Miracle-dz/UniFAR-FTUs huggingface dataset:
The Taming of the Stack: Isolating Stack Data from Memory Errors

—Despite vast research on defenses to protect stack objects from the exploitation of memory errors, much stack data remains at risk. Historically, stack defenses focus on the protection of code pointers, such as return addresses, but emerging techniques to exploit memory errors motivate the need for practical solutions to protect stack data objects as well. However, recent approaches provide an incomplete view of security by not accounting for memory errors comprehensively and by limiting the set of objects that can be protected unnecessarily. In this paper, we present the D ATA G UARD system that identiﬁes which stack objects are safe statically from spatial, type, and temporal memory errors to protect those objects efﬁciently. D ATA G UARD improves security through a more comprehensive and accurate safety analysis that proves a larger number of stack objects are safe from memory errors, while ensuring that no unsafe stack objects are mistakenly classiﬁed as safe. D ATA G UARD ’s analysis of server programs and the SPEC CPU2006 benchmark suite shows that D ATA G UARD improves security by: (1) ensuring that no memory safety violations are possible for any stack objects classiﬁed as safe, removing 6.3% of the stack objects previously classiﬁed safe by the Safe Stack method, and (2) blocking exploit of all 118 stack vulnerabilities in the CGC Binaries. D ATA G UARD extends the scope of stack protection by validating as safe over 70% of the stack objects classiﬁed as unsafe by the Safe Stack method, leading to an average of 91.45% of all stack objects that can only be referenced safely. By identifying more functions with only safe stack objects, D ATA G UARD reduces the overhead of using Clang’s Safe Stack defense for protection of the SPEC CPU2006 benchmarks from 11.3% to 4.3%. Thus, D ATA G UARD shows that a comprehensive and accurate analysis can both increase the scope of stack data protection and reduce overheads.
An Automated Model to Detect Fake Profiles and botnets in Online Social Networks Using Steganography Technique

At the present time, hundreds of millions of active users all around the world are using online social network, such as Facebook, Twitter, Tumblr and LinkedIn. This service turned out to be one of the most well- liked and accepted services on the Internet. With the quick development of information technology and networking, the users became able to share many things on the web such as pictures, videos, their daily activities, attended events and even their location. Nonetheless, the majorities of social networks have weak user to user authentication method, which is based on some basic information like displayed name, photo. These weaknesses make it effortless to misuse user's information and do identity cloning attack to form fake profile. Currently, Facebook has 955 million active users. Of this whole, approximately 8.7 percent (which is equal to 83 million users) is fake accounts. Fakes can introduce spam, manipulate online rating, or exploit knowledge extracted from the network. This enormous number of fake profiles can cause hazards for privacy and security such as spying, misusing personal information, identity thievery, and compromise privacy of users and their families. This paper will be discussing data hiding techniques to hide some information in profile pictures in order to detect botnets and fake profiles and finally will propose an automated model to detect fake profiles and botnets instead of current manual method which is costly and labor-intensive. Keywords: Fake book, Trust, Privacy, social Network, Steganography, Watermarking, botnets
IMPACTO DA EDUCAÇÃO SUPERIOR NO DESENVOLVIMENTO SÓCIO- ECONÔMICO IMPACTO DE LA EDUCACIÓN SUPERIOR EN EL DESARROLLO SOCIOECONÓMICO IMPACT OF HIGHER EDUCATION ON SOCIO-ECONOMIC DEVELOPMENT

In the article it is studied the influence of higher education on the development of regional socio-economic systems. Economic and statistical analysis revealed the impact of measures of higher education development on the GRP of the region. Highly significant factors were: salaries of faculty staff of the universities and internal research and development costs. These indicators indirectly reflect the degree of intellectual development in the region and show the impact of intellectual capital development on socio-economic development. University professors develop students' knowledge, which allows them to institutionalize higher education and spread it in the region. The quality of a university professor’s intelligence directly affects the formation of intellectual capital in the region. In addition, it was significant the number of students studied in bachelor's, specialty, and master's programs. Moreover, the higher the number of students per capita, the lower the GRP per capita, which is explained by their low incomes. In the article it was considered the differentiation of Federal districts and regions of the Russian Federation in terms of the development of the higher education system. Methods of dividing indicators and grouping were applied, which made it possible to make cartograms and visualize the differentiation of regions by the number of higher education institutions and by the number of graduates.
1217 Typeset-in progress

Background: Student-pharmacists forced into remote-learning by the COVID-19 pandemic participated in a Virtual Mock Trial (VMT). Objectives: Feasibility of VMTs was assessed by evaluating student VMT performance, student perceptions on technology and overall experiences. Methods: The VMT was implemented via video conferencing technology in April 2020. Faculty-judges and student-jurors observed/rated student performance using pre-established rubrics. A post-VMT survey was administered electronically. Descriptive analyses were performed, and WilcoxonMann-Whitney tests were conducted to compare programmes. Results: Fortysix students from Programme A (East Coast, USA) and 89 from Programme B (West Coast, USA) participated in the VMTs. The faculty-judges’ evaluation scores for student performance ranged from 85.0% to 96.7%, while the student-jurors’ evaluation scores ranged from 68.3% to 100%. Student perceptions on the four categories regarding technology use all had means > 5 on a 7-Point Likert Scale. More than 79.0% of students rated their VMT experience positively (i.e. 6 or 7). Conclusions: VMT is feasible for the current pandemic remote-learning environment, and it could be replicated in other pharmacy or healthcare programmes to enrich students' active learning in virtual education.
Wavelet Galerkin Methods for Elastic Multibody Systems

In the present paper we discuss simulation with a wavelet element method. The model problem is a simplified 2D-roller bearing model consisting of an elastic outer and inner ring and a roller. This model reflects typical properties of elastic multibody systems, eg. combination of gross motion with elastic deformation and contact problems. The wavelet element method is implemented in a similar way as the widely used finite element method. Its inherent advantages concerning model reduction will be demonstrated as well as the influence of model reduction on simulation time.
Pressure Side and Cutback Trailing Edge Film Cooling in a Linear Nozzle Vane Cascade at Different Mach Numbers

Tests on a specific designed linear nozzle guide vane cascade with trailing edge coolant ejection were carried out to investigate the influence of trailing edge bleeding on both aerodynamic and thermal performance. The cascade is composed of six vanes with a profile typical of a high pressure turbine stage. The trailing edge cooling features a pressure side cutback with film cooling slots, stiffened by evenly spaced ribs in an inline configuration. Cooling air is ejected not only through the slots but also through two rows of cooling holes placed on the pressure side, upstream of the cutback. The cascade was tested for different isentropic exit Mach numbers, ranging from M2is = 0.2 to M2is = 0.6, while varying the coolant to mainstream mass flow ratio MFR up to 2.8%. The momentum boundary layer behavior at a location close to the trailing edge, on the pressure side, was assessed by means of Laser Doppler measurements. Cases with and without coolant ejection allowed to identify the contribution of the coolant to the off the wall velocity profile. Thermochromic Liquid Crystals (TLC) were used to map adiabatic film cooling effectiveness on the pressure side cooled region. As expected, the cutback effect on cooling effectiveness, compared to the other cooling rows, was dominant.Copyright © 2011 by ASME
Development and External Validation of a Mixed-Effects Deep Learning Model to Diagnose COVID-19 from CT Imaging

Objectives To develop and externally geographically validate a mixed-effects deep learning model to diagnose COVID-19 from computed tomography (CT) imaging following best practice guidelines and assess the strengths and weaknesses of deep learning COVID-19 diagnosis. Design Model development and external validation with retrospectively collected data from two countries. Setting Hospitals in Moscow, Russia, collected between March 1, 2020, and April 25, 2020. The China Consortium of Chest CT Image Investigation (CC-CCII) collected between January 25, 2020, and March 27, 2020. Participants 1,110 and 796 patients with either COVID-19 or healthy CT volumes from Moscow, Russia, and China, respectively. Main outcome measures We developed a deep learning model with a novel mixed-effects layer to model the relationship between slices in CT imaging. The model was trained on a dataset from hospitals in Moscow, Russia, and externally geographically validated on a dataset from a consortium of Chinese hospitals. Model performance was evaluated in discriminative performance using the area under the receiver operating characteristic (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, calibration performance was assessed using calibration curves, and clinical benefit was assessed using decision curve analysis. Finally, the model's decisions were assessed visually using saliency maps. Results External validation on the large Chinese dataset showed excellent performance with an AUROC of 0.956 (95% CI: 0.943, 0.970), with a sensitivity and specificity, PPV, and NPV of 0.879 (0.852, 0.906), 0.942 (0.913, 0.972), 0.988 (0.975, 1.00), and 0.732 (0.650, 0.814). Conclusions Deep learning can reduce stress on healthcare systems by automatically screening CT imaging for COVID-19. However, deep learning models must be robustly assessed using various performance measures and externally validated in each setting. In addition, best practice guidelines for developing and reporting predictive models are vital for the safe adoption of such models.
Predicting clinical outcomes and hospitalization stay of hospitalized COVID-19 patients by using Deep Learning methods

Predicting outcomes and other critical clinical events of hospitalized COVID-19 patients may provide a valuable asset to healthcare and a chance to improve patient outcomes. Here, we have analyzed over 10,000 hospitalized COVID-19 patients in the Houston Methodist Hospital at the Texas Medical Center from the beginning of pandemics till April of 2020. This work extends our previous study analyzing longitudinal symptomatics of the hospitalized patients by seeking to understand how standard patient clinical data, like demographics and comorbidities, together with symptom data from early hospitalization can be used to predict the clinical outcomes and hospitalization stay. Deep Learning (DL) classification and regression methods were applied to quantify patient record importance and to perform predictions. The results suggest that patient outcome can be predicted with up to 75% accuracy. However, the prediction of hospitalization stay was more complex indicating deeper optimization of features.
Long Noncoding RNA Mediated Regulation in Human Embryogenesis, Pluripotency, and Reproduction

Long noncoding RNAs (lncRNAs), a class of noncoding RNAs with more than 200 bp in length, are produced by pervasive transcription in mammalian genomes and regulate gene expression through various action mechanisms. Accumulating data indicate that lncRNAs mediate essential biological functions in human development, including early embryogenesis, induction of pluripotency, and germ cell development. Comprehensive analysis of sequencing data highlights that lncRNAs are expressed in a stage-specific and human/primate-specific pattern during early human development. They contribute to cell fate determination through interacting with almost all classes of cellular biomolecules, including proteins, DNA, mRNAs, and microRNAs. Furthermore, the expression of a few of lncRNAs is highly associated with the pathogenesis and progression of many reproductive diseases, suggesting that they could serve as candidate biomarkers for diagnosis or novel targets for treatment. Here, we review research on lncRNAs and their roles in embryogenesis, pluripotency, and reproduction. We aim to identify the underlying molecular mechanisms essential for human development and provide novel insight into the causes and treatments of human reproductive diseases.
An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network

Alzheimer's disease (AD) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability. Deep learning models have shown outstanding performance in the diagnosis of AD, and these models do not need any handcrafted feature extraction over conventional machine learning algorithms. Since the 2012 AlexNet accomplishment, the convolutional neural network (CNN) has been progressively utilized by the medical community to assist practitioners to early diagnose AD. This paper explores the current cutting edge applications of CNN on single and multimodality (combination of two or more modalities) neuroimaging data for the classification of AD. An exhaustive systematic search is conducted on four notable databases: Google Scholar, IEEE Xplore, ACM Digital Library, and PubMed in June 2021. The objective of this study is to examine the effectiveness of classification approaches on AD to analyze different kinds of datasets, neuroimaging modalities, preprocessing techniques, and data handling methods. However, CNN has achieved great success in the classification of AD; still, there are a lot of challenges particularly due to scarcity of medical imaging data and its possible scope in this field.
Incidence and Associated Factors of Failed First Peripheral Intravenous Catheters among Adult Patients at Medical Surgical Wards in Public Referral  Hospitals of West Amhara, Ethiopia, 2021

Background Complications of peripheral intravenous catheters cause problems in clinical practice and bring high costs in terms of morbidity and mortality of patients. Therefore, this study aimed to assess the incidence and associated factors of failed first peripheral intravenous catheters among adult patients in selected Public Referral Hospitals of West Amhara Regional State, Ethiopia, 2021. Materials and Methods An institution-based prospective observational study was conducted among 423 adult patients from January to February 2021. Patients were selected using systematic random sampling techniques. The data were collected using interviewer-based, structured questionnaires and observational checklists. EPI-DATA 3.1 and SPSS version-23 were used for data entry and analysis, respectively. Frequency, percentages, and means were calculated. The outcome variable was determined and graded based on phlebitis and infiltration scales. Binary and multivariable logistics regressions were computed. Results Four hundred and seventeen first peripheral cannula sites from 418 patients were followed for 2,565 peripheral catheter hours. A failed first peripheral intravenous catheter was observed in 124 (29.7%, CI: 25.6–34) adult patients. Patients who were female (AOR = 0.4, 95% CI: 0.22–0.74) had cannula duration of 49–72 hours (AOR = 0.31, 95% CI: 0.14–0.7) and 73–96 hours (AOR = 0.39, 95% CI: 0.17–0.9), and patients who had been given electrolytes (AOR = 0.31, 95% CI: 0.11–0.86) were more likely to have failed first peripheral intravenous cannula. Conclusions Failed first peripheral intravenous cannula is much higher as compared to the acceptable rate of ≤5% by the Infusion Nurses Society. Hence, all patients with peripheral intravenous catheters are screened for catheter failure at least once a day. Providing appropriate nursing care and patient education is also required to reduce the risks.
Quadruped Capturability and Push Recovery via a Switched-Systems Characterization of Dynamic Balance

This article studies capturability and push recovery for quadruped locomotion. Despite the rich literature on capturability analysis and push recovery for legged robots, existing tools have been developed mainly with the requirement of reaching static or quasi-static balance following a push. In practice, this requirement commonly restricts capturability analysis to cases with simple dynamics and fails to encode the time dependence of capturable states for legged locomotion with time-based gaits. To address these issues, we apply switched systems to model quadruped locomotion and extend capturability notions through a novel specification of dynamic balance. We also provide an explicit model predictive control (EMPC) scheme to compute the dynamic balance and capturable tubes and offer a way of using the capturable tube to synthesize push recovery controllers. Such a generalization allows for a rigorous characterization of disturbance timing on the capturability of quadrupedal locomotion and opens the door of disturbance-timing-aware push recovery control strategies. Extensive simulation and hardware experiments illustrate the necessity of considering dynamic balance for quadrupedal push recovery, reveal how disturbance timing affects capturability, and demonstrate the significant improvement in disturbance rejection with the proposed strategy. Hardware experimental validations on a replica of the Mini Cheetah quadruped further verify that the proposed approach performs statistically better than the state-of-the-art baseline considered.
Pseudo-Differential Neural Operator: Generalized Fourier Neural Operator for Learning Solution Operators of Partial Differential Equations

Learning the mapping between two function spaces has garnered considerable research attention. However, learning the solution operator of partial differential equations (PDEs) remains a challenge in scientific computing. Fourier neural operator (FNO) was recently proposed to learn solution operators, and it achieved an excellent performance. In this study, we propose a novel \textit{pseudo-differential integral operator} (PDIO) to analyze and generalize the Fourier integral operator in FNO. PDIO is inspired by a pseudo-differential operator, which is a generalized differential operator characterized by a certain symbol. We parameterize this symbol using a neural network and demonstrate that the neural network-based symbol is contained in a smooth symbol class. Subsequently, we verify that the PDIO is a bounded linear operator, and thus is continuous in the Sobolev space. We combine the PDIO with the neural operator to develop a \textit{pseudo-differential neural operator} (PDNO) and learn the nonlinear solution operator of PDEs. We experimentally validate the effectiveness of the proposed model by utilizing Darcy flow and the Navier-Stokes equation. The obtained results indicate that the proposed PDNO outperforms the existing neural operator approaches in most experiments.
GTac: A Biomimetic Tactile Sensor With Skin-Like Heterogeneous Force Feedback for Robots

The tactile sensing capabilities of human hands are essential in daily activities. Simultaneously perceiving normal and shear forces via the mechanoreceptors integrated into the hands allow us humans to achieve daily tasks like grasping delicate objects. In this paper, we design and fabricate a biomimetic tactile sensor with skin-like heterogeneity that perceives normal and shear contact forces simultaneously. It mimics the multilayered structure of mechanoreceptors in human skin by combining an extrinsic layer (piezoresistive sensors) and an intrinsic layer (a Hall sensor) so that it can perform estimation of contact force directions, locations, and joint-level torque. By integrating our sensors, a robotic gripper can obtain contact force feedback at the fingertips; accordingly, robots can perform challenging tasks, such as using tweezers and grasping eggs. This insightful sensor design can be customized and applied in different areas of robotics and provide them with heterogeneous force sensing, potentially supporting robotics in acquiring skin-like tactile feedback.
Consolidated learning - a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IV

For many machine learning models, a choice of hyperparameters is a crucial step towards achieving high performance. Prevalent meta-learning approaches focus on obtaining good hyperparameters configurations with a limited computational budget for a completely new task based on the results obtained from the prior tasks. This paper proposes a new formulation of the tuning problem, called consolidated learning, more suited to practical challenges faced by model developers, in which a large number of predictive models are created on similar data sets. In such settings, we are interested in the total optimization time rather than tuning for a single task. We show that a carefully selected static portfolio of hyperparameters yields good results for anytime optimization, maintaining ease of use and implementation. Moreover, we point out how to construct such a portfolio for specific domains. The improvement in the optimization is possible due to more efficient transfer of hyperparameter configurations between similar tasks. We demonstrate the effectiveness of this approach through an empirical study for XGBoost algorithm and the collection of predictive tasks extracted from the MIMIC-IV medical database; however, consolidated learning is applicable in many others fields.
Impact of Phase-Noise and Spatial Correlation on Double-RIS-Assisted Multiuser MISO Networks

We study the performance of a phase-noise impaired double reconfigurable intelligent surface (RIS)-aided multiuser (MU) multiple-input single-output (MISO) system under spatial correlation at both RISs and base-station (BS). The downlink achievable rate is derived in closed-form under maximum ratio transmission (MRT) precoding. In addition, we obtain the optimal phase-shift design at both RISs in closed-form for the considered channel and phase-noise models. Numerical results validate the analytical expressions, and highlight the effects of different system parameters on the achievable rate. Our analysis shows that phase-noise can severely degrade the performance when users do not have direct links to both RISs, and can only be served via the double-reflection link. Also, we show that high spatial correlation at RISs is essential for high achievable rates.