Reviewerly/exHarmony huggingface dataset:
Sentiment analysis on Healthcare Tweets

In today's world variety of posting on social media offer huge information about the health issues, remedies, food and medication.Twitter is an online social networking service in which users can post their opinions about various topics and also cooperate with each other with messages known as "tweets".Tweets are very helpful in sharing the health related issues, medicines, hospitals information.Twitter helps the people to know about the disease symptoms, services and details about the hospital before they go for consultation.Sentiment analysis is a metric commonly used to investigate the positive or negative opinion within these messages.Sentiment analysis methods can be used in Twitter health care research.The analysis will help the users to better understand the alternative available.Sentiment analysis can also facilitate the healthcare industry to use reliable data for their growth by taking necessary measures.Sentiment analysis applies software to analyze the patient's tweets regarding their healthcare experiences regarding medicine, doctor, hospitals..It helps users as well as many healthcare organizations to understand their customers opinion and to take necessary measure to rectify the gaps.Ultimately, as more attention is given to such opinion analysis the health standard in the society will improve.Main aim of this paper is to build an algorithm that can accurately classify Twitter messages as positive or negative.
LSTM RNNs

In the previous chapter, you have discovered two types of Recurrent Neural Network cells called SimpleRNN and GRU (Gated Recurrent Unit). In this chapter, you’ll discover a third type of cell called LSTM, for Long Short-Term Memory.
Twitter Data Analysis using R

Big Data is an emerging concept in the field of Data mining. It has numerous applications in real life. Most data are coming from social media networking Websites comprising of structured and unstructured data including Text, video, images etc. The main characteristics can be understood by five v’s. Twitter is one among the major evolving social media. Twitter Data analysis can be give you a wide perspective of public opinion regarding any product, public opinion etc which can be used to mine the knowledge from the data. For example, prediction analysis, product review, favourite among people tweets about GST (Goods and service tax).
Distributions of sampling statistics

The distributions of sample statistics for normal populations are considered. The central limit theorem is introduced and used to find the approximate distribution of the sample mean when the data have an arbitrary distribution.
DXP Security

Digital experience platforms (DXPs) consist of various technologies such as web technologies, content technologies, and database technologies. Security is one of the fundamental attributes for the success of a DXP program. Lack of adequate security measures impacts the users’ trust and drastically impacts the application. Defining and maintaining DXP security is a continuous process.
Khasi to English Neural Machine Translation: an Implementation Perspective

Being able to translate and communicate consistently from one language to another would have been the ultimate goal of an intelligent system. With recent advancement of Neural Machine Translation (NMT), it has shown a promising solution to the problem of machine translation. NMT generally requires large size parallel corpora to obtained a good translation accuracy. In this paper, we would like to explore a Translation system from Khasi to English language using both supervised and unsupervised technique. Unsupervised was inspired to help attaining a better translation accuracy for low resource language. It was influenced by the recent advancement of unsupervised neural machine translation which primarily relies on monolingual corpora. In this work, Supervised NMT technique was also implemented and compared with the standard OpenNMT toolkit. Here, we also use Statistical Machine Translation (SMT) tools like Moses as a standard benchmark to compare the translation accuracy. When considering monolingual corpus, we obtain an accuracy of 0.23%. Given the small size monolingual corpus the result was lacking but showed promising rooms for improvement. We obtain much better accuracy of 35.35% and 41.87% when we use parallel corpus in supervised NMT and OpenNMT respectively. On comparison with SMT system with Blue score of 43.76%, the supervised NMT system was on par in its performance. Lastly, with improvement in corpus size and better adaptation of preprocessing steps on the source language (Khasi) the result can be tune to a better outcome.
Probabilistic Decision Based Average Trimmed Filter for the Removal of High-Density Salt and Pepper Noise

The paper focuses on the evacuation of salt and pepper noise from a contaminated image. A probabilistic decision based average trimmed filter (PDBATF) is proposed for both high and low noise density. The proposed algorithm addresses the issue related to even number of noise-free pixel in trimmed median filter for the calculation of processing pixel. The proposed average trimmed filter is incorporated for low noise density while the proposed patch else average trimmed filter is applied for high noise density. Finally, they are combined together to develop the proposed PDBATF. The proposed algorithm show an excellent noise removal capability compared to the recently developed algorithms in terms of peak signal to noise ratio, image enhancement factor, mean absolute error and execution time. It works very efficiently in de-noising contaminated medical images such as chest-x-ray and malaria-blood-smear.
Can internal marketing activities save small and medium industrial enterprises in a tough economy Back to the basics approach

Recently, and due to the economic pressure in Jordan, several small and medium enterprises have filed for bankruptcy, or downsized their business leading to the layout of hundreds of employees, which will deepen the economic problems in Jordan and increase the circle of effect. Small and medium enterprises can follow other courses of actions to address such issue. This paper aims to understand the possible impact of internal marketing practices and activities during economic problems on the employees' productivity in small and medium industrial enterprises. In order to achieve this aim, a descriptive analytical methodology was used, quantitative approach through survey strategy was applied and electronically administrated questionnaires were utilised to collect the primary data from a representative stratified random sample of 490 small and medium industrial enterprises out of 2,291 registered local small and medium industrial enterprises. 345 filtered and screened questionnaires were analysed using SPSS, the main results of simple and multiple regression analyses indicate that internal marketing mix has a strong positive direct effect on employees' productivity, where internal promotion has the most effect followed by workplace, internal product and internal price respectively. Discussion, conclusion, and recommendations are provided based on the results of the study.
Improved LZW Compression Technique using Difference Method

This work attempts to give a best approach for selecting one of the popular image compression algorithm. The proposed method is designed to find the best performance approach amongst the several compression algorithms. In this work existing lossless compression technique LZW (Lempel-Ziv-Welch) is redesigned to achieve better compression ratio. LZW compression technique works on the basis of repetition of data. In a situation where all the values are distinct or repetition of data does not present the LZW can’t work properly. To avoid this problem, difference method called difference matrix method which is actually calculate the difference between two consequence data and store it in a resultant matrix is used. In this case the matrix contains repetitive data which is more effective compared to LZW technique. Another problem of LZW is dictionary overflow, because of LZW works on ASCII character there is a limit of 256 dictionary length initially. In this work dynamic dictionary method is used without using the ASCII rather than this static method. As a result, this dictionary can contain the initial value anything in a range of -256 to 255. Here ASCII values are not used because the proposed method is applicable grayscale image, where the pixel values are between in range 0 to 255. Using these two changes the proposed improved LZW method becomes more powerful that can compress a non-repetitive set of data significantly. The proposed method is applied on many standard gray images found in the literature achieved 7% to 18% more compression the normal LZW keeping quality of the image same as existing.
Advanced security of two-factor authentication system using stego QR code

Many financial institutions are trying to protect their customers by offering improved and more secure technologies for authentication. One of the most common is two-factor authentication (2FA), which presents many vulnerabilities that allow attackers to retrieve confidential information such as mobile transaction authentication (mTAN). Thus, according to NIST (National Institute of Standards and Technology), 2FA based on SMS is deprecated and aims to find a secure communication channel other than SMS. Therefore in this paper, we propose a 2FA communication channel based on steganography in the QR-code. So, the mTAN can only be read by a specific scanner that implements the technique of extracting the hidden information while having the shared key and the public information in the QR-code readable by the standard scanners. Finally, we implement our proposed method and then do the test by simulating a line banking service.
Crooked functions

Crooked permutations were introduced twenty years ago since they allow to construct interesting objects in graph theory.The field of applications was extended later.Crooked functions, bijective or not, correspond to APN functions and to some optimal codes.We adopt an unified presentation of crooked functions, explaining the connection with partially-bent functions.We then complete some known results and derive new properties.For instance, we observe that crooked functions allow to construct sets of bent functions and define some permutations.
Study of Assistance Development of Thematic Learning Material by Integrating New Literacy and Disaster Literacy on Science Teachers

The 4.0 industrial revolution requires humans to possess 21st century skills. Education should be able to prepare students to have 21st century skills to be successful in their life. In addition, the geographical condition of Indonesia is a disaster-prone region. For this reason, teaching should be able to develop student literacy to face the challenges of the industrial revolution 4.0 and the geographical condition of the Indonesian region. The thematic science teaching is relevant to 21st century skills, essential principles of 21st century learning, and the geographical condition of Indonesia. But the real conditions show that the integration of literacy in science teaching is still lacking and limited. A solution to solve this problem is the assistance program the development of thematic learning material by integrating new literacy and disaster literacy for science teachers in junior high school. The objective of the research is to investigate the results of the assistance program the development of thematic learning material. Participants of program assistance were 30 science teachers who were members of the Science MGMP in East Agam district. The instrument for collecting data consisted of pretest and posttest and questionnaire sheet. The data were analyzed by descriptive statistics analysis and wilcoxon signed rank test. Based on the results of data analysis can be stated that: 1). in the assistance program activities for the development of thematic science teaching material can be produced 26 teaching materials in the form of student worksheet by integrating new literacy and disaster literacy, 2). the average value of the practicality of using thematic science worksheet according to students in two junior high schools is 86.41 so that this average value can be classified into very good category, 3). the average value of participant responses to the implementation of the assistance program is 85.65 and this average value can be entered into very good category, and 4). the assistance program for developing learning material by integrating new literacy and disaster literacy is effective to improve knowledge of science teachers about data literacy, technological literacy, human literacy, and disaster literacy.
Application of artificial intelligence in computer network technology in the era of big data

Today, with the progress of computer network technology, the advantages of artificial intelligence gradually began to show and become a new product under the development of the current era. At present, the strength of artificial intelligence technology lies in its data analysis and processing ability, because with the continuous development and progress of the society, the difficulty of processing data and information is becoming more and more complex for many years, and the processing ability of computers has begun to show shortcomings. The use of artificial intelligence can solve this problem well, and it can also put forward solutions according to the corresponding data problems, to timely deal with all kinds of data information. This paper mainly analyzes the application of artificial intelligence in computer network in the era of big data, and discusses the application and significance of artificial intelligence in big data.
A Recommendation System: Trends And Future

Recommendation system plays a key role in e-commerce universe and is used in many applications, websites and more. It has led to synergies between applications, created global village and growth of information. This paper represents the overview of approaches and techniques generated in recommendation systems. Recommendation system is categorized in two classes: Personalized and Non-personalized, which is further divided into various approaches and techniques. This paper discusses each of the methodology in detail highlighting their strengths and weaknesses.
A hybrid PSO optimised virtual machine scheduling algorithm in cloud computing

The service to the end user in cloud computing is offered as virtual machine instances as demanded for a specified duration of time and billed on pay per use basis. A major problem faced in cloud computing is the virtual machine scheduling problem. The existing algorithms efficiently cannot satisfy the requirements with respect to resource utilisation, bandwidth utilisation, and cost. Also, most of them are of the fixed type which leads to wastage of resources. To overcome these problems, the hybrid particle swarm optimisation (HPSO) algorithm is proposed by efficiently allocating the resources to the users. This algorithm combines the genetic algorithm (GA) and the variable neighbourhood search (VNS) algorithm with the particle swarm optimisation (PSO) technique to increase the utilisation rate of the virtual machine as well as to minimise the total completion time. The proposed system is evaluated by performing simulations. The experimental results show that the proposed algorithm minimises the total completion time and increase the resource utilisation than PSO, GA and VNS algorithm.
Kamal Transformation based Cryptographic Technique in Network Security Involving ASCII Value

From old times, information security has been an essential part of human life. Day to day developing new technologies and increasing number of users share information with each other, securing information is becoming more important. For securing information, there are several techniques out of which most common is cryptography. Cryptography is a technique involving securing transmission of messages in presence of adversaries. The basic aim is to hide the information from unauthorized users. Cryptography includes to phases: encryption of plain message and decrypting encrypted message. The aim of this paper is to use new integral transform "Kamal transform" and congruence modulo operator involving ASCII value for encryption and decryption of message.
Empower good governance with public assessed schemes by improved sentiment analysis accuracy

Many government schemes were unsuccessful because lack of proper feedback on the ongoing schemes, where billion dollars investment is going to be in vain. Sentiment analysis is one of best approach to analyse opinions of the peoples on various government schemes. Sentiment analysis and machine learning techniques emerged to analyse huge social media corpora to track people's views on government policies, products and services. Sentiment analysis process consists of various phases which include data discovery, data collection, data pre-processing, and data analysis. Stemming is a process to generate the morphemes in natural language sentences for various applications such as sentiment analysis, information retrieval, and domain analysis. The stemming process involved two major errors, which are over-stemming and under-stemming errors. Most of sentiment analysis natural languages processing applications used Lancaster and Porter stemming algorithms where more than one word inflected into same morpheme, which causes the etymology behaviour of the stemming word and prone to classify the tweets false positives and false negative. The proposed un-prejudice light stemming algorithm prevent etymology behaviour of morpheme and sustain its meaning during stemming process by selecting a word which has maximum number of synonyms in lexical database.
A ML and NLP based Framework for Sentiment Analysis on Bigdata

Big data as multiple sources and social media is one of them. Such data is rich in opinion of people and needs automated approach with Natural Language Processing (NLP) and Machine Learning (ML) to obtain and summarize social feedback. With ML as an integral part of Artificial Intelligence (AI), machines can demonstrate intelligence exhibited by humans. ML is widely used in different domains. With proliferation of Online Social Networks (OSNs), people of all walks of life exchange their views instantly. Thus they became platforms where opinions or people are available. In other words, social feedback on products and services are available. For instance, Twitter produces large volumes of such data which is of much use to enterprises to garner Business Intelligence (BI) useful to make expert decisions. In addition to the traditional feedback systems, the feedback (opinions) over social networks provide depth in the intelligence to revise strategies and policies. Sentiment analysis is the phenomenon which is employed to analyze opinions and classify them into positive, negative and neutral. Existing studies usually treated overall sentiment analysis and aspect-based sentiment analysis in isolation, and then introduce a variety of methods to analyse either overall sentiments or aspect-level sentiments, but not both. Usage of probabilistic topic model is a novel approach in sentiment analysis. In this paper, we proposed a framework for comprehensive analysis of overall and aspect-based sentiments. The framework is realized with aspect based topic modelling for sentiment analysis and ensemble learning algorithms. It also employs many ML algorithms with supervised learning approach. Benchmark datasets used in international SemEval conferences are used for empirical study. Experimental results revealed the efficiency of the proposed framework over the state of the art.
BDCPS — A Framework for Smart Manufacturing Systems using Blockchain Technology

Blockchain is going to be the most fundamental technology, and will change the world — going forward. In fact, the revolution has already begun. The birth of Industry 4.0 aka the Fourth Industrial Relution (I4.0), has created a need for autonomous and integrated, secure manufacturing systems. The current smart systems lack the decentralized decision making and real-time communication infrastructure, which is a condition for adaptive, smart manufacturing systems. In this paper, an autonomous, secure and collaborative platform based on Blockchain technology, is presented to adapt to such results. In support with Internet of Things (IoT) and cloud services, a Blockchain Driven Cyber Physical Production System (BDCPS) architecture is designed to communicate with machines, users, devices, suppliers and other peers. Using the Smart Contracts feature and trust-less peer-to-peer decentralized ledger feature, BDCPS will validate the claim with a small-scale real-life Blockchain with IoT system. This implementation case study will be running a private Blockchain on a single board computer, and bridged to a microcontroller containing IoT sensors. The applications of this system in automotive manufacturing industry are presented, to proceed towards Industry 4.0.
Accounting for Visual Bias in Tangible Data Design

Abstract Data engagement has become an important facet of engaged citizenship. While this is celebrated by those who advocate for expanding participatory channels in civic experience, others have rightfully expressed concern about the complicated dimensions of balancing access with data literacy. If engaged citizenship increasingly requires the ability to interpret civic data through city dashboards and open data portals, then there is a concomitant requirement for diverse populations to develop critical perspectives on data representation (what is commonly referred to as data visualisation, information graphics, etc.). Effective data representations are used to ground conversations, communicate policy ideas and substantiate arguments about important civic issues, but they are also frequently used to deceive and mislead. Expanding statistical, graphical, digital and media literacy is a necessary component of fostering a critical data culture, but who are the beneficiaries of expanded models of literacy and modes of civic engagement? Which communities are invalidated in the design of civic data interfaces? In this article, I summarise the results of a design study undertaken to inform the development of accessible data representation techniques. In this study, I conducted fourteen 2-h participatory design-inspired interview sessions with blind and visually impaired citizens. These sessions, in which I iteratively developed new physical data objects and assessed their interpretability, leveraged a public transit dataset made available by the City of Toronto through its open data portal. While ostensibly “open,” this dataset was initially published in a format that was exclusively visual, excluding blind and visually impaired citizens from engaging with it. What I discovered through the study was that the process of translating 2D, screen-based civic dashboards and data visualisations into tangible objects has the capacity to reintroduce visual biases in ways that data designers may not generally be aware of.
CREATING HARMONIOUS SCHOOL LEARNING ENVIRONMENT: WALDORF EDUCATION PERSPECTIVE.

07Jul 2019 CREATING HARMONIOUS SCHOOL LEARNING ENVIRONMENT: WALDORF EDUCATION PERSPECTIVE. Olena Mezentseva. Kyiv, Ukraine.
Exploring and Processing Text Data

This chapter discusses various methods and techniques to preprocess textual data and exploratory data analysis. It covers the following recipes.