Miracle-dz/UniFAR-FTUs huggingface dataset:
Schubert Using Logic Proofs to justify answers in Open-Domain Question-Answering system

In an open-domain question answering (QA) system, we can have any type of questions (e.g. factoid, list, definition, etc) on any subject topic (starting from sports to medical, news, programming language, tea, weather, cooking, literally anything! ). To understand the complex questions and to find the accurate answers for these questions, we need deeper text understanding among question, answer and world knowledge to justify if the answer is suitable in the context of the given question. Even though some QA system can answer simple fact seeking questions, but when it turns into a complex question, which needs deeper understanding of texts, then these systems cannot answer very accurately [Voorhees 1999]. D Moldovan and his group at Language Computer Cooperation [LCC] tried to take the state-of-the art QA systems to one step ahead by introducing the Logic Proof system in QA. For the logic proof they convert the world knowledge, question and prospective answer into logic forms and then do the rigorous logic proofs to justify the answer. In this survey we start by describing what Question Answering system is. After that we describe how a question answering system works and also give some examples of different question answering systems. At that point it should be clear to the reader that a question answering system need deeper text understanding to generate the answers. Then we present the question answering system by LCC [Moldovan et al 2002a] and [Moldovan et al 2003], with focus on their logic proof system that is used to justify the answer. We describe the result on their performance issues and error analysis of the system [Moldovan et al 2002b], more specifically why it cannot give answers to some questions, i.e. the reason behind failing in some cases. Text REtrieval Conference [TREC], co-sponsored by National Institute of Standards and Technology (NIST) and U.S. Department of Defense, introduced the evaluation of open-domain question answering system. Most of the research and result in the open domain question answering are based on the evaluation system by TREC [TREC]. Every year they also give an overview of what type of question the QA systems have to answer that year and which team performed how well and a comparative study is also available in those overview papers. D. Moldovan and his group introduced logic proof in their question answering system in 2002/2003 [Moldovan et al 2002 and 2003]. To compare how their system works compare to the other systems, we present the evaluation of different QA systems at TREC 2003 [Voorhees 2003]. We also present their recent performance compare to other groups [Dang et al 2006]. Finally we give some comments on what extra benefits LCC’s QA system achieves for adding the logic proof module in their QA system and we also give comments on how this addition make the system suitable as a next generation question answering system! Survey Paper for CSC 444 (Logical Foundation of AI), University of Rochester, Fall 2007 Instructed By: Lenhart K. Schubert
Dynamics of fertility and partnership in Europe : insights and lessons from comparative research

This two-volume set is based on the Fertility and Family Survey Flagship Conference, which took place in May 2000 in Brussels. The five themes of the conference were: partnership behavior, fertility behavior, partnership and fertility behaviors as interdependent processes, new approaches and methodological innovations in the study of these behaviors, and research and policy agenda for the future. The work includes papers presented at the five thematic sessions.
Evaluation of an ensemble of genetic models for prediction of a quantitative trait

Many genetic markers have been shown to be associated with common quantitative traits in genome-wide association studies. Typically these associated genetic markers have small to modest effect sizes and individually they explain only a small amount of the variability of the phenotype. In order to build a genetic prediction model without fitting a multiple linear regression model with possibly hundreds of genetic markers as predictors, researchers often summarize the joint effect of risk alleles into a genetic score that is used as a covariate in the genetic prediction model. However, the prediction accuracy can be highly variable and selecting the optimal number of markers to be included in the genetic score is challenging. In this manuscript we present a strategy to build an ensemble of genetic prediction models from data and we show that the ensemble-based method makes the challenge of choosing the number of genetic markers more amenable. Using simulated data with varying heritability and number of genetic markers, we compare the predictive accuracy and inclusion of true positive and false positive markers of a single genetic prediction model and our proposed ensemble method. The results show that the ensemble of genetic models tends to include a larger number of genetic variants than a single genetic model and it is more likely to include all of the true genetic markers. This increased sensitivity is obtained at the price of a lower specificity that appears to minimally affect the predictive accuracy of the ensemble.
Unbiased Feature Selection in Learning Random Forests for High-Dimensional Data

Random forests (RFs) have been widely used as a powerful classification method. However, with the randomization in both bagging samples and feature selection, the trees in the forest tend to select uninformative features for node splitting. This makes RFs have poor accuracy when working with high-dimensional data. Besides that, RFs have bias in the feature selection process where multivalued features are favored. Aiming at debiasing feature selection in RFs, we propose a new RF algorithm, called xRF, to select good features in learning RFs for high-dimensional data. We first remove the uninformative features using p-value assessment, and the subset of unbiased features is then selected based on some statistical measures. This feature subset is then partitioned into two subsets. A feature weighting sampling technique is used to sample features from these two subsets for building trees. This approach enables one to generate more accurate trees, while allowing one to reduce dimensionality and the amount of data needed for learning RFs. An extensive set of experiments has been conducted on 47 high-dimensional real-world datasets including image datasets. The experimental results have shown that RFs with the proposed approach outperformed the existing random forests in increasing the accuracy and the AUC measures.
Does psychological empowerment of boundary employee mediate the relationship between supervisor support and job satisfaction

This study investigates the mediation effects of the four components of psychological empowerment on the relationship between supervisor support and job satisfaction. To test this relationship, 350 boundary employees in automobile repair store were surveyed. Regression analysis findings demonstrated that psychological empowerment and its' four dimensions partially mediated the relationship. This study demonstrates that to improve job satisfaction, the managers need to be focused on while stimulated employees' choice and impact while ensuring job skill and significance.
Statistical vs. visual data generation in hand gesture recognition

A dataset with diverse training data is essence of the hand gesture recognition research. Most of the benchmarked datasets are limited in the number of signers and/or the number of each gesture try, which often result in over-fitting and poor generalization. Overcoming this challenge is often achieved by collecting a large number of exemplars for each hand gesture. This process is either expensive or impractical. Recently, synthetic data generation methods have been presented as a more reliable way to extend and enrich datasets. This paper proposes a comparative study of statistical and visual synthetic data generation. The visual synthetic data generation is executed by building synthetic 3D animated models using human figure design software. The experiments illustrate how the recognition accuracy will be changed when both methods used in enlarging the training data. The results show that in both cases recognition accuracy is enhanced, and in most cases, the visual synthetic data enlargement provides better improvement in the quality and diversity of the training data.
A Vision Based Emergency Forced Landing System for an Autonomous UAV

This paper introduces the forced landing problem for UAVs and presents the machine-vision based approach taken for this research. The forced landing problem, is a new field of research for UAVs and this paper will show the preliminary analysis to date. The results are based on video data collected from a series of flight trials in a Cessna 172. The aim of this research is to locate "safe" landing sites for UAV forced landings, from low quality aerial imagery. Output video image frames, will highlight the algorithm’s selected safe landing locations. The algorithms for the problem use image processing techniques and neural networks for the classification problem. It should be noted that although the system is being designed primarily for the forced landing problem for UAVs, the research can also be applied to forced landings or glider applications for piloted aircraft.
Parametric Assessment of Stress Development and Cracking in Restrained Mortars Containing Internal Curing Under Autogenous and Thermal Loading

A finite element model is used to examine how the properties of cementitious mortar are related to the stress development in the dual ring test. The results of this investigation are used to explain the thermal cracking behavior of mixtures containing prewetted lightweight aggregates (LWA) by quantifying the contribution of several material properties individually. In addition to the beneficial effects of using the LWA as an internal curing agent to reduce the autogenous shrinkage of concrete, the LWA also helps to reduce the potential for thermal cracking due to a lower elastic modulus and increased stress relaxation. The rate of stress development, age of cracking, and magnitude of the temperature drop necessary to induce cracking in a dual ring specimen are dependent on a variety of factors, including the coefficient of thermal expansion of both the cementitious mortar and the restraining rings, elastic modulus of the mortar, creep effect of the mortar, and rate of thermal loading. Depending on the rate of cooling, cracking may or may not occur. The slowest rate of cooling (2.5 • C/h) minimizes the effects of creep while cooling rates faster than 8 • C/h can produce a thermal gradient through the mortar cross-section that needs to be considered.
Load-bearing characteristics of polyethylene fo ' nation of structural and compression properties

Cellular foams used in shoe insoles come in a variety of structural configurations differing with respect to cell geometry. The compression of a cellular foam depends on cell reticulation and size. Examination of the compression behavior of closed cell polyethylene foams revealed distinct time-and nontime-related properties that occur during static and cyclic loading. Physical parameters were developed and determined to exactly specify pressure profiles that occur at the plantar interface. Evaluation of an interface material can be made based on peak pressures which are dependent on depth of compression, foam thickness, and physical properties of the foam. Sustained loading damages the mechanical integrity of the cellular structure such that thickness does not completely recover. Therefore, issuing several pairs of thick insoles for daily rotation is recommended for a particular foam.
Design of experiments to address manufacturing tolerances and process variation influencing cogging torque and back EMF in the mass production of the permanent magnet synchronous motors

A number of manufacturing challenges exist in creating a robust permanent magnet synchronous motor (PMSM) design to meet critical requirements for cogging torque and torque ripple in specific applications. In addition, reduction of back EMF harmonics that lead to torque ripple is also required during design. Due to manufacturing tolerances, maintaining capability with these requirements is very difficult especially for mass production of units. A design of experiments (DOE) that combines manufacturing tolerances with assembly process variations is proposed here to address the sensitivity of cogging torque and harmonics. The limits for the parts and the process variations are extracted from an actual production environment. The DOE is laid out according to one of the robust engineering matrices chosen from Taguchi's method. The findings of the DOE are then utilized to set the tolerance limits for manufacturing the rotor magnets and to set the allowance for the rotor assembly process variation. The method helps limit the cogging torque and harmonics within predefined bands and make the motor design robust. This paper also investigates the capability of maintaining the tolerances of magnets for a surface mounted PMSM. The analysis is done using finite-element method and verified with experimental results.
Measuring banking sector outreach

• A well developed financial system is necessary for economic development and poverty alleviation (Beck, Demirguc-Kunt and Levine 2004 and Honohan 2004a). The empirical relationship between finance and growth is well established in theoretical literature, as surveyed by Levine (2005). Financial market imperfections are particularly binding on poor or small entrepreneurs lacking collateral, credit history or networks. Credit constraints impede financing of high-return projects and hamper efficient resource allocation, as well as adversely impact economic growth and poverty reduction (Galor and Zeira, 1993). • Financial development facilitates entry of new firms which fosters economic growth (Klapper, Laeven and Rajan, 2006). Wide access to finance is seen not only as expanding opportunities beyond the rich and the connected, but also promoting democracy and market economy (Rajan and Zingales, 2003). • Access to finance can contribute to technological progress and innovation (Ayyagari, Demirguc-Kunt and V. Maksimovic 2006). • Finally, given its social (and economic) benefits, access to finance can be seen on a similar level as access to basic needs such as safe water, health services, and education (Peachey and Roe, 2004). Depth versus outreach So far the literature on financial sector development has focused primarily on the dimension of depth, its measurement, determinants and its economic impact. Little is known about the breadth or the outreach of financial systems across countries, its determinants and impact on development. This is partly due to a dearth of adequate data (see Honohan 2004b). However, when discussing outreach or access, distinction has to be made between access versus use since the two are not synonymous. Access to and the possibility of utilizing financial services is a different concept than actual usage.
The Design of Web Information Automatic Retrieval System Based on Multi-agent

This paper briefly introduces the concept and status quo of the information retrieving technique, Proposes an automatic Web information retrieving system based on Multi-agent. This system analyzes its model architecture and work principle, gives solution to critical problems, leads Support Vector Machine(SVM) into intelligent filter subsystem, therefore to realize intelligent classifying and retrieving information on the Web efficiently. Experiment results prove that this system can improve recall ratio and precision ratio effectively and then attain a satisfying personal retrieving effect.
Empirical Analysis of Seed Selection Criterion in Influence Mining for Different Classes of Networks

Recent years have seen social networks gain lot of popularity to share information, connecting millions of people from all over the world. Studying the spread of information, or Information Diffusion in these networks has shaped into a well known field of study with numerous applications in areas such as marketing, politics, and personality evaluation. Researchers have studied information diffusion under various models and opted centrality-based algorithms that offer better results over many other approaches. These algorithms try to select initial seed nodes effectively so as to maximize influence in a network in minimum time. However, since different networks follow different structural properties, motivating the need to study different diffusion strategies for networks with different structural properties. In this paper, we aim to empirically analyze four different measures of centrality to select seed vertices for influence mining on four classes of networks: small-World networks, scale-free networks, small world-scale free networks and random networks. These networks are generated equivalent in size to four semantically different real world social networks. We use two most frequently used diffusion models: Independent Cascade model and Linear Threshold model for analysis. Our results show interesting behavior of various centrality measures for the above said classes of networks.
Zentrum für Technomathematik Fachbereich 3 – Mathematik und Informatik A Wavelet Regularization for Nonlinear Diffusion Equations

We are concerned with a wavelet–based treatment of nonlinear diffusion equations in the context of image processing. In particular, we focus on the Perona– Malik model as a suitable instrument for smoothing images while preserving edges. We are not exploring a complete new method of solving the Perona– Malik equation but, inspired by Weickert et.al., we develop a new variant, based on wavelet technology, of regularizing this specific equation. By carefully choosing the generators, we are able to derive all inner products and integrals of the weak formulation with high efficiency. We prove that the proposed scheme overcomes the ill-posedness of the nonlinear Perona–Malik diffusion equation and illustrate the obtained results by some numerical experiments.
Interconnect sizing and spacing with consideration of buffer insertion for simultaneous crosstalk-delay optimization

As integrated circuits (ICs) are scaled into nanometre dimensions and operate in gigahertz frequencies, interconnects have become critical in determining system performance and reliability. In this paper we propose a new approach to investigate crosstalk reduction techniques which helps to have simultaneous optimization of interconnect delay and crosstalk noise in deep submicron VLSI circuits. The optimization problem is modelled by solving a new cost function to find a minimum cost for both crosstalk noise and delay which are conflicting in nature. Through MATLAB software, a system of three coupled wires is modelled as a RC distributed network. The results indicate the number of optimum available solutions including wire sizing, wire spacing and buffer insertion in which crosstalk reduction techniques can be useful for both crosstalk noise and delay.
Endocannabinoid-Independent Retrograde Signaling at Inhibitory Synapses in Layer 2/3 of Neocortex: Involvement of Vesicular Glutamate Transporter 3

Recent studies implicate dendritic endocannabinoid release from subsynaptic dendrites and subsequent inhibition of neurotransmitter release from nerve terminals as a means of retrograde signaling in multiple brain regions. Here we show that type 1 cannabinoid receptor-mediated endocannabinoid signaling is not involved in the retrograde control of synaptic efficacy at inhibitory synapses between fast-spiking interneurons and pyramidal cells in layer 2/3 of the neocortex. Vesicular neurotransmitter transporters, such as vesicular glutamate transporters (VGLUTs) 1 and 2, are localized to presynaptic terminals and accumulate neurotransmitters into synaptic vesicles. A third subtype of VGLUTs (VGLUT3) was recently identified and found localized to dendrites of various cell types. We demonstrate, using multiple immunofluorescence labeling and confocal laser-scanning microscopy, that VGLUT3-like immunoreactivity is present in dendrites of layer 2/3 pyramidal neurons in the rat neocortex. Electron microscopy analysis confirmed that VGLUT3-like labeling is localized to vesicular structures, which show a tendency to accumulate in close proximity to postsynaptic specializations in dendritic shafts of pyramidal cells. Dual whole-cell recordings revealed that retrograde signaling between fast-spiking interneurons and pyramidal cells was enhanced under conditions of maximal efficacy of VGLUT3-mediated glutamate uptake, whereas it was reduced when glutamate uptake was inhibited by incrementing concentrations of the nonselective VGLUT inhibitor Evans blue (0.5-5.0 μm) or intracellular Cl- concentrations (4-145 mm). Our results present further evidence that dendritic vesicular glutamate release, controlled by novel VGLUT isoforms, provides fast negative feedback at inhibitory neocortical synapses, and demonstrate that glutamate can act as a retrograde messenger in the CNS.
Combination Forecasting Model for Mid-long Term Load Based on Least Squares Support Vector Machines and a Mended Particle Swarm Optimization Algorithm

Mid-Long term load forecasting(MTLF) plays an important role in power system. With more factors involved, single forecasting method becomes hard to satisfy requirement. This paper proposes a new combination model for MTLF based on least squares support vector machines (LS-SVM) and particle swarm optimization (PSO) algorithm. LS-SVM is a new kind of SVM which regresses faster than standard, and a mended particle swarm optimization (MPSO) algorithm is employed to optimize the parameters of LS-SVM. With a real case test, the result shows proposed model outperforms tradition combination model.
a chair ” “ Stop robot ” “ Stopped following ” “ Back ” “ OK ” Req Contact Display contact FOLLOW FOLLOW DIRECTIVENO

The purpose of this paper is to motivate the need for modelling Contact and Perception feedback in Natural language user interfaces for Human-Robot Interaction. In a scenario where the user and the robot are co-present contact and perception feedback will increase the situational awareness of the user. One component of a such a model could be a kind of low-level user model aiming to decide the perceptual status of the user. Miscommunication in scenarios that is related to insufficient or ill-formed Contact and Perception feedback has been analysed. To pinpoint the source of feedback problems we provide an initial account for the type of information sources that a low-level user model of Contact and Perception should comprise. We also discuss design implications and present a design case for a multimodal user interfaces that motivates this explorative effort.
An Improved Method to Predict the Position of Maximum Power Point During Partial Shading for PV Arrays

For a photovoltaic (PV) system, it is widely assumed that the peaks of a partially shaded P-V curve are located at the multiples of 0.8Voc, where Voc is the open-circuit voltage of the PV module. However, this assumption - known as the of 0.8Voc model for partial shading - is not necessarily true. If the same model is used to design the maximum power point tracker (MPPT), it is very likely that the algorithm will scan the wrong region of the P-V curve and, hence, an incorrect global peak detection. This paper attempts to prove the inadequacy of the 0.8Voc model when the number of modules in the string increases. Further, the work suggests a simple relationship to predict the correct position of the peaks. It is found that for a string of 20 modules (Voc of 400 V), the maximum deviation between the actual and computed peak is less than 3 V, while for the 0.8Voc model, the deviation reaches up to 50 V. In addition, using the proposed method, the MPPT efficiency can be improved by approximately 2%.