Reviewerly/exHarmony huggingface dataset:
The problems of digital training of specialists for the agro-industrial complex

New trends in the agro-industrial complex require specialists working in agriculture to possess digital skills and competencies. There is an acute shortage of personnel capable of solving a wide range of tasks based on modern digital technologies. The article is devoted to the actual problem of training graduates in the context of the development of digital didactics as a new methodology for teaching in a digital environment with changes in technologies and means due to the digital transformation of all types of social life. In this study, using a questionnaire on a wide range of issues, an analysis was made of the readiness of teachers and students for the digital transition in education and their ability to use the digital educational space to organize and conduct the educational process. The strengths and weaknesses of the participants in educational activities, the positive and problematic aspects of digital education, the didactic difficulties of learning in the digital environment are noted. The emphasis is on assessing the state and problems of digital didactics in domestic and international educational practice. The factors that determine the effectiveness of training in the digital environment are described. New didactic principles are presented that contribute to improving the efficiency and effectiveness of educational activities, requiring digital literacy of teachers and students for their implementation.
Application of Augmented Reality Technology in the Teaching of Literacy at Low Learning Stage

In primary school Chinese education, literacy is a very important work. Literacy is an important part of primary school Chinese and also an important part of primary school Chinese. At present, literacy education is increasingly valued by teachers. However, there are still many problems to be solved in China’s literacy education. In traditional education and teaching, teachers should let students recite a large number of concepts, formulas, and rules in order to improve students’ learning effect. However, due to the lack of practical support, this has become a major obstacle in education and teaching. Therefore, the main purpose of this paper is to use augmented reality (AR) technology to explore the literacy teaching of junior students. From the result of the test of students’ literacy teaching, it can be seen that the number of people who scored 81–100 points in the experimental class and the control class before the experiment was 9 and 4, respectively. After the experiment, the number of people who scored 81–100 points in the experimental class and the control board was 15 and 7, respectively. Therefore, it is very necessary to use AR technology to carry out applied research on literacy teaching at the low learning stage.
Daily Prediction Model of Photovoltaic Power Generation Using a Hybrid Architecture of Recurrent Neural Networks and Shallow Neural Networks

In recent years, photovoltaic energy has become one of the most implemented electricity generation options to help reduce environmental pollution suffered by the planet. Accuracy in this photovoltaic energy forecasting is essential to increase the amount of renewable energy that can be introduced to existing electrical grid systems. The objective of this work is based on developing various computational models capable of making short-term forecasting about the generation of photovoltaic energy that is generated in a solar plant. For the implementation of these models, a hybrid architecture based on recurrent neural networks (RNN) with long short-term memory (LSTM) or gated recurrent units (GRU) structure, combined with shallow artificial neural networks (ANN) with multilayer perceptron (MLP) structure, is established. RNN models have a particular configuration that makes them efficient for processing ordered data in time series. The results of this work have been obtained through controlled experiments with different configurations of its hyperparameters for hybrid RNN-ANN models. From these, the three models with the best performance are selected, and after a comparative analysis between them, the forecasting of photovoltaic energy production for the next few hours can be determined with a determination coefficient of 0.97 and root mean square error (RMSE) of 0.17. It is concluded that the proposed and implemented models are functional and capable of predicting with a high level of accuracy the photovoltaic energy production of the solar plant, based on historical data on photovoltaic energy production.
Hierarchical Co-Attention Propagation Network for Zero-Shot Video Object Segmentation

Zero-shot video object segmentation (ZS-VOS) aims to segment foreground objects in a video sequence without prior knowledge of these objects. However, existing ZS-VOS methods often struggle to distinguish between foreground and background or to keep track of the foreground in complex scenarios. The common practice of introducing motion information, such as optical flow, can lead to overreliance on optical flow estimation. To address these challenges, we propose an encoder-decoder-based hierarchical co-attention propagation network (HCPN) capable of tracking and segmenting objects. Specifically, our model is built upon multiple collaborative evolutions of the parallel co-attention module (PCM) and the cross co-attention module (CCM). PCM captures common foreground regions among adjacent appearance and motion features, while CCM further exploits and fuses cross-modal motion features returned by PCM. Our method is progressively trained to achieve hierarchical spatio-temporal feature propagation across the entire video. Experimental results demonstrate that our HCPN outperforms all previous methods on public benchmarks, showcasing its effectiveness for ZS-VOS. Code and pre-trained model can be found at https://github.com/NUST-Machine-Intelligence-Laboratory/HCPN.
Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease

Given its progressive deterioration in the clinical course, noninvasive assessment and risk stratification for the severity of renal fibrosis in chronic kidney disease (CKD) are required. We aimed to develop and validate an end-to-end multilayer perceptron (MLP) model for assessing renal fibrosis in CKD patients based on real-time two-dimensional shear wave elastography (2D-SWE) and clinical variables.From April 2019 to December 2021, a total of 162 patients with CKD who underwent a kidney biopsy and 2D-SWE examination were included in this single-center, cross-sectional, and prospective clinical study. 2D-SWE was performed to measure the right renal cortex stiffness, and the corresponding elastic values were recorded. Patients were categorized into two groups according to their histopathological results: mild and moderate-severe renal fibrosis. The patients were randomly divided into a training cohort (n = 114) or a test cohort (n = 48). The MLP classifier using a machine learning algorithm was used to construct a diagnostic model incorporating elastic values with clinical features. Discrimination, calibration, and clinical utility were used to appraise the performance of the established MLP model in the training and test sets, respectively.The developed MLP model demonstrated good calibration and discrimination in both the training [area under the receiver operating characteristic curve (AUC) = 0.93; 95% confidence interval (CI) = 0.88 to 0.98] and test cohorts [AUC = 0.86; 95% CI = 0.75 to 0.97]. A decision curve analysis and a clinical impact curve also showed that the MLP model had a positive clinical impact and relatively few negative effects.The proposed MLP model exhibited the satisfactory performance in identifying the individualized risk of moderate-severe renal fibrosis in patients with CKD, which is potentially helpful for clinical management and treatment decision-making.
Deep facial emotion recognition model using optimal feature extraction and dual‐attention residual <scp>U‐Net</scp> classifier

Abstract Human facial emotion recognition (FER) system has become an active research area and it has attracted various research communities for its wide ranging and promising applications especially in the cybersecurity field. Recognizing various facial expressions corresponding to the emotional forms is considered as a significant task in the system of FER. Typically, the automated system of FER consists of two major and important steps like feature extraction, and facial emotion recognition. In this work, initially the input data is acquired and the features are extracted from the input facial image with the use of Fuzzy Eigen Weighted based feature extraction model (FEW‐FE). Among the extracted features, an optimal and best features are selected by means of feature selection technique, which employs Chaotic Spider Monkey Optimization algorithm (CSMO) so as to find best fitness function solution. The use of this optimization technique for the feature subset selection aids the enhancement of classifier performance. Then, the recognition process is carried using Dual‐attention residual U‐Net classifier framework. The performance evaluation of this proposed model is carried over three input datasets considered such as CK+, FER2013, and JAFFE in terms of recognition accuracy, precision, recall, and F‐measure. The comparison is made for the feature extraction and classifier model with that of various existing methodologies which shows the effectiveness of proposed system over other traditional schemes. This proposed design is applicable in the cybersecurity system to detect a person's emotional state from their expression.
Cryptanalysis of three quantum money schemes

We investigate the security assumptions behind three public-key quantum money schemes. Aaronson and Christiano proposed a scheme based on hidden subspaces of the vector space $$\mathbb {F}_2^n$$ in 2012. It was conjectured by Pena et al. (IACR international workshop on public-key cryptography, pp 194–213. Springer, 2015) that the hard problem underlying the scheme can be solved in quasi-polynomial time. We confirm this conjecture, and hence prove that the scheme is insecure, by giving a polynomial time quantum algorithm for the underlying problem. Our algorithm is based on computing the Zariski tangent space of a random point in the hidden subspace. Zhandry (Quantum lightning never strikes the same state twice 11, 2017. https://eprint.iacr.org/2017/1080/20171110:155027 ) proposed a scheme based on multivariate hash functions. We prove that Zhandry’s scheme is insecure by giving a polynomial time quantum algorithm for cloning a money state with high probability. Our algorithm uses the verification circuit of the scheme to produce a banknote from a given serial number. Kane et al. (Quantum money from quaternion algebras, 2021. arXiv:2109.12643 ) proposed a scheme based on quaternion algebras. The underlying hard problem in their scheme is cloning a quantum state that represents an eigenvector of a set of Hecke operators. We give a polynomial time quantum reduction from this hard problem to a linear algebra problem. Although our reduction does not break this scheme, the latter problem is much easier to understand, and we hope that our reduction opens new avenues to future cryptanalyses of the scheme.
A Hybrid Homomorphic Model with RSA Algorithm and Modified Enhanced Homomorphic Encryption Technique

Security for sending secret information over the network is the most demanding in today's reality for a wide range of applications. Many malicious attacks target confidential information. As a result, securing sensitive information on the Internet has become a serious challenge. Cryptographic algorithms are the most secure option when it comes to protecting against malicious assaults. Information can be safeguarded using a variety of approaches. Another big difficulty that still needs to be explored is safeguarding information in the cloud. Thus, the concept of homomorphic encryption technique arises to safely process the data in the encrypted format rather than on plain text. There are a variety of cryptography techniques, but this system utilizes Homomorphic Encryption to combine the new technique with the RSA algorithm, allowing us to conduct arbitrary operations on RSA while maintaining the security of the existing methodology. Our main goal in this system is to assure data security, so a new model suggested a method based on the RSA algorithm and Modified Homomorphic Encryption.
Large-margin representation learning for texture classification

This paper presents a novel approach combining convolutional layers (CLs) and large-margin metric learning for training supervised models on small datasets for texture classification. The core of such an approach is a loss function that computes the distances between instances of interest and support vectors. The objective is to update the weights of CLs iteratively to learn a representation with a large margin between classes. Each iteration results in a large-margin discriminant model represented by support vectors based on such a representation. The advantage of the proposed approach w.r.t. convolutional neural networks (CNNs) is two-fold. First, it allows representation learning with a small amount of data due to the reduced number of parameters compared to an equivalent CNN. Second, it has a low training cost since the backpropagation considers only support vectors. The experimental results on texture and histopathologic image datasets show that the proposed approach achieves competitive accuracy with lower computational cost and faster convergence compared to equivalent CNNs.
The Impact of Data Augmentation on Sentiment Analysis of Translated Textual Data

Sentiment analysis is an application of natural language processing that requires an abundance of data that may not be achieved sometimes for some reason. Data augmentation is one technique that deals with the lack of data by creating synthetic training data without adding new ones. It boosts model performance, especially with deep learning ones. Despite its influential role in boosting the model performance, it attracted very little attention from the researchers of the Arabic NLP community, specifically with scarce language resources such as Arabic and its dialects. In this study, one of the augmentation techniques called random swap was applied with LSTM deep learning model to classify three parallel datasets. The three parallel datasets are Bahraini dialects, Modern Standard Arabic and English. The results show an improvement in the LSTM model by 14.06%, 12.57%, and 11.04% on Bahraini dialects, Modern Standard Arabic, and English datasets, respectively, when applying the augmentation technique over that of no application.
Weapon detection system for surveillance and security

Weapon detection is a critical and serious topic in terms of public security and safety, however, it's a difficult and time-consuming operation. Due to the increase in demand for security, safety, and personal property protection, the requirement for deploying video surveillance systems capable of recognizing and interpreting scenes and anomalous occurrences plays an important role in intelligence monitoring. In certain regions of the globe, mass shootings and gun violence are on the increase. Timely detection of the presence of a gun is critical to prevent loss of life and property. Several object detection models are available, which struggle to recognize firearms due to their unique size and form, as well as the varied colors of the background. In this study, we proposed a state-of-the-art system based on a deep learning model YOLO V5 for weapon detection that will be sufficiently resilient in terms of affine, rotation, occlusion, and size. We evaluated the performance of our system on a publicly available dataset and achieved the F1-score of 95.43%. For the detection and segmentation of firearms, we implemented Instance segmentation or Pixel level segmentation which employed Mask-RCNN. The system achieved the detection accuracy (DC) of 90.66% and 88.74% Mean intersection over union (mIoU). The purposed methodology combined different data augmentation and preprocessing methods to improve the accuracy of the proposed weapon detection system.
A Systematic Literature Review of Blockchain for Higher Education

Blockchain is one of the most current technologies in recent times. Many Institutions in Higher Education aim to implement it for the management of certificates, the assessment of professional skills of students, the registration of non-academic activities, the distribution of educational resources, the management of academic degrees, the transfer of fees and credits, etc. The main purpose of this paper is to review the research carried out in relation to blockchain technology in Higher Education, specifically in relation to its applications in education, the main benefits, challenges, implementation which will help us find gaps and research in the future. To make this review, we extracted 763 articles from the IEEE Explore, ACM and Google Scholar databases during the years 2018–2022. After a detailed review based on the defined inclusion and exclusion criteria, we reviewed 33 papers.
Evaluating Gender Bias in Pre-trained Filipino FastText Embeddings

Past studies show that word embeddings can learn gender biases introduced by human agents into the textual corpora used to train these models. However, it has also been shown that some non-English embeddings may actually not capture such biases in their word representations. This study, therefore, aimed to answer the question: Does the publicly available Filipino FastText word embedding contain gender bias? Various iterations of the Word Embedding Association Test and principal component analysis were conducted on the embedding to answer this question. Results show that the Tagalog FastText embedding not only represents gendered semantic information properly but also captures biases about masculinity and femininity collectively held by Filipinos. Specifically, the embedding most strongly associates the female with nouns pertaining to domestic and caregiving roles and the male with verbs relating to strength and their bodies. The study's findings can help determine what next steps need to be undertaken to reduce or eliminate bias from Filipino embeddings.
A Comparative Analysis of GAN and VAE based Synthetic Data Generators for High Dimensional, Imbalanced Tabular data

Synthetic data has emerged as an acceptable solution in machine learning that overcomes the constraints of data availability due to data privacy restrictions. Other major challenge with machine learning is dealing with imbalanced dataset. Several techniques exist to deal with the data imbalance, however, the problem continues to exist when using synthetic data generators dealing with highly imbalanced datasets. Generative Adversarial Network is already proven to be an excellent model to generate synthetic data, especially for high-dimensional datasets. There are other deep learning models that use Variational Autoencoders and Recurrent Neural Networks which are also being explored. To understand how these generators perform when presented situations dealing with highly imbalanced datasets, we experimentally evaluate two deep-learning synthetic data generators, one is based on Generative Adversarial Network (CTGAN) and the other is on Variational Auto Encoder (TVAE). We assess how each of these performs when presented with two datasets of distinct characteristics. The datasets used are high dimensional, highly imbalanced tabular data with one dataset having 19.3% minority class and the other having only 5.68% of minority class. Our test results find that TVAE fails to generate minority data when the minority class is very small in number.
Secure Data Transmission using Cryptography for Internet of Things and Sensor Networks Applications

Recent years have seen tremendous advancements in human life thanks to IoT, which can be seen in healthcare applications as well as regular duties. Everyday items may now be linked to the web thanks to the IoT. Data collection and dissemination are key functions for these devices, thus they include sensors and actuators. However, there may be various privacy and security issues throughout the data’s transfer. In order to address this issue, we present a Secure Data Transmission Scheme (SDTS) that enhances the security of communications in WSNs that are organised into clusters (WSNs). Elliptic Curve Cryptography (ECC) is the foundation of the SDTS because it provides a high degree of security with a very small key size. Several security goals, such as privacy, integrity, and authentication, are addressed by the suggested approach. Not only that, but it’s secure against a wide variety of threats, including brute force assaults, replay attacks, and sinkhole attacks. The results of the performance study demonstrate that SDTS is cost-effective in its communications.
Educative Reality - Augmented Reality Application for Education

The study of augmented reality, also known as AR, is currently a high priority for multiple prestigious international universities and research institutions. This research paper explores the usage of augmented reality in the world of education. It has often been found that when presented visually, information is better understood by students in the classroom. AR is such a technology that can be used to provide interactive learning visual experience giving learners a sense of deeper immersion and significantly raising the learner’s appeal and interest. Augmented reality (AR) gaming can be utilized as an instructional tool, in addition to visual learning through animations and 3D models as it has been found a useful technique for improving understanding of new information. The purpose of this research is also to illustrate an educational application that uses AR and gaming to enhance conceptual understanding and adopts a different pedagogy from traditional methods. Blender and Unity3D were used to create the application that is discussed in the paper.
Image Registration using Shi-Tomasi and SIFT

The procedure of merging data from various sources into a single reference system/ reference frame is known as ‘Image Registration’. The data can be images taken from the sensors, multiple different images taken from different angles, images from different lapses of time, images from a different point of view/frame of reference, etc. This type of data is used in numerous applications such as medical imaging, computer vision, target detection in the military, analyzing and processing of satellite data, etc. To merge and differentiate this data obtained from various different sources, image registration is used. This paper introduces two algorithms well-balanced together for image registration. The two algorithms used are Shi-Tomasi Corner Detection Algorithm and Scale-Invariant Feature Transform (SIFT) Algorithm. The Shi-Tomasi Corner Detection Algorithm issued by J.Shi and C.Tomasi in the year 1994 is used for the corner detection in the reference image. The Scale-Invariant Feature Transform (SIFT) Algorithm is used for searching, outlining and equivalenting the corners/features in the images. The average error obtained after the estimation of the TRS for, Translation of (500,500), Rotation of angle -10, and Scale of 0.7 is $\theta=0.18\%,\Delta x=17.14\%,\Delta y=7.69\%,S=0.01\%$, and the average error obtained after the estimation of the TRS for, Translation of $(500,500)$, Rotation of angle 10 and Scale of 0.8 is $\Theta=0.14\%,\Delta\mathrm{x}=10.76\%,\Delta\mathrm{y}=7.95\%,\mathrm{~S}=0.01\%$