Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuity and availability in Dual-Frequency Multi-Constellation ARAIM

Future multi-constellation global navigation satellite systems (GNSS) will provide a greatly increased number of redundant ranging signals, which can improve integrity monitoring capability using receiver autonomous integrity monitoring (RAIM). Advanced RAIM (ARAIM) aims at providing localizer precision with vertical guidance down to 200 feet altitude (LPV-200) for worldwide aircraft landing navigation. This paper assesses the need of exclusion for continuity and availability in future dualfrequency, multi-constellation ARAIM. The first part of the paper is a three-part analysis of the need of faultexclusion at the aircraft for continuity. First, an interpretation of ‘average sense’ continuity is given, which is specified but not fully defined by the International Civil Aviation Organization (ICAO). Second, a critical satellite analysis is carried out to show that, for future multi-constellation GNSS, the impact of unscheduled satellite outages is small as compared to other sources of loss of continuity. Third, the paper shows that fault-exclusion at the aircraft is not required to meet the LPV-200 continuity risk requirement using dualconstellation GPS/Galileo ARAIM. The second part of the paper addresses the need of airborne exclusion for availability. Without airborne exclusion, ARAIM service outages due to fault detection can potentially last for one hour or longer. Such periods of continuous outage are highly undesirable. In response, fault exclusion methods can help reduce service outage periods at the cost of increased integrity risk due wrong exclusions. In this paper, availability is evaluated using ARAIM detectiononly as compared to using ARAIM detection-andexclusion. Results indicate that outage duration can be significantly reduced without substantial decrease in overall integrity performance.
Learning from Quality Audit in Higher Education Institutions: A Tool for Community Engagement Enhancement

Abstract Higher education institutions (HEIs) are asked more and more to be community engaged. In fact, governments have become more sensitive regarding the role and the contributions of these institutions in the development of the society in general and students’ skills in specific. Community engagement (CE) has also become an important part of the quality accreditation framework in several countries. Through this, researchers tried to demonstrate a positive correlation between the publication of the quality audit reports and the generation of positive outcomes in terms of CE. The study notified a certain decrease in terms of recommendations related to the standard of CE in comparison with the other areas of quality assessment. However, it was not possible to establish a positive correlation between the publication of quality reports and the decrease of the number of recommendations. Researchers concluded that the institutional engagement in CE activities appears to be a more strategic imposed tool made under the constraints of the quality accreditation standards, rather than the self-awareness and self-engagement of the HEIs.
A Framework for APP Discovery Based on Relation Mining

In this paper, we propose a framework of APP discovery for a given user based on APP relation mining. First, Personalized Trust Community (PTC) is constructed based on complex network techniques with “APP-User” data. Then we discover fresh APPs based on APPs latent relations which is mined through PTC analysis. We plan to experiment our framework with the data crawled from APP store.
Management and Recovery Patterns Following Sport-Related Mild Traumatic Brain Injury in Male and Female College Athletes

Purpose: We examined patterns in mild traumatic brain injury (mTBI) management and recovery exhibited by male and female athletes over the 19-year history of a Division 1 University Concussion Management Program. Methods: We examined 234 diagnosed mTBIs and compared time required to return to baseline on neurocognitive and self-report symptom measures for male and female athletes. Results: Males and females sustained 63.3% and 36.7% of mTBI, respectively. Athletes required on average 11.89 days to return to baseline on neurocognitive assessments and 11.83 days to report being symptom-free. There was a significant difference in the number of days until genders were symptom-free, with males and females requiring on average 10.11 days and 14.30 days, respectively. Discussion: More collegiate athletes received pre- and post-mTBI management than in the past. There is a continued need to examine patterns of neurocognitive and symptom recovery, which may lead to earlier detection of athletes at risk for persistent post-mTBI symptoms.
Evaluation of Altered Functional Connections in Male Children With Autism Spectrum Disorders on Multiple-Site Data Optimized With Machine Learning

No univocal and reliable brain-based biomarkers have been detected to date in Autism Spectrum Disorders (ASD). Neuroimaging studies have consistently revealed alterations in brain structure and function of individuals with ASD; however, it remains difficult to ascertain the extent and localization of affected brain networks. In this context, the application of Machine Learning (ML) classification methods to neuroimaging data has the potential to contribute to a better distinction between subjects with ASD and typical development controls (TD). This study is focused on the analysis of resting-state fMRI data of individuals with ASD and matched TD, available within the ABIDE collection. To reduce the multiple sources of heterogeneity that impact on understanding the neural underpinnings of autistic condition, we selected a subgroup of 190 subjects (102 with ASD and 88 TD) according to the following criteria: male children (age range: 6.5–13 years); rs-fMRI data acquired with open eyes; data from the University sites that provided the largest number of scans (KKI, NYU, UCLA, UM). Connectivity values were evaluated as the linear correlation between pairs of time series of brain areas; then, a Linear kernel Support Vector Machine (L-SVM) classification, with an inter-site cross-validation scheme, was carried out. A permutation test was conducted to identify over-connectivity and under-connectivity alterations in the ASD group. The mean L-SVM classification performance, in terms of the area under the ROC curve (AUC), was 0.75 ± 0.05. The highest performance was obtained using data from KKI, NYU and UCLA sites in training and data from UM as testing set (AUC = 0.83). Specifically, stronger functional connectivity (FC) in ASD with respect to TD involve (p < 0.001) the angular gyrus with the precuneus in the right (R) hemisphere, and the R frontal operculum cortex with the pars opercularis of the left (L) inferior frontal gyrus. Weaker connections in ASD group with respect to TD are the intra-hemispheric R temporal fusiform cortex with the R hippocampus, and the L supramarginal gyrus with L planum polare. The results indicate that both under- and over-FC occurred in a selected cohort of ASD children relative to TD controls, and that these functional alterations are spread in different brain networks.
Guarding the Golden Door: American Immigration Policy and Immigrants since 1882

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Maintaining Source Origin in a Modelica Compiler

Modelica is a modelling language used to describe a physical system and can be used to simulate the system. A Modelica compiler transforms a Modelica model into an equation system in order to simulate it. During this transformation the information about where the equations originates from is lost. This information could be useful to have when, e.g., trying to locate an error. In this thesis we solve this problem for the JModelica.org Modelica compiler by propagating information about the source origin through the compiler. This makes it possible for us to show the origin of an error. We have also made sure that the connections between the transformed model and the original model can be viewed even when no error has occurred in a HTML document. The increase in memory usage in the current implementation is well within acceptable levels, with the highest memory increase measured being only 0.18%. (Less)
Face Recognition based on Gabor Transform Combined with LBP

Traditional human feature extraction algorithm based on Gabor transform folds the human face image and multiple scales and multiple direction kernel function for Gabor to obtain Gabor human feature with sampling and cascade. The obtained Gabor feature dimension of human face is high, the recognition process wastes time rather without rotation invariance, and performance of human face figure decreases at plane rotation. LBP is used in operator for texture analysis due to algorithm thought is sample, computation complexity is low and discernment is strong, etc. The above-mentioned analyzed and used widely for several years, LBP operator has fortissimo gray invariance and rotation invariance to overcome the problems of rotation shifting and uneven illumination. This dissertation adopts feature extraction method for combination of Gabor wavelet and LBP to research human face recognition to put forward to LBP algorithm with uniform pattern for improving the accuracy of human face feature recognition and flexibility of practical operation. The dissertation provides optimization selection and integration for human face feature extracted by Gabor wavelet and LBP to put forward to improved algorithm: feature extraction methods combined with 2D-Gabor wavelet and uniform LBP. This dissertation algorithm makes progress to improve recognition rate and decrease data redundancy with flexibility and effectiveness for human face recognition.
A computational model of sound perception in a second language

This thesis reposts on an investigation based around a number of studies that use computational statistical pattern recognition and machine learning techniques to simulate listeners ? non-native consonant perception and learning development.More specifically, listeners? second language (L2) to native language (L1) consonant assimilations as well as the early stage development of learnes? L2 consonant acquisition have been modeled.The computational modeling approaches used in this thesis have been shown to provide quantitative predictions and data visualization for L2 perception studies, and as such act as a valuable complementary approach to those used in traditional behavioral and theoretical studies in sound acquisition.
Urdu Documents Classification using Naïve Bayes

Objectives: The purpose of this conceptual paper is to highlight the process involved in handling of editorials based on Urdu morphology for better classification purpose. Methods: The first step is to collect editorials belongs to different categories, Corpus will be formed by the collected data, preprocessing activities makes corpus more reliable and relevant. Naive Bayes will be used for classification purpose. Naive Bayes is identified as best technique for serving as a document classification model, it produces fastest and accurate results as well as very robust to irrelevant features. Findings: Handling Urdu morphology is one of the biggest tasks of our research, to handle this problem we need to encode corpus by using utf-8 encoding and thereby changing system locale Urdu easily appear in readable form. Application: The main purpose of this approach is to work on classification of Urdu documents, as Urdu is a South Asian Language, which is among the widely spoken in the sub-continent. Urdu document classification involves all the pre-processing activities such as Language processing tasks, labeling and tagging; the tool explored will be R. It will be very helpful for all the firms who manage and manipulate data in Urdu languages e.g. this approach can be implemented on all Urdu news editorials so that all the editorials will be classified to different sub-categories so that user can extract the information he is looking for. This research is still in progress and may very time to time.
ALEXNET FEATURE EXTRACTION AND MULTI-KERNEL LEARNING FOR OBJECTORIENTEDCLASSIFICATION

Abstract. In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.
Feasibility of Using Participant Owned Smartphone Features to Conduct Ecological Momentary Assessment of Planned Exercise Behavior in College-Aged Adults

Recommended Citation Sheridan, Lucas F. ; Toth, Lindsay; and Strohacker, Kelley (2019) "Feasibility of Using Participant Owned Smartphone Features to Conduct Ecological Momentary Assessment of Planned Exercise Behavior in College-Aged Adults," Pursuit The Journal of Undergraduate Research at the University of Tennessee: Vol. 9 : Iss. 1 , Article 1. Available at: https://trace.tennessee. edu/pursuit/vol9/iss1/1
Digital War of South Korean Netizens in New York City: From Tweets to a Billboard Advertisement, and an Alt-Right Online Community

This study explores how people share their political perspectives within online communities, construct online discourses on social and political issues in digital spaces, and finally utilize offline venues to grow their base of supporters as a form of social movement. The researcher examines the events related to a series of birthday wish ads for the South Korean president in Times Square in 2018 and the responses of Ilbe, an alt-right online community, to the occasion. I use three lines of inquiry: 1) How did the Ilbe online community users facilitate online discussions in order to lead to offline social action? 2) Why did the Ilbe members create the advertisement in Times Square? and 3) What are the implications of the billboard within political and social contexts? By closely examining the recent digital confrontation among different online groups in South Korea, this project yields insights into understanding the current social movements based on digital media, and in particular, an alt-right group. The findings of this research demonstrate how social action can be mobilized based on online communication, and how faithfully the Ilbe community performs its role as a representative alt-right community of South Korea during the political turmoil. Finally, the current study articulates the relations between the digital world and physical space.
Music technology and Future Work Skills 2020: An employability mapping of Australian undergraduate music technology curriculum

This study explored how Australian music technology courses teach employability skills. A curriculum mapping of 63 undergraduate courses was conducted with course learning outcomes aligned against two benchmarks. The first benchmark was the Ten Skills for the Future Workforce which identifies key employability skills graduates will require in the coming decade. The second benchmark was the Australian Qualifications Framework Specification for the Bachelor Degree which defines the generic skills graduates must obtain through Australian Bachelor Degrees. This curriculum mapping reveals that Australian music technology courses teach Novel and Adaptive Thinking, Computational Thinking, New Media Literacy, and Design Mindsets universally. However, this curriculum mapping also reveals a deficit in employability skills related to Cross-Cultural Competency, Transdisciplinarity, Virtual Collaboration, and Collaboration more generally. The implications of this mapping is that Australian music technology educators seem to be prioritizing specific technical and creative skills over higher-order applications of skills and knowledge which are contextualized in their broader social and cultural contexts. Finally, this article shows how curriculum mapping can be implemented to embed employability skills progressively across a program sequence using a case study from the School of Music, University of Queensland.
Kurdish Dialects and Neighbor Languages Automatic Recognition

Dialect recognition is one of the most hot topics in the speech analysis area. In this study a system for dialect and language recognition is developed using phonetic and a style based features. The study suggests a new set of feature using one-dimensional LBP feature. The results show that the proposed LBP set of feature is useful to improve dialect and language recognition accuracy. The acquired data involved in this study are three Kurdish dialects (Sorani, Badini and Hawrami) with three neighbor languages (Arabic, Persian and Turkish). The study proposed a new method to interpret the closeness of the Kurdish dialects and their neighbor languages using confusion matrix and a non-metric multi-dimensional visualization technique. The result shows that the Kurdish dialects can be clustered and linearly separated from the neighbor languages.
JythonMusic: An environment for teaching algorithmic music composition, dynamic coding and musical performativity

The practice of coding music live challenges computing conventions with regard to developmental agility. The computational representation of music likewise challenges musicians to articulate their practice in new ways. In this article we describe the development, teaching and use of the JythonMusic environment designed to meet these challenges head on. JythonMusic, written in Python, is an open source project for music making and creative programming activities intended for musicians and programmers, of all levels and backgrounds. JythonMusic supports algorithmic music composition, dynamic coding and musical performativity including live coding. This article examines the functionality of JythonMusic for composition and performance projects in the context of university courses that combine computer science and music. The advantages of this medium for music and programming instruction are demonstrated through several projects and a live coding case study, leading to a series of observations and proposals concerning the advantages of coding in music pedagogy.
Transfer learning and Local interpretable model agnostic based visual approach in Monkeypox Disease Detection and Classification: A Deep Learning insights

The recent development of Monkeypox disease among various nations poses a global pandemic threat when the world is still fighting Coronavirus Disease-2019 (COVID-19). At its dawn, the slow and steady transmission of Monkeypox disease among individuals needs to be addressed seriously. Over the years, Deep learning (DL) based disease prediction has demonstrated true potential by providing early, cheap, and affordable diagnosis facilities. Considering this opportunity, we have conducted two studies where we modified and tested six distinct deep learning models-VGG16, InceptionResNetV2, ResNet50, ResNet101, MobileNetV2, and VGG19-using transfer learning approaches. Our preliminary computational results show that the proposed modified InceptionResNetV2 and MobileNetV2 models perform best by achieving an accuracy ranging from 93% to 99%. Our findings are reinforced by recent academic work that demonstrates improved performance in constructing multiple disease diagnosis models using transfer learning approaches. Lastly, we further explain our model prediction using Local Interpretable Model-Agnostic Explanations (LIME), which play an essential role in identifying important features that characterize the onset of Monkeypox disease.
An Adopted Hybrid Approach for Encroachment Catching By Combining Neuro Fuzzy Clustering and SVM On KDD-Cup-Dataset

- With the impending era of internet, the network security has become the key foundation for lot of financial and business web applications. Intrusion detection is one of the looms to resolve the problem of network security. Imperfectness of intrusion detection systems (IDS) has given an opportunity for data mining to make several important contributions to the field of intrusion detection. In recent years, many researchers are using data mining techniques for building IDS. Here, we propose a new approach by utilizing data mining techniques such as neuro-fuzzy and radial basis support vector machine (SVM) for helping IDS to attain higher detection rate. The proposed technique has four major steps: primarily, k-means clustering is used to generate different training subsets. Then, based on the obtained training subsets, different neuro-fuzzy models are trained. Subsequently, a vector for SVM classification is formed and in the end, classification using radial SVM is performed to detect intrusion has happened or not. To illustrate the applicability and capability of the new approach, the results of experiments on KDD CUP 1999 dataset is demonstrated. Experimental results shows that our proposed new approach do better than BPNN, multiclass SVM and other well-known methods such as decision trees and Columbia model in terms of sensitivity, specificity and in particular detection accuracy.
Regression as Classification: Influence of Task Formulation on Neural Network Features

Neural networks can be trained to solve regression problems by using gradient-based methods to minimize the square loss. However, practitioners often prefer to reformulate regression as a classification problem, observing that training on the cross entropy loss results in better performance. By focusing on two-layer ReLU networks, which can be fully characterized by measures over their feature space, we explore how the implicit bias induced by gradient-based optimization could partly explain the above phenomenon. We provide theoretical evidence that the regression formulation yields a measure whose support can differ greatly from that for classification, in the case of one-dimensional data. Our proposed optimal supports correspond directly to the features learned by the input layer of the network. The different nature of these supports sheds light on possible optimization difficulties the square loss could encounter during training, and we present empirical results illustrating this phenomenon.