Reviewerly/exHarmony huggingface dataset:
FAIR Points: From sdo:LearningResource to FAIR Digital Object

The FAIRPoints organization, co-founded by the authors, aims to provide a platform for conversations to take place around realistic and pragmatic implementations of the FAIR (Findable, Accessible, Interoperable, Reusable) principles. The uniqueness of the FAIRPoints effort stems from an additional aim: to capture conversation contributions in the form of “bite-sized” objects – “points” – in a way that facilitates dynamic composition by instructors for the delivery of audience-customized training experiences. Thus, FAIRPoints aims to cultivate pragmatic learning resources to help realize the FAIR principles in practice, both through inviting speakers to prime and lead discussions focused on choices/challenges regarding FAIR, and amplifying downstream value potential by serializing “points” made during such events as FAIR resources. inviting speakers to prime and lead discussions focused on choices/challenges regarding FAIR, and amplifying downstream value potential by serializing “points” made during such events as FAIR resources. Currently, event outcomes are serialized as LearningResource-typed JSON-LD objects in the schema.org sense, i.e. sdo:LearningResource, where @prefix sdo: &amp;lt;https://schema.org/&amp;gt; ., and conform to the bioschemas.org TrainingMaterial profile. However, any differences in participant perspectives must be reconciled, via git revision control, towards a single “view” of a sdo:LearningResource. This situation is at odds with other explicit aims of the FAIRPoints organization such as including diverse voices and collecting heterogeneous input from a global perspective. Using the FAIR Digital Object (FDO) approach, a FAIRPoints sdo:LearningResource instance may be the Object to which an Identifier points, through an FDO Identifier Record, and sdo:LearningResource may be the FDO Type. Crucially, there may be a multiplicity of Metadata records pointed to by an FDO Identifier Record and thus a formal mechanism to cultivate and publish diverse perspectives. This presentation will outline FAIRPoints’ approach to FDO implementation for learning resources and its relation to published practice. Specifically, in relation to the FAIR Digital Twins approach*1, our approach may be seen as the stewardship of a “fluid graph” of learning-resource “knowlets” with support for “qua” projection in service of e.g. an instructor’s dynamic composition of training material for a targeted workshop.
Interactive Display of Images in Digital Exhibition Halls under Artificial Intelligence and Mixed Reality Technology

The attractiveness of traditional exhibition halls to young people is gradually decreasing. Combining modern digital technology to improve the display effect of the exhibition hall can effectively enhance the effect of cultural publicity. This article introduces the technology of image interaction and mixed reality (MR) to improve the historical and cultural propaganda level of the Shaanxi exhibition hall. The advantages of MR technology in applying digital exhibition halls are theoretically expounded. A theoretical plan for Shaanxi history and culture-related display areas is designed using artificial intelligence combined with MR technology. In addition, the survey respondent’s evaluation of the effect of the new exhibition hall is obtained using a questionnaire survey. The survey results show that 97% of people like the history and culture of Shaanxi but only 13% of the people say they know or know very well about the history and culture of Shaanxi. In addition, 60% of the tourists say they are satisfied with the cultural experience of Shaanxi, and only 27% of the tourists are very satisfied. Also, 96% of tourists are willing to experience Shaanxi’s history and culture through digital exhibition halls, and 93% are willing to participate in cultural experience activities based on MR technology. The survey results prove that tourists are satisfied with the effect of the new exhibition hall. Tourists want to add a distinctive form of cultural experience to the exhibition hall. They are willing to accept digital exhibition halls incorporating MR technology and are very happy to participate in the exhibition method of image interaction. This shows that the use of image interactive display based on MR technology in the layout of the exhibition hall is recognized by people and has strong feasibility. This article has reference significance for the digital upgrade of the exhibition hall and the development of the cultural tourism industry.
An Example of a Dynamic CPN Model to Obtain Routes in the Presence of Obstacles Detected Using Machine Learning Techniques

Obtaining routes has been a challenge during the recent history of software systems. GPS has helped this technology to be widely used. Some challenges still remain like providing routes in the presence of obstacles. The term obstacle differs depending on the scenario. It is not the same for wheelchair users than for a group of school children. In this paper, we study how to enable a set of technologies to bridge this gap. We propose to combine a graphical formalism with machine learning. Color Petri Nets are used to model a dynamic scenario where conditions, i.e. obstacles, change at real time, while machine learning is used for obstacle detection. Several techniques can be applied to obtain routes in this context. We use a simulation technique which provides fast routes in real time. We apply this technique in the context of a school visit to the Alhambra Generalife Gardens in Granada (Spain) providing a model of the gardens’ map which evolves dynamically depending on the real time data offered by the machine learning module. This module is in charge of detecting overcrowded areas in order to avoid them. Routes are obtained by simulating several users traversing the map simultaneously and the fastest is returned. The system provided can be adapted to other user necessities and scenarios.
SISTEM PAKAR UNTUK MENDIAGNOSIS HAMA PENYAKIT TANAMAN SEMANGKA MENGGUNAKAN METODE CERTAINTY FACTOR DAN METODE BAYES

Watermelon is one of the most popular fruits by the general public. Apart from the benefits of watermelon, watermelon also has several diseases that are difficult for farmers to know, such as fruit rot (anthracnose), fruit scabies (bacterial fruit blotch), and others. Although watermelon disease is common, there are still many farmers who do not know about this problem. Therefore, an expert system for watermelon disease detection can be used using the certainty factor algorithm and the Bayes method. Based on the problems that occurred, this study raised the title "Expert System for Diagnosing Pests and Diseases of Watermelon Plants using the Certainty Factor Method and the Bayes Method" to overcome the ignorance of farmers in detecting watermelon disease by entering various characteristics listed in the system based on plant events, the system will provide insight based on existing characteristics using a certainty factor algorithm which will then compare the results with the Bayes method
Coverless image steganography based on neural style transfer

Traditional image steganography often utilizes modifying the pixel values of the carrier image to embed secret image information. However, once the pixel value of the carrier image is modified, it will leave a trace of modification, which is easy to be discovered by a third party, thereby destroying the secret image information and causing the failure of the secret image transmission. This paper proposes a coverless image steganography algorithm based on style transfer, which uses the style of secret images to generate camouflage images and transmit them on public channels. This method does not need to modify the pixel values of carrier images, and the method can resist common attacks. Our method consists of two parts: sender and receiver. In the sending stage, we first use the style transfer technique to combine the style of the secret image with the content of the natural image to generate a camouflage image, and in the receiver stage we design a convolutional neural network (CNNSI) to extract the secret image. The training data set of CNNSI network consists of camouflage images subjected to various attacks. Experimental results show that, compared with the existing methods, the proposed method can still extract the secret image after the camouflage image is attacked. This method has better robustness and security.
Generative adversarial networks based on MLP

Inspired by the state-of-the-art performance of MLP-Mixer on image classification task, multilayer perceptron (MLP) model attracts a number of researcher's attention again. Although various MLP architectures have been proposed, most of them focus on image classification domain. In this paper, we extend MLP to the task of image generation based on generative adversarial network (GAN). The pure MLP model is not friendly to small dataset because it is a data-hungry architecture. Thus, we leverage a hybrid model to solve the problem which uses MLP blocks as generator and CNN blocks as discriminator. Experimental results demonstrate that our model outperforms the pure CNN network on CIFAR- 10 dataset in view of two different evaluation metrics. Besides, we apply eight widely popular MLP structures to our generator to validate which one achieves the most excellent performance in image generation task.
Dynamic three-dimensional measurement using convolution neural network and binocular structured light system

In this paper, a dynamic three-dimensional measurement method based on convolution neural network and binocular structured light system is proposed. We propose a convolution neural network to extract the real and imaginary terms of the first-order spectrum of a single frame fringe pattern. In our learning model, the loss function is established with output consistency, phase consistency and feature consistency as the joint constraints. And the dataset is built with actual deformed patterns of different scenes and frequencies. Furthermore, a dual frequency stereo phase unwrapping algorithm based on virtual plane is designed. Combined with the network, the absolute phase can be obtained by only two fringe projections in the measurement range, enabling the dynamic three-dimensional reconstruction of discontinuous or multiple isolated objects. The experimental results show the proposed network can significantly improve the accuracy of phase retrieve by 20 times compared to Fourier Transform Profilometry and the measurement error of the measurement system proposed in this paper for calibration sphere is less than 0.04mm. Furthermore, the measurement results of the dynamic process of palm unfolding verify the feasibility and the effectiveness of the proposed method.
Determinants of bank deposit growth in Rwanda

The paper empirically examined the relationship between selected variables and bank deposits growth in Rwanda.Empirical results imply that both macroeconomic and bank-specific factors play a role in determining bank deposits growth in Rwanda.The study used quarterly time series data spanning the period 2005Q1-2019Q4, to estimate the long run and a shortrun relationship.Results confirm a long run and a short-run relationship between bank deposits and its expected determinants.The relationship between selected variables and bank deposits in Rwanda has been empirically studied, and results of the model imply that macroeconomic factors and bank-specific factors play a role in determining the growth of bank deposits in Rwanda.The study used quarterly time series data spanning from 2005Q1 to 2019Q4, to estimate a long run and a short-run relationship.Results confirm a long run and a short-run relationship between bank deposits and its expected determinants.Empirical results show that gross domestic product, government expenditure, number of bank branches and deposit interest rate have a long run influence on bank deposit growth in Rwanda.In the short-run, findings also revealed that government expenditure and gross domestic product prove to be the main determinants of bank deposit growth.The number of bank branches and deposit interest rate were correctly signed albeit insignificant, implying that they have no short-run impact on bank deposits.Based on the study findings, the paper recommends thus, at the macro level, policy makers must ratify policies to improving the overall macroeconomic environment, particularly policies to enhancing economic growth.In addition, banks should fix the deposit interest rate based on the level of customer's deposits such that customers who deposit more of their surplus income should earn higher interest rate, and this will encourage people to save.Furthermore, banks must invest in viable projects to yield higher returns to be able to pay high interest on customers 'deposits.Finally, since branch expansion negatively affects bank deposits in the long run, banks may limit branch expansion and shift their focus to advancing more sophisticated electronic platforms to allow customers to transact easily.
Multi-frame DVC for temporal image sequences

Digital volume correlation (DVC) is a 3D image-based technique for displacement and strain computation. Traditionally, both (digital image correlation) DIC and DVC are methods based on two individual time frames; the estimation of the displacement and strain field is done using one reference and one moving frame as input. However, dynamic experiments generate more than two temporal frames. Therefore, with classical DVC techniques, only a subset of the available data is used. In this study, we propose a novel DVC method that can rely on more than two frames for the displacement and strain computation. The proposed method aims to be as general as possible; there is no constraint regarding the nature or the rate of the displacement (e.g., cyclic or linear). The aim of this method is to impose a temporal regularization that improves the self-consistency of the algorithm. The multi-frame DVC improves the quality of the registration in challenging situations. As an example, we investigate the dissolution of a pharmaceutical tablet in water, which undergoes three processes: swelling, gel formation, and material erosion. The accuracy of the registration—quantified by the sum of square differences (SSD)—has improved by 23% on an average with respect to the classical two-frame method. Classical DVC methods fail in registering images with structures that change appearance through time, such as the tablet that, in contact with water, reacts chemically, changing phase and becoming a gel. Moreover, we proved that multi-frame DVC is more robust in registering images with severe but realistic motion artefacts. As an example for this case, we apply the method to a series of μ -CT datasets of aluminum foam during a compression experiment. As seen with the tablets, we are in a situation where the appearance of the structures in the images changes through time, but in this case it is because of motion artefacts. Finally, the use of more than two frames makes the method more robust against noisy images, with an average improvement of 35% in registration accuracy obtained using the three-frame DVC method compared to the classical two-frame DVC method.
Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding

The scarcity of high-quality annotations in many application scenarios has recently led to an increasing interest in devising learning techniques that combine unlabeled data with labeled data in a network. In this work, we focus on the label propagation problem in multilayer networks. Our approach is inspired by the heat diffusion model, which shows usefulness in machine learning problems such as classification and dimensionality reduction. We propose a novel boundary-based heat diffusion algorithm that guarantees a closed-form solution with an efficient implementation. We experimentally validated our method on synthetic networks and five real-world multilayer network datasets representing scientific coauthorship, spreading drug adoption among physicians, two bibliographic networks, and a movie network. The results demonstrate the benefits of the proposed algorithm, where our boundary-based heat diffusion dominates the performance of the state-of-the-art methods.
The Role of Victim’s Resilience and Self-Esteem in Experiencing Internet Hate

Despite the growing prevalence of research on Internet hate, little is still known about the psychological factors that differentiate those who are negatively affected by being subjected to Internet hate and those who are not affected at all or only to a small degree. In the present studies, we aimed to verify if resilience and self-esteem could be predictors of such responses. A total of 60 public figures (politicians, athletes, and artists; 46.7% women) and 1128 ordinary Internet users (25.1% women) participated in the study. Participants completed The Brief Resilience Scale, The Self-Esteem Scale, and The Internet Hate Concern Scale, which was created for this study, and determined how often they experience hate online. The results showed that the public figures experience Internet hate more often but were less concerned with it than the ordinary Internet users, who received online hate less often, but were more worried about it. In both groups, high self-esteem and high resilience were negative predictors of greater concern with received online hate. Our study is the first step to understanding what makes the difference between people who cope well and are not particularly concerned, and people who are greatly affected by received Internet hate.
Enhancing Food Ingredient Named-Entity Recognition with Recurrent Network-Based Ensemble (RNE) Model

Food recipe sharing sites are becoming increasingly popular among people who want to learn how to cook or plan their menu. Through online food recipes, individuals can select ingredients that suit their lifestyle and health condition. Information from online food recipes is useful in developing food-related systems such as recommendations and health care systems. However, the information from online recipes is often unstructured. One way of extracting such information into a well-structured format is the technique called named-entity recognition (NER), which is the process of identifying keywords and phrases in the text and classifying them into a set of predetermined categories, such as location, persons, time, and others. We present a food ingredient named-entity recognition model called RNE (recurrent network-based ensemble methods) to extract the entities from the online recipe. RNE is an ensemble-learning framework using recurrent network models such as RNN, GRU, and LSTM. These models are trained independently on the same dataset and combined to produce better predictions in extracting food entities such as ingredient names, products, units, quantities, and states for each ingredient in a recipe. The experimental findings demonstrate that the proposed model achieves predictions with an F1 score of 96.09% and outperforms all individual models by 0.2% to 0.5% in percentage points. This result indicates that RNE can extract information from food recipes better than a single model. In addition, this information extracted by RNE can be used to support various information systems related to food.
Big Data in E-government: Classification and Prediction using Machine Learning Algorithms

Many countries have used big data to develop their institutions, such as Estonia in policing, India in health care, and the development of agriculture in the United Kingdom, etc. Data is very important as it is no longer oil that is the most valuable resource in the world, but data. This research examines ways to develop the Iraqi state institutions by using the big data of one of its institutions (electronic civil registry) (ECR) using the mining and analysis of this data. The pre-processing and analysis of this data are carried out depending on the needs of each institution and then using Machine Learning (ML) techniques. Its use has shown remarkable results in many areas, especially in data analysis, classification and forecasting. We applied five ML algorithms that are Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), and Naive Bayes (NB) carried out in the Orange Data Mining tool. According to the simulation results of the proposed system, the accuracy of the classifications was around 100%, 99%, and 100% for the military department by the SVM classifier, the social welfare department by the RF classifier, and the statistics-planning department by the SVM classifier, respectively.
Learning multi-agent cooperation

Advances in reinforcement learning (RL) have resulted in recent breakthroughs in the application of artificial intelligence (AI) across many different domains. An emerging landscape of development environments is making powerful RL techniques more accessible for a growing community of researchers. However, most existing frameworks do not directly address the problem of learning in complex operating environments, such as dense urban settings or defense-related scenarios, that incorporate distributed, heterogeneous teams of agents. To help enable AI research for this important class of applications, we introduce the AI Arena: a scalable framework with flexible abstractions for associating agents with policies and policies with learning algorithms. Our results highlight the strengths of our approach, illustrate the importance of curriculum design, and measure the impact of multi-agent learning paradigms on the emergence of cooperation.
Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

&lt;p&gt;We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.&lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt;