Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of College Students' Use of Social Networking Sites on Self-Concept Clarity: Mediating Role of Social Comparison

To explore the effect of college students' use of social networking sites and social comparison on self-concept clarity and its mechanism of action, the researchers used social networking websites use strength questionnaire, social comparison tendency questionnaire and selfconcept clear questionnaire and investigated 697 college students. The results showed that: (1) social networking sites use strength and social comparison was significantly positively related, and the intensity of social networking sites use and social comparison tendency and self-concept clarity has a significant negative correlation. (2) the use of social networking sites not only has a significant direct predictive effect on self-concept clarity but also has an indirect effect on self-concept clarity through the social comparison tendency.
Micropolitical insights into Assistant Regional Directors’ leadership in Queensland Education

The position of Assistant Regional Directors, School Performance (ARD-SP) was established by the Department of Education and Training (DET), the government provider of public education in Queensland, in 2010, to improve student learning across Queensland by providing close supervision of principals. Based on interviews with 18 ARDs-SP and two of their immediate supervisors, this paper explores their views about this relatively new position and their understandings of the role. Following Blase and Anderson (1995), it uses micropolitical leadership theory to analyse comments made by the participants. A key finding was a view of leadership based on a differentiated supervision model whereby ARDs-SP worked with principals to ensure they met the corporate agenda. Participants’ comments favoured a leadership approach that was both adversarial (drawing upon power over and power through) and facilitative (drawing upon power through and power over) and for those principals deemed under-performing, an authoritarian leadership approach was apparent.
Staphylococcal Enterotoxins

Staphylococcus aureus (S. aureus) is a Gram positive bacterium that is carried by about one third of the general population and is responsible for common and serious diseases. These diseases include food poisoning and toxic shock syndrome, which are caused by exotoxins produced by S. aureus. Of the more than 20 Staphylococcal enterotoxins, SEA and SEB are the best characterized and are also regarded as superantigens because of their ability to bind to class II MHC molecules on antigen presenting cells and stimulate large populations of T cells that share variable regions on the β chain of the T cell receptor. The result of this massive T cell activation is a cytokine bolus leading to an acute toxic shock. These proteins are highly resistant to denaturation, which allows them to remain intact in contaminated food and trigger disease outbreaks. A recognized problem is the emergence of multi-drug resistant strains of S. aureus and these are a concern in the clinical setting as they are a common cause of antibiotic-associated diarrhea in hospitalized patients. In this review, we provide an overview of the current understanding of these proteins.
Correcting for Missing Discrete Responses in Business Surveys

Businesses often need an accurate profile of their customers in order to better serve them, improve products and make advertising more effective. Unfortunately, customers do not always completely fill out the survey forms, especially those product registration cards. The forms often have multiple choice questions and customers may leave some questions blank. This paper presents a maximum likelihood method of correcting for the biased sample selection that occurs when the dependent variable is an incomplete categorical response where the data are not missing at random. The paper includes a test of the missing-at-random hypothesis.
Schooling returns, schooling decisions and educational finance

Given the presence of significant returns to education, it would seem logical to query why individuals choose to leave school early. This paper examines the evidence on this issue, dealing with both methodological and evidence-based findings. Drawing on existing research in the area of schooling returns, the evidence and deficiencies of the literature are explored in an effort to quantify the scale of the private return to education. The schooling decision is subsequently examined more closely, so as to investigate the effect of variables such as family income on that choice. Proposed educational finance solutions are then surveyed. Specifically, this paper reports on an experimental approach in the UK, which pays allowances to households and individual students for participation in education, thus reducing the opportunity cost of staying on at school. Finally, estimates are presented, based on an analysis of non-experimental UK data, of the probability of early school leaving, conditional on named variables.
The Functional and Structural Neural Basis of Individual Differences in Loss Aversion

Decision making under risk entails the anticipation of prospective outcomes, typically leading to the greater sensitivity to losses than gains known as loss aversion. Previous studies on the neural bases of choice-outcome anticipation and loss aversion provided inconsistent results, showing either bidirectional mesolimbic responses of activation for gains and deactivation for losses, or a specific amygdala involvement in processing losses. Here we focused on loss aversion with the aim to address interindividual differences in the neural bases of choice-outcome anticipation. Fifty-six healthy human participants accepted or rejected 104 mixed gambles offering equal (50%) chances of gaining or losing different amounts of money while their brain activity was measured with functional magnetic resonance imaging (fMRI). We report both bidirectional and gain/loss-specific responses while evaluating risky gambles, with amygdala and posterior insula specifically tracking the magnitude of potential losses. At the individual level, loss aversion was reflected both in limbic fMRI responses and in gray matter volume in a structural amygdala–thalamus–striatum network, in which the volume of the “output” centromedial amygdala nuclei mediating avoidance behavior was negatively correlated with monetary performance. We conclude that outcome anticipation and ensuing loss aversion involve multiple neural systems, showing functional and structural individual variability directly related to the actual financial outcomes of choices. By supporting the simultaneous involvement of both appetitive and aversive processing in economic decision making, these results contribute to the interpretation of existing inconsistencies on the neural bases of anticipating choice outcomes.
Unified motion planner for fishes with various swimming styles

We propose a unified motion planner that reproduces variations in swimming styles based on the differences in the fish skeletal structures or the variations in the swimming styles based on changes in environmental conditions. The key idea in our method, based on biology, is the following. We considered the common decision-making mechanism in fish that allows them to instantly decide "where and how to swim. " The unified motion planner comprises two stages. In the first stage, where to swim to is decided. Using a probability distribution generated by integrating the perceptual information, the short-term target position and target speed are decided. In the second stage, how to swim is decided. A style of swimming that matches the information for transitioning from the current speed to the target speed is selected. Using the proposed method, we demonstrate 12 types of CG models with completely different sizes and skeletal structures, such as manta ray, tuna, and boxfish, as well as a scene where a school of a few thousand fish swim realistically. Our method is easy to integrate into existing graphics pipelines. In addition, in our method, the movement characteristics can easily be changed by adjusting the parameters. The method also has a feature where the expression of an entire school of fish, such as tornado or circling, can be designated top-down.
Experimental Research on the Reflection Characteristics of Guided Waves from Defects in Steel Wires

The aim of this work is to develop a technique for inspecting parallel wire cables by a quantitative study of the interaction of guided waves with artificial defects in steel wires. The reflection of the L(0,1) mode from notches of varying axial extents and cross-sections in 7-mm diameter wires has been studied by laboratory experiments. Results show that the reflection coefficient of this mode around 50 kHz is a strong function of the defect axial extent, two maximum reflection coefficients occurring respectively at a defect length of about 25 percent of the wavelength and 75 percent, while a local minimum occurring at 50 percent. And the reflection coefficient is approximately linear with defect cross-section when the defect axial length is given. Results imply that the maximum and minimum reflection coefficients at many different frequencies can be utilized for sizing of the axial extent of the defect. If the defect axial extent is found, the reflection coefficient at any frequency can be used to estimate the cross-sectional area of the defect. These findings will provide a certain foundation for the detection of parallel wire cables if the reflection characteristics of guided waves in the wire bundles are similar to those in an individual wire.
SQL Injection: Detection and Prevention Techniques

As the use of internet is increasing rapidly, the attacks on web applications are also increasing as well. Nowadays SQL injection attack is a major issue of web applications. It allows unrestricted access to the database. The successful execution of SQL injection leads to a loss of integrity and confidentiality. In this paper, a review of different types of SQL injection attacks, their detection and prevention techniques are presented. This paper will help the researchers decide the technique of interest for further studies.
Iterative Error Bound Minimisation for AAM Alignment

The active appearance model (AAM) is a powerful generative method used for modelling and segmenting de-formable visual objects. Linear iterative methods have proven to be an efficient alignment method for the AAM when initialisation is close to the optimum. However, current methods are plagued with the requirement to adapt these linear update models to the problem at hand when the class of visual object being modelled exhibits large variations in shape and texture. In this paper, we present a new precomputed parameter update scheme which is designed to reduce the error bound over the model parameters at every iteration. Compared to traditional update methods, our method boasts significant improvements in both convergence frequency and accuracy for complex visual objects whilst maintaining efficiency
Verification and Validation of Activity Diagrams Using Graph Transformation

Graphical structures (like graphs, diagrams, visual sentences and others) are very useful to describe complex structures and systems. These structures are often augmented by formalisms which add a further dimension to the static description allowing for the modeling of the evolution of systems via any kind of transformation of such graphical structures. Graph transformation has been widely used for modeling. Graphs are well suited to describe the underlying structures of models. They provide a good method to carry out the analysis and verification activities and using the AGG toolset for designing them. A major goal of this paper is the ability to determine the correctness behavior and formal semantics of UML2.0 activity diagrams by Graph Transformation Systems.
Quantum Uncertainty Reduction (QUR) Theory of Attended Access and Phenomenal Consciousness

iv Acknowledgements . vi List of Figures . xii Chapter 1: Characterization of the Problems of Consciousness and Motivation of Thesis. 1 1.1 Easy and Hard Problems of Consciousness. 1 1.2 David Chalmers’s Dual Aspect Analysis of Consciousness. 3 1.3 Ned Block’s Concepts of Phenomenal and Access Consciousness. 8 1.4 Jesse Prinz’s Neurofunctionalist AIR Theory of Consciousness. 14 1.5 Thesis Motivation and the Plan . 19 Chapter 2: Information Representation and Processing in Mechanisms. Perceptual Information-Processing Mechanism based on Shannon Communication System that Combines Bayesian Predictive Processing and Modal Emulation. 25 2.1 Claude Shannon’s Mathematical Theory of Communication as statistical approach to information. Shannon Communication System with two sources of information. 25 2.2 Information Representation and Processing in Mechanisms. Causal and Mechanical Accounts of Computation in Physical Systems. 29 2.3 Computation as Information Processing within Mechanisms. Causal and Mechanical Accounts of Computation in Physical Systems. 32
Creating a Test Collection for Citation-based IR Experiments

We present an approach to building a test collection of research papers. The approach is based on the Cranfield 2 tests but uses as its vehicle a current conference; research questions and relevance judgements of all cited papers are elicited from conference authors. The resultant test collection is different from TREC's in that it comprises scientific articles rather than newspaper text and, thus, allows for IR experiments that include citation information. The test collection currently consists of 170 queries with relevance judgements; the document collection is the ACL Anthology. We describe properties of our queries and relevance judgements, and demonstrate the use of the test collection in an experimental setup. One potentially problematic property of our collection is that queries have a low number of relevant documents; we discuss ways of alleviating this.
Planning Behaviours

The Brain is a slow computer yet humans can skilfully play games where very fast reactions are required. A review of data on motor and cognitive effects of cerebellar lesions leads to propose that the brain minimises reaction time during skilled behaviour by eliminating online planning. Behaviour then results from direct mapping from perceptions to actions. Its is proposed that the cerebellum i) learns sensory-motor associations planned off-line and ii) enables the set of sensory-motor associations most appropriate for a given goal. In that framework, planning defines both the stimulus and the action that will be triggered. For producing effective behaviour, such a scheme requires in principle that, for each goal, a large set of sensory motor associations are prepared to cover a wide range of possible initial conditions and variations in execution. A neural network implementation of these principles is proposed with generalization properties obviating the need for planning multiple associations. After learning only the optimal plan, the NN exhibits a field of solutions that ensures robustness against variations in initial conditions and execution noise. The proposed scheme unifies purely behaviour-based approaches to robot control and approaches based on planning using internal representations. On the one hand sensory-motor associations enable fast reactions and on the other hand, being products of planning, these associations enables flexible goal adaptation. Such a principle is demonstrated with a simulated robot arm control problem and has been applied to the control of a real autonomous wheelchair.
Insynd : Privacy-Preserving Transparency Logging Using Balloons

Insynd is a cryptographic scheme for privacy-preserving transparency logging. In the setting of transparency logging, a service provider continuously logs descriptions of its data processing on its users’ personal data, where each description is intended for a particular user. Our work focuses on protecting the privacy of users. Insynd provides secrecy of messages, message integrity and authenticity, protection against recipient profiling, and publicly verifiable proofs of who sent what message to which recipient at what particular time. Our scheme is built on an authenticated data structure (Balloon) that enables the safe outsourcing of storage of messages to an untrusted server (such as commodity cloud services). The author of messages is in the forward-security model. Insynd provides stronger privacy protections than prior work in this setting, improved efficiency in terms of event generation, and increases the utility of all data sent through the scheme thanks to the publicly verifiable proofs. Our prototype implementation shows greatly improved performance over related work and competitive performance for more data-intensive settings like secure logging.
SPEECH ENHANCEMENT USING BETA-ORDER MMSE SPECTRAL AMPLITUDE ESTIMATOR WITH LAPLACIAN PRIOR

This report addresses the problem of speech enhancement employing the Minimum Mean-Square Error (MMSE) of β-order Short Time Spectral Amplitude (STSA). We present an analytical solution for β-order MMSE estimator where Discrete Fourier Transform (DFT) coefficients of (clean) speech are modeled by Laplacian distributions. Using some approximations for the joint probability density function and the Bessel function, we also present a closed-form version of the estimator (called β-order LapMMSE). The performance of the proposed estimator is compared to the state-of-the–art spectral amplitude estimators that assume Gaussian priors for clean DFT coefficients. Comparative results demonstrate the superiority of the proposed estimator in terms of speech enhancement/ noise reduction measures.
Mono-camera based pitch rate estimation in nighttime scenarios

The camera relative pose is essential information for driver assistance systems in general, and especially so for systems that aim to visualize obstacles or other relevant objects for the driver. In order to display objects that were not detected in the vision frame of the camera (e.g. road information of a digital map), the exact transformation from camera to the world has to be known for every time frame. The horizontal movement of the camera in the real world is well known due to sufficiently accurate inertial sensors and wheel speed sensors available in the car. In contrast, the vertical movement is both unknown and highly dynamic. Relative pose estimation is a widely used technique that determines a vehicle's movement and rotation from frame to frame. These techniques rely primarily on point correspondences computed beforehand. In the nighttime case, these methods fail because of the poor image quality and low number of correspondences. This paper presents a method that fuses vehicle movement information with the features extracted from a mono camera. By restricting the problem to pitch estimation only, this method allows robust and accurate estimation of the vehicle's pitch movements even in nighttime scenarios. A highly accurate inertial navigation system is used to evaluate the results obtained in challenging real world nighttime video sequences. Experiments show the feasibility and robustness of the proposed approach.
Adaptation of the Osmotolerant Yeast Zygosaccharomyces rouxii to an Osmotic Environment Through Copy Number Amplification of FLO11D

Copy number variations (CNVs) contribute to the adaptation process in two possible ways. First, they may have a direct role, in which a certain number of copies often provide a selective advantage. Second, CNVs can also indirectly contribute to adaptation because a higher copy number increases the so-called “mutational target size. ” In this study, we show that the copy number amplification of FLO11D in the osmotolerant yeast Zygosaccharomyces rouxii promotes its further adaptation to a flor-formative environment, such as osmostress static culture conditions. We demonstrate that a gene, which was identified as FLO11D, is responsible for flor formation and that its expression is induced by osmostress under glucose-free conditions, which confer unique characteristics to Z. rouxii, such as osmostress-dependent flor formation. This organism possesses zero to three copies of FLO11D, and it appears likely that the FLO11D copy number increased in a branch of the Z. rouxii tree. The cellular hydrophobicity correlates with the FLO11D copy number, and the strain with a higher copy number of FLO11D exhibits a fitness advantage compared to a reference strain under osmostress static culture conditions. Our data indicate that the FLO gene-related system in Z. rouxii has evolved remarkably to adapt to osmostress environments.
The influence of chemical composition on the properties of Cepheid stars. I. Period-Luminosity relation vs. iron abundance

We have assessed the influence of the stellar iron content on t he Cepheid Period-Luminosity (PL) relation by relating the V band residuals from the Freedman et al (2001) PL relation to (Fe/H) for 37 Galactic and Magellanic Clouds Cepheids. The iron abundances were measured from FEROS and UVES high-resolution and high-signal to noise optical spectra. Our data indicate that the stars become fainter as metallicity i ncreases, until a plateau or turnover point is reached at abo ut solar metallicity. Our data are incompatible with both no dependence of the PL relation on iron abundance, and with the linearly decreasing behavior often found in the literature (e.g. Kennicutt et al 1998, Sakai et al 2004). On the other hand, non-linear theoretical models of Fiorentino et al (2002) provide a fair ly good description of the data.
Learning Leading Indicators for Time Series Predictions

We consider the problem of learning models for forecasting multiple time-series systems together with discovering the leading indicators that serve as good predictors for the system. We model the systems by linear vector autoregressive models (VAR) and link the discovery of leading indicators to inferring sparse graphs of Granger-causality. We propose new problem formulations and develop two new methods to learn such models, gradually increasing the complexity of assumptions and approaches. While the first method assumes common structures across the whole system, our second method uncovers model clusters based on the Granger-causality and leading indicators together with learning the model parameters. We study the performance of our methods on a comprehensive set of experiments and confirm their efficacy and their advantages over state-of-the-art sparse VAR and graphical Granger learning methods.
Cell Zooming for Power Efficient Base Station Operation

With enormous growth in the telecommunication industry, energy efficiency has become a critical issue. Base stations and the core network account for a large amount of energy consumption today. Traditional energy saving techniques switch some base stations off completely during light loads to save energy. This creates problems for the backhaul network and also for quickly returning to full capacity when demand increases. In this paper, we propose novel cell zooming techniques to reduce energy consumption at base stations. With cell zooming, base stations dynamically adjust their coverage radius and hence their transmit powers based on user locations. The transmit power is set to the minimum required level depending on user locations, signal to interference and noise ratio (SINR) and quality of service (QoS) requirements of the users. Base stations are never completely switched off. Our simulations show that the proposed cell zooming algorithm reduces the energy consumption of base stations by up to 40% compared to traditional static-coverage-area base stations without compromising on the QoS requirements of any user in the cell.