Miracle-dz/UniFAR-FTUs huggingface dataset:
An Empirical Study on How iOS Developers Report Quality Aspects on Stack Overflow

Software developers around the globe are actively asking a question(s) and sharing solutions to the problems related to software development on Stack Overflow a social question and answer (Q&A) website. The knowledge shared by software developers on Stack Overflow contains useful information related to software development such as feature requests (functional/non-functional), code snippets, reporting bugs or sentiments. How to extract the functional and nonfunctional requirements shared by mobile application developers on social/programming Q&A website Stack Overflow has become a challenge and a less researched area. To understand the problems, needs, and trend in the iOS mobile application development, we evaluated the quality requirements or non-functional requirements (NFRs) on Stack Overflow posts. To this end, we applied Latent Dirichlet Allocation (LDA) topic models, to identify the main topics in iOS posts on Stack Overflow. Besides, we labeled the extracted topics with quality requirements or NFRs by using the wordlists to evaluate the trend, evolution, hot and unresolved NFRS in all iOS discussions. Our findings revealed that the highly frequent topics the iOS developers discussed are related to usability, reliability, and functionality followed by efficiency. Interestingly, the most problematic areas unresolved are also usability, reliability, and functionality though followed by portability. Besides, the evolution trend of each of the six different quality requirements or NFRs over time is depicted through comprehensive visualization.
Vascular PPARβ/δ Promotes Tumor Angiogenesis and Progression

Peroxisome proliferator-activated receptors (PPARs) are nuclear receptors, which function as transcription factors. Among them, PPARβ/δ is highly expressed in endothelial cells. Pharmacological activation with PPARβ/δ agonists had been shown to increase their angiogenic properties. PPARβ/δ has been suggested to be involved in the regulation of the angiogenic switch in tumor progression. However, until now, it is not clear to what extent the expression of PPARβ/δ in tumor endothelium influences tumor progression and metastasis formation. We addressed this question using transgenic mice with an inducible conditional vascular-specific overexpression of PPARβ/δ. Following specific over-expression of PPARβ/δ in endothelial cells, we induced syngenic tumors. We observed an enhanced tumor growth, a higher vessel density, and enhanced metastasis formation in the tumors of animals with vessel-specific overexpression of PPARβ/δ. In order to identify molecular downstream targets of PPARβ/δ in the tumor endothelium, we sorted endothelial cells from the tumors and performed RNA sequencing. We identified platelet-derived growth factor receptor beta (Pdgfrb), platelet-derived growth factor subunit B (Pdgfb), and the tyrosinkinase KIT (c-Kit) as new PPARβ/δ -dependent molecules. We show here that PPARβ/δ activation, regardless of its action on different cancer cell types, leads to a higher tumor vascularization which favors tumor growth and metastasis formation.
Confidential Assets on MimbleWimble

This paper proposes a solution for implementing Conﬁden-tial Assets on MimbleWimble, which allows users to issue and transfer multiple assets on a blockchain without showing transaction addresses, amounts, and asset types. We ﬁrst introduce the basic principles of Mim-bleWimble and then describe the implementation in detail.
Product modularity: Conceptualization, measurement, and consequences

Customized products help firms creating differentiated value for consumers in highly competitive markets. Modular product designs enable firms to fulfill market requirements for customization while balancing profitability targets. In modular product design, larger systems are broken down into independent sub-components, while the functionality of the entire system is preserved. While product modularity has received great attention from the literature in production and process optimization, research on consumer perceptions of modular products is still in its infancy. The present study develops a two-dimensional scale for product modularity as a basis for an online consumer survey, assessing their perceptions of modular product designs and potential effects of product modularity on product adoption intention. Particular value of this study lies in the application of a consumer research vantage point in the literature field of modular product design.
That’s how I feel: A Study of User’s Security Mental Model

Human behavior relates to many security breaches and plays a significant part in many security situations. It reduces the efficacy of the security models developed to ensure data protection. For instance, the lack of awareness of cyber threats and failure to comply with security policies can impact the security of an organization. The major link between human behavior and information security is the element of usability. Security and usability tend to be inversely related, but the role of usability in designing security models has often been ignored. The aim of this paper is to study users’ decisionmaking process when making considerations pertaining to security and usability through the mental model approach. To elicit and depict users’ security and usability mental models, crowd-sourcing techniques and a cognitive map method are utilized. We have also performed an experiment to evaluate our findings using Amazon Mechanical Turk (MTurk).
Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers

In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial–spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75).
Robust Resource Allocation of Independent Tasks in Heterogeneous Computing Systems via Probabilistic Task Pruning

In heterogeneous distributed computing system, diversity can be present both in the computational resources and in the types of arriving tasks. In an inconsistently Heterogeneous Computing (HC) system, different task types can have different performance characteristics (i.e., execution times) on heterogeneous machines. A mapping method is required to match arriving tasks with machines based on both machine availability and performance to maximize the number of tasks meeting their deadlines (known as robustness). In particular, for tasks with hard individual deadlines (e.g., live video streaming tasks), those that have missed their deadlines are dropped, as there is no value in executing them. The problem investigated in this research is how to maximize robustness of an HC system, specifically, when it is oversubscribed. The proposal is to prune (i.e., defer or drop) tasks with low probability of meeting their deadlines. Pruning low-chance tasks increases the probability of other tasks meeting their deadlines. To that end, first a model is provided to estimate the probability of meeting deadline for each task in the presence of task dropping. Second, a pruning mechanism is proposed to predictively defer or drop tasks in an effort to maximize the overall robustness of the HC system. Third, a mapping method is proposed that functions based on the pruning mechanism and improves robustness of the HC system. Fourth, to show a broad application, the pruning mechanism is applied to other mapping heuristics. Fifth, further development of the pruning mechanism is made from multiple fronts to improve robustness, engender fairness amongst completed task types, and examine the cost ramifications of using a pruning mechanism. Simulation results, harnessing a selection of mapping heuristics, show the efficacy of the proposed pruning mechanism can improve robustness of some oversubscribed HC system by more than 40%.
Evaluating HIV Prevention Interventions Among Drug Abusers: Validity Issues

ABSTRACT Recent literature identifies many difficulties in validly measuring sex and drug use behavior associated with HIV risk, particularly among adolescents. In evaluating HIV risk reduction interventions, this literature emphasizes that, in addition to the more obvious determinants of self-report bias (e.g., response style, interviewer characteristics, social desirability, the interview setting), other factors require careful consideration (e.g., whether the person is assigned to a treatment or comparison group; whether the interview occurred at in-take, in treatment, or post-discharge; and the severily of the respondent's drug use). It also emphasizes the importance of including toxicologic STD and drug use measures in treatment outcome research to validate respondents' self-reports of risk behavior and adjust them for under-reporting. Guided by the literature, this article suggests procedures for more validly measuring HIV risk reduction, particularly among drug abusing adolescents. These include me.
Autocorrelation Ensemble Average of Larger Amplitude Impact Transients for the Fault Diagnosis of Rolling Element Bearings

Rolling element bearings are one of the critical elements in rotating machinery of energy engineering systems. A defective roller of bearing moves in and out of the load zone during each revolution of the cage. Larger amplitude impact transients (LAITs) are produced when the defective roller passes the load zone centre and the defective area strikes the inner or outer races. A series of LAIT segments with higher signal to noise ratio are separated from a continuous vibration signal according to the bearing geometry and kinematics. In order to eliminate the phase errors between different LAIT segments that can arise from rotational speed fluctuations and roller slippages, unbiased autocorrelation is introduced to align the phases of LAIT segments. The unbiased autocorrelation signals make the ensemble averaging more accurate, and hence, archive enhanced diagnostic signatures, which are denoted as LAIT-AEAs for brevity. The diagnostic method based on LAIT separation and autocorrelation ensemble average (AEA) is evaluated with the datasets captured from real bearings of two different experiment benches. The validation results of the LAIT-AEAs are compared with the squared envelope spectrums (SESs) yielded based on two state-of-the-art techniques of Fast Kurtogram and Autogram.
GoodEnough forGovernmentWork ? An Incomplete Contracts Approach to theUse of Nonprofits inU

This paper examines the use of the nonprofit organizational form tomitigate the impact of incomplete contracts in the public sector Transaction costs economics (TCE) predicts that the expense of incomplete contractswill rise with contract complexity and asset specificity. Previous research shows that government agencies increase their use cost-plus style contracts to economize on these costs. However, cost-plus style contracts may also increase the propensity to inflate procurement costs, also known as gold-plating, when relationally specific investments are required. Consistent with this expectation, we find that federal agencies reduce their use of cost-plus style contracts as asset specificity rises. The paper then explores the use of nonprofit organizations as an alternative tool to reduce contracting costs. Using data from the Federal Procurement Data System, we examine the choice of organizational form by federal agencies, as contracts become more or less incomplete. Consistent with our hypotheses, we find that the use of nonprofit organizations increases with contract complexity. In contrast to cost-plus style contracts, we find that the use of nonprofits also increases with asset specificity. We apply this finding to support the conjecture that the nonprofit organization form is used by government agencies to mitigate contract incompleteness without the associated risk of cost inflation. We conclude by offering suggestions for why nonprofit contracts appear relatively infrequently in federal procurement data.
DeepClimGAN: A High-Resolution Climate Data Generator

Earth system models (ESMs), which simulate the physics and chemistry of the global atmosphere, land, and ocean, are often used to generate future projections of climate change scenarios. These models are far too computationally intensive to run repeatedly, but limited sets of runs are insufficient for some important applications, like adequately sampling distribution tails to characterize extreme events. As a compromise, emulators are substantially less expensive but may not have all of the complexity of an ESM. Here we demonstrate the use of a conditional generative adversarial network (GAN) to act as an ESM emulator. In doing so, we gain the ability to produce daily weather data that is consistent with what ESM might output over any chosen scenario. In particular, the GAN is aimed at representing a joint probability distribution over space, time, and climate variables, enabling the study of correlated extreme events, such as floods, droughts, or heatwaves.
An Inertial Sensor Based System for Real-Time Biomechanical Analysis during Running

Running is a popular form of physical exercise. However, runners often experience a high number of running-related injuries. This is partially due to the fact that runners are lack of knowledge about their running postures during running. This paper describes a preliminary study of using an Inertial Measurement Unit (IMU) based wearable system for real-time biomechanical analysis during Running. This system consisted four IMU sensors. Two were strapped to the toes of a subject’s shoes and another two were attached to the subject’s medial and lateral ASIS. The foot and pelvis positions were estimated by the IMU acceleration and a linear regression model. The lower limb joint angles were estimated by combining these position information with a planar 3R serial chain and solving its inverse kinematics. The results show that joint angles of lower limbs (i.e. hip, knee and ankle angles) can be accurately estimated by this wearable system. This study can benefit the future research on conducting complete lower limbs kinematics analysis with minimal and unobtrusive wearable sensors and provide real-time feed back during running exercise.
Shervin Shahrdar Thesis

Author: Shervin Shahrdar Title: New Structured Data Collection Approach for Real-Time Trust Measurement In Human-Autonomous Vehicle Interactions Institution: Florida Atlantic University Thesis Advisor: Dr. Mehrdad Nojoumian Degree: Master of Science Year: 2018 Most of recent studies indicate that people are negatively predisposed toward utilizing autonomous systems. These findings highlight the necessity of conducting research to better understand the evolution of trust between humans and growing autonomous technologies such as self-driving cars (SDC). This research therefore presents a new approach for real-time trust measurement between passengers and SDCs. We utilized a new structured data collection approach along with a virtual reality (VR) SDC simulator to understand how various autonomous driving scenarios can increase or decrease human trust and how trust can be re-built in the case of incidental failures. To verify our methodology, we designed and conducted an empirical experiment on 50 human subjects. The results of this experiment indicated that most subjects could rebuild trust during a reasonable timeframe after the system demonstrated faulty behavior. Furthermore, we discovered that the cultural background and past trust-related experiences of the subjects affect how they lose or regain their trust in SDCs. Our analysis showed that this model is highly effective for collecting real-time data from human subjects and lays the foundation for more-involved future research in the domain of human trust and autonomous driving.
An Overview of Advance Microcontroller Bus Architecture Relate on AHB Bridge

As microprocessor performance has relentlessly improved in recent years, it has become increasingly important to provide a high-bandwidth, low-latency memory subsystem to achieve the full performance potential of these processors. In the past years, improvements in memory latency and bandwidth have not kept pace with reductions in instruction execution time. Caches have been used extensively to patch over this mismatch, but some applications do not use caches effectively. The result is that the memory access time has been a bottleneck which limits the system performance. A Memory Controller is designed to cater to this problem. The Memory Controller is a digital circuit which manages the flow of data going to and from the main memory. It can be a separate chip or can be integrated into the system chipset. This paper revolves around building an Advanced Microcontroller Bus Architecture (AMBA) compliant Memory Controller as an Advanced High-performance Bus (AHB) slave. The whole design is captured using VHDL, simulated with ModelSim and configured to a FPGA target device belonging to the Virtex4 family using Xilinx.
MASTER'S THESIS Multipath Separation in Time of Flight- cameras

In this thesis, the SRA-algorithm (Sparse Reflections Analysis), proposed by Freedman et al. [1], is evaluated as a method of solving the multipath problem in ToF-cameras (Time of Flight). Multipath is an error source in ToF-cameras arising due to scattering within the scene, and correcting this error is important for accurate measurements. The SRA-algorithm was implemented, and a radiometric model was created in Matlab to simulate the ToF-operation. This model was used to evaluate the algorithm for different example cases. The cases are typical situations where multipath effects introduce errors to the measurement. The model was created such that other parts of ToF-operation can also be simulated. The SRA-algorithm was found to reduce the multipath error in all example cases. The performance of the algorithm is affected by the number of modulation frequencies and what frequencies that are used. A method of using the Gram matrix as a method of chosing frequencies is presented. How to best choose parameters, like the noise tolerance in the optimization and threshold for the peak detection, was also investigated. Some guidelines are presented, but additional work is needed to find more sophisticated methods for these choices.
Quantitative analysis of changes in mitral regurgitation after transcatheter aortic valve implantation

Patients with severe aortic stenosis undergoing transcatheter aortic valve implantation (TAVI) often have concomitant significant mitral regurgitation (MR). Quantification and time course characterization of changes in MR after TAVI remain unexplored. The present study evaluated quantitatively assessed changes of significant MR after TAVI. Fifty-nine patients (age 82±6 years, male 41%) with severe aortic stenosis and mild or more MR undergoing TAVI were evaluated retrospectively. All patients underwent pre-discharge echocardiography and 44 underwent repeat echocardiography at 12 months follow-up. Mitral regurgitation was quantitatively assessed. Improvement in MR, defined as ≥30% reduction in effective regurgitant orifice area (EROA), was assessed in the pre-discharge echocardiogram or at 12 months follow-up. Mitral regurgitant volume reduced from 35.6[2.2] to 18.0[1.8] and to 17.5[2.4] ml (p < 0.001), EROA from 20.5 [1.1] to 13.9[1.2] and to 10.6[1.2] mm2 (p < 0.001) and vena contracta from 5.0 [0.15] to 3.8 [0.18] and to 3.5 [0.24] mm (p < 0001) immediately after TAVI and at 12 months, respectively. Overall, 35 (59%) patients showed a pre-discharge improvement in MR and an additional 10 (17%) improved by 12 months post-TAVI. In multivariable analysis, baseline EROA (odds ratio 1.095, 95% confidence interval: 1.013-1.185, p = 0.023) and predominantly functional MR (odds ratio 4.714, 95% confidence interval: 1.105-20.112, p = 0.036) were independently related to acute postoperative improvement. In conclusion, immediately after TAVI, a significant improvement in MR is observed in two-thirds of patients undergoing TAVI and in an additional one-fifth at 12 months follow-up. Baseline EROA and predominantly functional MR pathology are associated with pre-discharge postoperative MR improvement. Quantitative analysis of changes in mitral regurgitation after transcatheter aortic valve implantation 99
Shiniphy-Visual Data Mining of movie recommendations

An interactive visualization system for browsing results of a movie recommendation system is presented. The movie recommendations are based on collaborative filtering data mining techniques on Netflix Challenge dataset. The visualization allows for two way feedback, from the recommendation system to the user, and from the user to the system. The user can intuitively change the results of recommendations over time. Dynamic querying filters are also provided.
FIELDABLE PLATFORM FOR LARGE-SCALE DEPOSITION OF CONCRETE STRUCTURES

Oak Ridge National Laboratory’s Manufacturing Demonstration Facility is developing a novel, large-scale additive manufacturing, or 3D printing, system. The Sky Big Area Additive Manufacturing (SkyBAAM) system will ultimately be a fieldable concrete deposition machine with pick and place abilities that will allow for full-scale, automated construction of buildings. The system will be implemented with existing construction equipment meaning conventional cranes will be used to suspend the print head. SkyBAAM will be cable-driven by four base stations and suspended from a single crane. The elimination of a gantry system, found commonly in large-scale additive manufacturing systems, will enable SkyBAAM to be quickly set up with minimal site preparation. The medium-scale version of SkyBAAM is currently in development. The system design, cable stiffness analysis, and tactics for freezing rotational degrees-of-freedom (DOF), detailed in this paper, will provide a basis for the final, large-scale version of the SkyBAAM system.
Robust Image Watermarking in the Wavelet Domain

The growth of new imaging technologies has created a need for techniques that can be used for copyright protection of digital images. In this paper, a new and robust spread spectrum basedwatermarking scheme has been proposed.The proposed scheme depend on both Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). First, we decompose the image by DWT into asingle level. Then, the approximation part is divided into blocks. The embedding is done in an adaptive fashion depending on the mean (M) of the block. A chaotic sequence of real numbers, depends on asecret key, is embedded as a watermark in the DCT coefficients of the selected blocks. Detection stage generates a watermark which would be compared with the original watermark, by the correlation measure, to determine the existing of the watermark or not. Different tests have been experimented to explain the transparency and the robust of the proposed scheme.