Reviewerly/exHarmony huggingface dataset:
Design and Implementation of an Android based New Student Admissions Classification System using the Vikor Method

Today's technological developments make all aspects of life easier. One form of convenience that can be found in the world of lectures is a digital student graduation information system. In this study the authors designed an android-based new student admission classification system using the vikor method. In simple terms, the vikor method can be used to perform multicriteria ranking and weighting of each assessment criterion. In the implementation of the North Sumatra State Islamic University Independent Examination, there are several materials to be tested such as Religious Sciences, General Sciences, Arabic, English, and Interviews where the system still uses a passing grade where its application is considered less efficient. The use of the vikor method in this study is intended to carry out the process of ranking student grades based on predetermined weights based on the criteria of several materials tested. In this study, the creation of an android application using the app inventor platform. The results in this study indicate that the vikor method can be implemented in the new student admission classification system and perform ranking based on the exam results that have been carried out using an android smartphone.
Study of Rescaling Mechanism Utilization in Binary Neural Networks

Single Image Super Resolution (SISR) is a common task on devices to enhance quality of visual data. Deep Convolutional Neural Networks (DCNN) have recently shown great results in this field. However, DCNN are not compatible with resource-limited devices, because they demand significant amounts of memory and computations. Binary neural networks (BNN) provide a promising approach to reduce computational complexity and speed up the inference of a model. SISR models are much more vulnerable to degradation in performance when decreasing the precision of weights, than image classification models, due to the complexity of the task that relies on dense pixel-level predictions. Hence, they suffer a significant quality drop when being binarized. The paper investigates the importance of restricting information in BNN and proposes several binary block modifications based on different rescaling mechanisms. We implement different rescaling modules into the binary block and prove them to increase model performance. We propose various binary block modifications and find the most effective rescaling block locations. Our method shows state-of-the-art results, quantitatively proving the importance of applying rescaling in the binary block. Our modifications outperform existing BNN on benchmark datasets, showing the importance of the rescaling mechanism for increasing BNN quality.
Russian Language Speech Generation from Facial Video Recordings Using Variational Autoencoder

This paper describes the use of a variational autoencoder (VAE) based generative adversarial neural network (GAN) to generate Russian language speech from facial video recordings. The proposed system uses the VAE to learn a low-dimensional representation of the input video frames and the corresponding speech signals. A discriminator is used for regularization during training. The model is trained on Russian-language speech and video recordings of a speaking person collected from video hosting sites. The results show that the proposed method can generate high quality speech signals that are close to the original speech. The system is also able to generate speech with different emotions and speaking styles, demonstrating its potential for use in speech synthesis applications. Overall, the proposed method provides a promising approach for generating speech from video recordings of a face, which could have important applications in areas such as the cinematography, the game development, virtual reality systems, rehabilitation, and medicine.
Estimating the Transfer Learning Ability of a Deep Neural Networks by Means of Representations

The basis of transfer learning methods is the ability of deep neural networks to use knowledge from one domain to learn in another domain. However, another important task is the analysis and explanation of the internal representations of deep neural networks models in the process of transfer learning. Some deep models are known to be better at transferring knowledge than others. In this research, we apply the Centered Kernel Alignment (CKA) method to analyze the internal representations of deep neural networks and propose a method to evaluate the ability of a neural network architecture to transfer knowledge based on the quantitative change in representations during the learning process. We introduce the Transfer Ability Score (TAs) measure to assess the ability of an architecture to effectively transfer learning. We test our approach using Vision Transformer (ViT-B/16) and CNN (ResNet, DenseNet) architectures in computer vision tasks in several datasets, including medical images. Our work is an attempt to explain the transfer learning process.
Impacts of climate change in the meteorological conditions during the period 2022 in Albania

The impacts of climate change on the meteorological conditions in our country, analysing the main climate indicators (temperatures, rainfall for the last year) will be addressed in this paper. In the last years, in our country, rising temperatures and seasonal rainfall anomalies were the two most encountered phenomena. The assessment of the meteorological conditions of each month during the last year and the comparison with the norm has enabled some conclusions to be drawn regarding different indicators of climate and its changes. Some of the results show an increase in the average annual air temperature by about +2.0°C, where in particular the summer season differ by +3.4°C, while precipitation marks a decrease around -13.1%, which is most pronounced in the season of winter by -30.4%. According to data analyses, the number of rainy days decreased by 18.8%. In recent years, due to the reduction in the number of rainy days and a lower cloud, there has been an increase of sunshine by about +5% in the area of Albania. The impact of these climate change indicators has had positive and negative impacts on various branches of the economy, such as energy, agriculture, transport, etc., but the most negative effects of the decrease of the precipitation and the increase of temperatures is related to droughts.
AR Based CALL Design for English Language Learners in China

With the development of digitalization of education, augmented reality (AR) is largely developed and used in second language teaching. This essay aims to introduce an AR technique design within Computer Assisted Language Learning (CALL) for English language learners in China to learn English in virtual environment. First, an elaborate analysis about this artefact design will be given. Chapter two introduces the rationale, function, mode and the learner context of the AR based design. Then how to evaluate AR technique design is illustrated. Finally the significance of the study is given. The analysis of this paper demonstrates the effective AR usage in second language learning and provides inspiration to future research on this topic.
The weight distributions of several classes of few-weight linear codes

In data storage systems, authentication codes, association schemes, and some other fields, linear codes with few weights play an important role. In this paper, we construct six classes of few weights linear codes over $ \mathbb F_{q} $ and use Gauss periods in the semi-primitive case to determine their weight distributions. There are some linear codes with few weights whose duals are almost Maximum Distance Separable (AMDS) codes and some Maximum Distance Separable (MDS) codes.
Polynomial representation of additive cyclic codes and new quantum codes

We give a polynomial representation for additive cyclic codes over $ \mathbb{F}_{p^2} $. This representation will be applied to uniquely present each additive cyclic code by at most two generator polynomials. We determine the generator polynomials of all different additive cyclic codes. A minimum distance lower bound for additive cyclic codes will also be provided using linear cyclic codes over $ \mathbb{F}_p $. We classify all the symplectic self-dual, self-orthogonal, and nearly self-orthogonal additive cyclic codes over $ \mathbb{F}_{p^2} $. Finally, we present ten record-breaking binary quantum codes after applying a quantum construction to self-orthogonal and nearly self-orthogonal additive cyclic codes over $ \mathbb{F}_{4} $.
Intelligent synthesis of hyperspectral images from arbitrary web cameras in latent sparse space reconstruction

&lt;abstract&gt;&lt;p&gt;Synthesizing hyperspectral images (HSI) from an ordinary camera has been accomplished recently. However, such computation models require detailed properties of the target camera, which can only be measured in a professional lab. This prerequisite prevents the synthesizing model from being installed on arbitrary cameras for end-users. This study offers a calibration-free method for transforming any camera into an HSI camera. Our solution requires no controllable light sources and spectrometers. Any consumer installing the program should produce high-quality HSI without the assistance of optical laboratories. Our approach facilitates a cycle-generative adversarial network (cycle-GAN) and sparse assimilation method to render the illumination-dependent spectral response function (SRF) of the underlying camera at the first part of the setup stage. The current illuminating function (CIF) must be identified for each image and decoupled from the underlying model. The HSI model is then integrated with the static SRF and dynamic CIF in the second part of the stage. The estimated SRFs and CIFs have been double-checked with the results by the standard laboratory method. The reconstructed HSIs have errors under 3% in the root mean square.&lt;/p&gt;&lt;/abstract&gt;
The Effect of Overlap Length on Adhesive Bonded Composite Joint Using Digital Image Correlation

In this paper, the effect of varying the overlap length of the adhesive-bonded single-lap composite joint has been studied using Digital Image Correlation (DIC). The study of surface strain and stress distribution with time steps helps to predict the behaviour of joints under the change in load magnitude. The samples of single lap adhesive bonded joints were prepared using unidirectional glass fiber reinforced polymer composite at 0° orientation as an adherend. The surface pre-treatment was done to remove dust particles and make the surface rough for good bonding strength. The structural adhesive 3m-DP8805ns was used to bond the varying overlap lengths of 10, 25, and 40 mm. Speckle patterns were formed on the surface of the samples. DIC was used to visualize the displacement and surface strain of the joint. The joint failure observed in all overlap lengths was cohesive failure. The maximum load was carried by the overlap length of 40 mm. The significant increase is because of uniform stress distribution, visualized using the DIC, and surface strains on the adherend help to predict future damage in the future for using the backface strain technique. The present work helps to understand the effect of varying overlap lengths of adhesive-bonded composite joints prepared using adhesive on strength, strain, and stress distribution. Therefore, the study concludes that strain would reduce and load-carrying capacity would rise in proportion to an increase in the bonded area. The failure load significantly increases from 25 mm, and carries higher strain. Hence the 25 mm is selected as optimal overlap length.
LAND MANAGEMENT

Innovative educational technologies: European experience and its application in training in economics and management : Proceedings of scientific and pedagogical internship (February 13 – March 26, 2023, Riga, Latvia). Riga, Latvia : “Baltija Publishing”, 2023.
Determinants of employees’ turnover intention

The growing competition for talent and increasing employee turnover intention pose challenges for organizations. This paper examines the effect of employees’ organizational trust, job satisfaction, and perception of organizational performance on intention to leave. The research method used was an online survey, with partial least square structural equation modeling (PLS-SEM). The findings reveal there is an insignificant direct influence of employee organizational trust on turnover intention. Moreover, the impact of job satisfaction and organizational performance on turnover intention was found to be both positive and significant. This paper, therefore, introduces fresh empirical evidence and perspective to augment the body of research on the turnover intentions of employees working at public sector institutions, particularly in the post-COVID-19 context.
Medical Strategies for Current Pandemic: Pros and Cons

The development of various strategies such as makeshift hospitals, telemedicine, mobile health apps and critical care during the COVID-19 pandemic has proven to be effective in preventing future pandemics. In this chapter, we have highlighted the advantages of various strategies adopted during the pandemic, which proved to be beneficial but also there were pitfalls observed over time. The rapid surge of cases led to the adoption of strategies that included development of mobile application tools or using artificial intelligence assessing data at a faster speed. The advantages and disadvantages of telemedicine for remote diagnosis and treatment and shortage of intensive care unit (ICU) beds during the early stages of COVID-19 pandemic proved beneficial in the long run. Lessons learned from the COVID helped in taking measures such as quarantine or social distancing policies, which helped in limiting the spread of the infection. The innovations implemented during the COVID-19 strengthened the healthcare system and helped curb the challenges faced during the pandemic to reduce mortality and morbidity of patients.
Communication-Efficient and Privacy-Aware Distributed Learning

Communication efficiency and privacy are two key concerns in modern distributed computing systems. Towards this goal, this article proposes partial sharing private distributed learning (PPDL) algorithms that offer communication efficiency while preserving privacy, thus making them suitable for applications with limited resources in adversarial environments. First, we propose a noise injection-based PPDL algorithm that achieves communication efficiency by sharing only a fraction of the information at each consensus iteration and provides privacy by perturbing the information exchanged among neighbors. To further increase privacy, local information is randomly decomposed into private and public substates before sharing with the neighbors. This results in a decomposition- and noise-injection-based PPDL strategy in which only a fraction of the perturbed public substate is shared during local collaborations, whereas the private substate is updated locally without being shared. To determine the impact of communication savings and privacy preservation on the performance of distributed learning algorithms, we analyze the mean and mean-square convergence of the proposed algorithms. Moreover, we investigate the privacy of agents by characterizing privacy as the mean squared error of the estimate of private information at the honest-but-curious adversary. The analytical results show a tradeoff between communication efficiency and privacy in proposed PPDL algorithms, while decomposition- and noise-injection-based PPDL improves privacy compared to noise-injection-based PPDL. Lastly, numerical simulations corroborate the analytical findings.
A Marginal Distributionally Robust MMSE Estimation for a Multisensor System With Kullback-Leibler Divergence Constraints

In this paper, we study a novel marginal distributionally robust minimum mean-squared error (MDR-MMSE) estimation problem for a random state vector in a multisensory system consisting of several sensors linked with a fusion center by employing the minimax viewpoint, which involves finding the estimator with the best performance under the least favourable distribution within an uncertainty set. In contrast to previous studies, the uncertainty set includes a family of densities whose marginal densities are located in a neighborhood defined by placing a threshold on the Kullback-Leibler (KL) divergence between actual and nominal Gaussian marginal densities, which is suitable for the scenario of the multisensor system. Despite the infinitedimensional optimization nature of the MDR-MMSE estimation problem, we prove that there exists an affine estimator and a corresponding Gaussian distribution constituting its saddle point solution, which can be obtained by solving a finite-dimensional convex optimization problem. In addition, the “partially affine” and “partially Gaussian” properties are also analysed for all saddle point solutions. Moreover, we proved that the optimal estimator for a reduced MDR-MMSE estimation problem with all inactive sensors removed is also optimal for the original problem with all sensors remaining, thus reducing the communication burden of sending the observations from inactive sensors to the fusion center. Additionally, three distinct criteria for evaluating the relationships between the active sensors and the thresholds are established. Numerical experiments are also provided to verify the superiority of our proposed model.
MDRL-IR: Incentive Routing for Blockchain Scalability With Memory-Based Deep Reinforcement Learning

Blockchain-based cryptocurrencies have developed rapidly in recent years, however, scalability is one of the biggest challenge. Payment channel networks (PCNs) are one of the important solutions to blockchain scalability and routing is the most critical problem in PCN. Routing algorithms in PCNs have evolved fast and achieved high throughput. However, most of these routing algorithms are designed from the perspective of technical feasibility, and few algorithms focus on the incentives of each off-chain participant, especially the economic incentives for intermediate routing nodes. Besides, due to the highly dynamic nature of off-chain channel deposits, existing routing algorithms rely heavily on channel deposit probing in order to ensure high throughput. In this paper, we design routing algorithms from an incentive perspective to improve the profit of intermediate nodes and use deep learning to reduce the dependency of off-chain routing on channel deposit probing. Our experiments show that under the same model, MDRL-IR can increase the profit of intermediate nodes by up to 1.87x and increase the throughput by up to 2.0x compared to the state-of-the-art routing algorithm, while ensuring that the user routing cost per unit throughput remains unchanged. Moreover, approximate performance can be achieved when deposit probing is greatly reduced.
Low-Light Image Enhancement via Implicit Priors Regularized Illumination Optimization

Low-light image enhancement is a very challenging problem due to insufficient or uneven illumination, complicated noise and low contrast. Retinex-based methods have shown to be effective in separating the illumination from the reflectance with well-designed priors. However, the commonly used hand-crafted priors may not model the piecewise smoothness of the illumination. In this paper, we propose a Retinex-based variational framework, which imposes an implicit prior on the illumination component. By formulating decomposition problems as an implicit prior regularized model, the regularized illumination term can be inferred by an adaptable mapping instead of using hand-crafted priors, which makes our model extremely versatile. In addition, an adaptive regularizer and the sparsity-enforcing regularization term are carefully designed, responsible for artifact-alleviation and noise suppression in realistic enhanced results. In order to accomplish an efficient numerical implementation, we propose a plug-and-play inspired algorithm to alternatively update the sought image and the illumination. Experimental results on four public datasets show the effectiveness of our method, which significantly outperforms the state-of-the-art methods in terms of visual quality and quantitative comparisons.
A Novel Framework for Credit Card Fraud Detection

Credit card transactions have grown considerably in the last few years. However, this increase has led to significant financial losses around the world. More than that, processing the enormous amount of generated data becomes very challenging, making the datasets highly dimensional and unbalanced. This means the collected data is suffering from two major problems. It is characterized by a severe difference in observation frequency between fraud and non-fraud transactions, and it contains irrelevant, inappropriate, and correlated data that negatively affects their prediction performance. Consequently, it has attracted the interest of machine learning (ML), which has become a significant actor in fraud detection. ML has provided methods such as Logistic Regression (LR), Support vector machines (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN). However, these methods cannot meet the outstanding performance required to detect and predict unusual fraud patterns. In this regard, the contribution of this paper is to propose a framework for fraud detection (FFD). At first, to overcome the unbalanced data problem, the framework uses an undersampling technique. Next, a feature selection (FS) mechanism is applied to select only relevant features. Then, a Support Vector Data Description (SVDD) is used to build the ML model. SVDD aims to create a tight boundary around regular data points to distinguish them from potential outliers or anomalies. In order to enhance optimization capability for its hyperparameters C and σ, a modified version of the Particle Swarm Optimization (PSO) algorithm, Polynomial Self Learning PSO (PSLPSO), is proposed. As a result, the framework’s effectiveness is shown in the experimental results on a real credit card transaction dataset.
Two-Phase Evolutionary Convolutional Neural Network Architecture Search for Medical Image Classification

Recently, convolutional neural networks (CNNs) have shown promising achievements in various computer vision tasks. However, designing a CNN model architecture necessitates a high-domain knowledge expert, which can be difficult for new researchers while solving real-world problems like medical image diagnosis. Neural architecture search (NAS) is an approach to reduce the human intervention by automatically designing CNN architecture. This study proposes a two-phase evolutionary framework to design a suitable CNN model for medical image classification named TPEvo-CNN. The proposed framework mainly focuses on architectural depth search and hyper-parameter settings of the layered architecture for the CNN model. In the first phase, differential evolution (DE) is applied to determine the optimal number of layers for a CNN architecture, which enhances faster convergence to achieve CNN model architectures. In the second phase, the genetic algorithm (GA) is used to fine-tune the hyper-parameter settings of the generated CNN layer architecture in the first phase. Crossover and mutation operations of GA are devised to explore the hyper-parameter search space. Also, an elitism selection strategy is introduced to select the potential hyper-parameters of the CNN architecture for the next generation. The suggested approach is experimented on six medical image datasets, including pneumonia, skin cancer, and four COVID-19 datasets, which are categorized based on image types and class numbers. The experimental findings demonstrate the superiority of the proposed TPEvo-CNN model compared to existing hand-crafted, pre-trained, and NAS-based CNN models in terms of classification metrics, confusion matrix, radar plots, and statistical analysis.