Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Memorable Tourism Experiences on Loyalty Behaviors: The Mediating Effects of Destination Image and Satisfaction

The current study develops a theoretical model of the effect of memorable tourism experiences (MTEs) on behavioral intentions by examining the structural relationships between destination image (DI), tourist satisfaction, revisit intention, and word-of-mouth (WOM) publicity. The results show that MTEs influence future behavioral intentions both directly and indirectly through DI and tourist satisfaction. Moreover, MTEs are found to be the most influential determinant of behavioral intentions. Thus, the results challenge those practitioners and researchers who perceive visitor satisfaction to be the most important indicator of destination performance. Theoretical and managerial implications are discussed based on the study results, and directions for future research are provided.
Speech enhancement combined with dereverberation and acoustic echo reduction for time varying systems

This paper deals with speech enhancement problems for highly time-varying systems. For high noise-reduction performance with little speech distortion, cascade methods of various linear/non-linear filters can be easily conceived of, but cascade methods are partial optimizations and cause interferences between filters, because an optimized output signal of a preceding filter is not always an optimized input signal of successive filters. To obtain total optimization, the proposed method adapts all filters under a unified likelihood function with an extended local Gaussian model, which consists of non-stationary multichannel speech model with time-varying acoustic transfer functions, an acoustic echo model with a time-varying echo path model, and stationary background noise model. Qualitatively, the residual reverberation or the residual acoustic echo after the preceding linear filtering keep desirable time-structures for the successive non-linear filtering. Experimental results show that the propose method is superior to the cascade methods.
Distributed optimization by myopic strategic interactions and the price of heterogeneity

This paper is concerned with the tradeoffs between low-cost heterogenous designs and optimality. We study a class of constrained myopic strategic games on networks which approximate the solutions to a constrained quadratic optimization problem; the Nash equilibria of these games can be found using best-response dynamical systems, which only use local information. The notion of price of heterogeneity captures the quality of our approximations. This notion relies on the structure and the strength of the interconnections between agents. We study the stability properties of these dynamical systems and demonstrate their complex characteristics, including abundance of equilibria on graphs with high sparsity and heterogeneity. We also introduce the novel notions of social equivalence and social dominance, and show some of their interesting implications, including their correspondence to consensus. Finally, using a classical result of Hirsch [1], we fully characterize the stability of these dynamical systems for the case of star graphs with asymmetric interactions. Various examples illustrate our results.
ON RELATIVISTIC DISK SPECTROSCOPY IN COMPACT OBJECTS WITH X-RAY CCD CAMERAS

X-ray charge-coupled devices (CCDs) are the workhorse detectors of modern X-ray astronomy. Typically covering the 0.3–10.0 keV energy range, CCDs are able to detect photoelectric absorption edges and K shell lines from most abundant metals. New CCDs also offer resolutions of 30–50 (E/ΔE), which is sufficient to detect lines in hot plasmas and to resolve many lines shaped by dynamical processes in accretion flows. The spectral capabilities of X-ray CCDs have been particularly important in detecting relativistic emission lines from the inner disks around accreting neutron stars and black holes. One drawback of X-ray CCDs is that spectra can be distorted by photon “pile-up,” wherein two or more photons may be registered as a single event during one frame time. We have conducted a large number of simulations using a statistical model of photon pile-up to assess its impacts on relativistic disk line and continuum spectra from stellar-mass black holes and neutron stars. The simulations cover the range of current X-ray CCD spectrometers and operational modes typically used to observe neutron stars and black holes in X-ray binaries. Our results suggest that severe photon pile-up acts to falsely narrow emission lines, leading to falsely large disk radii and falsely low spin values. In contrast, our simulations suggest that disk continua affected by severe pile-up are measured to have falsely low flux values, leading to falsely small radii and falsely high spin values. The results of these simulations and existing data appear to suggest that relativistic disk spectroscopy is generally robust against pile-up when this effect is modest.
An Empirical Study of Factors Affecting Electronic Commerce Adoption among SMEs in Malaysia

This study empirically examines determinants of E-commerce adoption by Malaysian Small and Medium-sized Enterprises. Research model for this study was drawn on the literature on information and communication technology, Information system, and electronic data interchange. This study tested seven hypotheses on factors that influence e-commerce adoption with empirical data from a sample of 200 SMEs in Malaysia. The findings show that relative advantage, compatibility, organizational readiness, manager's characteristics, and security have significant impact on e-commerce adoption. The study provides a clear understanding of manager's perception about e-commerce adoption in their businesses. This study is important in a global context, as SMEs in Malaysia are going for exporting their product in the global marketplace.
Cannabis and Amphetamine Use and Associated Factors Among School-Going Adolescents in Nine African Countries

ABSTRACT The aim of this study was to assess the prevalence of cannabis and amphetamine use and associated factors among adolescents in nine African countries. We analyzed cross-sectional data from 25,372 adolescents (mean age 14.3 years, SD = 1.6) from nine African countries that participated in the Global School-Based Student Health Survey (GSHS) in 2009–2013. Information was collected with a self-administered structured questionnaire. Overall, past-month cannabis use was 4.1% and lifetime amphetamine use was 5.1% among students. In multivariable unconditional regression analysis older age, personal attributes (suicidal ideation, current smoking, school truancy, and having had two or more sexual partners), smoking status of the father and/or mother, and environmental stressors (having experienced hunger, been bullied, having been in a physical fight and having been attacked) were associated with both past-month cannabis and lifetime amphetamine use. Measures to prevent and control cannabis and amphetamine use should include personal attributes (suicidal ideation, school truancy, and HIV sexual risk behavior) and environmental stressors (food insecurity and violence).
Multi-Algorithmic Face Authentication System

In this paper, multiple algorithm and score-level fusion for enhancing the performance of the face based Biometric Person Authentication System is discussed. Though many algorithms are conferred, several crucial issues are still involved in the face authentication. Most traditional algorithms are based on certain assumptions failing which the system will not give appropriate results. Due to the inherent variations in face with time and space, it is a big challenge to formulate a single algorithm based on the face biometric that works well under all variations. This paper addresses the problem of illumination and pose variations, by using three algorithms for face recognition, Block Independent Component Analysis (B- ICA), Discrete Cosine Transform (DCT) and Kalman Filter and weighted average based score level fusion to improve the results obtained of the system. An intensive analysis of the various algorithms has been performed and the results indicate an increase in accuracy of the proposed system.
RIBFIND: a web server for identifying rigid bodies in protein structures and to aid flexible fitting into cryo EM maps

MOTIVATION To better analyze low-resolution cryo electron microscopy maps of macromolecular assemblies, component atomic structures frequently have to be flexibly fitted into them. Reaching an optimal fit and preventing the fitting process from getting trapped in local minima can be significantly improved by identifying appropriate rigid bodies (RBs) in the fitted component. RESULTS Here we present the RIBFIND server, a tool for identifying RBs in protein structures. The server identifies RBs in proteins by calculating spatial proximity between their secondary structural elements. AVAILABILITY The RIBFIND web server and its standalone program are available at http://ribfind.ismb. lon.ac. uk. CONTACT a.pandurangan@mail.cryst. bbk.ac. uk SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Multiobjective genetic algorithm for demand side management of smart grid

Demand side management is one of the most effective methods to control the usage of energy so as to achieve reliability and sustainability in the smart grid. Conventional methods for generating the management scheme are generally based on one objective, which represents only the requirements of energy suppliers or only the energy consumers. In this paper, a multiobjective genetic algorithm (GA) is proposed for extending the optimization problems by considering the objectives from the two conflicting groups and some compromise solutions are provided. The performance of the multiobjective GA is compared with its single objective version by three different cases. The results show that the solutions obtained by the multiobjective GA are more reasonable and better solutions for a single objective can even be found.
IMPROVING SERVICE BY INFORMING CUSTOMERSABOUT ANTICIPATED

This paper studies alternative ways to manage a multi-server system such as a telephone call center. Three alternatives can be described succinctly by: (i) blocking, (ii) reneging and (iii) balking. The rst alternative { blocking { is to have no provision for waiting. The second alternative is to allow waiting, but neither inform customers about anticipated delays nor provide state information to allow arriving customers to predict delays. The second alternative tends to yield higher server utilizations. The rst alternative tends to reduce to the second, without the rst-come rstserved service discipline, when customers can easily retry, as with automatic redialers in telephone access. The third alternative is to both allow waiting and inform customers about anticipated delays. The third alternative tends to cause balking when all servers are busy (abandonment upon arrival) instead of reneging (abandonment after waiting). Birth-and-death process models are proposed to describe the performance with each alternative. Algorithms are developed to compute the conditional distributions of the time to receive service and the time to renege given each outcome. Algorithms are also developed to help the service provider predict customer waiting times before beginning service, given estimated service-time distributions and the elapsed service times of the customers in service. Better predictions may be obtained by classifying customers and thereby obtaining better estimates of their service-time distributions.
Learning to Diversify via Weighted Kernels for Classifier Ensemble

Classifier ensemble generally should combine diverse component classifiers. However, it is difficult to give a definitive connection between diversity measure and ensemble accuracy. Given a list of available component classifiers, how to adaptively and diversely ensemble classifiers becomes a big challenge in the literature. In this paper, we argue that diversity, not direct diversity on samples but adaptive diversity with data, is highly correlated to ensemble accuracy, and we propose a novel technology for classifier ensemble, learning to diversify, which learns to adaptively combine classifiers by considering both accuracy and diversity. Specifically, our approach, Learning TO Diversify via Weighted Kernels (L2DWK), performs classifier combination by optimizing a direct but simple criterion: maximizing ensemble accuracy and adaptive diversity simultaneously by minimizing a convex loss function. Given a measure formulation, the diversity is calculated with weighted kernels (i.e., the diversity is measured on the component classifiers' outputs which are kernelled and weighted), and the kernel weights are automatically learned. We minimize this loss function by estimating the kernel weights in conjunction with the classifier weights, and propose a self-training algorithm for conducting this convex optimization procedure iteratively. Extensive experiments on a variety of 32 UCI classification benchmark datasets show that the proposed approach consistently outperforms state-of-the-art ensembles such as Bagging, AdaBoost, Random Forests, Gasen, Regularized Selective Ensemble, and Ensemble Pruning via Semi-Definite Programming.
Traditional Values and Political Trust in China

Chinese traditional culture is viewed to sustain political trust in the authoritarian regime. Given that Chinese cultural traditions are complex and multi-dimensional, it is ineffective to deal with this notion by a single index. This study divides Chinese traditional values into a non-political dimension (traditional family and social values) and a political dimension (traditional political values). Then, I empirically test how different dimensions of Chinese cultural traditions shape the ordinary people’s orientations toward their political institutions and government officials. The results show that the impact of traditional values on political trust varies by its different dimensions. Traditional political values and social values are positively correlated with both institutional trust and trust in government officials. Traditional family values are positively correlated with trust in government officials but do not have an effect on institutional trust. Liberal democratic values negatively correlate with trust in government officials, but this effect on institutional trust is not significant.
Making Static Lessons Adaptive through Crowdsourcing & Machine Learning

Text components of digital lessons and problems are often static: they are written once and too often not improved over time. This is true for both large text components like webpages and documents as well as the small components that form the building blocks of courses: explanations, hints, examples, discussion questions/answers, emails, study tips, motivational messages. This represents a missed opportunity, since it should be technologically straightforward to enhance learning by improving text, as instructors get new ideas and data is collected about what helps learning. We describe how instructors can use recent work (Williams, Kim, Rafferty, Maldonado, Gajos, Lasecki, & Heffernan, 2016a) to make text components into adaptive resources that semi-automatically improve over time, by combining crowdsourcing methods from human computer interaction (HCI) with algorithms from statistical machine learning that use data for optimization.
An Updated Statistical Hurricane Intensity Prediction Scheme (SHIPS) for the Atlantic and Eastern North Pacific Basins

Abstract Updates to the Statistical Hurricane Intensity Prediction Scheme (SHIPS) for the Atlantic basin are described. SHIPS combines climatological, persistence, and synoptic predictors to forecast intensity changes using a multiple regression technique. The original version of the model was developed for the Atlantic basin and was run in near–real time at the Hurricane Research Division beginning in 1993. In 1996, the model was incorporated into the National Hurricane Center operational forecast cycle, and a version was developed for the eastern North Pacific basin. Analysis of the forecast errors for the period 1993–96 shows that SHIPS had little skill relative to forecasts based upon climatology and persistence. However, SHIPS had significant skill in both the Atlantic and east Pacific basins during the 1997 hurricane season. The regression coefficients for SHIPS were rederived after each hurricane season since 1993 so that the previous season’s forecast cases were included in the sample. Modificatio.
The role of facial movements in face processing and its development

Our visual processing system is largely shaped by the environment in which we live. Human faces, as one of the most commonly seen stimuli, exert unparalleled influence in our visual system and its development. In the past half century, researchers have extensively studied face processing, which has greatly broadened our understandings of visual perception. Most of these prior studies used static face images as experiment stimuli. In the real world, faces are mostly moving: people smile, talk, and nod. Prior studies reported unique contributions of facial movements in facilitating face recognition in infants and adults. The underlying perceptual mechanisms for processing moving faces, however, are still unclear. In particular, our current understanding about face processing is limited to evidence from studies using static faces. It is an open question as to whether our understanding about static face processing can help us understand face processing in the real world where faces are moving. The current thesis specifically addresses this theoretically important question by examining the roles of facial movements in the development of face perception in infancy, childhood, adolescence, and
Robust redesign of distributed adaptive consensus protocols for linear multi-agent systems

This paper revisits the distributed adaptive consensus protocols proposed in [1], [2]. It is shown that under the adaptive protocols in [1], [2] the parameter drift phenomenon takes place when external disturbances are present, implying that those adaptive protocols are not robust with respect to external disturbances. By using the σ modification technique, distributed robust adaptive protocols are designed, which can guarantee the ultimate boundedness of both the consensus error and the adaptive gains in the presence of bounded disturbances. The upper bounds of the consensus error are further derived. Simulation examples are finally presented for illustration.
STBH: A Spectrum-tree-based Hybrid Routing Protocol for Cognitive Network

This thesis proposes a spectrum-tree-based hybrid routing protocol STBH for cognitive network. By proposing ideas of architecture, we introduce the model of STBH, route metric and the algorithm for forming a spectrum tree. Then we propose the route discovery and recovery strategy for Cognitive Networks both in single spectrum and among several spectrums. At last, with simulating by NS2, the effectiveness and robustness of STBH will be proved with the comparison with CTBR.
Designing to support prescribed home exercises: understanding the needs of physiotherapy patients

Musculoskeletal disorders are a globally significant health problem affecting millions. Physiotherapy, including prescribed exercises performed independently by patients in their homes, is a key treatment for many sufferers. However, many patients fail to complete home exercises, prolonging recovery periods or accelerating decline. Pervasive health technologies, capable of monitoring users in their homes, are ideally suited to address this problem. This paper describes user research with a group of three physiotherapists and eleven current physiotherapy patients to understand the problems and user needs underlying non-compliance with home exercise regimes. The research adopted a speed dating approach and culminated with six insights and design recommendations relating to the form and type of feedback that should be used in such systems, to how scheduling and therapist-patient communication systems should be designed and to the role of privacy.
Detecting Intrusions through System Call Sequence and Argument Analysis

We describe an unsupervised host-based intrusion detection system based on system call arguments and sequences. We define a set of anomaly detection models for the individual parameters of the call. We then describe a clustering process that helps to better fit models to system call arguments and creates interrelations among different arguments of a system call. Finally, we add a behavioral Markov model in order to capture time correlations and abnormal behaviors. The whole system needs no prior knowledge input; it has a good signal-to-noise ratio, and it is also able to correctly contextualize alarms, giving the user more information to understand whether a true or false positive happened, and to detect global variations over the entire execution flow, as opposed to punctual ones over individual instances.
An Algorithm for Unsupervised Learningvia Normal Mixture

We consider the approach to unsupervised learning whereby a normal mixture model is tted to the data by maximum likelihood. An algorithm called NMM is presented that enables the normal mixture model with either restricted or unrestricted component covariance matrices to be tted to a given data set. The algorithm automatically handles the problem of the speciication of initial values for the parameters in the iterative tting of the model within the framework of the EM algorithm. The algorithm also has the provision to carry a test for the number of components on the basis of the likelihood ratio statistic.
Design of static resilient WDM mesh networks with multiple heuristic criteria

We present a heuristic method to optimize the capacity of a WDM mesh network assigning working and protection capacity to a set of static optical-connection requests under the link-disjoint constraint. The method can be applied to networks supporting either dedicated or shared path-protection. Routing and fiber and wavelength assignment (RFWA) are jointly performed for all the lightpaths with the aim of minimizing the number of fibers in the network. The employment of multiple prioritized heuristic criteria and an efficient link disjoint paths search algorithm are proposed. With less computational effort, the proposed heuristic approach allows one to obtain good suboptimal results compared to the exact integer-linear-programming optimization. After introducing our design approach, we discuss the optimization of particular case-study networks under various conditions and compare the results.
Existence of solutions for a nonlinear fractional order differential equation

Let D denote the Riemann-Liouville fractional differential operator of order α. Let 1 < α < 2 and 0 < β < α. Define the operator L by L = D − aD where a ∈ R. We give sufficient conditions for the existence of solutions of the nonlinear fractional boundary value problem Lu(t) + f(t, u(t)) = 0, 0 < t < 1, u(0) = 0, u(1) = 0.