Reviewerly/exHarmony huggingface dataset:
An Attributed Network Representation Learning Method Based on Biased Random Walk

Network representation learning(NRL) aims to learn the low-dimensional and continuous vector representations for all nodes in networks, which is used as the input feature for many complex networks analysis tasks. Random walk has a wide range of applications in structure-based network representation learning. In reality, the network contains a lot of attribute information, which brings opportunities and challenges to random walk. Combining attribute information can reconstruct the network better. Based on the above, we propose a robust NRL method called Attributed Network Representation Learning Based on Biased Random Walk (ANRLBRW). This method considers the structure and homogeneity of the network through a biased random walk. It uses the attribute random walk to take care of the node’s attribute information, and combines two random walk sequences to learn the network representation of the node using the skip-gram model. The experimental results on three real attributed networks show that the accuracy of node classification outperforms the state-of-the-art methods on the basis of our proposed network representation learning method.
Survey of Various Techniques used for Credit Card Fraud Detection

In today's era , technology is changing rapidly and with the increase in new innovations and advancements our way of life has changed . Government is making attempt to make India a digital country, So using an ATM or credit card is a convenient way of fulfilling the government's aim. Banks and its customers are suffering huge losses due to fraudulent activities. So, researchers are trying to find the solution by applying algorithms like Data Mining, Machine Learning and Deep Learning, to prevent the fraudulent transactions by building efficient system. This paper is prepared to disclose the survey of existing techniques.
Stopping Criterion Design for Recursive Bayesian Classification: Analysis and Decision Geometry

Systems that are based on recursive Bayesian updates for classification limit the cost of evidence collection through certain stopping/termination criteria and accordingly enforce decision making. Conventionally, two termination criteria based on pre-defined thresholds over (i) the maximum of the state posterior distribution; and (ii) the state posterior uncertainty are commonly used. In this paper, we propose a geometric interpretation over the state posterior progression and accordingly we provide a point-by-point analysis over the disadvantages of using such conventional termination criteria. For example, through the proposed geometric interpretation we show that confidence thresholds defined over maximum of the state posteriors suffer from stiffness that results in unnecessary evidence collection whereas uncertainty based thresholding methods are fragile to number of categories and terminate prematurely if some state candidates are already discovered to be unfavorable. Moreover, both types of termination methods neglect the evolution of posterior updates. We then propose a new stopping/termination criterion with a geometrical insight to overcome the limitations of these conventional methods and provide a comparison in terms of decision accuracy and speed. We validate our claims using simulations and using real experimental data obtained through a brain computer interfaced typing system.
ICARE Learning Model in Improving the Students Writing Ability

This research explored the use of the ICARE Model to studying the writing ability at the Senior high school in Parepare. ICARE includes five essential elements of a good learning experience, can be applied to children, teenagers, and parents, namely Introduction, Connection, Application, Reflection, and Extension. This research involved a population from Senior high school in Parepare in the academic year 2015/2016. The students consist of two classes, an experimental class, and a control class. Each class consists of 36 students. This research utilized a cluster random sampling technique to select the sample. The instrument used in this research is the writing test. The data were analyzed by using t-test. The research reveals that there was a significant increase in students writing ability. This finding indicates that the mean score of the post-test of the experimental group is getting much better than the control class. It meant that using ICARE developed the students' ability in writing, and the writing component increased the highest is in the vocabulary aspect.
Toward General Scene Graph: Integration of Visual Semantic Knowledge with Entity Synset Alignment

Scene graph is a graph representation that explicitly represents high-level semantic knowledge of an image such as objects, attributes of objects and relationships between objects.Various tasks have been proposed for the scene graph, but the problem is that they have a limited vocabulary and biased information due to their own hypothesis.Therefore, results of each task are not generalizable and difficult to be applied to other down-stream tasks.In this paper, we propose Entity Synset Alignment(ESA), which is a method to create a general scene graph by aligning various semantic knowledge efficiently to solve this bias problem.The ESA uses a large-scale lexical database, WordNet and Intersection of Union (IoU) to align the object labels in multiple scene graphs/semantic knowledge.In experiment, the integrated scene graph is applied to the image-caption retrieval task as a downstream task.We confirm that integrating multiple scene graphs helps to get better representations of images.
Regional Integration and Growth Spillovers: Is Africa an Economic Space?

Market integration is often associated with economic growth spillovers, to the extent that strengthened trade linkages would make economic growth in one country an opportunity for growth to its partners.This paper uses a spatial dynamic panel data approach to analyze the extent to which Africa and its major regional groupings have been able to generate shared growth.The results indicate that the continent as a whole is indeed a spatially integrated economic zone.Although of a weak proportion compared to advanced economies, the contemporaneous spatial growth effect is associated with strong growth convergence and a large rate of return to physical capital.The results also suggest that space has more to do with bilateral trade than geographical distance, and point to a great deal of heterogeneity: the positive growth spillover effect in regional groupings such as South African Development Authority, Common Market for Eastern and Southern African, and Economic Community of West African States, which happen to register larger intraregional trade shares, contrast with the absence of such an effect in Economic Community of Central African States and Arab Maghreb Union, two blocks which have achieved little in terms of economic integration.These results should be a welcome addition to the debate over the opportunity of the proposed continent-wide free trade area and its ability to foster inclusive and sustainable economic growth and development for African economies.
Approaches to improving the training of teachers working under the International Baccalaureate programs in the field of education informatization

Problem and goal. The article is devoted to studying the possibilities of working with the Moscow Electronic School project. The goal was to develop programs to improve the training of teachers in Moscow schools. This goal required to design a model for improving the training of teachers working under the International Baccalaureate programs in the field of education informatization, to develop the content of teacher training, to organize and conduct such training, based on an active study of the possibilities of the Moscow Electronic School project for teachers of International Baccalaureate schools, as well as evaluate the effectiveness of the proposed training program. Methodology. In the course of the study, a set of methods was used: analysis of normative documents related to the professional training of teachers, study of literature, including those presented by the International Baccalaureate Organization (IBO), search and analysis of the resources of the Moscow Electronic School project library, study of the experience of using services in the system education of the city of Moscow, a pedagogical experiment. Results. The article presents a fragment of a pedagogical experiment to improve the training of teachers working under the International Baccalaureate programs, aimed at developing skills in working with the Moscow Electronic School project. The study confirmed the relevance of the problem, which consists in the need to improve the training of teachers in Moscow schools working under the International Baccalaureate programs in the field of education informatization, including in the field of work with the resources of the Moscow Electronic School project. The results of the study allowed to find approaches to solving the identified problem. Conclusion. It is proposed to supplement the content of the discipline Modern Teaching Technologies in the framework of the masters training of teachers working under the International Baccalaureate programs, with topics related to studying the resources of the Moscow Electronic School project in the activities of a teacher working under the International Baccalaureate programs, as well as a system of practical work that takes into account the specifics of the International Baccalaureate school.
This History and Evolution of Virtual Reality

Virtual reality (VR) is a continuously evolving technology that is gradually being integrated into the teaching-learning process, within institutions of higher education. VR has the potential to transform the instructional process, enhance student learning, and engage students in a more interactive manner than has occurred historically. While technology integration within the instructional process has been initiated within institutions of higher education, mass adoption among faculty has not yet occurred. This chapter provides an introduction to VR, discusses the evolution of VR, applications in higher education and other fields, and a progression of features, tools, and functionality that can be used to innovate learning in higher education.
Hosting Social Touch in Public Space of Merging Realities

Is human hosting essential to social touch in the public space of merging realities? This paper explores the role of hosting in art and design for mediating social touch in public space, social robotics, virtual reality and tele-matic environments. The question of whether human hosting is essential to social touch was the focus of three experiments held during performance of artistic orchestrations designed for social touch in public space for which the effects of different hosting designs have been analyzed. These internationally presented orchestrations, Saving Face (2012) and Master Touch (2013), purposefully disrupt and re-orchestrate multi-sensory connections in unfamiliar and unpredictable ways, to evoke shared reflection and shared sense making in public space, mediated by a host. Saving Face was orchestrated internationally in museums, urban public spaces and theatres, including; 56th Venice Biennale 2015; Connecting Cities Network Ber-lin/Dessau 2013; 3th TASIE Art-Science exhibition, Science & Technology Museum Beijing 2013; Beijing Culture & Art Center BCAC 2015-2016. Master Touch was orchestrated at Rijksmuseum Amsterdam 2013. This paper extends the multi-sensory interaction model for social touch described in (Lancel et al. 2019e) to explicitly include the role of a host. The question this paper addresses is whether the host needs to be human. This paper calls for future design of disrupted social touch in merging realities to consider hosting processes of shared sense making. Such design should facilitate new forms of reciprocal embodied interaction, that support descriptive self-disclosure, dialogue and shared reflection on experience of social touch in merging realities.
Enhancement of digital signature algorithm in bitcoin wallet

Bitcoin is a peer-to-peer electronic cash system largely used for online financial transactions. It gained popularity due to its anonymity, privacy, and comparatively low transaction cost. Its wallet heavily relies on Elliptic Curve Digital Signature Algorithm (ECDSA). Weaknesses in such algorithms can significantly affect the safety and the security of bitcoin wallets. In this paper, a secure key management wallet was designed based on several changes in the wallet parts. In the cold wallet, we employed an image-based passphrase to achieve a strong entropy source of master seed. The hot wallet, the proposed key_ Gen algorithm is modifying to the key generation step of the ECDSA that it is to generate a fresh key pair at each transaction. The final part ensures recovering all keys on both hot and cold wallets without daily backups in case of losing the wallet. The findings prove that the proposed cold wallet is resisting against a dictionary attack and overcoming the memorizing problem. The proposed hot wallet model acquires good anonymity and privacy for bitcoin users by eliminating transaction likability without additional cost. The execution time for signing a transaction of the proposed model is~70 millisecond, which is then important in the bitcoin domain.
A Novel Text Features Jointing Model for Review Spam Filtering of Chinese

Review spam filtering of Chinese is a hot research topic in the field of natural language processing. In recent years, there have aroused a lot of neural network models for review spam filtering of Chinese, but these models mainly focus on utilizing text semantics or part-of-speech of a review without considering the variants of a sensitive word. In this paper, based on TextCNN, we propose a novel review spam filtering model of Chinese that embeds various features of the text. Considering the common types of variant words, we first extract three sets of vectors for each Chinese character of the review, including the single character vector, the pinyin vector, and the pinyin vector of each single character. Then, we use TextCNN to learn the features of these vectors, respectively. After that, the learned features are added as the comprehensive features of review. Finally, we input it to the softmax layer to get the final classification results. The experimental results show that the joint model performs better than classic classification methods in review spam filtering of Chinese. The recall and F1-score on review spam reach 92.4% and 93.6%.
Security of Wireless Sensor Nodes

Due to the large-scale development in satellite and network communication technologies, there is a significant demand for preserving the secure storage and transmission of the data over the internet and shared network environments. New challenges appeared that are related to the protection of critical and sensitive datafrom illegal usage and unauthorized access. In this paper, we address the issues described above and develop new techniques to eliminate the associated problems. To achieve this, we propose a design of a new sensor node for tracking the location of cars and collecting all information and all visited locations by cars, followed byencryption in a sensor node and saving in the database. A microcontroller of Arduino esp8266 Node MCU board and a GPS module are used. The cryptography uses the chaos-based symmetric-key encryption technique for data. This scheme utilizes a chaotic map (HÃ©non map) for robustness and security of data. The key sensitivity can be performed by statistical experiments to determine the safety, reliability, and speed of the algorithm. The proposed algorithm presents several exciting features, such as a high level of security, sufficient saving of the energy of the sensor network, and an acceptable encryption speed compared to Advanced Encryption Standard (AES) and Data Encryption Standard (DES).
The Visual Analytics Approach for Analyzing Trajectories of Critical Infrastructure Employers

Employees of different critical infrastructures, including energy systems, are considered to be a security resource, and understanding their behavior patterns may leverage user and entity behavior analytics and improve organization capabilities in information threat detection such as insider threat and targeted attacks. Such behavior patterns are particularly critical for power stations and other energy companies. The paper presents a visual analytics approach to the exploratory analysis of the employees’ routes extracted from the logs of the access control system. Key elements of the approach are interactive self-organizing Kohonen maps used to detect groups of employees with similar movement trajectories, and heat maps highlighting possible anomalies in their movement. The spatiotemporal patterns of the routes are presented using a Gantt chart-based visualization model named BandView. The paper also discusses the results of efficiency assessment of the proposed analysis and visualization models. The assessment procedure was implemented using artificially generated and real-world data. It is demonstrated that the suggested approach may significantly increase the efficiency of the exploratory analysis especially under the condition when no prior information on existing employees’ moving routine is available.
Computational state space model for intelligent tutoring of students in nursing subjects

Due to population ageing and the increase of chronic diseases, personnel in the nursing and health management sector is of high demand. Intelligent tutoring systems (ITS) can contribute in closing the personnel demand gap. Such tutoring systems should be efficient, sustainable and patient oriented. Currently, most tutoring models are either knowledge based or data-driven models. This paper introduces an ITS model that is both knowledge based and data driven to be able to integrate experts' knowledge and the provided data simultaneously. Moreover, the proposed tutoring system serves as a conversational agent for a student where the system provides questions for the student and evaluates the student's answers in order to ensure that the student understands different topics.
Code Comment Assessment Development for Basic Programming Subject using Online Judge

The computer program source code must have good documentation on it to be more understandable by others.One of the documentation is code comments.Therefore, university students need to learn and able to write comments.Some university students are familiar with an online judge as a tool for learning programming.An online judge usually does not check how the source code written instead of the output only.This research is aimed at implementing the code comment assessment module for online judge in the order it can check the comments in the source code which is uploaded by students.The comments on this module are categorized into two parts.There are header and process comments.
Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet

This work improves a LeNet model algorithm based on a signal's bispectral features to recognize the communication behaviors of a non-collaborative short-wave radio station. At first, the mapping relationships between the burst waveforms and the communication behaviors of a radio station are analyzed. Then, bispectral features of simulated behavior signals are obtained as the input of the network. With regard to the recognition neural network, the structure of LeNet and the size of the convolutional kernel in LeNet are optimized. Finally, the five types of communication behavior are recognized by using the improved bispectral estimation matrix of signals and the ameliorated LeNet. The experimental results show that when the signal-to-noise ratio (SNR) values are 8, 10, or 15 dB, the recognition accuracy values of the improved algorithm reach 81.5%, 94.5%, and 99.3%, respectively. Compared with other algorithms, the training time cost and recognition accuracy of the proposed algorithm are lower and higher, respectively; thus, the proposed algorithm is of great practical value.
The Lucas Paradox and Institutional Quality: Evidence from Emerging Markets

The neoclassical theory illustrates that the capital will flow from the capital-rich economies towards the capital-poor states. However, it is generally observed that the capital does not move from high-income to low-income economies. This contradictory behavior of global capital flows is called the Lucas paradox. According to Alfaro, Kalemli-Ozcan, &amp; Volosovych (AKV) model, the Lucas paradox can be entirely explained by the institutional quality. In the light of AKV notion, this paper examines the role of institutional quality in explaining the Lucas paradox. The empirical analysis involves 17 major emerging economies of the world by using panel data for the period 1999-2018. The GMM estimation reveals that the Lucas paradox is explained by the institutional quality in case of FDI flows only. However, institutional quality indicators generally remain unable to explain the paradox in case of portfolio equity capital flows. Moreover, financial development is found to be a significant determinant of portfolio equity flows in the emerging markets. Finally, we suggest that the financial flows to the emerging markets are sensitive to the nature of the capital flows.
Design of an Helical Spring using Single-solution Simulated Kalman Filter Optimizer

&#x0D; &#x0D; &#x0D; &#x0D; Optimization is one of the important process in solving engineering problems. Regrettably, there are numerous problems in practical optimization that cannot be solved flawlessly within reasonable computational effort. Thus, metaheuristic approach is often useful to get near-optimal solution when the best solution is not achievable. This paper demonstrates the usefullness of a metaheuristic algorithm called single-solution simulated Kalman filter (ssSKF) in helical spring design, which is an example of structural engineering design problem. The ssSKF is a single agent-based optimization algorithm based on the Kalman filtering. The solution obtained by the ssSKF is compared againsts the genetic algorithm, co-evolutionary particle swarm optimization, co-evolutionary differential evolution, bat algorithm, and artificial bee colony.&#x0D; &#x0D; &#x0D; &#x0D;
k-Means and GIS for Mapping Natural Disaster Prone Areas in Indonesia

The number of natural disasters in Indonesia is very high frequency.However, the data collected based on natural disasters has complex structures.One of the efforts to make prevention design is grouping the areas of natural disasters based on their similarities.The proposed methods are k-means to cluster areas and Geographical Information System (GIS) to improve visualization of yielded clusters.This result showed that the best cluster was seven clusters based on root mean square standard deviation (RMSD).Although kmeans obtained the best number of clusters, however, it was difficult to present the clusters of natural disaster areas in a map.Therefore, the GIS method can be a useful tool to improve the visualization of k-means.