Miracle-dz/UniFAR-FTUs huggingface dataset:
Backstepping Algorithm for Controlling of Quadrotor

In this paper, we consider the synthesis of a flight control system of an unmanned aerial vehicle like a quadrotor based on a backstepping procedure. The control object has a mathematical model in the form of the system of second-order differential equations, and it has essential nonlinear dynamics. The paper investigates a way of design the algorithm state feedback control based that stabilizing the final state. Some simulation results are presented, as well.
Simulation of misinformation spreading processes in social networks: an application with NetLogo

We introduce an agent-based framework (developed in NetLogo, one of most relevant simulation platforms) to simulate the diffusion of a piece of misinformation, according to a known compartmental model in which the fake news and its debunking compete in a social network. The tool allows to set different values for the spreading rate of the news, the hoax credibility, the probability of fact-checking and the forgetting rate of the agents. Moreover, it is possible to run the process over any given network. Since NetLogo is free and open source, our tool could be easily used and/or personalised by other researchers to explore different scenarios of fake news spreading.
Extending the SEMAT Kernel for the Practice of Designing and Implementing Microservice-Based Applications using Domain Driven Design

In the quest for organizations to undergo digital transformation, adoption of the microservice architecture has been a key enabler to re-imagine the application landscape. However, splitting the business domain into a distributed software architecture poses certain challenges. Applying proven practices through software processes can help us to address those. In this paper, we leverage SEMAT to scrutinize the approaches to develop applications with microservice architecture using Domain Driven Design (DDD). We introduce relevant alphas to extend the SEMAT Kernel to successfully adopt the practice of using microservices with DDD and deliver required software quality.
Credit Risk Evaluation Based on Data Mining and Integrated Feature Selection

Credit risk assessment is an important process for every financial institution. Most current studies in this area focus on the development of the classifiers. However, besides the classifiers, data analysis and mining also play an important role in improving the accuracy. In this paper, an integrated feature selection method is proposed combing the analysis of the data in one feature but belongs to different labels and the analysis of the relationship between a single feature and labels. Back propagation neural network is used as the classification model. Experiments are done on Australian credit approval dataset. The results show that the proposed integrated feature selection can improve the accuracy of assessment compared with other previous work.
A Method of SAS with Sparse Aperture Based on Compressive Sensing

High resolution synthetic aperture sonar (SAS) is limited by some factors such as size of the sonar carrier, carrier speed, underwater sound velocity and sound propagation characteristics, so obviously the mapping efficiency of SAS is less than that of synthetic aperture radar (SAR). In order to break through the problem of low mapping efficiency, this paper proposes a method of sampling sparse in azimuth direction to obtain higher operation efficiency. The signal is transformed into SAS imaging by stripe along the range direction to solve the effect of range migration caused by the motion of carrier. And the scattering coefficient is reconstructed by one of the compressed sensing (CS) method called best orthogonal matching pursuit (OMP) algorithm, in order to obtain the final SAS imaging. The simulation and experimental data imaging results show that the method can still realize the imaging without ambiguity under the condition of the enormous down sampling along the azimuth direction. It solves the problem of the grating lobe effect caused by sparse sampling, improves the mapping efficiency greatly and has strong effectiveness and practicability.
Users First: A Robust Two-Level Learning of Power Control in Uplink Ultra-Dense HetNets

In this article, power control of uplink connection in the ultra-dense heterogeneous networks (HetNets), which are studied as different types of access points (APs), is investigated. It is demonstrated that an efficient performance of users during the uplink transmission is limited to the issue of per-user power control. Although the per-user power control allows users to transmit with full power to maintain a stable connection, it also causes a higher outage probability during the uplink transmission. In light of this, we propose a robust distributed energy-efficient scheme for uplink power control in HetNets, which confronts the per-user power control problem and coordination of the multi-user interferences. Therefore, firstly, Jarvic-Patric (JP) algorithm is adopted for the users’ clustering. Unlike the traditional JP algorithm, conditions for the formation of users’ clustering are extended with a term named the degree of membership in this article. Secondly, the distributed energy efficiency (EE) problem is formulated as the mean-metric of the EE, i.e., a sum of users’ cooperative EE functions to address the cooperation problem among clustered interfering users at the local level and coordination of multi-user interferences at a global level. The formulation of the EE problem in this fashion reveals the interdependence of power optimization at local and global levels, which brings about the necessity of joint optimization. Hence, we propose a novel 2-level distributed cooperative learning (DCL) scheme, where users act as self-organized agents and optimize power control at local and global levels jointly. In the 2-level DCL scheme, clustered users are engaged in the cooperative game of power control at the local level to maximize the cooperative EE. Meantime, users communicate with each other to learn an online power control at the global level. Besides, a popular performance metric for a machine learning scheme named regret is demonstrated for the 2-level DCL scheme. Finally, the numerical results demonstrate that the proposed scheme significantly improves the EE compared to existing works.
A Deep Learning Approach to Predict Weather Data Using Cascaded LSTM Network

Weather prediction is a challenging research problem although the revolutionary advancement in deep learning, along with the availability of big data, has significantly alleviated this problem. Moreover, in terms of robustness and computational cost, this problem has currently interested many researchers to develop numerous models. This paper proposes a lightweight yet powerful deep learning architecture for weather forecasting that can outperform some of the existing well-known models. This architecture mainly uses the LSTM layers in a stacked fashion, with a different number of units in each layer. It takes in multiple weather variables as input features for a given time sequence to forecast the same weather parameters in a multi-input multi-output (MIMO) structure. The resulting models are tested to predict the wind speed, relative humidity, dew point and temperature in this study and experimented with different hyper-parameters consisting a number of LSTM layers, a variable learning rate, number of LSTM units. Two models have been built cascading the basic 1hour-ahead model which predicts the weather parameters for the 2 hours and 3 hours ahead. The obtained results show that the cascaded models perform significantly better than the standard LSTM or 1D convolution networks in shorter period prediction.
Effectiveness of dynamic image features for estimating daily conversation atmosphere

To achieve a conversation atmosphere estimation method, we confirm the usefulness of optical flow values for conversation atmosphere estimation. We extracted only the response scenes from a database of free conversation between elderly individuals, analyzed two-dimensional optical flow parameters, and calculated the standard deviation values (FX-SD, FY-SD) of all frames for each dimension. These values express the dynamic characteristics of conversation gestures. As a result of comparing the FX-SD values and the FY-SD values of “smooth conversation scene (not-need help (NNH))” with that of “not-smooth conversation scene (need help (NH))”, the “NNH scenes” values are smaller than those of the “NH scenes”. We also confirmed the identification rate using support vector machine (SVM). The average identification rate is 72.3%. These results suggest that optical flow parameters are useful for identification, but the performance is not sufficient. In the future, we should evaluate more conversation databases and provide more in-depth discussion of the effectiveness.
Multimodality Weight and Score Fusion for SLAM

Simultaneous Localization And Mapping (SLAM) is used to predict the trajectory by the Autonomous Navigation Robots (ANR), for instance Self-Driving Cars (SDC). It computes the trajectory through sensing the surroundings, like a visual perception of the environment. This work focuses on the performance improvements of a SLAM model using multimodal learning: (i), early fusion via layer weight enhancement of feature extractors, and (ii), late fusion via score refinement of the trajectory (pose) regressor. The comparative analysis on Apolloscape dataset shows that the proposed fusion strategies improve localization performance significantly. This work also evaluates applicability of various Deep Convolutional Neural Networks (DCNNs) for SLAM.
Evaluation and Detection of Gaps in Curved Sugarcane Planting Lines in Aerial Images

Sugarcane is one of the main crops in the world due to the economic value it promotes by selling its derivatives. A diversity of technologies has been developed to optimize agricultural activities and maximize the productivity of sugarcane crops. In this sense, our primary goal is to contribute to this research area by detecting planting lines and measuring their faults, including the evaluation of curved lines that substantially limit numerous solutions in practical applications. An automatic method that identifies and measures sugarcane planting lines through digital image processing techniques and machine learning algorithms is presented. The proposal is evaluated using a database of real scene images, which were classified by K-Nearest Neighbors (KNN) and prepared with the support of a small unmanned aerial vehicle (UAV). Experimental tests show a low relative error of approximately 1.65% compared to manual mapping in the planting regions. It means that our proposal can identify and measure planting lines accurately, which enables automated inspections with high precision measurements.
Stress Prediction from Head Motion

The measurement of cognitive stress has huge potential for advertising optimization (e.g., neuromarketing), optimization of recommendation systems, and applications in the fields of human-computer interaction and affective computing. Many studies have addressed stress prediction based on machine learning from the features measured by sensors attached to a subject’s body. Meanwhile, as virtual reality (VR) and augmented reality (AR) have increased in popularity, head motion data from users watching VR/AR contents have become ubiquitous. In addition, stress prediction from head motion can be valuable because it does not rely on skin condition (e.g., sweat, tattoo, and cosmetics) and is not detrimental to usability. However, the effectiveness of stress prediction based on head motion data is not well understood. In this study, we propose a method to predict stress from head motion and verify the performance of this method in multiple test situations.
A Wideband Cluster-Based SCM Vehicle-toVehicle Channel Model in Roadside Scattering Environments

A novel cluster-based spatial channel model (SCM) for vehicle-to-vehicle (V2V) communications is proposed. This model is set up for V2V roadside scattering environments. To efficiently illustrate the real-world scenarios and describe non-stationary V2V channels, we divide all effective scatterers into three categories of clusters in terms of relative position of the scattering object. A mathematical expression of channel impulse response (CIR) is derived from an extension SCM and cluster-based models. Furthermore, the spatial and frequency statistical properties of the proposed V2V channel model are thoroughly studied. Finally, numerical simulations are presented to demonstrate the effectiveness of SCM V2V model.
Comparison of generalized data assimilation method with an Ensemble Optimal Interpolation scheme

A previously developed original data assimilation method, which is called in the paper as Generalized Kalman Filter is applied in conjunction with the Hybrid-Coordinate Ocean Model to assimilate altimetry data from the Archiving, Validating and Interpolating Satellite Oceanography Data. The results of assimilation using this method have been compared with the assimilation by the well-known Ensemble Optimal Interpolation scheme. It is shown that Generalized Kalman filter has several advantages. It provides a better forecast and requires less computational cost. Also, it captures a synoptic variability of the input characteristics. The studied method allows estimating the error bars of the obtained analysis, this topic is discussed in conclusions.
VLD: Smartphone-assisted Vertical Location Detection for Vehicles in Urban Environments

As the most widely used outdoor navigation system, GPS can provide accurate localization on the horizontal plane. However, the vertical localization accuracy exhibits a poor performance. Vehicles on the elevated road usually receive wrong navigation instructions since GPS fails to detect vehicles' vertical location. In this paper, we present a vertical location system named VLD for vehicles in metropolises mainly leveraging smartphones' barometers, a low-power sensor found in an increasing number of smart devices. When initially on the ground, VLD combines the height and angle detection algorithms to confirm the vertical location with low complexity. Once vehicles have exited the ground and start to travel on the elevated road, a pressure-height model trained by a novel proposed sensor fusion algorithm is activated to measure vehicles' relative height. Then, it tracks the relative height in real time according to a pressure-temperature model which is calibrated by current weather. Comparing the relative height with the single-level elevated road's height, VLD can determine which level vehicles travel on in many highway interchanges and when they exit the elevated road. Experiments covering one month demonstrate that the detection accuracy of VLD exceeds 99% under different weather conditions, and it shows a more accurate relative height measurement compared to available literature, including Baidu Maps and one barometer based application-Altitude. Finally, VLD demonstrates a high efficiency with respect to both the detection delay and power consumption.
Dynamic Spectrum Access and Management in Cognitive Radio Networks: Introduction to cognitive radio

Are you involved in designing the next generation of wireless networks? With spectrum becoming an ever scarcer resource, it is critical that new systems utilize all available frequency bands as efficiently as possible. The revolutionary technology presented in this book will be at the cutting edge of future wireless communications. Dynamic Spectrum Access and Management in Cognitive Radio Networks provides you with an all-inclusive introduction to this emerging technology, outlining the fundamentals of cognitive radio-based wireless communication and networking, spectrum sharing models, and the requirements for dynamic spectrum access. In addition to the different techniques and their applications in designing dynamic spectrum access methods, you'll also find state-of-the-art dynamic spectrum access schemes, including classifications of the different schemes and the technical details of each scheme. This is a perfect introduction for graduate students and researchers, as well as a useful self-study guide for practitioners.
The high‐resolution X‐ray structure of vinca‐domain inhibitors of microtubules provides a rational approach for drug design

Tubulin vinca‐domain ligands can inhibit microtubule polymerization, causing cell death in mitosis, and their potential against multiple cancer types has been demonstrated. However, due to drug resistance and toxicities, development of novel vinca‐domain ligands is still needed. In this study, we determined the high‐resolution crystal structures of vinorelbine, YXD, and Phomopsin A in complex with tubulin at 2.5 Å. Additionally, we recapitulated all previously published high‐resolution crystal structures of the vinca binding site to reveal critical residues and the molecular mechanism of vinca‐domain ligands interacting with tubulin. Furthermore, we designed putatively novel triazolopyrimidine derivatives by introducing secondary amine groups to establish salt‐bridge and H‐bond interactions with Asp179β1 and Asn329α2. Our studies provided the structural basis for designing novel tubulin vinca‐domain ligands.
Perceptions of values over time and why they matter

Abstract Objective Extant research mostly treats values as being stable over time. Our research examined how people perceive values over time and whether or not these perceptions reflect motivational tensions between theoretically opposing values. We also assessed the viability of examining values over time to predict well‐being and future intentions. Method Four studies (N = 934) asked participants to report their values across past, present, and future settings. These temporal trajectories were analyzed across the four types of higher‐order values: self‐transcendence, self‐enhancement, openness, and conservation. Studies 3 and 4 assessed associations with well‐being. Study 4 assessed associations with self‐reported behavior over time. Results Across all four studies, participants perceived their values as being dynamic over time. Younger participants' trajectories did not reflect the motivational conflicts typically reported in values research, but Study 4 showed potential awareness in older age groups. Variability in temporal values correlated with well‐being, particularly for openness values. Future values predicted future intentions, even when controlling for present values. Conclusion This novel method of examining values provides new understanding into how people perceive the pursuit of values over time. Additionally, we show two ways that a temporal values measure can offer new insights into well‐being and future intentions.
Determinants of postpartum sleep duration and sleep efficiency in minority women.

STUDY OBJECTIVES To examine demographic, psychosocial, and behavioral determinants of postpartum sleep duration and sleep efficiency among a cohort of Black and Latina women. METHODS Data were from 148 women (67% Black, 32% Latina) at 5 months postpartum, recruited from an academic medical center in Philadelphia. Relevant demographic, psychosocial and behavioral predictors were assessed via questionnaire. Nocturnal sleep was objectively measured for 1 week using wrist actigraphy. Sleep duration was examined as a continuous variable and in categories (<7 versus ≥7 hours/night); sleep efficiency was examined as a continuous variable. Independent multiple linear regression models were built to evaluate significant determinants of sleep. RESULTS Adjusted models revealed that breastfeeding, having a bedtime after midnight, and being employed were associated with shorter sleep duration (-25-33 minutes, all p<0.05). Multiparity, being unmarried, being employed, breastfeeding, having a bedtime after midnight, bedsharing, and responding to infant awakenings by getting up immediately rather than waiting a few minutes to see if the infant fell back asleep, were all significant determinants of sleeping <7 h/night (OR varying: 2.29-4.59, all p<0.05). Bedsharing was the only variable identified from the multiple regression model that associated with poorer sleep efficiency (-3.8%, p<0.05). CONCLUSIONS Findings may inform interventions for improving postpartum sleep in socioeconomically disadvantaged, racial/ethnic minority postpartum women.
Object-Oriented Programming: Diagnosis Understanding by Identifying and Describing Novice Perceptions

This work in progress focuses on oriented-object programming (OOP) education. The purpose of this contribution is to validate a methodology for building a concept inventory (CI) dedicated to OOP. The long-term study presented here concerns the variable programming concept. Teaching programming is a major issue due to the misconceptions of the students, notably in the OOP paradigm. Teachers need to be aware of the existence of these misconceptions, their variety and when they appear in order to quickly and efficiently correct them. A CI can measure students’ understanding of a set of concepts at a precise time. To measure the evolution of student’s understanding over a semester, the CI has to be administrated several times along a specific timeline. In the context of a 60-hour OOP course, a preliminary qualitative study was conducted during the 2018-2019 academic year. Semi-structured interviews were conducted with 13 students following the OOP course. Each student was interviewed three times over a semester: Before the course, at mid-term over the semester, and after the course. Six students’ perceptions of the variable programming concept were identified and used to build the OOP CI. During the 2019-2020 academic-year, the CI was administred to 107 students following the OOP course. Three administrations were organized according to a specific timeline, aiming both to confirm the results of the preliminary study and to quantify the occurrence of the six perceptions in a class of students. Based on the results obtained, the methodology appears to be effective in building a OOP CI.
Circ_0109046 promotes the malignancy of endometrial carcinoma cells through the microRNA‐105/SOX9/Wnt/β‐catenin axis

Emerging evidence suggests the important involvements of circular RNAs (circRNAs) in cancer progression. This study focuses on the function of Circ_0109046 on the malignancy of endometrial carcinoma (EC) cells and the molecules involved. First, high expression of Circ_0109046 was found in EC tissues compared to the adjacent tissues, and it predicted unfavorable prognosis in patients. Similarly, high expression of Circ_0109046 was confirmed in EC cells relative to that in normal endometrial epithelial cells. Silencing of Circ_0109046 in AN3‐CA cells inhibited proliferation and aggressiveness but increased apoptosis of cells. Circ_0109046 was mainly sub‐localized in cytoplasm, and it mediated SOX9 expression through sponging microRNA (miR)‐105. The proliferation and aggressiveness of EC cells suppressed by Circ_0109046 downregulation was recovered upon SOX9 overexpression. SOX9 activated the Wnt/β‐catenin pathway. Furthermore, downregulation of Circ_0109046 reduced the growth of xenograft tumors in nude mice. This study evidenced that Circ_0109046 upregulates SOX9 expression through sponging miR105, leading to activation of Wnt/β‐catenin signaling and the malignant growth of EC. This study may offer novel understanding in EC treatment.