Dissertation Outline
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This document outlines the chapters of a dissertation on evaluating a multimedia application for smoking cessation. The dissertation will include an introduction outlining the research question and methodology. Chapter 2 will discuss relevant learning and behavior theories, including the Transtheoretical Model, Elaboration Likelihood Model, and Persuasion Theory. It will also cover examples of multimedia applications. Chapter 3 will develop a case study prototype. Chapter 4 will present, analyze, and discuss collected data. Chapter 5 will provide conclusions, discuss limitations, and suggest future research perspectives.
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Chapter two & three
This document outlines the structure and content of chapters 2 and 3 in a research paper. Chapter 2 will review relevant literature divided into subtopics on the dependent and independent variables. Chapter 3 describes the quantitative research design, including a sample of 120 respondents selected using either quota or convenient sampling. Data analysis methods like descriptive statistics, t-tests, and correlation will be used to analyze relationships between variables.
Experimental research data quality in
In materials sciences, a large amount of research data is generated through a broad spectrum of different
experiments. As of today, experimental research data including meta-data in materials science is often
stored decentralized by the researcher(s) conducting the experiments without generally accepted standards
on what and how to store data. The conducted research and experiments often involve a considerable
investment from public funding agencies that desire the results to be made available in order to increase
their impact. In order to achieve the goal of citable and (openly) accessible materials science experimental
research data in the future, not only an adequate infrastructure needs to be established but the question of
how to measure the quality of the experimental research data also to be addressed. In this publication, the
authors identify requirements and challenges towards a systematic methodology to measure experimental
research data quality prior to publication and derive different approaches on that basis. These methods are
critically discussed and assessed by their contribution and limitations towards the set goals. Concluding, a
combination of selected methods is presented as a systematic, functional and practical quality measurement
and assurance approach for experimental research data in materials science with the goal of supporting
the accessibility and dissemination of existing data sets.
Aims and steps
The document outlines four main aims of research: describe, predict, explain, and interpret. It then discusses various research methodologies including descriptive studies, correlational research, explanatory research, and interpretive research. The document also outlines the typical steps in a research process including formulating the research problem, determining the design, collecting and analyzing data, drawing conclusions, and reporting findings. It discusses phases of research including clarifying the issue, collecting data through various methods, and analyzing and interpreting the data. The document emphasizes the importance of research ethics in protecting participants and honestly reporting results.
SECONDARY DATA ANALYSIS
Secondary data analysis involves analyzing data that was collected by another researcher for a different study. It is usually done to test a new hypothesis without having to collect new data. This approach saves time and money compared to primary data collection. Secondary data analysis can be performed on both quantitative and qualitative data from previous studies to answer new research questions.
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This document provides definitions for key research methods terminology. It lists terms like case study, content analysis, triangulation, qualitative data, quantitative data, and more. Readers are asked to match each term with its corresponding definition. Understanding these terms is important for studying research methods.
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https://www.youtube.com/channel/UCvmyC56ovZ8vIspsFMwkBgA
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Grounded theory (2007 2010)
The document discusses grounded theory, which is a research methodology that involves developing theory inductively from data. Grounded theory was introduced in 1967 and involves systematically generating theory through rigorous data collection and comparative analysis. It allows researchers to conceptualize patterns and structures by constantly comparing data. The methodology is iterative and involves purposive sampling, collection of various data types, coding data through comparison to derive categories and properties, and developing theory that fits the situation and works.
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Chapter two & three
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In materials sciences, a large amount of research data is generated through a broad spectrum of different
experiments. As of today, experimental research data including meta-data in materials science is often
stored decentralized by the researcher(s) conducting the experiments without generally accepted standards
on what and how to store data. The conducted research and experiments often involve a considerable
investment from public funding agencies that desire the results to be made available in order to increase
their impact. In order to achieve the goal of citable and (openly) accessible materials science experimental
research data in the future, not only an adequate infrastructure needs to be established but the question of
how to measure the quality of the experimental research data also to be addressed. In this publication, the
authors identify requirements and challenges towards a systematic methodology to measure experimental
research data quality prior to publication and derive different approaches on that basis. These methods are
critically discussed and assessed by their contribution and limitations towards the set goals. Concluding, a
combination of selected methods is presented as a systematic, functional and practical quality measurement
and assurance approach for experimental research data in materials science with the goal of supporting
the accessibility and dissemination of existing data sets.
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SECONDARY DATA ANALYSIS
Secondary data analysis involves analyzing data that was collected by another researcher for a different study. It is usually done to test a new hypothesis without having to collect new data. This approach saves time and money compared to primary data collection. Secondary data analysis can be performed on both quantitative and qualitative data from previous studies to answer new research questions.
SociologyExchange.co.uk Shared Resource
This document provides definitions for key research methods terminology. It lists terms like case study, content analysis, triangulation, qualitative data, quantitative data, and more. Readers are asked to match each term with its corresponding definition. Understanding these terms is important for studying research methods.
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For detailed lectures with Urdu/Hindi explanation, subscribe to my YouTube channel.
https://www.youtube.com/channel/UCvmyC56ovZ8vIspsFMwkBgA
You can also follow us on Instagram, TikTok & Facebook via-@learnwithsamii
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The document discusses grounded theory, which is a research methodology that involves developing theory inductively from data. Grounded theory was introduced in 1967 and involves systematically generating theory through rigorous data collection and comparative analysis. It allows researchers to conceptualize patterns and structures by constantly comparing data. The methodology is iterative and involves purposive sampling, collection of various data types, coding data through comparison to derive categories and properties, and developing theory that fits the situation and works.
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This document summarizes the results of a content analysis of 322 dissertations submitted to the Department of Library and Information Science at S.G.B. Amravati University between 1993-2009. The analysis found that bibliometric studies were the most popular research area among students, accounting for 11.18% of dissertations. Evaluation studies and studies on library growth and development were also popular subjects. Professors R.B. Jatkar and M.R. Kherde guided the highest number of dissertations at 18.01% and 16.15%, respectively. Overall, the content analysis provided insights into trends in dissertation topics and advisors over the 16-year study period.
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MELJUN CORTES research seminar_1_theoretical_framework
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Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
What is an RPA CoE? Session 1 – CoE Vision
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
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zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
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Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
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Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
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8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
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I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
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The Microsoft 365 Migration Tutorial For Beginner.pptx
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
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HERE IS YOUR WEBINAR CONTENT! 'Mastering Customer Journey Management with Dr. Graham Hill'. We hope you find the webinar recording both insightful and enjoyable.
In this webinar, we explored essential aspects of Customer Journey Management and personalization. Here’s a summary of the key insights and topics discussed:
Key Takeaways:
Understanding the Customer Journey: Dr. Hill emphasized the importance of mapping and understanding the complete customer journey to identify touchpoints and opportunities for improvement.
Personalization Strategies: We discussed how to leverage data and insights to create personalized experiences that resonate with customers.
Technology Integration: Insights were shared on how inQuba’s advanced technology can streamline customer interactions and drive operational efficiency.
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"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
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zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
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inQuba Webinar Mastering Customer Journey Management with Dr Graham Hill
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Demystifying Knowledge Management through Storytelling
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
Dissertation Outline
- 1. CHANGING BEHAVIOUR THROUGH MULTIMEDIA: EVALUATION OF AN APPLICATION ON SMOKING CESSATION Dissertation Outline Outline Chapter I – Introduction 1.1 study pertinence 1.2 research question 1.3 research methodology 1.4 research model 1.5 methodological procedure 1.6 objectives 1.7 dissertation structure Chapter II – Theoretic Framework 2.1 Learning Theories 2.1.1 Behavourism 2.1.2 Cognitivism 2.1.3 Construtivism 2.2 Behaviour theories 2.2.1 Transtheoretical Model - model explanation - stages of therapeutic change - intervention techniques - model relevance - main aplications 2.2.2 Elaboration Likelihood Model (ELM) - model explanation - stages of therapeutic change
- 2. 2.2.3 Persuassion Theory 2.3 2.4 multimedia applications examples Chapter III - DEVELOPMENT OF RESEARCH - CASE STUDY ( prototype development ) 3.1 Context of the study (contact institutions / sample selection / characterization of the sample) 3.2 Tools to collect data 3.3 Timing of data collection 3.4 Occasions (equipment, study the subject, the scene, performance adult) Chapter IV - presentation, analysis and discussion of the collected data 4.1 presentation of the collected data 4.2 analysis of the collected data 4.3 discussion of the obtained results Chapter- V Final Considerations 5.1 conclusion 5.2 Research difficulties and limitations 5.3 Future research perspectives