Reviewerly/exHarmony huggingface dataset:
Coding theory package for Macaulay2

In this Macaulay2 \cite{M2} package we define an object called {\it linear code}. We implement functions that compute basic parameters and objects associated with a linear code, such as generator and parity check matrices, the dual code, length, dimension, and minimum distance, among others. We define an object {\it evaluation code}, a construction which allows to study linear codes using tools of algebraic geometry and commutative algebra. We implement functions to generate important families of linear codes such as Hamming codes, cyclic codes, Reed--Solomon codes, Reed--Muller codes, Cartesian codes, monomial--Cartesian codes, and toric codes. In addition, we define functions for the syndrome decoding algorithm and locally recoverable code construction, which are important tools in applications of linear codes. The package \textit{CodingTheory.m2} is available at \url{https://github.com/Macaulay2/Workshop-2020-Cleveland/tree/CodingTheory/CodingTheory}
A Reversible Convolutional Neural Network Model for Sign Language Recognition

Hand gestures and voice inputs have been measured as the vital communication component for the past few decades.Here, deep learning-based Reversible Convolutional Neural Network (Rev-CNN) is explicitly modelled to predict gesture-based sign language.Similarly, this work concentrates on modelling a reversible model to identify the voice gesture into sign language.It shows reversible representation by attaining superior accuracy with a lesser amount of model parameters and various CNN architecture.Here, the efficiency of the reversible model is evaluated with the prevailing G-CNN, VGG-11/16 model over the testing and training environment.Here, two diverse datasets like ROBITA Indian Sign Language Gesture Database and the standard voice-input dataset, is considered for evaluation purpose.The highest prediction accuracy of 94.38 % and 97.89 % is attained using the proposed reversible CNN model over the other approaches like GCNN, VGG-11 and VGG-16 model.The experimental outcomes and metrics like loss function, error rate and execution time are measured and compared with different methods like GCNN, VGG-11/16.Additionally, other efficiency metrics are utilized to determine the efficiency of the anticipated model.The model outperforms the existing approaches by categorizing the gestures with reduced error rate.The prediction accuracy of the reversible CNN (dataset 1) is 95.38 % and for dataset 2 is 96.69%.Similarly, the execution time is 5.5 minutes.
Image Encryption using Double Layer Chaos with Dynamic Iteration and Rotation Pattern

This research proposes an encryption method for images based on dual chaos systems and dual hash functions.SHA-256 and SHA-512 are hash functions chosen to process plain images and private keys.The purpose of using hash functions on plain and key images is to increase encryption resistance against differential attacks and perform local encryption in each image zone.The proposed method consists of two stages of encryption, the first involves dual hash functions and confusion with a chaotic system by performing encryption based on zoning and rotation of the image.The second stage is a diffuse process using a logistic map.The proposed method is proven to be strong against various attacks that have been tested with various measuring tools such as entropy, histogram analysis, chi-square, number of changing pixel rate (NPCR), unified averaged changed intensity (UACI), and the avalanche effect (AE).This method also proved to work better than the state-of-the-art method in this research, significantly based on entropy, UACI, and NPCR.
Research on Badminton Player’s Step Training Model Based on Big Data and IoT Networks

In view of the poor effect of badminton players’ physical fitness and response training, this paper puts forward the construction method of badminton players’ pace training model based on big data, constructs badminton players’ pace training index by combining big data technology, optimizes the pace training evaluation algorithm, and puts forward the corresponding Badminton Players’ pace training method to achieve the model design goal. Finally, experiments show that the badminton athlete’s pace training model based on big data has high practicability in the process of practical application and fully meets the research requirements.
Reducing Redundancy in the Bottleneck Representation of Autoencoders

Autoencoders (AEs) are a type of unsupervised neural networks, which can be used to solve various tasks, e.g., dimensionality reduction, image compression, and image denoising. An AE has two goals: (i) compress the original input to a low-dimensional space at the bottleneck of the network topology using an encoder, (ii) reconstruct the input from the representation at the bottleneck using a decoder. Both encoder and decoder are optimized jointly by minimizing a distortion-based loss which implicitly forces the model to keep only those variations of input data that are required to reconstruct the and to reduce redundancies. In this paper, we propose a scheme to explicitly penalize feature redundancies in the bottleneck representation. To this end, we propose an additional loss term, based on the pair-wise correlation of the neurons, which complements the standard reconstruction loss forcing the encoder to learn a more diverse and richer representation of the input. We tested our approach across different tasks: dimensionality reduction using three different dataset, image compression using the MNIST dataset, and image denoising using fashion MNIST. The experimental results show that the proposed loss leads consistently to superior performance compared to the standard AE loss.
Sequence-to-Sequence Learning-Based Conversion of Pseudo-Code to Source Code Using Neural Translation Approach

Pseudo-code refers to an informal means of representing algorithms that do not require the exact syntax of a computer programming language. Pseudo-code helps developers and researchers represent their algorithms using human-readable language. Generally, researchers can convert the pseudo-code into computer source code using different conversion techniques. The efficiency of such conversion methods is measured based on the converted algorithm&#x2019;s correctness. Researchers have already explored diverse technologies to devise conversion methods with higher accuracy. This paper proposes a novel pseudo-code conversion learning method that includes natural language processing-based text preprocessing and a sequence-to-sequence deep learning-based model trained with the SPoC dataset. We conducted an extensive experiment on our designed algorithm using descriptive bilingual understudy scoring and compared our results with state-of-the-art techniques. Result analysis shows that our approach is more accurate and efficient than other existing conversion methods in terms of several performances metrics. Furthermore, the proposed method outperforms the existing approaches because our method utilizes two Long-Short-Term-Memory networks that might increase the accuracy.
A Novel Method for Developing Post-quantum Digital Signature Algorithms on Non-commutative Associative Algebras

Introduction: Development of practical post-quantum signature algorithms is a current challenge in the area of cryptography. Recently, several candidates on post-quantum signature schemes, in which the exponentiation operations in a hidden commutative group contained in a non-commutative algebra is used, were proposed. Search for new mechanisms of using a hidden group, while developing signature schemes resistant to quantum attacks, is of significant practical interest. Purpose: Development of a new method for designing post-quantum signature algorithms on finite non-commutative associative algebras. Results: A novel method for developing digital signature algorithms on non-commutative algebras. A new four-dimensional finite non-commutative associative algebra set over the ground field GF(p) have been proposed as algebraic support of the signature algorithms. To provide a higher performance of the algorithm, in the introduced algebra the vector multiplication is defined by a sparse basis vector multiplication table. Study of the algebra structure has shown that it can be represented as a set of commutative subalgebras of three different types, which intersect exactly in the set of scalar vectors. Using the proposed method and introduced algebra, a new post-quantum signature scheme has been designed. The introduced method is characterized in using one of the elements of the signature (e, S) in form of the four-dimensional vector S that is computed as a masked product of two exponentiated elements G and H of a hidden commutative group: S = B-1GnHmC-1, where non-permutable vectors B and C are masking multipliers; the natural numbers n and m are calculated depending on the signed document M and public key. The pair &lt;G, H&gt; composes a minimum generator systems of the hidden group. The signature verification equation has the form R = (Y1SZ1)e(Y2SZ2)e2, where pairwise non-permutable vectors Y1, Z1, Y2, and Z2 are element of the public key and natural number e that is computed depending on the value M and the vector R. Practical relevance: Due to sufficiently small size of public key and signature and high, the developed digital signature scheme represents interest as a practical post-quantum signature algorithm. The introduced method is very attractive to develop a post-quantum digital signature standard.
Continuous Non-Invasive Assessment of Gait Speed Through Bluetooth Low Energy

In the near future, as a consequence of the increasing percentage of elder people with respect to the total population, developed countries will face significant stress on their healthcare systems. The onset of some diseases associated with aging, such as dementia or cognitive decline, has been associated with a number of factors that can be detected in advance, thus offering the possibility of an early intervention to delay their onset. Specifically, several studies have shown a correlation between slower gait speed and serious cognitive diseases such as Alzheimer’s disease. In this work we present a method, based on Bluetooth Low Energy (BLE), capable of detecting the user’s walking speed in a continuous and non-invasive way, providing a useful tool for the monitoring and early detection of this type of diseases. The proposed method estimates the gait speed with an average error of less than 10 cm/s, and is capable of providing continuous non-invasive monitoring of the gait speed of users while they conduct their usual life routines, without any additional requirements other than wearing a smartwatch or an activity band with inertial sensors and BLE capabilities.
SentiMedQAer: A Transfer Learning-Based Sentiment-Aware Model for Biomedical Question Answering

Recent advances have witnessed a trending application of transfer learning in a broad spectrum of natural language processing (NLP) tasks, including question answering (QA). Transfer learning allows a model to inherit domain knowledge obtained from an existing model that has been sufficiently pre-trained. In the biomedical field, most QA datasets are limited by insufficient training examples and the presence of factoid questions. This study proposes a transfer learning-based sentiment-aware model, named SentiMedQAer, for biomedical QA. The proposed method consists of a learning pipeline that utilizes BioBERT to encode text tokens with contextual and domain-specific embeddings, fine-tunes Text-to-Text Transfer Transformer (T5), and RoBERTa models to integrate sentiment information into the model, and trains an XGBoost classifier to output a confidence score to determine the final answer to the question. We validate SentiMedQAer on PubMedQA, a biomedical QA dataset with reasoning-required yes/no questions. Results show that our method outperforms the SOTA by 15.83% and a single human annotator by 5.91%.
E-readiness of EFL teachers

Integrating recent technologies into the education environment is one of the dynamic requirements for teachers of digital age learners. Such requirements oblige teachers to have adequate technology competence and know-how to integrate them into their pedagogical practices. Thus, the present study intends to illustrate English Language teachers' technology readiness profile in a city located on Turkey's southwestern Mediterranean coast. Adopting a mixed-method research methodology, the gathered data were examined to answer the research questions. Findings revealed that over half of the participants have decidedly negative attitudes towardstechnology integration, and they are not ready to use them in their teaching environments. Additionally, findings revealed a reverse relationship between teachers' daily technology use and e-readiness in their classroom practices. Thus, results showed that the e-readiness of mostEnglish Language teachers in the region is entirely below the expectations. Concerning the findings, we suggest that functional and pleasing training, either as in-service or certified promotions, should be organized to nurture positive attitudes towards technology use in the classroom and increase teachers' e-readiness and acquaintance with technology.
Evaluation of the Online and Offline Mixed Teaching Effect of MOOC Based upon the Deep Neural Network Model

This article is dedicated to discussing the online and offline mixed teaching evaluation of MOOC based on deep neural networks. Deep neural networks are an important means to solve various problems in various fields. It can evaluate the teaching attitude of teachers, the teaching content in the classroom, the teacher’s narrative ability, the teaching methods used by the teachers, and whether the teaching methods are rigorous. And it can train on a large number of datasets evaluated by students on a certain course and get results. This article first explains the advantages of the neural network model and explains the reasons for the emergence of MOOCs and the mixing with traditional classrooms. It also explains some deep neural network (DNN) models and algorithms, such as BP neural network model and algorithms. This model uses backpropagation. When there is an error between the output sample of the neural network and the target sample, the error can be backpropagated to adjust the threshold and weight to make the error reach the minimum. The algorithm steps include forward propagation and backpropagation and are substituted into the gradient descent method to obtain the weight change of the output layer and the hidden layer. It also explains the Gaussian model in DNNs. The given training data vector in the Gaussian mixture model and the configuration of GMM are used for expectation maximization training using an iterative algorithm, and the unsupervised clustering accuracy ACC is applied to evaluate its performance. Use pictures to describe the mixed-mode teaching mode in the MOOC environment. It is necessary to consider teaching practice conditions, time, location, curriculum resources, teaching methods and means, etc. It can cultivate students’ spatial imagination, engineering consciousness, creative design ability, drawing hand-made ability, and logical thinking abilities. It enables teachers to accept the fair and just evaluation of students. Finally, this article discusses the parallelization and optimization of GPU-based DNN models, splits the DNN models, and combines different models to calculate weight parameters. This article combines model training and data training in parallel to increase the processing speed under the same amount of data, increase the batch, increase the accuracy, and reduce the training shock. It can be concluded that its DNN model has greatly improved the training effect performance of the MOOC online and offline mixed course effect dataset. The calculation time is shortened, the convergence speed is accelerated, the accuracy rate is improved, and the acceleration ratio is increased, which compares with the same period of the previous year increase of more than 37.37%. The accuracy has increased, comparing with the same period of the previous year, an increase of more than 12.34%.
Transfer Learning Algorithm With Knowledge Division Level

One of the major challenges of transfer learning algorithms is the domain drifting problem where the knowledge of source scene is inappropriate for the task of target scene. To solve this problem, a transfer learning algorithm with knowledge division level (KDTL) is proposed to subdivide knowledge of source scene and leverage them with different drifting degrees. The main properties of KDTL are three folds. First, a comparative evaluation mechanism is developed to detect and subdivide the knowledge into three kinds—the ineffective knowledge, the usable knowledge, and the efficient knowledge. Then, the ineffective and usable knowledge can be found to avoid the negative transfer problem. Second, an integrated framework is designed to prune the ineffective knowledge in the elastic layer, reconstruct the usable knowledge in the refined layer, and learn the efficient knowledge in the leveraged layer. Then, the efficient knowledge can be acquired to improve the learning performance. Third, the theoretical analysis of the proposed KDTL is analyzed in different phases. Then, the convergence property, error bound, and computational complexity of KDTL are provided for the successful applications. Finally, the proposed KDTL is tested by several benchmark problems and some real problems. The experimental results demonstrate that this proposed KDTL can achieve significant improvement over some state-of-the-art algorithms.
Assessing the Impact of the MOOC Learning Platform on the Comprehensive Development of English Teachers at College Level under “Double First-Rate” by Utilization of the SWOT Analysis in Hunan Province, China

This research is to investigate the impact of massive open online course (MOOC) learning platforms on teacher development, promote the development and innovation of online learning models under the “Double First-Rate,” and especially expand the application of the MOOC platforms combining language learning with professional development paths. The MOOC online learning platform has a problem of a high abandonment rate. This paper first proposes a MOOC learning recommendation algorithm based on the learning sequence and similarity distance analysis as well as evaluates its accuracy. Then, the reliability test and the MOOC learning recommendation algorithm are used to evaluate the quality evaluation system of English teaching in the MOOC utilizing the structural equation model. Finally, the strengths, weaknesses, opportunities, and threats (SWOT) are determined to analyze the impact of the MOOC regarding the English teaching platform on teachers' comprehensive development. The results show that the MOOC platform-based learning recommendation algorithm has higher recommendation accuracy and efficiency, improving the learning effect with the utilization of the MOOC. Also, it can effectively reduce the abandonment rate and has a positive effect of resolving the interaction problem pertinent to characteristic differences and sequences in the learning recommendation. The quality evaluation system of online English teaching in the MOOC has higher reliability and convergent validity, which shows better stability and consistency in all dimensions. If teachers can actively learn from the resources of the MOOC platform, then they continuously update teaching concepts, improve online teaching, give full play to their language advantages, accurately locate student needs, and develop unique courses. Therefore, it will promote the overall development of their careers and improve innovation.
Issues and challenges with fake reviews in Digital Marketing

Digital marketing has been a key enabler for any business organization in today&#x0027;s digital world. It uses electronic means for promotion of products and services. Emergence and growth of ecommerce has been a big boost that has resulted in new techniques of digital marketing like product reviews. Customers like to buy online and product reviews play a key role in product selection by the customer. These product reviews are critical pieces of data that have significant impact on sales of products. Recent developments in Big Data Analytics based business intelligence (BI) and artificial intelligence (AI) use product reviews to understand customer sentiments. But fake product reviews may lead to a serious risk of losing customers. This paper is aimed to study the issues and challenges related to fake product reviews. The objective of the paper is to review the current research work in combating fake product reviews and to identify unaddressed areas and suitability of technology like blockchain to address the problem.
Least Absolute Shrinkage and Selection Operator (LASSO) and k-Nearest Neighbors (k-NN) Algorithm Analysis Based on Feature Selection for Diamond Price Prediction

Diamonds are the most expensive, rarest, and most complex gemstones globally. Diamond investing is a new lifestyle; however, diamond prices fluctuate and are difficult to predict. Predicting the price of diamonds can be done using a regression technique because the price is continuous. Regression is part of the field of machine learning. This study aims to find the most efficient and accurate model. The models used to predict diamond prices are k-Nearest Neighbors (kNN) and Least Absolute Shrinkage and Selection Operator (LASSO). The process is carried out by selecting features, considering the value of k from k-NN and alpha from LASSO to ensure optimal accuracy. The data of this research is public and taken from Kaggle. The number of datasets is around 54000 data and is divided into training data by 80% and testing data by 20%. The results showed that k-NN had the highest accuracy of 0.9066 compared to LASSO, which was 0.8801. Meanwhile, the RMSE level shows that k-NN has the smallest value, 926.06, compared to LASSO, 1049.59.
Comparative Evaluation of Machine Learning Algorithms in Breast Cancer

Breast cancer is one of the world's leading causes of mortality in women and is due to uncontrollable breast cell growth. Early detection and proper care are the only means of avoiding deaths from breast cancer. The precise characterization of tumors is a critical task in the medical profession. Because of their high precision and accuracy, machine learning methods are commonly used in identifying and classifying various forms of cancer. In this review article, the authors have tested different machine learning algorithms and implemented them, which can be used by doctors to identify cancer cells in an early and accurate way. This article introduces several algorithms, including support vector machine (SVM), Nave Bayes Classifier (NBC), artificial neural network (ANN), Random Forest (RF), decision tree (DT), and k-Nearest-Neighbor (KNN). These algorithms are trained with a collection of data containing tumor parameters for a person with breast cancer. After comparing the results, we found the highest accuracy of the Support Vector Machine and Random Forest and the highest precision of the Naive Bayes Classifier (NBC). In addition, we review the number of researches that provide machine learning algorithms for detecting breast cancer
Modalities and Parametric Adjoints

Birkedal et al. recently introduced dependent right adjoints as an important class of (non-fibered) modalities in type theory. We observe that several aspects of their calculus are left underdeveloped and that it cannot serve as an internal language. We resolve these problems by assuming that the modal context operator is a parametric right adjoint. We show that this hitherto unrecognized structure is common. Based on these discoveries we present a new well-behaved Fitch-style multimodal type theory, which can be used as an internal language. Finally, we apply this syntax to guarded recursion and parametricity.