Reviewerly/exHarmony huggingface dataset:
Detection of Representative Variables in Complex Systems with Interpretable Rules Using Core-Clusters

In this paper, we present a new framework dedicated to the robust detection of representative variables in high dimensional spaces with a potentially limited number of observations. Representative variables are selected by using an original regularization strategy: they are the center of specific variable clusters, denoted CORE-clusters, which respect fully interpretable constraints. Each CORE-cluster indeed contains more than a predefined amount of variables and each pair of its variables has a coherent behavior in the observed data. The key advantage of our regularization strategy is therefore that it only requires to tune two intuitive parameters: the minimal dimension of the CORE-clusters and the minimum level of similarity which gathers their variables. Interpreting the role played by a selected representative variable is additionally obvious as it has a similar observed behaviour as a controlled number of other variables. After introducing and justifying this variable selection formalism, we propose two algorithmic strategies to detect the CORE-clusters, one of them scaling particularly well to high-dimensional data. Results obtained on synthetic as well as real data are finally presented.
Contribution Of Deposit Money Banks To Economic Growth In Nigeria

Over the years, the Nigerian banking industry has become deep and experienced and there has been a high level of improvement and development, but the economic growth has not attained the same level attained by the banking industry, hence, the reason for this study which focussed on the relationship between deposit money banks (DMBs) and economic growth in Nigeria from 1994 to 2017.The study was carried out on secondary data obtained from real gross domestic product (RGDP), money supply (M 2 ), bank credit (BC) and interest rate (INT).Data was sourced from the Central Bank of Nigeria statistical bulletin and National Bureau of Statistics annual report.Regression analysis was applied to estimate the relationship between deposit money banks (DMBs) and economic growth, while Ordinary Least Square (OLS) method was used to estimate the model with the aid of econometric view (E-view).Results obtained from the study showed that deposit money banks through money supply, credit to private sectors and interest rate charged on lending to borrowers significantly impacts on economic growth in Nigeria.The study therefore recommends that Central Bank of Nigeria should direct deposit money banks to make bank charges and interest rate to be at moderate level to enable manufacturers and other productive sectors of the economy to have access to credit at affordable rate.The study further recommends that government through supervision and regulation should make people have banking confidence in the banking system of Nigeria.
Pupil Center Detection Approaches: A Comparative Analysis

In the last decade, the development of technologies and tools for eye tracking has been a constantly growing area. Detecting the center of the pupil, using image processing techniques, has been an essential step in this process. A large number of techniques have been proposed for pupil center detection using both traditional image processing and machine learning-based methods. Despite the large number of methods proposed, no comparative work on their performance was found, using the same images and performance metrics. In this work, we aim at comparing four of the most frequently cited traditional methods for pupil center detection in terms of accuracy, robustness, and computational cost. These methods are based on the circular Hough transform, ellipse fitting, Daugman's integro-differential operator and radial symmetry transform. The comparative analysis was performed with 800 infrared images from the CASIA-IrisV3 and CASIA-IrisV4 databases containing various types of disturbances. The best performance was obtained by the method based on the radial symmetry transform with an accuracy and average robustness higher than 94%. The shortest processing time, obtained with the ellipse fitting method, was 0.06 s.
Variational Invariant Learning for Bayesian Domain Generalization

Domain generalization addresses the out-of-distribution problem, which is challenging due to the domain shift and the uncertainty caused by the inaccessibility to data from the target domains. In this paper, we propose variational invariant learning, a probabilistic inference framework that jointly models domain invariance and uncertainty. We introduce variational Bayesian approximation into both the feature representation and classifier layers to facilitate invariant learning for better generalization across domains. In the probabilistic modeling framework, we introduce a domain-invariant principle to explore invariance across domains in a unified way. We incorporate the principle into the variational Bayesian layers in neural networks, achieving domain-invariant representations and classifier. We empirically demonstrate the effectiveness of our proposal on four widely used cross-domain visual recognition benchmarks. Ablation studies demonstrate the benefits of our proposal and on all benchmarks our variational invariant learning consistently delivers state-of-the-art performance.
Preimpact Fall Detection for Elderly Based on Fractional Domain

The aging population has become a growing worldwide problem. Every year, deaths and injuries caused by elderly people's falls bring huge social costs. To reduce the rate of injury and death caused by falls among the elderly and the following social cost, the elderly must be monitored. In this context, falls detecting has become a hotspot for many research institutions and enterprises at home and abroad. This paper proposes an algorithm framework to prealarm the fall based on fractional domain, using inertial data sensor as motion data collection devices, preprocessing the data by axis synthesis and mean filtering, and using fractional-order Fourier transform to convert the collected data from time domain to fractional domain. Based on the above, a multilayer dichotomy classifier is designed, and each node parameter selection method is given, which constructed a preimpact fall detection system with excellent performance. The experiment result demonstrates that the algorithm proposed in this paper can guarantee better warning effect and classification accuracy with fewer features.
Water quality assessment based on interval-valued data cluster analysis

The interval data cluster analysis method was adopted to evaluate the water quality of the Huaihe River.This method could reduce the dimension of water quality data.When the dimension reduced, the calculation could be easier.With this method, there is no loss of information.Twenty-six sites in the river were selected to take samples.At each sampling site, four indicators were recorded each week in 2012.First, the original 1,326 × 4 matrix was converted into a 26 × 4 matrix.The traditional number elements in matrix were replaced by interval-valued data.Then, the interval data were standardized.For the standardized data, Euclidean-Hausdorff distance was used for hierarchical cluster analysis.To determine the needed clusters, the paper employed corrected rand index (CRI).According to the value of CRI, 26 sites were divided into six clusters.Samples in different clusters had different interval radius and pollution levels.Samples in cluster 1 are mildly polluted and the fluctuation of indicators' concentrations is not as violent as those in other clusters.Though the samples in cluster 2 and 4 rank in all the middle levels in terms of pollution, pollution in cluster 4 is relatively stable.Cluster 5 and 6 have higher concentrations of DO, COD Mn , and NH 3 -N, due to insufficient DO.The interval data cluster analysis method based on Euclidean-Hausdorff distance classifies the sample sites into multiple clusters without loss of information.
Design and Development of High-Performance Intelligent Disaster Management System in IoT Paradigm A Novel Architecture and Review on Challenges in real time Hazards Monitoring

In recent years India is suffering from various natural disaster which have great effect on social life of people and large burden on disaster management authority. The high frequency disaster causes a serious loss of property, life and present more complex damage. Disaster management involves the detailed process of disaster response. A High-Performance Intelligent Disaster Management System can realize the complete disaster avoidance and reduction from satellite mission planning, data production, data acquisition, with the application of remote sensing and managing integrated rapid service. The main objective of proposed work is to overcome the limitations of disaster management with a novel design and development of IoT based platform for the application of disaster management system.
THE SYSTEM OF PREPARING FUTURE PHYSICS TEACHERS FOR THE ORGANIZATION OF STUDENTS’ INNOVATIVE ACTIVITIES.

The article presents conceptual provisions used for preparation of future physics teachers for the organization of students’ innovation activities, which form the basis for the development of a model for training future physics teachers for the organization of students' innovation activities in the educational process. The main idea of this model is to implement a two-component system of training future physics teachers for the organization of students’ innovative activities; its first (educational) component is related to the students’ targeted learning activity in the context of forming readiness for organizing students’ innovation activities; the second (professional) component is implemented in the future teacher’s quasi- professional activities, during which he or she is the organizer of students’ innovation activities. This idea implies the continuity of teacher's professional training and provides for: 1) updating the content of professional training for a future physics teacher by developing educational programs, curricula, programs of disciplines, teaching materials that should take into account the innovative nature of the professional activities accomplished by a future teacher; 2) the use of a three-level structure for preparing future physics teachers for organizing students' innovative activities creates conditions for: appropriate theoretical training of students (first level); acquiring practical skills and experience in implementing innovative activities in the learning process (second level); students aim at organizing their innovative search in the educational process in physics (third level); 3) organization of the educational environment in the conditions of quasi- professional activity of students provides for the approximation of educational activities of future teachers to the forms of their professional activity in order to imitate those elements of pedagogical activity that future physics teachers will have to perform; 4) the use of methods, forms and means of training in the process of vocational training that contribute to attracting students to innovative search during the class and extracurricular forms of work; 5) increasing the role of independent work of students in the educational process by involving them in research activities on the creation and implementation of innovative educational products; 6) creation of conditions for the involvement of students in the leadership of the research work in the process of their preparation for participation in national and international competitions. The model offers a three-level structure for the training of future teachers of physics for the organization of students' innovative activity.
Default Prediction of Automobile Credit Based on Support Vector Machine

Automobile credit business has developed rapidly in recent years, and corresponding default phenomena occur frequently. Credit default will bring great losses to automobile financial institutions. Therefore, the successful prediction of automobile credit default is of great significance. Firstly, the missing values are deleted, then the random forest is used for feature selection, and then the sample data are randomly grouped. Finally, six prediction models of support vector machine (SVM), random forest and k-nearest neighbor (KNN), logistic, decision tree, and artificial neural network (ANN) are constructed. The results show that these six machine learning models can be used to predict the default of automobile credit. Among these six models, the accuracy of decision tree is 0.79, which is the highest, but the comprehensive performance of SVM is the best. And random grouping can improve the efficiency of model operation to a certain extent, especially SVM.
GPR: Global Personalized Restaurant Recommender System Leveraging Billions of Financial Transactions

In this paper, we demonstrate our Global Personalized Recommender (GPR) system for restaurants. GPR does not use any explicit reviews, ratings, or domain-specific metadata but rather leverages over 3 billion anonymized payment transactions to learn user and restaurant behavior patterns. The design and development of GPR have been challenging, primarily due to the scale and skew of the data. Our system supports over 450M cardholders from over 200 countries and 2.5M restaurants in over 35K cities worldwide, respectively. Additionally, GPR being a global recommender system, needs to account for the regional variations in people's food choices and habits. We address the challenges by combining three different recommendation algorithms instead of using a single revolutionary model in the backend. The individual recommendation models are scalable and adapt to varying data skew challenges to ensure high-quality personalized recommendations for any user anywhere in the world.
Multi-criteria approach to pair-multiple linear regression models constructing

A pair-multiple linear regression model which is a synthesis of Deming regression and multiple linear regression model is considered. It is shown that with a change in the type of minimized distance, the pair-multiple regression model transforms smoothly from the pair model into the multiple linear regression model. In this case, pair-multiple regression models retain the ability to interpret the coefficients and predict the values of the explained variable. An aggregated quality criterion of regression models based on four well-known indicators: the coefficient of determination, Darbin – Watson, the consistency of behaviour and the average relative error of approximation is proposed. Using this criterion, the problem of multi-criteria construction of a pair-multiple linear regression model is formalized as a nonlinear programming problem. An algorithm for its approximate solution is developed. The results of this work can be used to improve the overall qualitative characteristics of multiple linear regression models.
Network Hypes and Asset Prices of Cryptocurrencies: Empirical Evidence Based on Google-Attention Approach

The rapid development of cryptocurrencies motivates us to examine the factors that drive cryptocurrency prices. Using Google attention innovation ($GAI$) to measure network hype or investor speculation, this paper investigates the effect of network hype in the cryptocurrency market and proposes a three-factor asset pricing model composed of the market, size, and network hype($GAI$) factors. This paper is the first empirical study on asset pricing in cryptocurrency markets and is a unique attempt to value the cryptocurrencies prices using a factor model. The empirical results show: (1) There is a positive and significant effect of network hype in the cryptocurrency market, and network hype plays a significant role in increasing cryptocurrency prices. (2) Average excess returns of cryptocurrencies are negatively correlated with their size and have a significant positive correlation with Google attention innovation. (3) Our three-factor model has strong explanatory power for excess cryptocurrency returns and can explain the momentum, reversal, and liquidity factors in the cryptocurrency market.
Data Journalism – Impact of Statistical Methods

Data journalism strongly depends on adequate data preparation and analysis. In this paper, we discuss the impact of statistical methods on data journalistic analysis. To this end, we re-analyze two data journalistic publications of SPIEGEL ONLINE with more advanced statistical methods and discuss pro and contra.
Factorization in Call-by-Name and Call-by-Value Calculi via Linear Logic

Abstract In each variant of the $$\lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>λ</mml:mi> </mml:math> -calculus, factorization and normalization are two key properties that show how results are computed. Instead of proving factorization/normalization for the call-by-name (CbN) and call-by-value (CbV) variants separately, we prove them only once, for the bang calculus (an extension of the $$\lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>λ</mml:mi> </mml:math> -calculus inspired by linear logic and subsuming CbN and CbV), and then we transfer the result via translations, obtaining factorization/normalization for CbN and CbV. The approach is robust: it still holds when extending the calculi with operators and extra rules to model some additional computational features.
Research on Improvisation in the Course of Piano and Nursery Rhyme Playing and Singing in Preschool Education Major

Nowadays, the training goals of the preschool education major are constantly expanding with the society's deep understanding of early childhood education, and the requirements for students' various skills and professionalism have been improved. Among them, piano improvisation is the teaching content of the course of piano and nursery rhyme playing and singing in this major, and it is also an ability of great practical significance. Therefore, teachers should actively explore the value of piano improvisation ability, reflect on the problems in the current teaching situation, and find ways to optimize the teaching effect, which are very helpful to future teaching practice.
Implementation of TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) in Determining Poverty Ranking of Village Communities as Recommendations for Government Assistance

Since the monetary crisis that hit Indonesia in 1998 it has become a scourge tense for the State of Indonesia. Every year it increases, this thing one of the reasons is the increasing price of food, clothing and food. In response to this, the government has provided several economic assistance, such asFamily Hope Program, KIP (Smart Indonesian Family) and assistance provided by the government. However, there is a lot of assistance the community, especially in the village, admitted to being poor to get this assistance. So that aid was not on target, causing unequal distribution of aid to the community who are indeed poor in accordance with government criteria, as happened, around 1,443 assistance was leveraged or double-checked because it was not right on target. Therefore, we need a ranking concept that is right on target and according to the criteria who are entitled to get assistance from the government by using the TOPSIS method as a recommendation who is entitled to assistance from the government. The fundamental reason for choosing this method is because of the level of complexity so that it has high accuracy when compared to others. And the method will be created using a website, so that it is easily accessible in all places. So, calculating the TOPSIS aims to see the poor in accordance with the criteria set by the government as a recommendation for providing assistance to truly poor people
Design of Obstacle Avoidance and Waypoint Navigation using Global position sensor/ Ultrasonic sensor

The objective of the research work to focus on a path planning aims to plan the route of an Unmanned Vehicle (UV) using Global Positioning System (GPS), and the most suitable path is selected avoiding the obstacles along the desired path. The coordinates of the starting point and destination are fed through programming. In addition, an obstacle avoidance algorithm is used and waypoints are given using the AVR Programming. Waypoint Navigation System for the Unmanned Ground Vehicle is use with GPS avoiding the obstacles in its path. The Waypoint Navigation System is the planning of the path of an object so that the path goes through some specified coordinates or benchmarks to the final destination thus avoiding any obstacles. The technology used to accomplish the aim includes Global Positioning System (GPS), Ultrasonic Sonic sensors and Micro-controller for programming. GPS will be the guiding medium from source to destination and two Ultrasonic Sensors will be used. These types of vehicles are used for many purposes and have been used by United States of America for Military and Defense purposes. In advanced cases, an Unmanned Aerial Vehicle may also be developed using these algorithms.
Advocating for Multiple Defense Strategies Against Adversarial Examples

It has been empirically observed that defense mechanisms designed to protect neural networks against $$\ell _\infty $$ adversarial examples offer poor performance against $$\ell _2$$ adversarial examples and vice versa. In this paper we conduct a geometrical analysis that validates this observation. Then, we provide a number of empirical insights to illustrate the effect of this phenomenon in practice. Then, we review some of the existing defense mechanisms that attempt to defend against multiple attacks by mixing defense strategies. Thanks to our numerical experiments, we discuss the relevance of this method and state open questions for the adversarial examples community.
Crime Detection System using Data Mining﻿

Data mining plays the key role in crime analysis. There are various number of different algorithm in previous research papers like virtual identifier, pruning strategy, support vector machines and apriori algorithms. "VID" (Virtual ID) is to find relationship between record. Then the "Apriori algorithm is used to around six hundred seconds to detect a mail bomb attack. Which is quite fast as we wanted and its very useful to achieve our goal. We used the "crime mapping analysis based on "KNN" (K-Nearest Neighbor) algorithm to simplify this process and the crime mapping is very essential research area to concentrate on because we can identify the most frequently crime occurring zone with the help of data mining techniques. We use the following steps to reduce the crime rate:i. Collect crime data.
Impossibility on the Schnorr Signature from the One-More DL Assumption in the Non-Programmable Random Oracle Model

The Schnorr signature is one of the representative signature schemes and its security was widely discussed. In the random oracle model (ROM), it is provable from the DL assumption, whereas there is negative circumstantial evidence in the standard model. Fleischhacker, Jager, and Schröder showed that the tight security of the Schnorr signature is unprovable from a strong cryptographic assumption, such as the One-More DL (OM-DL) assumption and the computational and decisional Diffie-Hellman assumption, in the ROM via a generic reduction as long as the underlying cryptographic assumption holds. However, it remains open whether or not the impossibility of the provable security of the Schnorr signature from a strong assumption via a non-tight and reasonable reduction. In this paper, we show that the security of the Schnorr signature is unprovable from the OM-DL assumption in the non-programmable ROM as long as the OM-DL assumption holds. Our impossibility result is proven via a non-tight Turing reduction.