Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multidimensional Framework of Group Productive Disciplinary Engagement

: This research is aimed at developing theory and methods to examine collaborative group engagement during classroom activity that integrates math and science disciplinary practices (i.e., modeling, argumentation). This work draws on a situative perspective, along with prior framings of individual and group engagement, to conceptualize engagement as shared and multidimensional. Case illustrations showcase the synergistic interrelations of multiple dimensions, together constituting productive disciplinary engagement. Application of the rubric includes instructional design and practice. This research is aimed at advancing theory and methods for understanding how collaborative groups come to productive forms of engagement in STEM activities. This research builds from Engle and Conant’s (2002) conceptualization of productive disciplinary engagement (PDE) as collective intellectual progress on core ideas and scientific and mathematics practices during authentic tasks by a) broadening the analytic focus to encompass collaborative groups’ collective engagement and b) describing groups’ engagement as multifaceted, with dimensions operating in synergy, drawing on extant conceptualizations of individual’s engagement (Fredricks, Blumenfeld & Paris, 2004). In alignment with a situative perspective, we conceptualize engagement as social and representative of the negotiated norms of activity, including shared group norms (Sinha, Rogat, Adams-Wiggins & Hmelo-Silver, 2015). A multidimensional conceptualization of engagement enables examination of individual engagement dimensions that together constitute PDE. This multifaceted approach is aligned with aims to understand the interconnections between social and cognitive dimensions during group activity (Barron, 2003) and
Crowdsourcing Explanations for Improving Assessment Content and Identifying Knowledge Components

: Refining assessment items to improve their clarity and identifying the intended knowledge components required to solve them is a time-consuming task. In this study, we present the results of crowdsourcing insights into the underlying concepts of problems in mathematics and English writing, as a step towards leveraging the crowd to expedite the task. This work demonstrates a method to use the crowd’s knowledge that can lead to knowledge component identification and improved assessments.
Antifungal Activity of Weed Extracts on Candida Albicans : An In-vitro Study

The genus Candida is caused morbidity and mortality in human beings. The virulence factors of the Candida albicans have the great role in the pseudohyphae formation by attached with epithelial cells and endothelial cells. The aim of the study was to evaluate the antifungal activity of weed extracts used in traditional herbal medicine. The weeds were selected on the basis of their reported ethnobotanical uses. Water and alcoholic weed extracts were screened in vitro for their antifungal activity against fungus C. albicans . 50μl concentration of minimal Inhibitory Concentration (MIC) of Mimosa pudica extract in alcohol and 70μl concentration of MIC of Oxalis corniculata extract shows in alcohol. We conclude from this that these extracts exhibit amazing fungicidal properties that support their traditional uses. The presence of phytochemicals in the extracts including, tannins, alkaloids, steroids, glycosides, triterpenoids, flavonoids, phenolic compounds and organic acids like, Malic acid, tartaric acid and citric acid may be responsible for these activities. The acetone extracts of plant are more efficient as compared to the water extract.
University of Glasgow Terrier Team (uogTr) at the TREC 2020 Incident Streams Track

In this paper, we describe runs submitted on behalf of the University of Glasgow Terrier Team (uogTr) for the TREC 2020 Incident Streams track. We detail our approach to addressing the challenges present in the classification of crisis and disaster management data in unstructured text. In particular, we explore the usage of pre-trained ELMo embeddings alongside descriptive metadata-level and event-level features for classification. We also utilise algorithms incorporating undersamplingtechniques inorderto mitigatethesig-nificant class imbalance in the dataset. We submitted a total of three official runs to the 2020A track: ELMO_ALL_BRF , ELMO_ALL_EEC , and ELMO_ALL_TFIDF_BRF with varying features and classifiers used. Our results show that our run, ELMO_ALL_BRF shows competitive performance, performing above the median across a number of track-specific metrics.
Photopolymérisation radicalaire contrôlée pour la micro-nanostructuration de polymères fonctionnels

La fabrication de surfaces polymeres complexes avec des chimies et des topographies controlees a l’echelle micro et nanometrique est en pleine expansion en raison de la large gamme d'applications. Une nouvelle methode prometteuse consiste a utiliser la photopolymerisation radicalaire controlee par les nitroxydes (NMP2) qui exploite une alcoxyamine photosensible (AA). Pour demontrer le potentiel de fabrication de surfaces de polymeres complexes, un film de polymere a d'abord ete forme en irradiant avec une formulation contenant un melange de monomere acrylique et l’alcoxyamine. Ensuite, le depot d'un second monomere acrylique sur ce film durci peut redemarrer une nouvelle reaction de photopolymerisation du fait de la presence d'alcoxyamines a la surface. Les radicaux peuvent etre reactives par exposition aux lampes UV et permettent de commencer un nouveau procede de polymerisation. Une autre alternative est d'utiliser l’ecriture directe par laser pour produire des structures en 2D ou 2.5D de polymere, en deplacant le faisceau laser sur la surface de l'echantillon.Un soin particulier a ete axe sur l'impact de parametres photoniques et chimiques sur le processus de repolymerisation. Les mecanismes moleculaires qui regissent la repolymerisation pourraient etre deduits de cette etude.Certaines applications montrent le potentiel de l'alcoxyamine pour generer des surfaces hydrophiles / hydrophobes ou fluorescentes pour des applications avancees.
UTILIZATION OF POMEGRANATE AND ORANGE PEELS TO PREPARE HEALTHY CAKES

Agricultural by-products originated from food processing factories represented one of the permanent environmental problems. New trends were raised to utilize these valuable wastes full of nutraceuticals and phytochemicals. Pomegranate (PPP) and orange peels (OPP) were used in the current study to assess their beneficial effects. The counts of microorganism were determined and found in the safe permitted level in food.Afterward peels were directed to prepare bakery products.Pomegranate peel contained iron, zinc, calcium, potassium, magnesium, phosphorus and sodium at levels of 12.75, 5.9, 202.7, 135.8, 45.46, 66.38 and 38.91 mg/100g dry matter, respectively. Orange peel valued 19.5, 6.7, 635.8, 785.9, 91.4, 57.55 and 32.34 mg/100g dry matter, respectively. Pomegranate peel contained vitamins B1 and D3 at levels of 0.014 g/Kg and 0.023 MIU/kg, respectively. While, in orange peel were 0.027 g/kg and 0.019 MIU/kg, respectively. Pomegranate peel had total, soluble and insoluble dietary fiber contents of 19.7%, 5.8% and 13.9%, respectively. Whereas, orange peel registered 33.9%, 6.5% and 27.4%, respectively. GC/MS analysis of orange peel showed the existence of Dihydrocarvone (30.25%) and 4',6-Dimethoxyisoflavone-7-O-β-Dglucopyranoside (20.83%). Phloroglucinol (28.52%), (S)-(-)-Citronellic acid (14.24%) werethe bioactive compounds. Sensory evaluation for overall acceptability showed that non-significant difference existed between T1 (Control); T2 (10%PPP) and T5 (10%OPP). The chemical composition of the resulting cake showed that by increasing the substitution levels of either PPP or OPP, the ash and dietary fiber content increased, the available carbohydrate and fat content decreased and the protein content was not affected compared to the control without addition. The decrease in caloric values ranged between 404.34-420.59 kcal/100 g compared to 453.66 kcal/100 g for the control sample. It was found that by increasing the replacement ratios, the texture properties were affected and the microbial content of the resulting cake samples decreased, which gives an indication of the possibility of increasing the preservation period. The results indicated that PPP and OPP by-products can serve as a good source of bioactive components. It could be recommended that incorporation of the mentioned raw material with wheat flour 72% extraction to obtain healthy bakery productshave high biological value.
A Multi-Peer, Low Cost Immersive Communication System for Pandemic Times

We present our multi-peer, low cost immersive communication system which targets to virtually bring people closer to each other during the current self-isolation situation consequence of the pandemic outbreak. Our fully immersive conferencing system allows remote users to virtually teleport where the media capturing system is placed and interact in real-time with other local users present there. This is achieved by a fully mobile, accessible and simple to use system specifically designed to ease the use even for the less experienced users. We accomplish this by avoiding any complex 3D user interface, allowing the remote users to directly connect to the currently active session by just running the application. Furthermore, we also allow the users to share content between each other in a very seamless manner, improving the virtual collaboration tools available in the state of the art.
A Survey on Mathematical Software Tools for Visually Impaired Persons: A Practical Perspective

There is no evidence that mathematical semantics cannot be understood due to blindness, the problem is the current access barrier to mathematical resources. In light of this problem, this survey aims at providing visually impaired persons (VIPs), or people close to them, with an overview of the currently available software tools for approaching mathematical content. These can be categorized into (a) tools for accessing mathematical documents (where VIPs are just consumers of content), and (b) tools that allow VIPs to become the creators of mathematical content and even to execute mathematical operations. We also explain the advantages and disadvantages of several key technologies used to interact with mathematics. Moreover, we discuss the necessity of the most common formats and languages behind these tools. Finally, we outline promising paths for future research and development towards blind-friendly mathematical resources. The authors hope that this survey may encourage researchers to engage with the still unsolved challenges of this topic.
A Blockchain-Based Scheme for Secure Storage and Sharing of Student Digital Profiles

With the development of education informati-zation in China, the student profiles are becoming digitized. However, currently different digital profiles of a student are still stored dispersedly in multiple databases, which results in information silos and vulnerability to security and privacy threats. Aiming at these deficiencies, this paper proposes a secure storage and sharing scheme of student digital profile based on blockchain technology. With the characteristics of decentralization, security, credibility, and tamper-resistance of the blockchain, the consensus of network nodes is achieved through the Delegated Byzantine Fault Tolerance (DBFT) protocol, and the sharing of student digital profile information is achieved through the access control mechanism. The public information of educational digital information is preserved on the blockchain, and the student profiles are encrypted and stored in a database or cloud, which facilitates the effective storage of personal digital profiles and information sharing among various systems. The proposed scheme utilizes blockchain technology for decentralized and tamper-proof management, and provides a solution to information silos and security issues of student digital profiles.
An Anomaly-based Technique for Fault Detection in Power System Networks

In recent years, fault detection in electrical power systems has attracted substantial attention from both research communities and industry. Although many fault detection methods and their modifications have been developed during the past decade, it remained very challenging in real applications. Moreover, one of the most important parts of designing a fault detection system is reliable data for training and testing which is rare. Accordingly, this paper proposes an anomaly-based technique for fault detection in electrical power systems. Furthermore, a One-Class Support Vector Machine (SVM) model and a Principal Component Analysis (PCA)based model are utilized to accomplish the desired task. The used models are trained and tested on VSB (Technical University of Ostrava) Power Line Fault Detection dataset which is a large amount of real-time waveform data recorded by their meter on Kaggle. Finally, performance and Receiver Operating Characteristic (ROC) curves analyses of our results are exploited to verify the effectiveness of the proposed technique in the fault detection problem.
Computation Of Stabilizing PID Controllers For Magnetic Levitation System With Parametric Uncertainties

The growing demand of light weight technology, cabin comfort, and better emission control and stability of the vehicles led to the increasing concern towards air magnetic suspension systems. Due to the high nonlinearity in the modeling process of such kind of systems, the stabilizing of magnetic levitation has been considered as a challenging task for many researchers in control engineering sector. This paper illustrates the calculating of all stabilizing PID controller gains for ED-4810 magnetic levitation system benchmark (Maglev) in the presence of uncertain parameters. Firstly, the linearized model of the uncertain plant is derived where the resultant characteristic polynomial is shown to be an unstable affine structure using Zero Exclusion Theorem and singular frequencies technique. Then all PID parameters values are demonstrated to establish robust stability using parameter space approach. The effectiveness of the proposed graphical method has been proved through simulation to achieve robust stability for magnetic levitation system.
Methodology for Creating a Reference Reflection Spectra Database for Space Objects Monitoring

The analysis of the influence of identical materials simultaneously stored in a special database on the accuracy of quantitative interpretation of reflection spectra in the framework of monitoring space objects is carried out. It is proposed to use a new classification of the reflection spectra of materials and coatings (according to the cross-correlation coefficient) and leave one material in each class of the generated database. The dependence of the identification performance on the threshold value of the correlation coefficient is shown.
Reinforcement Learning based Optimization of Line Switching off during Cascading failures in Power Grids

Nowadays, large-scale power outages caused by cascading failures occur frequently around the world. It is of great significance to study the control strategies for cascading failures to ensure the safety and reliability of power grids. During cascading failures, the shift of the flow from the broken elements to the others will further cause the overload of the other elements. After the initial failure occurs, if we immediately switch off a group of key lines to control the transfer of the flow, the cascading failure could be effectively mitigated. The line switching-off optimization method based on reinforcement learning is studied and the cascading failure's control methods for known failure scenes and random failure scenes are proposed respectively. Simulations results on IEEE 118-bus system validate our proposed method.
Compact Rectifier Array With Wide Input Power and Frequency Ranges Based on Adaptive Power Distribution

This study presents a compact rectifier array with an adaptive power distribution network (APDN). The rectifier array comprises rectifier cell 1 for both high-power mode and high-frequency band; rectifier cell 2 for both low-power mode and low-frequency band; and a power distribution network, which adaptively distributes power according to the input power and frequency. The proposed APDN is merged with input matching networks of the subrectifiers to achieve low insertion loss and high compactness with wideband characteristics. For validation, the proposed rectifier array was designed, implemented, and measured. The measurement results show that the power conversion efficiency (PCE) is over 50% within an input power range of 9.9–30.8 dBm. A peak PCE of over 50% is realized at a frequency range of 0.1–2.7 GHz.
A Practicable Method for Calibrating a Magnetic Sensor Array

Magnetic sensor array has become increasingly widespread in use. Because of the manufacturing process, magnetic sensors in an array inevitably experience calibration factor errors. Therefore, the calibration of a magnetic sensor array is very important before use. However, it lacks explicit and quantitative measures on how to calibrate a magnetic sensor array. By using magnetic fields produced by a standard magnetic source at different locations, a practicable method was proposed based on the least-squares method to calibrate a magnetic sensor array. The dimensions of sensor array are about $50\times 50\times50$ cm3 and the level of magnetic field aiming to be measured is ~nT. Both the simulation and experimental results demonstrate that the proposed method is effective in identifying the calibration factor of a magnetic sensor array.
Impact of Vehicle Headlights Radiation Pattern on Dynamic Vehicular VLC Channel

This article develops a statistical large-scale fading (path loss) model of a dynamic vehicular visible light communication (VVLC) system. The proposed model combines the impact of inter-vehicle spacing and the radiation intensity distribution as a function of the irradiance angle which changes with the traffic conditions. Three models (Lambertian, Gaussian, and empirical) are utilized to examine the impact of vehicles headlights radiation pattern on the statistical path loss of VVLC system. The analytical model of channel path loss is validated by Monte Carlo simulation with the headlight model simulated with a raytracing software. The path loss values of the Gaussian model differ by 2 dB compared to the Lambertian model, irrespective of the traffic conditions while it differs by 24.6 dB during late night and 8.15 dB during rush hours compared to the empirical model of a Toyota Altis headlight. This variation shows that the radiation intensity distribution should be modelled for each vehicle's headlights from each manufacturer to ensure accurate VVLC channel model. The proposed Gaussian model provides a close approximation to describe such radiation pattern and can easily be adapted to model for different manufacturers’ headlights.
The Dreamcatcher: Interactive Storytelling of Dreams

Sleep scientists have extensively validated the continuity hypothesis, according to which our dreams reflect what happens during our waking life. Yet, only a few attempts have been made to increase the general public’s awareness about the benefits of dream analysis in better understanding and improving our daily life. We designed “The Dreamcatcher,” an interactive visual tool that explores the link between dreams and waking life through a collection of dream reports. We conducted a user study with 154 participants and found a 25% increase in the number of people believing that dream analysis can improve our daily lives after interacting with our tool. The visualization informed people about the potential of the continuity hypothesis to a surprising extent, to the point that it increased their concerns about sharing their own dream reports, thus opening new questions on how to design privacy-aware tools for dream collection.
Key-Leakage Resilient Encrypted Data Aggregation With Lightweight Verification in Fog-Assisted Smart Grids

In this article, we analyze the inherent characteristics of smart grids, and point out that some electricity consumption data are very sensitive and should be encrypted. However, once the corresponding private key is compromised, the content of encrypted data would be leaked, thereby violating users’ privacy. Additionally, since a control center (CC) is always required to conduct accurate statistic analysis on these data for subsequent services, it is highly demanded for CC to check the integrity of encrypted data. To this end, based on a modified Boneh–Goh–Nissim (BGN) cryptosystem, we propose a key-leakage resilient encrypted data aggregation (KLR-EDA) scheme with lightweight verification in fog-assisted smart grids. KLR-EDA enables each fog node to aggregate first-level verifiable encrypted data from smart meters in the same grid area, and forward them to the cloud server (CS) for long-term storage. Upon receiving flexible challenging list of fog nodes from CC, CS produces second-level verifiable encrypted aggregated data and returns the results to CC. KLR-EDA enables CC to check the integrity of encrypted aggregated data efficiently, and further obtain the statistic analysis results on the aggregated data without learning any information of individual user. In particular, even the private key of CC is exposed or compromised, any adversary cannot break users’ privacy. We provide security analysis of KLR-EDA, and conduct performance evaluation to demonstrate its lightweight statistical analysis and verification advantages on the CC side.
Decomposed Meta Batch Normalization for Fast Domain Adaptation in Face Recognition

Face recognition systems are sometimes deployed to a target domain with limited unlabeled samples available. For instance, a model trained on the large-scale webfaces may be required to adapt to a NIR-VIS scenario via very limited unlabeled faces. This situation poses a great challenge to Unsupervised Domain Adaptation with Limited samples for Face Recognition (UDAL-FR), which is less studied in previous works. In this paper, with deep learning methods, we propose a novel training remedy by decomposing the model into the weight parameters and the BN statistics in the training phase. Based on decomposing, we design a novel framework via meta-learning, called Decomposed Meta Batch Normalization (DMBN) for fast domain adaptation in face recognition. DMBN trains the network such that domain-invariant information is prone to store in the weight parameters and domain-specific knowledge tends to be represented by the BN statistics. Specifically, DMBN constructs distribution-shifted tasks via domain-aware sampling, on which several meta-gradients are obtained by optimizing discriminative representations across different BNs. Finally, the weight parameters are updated with these meta-gradients for better consistency across different BNs. With the learned weight parameters, the adaptation is very fast since only the BN updating on limited data is needed. We propose two UDAL-FR benchmarks to evaluate the domain-adaptive ability of a model with limited unlabeled samples. Extensive experiments validate the efficacy of our proposed DMBN.
Design and Analysis of 5GHz Microwave Absorber

An economical, thin and compact microwave absorber at 5 GHz resonance has been proposed. The square copper conductive ring has a geometrical dimension of 9.35 mm length and 0.5 mm width and is designed over $11.35\ \text{mm}\times 11.35\ \text{mm}\times 0.78\ \text{mm}$ square dielectric base with epoxy resin. The ground reference has been created on the other side of the dielectric using PEC. The peak absorption coefficient with one has been calculated at its resonance and its parametric variation with the geometrical dimensions, dielectric media and metallic effect within the structure for the TE and TM polarization mode has been obtained and compared. The effect of the incidence angle for the field wave has also been obtained and compared. In addition the paper also provides impedance extraction mechanism for the absorber using the S-parameter.