Reviewerly/exHarmony huggingface dataset:
An Approach for Improving Similarity Measure Using Fuzzy Logic

An information retrieval system stores and indexes documents such that when users submit a query, the system gets relevant documents and assigns a score to each one. The higher the score, the more important the document is. IR systems typically yield vast result sets, and users must spend a significant amount of time sifting through them to identify the elements that are genuinely important. Different suggestions for applying evolutionary computing to the topic of information retrieval will be reviewed from the specialist literature. To do so, researchers looked at a variety of IR issues that were addressed using evolutionary algorithms. Some of the current ways will be detailed in detail; for example, when dealing with specialized domain knowledge, this challenge can be solved by embedding a knowledge base into existing information retrieval systems that illustrates the relationships between index words. The fuzzy set theory may be used to change the knowledge in the bases to cope with the ambiguity that is typical of human knowledge. In this work, a novel way for implementing a similarity measure utilizing fuzzy logic for IR is provided. A suggested similarity metric is based on many IR system attributes that boost IR system performance. This method's strength is that it can extract the majority of a document's characteristics. Fuzzy rules, which translated domain knowledge into fuzzy sets, were also designed to make this most effective. Our suggested similarity metric is validated using the CACM and CRAN benchmark datasets.
Applications and Blockchain Solution for Big Data Challenges

The term “big data” is related to data that is huge in amount, fast to access, or complex. A huge amount of data is generated day by day. The big data and its analysis play an important role in the world of IT with other applications like cloud computing, data mining, IoT, etc. As the internet and digital technologies are growing, the amount of data is also increasing day by day. But this amount of data cannot be processed by using traditional methods. A lot of effort is required to analyze this amount of data. But while ensuring data security and privacy, many challenges are there like protection against security breaches and data leakage, vulnerabilities in public databases, and third-party data sharing etc. The objective of this paper is to discuss various applications and security issues with blockchain solution of big data challenges.
Affine Transform Assisted Firefly Algorithm in Image Registry to MRI and CT Brain Images

Medical multimodality images create an essential need of Image registration. In this work, we focused on one of the components of the methods of which is the measure of similarity used to match the images. We are particularly interested in the iconic approach, which takes into account only the information carried by the intensities of the pixels of the images to be recalibrated. This approach has the advantage of being fully automatic, since no prior segmentation is necessary. The main contribution of this research work is to propose new measures of similarity based on cumulants and the development of edge worth and for some of them they approximate the Mutual Information, which is a measure of similarity of reference in registration. Tests on these new measures show their effectiveness for registration medical images. In addition, the generosity of the proposed approach allows the use of these measures in various situations. Further we proposed and analysed a novel multimodal image registration method for medical imaging using firefly algorithm (FF). We optimized the registration parameters of affine transform with firefly algorithm to register computed tomography (CT) brain image over magnetic resonance image (MRI) to maximize mutual information. We tested our method over images of different sizes and modality.
Identifying the impact of a technology-oriented strategy on the organizational performance of the banking system

The current research was conducted with the objective of identifying the effect of a technology-oriented strategy on the organizational performance of a banking system in Bank Tejarat. Regarding its objective, the present research is an applied study. Also, data collection was survey-oriented and cross-sectional, then the study was of survey-oriented correlational research. The statistical population of the study consisted of 532 Bank Tejarat employees in west Tehran branches. According to the Cochran's formula, 223 subjects were considered as the sample required for a population of 532 subjects. These individuals were selected using simple stratified sampling. For data collection, a standard questionnaire developed by Hao and Song (2015) and Miller (1996) was used. The research instrument validity was evidenced through divergent, convergent and content validities. The research instrument reliability was also verified via Cronbach's alpha coefficient, shared reliability and combined reliability. Data were analyzed using structural equation modeling and SmartPLS software. The results showed that technology-oriented strategy had a positive and significant effect on the organizational performance of the banking system (0.505). Also, both market-based strategy (0.560) and technology-oriented strategy (0.519) had a positive and significant effect on the organization performance.
Digital Image Watermarking Technology Based on Discrete Wavelet Transform and Discrete Cosine Transform

Digital watermarking has been proposed as a viable solution to the need of copyright protection and authentication of multimedia data in a networked environment, since it makes possible to identify the author, owner, distributor or authorized consumer of a document. In this paper a new watermarking technique to add a code to digital images is presented: the method operates in the frequency domain embedding a pseudo-random sequence of real numbers in a selected set of DCT coefficient and a new method for digital image watermarking which does not require the original image for watermark detection. The watermark is added in selected coefficients with significant image energy in the transform domain in order to ensure non-erasability of the watermark. Advantages of the proposed method include: improved resistance to attacks on the watermark, implicit visual masking utilizing the time-frequency localization property of wavelet transform and a robust definition for the threshold which validates the watermark. . Experimental results demonstrate that this proposed technique is robust to most of the signal processing techniques and geometric distortions.
A STUDY ON CRYPTOCURRENCY-IT IS THE TIME FOR BANKS SHOULD INVOLVE WITH CRYPTO ASSETS

“In today’s time internet is proved to be the hub for economic activities. It makes life of the human being easy and stress-free. Nowadays people are taking keen interest in trading of stocks and cryptocurrency, around 83% of Indians are aware of stock trading and 16% of them own it. In 2018 government motivated the public to get involve them self in stock &amp; crypto trading but on other hand RBI banned banks to have anything with virtual currency. Meanwhile world took huge step in adoption of cryptocurrency and now it’s time for bank to get evolved itself with crypto assets as these assets removal of intermediaries, fees and other roadblocks to large transaction, creating a faster and less expensive global payment network. Crypto assets will give more exposure to financial system by providing an alternative to traditional assets and taking the financial access to wider range. It will provide the transparency throughout the asset lifecycle. To avoid any left behind bank should adopt cryptocurrency rather than treating it as an enemy.” Keywords: Cryptocurrency, Trading, financial system, Roadblocks, Lifecycle, Global payment network, Bank.
Inverse kinematics in VR motion mirroring

The objective of this research is to develop a computer tool to perform body motion sensing, dynamics analysis, and realtime visualization based on a virtual reality system. The aimed system can enable effective motion detection, monitoring and instant visual highlights involving muscle contractions during one’s exercises. Experimental results are presented in projecting one’s movement onto a virtual skeleton by applying inverse kinematics.
A Study on the Design of Teaching Objective of Junior High School Teachers

This paper studies the design and description of teaching objective through the investigation of 38 instructional designs written by junior high school English teachers. Quantitative analysis was adopted to examine teachers' teaching objective writing based on the instructional designs of the 15th National Junior High School English Teaching Basic Skills Competition. The purpose of this study is to find out existing problems of teacher teaching objective design so as to improve junior high school English teachers' professional development. According to the teaching objective writing and data analysis, the existing problems in writing teaching aims among junior high schools are as follows: (1) only a few teaching objectives are written in line with the China's General Senior High School English Curriculum Standards for English published in 2018 which advocates promoting learners' English subject core competency; (2) the subject is not clear; (3) some descriptions performed by students are not observable and detecTable. This paper suggests that teachers should further study the China's General Senior High School English Curriculum Standards for English (2017) and change their traditional ways in designing teaching objectives to better assess and promote learners' learning and their English teaching ability.
Path Planning for Autonomous Cars

Abstract— Path planning plays a vital role in autonomous driving. It is the replication of the reasoning and decision-making of a human brain. This paper is about analyzing and optimizing a GitHub project which is related to path planning for autonomous cars. This work has fixed the program to drive a car on the simulated highway while avoiding collision, following traffic, safely changing lanes, and minimizing jerk. Additionally, more critical scenarios have been identified to make driving experiences safer and the present cost functions are optimized to make the car react effectively. Throughout the multiple experiments, a more efficient program has been produced that has reduced the time to finish one lap by roughly 10 seconds. Path planning is one of the extremely fundamental processes within autonomous driving. There are still challenges to make path planning safer and robust, such as behavior modelling on other cars, and more efficient path searching algorithm, etc.
A collision avoidance algorithm in Simultaneous Localization and Mapping problem for mobile platforms

A collision avoidance algorithm applicable in simultaneous localization and mapping (SLAM) has been developed with a prospect of an on-line application for mobile platforms to search and map the operation area and avoid contact with obstacles.The algorithm, which was implemented in MATLAB R ○ software, is based on a linear discrete-time state transition model for determination of the platform position and orientation, and a 'force' points method for collision avoidance and definition of the next-step of platform motion.The proposed approach may be incorporated into real-time applications with limited on-board computational resources.
Continuity of Memory Operation in Presence of Errors

Modern critical systems require continuity of operation which requires computing systems that utilize fault tolerance criteria.The operation of systems in the presence of faults is essential for safety and reliability such as in electric power distribution systems, telecommunications, medical life-support, nuclear reactor control, transportation, automotive, aircraft, and space vehicles.Continuity and reliability of service in such systems is essential.To meeting the severe reliability requirements inherent in certain future computer application, the technique of Triple Modular Redundant (TMR) is used.Essentially, this technique depends on voting two out of three system output levels.In this paper a fault-tolerant system is proposed using TMR configuration for memory module with spare model for -line self -reconfiguration.A voter is designed to pass reliable data and signals to the output.The voter has the ability to analyze the error and stop the system on the proper time.The proposed system is tested using MATLAB simulation.A set of different faults are injected in different modules of the system in different data pater.The simulation results demonstrate the capability and accuracy of the proposed system with in the presence of faults as well as the proposed system ability of errors handing.
Text To Speech Synthesis for Afaan Oromoo Language Using Deep Learning Approach

Text to speech synthesis (TTS) which generate input texts is generate to the speech from texts.TTS is very important in aiding impaired people, in teaching and learning process.But, to implemented TTS have a lot of challenging such as text processing, time to phoneme mapping and acoustic modeling for Afaan Oromoo language.So, Afaan Oromoo language mostly required to text to speech synthesis for development of this language.The application of Natural Language Processing is provide that input texts pair speech to generate the desired result outputs of speech in waveforms from prepared text corpus.The normalized text was used for linguistic features are extracted by using Festival toolkit for Afaan Oromoo TTS.The labeled texts are done using Festival toolkit, and generated the utterances of texts from scheme file parameters.The Festival toolkit is used for texts normalized in linguistic extraction from label phoneme alignment to match with speech corpus in trains and tests.The forced alignment is done by HTK toolkit for prepared environment, checked data extracting features within timestamps of state level alignment for acoustic feature extracted.So, this study focus on TTS approach deep learning model based on BLSTM-RNN for Afaan Oromoo language.The RNN model used from a given input feature sequence to extracted duration model and acoustic model.The implementation is done in BLSTM-based on RNN using pytorch library on jupyter notebook, create duration model and generated speech samples from trained acoustic model.We have prepared 1000 texts corpus their matching text transcription from Afaan Oromoo speech corpus by a female speaker dependent for training 700 sentences and tests 300 sentences from dataset domains.In this study, two evaluation techniques used.Frist, the Mean Opinion Score (MOS) evaluation technique is used for intelligibility and naturalness in TTS.The second is Mel Cepstral Distortion (MCD) which is highly used for objective evaluation in model approach for TTS.So, the performance of this model was measured and quality of synthesized speech is assessed in terms of intelligibility and naturalness which results are 3.77 and 3.76 respectively.The total average processed using objective evaluation technique the speech corpus on 16 kHz standards is generated by MCD BLSTM-based on RNN is 3.89 and merlin wave generated is 3.71 correspondingly.
Auto Climate Monitoring System

In this work we present a detailed conception of weather monitoring system which displays weather, cloud and air purity also, we represent it by using graphs and bar graph. In our web application a user can get the weather information upto 7 days. Here we used an Application programming interface (API). An application programming interface, or API, enables companies to open up their applications’ data and functionality to external third-party developers, business partners, and internal departments within their companies. In this framework the climate parameters estimations taken are temperature, moistness, wind course, and wind speed. In this proposed work we will monitor the live weather’s parameter of entire world. With the help of this proposed system, we measure the weather condition of whichever city entered in search bar. After getting results from API(Open weather map), it is observed that our proposed model achieves better results in comparison with the standard weather parameters
Research on the scale effect of multistatic sonar

This paper studies and presents the problem of scale effect of multistatic sonar. To study the detection performance of the multistatic sonobuoy system, the track-initiation probability is used as the detection probability of the target. The monostatic distributed and multistatic distributed detection modes are taken as examples. The influence of sonobuoy arrangements on sonar detection area is analyzed. The results of experiments show the method is available and compared with monostatic sonar system, multistatic system has scale effect. Moreover, the scale gain increases when the arrangement mode changes from triangular and square to hexagonal layout. Taking into account the flexible deployment of the multistatic sonobuoy system and the easy node expansion characteristics, we can make full use of the scale effect of multistatic detection, so as to effectively improve the capability for detecting submarine.
Quantum Key Distribution in the Service Provider Network

We review and discuss the practicalities of integrating Quantum Key Distribution within the service provider fiber network.
End‐to‐end motion segmentation and recognition with high accuracy using wearable devices

An important research direction in applying wearable devices in sports is motion recognition and segmentation. The authors propose an end-to-end motion recognition–segmentation method in basketball. First, data are collected using an arm guard with an inertial measurement unit (IMU) module. Next, a sliding window is used to extract the data features. The feature extraction method is a multi-model long short-term memory (LSTM) network. After feature extraction, a label vector is obtained and a result vector is formed after temporal matrix processing. The results of the experiment on the accuracy, precision and recall were 97.1% ± 1.6%, 94.4% ± 0.7% and 96.4% ± 1.1%, respectively, showing better performance among similar studies.
A Study on Evaluation of Teaching Methods to Amplify the Effectiveness of Management Education

The concept of management education, its growth, status, evolution and trends are analyzed based on secondary data. Bridging the gap between Education 4.0 and Industry 4.0 by reorientation of Learning and Teaching methods to amplify the effectiveness of management education. Lectures and tutorials have become most commonly used teaching techniques at business schools. However Practitioners and Educationists developed case study based, Corporate Games, Simulations and interactive teaching techniques to enhance the effectiveness of management education in business schools. Quantitative method is deployed by executing parametric tests. The Innovative, creative and practical based teaching methods can improve the quality of management students and institutions. Education 4.0 offers learning beyond the geographical boundaries and encourages cross domain and culture learning which boost the pragmatic application of learned concepts.
Impact of Blockchain Technology in the Financial Sector

Purpose: The purpose of the study was to examine the factors influencing the adoption of Blockchain Technology in Financial Sector in India. For this study, four factors namely, Regulatory Support, Trust, Social Influence, Perceived Usefulness, and Design were used to determine the consumer perception of Blockchain Technology in India.Methodology/Approach: An online survey was used to collect data from 161 consumers performing financial transactions in India. IBM SPSS and IBM SPSS AMOS were used to test the conceptual model and to validate and statistically analyze the results.Findings: The factors Regulatory Support and Design were found to be significant on behavioral intention, whereas Trust, Social Influence and Perceived Usefulness were found not to be significant.Implications: This study can be used by government agencies, banks, financial institutions, as well as fintech firms in India to strategize and to attract customers to adopt financial transactions using blockchain technology.
Popular Conceptions of Unidentified Flying Objects Can Undermine Scientific Inquiry

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Popular Conceptions of Unidentified Flying Objects Can Undermine Scientific InquiryAuthorsJacobHaqq-MisraiDRaviKopparapuiDSee all authors Jacob Haqq-MisraiDCorresponding Author• Submitting AuthorBlue Marble Space Institute of ScienceiDhttps://orcid.org/0000-0003-4346-2611view email addressThe email was not providedcopy email addressRavi KopparapuiDNASA Goddard Space Flight CenteriDhttps://orcid.org/0000-0002-5893-2471view email addressThe email was not providedcopy email address
Dynamic Differential Image Circle Diameter Measurement Precision Assessment: Application to Burning Droplets

Dynamic measurement precision assessment has been achieved for a differential circle measurement application. Differential circle diameter measurement, in image analysis, typically requires fitting a circle model that optimizes for image distortions, defects or occlusions. The differential task occurs when precise measurements of diameter change are required given object size variation with time. An automated system was designed to provide diameter measurements and associated measurement precision of images of a fuel droplet undergoing combustion in zero gravity for the FLEX-2 dataset. An image gradient-based, least-squares boundary point fitting method to a circle or ellipse model is used for diameter measurement. The presence of soot aggregates poses significant challenges for diameter measurements when it occludes part of the droplet boundary. The precision of the diameter measurements depends upon the image quality. Using synthetic image simulations that model the soot behavior, we developed a model based on image quality measures that assesses the measurement precision for each individual diameter measurement. Thus, diameter measurements with precision assessments were made available for follow-up scientific analysis. The algorithm's success rate for measurable runs was 98%. In cases of limited occlusion, a measurement precision of ±0.2 pixels for the FLEX-2 dataset was achieved.