Reviewerly/exHarmony huggingface dataset:
A BERT-Based Approach for Extracting Prerequisite Relations among Wikipedia Concepts

Concept prerequisite relation prediction is a common task in the field of knowledge discovery. Concept prerequisite relations can be used to rank learning resources and help learners plan their learning paths. As the largest Internet encyclopedia, Wikipedia is composed of many articles edited in multiple languages. Basic knowledge concepts in a variety of subjects can be found on Wikipedia. Although there are many knowledge concepts in each field, the prerequisite relations between them are not clear. When we browse pages in an area on Wikipedia, we do not know which page to start. In this paper, we propose a BERT-based Wikipedia concept prerequisite relation prediction model. First, we created two types of concept pair features, one is based on BERT sentence embedding and the other is based on the attributes of Wikipedia articles. Then, we use these two types of concept pair features to predict the prerequisite relations between two concepts. Experimental results show that our proposed method performs better than state-of-the-art methods for English and Chinese datasets.
Implementation Word2Vec for Feature Expansion in Twitter Sentiment Analysis

Abstract &#x0D; Twitter is a microblog-based social media site launched on July 13, 2006. In March 2020, 476.696 tweets about the government policy in COVID-19 spread on Twitter were captured by the Institute for Development of Economics and Finance (Indef). Government policy has a standard meaning, namely a decision systematically made by the government with specific goals and objectives relating to the public interest, whether carried out directly or indirectly. Sentiment analysis analyzes people’s opinions, sentiments, evaluations, attitudes, and emotions from written language. In this decade, Sentiment Analysis is has become a trendy research area. The purpose of this paper is to focus how to implement word2vec using similarity word as a feature expansion for minimize the vocabulary mismatch in Twitter Sentiment Analysis using “word embeddings”. This research contains 11.395 tweets for a dataset, where the dataset will be used in two classifications: Support Vector Machine Algorithm and Artificial Neural Network Algorithm. The output of Word2Vec will be used for feature expansion in this research, where the algorithm of expansion will check in each row in the corpus where has a similarity vector with that word and will replace the word with the similarity of this words if the value is 0. The dataset in Feature Expansion is using 142.545 articles from Indonesian media. The result of this research is ANN is better than SVM, where the ANN without feature expansion gets 68.89 % and using feature expansion gets 72.58 %. For SVM, the final accuracy without feature expansion is 63.95 %, and using feature expansion gets 68.56 %. This research proves that feature expansion can improve the final accuracy.
IMPROVING STUDENTS’ ABILITY IN READING COMPREHENSION THROUGH THE INTERACTIVE COMPENSATORY MODEL

This research aimed at improving the students' reading comprehension of the first grade at Wahdah Islamiyah Makassar junior high school, which contains 25 students used a learning model called the interactive compensatory model. This research used Classroom Action Research (CAR) with two cycles. Each cycle consists of steps to planning, acting, observing, and reflecting. The data had collected by the form of quantitative and qualitative data. Quantitative data obtained by analyzing the observation checklist result, while quantitative data obtained through score processing used a particular formula. This research’s findings proved that applied the instructional model called the interactive compensatory model successfully improved students' learning abilities, especially in reading comprehension. The test results in the first cycle, was 1,660 or 66,4% and increased to 2,120 or 84,8%. In the first cycle, 14 students (56%) out of 25 students got the point under the minimum standard score (KKM), while those who reached the minimum standard (KKM) score were only 44%. However, after being given treatment in the second cycle, it was seen that 100% of the students exceed the minimum standard (KKM) score. The observation checklist results showed that students’ participation in the first Cycle increased in the second cycle. They became more active and dared to expressed their opinion.
Forecast Breast Cancer Cells from Microscopic Biopsy Images using Big Transfer (BiT): A Deep Learning Approach

Now-a-days, breast cancer is the most crucial problem amongst men and women.A massive number of people are invaded with breast cancer all over the world.An early diagnosis can help to save lives with proper treatment.Recently, computer-aided diagnosis is becoming more popular in medical science as well as in cancer cell identification.Deep learning models achieve excessive attention because of their performance in identifying cancer cells.Mammography is a significant creation for detecting breast cancer.However, due to its complex structure, it is challenging for doctors to identify.This study provides a convolutional neural network (CNN) approach to detecting cancer cells early.Dividing benign and malignant mammography images can significantly improve detection and accuracy levels.The BreakHis 400X dataset is collected from Kaggle and DenseNet-201, NasNet-Large, Inception ResNet-V3, Big Transfer (M-r101x1x1); these architectures show impressive performance.Among them, M-r101x1x1 provides the highest accuracy of 90%.The main priority for this research work is to classify breast cancer with the highest accuracy with selected neural networks.This study can improve the systematic way of early-stage breast cancer detection and help physicians' decisionmaking.
Crowd Abnormality Detection Using Optical Flow and GLCM-Based Texture Features

Detection of abnormal crowd behavior is one of the important tasks in real-time video surveillance systems for public safety in public places such as subway, shopping malls, sport complexes and various other public gatherings. Due to high density crowded scenes, the detection of crowd behavior becomes a tedious task. Hence, crowd behavior analysis becomes a hot topic of research and requires an approach with higher rate of detection. In this work, the focus is on the crowd management and present an end-to-end model for crowd behavior analysis. A feature extraction-based model using contrast, entropy, homogeneity, and uniformity features to determine the threshold on normal and abnormal activity has been proposed in this paper. The crowd behavior analysis is measured in terms of receiver operating characteristic curve (ROC) &amp; area under curve (AUC) for UMN dataset for the proposed model and compared with other crowd analysis methods in literature to prove its worthiness. YouTube video sequences also used for anomaly detection.
Role of the ICT in Women Empowerment and Achieving SDGs: A Case Study of Women Labor Force in Developing Countries

The aim of the study is to analyze the ways women are empowered through information and communication technology (ICT) and fostering the process of achieving the Sustainable Development Goals (SDGs). Women in developing countries are facing economic and social hardships and have limited access to ICT, which in turn translate into their lack of skills, lower literacy rate and restricted social engagement. Therefore, present study explores the role of ICT on women empowerment in 51 developing countries by conducting panel data modeling over the period of 2000-17. The study finds that ICT has significant and positive contribution on female labor force participation rate by Generalized Method of Moment (GMM). Control variables like GDP growth and government expenditure on education have positive and statistically significant role to enhance women empowerment. Findings of the study imply that the ICT has an impact on women empowerment and which helps in fostering the process of achieving SDGs 2030.
Talking Face Generation Based on Information Bottleneck and Complementary Representations

Audio-driven talking face generation is an active research direction in the field of virtual reality. The main challenge is that the generated lip shape of the speaker is out of sync with the input audio. To address this challenge, we propose a novel solution to synthesize lip-synchronized, high-quality, realistic video given input audio. We first decompose the target person's video frames into 3D face model parameters, and the information bottleneck is inserted into the audio-to-expression network to learn the mapping between audio features and expression parameters. Then, we replace the expression parameters in the target video frame with the extracted expression parameters from audio and re-render the face. Finally, we add high-level audio embedding extracted from the raw audio and lip landmarks embedding in the neural rendering network. The 3D face shapes, 2D landmarks, and audio embedding provide complementary information for the neural rendering network which guarantees the generation of lip-synchronized high-quality video portraits from the synthesized rendered faces. Experimental results show that compared with other talking face generation methods, our method is the best concerning lip synchronization with high video definition.
Utilizing Entity-Based Gated Convolution and Multilevel Sentence Attention to Improve Distantly Supervised Relation Extraction

Distant supervision is an effective method to automatically collect large-scale datasets for relation extraction (RE). Automatically constructed datasets usually comprise two types of noise: the intrasentence noise and the wrongly labeled noisy sentence. To address issues caused by the above two types of noise and improve distantly supervised relation extraction, this paper proposes a novel distantly supervised relation extraction model, which consists of an entity-based gated convolution sentence encoder and a multilevel sentence selective attention (Matt) module. Specifically, we first apply an entity-based gated convolution operation to force the sentence encoder to extract entity-pair-related features and filter out useless intrasentence noise information. Furthermore, the multilevel attention schema fuses the bag information to obtain a fine-grained bag-specific query vector, which can better identify valid sentences and reduce the influence of wrongly labeled sentences. Experimental results on a large-scale benchmark dataset show that our model can effectively reduce the influence of the above two types of noise and achieves state-of-the-art performance in relation extraction.
Color Based Image Retrieval by Combining Various Features

Content based image retrieval system retrieve the images according to the strong feature related to desire as color, texture and shape of an image. Although visual features cannot be completely determined by semantic features, but still semantic features can be integrate easily into mathematical formulas. This paper is focused on retrieval of images within a large image collection, based on color projection by applying segmentation and quantification on different color models and compared for good result. This method is applied on different categories of image set and evaluated its retrieval rate in different models.
A multi-task learning based hybrid prediction algorithm for privacy preserving human activity recognition framework

There is ever increasing need to use computer vision devices to capture videos as part of many real-world applications. However, invading privacy of people is the cause of concern. There is need for protecting privacy of people while videos are used purposefully based on objective functions. One such use case is human activity recognition without disclosing human identity. In this paper, we proposed a multi-task learning based hybrid prediction algorithm (MTL-HPA) towards realising privacy preserving human activity recognition framework (PPHARF). It serves the purpose by recognizing human activities from videos while preserving identity of humans present in the multimedia object. Face of any person in the video is anonymized to preserve privacy while the actions of the person are exposed to get them extracted. Without losing utility of human activity recognition, anonymization is achieved. Humans and face detection methods file to reveal identity of the persons in video. We experimentally confirm with joint-annotated human motion data base (JHMDB) and daily action localization in YouTube (DALY) datasets that the framework recognises human activities and ensures non-disclosure of privacy information. Our approach is better than many traditional anonymization techniques such as noise adding, blurring, and masking.
Analytics on Anonymity for Privacy Retention in Smart Health Data

Advancements in smart technology, wearable and mobile devices, and Internet of Things, have made smart health an integral part of modern living to better individual healthcare and well-being. By enhancing self-monitoring, data collection and sharing among users and service providers, smart health can increase healthy lifestyles, timely treatments, and save lives. However, as health data become larger and more accessible to multiple parties, they become vulnerable to privacy attacks. One way to safeguard privacy is to increase users’ anonymity as anonymity increases indistinguishability making it harder for re-identification. Still the challenge is not only to preserve data privacy but also to ensure that the shared data are sufficiently informative to be useful. Our research studies health data analytics focusing on anonymity for privacy protection. This paper presents a multi-faceted analytical approach to (1) identifying attributes susceptible to information leakages by using entropy-based measure to analyze information loss, (2) anonymizing the data by generalization using attribute hierarchies, and (3) balancing between anonymity and informativeness by our anonymization technique that produces anonymized data satisfying a given anonymity requirement while optimizing data retention. Our anonymization technique is an automated Artificial Intelligent search based on two simple heuristics. The paper describes and illustrates the detailed approach and analytics including pre and post anonymization analytics. Experiments on published data are performed on the anonymization technique. Results, compared with other similar techniques, show that our anonymization technique gives the most effective data sharing solution, with respect to computational cost and balancing between anonymity and data retention.
Performance Analysis of Hybrid Deep Learning Models with Attention Mechanism Positioning and Focal Loss for Text Classification

Over the past few decades, text classification problems have been widely utilized in many real time applications. Leveraging the text classification methods by means of developing new applications in the field of text mining and Natural Language Processing (NLP) is very important. In order to accurately classify tasks in many applications, a deeper insight into deep learning methods is required as there is an exponential growth in the number of complex documents. The success of any deep learning algorithm depends on its capacity to understand the nonlinear relationships of the complex models within data. Thus, a huge challenge for researchers lies in the development of suitable techniques, architectures, and models for text classification. In this paper, hybrid deep learning models, with an emphasis on positioning of attention mechanism analysis, are considered and analyzed well for text classification. The first hybrid model proposed is called convolutional Bidirectional Long Short-Term Memory (Bi-LSTM) with attention mechanism and output (CBAO) model, and the second hybrid model is called convolutional attention mechanism with Bi-LSTM and output (CABO) model. In the first hybrid model, the attention mechanism is placed after the Bi-LSTM, and then the output Softmax layer is constructed. In the second hybrid model, the attention mechanism is placed after convolutional layer and followed by Bi-LSTM and the output Softmax layer. The proposed hybrid models are tested on three datasets, and the results show that when the proposed CBAO model is implemented for IMDB dataset, a high classification accuracy of 92.72% is obtained and when the proposed CABO model is implemented on the same dataset, a high classification accuracy of 90.51% is obtained.
Moth Flame Optimization Algorithm for Optimal FIR Filter Design

This paper presents the application of Moth Flame optimization (MFO) algorithm to determine the best impulse response coefficients of FIR low pass, high pass, band pass and band stop filters. MFO was inspired by observing the navigation strategy of moths in nature called transverse orientation composed of three mathematical sub-models. The performance of the proposed technique was compared to those of other well-known high performing optimization techniques like techniques like Particle Swarm Optimization (PSO), Novel Particle Swarm Optimization (NPSO), Improved Novel Particle Swarm Optimization (INPSO), Genetic Algorithm (GA), Parks and McClellan (PM) Algorithm. The performances of the MFO based designed optimized FIR filters have proved to be superior as compared to those obtained by PSO, NPSO, INPSO, GA, and PM Algorithm. Simulation results indicated that the maximum stop band ripples 0.057326, transition width 0.079 and fitness value 1.3682 obtained by MFO is better than that of PSO, NPSO, INPSO, GA, and PM Algorithms. The value of stop band ripples indicated the ripples or fluctuations obtained at the range which signals are attenuated is very low. The reduced value of transition width is the rate at which a signal changes from either stop band to pass band of a filter or vice versa is very good. Also, small fitness value in an indication that the values of the control variable of MFO are very near to its optimum solutions. The proposed design technique in this work generates excellent solution with high computational efficiency. This shows that MFO algorithm is an outstanding technique for FIR filter design.
Calibration of Ångström-Prescott Coefficients to Estimate Global Solar Radiation in Côte d’Ivoire

In this study, we used monthly mean daily global radiation data and sunshine durations from nine (9) weather stations in Côte d’Ivoire to determine the annual Ångström-Prescott coefficients. The calibration of the Ångström-Prescott equation has been done through linear regression using the least square method. The empirical coefficients obtained are utilized to predict the global horizontal irradiance over the nine (9) weather stations of interest. Estimated and measured global radiations were compared using the root mean square error (RMSE), the mean bias error (MBE), the mean absolute bias error (MABE), the mean percentage error (MPE), the Nash-Sutcliffe coefficient of efficiency (NSE), and the statistic -test (). The low values of the statistic t-test (from 0.10 to 1.07) and MPE (from -0.413 to 0.201) indicate a good performance of the model. The high values of the coefficient of determination R² (from 0.9776 to 0.9916) show a remarkable agreement between predicted and measured global solar radiations. This remark is also confirmed by the high values of NSE (from 0.8671 to 0.9819) closer to 1. The obtained values of MBE (from -18.17 to 8.69 kWh/m²), MABE (from 7.16 to 8.52 kWh/m²), and RMSE (69.1 to 167.3 kWh/m²) show a low deviation or bias between the estimate and the measurements. The Ångström-Prescott coefficients determinants are consistent and can be used to efficiently calculate the global horizontal irradiance. The model established can be recommended to be used in the nine (9) weather stations to accurately estimate global solar radiation on horizontal surfaces.
FAME: Feature-Based Adversarial Meta-Embeddings for Robust Input Representations

Combining several embeddings typically improves performance in downstream tasks as different embeddings encode different information.It has been shown that even models using embeddings from transformers still benefit from the inclusion of standard word embeddings.However, the combination of embeddings of different types and dimensions is challenging.As an alternative to attention-based meta-embeddings, we propose feature-based adversarial meta-embeddings (FAME) with an attention function that is guided by features reflecting word-specific properties, such as shape and frequency, and show that this is beneficial to handle subword-based embeddings.In addition, FAME uses adversarial training to optimize the mappings of differently-sized embeddings to the same space.We demonstrate that FAME works effectively across languages and domains for sequence labeling and sentence classification, in particular in lowresource settings.FAME sets the new state of the art for POS tagging in 27 languages, various NER settings and question classification in different domains.
A Common Framework for Artificial Intelligence in Higher Education (AAI-HE Model)

This research aims to 1) Develop a common framework for artificial intelligence in higher education (AAI-HE model) and 2) Assess the AAI-HE model. The research process is divided into two stages: 1) Develop an AAI-HE model, and 2) Assessment the model. The sample consists of five experts chosen through purposive sampling. The data is analyzed by means and standardized deviations statistically. The research result shows that 1) the AAI-HE model consists of seven key components which are 1.1) User Interactive Components and Technology of AI, 1.2) Components and Technology of AI, 1.3) Roles for Artificial Intelligence in Education 1.4) Machine Learning and Deep Learning 1.5) DSS Modules 1.6) Applications of Artificial Intelligence in Education, and 1.7) AI to enhance campus efficiencies, and 2) The result of the assessment of the AAI-HE model is rated as absolutely appropriate overall.