Miracle-dz/UniFAR-FTUs huggingface dataset:
Rationalization and Regret among Smokers in Thailand and Malaysia

iii ACKNOWLEDGEMENTS . ,. v DEDICATION . vi LIST OF TABLES . xi LIST OF FIGURES . xii 1.0 INTRODUCTION . 1 1.1 Cognitive Dissonance in Smokers . 2 1.2 Rationalization . 3 1.3 Regret . 7 1.4 Relation between Rationalization and Regret . 12 1.5 Social Norms and Cultural Values in Smoking. 15 1.6 Thailand and Malaysia . 22 1.6. 1 Tobacco policies in Thailand and Malaysia . 23 1.6. 2 Cultures in Thailand and Malaysia . 25 1.7 Summary . 27 2.0 GOALS OF STUDY . 29 3.0 METHOD . 30 3.1 The International Tobacco Control Policy Evaluation Survey . 30 viii 3.2 Participants . 31 3.3 Sampling Design . 31 3.4 Procedure . 33 3.5 Measures . 33 3.5.1 Intentions to quit smoking . 34 3.5.2 Rationalization . 34 3.5.3 Regret . 34 3.5.4 Perceived social norms . 35 3.5.5 Vertical collectivism. 35 3.5.6 Demographic variables . 36 3.5. 7 Smokingand quitting-relevant variables . 36 3.5. 8 Health-relevant variables . 38 3.5. 9 Perceived financial cost . 39 4.0 RESULTS . 39 4.1 Characteristics of the Sample . 40 4.2 Preliminary Analyses . 41 4.2. 1 Prevalence of rationalization, regret and intentions to quit across country . 41 4.2. 2 Relation between rationalization and regret . 45 4.3 Mediation Model of Intentions to Quit . 45 ix 4.4 Mediation Models of Rationalization and Regret . 48 4.4.1 Primary analyses of society norm, people norm and vertical collectivism . 49 4.4.2 Mediation model of rationalization . 51 4.4.3 Mediation model of regret . 55 4.5 Predictors of Rationalization: Logistic Regression Model . 60 4.5.1 Country variable . 61 4.5.2 Demographic variables . 62 4.5.3 Smokingand quitting-relevant variables . 63 4.5.4 Health-relevant variables . 65 4.5.5 Perceived financial cost . 66 4.5.6 Perceived social norms about smoking . 66 4.5. 7 Vertical collectivism . 67 4.6 Predictors of Regret: Logistic Regression Model . 73 4.6. 1 Country variable . 74 4.6. 2 Demographic variables . 74 4.6. 3 Smokingand quitting-relevant variables . 75 4.6. 4 Health-relevant variables . 77 4.6. 5 Perceived financial cost . 78 4.6. 6 Perceived social norms about smoking . 78 x 4.6. 7 Vertical collectivism . 78 4.7 Predictor Comparisons between Rationalization and Regret . 85 5.0 DISCUSSION . 93 5.1 Rationalization, Regret, and their Relation to Intentions to Quit Smoking . 94 5.2 Social Norms, Culture, and Their Relations to Rationalization and Regret . 102 5.3 Predictors of Rationalization and Regret . 106 5.4 Intervention and Policy Implications . 108 5.5 Limitations . 110 5.6 Conclusions . 112 6.0 REFERENCES . 113 7.0 FOOTNOTE . 126 8.0 APPENDIX . 127 8.1 Appendix A . 127 8.2 Appendix B . 132
webQDA — Qualitative data analysis software: Usability assessment

The use of qualitative analysis to support software has been growing. The integration of these types of tools in research is accompanied by an increase in the number of software packages available. Depending on the design and research questions, researchers can explore various solutions available in the market. Thus, it is urgent to ensure that these tools, apart from containing the necessary functionality for the purposes of research projects, are also usable. This study presents an assessment of the usability of the qualitative data analysis software webQDA® (version 2.0). To assess its usability, the System Usability Scale (SUS) was used. The results indicate that the current version is “acceptable” in terms of usability. We therefore hope, with this assessment, to make decisions so as to improve the usability of the new version of webQDA® (version 3.0).
Evidence for a Role of Truncated trkC Receptor Isoforms in Mouse Development

The trkC locus encodes several receptors for neurotrophin-3, including the well studied full-length tyrosine kinase isoform, in addition to receptor isoforms lacking the kinase active domain. TrkC receptors are widely expressed throughout mouse development in many different organs. To investigate the function of truncated receptors in vivo and to identify cell types that are biologically responsive to this gene product, we have overexpressed a physiological truncated trkC isoform in the mouse. Mice overexpressing this receptor develop to term but die in the first postnatal days. High levels of transgene expression result in severe developmental defects in the peripheral nervous system and in the heart. The severity of neuronal losses observed in these animals suggests that truncated receptors may act by sequestering neurotrophin, thus, closely relating this mouse model to the neurotrophin-3-deficient one. Lower levels of exogenous truncated receptor in transgenic mice result in a more modest phenotype and, in some neuronal populations, do not cause neural deficits. Taken together, these data suggest that truncated trkC receptor isoforms may have modulatory functions in development.
Video de-interlacing method based-on optical flow

This paper proposes to improve the accuracy of motion vectors for video de-interlacing by selectively using optical flow results to assist the traditional block-based motion estimation used in video codec. Firstly, a framework for performance evaluation is proposed so that the subjective performance of de-interlacing will not matter in applications such as video compression. Then pre-filtering is performed to remove potential noise, and a hierarchical structure is introduced for same-parity field pictures or opposite-parity field pictures to get more reliable optical flow results for compression. Finally the optical flow results are used to improve the accuracy of block-based methods for each partition of the to-be-interpolated image and the final motion vectors are determined. Experimental results show that the proposed solution can achieve both better peak signal-to-noise ratio (PSNR) performance for video de-interlacing and higher coding efficiency for interlaced videos.
High-performance neural network training on a computational cluster

In this paper, a high performance neural network for image segmentation is described which utilizes a Myrinet-based PC cluster to implement back propagation training on large data sets. Back propagation training of the very large neural network described here requires substantial computing resources that impact the time required for training. Work here shows that exploitation of modern microprocessor architectures as well as the distribution of processing over a high performance cluster can significantly impact training times required and effectively enlarge the envelop within which large neural networks remain computationally feasible. This work proposes a personal computer cluster solution that balances the distribution of computations while minimizing communication among cluster nodes. Results show that for a PC cluster with n nodes, the training of neural networks running on the cluster has a speedup of near (n-2) times when compared to training on a single node.
A Time-Reversal Space-Time Chip Semiblind Receiver for TR-STBC Downlink MIMO MC-CDMA Systems

In this paper, a time-reversal based space-time block code (TR-STBC) downlink multi-carrier code division multiple access (MC-CDMA) scheme for multiple-input multiple-output (MIMO) array antenna is presented. A TR space-time chip semiblind multiuser receiver with mixing parameterisation is proposed. The TR space-time shifted receive signal matrix using the diversity from space-time technique and multiantenna is formulated for updated receiver. The criterion to obtain the optimal mixing parameter value is addressed. The simulation results show that the proposed semiblind receiver with optimal mixing parameter outperforms in the symbol error rate (SER) over existing chip semiblind with fixed mixing parameter and training-based receivers in 3GPP MIMO frequency-selective fading channel models with Doppler speed of 120km/h.
Modeling therapy rehabilitation sessions using non-invasive serious games

The use of non-invasive methods for detecting motion of the human body in three dimensions has become very popular in recent years due to commercial availability of economically priced 3D sensors. In this paper, we present a web-based interactive serious game interface that uses noninvasive methods to recognize the movements of the body. Our framework provides a mechanism that can engage disabled children in playing serious games while tracking, in the background, their movements in the 3D axes. Motion data of the subjects is collected through multiple sensors in a noninvasive manner. This information can be displayed on screen in a live manner or recorded for offline replaying and data analysis. The system incorporates an intelligent authoring tool that allows the therapist to design a therapy and assign it to a patient. The subject as well as other members of the Community of Interest such as therapists, parents and caregivers can view the results at any time and thus could follow up with patient's progress.
Developing Elastic Software for the Cloud

Developing standalone applications running on a single computer is very different from developing scalable applications running on the cloud, such as data analytics applications that process terabytes of data, Web applications that receive thousands of requests per second, or distributed computing applications where components run simultaneously across many computers. Cloud computing service providers help facilitate the development of these complex applications through their cloud programming frameworks. A cloud programming framework is a software platform to develop applications in the cloud that takes care of nonfunctional concerns, such as scalability, elasticity, fault tolerance, and load balancing. Using cloud programming frameworks, application developers can focus on the functional aspects of their applications and benefit from the power of cloud computing. In this chapter, we will show how to use some of the existing cloud programming frameworks in three application domains: data analytics, Web applications, and distributed computing. More specifically, we will explain how to use MapReduce (Dean and Ghemawat, 2008) for data analytics, Google App Engine (Google, 2014) for Web applications, and SALSA (Varela and Agha, 2001) for distributed computing. The rest of the chapter is structured as follows. In section 50.2, we describe nonfunctional concerns supported at different levels of cloud services and go through existing cloud programming frameworks. In section 50.3, we explain MapReduce, Google App Engine, and Simple Actor Language System and Architecture (SALSA). In section 50.4, we illustrate how to use these three programming frameworks by showing example applications. Finally, we conclude the chapter in section 50.5. Developing Elastic Software for the Cloud
A New Perspective on Method Variance: A Measure-Centric Approach

A widespread methodological concern in the organizational literature is the possibility that observed results are due to the influence of common-method variance or mono-method bias. This concern is based on a conception of method variance as being produced by the nature of the method itself, and therefore, variables assessed with the same method would share common-method variance that inflates observed correlations. In this paper, we argue for a more complex view of method variance that consists of multiple sources that affect each measured variable in a potentially unique way. Shared sources among measures (common-method variance) act to inflate correlations, whereas unshared sources (uncommon-method variance) act to attenuate correlations. Two empirical examples, one from a simulation study and the other from a single-source survey, are presented to illustrate the complex action of multiple sources of method variance. A five-step approach is suggested whereby a theory of the measure is generated for each measured variable that would inform strategies to control for method variance by assessing and modeling the actions of identified method variance sources.
Efficient Deniably Authenticated Encryption and Its Application to E-Mail

Confidentiality and authentication are two main security goals in secure electronic mail (e-mail). Pretty good privacy (PGP) and secure/multipurpose internet mail extensions (S/MIME) are two famous secure e-mail solutions. Both PGP and S/MIME use digital envelope to provide message confidentiality and digital signature to provide message authentication. However, these methods have the following two weaknesses: 1) digital signature provides non-repudiation evidence of sender that is not desired in some e-mail applications and 2) efficiency is low, since these methods use two kinds of public key cryptographic primitives: public key encryption and digital signature. To overcome the above two weaknesses, we introduce a new concept called deniably authenticated encryption that can achieve confidentiality, integrity, and deniable authentication in a logical single step. We first propose a deniably authenticated encryption scheme and prove its security in the random oracle model. Then, we design a secure e-mail protocol using the proposed deniably authenticated encryption scheme. The deniable authentication property protects senders' privacy.
CXCR7 Is Involved in Human Oligodendroglial Precursor Cell Maturation

Differentiation of oligodendroglial precursor cells (OPCs), a crucial prerequisite for central nervous system (CNS) remyelination in diseases such as Multiple Sclerosis (MS), is modulated by a multitude of extrinsic and intrinsic factors. In a previous study we revealed that the chemokine CXCL12 stimulates rodent OPC differentiation via activation of its receptor CXCR7. We could now demonstrate that CXCR7 is also expressed on NogoA- and Nkx2.2-positive oligodendroglial cells in human MS brains and that stimulation of cultured primary fetal human OPCs with CXCL12 promotes their differentiation as measured by surface marker expression and morphologic complexity. Pharmacological inhibition of CXCR7 effectively blocks these CXCL12-dependent effects. Our findings therefore suggest that a specific activation of CXCR7 could provide a means to promote oligodendroglial differentiation facilitating endogenous remyelination activities.
Optimal N-term approximation by linear splines over anisotropic Delaunay triangulations

Anisotropic triangulations provide efficient geometrical methods for sparse representations of bivariate functions from discrete data, in particular from image data. In previous work, we have proposed a locally adaptive method for efficient image approximation, called adaptive thinning, which relies on linear splines over anisotropic Delaunay triangulations. In this paper, we prove asymptotically optimal N -term approximation rates for linear splines over anisotropic Delaunay triangulations, where our analysis applies to relevant classes of target functions: (a) piecewise linear horizon functions across α Holder smooth boundaries, (b) functions of Wα,p regularity, where α > 2/p−1, (c) piecewise regular horizon functions of Wα,2 regularity, where α > 1.
School Segregation and the Achievement Gap between Immigrant and Native Students

Our comparison of performance gaps in mathematics between native and immigrant students across 18 countries shows considerable cross-national variation in the size of performance differences between native and immigrant students. We hypothesize that countries with higher levels of school segregation by family socioeconomic status (SES) and immigrant status should display stronger effects of immigrant status. In countries with high levels of school segregation, immigrant students should be more likely to be sorted into schools that are poorer and/or have higher concentration of immigrant students, which may in turn negatively affect immigrant students’ achievement. Two-level hierarchical linear models show that countries’ native-immigrant gaps are systematically related with levels of school segregation by family SES but not with levels of school segregation by immigrant status. The finding implies that redistributing key educational resources to disadvantaged schools to moderate the negative consequences of attending low SES schools may facilitate educational integration of immigrant students.
Deep Web Entity Identification Method Based on Improved Jaccard Coefficients

There are a large number of accessible deep web websites on the internet. However, even if identical entity has different representation formats on different websites. So entity identification plays a crucial role in deep web data mining. This paper proposes an entity identification method in the field of Chinese books. First, using improved Jaccard coefficients to calculate similarity of text attributes. Second, AHP (Analytic Hierarchy Process) is used to obtain the weights, and using the sum of weights to calculate the entity similarity. Finally, it needs to integrate duplicate entity to achieve the entity identification. The experiment results demonstrate the approach has higher accuracy with good feasibility.
B-spline neural network based digital baseband predistorter solution using the inverse of De Boor algorithm

In this paper a new nonlinear digital baseband predistorter design is introduced based on direct learning, together with a new Wiener system modeling approach for the high power amplifiers (HPA) based on the B-spline neural network. The contribution is twofold. Firstly, by assuming that the nonlinearity in the HPA is mainly dependent on the input signal amplitude the complex valued nonlinear static function is represented by two real valued B-spline neural networks, one for the amplitude distortion and another for the phase shift. The Gauss-Newton algorithm is applied for the parameter estimation, in which the De Boor recursion is employed to calculate both the B-spline curve and the first order derivatives. Secondly, we derive the predistorter algorithm calculating the inverse of the complex valued nonlinear static function according to B-spline neural network based Wiener models. The inverse of the amplitude and phase shift distortion are then computed and compensated using the identified phase shift model. Numerical examples have been employed to demonstrate the efficacy of the proposed approaches.
Reducing Web Test Cases Aging by Means of Robust XPath Locators

In the context of web regression testing, the main aging factor for a test suite is related to the continuous evolution of the underlying web application that makes the test cases broken. This rapid decay forces the quality experts to evolve the test ware. One of the major costs of test case evolution is due to the manual effort necessary to repair broken web page element locators. Locators are lines of source code identifying the web elements the test cases interact with. Web test cases rely heavily on locators, for instance to identify and fill the input portions of a web page (e.g., The form fields), to execute some computations (e.g., By locating and clicking on buttons) and to verify the correctness of the output (by locating the web page elements showing the results). In this paper we present ROBULA (ROBUst Locator Algorithm), a novel algorithm able to partially prevent and thus reduce the aging of web test cases by automatically generating robust XPath-based locators that are likely to work also when new releases of the web application are created. Preliminary results show that XPath locators produced by ROBULA are substantially more robust than absolute and relative locators, generated by state of the practice tools such as Fire Path. Fragility of the test suites is reduced on average by 56% for absolute locators and 41% for relative locators.
Code querying by UML

The need to identify source code that exhibits particular characteristics is essential to program comprehension. In this paper we introduce Arabica, a tool for querying Java code using UML class and sequence diagrams. Our use of UML diagrams avoids the need for developers to familiarize themselves with yet another language. In contrast to tools that rely on dedicated query languages, Arabica encodes querying semantics in a dedicated, minimal UML profile. Stereotyped class and sequence diagrams, characterizing structural and behavioral properties respectively, are translated into logic program queries. Using examples from the JHotDraw framework, we illustrate the utility of Arabica in validating design invariants, finding design pattern implementations and exploring extension points. We present a pre/post-test quasi experiment as a preliminary assessment of our approach.
Educators’ beliefs about appropriate pedagogical models for Spanish-speaking ELLs who differ in home-language and English-language literacy abilities in the United States

ABSTRACT Survey research (n = 366) examined educators’ beliefs about the efficacy of five pedagogical models (English as a second language (ESL) self-contained, ESL push-in, ESL pullout, bilingual, and dual language) for English language learners who differ in English literacy proficiency and home-language abilities (delimited to Spanish in this research). Dual language was preferred when students have high English proficiency; this effect was extremely strong for students who are also proficient in Spanish, and moderate when students’ Spanish skills are low. Bilingual education was moderately favored when English is low and Spanish is high. ESL self-contained was moderately favored when students lack literacy proficiency in both English and Spanish. Language proficiency was a consistent predictor of model preferences, with effects sufficiently strong to likely have meaningful impact on actual student placements. Future research should determine the extent to which this pattern in teachers’ beliefs comports with educational outcomes.
Trinocular stereo image rectification in closed-form only using fundamental matrices

Trinocular stereo image rectification is a process to transform a set of three images into a new set so that the epipolar lines in the transformed images have the same direction as the image row or column and matching epipolar lines in different images have the same row or column indices to enable an efficient and reliable dense stereo matching. In this paper we propose a new closed-form method to rectify three-view stereo images just using fundamental matrices. The algorithm involves only direct and purely geometric transformation processes. No iteration or optimization process is involved in our method. Real images have been used for testing purposes, and accurate results have been obtained using our algorithm.
Acceleration of pedestrian detection algorithms on a multi-core vehicle computing platform

Vehicle-based pedestrian detection system receives more and more attentions in road safety applications of the modern intelligent transportation system. However, the existed detection algorithms are too computing extensive for single core vehicle-based processors. As the promising multi-core architecture provides both energy efficient and powerful computing solutions, it is relevant to evaluate the up-to-date pedestrian detection algorithm on such novel platforms. This paper implemented a popular template based pedestrian detection algorithm on such platforms and gave out computing bottlenecks by profiling. Furthermore, we proposed an application aware multithread accelerating technique. Experimental results showed that our design can achieve nearly 2 – 4x speedups on multi-core processors for critical function blocks, which reduces total execution time ranging from 26 to 41%.