Reviewerly/exHarmony huggingface dataset:
The Missing Voices of Disabled People

This paper presents the development process of a graph database that connects statements posted by disabled people on various web-based platforms with accessibility requirements in the Danish Building Regulations (BR18). The aim is to bring the lived experience of disabled people into a vocabulary of space-making for architects. By elevating the missing voices of disabled people - describing what matters, how and why - the project supports the decision-making processes of architects to make the built environment more inclusive. The developed database relates statements posted by disabled people with sentences from paragraphs of BR18 through specific architectural features of room, element, and object. Using the architectural features as point of reference, the database not only highlights some of the most common building situations encountered by disabled people, but also allows anyone interested to explore their relationship to the real lives of disabled users and the statutory requirements.
Exploiting Augmented Reality and Computer Vision for Healthcare Education: The Case of Pharmaceutical Substances Visualization and Information Retrieval

Augmented Reality (AR) is already used as the primary visualization and user interaction tool in several scientific and business areas. At the same time new AR technologies and frameworks considerably facilitate both the development of innovative applications and also their wide adoption in different domains of everyday life. In the area of healthcare AR solutions make use of mobile or wearable devices and glasses to support, among others, education and healthcare professionals training. The aim of this paper is to present a prototype mHealth app for education, which uses AR and computer vision technologies for pharmaceutical substances recognition on drug packaging. The conceptual design of the system includes three main components which are responsible for a) Text recognition, b) Drug identification and c) AR operations for interactivity. The prototype application is available in Android or iOS platforms and has been evaluated in real-world scenarios. Camera and screen of the mobile phones fulfill the text recognition and AR operations, which eliminates the need for special equipment, while PubChem and 3D Model databases provide assets required for the drug identification and AR visualizations. The results highlight the value of AR for educational purposes, especially when combined with advanced image recognition technologies to build interactive AR encyclopedias.
Potential Utilization of Bacterial Consortium of Symbionts Marine Sponges in Removing Pollutants Global Trends: A Review

Toxic materials in waste generally contain several components of global trending pollutant categories, especially PAHs and heavy metals. Bioremediation technology for managing waste utilizing microorganisms (bacteria) has not been fully capable of breaking down these toxic materials simple and environmentally friendly chemical products. This study examines the potential application of a marine sponge symbiont consortium with high performance and efficiency in removing PAHs and heavy metal contaminants. The method is carried out through a review of some related research articles by the author and published by other re-searchers. The study results concluded that bioremediation technology development GTP, can be carried out to improve remediation efficiency. Several types of marine sponge symbiont bacteria, hydrocarbonoclastic (R-1), metalloclastic (R-2), and metallohydro-carbonoclastic (R-3), have the potential to be applied to improve the removal performance of waste. Bacterial screening be done to find and categorize R-1 bacteria, R-2; R-3 to remediate GTP. Develop of R-1 bacteria, R-2; R-3 forms of the mobile formulation are needed in the future. A crystalline consortium of bacteria preparations is needed so that they can be quickly mobilized to locations exposed to GTP. Marine sponge symbiont bacteria be traced mainly to marine sponges whose body surface is covered with mucus.
Fast Learning Architecture for Neural Networks

This paper proposes a solution to minimize the learning time of a fully connected neural network.The paper presents a processing architecture in which the treatments applied to the examples of the learning base are strongly parallelized and anticipated, even before the parameters adaptation of the previous examples are completed.This strategy finally leads to a delayed adaptation and the impact of this delay on the learning performances is analysed through a simple replicable school case study.It is shown that a reduction of the adaptation step size could be proposed to compensate errors due to the delayed adaptation.Finally, the gain in processing time for the learning phase is analysed as a function of the network parameters chosen in this study.
Sentimental Analysis of Industry 4.0 Perspectives Using a Graph-Based Bi-LSTM CNN Model

Today’s modern society mainly depends on Internet for every fundamental task in their life, like sharing thoughts, education, business, and Industry 4.0, etc. Internet has strengthened the base of society digitally. Searching for the reviews and comments for a particular product from the former or present customers has become compulsory for making decision or a purchase, helping them to make a fair deal of the product by view from social media on Industry 4.0. The increased use of data over Internet has led to rise of many e-commerce websites where people can buy things as per their requirement without even stepping out of their house using analysis of text using natural language techniques in Industry 4.0. The graph-based modelling of sentiment analysis classifier needs to divide the textual data into training dataset and testing dataset. First, preprocessing work is performed to improve the data reliability by removing unwanted information and fixing typing errors. To train the models, the whole dataset is used and to measure the classification performance of the categorized models 10-fold validation technique is being used. The paper proposes a combined hybrid model for sentiment analysis using CNN and independent bidirectional LSTM networks to enhance sentiment knowledge in order to address the issues mentioned for sentiment analysis. The proposed CNN model uses global max-pooling for retrieving context information and to downsample the dimensionality. Lastly, to acquire long term dependencies, a distinctive bidirectional LSTM is used. To emphasise each word's learning ability, parts-of-speech (PoS) are tagged in the LSTM layer. In addition, the regularization techniques, batch normalization, and dropout are used to prevent the overfitting issue. The proposed model is compared with a collaborative classifier with six classifiers and each of them predicts the sentiments separately, and the majority class prediction is taken under consideration. The proposed Bi-LSTM CNN model achieves an accuracy of 98.61% along with PoS tagging of the sentiments.
A Covid-19 Positive Case Prediction and People Movement Restriction Classification

The world experienced a pandemic that changed people's daily life due to Coronavirus Disease 2019 (covid-19).In Jakarta, the covid-19 cases were discovered on March 18, 2020, and the case increased uncontrollably until the government conducted a movement restriction called pembatasan sosial berskala besar (PSBB).The effectivity of movement restriction was not evaluated in detail.Therefore, we investigated the covid-19 cases in the PSBB period to understand the contribution of movement restriction.Moreover, a prediction model is proposed to computerize the decision of movement restriction.The models are divided into regression and classification models.The regression model is developed to forecast the number of infected cases.At the same time, the classification model is used to identify the best movement restriction type.We utilize data transformation named Principal Component Analysis (PCA) to reduce the number of features.In our case, the best regression method is Multiple Linear Regression (MLP).Then, the best classification method is the Support Vector Machine (SVM).The MLP results are 148.38,37036.37,and 0.250336 for Mean Absolute Error (MAE), Mean Square Error (MSE), and R 2 , respectively.In contrast, the SVM achieved an accuracy of 84.81%.Moreover, the prediction system on the website were successfully deployed.
An Improved Arabic Sentiment Analysis Approach using Optimized Multinomial Naïve Bayes Classifier

Arabic sentiment analysis has emerged during the last decade as a computational process on Arabic texts for extracting people's attitudes toward targeted objects or their feelings and emotions regarding targeted events.Sentiment analysis (SA) using machine learning (ML) methods has become an important research task for developing various text-based applications.Among different ML classifiers, multinomial Naïve Bayes (MNNB) classifier is widely used for documents classification due to its ability for performing statistical analysis of text contents.It significantly simplifies textual-data classification and offers an alternative to heavy ML-based semantic analysis methods.However, the MNNB classifier has a number of hyper-parameters affects the classification task of texts and controls the decision boundary of the model itself.In this paper, an optimized MNNB classifier-based approach is proposed for improving Arabic sentiment analysis.A number of experiments are conducted on large sets of Arabic tweets to evaluate the proposed approach.The optimized MNNB classifier is trained on three datasets and tested on a different separated test set to show the performance of developed approach.The experimental results on the test set revealed that the optimized MNNB classifier of proposed approach outperforms the traditional MNNB classifier and other baseline classifiers.The accuracy rate of the optimization approach is increased by 1.6% compared with using the default values of the classifier's hyperparameters.
A Comparative Analysis of Generative Neural Attention-based Service Chatbot

Companies constantly rely on customer support to deliver pre-and post-sale services to their clients through websites, mobile devices or social media platforms such as Twitter.In assisting customers, companies employ virtual service agents (chatbots) to provide support via communication devices.The primary focus is to automate the generation of conversational chat between a computer and a human by constructing virtual service agents that can predict appropriate and automatic responses to customers' queries.This paper aims to present and implement a seq2seq-based learning task model based on encoder-decoder architectural solutions by training generative chatbots on customer support Twitter datasets.The model is based on deep Recurrent Neural Networks (RNNs) structures which are uni-directional and bi-directional encoder types of Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU).The RNNs are augmented with an attention layer to focus on important information between input and output sequences.Word level embedding such as Word2Vec, GloVe, and FastText are employed as input to the model.Incorporating the base architecture, a comparative analysis is applied where baseline models are compared with and without the use of attention as well as different types of input embedding for each experiment.Bilingual Evaluation Understudy (BLEU) was employed to evaluate the model's performance.Results revealed that while biLSTM performs better with Glove, biGRU operates better with FastText.Thus, the finding significantly indicated that the attention-based, bi-directional RNNs (LSTM or GRU) model significantly outperformed baseline approaches in their BLEU score as a promising use in future works.
A Novel Hybrid Sentiment Analysis Classification Approach for Mobile Applications Arabic Slang Reviews

Arabic language incurs from the shortage of accessible huge datasets for Sentiment Analysis (SA), Machine Learning (ML), and Deep Learning (DL) applications.In this paper, we present MASR, a simple Mobile Applications Arabic Slang Reviews dataset for SA, ML, and DL applications which comprises of 2469 Egyptian Mobile Apps reviews, and help app developers meet user requirements evolution.Our methodology consists of six phases.We collect mobile apps reviews dataset, then apply preprocessing steps, in addition perform SA tasks.To evaluate MASR datasets, first we apply ML classification techniques: K-Nearest Neighbors (K-NN), Support vector machine (SVM), Logistic Regression (LR), and Random Forest (RF), and DL classification technique: Multi-layer Perceptron Neural Network (MLP-NN).From the examination for pervious classification techniques, we adopted a hybrid classification approach combined from the top two ML classifier accuracy results (LR, RF), and DL classifier (MLP-NN).The findings prove the adequacy of a hybrid supervised classification approach for MASR datasets.
High Capacity Image Steganography System based on Multi-layer Security and LSB Exchanging Method

Data security is becoming an important issue because of the vast use of the Internet and data transfer from one place to another.Security of these data is essential, especially when these data represent critical information.There are several techniques used to hide these data, such as encryption.Steganography can be utilised as an alternative to encryption because encryption is susceptible to data modification during transmission.Steganography is the hiding data on a cover multimedia such as images, audio, and video.The technique allows security for data transmission so unwanted third parties cannot notice the hidden data.The challenge of steganography is the trade-off between the hidden data's payload capacity and the system's imperceptibility and robustness.If the hidden data increases, the imperceptibility and the robustness will be decreased.This case is a big challenge in this digital world where social media, Internet, and data transfer are used hugely.Because of this, this paper proposes using a modified Least Significant Bit (LSB) method for the embedding process called Multi-Layer Least Significant Bit Exchange Method (MLLSBEM).This proposed algorithm uses the AES encryption method to encrypt the secret text and then uses Huffman coding to compress the encrypted message as pre-processing data.The proposed study seeks to strike a compromise between important issues, provide maximum payload capacity, and retain high security, imperceptibility, and reliability for secret communication Using image processing and steganography techniques.Simulation findings demonstrate that the suggested method is superior for existing PSNR, SSIM, NCC, and payload capacity investigations.The proposed method is immune to the histogram, chi-square, and HVS attacks.
Use of Video/Listening Content Transcripts in English for Coronavirus Awareness and Information

Triangulation of data through a variety of data sources (data from multiple sources) / different angles /perspectives enriches the analysis of findings and validates the results of a study. Buch, et al. [1] defines data triangulation as “the elicitation of different types of data sources at dfferent times and situations.”
Thermodynamic analysis of primary and secondary minerals stability in melilite-nephelinite tuff with Australopithecus Afarensis footprints, Laetoli, Tanzania

The Laetoli area in northern Tanzania is an important palaeo-anthropological site, where the oldest footprints of Australopithecus afarensis reside. Aeolian tuffs are the major rock type at Laetoli and they are divided into Lower and Upper Laetolil Beds that were deposited at an interval of 4.36 and 3.63 million years. The Upper Laetolil Beds contain eight layers of air-fall tuffs known as marker tuffs. The Australopithecus afarensis footprints are observed on the surface of the white tuff, which is a part of the Upper Laetolil marker tuff 7, also known as the “Footprint Tuff.” The interpolated age of the marker tuff 7 is 3.66 million years. Two mineral assemblages are distinguished in the Upper Laetolil marker tuffs. The first assemblage consists of primary tuff minerals and includes clinopyroxene (diopside, augite, aegirine-augite), nepheline, melilite (åkermanite and alumoåkermanite), garnet (andradite and schorlomite), magnetite, and others. The second mineral assemblage consists of secondary minerals, montmorillonite, calcite, and phillipsite. They were formed during replacement of the primary minerals, volcanic glass, and ash cementation. Thermodynamic calculations show that the major primary tuff minerals (melilite and nepheline) are stable at variable sodium activity and pH values. Replacement of melilite and nepheline by montmorillonite is caused by a decrease of sodium activity in slightly alkaline, neutral and acidic conditions (рН &lt; 10). Montmorillonite is not present in the altered nephelinitic tuff of the Sadiman volcano (which is considered as a source of the Laetolil Beds) where kaolinite is the major secondary mineral. This is explained by the difference in H2O fugacity with higher lgfH2O values in Sadiman and lower values in Laetoli. Relationships between primary and secondary tuffs minerals on the lgaHCO3 vs pH plot suggest mineral transformation within the Laetolil Beds in slightly acid and neutral con- ditions (рН = 5–7) compared with more alkaline conditions at Sadiman (рН &gt; 10).
Industry 4.0 and Rami Model Based Art in Microindustry for Polishing Application

The paper presents a case study of the transformation of a manual-mechanized production process of a micro-company engaged in the production of decorative medallion tiles, into a production process that corresponds to trends defined by Industry 4.0 principles, and conclusions related to possible development trends in this type of company. We are integrating our work into the RAMI 4.0 framework system which is the basis of the industry 4.0 standard. The hardware of the old system has been replaced with the new elements. The basis element of the polishing system is the FANUC SCARA robot. The robot system is equipped whit sensor elements to receive the necessary information. The quality of the polishing is improved with signals from camera and from force sensor. The main goal of the project to integrate industry 4.0 standard in the micro industry.
The effect of digitalism on supply chain flexibility of food industry in Jordan

This study comes to ascertain the impact of digitalism on supply chain flexibility. A sample consisting of 350 employees was selected from manufacturing companies in the food industry to collect research data. One inclusion criterion was assumed in this research, which is employee knowledge of supply chain processes. Research data was gathered using a questionnaire distributed to the members of the sample. The final number of the questionnaires was 297 with a response rate of 85%. Analyzing data via SmartPLS 3.0, the results indicated that Internet of things (IOT) had a significant effect on supply chain flexibility. Such results confirmed that one dimension of digitalism, which is the Internet of things, has a significant effect on supply chain flexibility.
Corporate social responsibilities and financial reporting quality: Evidence from Jordanian manufacturing firms

The basic aim of the ongoing article is to examine the impact of social corporate responsibilities on the financial reporting quality of Jordanian manufacturing firms. For this purpose, data has been extracted from the top fifteen manufacturing companies by using corporate reports along with the Amman Stock Exchange from 2010 to 2020. The random effect model along with the generalized method of moment (GMM) has been executed for analysis purposes. The results revealed that social corporate responsibilities have positively impacted on the financial reporting quality of the Jordanian manufacturing firms. These findings have been guided to the regulators that they should provide the focus on the social corporate responsibilities that enhance the quality of financial reporting of the firm. The findings of the current study may be helpful for the manufacturing firm who wants to produce better quality of financial reporting along with the upcoming studies who want to investigate this area in their studies.
“Regulatory guillotine” in education: Issues of legal regulation and implementation practice

In the Russian Federation, as in all developed countries, reforming the activities of the political machine is a permanent process, where every phase has the emphasis on the most topical issues. If the administrative reform of 2004 solved the problem of structuring the system of executive authorities, then the reform of control supervision activity, which is currently ongoing, aims to revise the system of requirements for the main types of economic activity, reduce administrative barriers and other obstacles faced by individuals, who carried out such activity. Educational activity, as a single process of training and education, is one of the most important types of economic activity, and cannot keep out of the designated reforms. This article analyzes the practice of implementing reforms of control supervision activity, the provisions of the “regulatory guillotine” related to the education system. Attention is focused on such issues as mandatory requirements and mandatory requirements in education, systematization of legislation and the abolition of acts containing outdated norms and excess requirements. The concept of a system of mandatory requirements in educational activities is proposed, based on the basic principles of the reform of control supervision activity and the model of risks inherent in the education system. The conclusions mentioned in the article were prepared on the basis of changes in the legal regulation in the field of education over a period of two years (from 1 January 2020 to 1 January 2022).
A Two-Fold Cross-Validation Training Framework Combined with Meta-Learning for Code-Switching Speech Recognition

Although end-to-end based speech recognition research for Mandarin-English code-switching has attracted increasing interests, it remains challenging due to data scarcity. Meta-learning approach is popular with low-resource modeling using high-resource data, but it does not make full use of low-resource code-switching data. Therefore we propose a two-fold cross-validation training framework combined with meta-learning approach. Experiments on the SEAME corpus demonstrate the effects of our method.
Radial Neural Network Processing Applied to Athlete’s Personalized Psychological Regulation Detection System

Radial neural network is based in the world with the characteristics of active adaptation, active learning, active recognition, low error rate, and thought mapping and plays an important role in personalized regulation. However, in practical applications, due to the existence of pattern recognition, motion control, and a large amount of combined knowledge, the traditional methods are difficult to solve, even powerless, and cannot be effectively solved. Although the traditional BP network is more widely used, the BP neural network is easy to enter the regional minimum value during the training process, which leads to a lower training learning speed and low efficiency. The RBF network (radial neural network) is in a certain sense, and it can detect both known intrusions and unpredictable intrusions. At the same time, it is superior to BP neural network in data collection, pattern recognition, and personality customization. Through detection and comparison, it is found that the radial network has improved the analysis speed by about 20%, and the degree of privacy protection of athletes is as high as 99%, which is close to the full value, and the accuracy of the psychological control scheme is also improved by about 15% on the basis of the classic network. Athletes can be adjusted as soon as possible to achieve the best state; so, it will be an ideal choice to use a radial neural network for the psychological adjustment detection system.