Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Asymmetric Nonnegative Matrix Factorization

The problems that involve separation of grouped outliers and low rank part in a given data matrix have attracted a great attention in recent years in image analysis such as background modeling and face recognition. In this paper, we introduce a new formulation called (cid:96) ∞ -norm based robust asymmetric nonnegative matrix factorization (RANMF) for the grouped outliers and low nonnegative rank separation problems. The main advantage of (cid:96) ∞ -norm in RANMF is that we can control denseness of the low nonnegative rank factor matrices. However, we also need to control distinguishability of the column vectors in the low nonnegative rank factor matrices for stable basis. For this, we impose asymmetric constrains, i.e., denseness condition on the coefﬁcient factor matrix only. As a byproduct, we can obtain a well-conditioned basis factor matrix. One of the advantages of the RANMF model, compared to the nuclear norm based low rank enforcing models, is that it is not sensitive to the nonnegative rank constraint parameter due to the proposed soft regularization method. This has a signiﬁcant practical implication since the rank or nonnegative rank is difﬁcult to compute and many existing methods are sensitive to the estimated rank. Numerical results show that the proposed RANMF outperforms the state-of-the-art robust principal component analysis (PCA) and other robust NMF models in many image analysis applications.
Pengaruh Penggunaan Aplikasi Gojek Terhadap Perilaku Mahasiswa Universitas

– Currently, businesses engaged in online transportation are growing rapidly. The company really helps the community from doing daily activities to entertainment. The user only uses the phone, and the user can handle everything in one application. This software has many features from online food delivery, drug delivery, transportation, and so on. Corporate marketing is widespread and many people are free to use it, from high school students to adults. This research is to know the students' behavioral intentions. It is necessary to know the factors that attract users to Gojek application. The factors themselves are Performance Expectancy, Social Influence, Effort Expectancy, Facilitating Condition, Price Value, Hedonic Motivation, Habit to Behavioral Intention, and Use Behavior based on Unified Theory of Acceptance and Use of Technology (UTAUT 2) as a theoretical framework using Smart PLS -3, namely the acceptance of technology in the context of consumer use. Based on the results of research with 160 respondents among students in the city of Batam, there are 3 relationships that have positive and significant values. Variables that affect Behavioral Intention are Habit and Hedonic Motivation. then the variable that has an effect on Use Behavior is Behavioral Intention while the other variables have no significant effect. The results of the Coefficient of Determination (R2) test, the factors above have an effect on Behavioral Intention of 60.4% and the effect of Use Behavior is 54.4%.
Image Registration using Local Invariant Relational Gabor Features for Automated Underwater Images Mosaicing

—This paper describes a novel image registration in mosaic framework based on a new type of local invariant feature. Our features are located using Gabor-based features detector which gives spatial distribution of features. We sample a feature descriptor using a newly proposed Relational Gabor Features that adapts relational features onto Gabor magnitude using varying patch sizes obtained during features detection. We tested our proposed features for mosaicing underwater image sequences at ten meters depth taken in Redang Island, Terengganu, Malaysia. Based on the experimental results, the proposed method has produced in highly effective image registration method to obtain a coherent underwater image mosaic.
Smart Health Prediction System Using Machine Learning

: Diabetes is one of the deadliest diseases which causes an increase in blood sugar. Many problems can occur if diabetes remains undetected and unidentified. The lengthy identifying process results in visiting of a patient to a diagnostic center and consulting doctor. But the rise in machine learning approaches solves this difficult problem. The true objective of this study is to design a model which can determine the likelihood of diabetes in patients with maximum accuracy. Therefore, we are using machine learning classification algorithm, namely Decision Tree, in this experiment to detect diabetes at an early stage. However, early determination of diabetes is quite challenging process for medical practicing people due to complex interdependence on various factors as diabetes affects human organs such as kidney, eye, heart, nerves, foot, etc. Data science methods have the ability to benefit other scientific fields by finding new light on common questions. One such benefit is to help make predictions on medical data. Machine learning is an emerging scientific field in data science to deal with the ways in which machines learn from their experience.
THE IMPACT OF LAND COVER CHANGE ON THE HYDRAULIC CONDUCTIVITY IN TROPICAL PEATLANDS

Hydraulic conductivity is one of the most important parameters that describe peat properties related to water movement through the peat profile. In the application of peat hydrology, the information about saturated hydraulic conductivity is required to estimate the amount of water needed to maintain the saturation condition in the peat dome and is also required to simulate the peat water table depth and groundwater flow. However, some uncertainties regarding hydraulic conductivity in the tropical peatlands especially those related to the effect of peat swamp forest conversion on hydraulic conductivity remain. Hydraulic conductivity was estimated at four different land cover types in West Kalimantan, Indonesia. We utilized the slug test method to estimate the hydraulic conductivity at three depths (1.0, 3.5, and 6.0 m) and interpreted the data based on the Bouwer and Rice (1976) approach. In total, 324 wells were installed in 21 sites including five sites in shrubs, five sites in oil palm plantations and eight sites in forests and three sites in recently burnt forest with six replications for each site. The results show that forested peatlands have higher conductivity with an average of 0.63 ± 0.36 m d -1 (mean ± s.e.) compare with oil palm of 0.20 ± 0.13 m d -1 and shrubs of 0.16 ± 0.08 m d -1 . The relationship between hydraulic conductivity land cover types, measurement depth and peat properties such as carbon concentration, loss on ignition, bulk density, and degree of decomposition, were assessed using hierarchical linear models. Results depicted that hydraulic conductivity was likely be the best explained by the measurement depth, bulk density, and forests-non forests land cover. However, carbon concentration and loss on ignition provided a very small portion for explaining the variability of hydraulic conductivity.
DEVELOPING EVALUATION MODEL IN SEERAH TEACHING FOR UPPER SECONDARY ISLAMIC EDUCATION TEACHERS

Application of Historical Thinking Skills (TKPS) is needed in the field of history to make its teaching and learning process effective and meaningful. This raised a question whether Islamic Education teachers, who are untrained in historical discipline, are able to apply the five components of Historical Thinking Skills (KPS) when teaching Seerah Nabi (Biography of Prophet Muhammad P.B.U.H)). The purpose of this research to determine the number of dimensions for the items and build an evaluation model to measure the effects of the relationship between KPS, TPACK and TKPS in this area of study. Structural Equation Modelling (SEM) using IBM-SPSS AMOS Graphic software was employed for this purpose. The population for this research consisted of Islamic Education teachers who teach Upper Secondary Islamic Education subject in Kelantan, Malaysia. From the results of EFA and CFA analyses, 6 items have been deleted from the KPS construct leaving only 12 items at the end of the study. Similar results were also found for the Application of Historical Thinking Skills construct (TKPS) where items which were originally 13 were reduced to 10. For the TPACK component, as a result of the CFA analysis, the Technological sub-component was removed from the TPACK component due to the low factor loading (0.39). Based on the result of the EFA and CFA analyses, only 35 items with three main constructs (KPS, TKPS and TPACK) and two sub-constructs (Pedagogy and Content) were considered for SEM analysis.This study has successfully developed an evaluation model (SEM) to measure the effect of Historical Thinking Skills (KPS) and Technological, Pedagogical and Content Knowledge Skills (TPACK) on the application of KPS by Islamic Education teachers. Findings of the study found that the relationship of KPS effects on the application of KPS is significant with the regression coefficient value of 0.80. This proves that the teaching and learning process (PdPC) is dependent on the level of mastery of teachers in the KPS component. KPS component also has a significant effect on TPACK with a regression coefficient of 0.81. This means TPACK skills during the teaching and learning process (PdPC) were dependent on the level of mastery of teachers in the KPS component and TPACK Construct as a mediator for the relationship between KPS and TKPS.
Institutional Environments and Social Entrepreneurial Intentions: A Case of Thailand

This paper provides an empirically tested model that clarifies the relationships of institutional environment dimensions. This model also reveals the mediating role of perceived desirability and feasibility toward social entrepreneurial intentions. This paper is the first attempt to apply institutional environments into social entrepreneurial intentions in Thailand. A total of 530 undergraduate students from four universities in Thailand were analyzed. All hypotheses were tested using structural equation modeling. The results indicated that two institutional environments dimensions, which are regulatory and cognitive, have a positive, significant, and direct effect on social entrepreneurial intentions, but normative dimension has an insignificant direct effect on social entrepreneurial intentions. Perceived feasibility mediated all institutional environment dimensions, whereas perceived desirability insignificantly mediated all institutional environment dimensions. Therefore, this research presents a new understanding of the effects of institutional environments on social entrepreneurial intentions. It may also promote emerging and important global activities.
Functional Outcomes in Musculoskeletal Oncology

16 BACKGROUND . 18 METHODS . 18 RESULTS . 23 DISCUSSION . 24 CONCLUSIONS . 27 CHAPTER 4: PREDICTORS OF FUNCTIONAL RECOVERY AMONG MUSCULOSKELETAL ONCOLOGY PATIENTS UNDERGOING LOWER EXTREMITY ENDOPROSTHETIC RECONSTRUCTION . 31 ABSTRACT . 33 BACKGROUND . 35 METHODS . 36 RESULTS . 39 DISCUSSION . 41 CONCLUSIONS . 43. 33 BACKGROUND . 35 METHODS . 36 RESULTS . 39 DISCUSSION . 41 CONCLUSIONS . 43
Measures Of Population Stability and Instability

PSI measures distribution stability of a variable. A popularly used PSI is undesirable. Creating binned ranges for its calculation is a chore. The range boundaries and the number of bins could affect the PSI quantity. Bin ordering makes no difference thus all variables are treated as categorical. We build our PSI without requiring binning, distinguishes numeric shifts from categorical redistribution, and create a mechanism to unify the two for mixed ordinal/categorical variables. our PSI for numeric variables separate from categorical variables, and show a simple shift test and an overlap test. We create a mechanism to link the quantities for mixed variables (e.g. numeric with missing).
Climate Change, Cereal Production and Economic Growth: An Application of Generalized Method of Movement (GMM)

: Developing countries like Pakistan, India and Bangladesh shares various common challenges in addressing existing and future climate hazards. This study examines the impact of climate change and cereal production on economic growth in Pakistan, India and Bangladesh for the period 1971–2013. The variables employed in the study are carbon dioxide emissions, methane emissions, nitrous oxide emissions, total greenhouse gases emissions, cereal production and GDP growth rate. Generalized method of moments (GMM) analysis is used to evaluate different equations in relation to climatic factors and cereal production with economic growth in Pakistan, India and Bangladesh. The results show that effects of climate change variables have negative relationship with economic growth both in case of Pakistan and India. While the situation is different in case of Bangladesh. Carbon dioxide emissions and methane emissions are positively while nitrous oxide emissions and total greenhouse gases emissions are negatively related with economic growth. There is need to overcome the problem of climate change by expanding climatic and agricultural research to improve the economic situation of a country.
Phase Transitions Without Thermodynamic Limit,The Crucial R\^ole of Possible and Impossible Fluctuations, The Treatment of Inhomogeneous Scenaria in the Microcanonical Ensemble

Microcanonical thermodynamics (MCTh) is contrasted to canonical thermodynamics (CTh). At phase transitions of 1.order the two ensembles are NOT equivalent even in the thermodynamic limit . Energy fluctuations do not vanish and phase separations are suppressed in CTh. A proper treatment of fluctuations is neccessary. MCTh allows to address even isolated small systems where phase transitions can be clearly classified into first order and continuous ones. The microcanonical caloric curve T(E) determines the transition temperature, latent heat AND surface entropy/tension. For systems of ca.1000 Na-, K-, or Fe-atoms at 1 atm. all 3 quantities can be calculated. The three parameters approach with rising size the known bulk values. There is nothing that demands the use of the thermodynamic limit. Within microcanonical thermodynamics of finite systems there are fundamental differences between conserved extensive variables and ensemble related ones like entropy, temperature and pressure. This is discussed in detail.
AiCVT - FIRST CONTINUALLY VARIABLE TRANSMISSION (CVT) WITH POSSIBILITY OF AVERAGE EFFICIENCY ABOVE 100% AND WITHOUT LIMIT OF POWER

In this work a theoretical increase in the efficiency of the Automatic Inertial Continuously Variable Transmission (AiCVT) [1] was obtained using a modified torque converter, instead of a simple fluid coupler. In this modified torque converter the stator multiplies the torque in the pump (instead of in the turbine), but the pump is in the output shaft, so the AiCVT system will do something never done before, it will have an efficiency above 100% in the first gears, and last gear will achieve ≈100% efficiency. It will be the only power transmission system that besides normal losses, will also have gains, and the more slips between the pump and turbine, more losses, but also more gains. The AiCVT features the torque converter pump at the output of the transmission, and planetary inside the turbine, allowing power from the engine to have two parallel paths to traverse the transmission. The turning of the turbine serves to change the gear ratios. With these 2 features it becomes unnecessary to use any sensor, since changing gear ratios is a magnitude similar to the instantaneous Linear Momentum of the vehicle, and this also causes immediate responses. There is no torque limit and it does not depend on friction to transfer power.
A GENERAL GRAPH DECOMPOSITION AND MINIMUM CUTS COMPOSITION FRAMEWORK FOR MIN-CUT PROBLEM

A general graph decomposition and minimum cuts composition framework is proposed in this paper. We ﬁrst present a graph decomposition method. We prove that the minimum cuts of the original graph can be obtained by ﬁnding the minimum cuts of a sequence of subgraphs. Based on the graph decomposition method and minimum cuts composition theorem, we develop a general framework, recursive minimum cuts (RMC) framework, where any existing minimum cuts algorithm can be used to solve minimum cuts problem over the sub-graphs. The computational complexity of the proposed approach is O ( N log3log2 ) . We also design a parallel algorithm and run in approximate O ( N ) time. Some preliminary experiments are provided on some synthetic graphs from images to test the proposed RMC framework.
Improving quality management of pavement condition data by Bahareh Bazargani A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Major: Civil Engineering (Intelligent Infrastructure Engineering) Program of Study Committee

Pavement management, planning ahead for the maintenance and repair of the roadway, is dynamically developing with evolving technology. To collect reliable, accurate, and complete data, agencies must perform quality management (QM) on their pavement data. QM is defined as the process to achieve high-quality output. It is a procedure to ensure reliability, accuracy, and completeness of data. Pavement management engineers have been performing QM on pavement condition data for decades. To maintain a certain level of pavement quality, the Federal Highway Administration (FHWA) has asked state departments of transportation (DOTs) to report all the data processes they follow in their pavement management practices in a document called a data quality management plan (DQMP). A DQMP must contain information regarding the following sections: (1) data collection equipment calibration and certification, (2) certification process for persons performing manual data collection, (3) data quality control measures, (4) data sampling, review and checking processes, and (5) error resolution procedures and data acceptance criteria. Sampling plays a significant role in QM and it is one of the main parts of DQMP. It is necessary to select the appropriate and representative sample size to ensure that the budget and resources are not being excessively used for large sample sizes, and also the samples represent the population to be able to generalize from their results. Currently, in pavement management, QM sample size is selected based on experience, and using rules of thumb usually 5% or 10% of the population while sample size calculation should be independent of population size. This dissertation aims to improve the QM of pavement management data by reviewing multiple DOTs DQMPs and detecting the gaps and needs in the state of practice, and also by implementing the power analysis method to select a representative sample. For the first objective of this research, a survey was sent out to DOT pavement management engineers requesting information about their
Modelling the Pass-Through of Airline Operating Costs on Average Fares in the Global Aviation Market

This paper describes the development of a regression model that captures the effect of specific airline operating costs, as well as other key determining factors, on airfare. The model is estimated for a number of world regions. This allows for comparison of the variation in airline cost pass-through within and between regional markets, a topic that has thus far received little focus. Based on the results of model estimation, airfares are found to be the most responsive to changes in fuel cost, and the least elastic to increases in flight-based operating costs. Although similar patterns are found within each of the selected regional markets, the cost pass-through varies significantly across different markets. For example, passengers in Asia-Pacific regions would face higher fare increases than passengers in North America or Europe, given a same percentage increase in fuel costs. Frequently an airline will operate across multiple regional markets and our results have implications for the design of policies to incentivize airlines to reduce aviation emissions. The model described is a core component of the Aviation Integrated Model AIM2015 open source release.
Potentiale for overflytning af korte bilture til cykel og gang

Abstrakt Paperet indeholder en analyse af transportmiddelvalg på korte rejser. Det beskriver de faktorer, der har størst betydning for valg af cykel og gang henholdsvis bil på rejser på op til 22 km. Med en logistisk valgmodel belyses yderligere effekten af diverse virkemidler til at fremme overflytning af korte bilture til gang og cykel. Undersøgelsen viser, at der er et vist potentiale for at overflytte korte bilture især til cykelture, men at de færre bilture kun vil have en forholdsvis beskeden indvirkning på den samlede biltrafik.
Understanding Probabilistic Reasoning in Innovation

Organizational decision-making processes are characterized by high uncertainty and prone to decision-making biases. In this paper we explore the mechanisms and implications associated with what we call a scientific approach to decision making, based on probabilistic reasoning. We develop a structural model to disentangle and identify two separate but complementary effects of this approach. We estimate our structural model, based on data from two randomized control trials (RCTs) involving early stage start-ups and show that scientific entrepreneurs tend to be more conservative in assessing the value over their ideas, an effect that we call debiasing effect. We also show that, conditional on their decision to remain operational, scientific entrepreneurs tend to perform better, an effect that we call learning effect. We finally explore the implications associated with the use of this approach and show how the selection induced by the debiasing effect does not significantly increase the rate of false-negatives for treated firms compared to the control group. We discuss the implications for future research and practice.
Freight on Transit Delphi Study

The Freight on Transit Delphi Study was conducted to explore the concept of freight on transit – using public transit vehicles and infrastructure for transporting things other than people. Three rounds of web based surveys were conducted with a panel of 34 transportation experts to explore the main issues related to freight and transit integration and to build and evaluate potential freight on transit operations. Survey results were consistent with previous investigations and suggest that organizational disputes are a larger barrier to implementation than technical challenges. Traditional Delphi questions were used to determine the most important positive impacts, negative impacts, and challenges of moving freight on transit networks while survey responses combined with scenario building techniques were used to build and evaluate five potential freight on transit operating strategies using public transit networks in the GTHA.
Automatic Gesture Recognition for Human-Machine Interaction: An Overview

Summary With the increasing reliance of computing systems in our everyday life, there is always a constant need to improve the ways users can interact with such systems in a more natural, effective, and convenient way. In the initial computing revolution, the interaction between the humans and machines have been limited. The machines were not necessarily meant to be intelligent. This begged for the need to develop systems that could automatically identify and interpret our actions. Automatic gesture recognition is one of the popular methods users can control systems with their gestures. This includes various kinds of tracking including the whole body, hands, head, face, etc. We also touch upon a different line of work including Brain-Computer Interface (BCI), Electromyography (EMG) as potential additions to the gesture recognition regime. In this work, we present an overview of several applications of automated gesture recognition systems and a brief look at the popular methods employed.