Reviewerly/exHarmony huggingface dataset:
On the size of a linear combination of two linear recurrence sequences over function fields

Abstract Let $$ G_n $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>G</mml:mi> <mml:mi>n</mml:mi> </mml:msub> </mml:math> and $$ H_m $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>H</mml:mi> <mml:mi>m</mml:mi> </mml:msub> </mml:math> be two non-degenerate linear recurrence sequences defined over a function field F in one variable over $$ \mathbb {C}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>C</mml:mi> </mml:math> , and let $$ \mu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> be a valuation on F . We prove that under suitable conditions there are effectively computable constants $$ c_1 $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>c</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> and $$ C' $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>C</mml:mi> <mml:mo>′</mml:mo> </mml:msup> </mml:math> such that the bound $$\begin{aligned} \mu (G_n - H_m) \le \mu (G_n) + C' \end{aligned}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mtable> <mml:mtr> <mml:mtd> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>G</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>-</mml:mo> <mml:msub> <mml:mi>H</mml:mi> <mml:mi>m</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>≤</mml:mo> <mml:mi>μ</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>G</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>+</mml:mo> <mml:msup> <mml:mi>C</mml:mi> <mml:mo>′</mml:mo> </mml:msup> </mml:mrow> </mml:mtd> </mml:mtr> </mml:mtable> </mml:mrow> </mml:math> holds for $$ \max \left( n,m \right) &gt; c_1 $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>max</mml:mo> <mml:mfenced> <mml:mi>n</mml:mi> <mml:mo>,</mml:mo> <mml:mi>m</mml:mi> </mml:mfenced> <mml:mo>&gt;</mml:mo> <mml:msub> <mml:mi>c</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:mrow> </mml:math> .
Information Processing for Low Resource Processing Based Cognitive Psychology for Second Language Teaching by Opinion Mining using Deep Learning Architecture

Foreign language instruction is crucial and difficult in every nation. Effective teachers must consider students' attitudes, motivation, and knowledge. Quality teaching determines student success. This study presents an AI-based deep learning method for second language and English instruction. This dataset was collected from students' second language and English teaching preferences. Dimensionality reduction and missing value removal were done on the dataset. Fuzzy set-based clustering with stochastic gradient residual neural network (ResNet) architecture classified this processed data. Students' second language and English teaching opinions were collected using fuzzy rules. Fuzzy clustering and stochastic gradient ResNet architecture classified this data. Student opinion mining was used for experimental study of various datasets and the parametric analysis yielded 96% accuracy, 90% sensitivity, 92% specificity, 82% F-1 score, 72% Mean squared error (MSE), and 88% Area Under the Curve (AUC).
Evaluation of total solar radiation with daily breakdown based on regression models

The study aims to propose new regression models using available weather data by analyzing the data published on the development of regression models for evaluating the flux of total solar radiation. Following an analysis of literature sources, primary stages in developing regression models and approaches to their implementation are described. Models are developed and compared for accuracy based on weather data (maximum and minimal temperature, air humidity, overall and lower cloudiness) in Irkutsk over 2007–2019. For calibration and validation of the models, open databases of ground measurements of weather stations were used. Ten known and seven new regression models were calibrated and validated, including three models based on the support vector method. The new models based on air temperature and humidity, atmospheric pressure, as well as overall and lower cloudiness, showed the highest accuracy in evaluating the total solar radiation with daily breakdown. The maximum mean absolute error in evaluating daily total solar radiation over 2016–2019 comprised 627.52 W·h/m2·day for the analyzed known models, 504.7 W·h/m2·day for the newly proposed regression models, and 463.2 W·h/m2·day for the regression models based on the support vector method. The conducted analysis of the mean bias error revealed models having the highest accuracy in evaluating monthly and annual sums of total solar radiation were determined. These include a known regression model using air humidity data and a regression model based on the support vector method.
Online proctored exams and digital inequalities during the pandemic

Abstract Background The emergence of the COVID‐19 and the resulting global pandemic has ushered in far‐reaching changes for countries across the world, not least of which are changes to their education systems. With traditional location‐based exams no longer possible at universities, the uptake of online proctored exams (OPE) has occurred at a pace not seen prior to the pandemic. Students' experiences of online proctored exams during the pandemic are reasonably well‐understood in terms of digital access and ease of use of the technology. However, less is known about students' perceptions of digital confidence and competence to complete an online exam, both of which are important digital equity considerations. Objectives This study investigates students' digital confidence and competence to undertake online proctored exams to determine whether issues of equity exist for students. Methods This study reports the results of a survey ( N = 761) of one university's students' experiences of end‐of‐semester online proctored exams in New Zealand. Results Most students were positive about online exams, felt reasonably confident to complete an exam, and had the necessary digital access (i.e., devices, internet) and competence to succeed. However, digital inequalities were found between students' perceptions of digital competence based on ethnicity. While more time learning online equated to increasing self‐reported digital competence for most ethnic groups, this was not the case for Pacific learners. Takeaways Perceptions of low digital competence can contribute to digital inequalities. Educators can support learners to develop positive perceptions of digital competence by teaching digital literacy skills.
Feature Elimination through Data Complexity for Error-Correcting Output Codes based micro-expression recognition

Micro-Expression (ME) is a kind of short-lived and uncontrollable facial expressions. The MEs recognition task poses a great challenge to both the psychological and computer vision research communities. In this study, a Feature Elimination through Data Complexity-based Error-Correcting Output Codes (FEDC-ECOC) algorithm is proposed. In the generation of the coding matrix, a set of data complexity measures are utilized as the division criteria to form a coding matrix. Meanwhile, the sliding window and the greedy search algorithm are applied to improve the discriminative ability of the coding matrix for various emotion types. On the other hand, this study proposes a feature selection algorithm to identify essential features to enhance the performance of classifiers. Comprehensive experiments are conducted, and the results confirm the robustness and effectiveness of our FEDC-ECOC. Detailed analysis is given to further provide insights of the proposed method.
Hybrid feature learning framework for the classification of encrypted network traffic

The purpose of traffic classification is to allocate bandwidth to different types of data on a network. Application-level traffic classification is important for identifying the applications that are in high demand on the network. Due to the increasing complexity and volume of internet traffic, machine learning and deep learning methods are being used more frequently in traffic classification. The focus of this research is to evaluate the performance of the Support Vector Machine (SVM) in classifying network packets by application type, as well as classifying the type of data communicated within an application. The research considers encrypted network packets, including those from Virtual Private Networks (VPN) and the WhatsApp mobile application. Previous research has shown that deep learning methods are effective in the feature learning process, so this study uses a simple feed-forward Deep Neural Network (DNN) to improve the performance of the SVM algorithm. Additionally, various feature learning frameworks based on deep learning, such as DNN, Autoencoder and PCA, are compared. The study concludes that the DNN is able to improve the F1 score of the SVM classifier from 0.78 to 0.90. Furthermore, the study shows that using a hybrid framework of DNN with SVM can address the class imbalance problem often present in machine learning.
Multistatic Localization Algorithm for Moving Object with Constant Acceleration Eliminating Extra Variables

This correspondence aims to locate the unknown object moving with constant acceleration in multistatic systems by using bistatic range (BR), bistatic range rate (BRR), and derivative of bistatic range rate (DBRR) measurements obtained at a single time instant. Albeit previous studies can attain optimal performance under mild noise conditions, the localization model used to solely estimate the target position and velocity cannot be applied to some generalized situations with unknown constant acceleration. To address this problem, we start with the fundamental equations of traditional methods and propose an algebraic solution consisting of two stages. In the first stage, we obtain initial estimates of target position, velocity, and acceleration without nuisance variables by using the projection matrix. In the second stage, the estimation accuracy is improved by refining initial estimates. The proposed estimator is proven to achieve the Cramer-Rao lower bound (CRLB) accuracy analytically when the measurement noise is low. Simulation results validate the theoretical analysis and reveal that the proposed method can determine the target acceleration while outperforming the previous studies for position and velocity estimation performance.
Lesson Enactments: Maintenance in Everyday Educational Practice

Abstract This article explores lesson enactments as co-constitutive of human-technology relationality in everyday schooling, rather than neutral backdrops for educational activities. In doing so, the article introduces maintenance as its key concept, drawing on insights from maintenance studies and actor-network theory (ANT). Being both theoretically and empirically informed, maintenance means reconsidering lessons, and digital technologies, as part of lively and vulnerable objects achieved in sociomaterial practices and not merely stable in function and use. The empirical case of lesson enactments comes from fieldwork with an upper secondary school in Sweden during Covid-19. The article analyses situations of maintenance with online class calls and scheduling meetings. Herein, lessons turn into a topic of concern and mechanisms of maintenance enact educational order and prevent disorder. The article demonstrates how putting maintenance to work articulates and identifies so far neglected and mundane practices with digital technology in education. In light of this, the article argues for recognising maintenance in educational practice as too long overshadowed by use, reinforced by a persistent user-technology dichotomy. Finally, the article discusses how maintenance invites reconsiderations of the dominant before-after debate that the Covid-19 pandemic attracts and calls attention to the mundane maintenance of lessons regardless of breakdowns.
VIOLA jones algorithm with capsule graph network for deepfake detection

DeepFake is a forged image or video created using deep learning techniques. The present fake content of the detection technique can detect trivial images such as barefaced fake faces. Moreover, the capability of current methods to detect fake faces is minimal. Many recent types of research have made the fake detection algorithm from rule-based to machine-learning models. However, the emergence of deep learning technology with intelligent improvement motivates this specified research to use deep learning techniques. Thus, it is proposed to have VIOLA Jones's (VJ) algorithm for selecting the best features with Capsule Graph Neural Network (CN). The graph neural network is improved by capsule-based node feature extraction to improve the results of the graph neural network. The experiment is evaluated with CelebDF-FaceForencics++ (c23) datasets, which combines FaceForencies++ (c23) and Celeb-DF. In the end, it is proved that the accuracy of the proposed model has achieved 94.
A Multi-Party Functional Signatures Scheme for Private Blockchain

Digital signature technology is essential for ensuring the authenticity and unforgeability of transactions in a private blockchain framework. In some scenarios, transactions require verification from multiple parties, each of whom needs to authenticate different parts of the transaction. To address this issue, researchers have developed multi-party ECDSA (Elliptic Curve Digital Signature Algorithm) signature schemes. However, these schemes either need to consider the authentication of different parts of the transaction or generate an aggregated signature. This paper proposes a novel solution that combines functional signatures and multi-party ECDSA signatures to create a multi-party functional signature for private blockchains. Compared to previous constructions, the proposed scheme ensures that each part of the transaction is verified. Furthermore, when the aggregate signature of the entire transaction cannot be verified, this scheme identifies the specific part of the transaction for which the signature authentication fails instead of rejecting the entire transaction. This paper uses a smart contract to securely deploy the proposed scheme and authenticate the f in functional signatures. The constructed scheme also provides security under the existential unforgeability of the ECDSA signature, even if n−1 parties are corrupted, assuming a total of n parties. The scheme of this paper successfully conducted experiments on a personal computer, with three users taking approximately 343 ms, six users taking 552 ms, and nine users taking 791 ms.
Linguistic Methods of Image Division for Visual Data Security

This paper defines new classes of algorithms for securing and sharing visual information. Algorithms offering data protection against unauthorised access are cryptographic protocols for data sharing and splitting. These protocols ensure the division of information among a trusted group of secret holders, with every protocol participant being allocated a specified number of shares in the executed algorithm. Proposing and defining new solutions in the field of cryptographic algorithms for data sharing constitutes the main topic of this paper. This paper discusses a new class of algorithms for secret sharing with the use of linguistic formalisms dedicated to the processes of meaning interpretation and linguistic data sharing. Linguistic threshold schemes serve the processes of data protection in distributed systems; they are also used to distribute the shared secret parts in an optimum way, and to perform the meaning analysis and interpretation of various data sets. Semantic analysis as an element of the impact assessment of the meaning of the interpreted and analysed data will make it possible to take into consideration a much wider aspect of description and interpretation of the analysed phenomenon or data set; it will also enable the assessment of the core of the characterised sets in respect to other information with related meaning. The proposed protocols enhance the security of shared data, and allow the generation of any number of secret shares, which is greater than traditional secret sharing methods.
Measuring the effects of co-location on emotion perception in shared virtual environments: An ecological perspective

Inferring emotions from others’ non-verbal behavior is a pervasive and fundamental task in social interactions. Typically, real-life encounters imply the co-location of interactants, i.e., their embodiment within a shared spatial-temporal continuum in which the trajectories of the interaction partner’s Expressive Body Movement (EBM) create mutual social affordances. Shared Virtual Environments (SVEs) and Virtual Characters (VCs) are increasingly used to study social perception, allowing to reconcile experimental stimulus control with ecological validity. However, it remains unclear whether display modalities that enable co-presence have an impact on observers responses to VCs’ expressive behaviors. Drawing upon ecological approaches to social perception, we reasoned that sharing the space with a VC should amplify affordances as compared to a screen display, and consequently alter observers’ perceptions of EBM in terms of judgment certainty, hit rates, perceived expressive qualities (arousal and valence), and resulting approach and avoidance tendencies. In a between-subject design, we compared the perception of 54 10-s animations of VCs performing three daily activities (painting, mopping, sanding) in three emotional states (angry, happy, sad)—either displayed in 3D as a co-located VC moving in shared space, or as a 2D replay on a screen that was also placed in the SVEs. Results confirm the effective experimental control of the variable of interest, showing that perceived co-presence was significantly affected by the display modality, while perceived realism and immersion showed no difference. Spatial presence and social presence showed marginal effects. Results suggest that the display modality had a minimal effect on emotion perception. A weak effect was found for the expression “happy,” for which unbiased hit rates were higher in the 3D condition. Importantly, low hit rates were observed for all three emotion categories. However, observers judgments significantly correlated for category assignment and across all rating dimensions, indicating universal decoding principles. While category assignment was erroneous, though, ratings of valence and arousal were consistent with expectations derived from emotion theory. The study demonstrates the value of animated VCs in emotion perception studies and raises new questions regarding the validity of category-based emotion recognition measures.
Attributed Graph Embedding with Random Walk Regularization and Centrality-Based Attention

Graph-embedding learning is the foundation of complex information network analysis, aiming to represent nodes in a graph network as low-dimensional dense real-valued vectors for the application in practical analysis tasks. In recent years, the study of graph network representation learning has received increasing attention from researchers, and, among them, graph neural networks (GNNs) based on deep learning are playing an increasingly important role in this field. However, the fact that higher-order neighborhood information cannot be used effectively is a problem of most existing graph neural networks. Moreover, it tends to ignore the influence of latent representation and structural properties on graph embedding. In hopes of solving these issues, we introduce centrality encoding to learn the node properties, add an attention mechanism consideration to better distinguish the significance of neighboring nodes, and introduce random walk regularization to make sample neighbors that consistently satisfy predetermined criteria. This allows us to learn a representation of a potential node. We tested the performance of our model on node-clustering and link prediction tasks using three widely recognized benchmark datasets. The outcomes of our experiments demonstrate that our model significantly surpasses the baseline method in both tasks, indicating that the graph embedding it generates is highly expressive.
Establishing the Forecasting Model with Time Series Data Based on Graph and Particle Swarm Optimization

In recent years, a wide variety of fuzzy time series (FTS) forecasting models have been created and recommended to handle the complicated and ambiguous challenges relating to time series data from real-world sources. However, the accuracy of a model is problem-specific and varies across data sets. But a model’s precision varies between different data sets and depends on the situation at hand. Even though many models assert that they are better than statistics and a single machine learning-based model, increasing forecasting accuracy is still a challenging task. In the fuzzy time series models, the size of the intervals and the fuzzy relationship groups are thought to be crucial variables that affect the model’s forecasting abilities. This study offers a hybrid FTS forecasting model that makes use of both the graph-based clustering technique (GBC) and particle swarm optimization (PSO) for adjusting interval lengths in the universe of discourse (UoD). The suggested model’s forecasting results have been compared to those provided by other current models on a dataset of enrollments at the University of Alabama. For all orders of fuzzy relationships, the suggested model outperforms its counterparts in terms of forecasting accuracy.
Source, Contamination Assessment and Risk Evaluation of Heavy Metals in the Stream Sediments of Rivers around Olode Area SW, Nigeria

In order to investigate the source, contamination, and risk of heavy metals such as Pb, Zn, Cu, Ni, Co, Fe, Mn, and Cr, twelve (12) stream sediments and ten (10) rock samples were collected from pegmatite mining sites at Olode and its environs inside Ibadan, Southwestern Nigeria. The average values and order of abundance obtained followed the pattern: Mn (595.09) &gt; Ba (80) &gt; Cr (50.82) &gt; V (45.09) &gt; Zn (29.73) &gt; Cu (13.82) &gt; Co (13.82) &gt; Sr (10.46) &gt; Ni (9.73) &gt; Pb (9.09) &gt; Fe (1.59). These were greater than the background values, indicating that mining has a negative impact on the study area, as indicated by the high coefficient of variation and correlation values (&gt; 0.6) for Copper-Lead (0.929), Copper-Vanadium (0.970), Copper-Chromium (0.815), Lead-Vanadium (0.884), and others. On the basis of the enrichment factor (EF), the Olode sediments show extremely high enrichment for Mn and Ba in the research region. Cu and Ni are most likely to blame for the elevated contamination levels, according to CF values. The degree of contamination (CD), pollution load index (PLI), pollution index (PI), and modified pollution index (MPI) all revealed high levels of contamination in all stream sediment samples, whereas Igeo shows that the Olode stream sediments are “practically uncontaminated to extremely contaminated by Ni, Co, and Mn”. Ni and Cu are the major regulating factors that are most likely causing the possible Eri. As a result, these findings give important information for conducting appropriate ecological management research.