Reviewerly/exHarmony huggingface dataset:
Strategy of Development of the Regional Ecosystem “Education - Science – Industry”

The article is devoted to the consideration of the regional ecosystem "Education -Science -Industry", the definition of the role of regional universities and scientific institutions in the technological and economic development of regions.The author's vision of strategizing the regional ecosystem "Education -Science -Industry" and the socioeconomic system of the region is presented.The use of digital twins of the regional ecosystem "Education -Science -Industry" and the socio-economic system of the region in the development of strategies for their development is justified.The role of the laboratory of analysis and forecasting as an element of the institutional infrastructure of regional development in the process of strategizing the regional ecosystem "Education -Science -Industry" and the socio-economic system of the region is outlined.A unique methodology for assessing the mutual influence of the authors of the regional ecosystem "Education -Science -Industry" and the region has been developed.The classification and ranking of indicators of the methodology for assessing the contribution of the regional ecosystem "Education -Science -Industry" to the development of the region are presented.The results of the work can be useful in the formation of innovative scientific and technological centers, consortia of universities, scientific institutions, and enterprises of the real sector of the regional economy, determining the strategic prospects for their development.
Deep Wavelet Neural Network based Robust Text Recognition for Overlapping Characters

This paper presents a deep learning based intelligent text recognition system with touching and overlapped characters.The robustness and effectiveness in the proposed model are enhanced through the modified configuration of neural network known as Deep Wavelet Neural Network (DWNN).The capability of deep learning networks to learn efficiently from an unlabeled dataset has attracted the attention of many researchers over the last decade.However, the performance of these networks is subject to the quality of the dataset and invariant image representation.Numerous optical character recognition techniques have also been presented in the recent years, but the overlapped and touching characters have not been addressed much.The nonlinear and uncertain representation of image data in case of overlapped text adds severe complexity in the process of feature extraction and respective learning.The proposed architecture of DWNN uses fast decaying wavelet functions as activation function in place of conventional sigmoid function to cope up with the uncertainties and nonlinearity of the data representation in overlapped text images.It comprises of cascaded layered architecture of translated and dilated versions of wavelets as activation functions for the training and feature extraction at multiple levels.The local transformation and deformation variation in the visual data has also been taken care efficiently through the modified architecture of DWNN.Comprehensive experimental analysis has been performed over various test images to verify the effectiveness of the proposed text recognition system.The performance of the proposed method is assessed with the help of the metrics, namely, estimation error, cost function and accuracy.The proposed approach will be implemented in MATLAB.
Recurrence relation and DNA sequence: A state-of-art technique for secret sharing

During the transmission over the Internet, protection of data and information is an important issue. Efficient cryptographic techniques are used for protection but everything depends on the encryption key and robustness of encryption algorithm. Threshold cryptography provides the development of reliable and strong encryption and key management machine which can reconstruct the message even in the case of destruction of some particular numbers of shares and at the opposite the data cannot be reconstructed unless an allowable set of shares are been gathered. The earlier techniques available in literature result in high computational complexity in the course of both sharing and reconstructing of message. Our method employs a brand new easy protecting technique based totally on unit matrix. The simple AND operation is used for percentage generation and reconstruction can be finished by way of easy ORing the stocks with threshold cost. We are proposing a sharing approach in conjunction with conventional cryptography technique for key control to make the key greater sturdy and for encryption we have used a session key the use of the idea of recurrence relation and DNA series Different types of experimental results confirm authenticity, confidentiality, integrity and acceptance of our technique.
Activation ensemble generative adversarial network transfer learning for image classification

Transfer learning provides a useful solution to learn a new conceptual domain from few examples, which exploits prior knowledge from a related domain. We proposed a simple and yet effective transfer learning method for image classification that constructs an activation ensemble generative adversarial net (AE-GAN) to transfer knowledge from one dataset to another. The AE-GAN is mainly composed of three convolutional layers and adopts an ensemble of multiple activation functions. Experimental results on five benchmark datasets show that when only a few samples are available for training a target task, leveraging datasets from other related datasets by AE-GAN can significantly improve the performance for image classification with a small set of samples.
Analysis of SMOTE

The tremendous amount of data generated through IoT can be imbalanced causing class imbalance problem (CIP). CIP is one of the major issues in machine learning where most of the samples belong to one of the classes, thus producing biased classifiers. The authors in this paper are working on four imbalanced datasets belonging to diverse domains. The objective of this study is to deal with CIP using oversampling techniques. One of the commonly used oversampling approaches is synthetic minority oversampling technique (SMOTE). In this paper, the authors have suggested modifications in SMOTE and proposed their own algorithm, SMOTE-modified (SMOTE-M). To provide a fair evaluation, it is compared with three oversampling approaches, SMOTE, adaptive synthetic oversampling (ADASYN), and SMOTE-Adaboost. To evaluate the performances of sampling approaches, models are constructed using four classifiers (K-nearest neighbour, decision tree, naive Bayes, logistic regression) on balanced and imbalanced datasets. The study shows that the results of SMOTE-M are comparable to that of ADASYN and SMOTE-Adaboost.
A Blockchain-based Privacy Preserving Scheme for Vehicular Trust Management Systems

The Internet of Vehicles (IoV) trust management systems usually aims to provide a security guarantee for the interaction between vehicles, which has attracted many researchers' attention. However, most studies rarely consider data security and identity privacy protection issues brought about by trust management systems. Due to the untrusted environment, data dissemination among vehicles is easy to falsify. Meanwhile, the true identity information of vehicles may be exposed to message transmission and evaluation. To address these issues, this paper applies consortium blockchain to build a two-layer vehicular network architecture, and based on it, homomorphic encryption and pseudonym technology are introduced to protect data security while simultaneously preserving identity privacy of vehicles. For performance analysis, a novel compositional approach, named Performance Evaluation Process Algebra (PEPA), is applied to model the scheme.
Supplementary Material for 'A ReMeDI for Microstructure Noise'

This note contains the supplements to Li and Linton (2019). Section A contains extensive simulation studies. Section B presents the methodology and a detailed numerical algorithm to select the tuning parameters. Section C presents additional empirical studies. Section D contains all mathematical proofs.
Research on obtaining pseudo CT images based on stacked generative adversarial network

To investigate the feasibility of using a stacked generative adversarial network (sGAN) to synthesize pseudo computed tomography (CT) images based on ultrasound (US) images.The pre-radiotherapy US and CT images of 75 patients with cervical cancer were selected for the training set of pseudo-image synthesis. In the first stage, labeled US images were used as the first conditional GAN input to obtain low-resolution pseudo CT images, and in the second stage, a super-resolution reconstruction GAN was used. The pseudo CT image obtained in the first stage was used as an input, following which a high-resolution pseudo CT image with clear texture and accurate grayscale information was obtained. Five cross validation tests were performed to verify our model. The mean absolute error (MAE) was used to compare each pseudo CT with the same patient's real CT image. Also, another 10 cases of patients with cervical cancer, before radiotherapy, were selected for testing, and the pseudo CT image obtained using the neural style transfer (NSF) and CycleGAN methods were compared with that obtained using the sGAN method proposed in this study. Finally, the dosimetric accuracy of pseudo CT images was verified by phantom experiments.The MAE metric values between the pseudo CT obtained based on sGAN, and the real CT in five-fold cross validation are 66.82±1.59 HU, 66.36±1.85 HU, 67.26±2.37 HU, 66.34±1.75 HU, and 67.22±1.30 HU, respectively. The results of the metrics, namely, normalized mutual information (NMI), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR), between the pseudo CT images obtained using the sGAN method and the ground truth CT (CTgt) images were compared with those of the other two methods via the paired t-test, and the differences were statistically significant. The dice similarity coefficient (DSC) measurement results showed that the pseudo CT images obtained using the sGAN method were more similar to the CTgt images of organs at risk. The dosimetric phantom experiments also showed that the dose distribution between the pseudo CT images synthesized by the new method was similar to that of the CTgt images.Compared with NSF and CycleGAN methods, the sGAN method can obtain more accurate pseudo CT images, thereby providing a new method for image guidance in radiotherapy for cervical cancer.
Alteration Mapping for Lead-Zinc-Barium Mineralization in Parts of the Southern Benue Trough, Nigeria, Using ASTER Multispectral Data

The lead-zinc-barium deposits of the southern Benue Trough, Nigeria belong to a suite of clastic dominated fracture filling hydrothermal vein deposits. The alteration types and spread are poorly known yet required to aid exploration. Band ratio composites (BRC), Principal Component Analysis (PCA), and Minimum Noise Fraction (MNF) were applied to a full scene Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery covering the study area. Spectral analysis of sulphide minerals known in the area led to the development of the (B1+B3)/2 ratio, which provided a highly effective sulphide discriminant. PCA and MNF bands with high eigenvectors in the absorption features of target minerals qualified as colour composite candidates for alteration mapping. This study demonstrated the effectiveness of combining the BRC, PCA and MNF techniques in the discrimination of ferric-ferrous/sulphide and silica alteration zones in the Southern Benue Trough.
Central Moment and Multinomial Based Sub Image Clipped Histogram Equalization for Image Enhancement

The visual appearance of a digital image can be improved through image enhancement algorithm by reducing the noise in an image, improving the color, brightness and contrast of an image for more analysis.This paper introduces an image enhancement algorithm.The image histogram is processed through multinomial curvature fitting function to reduces the number of pixels for each intensity value through minimizing the sum of squared residuals.Then resampling is done to smooth out the computed data.After then histogram clipping threshold is computed by central moment processed on the resampled data value to restrict the over enhancement rate.Histogram is equally divided into two sub histograms.The sub histograms are equalized by transfer function to merged the sub images into one output image.The output image is further improved by reducing the environmental haze effect by applying Matlab imreducehaze method, which gives the final output image.Matlab simulation results demonstrate that the proposed method outperforms than other compared methods in terms of both quantitative and qualitative performance evaluation applied on colorfulness based PCQI (C-PCQI), and blind image quality measure of enhanced images (BIQME) image quality metrics.
Cell Detection for Asthma on Partially Annotated Whole Slide Images

Asthma is a chronic inflammatory disorder of the lower respiratory tract and naturally occurs in humans and animals including horses. The annotation of an asthma microscopy whole slide image (WSI) is an extremely labour-intensive task due to the hundreds of thousands of cells per WSI. To overcome the limitation of annotating WSI incompletely, we developed a training pipeline which can train a deep learning-based object detection model with partially annotated WSIs and compensate class imbalances on the fly. With this approach we can freely sample from annotated WSIs areas and are not restricted to fully annotated extracted sub-images of the WSI as with classical approaches. We evaluated our pipeline in a cross-validation setup with a fxed training set using a dataset of six equine WSIs of which four are partially annotated and used for training, and two fully annotated WSI are used for validation and testing. Our WSI-based training approach outperformed classical sub-image-based training methods by up to 15% mAP and yielded human-like performance when compared to the annotations of ten trained pathologists.
The influence of vergence facility on binocular eye movements during reading

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Optometrists regularly use binocular measurements in patients with asthenopic complaints when performing close-up work. The focus of this work was therefore on the correlation of optometric parameters and objective fixation disparity (FD) measured by an eye tracker. In our investigation, 20 participants (6 male, 14 female) were subjected to a classical optometric procedure. Subsequently, these subjects read various sentences on a screen and eye movements were registered by using a RED500 eye tracker. The experiment was performed under two reading distance conditions. In order to be comparable with previous work, the present study was conducted under dark illumination conditions (J. A. Kirkby, Blythe, Drieghe, Benson, &amp; Liversedge, 2013). FD values were deduced from objective eye tracking data during reading. Data analysis was done using linear mixed-effects models. &#x0D; FD was found to depend on vergence facility (t=3.3, p=0.004). Subjects with a low vergence facility showed more eso fixation disparity than subjects with a normal vergence facility. If studies of binocular coordination using eye tracking methods are performed under dark illumination conditions, vergence facility is an important parameter and should be accounted for. Neglecting this parameter may mask other important parameters. Vergence facility in context of reading difficulties may be important. &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
How Does the Attitude to New Media influence the Evaluation of a new e-Learning Platform Among Current and Future Teachers?

The aim of this study is to show the relationship between the attitude to new media in the group of future and current teachers and the evaluation of the new e-learning platform. The research problem is closely related to the development of digitally mediated education. It is the teachers who first experiment and are responsible for implementing new digital solutions in education. The research focuses on the juxtaposition of the motivation factor and evaluation of the new tool - learning environment - SELI (Smart Ecosystem for Learning and Inclusion) platform. The survey was conducted in Poland - Pedagogical University of Krakow in May-July 2020 on a sample of 226 people, mixing students (83.3%) and active teachers (16.7%) raising their professional qualifications. On the basis of the collected data, it was noted that: 1) positive attitude to media in one area of use, e.g. private is linear with positive attitude in another area, e.g. professional 2) attitude to new media is constant and does not change under the influence of testing new educational environments 3) the vast majority of teachers and future educators positively assess the use of ICT in education 4) only about 25% have mixed opinions about increasing motivation and involvement of students under the influence of ICT.
Efficient Parallel Implementation of CTR Mode of ARX-Based Block Ciphers on ARMv8 Microcontrollers

With the advancement of 5G mobile telecommunication, various IoT (Internet of Things) devices communicate massive amounts of data by being connected to wireless networks. Since this wireless communication is vulnerable to hackers via data leakage during communication, the transmitted data should be encrypted through block ciphers to protect the data during communication. In addition, in order to encrypt the massive amounts of data securely, it is essential to apply one of secure mode of operation. Among them, CTR (CounTeR) mode is the most widely used in industrial applications. However, these IoT devices have limited resources of computing and memory compared to typical computers, so that it is challenging to process cryptographic algorithms that have computation-intensive tasks in IoT devices at high speed. Thus, it is required that cryptographic algorithms are optimized in IoT devices. In other words, optimizing cryptographic operations on these IoT devices is not only basic but also an essential effort in order to build secure IoT-based service systems. For efficient encryption on IoT devices, even though several ARX (Add-Rotate-XOR)-based ciphers have been proposed, it still necessary to improve the performance of encryption for smooth and secure IoT services. In this article, we propose the first parallel implementations of CTR mode of ARX-based ciphers: LEA (Lightweight Encryption Algorithm), HIGHT (high security and light weight), and revised CHAM on the ARMv8 platform, a popular microcontroller in various IoT applications. For the parallel implementation, we propose an efficient data parallelism technique and register scheduling, which maximizes the usage of vector registers. Through proposed techniques, we process the maximum amount of encryption simultaneously by utilizing all vector registers. Namely, in the case of HIGHT and revised CHAM-64/128 (resp. LEA, revised CHAM-128/128, and CHAM-128/256), we can execute 48 (resp. 24) encryptions simultaneously. In addition, we optimize the process of CTR mode by pre-computing and using the intermediate value of some initial rounds by utilizing the property that the nonce part of CTR mode input is fixed during encryptions. Through the pre-computation table, CTR mode is optimized up until round 4 in LEA, round 5 in HIGHT, and round 7 in revised CHAM. With the proposed parallel processing technique, our software provides 3.09%, 5.26%, and 9.52% of improved performance in LEA, HIGHT, and revised CHAM-64/128, respectively, compared to the existing parallel works in ARM-based MCU. Furthermore, with the proposed CTR mode optimization technique, our software provides the most improved performance with 8.76%, 8.62%, and 15.87% in LEA-CTR, HIGHT-CTR, and revised CHAM-CTR, respectively. This work is the fastest implementation of CTR mode on ARMv8 architecture to the best of our knowledge.
Phone based acoustic modeling for automatic speech recognition for Punjabi language

Punjabi language is a tonal language belonging to an Indo-Aryan language family and has a number of speakers all around the world. Punjabi language has gained acceptability in the media &amp; communication and therefore deserves to have a place in the growing field of automatic speech recognition which has been explored already for a number of other Indian and foreign languages successfully. Some work has been done in the field of isolated word speech recognition for Punjabi language, but only using whole word based acoustic models. A phone based approach has yet to be applied for Punjabi language speech recognition. This paper describes an automatic speech recognizer that recognizes isolated word speech and connected word speech using a triphone based acoustic model on the HTK 3.4.1 speech Engine and compares the performance with acoustic whole word model based ASR system. Word recognition accuracy of isolated word speech was 92.05% for acoustic whole word model based system and 97.14% for acoustic triphone model based system whereas word recognition accuracy of connected word speech was 87.75% for acoustic whole word model based system and 91.62% for acoustic triphone model based system.
SETTING TIME, FLOWABILITY, AND SOLUBILITY OF EPOXY RESIN-BASED SEALER MIXED WITH CHITOSAN NANOPARTICLES

Objective: The purpose of this study was to investigate the effect of the incorporation of chitosan nanoparticles into epoxy resin-based sealer on its setting time, flowability and solubility. &#x0D; Methods: This study was divided into three evaluations: setting time, flowability, and solubility of sealers. Each study used 20 samples, which were divided into two groups. Group 1, epoxy resin-based sealers, and group 2, epoxy resin-based sealers mixed with chitosan nanoparticles. The Gilmore needle was used to observe the setting time, a simple press method based on ISO 6876 was employed to evaluate the flowability of the sealer, and the solubility test, which was according to ISO 6876 standard, was used to determine the solubility of sealers. The surface structure of the sealers before and after the solubility test was observed under Scanning Electron Microscopy (SEM). The data obtained from each study were analyzed by an unpaired t-test with a degree of significance of 95%.&#x0D; Results: The results showed that the setting time of epoxy resin and epoxy resin incorporated with chitosan were 567±30.20 and 572.5±27.91 min, flowability was 25.06±0.89 and 23.18±1.06 mm, and solubility was 0.0051%±0.0016 and 0.0045%±0.0018, respectively. No significant difference occurred between epoxy resin-based sealer and epoxy resin-based sealer mixed with chitosan nanoparticles in setting time, flowability, and solubility of sealers (*P&gt;0.05). &#x0D; Conclusion: The incorporation of chitosan nanoparticles produced a similar effect in setting time, flowability, and solubility as the epoxy resin-based sealer. Thus, epoxy resin-based sealer mixed with chitosan nanoparticles had acceptable properties in setting time, flowability, and solubility based on ISO 6876 standards.