
## Project Overview

I am designing and programming an indie game using the Unity engine. This game is a survival horror experience set in the unforgiving expanse of space. Drawing heavy inspiration from games like Darkwood, Duskers, and FTL, it combines elements of exploration, strategy, and survival in a top-down perspective.

The game focuses on engineering and resource management. The player's ship is controlled via a terminal console, reminiscent of retro computing interfaces. This terminal allows players to manage ship systems, monitor system health, and interact with the environment in a deeply immersive way.

The primary objective is to explore the cosmos, find, and document alien life forms. However, resources are scarce, and space is fraught with dangers. Players must navigate environmental hazards, repair broken machinery, combat alien pathogens, and overcome other challenges that threaten their mission's success.

## Current Progress

### Basic Movement

Significant strides have been made in developing the core mechanics of player movement and interaction. Currently, the player can:

- Move in all directions with smooth, responsive controls.

- Aim and turn to face different directions.

- Interact with objects within the game world, laying the foundation for more complex interactions and gameplay mechanics.

### Basic Terminal

A crucial feature of the game, the in-game terminal, has been implemented with essential functionality:

- Dynamic Directory System: The terminal uses a simple file parsing system to dynamically build and navigate directories, providing a realistic computing experience.

- Vim-Inspired Text Editor: Development is underway for a text editor inspired by the Vim text editor. This tool will allow players to edit system files, input commands, and manage their ship's functions directly through the terminal.

## Development Insights

### Inspiration and Design Philosophy

The game's design is rooted in the desire to blend the suspense and tension of survival horror with the strategic depth of resource management. The top-down perspective offers a unique vantage point, emphasizing the isolation and vulnerability of space exploration. By using a terminal console for ship control, the game aims to evoke a sense of nostalgia while providing a fresh and challenging gameplay mechanic.

### Challenges and Solutions

Developing an indie game presents numerous challenges, from technical hurdles to creative decisions. Some of the key challenges faced so far include:

- Creating Immersive Gameplay: Ensuring the terminal console is both functional and engaging requires careful balancing of complexity and usability. Iterative testing and player feedback will be crucial in refining this aspect.
- Balancing Resource Management: Crafting a resource management system that is challenging yet fair is a delicate process. Continuous adjustments are being made to resource scarcity and the impact of player decisions to maintain balance and tension.

### Future Plans
As development progresses, several key areas will be the focus:

- Expanded Terminal Functionality: Enhancing the terminal with more commands, interactive scripts, and deeper integration with the game's systems.
- Environmental Hazards and Alien Life: Developing diverse environmental hazards and alien life forms that will interact with the player and the ship's systems in unpredictable ways.
- Narrative Development: Crafting a compelling story that draws players into the world, with rich lore and engaging missions.


A living blog illustrating the creative and technical process of my most recent unity game

Unity Development Blog


## Intro to Neural Networks

Neural Networks are a type of artificial intelligence model that can infer information from a given text. They have evolved significantly over the years, becoming a cornerstone in various NLP tasks due to their ability to learn complex patterns and representations from large datasets.

### How they work

A Neural Network is a collection of interconnected nodes, each representing a simple equation. These nodes, or neurons, classify inputs based on weights that are adjusted during training.

During training, the network compares its output to a gold standard dataset to measure accuracy. It then propagates errors backward through the network (a process known as backpropagation) to update the weights and improve future predictions. This iterative process enables the network to learn from data. However, the features and weights used by neural networks are typically not interpretable by humans, which can be a limitation when understanding the model's decisions.



## Neural Networks for Natural Language Processing

Neural Networks are particularly well-suited for NLP tasks because they can capture nuances and contextual relationships in text that simpler algorithms might miss. For instance, in tasks such as sentiment analysis, translation, and text generation, neural networks, especially deep learning models like Recurrent Neural Networks (RNNs) and Transformers, have shown remarkable performance.

## Text Classification

Text classification involves tagging text with predefined labels. For NLP models, this task is fundamental yet essential, providing a basis for more complex operations. By using pre-tagged datasets, we can train models and easily verify their accuracy, making it a straightforward entry point into NLP applications.



## Sarcasm Detection

### Project Members

Alexander Sitzman

Chris Farrer

Maxwell Schultz

### Problem Statement

We are attempting to build a set of models which can accurately predict whether a news article title contains sarcasm or not. We are finding ways to effectively normalize and vectorize the data before finding optimal weights to a variety of ML models which will solve the binary classification problem.

### Ethical and Social Impact

This project has the potential to reach a deeper linguistic understanding of subtle pragmatic structures such as sarcasm. It will be able to show the potential of ML machinery paired with NLP methodologies, and how real social phenomena can be modeled in such a way.

### Expected Learning Outcome

We expect to learn more about the reliability of the given dataset, the difficulty of the problem, and the ability of the various models for this type of problem. We expect to better understand the process of setting up various models, and gain deeper insights to the methodology of hyperparameter tweaking.


### Datasets

News Headlines Dataset For Sarcasm Detection (kaggle.com)

A collection of over 28,000 news headlines from real and satirical news outlets. Each datapoint is tagged as either sarcastic or non sarcastic, as well as a link to the original article. The advantage of this data set is that it is  self contained, that is unlike a tweet set, there is not a connection between previous data points to provide context.

Sarcasm detection (kaggle.com)

Another popular dataset from Kaggle is the Sarcasm detection data set. It provides similar examples of headlines tagged by sarcastic and not sarcastic. This data set claims to be smaller and more refined and is newer. May be potentially interesting to see the comparisons between the two datasets

These two cannot both be used at the same time as they may have overlapping data. The strongest approach will be to divide the datasets in half to create a training set and a test set from each one and test the effectiveness of either set independently

### Technical Approach

To achieve the objective of accurately predicting sarcasm in news headlines, we will utilize multiple models to test model efficiency, including both traditional machine learning approaches and neural network approaches. The process will include the following steps:

#### Data Preprocessing

Normalization: Standardizing the text data by converting all headlines to lowercase, removing punctuation, and stemming/lemmatizing words to ensure uniformity.

Vectorization: Transforming text data into numerical vectors using techniques such as TF-IDF (Term Frequency-Inverse Document Frequency) and word embeddings like Word2Vec

#### Model Selection and Training

Traditional Machine Learning Models: We will implement models such as Logistic Regression and Support Vector Machines (SVM) to establish baseline performance metrics.

Neural Networks: We will explore more advanced models like Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs)

#### Model Evaluation

Performance Metrics: Evaluating models using accuracy, precision, recall, and F1 score

#### Comparative Analysis

We will compare the performance of different models to determine the most effective approach for sarcasm detection. Additionally, we will analyze the differences in model performance between the two datasets to gain insights into how dataset characteristics influence model efficacy.


## Our Findings

The project is currently in progress and has not yielded any specific results as of yet. After examining our dataset, we discovered that our dataset was flawed and more accurately described as fake news detection instead of sarcasm detection. Consequently, we adjusted our goal to focus on detecting the validity of news articles.

## Updated Goal: Detecting Fake News

After realizing the limitations of our initial dataset for sarcasm detection, we pivoted our model's objective to determining the validity of news articles. We acquired a new dataset tagged with fake or real news, which provides a more accurate context for our analysis. The dataset used headlines from The Onion, a satirical news website, as data points for sarcasm. However, we concluded that this context was flawed. Satire, while similar to sarcasm, is more akin to fake news because it presents a fabricated reaction to a fictional context, rather than a sarcastic reaction to a real context. A satirical headline paired with a fake article gives the impression of a genuine response rather than a sarcastic one.

To achieve a true sarcasm dataset, the data would need to come from a different source, such as forum posts marked with a "/s" postfix, which is a common tag for online users to denote sarcasm. Data collected in this manner could provide the necessary context for creating a functional sarcasm detection model.

As a result, we decided to pivot our current dataset focus to satire and fake news detection for the purposes of this model. We plan to revisit and reevaluate sarcasm detection in the future with a more appropriate dataset.

### Data Splitting: 
We split our Kaggle dataset into 85% training and 15% test sets, and further split the training set into 85% training and 15% validation sets. This approach allows us to have a validation set for hyperparameter tuning and an untouched test set to measure the final model's validity.

### Models Implemented:

Neural Bag of Words: Achieved 96.2% accuracy on the test set.

Recurrent Neural Network.

Transformer Model: Achieved 99.2% accuracy on the test set.
Model Performance: The Transformer model was the most accurate, correctly identifying real and fake news articles. However, it still showed some blind spots, particularly in detecting satire.

## Key Takeaways:

Sarcasm detection requires highly nuanced data. In practice, the language used in sarcasm can be quite similar to lies. What often separates sarcasm from lies is the delivery, which is challenging to convey through text alone. The only reliable indicator is the context provided, which was inadequate in our datasets.

Fake news detection also faces significant challenges due to the lack of real cultural context within the model. A fake news article can appear real to someone unfamiliar with the subject matter. This limitation can lead to false positives or negatives in the model's predictions.

Our Transformer model demonstrated high accuracy but had notable blind spots. For instance, it failed to identify the satirical nature of an article titled "Nation Forgets What It Was They Didn’t Like About O.J. Simpson." The article humorously suggested that 340 million Americans suddenly forgot why they disliked O.J. Simpson, portraying a fictional collective amnesia about his well-known criminal history. The model's inability to recognize the satirical context highlights its limitation in understanding deeper cultural and historical nuances.



## References

<a href="https://www.kaggle.com/datasets/rmisra/news-headlines-dataset-for-sarcasm-detection">News Headlines Dataset For Sarcasm Detection</a>


<a href="https://www.kaggle.com/datasets/saurabhbagchi/sarcasm-detection-through-nlp">Sarcasm Detection</a>


<a href="https://www.kaggle.com/datasets/saurabhshahane/fake-news-classification">Fake News Detection</a>

<a href="https://www.theonion.com/nation-forgets-what-it-was-they-didn-t-like-about-o-j-1851514220">The Onion</a>





A study to determine the effectiveness of different types of language models on sucessful tagging of text as either sarcastic or non-sarcastic

Sarcasm Detection Natural Language Processing


## Project Overview

The Virtual Internet project aims to provide comprehensive cybersecurity training to students in a safe, stable, and self-contained environment. This environment allows students to test common internet attacks without the risks associated with running such exercises on a standard network. Utilizing an open-source Python library called SEED Emulator, this project emulates real-world network device functionality and communication, providing a robust training ground for cybersecurity education.

## Vision Statement
The Virtual Internet serves as a virtualized environment specifically designed for Western Washington University (WWU) students. It allows them to conduct labs and exercises that would be unsafe or impractical in a typical computer lab or network of virtual machines. By abstracting the setup process through the SEED Emulator, students can focus on learning and practicing cybersecurity tasks. This project is integrated with WWU’s Cyber Range, offering a convenient and accessible platform for all students.

## Technology Utilized
- Debian-based Linux distribution
- Python3
- Docker
- SEED Emulator

## Hardware Requirements:
- 8GB RAM
- 20GB SSD/HDD
- 4 or more cores on Intel 7th-gen or AMD 2000-series or newer processor
- Ubuntu Horizon Platform: Hosting lab images on the cyber range

## Project Outcomes

### Added Features

- Automated User Manual: When the virtual machine (VM) launches, a script automatically opens a User Manual. This manual informs the end-user about the aliased commands they can issue in a terminal window to start various labs, along with providing a terminal window for these commands.
- Optimized Bash Scripts: Aliased commands run optimized bash scripts that manage the SEED Emulator's launch process. These scripts redirect terminal output into log files, detecting when the Emulator GUI is ready and handling common Docker bugs.
- New Labs: Full implementation and writing of BGP Attack and Botnet labs.
- Updated Network Build Script: The Python script for network building has been rewritten to be more extensible and explanatory, facilitating future groups in implementing fully extensible networks.

## Architecture

### System Architecture

The Virtual Internet project consists of several main components, each interacting seamlessly to provide a functional and secure environment for cybersecurity training.

### SEED Emulator Integration:

Python scripts, developed within the SEED framework, are compiled to generate Docker Containers representing network devices such as routers, switches, and user devices.
These Docker Containers create a virtual internet/network, which can be interacted with and visualized through a front-end emulator accessible via any web browser.

### Cyber Range Interaction:

The project is hosted on WWU’s Cyber Range, allowing easy access and deployment for students.
Lab images are hosted on the Ubuntu Horizon Platform, ensuring stability and accessibility.


### Deployment and Configuration

Component Connections: Each component is connected via Docker networks, enabling seamless communication between virtual devices.

Deployment Requirements: The project must be deployed on systems meeting the specified hardware requirements. Configuration details include setting up Docker and Python environments and ensuring proper permissions for running scripts.

Dependencies: Critical dependencies include the SEED Emulator, Docker, and Python libraries used in the project.

Security Mechanisms: The project incorporates standard cybersecurity practices, such as isolating the virtual environment from the host system and using secure communication protocols within the emulated network.


## Areas for Future Work

Extend Netbuild.py Script:

- Enable the creation of asymmetrical and more complex networks.

- Integrate SEED’s built-in DNS server, webserver functionality, and blockchain simulation.

- Include VPN simulation using SEED’s OpenVPN simulation.

Expand SEED Labs: Develop and publish additional labs to the Cyber Range, enhancing the variety and depth of training scenarios.

Cross VM Interactions: Enable interactions across virtual machines via the Cyber Range Virtual Internet Sub network as a stretch goal.




A virtual internet environment built in the SEED python library

Virtual Internet Emulation For Cybersecurity Training


## Project Overview

I set out to create a home network cloud service using the skills I have developed in the pursuit of my cloud administration certifications

The rack is composed of 5 Raspberry Pis and runs Kubernetes with MetalLB to evenly distribute the workload between nodes.

### In Progress

As of now the rack is not finished but does successfully host a local ad-blocker, a vpn for accessing the adblocker away from the home network, and a cloud photo vault to access photos without paying for hosted cloud storage


A server rack composed of Raspberry Pis used to run a Kubernetes Cluster

Create Design Build

Unity Development Blog

Sarcasm Detection Natural Language Processing

Virtual Internet Emulation For Cybersecurity Training

Pi Cluster