Reviewerly/exHarmony huggingface dataset:
Message from the Blockchain 2019 Program Chairs

As a promising technique to achieve decentralized consensus, blockchain has been successfully applied into digital currency, e.g., Bitcoin, for serving as a public ledger for transactions.Its secure design for supporting a distributed computing system with high fault tolerance is attracting wide attention all over the world.Blockchain has a great potential to create new foundations for our socio-economic systems by efficiently establishing trust among people and machines, reducing cost, and improving speed and efficiencies of transactions.On one hand, blockchain will play an important role for secure decentralization in such emerging fields as Internet of Things, Cyber Physical Systems, edge computing, social networking, crowdsourcing and next generation wireless communications, and even more other fields.On the other hand, its advance should be further evolved in terms of scalability, privacy, efficiency, flexibility, availability and high dependability.The technical program of Blockchain 2019 consists of 28 regular papers, 17 short papers, and 23 symposium papers selected by the program committee from 176 full submissions in response to the call for papers, as well as 10 workshop papers.This year, the acceptance rate of main conference regular papers is only 15.9%.All submissions were reviewed by the program committee members.These high-quality submissions cover many hot research topics, including smart contract and distributed ledger, blockchain and Bitcoin security, distributed consensus and fault tolerance mechanisms, applications with blockchain technique, blockchain-based lightweight data structures for IoT data, blockchain in social networking, blockchain in 5G, etc.
Various Real Time Chat Bots and Their Applications in Human Life

Chatbots are also referred to as virtual assistance devices. They are the basic forms of artificial intelligence software that can imitate human conversations. Chatbots are relatively a new technology. The main goal of this survey paper is to provide the information about the various existing chatbots and their history of evaluation, and the applications of various chatbots in different domains. Chatbots are applied in fields like medical, E-commerce, business, education, banking, customer services, entertainment, etc. Chatbots cab be analyzed and improved. Main goal of any chatbot is to allow the user to make a natural conversation with machine. A conversational system consists of dialogue management, speech recognition, speech synthesis and conversation generation.
Author Correction: Infectivity, susceptibility, and risk factors associated with SARS-CoV-2 transmission under intensive contact tracing in Hunan, China

A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-22487-4
UPAYA PEMERINTAH DALAM MENANGANI BENCANA ALAM TANAH LONGSOR Studi Kasus di Desa Cihanjuang Kecamatan Cimanggung Kabupaten Sumedang

Indonesia is famous for its areas that are prone to landslides. For example, in Cihanjuang Village, Cimanggung District, Sumedang Regency, which in early 2021 had experienced a serious landslide disaster. This landslide phenomenon requires an in-depth study so that the arrangement and management is carried out appropriately, where the role of the government here is very large to be able to take the right policy on this problem. Thus, the purpose of this paper is to determine the cause of the landslide disaster and to identify and analyze the government's efforts in dealing with the landslide disaster. This paper uses descriptive qualitative research methods using data derived from secondary data. Based on the results, it was found that the landslide that occurred in Cihanjuang Village was caused by high rainfall, steep slopes, poor drainage system and lack of vegetation. However, the government already has several efforts to be undertaken, including long-term, short-term post-disaster and mitigation. Based on the analysis that has been made, the recommendations for this problem are increasing awareness and socialization for the community, increasing mitigation and monitoring the policy that have been made.
Exploration of the Online Teaching Method of English Production and Education Integration Based on Big Data

In the context of my country’s sustained economic development, big data has also been developed to a certain extent, and has now become an issue of great concern to all industries. In the field of education, English is a very important subject. It carries out the entire study life of students. In order to cultivate English professionals, broaden students’ horizons, and achieve integration of production and education, it is necessary to draw lessons from big data. As a basis, online teaching methods are implemented in the process of English teaching to improve student learning efficiency and better realize the integration of production and education. Based on this, this article analyzes the online teaching of English production and education integration based on big data, hoping to provide a certain degree of reference for the research of latecomers.
Fraud Detection of Credit Card using Data Mining Techniques

The handling of credit card for online and systematic purchase is booming and scam associated with it. An industry of fraud detection where cumulative rise can have huge perk for banks and client. Numerous stylish techniques like data mining, genetic programming, neural network etc. are used in identify fraudulent transaction. In online transaction, Data mining acquire indispensable aspect in discovery of credit card counterfeit. This paper uses gradient boosted trees, neural network, clustering technique and genetic algorithm and hidden markov model for achieving upshot of the fraudulent transaction. These all model are emerging in identifying various credit card fraudulent detection. The indispensable aims to expose the fraudulent transaction and to corroborate test data for further use. This paper presents the look over techniques and pinpoint the top fraud cases.
Reconstruction of three-dimensional maps based on closed-form solutions of the variational problem of multisensor data registration

A closed-form solution is proposed for the problem of minimizing a functional consisting of two terms measuring mean-square distances for visually associated characteristic points on an image and meansquare distances for point clouds in terms of a point-to-plane metric. An accurate method for reconstructing three-dimensional dynamic environment is presented, and the properties of closed-form solutions are described. The proposed approach improves the accuracy and convergence of reconstruction methods for complex and large-scale scenes.
Dual Denoising Autoencoder Based on Neighbor-Attention Module for Implicit Feedback Recommendation

In recent years, the content-aware recommendation of implicit feedback data using denoising autoencoder has become the mainstream technology in the field of recommendation systems.However, the technology still faces the following main problems: the denoising autoencoder structure ignores the intrinsic structural duality property of the model, and the recommendation of traditional implicit feedback data based on the denoising autoencoder ignores the neighbor item information.To solve these problems, a dual denoising autoencoder based on neighbor-attention module (DDAENAM) was proposed.The encoder and the decoder were designed as a dual closed loop, and the dual attribute of the structure was used to train the encoder and the decoder at the same time, such that the feedback signal between the encoder and the decoder can be shared in the model.Then, a neighbor-attention module was proposed to extract word embedding and neighboring item information.Results show that in three standard implicit feedback datasets including CiteULike-a, MovieLens-20M, and Amazon-Books, DDAENAM achieves the best result compared with the state-of-the-art models, with an average improvement of 2.4%.Under the review of evaluation indices select recall rate, DDAENAM is slightly lower than the joint representation learning model (JRLM), except for the MovieLens-20M.The proposed DDAENAM in this study achieves the best result with the combination of three modules including attention module, termattention module, and neighbor-attention module, indicating the effectiveness of the DDAENAM module.
Finding Best Clustering For Big Networks with Minimum Objective Function by Using Probabilistic Tabu Search

Fuzzy C-means (FCM) is a clustering method used for collecting similar data elements within the group according to specific measurements. Tabu is a heuristic algorithm. In this paper, Probabilistic Tabu Search for FCM implemented to find a global clustering based on the minimum value of the Fuzzy objective function. The experiments designed for different networks, and clusterâ€™s number the results show the best performance based on the comparison that is done between the values of the objective function in the case of using standard FCM and Tabu-FCM, for the average of ten runs.
Scheduled Multi-task Learning for Neural Chat Translation

Neural Chat Translation (NCT) aims to translate conversational text into different languages.Existing methods mainly focus on modeling the bilingual dialogue characteristics (e.g., coherence) to improve chat translation via multi-task learning on small-scale chat translation data.Although the NCT models have achieved impressive success, it is still far from satisfactory due to insufficient chat translation data and simple joint training manners.To address the above issues, we propose a scheduled multi-task learning framework for NCT.Specifically, we devise a three-stage training framework to incorporate the large-scale in-domain chat translation data into training by adding a second pre-training stage between the original pre-training and fine-tuning stages.Further, we investigate where and how to schedule the dialogue-related auxiliary tasks in multiple training stages to effectively enhance the main chat translation task.Extensive experiments on four language directions (English↔Chinese and English↔German) verify the effectiveness and superiority of the proposed approach.Additionally, we will make the large-scale indomain paired bilingual dialogue dataset publicly available for the research community.
Design of Band-Pass Fir Filter Using Modified Social Group Optimization Algorithm and its Implementation on FPGA

This paper proposes a method for the design of a type-I band-pass Finite Impulse Response (FIR) filter using Modified Social Group Optimization. To achieve better performance in filter response, few modifications were made in the basic SGO algorithm. When compared with the well-known Parks- McClellan (PM) and improved Sine Cosine algorithm, this modified version shows better results in terms of pass-band ripple and stop-band attenuation. Moreover, this paper discusses the hardware implementation aspect of filters in the FPGA platform.
BRIGHTEARTH: PIPELINE FOR ON-THE-FLY 3D RECONSTRUCTION OF URBAN AND RURAL SCENES FROM ONE SATELLITE IMAGE

Abstract. With the growth of the availability and quality of satellite images, automatic 3D reconstruction from optical satellite images remains a popular research topic. Numerous applications, such as telecommunications and defence, directly benefit from the use of 3D models of both urban and rural scenes. While most of the state-of-the-art methods use stereo pairs for 3D reconstruction, such pairs are not immediately available anywhere in the world. In this paper, we propose an automatic pipeline for very-large-scale 3D reconstruction of urban and rural scenes from one high-resolution satellite image. Convolutional neural networks are trained to extract key semantic information. The extracted information is then converted into GIS vector format, and enriched by both terrain and object height information. The final classification step is applied, yielding a 16-class 3D map. The presented pipeline is operational and available for commercial purposes under the BrightEarth trademark.
Forecasting the Number of Students in Multiple Linear Regressions

The most important element of higher education was students, therefore every university must continue to improve services in the future, and one of them was by using decision support. This case could be done by utilizing the University of Big Data. Predicting the number of prospective students in higher education was done by utilizing data mining and multiple linear regression approaches. By using 2 independent variables, namely administration costs (X1), accreditation score (X2), and the number of students who was registered each year as dependent variable (Y). For the test data, it used database for the last 13 years. By using multiple linear regression, the intercept value was sought and the coefficient of determination until the regression coefficient was obtained with the equation Y = 45.28 + -0.02.X1 + 121.58.X2, noted that if X2 was constant, the increasing of one unit was in X1 would have the effect of increasing -0.02 units on Y. Secondly, if X1 was constant, the increasing of one unit was in X2, would have the effect of increasing 121.58 units in Y. Thirdly, if X1 and X2 were equal to zero, the magnitude of Y was 45.28 units. Therefore, the proposed approach could be provided the acceptable predictive results.
EvoRobogami

We study the effects of injecting human-generated designs into the initial population of an evolutionary robotics experiment, where subsequent population of robots are optimised via a Genetic Algorithm and MAP-Elites. First, human participants interact via a graphical front-end to explore a directly-parameterised legged robot design space and attempt to produce robots via a combination of intuition and trial-and-error that perform well in a range of environments. Environments are generated whose corresponding high-performance robot designs range from intuitive to complex and hard to grasp. Once the human designs have been collected, their impact on the evolutionary process is assessed by replacing a varying number of designs in the initial population with human designs and subsequently running the evolutionary algorithm. Our results suggest that a balance of random and hand-designed initial solutions provides the best performance for the problems considered, and that human designs are most valuable when the problem is intuitive. The influence of human design in an evolutionary algorithm is a highly understudied area, and the insights in this paper may be valuable to the area of AI-based design more generally.
Teleoperation of High-Speed Robot Hand with High-Speed Finger Position Recognition and High-Accuracy Grasp Type Estimation

This paper focuses on the teleoperation of a robot hand on the basis of finger position recognition and grasp type estimation. For the finger position recognition, we propose a new method that fuses machine learning and high-speed image-processing techniques. Furthermore, we propose a grasp type estimation method according to the results of the finger position recognition by using decision tree. We developed a teleoperation system with high speed and high responsiveness according to the results of the finger position recognition and grasp type estimation. By using the proposed method and system, we achieved teleoperation of a high-speed robot hand. In particular, we achieved teleoperated robot hand control beyond the speed of human hand motion.
GLC-STATISTICS: A WEB-BASED SPATIAL STATISTICS SYSTEM FOR GLOBAL LAND COVER DATA

Abstract. Land cover data is one of the principle sources to understand changes of the earth, develop sustainable globe, conduct environment change studies, land source management, and many other societal benefit areas. Globeland30 is the first multi-temporal (for years 2000 and 2010) global land cover datasets at 30-meter resolution. Based on this dataset, an online geo-statistical system has been developed named “GlobeLand30 GSA”. It helps users from different technical expertise backgrounds to visually explore and quantitatively analyse global land cover data. Different from other Web geoprocessing services and applications or desktop GIS software, this system has developed algorithms to correct multi-type errors in computing global land cover statistics, and applied optimal strategies of pre-organizing geography and auxiliary datasets in order to provide rapid response to multiple co-occurrence user-requests. This paper presents the system architecture and the developed data processing strategies, and also the related accuracy and efficiency assessment of GlobeLand30 GSA. The result shows that the system can provide accurate and efficient land cover statistical analysis for various size and shape of user requested areas, and is capable of handling as much as 200 concurrent user-requests with reasonable response time using a low-end desktop computer system environment.
The Effect of DuoLingo and SPADA to Teach Listening to Students with Different Achievement Levels

There are various levels of students’ achievement in English listening classrooms. Students with different achievement levels (i.e. high achieveing students and low achieveing students) respond differently towards teaching media as well. The aim of this research was to examine the difference of students’ performance, both before and after they were taught by using two English listening teaching media namely DuoLingo mobile application and SPADA (Sistem Pembelajaran Daring Indonesia) platform. As a quantitative research it employed the Experimental factorial 2 x 2 design. Students were divided into two groups based on their achievement classification. The two groups involved in this study were high achieveing students and low achieveing students, and each group were further divided into another two group to be taught with DuoLingo mobile application a for one group and with Sistem Pembelajaran Daring Indonesia platform for the other group. It was found that both Duo Lingo mobile application and Sistem Pembelajaran Daring Indonesia platform does contribute to an increase in students’ performance for both high achievers and low achievers. This research is expected to contribute as a reference for English teachers to choose teaching media to teach listening to students with different achievement levels, and also as a further research regarding media usage for teaching listening to students with different achievement levels.
Tvorba modelu kľúčových kompetencií lídra v edukácii

Leadership in education has become a priority issue in the educational policy of countries all over the world. As countries try to attune their educational systems to present-day needs, they also modify their expectations concerning schools and their management. In regard to needs and challenges related to the transformational process of schools in the Slovak Republic, the preparedness of school managers and teachers cannot be considered as sufficient. In this context, foreign approaches to the conceptualization of standards and requirements placed on leaders in the educational context can be considered as well elaborated, inspirational and relevant for the Slovak context. One of the objectives of the applied research project APVV 17-0557 (Psychological Approach to Cre‑ ation, Implementation, and Verification of Educational Leader›s Competence Model Development) was to develop a theoretically based and empirically evaluated competency model for leaders in the educational context, as a conceptual framework for defining professiona competencies of school managers and teachers for leader competencies relevant for the educational context. For the process of its development, the methodological procedure for producing a competency model proposed by Marrelli et al. (2005) has been applied. After defining the objectives and analyses of both foreign and Slovak standards as well as competency standards and reviewing research findings on effective leadership within the educational context, the Model of key competencies for school leaders and teacher leaders was specified. The three dimensional model consists of five key competencies ([1] openness to change and managing change; [2] focus and commitment to the permanent development of the self and the organization; [3] effective communication and interactions; [4] supporting collaborative culture; [5] building external networks and partnership) applied within five domains that correspond to the roles of leaders in education ([1] vision, values and culture; [2] professional skills and problem solving; [3] leader in relation to the self – personal skills, abilities and values; [4] leader in relation to the other – focusing on others and leading others; [5] leader in relation to the system – strategic leadership and leadership of change). The key competencies within individual domains are specified on the empirical level of numerous indicators (as demonstrated within one specific key competency). The paper discusses the applicability of the proposed design into educational programs development aimed at supporting key competencies of educational leaders, as well as to the identification of educational needs of education participants and to evaluating effects of educational programs administered. The paper also reports on the present experience of the research team with the abovementioned applications of the model used in the development and implementation of the educational program for school leaders teacher leaders and undergraduate future teachers, as well as further steps planned for verifying its validity.
Technology Assessment as a Field of Professional Competency of Technoscience Society Engineers

The article is devoted to the competency model analysis of a technical university graduate and its compliance for the technoscience society. Due to the transition to the society of technoscience, we may notice the shift toward requirements for the future technical specialist in conditions of the society&#x2019;s ongoing digital transformation. Central technoscience drivers are information and communication technologies, Artificial Intelligence, and Big Data. The paper focuses on the fact that modern engineer is in demand for the development, design, and manufacture of industrial products and sophisticated social engineering systems. Along with technical and technological optimality, organizational and managerial rationality, and economic efficiency, their development must necessarily include an extensive socio-humanitarian component the human dimension itself. In the digital space, a modern specialist should be able to model and calculate the impact of new technology on a person, their living conditions, environment, social risks and possible consequences of the complex high-tech systems operation. According to the authors&#x2019; research and a wide range of evidence, the paper shows that the modern labor market is already demonstrating these trends through changes in the role behavior of an engineer in the new economy requirements. An engineer&#x2019;s competence model must necessarily include a whole set of social knowledge and skills to contribute to its effective integration into modern high-tech digital technologies. An engineer of the new digital reality must recognize social foundations and social conditionality development of complex technical systems and technologies. He should also be able to identify the problem area in the interaction of nature, technology, and humanity, apply standard analysis procedures to the problems of this interaction, find ways to overcome these problems, and possess methods of analysis and risk forecasting.