Reviewerly/exHarmony huggingface dataset:
Sedimentological Origin and Environmental Impact of El Zayat Shales, East Dakhla Oasis, Western Desert, Egypt

Twenty representative shale samples were collected from a vertical lithostratigraphic section including (Quseir Shale Member of Nubia Formation, Duwi Formation and Dakhla Formation) of El Zayat area on the southern scarp at the central part of the Abu Tartur Plateau.The chemical analyses data show that the average values of V/Cr in the studied shales are higher than 2, which may indicate that all of the studied sediments were deposited under anoxic depositional conditions.The Cr/Ni ratios of El Zayat shales are 1.5, may be reflect these sediments are derived from mafic source rocks.The tectonic setting studies indicated that the investigated studied shales were mainly derived from volcanic rocks with province origin.The chemical Index of alteration (CIA) was calculated for all the samples and revealed that El Zayat shales describe the moderate chemically weathered terrain.Beside, the fluviatile of post deposition contributions from local sources, such as the basement rocks in the Egyptian South Western Desert, can be considered as subordinate sources.The immobile elements are also indicators of the origin of the provenance.Therefore, the presence of trace elements Cr, V and Ni in addition to Ti in El Zayat shales suggests a basaltic source origin.Additional environmental investigations were also undertaken in order to explore any environmental radiation impact that may arise from the utility of these shales in any domestic purposes by the locals and/or industry.The calculation of geo-accumulation indices (Igeo) indicate that the investigated sediment in El Zayat area is mainly low to moderately pollute.However, their utility in domestic purposes is safe if the dilution factor in the final product is proper.
Secure Architecture for E-commerce Websites

With the rapid growth of Information Technology(IT), the current markets are being transformed into e-markets/ecommerce.This has created a need for a secure approach toimplement e-commerce and to keep its security. To meet thisneed, Service Oriented Architecture (SOA), is one of the bestproficiencies to opt to, because it is flexible and easy to use.However, together with the advantages of SOA, there is also apossibility of unauthorized access of data, and interfering withkey data is also easy. It is therefore a challenge for someorganizations to implement secure e-commerce. This paper willexplore/highlight the significance of SOA for e-commerce andalso the issues/faults in the current e-commerce systems. Animplementation design of a secure architecture for an ecommercesystem supported by SOA has also been suggested inthis paper.
Analysis of Breast Cancer dataset using Supervised Machine Learning Classifiers

We Have Extracted Our Dataset From Kaggle. Our Study Is About Breast Cancer Diagnosis Based On 31 Input Attributes To Produce One Output Attribute That Is The Type Of Breast Cancer. Our Analysis Is On Two Major Aspects That Are Malignant And Benign On The Basis Of 10 Attributes That Is Texture, Perimeter, Area, Smoothness, Compactness, Concavity, Symmetry, Fractal Dimension, Concave Points And Radius.
An Architecture to Implement the Internet-of-Things using the Prometheus Methodology

The rapid expansion of the Internet of Things (IoT) will generate a diverse range of data types that needs to be handled, processed and stored. This paper aims to create a multi-agent system that suits the needs introduced by the IoT expansion, thus being able to oversee the Big Data collection and processing and also to maintain the semantic links between the data sources and data consumers. In order to build a complex agent oriented architecture, we have assessed the existing agent oriented methodologies searching for the best solution that is not bound to a specific programming language of framework, and it is flexible enough to be applied in such a divers domain like IoT. As complex scenario, the proposed approach has been applied to medical diagnosis and motoring of mental disorders.
Feature engineering-based predictive modelling using a non-imputed dataset

This paper presents random investigations of different regression algorithms of machine learning techniques by considering some of the feature engineering principles using a major nutrient-based soil dataset containing missing values and noisy instances. Here, one of the feature engineering techniques like imputation is considered and emphasised for the efficient random investigation analysis using seven regression machine learning algorithms. The subsequent dataset then participates without losing its basic features and negotiating accuracy results. Finally, the transformed input dataset tuned by feature engineering principles is used by the different machine learning algorithms for random investigations. The result obtained from the different experiments ensures that the feature engineering-based linear regression, Ridge regression, and Classification and Regression tree regression algorithms powered by explanatory are one of the significant approaches in the field of machine learning in building a potent explanatory model with improved performances.
The effect of scenario planning on non-financial performance in Malaysia hotel industry

This study examines the effects of scenario planning on non-financial performance of business organisations in the hospitality industry. This has the capacity to promote the understanding of regional and global tourism environment through additional alternative methods. Several studies on organisational performance have deployed the conventional planning and strategies to determine the best alternative that could accelerate business performance and sustain its environment. In order to determine the cause and effect relationship between the two variables, the study applies the VB-SEM using Smart-PLS to 82 samples. Evidence from the study reveals that scenario planning has a direct positive effect on non-financial performance of business organisations in the hospitality industry. Thus, the study emphasises that scenario planning is fundamental and practical strategic tools that could be deployed to accelerate non-financial performance.
A Bonesetter Medicinal Services Administrations to Enhance Individuals' Wellbeing in a Single Touch via Android Application

These days, advanced mobile phones have achieved each hand and each home. Thus, individuals are making utilization of the gainful portable applications to make their regular day to day existence less demanding. According to the insights, 27% of Indians kick the bucket without legitimate restorative help. Individuals have begun to concentrate on their wellbeing, yet at the same time they are looking for solid proposals. To decrease such multifaceted nature and memory required for each different application another application is required. The application, for example, incorporated across the board. In early proposed framework, it is accessible just to discover the ailment dependent on our indications. There are no further proposals about the supporting measure. The possibility of our undertaking is to give restorative help to individuals at their single touch. This application will give medicinal services administrations to enhance individuals' wellbeing. Utilizing this application individuals can get various advantages like data on blood accessibility, Body Mass Index number cruncher, issue discoverer, patient history etc.
On the Seidel Energy of Certain Mesh Derived Networks

The energy of graph G is defined as the sum of the absolute values of eigenvalues of the adjacency matrix A(G). The manual calculation of energy of graphs consumes several man hours. In this paper, we use MATLAB to generate the Seidel matrix and hence calculate the Seidel energy of some mesh derived networks.
Final Remarks and Further Work for the Hybrid-AutoML System

This chapter addresses that the various use cases have proved that the aims and contributions of this research to conceptualise, design, and develop a scalable and flexible toolkit for automatic big data ML mode and model selection, on single or multi-varying datasets has been achieved. A major benefit of the hybrid-autoML toolkit is that it reduces the time data scientists and researchers in the field spend, searching through the algorithm selections and hyper parameter space. This advantage was discussed in Section 5.2 where the authors compared the hybrid-autoML tool with autoWeka on about 35 datasets using measures such as accuracy, mean absolute error (MAE), and time.
Text Manipulation

Automation of UNIX-based systems often involves text manipulation. Many programs are configured with textual configuration files. Text is the output format, and the input format, of many systems. While tools like sed, grep, and awk have their place, Python is a powerful tool for sophisticated text manipulation.
Semantic Web Services: An Empirical Methodology to Security

In today's modern era, the Semantic Web is progressing at a tremendous speed. The Semantic Web services have opened new possibilities through which any one can avail various services. But if these technologies are not properly protected, then their use can put users in danger. Many people have to face difficulty in assessing the security hazards allied with Semantic Web services. Many Web services such as e-commerce stores various types of data on their database servers. These database servers are distributed across the globe. The e-commerce may use the unalike assortments of database for storing different information. Web services such as e-commerce have to handle assorted types of data. Unalike Web services like e-commerce have to handle assorted types of data. A single database program will not be suitable for storing and processing mixed types of data as it will increase the processing overhead and ultimately reduce the performance of the web service. So there is need arises to define an apposite subclass of databases for each document type.This paper insinuates a set of databases for each type of documents stored on an e-commerce platform. An exertion has been made to define a proper set of database programs for a web service such as e-commerce that adequately manages the requirements of a particular type of data. The tryouts have been steered for an e-commerce Web service for weighing the efficacy of the proposed approach. The results shows that if well-structured database has been used for a Web service such as e-commerce then response can be very quick and chances of packet loss will also be very low. If network attacks such as DoS and man-in-the-middle attack exist within the request, the performance of the Web service severely affected. A network attack may lead to the more chances of packet loss. The paper also delineates the impact of network attacks on web service performance.
DENOISING ENCODER WITH SEMANTICS AND EXCLUSION FOR SPOTTING CYBERBULLY

The rapid growth of social networking is supplementing the progression of cyberbullying activities. Most of the individuals involved in these activities belong to the younger generations, especially teenagers, who are at more risk of suicidal attempts. Cyberbullying is the process of using the Internet, cell phones, or other devices to send or post text or images intended to hurt or embarrass another person. Through machine learning techniques, we can detect language patterns used by bullies and their victims, and develop rules to automatically detect cyberbullying content. Here, we introduce a new machine learning method to deal with this problem. Our method named Semantic-Enhanced Marginalized Stacked Denoising Auto-Encoder (smSDA) is developed via a semantic extension of the popular deep learning model. The smSDA method detects the hidden attributes of the bullying information. Our approach experiments on two public cyberbullying corpora i.e. twitter and MySpace. The outcome of our proposed method is better than the other text representation learning methods.
An Ensemble of IoT with Cloud Enabled Distributed Disease Diagnosis System using Optimal Kernel Extreme Learning Machine Model

In recent times, Internet of Things (IoT) and cloud computing (CC) technologies provides diverse applications and services in the healthcare sector.In a distributed healthcare management, manyIoT devices are used to monitor the health status of the patients and send the data to the cloud server for processing.This paper devises a new IoT and cloud enabled intelligent distributed disease diagnosis model using Elephant Herd Optimization (EHO) with Kernel Extreme Learning Machine (KELM), called EHO-KELM model.The proposed EHO-KELM model initially performs the data acquisition process to collect the medical data of the patients from different sources.Then, the collected data is transmitted to the cloud server for further processing, where the EHO-KELM model gets executed to classify the patient data to identify the existence of diseases.The application of EHO algorithm helps to tune the parameters of the KELM model for better classification performance.The performance of the EHO-KELM model has been validated using two medical dataset namely diabetes and heart disease.The simulation outcome indicated that the EHO-KELM model has demonstrated effective outcome with the maximum classification accuracy of 94.25% on the applied heart disease respectively.
Build

In most cases, programmers don’t have complete knowledge of the behavioral situations they wish to change in some way. There may be a rather abstract sense that many people would like to exercise more or take advantage of breast screening exam services, but little knowledge of what kinds of things would induce people to leave their current patterns of behavior behind, to adopt a new daily routine incorporating exercise, or actually go out to get a mammogram. This chapter outlines numerous ways that programmers can learn the information they need to produce creative campaigns and interventions.
MMSports'21

The fourth ACM International Workshop on Multimedia Content Analysis in Sports (ACM MMSports'21) is part of the ACM International Conference on Multimedia 2021 (ACM Multimedia 2021). Exceptionally, due to the corona pandemic, the workshop is held virtually. The goal of this workshop is to bring together researchers and practitioners from academia and industry to address challenges and report progress in mining, analyzing, understanding and visualizing the multimedia/multimodal data in sports, sports broadcasts, sports games and sports medicine. The combination of sports and modern technology offers a novel and intriguing field of research with promising approaches for visual broadcast augmentation and understanding, statistical analysis and evaluation, and sensor fusion during workouts as well as competitions. There is a lack of research communities focusing on the fusion of multiple modalities. We are helping to close this research gap with this workshop series on multimedia content analysis in sports.
Research on highway night traffic event detection method based on video processing

At present, most highways in China have established full-coverage video systems, but highway traffic event detection still uses alarms and manual inspections. Especially, the high incidence of nighttime freeway traffic events makes inspection difficult, which is a difficult point in freeway emergency management. We develop a nighttime traffic event detection algorithm that can be integrated into existing highway video surveillance systems, which has three components: video initial processing, vehicle detection and vehicle tracking speed measurement. Among them, the Hom-Schunck optical flow algorithm is used for vehicle detection and Deepsort’s multi-target vehicle tracking algorithm is used for vehicle tracking and speed measurement. Finally comparing the vehicle detection part using background difference method, yolo algorithm and the algorithm in this paper, the algorithm in this paper has higher detection accuracy, while the detection speed meets the system working requirements.
Database Consistency

Databases involve lots of IO. When you have a lot of IO, you inherently run the risk of corruption. Your primary defense against database corruption is to take regular backups of your database and to periodically test that these backups can be restored. You need to look out for database corruption, however, and SQL Server provides tools you can use to check the consistency of your database as well as to resolve consistency issues if backups are not available. This chapter will look at the options you have for both checking and fixing consistency issues.
Strategies to Improve Speed Reading and Its Effect on Comprehension in Student Teachers of EnglishAn Experimental Study

Reading is not only one of the most important skills in language learning, but also one of the main objectives of learning English in general. Many factors such as students’ background knowledge, motivation, interest, organization of the texts and study skills may affect reading skill. This paper describes an experimental study to identify the effect of the significance of speed reading on comprehension. The results indicate that there are significant gains as a result of the experiment. Reading became faster, scientific and systematic
Mining Data Streams

Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the MapReduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream-processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets, and clustering. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.
Steganography using Spatial Domain Techniques

In present world data transfer using the internet is growing. It is very easy and fast way to transfer information like confidential documents, economic transactions, business applications and other covert information over internet. With the advent and growth of internet, people are more concerned about security of information. Data Security is important while data is transferred over internet because any illegal user can access important and private data also make it worthless. Research in data security area will help government agencies, military organization and private companies to securely transmit their confidential data over internet. From past few years various steganography techniques have been developed to hide secret message using various multimedia objects having large amount of redundant data to support steganography. In this paper introduction about steganography, related concepts and implementation of commonly used spatial domain techniques like LSB(Least Significant Bit Technique) with modulus, PVD(Pixel Value Difference) with LSB replacement and adaptive data hiding over edges with LSB are considered. It is observed(while visual, statistical analysis and experiments were carried out) with benchmark cover and stego objects that embedding same amount of secret data in each pixel leads to more visible distortions in a stego image because all pixels do not bear same amount of changes and this effect is more observed in smooth area then edges. Improving stego image imperceptibility and adjusting hiding capacity adaptively are major related research challenges about spatial domain techniques.
Improving Accuracy of Sentiment Analysis for Depression Recommendation using Multi-Domain Fuzzy Rules

Social media &amp; e-commerce has opened up the doors for human behavioral analysis in ways which were not possible before. Companies have the ability to track user’s mood and suggest advertisements which can trigger buying decisions based on it. This is possible due to user’s real time social media updates. Users nowadays are willing to provide information like their location, their age, nearby friends information, their mood, their buying patterns, etc. Companies do not intentionally collect all this information, but it has become a matter of social pride to post it as social media status and updates. The information available can be put to use in multiple forms- predict election results, movie success, product liking/disliking, travel destination recommendation, health care, etc. In our work, we utilize this textual information posted by different users and analyze their depression level focusing on negative sentiments. In order to perform this task, we have considered user’s tweets, any links which they might have posted, the time of the tweet, their age group and any previous depression history of the user. All these parameters are given to a novel fuzzy decision tree that uses sentiment analysis and game theory-based scoring in order to evaluate the depression score for the user. We analyzed the system on different real-time users, and observed that the system predicts depression level with more than 90% accuracy. Our work can be used to generate a prototype to identify if a person is in a depressive state and figure out the intensity of his/her depression.
A Study of Aptitude and General Knowledge of Trainees of Teacher- Training Colleges

Many radical changes have been taken place in the educational journey from the Gurukul practice to today's Luxurious digital education center. According to which in order to test Aptitude and General Knowledge of the Education Graduates which are essential for survival in the present condition, a study was conducted regarding the gender, area, and stream of the Trainees of Teacher Training colleges affiliated to Veer Narmad South Gujarat University. According to which data was collected face to face from about 574 trainees of 10 teacher training colleges affiliated to Veer Narmad South Gujarat University with the help of teacher made educational achievement test and general knowledge test. In a study do interpretation with the help of statistical tools average and correlation observed that there was a very high correlation between aptitude and general knowledge. In presented research tried to reach the depth of the study by deducing several such findings which are presented here.I.