Reviewerly/exHarmony huggingface dataset:
How to Deeply Analyze the Content of Online Newspapers Using Clustering and Correlation

The increase in the number of visitors is one of the keys to increasing income for online newspapers, whether to increase the number of ads, Google AdSense, or customer trust. Therefore, finding which news categories increase the number of visitors needs to be known and analyzed more deeply. Because it is very common to add content to online newspaper sites every day, even for hours, this pattern analysis is not the same as analyzing regular website content patterns. This study intends to add methods in the world of research on how to analyze website content, especially online news, by using the clustering method to classify what news categories bring high, medium, or a low number of visitors and then analyzing the correlation to explore the depth of the relationship between the variables, namely which parameters have a large or low effect on the increase in the number of visitors. A local Batam-based online newspaper company is used as a case study for this research. Data is collected, preprocessed first, and analyzed using the clustering and correlation method. This analysis of the news content readership suggests what news categories should be optimized because it provides an increase in the number of visitors. A summary of the analysis steps in this study is presented. We also provided some suggestions if other online newspaper owners or researchers are interested in a similar analysis of online news content.
A Framework for Personalized Training at Home Based on Motion Capture

As the number of single-person households continues to increase, contents related to exercise at home are increasing. Therefore, this paper proposes a framework for efficient home fitness. The home fitness framework proposed in this paper is based on a method of acquiring expert motion information and providing it to users. To this end, content creation and provision are implemented by using Kinect and Unreal game engine. In addition, it is possible to provide customized home fitness in consideration of the user's athletic ability. The proposed system first captures the expert's motion and stores it. We propose a method that can efficiently store stop motion and dynamic motion storage. In the case of each user, since there is a difference in exercise ability for each individual, an adverse effect may occur if an excessively accurate motion is requested. Therefore, to solve these problems, we present the parts that can be considered for each joint. For the exercise motion provided by the expert, a method was provided that allows the user to adjust the degree of matching for each joint in consideration of user's own exercise ability. That is, joint parts that do not require exact matching could be completely excluded from matching. For parts that would be subject to large changes, a range of errors is specified. As the training progresses, the error range is reduced, and the excluded parts are presented to be matched gradually. Such adjustments are made based on expert feedback. In this way, it was possible to improve the exercise effect gradually. This paper proposes an effective method for personalized home fitness according to the user's athletic ability. This will apply to various fields besides home fitness.
Information Technology Control and Fraud Risk Detection in Deposit Money Banks (DMBs) in Nigeria

The banking sector has been considered as a fulcrum for economic growth in any country because of the intermediary role the banks play in collecting funds at interest from the surplus household and giving it out at a markup to the deficit household.The smooth operations of banks however had been faced with the risk of fraud which has led to the loss of a considerable amount of money running into billions of naira annually.This challenge had brought a bigger problem of how to properly manage the risk of fraud and boast customers' confidence in the banking sector in Nigeria and globally.Information Technology Control had been perceived to influence fraud risk management.Therefore, this study reviewed the effect of Information Technology Control on Fraud Risk Detection in Deposit Money Banks (DMBs) in Nigeria.The study employed the survey research design with a study population of 1,030 which comprised staff in the Internal Control, Internal Audit and Information Technology departments of all Deposit Money Banks (DMBs) in Nigeria as contained on the CBN website.The 13 listed banks were used as samples for the study and the Taro Yamane formula was used to obtain a sample size of 288.The purposive sampling technique was subsequently used in administering the questionnaire to the respondents.The reliability of Cronbach-alpha coefficients ranged from 0.864 to 0.952.Descriptive and inferential statistics were used to analyze the data.Similarly, the study showed that IT control has a significant effect on fraud risk detection in Deposit Money Banks (DMBs) in Nigeria with F287=30.690,Df = 3 & 265, adj.R 2 =0.209, p-value=0.000<0.05.The study, therefore, concluded that IT Controls have a significant effect on fraud risk detection in DMBs in Nigeria.The study, therefore, recommended that Banks should give priority to the implementation of information technology controls across all their digital channels or platforms as this will help to promote adequate fraud risk detection on the platforms and boost customers' confidence in the banking sector.
Conversational Banking

For the past 20 years, banks have attempted to build technology to engage customers in human and human-like conversations. They have been using call centres and IVR as voice channels for the past two decades. However, they have not been able to provide human-like conversation, mainly due to technology limitations. Conversational banking aims to change all this and enable human-like conversation with the machines of the bank, by the customers, employees, and prospective customers.
Resource Management and Scheduling Algorithm in Hybrid Cloud Environment

In the development of cloud computing, public cloud and private cloud are two mature ways to provide cloud computing services, but the public cloud is trans-parent to users, and the cost of private cloud is high, so many users choose to use hybrid cloud.It can also solve the problem of peak workload.
Performance of Simultaneous Perturbation Stochastic Approximation for Feature Selection

Feature Selection (FS) is an important process in the field of machine learning where complex and large-size datasets are available. By extracting unnecessary properties from the datasets, FS reduces the size of datasets and evaluation time of algorithms and also improves the performance of classification algorithms. The main purpose of the FS is achieving a minimal feature subset from the initial features of the given problem dataset where the minimal feature subset should show an acceptable performance in representing the original dataset. In this study, to generate subsets we used simultaneous perturbation stochastic approximation (SPSA), migrating birds optimization and simulated annealing algorithms. Subsets generated by the algorithms are evaluated by using correlation-based FS and performance of the algorithms is measured by using decision tree (C4.5) as a classifier. To our knowledge, SPSA algorithm is applied to the FS problem as a filter approach for the first time. We present the computational experiments conducted on the 15 datasets taken from UCI machine learning repository. Our results show that SPSA algorithm outperforms other algorithms in terms of accuracy values. Another point is that, all algorithms reduce the number of features by more than 50%.
The Photovoltaic Facade and its Impact on the Energy Economy of Buildings Located in Semi-arid Areas

it's clear to the world that the eastern Japan earthquake was the biggest disaster the modern world had ever came to face, that's why it was of great importance that we took crucial lessons of it on every aspect possible so that we can be more ready for future disasters, no matter what is their scale, because as we know disaster are going to come, in all shapes and kinds, and a major disaster like the Great Eastern Japan Earthquake is going to be upon us every few decades, so being ready, as calculations have proofed, spending and providing for the preparedness phase, is 7 times better, safer and cheaper, than trying to solve the problems in the recovery phase, after the disaster happens, in this study we'll be doing an analysis of the event, and suggesting numerous solutions to prevent the likelihood of a similar scenario in the future.
Smart Bus Concept of COVID-19 Prevention Using FaceBio Keys

The COVID-19 surge has impacted surge has an impact of each and every part of world. Vaccination is going on but still some measures are required to maintain the social distances to reduce transmission of virus. The transportation is the rapid source of transmission of diseases as daily number of people makes use of public transportation. We have proposed the concept of the smart bus will can contribute a lot in controlling its massive spread. So, we will be proposing the new design for the smart bus for promoting the contact-less journey. In the case, the door entry will be based on the face recognition concept, for the purpose of the face recognition, we will use of the concept of the facial landmark analysis and with the concept of the machine learning. For the transportation purpose, the face parameters are linked with the smart bus database, which require the passenger to take a pre-paid card, in order to enjoy the travel. The bus will have the camera which recognize in the passenger face and give the entry and the bus will be automated driving mode using the google navigation system and traffic sensing system. The bus will be driver less and the bus will have sensors in the seats which operate on the pressure concept, so the seating arrangement in the bus is also on basis of the social distancing. If the social distancing, is not maintained as passenger tries to sit on the wrong seat and the alarm them will sound in the bus and the bus will not move further. So, using these concepts, we can reduce the COVID-19 chances of spreading due to the usage of public transportation services.
Localization of Magnetocardiographic Sources for Myocardial Infarction Cases Using Deterministic and Bayesian Approaches

In this paper, the inverse problems of cardiac sources using analytical and probabilistic methods are solved and discussed without committing the ‘inverse crime’. The standard Tikhonov regularization technique is solved initially to estimate the under-determined heart surface potentials from Magnetocardiographic (MCG) signals. The results of the deterministic method subjected to noise in the measurements are discussed and compared with the probabilistic models. Hierarchical Bayesian modeling with fixed Gaussian prior is employed to quantify the uncertainties in source reconstructions. We present a novel application of the Variational Bayesian inference approach to estimate the heart sources. The reconstruction results of the Variational Bayesian model with nonstationary priors are compared with the solutions of the simplistic Bayesian approach; and the performances are evaluated using Root Mean Square Error (RMSE) and correlation co-efficient metrics. The solutions in the study are extended to localize the MCG sources for two types of Myocardial infarction cases.Funding Information: The authors would like to thank the department of Electronics and Communication Engineering at Manipal Academy of Higher Education, Manipal for their resources and support. Declaration of Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Face detection of migrating learning based on constrained scene

Face detection places an important role in face recognition which is a popular choice for biometric systems. To solve the low face detection rate problem of face detection in constrained scenario, an efficient face detection method based on migration learning was proposed in this paper. In the proposed facial detection approach, data-cleaning was firstly used to optimize the face database. Then the Visual Geometry Group 16 (VGG16) deep learning network was improved to realize migration learning by replacing the softmax regression layer with the multi-scale feature detection layer. Finally, the constrained scene face images for testing were detected and labeled by the trained migration learning model. The WIDER FACE dataset was used for experiments. Experiment results showed that the proposed method can successfully perform face detection in the WIDER FACE dataset and obtain more than 90% detection rate.
Titlegen-Fl: Quality Prediction-Based Filter for Automated Issue Title Generation

A high-quality issue title should provide a concise and precise summarization for the issue body. However, the quality of the issue titles in open-source projects is still far from satisfactory. To solve this problem, researchers first formulated the issue title generation problem into a one-sentence summarization problem and then proposed an effective method iTAPE. However, after analyzing the quality of the titles generated by the method iTAPE, which is measured by ROUGE-L, we find the ROUGE-L scores of only 42.7% of titles can exceed 0.3. This means the quality of the generated titles is not satisfactory and can limit the practicability of the method iTAPE. Therefore, we propose a quality predictionbased filter TitleGen-FL, which includes the deep learning-based (DL) module and the information retrieval-based (IR) module. If both of these two modules predict that the high-quality title cannot be generated by iTAPE, TitleGen-FL can automatically filter this issue and return a warning message. To evaluate the effectiveness of our proposed filter, we select the benchmark dataset gathered from real-world open-source projects as our experimental subject. Both automatic evaluation and human study show that our proposed filter TitleGen-FL can effectively filter the issues, which cannot generate high-quality titles by iTAPE.
Code-Level Verification

The verification effort described so far ensures that the SCION protocol guarantees the intended correctness and security properties. These design-level guarantees are essential, but by themselves do not guarantee that a SCION network actually works as intended. Errors in the protocol’s implementation could compromise its availability, correctness, or security—for instance, by causing a component to crash, skip essential checks, or leak confidential information. To guarantee the absence of errors in the SCION implementation, we employ code-level verification. Design-level and code-level verification complement each other and target different abstractions of the overall system. In particular, design-level verification provides specifications for many of the properties that need to hold for the implementation to be correct, for instance, the intended I/O behavior of each component. Other properties, such as memory safety, data-race freedom, and the absence of backdoors are specified directly on the code level.
A Classification Schema for Designing Augmented Reality Experiences

Aim/Purpose: Designing augmented reality (AR) experiences for education, health or entertainment involves multidisciplinary teams making design decisions across several areas. The goal of this paper is to present a classification schema that describes the design choices when constructing an AR interactive experience. Background: Existing extended reality schema often focuses on single dimensions of an AR experience, with limited attention to design choices. These schemata, combined with an analysis of a diverse range of AR applications, form the basis for the schema synthesized in this paper. Methodology: An extensive literature review and scoring of existing classifications were completed to enable a definition of seven design dimensions. To validate the design dimensions, the literature was mapped to the seven-design choice to represent opportunities when designing AR iterative experiences. Contribution: The classification scheme of seven dimensions can be applied to communicating design considerations and alternative design scenarios where teams of domain specialists need to collaborate to build AR experiences for a defined purpose. Findings: The dimensions of nature of reality, location (setting), feedback, objects, concepts explored, participant presence and interactive agency, and style describe features common to most AR experiences. Classification within each dimension facilitates ideation for novel experiences and proximity to neighbours recommends feasible implementation strategies. Recommendations for Practitioners: To support professionals, this paper presents a comprehensive classification schema and design rationale for AR. When designing an AR experience, the schema serves as a design template and is intended to ensure comprehensive discussion and decision making across the spectrum of design choices. Recommendations for Researchers: The classification schema presents a standardized and complete framework for the review of literature and AR applications that other researchers will benefit from to more readily identify relevant related work. Impact on Society: The potential of AR has not been fully realized. The classification scheme presented in this paper provides opportunities to deliberately design and evaluate novel forms of AR experience. Future Research: The classification schema can be extended to include explicit support for the design of virtual and extended reality applications.
Usability Evaluation of Engineering Research Center for Compact Efficient Fluid Power Website

This study aims to investigate usability problems of the National Science Foundation engineering research center for compact and efficient fluid power (CCEFP) website and provides design recommendations to improve it. A discounted usability evaluation technique, heuristic evaluation was applied and followed by a usability testing. Usability metrics were then analyzed. Top usability problems were discussed in detail. Findings from this study can help CCEFP improve its effectiveness and efficiency of their website and better serve the fluid power community.
Automatic Classification of Bug Reports Based on Multiple Text Information and Reports’ Intention

With the rapid growth of software scale and complexity, a large number of bug reports are submitted to the bug tracking system. In order to speed up defect repair, these reports need to be accurately classified so that they can be sent to the appropriate developers. However, the existing classification methods only use the text information of the bug report, which leads to their low performance. To solve the above problems, this paper proposes a new automatic classification method of bug reports. The innovation is that when categorizing bug reports, in addition to using the text information of the report, the intention of the report (i.e. “suggestion” or “explanation”) is also considered, thereby improving the performance of the classification. First, we collect bug reports from four ecosystems (Apache, Eclipse, Gentoo, Mozilla) and manually annotate them to construct an experimental data set. Then, we use Natural Language Processing technology to preprocess the data. On this basis, BERT and TF-IDF are used to extract the features of the intention and the multiple text information. Finally, the features are used to train the classifiers. The experimental result on five classifiers (including K-Nearest Neighbor, Naive Bayes, Logistic Regression, Support Vector Machine and Random Forest) show that our proposed method achieves better performance and its F-Measure achieves from 87.3% to 95.5%.
Improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -learning is one of the most well-known reinforcement learning algorithms. There have been tremendous efforts to develop this algorithm using neural networks. Bootstrapped deep <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -learning network is amongst them. It utilizes multiple neural network heads to introduce diversity into <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -learning. Diversity can sometimes be viewed as the amount of reasonable moves an agent can take at a given state, analogous to the definition of the exploration ratio in RL. Thus, the performance of bootstrapped deep <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -learning network is deeply connected with the level of diversity within the algorithm. In the original research, it was pointed out that a random prior could improve the performance of the model. In this article, we further explore the possibility of replacing priors with noise and sample the noise from a Gaussian distribution to introduce more diversity into this algorithm. We conduct our experiment on the Atari benchmark and compare our algorithm to both the original and other related algorithms. The results show that our modification of the bootstrapped deep <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -learning algorithm achieves significantly higher evaluation scores across different types of Atari games. Thus, we conclude that replacing priors with noise can improve bootstrapped deep <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -learning's performance by ensuring the integrity of diversities.
DETECTION OF LIVER CANCER DISEASE USING MICROSCOPIC IMAGES

The detection of liver cancer in its early stages is very difficult and more time-consuming. The proposed system collects microscopic images as input from the patients and preprocesses them to extract features. Once the feature extraction stage is completed the classification of the image needs to be done on them. The proposed system uses the classifier support vector machine (SVM) technique to classify the images into their respective classes. The classifier in the proposed system uses the normal approach of classification i.e., a classifier has normally two stages one is training and then testing. Each of these classifiers goes through both stages. Firstly, the training stage involves the system learning about the images and their respective category which is already known from the expert advice. In this way, a series of images are given in the form of input with their actual category. The classifier learns from this and then in the testing phase a new image is given for classification to the system. The system uses the prior knowledge that it has learned during the training phase to predict the category for the image.
An Overview of Various Security Issues and Application Challenges of the Attacks in Field of Blockchain Technology

In present world, everyone seems to be specializing in technologies however they’re not worrying concerning security. Most users and researchers are giving more attention of the Blockchain applications and technology itself, however less attention towards security. In this paper, we represent overview of the various cyber security challenges and the privacy of various Blockchain applications. From the essential data security and the very effective privacy protection which can make the Blockchain to be used widely in the industries. From the expansion of Blockchain applications many of the security attacks are targeted to the Blockchain. This paper for the most part centers around the security examination of the innovation and uses of the Blockchain, and condenses the vulnerabilities and anticipates potential assaults and furthermore outlines the present status of Blockchain security protection.