Reviewerly/exHarmony huggingface dataset:
Application of Nanomaterials in Subgrade and Pavement

With the continuous progress of social science and technology, there are increasingly more kinds of high-tech materials that can be used in the construction of subgrade and pavement. Among them, the application of nanomaterials plays a commendatory role, improving the quality of pavement. Based on this, this paper analyzes the characteristics of nanomaterials and puts forward specific measures for the application of nanomaterials on the basis of further exploring the working principle of nanomaterials in subgrade and pavement, so as to promote the further development of subgrade and pavement construction.
The prediction management framework: ethical, governable, and interpretable deployment of artificial intelligence/machine learning systems

As defense organizations integrate artificial intelligence (AI) into evermore critical operations, especially those near the tactical edge with real-time decision making, the necessity of a standardized, robust framework for deployment and management of AI systems is increasing. In this paper, we propose a Prediction Management Framework (PMF) that aligns with the Department of Defense’s Ethical Principles for AI for ethical, governable, and interpretable deployments. We explore different requirements for the framework with inspiration drawn from various regulatory, safety, and communication standards. In support of these requirements, we offer recommendations and implementation guidance to provide comprehensive visibility into the system.
Website-Based Budget Adjustment Information System at PT. Taspen (Persero) Denpasar Branch Office

The activities of the budget adjustments in the manufacture of the allocation of procurement of goods still have not been done optimally. This leads to lack of control over spending budget. The purpose of this research is to make the Information Systems Budget Adjustments Purchase Website Based on PT. Taspen (Persero) Kantor Cabang Denpasar, which is the solution of the weakness of the existing system. This study aims to produce a system that will simplify and accelerate the employees of PT. Taspen (Persero) Denpasar in adjusting the budget the purchase of equipment and supplies so as to produce the management of the orderly, effective, and efficient. The stages in achieving this goal based on the methods of the waterfall includes Flowmap, Context Diagram, Data Flow Diagram, Entity Relationship Diagram, and database design using software package xampp and MySQL. Testing methods carried out using black box testing. The results obtained in the form of the establishment of a system that supports the process of inputting the data of the budget, the calculation of the adjustment of the budget, and reporting the data required as an accountability report
Gaussian Perturbations in ReLU Networks and the Arrangement of Activation Regions

Recent articles indicate that deep neural networks are efficient models for various learning problems. However, they are often highly sensitive to various changes that cannot be detected by an independent observer. As our understanding of deep neural networks with traditional generalisation bounds still remains incomplete, there are several measures which capture the behaviour of the model in case of small changes at a specific state. In this paper we consider Gaussian perturbations in the tangent space and suggest tangent sensitivity in order to characterise the stability of gradient updates. We focus on a particular kind of stability with respect to changes in parameters that are induced by individual examples without known labels. We derive several easily computable bounds and empirical measures for feed-forward fully connected ReLU (Rectified Linear Unit) networks and connect tangent sensitivity to the distribution of the activation regions in the input space realised by the network.
Monte Carlo Simulation Of Estimating Clean Water Supplies

Estimates are important tools in effective and efficient planning for predicting future events. Identical estimates of the future values of a variable for planning or decision making of a situation to estimate future values. Monte Carlo simulation is a simulation model that involves a series of random and sampling with a probability distribution that can be known and determined, then this simulation can be used. In this study, data is taken from the amount of water usage in PDAM Tirtanadi H.M branch. Yamin, North Sumatra from January 2018 to June 2019. Then, the data is processed and analyzed using Monte Carlo Simulation to determine the forecast results in the years that follow. The result is an estimated amount of water usage in 2019 and 2020 at PDAM Tirtanadi H.M branch. Yamin, North Sumatra is 8,604,556 and 8,592,873. The estimated amount of water use is down from the amount of water use in 2018 which reached 8,685,356. The amount of water usage in 2018, 2019 and 2020 decreases by about .
Neural Network Algorithm for Budget Expenditure Prediction in LPP RRI Gorontalo

In this Data Mining research, the researcher uses the Neural Network Algorithm to predict budget expenditures at LPP RRI Gorontalo, the goal is to find out how much cash spending at LPP RRI Gorontalo is on average in each month, so it will make it easier for the Treasurer to control cash disbursements in each month. month. Using 412 Expenditure Records Data from 2013 to 2021, the lowest RMSE value is at Hiden Layer 11, Training Cyle 400, Learning Rate 0.1 and Momentum 0.1 with RMSE 0.142. Prediction results look better because they are closer to Real Data, so Neural Networks can be used to predicting spending at LPP RRI Gorontalo.
ROMANIAN CONSUMERS’ PERCEPTIONS TOWARDS HUMANOID ROBOTS INVOLVED IN VARIOUS FIELDS – STUDY IMPLYING A NEUROMARKETING TOOL (EYE TRACKING)

E. Goga1, M.C. Orzan 1, E.F. Grejdan1, I.I. Gradinescu1, A. Radulian1, A.O. Orzan2 1Bucharest University of Economic Studies (ROMANIA) 2“Carol Davila” Bucharest University of Medicine and Pharmacy (ROMANIA)
Preserving Women Public Restroom Privacy using Convolutional Neural Networks-Based Automatic Gender Detection

Personal safety and privacy have been the significant concerns among women to use and access public restrooms/toilets, especially in developing countries such as Indonesia. Privacy-enhancing designs are unquestionably expected to ensure no men entering the rooms neither intentionally nor accidentally without prior notice. In this paper, we propose a facial recognition approach to ensure women's safety and privacy in public restroom areas using Convolutional Neural Networks (CNN) model as a gender classifier. Our main contributions are as follows: (1) a webcam feed automatic gender detection model using CNN which may further be connected to a security alarm (2) a publicly available gender-annotated image dataset that embraces Indonesian facial recognition samples. Supplementary Indonesian facial examples are taken from a government-affiliated college, Politeknik Statistika STIS students' photo datasets. The experimental results show a promising accuracy of our proposed model up to 95.84%. This study could be beneficial and useful for wider implementation in supporting the safety system of public universities, offices, and government buildings.
Analysis of the Effectiveness of Various Machine Learning, Artificial Neural Network and Deep Learning Methods in Detecting Fraudulent Credit Card Transactions

A credit card is an important financial tool that has emerged in parallel with the developments in technology from the past to the present and has become an indispensable part of human life. The credit card has many advantages that can be listed as facilitating online shopping, providing installments in purchases, and preventing cash dependence. This is why the rate of use of credit cards worldwide is increasing day by day. On the other hand, there are some risks of the credit cards highlighted by security concerns. The fraudsters who access the identity and credit card information of the consumers through different means use it to shop online without the consumer’s knowledge and gain an unfair advantage. Therefore, it is crucial to eliminate this security vulnerability that the fraudsters exploit and to develop an effective solution to the customer victimization experienced by e-commerce companies due to the fraudulent credit card transactions. With this motivation, the performance of the methods from different research fields was examined to explore the solution space in detail in terms of the problem at hand within the scope of this study. For this purpose, three machine learning algorithms (K-Nearest Neighbor, Naive Bayes, Support Vector Machine), two artificial neural network algorithms (Binary Classifier, Autoencoder), and two deep learning algorithms (Deep Autoencoder and Deep Neural Network Classifier) were implemented. The effectiveness of the algorithms in question was tested with a famous dataset widely used in the literature. Experimental results showed that the Deep Neural Network Classifier outperformed the other algorithms used in this study and the best study ever reported in the literature in detecting fraudulent credit card transactions when accuracy and AUROC performance criteria were taken into account.
Integrating online Teaching English into the system of higher education

The article discusses current problems in organizing and conducting online classes. The effectiveness and compliance of online teaching English with the modern competence approach in the process of evaluation of effectiveness of the selected learning model. The purpose of the article is to justify effectiveness of managing the student’s learning activities in the process of online learning through an optimal combination of possibilities of Moodle electronic educational environment and the use of interactive pedagogical technologies. Methodology and methods of research. The study was conducted on the basis of the implementation of the model of online English course as a process of building up students’ professionally significant competencies, development of students’ self-regulation strategies and improvement of their soft skills and abilities. The research methods used were the analysis of pedagogical, socio-psychological, scientific-methodological and special professional literature on the problem of research; purposeful observation of the process of development and training of students, analysis of experimental data, questionnaires. The developed model of online English course was implemented as part of an experimental study of the discipline “The English Language”. Conclusion. It is worth noting that the implementation of online teaching model based on the concepts of Edutainment, Self-Regulative Strategies of learning, as well as communicative approach with the use of interactive pedagogical technologies integrated into content-based learning corresponds to the modern competence-based approach implemented in the educational process of the discipline “The English Language”.
Towards waveform seismic filtering of mantle convection models

&amp;lt;p&amp;gt;Earth science has been heavily data-driven due to the abundance in data. Yet, when there are relatively a small number of hypotheses to verify, the inverse problem becomes a classification problem. It is then worth directly examining observed seismic data against predicted data. Concretely, we chain forward modelling from geodynamics&amp;amp;#160;&amp;amp;#160;to seismology. We call this process &amp;amp;#8216;waveform Seismic Low Filtering of Earth&amp;amp;#8217;s models&amp;amp;#8217; (SeLFiE). We take seismic signals of the snapshots of forwardly generated Earth models with that of the actual Earth, as if we took a photo of ourselves. Although there have several studies on how the seismological tomographic technique can perceive the geodynamical models, there are few studies on the seismic waveform sensitivity to geodynamical or petrological parameters. A pilot test of our&amp;amp;#160;&amp;lt;em&amp;gt;SeLFiE&amp;lt;/em&amp;gt;&amp;amp;#160;methodology was encouraging, since we used only one seismic station to constrain the melt transportation manner beneath the R&amp;amp;#233;union island (Franken et al. 2020). Here in this contribution we present our strategy and developed tools towards the waveform filtering that have been developed during and after the CLEEDI week in August, 2020.&amp;lt;/p&amp;gt;
ROLE OF SOCIAL CAPITAL IN PROFESSIONAL SOCIALIZATION OF PERSONALITY

The article analyzes the influence of social capital on the process and result of professional socialization of the individual, argues that in the mechanism of professional socialization social capital largely determines the choice by the individual of identification objects and value-normative system that will regulate his or her behaviour. It is emphasized that social capital of the professional environment in which the individual finds himself can not only promote but also hinder the accumulation of socially acceptable cultural skills, knowledge and information. This is due to the social and moral orientation of the value system on which it is based.
Gaze-FTNet: A feature transverse architecture for predicting gaze attention

The dynamics of gaze coordination in natural contexts are affected by various properties of the task, the agent, the environment, and their interaction. Artificial Intelligence (AI) lays the foundation for detection, classification, segmentation, and scene analysis. Much of AI in everyday use is dedicated to predicting people's behavior. However, a purely data-driven approach cannot solve development problems alone. Therefore, it is imperative that decision-makers also consider another AI approach—causal AI, which can help identify the precise relationships of cause and effect. This article presents a novel Gaze Feature Transverse Network (Gaze-FTNet) that generates close-to-human gaze attention. The proposed end-to-end trainable approach leverages a feature transverse network (FTNet) to model long-term dependencies for optimal saliency map prediction. Moreover, several modern backbone architectures are explored, tested, and analyzed. Synthetically predicting human attention from monocular RGB images will benefit several domains, particularly humanvehicle interaction, autonomous driving, and augmented reality.
AR needs display revolution

Eyes are by far our main input from the real and digital worlds. The future of Augmented Reality therefore vitally depends on a display technology that can merge the two worlds into one. The real world is however 3D while today’s “3D displays” are not - they lack focal depth. True 3D needs a display revolution. Light-field does it. Contrary to widespread notion, light-field can provide genuinely 3D imagery while it is equally or more efficient as an AR display than conventional raster displays. This talk will introduce why and how.
Depth-based sparse bundle adjustment

It is demonstrated in this paper that due to error model inconsistency, a certain degree of accuracy loss would be incurred to the estimated parameters when the traditional bundle adjustment method is directly applied to the scenario where a fraction of observations is implicitly error free (e.g., the reference image points in commonly used least squares matching refinement). To this end, a depth-based object point model and corresponding depth-based sparse bundle adjustment method are proposed in this paper, in which the position of an object point is represented by its 1D depth relative to its reference image. A corresponding projection model is derived, the sparse block structures of normal equations are studied depending on whether there are shared image parameters to be optimized or not, and corresponding sparse solutions of the normal equations and parameter covariance matrices are derived. Compared with the traditional sparse bundle adjustment method, simulated experiments demonstrate that our method matches the error model of the target scenario, and thus can avoid further accuracy loss. Moreover, both simulated and real data experiments demonstrate that our method can effectively improve computational efficiency.
Ukrainians rush to save ‘irreplaceable’ collections

Researchers hide ancient weapons and bat skulls, upload data as bombs fall.
Element redistribution by greisenization in rhyolite, Zinnwald/Cinovec

&amp;lt;p&amp;gt;The Zinnwald/Cinovec Sn-W-Li deposit on the border between Germany and Czech Republic in the eastern part of Kru&amp;amp;#353;n&amp;amp;#233; Hory/Erzgebirge represents a fluorine-rich hydrothermal alteration of a granite-rhyolite association. The host rock can be divided into two parts: 1) zinnwaldite granite (upper part) with massive greisen body and various hydrothermal veins and 2) homogeneous protolithionite granite (lower part). The basement is the Kru&amp;amp;#353;n&amp;amp;#233; Hory/Erzgebirge crystalline complex with different metamorphic grades, overlain by the Teplice rhyolite, which also contains greisen veins and is the focus of this study.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study, we focus on the effects of fluid-rock interaction on distal rhyolites of the deposit. We combine whole rock chemistry with petrological data to constrain mass gain or loss of economically interesting elements. The samples were selected from the upper contact zone between granite and rhyolite. Three distinct zones of high- and low-degree greisenization (HG and LG) and albitization (A) developed with different textures, mineral assemblages and mineral compositions. Beyond the albitization zone, a continuous transition to the least altered rhyolite was observed. In the greisen part, the predominant minerals are quartz (~80 vol%) and topaz (~10 vol%) with minor biotite (~5 vol%). The albitization zone contains mostly albite (~40 vol%), quartz (~25 vol%), orthoclase (~25 vol%), and biotite (~10 vol%). In comparison to the composition of the rhyolite wall rock, mass balance calculations show that the greisen has 50%-100% loss of LILEs, 15%-20% loss of HREEs, and 7%-11% gain of LREEs.&amp;amp;#160; The Th/U, Zr/Hf, Y/Ho, and La/Yb ratios are similar between the rhyolite and the greisen zone but very different for the albitization zone. This suggests a dynamic dissolution/precipitation process in the albitization zone caused by the particularly high F- and Na-activity in this zone compared to the unaltered rhyolite but also the greisen (where F is precipitated as topaz and fluorite whereas Na is lost to the fluid). The chemical changes show that the F-rich fluid carried LILEs and LREES to the greisen, and also resulted in the loss of HREEs and alkaline elements.&amp;lt;/p&amp;gt;
APPLICATION OF THE CHALLENGE-BASED LEARNING METHODOLOGY FOR LEARNING 8D PROBLEM-SOLVING PROCESS

O. Trull Dominguez, A. Peiró-Signes, J.C. García Díaz, C. Gómez PalaciosUniversitat Politècnica de València (SPAIN)
Using the Baldwin Effect to Accelerate a Genetic Algorithm

The standard Genetic Algorithm, originally inspired by natural evolution, has displayed its effectiveness in solving a wide variety of complex problems. This paper describes the use of the natural phenomenon known as the “Baldwin Effect” (or cross-generational learning) as an enhancement to the standard Genetic Algorithm. This is facilitated via the use of artificial neural networks to store aspects of the population’s history, and through the use of elitism in conjunction with a local heuristic. It also describes a method by which the negative side effects of a large elite sub-population can be counter-balanced by using an aging coefficient in the fitness calculation. The new algorithm is then tested on three standard genetic algorithm test functions, and on one commercial application of the genetic algorithm.
Hate Speech and Ethno-Religious Violence in Nigeria

Hate speech is the precursor to ethno-religious violence in Nigeria. In recent times, no issue has generated widespread public comment in Nigeria more than the issues of hate speech and ethno-religious violence rocking the country and its sad consequences on national integration. Though, on the issue of hate speech, the provisions of Nigeria constitution and Nigeria Electoral Act are clear but the question is where to draw the line between political statements and hate speech. Therefore, this study explored the effect of hate speech on ethno-religious violence in Nigeria. A descriptive method was adopted and data was collected via a survey of 600 respondents comprises of Traditional Leaders, Christian leaders, Muslim leaders, Leaders of civil society groups and Youth leaders in South-South, South-West, South-East, North-West, North-central and North-East geopolitical zones of Nigeria. Data collected were analyzed using correlation and linear regression analysis with the aid of Statistical Package for Social Science (SPSS) version 21. The study found that there is significant relationship between hate speech and ethno-religious violence in Nigeria. The study submits that, hate speeches in Nigeria are mostly credited to political leaders and their ethnic, religious or regional supporters. Requisite recommendations and conclusion were provided in the light of empirical and theoretical findings.
Machine Learning in Healthcare

Healthcare as a sector has seen a phenomenal overhaul in the past centuries, and has been one of the fastest growing sector. With the enormous amount of data generated and used in healthcare, machine learning, which at its core is a data-driven domain, provides myriad technological innovations and applications. This chapter aims to give an overview of the basics of machine learning, evaluates its importance of use over the statistical methods and provides brief information about different machine learning techniques. It demonstrates the key segments of healthcare, which in itself is a multidisciplinary industry. Further, the chapter gives a broad coverage about the numerous application of machine learning in prognosis and medical diagnosis. Electronic health records (EHRs), which are an important application in a smart healthcare setup, have the potential to aid better management of medical systems is cited and the proliferation of EHRs in various clinical applications can provide better clinical decision support. This chapter encapsulates application of machine learning in medical image analysis and natural language processing (NLP) of medical documents and literature. Machine learning in healthcare can be used for combatting the pandemic at both levels of primary prediction and for post-pandemic control. The main objective of this chapter is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. This chapter also highlights the integration of machine learning with cutting-edge technologies like big data and Internet of Things “IoT”, biometrics and cloud computing, which have the potential to provide remarkable results and carve a new path for smart healthcare.
Radar emitter individual identification

Radar Emitter Individual Identification is a technique to extract the radio fingerprint features of radar by means of external feature measurement of the radar signal, in order to identify radar emitter individuals. In the past few years, the related theories and practical applications of radar emitter individual identification technology had been continuously improved, and the research on radio frequency fingerprint extraction methods has made great progress. Based on domestic and foreign academic achievements, the current research status is classified by deep learning methods, which are specifically divided into two types of traditional SEI methods and deep learning SEI methods. Finally, several potential research directions for Radar Emitter Individual Identification are analyzed and explored.