Reviewerly/exHarmony huggingface dataset:
A novel approach for query over encrypted data in database

Database management is considered an essential component of many information systems to store data. Some database system contains secure data; these data are protected by using encryption techniques. The query performance is affected by encryption techniques. Therefore; should be a balance between the security and performance. A new technique in this research work has been proposed to enhance the query performance over the encrypted fields in a database system. This technique is based on producing a unique hash value for each secure data and transforms the SQL query into an appropriate formula to be executed over the hash value fields. The proposed approach has eliminated any statistical relationship between encrypted and hash value fields. The time of execution encryption/decryption is reduced to enhance the performance of the query over encrypted secure data in the proposed technique. A set of experiments are carried out, and the results indicate that the performance of SQL-query is enhanced by reducing the average response time to 14 compared with others related approaches.
HAND GESTURE DETECTION USING ARDUINO AND PYTHON FOR SCREEN CONTROL

Screen Controlled of the device is a one which can be dignified by physical programmed of human hand which is called as simple gesture.The user just necessities to apparel a gesture device which embraces a sensor.The sensor will record the movement of hand in a specific direction which will result in the movement of the robot in the respective direction.The robot and the Gesture device are connected wirelessly via radio waves .Gesture commands freely trainable by the user can be used for supervisory external devices with handheld wireless sensor unit, The persistence gesture acknowledgment in Computers has always been the minimization of the distance between the physical world and the digital world.The way humans interrelate among themselves could be employed in communication with the digital world by understanding gestures control via mathematical algorithm.Numerous ways and algorithms have been suggested and executed to achieve the goal of gesture recognition and its use in interconnecting with the digital world.Gestures can be tracked using hand activities, accelerometers and more.The controller is interfaced with sensor to manipulate the path of the hand movement, The accelerometer depends upon the gestures of hand.Through accelerometer, a passage of data signal is received and it is processed with the help of Arduino microcontroller This paper content with the organized motion detection system and implementation of a gesture precise computer using Arduino, Uno ARDUINO with HCSR04, processor and a laptop loaded with Windows 10 operating system along with low cost hardware necessities.The system can be generally classified into two components: The Hardware part consisting of Arduino Microcontroller, the ultrasonic sensors HC-SR04 and a computer, preferably a laptop, and the software part consists of Arduino IDE and Python 3.8.2IDLE with GUI module installed.The wireless message allows the user to interrelate with the advance technology
An Efficient Framework for Detecting Various Moods in Hinglish and English Dataset

Due to the paced growth in web technologies and natural language processing, research on Sentiment Analysis (SA) has become very popular in recent times. In recent years most of the research papers have focused on sentiment analysis based on polarity (positive and negative sentiments). This paper presents an effective framework for identification of various moods of person from its written text or sentences. The paper focuses on the mood detection in given text written in mixed language called “Hinglish”. Hinglish is actually a fusion of two languages, English with the Hindi language. The major goal of this research is to propose a methodology for extracting information of emotions from a given text in Hinglish. The framework tested on 700 sentences containing Hinglish data. Seven emotions anger, happy, joy, confidence, sadness, tentativeness and fear have been used for generation of results. The proposed approach yielded an accuracy of 93.96%.
OpTrak

This article describes the structure and functionality of OpTrak, a decentralized app implemented using the Ethereum blockchain that targets the opioid epidemic currently plaguing the United States. Over-prescription and distribution of opioids cost the national healthcare system over $78 billion every year. Problems persist in every stage of the process, from doctors prescribing the medication to the pharmacists fulfilling prescriptions. These problems arise from a combination of factors, including lack of accountability, transparency, and reliability in the current prescription drug monitoring programs. This work provides three key contributions to research on a technical approach to mitigate the opioid epidemic. First, the authors pinpoint key problems in the current opioid prescription system. Second, they propose an integrated approach for addressing the problems by leveraging distributed ledgers, focusing on blockchain technology. Third, the authors describe the structure and functionality of OpTrak that allows a consortium of care providers to exchange patient prescription data securely.
Assessing the impact of brand equity and customer experience on brand loyalty in the United Arab Emirates' hotel industry

Contributing to the existing literature regarding the hotel industry of the United Arab Emirates, this research study aimed to assess the impact of brand equity and the quality of customer experience on brand loyalty. In particular, the study examined the relationship between brand equity and the quality of customers' experiences with some of the prominent players in the hotel industry of the United Arab Emirates. The sample for the study included a total of 694 participants. Five hotel chains were included in the data collection: Atlantis, The Palm, Movenpick Hotel, Sheraton Hotel, Crowne Plaza Hotel, and Hotel Ibis. Study findings revealed a significant impact of the brand equity and customer experience on the development of brand loyalty among consumers.
Gradual Weight Updating for Sentiment Mining

Nowadays, many people prefer the use of social media for communicating and exchanging opinions with each other over face to face communication. This has lead to a generation of a tremendous amount of textual opinioned data. Understanding this opinioned data is useful from all perspectives. But the major challenge exists here is how to extract the exact sentiment hidden behind this huge data. To solve this problem, keyword spotting or dictionary-based approaches are followed. In this paper, we present a Gradual Weight Updating for sentiment mining. It not only considers the polarity of each word similar to the unigram methodology but, it also focuses on the entire cluster of words that contains the unigram. The different steps it follows for sentiment extraction of the word are polarity fetching, cluster marking, weight tagging, valence shifter, adversative conjunction handling, and final score generation. The paper contributions in the area of domain independent opinioned word extraction and accurate polarity mining with the help of context marking approach. We used the various opinionated datasets to compare and illustrate the performance of our proposed system.
Perplexed Bayes classifier-based secure and intelligent approach for aspect level sentiment analysis

In this work, we are using machine learning methods to classify a review document. We are using two machine learning methods - Naive Bayes classifier and perplexed Bayes classifier. First we will briefly introduce the Naive Bayes classifier, its shortcomings and perplexed Bayes classifier. Further, we will be training the classifiers using a small training set and will use a test set with reviews having dependency among its features. We will then show that how Naive Bayes classifier fails to classify such reviews and will be showing that perplexed Bayes classifier can be used to classify the given test set, having dependency among its features.
Hash-based and privacy-aware movie recommendations in a big data environment

Movie recommendation is an important activity in the people's daily entertainment. Typically, through analysing the users' ever-watched movie list, a movie recommender system can recommend appropriate new movies to the target user. However, traditional movie recommendation techniques, e.g., collaborative filtering (CF) often face the following two challenges. First, as CF is essentially a traversal technique, the recommendation efficiency is often low. Second, traditional movie recommender systems often assume that the users' ever-watched movie list for decision-making is centralised, which makes it hard to be applied to the distributed movie recommendation scenarios. In view of these challenges, in this paper, we bring forth an efficient and privacy-aware online movie recommendation approach based on hashing technique. Through experiments on famous MovieLens dataset, we show that our proposal shows a better performance compared with other approaches in terms of recommendation efficiency and accuracy while users' private information is protected.
Enhancing Network Security: an SDN (Software Defined Networking) Method

Today's IT infrastructure, featuring a mobile workforce, IoT apps, digital business transitions and the cloud, is developing at a rate that exceeds traditional technology approaches capabilities. Network security has proven insufficient and lacks the visibility, control and intelligence necessary to meet changing needs. SDN became a subject of interest for academia and industry to discuss the network on-demand relevant aspects of applying real time network policy. The article is an assessment the security situation and what to do to strengthen it. In this paper we introduced SDN-based approach functionality not available in traditional approaches to security. This show how the proposed security architecture can be used fortify the overall protection of today's network.
Evaluation of Sentiment Data using Classifier Model in Rapid Miner Tool

Evaluation of internet and the usage of internet as websites which is for penetrating to gain a specific requirements, like group communication as social networks (such as face book, twitter,etc.,) ,blogs for opinions, online portals (such as iGoogle, MSN) for communication, experience as reviews, suggestions as opinions, combination of reviews and opinions as recommendations, ratings and feedbacks which is identified and elevating in almost all the field now-a-days. The writers of online portal, review, opinion and recommendation in any social media take measures as beneficial factor for the improvement of businesses, organization, governments and mostly individuals. When this content boost up the study of content and the need of data mining, text mining techniques and sentiment analysis is inescapable. Natural language processing and text analysis techniques are used in sentiment analysis to recognize and extract information from the text [1]. This paper provides a result of sentiment analysis with the intellectual tool named Rapid Miner to show the sentiment comments about the contents in the online traders.
Mastering Interpersonal and Virtual Communication Skills of the Education Process Participants in E-learning

Social communication is carried out in two environments - real (interpersonal communication) and electronic (virtual communication). Both types of communication have become essential nowadays for specialists. Therefore, education should focus on mastering universal communicative competence. Online education is favorable for mastering both interpersonal and virtual communication skills of all the educational process participants. The article investigates a way to master communicative skills of two groups of education process participants. The first group incorporates learners, educators and parents who traditionally interact in the educational process. The second group involves interacting representatives of different cultures, and it becomes the feature of a teacher-student interaction in internationalization of tertiary education. The article gives theoretical background of the research studying different kinds of communication of education process participants in online learning environment, identifies the specific character of their communication and provides evidence that e-learning impacts the mastering of communicative skills for both groups of participants.
Introduction

In this introduction, the coeditors of this issue introduce, discuss, and provide a relevant framework for the work found herein. Special attention is given to the language of disability and the preferences of those both within and outside of the disability community. A brief explanation is given for each of the theoretical frameworks used to model disability in the existing literature to help provide important context for this issue. Lastly, we include an introduction to each of the articles and highlight some of the common themes they reveal.
Research trends in Hand Gesture Recognition techniques

Over years’ key board and mouse are among the most effective input devices to the computers. Not much improvement has happened for other modes of inputs like voice and gestures. These kinds of inputs explore the limitations caused by the key board and mouse, which needs actual physical contact. With gestures and voice the physical proximity can be little relaxed and can be used by specially-abled people. Exploring effective Human Computer Interactions (HCI) has always been of great interest for development of new techniques and methods. Gestures are considered as more easy, natural, and intuitive method for communicating with systems. Hand recognition and analysis forms the core of these systems. This paper provides comparative study on state of art technologies that work in this area. The different representation, detection and recognition techniques are discussed. The strength and challenges of different techniques are highlighted. The main aim of this research is to summarize the research progress done till date with different technologies for gesture recognition algorithms, identify areas which need further research.
Tree-Based Learners

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.
Abstract 1396: Detection and local histological staging of prostate cancer foci in H&amp;E whole slide images using convolutional neural networks

Abstract A pathologist’s histological diagnosis is the gold standard of prostate cancer diagnostic measures. In addition to cancer grade, the stage of cancer determines the follow-up and possible adjuvant therapies after surgery. Not only glandular pattern recognition but also the assessment of three-dimensional extent and size of cancer, in relation to the whole organ, are parts of the subjective diagnosis, which may vary among pathologists. Furthermore, evaluation of lymph nodes for any possible metastases is time-consuming and missing cancer in lymph nodes can lead to undertreatment of the patient. For the abovementioned reasons, our team is developing an algorithm that calculates the amount of cancer tissue objectively, assisting the pathologist in the diagnostic procedure. In addition, we are creating an assistant for cancer detection in lymph nodes, which are removed for histological evaluation. All in all, we aim to create a tool that will help pathologists to a better, faster, more secure and more accurate diagnosis. Our material consists of full sets of scanned whole slide images from 302 prostates that were retrieved by radical prostatectomy. After supervised learning procedure, convolutional neural networks were employed for the classification of cancerous and non-cancerous regions in the images. A tiling based approach was used in which a slide was divided into square shaped small tiles. Millions of cancerous and non-cancerous tiles were sampled from the dataset for training and validation. During the convolutional neural network training, several different tile sizes were used, i.e., 256x256, 512x512, 1024x1024 pixels. Four different types of architectures were fine-tuned and trained for the task of tile-wise binary classification, namely InceptionV3, Xception, ResNet50 and a custom convolutional neural network architecture. In our preliminary assays for cancer detection, in both pixel-wise and tile-wise evaluation, InceptionV3 performed outstandingly well with an AUC score of 0.97 and 0.951, respectively. In conclusion, our algorithm has developed very well thus far with an accuracy in cancer detection of 97%. It is not only a versatile assisting tool, aiding pathologists to a more objective, standardized and accurate diagnosis, but also serves as a second opinion in difficult and challenging diagnostic cases. Citation Format: Carolin Stürenberg, Umair Khan, Kevin Sandeman, Oguzhan Gencoglu, Adrian Malen, Andrew Erickson, Timo Heikkinen, Antti Rannikko, Tuomas Mirtti. Detection and local histological staging of prostate cancer foci in H&amp;E whole slide images using convolutional neural networks [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1396.
The Effect of Work Plateau on Intention to Remain: Mediating Role of Organizational Commitment

In the present study, the the impact of work plateau on intention to remain at work with the mediating role of organizational commitment has been studied. The statistical population of this research is all employees of Ferdowsi University of Mashhad. The research method is descriptive-survey. According to statistics of personnel management office, the number of employees is 1100 in 2017. In order to determine the sample size, due to the limited population, Morgan table was used and 285 people were selected from population through random sampling. In order to collect the data, Alan and Meyer (1990) questionnaire was used to assess organizational commitment, a researcher-made questionnaire was used to investigate the intention to remain at job and Miliaman's questionnaire (1992) was used to investigate career plateauing. The validity of these three questionnaires has been confirmed by experts. Reliability of the questionnaire is confirmed by Cronbach’s alpha coefficient of 86%, 91% and 84%, respectively. The statistical methods used in this research to test the hypotheses are T value and structural equation modeling. Structural equation model in this study was determined using Liserl software, according to which the content plateau and structural plateau, considering the mediator role of organizational commitment, has a negative and significant impact on employees' intention to remain at work.
A Study on the Learning Satisfaction and Learning Immersion of Blended Learning (face-to-face) Classes between Korean and foreign college students after covid-19 -To H University in Seoul, Department of Beauty Design Management-

This study aims to compare and analyze learning satisfaction and learning immersion among Koreans and foreign students in the Corona 19 era and to prepare the need and alternatives for blended learning classes (face-to-face + non-face) among various class methods. In this study, 208 questionnaires were distributed to every students in the Department of Beauty Design Management at H University in Seoul, and 206 questionnaires were used for the final analysis. The statistical processing was analyzed using SPSS 21.0 as follows: First, a frequency analysis was conducted to identify the general characteristics of the subjects surveyed; second, an exploratory factor analysis was conducted for the verification of the subjects surveyed; and third, a reliability test was conducted to verify the validity of the measurement tool. Fourth, we conducted t-test, ANOVA to find differences in learning satisfaction and immersion according to demographic characteristics, and conducted a Scheme-test as a post-test of ANOVA. Female students showed higher proportion than male students, and the proportion of Korean and foreign students was about the same. The Blended Learning course found that 39.8% of students take more than five courses, meaning that online classes alone are not enough to satisfy students' needs. Foreign students showed higher satisfaction in learning according to nationality than Korean students, and foreign students also showed higher immersion in learning than Korean students.
Classification of Red Blood Cells using Principal Component Analysis Technique

Principal component analysis (PCA) is based feature reduction that reduces the correlation of features. In this research, a novel approach is proposed by applying the PCA technique on various morphologies of red blood cells (RBCs). According to hematologists, this method successfully classified 40 different types of abnormal RBCs. The classification of RBCs into various distinct subtypes using three machine learning algorithms is important in clinical and laboratory tests for detecting blood diseases. The most common abnormal RBCs are considered as anemic. The RBC features are sufficient to identify the type of anemia and the disease that caused it. Therefore, we found that several features extracted from RBCs in the blood smear images are not significant for classification when observed independently but are significant when combined with other features. The number of feature vectors is reduced from 271 to 8 as time resuming in training and accuracy percentage increased to 98%.
Computing the optimal Ate pairing over elliptic curves with embedding degrees 54 and 48 at the 256-bit security level

Due to recent advances in the computation of finite fields discrete logarithms, the Barreto-Lynn-Scott family of elliptic curves of embedding degree 48 became suitable for instantiating pairing-based cryptography at the 256-bit security level. Observing the uncertainty around determining the constants that govern the best approach for computing discrete logarithms, Scott and Guillevic consider pairing-friendly elliptic curves of embedding degree higher than 50, and discovered a new family of elliptic curves with embedding degree 54. This work aims at investigating the theoretical and practical cost of both the Miller algorithm and the final exponentiation in the computation of the optimal ate pairing on the two aforementioned curves. Both our theoretical results, based on the operation counts of base-field operations, and our experimental observations collected from a real implementation, confirm that BLS48 curves remain the faster curve in the computation of the optimal ate pairing at the 256-bit security level.
Machine Learning

We explain the foundations of supervised and unsupervised machine learning, including the central ingredients of data, models and cost functions, as well as the basic concepts of training, regularisation and generalisation. A number of important models—such as linear models, neural networks, probabilistic graphical models and kernel methods—are introduced as a foundation and reference for later chapters.