Miracle-dz/UniFAR-FTUs huggingface dataset:
Log: It's Big, It's Heavy, It's Filled with Personal Data! Measuring the Logging of Sensitive Information in the Android Ecosystem

Android offers a shared system that multiplexes all logged data from all system components, including both the operating system and the console output of apps that run on it. A security mechanism ensures that user-space apps can only read the log entries that they create, though many “privileged” apps are exempt from this restriction. This includes preloaded system apps provided by Google, the phone manufacturer, the cellular carrier, as well as those sharing the same signature. Consequently, Google advises developers to not log sensitive information to the system log. In this work, we examined the logging of sensitive data in the Android ecosystem. Using a field study, we show that most devices log some amount of user-identifying information. We show that the logging of “activity” names can inadvertently reveal information about users through their app usage. We also tested whether different smartphones log personal identifiers by default, examined preinstalled apps that access the system logs, and analyzed the privacy policies of manufacturers that report collecting system logs.
A Kindergarten Lottery Evaluation of Core Knowledge Charter Schools:

The Core Knowledge curriculum is a K-8 curriculum focused on building students General Knowledge about the world they live in that is hypothesized to increase reading comprehension and Reading/English-LA achievement. This study utilizes an experimental design to evaluate the long term effects of attending Charter schools teaching the Core Knowledge curriculum. Fourteen oversubscribed kindergarten lotteries for enrollment in nine Core Knowledge Charter schools using the curriculum had 2310 students applying from parents in predominately middle/high income school districts. State achievement data was collected at 3rd6th grade in Reading/English-LA and Mathematics and at 5th Grade in Science. A new methodology addresses two previously undiscovered sources of bias inherent in kindergarten lotteries that include middle/high income families. The unbiased confirmatory Reading-English-LA results show statistically significant ITT (0.241***) and TOT (0.473***) effects for 3rd-6th grade achievement with statistically significant ITT and TOT effects at each grade. Exploratory analyses also showed significant ITT (0.15*) and TOT (0.300*) unbiased effects at 5th grade in Science. A CK-Charter school in a low income school district also had statistically significant, moderate to large unbiased ITT and TOT effects in English Language Arts (ITT= 0.944**; TOT = 1.299**), Mathematics (ITT= 0.735*; TOT = 0.997*) and positive, but insignificant Science effects (ITT= 0.468; TOT = 0.622) that eliminated achievement gaps in all subjects.
FIXED POINT THEOREMS FOR α-ψ-CONTRACTIONS ON METRIC SPACES WITH A GRAPH

In 2008 Jachymski used the context of metric spaces endowed with a graph in theory of fixed points in some general structures and In 2012, Samet introduced a new concept of α-ψ-contractive type mappings and established fixed point theorems for such mappings in complete metric spaces. The purpose of this article is to compose this two contexts and present some new fixed point results for α-ψ-contraction on complete metric spaces endowed with a graph and in finally, as a consequence of our main result, we obtain fixed point theorems for cyclic contractive mappings.
Adiabatic Quantum Computing with QUBO Formulations

We study two types of problems in this thesis, graph covering problems including the Dominating Set and Edge Cover which are classic combinatorial problems and the Graph Isomorphism Problem with several of its variations. For each of the problems, we provide efficient quadratic unconstrained binary optimization (QUBO) formulations suitable for adiabatic quantum computers, which are viewed as a real-world enhanced model of simulated annealing. The number of qubits (dimension of QUBO matrices) required to solve the graph covering problems are O(n + n lg n) and O(m + n lg n) respectively, where n is the number of vertices and m is the number of edges. We also extend our formulations for the Minimum VertexWeighted Dominating Set problem and Minimum Edge-Weighted Edge Cover problem. For the Graph Isomorphism Problem, we provide two QUBO formulation through two approaches both requiring O(n) variables. We also provide several different formulations for two extensions of the Graph Isomorphism Problems each requiring a different number of variables ranging from O(n1n2) to O((n1 + 1)n2). We also provide some experimental results using a D-Wave 2X quantum computer with 1098 active qubit-coupled processors on the problems studied here for a selection of known common graphs.
Enhanced Efficiency of Advanced Encryption Standard (EE-AES) Algorithm

: Encryption is a method of coding information or sensitive data or asset, to prevent unauthorized users from accessing it. Currently, it is essential to secure data that is at rest in our computer or is transmitted via web against attacks. Several of cryptographic techniques are being used to preserve security and could be classified as: symmetric and asymmetric
An integrated approach to planetary gearbox fault diagnosis using deep belief networks

Aiming at improving the accuracy of planetary gearbox fault diagnosis, an integrated scheme based on dimensionality reduction method and deep belief networks (DBNs) is presented in this paper. Firstly, the acquired vibration signals are decomposed into mono-component called intrinsic mode functions (IMFs) through ensemble empirical mode decomposition (EEMD), and then Teager–Kaiser energy operator (TKEO) is used to track the instantaneous amplitude (IA) and instantaneous frequency (IF) of a mono-component amplitude modulation (AM) and frequency modulation (FM) signal. Secondly, a high dimensional feature set is constructed through extracting statistical features from six different signal groups. Then, an integrated dimensionality reduction method combining feature selection and feature extraction techniques is proposed to yield a more sensitive and lower dimensional feature set, which not only reduces the computation burden for fault diagnosis but also improves the separability of the samples by integrating the label information. Further, the low dimensional feature set is fed into DBNs classifier to identify the fault types using the optimal parameters selected by particle swarm optimization algorithm (PSO). Finally, two independent cases study of planetary gearbox fault diagnosis are carried out on test rig, and the results show that the proposed method provides higher accuracy in comparison with the existing methods.
Membrane and microtubule rapid instance segmentation with dimensionless instance segmentation by learning graph representations of point clouds

Point clouds are an increasingly common spatial data modality, being produced by sensors used in robotics and self-driving cars, and as natural intermediate representations of objects in microscopy and other bioimaging domains (e.g., cell locations over time, or filaments, membranes, or organelle boundaries in cryo-electron micrographs or tomograms). However, semantic and instance segmentation of this data remains challenging due to the complex nature of objects in point clouds. Especially in bioimaging domains where objects are often large and can be intersecting or overlapping. Furthermore, methods for operating on point clouds should not be sensitive to the specific orientation or translation of the point cloud, which is often arbitrary. Here, we frame the instance segmentation problem as a graph learning problem in which we seek to learn a function that accepts the point cloud as input and outputs a probability distribution over neighbor graphs in which connected components of the graph correspond to individual instances. We introduce the Dimensionless Instance Segmentation Transformer (DIST), a deep neural network for spatially invariant instance segmentation of point clouds to solve the point cloud-to-graph problem. DIST uses an SO(n) invariant transformer layer architecture to operate on point clouds of arbitrary dimension and outputs, for each pair of points, the probability that an edge exists between them in the instance graph. We then decode the most likely set of instances using a graph cut. We demonstrate that the DIST has the power to segment biomolecules in cryo-electron micrographs and tomograms, far surpassing existing methods for membrane and filament segmentation in our evaluation. We anticipate that DIST will underpin a new generation of methods for point cloud segmentation and that our general model and approach will provide useful insights for point cloud segmentation methods in
Investigating Adiposity-Related Metabolic Health Phenotypes in Patients with Hidradenitis Suppurativa: A Cross-Sectional Study

Background: Obesity and hidradenitis suppurativa (HS) are related through meta-inflammation and are both associated with increased cardiometabolic risk. Notwithstanding, cardiometabolic pathology is not uniform in obesity and a subset of individuals with excess adiposity exhibit a healthy metabolic profile. Whilst the incidence of cardiometabolic endpoints and transitions across different adiposity-related body composition phenotypes within several populations and across different ethnicities have been investigated, data regarding metabolic health (MetH) and body composition phenotypes in individuals with HS are lacking. The objective of this study was to evaluate the relationship between different body composition phenotypes in individuals with HS. Methods: This was a cross-sectional study of 632 individuals with and without HS from a population with a high prevalence of both obesity and HS. A total of four body composition phenotypes were generated based on BMI and metabolic status (defined using either the metabolic syndrome definition or the homeostasis model of insulin resistance (HOMA-IR)): metabolically healthy overweight/obese (MHOWOB), metabolically unhealthy overweight/obese (MUOWOB), metabolically healthy normal weight (MHNW), and metabolically unhealthy normal weight (MUNW). Results: Generally, subjects with HS exhibited a worse metabolic profile with higher levels of indices of central adiposity measures (including Visceral Adiposity Index and waist circumference), systolic blood pressure and markers of insulin resistance, as well as a higher prevalence of the metabolic syndrome. Moreover, when sub-stratified into the different body composition phenotypes, individuals with HS typically also demonstrated adverse metabolic characteristics relative to controls matched for both adiposity and metabolic health, particularly in the normal weight category and despite being classified as metabolically healthy. Being metabolically unhealthy in addition to being overweight/obese increases an individual’s risk of HS. Conclusions: Metabolic risk-assessment should be prioritized in the clinical management of individuals with HS even in those who are lean. Patients attending HS clinics provide a valuable opportunity for targeted cardiovascular risk reduction with respect to the management of both obesity and metabolic health.
TAVR Interventions and Coronary Access: How to Prevent Coronary Occlusion

Due to technological advancements during the past 20 years, transcatheter aortic valve replacements (TAVRs) have significantly improved the treatment of symptomatic and severe aortic stenosis, significantly improving patient outcomes. The continuous evolution of transcatheter valve models, refined imaging planning for enhanced accuracy, and the growing expertise of technicians have collectively contributed to increased safety and procedural success over time. These notable advancements have expanded the scope of TAVR to include patients with lower risk profiles as it has consistently demonstrated more favorable outcomes than surgical aortic valve replacement (SAVR). As the field progresses, coronary angiography is anticipated to become increasingly prevalent among patients who have previously undergone TAVR, particularly in younger cohorts. It is worth noting that aortic stenosis is often associated with coronary artery disease. While the task of re-accessing coronary artery access following TAVR is challenging, it is generally feasible. In the context of valve-in-valve procedures, several crucial factors must be carefully considered to optimize coronary re-access. To obtain successful coronary re-access, it is essential to align the prosthesis with the native coronary ostia. As part of preventive measures, strategies have been developed to safeguard against coronary obstruction during TAVR. One such approach involves placing wires and non-deployed coronary balloons or scaffolds inside an at-risk coronary artery, a procedure known as chimney stenting. Additionally, the bioprosthetic or native aortic scallops intentional laceration to prevent iatrogenic coronary artery obstruction (BASILICA) procedure offers an effective and safer alternative to prevent coronary artery obstructions. The key objective of our study was to evaluate the techniques and procedures employed to achieve commissural alignment in TAVR, as well as to assess the efficacy and measure the impact on coronary re-access in valve-in-valve procedures.
Pancreatic Stellate Cells and the Targeted Therapeutic Strategies in Chronic Pancreatitis

Chronic pancreatitis (CP) is a disease characterized by inflammatory recurrence that accompanies the development of pancreatic fibrosis. As the mystery of CP pathogenesis is gradually revealed, accumulating evidence suggests that the activation of pancreatic stellate cells (PSCs) and the appearance of a myofibroblast-like phenotype are the key gatekeepers in the development of CP. Targeting PSCs to prevent their activation and conversion to a myofibroblast-like phenotype, as well as increasing antioxidant capacity to counteract ongoing oxidative stress, are effective strategies for preventing or treating CP. Therefore, we reviewed the crosstalk between CP and pancreatic fibrosis, summarized the activation mechanisms of PSCs, and investigated potential CP therapeutic strategies targeting PSCs, including, but not limited to, anti-fibrosis therapy, antioxidant therapy, and gene therapy. Meanwhile, the above therapeutic strategies are selected in order to update the available phytopharmaceuticals as novel complementary or alternative approaches for the prevention and treatment of CP to clarify their potential mechanisms of action and their relevant molecular targets, aiming to provide the most comprehensive therapeutic treatment direction for CP and to bring new hope to CP patients.
Split-nanotube-based negative index metamaterial for midinfrared wavelengths

Abstract. A design of split-nanotube-based negative index metamaterial for the infrared spectrum has been proposed. The proposed design and its operation are similar to that of a split-ring resonator (SRR) without inheriting the fabrication difficulties associated with conventional SRR. A negative refractive index has been achieved using a split-nanotube in combination with a periodic array of metallic wires between 1.5 and 3.3 μm.
The Role of Perceived Fit, Attitude, and Need for Uniqueness on Intention to Purchase Co-Branded Product in Indonesia

This research was conducted to empirically examine the role of perceived fit, attitude, and need for uniqueness to increase intention to purchase co-branded product in Jakarta. A total of 202 respondents were collected with non-probability convenience sampling technique, but only 150 data were usable as samples. An online questionnaire was distributed using Google Form with 5-point Likert scale for each indicator. All data were analyzed using PLS-SEM method. The results showed that perceived fit does not positively and significantly impact intention to purchase co-branded product. In addition, attitude and need for uniqueness can positively and significantly impact intention to purchase co-branded product. The results of this study contribute to get a better understanding of co-branding and to help the marketers formulate better co-branding strategies.
Improved Magnus form approximation of saturation vapor pressure

Algorithms, based on Magnus's form equations, are described that minimize the difference between several relationships between temperature and water vapor pressure at saturation that are commonly used in archiving data. The work was initiated in connection with the development of a unified upper-air dataset that will use measurements gathered from the late 1930s to the present and archived in several data centers. The conversion of field measurements to archived humidity values within the databases that are being used have not been consistent and in some cases are unknown. A goal of this work was to develop a uniform and accurate method to convert these data to various humidity variables without regard to the equations used in archiving the original data. Archived temperature values are recorded to 0.1°C. This precision creates a temperature dependent range in uncertainty in saturation vapor pressure. A procedure was developed to take this into account when the error minimizing equations were derived.
Unveiling the Impact of the Omicron Variant: Insights from Genomic Surveillance in Mato Grosso do Sul, Midwest Brazil

Genomic surveillance has emerged as a crucial tool in monitoring and understanding the dynamics of viral variants during the COVID-19 pandemic. In the Midwest region of Brazil, Mato Grosso do Sul has faced a significant burden from the SARS-CoV-2 epidemic, with a total of 613,000 confirmed cases as of June 2023. In collaboration with the Central Public Health Laboratory in the capital city of Campo Grande, we conducted a portable whole-genome sequencing and phylodynamic analysis to investigate the circulation of the Omicron variant in the region. The study aimed to uncover the genomic landscape and provide valuable insights into the prevalence and transmission patterns of this highly transmissible variant. Our findings revealed an increase in the number of cases within the region during 2022, followed by a gradual decline as a result of the successful impact of the vaccination program together with the capacity of this unpredictable and very transmissible variant to quickly affect the proportion of susceptible population. Genomic data indicated multiple introduction events, suggesting that human mobility played a differential role in the variant’s dispersion dynamics throughout the state. These findings emphasize the significance of implementing public health interventions to mitigate further spread and highlight the powerful role of genomic monitoring in promptly tracking and uncovering the circulation of viral strains. Together those results underscore the importance of proactive surveillance, rapid genomic sequencing, and data sharing to facilitate timely public health responses.
A piecewise linear programming algorithm for sparse signal reconstruction

In order to recover a signal from its compressive measurements, the compressed sensing theory seeks the sparsest signal that agrees with the measurements, which is actually an l norm minimization problem. In this paper, we equivalently transform the l norm minimization into a concave continuous piecewise linear programming, and propose an optimization algorithm based on a modified interior point method. Numerical experiments demonstrate that our algorithm improves the sufficient number of measurements, relaxes the restrictions of the sensing matrix to some extent, and performs robustly in the noisy scenarios.
Can App Reviews Help Developers to Improve Mobile User Interface Design?

For mobile user interface (M-UI) design, it has an important impact on app user’s usage. However, M-UI design is limited by subjective factors, even professional developers can’t determine whether the M-UI design is good or bad. App reviews provide an opportunity to proactively collect user complaints and promptly improve the user experience of apps. Therefore, it is meaningful to explore whether app reviews can help developers to improve M-UI design. In this article, we randomly select six different categories of apps from Google Play Store and App Store, with over 160000 reviews, and conduct a preliminary empirical study to answer the question. Specially, we gather M-UI-related reviews, and compare the average rating of M-UI-related reviews and total reviews of each app. We observe that the M-UI is concerned by users and the average rating for M-UI-related reviews is lower than the average rating for total reviews. By extracting the topics of M-UI-related reviews, we estimate the sentiment of the M-UI-related topics. The results show that the number of M-UI-related topics are about three or four, and the sentiment of M-UI-related topics is related to the app itself. Further, by investigating the relation between the M-UI-related topics and M-UI design. We observe that users are concerned about the M-UI usability the most, and it is the various aspects of the M-UI that are causing user frustration. In particular, our findings show that M-UI-related reviews reflect the severity of M-UI-related issues and app reviews can help developers to improve M-UI design about appearance, usability, fault-tolerance, of which usability deserves the most attention.
Moobot: A Miniature Origami Omnidirectional Jumping Robot With High Trajectory Accuracy

Trajectory control is important for jumping robots because it helps them to overcome different obstacles; however, integrating actuators and sensors exhibiting small scale and high accuracy remains challenging. No insect-scale jumping robots have yet demonstrated accurate trajectory following. Here, we propose a miniature origami omnidirectional jumping robot (Moobot) capable of high-accuracy feedback control of its jumping trajectory. The robot is based on a four-bar origami structure and shape memory alloys. Two strain gauges and an inertial measurement unit were used to control the jumping force and takeoff angle, respectively, and a reliable jumping mechanism and novel supporting leg were designed to improve jumping stability and trajectory accuracy. Unlike typical externally powered miniature robots, we constructed a 5 cm, 6 g untethered prototype. The error and the standard deviation of the jumping distances were both less than 1.5 cm; this represents a significant improvement relative to all previous insect-scale jumping robots. Furthermore, we integrated the yaw control and conducted omnidirectional jumping experiments, showing that Moobot was capable of accurately traversing different platforms from all directions.
Analysis of Pediatric Respiratory Disease Trends Using the 2016 KIDs' Inpatient Database

Many infants and young children worldwide have been affected by chronic respiratory conditions. In this paper, the authors performed an exploratory and predictive analysis of the 2016 KID data set to examine respiratory disease trends among children. They applied the multiple linear regression and random forest regression methods to build a predictive model for the length of stay (LOS) for children with respiratory problems. The tree approach implemented using random forest is found to be a better approach for predicting the length of stay (LOS). In addition, they performed an exploratory analysis of significant fields from the data set. From the analysis, it is found that the winter season has the highest number of inpatient admissions of children having chronic respiratory illnesses. Further, it is found that newborns and infants are more prone to respiratory diseases, with bronchitis being the leading cause of respiratory diseases among children.