Optimization Methods in Emotion Recognition System

Emotions play big role in our everyday communication and contain important information. This work describes a novel method of automatic emotion recognition from textual data. The method is based on well-known data mining techniques, novel approach based on parallel run of SVM (Support Vector Machine) classifiers, text preprocessing and 3 optimization methods: sequential elimination of attributes, parameter optimization based on token groups, and method of extending train data sets during practical testing and production release final tuning. We outperformed current state of the art methods and the results were validated on bigger data sets (3346 manually labelled samples) which is less prone to overfitting when compared to related works. The accuracy achieved in this work is 86.89% for recognition of 5 emotional classes. The experiments were performed in the real world helpdesk environment, was processing Czech language but the proposed methodology is general and can be applied to many different languages

Mining Helpdesk Databases For Professional Development Topic Discovery

This single-site, instrumental case study created and tested a methodological road map by which academic institutions can use text data mining techniques to derive technology skillset weaknesses and professional development topics from the site’s technical support helpdesk database. The methods employed were described in detail and applied to the helpdesk database of an independent, co-educational boarding high school in the northeastern United States. Standard text data mining procedures, including the formation of a wordlist (frequently occurring terms), and the creation and application of clustering (automated data grouping) and classification (automated data labeling) models generated meaningful and revealing themes from the helpdesk database. The results of the text mining procedures were bolstered and analyzed using human interpretation and spreadsheet-based summaries. Major findings included the discovery of four prominent technologies that warranted professional development at the site and a universally-applicable approach to undertaking successful helpdesk data mining endeavors. The case study’s conclusions included a call to action for researchers to leverage the methodology at other locations. Future data mining studies may yield practical and applicable knowledge at research sites. Shared methods, approaches, and findings from such studies will advance the field of helpdesk data mining used to glean professional development topics for the very people who have submitted technological support requests to helpdesk providers

Countering Online Misinformation, Hate Speech or Extremist Narratives in the Global South

The widespread expansion of social media outlets has enabled the spread of mis/disinformation, hate speech and extremist narratives online. Internet-based technologies can also be used to confront these types of communication. This report focuses on counter-messaging efforts (also referred to as strategic communications) through online platforms. This can comprise counter-narratives that challenge false information or existing narratives (e.g. undermining the credibility of an extremist group); or alternative narratives that seek to replace existing narratives, rather than directly confront them (e.g. introducing messages of coexistence).Foreign, Commonwealth and Development Office (FCDO

Using a Hybrid Technology Acceptance Model to Explore How Security Measures Affect the Adoption of Electronic Health Record Systems

While the adoption of computer systems is pervasive in most industries, few healthcare organizations have implemented electronic health record systems. Security is a major issue for these healthcare organizations. Security concerns include breaches of privacy and medical identity theft. This article uses a hybrid technology acceptance model (TAM) to explore why healthcare organizations are slow to adopt an EHR and slower to adopt biometric technology and single sign-on functionality despite the benefits of these systems. This paper advocates that healthcare organizations should adopt biometrics for authentication purposes, allow for multiple connections by each healthcare provider, and use single sign-on systems when implementing EHR systems. This research will also determine how costs, compliance issues, and security issues impact an individual’s attitude when asked to use EHR systems

Machine Learning-Based Sentiment Analysis of Incoming Calls on Helpdesk

In today's daily life we are getting so many anonymous calls. Some calls are related to loan marketing and finance. As per the survey, one person is getting 26% spam calls in a day. The proposed methodology accepts user calls and based on the conversation the spam numbers are identified and the same information is provided to the other callers. This is possible because of machine learning-based sentiment analysis. Sentiment analysis is the subdomain of machine learning. The goal of this research is to propose an adaptive methodology for incoming calls. The sentiment-based incoming calls help desk works with freely available lexical resources WordNet, SemCor, and OMSTI. The discussed methodology accepts user conversations in audio format the speech-to-text conversion of the audio will be done. After pre-processing the keyword is detected from the statement. The word2Vec word embedding technique is used for representing words from document space to vector space. The 150-200 dimensional word vector is generated. The WordNet is used for sense mapping and keyword identification. Based on the sentiment analysis of input calls the decision is taken whether to accept or reject calls. This methodology is generating superior results for supervised machine learning models

Design and implementation of a software agent platform applied in E-learning and course management

Text in English; Abstract: English and TurkishIncludes bibliographical references (leaves 86-89)xi, 115 leavesIn this thesis, we report an experience on constructing a software agent platform for development and implementation of software agent systems running with integrated e-learning and course management applications which are developed and running under different technologies. The proposed platform consists of an agent development framework namely JADE (Java Agent Development Environmet), a common database infrastructure serving to many different applications and the applications infrastructure running on different platforms. An example e-university application module which is an integrated course management software running on the proposed platform namely Course ON-LINE and an agent application running as an add-on utility to this application namely GAIA is explained in detail to demonstrate the use of the proposed application.Bu çalışmada farklı teknolojiler kullanılarak geliştirilen ve farklı platformlarda çalıştırılmakta olan ve tümleşik yapıdaki uzaktan eğtim ve ders yönetimi araçları uygulamalarla birlikte çalışabilecek yazılım etmen sistemlerinin geliştirilebilmesini sağlayan bir yazılım geliştirme ve çalıştırma ortamı inşa etme deneyimi aktarılmıştır. Önerilen ortam JADE (Java Agent Development Environmet), isimli bir etmen geliştirme aracı, etmen sistemleri dahil tüm uygulamaların ortak kullandıkları bir veritabanı altyapısı, ve farklı ortamlarda çalışan ve farklı teknolojilerle geliştirilmiş uygulamaların altyapısından oluşmaktadır. Önerilen ortamın kullanılışını göstermek için tümleşik ders web sayfaları yönetim aracı olan ve e-üniversite uygulamalarının bir parçası olan Course ON-LINE ve onunla birlikte çalışan bir yazılım etmeni uygulaması olan GAIA uygulamaları detaylıca sunulmuştur

Effective IT Use Among Residential Caregivers: The Role of Autonomy, Competence and Relatedness

Technological innovation in the healthcare sector is increasing, but integration of information technology (IT) in the care process is difficult. Healthcare workers are important agents in this IT integration. The purpose of this study is to explore factors that feed motivation to use IT. Self-determination theory (SDT) is applied to study how motivational factors impact effective IT use among frontline caregivers in residential care settings. As the team is very important to these caregivers, the team is our unit of analysis. In an embedded single case study design, interviews were conducted with all nine members of a team effectively using IT. All three basic psychological needs from SDT - autonomy, competence and relatedness - were found to have impact on effective IT use, though autonomy was primarily experienced at team level. Conversely, the effective use of an IT collaboration tool influences relatedness

Health Information Security and Privacy: A Social Science Exploration of Nurses\u27 Knowledge and Risk Behaviors with Security and Privacy Issues Focusing on Mobile Device Usage

Background. Health information system security and privacy are critical issues that impact the wide use of the Electronic Health Record (EHR) in healthcare including hospitals, providers and health systems (Breaches Affecting 500 or More Individuals, 2017). These issues have been researched from a technology standpoint in this era of accelerated electronic health record adoption, but less has been done related to the EHR users in the United States. Most of the literature related to security and privacy explores research topics, peripheral and direct, regarding policy adherence mechanisms. Yet to be studied is a social science exploration of nurses’ risk knowledge and risk behaviors associated with security and privacy issues. Purpose. This dissertation examines characteristics related to cybersecurity practices of new nurses a year following graduation from nursing school where they may have been prepared to work in environments with EHRs. The study will explore their understanding of cybersecurity as it relates to use and protection of the sources of information in the EHRs, and their own personal risk behaviors with mobile technologies that may put them at risk to outside hacking or misuse of information. The questions that drive the study are the associations with nurses’ knowledge of information system security, risk behaviors specifically with mobile device use, and their threat appraisal that may influence their personal habits and their concern for potential misuse of their own electronic health information. Method. A web-based survey was emailed to a sample of new graduates who completed the National Student Nurses’ Association (NSNA) Annual Survey and gave their permanent email address voluntarily to be contacted again for additional surveys. The survey designed in SurveyMonkey®, the same approach used with this sample in prior studies, was sent to a list of 3,000 addresses. The variables of interest are Knowledge of Information System Security (KISS), ii Risk Behaviors (RB), Personal Technology Practices (PTP), Mobile Device Habits (MDH), Threat Appraisal (Internal and External), Concern for Information Privacy (CFIP), and Information Privacy Protection Response (IPPR). Pilot Testing. Several measures developed for the study were tested on a sample of senior graduating nursing students (n=167) to assess their validity and reliability, including KISS, RB and PTP. Prior to data collection, the new items were assessed for content validity by five judges in preparation to be tested for reliability analysis. A paper-pencil version of the new items was distributed to the nursing students just prior to their graduation. Their responses were entered and analyzed using SPSS, which yielded a final set of items with acceptable reliability (α = .700), These new items were combined with the other variables of previously studied items, slightly modified, for integration on the final tool. Additional demographic questions and mobile device usage were added. Procedures. The final survey was distributed to the list of participants (n=3,000), anticipating a 10 - 20% return rate that would yield 300 - 600 subjects. A reminder was sent every 2 weeks for 6 weeks while the study remained open. Participants were offered an incentive of being eligible for a $250 drawing at the conclusion of the study. Analysis. The first level of analysis included an extensive descriptive analysis of the frequencies and measures of central tendency for subject self-reported mobile device frequency and types of use. The subsequent analysis included a series of correlations calculated on the variables of interest to determine the relationships of predicted relationships. The model did not support the predictions and an adjusted model was proposed for future studies on the measured variables and demographic variables of interest. iii Limitations. The pilot study was distributed in a paper format whereas the proposed format for the national study used an electronic medium. Conclusions. This study provided information about the relationship between the core variables and demographic components. These findings could inform educators and employers about new nurses’ knowledge and risk behaviors related to information system security