Reviewerly/exHarmony huggingface dataset:
Embedding Extraction for Arabic Text Using the AraBERT Model

Nowadays, we can use the multi-task learning approach to train a machine-learning algorithm to learn multiple related tasks instead of training it to solve a single task. In this work, we propose an algorithm for estimating textual similarity scores and then use these scores in multiple tasks such as text ranking, essay grading, and question answering systems. We used several vectorization schemes to represent the Arabic texts in the SemEval2017-task3-subtask-D dataset. The used schemes include lexical-based similarity features, frequency-based features, and pre-trained model-based features. Also, we used contextual-based embedding models such as Arabic Bidirectional Encoder Representations from Transformers (AraBERT). We used the AraBERT model in two different variants. First, as a feature extractor in addition to the text vectorization schemes’ features. We fed those features to various regression models to make a prediction value that represents the relevancy score between Arabic text units. Second, AraBERT is adopted as a pre-trained model, and its parameters are fine-tuned to estimate the relevancy scores between Arabic textual sentences. To evaluate the research results, we conducted several experiments to compare the use of the AraBERT model in its two variants. In terms of Mean Absolute Percentage Error (MAPE), the results show minor variance between AraBERT v0.2 as a feature extractor (21.7723) and the fine-tuned AraBERT v2 (21.8211). On the other hand, AraBERT v0.2-Large as a feature extractor outperforms the fine-tuned AraBERT v2 model on the used data set in terms of the coefficient of determination () values (0.014050,−0.032861), respectively.
Deep Learning: Why Neural Networks Are State of the Art

This chapter provides a brief introduction of deep learning, an emerging subfield of artificial intelligence with a focus on neural networks, their composition, and their application. After explaining the fundamental structure of neural networks including their neurons and hidden layers, similarities between neural networks and the human brain are presented. To explain how neural networks are trained, cost functions, gradient descent, and backpropagation are briefly introduced. Following an explanation of the structure and function of deep neural networks, the chapter describes the “hierarchy of concepts” – a notion core to the fundamental potential of deep learning. Importantly, neural networks designed for specific tasks like image processing or translation have revolutionized numerous fields. As a result, various deep neural network architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are discussed. Finally, after mentioning various successful applications of deep neural networks, their limitations, including bias and the “black box” problem, are explained.
Analysis on the Effectiveness of Transfer Learned Features for X-ray Image Retrieval

A computer-assisted system for retrieving medical images of identical contents can be used as a data processing method for managing and mining large amount of medical data, as well as in clinical decision support systems. This paper studies the effectiveness of deep features extracted from state-of-the-art deep learning models for the retrieval of X-ray images. The first part of the study explores the effectiveness of transfer learned features generated from DenseNet model, Inception model and Inception-ResNet model for image retrieval. The performance of transfer learned features for image retrieval was analyzed based on the retrieval accuracy. The second part of the paper analyzes the effect of preproccesing using adaptive histogram equalization on image retrieval. The experiment is carried out on a publicly available musculoskeletal radiographs (MURA) dataset which consists of nearly 40,561 bone X-ray images of different body parts in varied angles with 7 classes.
A Framework for Blockchain Technology Adoption in Food Supply Chain

With its several unique and useful applications, blockchain has proven to be a positively disruptive technology in various sectors. Blockchain applications for the food sector had been successfully developed and practised numerous times. The stakeholders of the food supply chainFood supply chain (FSC) are actively exploring the diffusion strategy for the blockchain. The present chapter identifies and analyses the Critical Success Factors (CSFs) that can facilitate the successful adoption of Block Chain Technology (BCT) in FSC. The results of the study highlight that the “Technological maturity (CSF3)” and “Resiliency (CSF5)” are the potential driving CSFs while “Information security and reliability (CSF1)” and “Trust and collaboration (CSF9)” are the most dependent CSFs for BCT adoption in FSC. The analysis from the study draw the contextual and hierarchical relationships among the identified CSFs of BCT adoption in FSC. The outcomes can assist the policymakers in the successful dissemination of BCT and enhancing the performance of FSC.
Research on Intelligent Estimation Method of Human Moving Target Pose Based on Adaptive Attention Mechanism

In daily physical education, posture performance is an important basis for making excellent results. This paper explores an intelligent method to estimate the target pose based on adaptive attention mechanism. First, the regional attention is iteratively generated from a global level to a local level based on the attention mechanism. Human decision-making patterns are imitated to evaluate the effectiveness of regional attention in real time. The level of attention mechanism is adaptively adjusted and focused layer by layer to achieve precise target detection and tracking. Second, with the target frame obtained from each frame, the pose estimation algorithm finds the key points of human body, enabling the human body pose optimization strategy to solve the crossover problem of the key points. Results of experiments on sports video images show that the proposed method has a higher accuracy in pose estimation than other algorithms and can help sportsmen adjust their training methods scientifically.
Blockchain and population health

Blockchain technology has made great strides in many industries but has yet to impact the world of public health. Population health issues such as outbreak surveillance and controlled substance tracking during emergencies all require a secure, easily accessible database. While the healthcare industry is typically slow to adapt to change, blockchain technology lends itself well to many healthcare issues.We utilized a 3D framework using difficulty, novelty and necessity to examine the adoption of blockchain technology in population health, based on the 2D framework of difficulty and novelty as driving factors for the development of foundational technologies in the world of business by Iansiti and Lakhani in The Harvard Business Review.We find that by implementing the third dimension of necessity into an evaluation framework, we can better predict the adoption of technology. We found how different areas of population health fit into the evaluation framework and how necessity can eliminate barriers from implementing novel technologies.The byproduct of this paper will be the extension of the Iansiti and Lakhani framework. We will show that blockchain, in all of these domains, shows promise to improve population health as we move past COVID-19 and into the future of healthcare.
On Non-Linear Non-Gaussian Autoregressive Model with Application to Daily Exchange Rate

Abstract The most often used distribution in statistical modeling follows Gaussian distribution. But many real-life time series data do not follow normal distribution and assumptions; therefore, inference from such a model could be misleading. Thus, a reparameterized non-Gaussian Autoregressive (NGAR) model that has the capabilities of handling non-Gaussian time series was proposed, while Anderson Darling statistics was used to identify the distribution embedded in the time series. In order to determine the performance of the proposed model, the Nigerian monthly exchange rate (Dollar-Naira Selling Rate) was analyzed using proposed and classical autoregressive models. The proposed model was used to determine the joint distribution of the observed series by separating the marginal distribution from the serial dependence. The maximum Likelihood (MLE) estimation method was used to obtain an optimal solution in estimating the generalized gamma distribution of the proposed model. The selection criteria used in this study were Akaike Information Criterion (AIC). The result revealed through the value of the Anderson Darling statistics that the data set were not normally distributed. The best model was selected using the minimum values of AIC value. The study concluded that the proposed model clearly shows that the non-Gaussian Autoregressive model is a very good alternative for analyzing time series data that deviate from the assumptions of normality and, in particular, for the estimation of its parameters.
A formalization of abstract argumentation in higher-order logic

Abstract We present an approach for representing abstract argumentation frameworks based on an encoding into classical higher-order logic. This provides a uniform framework for computer-assisted assessment of abstract argumentation frameworks using interactive and automated reasoning tools. This enables the formal analysis and verification of meta-theoretical properties as well as the flexible generation of extensions and labellings with respect to well-known argumentation semantics.
Novel Sentiment Analysis Model with Modern Bio-NLP Techniques Over Chronic Diseases

Large number of People are suffering from Chronic diseases these days. Numerous medications are available in the market for respective chronic diseases. Most of these drugs lead to Adverse Drug Reactions (ADR). Western countries USA, UK track the health of their citizens using data generated over social media platforms. Using reviews from the drug consumers we can determine the performance/ADR of the drug by using sentiment analysis. Most of the sentiment analysis models use SentiDocVec or SentiWordVec in their sentiment analysis models. But these consist of biological words like Asystole which will carry a large meaning but unidentified by popular vectorizers. Hence, we plan to develop a Novel Sentiment Analysis model with modern Bio-NLP techniques (Bio-SentVec and Bio-WordVec) using these techniques the sentiment analysis can be done more efficiently.
RFNet: Toward High-Quality Object Detection in Aerial Images

Due to the object overlap and multi-scale transformation problems in aerial image detection, the detection system has false and missed detections. In this paper, we propose feature Re-Fusion Network, named RFNet. Specifically, the recurses multi-layer feature maps into a pyramid network, cross fusion of upper and lower features to enhancing information flow. Next, we introduced a lightweight module that connects feature semantic information for fusion to improve the feature fusion ability. Finally, we optimized the parameters of attention mechanism block to adapt to aerial images detection. We equipped it to the modern detector Cascade R-CNN on the VisDrone dataset, which improved the mean Average Precision(mAP) by 1.1%, increased middle-size object detection accuracy by 2.1%, and increased the accuracy of the truck class by 2.4%.
Implementation of A* Algorithm for Optimal Path Planning for Mobile Robots

Automation has widespread applications and uses in industries and is gradually finding its way into our daily lives. One of the implementations of automation is in an automated path planning system, which is still being extensively researched. The system can plan a path from a starting point while detecting its surroundings to navigate to the endpoint given to it. The prime example of automatic path planning that is being heavily researched and invested in is automated cars, where a car can be driven on the road without a human driver. In this paper, we aim to show our work in creating a bot that uses the A* algorithm to create an automated path planning robot. We have prepared a bot that is programmed to be able to move in a path free of collision in an environment of dynamic obstacles, which is a realistic scenario if the bot were to be used practically. The environment would resemble a real-world scenario house and multiple rooms or locations in the floor plan. The bot will track the obstacle data in real time and navigate through the area to reach the given goal location autonomously.
On the non-existence of extended 1-perfect codes and MDS codes

We prove the non-existence of extended perfect codes in H(n,q), where q=3,4 and n>q+2 or both n and q are odd. In particular, we prove the non-existence of non-linear (q+2,qq−1,4)q MDS codes when q is odd.
The Effects of Interactivity on Learners’ Experience in a Visually Immersive Display Context

This research reports empirical evidence of the pedagogical benefits of using two interactive types of immersive spherical dome screens (large and small). Results are drawn from an experiment measuring the emotional, cognitive, perceptual/attitudinal states, and learning outcomes of 102 participants divided into control/experimental groups and group/individual pedagogical contexts. Specifically, this paper aimed to investigate the extent to which using interactivity in immersive technologies has positive learning outcomes and provide a positive learning experience. Building upon the INTERACT model, our results show that, in a smaller dome, the learner’s cognitive state, perception and attitudes play a mediating role regarding the impact of interactivity on learning outcomes. However, we did not observe a difference between the larger and smaller dome as for the learning outcomes. These results bring further insight into optimal educational practices in an immersive context and are relevant for pedagogical designers and immersive technologies manufacturers.
Research of the Particle Swarm Method for Generating Nonlinear Substitutions

Nonlinear substitutions (S-boxes) have an important role in ensuring security of symmetric cryptographical transformations. In this article, we consider a computational particle swarm optimization method (PSO) to generate S-boxes. We have implemented two algorithms for generating substitutions that reproduce a certain simulation of particle motion in the space of possible states to make experimental research. We perform a computational search and show the efficiency of these two implementations. Our proposed improved implementation allows us to confidently generate S-boxes with nonlinearity 104, delta uniformity 8, linear redundancy 0 and algebraic immunity 3.
Machine Automatic Translation Quality Evaluation Model Based on Recurrent Neural Network Algorithm

In real life, the evaluation of translation quality is widely used. Not only translation activities need evaluation and comparison, but also translation quality evaluation in the editing and publishing of foreign language works, text translation teaching and other fields. The purpose of this paper is to study the quality evaluation model of machine automatic translation based on the recurrent neural network algorithm. First introduced the research background of this article, discussed the importance of research, and then briefly reviewed the research and development of machine translation evaluation, focusing on the current situation of recurrent neural network and evaluation method automation, and summarized the translation evaluation of automated machine technology. Finally, the main content of this article is summarized. The language model based on iterative neural network algorithm is studied. Basic experiments are carried out on the repetitive neural network translation quality evaluation model for machine translation quality evaluation. On the basis of basic experiments, 100 sentences were randomly selected to evaluate the performance of the translation system. The experimental results show that the translation quality estimation system of the machine automatic translation quality evaluation model based on the recurrent neural network algorithm established in this paper is 0.1 points better than the QuEst system.
RNN Language Processing Model-Driven Spoken Dialogue System Modeling Method

Speech recognition and semantic understanding of spoken language are critical components in determining the dialogue system's performance in SDS. In the study of SDS, the improvement of SLU performance is critical. By influencing the factors before and after the input text sequence information, RNN predicts the next text information. The RNN language model's probability score is introduced, and the recognition's intermediate result is rescored. A method of combining cache RNN models to optimize the decoding process and improve the accuracy of word sequence probability calculation of language model on test data is proposed to address the problem of mismatch between test data and training data in recognition. The results of the experiments show that the method proposed in this paper can effectively improve the recognition system's performance on the test set. It has the potential to achieve a higher SLU score. It is useful for future research on spoken dialogue and SLU issues.
Reduced O3 subsequence labelling: a stepping stone towards optimisation sequence prediction

Finding a good compiler autotuning methodology, particularly for selecting the right set of optimisations and finding the best ordering of these optimisations for a given code fragment has been a long-standing problem. As the rapid development of machine learning techniques, tackling the problem of compiler autotuning using machine learning or deep learning has become increasingly common in recent years. There have been many deep learning models proposed to solve problems, such as predicting the optimal heterogeneous mapping or thread-coarsening factor; however, very few have revisited the problem of optimisation phase tuning. In this paper, we intend to revisit and tackle the problem using deep learning techniques. Unfortunately, the problem is too complex to be addressed in its full scope. We present a new problem, called reduced O3 subsequence labelling – a reduced O3 subsequence is defined as a subsequence of O3 optimisation passes that contains no useless passes, which is a simplified problem and expected to be a stepping stone towards solving the optimisation phase tuning problem. We formulated the problem and attempted to solve the problem by using genetic algorithms. We believe that with mature deep learning techniques, a machine learning model that predicts the reduced O3 subsequences or even the best O3 subsequence for a given code fragment could be developed and used to prune the search space of the problem of the optimisation phase tuning, thereby shortening the tuning process and also providing more effective tuning results.
An Overview of Methods for Control and Estimation of Capacity in COVID-19 Pandemic from Point Cloud and Imagery Data

The main actions to control the COVID-19 pandemic and prevent the spread of the virus have focused on population control and social distancing. Over the years, many applications of sensing technologies have shown their effectiveness in solving problems related to the acquisition, identification and modelling of the environment, although not always from a human-centred approach. This chapter compiles sensing techniques from point cloud and imagery data related to population control and estimation of the capacity: people counting, biometric identification, monitoring of activities, distance measurement and 3D modelling. The current state-of-the-art techniques and the most common algorithms are summarized. Finally, the advantages and disadvantages of point cloud data and imagery are discussed, as well as the current trends of the predominant technology in each field.
Research on Missing Data Imputation Based on Conditional Variational Autoencoder

With the development of artificial intelligence technology, data has become an important resource with much attention being attracted. However, in the process of data collection, transmission and storage, data missing happens inevitably due to various reasons. In order to reduce the impact of missing data on data application, this paper proposes a multiple imputation model for missing data based on conditional variational autoencoder. The model is trained on an incomplete dataset, using multiple sampling from the latent space to generate multiple estimates. In the model accuracy of the estimates has been improved, constrained by classification label of the data. Experiments were carried out on open source datasets and evaluated with existing imputation methods.
Oriented Target Detection Algorithm Based on Transformer

Oriented target detection is an important task in the field of remote sensing target detection, which has great application prospects in geography, agriculture and military. However, the current popular algorithms of oriented target detection do not achieve the best in speed, accuracy and computational complexity. In this paper, an oriented target detection algorithm based on Transformer is proposed, which uses the backbone(CSPNet) and neck(FPN + PAN) of YOLOV5 model to realize Feature extraction and multi-scale feature fusion. The multi-scale feature map is input into Transformer module for classification and regression. By introducing the transformer module into the oriented target detection task, the attention mechanism modeling at the scene level can be realized, and the detection accuracy can be improved; Using multi-scale feature map for self-attention modeling can improve the detection performance of the model for targets of different sizes; In addition, the multi-layer feature map is cut by region in the Sequence encoding, which can reduce the calculation cost and improve the detection speed.
Authentication System for Automobiles using Face Recognition Methodology

There is a threat of losing cars these days due to various theft activities. The cars are being exchanged by car drivers in many social networking pols like Uber, Ola, and so on. During the exchange of cars among the drivers, there is a possibility for a theft activity to take place. In order to prevent the activity, safety measures are to be taken. In this paper, the real time face authentication for rental cars has been developed. An efficient face detection and authentication system for improving the security in rental cars is proposed. This is done with the help of image processing by using Advanced Haar cascade and Adaboost algorithm. This system can be used in automobiles by the owners of the rental companies to secure their vehicles as well as the customers. This system works on the decision of the owners. The car get started only if the recognized driver drives the car, whereas for further permissible drivers, the owner's approval is needed. If any accident occurs, immediately the owner gets a message to his mail and also to his mobile number. Also, this message is passed to the emergency ambulance. The location of the cars are displayed by means of Android studio app, which makes the owner to track the location of the car easily. The whole system is controlled by advanced microprocessor.