Miracle-dz/UniFAR-FTUs huggingface dataset:
Using information and communication technologies for the assessment of a large number of students

Many examinations with thousands of participating students are organized worldwide every year. Usually, this large number of students sit the exams simultaneously and answer almost the same set of questions. This method of learning assessment requires tremendous effort and resources to prepare the venues, print question books and organize the whole process. Additional restrictions and obstacles may appear in conditions similar to those during the COVID-19 pandemic. One way to obviate the necessity of having all the students take an exam during the same period of time is to use a computer-assisted assessment with random item selection, so that every student receives an individual set of questions. The objective of this study is to investigate students’ perceptions of using random item selection from item banks in order to apply this method in large-scale assessments. An analysis of the responses of more than 1000 surveyed students revealed that most of them agree or completely agree with using the proposed method of assessment. The students from natural science departments showed more tolerance of this method of assessment compared with students from other groups. Based on the findings of this study, the authors concluded that higher-education institutions could benefit from implementing the abovementioned assessment method.
Tailoring multi-order absorptions of a Salisbury screen based on dispersion engineering of spoof surface plasmon polariton

In this paper, a comprehensive scheme of a Salisbury screen is proposed which makes full use of dispersion engineering of spoof surface plasmon polariton (SSPP) to tailor its multi-order absorptions. As a proof, the symmetrical corrugated groove structures made of metal strips are adhered on dielectric grid periodically as plasmonic metamaterial (PM), and then used as a spacer in a Salisbury screen. Owing to the enhanced k-vector as well as nonlinear k-dispersion relationship inspired by PM spacer, the multi-order absorptions of the proposed SSPP Salisbury screen can be adjusted to lower frequency in a nonlinear scale. Simulation and experimental measurement demonstrate that the fundamental and first-order absorptions can be adjusted from 7.5 and 22.5 GHz to 4.7 and 9.0 GHz, and their according frequency ratio is also changed from 1.0:3.0 to 1.0:1.9. The proposed attempt opens a door for further optimization of conventional absorbing structure based on dispersion engineering of SSPP, enabling a wide range of applications.
A structural view of approximation

The discovery of efficient probabilistically checkable proofs for NP, and their surprising connection to hardness of approximation, has resulted in a quantum leap in our understanding of approximability of many important optimization problems. Yet much of this research has stayed focused on problem-specific results. Building on many such results, this dissertation develops frameworks which unify a variety of seemingly different results and thereby highlights the intrinsic structural properties which govern the approximability of optimization problems. Broadly speaking, our work comprises of three distinct parts. In the first part, we develop a structural basis for computationally-defined approximation classes such as APX and poly-APX. We show that there exist canonical transformations whereby every problem in an approximation class can be "expressed" as a problem in a syntactically-defined optimization class. Thus, for example, we show that every problem in APX is expressible as a problem in the syntactic class MAX SNP. We also study the possibility that the core of PTAS may be characterized via a combination of syntactic constraints and restrictions on the input instances. In the second part, we study two natural classes of maximization problems defined via constraint satisfaction requirements. We discover classification theorems which, quite surprisingly, allow us to determine the approximability of every problem in both these classes merely from the description of the problem. Our results provide insight into questions such as: Why are there no natural maximization problems that exhibit a log n threshold for approximability? Why do all the known MAX SNP problems that are NP-hard, turn out also to be MAX SNP-hard? and, so on. Finally, in the last part of this work, we study the problem of efficiently coloring a 3-colorable graph. Since Karp's original NP-hardness result for this problem, no better lower bounds have been obtained towards the approximability of this fundamental optimization problem. We show that it is NP-hard to color a 3-colorable graph with 4 colors and thereby establish that a k-colorable graph cannot be colored with less than $k + 2 \lfloor k/3\rfloor$ colors, unless P = NP.
Backpacker Rohani: An Effort to Cultivate Moderate Insights Based on Islamic Religious Education Teaching Materials

The learning process requires teaching materials that are useful for making it easier for educators to convey information on teaching materials in the form of materials. The development of teaching materials is needed in order to support learning needs. This study describes the development of spiritual backpacker-based teaching materials as an effort to inculcate the value of religious moderation. The research process is field research using a phenomenological approach. MI Alam Alfa Kids was used as the research location to collect concrete data. sources of data in the form of interviews, observations, and documentation as the main data. The analysis was carried out by presenting data, reducing it, and concluding. The results of this study are: 1) the development of spiritual backpacker-based teaching materials is the development of teaching materials that are adapted to the learning designs at MI Alam Alfa Kids, namely spiritual backpacker-based learning as an effort to instill the value of religious moderation; 2) Spiritual backpacker teaching materials give participants firsthand experiences of being directly connected to the community environment, giving them open insight into how to be moderate. 3) Teaching materials have advantages, including an integrated learning process with the environment, fostering student inquiry, and training students to live tolerantly in community. The drawbacks of this model are that not all subjects can be developed using it; it requires special time; not all ages can apply spiritual backpacker-based teaching materials; and the implementation of teaching materials must be accompanied by more than one teacher.
Robust Learning from Multiple Information Sources

In the big data era, the ability to handle high-volume, high-velocity and high-variety information assets has become a basic requirement for data analysts. Traditional learning models, which focus on medium size, single source data, often fail to achieve reliable performance if data come from multiple heterogeneous sources (views). As a result, robust multi-view data processing methods that are insensitive to corruptions and anomalies in the data set are needed. This thesis develops robust learning methods for three problems that arise from realworld applications: robust training on a noisy training set, multi-view learning in the presence of between-view inconsistency and network topology inference using partially observed data. The central theme behind all these methods is the use of information-theoretic measures, including entropies and information divergences, as parsimonious representations of uncertainties in the data, as robust optimization surrogates that allows for efficient learning, and as flexible and reliable discrepancy measures for data fusion. More specifically, the thesis makes the following contributions: 1. We propose a maximum entropy-based discriminative learning model that incorporates the minimal entropy (ME) set anomaly detection technique. The resulting probabilistic model can perform both nonparametric classification and anomaly detection simultaneously. An efficient algorithm is then introduced to estimate the posterior distribution of the model parameters while selecting anomalies in the training data. 2. We consider a multi-view classification problem on a statistical manifold where class labels are provided by probabilistic density functions (p.d.f.) and may not be con-
A novel 3D spheroid model of rheumatoid arthritis synovial tissue incorporating fibroblasts, endothelial cells, and macrophages

Objective Rheumatoid Arthritis (RA) is a progressive and systemic autoimmune disorder associated with chronic and destructive joint inflammation. The hallmarks of joint synovial inflammation are cellular proliferation, extensive neoangiogenesis and infiltration of immune cells, including macrophages. In vitro approaches simulating RA synovial tissue are crucial in preclinical and translational research to evaluate novel diagnostic and/or therapeutic markers. Two-dimensional (2D) settings present very limited in vivo physiological proximity as they cannot recapitulate cell-cell and cell-matrix interactions occurring in the three-dimensional (3D) tissue compartment. Here, we present the engineering of a spheroid-based model of RA synovial tissue which mimics 3D interactions between cells and pro-inflammatory mediators present in the inflamed synovium. Methods Spheroids were generated by culturing RA fibroblast-like-synoviocytes (RAFLS), human umbilical vein endothelial cells (ECs) and monocyte-derived macrophages in a collagen-based 3D scaffold. The spheroids were cultured in the presence or absence of vascular endothelial growth factor (VEGF) and fibroblast growth factor 2 (bFGF) or RA synovial fluid (SF). Spheroid expansion and cell migration were quantified for all conditions using confocal microscopy and digital image analysis. Results A novel approach using machine learning was developed to quantify spheroid outgrowth and used to reexamine the existing spheroid-based model of RA synovial angiogenesis consisting of ECs and RAFLS. A 2-fold increase in the spheroid outgrowth ratio was demonstrated upon VEGF/bFGF stimulation (p<0.05). The addition of macrophages within the spheroid structure (3.75x104 RAFLS, 7.5x104 ECs and 3.0x104 macrophages) resulted in good incorporation of the new cell type. The addition of VEGF/bFGF significantly induced spheroid outgrowth (p<0.05) in the new system. SF stimulation enhanced containment of macrophages within the spheroids. Conclusion We present a novel spheroid based model consisting of RAFLS, ECs and macrophages that reflects the RA synovial tissue microenvironment. This model may be used to dissect the role of specific cell types in inflammatory responses in RA, to study specific signaling pathways involved in the disease pathogenesis and examine the effects of novel diagnostic (molecular imaging) and therapeutic compounds, including small molecule inhibitors and biologics.
Modeling and experimental validation for 3D mm-wave radar imaging

of the Dissertation Modeling and Experimental Validation for 3D mm-wave Radar Imaging by Galia Ghazi Doctor of Philosophy in Electrical and Computer Engineering Northeastern University, August 2017 Dr. Jose A. Martinez Lorenzo, Adviser As the problem of identifying suicide bombers wearing explosives concealed under clothing becomes increasingly important, it becomes essential to detect suspicious individuals at a distance. Systems which employ multiple sensors to determine the presence of explosives on people are being developed. Their functions include observing and following individuals with intelligent video, identifying explosives residues or heat signatures on the outer surface of their clothing, and characterizing explosives using penetrating X-rays, terahertz waves, neutron analysis, or nuclear quadrupole resonance. At present, mm-wave radar is the only modality that can both penetrate and sense beneath clothing at a distance of 2 to 50 meters without causing physical harm. Unfortunately, current mm-wave radar systems capable of performing high-resolution, real-time imaging require using arrays with a large number of transmitting and receiving modules; therefore, these systems present undesired large size, weight and power consumption, as well as extremely complex hardware architecture. The overarching goal of this thesis is the development and experimental validation of a next generation inexpensive, high-resolution radar system that can distinguish security threats hidden on individuals located at 2-10 meters range. In pursuit of this goal, this thesis proposes the following contributions: (1) Development and experimental validation of a new current-based, high-frequency computational method to model large scattering problems (hundreds of wavelengths) involving lossy, penetrable and multi-layered dielectric and conductive structures, which is needed for an accurate characterization of the wave-matter interaction and EM scattering in the target region; (2) Development of combined Norm-1, Norm-2 regularized imaging algorithms, which are needed for enhancing the resolution of the images while using a minimum number of transmitting and receiving antennas; (3) Implementation and experimental validation of new calibration techniques, which are needed for coherent imaging with multistatic configurations; and (4) Investigation of novel compressive antennas, which spatially modulate the wavefield in order to enhance the information transfer efficiency between sampling and imaging regions and use of Compressive Sensing algorithms.
Reconfigurable Nanophotonic Cavities with Nonvolatile Response

The use of phase-change materials on waveguide photonics is presently being purported for a range of applications from on-chip photonic data storage to new computing paradigms. Photonic integrated circuits in combination with phase-change materials provide on-chip control handles, featuring nonvolatility and operation speeds down to the nano- and picosecond regime. Besides ultrafast control, efficient operation of nonvolatile elements is crucial and requires compact photonic designs. Here we embed phase-change materials in photonic crystal cavities to realize tunable nanophotonic devices which can be reconfigured on demand. The devices exploit strong light matter interactions between the resonant modes of the cavity and the evanescently coupled phase-change material cell. This results in an increased transmission contrast and a power reduction of 520% over conventional phase-change nanophotonic devices when reversibly switched with optical pulses. Such designs can thus open up new areas of reconfigurable .
RDRGSE: A Framework for Noncoding RNA-Drug Resistance Discovery by Incorporating Graph Skeleton Extraction and Attentional Feature Fusion

Identifying noncoding RNAs (ncRNAs)-drug resistance association computationally would have a marked effect on understanding ncRNA molecular function and drug target mechanisms and alleviating the screening cost of corresponding biological wet experiments. Although graph neural network-based methods have been developed and facilitated the detection of ncRNAs related to drug resistance, it remains a challenge to explore a highly trusty ncRNA-drug resistance association prediction framework, due to inevitable noise edges originating from the batch effect and experimental errors. Herein, we proposed a framework, referred to as RDRGSE (RDR association prediction by using graph skeleton extraction and attentional feature fusion), for detecting ncRNA-drug resistance association. Specifically, starting with the construction of the original ncRNA-drug resistance association as a bipartite graph, RDRGSE took advantage of a bi-view skeleton extraction strategy to obtain two types of skeleton views, followed by a graph neural network-based estimator for iteratively optimizing skeleton views aimed at learning high-quality ncRNA-drug resistance edge embedding and optimal graph skeleton structure, jointly. Then, RDRGSE adopted adaptive attentional feature fusion to obtain final edge embedding and identified potential RDRAs under an end-to-end pattern. Comprehensive experiments were conducted, and experimental results indicated the significant advantage of a skeleton structure for ncRNA-drug resistance association discovery. Compared with state-of-the-art approaches, RDRGSE improved the prediction performance by 6.7% in terms of AUC and 6.1% in terms of AUPR. Also, ablation-like analysis and independent case studies corroborated RDRGSE generalization ability and robustness. Overall, RDRGSE provides a powerful computational method for ncRNA-drug resistance association prediction, which can also serve as a screening tool for drug resistance biomarkers.
Audiological Tests Used in the Evaluation of the Effects of Solvents on the Human Auditory System: A Mixed Methods Review

Abstract This study aimed to scope the literature, identify knowledge gaps, appraise results, and synthesize the evidence on the audiological evaluation of workers exposed to solvents. We searched Medline, PubMed, Embase, CINAHL, and NIOSHTIC-2 up to March 22, 2021. Using Covidence, two authors independently assessed study eligibility, risk of bias, and extracted data. National Institute of Health Quality Assessment Tools was used in the quality evaluation of included studies; the Downs and Black checklist was used to assess the risk of bias. Of 454 located references, 37 were included. Twenty-five tests were studied: two tests to measure hearing thresholds, one test to measure word recognition in quiet, six electroacoustic procedures, four electrophysiological tests, and twelve behavioral tests to assess auditory processing skills. Two studies used the Amsterdam Inventory for Auditory Disability and Handicap. The quality of individual studies was mostly considered moderate, but the overall quality of evidence was considered low. The discrepancies between studies and differences in the methodologies/outcomes prevent recommending a specific test battery to assess the auditory effects of occupational solvents. Decisions on audiological tests for patients with a history of solvent exposures require the integration of the most current research evidence with clinical expertise and stakeholder perspectives.
Delineating the Prostate Boundary on TRUS Image Using Predicting the Texture Features and its Boundary Distribution

Generally, the doctors manually delineated the prostate boundary seeing the image by their eyes, but the manual method not only needed quite much time but also had different boundaries depending on doctors. To reduce the effort like them the automatic delineating methods are needed, but detecting the boundary is hard to do since there are lots of uncertain textures or speckle noises. There have been studied in SVM, SIFT, Gabor texture filter, snake-like contour, and average-shape model methods. Besides, there were lots of studies about 2 and 3 dimension images and CT and MRI. But no studies have been developed superior to human experts and they need additional studies. For this, this paper proposes a method that delineates the boundary predicting its texture features and its average distribution on the prostate image. As result, we got the similar boundary as the method of human experts.
Morpho-Physiological Responses of Three Italian Olive Tree (Olea europaea L.) Cultivars to Drought Stress

Water scarcity in agriculture can limit crop production and trigger the need for more effective water resource management. As a result, it is critical to identify new crop genotypes that are more drought tolerant and perform better under low irrigation or even rain-fed conditions. The olive tree is a high-value crop that is well adapted to dry Mediterranean conditions. However, different genotypes may have developed different mechanisms of tolerance to water stress. To investigate such mechanisms, we examined three Italian olive cultivars (‘Giarraffa’, ‘Leccino’, and ‘Maurino’) grown in a greenhouse under drought stress. We found that single genotypes responded differently to the drought, though not all parameters revealed significant differences. The first major difference among the cultivars was in transpiration: the lower stomatal density and stomatal conductance of ‘Giarraffa’ allow this cultivar to use water more conservatively. In parallel with the reduction in stomatal and mesophyll conductance, the drought-stressed group of ‘Giarraffa’ maintained the electron transport rate and effective efficiency levels of photosystem II similar to those of the control until the fourth week of stress. The fluorescence parameters revealed the earlier closure of reaction photosynthetic centres in ‘Leccino’. Finally, the higher rate of electrolyte leakage in ‘Maurino’ indicated a significant ions loss in this cultivar when it was subjected to the drought. Both water management under stress conditions and the effect of drought on photosynthesis make ‘Giarraffa’ interesting to researchers studying its use in breeding or water-saving programmes.
Supportive parent-adolescent relationships as a foundation for adolescent emotion regulation and adjustment

Introduction The purpose of this investigation was to examine the influence of supportive parent-adolescent relationships on adolescent adjustment (i.e., prosocial behavior, aggression, depressive symptoms) both directly and indirectly (via adolescent emotion regulation). Scholars have posited that adolescent emotion regulation (ER) may serve as an underlying mechanism in the link between parenting and adolescent adjustment. Supportive parent-adolescent relationships (i.e., openness, acceptance, emotional responsiveness) may be a key emotion socialization mechanism influencing adolescent ER. Methods The sample included 206 adolescents (Age Range= 10–18years; 51% female; 70.4% ethnic minorities) and one primary caregiver (83.3% biological mothers, 38.7% single parents). Structural equation modeling (SEM) was implemented to investigate the direct and indirect (via adolescent ER) effects of supportive parent-adolescent relationships on adolescent adjustment. We also explored whether these indirect and direct effects varied by adolescent sex and age. Results Results suggested direct and indirect (via adolescent ER) links between supportive parent-adolescent relationships and adolescent prosocial behavior, aggressive behavior, and depressive symptoms. Moreover, evidence indicated that many of these pathways were significant for boys but not girls. No evidence of youth age as a moderator was found. Discussion These findings highlight the important role supportive parent-adolescent relationships play in adolescent emotional and behavioral adjustment. Parenting programs could focus on facilitating a mutually responsive parent-adolescent relationship with a specific focus on the dynamic nature of emotion socialization during adolescence.
Many-Body Localization in the Massless Schwinger Model

The massless Schwinger model, a one-dimensional system of massless fermions with a longrange Coulombic interaction, is equivalent to a non-interacting bosonic field with a mass term in the bosonization regime. This begs the question: does the massless Schwinger model many-body localize upon the addition of disorder? We answer this question by calculating the level statistics ratio for the disordered massless Schwinger model in the low-interaction regime, where bosonization applies. Our results suggest that the system many-body localizes in the bosonization regime at low energy densities. We present numerical evidence from finite size studies of a lattice version of the model in support of this conclusion.