Reviewerly/exHarmony huggingface dataset:
Splicing ET (error-tolerant) Quantum Computing System

In this paper, we develop a system with redundant splicing topology by studying the ET (error-tolerant) system, and study its unique advantages in topology. And tapped the potential of application in quantum computing. Through theoretical analysis and modeling, it reveals various possibilities for the design of two-dimensional and even three-dimensional quantum computing systems, which are widely neglected in the field of quantum computing, and combines some features of the previous ET system design. And the design methodology is discussed; the network design of the novel quantum computing system can be further introduced into the unique topology.
A Novel Rubik’s Cube Problem Solver by Combining Group Theory and Genetic Algorithm

Rubik’s cube is one of the most challenging problems in the field of evolutionary algorithm and group theory. Several algorithms have been considered to solve this problem in recent years. The solution of Rubik’s cube is different from other search algorithms. Finding the goal state is the target of the normal search algorithms. However, in this problem, the start (origin) and goal (destination) states are known and the challenge is to provide a convenient and efficient method for finding the shortest path between origin and destination. Existing solutions can be divided into several categories. In the first category, by dividing problem into sub-problems, researchers have tried to simplify and solve the cube. Apart from the divide and conquer method, the second category by creating a similar concept to the lookup tables tries to reduce computational costs with dynamic programming. Last category focused on the evolutionary algorithms to solve the problem. Regarding the fact that Rubik’s cube is one of the models in the field of group theory, we have proposed a novel method for solving this problem by a combination of group theory and genetic algorithm. The main idea of the proposed algorithm is finding and removing repeated states by group theory, because there are several repeated individuals (states) in different generations of genetic algorithm. The experimental results show a huge sieve in individuals per generations. In these experiments, by a significant reduction in the populations size, the speed of finding solutions has been increased by more than $$64\%$$.
Study on Arrangement of Waterlogging Control Storage Tank in City Village

Abstract In recent years, the problem of waterlogging in Chinese cities has become increasingly serious, and it is extremely urgent to solve the problem of waterlogging. Urban rainwater storage plays an important role in the prevention and control of waterlogging disasters. The control effect of the internal control of the storage tank is closely related to its layout position. On the basis of comprehensive consideration of the hydrological mechanism for reducing rainwater floods and the requirements for urban flood control, the Huangbian City Village of Panyu District, Guangzhou City was taken as an example to construct a one-dimensional, two-dimensional, pipe network coupled hydrological hydrodynamic model to study urban locusts and the optimal arrangement of the storage tank. The results show that compared with the centralized storage tank is arranged at the lower section of the pipe network bottleneck section and waterlogging point, arranging the storage tank in the upper section of the bottle neck section and the upstream of waterlogging point can not only significantly reduce the drainage pressure of the pipe network, reduce the flood area of waterlogging, but also reduce the storage capacity of a single storage tank and improve the feasibility of engineering construction. The arrangement method can effectively realize the storage function of the storage tank.
Shallow Convolutional Neural Network for Image Recognition

Deep convolutional neural networks (DCNNs) have achieved state-of-the-art results for image recognition.However, these DCNNs with complex structure consist of many layers like convolutional layers, which require high time and computational complexity for training.Therefore, we propose a novel shallow convolutional neural network (SCNND) with dropout to address the problems of the DCNNs for image recognition.The SCNND with 4 layers can fast learning the features of the images, using dropout technology between two convolutional layers to improve recognition performance.Compared to the SCNNs, our SCNND includes 4 layers, with low time complexity and parameters.Experimental results show that our SCNND outperforms shallow CNN methods on Fashion-MNIST dataset.
Lattice Based Verifiably Encrypted Double Authentication Preventing Signatures

Verifiably encrypted signatures (VES) have been widely used in various applications, such as signing contract, fair exchange and so on. However, most of them do not provide deterable function and with the development of quantum computers, they face new challenges. Verifiably encrypted double authentication preventing signatures (VEDAPS) with deterable function, have a property of punishing misbehavior parties by extracting their secret keys. In this paper, we propose a concrete VEDAPS based on Ring-SIS problem and Ring-LWE problem. Security analysis shows that our proposed scheme satisfies security properties including completeness, opacity, unforgeability and punishability in the random oracle model. Theory and parameter analyses demonstrate that our construction has reasonable efficiency for real applications.
Hybrid of Particle Swarm and Levenberg Marquardt Optimization in Neural Network Model for Rainfall Prediction

Abstract Neural network model has undergone many modifications from the original model through the development of network architecture and optimization methods. By default, the gradient-based optimization method is a method used to find network weights. But some of the weaknesses and limitations of these methods inspired many researchers to try to use other methods. Non-gradient based heuristic method is a reasonable choice considering the learning algorithm in artificial neural networks is inspired by the characteristics of creatures so that optimization methods that also mimic life patterns in nature will be appropriate. One disadvantage of the heurisic method is the length of the iteration process. In this paper, a method which combines heuristic optimization methods and gradient-based methods is applied, namely particle swarm optimization (PSO) and Levenberg Marquardt. The weight obtained from the PSO method becomes the initial weight for the Levenberg Marquardt method. The proposed procedure is applied to the rainfall data in Cokrotulung Klaten. The results showed that this procedure succeeded in providing better predictions than the Levenberg Marquardt method.
Virtual Brain Masterclass

The Inside Explorer; Virtual Brain Masterclass is focused on Unit 1 of VCE Psychology; How are behaviour and mental processes shaped? and relates to outcome 1: Role of the brain in mental processes and behaviour.
Derivation of Modified Input Estimation Filter Using Bayesian Framework

Abstract A new approach to derive the modified input estimation is proposed using Bayesian framework. Tracking manoeuvring targets is a tough problem and many algorithms are proposed to deal with it. Among these algorithms, modified input estimation (MIE) technique has been proved to be an effective method. MIE technique treats acceleration as additive input term in the state vector and estimates the original state and acceleration vectors simultaneously with a standard Kalman filter. Analogous to standard Kalman filter, however, the MIE filter equations which have a modification on Kalman gain is given without a cohesive derivation. Using a Bayesian framework, we will present a conceptually cohesive roadmap that starts at first principles and leads directly to derivation of MIE filter equations.
Camera Calibration for Color Classification

This paper is about the optimization of camera parameters to achieve good color classification for a limited amount of colors. In the context of RoboCup it is very important that the robots recognize the field, the ball and the competitors. This recognition can be done using the objects' colors. All approaches to color classification are dependent on the quality of the provided camera picture. A manual optimization of camera parameters is time consuming and we noticed that optimal parameters can be very different from manually chosen ones. Therefore we developed a general automatic approach to optimize camera parameters for color classification, based on evolutionary algorithms.
Model Prediction Of The Next Runway Model Using Decision Tree And Random Forest (case Study: Big Four Fashion Week)

Abstract Fashion industry contributes 4% of global market share with USD 385.7 billion-dollar market value worldwide. Fashion Week, the most prominent event in fashion industry, is a combination between art and commerce. Fashion models is the talent that plays a big role during the fashion week, in which their aesthetic is highly associated in presenting designersâ€™ work. However, their appearance in social network has built a deeper relationship to the industry. Choosing the talent out hundreds of faces is a challenge for casting director. The fast movement of fashion business has never evolved closely to digital utilization. This study aims to imitate the wisdom of traditional talent scouting process into an automation model based on machine learning practice by implementing Decision Tree, and Random Forest. In the era of fashion 4.0, shifting the traditional system into an automation model leads to revolution of fashion production, in this case fashion show production. Our framework is able to imitate talent scouting process to select the upcoming fashion model models appear on 2019 Fall/Winter Fashion Week with accuracy 74.57% using Random Forest. Keywords: Fashion Industry, Machine Learning, Prediction, Decision Tree, Random Forest.
ENCODINGS FOR THE CALCULATION OF THE PERMUTATION HYPOENTROPY AND THEIR APPLICATIONS ON FULL-SCALE COMPARTMENT FIRE DATA

Based on the data collected during a full-scale experiment, the order/disorder characteristics of a compartment fire are researched. We discuss methods, algorithms and the novelty of our entropic approach. From our analysis, we claim that the permutation type hypoentropies can be successfully used to detect unusual data and to perform relevant analysis of fire experiments.
Geochemistry and Mineralogy of the Upper Cretaceous-Paleocene Marine Series, Iullemmeden Basin, Niger Republic

Major and trace elements, mineralogy as well as the total organic content of the Upper Cretaceous-Paleocene Marine Series of the Iullemmeden basin in Niger were determined in order to assess their composition, the chemistry of the paleoenvironment and the nature of the parent crystalline rocks that they evolved from.The major and trace elements were analyzed using a Bruker Tracer IV energy-dispersive hand held X-ray fluorescence spectrometer, the mineralogical analyses were done using a Bruker AXS D8 Discover, while the total organic carbon was calculated by subtraction of inorganic carbon from the total carbon, determined using a CM5014 coulometer (UIC, Joliet, IL, USA) and a LECO SC-144DR instrument.Geochemical indices calculated from major oxides such as CIA, PIA, ICV and CIW have been computed and the results show that the Marine Series are made essentially of clay minerals of the smectite group (montmorillonite), quartz and other accessory minerals like rutile and calcite.Computed ratios of some trace elements such as V/(V + Ni) and V/(V + Cr) showed that the paleoenvironment was dysoxic-oxic and the low values (<2) of the Total Organic Carbon found in the sediments are justified by those poor preservative conditions.Based on Al 2 O 3 /TiO 2 ratios, the sediments were derived from intermediate to acidic igneous rocks surrounding the basin.
Research and Implementation of Cache Optimized Query Privacy Protection for Spatio-Temporal Data Based on PGSRQ-PIR

Abstract In view of the high redundancy of the nearest node of the Spark based user privacy protection query algorithm CRIP-V, the caching optimization algorithm PCPIR-V-CA is proposed. PCPIR-V has two parallel strategies, row strategy and bit strategy. The bit strategy divides the row into smaller pieces and improves the efficiency while the grid division is to small. The cache optimized algorithm of PCPIR-V first clusters the CPIR data transforms the data into tuples and caches them in each cluster. Finally it calulates the result based on the cached tuples and data. It has about 20 percents improvement than PGSRQ-PIR.
Aspect-Level Sentiment Classification with Dependency Rules and Dual Attention

Aspect-level sentiment classification aims to predict the sentiment polarity towards the given aspects of sentences. Neural network models with attention mechanism have achieved great success in this area. However, existing methods fail to capture enough aspect information. Besides, it is hard for simple attention mechanism to model complex interaction between aspects and contexts. In this paper, we propose a Segment Model with Dual Attention (SegM-DA) to tackle these problems. We combine deep learning models with traditional methods by defining dependency rules to extract auxiliary words, which helps to enrich aspect information. In addition, in order to model structural relation between aspects and contexts, we introduce dependent attention mechanism. Coupled with standard attention mechanism, we establish the dual attention mechanism, which models the interaction from both word- and structure- dependency. We perform aspect-level sentiment classification experiments on two real datasets. The results show that our model can achieve the state-of-the-art performance.
Trapezoidal Sketch: A Sketch Structure for Frequency Estimation of Data Streams

Abstract The sketch is one of the typical and widely used data structures for estimating the frequencies of items in data streams. However, since the counter sizes in traditional rectangular sketch (r-sketch) are the same, it is hard to achieve small space usage, high capacity (i.e. the maximum frequency can be recorded) and high estimated accuracy simultaneously. Moreover, when considering the high skewness of data streams, this problem will become even worse. Consequently, we propose the trapezoidal sketch (t-sketch) in this paper. In the t-sketch, different from the r-sketch, the counter sizes in different layers are different. Therefore, the low space usage and high capacity can be achieved simultaneously in the t-sketch. Moreover, based on the basic t-sketch, we propose the space-saving t-sketch and the capacity-improvement t-sketch and analyze the properties of these two t-sketches. Finally, for improving the estimation accuracy of the t-sketch further, we propose the probabilistic-based estimation error-reducing algorithm. Compared with the CM sketch, CU sketch, C sketch and A sketch, the simulation results show that the performances on space usage, capacity and estimation accuracy are improved successfully by the space-saving t-sketch and the capacity-improvement t-sketch.
Fréchet Metric in Neural Network Theory

Abstract This paper deals with application of a modified Fréchet metric to self-organizing neural networks, called Kohenen maps. The methodology used allows us to put more emphasis on the selected parameters in the input data. It can simplify finding the minimal distance dF j , since dF j ∈ 〈0,1〉
Scalable multidimensional relations between features of calls to emergency services

Quite frequent calls to emergency telephone services with false messages form a real problem. They mainly come from unreliable speakers or are subject of multiple mutually inconsistent reports of the same event. The paper presents an automated support for recognition of such calls. In order to reflect real cases precisely, we propose and formulate multidimensional call features correlations. This approach significantly improves the quality of results, proved by experiments. The more features are taken into comparison, the better the results reflect the real cases. With the proposed approach, it is possible to scale the accuracy of recognition. The phone operator, adequately to a defined reliability of the input data, can choose a properly needed level of accuracy. The proposed analysis can be applied not only in the emergency services, but also in many other fields of interest, in which multidimensional data should be compared.
On Probability Calibration of Recurrent Text Recognition Network

Optical text recognition has seen continual improvement in character accuracy over the past decade. However, as error persists, it is crucial to know when and where a recognition error occurs. Studies have shown that recent development of deep convolutional neural networks tends to increase calibration errors, compared to traditional classifiers such as SVM. Yet, the calibration error in deep neural networks for sequential text recognition has not been studied in the literature. This paper addresses the probability misalignment problem in unsegmented text recognition models. We analyze the causes of probability misalignment in the popular recurrent text recognition model, the attention encoder-decoder model, and propose a novel probability calibration algorithm for individual character predictions. Experiments show that the proposed methods not only reduce expected calibration error, but also improve the character prediction accuracy. In our experiments, calibration error on authentic industrial datasets improved as much as 68% compared to original text recognizer outputs.
Fuzzy Mapping Model For Classification In Supervised Learning

This paper proposes a method to improve the existing methods to classify documents in to categories based on supervised machine learning technique. It includes converting the unstructured text data into a numerical vector form for representational learning. The key issue in text analysis and learning is to find an effective representational model. In case of large data sets the classical Bag of Words model outperforms the existing document representational learning methods in its simplicity in processing and accuracy. An improvement to this method using a fuzzy mapping model based on semantic similarity among words, beats the existing methods for classification. The method gives a comparatively simple representation and higher accuracy model. The results on real world data sets show an improved accuracy using the proposed method in supervised learning methods for classification.
Image Cryptography using Complex Quadratic Map

Computer security holds three fundamental aspects namely confidentiality, integrity and availability. In order to deal with confidentiality, message encryption approach is one of the methods that adequate to secure the data. In this research, we propose complex quadratic map as an encryption method using a digital image as the plaintext. Our conducted experimental results conclude that complex quadratic map can be used as encryption method because it will approximately take 257 years for a supercomputer to crack the key, it also needs absolute error less than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-15</sup> in order to get adequate image decryption result which is challenging to be done by brute force approach.
Secure random number generation from parity symmetric radiations

The random number generators (RNGs) are an indispensable tool in cryptography. Of various types of RNG method, those using radiations from nuclear decays (radioactive RNG) has a relatively long history but their security has never been discussed rigorously in the literature. In this paper we propose a new method of the radioactive RNG that admits a simple and rigorous proof of security. The security proof is made possible here by exploiting the parity (space inversion) symmetry arising in the device, which has previously been unfocused but is generically available for a nuclide which decays by parity-conserving interactions.
Introduction to the Future of Education and Labor

Due to an increased and increasing automatization (robotic manufacturing) and digitalization and a more widespread and increased use of AI (artificial intelligence and logarithms), the character of labor and work in more general will change dramatically in the near future. While the significant transition from an agricultural society toward an industrial society took almost a century and was limited to certain regional parts of the globe, the speed of implementing robots and artificial intelligence is tremendously high and spreads all over the globe. There are estimations that about half of the present employment in the United States is at risk, and the situation in other countries is comparable. The way social and economic processes are working has become significantly complex. Combining knowledge in a creative way is at least as necessary as the increasing and collecting of knowledge. The educational system does not reflect this sufficiently. Cross-disciplinary (interdisciplinary and transdisciplinary) thinking and learning is not in the main focus of our educational systems. Educational systems must change now – when the risk of economic and societal collapse should be avoided.
Current Status and Tasks of Grammar Performance Assessment - focused on middle school Korean language department evaluation plan

This study aims to critically analyze the implementation of grammar performance assessments and to explore the tasks related to performance assessments in the grammar domain. For this purpose, the evaluation plan of 384 middle schools in Seoul was used as a research subject, and data were collected to explore aspects of performance evaluation of actual unit schools. For data analysis, basic analysis criteria were prepared based on the performance assessment examples of the student assessment support portal, and then refined the framework and materialized the analysis elements. Accordingly, the current implementation of grammar performance assessments as well as the content and composition of the same were analyzed in both the external and internal dimensions. Based on the result data, this study explored the meaning of grammar performance evaluation in the context of performance assessment and suggested the direction of advanced grammar performance assessment.
Classifying Three-input Boolean Functions by Neural Networks

Neural networks decide or predict something by learning examples. Recently, improving equips and enlarging data enable the deep learning to perform complex decision. However, some objects require a lot of learning time to improve the precision.In this study, we investigate the number of required epochs for neural networks to study three-input Boolean functions. Then, we find that the number of required epochs depends on each of Boolean functions. Thus, by focusing on the structure of Boolean functions, we find that we can estimate the minimum number of required epochs according to the property of disjunctive normal form of the Boolean function.
Morphological Segmentation for Identifying Backbone Area

Abstract Segmentation in images of abdominal CT scan is an important step in the processing and interpretation of medical images. This is not easy, because the organs are close together and the color intensity is similar. Various methods have been developed, but the problem of locating the position of the backbone is still difficult to solve. The purpose of this study is to develop a segmentation method to determine the location of the backbone using a morphological method with a binarization approach. Testing of 11 CT Scan images shows that the method developed can determine the area and position of the backbone. Thus, it can be concluded that the method of backbone segmentation using a morphological method with a binary approach is successfully developed so that it can be applied to the advanced analysis process.