Reviewerly/exHarmony huggingface dataset:
SD-DSAN: Saliency-Driven Dense Spatial Attention Network for Pan-Sharpening

The demands for spectral and spatial quality in remote sensing (RS) images vary from region to region. Saliency detection is an effective tool to distinguish different regions with different demands. In this paper, we introduce saliency detection to satisfy these demands and propose a novel saliency-driven pan-sharpening network to further improve the fusion quality. Firstly, we combine foreground distribution with background prior to generate the initial saliency map, and implement least-square optimization to improve the detection accuracy. Then, we construct a dense spatial attention network trained through a new spatial-spectral-based loss function designed by saliency to meet diverse spectral and spatial needs of different regions. Thus, accurate fused images can be predicted. Experiments on SPOT-5 dataset indicate that our proposal has excellent properties with respect to the unified spatial-spectral quality against state-of-the-art methods.
Blockchain in Dynamic Networks

We consider blockchain in dynamic networks. We define the Blockchain Decision Problem. It requires miners that maintain the blockchain to confirm whether a particular block is accepted. We establish the necessary conditions for the existence of a solution. We, however, prove that the solution, even under these necessary conditions is, in general, impossible. We then present two algorithms that solve the Blockchain Decision Problem under either the knowledge of the maximum source pool propagation time or the knowledge of the source pool membership. We evaluate the performance of the two algorithms.
A Rotating Multi-secret Color Visual Cryptography Scheme Based on Meaningful Shares

The multi-secret visual cryptography scheme (MVCS) can recover different secret images according to multiple stacking methods of shares. The rotating multi-secret visual cryptography scheme can restore additional secrets by stacking the share with another share after rotating a specific angle. The existing rotating multi-secret scheme is associated with the problem of meaningless noise-like shares, shares poor camouflage and security risks in the process of storage and transmission. At the same time, there are many problems in the existing schemes, such as no color image, low contrast of the recovered image and poor scalability of the algorithm. Therefore, this paper proposes a rotating multi-secret color visual cryptography scheme based on meaningful shares. The secret images are encrypted respectively, and the secret information is embedded into the cover images, and the cover images are processed by halftoning technology to obtain the meaningful shares, which improves the security of the rotating multi-secret scheme. The experimental results show that the scheme has the characteristics of gray and color secret images and cover images, the recovered images have no cross interference of cover images and extra secret, the shares and the recovered images have good visual effect, and the scheme meets the security requirements.
Spatial-Temporal Contextual Feature Fusion Network for Movie Description

The movie description task aims to generate narrative textual descriptions that match the content of the movie. Most of the current methods lack the ability to consider comprehensive visual content analysis and contextual information utilization simultaneously, resulting in inaccurate or incoherent in the generated descriptions. In order to tackle the problem, we propose a new method called spatial-temporal contextual feature fusion network (ST-CFFNet) to capture both spatial-temporal and contextual information in movie by building the stacked visual graph attention encoding unit and the contextual feature fusion module. We also propose a spatial-temporal context loss to constrain the effectiveness of ST-CFFNet in spatial-temporal relation analysis and context modeling. The experimental results on LSMDC dataset show that our method achieves more accurate and coherent movie descriptions.
E-COMMERCE PADA MASDOKUSNACK.ID

MASDOKUSNACK.ID is one of the UMKM that sell snack and in promotions using brochures and sales that have to wait for the arrival of customers to place. Is there a problem that is a small promotion and requires a large cost, difficult to increase sales and explanation of product information is less clear. Therefore, this research aims to find PHP e-commerce system and MySQL database where the researcher develops system with waterfall method and uses unified model language model system approach using usecase diagram, activity diagram, class diagram and flow diagram diagram. The new system has input data ie admin data input, category data, product data, expedition data, sales input and output that generate customer data, product data, order data, data delivery of goods, payment data and forum data arranged to facilitate MASDOKUSNACK.ID in promotion and can increase sales with big amount, giving facility for member to order product, giving facility for employees to processing product data.
Analysing the Sentiments in e-newspaper Contents using Novel Bidirectional Encoder Representation for Transformation - BERT over Linear Regression Algorithm

Aim: The aim of the study is to detect sentiment analysis from the good ones and improve the false positivity rate by using the proposed Novel Bidirectional Encoder Representation for Transformers-BERT over Linear Regression Algorithm.Materials and Methods: Sample groups that are considered in the project can be classified into two, one for Novel BERT over Linear Regression, which are tested using 0.80 for G-power to determine the sample size 20 and for t-test analysis.23000 BBC e-news dataset that collected data from NewsBrief and MediSys.Results: The automatic feature selection of the BERT algorithm splits the data with best fit, which has an average accuracy of 83.50%, which by far seems to be better than the Linear regression which gives around 77.80%.The significance is around 0.039 (p<0.05) and therefore there is a statistical insignificant difference among the study group.Conclusion: Novel BERT seems to be better in finding the Sentiment in e-newspaper content of BBC e-news dataset over the Linear Regression Algorithm.
Reconstructing Negative Survey with Least Squares Criterion

Inspired by artificial immune system, negative survey is a cost-effective privacy protection method for multiple choice question-answering with the advantage of decentralization. Present reconstruction methods for negative surveys can obtain positive survey without negative values and achieve high efficiency, but one important assumption is each negative category owns an equal chance of being selected. However, there are many situations where each negative category is selected with predefined probability. In this case, the current methods are difficult to reconstruct positive surveys. In this paper, we propose to extend current reconstruction methods to estimate positive surveys from negative surveys, in which each negative category is selected with any given probability. Our new method is to construct a system of contradictory equations and find the nonnegative least square solutions. We show that in many cases, can not only obtain positive survey without negative values, but also suitable for single choice negative survey in which each negative category is selected with any given probability. Experimental results show that this effective method can return non-negative values and have a smaller squared error.
A Comparative Study of Question Answering over Knowledge Bases

Question answering over knowledge bases (KBQA) has become a popular approach to help users extract information from knowledge bases. Although several systems exist, choosing one suitable for a particular application scenario is difficult. In this article, we provide a comparative study of six representative KBQA systems on eight benchmark datasets. In that, we study various question types, properties, languages, and domains to provide insights on where existing systems struggle. On top of that, we propose an advanced mapping algorithm to aid existing models in achieving superior results. Moreover, we also develop a multilingual corpus COVID-KGQA, which encourages COVID-19 research and multilingualism for the diversity of future AI. Finally, we discuss the key findings and their implications as well as performance guidelines and some future improvements. Our source code is available at https://github.com/tamlhp/kbqa .
Trailblazers deliver new standard

Children and Young People NowVol. 2019, No. 5 AnalysisTrailblazers deliver new standardDerren HayesDerren HayesSearch for more papers by this authorDerren HayesPublished Online:5 Jul 2019https://doi.org/10.12968/cypn.2019.5.14AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 2 May 2019Volume 2019Issue 5ISSN (print): 1755-8093ISSN (online): 2515-7582 Metrics History Published online 5 July 2019 Published in print 2 May 2019 Information© MA Education LimitedPDF download
Socio-psychological characteristics of self-fulfillment of youth and its resilience: internal and external dimensions

The article examines the socio-psychological features of youth self-realization and its sustainability in internal and external dimensions. The specifics and difficulties of studying the problem of self-realization of young people are determined by the growing mediation of the social realization of young people by socio-cultural factors. In this study of the problems of social and personal development of youth, it is appropriate to use a sociocultural approach, within which social processes are considered from the standpoint of their determination by elements of the cultural process (values, norms, models, beliefs, knowledge). It is the sociocultural approach that makes it possible to study the self-realization of youth through the analysis of the value orientations of the individual as the main motivational and meaningful mechanisms of self-affirmation that direct and regulate their life activities, which becomes especially relevant in the era of social changes and the complication of public life. The study of social and cultural factors affecting the process of self-realization of an individual creates opportunities for adequate generalization of disparate facts in the field of social development of youth and identification of trends in the implementation of social self-realization of modern young people. Realization of personal values is carried out in the process of life, the main spheres of which are educational and professional activity, socio-political activity, the sphere of family and household relations and leisure. Hierarchy of values-goals of the individual, concentrated in each of the spheres of life, determines the inequality of these spheres for its self-realization. The most significant spheres of self-realization of young people are the family-household and educational-professional spheres related to the vital needs of young people. Key words: youth, self-realization, hardiness.
Information fusion via symbolic regression: A tutorial in the context of human health

This tutorial paper provides a general overview of symbolic regression (SR) with specific focus on standards of interpretability. We posit that interpretable modeling, although its definition is still disputed in the literature, is a practical way to support the evaluation of successful information fusion. In order to convey the benefits of SR as a modeling technique, we demonstrate an application within the field of health and nutrition using publicly available National Health and Nutrition Examination Survey (NHANES) data from the Centers for Disease Control and Prevention (CDC), fusing together anthropometric markers into a simple mathematical expression to estimate body fat percentage. We discuss the advantages and challenges associated with SR modeling and provide qualitative and quantitative analyses of the learned models.
Phishing Detection in E-mails using Machine Learning

Emails are extensively utilized for both personal and business purposes as a form of communication. Information such as financial information, credit reports, log in details, and other sensitive and personal information is frequently shared over email. The transition time of email from sender to a receiver allows cybercriminals to exploit or breach the data shared, hence causing harm to the integrity of the data. Phishing remains a technique rummage-sale through con artists to gain subtle data from persons through impersonating well-known entities. The sender of a phished email strength persuades users to submit personal information based on fake data. The identification of a phished electronic mail is treated by way of an arrangement issue in this experiment, this tabloid explains the usage of mechanism knowledge techniques to categorize electronic mail as phished or ham. SVM and Random Forest classifiers reach a maximum accuracy of 99.87 percent in email categorization.
Watermarking method based on discrete wavelet transform

Improving the invisibility and robustness of watermarking, increasing the embedding capacity of watermarking and reducing the complexity of watermarking algorithms are becoming hot topics and difficulties in the research of watermarking algorithms nowadays. In this paper, we propose a digital watermarking technique based on discrete wavelet transform for the protection of author’s copyright. First, the watermark is pre-processed, specifically, then the Arnold transform is used for encryption to ensure the security of watermark. Then the wavelet coefficients are obtained by discrete wavelet transform of the original image. Finally, the watermark information is embedded into the wavelet coefficients of the image. The watermark embedding experiments indicate that the algorithm is reversible and the invisibility of watermark is excellent. The watermark extraction experiments demonstrate that our method has strong robustness to against various external attacks.
Research on Security of Business Scenario in Computing Force Network

With the development of the digital economy era, emerging technologies and applications have brought about the continuous growth of data volume, and all walks of life have put forward more urgent needs for computing power and network.Facing the in creasing demand for computing power and network, computing force network came into being.However, the integration of multi-source computing power and network has broken the traditional security domain, and the computing force network will face more serious security and privacy problems.According to the type of computing power, this paper divides the business scenarios of computing force network into three categories: storage task, computing task and training task, and studies the security work of computing force network respectively.
A Unified Framework for Covariance Adaptation with Multiple Source Domains

This paper addresses the problem of unsupervised domain adaptation in a setup where a single source is not sufficient for training the model. In this situation, a hybrid, multi-source driven training dataset is used. This calls for the need of an effective method to align the geometrically quasi-related source domains which would help prepare a better ground for aligning the unlabeled target dataset. We propose a robust framework that helps in better domain adaptation by reducing the probabilistic and subspace shift between the domains without compromising with their distributional information, and diminishing the distance of the between-class and within-class scatter of the domains collectively. The algorithm generates pseudo-labels after each iteration to update its objective function, thus helping it to perform better than conventional methods. The proposed framework tackles non-linear divergence by projecting the features into the kernel space. Computational experiments and their analysis show that the proposed algorithm performs better than other state-of-the-art domain adaptation methods on various visual recognition tasks.
UIT-VLFC: Vietnamese Lipstick Feedbacks Corpus

Sentiment analysis for Vietnamese text can be applied in business fields such as sales, entertainment, advertisement, etc. Especially in the E-commerce era, thousands of products are sold online daily, followed by millions of users’ comments on the E-commerce platforms. For that reason, this research introduces the UIT-VLFC (UIT-Vietnamese Lipstick Feedbacks Corpus), a data set of users’ comments of lipstick products on E-commerce platforms. This aims to contribute a data set to the Vietnamese sentiment analysis field, even the sentiment-analysis recommendation system problem. The research also gives some experimental results on machine learning methods (Multinomial Naïve Bayes, Random forests, Logistic regression, Support vector machine (SVM)) and deep learning method (Bidirectional-Long short-term memory), etc. in order to assess the UIT-VLFC. The experimental results show that the UIT-VLFC has lots of implicit potentials that can be explored for many applications.
Sentiment Analysis and Classification Using Convolutional Neural Network Architecture

Nowadays, many companies sell their products and services on social media; they can get ideas directly from the end-users on these social media. Manually reading each text is time-consuming, so by analyzing the emotions of all the text, companies can roughly know how many positive or negative users there are on a particular topic. It also helps estimate the effect of organizations' social promoting systems by distinguishing the public view of an item or item-related occasion. A large portion of the exploration done as such far centers around getting profound properties by examining the syntactic and dress properties of plainly communicated words, outlines, and other extraordinary images. Propose a Convolutional Neural Network (CNN) to deal with opinion investigation of item surveys utilizing profound learning. Dissimilar to conventional DL methods, profound learning models don't depend on highlight extraction. These highlights are advanced straightforwardly during the preparation series. The fundamental thought of this assignment is to use convolutional brain organizations to prepare and arrange feeling classes in item surveys. It can utilize this CNN model to foresee the opinion of audits of new items.
PENGARUH MOTIVASI DAN KECERDASAN EMOSIONAL TERHADAP KEMANDIRIAN BELAJAR SISWA PADA PEMBELAJARAN DARING SEKOLAH BATAM (STUDI PADA SDN 005 SEKUPANG BATAM)

This study aims to analyze the motivation and emotional intelligence of students' learning independence in Online Learning Schools in Batam. The research method uses a quantitative approach, correlational design. The population of this study were 339 students. Sampling technique using disproportionate stratified random sampling set a sample of 183 respondents. Data collection techniques through questionnaires, interviews, and documentation. The results showed that the variables of motivation and emotional intelligence had a significant and simultaneous effect on students' learning independence. The results of the t test show that the motivation variable has a significant effect on student learning independence, and emotional intelligence also has a significant effect on learning independence. The coefficient of determination is 0.315 or shows that 31.5% of students' learning independence is influenced simultaneously by the variables of motivation and emotional intelligence.
Commemorative Stamps as a Recognition Tool: A Cross-Sectional Analysis

Commemorative stamps have a place in the cultural, economic, and geopolitical status of a nation; and the nature of what is printed on stamps is very much a reflection of the culture, regime, and values of a country. Whilst commemorative stamps can be a source of celebration for monuments, flora and fauna, and key points in a nation’s history; they are also a source of recognition, designed to publicly celebrate and showcase the contribution of key figures based on their performance in areas deemed significant to the nation’s culture and evolution. In this empirical study, we analyse commemorative stamp data in a cross-sectional setting to understand key determinants that shape their provision. The award literature has developed some propositions that so far have not been tested empirically. We find strong evidence that the political process strongly influences the use of postage stamps as a recognition tool. The political regime as well as governance matter. For example, controlling for regime, more corrupt countries are more likely to use stamps to recognize personalities. On the other hand, more democratic regimes are more frequently using stamps as a recognition tool. We also find that cultural factors and ideologies show substantial heterogeneity in their influence while factors such as fractionalization or income inequality do not seem to matter. Factors such as personal and impersonal pro-sociality or creativity seemed to reduce the need for stamps as a recognition tool, while other factors such as market orientation, individualism, or traditionalism encourage the use of stamps to recognize individuals. Overall, we find that some results are consistent with the theoretical propositions while others are not, requiring more empirical evidence around awards and a better understanding of how stamps differ from other recognitions.
Formal theory: status and outlook

A very brief overview is presented on some of the current most active areas of research in formal aspects of High Energy Physics and Cosmology.Including the recent breakthrough on the black hole information paradox, developments on amplitudes, the bootstrap and swampland programmes as well as progress towards realistic UV complete models of particle physics and cosmology.Perspectives on future contact with observations are discussed emphasising the long term prospect for ultra high frequency gravitational waves to test early universe physics beyond the Standard Model.
Neural Network Interpretability

Neural networks are powerful tools that can be used to solve a host of difficult tabular data modeling challenges. However, they’re also less obviously interpretable than other alternatives to modeling tabular data, like Linear Regression or decision trees – from which the model’s processing of the data can be more or less directly read off of the learned parameters. The same is not true for neural network architectures, which are significantly more complex and therefore more difficult to interpret. At the same time, it is important to interpret any model used in production to verify that it is not using cheap tricks or other exploitative measures, which can lead to poor behavior in production.