical dynamics of the hypernudge. I argue that these three burdens can be understood through two concepts derived from nudge theory. The first of these concepts I call the assumption of error, whereby all decisions are assumed to be subject to some error which might be reduced through more nudging and more personalised nudging. The second of these concepts I call behavioural logic, which describes a system of logic – emergent from the assumption of error – which justifies various hypernudging practices. While I do not argue that either the assumption of error or behavioural logic are correct, I argue that insofar as a choice architect may believe them to be correct, they serve as a robust rationale for understanding the nature of hypernudges.

Analyzing periodic effects of PPI and REER index on Manufacturing Industry exports: Turkey case

The aim of this paper is to investigate the effects of Producer Price Index and Real Effective Exchange Rate on export of Turkey’s manufacturing industry. In this framework, a data set consisting of monthly data covering the period between 01/2003 - 12/2019 was used. In order to understand the effects of global and local scale events on exports in the scope of the study period in detail, the data were analyzed in 2003 - 2007 and 2009 - 2019 periods separately. Considering that the data used in the study are time series, firstly their stationarity was tested, then models were created by FMOLS method in order to determine the relationship between the variables that affect the export of the manufacturing industry. Then, error assumptions were tested in order to obtain statistically correct interpretation of the results. As a result of the study, while the effects of variables on exports in the long term give meaningful results, it was observed that the statistical effects of the variables changed in short term analysis.

Ring Signature With Unconditional Anonymity in the Plain Model

Ring signature allows a user to sign messages as a member of a set of users, which is called the ring. In addition to an unforgeability, ring signature ensures that nobody can detect which member in the ring signs the message. In this paper, we propose the first generic construction of ring signature with unconditional anonymity in the plain model based on the standard assumption. The plain model means that we do not need any setup procedure by trusted third party and not depends on a random oracle methodology. Our scheme is constructed based on a statistical ZAP argument, a lossy encryption scheme, and a standard signature scheme. It is known that all of our building blocks can be instantiated based on the quasi-polynomial learning with errors assumption.

US Policy Responses to the COVID-19 Pandemic and Sectoral Stock Indices: A Fractional Integration Approach

This paper uses fractional integration methods to assess the impact of US policy responses (containment and health measures, income support policy, debt-relief policy, changes in the Effective Federal Funds Rate, monetary and fiscal announcements) to the COVID-19 pandemic on US sectoral stock indices for Technology, Telecom, Health Care, Real Estate, Consumer Staples, Consumer Discretionary, Industrials, Basic Materials, Energy and Utilities from 1 January 2020 to 11 June 2021. The results provide evidence of mean reversion for seven sectoral stock indices (Consumer Discretionary, Consumer Staples, Health, Industrials, Technology, Telecom and Utilities), with orders of integration significantly below (though close to) 1 under the assumption of white noise errors. By contrast, three indices (Basic Materials, Energy and Real Estate) are found to be highly persistent (d ≥ 1), with shocks having permanent effects. As for the policy responses, it appears that the containment and health restrictions, income support policy, and debit relief policy have had no impact. By contrast, changes in the Effect Federal Funds Rate have had a significant and positive effect on all sectors except Energy and Industrial, and similarly monetary and fiscal announcements have had a positive and significant effect in most cases. Finally, the higher mortality rate caused by the Covid-19 pandemic has affected negatively most sectoral stock indices.

The COVID-19 Pandemic and the Degree of Persistence of US Stock Prices and Bond Yields

This paper analyses the possible effects of the Covid-19 pandemic on the degree of persistence of US monthly stock prices and bond yields using fractional integration techniques. The model is estimated first over the period January 1966-December 2020 and then a recursive approach is taken to examine whether or not persistence has changed during the following pandemic period. We find that the unit root hypothesis cannot be rejected for stock prices while for bond yields the results differ depending on the maturity date and the specification of the error term. In general, bond yields appear to be more persistent, although there is evidence of mean reversion in case of 1-year yields under the assumption of autocorrelated errors. The recursive analysis shows no impact of the Covid-19 pandemic on the persistence of stock prices, whilst there is an increase in the case of both 10- and 1- year bond yields but not of their spread.

Identity-Based Proxy Re-encryption over NTRU Lattices for Cloud Computing

Identity-based proxy re-encryption (IB-PRE) is a kind of public key cryptography that allows a proxy to convert the ciphertext based on Alice’s identity into another ciphertext of the same message for Bob. It has important applications in cloud computing, distributed systems and secure e-mail forwarding. So far, the existing lattice-based IB-PRE schemes are designed on the general lattice and their security is based on the learning with errors (LWE) assumption. In this paper, we propose an IB-PRE scheme over NTRU lattices with the desirable properties of anonymity, unidirectionality, backward collusion safeness and multi-hop. Our IB-PRE scheme is provable secure under adaptive chosen-plaintext and adaptive chosen-identity attacks. And, in the random oracle model, the security of our scheme is based on the decisional Ring-LWE assumption. Besides, the key and ciphertext sizes of our IB-PRE scheme are short, only one or two ring elements, and our scheme could encrypt n bits in each encryption process.

Smooth Tests for Normality in ANOVA

The normality assumption for errors in the Analysis of Variance (ANOVA) is common when using ANOVA models. But there are few people to test this normality assumption before using ANOVA models, and the existent literature also rarely mentions this problem. In this article, we propose an easy-to-use method to testing the normality assumption in ANOVA models by using smooth tests. The test statistic we propose has asymptotic chi-square distribution and our tests are always consistent in various different types of ANOVA models. Discussion about how to choose the dimension of the smooth model (the number of the basis functions) are also included in this article. Several simulation experiments show the superiority of our method.

Identification for partially linear regression model with autoregressive errors

The semiparametric partial linear models are often used in real data analysis for its flexibility and parsimony. Statistical inference of this model is restricted with two conditions: (i) the linear and nonlinear parts are known in advance, (ii) the errors are independent. However, in practice, this is unreasonable to artificially determine which subset of variables have linear effect on the response and which have nonlinear effect. In addition, the assumption of errors being independent may be incorrect for time series data. Therefore, it is of great interest to develop some efficient methods to distinguish linear components from nonlinear ones with correlated errors. In this paper, we develop a method for identifying linear and nonlinear components, and estimate the coefficients of error structure. The performance of the proposed method is examined by simulation study and analyses a real data set for an illustration.

LWEDM: Learning with error based secure mobile digital rights management system

AbstractMobile multimedia security is essential to ensure a secure communication in industries, businesses, and organizations. Mobile multimedia is a new concept for an enterprise, where organizations need to be secure. In the last decade, a number of anonymous authentication protocols have been designed based on factorization and discrete logarithm assumptions. Shor's algorithm solves a number‐theoretic assumption in polynomial time using postquantum computers that creates a trouble. However, learning with errors is an advanced hard assumption that cannot be solved by any known existing quantum algorithm. This inspires one to design an anonymous postquantum authentication protocol for the mobile digital rights management system. The protocol has been proved secure in a random oracle model under learning with errors assumption. Moreover, the article contributes an informal security discussion, an implementation using automated validation of internet security protocols and applications. The performance analysis ensures its efficiency even in the postquantum era.

An effective approach towards efficient estimation of general linear model in case of heteroscedastic errors

Aiming at minimizing the ratio of error with respect to the response variable, the least squares ratio is a relatively new method for estimating the regression parameters. In the current article, the performance of this new approach is compared with the traditional OLS approach in the case when homoscedasticity of errors assumption is violated. A comparison is made through a simulation study using mean square error, mean absolute percentage error, and false acceptance rate as performance measures. It is observed that the least square ratio method outperforms the OLS method in case of moderate or severe heteroscedasticity for all sample sizes and in case of weak or mild heteroscedasticity for relatively small samples. Generally, it is noted that the efficiency of the least squares ratio method increases with an increase in the severity of heteroscedasticity as well as an increase in values of common error variance. The use of the least squares ratio method is recommended in case of mild or moderate to severe heteroscedasticity. Similar results were obtained from two real-life examples.

Water prices: persistence, mean reversion and trends

Abstract Time series referring to water prices at different regions all over the world are examined in this paper by using fractionally integrated methods. We look at series corresponding to the following regions: Asia Pacific and Russia, Europe, United States and Latin America as well as global data. The results indicate large degrees of persistence, with the values of the differencing parameter being close to one in all cases and higher under the assumption of uncorrelated errors. If autocorrelation is permitted, a small degree of mean reversion is found in all except the Latin American series. The possibility of structural breaks is also investigated and the results indicate the presence of multiple breaks in the data: three in the case of Latin America and global data; four in Europe and USA and five for the Asian Pacific and Russia. Nevertheless, we do not observe a significant change in the degree of persistence across subsamples and once more mean reversion is found if autocorrelation is permitted.

Linkage-Data Linear Regression

AbstractData linkage is increasingly being used to combine data from different sources with the aim of identifying and bringing together records from separate files, which correspond to the same entities. Usually, data linkage is not a trivial procedure and linkage errors, false and missed links, are unavoidable. In these cases, standard statistical techniques may produce misleading inference. In this paper, we propose a method for secondary linear regression analysis, where the linked data have to be prepared by someone else, and neither the match-key variables nor the unlinked records are available to the analyst. We develop also a diagnostic test for the assumption of non-informative linkage errors, which is required for all existing secondary analysis adjustment methods. Our approach provides important advantages: it relies on the realistic assumption that the probabilities of correct linkage vary across the records but it does not assume that one is able to estimate the probability of correct linkage for each individual record. Moreover, it accommodates in a simple manner the general situation where the files are of different sizes and none of them is a subset of another. The proposed methodology of adjustment and testing is studied by simulation and applied to real data.

Land-Use Regression Variable Selection with Spatiotemporal Dependent Errors and Large Sample Size: A National and Daily Ground-Level NO2 Model

Nitrogen dioxide (NO2) is a primary constituent of traffic-related air pollution and has well established harmful environmental and human health impacts. Knowledge of the spatiotemporal distribution of NO2 is critical for assessing exposure and subsequent risk assessment. A common approach for assessing exposure to outdoor air pollution is linear regression involving spatially referenced covariates, known as land-use regression (LUR). While LURs have undoubtedly been useful for many exposure and risk assessment studies, the typical assumption of independent errors is usually violated because the spatial dependence in the response cannot be captured fully by the covariates, resulting in biased covariate estimates and decreased sensitivity and specificity in the model-selection process. Here, we develop an approach for simultaneous variable selection and estimation of LUR models with spatiotemporal correlated errors that is feasible with large sample sizes. We employ a general-Vecchia approximation, which is highly accurate and guarantees linear complexity with respect to the sample size. We demonstrate this new approach with spatiotemporal random field simulations and with the case study of daily ground-level NO2 in the United States. The simulations show that our approach results in consistently better prediction as measured by the cross-validation mean squared error (MSE) compared to the competing methods. Additionally, the model selection false positive and negative rates are lower across most simulation scenarios. For NO2, our approach has at least a 10 percent improvement in prediction MSE over all of the competing methods and results in significantly more sparse models. US-wide, daily NO2 predictions and R-code are freely available for use.

Revocable identity-based encryption with bounded decryption key exposure resistance: Lattice-based construction and more

In general, identity-based encryption (IBE) does not support an efficient revocation procedure. In ACM CCS'08, Boldyreva et al. proposed revocable identity-based encryption (RIBE), which enables us to efficiently revoke (malicious) users in IBE. In PKC 2013, Seo and Emura introduced an additional security notion for RIBE, called decryption key exposure resistance (DKER). Roughly speaking, RIBE with DKER guarantees that the security is not compromised even if an adversary gets (a number of) short-term decryption keys. Therefore, DKER captures realistic scenarios and is an important notion.In this paper, we introduce bounded decryption key exposure resistance (B-DKER), where an adversary is allowed to get a-priori bounded number of short-term decryption keys in the security game. B-DKER is a weak version of DKER, but it seems to be sufficient for practical use. We obtain the following results:•We propose a lattice-based (anonymous) RIBE scheme with B-DKER, which is the first lattice-based construction resilient to decryption key exposure. Our lattice-based construction is secure under the learning with errors assumption. A previous lattice-based construction satisfies anonymity but is vulnerable even with a single decryption key exposure.•We propose the first pairing-based RIBE scheme that simultaneously realizes anonymity and B-DKER. Our pairing-based construction is adaptively secure under the symmetric external Diffie-Hellman assumption.Our two constructions rely on cover free families to satisfy B-DKER, whereas all the existing works rely on the key re-randomization property to achieve DKER.

Pengaruh Kepemimpinan Diri Sendiri Terhadap Sikap Wirausaha Pada Usaha Makanan Tradisional Di Desa Palung Raya Kecamatan Tambang Kabupaten Kampar

Lopek Bugi Desa Palung Raya, District Kampar is a home-based traditional food business. In running a business requires self-leadership that requires a willing attitude to accept change. Spread the list of businesses in 2018 as many as 24 businesses with 140 owners and workers. The sampling technique was conducted random sampling of 44 samples by collecting data using questionnaires. The results showed that the Lieliefors L Linearity Test calculated (0.0167) < L tables (0.0974) and sig (p-value) = 200 > 0.05 or Y estimate errors over normal randomized error X or H0 count received. Heteroskedastisity tests observed that residual points were around random horizontal lines (Almost positive and negative inters intervals). Thus, it can be concluded narratively that the assumption of mean error equals 0 is met. Hypothesized testing in linieraitas test Can be concluded that, there is a linear relationship between X and Y because the level of significance of α = 0.05 then H0 is accepted because sig 0.850 > 0.05. Test f states that, It is known that f count= 20,205 and f table= 4.07, meaning f count> f table means significant between variables selfleadership affects Entrepreneurial Attitudes. While in the t test, the correlation between independent selfleadership variables (X) affects entrepreneurial attitudes (Y). This indicates that the higher self-leadership will lead to increased Entrepreneurial Attitudes. Thus this hypothesis states that Self-Leadership has a significant effect on entrepreneurial attitudes in small businesses traditional food "Lopek Bugi" In The Village Trough Raya District Mines Kampar District acceptable.

On PRESS and deletion bootstraps in linear regression

Bootstrap technique has an extensive use in almost all branches of statistics including regression analysis. The classical regression theory heavily depends on normality assumption of errors, which does not often hold in practice. Bootstrap technique can be readily applied to a situation when we do not assume any distribution for the error and that is why bootstrapping in regression is becoming very popular. In reality the true errors are not known, in that case we need a proper substitute of the residuals. The OLS residuals are being used as substitutes of errors from the very beginning but it is now evident that they may break down easily in the presence of unusual observations. In our bootstrap study we consider some other types of residuals such as PRESS and observed that it may produce results even worse than the OLS in the presence of outliers. Then we have considered deletion residuals based on robust fits. The usefulness and/or limitations of bootstrap techniques based on different types of residuals such as PRESS and deletion residuals are studied through some real life examples and a Monte Carlo simulation experiments under a variety of situations.

Modified multi-key fully homomorphic encryption based on NTRU cryptosystem without key-switching

The Multi-Key Fully Homomorphic Encryption (MKFHE) based on the NTRU cryptosystem is an important alternative to the post-quantum cryptography due to its simple scheme form, high efficiency, and fewer ciphertexts and keys. In 2012, Lopez-Alt et al. proposed the first NTRU-type MKFHE scheme, the LTV12 scheme, using the key-switching and modulus-reduction techniques, whose security relies on two assumptions: the Ring Learning With Error (RLWE) assumption and the Decisional Small Polynomial Ratio (DSPR) assumption. However, the LTV12 and subsequent NTRU-type schemes are restricted to the family of power-of-2 cyclotomic rings, which may affect the security in the case of subfield attacks. Moreover, the key-switching technique of the LTV12 scheme requires a circular application of evaluation keys, which causes rapid growth of the error and thus affects the circuit depth. In this paper, an NTRU-type MKFHE scheme over prime cyclotomic rings without key-switching is proposed, which has the potential to resist the subfield attack and decrease the error exponentially during the homomorphic evaluating process. First, based on the RLWE and DSPR assumptions over the prime cyclotomic rings, a detailed analysis of the factors affecting the error during the homomorphic evaluations in the LTV12 scheme is provided. Next, a Low Bit Discarded & Dimension Expansion of Ciphertexts (LBD&DEC) technique is proposed, and the inherent homomorphic multiplication decryption structure of the NTRU is proposed, which can eliminate the key-switching operation in the LTV12 scheme. Finally, a leveled NTRU-type MKFHE scheme is developed using the LBD&DEC and modulus-reduction techniques. The analysis shows that the proposed scheme compared to the LTV12 scheme can decrease the magnitude of the error exponentially and minimize the dimension of ciphertexts.

Modified Extragradient Method for Pseudomonotone Variational Inequalities in Infinite Dimensional Hilbert Spaces

In this paper, we prove the weak convergence of a modified extragradient algorithm for solving a variational inequality problem involving a pseudomonotone operator in an infinite dimensional Hilbert space. Moreover, we establish further the R-linear rate of the convergence of the proposed algorithm with the assumption of error bound. Several numerical experiments are performed to illustrate the convergence behaviour of the new algorithm in comparisons with others. The results obtained in the paper have extended some recent results in the literature.