Miracle-dz/UniFAR-FTUs huggingface dataset:
FLUX: Finding Bugs with LLVM IR Based Unit Test Crossovers

Optimizing compilers are as ubiquitous as they are crucial to software development. However, bugs in compilers are not uncommon. Among the most serious are bugs in compiler optimizations, which can cause unexpected behavior in compiled binaries. Existing approaches for detecting such bugs have focused on end-to-end compiler fuzzing, which limits their ability for targeted exploration of a compiler's optimizations. This paper proposes FLUX (Finding bugs with LLVM IR based Unit test cross(X)overs), a fuzzer that is designed to generate test cases that stress compiler optimizations. Previous compiler fuzzers are overly constrained by having to construct well-formed inputs. FLUX sidesteps this constraint by using human-written unit test suites as a starting point, and then selecting random combinations of them to generate new tests. We hypothesize that tests generated this way will be able to explore new execution paths through compiler optimizations and find new bugs. Our evaluation of FLUX on LLVM indicates that it is able to increase path coverage over the baseline LLVM unit test suite and explores more edge coverage than previous work. Further, we demonstrate FLUX's ability to generate miscompiled and crash-producing IR on LLVM's optimizations. After a month of fuzzing, FLUX found 28 unique bugs in LLVM's active development branch. We have reported 11 of these bugs which led to 6 of them being patched by LLVM developers. 22 of these are crashes that are triggered by well-formed input programs, and 6 of these are miscompilation bugs that silently produced incorrect code.
The lesser wanderer butterfly, Danaus petilia (Stoll 1790) stat. rev.(Lepidoptera: Danainae), reinstated as a species

The lesser wanderer butterfly, Danaus (Anosia) chrysippus petilia (Stoll) (Lepidoptera: Danainae), has been treated as a subspecies for the last 100 years. New mitochondrial DNA sequence data for D. petilia, in conjunction with allozyme, structural, morphometric and pattern characters, constitute a compelling case for its specific rank. The holotype of D. petilia has never been located and, as the type location is uncertain, a neotype is designated. Fresh material was collected and examined for this project. Danaus petilia and D. chrysippus have been separated at Lydekker's Line for an estimated 1.1 million years, and they remain interfertile. However, as an allopatric taxon, diagnosable from D. chrysippus, D. petilia merits specific status under the phylogenetic species concept. The following pairs of D. chrysippus subspecies are considered to be synonyms, the first member of each pair having priority: chrysippus + aegyptius (subspecies), klugii + infumata (hybrid phenotypes) and orientis +liboria (subspecies).
Evaluation of Airborne Visible/Infrared Imaging Spectrometer Data of the Mountain Pass, California carbonatite complex

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data of the Mountain Pass, California carbonatite complex were examined to evaluate the AVIRIS instrument performance and to explore alternative methods of data calibration. Although signal-to-noise estimates derived from the data indicated that the A, B, and C spectrometers generally met the original instrument design objectives, the S/N performance of the D spectrometer was below expectations. Signal-to-noise values of 20 to 1 or lower were typical of the D spectrometer and several detectors in the D spectrometer array were shown to have poor electronic stability. The AVIRIS data also exhibited periodic noise, and were occasionally subject to abrupt dark current offsets. Despite these limitations, a number of mineral absorption bands, including CO3, Al-OH, and unusual rare earth element bands, were observed for mine areas near the main carbonatite body. To discern these bands, two different calibration procedures were applied to remove atmospheric and solar components from the remote sensing data. The two procedures, referred to as the single spectrum and the flat field calibration methods gave distinctly different results. In principle, the single spectrum method should be more accurate; however, additional fieldwork is needed to rigorously determine the degree of calibration success.
Teachers’ Emotional Labor Behaviors: The Role of Positive Psychological Capital and Organizational Commitment Öğretmenlerin Duygusal Emek Davranışı: Pozitif Psikolojik Sermaye ve Örgütsel Bağlılığın

: The main aim of this research was to reveal the role of the positive psychological capital and organizational commitment of teachers working in high schools on their emotional labor behaviors. The research sample consisted of a total of 711 teachers, 561 of whom were from public schools and 150 of whom are from private secondary schools, all working in the city of Mersin. The data were collected using the "Positive Psychological Capital Scale" developed by Tösten and Özgan (2014), the "Emotional Labor Scale" adapted for teachers by Kıral (2016), and the "Organizational Commitment Scale" adapted into Turkish by Al (2007). The data were analyzed using multiple regression analysis. The analysis conducted on the entire sample revealed that teachers' positive psychological capital and organizational commitment explain 12% of the total variance in emotional labor. The analysis conducted on the sample from public schools revealed that these two variables together predict 11.6% of the total variance in emotional labor, while the analysis conducted on the sample from private schools revealed that organizational commitment did not significantly predict emotional labor, and that positive psychological capital alone predicted 21% of the total variance in emotional labor.
High-level strategies for parallel shared-memory sparse matrix – vector multiplication

The sparse matrix–vector multiplication is an important kernel, but is hard to efficiently execute even in the sequential case. The problems –namely low arithmetic intensity, inefficient cache use, and limited memory bandwidth– are magnified as the core count on shared-memory parallel architectures increases. Existing techniques are discussed in detail, and categorised chiefly based on their distribution types. Based on this new parallelisation techniques are proposed. The theoretical scalability and memory usage of the various strategies are analysed, and experiments on multiple NUMA architectures confirm the validity of the results. One of the newly proposed methods attains the best average result in experiments, in one of the experiments obtaining a parallel efficiency of 90 percent.
Observer‐based fuzzy adaptive quantized control for uncertain nonlinear multiagent systems

This paper focuses on consensus quantized control design problem for uncertain nonlinear multiagent systems with unmeasured states. Every follower can be denoted through a system with unmeasurable states, hysteretic quantized input, and unknown nonlinearities. Fuzzy state observer and Fuzzy logic systems are employed to estimate unmeasured states and approximate unknown nonlinear functions, respectively. The hysteretic quantized input can be split into two bounded nonlinear functions to avoid chattering problem. By combining adaptive backstepping and first‐order filter signals, an observer‐based fuzzy adaptive quantized control scheme is designed for each follower. All signals exist in closed‐loop systems are semiglobally uniformly ultimately bounded, and all followers can accomplish a desired consensus results. Finally, a numerical example is employed to elaborate the effectiveness of proposed control strategy.
Assessment of Mindfulness by Self-Report

A self-report inventory for the assessment of mindfulness skills was developed, and its psychometric characteristics and relationships with other constructs were examined. Participants included three samples of undergraduate students and a sample of outpatients with borderline personality disorder. Based on discussions of mindfulness in the current literature, four mindfulness skills were specified: observing, describing, acting with awareness, and accepting without judgment. Scales designed to measure each skill were developed and evaluated. Results showed good internal consistency and test-retest reliability and a clear factor structure. Most expected relationships with other constructs were significant. Findings suggest that mindfulness skills are differentially related to aspects of personality and mental health, including neuroticism, psychological symptoms, emotional intelligence, alexithymia, experiential avoidance, dissociation, and absorption.
Perceived Barriers and Opportunities to Offering Ivermectin Continuously in the Primary Health Care System: An Exploratory Study of Stakeholders in Cameroon

Onchocerciasis is endemic in about 60% of health districts in Cameroon despite decades of ivermectin (IVM) treatment. Absences during mass drug administration campaigns and community drug distributors failing to deliver the treatment in households were key determinants of this implementation gap. We conducted a study of the challenges and opportunities to partial integration of IVM distribution into the primary health care (PHC) system. This was a mixed-methods (qualitative and quantitative) study using in-depth interviews to capture information from stakeholders at all levels and focus group discussions at the community level. Quantitative data on cost and drug coverage were extracted from campaign reports of districts. We included 130 participants from five regions of Cameroon. Partial integration was perceived as a simple and easy-to-implement approach by stakeholders at different levels of the health system, health workers, and community members, and they were also highly motivated to try it. It also represented an opportunity to sustain IVM administration in case of funding shortages. Management of adverse events and risk of misuse of IVM were the key challenges identified. Treatment of one person costs about 53 CFA Francs (95% CI: 32.8 – 73.0). Trials to assess the real potential impact of this integration with the primary health care system are highly needed.
Kernel methods and Gaussian processes for system identiﬁcation and control

This article reviews some kernel-based approaches for system identiﬁcation and learning-based control. In the ﬁrst part, the presentation moves from classic linear system identiﬁcation, to nonlinear system identiﬁcation in Reproducing Kernel Hilbert Spaces. The kernel-based regularization methods are illustrated in a tutorial manner. Moreover, the probabilistic (Bayesian) interpretation of kernels is also introduced, with focus on the Gaussian Processes (GPs) framework, a special case of great practical interest. The second part touches upon the problem of quantifying the uncertainty of the estimated dynamic systems from diﬀerent points of views (deterministic, probabilistic, probabilistic and robust). The ﬁnal part of the article surveys the applications of GPs in robust control, adaptive control, model predictive control, feedback linearization and reinforcement learning
Optimistic Bayesian Sampling in Contextual-Bandit Problems

In sequential decision problems in an unknown environment, the decision maker often faces a dilemma over whether to explore to discover more about the environment, or to exploit current knowledge. We address the exploration-exploitation dilemma in a general setting encompassing both standard and contextualised bandit problems. The contextual bandit problem has recently resurfaced in attempts to maximise click-through rates in web based applications, a task with significant commercial interest. In this article we consider an approach of Thompson (1933) which makes use of samples from the posterior distributions for the instantaneous value of each action. We extend the approach by introducing a new algorithm, Optimistic Bayesian Sampling (OBS), in which the probability of playing an action increases with the uncertainty in the estimate of the action value. This results in better directed exploratory behaviour. We prove that, under unrestrictive assumptions, both approaches result in optimal behaviour with respect to the average reward criterion of Yang and Zhu (2002). We implement OBS and measure its performance in simulated Bernoulli bandit and linear regression domains, and also when tested with the task of personalised news article recommendation on a Yahoo! Front Page Today Module data set. We find that OBS performs competitively when compared to recently proposed benchmark algorithms and outperforms Thompson's method throughout.
Randomized Study on the Effect of Octreotide LAR in the Control of Tumor Growth in Patients with Metastatic

Procedure Appropriateness Category Liver transplantation Usually Appropriate Percutaneous ablation liver Usually Appropriate Surgical liver resection Usually Appropriate Combination locoregional therapy May Be Appropriate External beam radiation therapy May Be Appropriate Transarterial chemoembolization May Be Appropriate Transarterial radioembolization May Be Appropriate Bland transarterial embolization May Be Appropriate Systemic therapies Usually Not Appropriate
The Dark Side of Creativity: Original Thinkers Can Be More Dishonest

Creativity is a common aspiration for individuals, organizations, and societies. Here, however, we test whether creativity increases dishonesty. We propose that a creative personality and a creative mindset promote individuals’ ability to justify their behavior, which, in turn, leads to unethical behavior. In five studies, we show that participants with creative personalities tended to cheat more than less creative individuals, and that dispositional creativity is a better predictor of unethical behavior than intelligence (Experiment 1). In addition, we find that participants who were primed to think creatively were more likely to behave dishonestly than those in a control condition (Experiment 2) and that greater ability to justify their dishonest behavior explained the link between creativity and increased dishonesty (Experiments 3 and 4). Finally, we demonstrate that dispositional creativity moderates the influence of temporarily priming creativity on dishonest behavior (Experiment 5). The results provide evidence for an association between creativity and dishonesty, thus highlighting a dark side of creativity.
The Dark Side of Creativity : Original Thinkers Can Be More Dishonest

Creativity is a common aspiration for individuals, organizations, and societies. Here, however, we test whether creativity increases dishonesty. We propose that a creative personality and a creative mindset promote individuals’ ability to justify their behavior, which, in turn, leads to unethical behavior. In five studies, we show that participants with creative personalities tended to cheat more than less creative individuals, and that dispositional creativity is a better predictor of unethical behavior than intelligence (Experiment 1). In addition, we find that participants who were primed to think creatively were more likely to behave dishonestly than those in a control condition (Experiment 2) and that greater ability to justify their dishonest behavior explained the link between creativity and increased dishonesty (Experiments 3 and 4). Finally, we demonstrate that dispositional creativity moderates the influence of temporarily priming creativity on dishonest behavior (Experiment 5). The results provide evidence for an association between creativity and dishonesty, thus highlighting a dark side of creativity.
B-Spline and reproducing polynomial particle shape functions for linear and nonlinear elasticity problems

Abstract. In this paper, B-Spline functions and the reproducing polynomial particle (RPP) shape functions are used for solving 2-D linear and nonlinear elasticity problems. Tensor product approach is used to construct 2-D base functions. A structured grid with proper scale and implicit equations representing the boundaries of the analysis domain are used to divide the analysis domain into boundary elements and internal elements. Complete approximation functions used for the analysis domain can be determined based on information of boundary elements and internal elements. The boundary elements require further subdivisions for high accuracy of integration if the scale is large. Essential boundary conditions can be imposed directly in regular domain and by Lagrange multipliers method, couple method or penalty method in general domain. The two kinds of base functions constructed with different parameters are used in simulation of several 2-D linear and nonlinear elasticity problems.
Understanding The E-Banking Adoption on MSMEs: An UTAUT Approach

This study analyzes the acceptance and use of e-banking among MSMEs in Kebumen Regency using the UTAUT model approach. The UTAUT model suggests that the factors that play an important role in user acceptance include job expectations, business expectations, social influences, and facilitating conditions. This type of research is quantitative research and the data source used is primary data. The sampling technique in this study was using purposive sampling. Respondents in this study were 50 MSME actors in Kebumen Regency who already used e-banking. The hypothesis in this study was tested using SEM-PLS with WarpPLS 7.0. The results in this study indicate that the first model, namely performance expectancy, effort expectancy, and social influence are factors that influence behavioral intention. The second model of behavioral intention is a factor that affects Use behavioral, while facilitating conditions do not affect Use behavioral in the use of e-banking for MSMEs in Kebumen Regency.
DAMAGE DETECTION IN A STEEL BEAM USING VIBRATION RESPONSE

Changes in vibration signals, natural frequency and modal parameters were used for damage detection in a steel beam. Several tests were performed on simply supported steel beams having open transverse cracks with varying depths and locations. Frequencies for the first three vibration modes were generated from the analytical, numerical, and experimental models. Analytical frequencies were calculated assuming Euler-Bernoulli beam and solving partial differential equations. Vibration signals were collected using a portable digital vibrometer and analyzed through Fast Fourier Transform. The finite element beam models were calibrated using experimental results. The graphical plots of the normalized frequency from the experimental study and FE models showed similar pattern for the first mode. The nodal points were identified as the locations where the damaged frequency equals the frequency of an intact beam. The existence of damage in the beam specimen was confirmed by comparing the natural frequency of a beam at its intact and damaged states. Equations relating normalized frequency with varying crack depth and location were proposed.
Crohn’s Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome

Human microbiota refers to the trillions of microorganisms that inhabit our bodies and have been discovered to have a substantial impact on human health and disease. By sampling the microbiota, it is possible to generate massive quantities of data for analysis using Machine Learning algorithms. In this study, we employed several modern Machine Learning techniques to predict Inflammatory Bowel Disease using raw sequence data. The dataset was obtained from NCBI preprocessed graph representations and converted into a structured form. Seven well-known Machine Learning frameworks, including Random Forest, Support Vector Machines, Extreme Gradient Boosting, Light Gradient Boosting Machine, Gaussian Naïve Bayes, Logistic Regression, and k-Nearest Neighbor, were used. Grid Search was employed for hyperparameter optimization. The performance of the Machine Learning models was evaluated using various metrics such as accuracy, precision, fscore, kappa, and area under the receiver operating characteristic curve. Additionally, Mc Nemar’s test was conducted to assess the statistical significance of the experiment. The data was constructed using k-mer lengths of 3, 4 and 5. The Light Gradient Boosting Machine model overperformed over other models with 67.24%, 74.63% and 76.47% accuracy for k-mer lengths of 3, 4 and 5, respectively. The LightGBM model also demonstrated the best performance in each metric. The study showed promising results predicting disease from raw sequence data. Finally, Mc Nemar’s test results found statistically significant differences between different Machine Learning approaches.
Performance Analysis of Lambda Architecture-Based Big-Data Systems on Air/Ground Surveillance Application with ADS-B Data

This study introduces a novel methodology designed to assess the accuracy of data processing in the Lambda Architecture (LA), an advanced big-data framework qualified for processing streaming (data in motion) and batch (data at rest) data. Distinct from prior studies that have focused on hardware performance and scalability evaluations, our research uniquely targets the intricate aspects of data-processing accuracy within the various layers of LA. The salient contribution of this study lies in its empirical approach. For the first time, we provide empirical evidence that validates previously theoretical assertions about LA, which have remained largely unexamined due to LA’s intricate design. Our methodology encompasses the evaluation of prospective technologies across all levels of LA, the examination of layer-specific design limitations, and the implementation of a uniform software development framework across multiple layers. Specifically, our methodology employs a unique set of metrics, including data latency and processing accuracy under various conditions, which serve as critical indicators of LA’s accurate data-processing performance. Our findings compellingly illustrate LA’s “eventual consistency”. Despite potential transient inconsistencies during real-time processing in the Speed Layer (SL), the system ultimately converges to deliver precise and reliable results, as informed by the comprehensive computations of the Batch Layer (BL). This empirical validation not only confirms but also quantifies the claims posited by previous theoretical discourse, with our results indicating a 100% accuracy rate under various severe data-ingestion scenarios. We applied this methodology in a practical case study involving air/ground surveillance, a domain where data accuracy is paramount. This application demonstrates the effectiveness of the methodology using real-world data-intake scenarios, therefore distinguishing this study from hardware-centric evaluations. This study not only contributes to the existing body of knowledge on LA but also addresses a significant literature gap. By offering a novel, empirically supported methodology for testing LA, a methodology with potential applicability to other big-data architectures, this study sets a precedent for future research in this area, advancing beyond previous work that lacked empirical validation.
Blood C‐peptide concentration as a proxy marker of cardiovascular disease: An observational cross‐sectional study

Cardiovascular diseases (CVDs) are among the leading causes of disability and early death in sub‐Saharan Africa. Most of the current blood tests for CVD diagnosis involve performing about three test profiles; often at additional cost to patients. C‐peptide, a cleavage product of proinsulin, is a promising marker that has the potential to serve as a proxy marker for diagnosing CVDs in resource‐poor settings.