Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Improved Class Vector for Multi-Class Question Type Classification

Recent research in NLP has exploited word embedding to achieve outstanding results in various tasks such as; spam filtering, text classification and summarization and others. Present word embedding algorithms have power to capture semantic and syntactic knowledge about word, but not enough to portray the distinct meaning of polysemy word. Many work has utilized sense embeddings to integrate all possible meaning to word vector, which is computationally expensive. Context embedding is another way out to identify word’s actual meaning, but it is hard to enumerate every context with a small size dataset. This paper has proposed a methodology to generate improved class-specific word vector that enhance the distinctive property of word in a class to tackle light polysemy problem in question classification. The proposed approach is compared with baseline approaches, tested using deep learning models upon TREC, Kaggle and Yahoo questions datasets and respectively attain 93.6%, 91.8% and 89.2% accuracy.
ENSEMBLE CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES FOR LAND USE CLASSIFICATION IN ECONOMIC CROPS AERIAL IMAGES

The analysis of land use and land cover is a task of remote sensing and geographic information systems. Nowadays, deep learning techniques can analyze land use and land cover with high performance. In this paper, we focus on the classi(cid:12)cation of land use for Thailand’s economic crops based on the convolutional neural network (CNN) technique. We evaluated the ensemble CNN framework on Thailand’s economic crops aerial image dataset called the EcoCropsAID dataset. Five economic crops categories, including rice, sugarcane, cassava, rubber, and longan, were collected using the Google Earth program. Economic crops aerial images obtained between 2014 and 2018 were considered. There were 5,400 images with approximately 1,000 images per class. Due to the ensemble CNN framework, we (cid:12)rst proposed to use eight pre-trained CNN models consisting of InceptionResNetV2, MobileNetV2, DenseNet201, Xception, ResNet152V2, NASNetLarge, VGG16, and VGG19 to discover the best baseline CNN model. Second, three simplistic data augmentation techniques (rotation, width shift, and height shift) are applied to increasing the accuracy of the CNN models. As a result, we found that the three best models were VGG16, VGG19, and NASNetLarge architectures, respectively. Finally, we created an ensemble CNN framework that consisted of 3 CNNs based on the best CNN models. We also compared three ensemble methods, that were weighted average, unweighted average, and unweighted majority vote. From our experiments, the results show that the VGG16 outperforms other CNN models. Consequently, the classi(cid:12)-cation performance on Thailand’s economic crops aerial image dataset was signi(cid:12)cantly improved when the weighted average ensemble method was employed.
Development of agro-climatic grape yield model with future prospective

Agriculture sector is most vulnerable to climate change. To predict the crop yield in accordance with the changing climate is a need of hour than choice. To know the climate in advance is crucial for grape growing farmers and grape export agencies for its better planning and security of grape industries from climate change perspective. In the present study, the Agro-Climatic Grape Yield (ACGY) model is developed on monthly scale climatic parameters using correlation, significance and multi-regression analysis approach. The developed model is statistically tested for its predictive ability. The discrepancy ratio, the standard deviation of discrepancy ratio, mean percentage error and standard deviation of mean percentage error for the developed model is obtained as 1.03, 0.19, 0.03% and 0.19 respectively. Sensitivity analysis is carried out for the developed ACGY model using the parametric sensitivity method. In order to know the grape yield for future using developed ACGY model, climate scenarios are generated under Canadian Earth System Model (CanESM2) for three emissions Representative Concentration Pathways (RCP) as RCP2.6, RCP4.5, and RCP8.5. Model response variability is carried out to understand the variation of grape yield. It is observed that grape yield is showing adverse variation with the increase in minimum temperature in January and November months, and precipitation in August and November months. Whereas, minimum temperature in April and sum of monthly mean evapotranspiration showing accordance effect on the grape yield. It is recommended the use of ACGY model for grape yield estimations applicable for the present and future climate of the study area based on the predictive capability of developed model.
Improving NGSS focused model-based learning curriculum through the examination of students’ experiences and iterated models

ABSTRACT Background It is crucial to support students in better understanding water and sustainability issues because water plays a vital role in maintaining global ecosystems, including human life. A wide range of curricular and instructional supports like those embodied in model-based learning (MBL) are necessary for teachers to engage students in the core epistemic commitments of the Next-Generation Science Standards (NGSS). Purpose The main purpose of this study is to inform theory about students’ early attempts to engage in the complex kinds of sensemaking experiences inherent in the Framework for K-12 Science Education and the NGSS. Sample Data for this study was collected from 74 10th grade students in a high school in the Northwest region of the New England state. Design and Methods An explanatory sequential mixed-method research design was used to examine students’ learning outcomes, and to better understand these outcomes in connection to their experiences engaging in modeling in the MBL curriculum unit. Results The results indicated that students’ model scores, the number of concepts in models, and the coherence and sophistication of models improved between their initial and final models. Additionally, the following patterns emerged related to ways in which students engaged in the practice of modeling: (1) students attempted to directly represent what they observed, (2) they struggled to pictorially express complex patterns or mechanisms, and (3) students experienced difficulties representing models from a diverse range of perspectives. Conclusion The patterns identified across student models, as well as their reports of experiences related to the MBL unit implementation, provided insight into student experiences with models, while also providing meaningful implications for the refinement of the MBL curriculum unit investigated in this research specifically, while informing approaches MBL curricular units aimed at supporting NGSS implementation efforts more generally.
Application of block matching method-based Echocardiography combined with serum NT-PROBNP level detection in the early prediction of PDA in premature infants

Objectives: The paper uses block matching method combined with echocardiography to explore the value of N-terminal pro-brain natriuretic peptide (NT-proBNP) in predicting symptomatic patent ductus arteriosus (PDA) in preterm infants. Methods: We selected premature infants born between February 2019 and March 2020, gestational age ≤32 weeks, and echocardiography within 48 hours to determine the presence of arterial ducts as the research object, monitor their clinical manifestations, and detect serum at three and five days after birth The level of NT-proBNP was checked with echocardiography, and the children were divided into PDA group and asymptomatic patent ductus arteriosus (aPDA) group according to the clinical manifestations and echocardiographic. Results: The area under the ROC curve of PDA predicted by serum NT-proBNP level at 3 days after birth was 0.949, the cut-off value was 27035pg/mL, the sensitivity was 92.3%, and the specificity was 94.6%; serum NT-proBNP level at 5 days after birth predicted the ROC curve of PDA The lower area is 0.924, the critical value is 6411 pg/mL, the sensitivity is 92.3%, and the specificity is 92.9%. Conclusion: NT-proBNP may be a quantitative indicator of arterial duct shunt; the detection of serum NT-proBNP levels at 3 and 5 days after birth is helpful for early prediction of PDA.
Educational Data Mining Using Analysis Student Learning Process

: Higher education institutions are often very interested to know around the success frequency of the students throughout their study. It is vital to study and analysis educational data especially students’ performance analysis. Educational Data Mining (EDM) is the field of study disturbed with mining educational data to find out interesting outlines and knowledge in educational administrations. This learning is equally concerned with this subject, specifically, the students’ performance. It contains machine learning algorithms and statistical techniques to help the user for understanding of student’s learning habits, their academic performance and further enhancement if required. In this study explores several factors hypothetically assumed to move student performance in higher education, and finds a qualitative model which best naïve bayes classifies and predicts the students’ performance. In this paper will deliberate various techniques of data mining which are useful for predicting performance level of students.
THE CONNECTION BETWEEN DIGITAL DIVIDE AND SOCIAL EXCLUSION: IMPLICATIONS FOR SOCIAL WORK

Purpose of the study: This paper aims to ascertain the nexus between the digital divide and social exclusion (inequalities) - directly and indirectly, as digital technology enables people to socialise, communicate, work, study, and conduct business. Methodology: To do this, the literature review is employed as a methodology. Specifically, the narrative or semi-systematic literature review was conducted on all sources germane to the topic under investigation. Main findings: The paper has found that the digital divide has adverse effects on the employment, education, health, social services, and socio-economic development of digitally excluded people. In addition, it has emerged that the digital divide exacerbates social exclusion. Applications of this study: To this end, Social Workers and associated professionals are empowered with knowledge about the role of the digital divide in social exclusion, thus they are expected to play active assume advocacy roles as far as Information Communication Policies are concerned. Novelty/Originality of this study: Digitalisation has embedded factors inducing poverty, inequity, and discrimination unintentionally; thereby this paper replenishes literature in Social Science to enable Social Scientists to utilise relevant information –in their research, intervention, and commentary.
Computational Vacuum Forming with Conformal Mapping

Vacuum forming is a popular manufacturing technique which conforms a 2D plastic sheet to a 3D mold. However, vacuum forming plastic with 2D texture faces a same challenge found in texture mapping: when applying a texture an arbitrary 3D surface, its scale, shape, and angle is not preserved. To address this, we simulate vacuum forming the 2D texture using a mass-springs cloth simulation with gravity and vacuum forces, then create a conformal map via global parameterization of the mesh to pre-distort the texture. This solution enables applications such as creating undistorted MRI coils closely conforming to any 3D scanned head shape or thermoforming other plastic objects such as figurines or models
Forecasting COVID-19 cases in santa catarina cities with recurrent neural networks

Objective: evaluate the forecasting of COVID-19 daily incidence in the cities of Santa Catarina, through a machine learning algo-rithm, within a time horizon of 14 days. Method: a recurrent neural network was applied to model a regression problem with a predictive purpose, using a retrospective longitudinal epidemiological study of COVID-19 cases in the analyzed cities. Results: the data model obtained with a machine learning algorithm presented an RMSE of 20.74, less than the baseline established through a persistence model. Conclusion: from the result achieved by the data model, it follows that the Artificial Intelligence tools used in the research are important instruments to face the COVID-19 pandemic, providing the management improvement of health resources, which need a suitable allocation for an adequate sanitary response to the disease progress.
High Area/Energy Efficiency RRAM CNN Accelerator with Pattern-Pruning-Based Weight Mapping Scheme

Resistive random access memory (RRAM) is an emerging device for processing-in-memory (PIM) architecture to accelerate convolutional neural network (CNN). However, due to the highly coupled crossbar structure in the RRAM array, it is difficult to exploit the CNN sparsity feature to improve the performance in RRAM-based CNN accelerator. To optimize the weight mapping of sparse network in the RRAM array and improve area and energy efficiency, we propose a novel weight mapping scheme and corresponding RRAM-based CNN accelerator architecture based on pattern pruning and operation unit(OU) mechanism. Experimental results show that our work can achieve 4.16x-5.20x crossbar area efficiency, 1.98x2. 15x energy efficiency, and 1.15x-1.35x performance speedup in comparison with the traditional method.
Footvector Representation of Curves and Surfaces

This paper proposes a foot mapping-based representation of curves and surfaces which is a geometric generalization of signed distance functions. We present a first-order characterization of the footvector mapping in terms of the differential geometric invariants of the represented shape and quantify the dependence of the spatial partial derivatives of the footvector mapping with respect to the principal curvatures at the footpoint. The practical applicability of foot mapping representations is highlighted by several fast iterative methods to compute the exact footvector mapping of the offset surface of CSG trees. The set operations for footpoint mappings are higher-order functions that map a tuple of functions to a single function, which poses a challenge for GPU implementations. We propose a code generation framework to overcome this that transforms CSG trees to the GLSL shader code.
Does frequent leadership changes influence firm performance? Insights from China

This letter is a first attempt to investigate the relationship between frequent leadership changes during the year and firm performance. We analyze how CEO frequency during one-year period impact performance indicators of Chinese listed firms. The results of panel fixed-effect regression reveal that CEO turnover leads to a decline in corporate performance measured by ROA and ROE. Moreover, with an increase in annual turnover frequency, the degree of performance decline gets more pronounced. These results remain robust after controlling for endogeneity using the alternate econometric specification of 2SLS. The study findings assert that frequent CEO changes are not conducive to firm performance. Hence, stability in the CEO tenure is essential to sustain and optimize financial performance of an enterprise.
Disease Detection of Dragon Fruit Stem Based on The Combined Features of Color and Texture

Dragon fruit is one of the favorite commodities in Banyuwangi Regency's agriculture. In 2019, this commodity had the fourth largest harvest area among other fruit commodities in Banyuwangi until it was exported to China. However, disease attacks often appeared in several dragon fruit plantations in Banyuwangi, and the identification system was still conventional. Many farmers did not know the types of disease and how to handle it, causing the quality and quantity of their crops to decline. Therefore, this study implemented two feature extraction methods. Both methods include color feature extraction using the color moments method and texture feature extraction using gray level co-occurrence matrices (GLCM). The methods used to develop a system that recognized or detected the three types of dragon fruit stem based on digital image processing using Support Vector Machine and k-Nearest Neighbors methods as comparison methods. The results obtained from this study indicated that the combination of the two proposed feature extraction methods could distinguish between stem rot, smallpox, and insect stings with an optimal accuracy score of 87.5% obtained by using Support Vector Machine as a classification method.
Presence and Antibiogram of Inhabiting Marine Bacterial Population at the Upper Sub-surface Water of the South East Coast of Bangladesh, Northern Bay of Bengal

The present study was conducted to assess the presence of bacterial populations at the upper sub-surface water from the offshore areas near the South East Coast of Bangladesh, the northern Bay of Bengal and to evaluate their antibiotic resistance pattern. Water samples were collected from 5 stations near the South East Coast of Bangladesh, the northern Bay of Bengal. About 38 marine isolates were primarily identified using conventional cultural methods (Mannitol Salt Agar, Chromocult Coliform Agar and Cetrimide Agar). Among them, two gram negative viz. Pantoea spp. and Pseudomonas aeruginosa and one gram positive viz. Micrococcus spp. were identified and confirmed by biochemical and analytical profiling index techniques. The antibiogram results showed that Pantoea spp. and Micrococcus spp. were sensitive to ciprofloxacin, tetracyclin, erythromycin and chloramphenicol, whereas P. aeruginosa was sensitive to ciprofloxacin, azithromycin and streptomycin. However, Pantoea spp. and Micrococcus spp. were resistant to at least 3 antibiotics (oxacillin, cefixime and polymyxin B). But P. aeruginosa was resistant to a number of antibiotics such as oxacillin, cefixime, ampicillin, novobiocin, cephalexin, tetracycline, amoxicillin and kanamycin. Increasing drug resistance potential of microbial organisms gives us signal to go for immediate necessary action on the maintenance of water quality of estuarine and coastal areas. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 9(2), 2020, P 35-44
PinText 2: Attentive Bag of Annotations Embedding

Word embedding is a basic building block in text processing tasks like classification, retrieval, and ranking. However, it is not a trivial task to aggregate a collection of word embeddings to a single vector representation. In particular, we have an annotation system at Pinterest where a set of concrete annotation terms are used to describe the content of a pin. Although we use a pre-trained PinText model [40] to derive the annotation embeddings, using the average of annotation embeddings as the pin’s embedding is not always optimal for a particular application. This leads to a common choice that practitioners often have to make: pre-train a unified word embeddings and fine-tune it everywhere, or learn the embeddings end-to-end together with the task-dependent machine learning model parameters. In this paper, we focus on the best pin-level embeddings given pins’ annotation set as the text input. We extend PinText to the second version by the following improvements applicable to scenarios where bag of text snippets are input: 1) extend the word embedding dictionary in the multitask learning setting to include phrase-level embeddings using a data-driven approach to identify the most important phrase dictionary; 2) propose a deep neural networks model architecture with attention mechanisms to best combine pin’s annotation embeddings; 3) conduct thorough study on the performance gap between pre-trained and end-to-end embeddings
Analysis of non-uniform hexagonal cross-sections for thin-walled functionally graded beams using artificial neural networks

We study static mechanical behavior of non-uniform hexagonal cross-sections for thin-walled functionally graded beams using a non-traditional computational approach based on artificial neural network. One of the main objectives of our approach is to save the computational cost for the optimization process, which is usually time-consuming by using traditional methods such as finite element method (FEM). In this study, 1000 data sets randomly generated by the FEM through iterations are used for the training process to get optimal weights. Based on these obtained optimal weights, beam behaviors under the changes in material distribution through thickness could then be predicted. In this model, the ANN's inputs are the gradation index of the power-law distribution and thickness, while the outputs are compliance and beam displacements. The computed results are verified against those derived from the FEM.
Difficulties of Grade X students in learning Trigonometry

Abstract This paper aims to explain the difficulties of grade X students in learning trigonometry that were explored from an empirical study in the year 2017 AD. The researcher used explanatory mixed research design where 155 students of grade X were selected randomly from the four schools of Kirtipur municipality to conduct initial survey to find low achievers and most difficult content areas of trigonometry by using achievement test. Then five low achievers and respective three mathematics teachers were selected and interviewed with the help of semi-structured interview guidelines to explore what difficulties had they experienced and encountered in learning trigonometry. The results revealed that mean achievement score of students in institutional schools was greater than that of students of community schools. However, there are some common areas of difficulties of students in both types of schools for learning trigonometry including the difficulties related to memorization of facts; comprehension of new concepts; transformation of verbal problems; derivation and proofs of trigonometric relations and unfamiliarity with the solving process of problems in application level. These difficulties are due to several reasons including abstract nature of contents, poor prerequisites among students, demonstration of teacher centered instruction, and focusing on rote memorization rather than conceptual understanding. Hence, it is recommended that both students and teachers need to focus on conceptual learning rather than rote memorization for meaningful understanding in trigonometry.
Impacts of Binge-Watching on Netflix during the COVID-19 pandemic

PurposeThe purpose of the study is to dive into various binge-watching habits of Netflix users amidst the COVID-19 pandemic. Consumers find themselves amidst the COVID-19 lockdown with more free time to indulge in these viewing habits. This study investigates motivational factors, amount of media consumption and negative attributes associated with binge-watching on Netflix during the COVID-19 outbreak.Design/methodology/approachThis study has employed an exploratory research design and obtained primary data via an online survey using a semistructured questionnaire. Convenience sampling has been used to choose a sample (n = 105) of Netflix binge-watchers during the COVID-19 pandemic. Both sample selection and survey administration have been done through social media messaging services owing to the COVID-19 lockdown measures.FindingsThe results indicate that most of the respondents use smartphones for binge-watching on Netflix. Moreover, they have expressed that a wide range of shows available on Netflix incline them to engage in marathon viewing. However, the respondents spend just over 70 h per month binge-watching on Netflix. Finally, the majority of respondents have flagged “one more episode” syndrome as the most challenging aspect of being marathon viewers on Netflix during the COVID-19 pandemic.Originality/valueThis is one of the few papers to exclusively focus on the impacts of binge-watching on Netflix during the COVID-19 pandemic. The findings will originate the value with novelty and important implications to the Netflix consumers, telecom service providers and payment gateways.