Miracle-dz/UniFAR-FTUs huggingface dataset:
An Experiment on Public Speaking Anxiety in Response to Three Different Types of Virtual Audience

This paper describes an experiment to assess the anxiety responses of people giving 5 min. presentations to virtual audiences consisting of eight male avatars. There were three different types of audience behavior: an emotionally neutral audience that remained static throughout the talk, a positive audience that exhibited friendly and appreciative behavior towards the speaker, and a negative audience that exhibited hostile and bored expressions throughout the talk. A second factor was immersion: half of the forty subjects experienced the virtual seminar room through a head-tracked, head-mounted display and the remainder on a desktop system. Responses were measured using the standard Personal Report of Confidence as a Public Speaker (PRCS), which was elicited prior to the experiment and after each talk. Several other standard psychological measures such as SCL-90-R (for screening for psychological disorder), the SAD, and the FNE were also measured prior to the experiment. Other response variables included subjectively assessed somaticization and a subject self-rating scale on performance during the talk. The subjects gave the talk twice each to a different audience, but in the analysis only the results of the first talk are presented, thus making this a between-groups design. The results show that post-talk PRCS is significantly and positively correlated to PRCS measured prior to the experiment in the case only of the positive and static audiences. For the negative audience, prior PRCS was not a predictor of post-PRCS, which was higher than for the other two audiences and constant. The negative audience clearly provoked an anxiety response irrespective of the normal level of public speaking confidence of the subject. The somatic response also showed a higher level of anxiety for the negative audience than for the other two, but self-rating was generally higher only for the static audience, each of these results taking into account prior PRCS.
Color space selection for human skin detection using color-texture features and neural networks

Skin color is a robust cue in human skin detection. It has been widely used in various human-related image processing applications. Although many researches have been carried out for skin color detection, there is no consensus on which color space is the most appropriate for skin color detection because many researchers do not provide strict justification of their color space choice. In this paper, a comprehensive comparative study using the Multilayer Perceptron artificial neural network (MLP), which is a universal classifier, is carried out to evaluate the overall performance of different color-spaces for skin detection. It aims at determining the most optimal color space using color and color-texture features separately. The study has been carried out using images of different databases. The experimental results showed that the YIQ color space gives the highest separability between skin and non-skin pixels among the different color spaces tested using color features. Combining color and texture eliminates the differences between color spaces but leads to much more accurate and efficient skin detection.
COPAS: dynamic contention-balancing to enhance the performance of TCP over multi-hop wireless networks

Most studies on TCP over multi-hop wireless ad hoc networks have only addressed the issue of performance degradation due to temporarily broken routes, which results in TCP inability to distinguish between losses due to link failures or congestion. This problem tends to become more serious as network mobility increases. We tackle the equally important capture problem to which there has been little or no solution, and is present mostly in static and low mobility multihop wireless networks. This is a result of the interplay between the MAC layer and TCP backoff policies, which causes nodes to unfairly capture the wireless shared medium, hence preventing neighboring nodes to access the channel. This has been shown to have major negative effects on TCP performance comparable to the impact of mobility. We propose a novel algorithm, called COPAS (COntention-based PAth Selection), which incorporates two mechanisms to enhance TCP performance by avoiding capture conditions. First, it uses disjoint forward (sender to receiver for TCP data) and reverse (receiver to sender for TCP ACKs) paths in order to minimize the conflicts of TCP data and ACK packets. Second, COPAS employs a dynamic contention-balancing scheme where it continuously monitors and changes forward and reverse paths according to the level of MAC layer contention, hence minimizing the likelihood of capture. Through extensive simulation, COPAS is shown to improve TCP throughput by up to 90% while keeping routing overhead low.
Non-Linear State estimation using pre-trained neural networks

This article presents a method to track non-Gaussian parametric probability density functions under nonlinear transformations and posterior calculations. The optimal set of parameters for the transformed distribution is a function of the parameters for the prior distribution and any other variables effecting the transformation. This function is approximated by a neural network using offline training. The training is based on monte carlo sampling. A way to obtain parametric distributions of flexible shape to be used easily with these networks is also presented. The method can also be used to improve other parametric methods around regions with strong non-linearities by including them inside the network.
Attack pattern-based combinatorial testing

The number of potential security threats rises with the increasing number of web applications, which cause tremendous financial and existential implications for developers and users as well. The biggest challenge for security testing is to specify and implement ways in order to detect potential vulnerabilities of the developed system in a never ending quest against new security threats but also to cover already known ones so that a program is suited against typical attack vectors. For these purposes many approaches have been developed in the area of model-based security testing in order to come up with solutions for real-world application problems. These approaches provide theoretical background as well as practical solutions for certain security issues. In this paper, we partially rely on previous work but focus on the representation of attack patterns using UML state diagrams. We extend previous work in combining the attack pattern models with combinatorial testing in order to provide concrete test input, which is submitted to the system under test. With combinatorial testing we capture different combinations of inputs and thus increasing the likelihood to find weaknesses in the implementation under test that can be exploited. Besides the foundations of our approach we further report on first experiments that indicate its practical use.
Probabilistic graphicalmodels for human motion tracking

Graphical models have proved to be very efficient models for labeling image data. In particular, they have been used to label data samples fromhuman body images. In this paper, a DTG-based graphical model is studied for human-body landmark localization and tracking along the image sequence. Experimental results on human motion databases are shown.
Real-Time Client-Side Phishing Prevention Add-On

Since existing solutions for steering users away from phishing websites are typically server-based, they have several drawbacks: they compromise user privacy, are not robust against adaptive attackers who serve different content at different times, and do not provide any guidance to users after flagging a website as a phish. To address these limitations, we present a new phishing prevention system implementing a fast and effective phishing detection technique we developed recently [1]. It is implemented as a client-side application and a browser add-on. It uses information extracted from website visited by the user to detect if it is a phish and warn the user. It also determines the target of the phish and offers to redirect the user there.
Object tracking based on multi-bandwidth mean shift with convergence acceleration

A multi-bandwidth based tracking algorithm was proposed to search for the global kernel mode when the probability density has multiple peak modes. Firstly, a monotonically decreasing sequence of bandwidths was fixed according to the target scale. At each bandwidth, using mean shift to find out the maximum probability, and starting the next iteration at the previous convergence location. Finally, the best optimal mode could be obtained at the last bandwidth. To accelerate the convergence, over-relaxed strategy was introduced to enlarge the step size. Under the convergence rule, the learning rate was adaptively adjusted by Bhattacharyya coefficients of consecutive iteration convergence. The experimental results show that the proposed multi-bandwidth mean shift tracker is robust in high-speed object tracking, and perform well in occlusions. The adaptive over-relaxed strategy is effective to lower the convergence iterations by enlarging the step size.
Linear Geometry Hall Thruster with Boron Nitride and Diamond Walls

* Presented as Paper IEPC-01-39 at the 27 International Electric Propulsion Conference, Pasadena, CA, 15-19 October 2001. † Copyright © 2001 by Stanford University. Published by the Electric Rocket Propulsion Society with permission. A preliminary assessment of the sputter resistance of polycrystalline diamond plates against xenon ion bombardment is reported, and first studies of integrating diamond walls into a Hall discharge channel are performed. The polycrystalline diamond is found to have a 25% better resistance to sputtering than boron nitride, a more commonly used Hall thruster wall material. The diamond plates were integrated into a linear Hall thruster with an open electron drift. In comparison to a similar Hall thruster with boron nitride walls, the diamond containing discharge had an overall lower discharge current, but resulted in a slightly lower propellant utilization. However, unlike the case with boron nitride walls, operation with the diamond walls was limited to 200V or higher, due to the emergence of large-scale fluctuations at lower voltages.
Research collaboration as social action: constructing meaning and interrogating relationship-building in an outcomes-based approach

In 2009 the Faculty of Education at the University of Southern Queensland began an ambitious agenda to improve both the quality and the quantity of its research outcomes. It encouraged the establishment of small, informal research teams with some financial incentives to support a research agenda. In this chapter, three members of one such team consider their experiences of research collaboration in relation to collective mindfulness, a term that one of the researchers used during a focused conversation. The analysis articulates and then synthesises the authors’ understandings and experiences of the term, which is posited as a useful theoretical and practical device for helping research teams to maximise their outcomes and at the same time to contribute positively to relevant social action.
Evaluation of Decentralization Outcomes in Indonesia: Analysis of Health and Education Sectors

In presenting this dissertation as a partial fulfillment of the requirements for an advanced degree from Georgia State University, I agree that the Library of the University shall make it available for inspection and circulation in accordance with its regulations governing materials of this type. I agree that permission to quote from, to copy from, or to publish this dissertation may be granted by the author or, in his or her absence, the professor under whose direction it was written or, in his or her absence, by the Dean of the Andrew Young School of Policy Studies. Such quoting, copying, or publishing must be solely for scholarly purposes and must not involve potential financial gain. It is understood that any copying from or publication of this dissertation which involves potential gain will not be allowed without written permission of the author. ____________________________________ Signature of the Author NOTICE TO BORROWERS All dissertations deposited in the Georgia State University Library must be used only in accordance with the stipulations prescribed by the author in the preceding statement. Users of this dissertation not regularly enrolled as students at Georgia State University are required to attest acceptance of the preceding stipulations by signing below. Libraries borrowing this dissertation for the use of their patrons are required to see that each user records here the information requested. ACCEPTANCE This dissertation was prepared under the direction of the candidate " s Dissertation Committee. It has been approved and accepted by all members of that committee, and it has been accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Economics in vi ACKNOWLEDGMENTS First, I would like to praise God for the strength, guidance and blessings that I have received with every step of my journey. Thank You for being a loving, patience and merciful Father to me. I am fortunate to have not one, but two mentors during my time at Georgia State University. My deepest gratitude goes to Dr. Jamie Boex who taught me the skills and tools to do research since my first time working as his research assistant. More importantly, I thank Dr. Boex for encouraging me to grow as an academic and a researcher. I am deeply indebted to Dr. Jorge Martinez-Vazquez for the opportunity to work with him, which over the years add greatly to my education and experience. I am especially grateful to Dr. …
Dynamic objects tracking with a mobile robot using passive UHF RFID tags

Recent research deals more and more with the application of ultra high frequency (UHF) radio-frequency identification (RFID) on mobile robots. However, the sensing characteristics between the reader and the tag (i.e. detections and signal strength) are challenging to model due to the influence of environmental effects (e.g. tag density, reflection, diffraction, or absorption). In this paper, we address the problem of dynamic objects tracking with a mobile agent using the signal strength from UHF RFID tags attached to objects. Our solution estimates the positions of RFID tags under a Bayesian framework. More precisely, we combine a two stage dynamic motion model with the dual particle filter, to capture the dynamic motion of the object and to quickly recover from failures in tracking. This approach is then tested on a Scitos G5 mobile robot through various experiments.
α-Linolenic Acid Reduces TNF-Induced Apoptosis in C2C12 Myoblasts by Regulating Expression of Apoptotic Proteins

Impaired regeneration and consequent muscle wasting is a major feature of muscle degenerative diseases. Nutritional interventions such as adjuvant strategy for preventing these conditions are recently gaining increasing attention. Ingestion of n3-polyunsaturated fatty acids has been suggested as having a positive impact on muscle diseases. We recently demonstrated that a diet enriched with plant derived n3-fatty acid, α-linolenic acid (ALA), exerts potent beneficial effects in preserving skeletal muscle regeneration in models of muscle dystrophy. To better elucidate the underlying mechanism we here investigate on the expression level of the anti- and pro-apoptotic proteins, as well as caspase-3 activity, in C2C12 myoblasts challenged with pathological levels of tumor necrosis factor-α (TNF). The results demonstrated that ALA protective effect on C2C12 myoblasts was associated with a decrease in caspase-3 activity and an increase of the Bcl-2/Bax ratio. Indeed, the effect of ALA was directed to rescuing Bcl-2 expression and to revert Bax translocation to mitochondria both affected in an opposite way by TNF, a major pro-inflammatory cytokine expressed in damaged skeletal muscle. Therefore, ALA counteracts inflammatory signals in the muscle microenvironment and may represent a valuable strategy for ameliorating skeletal muscle pathologies.
The Unified Logging Infrastructure for Data Analytics at Twitter

In recent years, there has been a substantial amount of work on large-scale data analytics using Hadoop-based platforms running on large clusters of commodity machines. A less-explored topic is how those data, dominated by application logs, are collected and structured to begin with. In this paper, we present Twitter's production logging infrastructure and its evolution from application-specific logging to a unified "client events" log format, where messages are captured in common, well-formatted, flexible Thrift messages. Since most analytics tasks consider the user session as the basic unit of analysis, we pre-materialize "session sequences", which are compact summaries that can answer a large class of common queries quickly. The development of this infrastructure has streamlined log collection and data analysis, thereby improving our ability to rapidly experiment and iterate on various aspects of the service.
A New Joint Source Channel Decoding Scheme Based on Speech Signal

The study proposes a joint source channel decoding scheme with the speech source residual redundancy without changing the complexity of the decoding algorithm. As the speech parameter index could be used to calculate the transition probability of the speech coding parameter index, we can get transition matrixes on different speech parameters according to the statistics of the speech signal. Using the transition matrix, we can get new priori information for the channel decoder which will finally lead to a better decoding result. The method updates the priori information for channel decoding before the channel decoding starts, aiming to get a better channel decoding result as well as reducing the iteration times of the decoding process. As shown by the simulating results, the joint source channel decoding scheme can reduce the bit error rate of the channel decoding even under bad channel circumstance.
Poor starting points in machine learning

Poor (even random) starting points for learning/training/optimization are common in machine learning. In many settings, the method of Robbins and Monro (online stochastic gradient descent) is known to be optimal for good starting points, but may not be optimal for poor starting points -- indeed, for poor starting points Nesterov acceleration can help during the initial iterations, even though Nesterov methods not designed for stochastic approximation could hurt during later iterations. The common practice of training with nontrivial minibatches enhances the advantage of Nesterov acceleration.
A Simple and Rapid Lights-up Solver

This study proposes a solver that mix pattern matching method with elimination search to solve Lights-up puzzles. First, the pattern matching method is used to solve some parts of puzzle in a very short time. Then, the elimination search method is applied to solve the part in which the former method cannot be applied. The solver is authenticated by solving problems taken from Internet and compared with methods in other studies. The proposed approach is fast and efficient.
A library writer's guide to shortcut fusion

There are now a variety of shortcut fusion techniques in the wild for removing intermediate data structures in Haskell. They are often presented, however, specialised to a specific data structure and interface. This can make it difficult to transfer these techniques to other settings. In this paper, we give a roadmap for a library writer who would like to implement fusion for his own library. We explain shortcut fusion without reference to any specific implementation by treating it as an instance of data refinement. We also provide an example application of our framework using the features available in the Glasgow Haskell Compiler.
Multivariate Statistical Analysis using the R package chemometrics

In multivariate data analysis we observe not only a single variable or the relation between two variables but we consider several characteristics simultaneously. For a statistical analysis of chemical data (also called chemometrics) we have to take into account the special structure of this type of data. Classic model assumptions might not be ful lled by chemical data, for instance there will be a large number of variables and only few observations, or correlations between the variables occur. To avoid problems arising from this fact, for chemometrics classical methods have to be adapted and new ones developed. The statistical environment R is a powerful tool for data analysis and graphical representation. It is an open source software with the possibility for many individuals to assist in improving the code and adding functions. One of those contributed function packages chemometrics implemented by Kurt Varmuza and Peter Filzmoser is designed especially for the multivariate analysis of chemical data and contains functions mostly for regression, classi cation and model evaluation. The work at hand is a vignette for this package and can be understood as a manual for its functionalities. The aim of this vignette is to explain the relevant methods and to demonstrate and compare them based on practical examples.
Numerical Models of Binary Neutron Star System Mergers. I. Numerical Methods and Equilibrium Data for Newtonian Models

The numerical modeling of binary neutron star mergers has become a subject of much interest in recent years. While a full and accurate model of this phenomenon would require the evolution of the equations of relativistic hydrodynamics along with the Einstein field equations, a qualitative study of the early stages on inspiral can be accomplished by either Newtonian or post-Newtonian models, which are more tractable. However, even purely Newtonian models present numerical challenges that must be overcome in order to have accurate models of the inspiral. In particular, the simulations must maintain conservation of both energy and momenta and otherwise exhibit good numerical behavior. A spate of recent papers have detailed the results for Newtonian and post-Newtonian models of neutron star coalescence from a variety of groups who employ very different numerical schemes. These include calculations that have been carried out in both inertial and rotating frames, as well as calculations that employ both equilibrium configurations and spherical stars as initial data. However, only a few papers have given attention to the issue of the accuracy of the models and the dependence of the results on the computational frame and the initial data. In this paper we offer a comparison of results from both rotating and nonrotating (inertial) frame calculations. We find that the rotating frame calculations offer significantly improved accuracy as compared with the inertial frame models. Furthermore, we show that inertial frame models exhibit significant and erroneous angular momentum loss during the simulations that leads to an unphysical inspiral of the two neutron stars. We also examine the dependence of the models on initial conditions by considering initial configurations that consist of spherical neutron stars as well as stars that are in equilibrium and are tidally distorted. We compare our models to those given by Rasio & Shapiro in 1992 and 1994 and by New & Tohline in 1997. Finally, we investigate the use of the isolated star approximation for the construction of initial data.