Reviewerly/exHarmony huggingface dataset:
Document classification using deep neural network with different word embedding techniques

Document classification has played a major role in many fields like information retrieval, data mining, etc. where machine learning and deep learning models can be applied. But, before applying any model for classification, textual data must be converted into a numerical measure, where word embedding can help. The selection of appropriate word embedding techniques plays a vital role in classification. So, we analysed the classification performance by widely used deep learning models long short-term memory (LSTM) and convolution neural network (CNN) with various word embedding techniques on five benchmark datasets. The pre-processed dataset is converted into vector representation using a word embedding techniques TF-IDF, Word2Vec, and Doc2Vec. The output is given to the LSTM and CNN classifier and documents are classified as per their context. The CNN classifier with Doc2Vec word embedding technique achieves almost 12% more accuracy as compared to other word embedding techniques on all the datasets.
The imbrication of media and international law

Journal Article The imbrication of media and international law Get access Daniel Joyce Daniel Joyce Associate Professor, Faculty of Law and Justice, UNSW Sydney; Affiliated Research Fellow, Erik Castrén Institute, University of Helsinki. Email: daniel.joyce@unsw.edu.au. Search for other works by this author on: Oxford Academic Google Scholar London Review of International Law, Volume 10, Issue 2, July 2022, Pages 325–334, https://doi.org/10.1093/lril/lrac012 Published: 16 September 2022
Predictive Power of An Ensemble Model for Cryptocurrency Forecasting

Cryptocurrencies have received much attention amongst investors and policymakers due to the innovative features and simplicity. However, prices of the cryptocurrencies are nonlinear and volatile, which creates challenges for the investors to forecast the cryptocurrency prices. The present study takes the price data of two important cryptocurrencies, i.e., Bitcoin and Ripple, for 2013 to 2020. The study presents the forecasting accuracy of statistical models such as random walk (RW) and autoregressive integrated moving average (ARIMA), and machine learning models such as artificial neural network (ANN) and ensemble model. The study develops the ensemble of RW, ARIMA, and ANN. The study compares the predictive power of all the models and demonstrates that the forecasting accuracy of the ensemble model is better than all the component models, i.e., RW, ARIMA, and ANN. The results of the study have several implications for investors, traders, and policymakers.
AdaCB: An Adaptive Gradient Method with Convergence Range Bound of Learning Rate

Adaptive gradient descent methods such as Adam, RMSprop, and AdaGrad achieve great success in training deep learning models. These methods adaptively change the learning rates, resulting in a faster convergence speed. Recent studies have shown their problems include extreme learning rates, non-convergence issues, as well as poor generalization. Some enhanced variants have been proposed, such as AMSGrad, and AdaBound. However, the performances of these alternatives are controversial and some drawbacks still occur. In this work, we proposed an optimizer called AdaCB, which limits the learning rates of Adam in a convergence range bound. The bound range is determined by the LR test, and then two bound functions are designed to constrain Adam, and two bound functions tend to a constant value. To evaluate our method, we carry out experiments on the image classification task, three models including Smallnet, Network IN Network, and Resnet are trained on CIFAR10 and CIFAR100 datasets. Experimental results show that our method outperforms other optimizers on CIFAR10 and CIFAR100 datasets with accuracies of (82.76%, 53.29%), (86.24%, 60.19%), and (83.24%, 55.04%) on Smallnet, Network IN Network and Resnet, respectively. The results also indicate that our method maintains a faster learning speed, like adaptive gradient methods, in the early stage and achieves considerable accuracy, like SGD (M), at the end.
MRF-Net: A multi-branch residual fusion network for fast and accurate whole-brain MRI segmentation

Whole-brain segmentation from T1-weighted magnetic resonance imaging (MRI) is an essential prerequisite for brain structural analysis, e.g., locating morphometric changes for brain aging analysis. Traditional neuroimaging analysis pipelines are implemented based on registration methods, which involve time-consuming optimization steps. Recent related deep learning methods speed up the segmentation pipeline but are limited to distinguishing fuzzy boundaries, especially encountering the multi-grained whole-brain segmentation task, where there exists high variability in size and shape among various anatomical regions. In this article, we propose a deep learning-based network, termed Multi-branch Residual Fusion Network, for the whole brain segmentation, which is capable of segmenting the whole brain into 136 parcels in seconds, outperforming the existing state-of-the-art networks. To tackle the multi-grained regions, the multi-branch cross-attention module (MCAM) is proposed to relate and aggregate the dependencies among multi-grained contextual information. Moreover, we propose a residual error fusion module (REFM) to improve the network's representations fuzzy boundaries. Evaluations of two datasets demonstrate the reliability and generalization ability of our method for the whole brain segmentation, indicating that our method represents a rapid and efficient segmentation tool for neuroimage analysis.
DETECTION OF STEGOSYSTEM USING EMBEDDING INTO CONTOURS OF IMAGES

Introduction: it is well known that the use of stegosystems increases the information security of storage and trans mission of confidential information, ensuring the concealment of the content and the very fact of the presence of classified data in "innocent" covering objects, the most important of which are images, sound and text files, and Internet protocols. The stegosystem is used not only by government agencies, but also in the business community to ensure organizational or technical confidentiality. On the other hand, the technology of detecting stegosystems is also relevant, since it allows organizations and companies to protect classified information from its leakage into the external circuit, as is usually done in the Data Leakage Prevention system. Purpose of the study: the purpose is to study methods for detecting a promising stegosystem with the immersion of confidential information into the contours of a still image with grayscale. Results: a method for finding image contours and an algorithm for immersing information in them are described, an assessment of the amount of embedded information, as well as the possibility of detecting an embedding by visual and histogram methods, is carried out. An approach based on pseudo-randomness tests was used as a more efficient detection method. The results of the experiment convincingly showed that this method of steganalysis has an advantage over traditional methods of detecting stegosystems, especially if it is combined with the Support Vector Machine technique. Discussion: however, when information is embedded only in a certain part of pixels using a stegokey, the considered steganalysis method gives only small improvements in detectability, which indicates a fairly high resistance of stegosystems with embedding in image contours
Supplemental Material: Validation of earthquake ground-motion models in southern California, USA, using precariously balanced rocks

&lt;p&gt;All the fragility, fragility age, and hazard validation results in color for each precariously balanced rock, and all the input and output cosmogenic-nuclide exposure dating data.&lt;/p&gt;
Characterizing covariational reasoning in physics modeling

Covariational reasoning-considering how changes in one quantity affect another, related quantity-is a foundation of quantitative modeling in physics.Understanding quantitative models is a learning objective of introductory physics instruction at the college level.Prior work suggests that covariational reasoning in physics contexts differs from the reasoning about functions and graphs in purely mathematical contexts that students develop in math courses; this reasoning is effortful in physics even for mathematically well-prepared students.In order to improve physics students' covariational reasoning, we must first characterize covariational reasoning with physics quantities.To this end, we present a framework of covariational reasoning in physics contexts, to describe the ways that covariational reasoning is used in physics modeling.The framework can be used as a lens through which to analyze student reasoning, and can help inform instructional interventions.We describe an application of this framework in the development of a set of computer-based training assignments.
New Forms of Work with Juniors in the International Environment of Olympiad Informatics

The article describes the problem of creating an accessible Olympiad environment to support the creativity of students in grades 5-10 based on digital programming competencies and digital project tracks in specialized subjects based on international experience in partnership with the participating countries of the International Olympiad in Informatics (IOI).&#x0D; The infrastructure of the International Junior School of Informatics (ISIJ) and the organizational and technological model of the Olympiad environment are presented as an online network of reference sites in the regions of Russia and partner countries of the Olympiad environment not only for participation in the ISIJ Cup, but also for the systematic preparation of students and coaches to participate in the international level Olympiad movement.
Winter at Koltushi

Abstract Pavlov spent his final winter at his favorite place—his science village in Koltushi, just outside Leningrad. There he struggled to complete two manuscripts—an article on the physiology of psychological processes and a letter to Joseph Stalin’s right-hand man, Vyacheslav Molotov, about the relationship between science, Communism, and Christianity. These manuscripts expressed his lifelong quest to replace uncertainty and chance with scientific certainty. A talented experimenter and driven truth seeker, Pavlov’s scientific style and career were shaped by his personality, the exciting scientific developments of his time, and his biographical trajectory through turbulent times for his beloved Russia. That distinctive scientific style took form in his Nobel Prize–winning studies of digestion and the three decades of research on conditional reflexes that made him a famous, though widely misunderstood, symbol of twentieth-century science.
Restructuring Guidelines for Teaching, Learning, and Evaluation in the National Mathematics Curriculum

This study attempts to restructure teaching, learning and evaluation guidelines in the national mathematics curriculum based on a group of experts’ opinion collected by conducting a survey and focused-group discussions. Three key points of distance learning, mathematical competence reinforcement, and future-oriented mathematics education were drawn from the literature to be investigated. Semantic network analyses were used to extract significant topics and meanings from the survey data. We further investigated experts’ suggestions in their vivid voices on reforming teaching, learning, and evaluation guidelines. The results suggest that it is necessary to provide detailed information on how to utilize distance learning environment with specific guides to new technologies including various platforms for building effective teaching, learning, and evaluation. Secondly, it is suggested to provide clarified concepts and specific components of mathematical competence with differentiation by school level. Thirdly, majority of experts pointed out the necessity of reinforcing process-oriented assessments and adaptive learning. The findings raised an issue of balancing between pursuing teacher autonomy and providing detailed guidelines in the national mathematics curriculum. Further findings and implications are discussed in terms of possible innovations and constraints in teaching, learning, and evaluation policies of the mathematics curriculum document.
The Effect of Mathematics Learning Using Faded Worked Examples on Achievement, Cognitive Load, Self-efficacy and Interest

The purpose of this study was to check whether there is a difference in the effect of the strategy of gradually fading the help of the worked examples on the mathematics achievement, cognitive load, self-efficacy and interest of middle school students. In addition, through interviews, we tried to examine students' perception of the faded worked examples strategy. To this end, 269 middle school sophomores were divided into two groups (faded worked examples strategy, example-problem strategy) and provided the task of the unit of application of simultaneous linear equations. The study findings are as follows. First, the faded worked examples strategy was found to have a positive role in increasing students' mathematical achievement. Second, it was found that the faded worked examples strategy did not affect cognitive load, self-efficacy and interest. Third, the preferred method of providing the worked examples was different according to the achievement level of the learner. Finally, for the faded worked examples strategy, high-achieving students perceived that it was helpful in solving the narrative problems, and low-achieving students perceived that it was a clue to problem-solving.
Market static model for software development based on a transport problem with quadratic cost additions

The authors propose to formulate a market continuous static model for software development based on the transport problem with quadratic cost additions. In contrast to the existing transport problem with fixed cost surcharges, the authors propose a statement that minimizes the sum of transportation costs, which may contain non-fixed additions proportional to the squares of the volumes of appointments. Thus, a quadratic formulation of the transport problem with non-fixed additions is proposed. It is shown that transport problem with quadratic additions can be reduced to a convex programming problem, which can be solved numerically by the subgradient method, or by the conjugate gradient method for the dual problem. The authors describe the interpretation of transport problem with quadratic additions as an assignment problem with non-fixed price discounts, taking into account the difference between the wholesale and retail prices. This makes possible to apply the set task to build digital platforms in the software development market for loading assignments to performers.
STUDY ON CLOUD COMPUTING SERVICES IN THE MODERN WORLD

Cloud computing is playing a vital role in giving their services to the customers on demand and paying only for the service they are using over the internet .cloud computing provides the infrastructure, tools ,applications etc to the customer according to their requirement. In this paper, we study about different type of services offered by cloud service provider. We will discuss about the ranking method in order to choose the best cloud service provider. Application of the cloud computing is also discussed in this paper such as cloud computing in E-learning and healthcare. We believe that in the way cloud is making every business to run smoothly without the installation of infrastructure on premises, it is also very important to know about its advantages and disadvantages and to select them accordingly.
Identification of Elastic Properties for Transversely Isotropic Materials Via Deep Learning Exploiting the Kinematic Fields

Determining the stress-strain relationship is challenging, especially when the material presents complex behaviors such as anisotropy. Here, the developed method is based on using Artificial Intelligence (AI) via the deep learning (DL) technique. The input data is the displacement field, which could be obtained from an experimental point of view by image correlation. Here, the kinematics field is generated by Finite Element Method (FEM) calculations using transversely isotropic materials. The main purpose of this paper is to identify the parameters of the behavior law for these materials. By using the proposed method, encouraging results are obtained. The advantage of this method is the quasi-instantaneousness solution. Also, the average errors obtained are less than 3% on all the identified parameters. The architecture of the AI is of paramount importance, where the multi-scale notion must be considered. For this reason, an encapsulated architecture is used to determine the mechanical properties.
Principles Of Cross-Linguistic Cultural Comparison Of Armenian And English Emotional-Attitudinal Expressions

Historically, there have been two approaches to interjections. First, as spontaneous verbal expressions that denoted emotional or mental states, later they were seen within the framework of verbal description. And indeed, interjections have natural expressive and symbolic-relative and communicative contents, which theoretically need to be reconciled to replace the contradiction between the mentioned approaches with a transition. For this purpose, it is necessary to examine emotional-attitudinal expressions (hereinafter EAEs) not only from the point of view of a single language but also from the point of view of cross-linguistic and intercultural comparison.
A Varifocal Camera System for Light Field Display Calibration

This paper presents the design and characterization of a varifocal camera system that is specially developed for head-mounted light field display calibration.
Upgrading Interferometric Focal Length Measurement Techniques with Adaptive Optics

In this paper, we attempt to automate, compactify, and forego bulk optical motion in existing ruling-based, double frequency-based, and Twyman-Green interferometer-based interferometric focal length measurement techniques via an addition of an adaptive optics element.
Video From Coded Motion Blur Using Dynamic Phase Coding

We present a method for video reconstruction of the scene dynamics from a single image using coded motion blur. Our approach addresses the limitations of the ill-posed task and utilizes a learned optical coding approach.
Theoretical optimization of electromechanical system to obtain uniform phase-steps for a dual-aperture common path interferometer with a gear system design

In this work, a mechanical optimization to generate uniforms spaced phase-shifts for extracting the phase difference between a reference and a probe object in a double aperture common path interferometer, is proposed. Analytical calculations and simulations of the mechanical gear system are shown. The transmission system was designed to increase the precision and control of the transverse translation of a diffraction grating in order to obtain homogenous phase-shifting.
Effect of Governance on Macroeconomic Instability in Sub-Saharan Africa: Does Regional Analysis Matter?

Macroeconomic instability is a growth blockage in any contemporary societies, businesses, and governments. However, how to achieve macroeconomic stability remains a gap in extant literature for most developing countries. This study examines the effect of governance on macroeconomic instability in a sample of 42 Sub-Saharan African countries over the period 1996-2019. Four governance indicators, namely political governance, economic governance, institutional governance and general governance are constructed with the use of principal component analysis. Empirical evidence based on the Generalized Method of Moments and alternatively by Two-Stage least Squares technique for regional analysis reveals that the selected governance indicators do not have the same reducing influence on macroeconomic instability in Sub-Saharan Africa. Considering sub-regional analysis, evidence of a positive effect of political governance over growth instability in Central Africa is sparingly apparent in Voice and Accountability, and liberal democracy. This finding therefore recommend, improved local contents of governance policies toward economic sustainability, redesigning and returning the content of democracy in line with the demands, peculiarities and realities of central Africa state.
The first international conference on sustainable energy, blockchain, and cryptocurrency (SEBC-2022)

The first international conference on sustainable energy, blockchain, and cryptocurrency (SEBC-2022) was an online conference organized by “Future Publishing LLC” on August 01-03, 2022. The SEBC-2022 was supported by Ocean Falls Blockchain, Guidehouse Insights, CoinPedia Fintech News, and WAM. The SEBC conferences are platforms for sophisticated professors, researchers, engineers, and managers from the academic world, financial and blockchain companies, and digital coin developers to demonstrate their constructive activities and discuss “how to make a clean and environmentally friendly digital world?” The objective of the SEBC-2022 conference was to provide a forum for scientists and researchers from academia and industry to discuss critical challenges in sustainable energy, blockchain, and cryptocurrency.
A novel dictionary learning-based approach for Ultrasound Elastography denoising

&lt;abstract&gt; &lt;p&gt;Ultrasound Elastography is a late-model Ultrasound imaging technique mainly used to diagnose tumors and diffusion diseases that can't be detected by traditional Ultrasound imaging. However, artifact noise, speckle noise, low contrast and low signal-to-noise ratio in images make disease diagnosing a challenging task. Medical images denoising, as the first step in the follow-up processing of medical images, has been concerned by many people. With the widespread use of deep learning technique in the research field, dictionary learning method are once again receiving attention. Dictionary learning, as a traditional machine learning method, requires less sample size, has high training efficiency, and can describe images well. In this work, we present a novel strategy based on K-clustering with singular value decomposition (K-SVD) and principal component analysis (PCA) to reduce noise in Ultrasound Elastography images. At this stage of dictionary training, we implement a PCA method to transform the way dictionary atoms are updated in K-SVD. Finally, we reconstructed the image based on the dictionary atoms and sparse coefficients to obtain the denoised image. We applied the presented method on datasets of clinical Ultrasound Elastography images of lung cancer from Nanjing First Hospital, and compared the results of the presented method and the original method. The experimental results of subjective and objective evaluation demonstrated that presented approach reached a satisfactory denoising effect and this research provides a new technical reference for computer aided diagnosis.&lt;/p&gt; &lt;/abstract&gt;