Miracle-dz/UniFAR-FTUs huggingface dataset:
Argumentation Models for Usability Problem Analysis in Individual and Collaborative Settings

ABSTRACT Consolidating usability problems (UPs) from problem lists from several users can be a cognitively demanding task for evaluators. It has been suggested that collaboration between evaluators can help this process. In an attempt to learn how evaluators make decisions in this process, the authors studied what justification evaluators give for extracting UPs and their consolidation when working both individually and collaboratively. An experiment with eight novice usability evaluators was carried out where they extracted UPs and consolidated them individually and then collaboratively. The data were analyzed by using conventional content analysis and by creating argumentation models according to the Toulmin model. The results showed that during UP, extraction novice usability evaluators could put forward warrants leading to clear claims when probed but seldom added qualifiers or rebuttals. Novice usability evaluators could identify predefined criteria for a UP when probed and this could be acknowledged as a backing to warrants. In the individual settings, novice evaluators had difficulty in presenting claims and warrants for their decisions on consolidation. Although further study is needed, the results of the study indicated that collaborating pairs had a tendency to argue slightly better than individuals. Through the experiment novice evaluators’ reasoning patterns during problem extraction and consolidation as well as during their assessment of severity and confidence could be identified.
Efficient Heuristics for Structure Learning of k-Dependence Bayesian Classifier

The rapid growth in data makes the quest for highly scalable learners a popular one. To achieve the trade-off between structure complexity and classification accuracy, the k-dependence Bayesian classifier (KDB) allows to represent different number of interdependencies for different data sizes. In this paper, we proposed two methods to improve the classification performance of KDB. Firstly, we use the minimal-redundancy-maximal-relevance analysis, which sorts the predictive features to identify redundant ones. Then, we propose an improved discriminative model selection to select an optimal sub-model by removing redundant features and arcs in the Bayesian network. Experimental results on 40 UCI datasets demonstrate that these two techniques are complementary and the proposed algorithm achieves competitive classification performance, and less classification time than other state-of-the-art Bayesian network classifiers like tree-augmented naive Bayes and averaged one-dependence estimators.
Virtual Network Embedding Based on Topology Potential

To improve the low acceptance ratio and revenue to cost ratio caused by the poor match between the virtual nodes and the physical nodes in the existing virtual network embedding (VNE) algorithms, we established a multi-objective optimization integer linear programming model for the VNE problem, and proposed a novel two-stage virtual network embedding algorithm based on topology potential (VNE-TP). In the node embedding stage, the field theory once used for data clustering was introduced and a node embedding function designed to find the optimal physical node. In the link embedding stage, both the available bandwidth and hops of the candidate paths were considered, and a path embedding function designed to find the optimal path. Extensive simulation results show that the proposed algorithm outperforms other existing algorithms in terms of acceptance ratio and revenue to cost ratio.
Robust stairway-detection and localization method for mobile robots using a graph-based model and competing initializations

One of the major challenges for mobile robots in human-shaped environments is navigating stairways. This study presents a method for accurately detecting, localizing, and estimating the characteristics of stairways using point cloud data. The main challenge is the wide variety of different structures and shapes of stairways. This challenge is often aggravated by an unfavorable position of the sensor, which leaves large parts of the stairway occluded. This can be further aggravated by sparse point data. We overcome these difficulties by introducing a three-dimensional graph-based stairway-detection method combined with competing initializations. The stairway graph characterizes the general structural design of stairways in a generic way that can be used to describe a large variety of different stairways. By using multiple ways to initialize the graph, we can robustly detect stairways even if parts of the stairway are occluded. Furthermore, by letting the initializations compete against each other, we find the best initialization that accurately describes the measured stairway. The detection algorithm utilizes a plane-based approach. We also investigate different planar segmentation algorithms and experimentally compare them in an application-orientated manner. Our system accurately detects and estimates the stairway parameters with an average error of only 2 . 5 mm for a variety of stairways including ascending, descending, and spiral stairways. Our method works robustly with different depth sensors for either small- or large-scale environments and for dense and sparse point cloud data. Despite this generality, our system’s accuracy is higher than most state-of-the-art stairway-detection methods.
Mixed reality emergency management: bringing virtual evacuation simulations into real-world built environments

ABSTRACT Computer-based evacuation simulations are important tools for emergency managers. These simulations vary in complexity and include 2D and 3D GIS-based network analyses, agent-based models, and sophisticated models built on documented human behaviour and particle dynamics. Despite the influential role of built environments in determining human movement, a disconnect often exists between the features of the real world and the way they are represented within these simulation environments. The proliferation of emergency management location-aware mobile devices, along with a recent infatuation for augmented reality (AR), has resulted in new wayfinding and hazard assessment tools that bridge this gap, allowing users to visualize geospatial information superimposed on the real world. In this paper, we report research and development that has produced AR geovisual analytical systems, enabling visual analysis of human dynamics in multi-level built environments with complex thoroughfare network infrastructure. We demonstrate prototypes that show how mixed reality visual analysis of intelligent human movement simulations built in virtual spaces can become part of real space. This research introduces a fundamentally new way to view and link simulations of people with the real-world context of the built environment: mixed reality crowd simulation in real space.
Exact bootstrap confidence intervals for regression coefficients in small samples

ABSTRACT Regression analysis is one of the important tools in statistics to investigate the relationships among variables. When the sample size is small, however, the assumptions for regression analysis can be violated. This research focuses on using the exact bootstrap to construct confidence intervals for regression parameters in small samples. The comparison of the exact bootstrap method with the basic bootstrap method was carried out by a simulation study. It was found that on a very small sample (n ≈ 5) under Laplace distribution with the independent variable treated as random, the exact bootstrap was more effective than the standard bootstrap confidence interval.
Sensitivity analysis of reliability functions of the exponential power series lifetime distribution

ABSTRACT Hazard rate functions are often used in modeling of lifetime data. The Exponential Power Series (EPS) family has a monotone hazard rate function. In this article, the influence of input factors such as time and parameters on the variability of hazard rate function is assessed by local and global sensitivity analysis. Two different indices based on local and global sensitivity indices are presented. The simulation results for two datasets show that the hazard rate functions of the EPS family are sensitive to input parameters. The results also show that the hazard rate function of the EPS family is more sensitive to the exponential distribution than power series distributions.
Simultaneous self-calibration and navigation using trajectory optimization

We describe a trajectory optimization framework that maximizes observability of one or more user-chosen states in a nonlinear system. Our framework is based on a novel metric for quality of observability that is state-estimator agnostic and offers improved numerical stability over prior methods in some cases where the states of interest do not appear directly in the observation. We apply this metric to trajectory optimization problems for closed-loop self-calibration, maintaining observability while navigating through an environment, and rapidly modifying an already-planned trajectory for online recalibration. We include a statistical procedure to balance observability of several states with heterogeneous units and magnitudes. As an example, we apply our framework to online calibration of GPS–IMU and visual–inertial navigation systems on a quadrotor helicopter. Extensive simulations and a real-robot experiment demonstrate the effectiveness of our framework, showing better convergence of the states and the resulting higher precision in navigation.
KStable: A Computational Method for Predicting Protein Thermal Stability Changes by K-Star with Regular-mRMR Feature Selection

Thermostability is a protein property that impacts many types of studies, including protein activity enhancement, protein structure determination, and drug development. However, most computational tools designed to predict protein thermostability require tertiary structure data as input. The few tools that are dependent only on the primary structure of a protein to predict its thermostability have one or more of the following problems: a slow execution speed, an inability to make large-scale mutation predictions, and the absence of temperature and pH as input parameters. Therefore, we developed a computational tool, named KStable, that is sequence-based, computationally rapid, and includes temperature and pH values to predict changes in the thermostability of a protein upon the introduction of a mutation at a single site. KStable was trained using basis features and minimal redundancy–maximal relevance (mRMR) features, and 58 classifiers were subsequently tested. To find the representative features, a regular-mRMR method was developed. When KStable was evaluated with an independent test set, it achieved an accuracy of 0.708.
Out-of-pocket health care expenditures and household food insecurity among families with children

approved:___________________________________________ Stephanie Bernell Since the late 1990s accelerated growth in health care spending coupled with a cost shift of health insurance from employers to employees has created an increased financial burden for many families. Past research suggests that financial burden due to out-of-pocket (OOP) health care costs limits access to health care and may reduce spending on other basic needs, such as food. The primary objective of this study was to assess the relationship between out-ofpocket health care expenditures and food insecurity among families with children. Secondarily, this study examined the relationship between the health status of children and household food security. This study used data from the Panel Study of Income Dynamics (PSID, 2003) to test whether higher out of pocket health care expenditures increase household food insecurity for families with children. Respondents reported out of pocket expenditures for both medical services and insurance premiums in 2001 and 2002. Food insecurity was measured for the previous 12 months using the 18-item USDA Food Security Survey Module. Multivariate weighted logit analysis was conducted to model the relationship between OOP health care costs and household food security status; and child health status and household food security. There was no evidence that higher OOP health care costs were associated with household food insecurity. However, among families earning less than 300 percent of the federal poverty threshold, having private insurance did increase the likelihood of experiencing food insecurity (OR =4.77, 95% CI = 0.05 1.02). Households with a child in poor health were not more likely to experience food insecurity; however having a wife in poor health was associated with food insecurity (OR = 4.00, 95% CI =1.679.52). The findings from this study suggest that programs designed to limit OOP health care spending among moderate and low income families should evaluate the impact on household food security. ©Copyright by Megan M. Patton-Lopez June 14, 2012 All Rights Reserved Out-of-Pocket Health Care Expenditures and Household Food Insecurity among Families with Children by Megan M. Patton-Lopez A DISSERTATION Submitted to Oregon State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy Presented June 14, 2012 Commencement June 2013 Doctor of Philosophy dissertation of Megan M. Patton-Lopez presented on June 14, 2012.
Clinical Nurses' Perception on Barriers to Research Utilization

Purpose: Although previous studies reported that actual application of research findings into the clinical setting was still poor, research utilization is a major component for implementation of Evidence-based Nursing Practice (EBNP). This study was conducted to identify the barriers to research utilization in clinical nurses. Methods: A descriptive survey design was used. Participants were 392 clinical nurses who were interviewed using a structured questionnaire. Results: Priority factors of barriers were setting, communication, research, and nurses. Five of the top 10 barriers were related to `setting. ` The first barrier was difficulty in the comprehension of statistical analyses. Nurses who worked in secondary hospitals and regularly read research articles perceived significantly lower barriers to research utilization compared with other nurses. There were statistically differences in research utilization among the groups by level of searching skills related to research documents and degree of understanding of EBNP. Conclusion: The study findings identified the top barriers to research utilization and key factors for nursing administrators and educators to consider in developing strategies to facilitate the utilization of research findings in clinical settings.
Reproducing room appearance on displays

This paper aims to successfully reproduce a real lit environment as an image on a self‐luminous computer display. The International Commission on Illumination‐recommended color appearance model CIECAM02 was used as the core of the whole reproduction system. Two experiments were conducted: to verify the values of the surround parameters specified in CIECAM02 and to produce equal perceived brightness between the room and the computer display by adjusting the illuminance level of the lighting in the room to match that of the displayed image.
The effect of DC offset on instantaneous operating characteristics of low-voltage circuit breakers

This paper addresses the effect of asymmetrical currents on factory adjustments and field verification of low-voltage circuit breaker instantaneous trip units. Both MCCBs and power circuit breakers are pertinent. Factory adjustment of low-voltage circuit breakers, based on test results using asymmetrical current, may cause the circuit breaker instantaneous unit to perform improperly and result in inadequate protection and coordination of electrical system apparatus. Similarly, field testing conducted to verify proper performance of low-voltage circuit breakers, using asymmetrical current, may lead to the erroneous conclusion that the circuit breaker's instantaneous unit is out of specification. In addition to addressing the difficulties presented by this phenomenon, this paper also proposes an approach to identifying and overcoming these testing difficulties.
The Interdisciplinary Collaborative Competency in Music Therapy: Terminology, Definitions, and Teaching Approaches

SPRING, ERIN K. , M.M. , June 2010, Music Therapy The Interdisciplinary Collaborative Competency in Music Therapy: Terminology, Definitions, and Teaching Approaches (90 pp.) Director of Thesis: Anita Louise Steele While a majority of music therapists report using collaboration in their music therapy practices, little information exists regarding how the AMTA competency on interdisciplinary collaboration is addressed within music therapy training programs. In order to investigate the teaching of this competency at the undergraduate level, a survey was sent to 65 music therapy program directors at AMTA approved institutions. The survey sought to identify and define the terms and teaching models used to address this competency, as well as its value and placement within the undergraduate curriculum. Interdisciplinary was the most commonly selected term to address the competency over the terms collaboration, multidisciplinary, consultation, transdisciplinary, and interprofessional. Definitions selected by respondents indicated confusion continues to exist regarding the definitions and use of collaborative terminology. Additionally, results showed that program directors strongly value the competency and that the preparation for interdisciplinary collaboration may benefit from more information on this topic. Collaborative definitions and teaching approaches are recommended. Approved: _____________________________________________________________ Anita Louise Steele Associate Professor of Music
Study on the Algorithm for Evaluation of Converged Network Performance Based on QoE

In converged networking, the evaluation of network performance is completed by analyzing the key performance indexes which can reflect the states of network from the point of user view and integrated results should be evaluated by user or quality of experiences. This paper proposes the algorithm for evaluation of network performance based on the fuzzy analytic hierarchy process that utilizes strategy data and key performance indexes to calculate the integrated results of evaluation. The result provides the necessary reference for the real-time monitoring of network.
The Practices of Best High-School Teachers

During the past school year I had at my disposal part of the time of an intelligent, but not a professionally trained, graduate student. At my direction he visited twenty-one public high schools in New York and eleven suburban cities, taking with him a letter from me requesting that he be permitted to observe the work of the best teachers of any subjects. Without prejudice and with few instructions he made reports varying from 70 to 1,500 words, averaging about 6oo, on what he observed in 104 classrooms. (The courtesy of the principals and teachers is gratefully acknowledged.) It would be illuminating of practices to present the reports in full, but they would require too much space. Instead, I shall give some analysis of them and some critical comments about them. To those accustomed to "model" or demonstration lessons or to illustrations used in textbooks of theory, the reports of these observed lessons may be disappointing. Hardly one could be used to exemplify ideal teaching, but they may fairly be considered as representative of current practices of teachers in the metropolitan area who are considered by the principals as their best teachers. There is no assumption that the reported practices are typical of those of the best teachers in other parts of the country, and no data were gathered to show how they differ from the practices of teachers in the same schools who are considered mediocre or poor. It is important that educators who are planning for the betterment of secondary education know what is actually being done in teaching, and principals who have shirked the responsibility for supervision, procrastinating it for easier and more immediately pressing duties of administration, may find in the description of these reports a challenge and a direction of what they can and should attack. The reports are, of course, incomplete and imperfect. Studying
Mathematics Teachers' Development, Exploration, and Advancement of Technological Pedagogical Content Knowledge in the Teaching and Learning of Algebra

This article describes experiences from a professional development project designed to prepare in-service eighth-grade mathematics teachers to develop, explore, and advance technological pedagogical content knowledge (TPCK) in the teaching and learning of Algebra I. This article describes the process of the participating teachers’ mathematical activities and teaching and learning tasks, each of which required a TPCK framework. Sessions were organized to transform content through strategies that integrate technology with the teachers’ content and pedagogical knowledge. Content of the professional development sessions ranged from analyzing algebraic learning activities to examining appropriate uses of technology in the teaching and learning of algebra. Teachers participated in 60 hours of summer sessions and 60 hours of academic year sessions. Results revealed the need to provide teachers with opportunities to develop and explore an integration of technological, pedagogical, and content knowledge in the teaching and learning of algebra.
Optimization algorithm of redundant data classification in distributed database

Abstract Large-scale distributed database includes a large number of redundant data, which will lead to low correlation between the data, resulting in lower network efficiency. This paper proposes a method for optimizing redundant data classification in distributed databases. Firstly, the redundant data attributes are extracted to provide the accurate data base for the classification of redundant data. Then the optimal classification threshold is obtained according to the weight probability theory, and the redundant data classification is realized. The experimental results show that the algorithm can improve the accuracy of classification and achieve satisfactory results.