Reviewerly/exHarmony huggingface dataset:
Auto-generation of the customer questions and their ranking in e-commerce system

Abstract The paper proposes the framework for auto-generation of the customer questions to help customers make choice of the best products based on their needs. The task of the optimal search organisation from millions of products is crucial for e-commerce systems. We propose the approach based on the following stages: the web-scraping stage of products reviews sites, pre-processing stage to detect key-phrases based on TextRank algorithm and POS-tagging, the subjective probabilities estimation stage to detect questions estimations and their ranking based on TextRank algorithm and Bayesian rule.
KSC: A Fast and Simple Spectral Clustering Algorithm

Spectral clustering (SC) has been successfully used in the field of machine learning and data mining. Although it has been widely used, one significant drawback of SC is its expensive computation cost. As data size and dimensions increase, the time overhead of SC is unbearable. In order to overcome this issue, we propose a novel SC algorithm KSC to reduce the time cost. In this paper, we use Markov chain Monte Carlo sampling to approximate the seeding step of k-means++, and get the initial cluster centers of k-means. At the same time, we redefine the data set of the SC by calculating the distance from the sample point to the center point to achieve dimensionality reduction to reduce time overhead. At last, we perform extensive experiments comparing KSC against other methods both real and synthetic datasets. Our results show that KSC has significantly improved its speed while maintaining clustering accuracy.
Back propagation network based prediction on map-reduce platforms

In this paper, we provide a significant model illustrating the challenges to handle terabytes of data efficiently. Proposed paper aims to develop a model which predicts the success rate of a product before its in launch into the market. Artificial Neural Network using back propagation (BANN) has been implemented for product prediction. Huge volume of e-commerce customer reviews and rating dataset is used for testing this model. This nonlinear method uses Broyden-Fletcher-Goldforb-Shanno (BFGS) training on multiple network layer. Our method gives improved prediction accuracy. The results of this model is to introduce a sustainable and successful product into the market, which in turn helps to improve the quality of the product.
The Rumyantsev Museum’s History in Russian Memoir Sources

The article provides an analytical review of the memoirs on the history of the Moscow Public and Rumyantsev Museum (now the Russian State Library), which started with the collections of count N.P. Rumyantsev. The author considers the memories on the Rumyantsev Museum since its foundation in St. Petersburg in 1828, its transfer to Moscow, the activities of the Moscow Public and Rumyantsev museums from 1862 to 1917. The author notes the role of memoir literature for the study of the history of librarianship. The article gives description of the sources containing the unique bibliogra­phic information, which reflects publications of me­moirs, diaries, letters about the Rumyantsev Museum in St. Petersburg and Moscow. The article presents the history of the transfer of the Rumyantsev Museum from St. Petersburg to Moscow and gives fragments from the memoirs of V.V. Stasov and “Diary” of V.F. Odoevsky about this event. The author also presents publications dedicated to the leaders of the Rumyantsev Museum and analytically discloses the memoirs placed in them. The article gives excerpts from memoirs, diaries, letters, which reflect the events and facts from the history of foundation and formation of the Rumyantsev Museum, the role of individuals who have made great contribution to the development of the Museum and its library. The author presents information from the memoirs, diary entries of readers about visiting the Rumyantsev library, their contribution to the accession of collections. The article also gives information about the publication of memoirs of the descendants of V.D. Golitsyn, the last Director of the Moscow Public and Rumyantsev Museums who became its first Soviet Director. The author reveals the potential of memoir materials for further research of the history of the Rumyantsev Museum and its library, its role in the history of culture and spiritual life of Russian society.
Enhancing Artificial Bee Colony Algorithm with Directional Information

Artificial bee colony (ABC) algorithm is a swarm intelligence based optimization technique, which has attracted wide attention from different research fields. In the basic ABC, however, the same solution search equation is used in both of the employed bee phase and onlooker bee phase, which performs well in exploration but poorly in exploitation. To address this concerning defect, in this paper, we propose an improved ABC variant by designing a mechanism of utilizing directional information. In this mechanism, we first construct a pool of differential vectors in the employed bee phase, and then utilize a differential vector randomly selected from the pool as directional information to guide search in the onlooker bee phase. Furthermore, we propose two novel solution search equations based on the current best solution and some good solutions with the aim of balancing exploration and exploitation. Experiments are conducted on a set of 22 well-known benchmark functions, and the results demonstrate that our proposed approach shows promising performance.
THE USE OF ROBOTICS AND 3D TECHNOLOGIES IN THE STEM EDUCATION DEVELOPMENT

The paper is devoted to the issues of exploring the possibilities of using robotics and 3D technologies in the context of STEM education development. Prospects for the development of robotics and 3D technologies in terms of Industry 4.0 concept formation are considered to solve the research objectives. The analysis of the robotics and 3D technology global trends has shown that these industries are currently dynamically developing and are already being used in many areas of human activity. This leads to the conclusion that there is a need to train relevant specialists, which in turn leads to the need to update the content of school and university education in accordance with the current requirements. Therefore, the question of implementation of the robotics and 3D technologies into the learning process of secondary and higher education institutions as an indispensable component of future specialists training in the field of high technologies is of particular importance. The paper provides examples of integration of 3D technologies and robotics in science and technology. The ways of using robotics and 3D technologies in the learning process, in particular through project activities, are outlined. Robotics and 3D technologies are popular and effective methods for the study of important fields of science, design and based on the active use of modern technologies in production, ICT and high intellectual level of professionals who will work in an innovative economy. The study examines the use of 3D technologies and robotics in the implementation of a research training STEM project. The use of project activities contributes to the development of students' teamwork skills, the development of independent search and creative activity, the formation of cross-curricular competences. The results of the study have shown the importance of developing modern approaches to teaching robotics and 3D technologies as promising fields of STEM education.
Community-based organizations and the provision of agricultural inputs for rural development in cross River State, Nigeria

With the rapid growth in Nigeria’s population, there has also been an increase in the demand for food. A major way of ensuring that food supply does not exceed demand is to invest in agriculture. Successive Nigerian governments have placed emphasis on agriculture as a means of ensuring food security. However, the extent to which this has translated into practice is debatable. Therefore, other agencies, of necessity, have begun to contribute to agricultural development. Such agencies include non-governmental organizations and community-based associations. This study therefore examined the role of such associations in rural development through the provision of agricultural inputs. The study sample was 807 accidentally selected samples of associates of community based associations in Cross River State, Nigeria. Data collection was done through a structured questionnaire, while the analysis was done using the simple regression statistical technique. .The result showed that there is a strong positive relationship between the activities of community based associations in the provision of agricultural input and rural development.Keywords: Community-based Associations, agricultural input, rural development, food production, population
Info-Detection: An Information-Theoretic Approach to Detect Outlier

Outlier detection is one of major task in unsupervised learning. We propose a cluster analysis based outlier detection method called Info-Detection. Info-Detection determines the number of outliers automatically and captures the global property of the provided data. To implement Info-Detection and overcome the global computational complexity, we use principal sequence of partition, which we improve one order of magnitude faster than the original version. Experiments show that compared with other outlier detection methods, Info-Detection achieves better accuracy with an affordable time overhead.
Implicit-Key Attack on the RSA Cryptosystem

In this paper, we address the security evaluation issue of the RSA cryptosystem with implicitly related private keys. We formulate the attack scenario and propose a novel implicit-key attack using the lattice-based method. When given public information $$(N_1,e_1)$$, $$(N_2,e_2)$$ and the amount of shared bits of the private keys $$d_1$$ and $$d_2$$, one can conduct the implicit-key attack to factor $$N_1, N_2$$ in polynomial time under a certain condition. We show that the RSA cryptosystem is more insecure when taking the implicitly related keys into consideration. The experimental results are provided to verify the validity of our proposed attack.
Research on 3D Visualization Design of Industrial Production Management Software

Abstract Affected by the wave of Industry 4.0, the Internet and IoT technologies are becoming more mature. However, when selecting industrial production management software, many companies still use traditional software management methods. This article describes a 3D visualization upgrade of enterprise implementation management software that describes and demonstrates the important role of 3D visualization throughout the production management process. In addition, this article studies the role of 3D visualization in the management software interface from the perspective of academic research, so as to achieve the purpose of improving the efficiency and operational efficiency of workshop management. And as a reference for industrial production enterprises in 3D visual design.
Comparison of Motion Similarity Using Image

Recent motion recognition devices are widely used as inputs to analyze the interaction with motion and sports and entertainment content. However, the performance and efficiency of the conventional motion recognition apparatus are degraded despite the good quality information using a plurality of cameras and sensors. In this paper, we propose an algorithm that can be used as input means for posture analysis, posture supplementation, and interaction with contents in sport and entertainment by overcoming limitations of conventional motion recognition devices and using specific posture similarity judgment.
Securing Continuity of Natural Disaster Statistics to Improve the Utilization of Time Series Analysis

As the planet's average temperature continues to rise because of global warming, climate change is causing an increasing number of unpredictable weather-related disasters, which correspondingly cause increasing damages.Natural disaster statistics mainly include data on lives lost, property damaged, and recovery; however, because of changes in administrative and statistical survey items, natural disaster statistics are currently not collected as time series statistics that include historical data.Therefore, this study examines the method securing continuity of natural disaster statistics to improve the utilization of time series analysis.The results are expected to increase the continuity of natural disaster statistics and serve as more accurate basic data that facilitate direct comparisons and analysis of data from different times, which was previously impossible.
Klasifikasi Kepribadian Berdasarkan Data Twitter dengan Menggunakan Metode Support Vector Machine

Abstrak Jejaring sosial menjadi media yang banyak digunakan dan populer untuk penyebaran informasi serta fasilitator interaksi sosial. Interaksi pengguna memberikan wawasan berharga tentang karakteristik dan perilaku individu. Twitter merupakan layanan microblogging. Aktifitas utama twitter adalah mem-posting teks yang pendek (tweet) melalui web atau mobile.Kepribadian merupakan sebuah sikap yang unik terhadap seseorang dalam berperilaku. Untuk mengetahui kepribadian seseorang berdasarkan statusstatus (tweet) yang mereka tulis di Twitter dilakukan teknik klasifikasi teks menggunakan metode Multiclass Support Vector Machine. Bahasa yang digunakan adalah Bahasa Indonesia. Hasil pengujian dengan menggunakan 10 Fold Cross Validation menunjukan Akurasi yang didapatkan dengan menggunakan strategi One-against-One dan One-against-All dan membandingkan penggunaan dengan dan tanpa Symbol removing. Hasil percobaan didapatkan menggunakan One-against-One dengan dan tanpa Symbol removing menghasilkan akurasi 86.55% dan 86.88%, sedangkan menggunakan One-against-All dengan dan tanpa Symbol removing 85.24% dan 85.07%. Kata kunci : Kepribadian, Twitter, klasifikasi Teks Abstract Social networks have become a widely used and popular media for disseminating information and facilitating social interaction. User interaction provides valuable insights about individual characteristics and behavior. Twitter is a microblogging service. The main activity of Twitter is posting short texts (tweets) via the web or mobile. Personality is a unique attitude towards someone in their behavior. To find out someone's personality based on the statuses (tweets) they wrote on Twitter, a text classification technique was performed using the Multiclass Support Vector Machine method. The language used is Indonesian. The test results using 10 Fold Cross Validation show the accuracy obtained by using the One-against-One and One-against-All strategies and compare the use with and without Symbol removing. The experimental results obtained using One-against-One with and without Symbol removing produces an accuracy of 86.55% and 86.88%, while using One-against-All with and without Symbol removing 85.24% and 85.07%. Keywords: personality,Twitter,text classification
TNT: An Effective Method for Finding Correlations Between Two Continuous Variables

Determining whether two continuous variables are relevant, either linearly or non-linearly correlated, is a fundamental problem in data science. To test whether two continuous variables have a linear correlation is simple and has a much complete solution, but to judge whether they are in nonlinear correlation is far more difficult. Here, we propose a novel method, Tight Nearest-neighbor prediction correlation Test (TNT), to determine whether two continuous variables are nonlinearly correlated. TNT first use the values of one variable to construct a tight neighborhood structure to predict the value of the other variable and then use the sum of squared errors to measure how well the prediction is. A permutation test based on the sum of squared errors is employed to determine whether two continuous variables are relevant. To evaluate the performance of TNT, we performed extensive simulations comparing with seven existing methods. The results on both simulation and real data demonstrate that TNT is an efficient method to test nonlinear correlations, particularly for some nonlinear correlation which existing methods cannot solve, such as “ring”.
Edge based Crime Assistance System with Cloud Computing

Nowadays, the level of crime is increasing more and more in every corner of the world. With the rising rate of criminal activities, the identification process of the person who committed crime takes a lot of time. Most of the crime reporting systems currently are reported personally. In order to create a fast and reliable crime reporting system, the face identification in crime assistance system between edge and cloud computing is proposed. The processing power of ubiquitous devices (e.g. smartphones, sensors, and actuators) are very powerful today. They allow capturing the image of the criminal anywhere at any time. They also have the capability of detecting faces from captured image. Therefore, the face detection process using Haar face detection and face identifier generation process using Local Binary Pattern (LBP) from the captured image can take place on the edge devices. Face identifier matching is proceeded on the cloud. The paper proposed the edge-based face identification system and represents the analysis result of face detection time, matching time and total processing time of the proposed system with the increasing number of test image data set. The results show the effective usage of edge devices and improve the efficiency of face identification.
Rhythm Tap Technique for Cross-Device Interaction Enabling Uniform Operation for Various Devices

This paper proposes a novel interaction technique to transfer data across various types of digital devices in uniform a manner and to allow specifying what kind of data should be sent. In our framework, when users tap multiple devices rhythmically, data corresponding to the rhythm (transfer type) are transferred from a device tapped in the first tap (source device) to the other (target device). It is easy to operate, applicable to a wide range of devices, and extensible in a sense that we can adopt new transfer types by adding new rhythms. Through a subjective evaluation and a simulation, we had a prospect that our approach would be feasible. We also discuss suggestions and limitation to implement the technique.
PbindFx

The class PbindFx, contained in miSCellaneous_lib [1], an extension of SuperCollider (SC, [5]), enables the sequencing of arbitrary audio effect (fx) graphs and corresponding parameter sequences. In this context an fx graph means a graph of SC SynthDefs, i.e. synthesis and/or processing instruments, which themselves are naturally defined by graph structures. Therefore PbindFx can be understood as a flexible meta-synthesis tool, including the possibility of realtime replacement of all of its constitutive elements. When applied with sequencing of short events unusual variants of granular synthesis can be achieved. This special application has been demonstrated in my artistic research project kitchen studies, which is documented in the artistic research database Research Catalogue [2], miSCellaneous_lib includes the source code of the fixed media piece of the same name.
Two new operations on intuitionistic fuzzy matrices

Besides the various basic operations for intuitionistic fuzzy matrices already available in literature, two new operations are introduced in this paper. By using these operations scalar multiplication and power operations are constructed for an intuitionistic fuzzy matrix and some new relations are established. Also, the behavior of modal operators over these operations are studied.
Method of Development the Behavior pattern

The paper presents methods for creating and understanding user behavior patterns. Kmeans algorithm is used for users grouping. After that, the sequential associative rules are built for each cluster separately. The concept of association rules is introduced; the method of finding dependencies is developed. The accuracy of model is evaluated.
THE READINESS TEACHERS OF NATURAL SUBJECTS TO USE MOBILE TECHNOLOGIES FOR TEACH STUDENTS

The article deals with the features of the mobile learning technology emergence. The genesis of mobile learning in the field of distance and e-learning and its development in the field of blended learning are clarified. The foreign experience and research of domestic scientists on the introduction of mobile learning in the educational process of Ukrainian educational institutions are analyzed. The relevance of the application of this innovative learning technology is clarified according to carried out the literary analysis, but there is a lack of study of mobile technologies using in the educational process, in particular in natural sciences study. The author conducted an experimental study in the form of questionnaires to find out the state of mobile education in general secondary education and to identify the readiness of natural subjects’ teachers to use mobile technologies to teach students. Teachers use mobile technology in lessons, mainly for organizing work with e-textbooks and searching for material online, and less often for demonstrating video experiences in the classroom. The barriers to the use of mobile technologies are the follows: lack of information about the educational opportunities of mobile technologies, lack of methods for its use, the influence of administration policy on the educational process, as well as the age of teachers. The article presents examples of mobile technologies that are appropriate to use in teaching natural sciences. Mobile applications are analyzed and their didactic capabilities are identified during specific lesson activities. The results of the study confirmed teachers' readiness to use mobile learning, in particular by using mobile technologies during the lesson. This paper describes the advantages of mobile devices in the application of its distance learning capabilities. The prospects of using mobile technologies in the educational process during pupils study are proved. The article outlines the prospects for future research of the methodology development for using mobile devices in teaching natural subjects.
A Robust People Tracking Method in Multiple Cameras

This paper proposes a robust people tracking method on videos obtained from multiple cameras. Yoshida et al. proposed a method that combines re-identification and video tracking technologies to track people in multiple cameras. However, this method is not robust enough because it uses only one type of features. The method fails to track people when some type of features is not available or when we use features of which characteristics can be easily changed with time or place. We propose a method to enhance the robustness by utilizing multiple types of features in this paper. We can track people even if some type of features are not available. Experimental results showed that 76% of images obtained from the same person were identified in average. In this demo, we show the results of the proposed method.
Photo Filter Recommendation Through Analyzing Objects, Scenes and Aesthetics

Photo filters are widespread-they give photos a stylized look without requiring the user to have professional knowledge of image processing. However, with the increasing number of photo filters and limited display size of mobile devices, selecting a proper filter for a given photo can be a tedious task for camera phone users. In this paper, we present a photo filter recommendation approach to address this problem. In particular, we rely on state-of-the-art deep learning approaches to extract objects, scenes, and image aesthetics reliably and represent them using deep features as the observations of our recommendation model. Furthermore, we collect 68,400 filtered photos from Instagram to learn the relationships among objects, scenes, aesthetics, and filter types. Experimental results using the FACD benchmark dataset demonstrate the state-of-the-art recommendation performance of the proposed approach; these results show that objects, scenes, and aesthetic attributes influence filter preference.