3.Project Presentation in Computing Research

3.Project Presentation in Computing Research

Introduction:

Welcome to the fifth and final lecture of "Research Method in Computing." In this tutorial, we will guide you through the crucial process of presenting your computing research project. A well-prepared presentation effectively communicates your project design, implementation, findings, and contributions to an audience. This tutorial provides detailed steps, tips, and resources to ensure a compelling and impactful project presentation.

Tutorial Steps:

Step 1: Understand Your Audience:

Identify the target audience for your presentation (e.g., peers, faculty, industry professionals).

Tailor your content and language to suit the knowledge level and interests of your audience.

Example: If presenting to a mixed audience of technical and non-technical individuals, strike a balance between technical details and broader implications.

Step 2: Structure Your Presentation:

Follow a logical structure with a clear introduction, main content, and conclusion.

Divide your presentation into sections such as Project Overview, Methodology, Results, and Contributions.

Example: Use a slide structure that mirrors the sections of your final report.

Step 3: Craft an Engaging Introduction:

Begin with a captivating introduction to grab the audience's attention.

Clearly state the problem you addressed and the objectives of your project.

Example: Start with a relatable scenario or statistic related to your sentiment analysis project.

Step 4: Detail Project Design and Methodology:

Explain the overall design of your project.

Walk through the methodology, emphasizing key decisions and approaches.

Example: Use visuals or flowcharts to illustrate the architecture of your sentiment analysis project.

Step 5: Showcase Implementation:

Demonstrate the implementation of your project.

If applicable, showcase the user interface or interactive components.

Example: Run a live demonstration of your sentiment analysis model on sample data.

Step 6: Present Results and Findings:

Clearly present the results obtained from your project.

Use visuals, graphs, or charts to illustrate key findings.

Example: Display accuracy metrics, visualizations of sentiment distribution, or any noteworthy outcomes.

Step 7: Discuss Contributions:

Articulate the contributions your project makes to the field.

Emphasize the innovative aspects and potential impact.

Example: Discuss how your sentiment analysis project contributes to advancements in natural language processing.

Step 8: Prepare for Questions:

Anticipate potential questions from the audience.

Be prepared to provide additional details or insights.

Example: Consider questions related to the choice of algorithms, dataset selection, or ethical considerations in sentiment analysis.

Step 9: Practice Delivery:

Rehearse your presentation multiple times.

Pay attention to pacing, clarity, and transitions between slides.

Example: Practice with a peer or mentor to receive feedback on your delivery.

Step 10: Use Visual Aids Effectively:

Use visuals aids, such as slides or multimedia, to enhance your presentation.

Ensure visuals are clear, relevant, and complement your verbal explanations.

Example: Include screenshots, diagrams, or short video clips to illustrate key points.

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1. Project Implementation in Computing Research

Tutorial Steps:

Step 1: Define Project Scope and Goals:

Clearly define the scope and goals of your project based on the formulated problem statement.

Specify the functionalities and outcomes you aim to achieve.

Example: For a sentiment analysis project, define whether the focus is on specific emotions, platforms, or user demographics.

Step 2: Set Up a Version Control System (VCS):

Create a GitHub repository for your project.

Use Git as a version control system to track changes and collaborate efficiently.

Example: Initialize a GitHub repository named "SentimentAnalysisProject" and set up Git for version control.

Step 3: Plan Project Architecture:

Outline the architecture and structure of your project.

Define modules, dependencies, and data flow.

Example: Plan modules for data preprocessing, model development, and result visualization in a sentiment analysis project.

Step 4: Code Implementation:

Begin coding based on the planned architecture.

Follow best coding practices and document your code thoroughly.

Example: Implement data preprocessing functions to clean and format input text data.

Step 5: Integrate Machine Learning Models:

If applicable, integrate machine learning models into your project.

Train models using relevant datasets.

Example: Implement a sentiment analysis model using a machine learning library like scikit-learn or TensorFlow.

Step 6: Implement Data Visualization:

If part of your project, implement visualization components.

Use libraries like Matplotlib or Plotly for effective visualization.

Example: Visualize sentiment analysis results through interactive plots or graphs.

Step 7: Continuous Integration (CI):

Set up continuous integration tools to automate testing.

Ensure that your codebase remains stable with each update.

Example: Use GitHub Actions to run automated tests on your sentiment analysis project.

Step 8: Document Progress in Markdown (ReadMe):

Maintain a detailed ReadMe file in Markdown format in your GitHub repository.

Document project progress, goals, and instructions for users.

Example: Use the ReadMe to describe the sentiment analysis project, its objectives, and steps for running the code.

Step 9: Collaborate and Seek Feedback:

Collaborate with peers or mentors on GitHub.

Seek feedback through pull requests and discussions.

Example: Invite collaborators to review and provide feedback on your sentiment analysis project.

Step 10: Version Control and Tagging:

Use Git tags to mark important project milestones.

Ensure that your GitHub repository reflects different versions of your code.

Example: Create a Git tag for the first stable version of your sentiment analysis project.

Step 11: Address Issues and Enhancements:

Regularly check and address issues raised on GitHub.

Implement enhancements and features based on feedback.

Example: Respond to user-reported issues related to the sentiment analysis model's accuracy.

Extended Tips:

Tip 1: Code Modularity:

Design your code with modularity in mind.

Separate functionalities into distinct modules for maintainability.

Example: Create separate Python files for data preprocessing, model training, and result visualization in your sentiment analysis project.

Tip 2: Testing and Validation:

Implement thorough testing for each module.

Validate results against expected outcomes.

Example: Test sentiment analysis model predictions against a manually annotated dataset to ensure accuracy.

Tip 3: Documentation Standards:

Follow documentation standards for your chosen programming language.

Include comments in your code for clarity.

Example: Document function parameters, return values, and usage in your sentiment analysis project code.

Tip 4: User-Friendly ReadMe:

Craft a ReadMe that is user-friendly and accessible.

Include installation instructions, usage guidelines, and troubleshooting tips.

Example: Provide clear steps for users to clone the GitHub repository, install dependencies, and run the sentiment analysis project.

Tip 5: Regular Backups:

Regularly back up your codebase and important project files.

Ensure that you can recover previous versions if needed.

Example: Use GitHub's version history to revert to previous code versions in case of unexpected issues.

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2. Creating a Final Report in Computing Research

Tutorial Steps:

Step 1: Understand Reporting Guidelines:

Familiarize yourself with any specific reporting guidelines from your institution or conference.

Adhere to any formatting requirements and guidelines for academic writing.

Example Resource: APA Style GuideLinks to an external site.

Step 2: Structure Your Report:

Follow a standard structure for academic reports.

Include sections such as Introduction, Literature Review, Methodology, Results, Discussion, and Conclusion.

Example Resource: Structure of a Research PaperLinks to an external site.

Step 3: Write an Engaging Introduction:

Clearly state the problem, objectives, and significance of your research.

Engage readers with a compelling introduction.

Example Resource: How to Write a Research IntroductionLinks to an external site.

Step 4: Review and Integrate Literature:

Summarize relevant literature and integrate it into your report.

Discuss how your work contributes to or builds upon existing research.

Example Resource:

Literature Review Guide - PURDUELinks to an external site.

Literature Reviews - UNCLinks to an external site.

Step 5: Describe Your Methodology:

Clearly explain your research design, methods, and data collection process.

Include details on any tools or technologies used.

Example Resource: Guidelines for Describing MethodsLinks to an external site.

Step 6: Present Results Effectively:

Use clear visuals (tables, graphs) to present results.

Include statistical analyses if applicable.

Example Resource: Presenting Your FindingsLinks to an external site.

Step 7: Engage in In-Depth Discussion:

Interpret your results and discuss their implications.

Relate findings back to the research questions or objectives.

Example Resource: Guidelines for Writing a DiscussionLinks to an external site.

Step 8: Craft a Strong Conclusion:

Summarize key findings and their importance.

Discuss any limitations and suggest areas for future research.

Example Resource: Writing a ConclusionLinks to an external site.

Step 9: Cite Sources Appropriately:

Ensure proper citation of all sources using a standard citation style (APA, MLA, etc.).

Use citation management tools for accuracy.

Example Resource:

Citing SourcesLinks to an external site.

In-Text Citations: The BasicsLinks to an external site.

Step 10: Proofread and Edit:

Carefully proofread your report for grammar, punctuation, and style.

Seek feedback from peers or mentors on the clarity of your writing.

Example Resource: Editing and Proofreading TipsLinks to an external site.

Step 11: Create an Executive Summary:

Craft a concise executive summary at the beginning of your report.

Summarize key points for readers who may not read the entire document.

Example Resource: How to Write an Executive SummaryLinks to an external site.

Extended Tips:

Tip 1: Clarity and Conciseness:

Strive for clarity and conciseness in your writing.

Clearly articulate your ideas without unnecessary complexity.

Example: Use plain language to explain complex concepts, ensuring accessibility for a broad audience.

Tip 2: Review Style Guidelines:

Follow the style guidelines specified by your academic institution or conference.

Ensure consistency in formatting, font, and citation style.

Example: Refer to the APA Style Guide for specific guidelines on writing and formatting.

Tip 3: Peer Review:

Share drafts of your report with peers or mentors.

Collect feedback on the overall structure, clarity, and coherence of your writing.

Example: Establish a peer review group to exchange constructive feedback on each other's reports.

Tip 4: Proofreading:

Proofread your report thoroughly for grammatical errors and typos.

Consider using proofreading tools or seeking assistance from others.

Example: Use online tools like Grammarly for automated proofreading.

Tip 5: Visual Enhancements:

Include visuals to enhance the presentation of results.

Ensure all visuals are labeled and explained in the text.

Example: Use Matplotlib or Excel for creating clear and informative graphs.

Tip 6: Reflect on Contributions:

Clearly articulate your contributions to the field.

Reflect on how your research advances existing knowledge.

Example: Emphasize the novel aspects of your sentiment analysis approach and its potential impact on real-world applications.

ENHANCING LEGAL CHATBOTS TO COMBAT SEXUAL VIOLENCE: A PROJECT PROPOSAL

Introduction

Sexual violence is a widespread issue and has affected millions at the global level. Still, survivors encounter a lot of hurdles in putting their case before a judge to seek justice. Chatbots create an unprecedented chance for CASES of abuse survivors to receive help in a formal, safe, and non-judgmental environment. This study intends to develop a chatbot using AI called "E-Law", which will provide legal help depending on the law and regulations of sexual violence in the United States.

Objectives

The primary objectives of this project are:

1. To design a conversational agent architecture optimized for the legal guidance domain.

2. To develop the chatbot's capability for natural dialog aligned with US laws on sexual violence.

3. To enhance the chatbot's empathy, emotional intelligence, and privacy protections.

4. To evaluate the chatbot's effectiveness through user studies with survivors and legal experts

Literature Review Summary

Recent advances in artificial intelligence and natural language processing have enabled new applications of chatbots and conversational agents for legal assistance. As highlighted by Juro (2024) [1], there are now various legal AI chatbots emerging to support tasks like legal research, document review, and client intake. The American Bar Association Journal (2024) [2] profiles how innovators like Tom Martin have combined law and technology to design custom legal chatbots for law firms to boost productivity. However, as noted by Kathrani (2017) [6], many existing legal chatbots rely on simple pattern matching and lack sophisticated natural language capabilities to address complex queries.

There is a significant opportunity to apply AI advancements to develop chatbots tailored for providing specialized legal advice on sensitive issues like domestic violence and sexual assault. For instance, PR Newswire (2023) [3] reports on a new chatbot powered by D-ID's generative AI to assist victims of domestic violence by gathering evidence safely and anonymously. According to NoCamels (2023) [4], the chatbot named Sophia aims to be the first specialized chatbot to support survivors of domestic abuse globally. However, experts cited point out limitations around Sophia's intelligence, as the responses rely on pre-written scripts rather than dynamic AI capabilities. This may be insufficient to handle the nuances of legal matters related to sexual violence cases (NoCamels, 2023).

Similar efforts are being made to leverage AI chatbots for providing personalized legal guidance to survivors of sexual violence in specific countries. As described in IEEE Access, the chatbot LAW-U was developed in Thailand to offer 24/7 legal support to survivors of sexual assault in alignment with regional laws [5]. While it focuses on anonymity and a victim-centered approach, LAW-U is also limited by scripted responses and basic conversational capabilities as per the analysis in the University of Westminster Journal (Kathrani, 2017) [6].

The proposed E-Law chatbot aims to address gaps in legal chatbots by providing personalized assistance tailored to US laws on sexual violence. However, as highlighted in the Research Handbook on the Law of Artificial Intelligence (Barfield & Pagallo, 2020) [7], inherent limitations around current AI natural language capabilities would need to be considered. Chatbots cannot fully replace human legal expertise in handling complex and nuanced sexual assault cases. There are also significant ethical and privacy concerns around collecting sensitive data from survivors that must be addressed through proper security controls.

Problem Formulation

While chatbots present a promising opportunity for providing specialized legal guidance to survivors of sexual violence, several key challenges need to be addressed:

1. Lack of legal knowledge: Existing chatbots lack comprehensive knowledge of regional laws, processes, and resources related to sexual violence cases.

2. Limited natural language capabilities: Most chatbots rely on simple pattern matching rather than more sophisticated NLP techniques needed for complex legal dialogues.

3. Absence of personalized guidance: Chatbots need to provide responses tailored to factors like user demographics, type of sexual violence, relationship to perpetrator, etc.

4. Lack of empathy and emotional intelligence: Chatbots need to detect user emotions and generate empathetic responses to handle sensitive conversations.

5. Privacy and security concerns: Robust protections are needed to maintain user anonymity and data privacy when discussing confidential legal matters.

6. Evaluation with real-world users: Rigorous testing is required with target users like survivors and legal experts to refine the chatbot and prove its real-world efficacy.

This project aims to address these gaps by developing an intelligent conversational agent called E-Law specifically optimized to provide personalized legal guidance to survivors of sexual violence in alignment with regional laws in the US.

Methodology Overview

The development of E-Law will proceed through the following phases (Figure 1): The development of E-Law will proceed through the following phases:

Knowledge Base Creation: A comprehensive knowledge base will be developed, including vital details on American law, processes, and resources concerning sexual misconduct. With all the relevant statutes, legislations, legal definitions, victim's rights, reporting procedures, and support services accumulated from different states, the chatbot will be able to possess rich legal knowledge.

Conversational Framework Design: The capability of conversation for E-Law would be based on a hybrid architecture based on combining rule-based dialog with machine learning components. The modular architectural design of Rasa API will let us integrate different models like intent tagging, entity extraction, and response retrieval models to achieve natural language understanding and generation.

User Persona Modeling: Personalized user guidance will be the core of the E-system with distinct survivor personas and conversation flows based on factors like age, type of sexual violence, relationship with the perpetrator, etc. To be able to provide context-sensitive responses these personas and flows will be created.

Empathetic Response Capabilities: Sentiment analysis would be employed to extract user emotions to return the specific empathetic answers. The functions of memory networks and the affective dialog models will be explored for E-Law to be more emotionally intelligent and sensitive.

Privacy and Security: Robust security features including data encryption, access control, and anonymous networking would be deployed to defend users' privacy. Group identification will limit the disaster information access to only those who are permitted to access it.

Testing and Evaluation: After the E-Law ready-made prototype is developed, user studies will take place by deploying the E-Law and allowing survivors and legal experts to react to it. The evaluation and refinement of a chatbot will be facilitated by using quantitative metrics and qualitative feedback to ensure its reliable performance.

Figure 1. The flow chart.

Expected Contributions

The expected outcomes of this project are:

An intelligent conversational agent capable of providing personalized legal guidance to survivors of sexual violence based on US law.

Increased accessibility to legal information through a private, secure chatbot interface.

Empirical evaluation of the chatbot's real-world efficacy in assisting survivors.

A reusable architecture and methodology for developing AI chatbots for legal guidance applicable globally.

Schedule

The project timeline spanning over 6 months is outlined below:

Months 1-2: Literature review, requirements gathering

Months 2-3: Knowledge base creation, system design

Months 3-4: Prototype implementation

Months 4-5: Testing and refinement

Month 6: Evaluation, documentation

Conclusion

The goal of this project is to investigate the opportunity of AI-enabled conversational agents offering legal aid with a trauma-informed approach, which may change according to survivors' needs and the relevant laws in each region. The results could limit access barriers and impact survivors of sexual violence in the world.

References

Juro, "7 Best Legal AI Chatbots for 2024," Juro. [Online]. Available: https://juro.com/learn/legal-ai-chatbot. [Accessed: Feb. 19, 2024].

ABA Journal, "Building Bots: Tom Martin Merged a Love of Law and Technology to Design Custom Legal Chatbots," ABA Journal. [Online]. Available: https://www.abajournal.com/legalrebels/article/tom-martin. [Accessed: Feb. 19, 2024].

D-ID and Spring ACT, "D-ID's Generative AI to Power Online Chatbot for Victims of Domestic Violence," PR Newswire, Mar. 8, 2023. [Online]. Available: https://www.prnewswire.com/il/news-releases/d-ids-generative-ai-to-power-online-chatbot-for-victims-of-domestic-violence-301765291.html. [Accessed: Feb. 19, 2024].

NoCamels, "Meet Sophia, The Worlds First Chatbot To Battle Domestic Abuse," NoCamels, Mar. 2023. [Online]. Available: https://nocamels.com/2023/03/meet-sophia-the-worlds-first-domestic-violence-chatbot/. [Accessed: Feb. 19, 2024].

DOI 10.1109/ACCESS.2021.3113172, IEEE Access. "LAW-U: Legal Guidance Through Artificial Intelligence Chatbot for Sexual Violence Victims and Survivors." [Online]. Available: https://www.academia.edu/57352504/LAW_U_Legal_Guidance_Through_Artificial_Intelligence_Chatbot_for_Sexual_Violence_Victims_and_SurvivorsP. Kathrani, "The potential Legal Chat Bots have in the context of Access to Justice," U. of Westminster Journal, 2017. [Online]. Available: http://arno.uvt.nl/show.cgi?fid=159847.

W. Barfield and U. Pagallo, "Research Handbook On The Law Of Artificial Intelligence," E-elgar.com, 2020. [Online]. Available: https://www.e-elgar.com/shop/gbp/research-handbook-on-the-law-of-artificial-intelligence-9781786439048.html.