Reviewerly/exHarmony huggingface dataset:
Detection of Fraud in Mobile Advertising using Machine Learning

With ongoing advancements in the field of technology, mobile advertising has emerged as a platform for publishers to earn profit from their free applications. An online attack commonly known as click fraud or ad fraud has added up to the issue of concerns surfacing mobile advertising. Click fraud is the act of generating illegitimate clicks or data events in order to earn illegal income. Generally, click frauds are generated by infusing the genuine code with some illegitimate bot, which clicks on the ad acting as a potential customer. These click frauds are usually planted by the advertisers or the advertising company so that the number of clicks on the ad increases which will give them the ability to charge the publishers with a hefty sum per number of clicks. A number of studies have determined the risks that click fraud poses to mobile advertising and a few solutions have been proposed to detect click frauds. The solution proposed in this paper comprises of a social network analysis model – to detect and categorize fraudulent clicks and then test sample datasets. This social network analysis model takes into consideration a wide range of parameters from a large group of users. A detailed study is conducted for analyzing these parameters in order to separate the parameters, which affect the click fraud generation process largely. These parameters are then tested and categorized into sample datasets. The mobile advertising industry forms a large part of the revenue generated by the advertising industry. Hence, detection of click fraud in mobile advertising is important to ensure that no illegitimate sources are used to generate this revenue. To be precise, the proposed method touches an accuracy of about 92%.
Web Based Multilingual Real Time Speech Transcription Transliteration and Translation System

As we all know that, communication is a weapon to conveying our thought to another person or group of people. But at the same time if we don't understand the language of users then it creates lots of difficulties. Language Translator can overcome this barrier of language. Language Translator translates from source language to selected target language. With the help of this application we convert language in any form (like voice or textual form) it depends on end users. In this paper, we are using Google translate libraries for translation and for recognition. Recognition library is responsible for input (voice/text) which is done by ASR (Automatic Speech Recognition) and here we are more focusing on translation part.
Influence of Interval-Valued Measures on Classification Methods with Missing Values

In this paper, the basic version of the k-nearest neighbors (k-NN) algorithm has been extended to include similarity and indistinguishability measures that reflect uncertainty. The measures used take into account the uncertainty in the decision-making process when classifying objects. The algorithm has been adapted to the processing of attribute values stored as interval values. The algorithm was tested on data with missing values. Three approaches were tested to determine the missing value of individual objects.
Collaborative Software Engineering Model Dependent on Deep Recursive Least Squares

With the continuous development and maturity of information technology, the open methods and development results of software are enriched constantly. There are uncertain factors that may affect the cost of software development. The control of demand, the determination of the logical framework, and the vagueness and inaccuracy of business logic can all affect the development cycle of software. In this paper, the bit error rate and complexity are calculated by using the related algorithm based on the deep recursive least squares algorithm through the establishment of a collaborative engineering tool evaluation system. Under the same preconditions, the estimation performance of the deep recursive least squares algorithm is compared with that of the ideal channel based on the same foundation, and the deep recursive least squares algorithm is simulated through the simulation curve. In addition, the performance of the algorithm is analyzed. The evaluation criteria of four indexes for perception, synchronization, product, and coordination were put forward. The results of the study indicate that the proposed algorithm is effective and can support the engineering modeling of collaborative software.
Design and Research of Physical Education Platform Based on Artificial Intelligence

With the continuous development of information technology, conventional physical education teaching methods are no longer applicable. In order to ensure the objectivity of college physical education evaluation, this paper designs a set of college physical education management information system based on artificial intelligence technology. The weighting algorithm in the student performance evaluation module and teacher performance evaluation module in the system adopts the intelligent algorithm based on FNN neural network. Experimental verification shows that the intelligent algorithm based on the FNN neural network can effectively predict the students’ score in the national college physical education examination, which can provide a more objective basis for teacher performance evaluation.
Fracture Orientation Significance in the Study of Pb-Zn Mineralization of Lower Benue Trough, Abakaliki Se Nigeria

The Lower Benue Trough, in South-eastern Nigeria, hosts several lead-zinc deposits. The mineralization is frequently hosted by Cretaceous black shales that appear to have undergone low grade metamorphism, and subordinate sandstones. Most parts of the deposits have been epigenetically formed, and occur as blocky shaped, discordant veins, fracture, and possibly open-space fillings, of hydrothermal origin. For this reason, structural evaluation constitutes an important criterion for the investigation of the mineralization. In particular, the orientation of fractures occurring at the Pb-Zn deposits can be useful in the differentiation of the relative ages of the mineral vein deposits. A total of one thousand, one hundred and forty-seven fractures around mineralized zones, at the study localities, were measured and analysed. Results indicate that the vein deposits are structurally controlled, have four main orientations, and most likely formed by crack-seal process. The most prominent fracture sets show NW-SE, NNE-SSW to N-S orientations, while the less prominent ones are displayed in the NE-SW and ESE-WNW directions. The NW-SE, NNE-SSW to N-S fracture sets is interpreted as ‘ac’ extension fractures from two different deformation episodes that affected the trough, while the NE-SW, ESE-WNW to E-W sets is the two ‘bc’ tensile fractures, respectively parallel to the axes of F1 and F2 folds which occur in the trough. Small variations of the NNE-SSW and ESE-WNW brought about the occurrences of N-S and E-W sets respectively. Indication are that the two deformation episodes affected the trough: the first episode produce the NE-SW (F1) fold axes, while the second less intensity episode produce the ESE-WNW (F2) folds. The most dominant mineral vein trends in the study area are the NW-SE and NNE-SSW to N-S orientations, in which mineral veins are loaded in the ‘ac’ extension fractures. The less dominant mineral vein trends are the NE-SW and ESE-WNW orientations, which are in the ‘bc’ tensile fractures. At the NW-SE trend, vein minerals mainly occur along 150-160o from N, although it ranges from 135-175o from N. Vein width up to 7.0m can be obtained from both the NW-SE and NNE-SSW directions. The Lower Cretaceous sediments appear to be characterized by the NW-SE (major) and NE-SW (minor) sets of veins, while the upper Cretaceous sequence is most likely characterized by the NNE-SSW (major) and ESE-WNW (minor) vein sets. The widest Pb-Zn vein in the study area is 11.5m wide, located in the Enyigba axes sub-area and trends in the NE-SW direction with evidence of slickensides which indicates faulting.
Multi-Carrier Signal Recognition Method Based on Multi-Feature Input and Hybrid Training Neural Network

In order to achieve automatic identification of modulation formats in orthogonal frequency division multiplexing (OFDM) subcarrier signals, a recognition method based on multiple feature inputs and a Hybrid Training Neural Network (HTNN) is proposed, in which an HTNN structure is designed to obtain high-order statistical correlation features and constellations of OFDM subcarriers. The recognition performance of the proposed method in free space channel transmission and atmospheric time-varying channel transmission is studied by simulation. Simulation results show that the overall identification accuracy of the recognition model based on the proposed method exceeded 93.37% in the wide Signal-to-Noise Ratio (SNR) range of the free space channel. With an SNR higher than 7.5 dB, identification accuracy performance of the learning model culminated, achieving 100% identification accuracy of OFDM subcarrier signals. Under weak turbulent atmospheric and time-varying channel conditions, the overall identification accuracy curve tended to increase as SNR increased and stabilized at more than 95%. Compared with the two comparison methods, the proposed method reduced the sensitivity to channel variations and improved the convergence speed on the basis of the guaranteed identification accuracy, and enabled reliable identification of OFDM subcarrier signals in a wide SNR range.
Characteristic Sequence Analysis of Giant Panda Voiceprint

By analyzing the voiceprint characteristics of giant panda’s voice, this study proposes a giant panda individual recognition method based on the characteristics of the composite Mel composite frequency cepstral coefficient (CMFCC) and proves that the characteristic sequence of the CMFCC has long-range dependent characteristics. First, the MFCC (Mel composite frequency cepstral coefficient) with a low frequency resolution is obtained by the Mel filter bank; then, the inverse Mel frequency cepstral coefficient (IMFCC) features of giant panda calls are extracted. The CMFCC characteristic sequence of giant panda voice composed of the MFCC and IMFCC improves the resolution of high- and low-frequency resolution characteristics of giant panda voice. Finally, the first-order difference characteristic parameters of the MFCC are integrated to obtain the difference characteristics between frames. Through experiments, the improvement of the system recognition effect is verified, and the recognition accuracy meets the theoretical expectation.
Estimation of the CHSH Parameter Using HOM Interference

The Clauser–Horne–Shimony–Holt (CHSH) experiment is an essential test of nonlocality in quantum mechanics and can be used to validate the principle of entanglement. In addition to verifying entanglement, the measurable CHSH parameter can also be used to gauge the quality of the entanglement present in a system. The measurement of Hong–Ou–Mandel (HOM) interference is another important fundamental experiment in quantum optics that measures the indistinguishability of a pair of photons. In this article, we demonstrate how the results of a HOM interference experiment, a relatively simple experiment, can be used to obtain an estimate for the value of the CHSH <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$S$</tex-math></inline-formula> parameter, which is a more complicated measurement. We experimentally demonstrate that the HOM interference technique is capable of providing an estimate of the value of the CHSH parameter that is within one standard deviation of measurement error when spectral impairments are present. We expect that this technique will aid in the calibration of quantum optical systems.
Self-Regulated Learning and Prestasi Akademik Siswa Program Akselerasi Berdasarkan Jenis Kelamin

The study aimed to examine the correlation between self-regulated learning (SRL) and academic achievement; examine the correlation between cognitive, motivational, and behavioral regulations on academic achievement; and examine the difference of SRL level and academic achievement on male and female students of accelerated program in Senior High School of Malang. &#x0D; To analyze the data correlation of Pearson, multiple regression and t-test were applied. The findings revealed that 1) there was a significant positive correlation between SRL and academic achievement (r=.777); 2) there was a significant correlation between motivational and behavioral regulations and academic achievement, and 3) there was no significant difference between SRL level and academic achievement on male and female students on an accelerated program.
Using Statistics to Solve Practical Vocabulary Problems

Most of us who do research on language acqusition have had to use statistics to evaluate the results of experiments. Some may use only the statistical procedures they learned in graduate school and may thus miss out on new advances in statistics that might shed light on some problems in a more straightforward way. The three papers that conduct empirical studies that I will discuss today have used statistical procedures that you may not be very familiar with—bootstrapping, Monte Carlo simulations, and Rasch (or item response theory [IRT]) analysis. Their use of these procedures, however, means that they are able to give quite precise and interesting answers to the questions that they have asked. The fourth paper I will discuss is not an empirical study but a review of studies and call for future research going forward.
A New Framework for Systematic Analysis and Classification of Inconsistencies in Multi-Viewpoint Ontologies

Plurality of beliefs and theories in different knowledge domains calls for modelling multi-viewpoint ontologies and knowledge organization systems (KOS). A generic theoretical approach recently proposed for heterogeneity representation in KOS was linking each ontological statement to a specific validity scope to determine a set of conditions under which the statement is valid. However, the practical applicability of this approach has yet to be empirically assessed. In addition, there is still a need to investigate the types of inconsistencies that might arise in multi-viewpoint ontologies as well as their possible causes. This study proposes a new framework for systematic analysis and classification of inconsistencies in multi-viewpoint ontologies. The framework is based on eight generic logical structures of ontological statements. To test the validity of the proposed framework, two ontologies from different knowledge domains were examined. We found that only three of the eight structures led to inconsistencies in both ontologies, while the other two structures were always present in logically consistent statements. The study has practical implications for building diversified and personalized knowledge systems.
Metalearning for Hyperparameter Optimization

Summary This chapter describes various approaches for the hyperparameter optimization (HPO) and combined algorithm selection and hyperparameter optimization problems (CASH). It starts by presenting some basic hyperparameter optimization methods, including grid search, random search, racing strategies, successive halving and hyperband. Next, it discusses Bayesian optimization, a technique that learns from the observed performance of previously tried hyperparameter settings on the current task. This knowledge is used to build a meta-model (surrogate model) that can be used to predict which unseen configurations may work better on that task. This part includes the description sequential model-based optimization (SMBO). This chapter also covers metalearning techniques that extend the previously discussed optimization techniques with the ability to transfer knowledge across tasks. This includes techniques such as warm-starting the search, or transferring previously learned meta-models that were trained on prior (similar) tasks. A key question here is how to establish how similar prior tasks are to the new task. This can be done on the basis of past experiments, but can also exploit the information gained from recent experiments on the target task. This chapter presents an overview of some recent methods proposed in this area.
RGBeat: A Recoloring Algorithm for Deutan and Protan Dichromats

Deutan and protan dichromats only see exactly two hues in the HSV color space, 240-blue (240º) and 60-yellow (60º).Consequently, they see both reds and greens as yellows; therefore, they cannot distinguish reds from greens very well.Thus, their color space is 2D and results from the intersection between the HSV color cone and the 60º-240º plane.The RGBeat recoloring algorithm's main contribution here is that it is the first recoloring algorithm that enhances the color perception of deutan and protan dichromats but without compromising the lifelong color perceptual learning.Also, as far as we know, this is the first HTML5-compliant web recoloring approach for dichromat people that considers both text and image recoloring in an integrated manner.
New Models for Estimation of Solar Radiation for Five Cities of Pakistan

The increasing demand for electricity due to the ordinary person's lifestyle has put immense pressure on fossil fuels.The rapid depletion of fossil fuels has made scientists and engineers search and adopt alternative sources of energy.Renewable energies, e.g., wind, hydro, biogas, solar, etc., are an excellent alternative to conventional energy sources.In this paper, we developed three models for direct beam solar radiation (BSR), diffuse solar radiation (DSR), and global solar radiation (GSR) for five cities (Karachi, Hyderabad, Lahore, Quetta, and Peshawar) of Pakistan.In these models, BSR, DSR, and GSR are given as a function of day-number.Three types of solar radiations have been calculated through the models developed in this study.The results are compared with the corresponding values calculated by the standard clear sky ASHRAE model.A good agreement is found between these values.For validation of results, Root means square error (RMSE), Mean Absolute error (MABE), Mean Absolute percent error (MAPE), and chi-square (χ 2 ), and coefficient of determination are calculated and found to be in the accepted range.It has also been found that DSR, BSR, and GSR have an inverse relation with Latitude.
Tele-Operation Systems for Robotic Arm Manipulator using IoT

The is paper has style and development of a robotic arm manipulator victimisation net of things with low value. Tele-robotics refers to regulate a mechanism from an overseas finish. The robotic arm has controlled by wirelessly is incredibly useful for broad vary of applications that starting from medical fields, automations in industries. With the recognition and widespread use of net, it becomes a straight forward task for anyone to regulate and monitor the robots from an overseas finish. During this project, a robotic arm is meant to be controlled by a licensed person at any time and from anywhere victimisation the online technology. With an internet server, it becomes simple to regulate devices from an overseas web site. The net based mostly robotic Tele-operations realize nice utility in applications that are dangerous, hostile, and inaccessible to humans and areas of labor no matter geographical locations. Thanks to the growing awareness, technological support, knowledge transmission protocols, easy use and low value the net has well-tried to be the foremost appropriate infrastructure for worldwide access and knowledge exchange.
Development of Artificial Intelligence Based Chatbot Using Deep Neural Network

No matter how well-known the colleges are, there will always be concerns that people have during the application process and even after they have been accepted. The college hosts a variety of events, ranging from departmental activities to club activities. Not everyone is likely aware of all events. The chatbot bridges the gap between people and information. The world is becoming more automated, and people expect services to become more automated as well. Using a chatbot is the best approach to serve because, unlike a human, they are accessible 24 hours a day, seven days a week. A chatbot is a software that responds to user questions and provides information from a knowledge base. The purpose of this project is to create a chatbot for VNRVJIET that will answer queries raised about fests, departmental activities, events, clubs, infrastructure, placement data, admission procedure, and other topics. The proposed methodology consists of a chatbot built using Deep Neural Networks and speech recognition capabilities. The information is delivered in both speech and text modes using the proposed methodology. Data is collected and formatted in JSON format initially. The prepared data is pre-processed, and then the bag of words (BoW) algorithm is applied to it. The bag of words algorithm is most influential method for object categorization. The key aspect of using this algorithm is for converting the word vector to a numerical dataset for machine to do a deeper analysis. A deep neural network is created using tensorflow API, and the speech recognition function is de-fined for the input query and output response. Finally, the chatbot function is defined and utilized for generating responses for any given query.
Machine Learning Applied to Health Information Exchange

The interest in artificial intelligence (AI) has grown in the last few years. The healthcare community is no exception. The present work is focused on the exchange of medical information, using the Health Level Seven (HL7) international standards. The main objective of the present work is to develop an AI model capable of inferring if for a given hour exists a peak in the number of exchanged messages. To accomplish that, two different deep learning models were created, an artificial neural networks (ANN) and long short-term memory (LSTM). The intention is to observe which is capable to perceive the situation better considering the environment and features of a healthcare facility. Using laboratory-generated data, it was possible to simulate variations and differences in “traffic.” Comparing the LSTM vs. ANN model, the first is capable of outputting peaks better but for considered mean values that do not happen. For the given context, predicting the peak is essential, so the LSTM is the right choice and uses fewer features that are good regarding performance.
Analisis Sentimen Pada Agen Perjalanan Online Menggunakan Naïve Bayes dan K-Nearest Neighbor

Social media has impact for decision maker to get more insights broadly. Including for online travel agent company, where costumer’s interest to use online travel agent for their chosen agent will grows along with the high number of customer’s satisfaction. As a one of the most important point in distribution, company provides a platform that reliable and effective to purchase a trip and share information of their experience through Online travel agent. It is important to know how consumer considerate which one the online travel agent they choose. One of their method is looking at the reviews. Facebook is one of social media that provide numerous reviews through comments sections. The research purposes are twofold, algorithm comparison and reveal the effect of uppercase as well as punctuation mark. The accuracy comparison between Naïve Bayes and K-Nearest Neighbor is provided against the datasets. This research collects the data from user comments on Facebook about the biggest three online travel agents in Indonesia. We classify the comments into three categories which are positive, negative, and neutral. The result of this research is found that K-Nearest Neighbor have slightly higher accuracy than the Naïve Bayes. Moreover, lowercase text without punctuation achieves better accuracy for both of algorithm.